Background Polycystic ovary syndrome (PCOS) is the most common endocrinopathy among reproductive age women. Although its cardinal manifestations include hyperandrogenism, oligo/anovulation, and/or polycystic ovarian morphology, PCOS women often display also notable metabolic comorbidities. An array of pathogenic mechanisms have been implicated in the etiology of this heterogeneous endocrine disorder; hyperandrogenism at various developmental periods is proposed as a major driver of the metabolic and reproductive perturbations associated with PCOS. However, the current understanding of the pathophysiology of PCOS-associated metabolic disease is incomplete, and therapeutic strategies used to manage this syndrome's metabolic complications remain limited. Scope of review This study is a systematic review of the potential etiopathogenic mechanisms of metabolic dysfunction frequently associated with PCOS, with special emphasis on the metabolic impact of androgen excess on different metabolic tissues and the brain. We also briefly summarize the therapeutic approaches currently available to manage metabolic perturbations linked to PCOS, highlighting current weaknesses and future directions. Major conclusions Androgen excess plays a prominent role in the development of metabolic disturbances associated with PCOS, with a discernible impact on key peripheral metabolic tissues, including the adipose, liver, pancreas, and muscle, and very prominently the brain, contributing to the constellation of metabolic complications of PCOS, from obesity to insulin resistance. However, the current understanding of the pathogenic roles of hyperandrogenism in metabolic dysfunction of PCOS and the underlying mechanisms remain largely incomplete. In addition, the development of more efficient, even personalized therapeutic strategies for the metabolic management of PCOS patients persists as an unmet need that will certainly benefit from a better comprehension of the molecular basis of this heterogeneous syndrome.

Metabolic dysfunction in polycystic ovary syndrome: Pathogenic role of androgen excess and potential therapeutic strategies.

Over recent years, many environmental pollutant chemicals have been shown to possess the ability to interfere in the functioning of the endocrine system and have been termed endocrine disrupting chemicals (EDCs). These compounds exist in air as volatile or semi-volatile compounds in the gas phase or attached to particulate matter. They include components of plastics (phthalates, bisphenol A), components of consumer goods (parabens, triclosan, alkylphenols, fragrance compounds, organobromine flame retardants, fluorosurfactants), industrial chemicals (polychlorinated biphenyls), products of combustion (polychlorinated dibenzodioxins/furans, polyaromatic hydrocarbons), pesticides, herbicides, and some metals. This review summarizes current knowledge concerning the sources of EDCs in air, measurements of levels of EDCs in air, and the potential for adverse effects of EDCs in air on human endocrine health.

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Overview of air pollution and endocrine disorders.

Bisphenol-A (BPA) has been reported to be associated to female infertility. Indeed, BPA has been found to be more frequently detected in infertile women thus leading to hypothesize a possible effect of BPA on natural conception and spontaneous fecundity. In addition, in procedures of medically assisted reproduction BPA exposure has been found to be negatively associated with peak serum estradiol levels during gonadotropin stimulation, number of retrieved oocytes, number of normally fertilized oocytes and implantation. BPA deleterious effects are more critical during perinatal exposure, causing dysregulation of hypothalamic-pituitary-ovarian axis in pups and adults, with a precocious maturation of the axis through a damage of GnRH pulsatility, gonadotropin signaling and sex steroid hormone production. Further, BPA exposure during early lifestage may have a transgenerational effect predisposing the subsequent generations to the risk of developing BPA related disease. Experimental studies suggested that prenatal, perinatal and postnatal exposure to BPA can impair several steps of ovarian development, induce ovarian morphology rearrangement and impair ovarian function, particularly folliculogenesis, as well as can impair uterus morphology and function, in female adult animal and offspring. Finally, studies carried out in animal models have been reported the occurrence of endometriosis-like lesions after BPA exposure. Moreover, BPA exposure has been described to encourage the genesis of PCOS-like abnormalities through the impairment of the secretion of sex hormones affecting ovarian morphology and functions, particularly folliculogenesis. The current manuscript summarizes the evidence regarding the association between BPA exposure and female infertility, reviewing both clinical and preclinical studies.

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Bisphenol A: an emerging threat to female fertility

This meta-analytic study explored the relationship between the risk of type 2 diabetes mellitus (T2DM) and bisphenol A concentrations. The Embase and Medline (PubMed) databases were searched, using relevant keywords, for studies published between 1980 and 2018. A total of 16 studies, twelve cross-sectional, two case-control and one prospective, were included in the meta-analysis. The odds ratio (OR) and its 95% confidence interval (CI) were determined across the sixteen studies. The OR and its 95% CI of diabetes associated with bisphenol A were estimated using both fixed-effects and random-effects models. A total of 41,320 subjects were included. Fourteen of the sixteen studies included in the analysis provided measurements of urine bisphenol A levels and two study provided serum bisphenol A levels. Bisphenol A concentrations in human bio-specimens showed positive associations with T2DM risk (OR 1.28, 95% CI 1.14, 1.44). A sensitivity analysis indicated that urine bisphenol A concentrations were positively associated with T2DM risk (OR 1.20, 95% CI 1.09, 1.31). This meta-analysis indicated that Bisphenol A exposure is positively associated with T2DM risk in humans.

