Potential Adverse Events of Fluoxetine: A Real-world Study Based on FAERS Database.

Objective: This study aimed to mine and analyze adverse event signals of fluoxetine using the FAERS database.

Methods: This study focused on suspected drug adverse reaction reports from the first quarter of 2004 to the second quarter of 2023, with fluoxetine as the primary suspected drug. Four signal mining and analysis methods were employed to comprehensively assess adverse event signals.

SIBW: A Swarm Intelligence-Based Network Flow Watermarking Approach for Privacy Leakage Detection in Digital Healthcare Systems.

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems.

Clinical outcomes of complex rhegmatogenous retinal detachment treated with a 25-G, 10,000-cpm beveled-tip cutter probe.

Background: Much effort has been exerted to improve the vitreous cutter probe. This study sought to investigate the clinical outcomes of using a novel 25-gauge (25-G), 10,000-cuts-per-minute (cpm) beveled-tip cutter probe (BTCP) to treat complex rhegmatogenous retinal detachment (RRD).

Methods: The preoperative characteristics, intraoperative surgical information, and postoperative recovery data of a prospective case series of 60 consecutive eyes that underwent primary pars plana vitrectomy (PPV) for complex RRD between March and October 2021 were recorded, and the correlations among these factors were examined.

Multi-omics analysis reveals mechanisms of FMT in Enhancing antidepressant effects of SSRIs.

Objective: This study explores the behavioral and molecular biological impacts of Fecal Microbiota Transplantation (FMT) on depressive mice unresponsive to treatment with Selective Serotonin Reuptake Inhibitors (SSRIs).

Methods: Healthy male C57BL/6 mice were used to establish a depression model through chronic restraint stress, treated with fluoxetine, and categorized into Response and Non-response groups. An FMT treatment was added to the Non-response group.

Analysis of antibacterial drug use and bacterial resistance in psychiatric hospital in the epidemic.

Analyze the use of antibacterial drugs and bacterial resistance in psychiatric hospital during the epidemic. Using the hospital information system and the National Antibacterial Drug Clinical Application Monitoring Network, we retrospectively collected data on the use of antibacterial drugs and bacterial resistance in psychiatric hospitals during the 2022 epidemic. During the 2022 epidemic, our hospital had an antibiotic use rate of 5.

Influencing factors of the participation of patients with severe mental disorders in community health service management.

Objective: This study aimed to explore the participation of patients with severe mental disorders in community health service management and its influencing factors in Wuxi.

Methods: The data of patients with severe mental disorders were extracted from the Jiangsu Provincial Management System between 2019 and 2021. The participation of patients in community health service management was analyzed.

SRF and CBP jointly regulate integrin β6 overexpression in head and neck squamous cell carcinomas.

Overexpression of integrin β6 (ITGB6) is crucially linked to the invasion and metastasis of head and neck squamous cell carcinoma (HNSCC). The molecular mechanisms driving ITGB6 upregulation in HNSCC are not well understood. Our study comprehensively analyzed the transcriptional regulation and epigenetic modification mechanisms affecting ITGB6 transcription.

The impact of cortical and subcortical volumes on major depression risk: A genetic study.

Objective: This study aimed to explore the causal relationship between brain cortical and subcortical structures and major depressive disorder (MDD) using the Mendelian Randomization (MR) method.

Methods: Single nucleotide polymorphisms (SNPs) were used as instrumental variables to analyze subcortical brain volume, cortical thickness, and surface area as exposure factors, with MDD as the outcome. Multiple sensitivity analyses were conducted to validate the robustness of the results.

Correlation Study Between Dietary Behaviors, Lifestyle, and Psychological Problems in Chinese Children Aged 3-7.

Objective: This study aimed to assess the psychological health status of children aged 3-7 years in Wuxi and analyze the correlation between dietary behaviors, lifestyle, and psychological problems.

Methods: Using a stratified cluster random sampling method, 3-7-year-old children from 18 kindergartens across Wuxi were selected as the survey subjects. An online survey was conducted to collect demographic information about children and their parents, dietary information, lifestyle data, and family backgrounds.

Characteristics of adverse events and clinical risks of Lecanemab based on FAERS data.

Objective: This study aims to analyze the distribution of adverse events (AEs) related to Lecanemab in real-world settings based on FAERS database data.

Methods: Using the FAERS database, AE data related to Lecanemab was collected from Q3 2023 to Q2 2024. Signal mining was conducted using frequency and Bayesian methods to identify positive signals associated with Lecanemab.

Association Between Coffee Intake and Common Mental Disorders: Insights From Genetic Analysis.

The study found a significant causal relationship between coffee intake and obsessive-compulsive disorder, showing a negative correlation. There was no causal relationship between coffee intake and other mental disorders. The sensitivity analysis test found no pleiotropy affecting the results, and no single nucleotide polymorphism had a major impact on the robustness of the results, indicating that the results are stable and reliable.

Exploration and evaluation of adverse event signals of droxidopa based on the FAERS database.

Background: This study aims to utilize the FDA's Adverse Event Reporting System (FAERS) for data analysis to explore the potential adverse events associated with Droxidopa in real-world settings, thereby providing reference information for clinical practice.

Methods: Adverse event reports where Droxidopa was the primary suspected drug were collected from the FAERS database from the third quarter of 2014 to the fourth quarter of 2023. Multiple signal quantification techniques were employed, including ROR, PRR, BCPNN, and MGPS.

Construction of a troublemaking risk assessment tool for patients with severe mental disorders in community of China.

Objective: Construction a troublemaking risk assessment tool to predict the risk of troublemaking for patients with severe mental disorders in the community of China.

Methods: 28,000 cases registered in the Jiangsu Provincial Severe Mental Disorder Management System from January 2017 to December 2019 were collected. The risk factors of troublemaking among patients with severe mental disorders in the community were analyzed through Logistic regression analysis, then the troublemaking risk assessment tool was established and verified.

The role of gut microbiota and metabolomic pathways in modulating the efficacy of SSRIs for major depressive disorder.

This study aims to explore the mechanism by which gut microbiota influences the antidepressant effects of serotonin reuptake inhibitors (SSRIs) through metabolic pathways. A total of 126 patients were analyzed for their gut microbiota and metabolomics. Patients received SSRI treatment and were categorized into responder and non-responder groups based on changes in their Hamilton Depression Rating Scale (HAMD-17) scores before and after treatment.

Corrigendum: Predicting 24-hour intraocular pressure peaks and averages with machine learning.

[This corrects the article DOI: 10.3389/fmed.2024.

Predicting 24-hour intraocular pressure peaks and averages with machine learning.

Purpose: Predicting 24-hour peak and average intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma. This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. We also aimed to identify relevant features and provide insights into the prediction results to better inform clinical practice.

Drug discovery of N-methyl-pyrazole derivatives as potent selective estrogen receptor degrader (SERD) for the treatment of breast cancer.

Nowadays, ERα is considered to be a primary target for the treatment of breast cancer, and selective estrogen receptor degraders (SERDs) are emerging as promising antitumor agents. By analysing ERα-SERDs complexes, the pharmacophore features of SERDs and the crucial protein-ligand interactions were identified. Then, by utilizing the scaffold-hopping and bioisosteres strategy, 23 novel derivatives were designed, synthesized and biologically evaluated.

Analysis of the health status and its influencing factors of the low-income populations in Wuxi, China.

Objective: To understand the physical health condition and its influencing factors among the low-income population.

Method: Low-income residents who visited or consulted at our Hospital during 2022 were selected for this study. Through telephone or face-to-face interviews, a self-made basic information questionnaire was used for data collection.