APPARATUS AND METHOD FOR OOCYTE RESCUE IN VITRO POST STIMULATION

Abstract

An apparatus and method for oocyte rescue in vitro post stimulation, the apparatus including a computing device, wherein the computing device includes at least a processor; and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to: receive biological sample data from a biological sample relating to a user, including at least an oocyte; determine a maturity level of the at least an oocyte; assign the at least an oocyte to a culture protocol as a function of the maturity level; receive culture data relating to the at least an oocyte as a function of the culture protocol; and calculate a scoring metric as a function of the culture data.

Description

FIELD OF THE INVENTION

The present invention generally relates to the field of in vitro maturation. In particular, the present invention is directed to an apparatus and method for oocyte rescue in vitro post stimulation.

BACKGROUND

One in ten women struggle with infertility, requiring assisted reproductive technology such as in vitro fertilization (IVF). Challenges remain with maintaining oocyte health in culture, resulting in low oocyte quality and subsequently poor embryo quality. Furthermore, oocytes that are developmentally immature are traditionally discarded, constricting the available oocyte pool for IVF.

SUMMARY OF THE DISCLOSURE

In an aspect, an apparatus for aiding in oocyte rescue in vitro post stimulation, the apparatus including: a computing device, wherein the computing device includes at least a processor; and a memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to: receive biological sample data from a biological sample relating to a user, including at least an oocyte; determine a maturity level of the at least an oocyte; assign the at least an oocyte to a culture protocol as a function of the maturity level; receive culture data relating to the at least an oocyte as a function of the culture protocol; and calculate a scoring metric as a function of the culture data.

In another aspect, a method for oocyte rescue in vitro post stimulation, the method including receiving a biological sample relating to a user, including at least an oocyte; determining a maturity level of the at least an oocyte; assigning the at least an oocyte to a culture protocol as a function of the maturity level; culturing the at least an oocyte as a function of the culture protocol; and calculating a scoring metric as a function of the culture data.

These and other aspects and features of non-limiting embodiments of the present invention will become apparent to those skilled in the art upon review of the following description of specific non-limiting embodiments of the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspects of one or more embodiments of the invention. However, it should be understood that the present invention is not limited to the precise arrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is an exemplary embodiment of an apparatus 100 for aiding in oocyte rescue in vitro post stimulation;

FIG. 2A is an exemplary embodiment of a machine-learning module;

FIG. 2B is an exemplary table of training data

FIG. 3 is an exemplary flow chart of oocyte denudation;

FIG. 4 is an exemplary table of metabolite formulations;

FIG. 5 is an exemplary flow-chart for preparing a granulosa co-culture;

FIG. 6A is an exemplary embodiment of a co-cultured oocyte;

FIG. 6B is an exemplary embodiment of a control culture of immature oocytes

FIG. 7 is an exemplary flow diagram illustrating a method for oocyte rescue in vitro post stimulation; and

FIG. 8 is a block diagram of a computing system that can be used to implement any one or more of the methodologies disclosed herein and any one or more portions thereof.

The drawings are not necessarily to scale and may be illustrated by phantom lines, diagrammatic representations and fragmentary views. In certain instances, details that are not necessary for an understanding of the embodiments or that render other details difficult to perceive may have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed to apparatuses and methods for oocyte rescue in vitro post stimulation.

Aspects of the present disclosure can be used to increase the overall pool of available healthy oocytes in women for use in IVF. Aspects of the present disclosure can also be used to significantly reduce FSH dosing in patients during egg retrieval and improve oocyte quality in culture. This may greatly expand access to reproductive technology, make the duration of a single cycle significantly shorter and require less cycles overall to achieve pregnancy.

Aspects of the present disclosure allow for exemplary embodiments illustrating aspects of the present disclosure are described below in the context of several specific examples.

Referring now to FIG. 1, an exemplary embodiment of an apparatus 100 for aiding in oocyte rescue in vitro post stimulation is illustrated. Apparatus 100 includes a computing device 104. Computing device 104 includes a processor 108 and a memory 112 communicatively connected to the processor 108, wherein memory 112 contains instructions configuring processor 108 to carry out the linking process. Processor 108 and memory 112 is contained in a computing device 104. Computing device 104 may include any computing device as described in this disclosure, including without limitation a microcontroller, microprocessor, digital signal processor (DSP) and/or system on a chip (SoC) as described in this disclosure. Computing device may include, be included in, and/or communicate with a mobile device such as a mobile telephone or smartphone. Computing device 104 may include a single computing device operating independently, or may include two or more computing device operating in concert, in parallel, sequentially or the like; two or more computing devices may be included together in a single computing device or in two or more computing devices. Computing device 104 may interface or communicate with one or more additional devices as described below in further detail via a network interface device. Network interface device may be utilized for connecting computing device 104 to one or more of a variety of networks, and one or more devices. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software etc.) may be communicated to and/or from a computer and/or a computing device. Computing device 104 may include but is not limited to, for example, a computing device or cluster of computing devices in a first location and a second computing device or cluster of computing devices in a second location. Computing device 104 may include one or more computing devices dedicated to data storage, security, distribution of traffic for load balancing, and the like. Computing device 104 may distribute one or more computing tasks as described below across a plurality of computing devices of computing device, which may operate in parallel, in series, redundantly, or in any other manner used for distribution of tasks or memory between computing devices. Computing device 104 may be implemented using a “shared nothing” architecture in which data is cached at the worker, in an embodiment, this may enable scalability of system 100 and/or computing device.

With continued reference to FIG. 1, computing device 104 may be designed and/or configured to perform any method, method step, or sequence of method steps in any embodiment described in this disclosure, in any order and with any degree of repetition. For instance, computing device 104 may be configured to perform a single step or sequence repeatedly until a desired or commanded outcome is achieved; repetition of a step or a sequence of steps may be performed iteratively and/or recursively using outputs of previous repetitions as inputs to subsequent repetitions, aggregating inputs and/or outputs of repetitions to produce an aggregate result, reduction or decrement of one or more variables such as global variables, and/or division of a larger processing task into a set of iteratively addressed smaller processing tasks. Computing device 104 may perform any step or sequence of steps as described in this disclosure in parallel, such as simultaneously and/or substantially simultaneously performing a step two or more times using two or more parallel threads, processor cores, or the like; division of tasks between parallel threads and/or processes may be performed according to any protocol suitable for division of tasks between iterations. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which steps, sequences of steps, processing tasks, and/or data may be subdivided, shared, or otherwise dealt with using iteration, recursion, and/or parallel processing.

With continued reference to FIG. 1, additionally, computing device 104 may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes 116. A “machine learning process,” as used in this disclosure, is a process that automatedly uses a body of data known as “training data” and/or a “training set” (described further below) to generate an algorithm that will be performed by a computing device/module to produce outputs given data provided as inputs; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language. Machine-learning process 116 may utilize supervised, unsupervised, lazy-learning processes and/or neural networks, described further below.

Still referring to FIG. 1, computing device 104 is configured to aid in oocyte rescue in vitro post stimulation. An “oocyte rescue” is the process of maturing and/or growing an oocyte cell in vitro. An “oocyte,” as used in this disclosure, is a reproductive cell originating in an ovary. Post simulation may refer to standard in vitro fertilization IVF stimulation protocols performed on a user. As used in this disclosure, a “stimulation protocol” is a medication injection process spanning over a specified period of time to induce ovaries in producing one or more oocytes. As used in this disclosure, a “user” is a living organism such as a human being, plant, animal, and all other organisms composed of cells. In some embodiments, post stimulation may refer to a minimal stimulation protocol. As used in this disclosure, a “minimal stimulation protocol” is a stimulation process spanning over a shortened period of time, compared to average in vitro fertilization (IVF) stimulation protocols, to aid in inducing an ovary to produce an egg. Typically, the average span of time for a stimulation protocol using standard IVF is around 8-14 days. The minimal stimulation protocol may induce the release of a cell in of 2-6 days, which is a shorted period of time compared to 8-14 days. The average time for performing minimal stimulation protocol may be 3 days. The max time may be 6 days and the minimal amount of time may be 2 days. In an embodiment, timing as to when minimal stimulation may be initiated by a patient may be determined by a patient's contraception status as described above in more detail. For example, a patient who is not taking contraception may begin stimulation with rFSH on the second day of the patient's menstrual cycle. In yet another non-limiting example, a patient who is taking contraception such as a combined oral contraception (COC) pill may begin stimulation 5 days after the last pill was consumed. Drug dosage and selection may be determined by one or more lab tests such as a blood test taken on the second day of a patient's menstrual cycle to determine blood levels of E2, FSH, LH, p4, and/or AMH. One or more measurements may be utilized to determine ovarian reserve health, circulating hormone levels, and/or fertility status.

