Identifying behavior regulatory leverage over mental disorders transcriptomic network hubs toward lifestyle-dependent psychiatric drugs repurposing DOI Creative Commons

Mennatullah Abdelzaher Turky,

Ibrahim Youssef, Azza El Amir

и другие.

Human Genomics, Год журнала: 2025, Номер 19(1)

Опубликована: Март 19, 2025

Abstract Background There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems the regulatory leverage lifestyle health. Methods This study analyzed coexpression modules MDD PTSD blood transcriptomic profile using modularity optimization method, first runner-up Disease Module Identification DREAM challenge . The top genes both were detected random forest model. Afterward, signature two predominant habitual phenotypes, diet-induced obesity smoking, identified. These transcription/translation regulating factors ( TRFs ) signals transduced toward disorders’ genes. A bipartite drugs that target TRFS together or hubs was constructed. Results research revealed one hub, CENPJ, which known influence intellectual ability. observation paves way for additional investigations potential CENPJ as novel therapeutic agents development. Additionally, most predicted associated multiple carcinomas, notable SHCBP1. SHCBP1 risk factor glioma, suggesting importance continuous monitoring patients mitigate cancer comorbidities. signaling illustrated three biomarkers co-regulated by phenotype TRFs. 6-Prenylnaringenin Aflibercept identified candidates targeting hubs: ATP6V0A1 PIGF. However, have no over Conclusion Combining biology succeeded revealing notoriously spreading PTSD. approach offers non-invasive diagnostic pipeline identifies drug targets could be repurposed under further investigation. findings contribute our understanding complex interplay between habits, interventions, thereby facilitating more targeted personalized treatment strategies.

Язык: Английский

Effect of a mindfulness program on stress, anxiety, depression, sleep quality, social support, and life satisfaction: a quasi-experimental study in college students DOI Creative Commons
Paúl Alan Arkin Alvarado-García, Marilú Roxana Soto Vásquez, Francisco Mercedes Infantes Gomez

и другие.

Frontiers in Psychology, Год журнала: 2025, Номер 16

Опубликована: Фев. 12, 2025

Introduction The university experience often brings various personal and academic challenges that can negatively impact students’ mental health. This research aimed to evaluate the effect of a mindfulness program on stress, anxiety, depression, sleep quality, social support, life satisfaction among students. Methods A quasi-experimental study was conducted with 128 participants, divided into experimental waiting list control groups. group participated in meditation consisting 12 weekly sessions. Pre-test post-test measurements were performed using Perceived Stress Scale (PSS-10), Zung Self-Rating Anxiety (SAS), Depression (SDS), Pittsburgh Sleep Quality Index (PSQI), Medical Outcomes Study Social Support Survey (MOS-SS), Satisfaction Life (SWLS) assess variables. Results showed statistically significant differences between phases groups after intervention for all variables examined ( p < 0.05). sizes calculated HC3 model stress η 2 = 0.376), anxiety 0.538), depression 0.091), quality 0.306), support 0.704), 0.510). shown be effective reducing levels while also improving college Conclusion These findings indicate may valuable enhancing psychological well-being educational settings.

Язык: Английский

Процитировано

0
Exploring the relationship between physical activity and smartphone addiction among college students in Western China DOI Creative Commons
Chun Sing Lai, Peiling Cai, Junyi Liao

и другие.