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Bisphenol A exposure and type 2 diabetes mellitus risk: a meta-analysis.

A realistic picture of our world shows that it is heavily polluted everywhere. Coastal regions and oceans are polluted by farm fertilizer, manure runoff, sewage and industrial discharges, and large isles of waste plastic are floating around, impacting sea life. Terrestrial ecosystems are contaminated by heavy metals and organic chemicals that can be taken up by and accumulate in crop plants, and water tables are heavily contaminated by untreated industrial discharges. As deadly particulates can drift far, poor air quality has become a significant global problem and one that is not exclusive to major industrialized cities. The consequences are a dramatic impairment of our ecosystem and biodiversity and increases in degenerative or man-made diseases. In this respect, it has been demonstrated that environmental pollution impairs fertility in all mammalian species. The worst consequences are observed for females since the number of germ cells present in the ovary is fixed during fetal life, and the cells are not renewable. This means that any pollutant affecting hormonal homeostasis and/or the reproductive apparatus inevitably harms reproductive performance. This decline will have important social and economic consequences that can no longer be overlooked.

Female Fertility and Environmental Pollution.

Objective: To investigate the association between bisphenol A (BPA) and polycystic ovary syndrome (PCOS).Methods: A systematic review and meta-analysis using STATA software for observational studie...

The association between the environmental endocrine disruptor bisphenol A and polycystic ovary syndrome: a systematic review and meta-analysis

Purpose To conduct a prospective observational study across 12 U.S. hospitals to evaluate real-time performance of an interpretable artificial intelligence (AI) model to detect COVID-19 on chest radiographs. Materials and Methods A total of 95 363 chest radiographs were included in model training, external validation, and real-time validation. The model was deployed as a clinical decision support system, and performance was prospectively evaluated. There were 5335 total real-time predictions and a COVID-19 prevalence of 4.8% (258 of 5335). Model performance was assessed with use of receiver operating characteristic analysis, precision-recall curves, and F1 score. Logistic regression was used to evaluate the association of race and sex with AI model diagnostic accuracy. To compare model accuracy with the performance of board-certified radiologists, a third dataset of 1638 images was read independently by two radiologists. Results Participants positive for COVID-19 had higher COVID-19 diagnostic scores than participants negative for COVID-19 (median, 0.1 [IQR, 0.0–0.8] vs 0.0 [IQR, 0.0–0.1], respectively; P < .001). Real-time model performance was unchanged over 19 weeks of implementation (area under the receiver operating characteristic curve, 0.70; 95% CI: 0.66, 0.73). Model sensitivity was higher in men than women (P = .01), whereas model specificity was higher in women (P = .001). Sensitivity was higher for Asian (P = .002) and Black (P = .046) participants compared with White participants. The COVID-19 AI diagnostic system had worse accuracy (63.5% correct) compared with radiologist predictions (radiologist 1 = 67.8% correct, radiologist 2 = 68.6% correct; McNemar P < .001 for both). Conclusion AI-based tools have not yet reached full diagnostic potential for COVID-19 and underperform compared with radiologist prediction. Keywords: Diagnosis, Classification, Application Domain, Infection, Lung Supplemental material is available for this article.. © RSNA, 2022

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study

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Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

Empirical robustness evaluation (RE) of deep learning models against adversarial perturbations entails solving nontrivial constrained optimization problems. Existing numerical algorithms that are commonly used to solve them in practice predominantly rely on projected gradient, and mostly handle perturbations modeled by the $\ell_1$, $\ell_2$ and $\ell_\infty$ distances. In this paper, we introduce a novel algorithmic framework that blends a general-purpose constrained-optimization solver PyGRANSO with Constraint Folding (PWCF), which can add more reliability and generality to the state-of-the-art RE packages, e.g., AutoAttack. Regarding reliability, PWCF provides solutions with stationarity measures and feasibility tests to assess the solution quality. For generality, PWCF can handle perturbation models that are typically inaccessible to the existing projected gradient methods; the main requirement is the distance metric to be almost everywhere differentiable. Taking advantage of PWCF and other existing numerical algorithms, we further explore the distinct patterns in the solutions found for solving these optimization problems using various combinations of losses, perturbation models, and optimization algorithms. We then discuss the implications of these patterns on the current robustness evaluation and adversarial training.

Optimization and Optimizers for Adversarial Robustness

Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Existing FL works mostly focus on designing advanced FL algorithms to improve the model performance. However, the economic considerations of the clients, such as fairness and incentive, are yet to be fully explored. Without such considerations, self-motivated clients may lose interest and leave the federation. To address this problem, we designed a novel incentive mechanism that involves a client selection process to remove low-quality clients and a money transfer process to ensure a fair reward distribution. Our experimental results strongly demonstrate that the proposed incentive mechanism can effectively improve the duration and fairness of the federation.

Le Peng

Papers

The association between the environmental endocrine disruptor bisphenol A and polycystic ovary syndrome: a systematic review and meta-analysis

Performance of a Chest Radiograph AI Diagnostic Tool for COVID-19: A Prospective Observational Study

Evaluation of federated learning variations for COVID-19 diagnosis using chest radiographs from 42 US and European hospitals

Optimization and Optimizers for Adversarial Robustness

Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective