Evidence-based clinical guidelines for chronic constipation 2023.

In July 2023 the Japan Gastroenterological Association published the first version of its clinical guidelines for chronic constipation 2023. Based on the latest evidence, these guidelines describe the definition, classification, diagnostic criteria, diagnostic testing methods, epidemiology, pathophysiology, and treatment of chronic constipation. They include flowcharts for both diagnosis and treatment of chronic constipation. In the treatment of chronic constipation, the first step involves differentiating between secondary forms, such as organic disease-associated constipation, systemic disease-associated constipation, and drug-induced constipation. The next step is to determine whether the chronic constipation stems from a motility disorder, a form of primary chronic constipation. For functional constipation and constipation-predominant irritable bowel syndrome, treatment should be initiated after evaluating symptoms like reduced frequency of bowel movement frequency type or defecation difficulty type. The first line of treatment includes improvement of lifestyle habits and diet therapy. The first drugs to consider for oral treatment are osmotic laxatives. If these are ineffective, secretagogues and ileal bile acid transporter inhibitors are candidates. However, stimulant laxatives are exclusively designated for as-needed use. Probiotics, bulk-forming laxatives, prokinetics, and Kampo medicine, for which there is insufficient evidence, are considered alternative or complementary therapy. Providing the best clinical strategies for chronic constipation therapy in Japan, these clinical guidelines for chronic constipation 2023 should prove useful for its treatment worldwide.

Detection of Adverse Event Signals with Severity Grade Classification from Cancer Patient Narrative.

Adverse event (AE) management is crucial to improve anti-cancer treatment outcomes, but it is reported that some AE signals can be missed in clinical visits. Thus, monitoring AE signals seamlessly, including events outside hospitals, would be helpful for early intervention. Here we investigated how to detect AE signals from texts written by cancer patients themselves by developing deep-learning (DL) models to classify posts mentioning AEs according to severity grade, in order to focus on those that might need immediate treatment interventions. Using patient blogs written in Japanese by cancer patients as a data source, we built DL models based on three approaches, BERT, ELECTRA, and T5. Among these models, T5 showed the best F1 scores for both Grade ≥ 1 and ≥ 2 article classification tasks (0.85 and 0.53, respectively). This model might benefit patients by enabling earlier AE signal detection, thereby improving quality of life.

Response caracteristics of thick sedimentary layers based on the deep vertical array observation

This paper describes the results of deep vertical array observation perfomed to understand responses of thick sedimentary horizontal layers. Acceleration responses are evaluated at 9 different levels from the bottom of the boring hole (about-500m) to the ground surface. Moderate nonlinear responses of the near surface layer are observed with the maximum surface acceleration of about 0.1G. Giving the deepest record, numerical simulations are carried out to validate a one-dimensional wave propagation analysis. Moreover, a comparison between an equivalent linear analysis and an effective stress analysis is made to observe the difference due to modelling of soil nonlinearity.(AU)