Frontiers in Public Health, Год журнала: 2025, Номер 13

Опубликована: Фев. 21, 2025

Smartphone addiction (SA) refers to a behavioral disorder characterized by an irresistible compulsion excessively engage with mobile devices. Currently, the evidence regarding relationship between physical activity (PA), exercise intensity (EI), and SA is limited, particularly within Chinese populations. This study aims explore correlation PA, EI, SA, specifically investigating how PA EI impact better understand nature of this relationship. A cross-sectional was conducted involving college students from over 20 universities in Western China. Data were collected on participants' engagement SA. Additionally, covariates such as age, gender, ethnicity, academic classification, university location, discipline, year study, hometown region, sibling status, social interactions recorded. Multivariate logistic regression models used assess association Stratified interaction analyses performed examine whether remained consistent across different subgroups. Of 3,506 surveyed, 1,905 (54.3%) experienced The prevalence 11.3% lower group that engaged compared those who did not. In fully adjusted model, negatively associated (OR = 0.70, 95% CI: 0.59-0.82, p < 0.001). also inversely Moderate- vigorous-intensity had odds ratios 0.81 (95% 0.67-0.98, 0.034) 0.83 0.68-1.00, 0.046), respectively, low-intensity exercise. Similar patterns observed subgroup (all values for >0.05). findings indicate significant negative highlighting potential promoting higher strategies reduce among students.

Язык: Английский

Процитировано

0
Understanding the complex network of anxiety, depression, sleep problems, and smartphone addiction among college art students using network analysis DOI Creative Commons
John Luo, Jianjin Xu, Yifei Lin

и другие.

Frontiers in Psychiatry, Год журнала: 2025, Номер 16

Опубликована: Март 4, 2025

Background This study employs a network analysis approach to explore the interconnections between anxiety, depression, and sleep problems smartphone addiction among college students using analysis, offering new perspective on these prevalent mental health issues. Methods A cross-sectional was conducted art at public university in province of Fujian, China. Data were collected Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-9, Pittsburgh Sleep Quality Index, Mobile Phone Addiction Index. The R package used for statistical information multi-stage sampling as well stratified sampling. Network utilized identify bivariate associations symptoms, core components, co-occurring patterns, key nodes within network. stability accuracy assessed bootstrap method, comparisons across subgroups based gender, residential condition, sibling status. Results included 2,057 participants. revealed uncontrollable worry most central symptom, with low energy excessive also identified symptoms Bridge such daytime dysfunction, self-harm or suicidal ideation, abnormal behavior speech, sensory fear found be critical linking problems. comorbid highlighted inefficiency loss control factors influencing health. No significant differences characteristics subgroups, suggesting universality structure. Conclusion delineates intricate problems, students, identifying symptomatic intersections their implications

Язык: Английский

Процитировано

0
Identifying behavior regulatory leverage over mental disorders transcriptomic network hubs toward lifestyle-dependent psychiatric drugs repurposing DOI Creative Commons

Mennatullah Abdelzaher Turky,

Ibrahim Youssef, Azza El Amir

и другие.

Human Genomics, Год журнала: 2025, Номер 19(1)

Опубликована: Март 19, 2025

Abstract Background There is a vast prevalence of mental disorders, but patient responses to psychiatric medication fluctuate. As food choices and daily habits play fundamental role in this fluctuation, integrating machine learning with network medicine can provide valuable insights into disease systems the regulatory leverage lifestyle health. Methods This study analyzed coexpression modules MDD PTSD blood transcriptomic profile using modularity optimization method, first runner-up Disease Module Identification DREAM challenge . The top genes both were detected random forest model. Afterward, signature two predominant habitual phenotypes, diet-induced obesity smoking, identified. These transcription/translation regulating factors ( TRFs ) signals transduced toward disorders’ genes. A bipartite drugs that target TRFS together or hubs was constructed. Results research revealed one hub, CENPJ, which known influence intellectual ability. observation paves way for additional investigations potential CENPJ as novel therapeutic agents development. Additionally, most predicted associated multiple carcinomas, notable SHCBP1. SHCBP1 risk factor glioma, suggesting importance continuous monitoring patients mitigate cancer comorbidities. signaling illustrated three biomarkers co-regulated by phenotype TRFs. 6-Prenylnaringenin Aflibercept identified candidates targeting hubs: ATP6V0A1 PIGF. However, have no over Conclusion Combining biology succeeded revealing notoriously spreading PTSD. approach offers non-invasive diagnostic pipeline identifies drug targets could be repurposed under further investigation. findings contribute our understanding complex interplay between habits, interventions, thereby facilitating more targeted personalized treatment strategies.

Язык: Английский

Процитировано

0