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Polycystic ovary syndrome (PCOS) is the most common endocrine disorder of reproductive age women. The syndrome is caused by a combination of environmental influences and genetic predisposition. Despite extensive efforts, the heritable factors contributing to PCOS development are not fully understood. The objective of this study was to test the hypothesis that genetic background contributes to the development of a PCOS-like reproductive and metabolic phenotype in mice exposed to excess DHEA during the pubertal transition. We tested whether the PCOS phenotype would be more pronounced on the diabetes-prone C57BL/6 background than the previously used strain, BALB/cByJ. In addition, we examined strain-dependent upregulation of the expression of ovarian and extra-ovarian candidate genes implicated in human PCOS, genes containing known strain variants, and genes involved with steroidogenesis or insulin sensitivity. These studies show that there are significant strain-related differences in metabolic response to excess androgen exposure during puberty. Additionally, our results suggest the C57BL/6J strain provides a more robust and uniform experimental platform for PCOS research than the BALB/cByJ strain.

We previously reported that leukocyte specific β2 integrins contribute to hypertrophy after muscle overload in mice. Because intercellular adhesion molecule-1 (ICAM-1) is an important ligand for β2 integrins, we examined ICAM-1 expression by murine skeletal muscle cells after muscle overload and its contribution to the ensuing hypertrophic response. Myofibers in control muscles of wild type mice and cultures of skeletal muscle cells (primary and C2C12) did not express ICAM-1. Overload of wild type plantaris muscles caused myofibers and satellite cells/myoblasts to express ICAM-1. Increased expression of ICAM-1 after muscle overload occurred via a β2 integrin independent mechanism as indicated by similar gene and protein expression of ICAM-1 between wild type and β2 integrin deficient (CD18-/-) mice. ICAM-1 contributed to muscle hypertrophy as demonstrated by greater (p

Clinical research shows an association between polycystic ovary syndrome (PCOS) and chronic inflammation, a pathological state thought to contribute to insulin resistance. The underlying pathways, however, have not been defined. The purpose of this study was to characterize the inflammatory state of a novel mouse model of PCOS. Female mice lacking leptin and insulin receptors in pro-opiomelanocortin neurons (IR/LepR(POMC) mice) and littermate controls were evaluated for estrous cyclicity, ovarian and adipose tissue morphology, and body composition by QMR and CT scan. Tissue-specific macrophage infiltration and cytokine mRNA expression were measured, as well as circulating cytokine levels. Finally, glucose regulation during pregnancy was evaluated as a measure of risk for diabetes development. Forty-five percent of IR/LepR(POMC) mice showed reduced or absent ovulation. IR/LepR(POMC) mice also had increased fat mass and adipocyte hypertrophy. These traits accompanied elevations in macrophage accumulation and inflammatory cytokine production in perigonadal adipose tissue, liver, and ovary. These mice also exhibited gestational hyperglycemia as predicted. This report is the first to show the presence of inflammation in IR/LepR(POMC) mice, which develop a PCOS-like phenotype. Thus, IR/LepR(POMC) mice may serve as a new mouse model to clarify the involvement of adipose and liver tissue in the pathogenesis and etiology of PCOS, allowing more targeted research on the development of PCOS and potential therapeutic interventions.

