Network analysis of the urban-rural differences in depressive symptoms among older adults with multiple chronic conditions: Evidence from a national survey.

BACKGROUND: Evidence on the differences in depressive symptoms among older adults with multiple chronic conditions (MCCs) in urban and rural areas is limited. METHODS: Measures of depressive symptoms (Center for Epidemiologic Studies Depression Scale-10) and demographic factors (age, gender, and urban-rural distribution) were used. RESULTS: A total of 4021 older adults with MCCs were included in this study. Significant differences were observed in both network global strength (Urban: 3.989 vs. Rural: 3.703, S = 0.286, p = 0.003) and network structure (M = 0.139, p = 0.002) between urban and rural residents. CONCLUSIONS: The study highlights the need for region-specific approaches to understanding and addressing depression and holds the potential to enhance understanding of the psychological health status of older adults with MCCs in urban and rural settings.

Gender difference in the relationship between clinical symptoms, thyroid hormones, and metabolic parameters in young, first-episode and drug-naïve major depressive disorder patients with suicide attempts: A network analysis perspective.

BACKGROUND: Suicide attempts are one of the most serious comorbidities in patients with major depressive disorder (MDD), and the prevalence of suicide attempts is higher in younger people compared to older people, with significant gender differences. This study aimed to investigate the relationship between suicide attempts, clinical symptoms, thyroid hormones, and metabolic parameters in young first-episode and drug-naïve (FEND) MDD patients of different genders. METHODS: A total of 1289 FEND MDD patients were recruited. Depression, anxiety, and psychotic symptoms were assessed using the Hamilton Depression Rating Scale (HAMD), Hamilton Anxiety Rating Scale (HAMA), and the Positive and Negative Syndrome Scale (PANSS) positive subscale, respectively. Thyroid hormones and glucolipid metabolism indicators were also tested. Network analysis was employed to delineate the interplay between thyroid dysfunction, clinical symptoms, and metabolic disorders. RESULTS: Among young FEND MDD patients, the rate of suicide attempts was 17.4% in males and 19.8% in females, showing no significant gender difference in the incidence of suicide attempts (χ2 = 1.06, p = 0.303). In the network model, PANSS positive subscale (Expected Influence = 0.578) and HAMD scores (Expected Influence = 0.576) were identified as the individual symptoms that most affected male patients, whereas TSH (Thyroid-Stimulating Hormone) (Expected Influence = 0.972) and PANSS positive subscale (Expected Influence = 0.937) were identified as the individual symptoms that most affected female patients. In addition, we found that TSH (Expected Influence = 0.438) was a pivotal node connecting metabolic disturbances and clinical symptoms. CONCLUSION: Our findings emphasize the important role of psychotic symptoms in young MDD patients with suicide attempts. Moreover, our results highlight the pivotal role of serum TSH levels in the pathophysiology of young female MDD patients with suicide attempts.

Do Adolescents Adopt the Prosocial Behaviors of the Classmates They Like? A Social Network Analysis on Prosocial Contagion.

While the influence of high-status peers on maladaptive behaviors is well-documented, socialization processes of prosocial behavior through high-status peers remain understudied. This study examined whether adolescents' prosocial behavior was influenced by the prosocial behavior of the peers they liked and whether this effect was stronger when the peers they liked were also well-liked by their classmates. Three waves of data, six months apart, were collected among Chilean early adolescents who completed peer nominations and ratings at Time 1 (n = 294, Mage = 13.29, SD = 0.62; 55.1% male), Time 2 (n = 282), and Time 3 (n = 275). Longitudinal social network analyses showed that adolescents adopted the prosocial behavior of the classmates they liked - especially if these classmates were well-liked by peers in general. In addition, adolescents low in likeability were more susceptible to this influence than adolescents high in likeability. The influence resulted both in increases and - especially - decreases in prosocial behavior, depending on the level of prosociality of the liked peer. Findings suggest that likeability represents an important aspect of peer status that may be crucial for understanding the significance of peer influence with respect to prosocial behaviors during adolescence. Pre-Registration: https://osf.io/u4pxm .

Network analysis of occupational stress and job satisfaction among radiologists.

