Proteomics and metabolomics analyses of Streptococcus agalactiae isolates from human and animal sources.

Streptococcus agalactiae (S. agalactiae), group B Streptococcus (GBS), a major cause of infection in a wide variety of diseases, have been compared in different human and animal sources. We aimed to compare the bacterial proteome and metabolome profiles of human and animal S. agalactiae strains to delineate biological interactions relevant to infection. With the innovative advancement in mass spectrometry, a comparative result between both strains provided a solid impression of different responses to the host. For instance, stress-related proteins (Asp23/Gls24 family envelope stress response protein and heat shock protein 70), which play a role in the survival of GBS under extreme environmental conditions or during treatment, are highly expressed in human and animal strains. One human strain contains ꞵ-lactamase (serine hydrolase) and biofilm regulatory protein (lytR), which are important virulence regulators and potential targets for the design of novel antimicrobials. Another human strain contains the aminoglycosides-resistance bifunctional AAC/APH (A0A0U2QMQ5) protein, which confers resistance to almost all clinically used aminoglycosides. Fifteen different metabolites were annotated between the two groups. L-aspartic acid, ureidopropionic acid, adenosine monophosphate, L-tryptophan, and guanosine monophosphate were annotated at higher levels in human strains. Butyric acid, fumaric acid, isoleucine, leucine, and hippuric acid have been found in both human and animal strains. Certain metabolites were uniquely expressed in animal strains, with fold changes greater than 2. For example, putrescine modulates biofilm formation. Overall, this study provides biological insights into the substantial possible bacterial response reflected in its macromolecular production, either at the proteomic or metabolomic level.

Prediction of intraperitoneal adhesions using striae gravidarum and scar characteristics in women undergoing repeated cesarean sections.

BACKGROUND: The current fact of increasing rates of cesarean deliveries is a catastrophe. Recurrent cesareans result in intraperitoneal adhesions that would lead to maternal morbidity during delivery. Great efforts are directed towards the prediction of intraperitoneal adhesions to provide the best care for laboring women. The aim of the current study was to evaluate the role of abdominal striae and cesarean scar characters in the prediction of intraperitoneal adhesions. METHODS: This was a case- control study conducted in the emergency ward of the obstetrics and gynecology department of a tertiary hospital from June to December 2019. The study was carried on patients admitted to the ward fulfilling particular inclusion and exclusion criteria. The study included two groups, group one was assessed for the presence of striae, and the degree of intraperitoneal adhesions was evaluated during the current cesarean section. Group two included patients without evidence of abdominal striae. They were evaluated for the severity of adhesions also after evaluation of the previous scar. Evaluation of the striae was done using Davey's scoring system. The scar was assessed using the Vancouver Scar Scale. The modified Nair's scoring system was used to evaluate intraperitoneal adhesions. RESULTS: The study group included 203 women, while the control group included 205 women. There were significant differences in the demographic characters of the recruited patients (p-value 0.001 for almost all variables). The mean Davey score in those with mild, moderate, and severe striae was 1.82 ± 0.39, 3.57 ± 0.5, and 6.73 ± 0.94, respectively (p-value < 0.001). Higher scores for the parameters of the Vancouver scale were present in patients with severe striae (1.69 ± 1.01, 1.73 ± 0.57, 2.67 ± 1.23, and 1.35 ± 1.06 for scar vascularity, pigmentation, pliability, and height respectively with a p-value of < 0.001 each). Thick intraperitoneal adhesions were noted significantly in women with severe striae [21 (43.75%), p-value < 0.001)]. The Davey's and Vancouver scores showed highly significant predictive performance in the prediction of intraperitoneal adhesions (p-value < 0.001). CONCLUSION: Abdominal striae and cesarean scar were significant predictors for intraperitoneal adhesions.

Diosmin Mitigates Cyclophosphamide Induced Premature Ovarian Insufficiency in Rat Model.

The current study was designed to investigate the protective role of diosmin against cyclophosphamide-induced premature ovarian insufficiency (POI). Female Swiss albino rats received a single intraperitoneal dose of cyclophosphamide (200 mg/kg) followed by 8 mg/kg/day for the next 15 consecutive days either alone or in combination with oral diosmin at 50 or 100 mg/kg. Histopathological examination of ovarian tissues, hormonal assays for follicle stimulating hormone (FSH), estradiol (E2), and anti-Mullerian hormone (AMH), assessment of the oxidative stress status, as well as measurement of the relative expression of miRNA-145 and its target genes [vascular endothelial growth factor B (VEGF-B) and regulator of cell cycle (RGC32)] were performed. Diosmin treatment ameliorated the levels of E2, AMH, and oxidative stress markers. Additionally, both low and high diosmin doses significantly reduced the histopathological alterations and nearly preserved the normal ovarian reserve. MiRNA-145 expression was upregulated after treatment with diosmin high dose. miRNA-145 target genes were over-expressed after both low and high diosmin administration. Based on our findings, diosmin has a dose-dependent protective effect against cyclophosphamide-induced ovarian toxicity in rats.

Structured crowdsourcing enables convolutional segmentation of histology images.

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.