Longitudinal integrated proteomic and metabolomic skin changes in atopic dermatitis patients treated with dupilumab.

Background: Inhibition of IL-4/IL-13 driven inflammation by dupilumab has shown significant clinical benefits in treatment of atopic dermatitis (AD).

Objective: To assess longitudinal protein and metabolite composition in AD skin during dupilumab treatment.

Methods: Skin tape strip (STS) were collected from lesional/non-lesional skin of 20 AD patients during 16-week dupilumab treatment and from 20 healthy volunteers (HV) followed for 16-weeks.

The effect of a 14-day Gymnema sylvestre intervention to reduce sugar intake in people self-identifying with a sweet tooth.

Gymnema sylvestre (GS) contains gymnemic acids which can reversibly suppress sweet taste responses. This randomised crossover study aimed to investigate whether supplemental GS use can reduce sugar cravings, sweet food desire and consumption among adults that identify as high sweet food consumers (having a 'sweet tooth'). Participants were told three different mints were trialled to avoid bias.

3D modeling to predict vascular involvement in resectable pancreatic adenocarcinoma.

Background: Current management of patients with borderline resectable pancreatic adenocarcinoma (BR-PDAC) depends on the degree of involvement of the major arterial and venous structures. The aim of this study was to evaluate 3D segmentation and printing to predict tumor size and vascular involvement of BR-PDAC to improve pre-operative planning of vascular resection and better select patients for neoadjuvant therapy.

Methods: We retrospectively evaluated 16 patients with BR-PDAC near vascular structures who underwent pancreatoduodenectomy (PD) with or without vascular resection between 2015 and 2021.

The Life and Work of Prof. Dr. Antapur Venkoba Rao: The Enduring Legacy of an Innovative Mind.

Dr. Antapur Venkoba Rao, born on August 20, 1927, in Andhra Pradesh, was a pioneering figure in Indian psychiatry, often recognized as the "Father of Indian Psychiatry" and the "Father of Geriatric Mental Health." His exceptional academic achievements led him to specialize in psychiatry, where he made substantial contributions, particularly in the study of depressive disorders.

Machine Learning-driven Identification of the Honeymoon Phase in Pediatric Type 1 Diabetes and Optimizing Insulin Management.

Objective: The honeymoon phase in Type 1 Diabetes (T1D) presents a temporary improvement in glycemic control, complicating insulin management. This study aims to develop and validate a machine learning-driven method for accurately detecting this phase to optimize insulin therapy and prevent adverse outcomes.

Methods: Data from pediatric T1D patients aged 6-17 years, including continuous glucose monitoring (CGM) data, Glucose Management Indicator (GMI) reports, HbA1c values, and patient medical history, were used to train machine learning models.