Background: Occupational stress and job satisfaction significantly impact the well-being and performance of healthcare professionals, including radiologists. Understanding the complex interplay between these factors through network analysis can provide valuable insights into intervention strategies to enhance workplace satisfaction and productivity. Method: In this study, a convenience sampling method was used to recruit 312 radiologists for participation. Data on socio-demographic characteristics, job satisfaction measured by the Minnesota job satisfaction questionnaire revised short version (MJSQ-RSV), and occupational stress assessed using the occupational stress scale. Network analysis was employed to analyze the data in this study. Results: The network analysis revealed intricate patterns of associations between occupational stress and job satisfaction symptoms among radiologists. Organizational management and occupational interests emerged as crucial nodes in the network, indicating strong relationships within these domains. Additionally, intrinsic satisfaction was identified as a central symptom with high connectivity in the network structure. The stability analysis demonstrated robustness in the network edges and centrality metrics, supporting the reliability of the findings. Conclusion: This study sheds light on the complex relationships between occupational stress and job satisfaction in radiologists, offering valuable insights for targeted interventions and support strategies to promote well-being and job satisfaction in healthcare settings.

Gender dynamics and academic rank in emergency medicine collaboration networks: A social network and gender propensity analysis.

BACKGROUND: Academic productivity is bolstered by collaboration, which is in turn related to connectivity between individuals. Gender disparities have been identified in academics in terms of both academic promotion and output. Using gender propensity and network analysis, we aimed to describe patterns of collaboration on publications in emergency medicine (EM), focusing on two Midwest academic departments. METHODS: We identified faculty at two EM departments, their academic rank, and their publications from 2020 to 2022 and gathered information on their co-authors. Using network analysis, gender propensity and standard statistical analyses we assessed the collaboration network for differences between men and women. RESULTS: Social network analysis of collaboration in academic emergency medicine showed no difference in the ways that men and women publish together. However, individuals with higher academic rank, regardless of gender, had more importance to the network. Men had a propensity to collaborate with men, and women with women. The rates of gender propensity for men and women fell between the gender ratios of emergency medicine (65%/35%) and the general population (50%/50%), 59.6% and 44%, respectively, suggesting a tendency toward homophily among men. CONCLUSION: Our study aims to use network analysis and gender propensity to identify patterns of collaboration. We found that further work in the area of network analysis application to academic productivity may be of value, with a particular focus on the role of academic rank. Our methodology may aid department leaders by using the information from local analyses to identify opportunities to support faculty members to broaden and diversify their networks.

A network analysis of positive psychosocial factors and indication of suboptimal HIV care outcomes among Black women living with HIV.

Black women living with HIV (BWLWH) face barriers that impact health outcomes. However, positive psychosocial indicators may influence HIV care outcomes. Among this cross-sectional study of 119 BWLWH, a network analysis was utilized to examine relationships between positive psychosocial factors and HIV-related health outcomes. A preliminary polychoric analysis was conducted to examine correlations between the variables, and the network analyzed connections between resilience, self-efficacy, self-esteem, perceived social support, religious coping, post-traumatic growth, and an indicator variable for suboptimal HIV care outcomes (low medication adherence, detectable viral load, and missed HIV-related health visits) and determined the centrality measures within the network. Seven significant associations were found among the factors: self-efficacy and self-esteem, post-traumatic growth and resilience, post-traumatic growth and self-efficacy, post-traumatic growth and religious coping, perceived social support and resilience, self-esteem and resilience, self-esteem and perceived social support (bootstrapped 95% CI did not contain zero). Self-efficacy was the strongest indicator associated with the other factors. Although not statistically significant, the indicator for suboptimal HIV care outcomes was negatively associated with perceived social support and religious coping. Future interventions incorporating self-efficacy may be beneficial to the overall well-being of Black women.

Contemporaneous Symptom Networks of Breast Cancer-Related Upper Limb Lymphedema: A Network Analysis.