Still referring to FIG. 1, computing device 104 is configured to receive biological sample data 120 from a biological sample relating to a user, including at least an oocyte. As used in this disclosure, a “biological sample” is any information relating to a user. Biological sample may include bodily fluids and/or data describing bodily fluids including blood, saliva, urine, semen (seminal fluid), vaginal secretions, cerebrospinal fluid (CSF), synovial fluid, pleural fluid (pleural lavage), pericardial fluid, peritoneal fluid, amniotic fluid, saliva, nasal fluid, optic fluid, gastric fluid, breast milk, cell culture supernatants, and the like. Biological sample data may include information describing a characterization of the biological, genetic, biochemical and/or physiological properties, compositions, or activities of biological samples. A biological sample may include a biological laboratory specimen held by a biorepository for research. A biological sample may include data describing one or more biological laboratory specimens. A biological sample may include data describing any information relating to a user. A biological sample may include a medical diagnosis, user input describing how a user is feeling and/or a symptomatic complaint, information collected from a wearable device pertaining to a user and the like. A biological sample may be collected from a user based on a survey, questionnaire, health history form, medical record, consultation, interview and the like collected from a user, third party such as a spouse or caregiver, and/or a medical professional such as a medical doctor, nurse, physician assistant, and the like. A “wearable device,” as used in this disclosure, is any device worn close to and/or on the surface of the skin of a user. A wearable device may detect, analyze, and/or transmit information concerning one or more body signals of a user including but not limited to vital signs, ambient, data, biofeedback data and the like. A wearable device may include an activity tracker. A wearable device may receive and/or transmit data utilizing the internet of things (TOT). For example, a biological sample may include information obtained from a visit with a medical professional such as a health history. In yet another non-limiting example, a biological sample may include information such as data collected from a wearable device worn by a user and designed to collect information relating to a user's sleep patterns, exercise patterns, and the like. In some embodiments, an oocyte may be an immature oocyte. An “immature oocyte” as used in this disclosure is one or more immature reproductive cells originating in the ovaries. In some embodiments, an immature oocyte may be an oocyte including but not limited to germinal vesicle (GV) and Metaphase 1 (M1) oocytes, as described further below. In some embodiments, an immature oocyte may be a plurality of oocytes. An immature oocyte may be immature cumulus-oocyte-complexes (COCs) taken from a patient. A “cumulus oocyte complex,” as used in this disclosure, is an oocyte containing one or more surrounding cumulus cells. A COC may contain an immature oocyte. A COC may contain a mature oocyte. A “mature oocyte” as used in this disclosure, is one or more mature reproductive cells originating in the ovaries. Once retrieved, a COC may rest for 2-3 hours to allow for equilibration to in vitro conditions. granulosa cell. In an embodiment, an oocyte may be combined with a granulosa cell. A “granulosa cell” is a cumulus cell surrounding the oocyte to ensure healthy oocyte and embryo development. In an embodiment, a granulosa cell may be sourced from a human donor. In an embodiment, a granulosa cell may be sourced from a stem cell such as a pluripotent stem cell. In an embodiment, a granulosa cell may be sourced from an embryonic tissue, a fetal tissue, an adult tissue, and/or a differentiated somatic cell. In an embodiment, a granulosa cell may be grown and sourced by artificial means such as in a laboratory. In some embodiments, the immature oocyte may contain an oocyte wherein the granulosa cell is added to mature the oocyte in a cell culture and thus create a COC. In some embodiments, the biological sample may include bodily fluids including blood, saliva, urine, semen (seminal fluid), vaginal secretions, cerebrospinal fluid (CSF), synovial fluid, pleural fluid (pleural lavage), pericardial fluid, peritoneal fluid, amniotic fluid, nasal fluid, optic fluid, gastric fluid, breast milk, cell culture supernatants, and the like. The biological sample may be collected at a particular date and/or time of a user's menstrual cycle. For instance and without limitation, the biological sample may be collected on the second day of a user's menstrual cycle to evaluate one or more hormone levels such as E2, FSH, LH, P4, and/or AMH. The biological sample may be utilized to determine one or more markers of a user's overall health including but not limited to ovarian reserve health and/or circulating hormone levels. This information may be utilized for example by a health care professional to monitor cycle progression and inform protocol and/or drug selection.

Still referring to FIG. 1, computing device 104 may receive biological sample data 120 from a biological sample relating to a user post stimulation from a biological sample database 124. A as used in this disclosure a “biological sample database” is a database containing all data related biological samples of users containing analytic information. In some embodiments, biological sample database 124 may contain a stimulation protocol index. A “stimulation protocol index,” as used in this disclosure, is data structure correlating user information regarding the completion of a stimulation protocol such as: user age, user BMI, number of COCs retrieved, number of mature MII oocytes, number of immature MI oocytes, number of immature GV oocytes, AMH Levels (ug/L), antral follicle count (AFC) at last ultrasound, user oocyte retrieval day E2 Levels (ng/L), user oocyte retrieval day P4 Levels (ng/L), user oocyte retrieval day LH (IU/L), user oocyte retrieval day FSH (IU/L), Days of stimulation, gonadotropin used, and total injected dose (IU). In some embodiments, biological sample database 124 may contain a systemic hormone index. A “systemic hormone index,” as used in this disclosure, is data structure correlating medical knowledge regarding systemic hormone therapy. For example, systemic hormone index may include side effects and risks, proper methods of administering hormones, correlations between E2, LH, FSH, and/or P4 deficiency and systemic hormones and the like. In some embodiments, biological sample database 124 may be communicatively connected to computing device 104. As used in this disclosure, “communicatively connected” means connected by way of a connection, attachment, or linkage between two or more relata which allows for reception and/or transmittance of information therebetween. For example, and without limitation, this connection may be wired or wireless, direct, or indirect, and between two or more components, circuits, devices, systems, and the like, which allows for reception and/or transmittance of data and/or signal(s) therebetween. Data and/or signals therebetween may include, without limitation, electrical, electromagnetic, magnetic, video, audio, radio, and microwave data and/or signals, combinations thereof, and the like, among others. A communicative connection may be achieved, for example and without limitation, through wired or wireless electronic, digital, or analog, communication, either directly or by way of one or more intervening devices or components. Further, communicative connection may include electrically coupling or connecting at least an output of one device, component, or circuit to at least an input of another device, component, or circuit. For example, and without limitation, via a bus or other facility for intercommunication between elements of a computing device. Communicative connecting may also include indirect connections via, for example and without limitation, wireless connection, radio communication, low power wide area network, optical communication, magnetic, capacitive, or optical coupling, and the like. In some instances, the terminology “communicatively coupled” may be used in place of communicatively connected in this disclosure.

With continued reference to FIG. 1, In an embodiment, selection of a stimulation protocol may be done utilizing information obtained from an ultrasound. An “ultrasound,” as used in this disclosure, is any procedure that utilizes sound waves to generate one or more images of a user's body. For example, an ultrasound may be utilized to obtain an image of a woman's reproductive organs and/or tissues. In an embodiment, an ultrasound may be performed at a particular time of a woman's menstrual cycle. For example, a woman may receive an ultrasound on day 2 of her cycle and this may be utilized to determine follicle size and/or follicle count. Selection of a stimulation protocol and/or adjustment to a stimulation protocol may be made utilizing this information. For example, a woman with an ultrasound that shows polycystic ovarian syndrome (PCOS) may have a dose adjustment made to one or more medications received and/or utilized during a stimulation protocol. In addition, the length of her stimulation protocol may be modified based on her PCOS diagnosis. In an embodiment, an ultrasound may be repeated one or more times throughout a woman's stimulation protocol, and information obtained may be utilized to adjust her stimulation protocol in real time. In addition, a woman's contraception usage may affect assignment of a stimulation protocol. “Contraception,” as used in this disclosure, is any method and/or device utilized to prevent pregnancy as a consequence of sexual intercourse. This may include but is not limited to any medication, technique, device, and/or birth control utilized by a woman. A woman's use of contraception or not may aid in determining at what point in the woman's menstrual cycle she should begin her stimulation protocol. For instance and without limitation, a woman who is not using any form of contraception may begin her stimulation protocol with recombinant follicle stimulating hormone (rFSH) on the second day of her menstrual cycle. In yet another non-limiting example, a woman who is using contraception may begin her stimulation protocol with rFSH 5 days after consuming her last oral contraception pill. In an embodiment, rFSH stimulation may be utilized for 2-3 days, depending on a patient's tolerance, follicle size, and/or growth dynamics. After this 2-3 day window, a coasting period of 1-2 days may be utilized to monitor follicle size and allow for further follicle maturation and development. A “coasting period,” as used in this disclosure, is any period of time when a medication used throughout a stimulation protocol is not administered and/or consumed. A coasting period may last for example for 1 day, 2 days, 3 days, and the like. During a coasting period, a patient may continue to receive one or more ultrasounds to monitor her progression. Once a follicle size has reached anywhere from between 8-10 mm, a patient may be triggered with a dose of human chorionic gonadotropin (hcG). In an embodiment, a double hcG injection may be utilized, to induce follicle maturation to prepare one or more follicles for retrieval. A blood test for one or more hormone levels such as E2, P4, and LH may be performed on trigger day of double dose of human chorionic gonadotropin (hcG) injection to monitor hormone levels. After the day of the double dose of hcG, one or more hormone levels may be measured such as for example a blood test to determine and examine doses of E2, P4, and LH. Approximately 24-48 hours after the dose of hcG that is administered, a patient may undergo an oocyte retrieval. On the day of oocyte retrieval, a blood test for one or more hormone levels such as E2, LH, and/or P4 may be performed to ensure quality metrics, hormone levels are within range, and/or that hcG dose was ingested

Still referring to FIG. 1, biological sample database 124, and all other databases described throughout this disclosure, may be implemented, without limitation, as a relational database, a key-value retrieval database such as a NOSQL database, or any other format or structure for use as a database that a person skilled in the art would recognize as suitable upon review of the entirety of this disclosure. Biological sample database 124 may alternatively or additionally be implemented using a distributed data storage protocol and/or data structure, such as a distributed hash table or the like. Biological sample database 124 may include a plurality of data entries and/or records as described above. Data entries in a database may be flagged with or linked to one or more additional elements of information, which may be reflected in data entry cells and/or in linked tables such as tables related by one or more indices in a relational database. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various ways in which data entries in a database may store, retrieve, organize, and/or reflect data and/or records as used herein, as well as categories and/or populations of data consistently with this disclosure.

Still referring to FIG. 1, in some embodiments, a biological sample containing an oocyte may be retrieved from a user post simulation by a medical professional, such as a doctor inserting an extraction device into the follicle containing an egg and extracting the egg and surrounding fluid. An “extraction device” is a device and/or tool capable of obtaining, recording and/or ascertaining a measurement associated with a sample. The extraction device may include a needle, syringe, vial, lancet, Evacuated Collection Tubes (ECT), tourniquet, vacuum extraction tube systems, any combination thereof and the like. For example, the extraction device may comprise a butterfly needle set. Oocyte retrieval may include retrieval of immature oocytes, mature oocytes, COCs, and any other type of cell involved in reproduction found in the ovaries.

