Polycystic ovary syndrome: clinical and laboratory variables related to new phenotypes using machine-learning models.

PURPOSE: Polycystic Ovary Syndrome (PCOS) is the most frequent endocrinopathy in women of reproductive age. Machine learning (ML) is the area of artificial intelligence with a focus on predictive computing algorithms. We aimed to define the most relevant clinical and laboratory variables related to PCOS diagnosis, and to stratify patients into different phenotypic groups (clusters) using ML algorithms. METHODS: Variables from a database comparing 72 patients with PCOS and 73 healthy women were included. The BorutaShap method, followed by the Random Forest algorithm, was applied to prediction and clustering of PCOS. RESULTS: Among the 58 variables investigated, the algorithm selected in decreasing order of importance: lipid accumulation product (LAP); abdominal circumference; thrombin activatable fibrinolysis inhibitor (TAFI) levels; body mass index (BMI); C-reactive protein (CRP), high-density lipoprotein cholesterol (HDL-c), follicle-stimulating hormone (FSH) and insulin levels; HOMA-IR value; age; prolactin, 17-OH progesterone and triglycerides levels; and family history of diabetes mellitus in first-degree relative as the variables associated to PCOS diagnosis. The combined use of these variables by the algorithm showed an accuracy of 86% and area under the ROC curve of 97%. Next, PCOS patients were gathered into two clusters in the first, the patients had higher BMI, abdominal circumference, LAP and HOMA-IR index, as well as CRP and insulin levels compared to the other cluster. CONCLUSION: The developed algorithm could be applied to select more important clinical and biochemical variables related to PCOS and to classify into phenotypically different clusters. These results could guide more personalized and effective approaches to the treatment of PCOS.

Two novel pathogenic variants in MED13L: one familial and one isolated case.

BACKGROUND: Genetic variants involving the MED13L gene can lead to an autosomal dominant syndrome characterised by intellectual disability/developmental delay and facial dysmorphism. METHODS: We investigated two cases (one familial and one isolated) of intellectual disability with speech delay and dysmorphic facial features by whole-exome sequencing analyses. Further, we performed a literature review about clinical and molecular aspects of MED13L gene and syndrome. RESULTS: Two MED13L variants have been identified [MED13L(NM_015335.5):c.4417C>T and MED13L(NM_015335.5):c.2318delC] and were classified as pathogenic according to the ACMG (American College of Medical Genetics and Genomics) guidelines. One of the variants was present in sibs. CONCLUSIONS: The two pathogenic variants identified have not been previously reported. Importantly, this is the first report of a familial case of MED13L nonsense mutation. Although the parents of the affected children were no longer available for analysis, their apparently normal phenotypes were surmised from familial verbal descriptions corresponding to normal mental behaviour and phenotype. In this situation, the familial component of mutation transmission might be caused by gonadal mosaicism of a MED13L mutation in a gonad from either the father or the mother. The case reports and the literature review presented in this manuscript can be useful for genetic counselling.

Microparticles: Inflammatory and haemostatic biomarkers in Polycystic Ovary Syndrome.

Polycystic Ovary Syndrome (PCOS) is associated with a chronic low-grade inflammation and predisposition to hemostatic and atherosclerotic complications. This case-control study evaluated the microparticles (MPs) profile in patients with the PCOS and related these MPs to clinical and biochemical parameters. MPs derived from platelets (PMPs), leuckocytes (LMPs) and endothelial cells (EMPs) were evaluated, as well as MPs expressing tissue factor (TFMPs), by flow cytometry, comparing women with PCOS (n = 50) and a healthy control group (n = 50). PCOS women presented increased total MPs, PMPs, LMPs and EMPs levels when compared to control group (all p < 0.05). TFMPs was similar between the groups (p = 0.379). In conclusion, these MPs populations could be useful biomarkers for association with thrombosis and cardiovascular disease in PCOS women.

A checklist of arthropods associated with pig carrion and human corpses in Southeastern Brazil

Necrophagous insects, mainly Diptera and Coleoptera, are attracted to specific stages of carcass decomposition, in a process of faunistic succession. They are very important in estimating the postmortem interval, the time interval between the death and the discovery of the body. In studies done with pig carcasses exposed to natural conditions in an urban forest (Santa Genebra Reservation), located in Campinas, State of São Paulo, southeastern Brazil, 4 out of 36 families of insects collected - Calliphoridae, Sarcophagidae, Muscidae (Diptera) and Dermestidae (Coleoptera) - were considered of forensic importance, because several species were collected in large numbers both visiting and breeding in pig carcasses. Several species were also observed and collected on human corpses at the Institute of Legal Medicine. The species belonged to 17 different families, 6 being of forensic importance because they were reared from human corpses or pig carcasses: Calliphoridae, Sarcophagidae, Muscidae, Piophilidae (Diptera), Dermestidae, Silphidae and Cleridae (Coleoptera). The most important species were: Diptera - Chrysomya albiceps, Chrysomya putoria, Hemilucilia segmentaria, Hemilucilia semidiaphana (Calliphoridae), Pattonella intermutans (Sarcophagidae), Ophyra chalcogaster (Muscidae), Piophila casei (Piophilidae); Coleoptera - Dermestes maculatus (Dermestidae), Oxyletrum disciolle (Silphidae) and Necrobia rufipes (Cleridae).