Leveraging machine learning to predict drug permeation: impact of menthol and limonene as enhancers.

The present study aimed to develop robust machine learning (ML) models to predict the skin permeability of poorly water-soluble drugs in the presence of menthol and limonene as penetration enhancers (PEs). The ML models were also applied in virtual screening (VS) to identify hydrophobic drugs that exhibited better skin permeability in the presence of permeation enhancers i.e.

Gestational Diabetes and Analysis of Maternal and Fetal Outcomes Among Pregnant Women: A Cross-Sectional Study.

Purpose Of The Study: To determine the occurrence of gestational diabetes mellitus and analyze the maternal and fetal outcomes among pregnant women.

Methods: A prospective cross-sectional study was conducted for a duration of 7 months in the Department of Gynecology and Obstetrics at tertiary care hospital where 518 pregnant women participants of age above 18 years who had come for labor and undergone DIPSI (diabetes in pregnancy study of India) test were included in the study after the ethical clearance and obtaining written informed consent, whereas pregnant women with pre-existing diabetes mellitus and on other medication that alter glucose metabolism were excluded from the study.

Results: The occurrence of GDM was found to be 15.

Association of Unsafe Sexual Behavior with Head and Neck Cancer: A Bibliometric Analysis.

Inappropriate sexual practice like oral sex has been linked with head and neck cancer (HNC). The recent data revealed that about 20% oropharyngeal caner among Indians are associated with oral sex. Thus, we aimed to comprehensively assess the trend of research in oral sex associated with HNC and to predict the research frontiers and potential hotspots in the near future.

Deep-learning generated B-line score mirrors clinical progression of disease for patients with heart failure.

Background: Ultrasound can detect fluid in the alveolar and interstitial spaces of the lung using the presence of artifacts known as B-lines. The aim of this study was to determine whether a deep learning algorithm generated B-line severity score correlated with pulmonary congestion and disease severity based on clinical assessment (as identified by composite congestion score and Rothman index) and to evaluate changes in the score with treatment. Patients suspected of congestive heart failure underwent daily ultrasonography.