The impact of clomiphene citrate on the endometrium in comparison to gonadotropins in intrauterine insemination cycles: is it thinner and does it matter?

Objective: To determine whether endometrial thickness (EMT) differs between i) clomiphene citrate (CC) and gonadotropin (Gn) utilizing patients as their own controls, and ii) patients who conceived with CC and those who did not. Furthermore, to investigate the association between late-follicular EMT and pregnancy outcomes, in CC and Gn cycles.

Methods: Retrospective study.

Trophectoderm grade as a predictor of beta human-chorionic gonadotropin rise in early pregnancy.

Purpose: To evaluate the association, if any, between the grade of the trophectoderm (TE) and the rate at which β-human-chorionic gonadotropin (β-HCG) rises in early pregnancy.

Methods: This is a retrospective cohort study including 1116 singleton clinical pregnancies resulting from in vitro fertilization with single day 5 blastocyst transfer at an academic fertility center. TE quality was assessed by trained embryologists employing standard criteria.

Current applications of artificial intelligence in assisted reproductive technologies through the perspective of a patient's journey.

Purpose Of Review: This review highlights the timely relevance of artificial intelligence in enhancing assisted reproductive technologies (ARTs), particularly in-vitro fertilization (IVF). It underscores artificial intelligence's potential in revolutionizing patient outcomes and operational efficiency by addressing challenges in fertility diagnoses and procedures.

Recent Findings: Recent advancements in artificial intelligence, including machine learning and predictive modeling, are making significant strides in optimizing IVF processes such as medication dosing, scheduling, and embryological assessments.

Live birth outcomes following single-step blastocyst warming technique - optimizing efficiency without impacting live birth rates.

Purpose: To evaluate the impact of a single-step (SS) warming versus standard warming (SW) protocol on the survival/expansion of vitrified blastocysts and their clinical outcomes post-frozen embryo transfer (FET).

Methods: Retrospective analysis was performed on 200 vitrified/warmed research blastocysts equally divided amongst two thawing protocols utilizing the Fujifilm Warming NX kits (Fujifilm, CA). SW utilized the standard 14-minute manufacturer's guidelines.

Predicting polycystic ovary syndrome with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis.

Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016.

Pharmacologic Characterization of LTGO-33, a Selective Small Molecule Inhibitor of the Voltage-Gated Sodium Channel Na1.8 with a Unique Mechanism of Action.

Discovery and development of new molecules directed against validated pain targets is required to advance the treatment of pain disorders. Voltage-gated sodium channels (Nas) are responsible for action potential initiation and transmission of pain signals. Na1.

The Role of Artificial Intelligence and Machine Learning in Assisted Reproductive Technologies.

Artificial intelligence (AI) and machine learning, the form most commonly used in medicine, offer powerful tools utilizing the strengths of large data sets and intelligent algorithms. These systems can help to revolutionize delivery of treatments, access to medical care, and improvement of outcomes, particularly in the realm of reproductive medicine. Whether that is more robust oocyte and embryo grading or more accurate follicular measurement, AI will be able to aid clinicians, and most importantly patients, in providing the best possible and individualized care.

Predicting polycystic ovary syndrome (PCOS) with machine learning algorithms from electronic health records.

Introduction: Predictive models have been used to aid early diagnosis of PCOS, though existing models are based on small sample sizes and limited to fertility clinic populations. We built a predictive model using machine learning algorithms based on an outpatient population at risk for PCOS to predict risk and facilitate earlier diagnosis, particularly among those who meet diagnostic criteria but have not received a diagnosis.

Methods: This is a retrospective cohort study from a SafetyNet hospital's electronic health records (EHR) from 2003-2016.

Temporal trends in urinary concentrations of phenols, phthalate metabolites and phthalate replacements between 2000 and 2017 in Boston, MA.

Endocrine disrupting chemicals (EDCs) can adversely affect human health and are ubiquitously found in everyday products. We examined temporal trends in urinary concentrations of EDCs and their replacements. Urinary concentrations of 11 environmental phenols, 15 phthalate metabolites, phthalate replacements such as two di(isononyl)cyclohexane-1,2-dicarboxylate (DINCH) metabolites, and triclocarban were quantified using isotope-dilution tandem mass spectrometry.

Noninvasive genetic screening: current advances in artificial intelligence for embryo ploidy prediction.