Still referring to FIG. 1, computing device 104 is configured to determine a maturity level 128 of the at least an oocyte. A “maturity level,” as used in this disclosure, is a datum representing an assessment of the oocyte stage of oogenesis; maturity level may include a quantitative element and/or field such as a number of days, hours, or the like of maturity, a stage of maturity, and/or a score representing a degree of maturity. In some embodiments, maturity level 128 may be an assessment of the oocyte maturation stage of oogenesis. Oocyte maturation refers to a release of meiotic arrest that allows oocytes to advance from prophase Ito metaphase II of meiosis. Determining maturity level 128 of the oocyte may include denuding the oocyte. “Denuding,” as used in this disclosure, is any process in which a cell may be removed from an oocyte. Denuding may include any mechanical and/or enzymatic process. For instance and without limitation denuding may include removing granulosa cells and/or cumulus cells from an oocyte. This may be performed mechanically and/or with one or more chemicals such as an enzyme to aid in the separation. Oocyte denudation refers to the removal of the somatic cell layers that surround the oocytes. For example, a COC may be denuded to remove the layer of granulosa cells surrounding the oocyte in order to determine the nuclear maturity of the oocyte. Oocyte denudation may include enzymatic and mechanical methods with the help of hyaluronidase and sterile glass pipettes as described further below.

Still referring to FIG. 1, in some embodiments, determining maturity level 128 may include determination of the maturity level 128 using a machine-learning process 116. For instance, and without limitation, a machine learning process 116 may be used to generate and train a machine learning model containing a classifier; the classifier may classify the oocyte to a maturity level as a function of biological sample data. A “classifier,” as used in this disclosure is a machine-learning model, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Computing device 104 and/or another device may generate maturity classifier 132 using a classification algorithm. As used in this disclosure, maturity classifier” is a classifier trained to intake biological sample data 120 and output a maturity level 128. Additionally or alternatively, maturity classifier 132 may be trained to intake oocyte denudation data to output a maturity level 128. As used in this disclosure, “oocyte denudation data” is data that provides a characterization of the biological, genetic, biochemical and/or physiological properties, compositions, or activities of a denuded oocyte. Computing device 104 may receive oocyte denudation data from biological sample database. In some embodiments, computing device 104 may train maturity classifier 132 with training data including, correlations between an oocyte to a maturity level, for instance and without limitation in the form of training examples. Training examples may be derived from a maturity level index, and feedback index contained in a biological sample database. As used in this disclosure, a “maturity level index” is data structure correlating biological knowledge relating to the stages of oocyte oogenesis, such as the stages of oocyte maturation. As used in this disclosure, feedback index” is a data structure correlating past maturity level 128 assessments performed by the computing device and communication from a third party. As used in this disclosure, third party” is a qualified person or organization, such as an embryologist, statistician, and the like. In some embodiments, maturity classifier 132 training data may be received from biological sample database. For example, biological sample data 120 may contain data relating to denuded immature COCs, and with the training data, determine, as maturity level 128, the oocytes resulting from denudation are GV and or MI oocytes. In some embodiments, a machine learning model containing a classifier trained to classify the oocyte to a maturity level as a function of biological sample data may also calculate a maturity level, a score, or other numerical and/or quantitative field related to the oocyte using machine learning processes 116.

Still referring to FIG. 1, classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers. Computing device 104 may be configured to generate a classifier using a Naïve Bayes classification algorithm. Naïve Bayes classification algorithm generates classifiers by assigning class labels to problem instances, represented as vectors of element values. Class labels are drawn from a finite set. Naïve Bayes classification algorithm may include generating a family of algorithms that assume that the value of a particular element is independent of the value of any other element, given a class variable. Naïve Bayes classification algorithm may be based on Bayes Theorem expressed as P(A/B)=P(B/A) P(A)±P(B), where P(AB) is the probability of hypothesis A given data B also known as posterior probability; P(B/A) is the probability of data B given that the hypothesis A was true; P(A) is the probability of hypothesis A being true regardless of data also known as prior probability of A; and P(B) is the probability of the data regardless of the hypothesis. A naïve Bayes algorithm may be generated by first transforming training data into a frequency table. Computing device 104 may then calculate a likelihood table by calculating probabilities of different data entries and classification labels. Computing device 104 may utilize a naïve Bayes equation to calculate a posterior probability for each class. A class containing the highest posterior probability is the outcome of prediction. Naïve Bayes classification algorithm may include a gaussian model that follows a normal distribution. Naïve Bayes classification algorithm may include a multinomial model that is used for discrete counts. Naïve Bayes classification algorithm may include a Bernoulli model that may be utilized when vectors are binary.

With continued reference to FIG. 1, computing device 104 may be configured to generate a classifier using a K-nearest neighbors (KNN) algorithm. A “K-nearest neighbors algorithm” as used in this disclosure, includes a classification method that utilizes feature similarity to analyze how closely out-of-sample-features resemble training data to classify input data to one or more clusters and/or categories of features as represented in training data; this may be performed by representing both training data and input data in vector forms, and using one or more measures of vector similarity to identify classifications within training data, and to determine a classification of input data. K-nearest neighbors algorithm may include specifying a K-value, or a number directing the classifier to select the k most similar entries training data to a given sample, determining the most common classifier of the entries in the database, and classifying the known sample; this may be performed recursively and/or iteratively to generate a classifier that may be used to classify input data as further samples. For instance, an initial set of samples may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship, which may be seeded, without limitation, using expert input received according to any process as described herein. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data. Heuristic may include selecting some number of highest-ranking associations and/or training data elements.

With continued reference to FIG. 1, generating k-nearest neighbors algorithm may generate a first vector output containing a data entry cluster, generating a second vector output containing an input data, and calculate the distance between the first vector output and the second vector output using any suitable norm such as cosine similarity, Euclidean distance measurement, or the like. Each vector output may be represented, without limitation, as an n-tuple of values, where n is at least two values. Each value of n-tuple of values may represent a measurement or other quantitative value associated with a given category of data, or attribute, examples of which are provided in further detail below; a vector may be represented, without limitation, in n-dimensional space using an axis per category of value represented in n-tuple of values, such that a vector has a geometric direction characterizing the relative quantities of attributes in the n-tuple as compared to each other. Two vectors may be considered equivalent where their directions, and/or the relative quantities of values within each vector as compared to each other, are the same; thus, as a non-limiting example, a vector represented as [5, 10, 15] may be treated as equivalent, for purposes of this disclosure, as a vector represented as [1, 2, 3]. Vectors may be more similar where their directions are more similar, and more different where their directions are more divergent; however, vector similarity may alternatively or additionally be determined using averages of similarities between like attributes, or any other measure of similarity suitable for any n-tuple of values, or aggregation of numerical similarity measures for the purposes of loss functions as described in further detail below. Any vectors as described herein may be scaled, such that each vector represents each attribute along an equivalent scale of values. Each vector may be “normalized,” or divided by a “length” attribute, such as a length attribute 1 as derived using a Pythagorean norm: l=√{square root over (Σ_i=0ⁿa_i²)}, where ai is attribute number i of the vector. Scaling and/or normalization may function to make vector comparison independent of absolute quantities of attributes, while preserving any dependency on similarity of attributes; this may, for instance, be advantageous where cases represented in training data are represented by different quantities of samples, which may result in proportionally equivalent vectors with divergent values. Computing device 104 may train any classifier or other machine-learning model using training data. In some embodiments, training data may include correlations between biological sample data and or biological samples extracted from users to maturity levels, scores, or other numerical and/or quantitative fields related to the oocyte, for instance and without limitation in the form of training examples. Training examples may be entered by one or more experts. An “expert” as used in this disclosure is a person or organization skilled in the art. Expert knowledge may be retrieved from the feedback index in biological sample database 124.

Still referring to FIG. 1, computing device 104 is configured to assign the oocyte to a culture protocol 136 as a function of maturity level 128. In some embodiments, the assigned oocyte may be a denuded oocyte. A “culture protocol,” as used in this disclosure, is a cell culture process by which cells are grown under controlled conditions. Culture protocol 136 may include cell culture metabolites selected as a function of maturity level 128; and cell culture mediums selected as a function of maturity level 128. As used in this disclosure, a “cell culture metabolite” is a substance involved in cell metabolism that optimize the synthesis of new molecules in a cell culture. For example, a cell culture metabolite may include doxycycline. Computing device 104 may generate and train a culture classifier 140 using training data that may include correlations between the oocyte and or maturity level to cell culture protocols, for instance and without limitation in the form of training examples. Training examples may be derived from a metabolite index, cell medium index, protocol index, feedback index contained in a culture database. For example, culture classifier 140 may take determined GV oocytes from maturity classifier 132 and assign the oocyte a culture protocol 136 containing IVM media and 500 ng/ml of androstenedione. Culture classifier 140 training data may be received from a Culture database 144 and the biological sample. As used in this disclosure, Culture database 144″ is a database correlating scientific knowledge relating to cell culture processes. Culture database 144 may be communicatively connected to computing device 104 and implemented as described above. Culture database 144 may include a metabolite index, cell medium index, and a protocol index. As used in this disclosure, metabolite index” is a data structure relating to scientific knowledge concerning metabolites. The metabolite index may contain data regarding the effect of particular metabolites in a cell culture as it relates to maturity level 128 of the oocyte, along with dosing requirements and preparations. As used in this disclosure, a “cell medium index” is a data structure relating to scientific knowledge concerning cell culture mediums. For examples the cell medium index may contain data regarding optimal mediums used in cell cultivation and methods on preparation/storage. As used in this disclosure, a “protocol index” is a data structure correlating scientific knowledge regarding IVM cell culture procedures. For examples the protocol index may include co-culture and group culture methods used in IVM. A “group culture” is an extracted COC combined with one or more additional cells. An additional cell may include any cell grown together with an extracted COC. An additional cell may include a granulosa cell. In an embodiment, a group culture may be cultured and/or incubated for a particular length of time, such as from between 12-120 hours

