Mapping the role of sexuality in adolescent mental health and substance use.

Individuals who belong to a sexual minority are at greater risk of adverse health and social outcomes. These effects are observed during adolescence when many mental health problems, such as depression, first emerge. Here, we used a network analytic approach to better understand the role that sexual minority status plays in the association between depression, interpersonal difficulties and substance use in a large sample of mid-adolescents. In doing so, we used data from 8017 fourteen year olds from the UK's Millennium Cohort Study, of which 490 self-identified as belonging to a sexual minority. We found that sexual minority status was highly central in the network and connected to multiple adverse outcomes, sometimes directly and sometimes indirectly. The largest single association was between sexual minority status and depression, and this link mediated multiple negative associations with being in a sexual minority. The shortest path to drinking, poor social support and closeness with parents and victimization occurred via depression. The shortest path to smoking and drug use occurred via conduct problems. We also identified three distinct profiles of adverse outcomes among those belonging to a sexual minority, highlighting the heterogeneous nature of this group.

Spiritual Care in Palliative Medicine and End of Life: A Bibliometric Network Analysis.

Background and Objectives: Spiritual care is an essential component of care for the terminally ill, because of its potential to positively impact patient perception of quality of life and dignity. However, it continues to be the least cultivated or even most overlooked aspect of palliative care and end of life. We performed a methodological review using bibliometric analysis to provide a holistic view of the scientific output published on this topic in the literature at the same time outlining present perspectives and research trends. Methods: In accordance with the BIBLIO checklist for reporting the bibliometric reviews of the biomedical literature, pertinent articles were retrieved from the Web of Science (WOS) database. The search string included "spiritual care," "end of life," and their synonyms. The VOSviewer (version 1.6.17) software was used to conduct comprehensive analyses. Semantic and research networks, bibliographic coupling, and journal analysis were examined. Results: A total of 924 articles were identified in WOS, and 842 were retrieved. An increasing trend in the number of publications is observed from 1981 to date, with a peak in the 2019-2021 timeframe. Most articles focused on palliative care, spirituality, spiritual care, religion, end of life, and cancer. The Journal of Pain and Symptom Management contributed the highest number of published documents, while the Journal of Palliative Medicine was the top-cited journal. The highest number of publications originated from collaborations of authors from the United Kingdom, the United States, and Australia. Conclusion: The remarkable increase in the number of publications on spiritual care observed in the years of the COVID-19 pandemic likely reflected global concerns, reasserting the importance of prioritizing spiritual care for whole-person palliation. Spiritual care is integrated with palliative care, in line with the latter's holistic nature and the recognition of spirituality as a fundamental aspect of end-of-life care. Nurses and chaplains exhibited more involvement in palliative-spiritual care than physicians reflecting the belief that chaplains are perceived as specialized providers, and nurses, owing to their direct exposure to spiritual suffering and ethos, are deemed suitable for providing spiritual care.

Bayesian network analysis of drug treatment strategies for thyroid associated ophthalmopathy.

BACKGROUND: The first line treatment for moderate to severe active thyroid associated ophthalmopathy is glucocorticoid pulse therapy, but for patients with contraindications to hormone therapy or hormone resistance, it is urgent to find a suitable treatment plan. AIMS: To find a reliable alternative to hormone pulse therapy for thyroid associated ophthalmopathy by comparing the efficacy with first-line treatment regimens. METHODS: Search PubMed, Ovid, Web of science, Cochrane library, and Clinical Trials.gov for randomized controlled trials on the treatment of thyroid associated ophthalmopathy published as of July 7, 2024. Quality evaluation and Bayesian network analysis were conducted using RevMan 5.3 software, STATA15.0 software, and ADDIS 1.16.8 software. RESULTS: A total of 666 patients were included in 11 studies and 8 interventions. Network analysis showed that the three interventions of mycophenolate mofetil combined with glucocorticoids, Teprotumumab and 99Tc-MDP were superior to glucocorticoid pulse therapy in improving clinical activity scores and proptosis. The regimen of glucocorticoids combined with statins can improve the quality of life score and diplopia score of patients. Neither methotrexate combined with glucocorticoids nor rituximab alone showed additional advantages when compared with glucocorticoid pulse therapy. CONCLUSION: Mycophenolate mofetil combined with glucocorticoid therapy is very beneficial for moderate to severe active thyroid associated ophthalmopathy. Mycophenolate mofetil may be a good choice when patients have contraindications to hormone use or hormone resistance. Teprotumumab is very promising and may be able to avoid patients undergoing orbital decompression surgery. The durability and safety of its long-term efficacy need to be further observed.

A protocol for assessing bias and robustness of social network metrics using GPS based radio-telemetry data.

BACKGROUND: Social network analysis of animal societies allows scientists to test hypotheses about social evolution, behaviour, and dynamic processes. However, the accuracy of estimated metrics depends on data characteristics like sample proportion, sample size, and frequency. A protocol is needed to assess for bias and robustness of social network metrics estimated for the animal populations especially when a limited number of individuals are monitored. METHODS: We used GPS telemetry datasets of five ungulate species to combine known social network approaches with novel ones into a comprehensive five-step protocol. To quantify the bias and uncertainty in the network metrics obtained from a partial population, we presented novel statistical methods which are particularly suited for autocorrelated data, such as telemetry relocations. The protocol was validated using a sixth species, the fallow deer, with a known population size where ∼ 85 % of the individuals have been directly monitored. RESULTS: Through the protocol, we demonstrated how pre-network data permutations allow researchers to assess non-random aspects of interactions within a population. The protocol assesses bias in global network metrics, obtains confidence intervals, and quantifies uncertainty of global and node-level network metrics based on the number of nodes in the network. We found that global network metrics like density remained robust even with a lowered sample size, while local network metrics like eigenvector centrality were unreliable for four of the species. The fallow deer network showed low uncertainty and bias even at lower sampling proportions, indicating the importance of a thoroughly sampled population while demonstrating the accuracy of our evaluation methods for smaller samples. CONCLUSIONS: The protocol allows researchers to analyse GPS-based radio-telemetry or other data to determine the reliability of social network metrics. The estimates enable the statistical comparison of networks under different conditions, such as analysing daily and seasonal changes in the density of a network. The methods can also guide methodological decisions in animal social network research, such as sampling design and allow more accurate ecological inferences from the available data. The R package aniSNA enables researchers to implement this workflow on their dataset, generating reliable inferences and guiding methodological decisions.