BACKGROUND: Upper limb lymphedema (ULL) is a common and deliberating complication for breast cancer survivors (BCSs). Breast cancer survivors with ULL reported a wide range of symptoms. However, little is known about symptom patterns and interrelationships among them. This study was designed to explore symptom clusters and construct symptom networks of ULL-related symptoms among BCSs and to identify the core symptoms. METHODS: This study is a secondary data analysis using datasets from three cross-sectional studies of BCSs in China. A total of 341 participants with maximum interlimb circumference ≥2 cm and complete responses in Part I of the Breast Cancer and Lymphedema Symptom Experience Index were included. Symptom clusters were identified through principal component analysis, and multiple linear regression analysis was employed to explore factors associated with severity of overall ULL-related symptoms. A contemporaneous network with 20 frequently reported symptoms were constructed after controlling for covariates. RESULTS: Three symptom clusters, including lymph stasis symptom cluster, nerve symptom cluster, and movement limitation symptom cluster, were identified. Postsurgery time, axillary lymph node dissection, and radiotherapy were associated with the severity of ULL-related symptoms. Tightness (rs = 1.379; rscov = 1.097), tingling (rs = 1.264; rscov = 0.925), and firmness (rs = 1.170; rscov = 0.923) were the most central symptoms in both networks with and without covariates. CONCLUSIONS: Breast cancer survivors with ULL experienced severe symptom burden. Tightness, tingling, and firmness were core symptoms of ULL among BCSs. Our findings demonstrated that the assessment and targeted intervention of specific core symptoms might help to relive effectively the burden of ULL-related symptom among BCSs.

Exploring stroke discourse on Twitter through content and network analysis among Indian users.

The study aimed to understand stroke-related Twitter conversations in India, focusing on topics, message sources, reach, and influential users to provide insights to stakeholders regarding community needs for knowledge, support, and interventions. Geo-tagged Twitter posts focusing on stroke originating from India and, spanning from November 7, 2022, to February 28, 2023, were systematically obtained via the Twitter application programming interface, using keywords and hashtags sourced through Symplur Signals. Preprocessing involved the removal of hashtags, stop words, and URLs. The Latent Dirichlet Allocation (LDA) topic model was used to identify recurring stroke-related topics, while influential users were identified through social network analysis. About half of the tweets about stroke in India were about seeking support and post-stroke bereavement sharing and had the highest reachability. Four out of 10 tweets were from the individual twitter users. Tweets on the topic risk factors, awareness and prevention (14.6%) constituted the least proportion, whereas the topic management, research, and promotion had the least retweet ratio. Twitter demonstrates significant potential as a platform for both disseminating and acquiring stroke-related information within the Indian context. The identified topics and understanding of the content of discussion offer valuable resources to public health professionals and organizations to develop targeted educational and engagement strategies for the relevant audience.

Metabolomic and transcriptomic analyses of peach leaves and fruits in response to pruning.

BACKGROUND: Pruning is an important cultivation management option that has important effects on peach yield and quality. However, the effects of pruning on the overall genetic and metabolic changes in peach leaves and fruits are poorly understood. RESULTS: The transcriptomic and metabolomic profiles of leaves and fruits from trees subjected to pruning and unpruning treatments were measured. A total of 20,633 genes and 622 metabolites were detected. Compared with those in the control, 1,127 differentially expressed genes (DEGs) and 77 differentially expressed metabolites (DEMs) were identified in leaves from pruned and unpruned trees (pdLvsupdL), whereas 423 DEGs and 29 DEMs were identified in fruits from the pairwise comparison pdFvsupdF. The content of three auxin analogues was upregulated in the leaves of pruned trees, the content of all flavonoids detected in the leaves decreased, and the expression of almost all genes involved in the flavonoid biosynthesis pathway decreased. The phenolic acid and amino acid metabolites detected in fruits from pruned trees were downregulated, and all terpenoids were upregulated. The correlation analysis revealed that DEGs and DEMs in leaves were enriched in tryptophan metabolism, auxin signal transduction, and flavonoid biosynthesis. DEGs and DEMs in fruits were enriched in flavonoid and phenylpropanoid biosynthesis, as well as L-glutamic acid biosynthesis. CONCLUSIONS: Pruning has different effects on the leaves and fruits of peach trees, affecting mainly the secondary metabolism and hormone signalling pathways in leaves and amino acid biosynthesis in fruits.

Applying Social Network Theory to Vaping in High School: Implications for Person-Centered Intervention.