Still referring to FIG. 1, in some embodiments, culture protocol 136 may include culturing the oocyte with a with a granulosa co-culture containing granulosa cells sourced from human induced pluripotent stem cells (hiPSCs). The cultured oocyte may be a denuded oocyte. As used in this disclosure, a “co-culture” is a cell cultivation set-up, in which two or more different populations of cells are grown with some degree of contact between them. hiPSCs may be produced using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology. “CRISPR” is programmable technology that targets specific stretches of genetic code to edit DNA at precise locations. CRISPR technology may include CRISPR-CAS 9. Cas9 (or “CRISPR-associated protein 9”) is an enzyme that uses CRISPR sequences as a guide to recognize and cleave specific strands of DNA that are complementary to the CRISPR sequence. Cas9 enzymes together with CRISPR sequences form the basis of a technology known as CRISPR-Cas9 that can be used to edit genes within organisms. CRISPR technology may include Class 1 CRISPR systems including type I (cas3), type III (cas10), and type IV and 12 subtypes. CRISPR technology may include Class 2 CRISPR systems including type II (cas9), type V (cas12), type VI (cas13), and 9 subtypes. In some embodiments, CRISPR technology may involve CRISPR-Cas design tools which are computer software platforms and bioinformatics tools used to facilitate the design of guide RNAs (gRNAs) for use with the CRISPR/Cas gene editing system. For example, CRISPR-Cas design tools may include: CRISPRon, CRISPRoff, Invitrogen TrueDesign Genome Editor, Breaking-Cas, Cas-OFFinder, CASTING, CRISPy, CCTop, CHOPCHOP, CRISPOR, sgRNA Designer, Synthego Design Tool, and the like. CRISPR technology may also be used as a diagnostic tool. For example, CRISPR-based diagnostics may be coupled to enzymatic processes, such as SHERLOCK-based Profiling of IN vitro Transcription (SPRINT). SPRINT can be used to detect a variety of substances, such as metabolites in patient samples or contaminants in environmental samples, with high throughput or with portable point-of-care devices.

Still referring to FIG. 1, in some embodiments, a user may donate hiPSCs. hiPSCs donation may follow an oocyte retrieval process as discussed above. A user participating in hiPSCs donation may be different, or the same, from the user related to the biological sample. In some embodiments, hiPSCs donation user may undergo a stimulation protocol as disclosed above. In some embodiments regarding the assigned culture protocol 136, hiPSCs, granulosa cells, cumulus cells, oocytes, GV oocytes, MI, oocytes, and all other types of cells described through this disclosure may be lysed, extracted for genomic material and flash frozen as the final step in the culture process. For example, cells may undergo enzymatic cell lysis using enzymes such as lysozyme, lysostaphin, zymolase, cellulose, protease or glycanase, and the like. Other lysis methods may be applied such as, chemical lysis, detergent lysis, alkaline lysis, mechanical lysis, thermal lysis, acoustic lysis, physical lysis, non-mechanical lysis and the like. In some embodiments, culture media may be flash frozen. Freezing methods may include using a cryoprotective agent such as dimethyl sulfoxide and/or any other freezing method described throughout this disclosure.

Still referring to FIG. 1, in embodiments regarding the culture of the oocyte, cell culture media may include LAG media. For example, LAG media may be used for the incubation of COCs post-retrieval from a stimulation protocol. Package size may be a 10 ml vial. Storage may be at 2-8° C. away from light up to one month. Media equilibration may be 18 to 24 hours pre-culture, include a seed 100 ul droplet and placed into 37 C incubator with 6% O2 and proper CO2. In some embodiments, cell culture media may include IVM media. For example, a modified-MediCult IVM media may be used a baseline control during the culturing process. Package size may be a 10 ml vial. Storage may be at 2-8° C. away from light up to one month. In some embodiments, the cell culture mediums may include metabolites. For example the modified-MediCult IVM media may include human serum albumin, FSH, hCG, Androstenedione, Doxycycline and other compounds. Other cell culture material and equipment may include: liquid nitrogen, hyaluronidase, dPBS, IVF-Qualified mineral oil, universal GPS dishes, G-NOPS plus media, micropipettes, stripper pipettors, camera-equipped inverted ICSI Microscope, Dry Inject Tabletop incubators, saturated humidity incubators, EmbryoScope, microcentrifuge, refrigerator, −20° C. freezer to 100° C. freezer, liquid nitrogen storage dewer, 35 mm dishes for denuding, stripper pipette tips, and other components one skilled in the art would understand to be included in the cell culture process.

Still referring to FIG. 1, computing device 104 is configured to receive culture data 148 as a function of culture protocol 136. “Culture data 148” is data that provides a characterization of the biological, genetic, biochemical and/or physiological properties, compositions, or activities of cell cultured biological samples. Culture data 148 may include recording data and identifying growth trends of the COCs formed as a result of adding the granulosa cells, such as hiPSCs, to the denuded or non-denuded oocyte. Culture data 148 may be received from an oocyte index contained in Culture database 144. As used in this disclosure, a “culture sample index” is a database including analytical data regarding the culturing process and methods of the oocyte. For example, culture data 148 may be images of the cultured oocytes, wherein computing device 104 may be configured to analyze the images for results, objectives, and the like. In some embodiments, Culture database 144 may include an oocyte index. An “oocyte index” is a data structure containing, rubrics, analytical methods, and approaches to analyzing cell culture media. For example, the oocyte analytical index may include methods to oocyte scoring, outcome analysis, cofounding variable analysis, and the like. In some embodiments, computing device 104 may receive data such as embryologist notes regarding the process, results, objectives, and the like of the cultured oocyte from a feedback index in Culture database 144. Computing device 104 may train a classifier or other machine-learning models configured to calculate a scoring metric using training data. In some embodiments, training data may include correlations between culture data to oocyte scoring, outcome analysis, cofounding variable analysis for instance and without limitation in the form of training examples. Training examples be derived from data in the oocyte index and feedback index retrieved from culture database 114.

Still referring to FIG. 1, computing device 104 is configured to calculate a scoring metric 152 as a function of culturing the oocyte. A “scoring metric,” as used in this disclosure, is a measure of quantitative assessment used for comparing, and tracking performance or production of oocyte maturation. Computing device 104 may receive data from biological sample database and Culture database 144 to calculate scoring metric 152. Calculating scoring metric 152 may include computing device 104 analyzing imaged co-cultures and control cultures. Computing device 104 may receive a: pre-culture oocyte image, post-culture oocyte image, and a post-culture denuded oocyte image. In an embodiment, scoring metric may include assessing a developmentally mature oocyte via microscopy for presence of a polar body. If a polar body is found, then the oocyte may be selected and utilized for intracytoplasmic sperm injection (ICSI) fertilization and/or oocyte freezing. In some embodiments, images may be of frozen lysates and cell culture media. In some embodiments, images may be sent to a third party for scoring calculation. Additionally, computing device 104 may perform any determinations, classification, and/or analysis steps, methods, processes, performed by a third party. In some embodiments scoring metric 152 may include total oocyte scoring (TOS) as a function of analyzing the imaged cultures. For example, each oocyte image may be subjected to a total oocyte scoring (TOS) system, which measures oocyte health via a 6-point qualitative scale. The mature egg formation rate and TOS may be analyzed to assess the increases in the percentage of MII oocytes obtained in in a granulosa co-culture and the average TOS of oocytes compared to oocytes matured in the oocyte culture. This system may be utilized to establish effect size and future cohort sizes for proper statistical analysis of outcomes in follow up studies, such as other scoring metric disclosed below. TOS data may be reported as a percentage of MII oocytes and median TOS. A Student's t-test may be used to compare TOS outcomes which may follow parametric distributions, and chi-squared analysis may be utilized to compare IVM rates, with two-sided p-values of less than p<0.05 considered significant.

Still referring to FIG. 1, the scoring metric may include oocyte scoring. Oocyte scoring may include metrics such as shape, size, ooplasm characteristics, structure of the perivitelline space (PVS), zona pellucida (ZP), polar body (PB) morphology, and the like. “Oocyte scoring,” as used in this disclosure, is a grading system assessing the production and quality of matured human oocytes. For example, computing device 104 may be configured to perform the total oocyte scoring on both pre and post culture oocyte images for generation of the TOS metric on a scale system of −6 to +6. Computing device 104 may generate and/or train a machine-learning model including a classification algorithm such as an image classifier to perform the total oocyte scoring. An “image classifier” is a classifier trained to output a scoring metric based on culture data 148. The training data may include any data described throughout this disclosure, such as patient information, follicular dynamics information, oocyte scoring metric, study sample sheet (such as oocyte scoring metric instruction set). Image classifier may take the co-culture images as an input, and by utilizing the training data, output the total oocyte score. Training data may be received from biological sample database and Culture database 144 as described above.

