Prediction of the minimum amount of anti-adhesive agent required for entire intra-abdominal cavity using fluorescent dye.

Purpose: Studies on the appropriate amount of anti-adhesive agents for preventing postoperative adhesion are lacking. This animal study aimed to investigate the distribution of an anti-adhesive agent in the abdominal cavity and estimate the necessary amount to cover the entire cavity.

Methods: Fluorescent dye Flamma-552 was conjugated to Guardix-sol to create Guardix-Flamma, which was laparoscopically applied to the abdominal cavity of two 10-kg pigs in different amounts: 15 mL for G1 and 35 mL for G2.

Establishment of an image evaluation grading criteria for experimental stifle joint osteoarthritis in dogs: an X-ray and CT imaging study.

Background: This study aimed to establish an image evaluation grading criteria for experimental stifle joint osteoarthritis (OA) in anterior cruciate ligament transection induced OA beagle dog models. The severity of OA was assessed using X-ray and computed tomography (CT) imaging.

Results: A total of 32 dogs (8 controls and 24 OA-induced dogs) were included in the study.

A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor.

As a type of stress hormone, glucocorticoids (GCs) affect numerous physiological pathways by binding to the glucocorticoid receptor (GR) and regulating the transcription of various genes. However, when GCs are dysregulated, the resulting hypercortisolism may contribute to various metabolic disorders, including obesity. Thus, attempts have been made to discover potent GR antagonists that can reverse excess-GC-related metabolic diseases.

OptNCMiner: a deep learning approach for the discovery of natural compounds modulating disease-specific multi-targets.

Background: Due to their diverse bioactivity, natural product (NP)s have been developed as commercial products in the pharmaceutical, food and cosmetic sectors as natural compound (NC)s and in the form of extracts. Following administration, NCs typically interact with multiple target proteins to elicit their effects. Various machine learning models have been developed to predict multi-target modulating NCs with desired physiological effects.