BACKGROUND: Peer influence on risky behavior is particularly potent in adolescence and varies by gender. Smoking prevention programs focused on peer-group leaders have shown great promise, and a social influence model has proven effective in understanding adult smoking networks but has not been applied to adolescent vaping until 2023. This work aims to apply a social influence model to analyze vaping by gender in a high school network. METHODS: A high school's student body was emailed an online survey asking for gender, age, grade level, vape status, and the names of three friends. Custom Java and MATLAB scripts were written to create a directed graph, compute centrality measures, and perform Fisher's exact tests to compare centrality measures by demographic variables and vape status. RESULTS: Of 192 students in the school, 102 students responded. Students who vape were in closer-knit friend groups than students who do not vape (p < .05). Compared to males who vape, females who vape had more social ties to other students who vape, exhibiting greater homophily (p < .01). Compared to females who do not vape, females who vape were in closer-knit friend groups (p < .05) and had more ties to other students who vape (p < .01). CONCLUSION: Differences in vaping by social connectedness and gender necessitate school and state policies incorporating the social aspect of vaping in public health initiatives. Large-scale research should determine if trends can be generalized across student bodies, and more granular studies should investigate differences in motivations and social influence by demographic variables to individualize cessation strategies.

Severity of depressive and anxious symptoms and its association with birth outcomes among pregnant women during the COVID-19 pandemic: a prospective case-control study.

AIM: Comparing the anxiety and depression severity and their impact on subsequent birth outcomes in pregnant women before and during Omicron wave in Shanghai in 2022. METHODS: The depression-anxiety symptoms networks were compared between the pregnant women during the outbreak period (outbreak group; n = 783) and a matched control group of pregnant women before the outbreak (pre-outbreak group; n = 783). The impact of baseline mental state on follow-up pregnancy and neonatal outcomes was also explored by logistic regression. FINDINGS: Levels of depression and anxiety between the two groups were not significant different. Network analysis showed that central symptom "trouble relaxing" and bridge symptom "depressed mood" shared by both groups. Different symptom associations in different periods of the pandemic. Total scores and sub-symptom scores of prenatal depressive and anxious severities increased the odds ratios of maternal and neonatal syndromes. The influence of mental state on gestational and neonatal outcomes differed across different pandemic periods. CONCLUSION: The Omicron wave did not have a significant negative impact on the depressive and anxious mood in pregnant women. Targeting central and bridge symptoms intervention may be effective in reducing their adverse effects on co-occurring of anxious and depressive mood and birth outcomes.

The influence of service performance in China's sci-tech commissioner system: Using social network analysis and interpretable machine learning.

The Sci-Tech Commissioner System (SCS) is a result of exploratory efforts by the Chinese government to use science and technology to strengthen the agricultural sector. Social network analysis (SNA) and machine learning (ML) techniques make it feasible to assess the service performance in China's SCS by using indicators such as group types and structure features. In this study, SNA and a clustering algorithm were employed to categorize service group types of sci-tech commissioners. By comparing the accuracy of different classification algorithms in predicting the clustering results, LightGBM algorithm was finally select to determine the clustering features of sci-tech commissioners and establish an interpretable ML model. Then, the SHAP was used to algorithm to analyze influences affecting service performance. Results show that the service forms of sci-tech commissioners are group-oriented, and that group types include small groups of young commissioners with close cooperation, larger groups of young and middle-aged commissioners, small groups of middle-aged and old commissioners with close cooperation, and isolated points of highly-influential commissioners. Furthermore, while group size is not the determinant of a commissioner's average performance, group structure and coordination ability were found to be more critical. Moreover, while differences in distinct types of service performance are caused by various factors, but good group structures and extensive social contacts are essential for high service performance.

Symptom cluster study undergoing chemotherapy in breast cancer patients: Latent class analysis and contemporaneous network analysis.