Background and Objective: Disorders of consciousness include coma (cannot be aroused, eye remain closed), vegetative state-VS (can appear to be awake, but unable to purposefully interact) and minimally conscious state-MCS (minimal but definite awareness). The objective of this study is to assess the impact of the SARS-CoV-2 infection on the Disorder of Consciousness (DOC) Rehabilitation Unit., Methods: This is a retrospective, longitudinal, descriptive, observational, pilot study. We consecutively enrolled 18 patients (age range: 40-72 years, 9 females and 9 males), from three to five months after a brain injury. They were grouped into VS (n = 8) and MCS (n = 10). A confirmed case of COVID-19 was defined as a positive result on high-throughput sequencing or real-time reverse-transcription polymerase chain reaction analysis of throat swab specimens. We collected data of lung Computed Tomography (CT) and laboratory exams. DOC patients who were positive for SARS-CoV-2 were classified into severe and no severe infected group, according to the American Thoracic Society guidelines., Results: A total of 18 hospitalized patients with (16) and without confirmed (2) SARS-CoV-2 infection were included in the analysis. After one month, a follow-up clinical evaluation reported that one patient died, one patient was transferred from Covid Unit to Emergency Unit and 3 patients were resulted negative to double swab and they returned to Rehabilitative Unit. Significant differences were reported about hypertension, cardiac disease and respiratory problems between the patients with severe infection and patients without severe infection (P< 0.001). The laboratory findings, such as blood cell counts (P < 0.001), C-reactive protein, D-dimer, potassium and vitamin D levels, seemed to be considered as useful prognostic predictors., Conclusions: To our knowledge, this is the first longitudinal study on a sample of chronic DOC patients affected by SARS-CoV-2. This study may offer important new clinical information on COVID-19 for management of DOC patients. Our findings showed that for the subjects with severe infection due to COVID-19, rapid clinical deterioration or worsening could be associated with clinical and laboratory findings, which could contribute to high mortality rate. During the COVID-19 epidemic period, the clinicians should consider all the reported risk factors to avoid delayed diagnosis or misdiagnosis and to prevent the infection transmission in DOC Rehabilitation Unit., Competing Interests: The authors have declared that no competing interests exist.

On March 2019 the World Health Organization declared Coronavirus disease (COVID-19) pandemic. Several recent reports disclose that the outcome of the infection is related to age, sex and can be influenced by underlying clinical conditions. Parkinson's disease (PD) and other parkinsonisms are the most common chronic disease which can cause, directly or indirectly, the patient to be more exposed to other diseases, mostly respiratory system's ones. Our primary outcome is to evaluate if PD patients are more susceptible than non-PD to take COVID-19 infection. Second, to detect if the infection course is worse in PD-COVID+ patients versus non-PD. This is a retrospective observational study on a cohort of 18 patients (13 PD- 5 non-PD), hospitalized in a Rehabilitative Unit during the occurrence of SARS-CoV2 epidemic outbreak. All patients performed laboratory tests, lung Computed Tomography (CT) and have been tested for COVID-19 thorough pharyngeal swab. PD and non-PD groups were comparable for age, gender and Hoehn and Yahr stage. Seventy-seven (77)% of PD and 60% of non-PD resulted positive for COVID-19. PD-COVID+ and PD-COVID- did not differ for age, disease duration and L-dopa daily dose. PD COVID-19+ subjects were mainly asymptomatic (50%) while non-PD ones were all symptomatic, mostly with respiratory difficulties. PD doesn't seem to be a risk factor to take SARS-COV2 infection, even if our study is related to a limited sample size. Our results, together with those of other recent studies, highlight the need to evaluate the actual susceptibility of patients with Parkinson's disease to develop COVID-19 disease, and how the infection may influence the risk of clinical worsening and increase of mortality., Competing Interests: The authors have declared that no competing interests exist.