This review discusses the use of artificial intelligence (AI) algorithms in noninvasive prediction of embryo ploidy status for preimplantation genetic testing in in vitro fertilization procedures. The current gold standard, preimplantation genetic testing for aneuploidy, has limitations, such as an invasive biopsy, financial burden, delays in results reporting, and difficulty in results reporting, Noninvasive ploidy screening methods, including blastocoel fluid sampling, spent media testing, and AI algorithms using embryonic images and clinical parameters, are explored. Various AI models have been developed using different machine learning algorithms, such as random forest classifier and logistic regression, have shown variable performance in predicting euploidy.

The impact of cryopreserved sperm on intrauterine insemination outcomes: is frozen as good as fresh?

Introduction: Frozen sperm utilization might negatively impact cycle outcomes in animals, implicating cryopreservation-induced sperm damage. However, fertilization and intrauterine insemination (IUI) in human studies are inconclusive.

Methods: This study is a retrospective review of 5,335 IUI [± ovarian stimulation (OS)] cycles from a large academic fertility center.

Applications of artificial intelligence in ovarian stimulation: a tool for improving efficiency and outcomes.

Because of the birth of the first baby after in vitro fertilization (IVF), the field of assisted reproductive technologies (ARTs) has seen significant advancements in the past 40 years. Over the last decade, the healthcare industry has increasingly adopted machine learning algorithms to improve patient care and operational efficiency. Artificial intelligence (AI) in ovarian stimulation is a burgeoning niche that is currently benefiting from increased research and investment from both the scientific and technology communities, leading to cutting-edge advancements with promise for rapid clinical integration.

Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade.

The integration of artificial intelligence (AI) and deep learning algorithms into medical care has been the focus of development over the last decade, particularly in the field of assisted reproductive technologies and in vitro fertilization (IVF). With embryo morphology the cornerstone of clinical decision making for IVF, the field of IVF is highly reliant on visual assessments that can be prone to error and subjectivity and be dependent on the level of training and expertise of the observing embryologist. Implementing AI algorithms into the IVF laboratory allows for reliable, objective, and timely assessments of both clinical parameters and microscopy images.

The use of voting ensembles to improve the accuracy of deep neural networks as a non-invasive method to predict embryo ploidy status.

Purpose: To determine if creating voting ensembles combining convolutional neural networks (CNN), support vector machine (SVM), and multi-layer neural networks (NN) alongside clinical parameters improves the accuracy of artificial intelligence (AI) as a non-invasive method for predicting aneuploidy.

Methods: A cohort of 699 day 5 PGT-A tested blastocysts was used to train, validate, and test a CNN to classify embryos as euploid/aneuploid. All embryos were analyzed using a modified FAST-SeqS next-generation sequencing method.

Advancements in the future of automating micromanipulation techniques in the IVF laboratory using deep convolutional neural networks.

Purpose: To determine if deep learning artificial intelligence algorithms can be used to accurately identify key morphologic landmarks on oocytes and cleavage stage embryo images for micromanipulation procedures such as intracytoplasmic sperm injection (ICSI) or assisted hatching (AH).

Methods: Two convolutional neural network (CNN) models were trained, validated, and tested over three replicates to identify key morphologic landmarks used to guide embryologists when performing micromanipulation procedures. The first model (CNN-ICSI) was trained (n = 13,992), validated (n = 1920), and tested (n = 3900) to identify the optimal location for ICSI through polar body identification.

Quality assurance (QA) for monitoring the performance of assisted reproductive technology (ART) staff using artificial intelligence (AI).

Purpose: Deep learning neural networks have been used to predict the developmental fate and implantation potential of embryos with high accuracy. Such networks have been used as an assistive quality assurance (QA) tool to identify perturbations in the embryo culture environment which may impact clinical outcomes. The present study aimed to evaluate the utility of an AI-QA tool to consistently monitor ART staff performance (MD and embryologist) in embryo transfer (ET), embryo vitrification (EV), embryo warming (EW), and trophectoderm biopsy (TBx).

Female pattern hair loss and polycystic ovarian syndrome: more than just hirsutism.

Purpose Of Review: To explore the recent updates in the diagnosis, management, and clinical implications of androgenic alopecia among patients diagnosed with polycystic ovarian syndrome (PCOS).

Recent Findings: PCOS diagnosis continues to be the most common cause of infertility among reproductively aged women, serving as the most common endocrinopathy among this population. Female pattern hair loss (FPHL) has been seen to be associated and more common among patients with PCOS, however, there are limited studies examining the impact of FPHL among PCOS patients.