Objective: This study aims to explore the subgroups and networks of symptom clusters in breast cancer patients undergoing chemotherapy, and to provide effective interventions for the core symptoms. Methods: A cross-sectional survey was conducted at four comprehensive hospitals in Foshan City, China, from August to November 2023. A total of 292 participants completed the social determinants of health questionnaire, the numerical rating scale (NRS), the Pittsburgh sleep quality index (PSQI), the Chinese version of the cancer fatigue scale (CFS), and the hospital anxiety and depression Scale (HADS). Latent class analysis (LCA) was utilized to distinguish subgroups, and network analysis was utilized to identify core symptoms among different subgroups. Results: Breast cancer patients undergoing chemotherapy exhibit symptoms were divided into two subgroups: the high burden group of symptoms (72.3%, Class 1) and the low burden group of symptoms (27.7%, Class 2). Education attainment, work status, family monthly income per capita, and daily sleep duration (hours) were associated with subgroup membership. "Panic feelings" (# HADS-A11) were the core symptom in both the full sample and Class 2, while "tension or pain" (# HADS-A1) was the core symptom in Class 1. Conclusions: The core symptoms of fear, enjoyment, nervousness, and pain varied across subgroups of patients and could inform the current strategies for symptom management in breast cancer chemotherapy patients.

Identifying key genes and functionally enriched pathways in acute myeloid leukemia by weighted gene co-expression network analysis.

Acute myeloid leukemia (AML) is characterized by the uncontrolled proliferation of myeloid leukemia cells in the bone marrow and other hematopoietic tissues and is highly heterogeneous. While with the progress of sequencing technology, understanding of the AML-related biomarkers is still incomplete. The purpose of this study is to identify potential biomarkers for prognosis of AML. Based on WGCNA analysis of gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases were employed for investigating potential biomarkers for the prognosis of AML. This study screened a total of 6153 genes by analyzing various changes in 103 acute myeloid leukemia (AML) samples, including gene mutation expression, methylation level distribution, mRNA expression, and AML-related genes in public databases. Moreover, seven AML-related co-expression modules were mined by WGCNA analysis, and twelve biomarkers associated with the AML prognosis were identified from each top 10 genes of the seven co-expression modules. The AML samples were then classified into two subgroups, the prognosis of which is significantly different, based on the expression of these twelve genes. The differentially expressed 7 genes of two subgroups (HOXB-AS3, HOXB3, SLC9C2, CPNE8, MEG8, S1PR5, MIR196B) are mainly involved in glucose metabolism, glutathione biosynthesis, small G protein-mediated signal transduction, and the Rap1 signaling pathway. With the utilization of WGCNA mining, seven gene co-expression modules were identified from the TCGA database, and there are unreported genes that may be potential driver genes of AML and may be the direction to identify the possible molecular signatures to predict survival of AML patients and help guide experiments for potential clinical drug targets.

Tele-connection of embodied carbon emissions from industries in China's trade: A complex network analysis.

Regions can meet their development demands through trade, with the attendant environmental costs being shifted to other regions, and carbon emissions emitted from different industries could be transferred over long distances through the increasingly diversified trade network. However, it remains unclear how regional trade leads to the tele-connection and transfer of embodied carbon emissions form industries, and what is the structure and characteristics of the transfer. Thus, multiregional input‒output models and complex network analysis are employed to reveal the tele-connection of carbon emissions from industries in China. The results show that embodied carbon emissions from trade increased by 869.47 million tons during in five years, with North China being the largest outflow area, while the coastal regions being the inflow areas. Moreover, the secondary industry is the highest source of embodied carbon emissions, accounting for 96.68 % of the volume, and the transfer of carbon emissions mainly occurs in North and East China. In carbon emissions networks, North China holds a controlling position, as analysed by degree and strength. The first 23.3%-30% of nodes carry about 62.6%-72.4% of the entire carbon emissions flow, and the network conforms to scale-free features. Centrality further reveals that northern and coastal areas occupy core positions, with interregional carbon flows dominating the critical pathways in the network. The number of clusters evolved from three to four communities during 2012-2017 in the network, demonstrating that the carbon flow network is developing towards multipolarity and modularity. This study underscores the urgency of mitigating carbon emissions in industrial trade by identifying key nodes and cluster structures in emission networks.

"Using network analysis modularity to group health code systems and decrease dimensionality in machine learning models".