Health advances are contingent on continuous development of new methods and approaches to foster data-driven discovery in the biomedical and clinical sciences. Open-science and team-based scientific discovery offer hope for tackling some of the difficult challenges associated with managing, modeling, and interpreting of large, complex, and multisource data. Translating raw observations into useful information and actionable knowledge depends on effective domain-independent reproducibility, area-specific replicability, data curation, analysis protocols, organization, management and sharing of health-related digital objects. This study expands the functionality and utility of an ensemble semi-supervised machine learning technique called Compressive Big Data Analytics (CBDA). Applied to high-dimensional data, CBDA (1) identifies salient features and key biomarkers enabling reliable and reproducible forecasting of binary, multinomial and continuous outcomes (i.e., feature mining); and (2) suggests the most accurate algorithms/models for predictive analytics of the observed data (i.e., model mining). The method relies on iterative subsampling, combines function optimization and statistical inference, and generates ensemble predictions for observed univariate outcomes. The novelty of this study is highlighted by a new and expanded set of CBDA features including (1) efficiently handling extremely large datasets (>100,000 cases and >1,000 features); (2) generalizing the internal and external validation steps; (3) expanding the set of base-learners for joint ensemble prediction; (4) introducing an automated selection of CBDA specifications; and (5) providing mechanisms to assess CBDA convergence, evaluate the prediction accuracy, and measure result consistency. To ground the mathematical model and the corresponding computational algorithm, CBDA 2.0 validation utilizes synthetic datasets as well as a population-wide census-like study. Specifically, an empirical validation of the CBDA technique is based on a translational health research using a large-scale clinical study (UK Biobank), which includes imaging, cognitive, and clinical assessment data. The UK Biobank archive presents several difficult challenges related to the aggregation, harmonization, modeling, and interrogation of the information. These problems are related to the complex longitudinal structure, variable heterogeneity, feature multicollinearity, incongruency, and missingness, as well as violations of classical parametric assumptions. Our results show the scalability, efficiency, and usability of CBDA to interrogate complex data into structural information leading to derived knowledge and translational action. Applying CBDA 2.0 to the UK Biobank case-study allows predicting various outcomes of interest, e.g., mood disorders and irritability, and suggests new and exciting avenues of evidence-based research in the context of identifying, tracking, and treating mental health and aging-related diseases. Following open-science principles, we share the entire end-to-end protocol, source-code, and results. This facilitates independent validation, result reproducibility, and team-based collaborative discovery., Competing Interests: The authors have declared that no competing interests exist.

The theoretical foundations of Big Data Science are not fully developed, yet. This study proposes a new scalable framework for Big Data representation, high-throughput analytics (variable selection and noise reduction), and model-free inference. Specifically, we explore the core principles of distribution-free and model-agnostic methods for scientific inference based on Big Data sets. Compressive Big Data analytics (CBDA) iteratively generates random (sub)samples from a big and complex dataset. This subsampling with replacement is conducted on the feature and case levels and results in samples that are not necessarily consistent or congruent across iterations. The approach relies on an ensemble predictor where established model-based or model-free inference techniques are iteratively applied to preprocessed and harmonized samples. Repeating the subsampling and prediction steps many times, yields derived likelihoods, probabilities, or parameter estimates, which can be used to assess the algorithm reliability and accuracy of findings via bootstrapping methods, or to extract important features via controlled variable selection. CBDA provides a scalable algorithm for addressing some of the challenges associated with handling complex, incongruent, incomplete and multi-source data and analytics challenges. Albeit not fully developed yet, a CBDA mathematical framework will enable the study of the ergodic properties and the asymptotics of the specific statistical inference approaches via CBDA. We implemented the high-throughput CBDA method using pure R as well as via the graphical pipeline environment. To validate the technique, we used several simulated datasets as well as a real neuroimaging-genetics of Alzheimer's disease case-study. The CBDA approach may be customized to provide generic representation of complex multimodal datasets and to provide stable scientific inference for large, incomplete, and multisource datasets., Competing Interests: The authors have declared that no competing interests exist.

Multiple sclerosis (MS) is a chronic inflammatory disease and one of the leading causes of disability in young adults. Functional markers able to predict MS progression are still lacking. It is recognized that olfactory dysfunction may be an early symptom in MS. The aim of this study was to investigate whether alterations in olfactory event-related potentials could play a prognostic role in MS. Thirty patients affected by MS relapsing-remitting underwent an olfactory potential examination (T0). Three years after baseline (T1), 28 of 30 patients were clinically evaluated by expanded disability status scale. In addition, the number of Disease Modifying Therapies (DMTs) and the total number of relapses occurred in the last 3 years were collected. At T1, we observed a negative correlation between presence/absence of olfactory potentials and expanded disability status scale scores (rpb = -0.48; p = 0.009). A significant trend for a negative correlation between presence/absence of olfactory potentials and disease duration (rpb = -0.36; p = 0.06) and total number of relapses (rpb = -0.34; p = 0.08) was found. Only patients with olfactory potential absence showed a significant trend in the difference of the disability status scale (p = 0.06) between T0 and T1. In the sub-group of patients with reduced olfactory potential amplitude, we detected a trend for a negative correlation between the disability status scale and the amplitude of N1-P2 components more marked at T1 (r = -0.52; p = 0.06) than T0 (r = -0.47; p = 0.09). This is the first study that evaluated the prognostic role of olfactory event-related potentials in MS. Our results highlighted that olfactory alterations of MS patients were related to disability progression and, to a lesser extent, disease activity. The analysis of olfactory potential parameters confirmed the involvement in olfactory network damage of inflammatory and/or neurodegeneration processes which could predict the progressive course of the disease.