With continued reference to FIG. 1, a detailed disclosure of the machine learning model is described in further detail below. Regarding oocyte shape, if oocyte morphology is poor (dark general oocyte coloration and/or ovoid shape), it may be assigned a value of −1; if almost normal (less dark general oocyte coloration and less ovoid shape), it may be assigned a value of 0; if it is judged to be normal, it may be assigned a value of +1. Regarding oocyte size: if oocyte size is defined as abnormally small or large, it may be assigned a value −1 if size is below 120μ or greater 160μ. If the size is almost normal, i.e., did not deviate from normal by more than 10μ, a value of 0 may be assigned, and a value of +1 may be assigned if oocyte size is within normal range >130μ and <150μ. Regarding ooplasm characteristics, if the ooplasm is very granular and/or very vacuolated and/or demonstrated several inclusions, a value of −1 may assigned. If it is only slightly granular and/or demonstrated only few inclusions, a value of 0 may be assigned. Absence of granularity and inclusions may result in a +1 value. Regarding structure of the perivitelline space (PVS), the PVS may defined as −1 with an abnormally large PVS, an absent PVS or a very granular PVS. It may be assigned a value of 0 with a moderately enlarged PVS and/or small PVS and/or a less granular PVS. A value of +1 may be assigned to a normal size PVS with no granules. Regarding, zona pellucida (ZP), if ZPs is very thin or thick (<10μ or >20μ) the oocyte may be assigned a −1. If the ZP does not deviate from normal by more than 2μ it may be assigned 0. A normal zona (>12μ and <18μ may be assigned a +1. Regarding polar body (PB) morphology, PB morphology is defined as follows: Flat and/or multiple PBs or zero PBs, granular and/or either abnormally small or large PBs is designated as −1. PBs, judged a fair but not excellent may be designated as 0, and a designation of +1 may be given to PBs of normal size and shape. In some embodiments, MII oocytes PB score may not be aggregated into TOS. In some embodiments, the TOS calculated by computing device 104 may be crossed checked against an embryologist or a similar person skilled in the art to solidify that the quality scoring was biased by image quality. Feedback relating to correction by a professional, adjustments, correlations may be added to the training data of the machine-learning model. Computing device 104 may train a classifier or other machine-learning models configured to calculate TOS using training data. In some embodiments, training data may include correlations between culture data to image quality and a 6-point qualitative scale for instance and without limitation in the form of training examples. Training examples be derived from data in the oocyte scoring index and feedback index retrieved from culture database 144. Still referring to FIG. 1, scoring metric may include performing an outcome analysis as a function of the TOS. An “outcome analysis,” as used in the disclosure, is a measurement of the maturation rate and oocyte quality scores between the control culture and co-culture. Parametric or non-parametric tests may be applied to determine the significance of findings during the analysis. Computing device 104 may use a classification algorithm using methods described above to determine GV to MII oocyte maturation rate; GV to MI oocyte maturation rate; MI to MII oocyte maturation rate; Average Total Oocyte Score; Average Oocyte Shape; Average Oocyte Size; Average Ooplasm quality; Average PVS quality; Average ZP quality; Average Polar Body quality, and the like. In some embodiments these outcomes may reported as a as mean, median, and deviation.

In some embodiments, and with continued reference to FIG. 1, computing device 104 may conduct the outcome analysis using machine learning processes 116 and/or models as described throughout this disclosure. For example, computing device 104 may train a machine-learning model to output an outcome analysis based on inputted culture images, wherein the training data includes oocyte scoring metrics, study sample sheet, patient information, feedback from computing device 104 programmers/third parties, data from the stimulation protocol, and any other form of data described through this disclosure. Training data may come from biological sample database and culture database 144. In some embodiments, communications from a third party may be inputted into a machine learning process 116 to create a machine-learning model to generate scoring metric 152. For example, a third-party communication may contain embryologist notes related to total oocyte scoring, wherein the notes are inputted into a machine-learning model containing a classifier to generate the outcome analysis using training data, received from biological sample database and Culture database 144, containing patient information, data from image classifier, data from the stimulation protocol, study sample sheet, and any other form of data described throughout this disclosure. Additionally or alternatively, communications relating to scoring metrics generated by the computing device may be sent to a third party may, using machine learning processes 116. For example, oocyte scoring metrics may be sent a third party operated remote computing device communicatively connected to computing device 104, wherein the third party may conduct further analysis such as the outcome analysis. Furthering this example, a third-party response to communications generated by computing device 104 may be uploaded into a database communicatively connected to computing device 104 and be used as feedback in training data.

Still referring to FIG. 1, in some embodiments, scoring metric may include an Omics-based analysis. Omics are novel, comprehensive approaches for analysis of complete genetic or molecular profiles of humans and other organisms. For example, in contrast to genetics, which focuses on single genes, genomics focuses on all genes (genomes) and their inter-relationships. In some embodiments, an omics-based analysis may include, genomics, proteomics, transcriptomics, pharmacogenomics, epigenomics, microbiomics, lipidomics, glycomics, transcriptomics culturomics, and/or any other omics one skilled in the art would understand as applicable. In some embodiments, after cultivation, an oocyte that has failed to mature, showing GV or MI characteristics, may be harvested for single cell RNA-sequencing, along with their associated granulosa cells from their culture. For this, oocytes and granulosa cells may be flash frozen and for library preparation. Of the oocytes that display MII oocyte development, half may be harvested for single cell RNA-sequencing along with their associated granulosa cells using the above flash freeze methods described throughout this disclosure. The remaining half of MII oocytes may be utilized for proteomic studies. The culture media for all conditions may additionally be flash frozen and utilized for metabolomics and proteomics to identify cholesterol metabolite levels and paracrine protein production. For example, frozen cell lysates and cell culture mediums may be analyzed for bulk RNA-sequencing, whole genome bisulfite sequencing (WGBS), mass spectrometry-based proteomics and metabolomics. Cell culture media may be utilized for metabolomics analysis to determine changes in molecular content of media following co-culture compared to pre-culture media controls. This may be utilized by computing device 104 to profile dynamic changes in paracrine signaling between granulosa cells and oocytes. As the media components are flash frozen, the sample is effectively quenched and amenable to metabolic assessment. The data gathered may then be aggregated for downstream analysis for determination of changes in epigenetic state, metabolite presence, and gene expression between different co-culture conditions and controls.

Referring now to FIG. 2A, an exemplary embodiment of a machine-learning module 200 that may perform one or more machine-learning processes as described in this disclosure is illustrated. Machine-learning module may perform determinations, classification, and/or analysis steps, methods, processes, or the like as described in this disclosure using machine learning processes. A “machine learning process,” as used in this disclosure, is a process that automatedly uses training data 204 to generate an algorithm that will be performed by a computing device/module to produce outputs 208 given data provided as inputs 212; this is in contrast to a non-machine learning software program where the commands to be executed are determined in advance by a user and written in a programming language.

Still referring to FIG. 2A, “training data,” as used herein, is data containing correlations that a machine-learning process may use to model relationships between two or more categories of data elements. For instance, and without limitation, training data 204 may include a plurality of data entries, each entry representing a set of data elements that were recorded, received, and/or generated together; data elements may be correlated by shared existence in a given data entry, by proximity in a given data entry, or the like. Multiple data entries in training data 204 may evince one or more trends in correlations between categories of data elements; for instance, and without limitation, a higher value of a first data element belonging to a first category of data element may tend to correlate to a higher value of a second data element belonging to a second category of data element, indicating a possible proportional or other mathematical relationship linking values belonging to the two categories. Multiple categories of data elements may be related in training data 204 according to various correlations; correlations may indicate causative and/or predictive links between categories of data elements, which may be modeled as relationships such as mathematical relationships by machine-learning processes as described in further detail below. Training data 204 may be formatted and/or organized by categories of data elements, for instance by associating data elements with one or more descriptors corresponding to categories of data elements. As a non-limiting example, training data 204 may include data entered in standardized forms by persons or processes, such that entry of a given data element in a given field in a form may be mapped to one or more descriptors of categories. Elements in training data 204 may be linked to descriptors of categories by tags, tokens, or other data elements; for instance, and without limitation, training data 204 may be provided in fixed-length formats, formats linking positions of data to categories such as comma-separated value (CSV) formats and/or self-describing formats such as extensible markup language (XML), JavaScript Object Notation (JSON), or the like, enabling processes or devices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 2A, training data 204 may include one or more elements that are not categorized; that is, training data 204 may not be formatted or contain descriptors for some elements of data. Machine-learning algorithms and/or other processes may sort training data 204 according to one or more categorizations using, for instance, natural language processing algorithms, tokenization, detection of correlated values in raw data and the like; categories may be generated using correlation and/or other processing algorithms. As a non-limiting example, in a corpus of text, phrases making up a number “n” of compound words, such as nouns modified by other nouns, may be identified according to a statistically significant prevalence of n-grams containing such words in a particular order; such an n-gram may be categorized as an element of language such as a “word” to be tracked similarly to single words, generating a new category as a result of statistical analysis. Similarly, in a data entry including some textual data, a person's name may be identified by reference to a list, dictionary, or other compendium of terms, permitting ad-hoc categorization by machine-learning algorithms, and/or automated association of data in the data entry with descriptors or into a given format. The ability to categorize data entries automatedly may enable the same training data 204 to be made applicable for two or more distinct machine-learning algorithms as described in further detail below. Training data 204 used by machine-learning module 200 may correlate any input data as described in this disclosure to any output data as described in this disclosure.

Further referring to FIG. 2A, training data may be filtered, sorted, and/or selected using one or more supervised and/or unsupervised machine-learning processes and/or models as described in further detail below; such models may include without limitation a training data classifier 216. Training data classifier 216 may include a “classifier,” which as used in this disclosure is a machine-learning model as defined below, such as a mathematical model, neural net, or program generated by a machine learning algorithm known as a “classification algorithm,” as described in further detail below, that sorts inputs into categories or bins of data, outputting the categories or bins of data and/or labels associated therewith. A classifier may be configured to output at least a datum that labels or otherwise identifies a set of data that are clustered together, found to be close under a distance metric as described below, or the like. Machine-learning module 200 may generate a classifier using a classification algorithm, defined as a process whereby a computing device and/or any module and/or component operating thereon derives a classifier from training data 204. Classification may be performed using, without limitation, linear classifiers such as without limitation logistic regression and/or naive Bayes classifiers, nearest neighbor classifiers such as k-nearest neighbors classifiers, support vector machines, least squares support vector machines, fisher's linear discriminant, quadratic classifiers, decision trees, boosted trees, random forest classifiers, learning vector quantization, and/or neural network-based classifiers.

Still referring to FIG. 2A, machine-learning module 200 may be configured to perform a lazy-learning process 220 and/or protocol, which may alternatively be referred to as a “lazy loading” or “call-when-needed” process and/or protocol, may be a process whereby machine learning is conducted upon receipt of an input to be converted to an output, by combining the input and training set to derive the algorithm to be used to produce the output on demand. For instance, an initial set of simulations may be performed to cover an initial heuristic and/or “first guess” at an output and/or relationship. As a non-limiting example, an initial heuristic may include a ranking of associations between inputs and elements of training data 204. Heuristic may include selecting some number of highest-ranking associations and/or training data 204 elements. Lazy learning may implement any suitable lazy learning algorithm, including without limitation a K-nearest neighbors algorithm, a lazy naïve Bayes algorithm, or the like; persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various lazy-learning algorithms that may be applied to generate outputs as described in this disclosure, including without limitation lazy learning applications of machine-learning algorithms as described in further detail below.

Alternatively or additionally, and with continued reference to FIG. 2A, machine-learning processes as described in this disclosure may be used to generate machine-learning models 224. A “machine-learning model,” as used in this disclosure, is a mathematical and/or algorithmic representation of a relationship between inputs and outputs, as generated using any machine-learning process including without limitation any process as described above and stored in memory; an input is submitted to a machine-learning model 224 once created, which generates an output based on the relationship that was derived. For instance, and without limitation, a linear regression model, generated using a linear regression algorithm, may compute a linear combination of input data using coefficients derived during machine-learning processes to calculate an output datum. As a further non-limiting example, a machine-learning model 224 may be generated by creating an artificial neural network, such as a convolutional neural network comprising an input layer of nodes, one or more intermediate layers, and an output layer of nodes. Connections between nodes may be created via the process of “training” the network, in which elements from a training data 204 set are applied to the input nodes, a suitable training algorithm (such as Levenberg-Marquardt, conjugate gradient, simulated annealing, or other algorithms) is then used to adjust the connections and weights between nodes in adjacent layers of the neural network to produce the desired values at the output nodes. This process is sometimes referred to as deep learning.

Still referring to FIG. 2A, machine-learning algorithms may include at least a supervised machine-learning process 228. At least a supervised machine-learning process 228, as defined herein, include algorithms that receive a training set relating a number of inputs to a number of outputs, and seek to find one or more mathematical relations relating inputs to outputs, where each of the one or more mathematical relations is optimal according to some criterion specified to the algorithm using some scoring function. For instance, a supervised learning algorithm may include as described above as inputs, as described above outputs, and a scoring function representing a desired form of relationship to be detected between inputs and outputs; scoring function may, for instance, seek to maximize the probability that a given input and/or combination of elements inputs is associated with a given output to minimize the probability that a given input is not associated with a given output. Scoring function may be expressed as a risk function representing an “expected loss” of an algorithm relating inputs to outputs, where loss is computed as an error function representing a degree to which a prediction generated by the relation is incorrect when compared to a given input-output pair provided in training data 204. Persons skilled in the art, upon reviewing the entirety of this disclosure, will be aware of various possible variations of at least a supervised machine-learning process 228 that may be used to determine relation between inputs and outputs. Supervised machine-learning processes may include classification algorithms as defined above.

Further referring to FIG. 2A, machine learning processes may include at least an unsupervised machine-learning processes 232. An unsupervised machine-learning process, as used herein, is a process that derives inferences in datasets without regard to labels; as a result, an unsupervised machine-learning process may be free to discover any structure, relationship, and/or correlation provided in the data. Unsupervised processes may not require a response variable; unsupervised processes may be used to find interesting patterns and/or inferences between variables, to determine a degree of correlation between two or more variables, or the like.

Still referring to FIG. 2A, machine-learning module 200 may be designed and configured to create a machine-learning model 224 using techniques for development of linear regression models. Linear regression models may include ordinary least squares regression, which aims to minimize the square of the difference between predicted outcomes and actual outcomes according to an appropriate norm for measuring such a difference (e.g., a vector-space distance norm); coefficients of the resulting linear equation may be modified to improve minimization. Linear regression models may include ridge regression methods, where the function to be minimized includes the least-squares function plus term multiplying the square of each coefficient by a scalar amount to penalize large coefficients. Linear regression models may include least absolute shrinkage and selection operator (LASSO) models, in which ridge regression is combined with multiplying the least-squares term by a factor of 1 divided by double the number of samples. Linear regression models may include a multi-task lasso model wherein the norm applied in the least-squares term of the lasso model is the Frobenius norm amounting to the square root of the sum of squares of all terms. Linear regression models may include the elastic net model, a multi-task elastic net model, a least angle regression model, a LARS lasso model, an orthogonal matching pursuit model, a Bayesian regression model, a logistic regression model, a stochastic gradient descent model, a perceptron model, a passive aggressive algorithm, a robustness regression model, a Huber regression model, or any other suitable model that may occur to persons skilled in the art upon reviewing the entirety of this disclosure. Linear regression models may be generalized in an embodiment to polynomial regression models, whereby a polynomial equation (e.g. a quadratic, cubic or higher-order equation) providing a best predicted output/actual output fit is sought; similar methods to those described above may be applied to minimize error functions, as will be apparent to persons skilled in the art upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 2A, machine-learning algorithms may include, without limitation, linear discriminant analysis. Machine-learning algorithm may include quadratic discriminate analysis. Machine-learning algorithms may include kernel ridge regression. Machine-learning algorithms may include support vector machines, including without limitation support vector classification-based regression processes. Machine-learning algorithms may include stochastic gradient descent algorithms, including classification and regression algorithms based on stochastic gradient descent. Machine-learning algorithms may include nearest neighbors algorithms. Machine-learning algorithms may include various forms of latent space regularization such as variational regularization. Machine-learning algorithms may include Gaussian processes such as Gaussian Process Regression. Machine-learning algorithms may include cross-decomposition algorithms, including partial least squares and/or canonical correlation analysis. Machine-learning algorithms may include naïve Bayes methods. Machine-learning algorithms may include algorithms based on decision trees, such as decision tree classification or regression algorithms. Machine-learning algorithms may include ensemble methods such as bagging meta-estimator, forest of randomized tress, AdaBoost, gradient tree boosting, and/or voting classifier methods. Machine-learning algorithms may include neural net algorithms, including convolutional neural net processes.

Referring now to FIG. 2B, an exemplary table 236 of training data 204. Training data 204 may include any data described throughout this disclosure. For example, training data 204 may contain de-identified user information. A user may be referred to as a patient in this disclosure. De-identified patient information may include patient Age, patient BMI, number of COCs retrieved, AMH Levels (ug/L), antral follicle count (AFC) at last ultrasound, patient oocyte retrieval day E2 Levels (ng/L), patient oocyte retrieval day P4 Levels (ng/L), patient oocyte retrieval day LH (IU/L), patient oocyte retrieval day FSH (IU/L), days of stimulation, gonadotropin used, total injected dose (IU), and the like. Additionally or alternatively, training data 204 may include pre-co-culture COC images, post-co-culture COC images, post-culture denuded oocyte images, third party notes such as embryologist notes, machine-learning feedback, follicular dynamics information, study sample sheet, frozen oocyte cell lysate data, frozen granulosa cells lysate data, frozen cell culture media data, data from systemic hormone index, data from maturity index, data from biological sample database 124, data from culture database 144, and the like.

Referring now to FIG. 3, is an exemplary flow chart of oocyte denudation. At step 305, method 300 may include receiving COCs from a biological sample. COCs may be received following oocyte retrieval methods disclosed above. At step 310, method 300 may include oocyte denudation of the COCs. In some embodiments, denudation may occur in a IVM well, by gently mechanically disassociating cells by pipetting to remove most cumulus and/or granulosa cells. If enzymatic disassociation is needed, the cells may be transferred to a separate dish for hyaluronidase treatment. At step 315, COCs may be stripped with stripper tips and washed in IVM media or MOPS plus media to clean the oocyte for imaging and if needed inactivate hyaluronidase. Stripper tips may include 200 micron and or 400 microns for fine cleaning. In some embodiments, GVs and MI oocytes may be formulated and utilized in cultivation as a result of the denudation of the COCs. At step 320, method 300 may include transferring denuded COCs to a culture dish for imaging.

Referring now to FIG. 4, is an exemplary table 400 of metabolite formulations that may be included in cultures. Metabolite 404 column lists exemplary metabolites that may be use as cell culture metabolite. A “cell culture metabolite,” as used in this disclosure, is a substance involved in cell metabolism that optimize the synthesis of new molecules in a cell culture. Stock Solution Preparation Concentration 408 column lists exemplary concentrations for cell culture metabolites. Final Concentration in IVM Media 412 column list exemplary concentrations of cell culture metabolites in a IVM media for coculturing of second biological sample 136. For examples, 10 mg/ml of HSA may be added to an IVM media for culturing. 75 mlU/ml of FSH may be added to an IVM media for culturing. 100 mlU/m1 of hCG may be added to an IVM media for culturing. 500 ng/ml of androstenedione may be added to an IVM media for culturing. 1 ug/ml of doxycycline may be added to an IVM media for culturing. In some embodiments, steroidogenic granulosa cells, derived from human induced pluripotent stem cells (hiPSCs), may be co-cultured with denuded or non-denuded immature oocytes, thereby reconstituting the follicular niche in vitro to promote rapid and efficient oocyte maturation in a manner that reinforces oocyte health and developmental competence. As used in this disclosure, a “steroidogenic granulosa cell” is a granulosa cell expressing high levels of steroidogenic enzymes, such as estradiol. For example, a steroidogenic granulosa cell may be a mural granulosa cell extracted from the antral follicle. Applying steroidogenic granulosa cells in the co-cultures of oocytes may increase oocyte maturation in vitro after egg/oocyte retrieval, allowing for utilization of all retrieved eggs/oocyte by directly supplying nutrients, raw materials, and mechanical support to oocytes throughout gametogenesis and folliculogenesis. Steroidogenic granulosa cells may grow and perform oocyte maturation of immature oocytes in standard IVF and IVM media as described further below. This may increase the overall pool of available, healthy oocytes for use in IVF and reduce the number of egg/oocyte retrieval procedures a user is subjected to.