Using artificial intelligence to avoid human error in identifying embryos: a retrospective cohort study.

Purpose: To determine whether convolutional neural networks (CNN) can be used to accurately ascertain the patient identity (ID) of cleavage and blastocyst stage embryos based on image data alone.

Methods: A CNN model was trained and validated over three replicates on a retrospective cohort of 4889 time-lapse embryo images. The algorithm processed embryo images for each patient and produced a unique identification key that was associated with the patient ID at a timepoint on day 3 (~ 65 hours post-insemination (hpi)) and day 5 (~ 105 hpi) forming our data library.

Examining Reproductive Health Outcomes in Females Exposed to Polychlorinated Biphenyl and Polybrominated Biphenyl.

In 1973, accidental contamination of Michigan livestock with polybrominated biphenyls (PBBs) led to the establishment of a registry of exposed individuals that have been followed for > 40 years. Besides being exposed to PBBs, this cohort has also been exposed to polychlorinated biphenyls (PCBs), a structurally similar class of environmental pollutants, at levels similar to average US exposure. In this study, we examined the association between current serum PCB and PBB levels and various female reproductive health outcomes to build upon previous work and inconsistencies.

Thyroid hormone levels associate with exposure to polychlorinated biphenyls and polybrominated biphenyls in adults exposed as children.

Background: Michigan residents were directly exposed to endocrine-disrupting compounds, polybrominated biphenyl (PBB) and polychlorinated biphenyl (PCB). A growing body of evidence suggests that exposure to certain endocrine-disrupting compounds may affect thyroid function, especially in people exposed as children, but there are conflicting observations. In this study, we extend previous work by examining age of exposure's effect on the relationship between PBB exposure and thyroid function in a large group of individuals exposed to PBB.

Comparative Analysis of Renal Functional Outcomes and Overall Survival of Elderly vs Nonelderly Patients Undergoing Radical Nephrectomy.

Purpose: To evaluate changes in renal function and overall survival in elderly vs nonelderly patients undergoing radical nephrectomy (RN) for renal masses.

Patients And Methods: We reviewed available records of 392 patients undergoing RN from 2008 through 2013. Patients were divided into elderly, defined as ≥70 years old (n = 110), or nonelderly (n = 282) at the time of nephrectomy.

Measuring Poverty in Southern India: A Comparison of Socio-Economic Scales Evaluated against Childhood Stunting.

Introduction: Socioeconomic status (SES) scales measure poverty, wealth and economic inequality in a population to guide appropriate economic and public health policies. Measurement of poverty and comparison of material deprivation across nations is a challenge. This study compared four SES scales which have been used locally and internationally and evaluated them against childhood stunting, used as an indicator of chronic deprivation, in urban southern India.

Ethylene Contributes to maize insect resistance1-Mediated Maize Defense against the Phloem Sap-Sucking Corn Leaf Aphid.

Signaling networks among multiple phytohormones fine-tune plant defense responses to insect herbivore attack. Previously, it was reported that the synergistic combination of ethylene (ET) and jasmonic acid (JA) was required for accumulation of the maize insect resistance1 (mir1) gene product, a cysteine (Cys) proteinase that is a key defensive protein against chewing insect pests in maize (Zea mays). However, this study suggests that mir1-mediated resistance to corn leaf aphid (CLA; Rhopalosiphum maidis), a phloem sap-sucking insect pest, is independent of JA but regulated by the ET-signaling pathway.

Investigation of an Outbreak of Variant Influenza A(H3N2) Virus Infection Associated With an Agricultural Fair-Ohio, August 2012.

Background: In 2012, one third of cases in a multistate outbreak of variant influenza A(H3N2) virus ([H3N2]v) infection occurred in Ohio. We conducted an investigation of (H3N2)v cases associated with agricultural Fair A in Ohio.

Methods: We surveyed Fair A swine exhibitors and their household members.

Outbreak of variant influenza A(H3N2) virus in the United States.

Background: Variant influenza virus infections are rare but may have pandemic potential if person-to-person transmission is efficient. We describe the epidemiology of a multistate outbreak of an influenza A(H3N2) variant virus (H3N2v) first identified in 2011.

Methods: We identified laboratory-confirmed cases of H3N2v and used a standard case report form to characterize illness and exposures.