Background: Several studies reported olfactory dysfunction in patients with multiple sclerosis. The estimate of the incidence of olfactory deficits in multiple sclerosis is uncertain; this may arise from different testing methods that may be influenced by patients' response bias and clinical, demographic and cognitive features., Aims: To evaluate objectively the olfactory function using Olfactory Event Related Potentials., Materials and Methods: We tested the olfactory function of 30 patients with relapsing remitting multiple sclerosis (mean age of 36.03±6.96 years) and of 30 age, sex and smoking-habit matched healthy controls by using olfactory potentials. A selective and controlled stimulation of the olfactory system to elicit the olfactory event related potentials was achieved by a computer-controlled olfactometer linked directly with electroencephalograph. Relationships between olfactory potential results and patients' clinical characteristics, such as gender, disability status score, disease-modifying therapy, and disease duration, were evaluated., Results: Seven of 30 patients did not show olfactory event related potentials. Sixteen of remaining 23 patients had a mean value of amplitude significantly lower than control group (p<0.01). The presence/absence of olfactory event related potentials was associated with dichotomous expanded disability status scale (p = 0.0433), as well as inversely correlated with the disease duration (r = -0.3641, p = 0.0479)., Conclusion: Unbiased olfactory dysfunction of different severity found in multiple sclerosis patients suggests an organic impairment which could be related to neuroinflammatory and/or neurodegenerative processes of olfactory networks, supporting the recent findings on neurophysiopathology of disease.

Satellite cells are the resident stem cell population of the adult mammalian skeletal muscle and they play a crucial role in its homeostasis and in its regenerative capacity after injury. We show here that the Polycomb group (PcG) gene Bmi1 is expressed in both the Pax7 positive (+)/Myf5 negative (-) stem cell population as well as the Pax7+/Myf5+ committed myogenic progenitor population. Depletion of Pax7+/Myf5- satellite cells with reciprocal increase in Pax7+/Myf5+ as well as MyoD positive (+) cells is seen in Bmi1-/- mice leading to reduced postnatal muscle fiber size and impaired regeneration upon injury. Bmi1-/- satellite cells have a reduced proliferative capacity and fail to re-enter the cell cycle when stimulated by high serum conditions in vitro, in keeping with a cell intrinsic defect. Thus, both the in vivo and in vitro results suggest that Bmi1 plays a crucial role in the maintenance of the stem cell pool in postnatal skeletal muscle and is essential for efficient muscle regeneration after injury especially after repeated muscle injury.

Background: The NuRD/Mi2 chromatin complex is involved in histone modifications and contains a large number of subunits, including the p66 protein. There are two mouse and human p66 paralogs, p66alpha and p66beta. The functions of these genes are not clear, in part because there are no mutants available, except in invertebrate model systems., Methodology: We made loss of function mutants in the mouse p66alpha gene (mp66alpha, official name Gatad2a, MGI:2384585). We found that mp66alpha is essential for development, as mutant embryos die around day 10 of embryogenesis. The gene is not required for normal blastocyst development or for implantation. The phenotype of mutant embryos and the pattern of gene expression in mutants are consistent with a role of mp66alpha in gene silencing., Conclusion: mp66alpha is an essential gene, required for early mouse development. The lethal phenotype supports a role in execution of methylated DNA silencing.