Referring now to FIG. 5, is an exemplary flow-chart 500 for preparing a granulosa co-culture is illustrated. Granulosa cells in a woman's ovaries play a key role in the female reproductive system. These cells release estrogen, progesterone and other hormones which drive oocyte maturation in the ovary, making them a logical tool for application in IVM. Furthermore, granulosa cells provide the developmental niche for follicle and oocyte development, directly supplying nutrients, raw materials, and mechanical support to oocytes throughout gametogenesis and folliculogenesis. At step 505, a liquid nitrogen frozen granulosa cell cryovial may be thawed and incubated. In some embodiments thawing and incubation may occur up to 72 hours before oocyte retrial from a user. The granulosa cell cryovial may be a 1 mL cryovial containing 50,000 to 500,000 granulosa cells. In some embodiments, the granulosa cell cryovial may be a 1 mL cryovial containing 100,000 granulosa cells. Thawing may occur by placing the granulosa cell cryovial in a water bath or a dry bead bath. The granulosa cell cryovial may be incubated for 3 to 5 minutes. At step 510, IVM media may be added to the cryovial for cell suspension. In some embodiments, 0.5 ml of ICM media may be added to induce cell suspension. At step 515, the cell suspension is transferred to a tube and centrifuged. In some embodiments, 1 mL of cell suspension may be transferred to a 1.5 mL tube, wherein the tube is centrifuged at 300×g for 5 minutes. At step 520, a pipette may be used to remove a supernatant. Pipette may be a p1000 pipette. At step 525, cells pellet formed as a function of centrifuged tube containing the cell suspension, may be resuspended in a IVM media and centrifuged. IVM media may be a 1 mL IVM media. The tube may be centrifuged as described above. At step 530, a pipette may be used to remove a supernatant. Pipette may be a p1000 pipette. At step 535 cells pellet may be resuspend in a IVM media. IVM media may be a 0.1 mL IVM media. At this step, granulosa cell may now be at 1,00 cells per 1 ul. In some embodiments 10 ul of the cell suspension may be utilized per oocyte in second biological sample 136 related to a user.

Referring now to FIG. 6A, is an exemplary embodiment of a co-cultured oocyte including immature oocytes related to the user. In some embodiments, oocytes may be denuded oocytes. Oocytes received after oocyte retrieval from a follicular aspirate relating to the user may be randomly divided in half to into a media, such as a LAG media of a granulosa cell plate, and the other half may go into a LAG media of a no-co-culture plate. Alternatively, some oocytes for culture may be pre-frozen, in which case they may be first be thawed before culture placement. Oocytes may be incubated in the LAG media at 37 C for 2 hours. Granulosa cells may be prepared as described in FIG. 5. The prepared granulosa cells may be added to the surrounding 50 ul wells that contain IVM media, adding 10,000 granulosa cells per oocyte that may be cultured. The dish with the granulosa cell may then be placed back in the incubator until use. After the 2-hour incubation period, the oocyte in the LAG media may be transferred to the IVM media in the granulosa cell dish with a Pasteur pipette. In the granulosa co-culture, after 18 to 48 hours incubation, the culture plate may be removed, and the oocytes and granulosa cells may be imaged in their individual wells. In some embodiments the culture may be imaged after 24 hours.

Referring now to FIG. 6B, is an exemplary embodiment of a control culture of immature oocytes related to the user. Oocytes may be incubated in a LAG media at 37 C for 2 hours. After the 2-hour incubation period, the oocyte in the LAG media may be transferred to a IVM media in a control dish with a Pasteur pipette. In the control culture, after 10 to 48 hours incubation, the culture plate may be removed, and the oocytes may be imaged in their individual wells. In some embodiments the culture may be imaged after 24 hours.

Referring now to FIG. 7, is an exemplary flow diagram illustrating a method for oocyte rescue in vitro post stimulation, for example and with reference to FIG. 1-6. Method 700 may include using computing device 104 or any computing device described through this disclosure. In some embodiments, method 700 may include using a third party as defined and described in FIG. 1. At step 705, method includes receiving a biological sample relating to a user, including at least an oocyte, for example n with reference to FIGS. 1-6. In some embodiments, an oocyte may be an immature oocyte. In some embodiments, an immature oocyte may be a plurality of oocytes. An immature oocyte may be immature cumulus-oocyte-complexes (COCs) taken from the mother. In some embodiments, the immature oocyte may contain an oocyte wherein the granulosa cell is added to mature the oocyte in a cell culture and thus create a COC. In some embodiments, the biological sample may include bodily fluids including blood, saliva, urine, semen (seminal fluid), vaginal secretions, cerebrospinal fluid (CSF), synovial fluid, pleural fluid (pleural lavage), pericardial fluid, peritoneal fluid, amniotic fluid, nasal fluid, optic fluid, gastric fluid, breast milk, cell culture supernatants, and the like. In some embodiments, and with reference to FIG. 1, a biological sample containing an oocyte may be retrieved from a user post simulation by a medical professional, such as a doctor inserting an extraction device into the follicle containing an egg and extracting the egg and surrounding fluid. The extraction device may include a needle, syringe, vial, lancet, Evacuated Collection Tubes (ECT), tourniquet, vacuum extraction tube systems, any combination thereof and the like. For example, the extraction device may comprise a butterfly needle set. Oocyte retrieval may include retrieval of immature oocytes, mature oocytes, COCs, and any other type of cell involved in reproduction found in the ovaries.

Still referring to FIG. 7, at step 710, method 700 includes determining a maturity level of the at least an oocyte, for example and with reference to FIGS. 1 and 3. In some embodiments, the maturity level may be an assessment of the oocyte maturation stage of oogenesis. Determining the maturity level of the oocyte may include denuding the oocyte, for example and with reference to FIG. 1. Oocyte denudation may include enzymatic and mechanical methods with the help of hyaluronidase and sterile glass pipettes as described in FIGS. 1 and 3. At step 715, method 700 includes assigning the oocyte to a culture protocol as a function of the maturity level. The culture protocol may include cell culture metabolites selected as a function of the maturity level; and cell culture mediums selected as a function of maturity level, for example and with reference to FIG. 1. In some embodiments, the culture protocol may include culturing the oocyte with a with a granulosa co-culture containing granulosa cells sourced from human induced pluripotent stem cells (hiPSCs). The cultured oocyte may be a denuded oocyte. hiPSCs may be produced using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology as described above. in some embodiments, a user may donate hiPSCs. hiPSCs donation may follow an oocyte retrieval process as discussed above. A user participating in hiPSCs donation may be different, or the same, from the user related to the biological sample. In some embodiments, hiPSCs donation user may undergo a stimulation protocol as disclosed above. In some embodiments regarding the assigned culture protocol, hiPSCs, granulosa cells, cumulus cells, oocytes, GV oocytes, MI, oocytes, and all other types of cells described throughout this disclosure may be lysed, extracted for genomic material and flash frozen as the final step in the culture process, for example and with reference to FIGS. 1-6.

Still referring to FIG. 7, at step 720, method 700 includes culturing the at least an oocyte as a function of the culture protocol, for example and with reference to FIGS. 1-6. In embodiments regarding the culture of the oocyte, cell culture media may include LAG media. In some embodiments, cell culture media may include IVM media. In some embodiments, the cell culture mediums may include metabolites. At step 725, method 700 includes calculating a scoring metric as a function of the cultured oocyte, for example and with reference to FIG. 1. Calculating the scoring metric may include analyzing imaged co-cultures and control cultures. Images may contain a: pre-culture oocyte image, post-culture oocyte image, and a post-culture denuded oocyte image. In some embodiments, images may be of frozen lysates and cell culture media. In some embodiments the scoring metric may include total oocyte scoring (TOS) as a function of analyzing the imaged cultures. For example, each oocyte image may be subjected to a total oocyte scoring (TOS) system, which measures oocyte health via a 6-point qualitative scale as described above. In some embodiments, the scoring metric may include oocyte scoring. Oocyte scoring may include metrics such as shape, size, ooplasm characteristics, structure of the perivitelline space (PVS), zona pellucida (ZP), polar body (PB) morphology, and the like. In some embodiments, scoring metric may include performing an outcome analysis as a function of the TOS. An “outcome analysis,” as used in the disclosure, is a measurement of the maturation rate and oocyte quality scores between the control culture and co-culture. Parametric or non-parametric tests may be applied to determine the significance of findings during the analysis. The outcome analysis may determine GV to MII oocyte maturation rate; GV to MI oocyte maturation rate; MI to MII oocyte maturation rate; Average Total Oocyte Score; Average Oocyte Shape; Average Oocyte Size; Average Ooplasm quality; Average PVS quality; Average ZP quality; Average Polar Body quality, and the like. In some embodiments these outcomes may reported as a as mean, median, and deviation. In some embodiments, scoring metric may include an Omics-based analysis. In some embodiments, after cultivation, an oocyte that has failed to mature, showing GV or MI characteristics, may be harvested for single cell RNA-sequencing, along with their associated granulosa cells from their culture. For this, oocytes and granulosa cells may be flash frozen and for library preparation. Of the oocytes that display MII oocyte development, half may be harvested for single cell RNA-sequencing along with their associated granulosa cells using the above flash freeze methods described throughout this disclosure. The remaining half of MII oocytes may be utilized for proteomic studies. The culture media for all conditions may additionally be flash frozen and utilized for metabolomics and proteomics to identify cholesterol metabolite levels and paracrine protein production. Cell culture media may be utilized for metabolomics analysis to determine changes in molecular content of media following co-culture compared to pre-culture media controls. This may be utilized to profile dynamic changes in paracrine signaling between granulosa cells and oocytes. The data gathered may then be aggregated for downstream analysis for determination of changes in epigenetic state, metabolite presence, and gene expression between different co-culture conditions and controls.

It is to be noted that any one or more of the aspects and embodiments described herein may be conveniently implemented using one or more machines (e.g., one or more computing devices that are utilized as a user computing device for an electronic document, one or more server devices, such as a document server, etc.) programmed according to the teachings of the present specification, as will be apparent to those of ordinary skill in the computer art. Appropriate software coding can readily be prepared by skilled programmers based on the teachings of the present disclosure, as will be apparent to those of ordinary skill in the software art. Aspects and implementations discussed above employing software and/or software modules may also include appropriate hardware for assisting in the implementation of the machine executable instructions of the software and/or software module.