Background: Machine learning (ML) prediction models in healthcare and pharmacy-related research face challenges with encoding high-dimensional Healthcare Coding Systems (HCSs) such as ICD, ATC, and DRG codes, given the trade-off between reducing model dimensionality and minimizing information loss. Objectives: To investigate using Network Analysis modularity as a method to group HCSs to improve encoding in ML models. Methods: The MIMIC-III dataset was utilized to create a multimorbidity network in which ICD-9 codes are the nodes and the edges are the number of patients sharing the same ICD-9 code pairs. A modularity detection algorithm was applied using different resolution thresholds to generate 6 sets of modules. The impact of four grouping strategies on the performance of predicting 90-day Intensive Care Unit readmissions was assessed. The grouping strategies compared: 1) binary encoding of codes, 2) encoding codes grouped by network modules, 3) grouping codes to the highest level of ICD-9 hierarchy, and 4) grouping using the single-level Clinical Classification Software (CCS). The same methodology was also applied to encode DRG codes but limiting the comparison to a single modularity threshold to binary encoding.The performance was assessed using Logistic Regression, Support Vector Machine with a non-linear kernel, and Gradient Boosting Machines algorithms. Accuracy, Precision, Recall, AUC, and F1-score with 95% confidence intervals were reported. Results: Models utilized modularity encoding outperformed ungrouped codes binary encoding models. The accuracy improved across all algorithms ranging from 0.736 to 0.78 for the modularity encoding, to 0.727 to 0.779 for binary encoding. AUC, recall, and precision also improved across almost all algorithms. In comparison with other grouping approaches, modularity encoding generally showed slightly higher performance in AUC, ranging from 0.813 to 0.837, and precision, ranging from 0.752 to 0.782. Conclusions: Modularity encoding enhances the performance of ML models in pharmacy research by effectively reducing dimensionality and retaining necessary information. Across the three algorithms used, models utilizing modularity encoding showed superior or comparable performance to other encoding approaches. Modularity encoding introduces other advantages such as it can be used for both hierarchical and non-hierarchical HCSs, the approach is clinically relevant, and can enhance ML models' clinical interpretation. A Python package has been developed to facilitate the use of the approach for future research.

Temporal dynamics of depressive symptoms and cognitive decline in the oldest old: dynamic time warp analysis of the Leiden 85-plus study.

BACKGROUND: The prevalence of depressive symptoms and cognitive decline increases with age. We investigated their temporal dynamics in individuals aged 85 and older across a 5-year follow-up period. METHODS: Participants were selected from the Leiden 85-plus study and were eligible if at least three follow-up measurements were available (325 of 599 participants). Depressive symptoms were assessed at baseline and at yearly assessments during a follow-up period of up to 5 years, using the 15-item Geriatric Depression Scale (GDS-15). Cognitive decline was measured through various tests, including the Mini Mental State Exam, Stroop test, Letter Digit Coding test and immediate and delayed recall. A novel method, dynamic time warping analysis, was employed to model their temporal dynamics within individuals, in undirected and directed time-lag analyses, to ascertain whether depressive symptoms precede cognitive decline in group-level aggregated results or vice versa. RESULTS: The 325 participants were all 85 years of age at baseline; 68% were female, and 45% received intermediate to higher education. Depressive symptoms and cognitive functioning significantly covaried in time, and directed analyses showed that depressive symptoms preceded most of the constituents of cognitive impairment in the oldest old. Of the GDS-15 symptoms, those with the strongest outstrength, indicating changes in these symptoms preceded subsequent changes in other symptoms, were worthlessness, hopelessness, low happiness, dropping activities/interests, and low satisfaction with life (all P's < 0.01). CONCLUSION: Depressive symptoms preceded cognitive impairment in a population based sample of the oldest old.

Integrative analysis of blood transcriptome profiles in small-cell lung cancer patients for identification of novel chemotherapy resistance-related biomarkers.

