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. After 24 hours, the distribution of Guardix-Flamma was examined under the near-infrared mode of the laparoscope, and the thickness was measured in tissues from the omentum, small, and large intestine by immunohistochemistry. RESULTS: The average area of the abdominal cavity in 10 kg pigs was 2,755 cm2. Guardix-Flamma fluorescence was detected in the greater omentum, ascites in the pelvis, and right quadrant area in G1, whereas in G2, it was detected everywhere. On average, the total thickness of G1 and G2 were 12.68 ± 9.80 µm and 18.16 ± 15.57 µm, respectively. Guardix-Flamma thickness applied to the omentum, small, and large intestines of G2 were 1.31-, 1.45-, and 1.49-times thicker than those of G1, respectively, and were all statistically significant (P < 0.05). CONCLUSION: The entire abdominal cavity of the 10 kg pig was not evenly covered with 15 mL of Guardix. Although 35 mL of Guardix is sufficient to cover the same area with an average thickness of 18 µm, further studies should evaluate the minimum thickness required for an effective anti-adhesive function.

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. The OA-induced group showed significantly higher manual joint palpation, gait analysis, and OA severity scores than the control group. Based on these two results, we calculated correlation coefficients. There was a strong positive correlation between manual joint palpation scores and OA severity on diagnostic imaging and between gait analysis scores and OA severity. CONCLUSIONS: The developed grading criteria based on radiographic evaluation correlated with clinical assessments. The study also employed CT imaging to enhance the accuracy and sensitivity of early-stage OA change detection in the stifle joint. However, further studies with larger sample sizes and multiple evaluators are recommended for the validation and generalizability of this grading system. These established image evaluation grading criteria can help evaluate and monitor the efficacy of interventions and changes in OA lesions in canine models.

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. Phytochemicals are a collection of valuable bioactive compounds that are known for their wide variety of chemotypes. Recently, various computational methods have been developed to obtain active phytochemicals that can modulate desired target proteins. In this study, we developed a workflow comprising two consecutive quantitative structure-activity relationship-based machine learning models to discover novel GR-antagonizing phytochemicals. These two models collectively identified 65 phytochemicals that bind to and antagonize GR. Of these, nine commercially available phytochemicals were validated for GR-antagonist and anti-obesity activities. In particular, we confirmed that demethylzeylasteral, a phytochemical of the Tripterygium wilfordii Radix, exhibits potent anti-obesity activity in vitro through GR antagonism.

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. However, due to deficiencies with existing chemical-protein interaction datasets, which are mostly single-labeled and limited, the existing models struggle to predict new chemical-protein interactions. New techniques are needed to overcome these limitations. RESULTS: We propose a novel NC discovery model called OptNCMiner that offers various advantages. The model is trained via end-to-end learning with a feature extraction step implemented, and it predicts multi-target modulating NCs through multi-label learning. In addition, it offers a few-shot learning approach to predict NC-protein interactions using a small training dataset. OptNCMiner achieved better prediction performance in terms of recall than conventional classification models. It was tested for the prediction of NC-protein interactions using small datasets and for a use case scenario to identify multi-target modulating NCs for type 2 diabetes mellitus complications. CONCLUSIONS: OptNCMiner identifies NCs that modulate multiple target proteins, which facilitates the discovery and the understanding of biological activity of novel NCs with desirable health benefits.