Such software may be a computer program product that employs a machine-readable storage medium. A machine-readable storage medium may be any medium that is capable of storing and/or encoding a sequence of instructions for execution by a machine (e.g., a computing device) and that causes the machine to perform any one of the methodologies and/or embodiments described herein. Examples of a machine-readable storage medium include, but are not limited to, a magnetic disk, an optical disc (e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-only memory “ROM” device, a random access memory “RAM” device, a magnetic card, an optical card, a solid-state memory device, an EPROM, an EEPROM, and any combinations thereof. A machine-readable medium, as used herein, is intended to include a single medium as well as a collection of physically separate media, such as, for example, a collection of compact discs or one or more hard disk drives in combination with a computer memory. As used herein, a machine-readable storage medium does not include transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as a data signal on a data carrier, such as a carrier wave. For example, machine-executable information may be included as a data-carrying signal embodied in a data carrier in which the signal encodes a sequence of instruction, or portion thereof, for execution by a machine (e.g., a computing device) and any related information (e.g., data structures and data) that causes the machine to perform any one of the methodologies and/or embodiments described herein.

Examples of a computing device include, but are not limited to, an electronic book reading device, a computer workstation, a terminal computer, a server computer, a handheld device (e.g., a tablet computer, a smartphone, etc.), a web appliance, a network router, a network switch, a network bridge, any machine capable of executing a sequence of instructions that specify an action to be taken by that machine, and any combinations thereof. In one example, a computing device may include and/or be included in a kiosk.

FIG. 8 shows a diagrammatic representation of one embodiment of a computing device in the exemplary form of a computer system 800 within which a set of instructions for causing a control system to perform any one or more of the aspects and/or methodologies of the present disclosure may be executed. It is also contemplated that multiple computing devices may be utilized to implement a specially configured set of instructions for causing one or more of the devices to perform any one or more of the aspects and/or methodologies of the present disclosure. Computer system 800 includes a processor 804 and a memory 808 that communicate with each other, and with other components, via a bus 812. Bus 812 may include any of several types of bus structures including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combinations thereof, using any of a variety of bus architectures.

Processor 804 may include any suitable processor, such as without limitation a processor incorporating logical circuitry for performing arithmetic and logical operations, such as an arithmetic and logic unit (ALU), which may be regulated with a state machine and directed by operational inputs from memory and/or sensors; processor 804 may be organized according to Von Neumann and/or Harvard architecture as a non-limiting example. Processor 804 may include, incorporate, and/or be incorporated in, without limitation, a microcontroller, microprocessor, digital signal processor (DSP), Field Programmable Gate Array (FPGA), Complex Programmable Logic Device (CPLD), Graphical Processing Unit (GPU), general purpose GPU, Tensor Processing Unit (TPU), analog or mixed signal processor, Trusted Platform Module (TPM), a floating point unit (FPU), and/or system on a chip (SoC).

Memory 808 may include various components (e.g., machine-readable media) including, but not limited to, a random-access memory component, a read only component, and any combinations thereof. In one example, a basic input/output system 816 (BIOS), including basic routines that help to transfer information between elements within computer system 800, such as during start-up, may be stored in memory 808. Memory 808 may also include (e.g., stored on one or more machine-readable media) instructions (e.g., software) 820 embodying any one or more of the aspects and/or methodologies of the present disclosure. In another example, memory 808 may further include any number of program modules including, but not limited to, an operating system, one or more application programs, other program modules, program data, and any combinations thereof.

Computer system 800 may also include a storage device 824. Examples of a storage device (e.g., storage device 824) include, but are not limited to, a hard disk drive, a magnetic disk drive, an optical disc drive in combination with an optical medium, a solid-state memory device, and any combinations thereof. Storage device 824 may be connected to bus 812 by an appropriate interface (not shown). Example interfaces include, but are not limited to, SCSI, advanced technology attachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and any combinations thereof. In one example, storage device 824 (or one or more components thereof) may be removably interfaced with computer system 800 (e.g., via an external port connector (not shown)). Particularly, storage device 824 and an associated machine-readable medium 828 may provide nonvolatile and/or volatile storage of machine-readable instructions, data structures, program modules, and/or other data for computer system 800. In one example, software 820 may reside, completely or partially, within machine-readable medium 828. In another example, software 820 may reside, completely or partially, within processor 804.

Computer system 800 may also include an input device 832. In one example, a user of computer system 800 may enter commands and/or other information into computer system 800 via input device 832. Examples of an input device 832 include, but are not limited to, an alpha-numeric input device (e.g., a keyboard), a pointing device, a joystick, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), a cursor control device (e.g., a mouse), a touchpad, an optical scanner, a video capture device (e.g., a still camera, a video camera), a touchscreen, and any combinations thereof. Input device 832 may be interfaced to bus 812 via any of a variety of interfaces (not shown) including, but not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a direct interface to bus 812, and any combinations thereof. Input device 832 may include a touch screen interface that may be a part of or separate from display 836, discussed further below. Input device 832 may be utilized as a user selection device for selecting one or more graphical representations in a graphical interface as described above.

A user may also input commands and/or other information to computer system 800 via storage device 824 (e.g., a removable disk drive, a flash drive, etc.) and/or network interface device 840. A network interface device, such as network interface device 840, may be utilized for connecting computer system 800 to one or more of a variety of networks, such as network 844, and one or more remote devices 848 connected thereto. Examples of a network interface device include, but are not limited to, a network interface card (e.g., a mobile network interface card, a LAN card), a modem, and any combination thereof. Examples of a network include, but are not limited to, a wide area network (e.g., the Internet, an enterprise network), a local area network (e.g., a network associated with an office, a building, a campus or other relatively small geographic space), a telephone network, a data network associated with a telephone/voice provider (e.g., a mobile communications provider data and/or voice network), a direct connection between two computing devices, and any combinations thereof. A network, such as network 844, may employ a wired and/or a wireless mode of communication. In general, any network topology may be used. Information (e.g., data, software 820, etc.) may be communicated to and/or from computer system 800 via network interface device 840.

Computer system 800 may further include a video display adapter 852 for communicating a displayable image to a display device, such as display device 836. Examples of a display device include, but are not limited to, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasma display, a light emitting diode (LED) display, and any combinations thereof. Display adapter 852 and display device 836 may be utilized in combination with processor 804 to provide graphical representations of aspects of the present disclosure. In addition to a display device, computer system 800 may include one or more other peripheral output devices including, but not limited to, an audio speaker, a printer, and any combinations thereof. Such peripheral output devices may be connected to bus 812 via a peripheral interface 856. Examples of a peripheral interface include, but are not limited to, a serial port, a USB connection, a FIREWIRE connection, a parallel connection, and any combinations thereof.

The foregoing has been a detailed description of illustrative embodiments of the invention. Various modifications and additions can be made without departing from the spirit and scope of this invention. Features of each of the various embodiments described above may be combined with features of other described embodiments as appropriate in order to provide a multiplicity of feature combinations in associated new embodiments. Furthermore, while the foregoing describes a number of separate embodiments, what has been described herein is merely illustrative of the application of the principles of the present invention. Additionally, although particular methods herein may be illustrated and/or described as being performed in a specific order, the ordering is highly variable within ordinary skill to achieve methods, systems, apparatuses and software according to the present disclosure. Accordingly, this description is meant to be taken only by way of example, and not to otherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in the accompanying drawings. It will be understood by those skilled in the art that various changes, omissions and additions may be made to that which is specifically disclosed herein without departing from the spirit and scope of the present invention.

Claims

1. An apparatus for aiding in oocyte rescue in vitro post stimulation, the apparatus comprising: a computing device, wherein the computing device comprises: at least a processor; anda memory communicatively connected to the at least processor, the memory containing instructions configuring the at least processor to:receive biological sample data from a biological sample relating to a user, wherein the biological sample comprises at least an oocyte;determine a maturity level of the at least an oocyte as a function of the biological sample data;assign the at least an oocyte to a culture protocol as a function of the maturity level;receive culture data relating to the at least an oocyte as a function of the culture protocol; andcalculate a scoring metric as a function of the culture data.

2. The apparatus of claim 1, wherein the at least an oocyte comprises a cumulus-oocyte-complex.

3. The apparatus of claim 1, wherein determining the maturity level of the at least an oocyte further comprises denuding the oocyte.

4. The apparatus of claim 1, wherein determining the maturity level of the at least oocyte further comprises using a machine learning process.

5. The apparatus of claim 1, wherein assigning the culture protocol further comprises selecting a cell culture metabolite as a function of the maturity level.

6. The apparatus of claim 1, wherein assigning the culture protocol further comprises selecting a cell culture medium as a function of the maturity level.

7. The apparatus of claim 1, wherein the culture protocol further comprises culturing the at least an oocyte with a granulosa co-culture containing granulosa cells sourced from human induced pluripotent stem cells (hiPSCs)

8. The apparatus of claim 7, wherein the culture protocol further comprises culturing the at least oocyte with a granulosa co-culture for 24 hours.

9. The apparatus of claim 1, wherein the scoring metric comprises omics-based analysis.

10. The apparatus of claim 9, wherein omics-based analysis includes genomics.

11. A method for oocyte rescue in vitro post stimulation, the method comprising: receiving a biological sample relating to a user, comprising at least an oocyte;determining a maturity level of the at least an oocyte;assigning the at least an oocyte to a culture protocol as a function of the maturity level;culturing the at least an oocyte as a function of the culture protocol; andcalculating a scoring metric as a function of the cultured oocyte.

12. The method of claim 11, wherein the at least an oocyte comprises a cumulus-oocyte-complex.

13. The method of claim 11, wherein determining the maturity level of the at least an oocyte further comprises denuding the oocyte.

14. The method of claim 11, wherein assigning the culture protocol further comprises selecting a cell culture metabolite as a function of the maturity level.

15. The method of claim 11, wherein assigning the culture protocol further comprises selecting a cell culture medium as a function of the maturity level.

16. The method of claim 11, wherein the culture protocol further comprises culturing the at least an oocyte with a granulosa co-culture containing granulosa cells sourced from human induced pluripotent stem cells (hiPSCs)

17. The method of claim 17, wherein the culture protocol further comprises culturing the at least oocyte with a granulosa co-culture for 24 hours.

18. The method of claim 11, wherein the culture protocol further comprises flash freezing the culture media.

19. The method of claim 11, wherein the scoring metric comprises omics-based analysis.

20. The method of claim 10, wherein omics-based analysis includes genomics.