Introduction: Chemoresistance constitutes a prevalent factor that significantly impacts thesurvival of patients undergoing treatment for smal-cell lung cancer (SCLC). Chemotherapy resistance in SCLC patients is generally classified as primary or acquired resistance, each governedby distinct mechanisms that remain inadequately researched. Methods: In this study, we performed transcriptome screening of peripheral blood plasma obtainedfrom 17 patients before and after receiving combined etoposide and platinum treatment. We firs testimated pseudo-single-cell analysis using xCell and ESTIMATE and identified differentially expressed genes (DEGs), then performed network analysis to discover key hub genes involved in chemotherapy resistance. Results: Our analysis showed a significant increase in class-switched memory B cell scores acrossboth chemotherapy resistance patterns, indicating their potential crucial role in mediatingresistance. Moreover, network analysis identifed PRICKLE3, TNFSFI0, ACSLl and EP300 as potential contributors to primary resistance, with SNWl, SENP2 and SMNDCl emerging assignificant factors in acquired resistance, providing valuable insights into chemotherapy resistancein SCLC. Discussion: These findings offer valuable insights for understanding chemotherapy resistance and related gene signatures in SCLC, which could help further biological validation studies.

Evidence-based practice in traditional persian medicine (TPM): a stakeholder and social network analysis.

BACKGROUND: The utilization of complementary and alternative medicine (CAM) is experiencing a global surge, accompanied by the adoption of national CAM policies in numerous countries. Traditional Persian medicine (TPM) is highly used as CAM in Iran, and the ongoing scientific evaluation of its interventions and the implementation of evidence-based medicine (EBM) encounters various barriers. Therefore, comprehending the characteristics and interactions of stakeholders is pivotal in advancing EBM within TPM policies. In this study, we utilized both classical stakeholder analysis and social network analysis to identify key stakeholders and potential communication patterns, thereby promoting EBM in TPM policy-making. METHODS: A cross-sectional nationwide stakeholder analysis was conducted in 2023 using snowball sampling. The interviews were carried out using a customized version of the six building blocks of health. Data were collected through semi-structured interviews. Stakeholders were assessed based on five factors (power, interest, influence, position, and competency). The connections and structure of the network were analyzed using degree, betweenness, closeness centrality, and modularity index to detect clusters of smaller networks. RESULTS: Among twenty-three identified stakeholders, the Ministry of Health and Medical Education (MOHME) and the Public were the most powerful and influential. The Iranian Academy of Medical Sciences was the most competent stakeholder. Social network analysis revealed a low density of connections among stakeholders. Pharmaceutical companies were identified as key connectors in the network, while the Public, supreme governmental bodies, and guilds acted as gatekeepers or brokers. The MOHME and Maraji were found to be high-ranking stakeholders based on four different centrality measures. CONCLUSION: This study identifies powerful stakeholders in the network and emphasizes the need to engage uninterested yet significant stakeholders. Recommendations include improving competence through education, strengthening international relations, and fostering stronger relationships. Engaging key connectors and gatekeepers is essential for bridging gaps in the network.

Comprehensive analysis of disulfidptosis-related genes reveals the effect of disulfidptosis in ulcerative colitis.

Ulcerative colitis (UC) is a chronic inflammatory condition of the intestinal tract. Various programmed cell death pathways in the intestinal mucosa are crucial to the pathogenesis of UC. Disulfidptosis, a recently identified form of programmed cell death, has not been extensively reported in the context of UC. This study evaluated the expression of disulfidptosis-related genes (DRGs) in UC through public databases and assessed disulfide accumulation in the intestinal mucosal tissues of UC patients and dextran sulfate sodium (DSS)-induced colitis mice via targeted metabolomics. We utilized various bioinformatics techniques to identify UC-specific disulfidptosis signature genes, analyze their potential functions, and investigate their association with immune cell infiltration in UC. The mRNA and protein expression levels of these signature genes were confirmed in the intestinal mucosa of DSS-induced colitis mice and UC patients. A total of 24 DRGs showed differential expression in UC. Our findings underscore the role of disulfide stress in UC. Four UC-related disulfidptosis signature genes-SLC7A11, LRPPRC, NDUFS1, and CD2AP-were identified. Their relationships with immune infiltration in UC were analyzed using CIBERSORT, and their expression levels were validated by quantitative real-time PCR and western blotting. This study provides further insights into their potential functions and explores their links to immune infiltration in UC. In summary, disulfidptosis, as a type of programmed cell death, may significantly influence the pathogenesis of UC by modulating the homeostasis of the intestinal mucosal barrier.