Phylogenetic systematics of Vigna sensu stricto in the context of Physostigma and allies.

PREMISE: Vigna includes economically vital crops and wild species. Molecular systematic studies of Vigna species resulted in generic segregates of many New World (NW) species. However, limited Old World (OW) sampling left questions regarding inter- and intraspecific relationships in Vigna s.s. METHODS: African species, including the putative sister genus Physostigma, were comprehensively sampled within the context of NW relatives. Maximum likelihood and Bayesian inference analyses of the chloroplast matK-trnK and nuclear ribosomal ITS/5.8 S (ITS) DNA regions were undertaken to resolve OW Vigna taxonomic questions. Divergence dates were estimated using BEAST to date key nodes in the phylogeny. RESULTS: Analyses of matK and ITS data supported five clades of Vigna s.s.: subg. Lasiospron, a reduced subg. Vigna, subg. Haydonia, subg. Ceratotropis, an enlarged subg. Plectrotropis, and a clade including V. kirkii and V. stenophylla. Genome size estimates of 601 Mb for V. kirkii are near the overall mean of the genus, whereas V. stenophylla had a larger genome (810 Mb), similar to some Vigna subg. Ceratotropis or Plectrotropis species. CONCLUSIONS: Former subg. Vigna is reduced to yellow- and blue-flowered species and subg. Plectrotropis is enlarged to mostly all white-, pink-, and purple-flowered species. The age of the split between NW and OW Vigna lineages is ~6-7 Myr. Genome size estimates cannot rule out a polyploid or hybrid origin for V. stenophylla, potentially involving extinct lineage ancestors of Vigna subg. Ceratotropis or Plectrotropis, as indicated by network and phylogenetic analyses. Taxonomic revisions are suggested based on these results.

Weighted gene coexpression network analysis and machine learning for the determination of tfh cell and B cell infiltrating biomarkers in thymoma-associated myasthenia gravis.

Patients with thymoma (THYM)-associated myasthenia gravis (MG) typically have a poor prognosis and recurring illness. This study aimed to discover important biomarkers associated with immune cell infiltration and THYM-associated MG (THYM-MG) development. Gene expression microarray data were downloaded from The Cancer Genome Atlas website (TCGA) and Gene Expression Omnibus (GEO). A total of 102 differentially expressed genes were investigated. According to the immune infiltration data, the distribution of Tfh cells, B cells, and CD4 T cells differed significantly between the THYM-MG and THYM-NMG groups. WGCNA derived 25 coexpression modules; one hub module (the blue module) strongly correlated with Tfh cells. Combining differential genes revealed 21 intersecting genes. LASSO analysis subsequently revealed 16 hub genes as potential THYM-MG biomarkers. ROC curve analysis of the predictive model revealed moderate diagnostic value. The association between the 16 hub genes and infiltrating immune cells was further evaluated in TIMER2.0 and the validation dataset. Draggability analysis identified the therapeutic target genes PTGS2 and ALB, along with significant drugs including Firocoxib, Alclofenac, Pyridostigmine, and Stavudine. This was validated through MD simulation, PCA, and MM-GBSA analyses. The interaction between numerous activated B cells and follicular helper T cells is closely associated with THYM-MG pathogenesis from a bioinformatics perspective. Hub genes (including SP6, SCUBE3, B3GNT7, and MAGEL2) may be downregulated in immune cells in THYM-MG and associated with progression.

Myrrh Essential Oil Improves DSS-Induced Colitis by Modulating the MAPK Signaling Pathway: In vitro and in vivo Studies.

Objective: To explore the mechanism and active components of the anti-colitis effects of myrrh essential oil (MEO). Methods: In this study, we investigated the anti-inflammatory effects and molecular mechanisms of MEO on dextran sulfate sodium (DSS)-induced colitis with in vitro cell experiments, RNA-seq (RNA Sequencing), Weighted gene co-expression network analysis (WGCNA), combined with "weighting coefficient" network pharmacology, as and in vivo pharmacodynamic experiments. A 3% DSS solution was used to induce colitis in BALB/c mice and MEO was administered orally. We performed gas chromatography-mass spectrometry (GC-MS) analysis of the MEO components. The disease activity index (DAI) was evaluated by observing body weight, fecal characteristics, and blood in the stool of mice. The levels of inflammatory cytokines (TNF-α and IL-1ß) in mouse serum were measured using ELISA (Enzyme-linked immunosorbent assay) kits. Additionally, the expression of MAPK-related proteins (JNK, p-JNK, ERK, and p-ERK) in mouse colonic tissues was detected by Western blotting and immunohistochemistry. Results: MEO (0.0625-0.125µg/g, p.o). significantly inhibited the expression of the inflammatory mediator Nitric oxide (NO) in lipopolysaccharide (LPS)-induced RAW264.7 macrophages. After treatment, there was a significant increase in body weight and alleviation of diarrhea and bloody stools in colitis mice. It also reduced inflammatory cell infiltration. Furthermore, it decreased the serum levels of TNF-α and IL-1ß, and reduced the activity of p-JNK and p-ERK in the MAPK pathway. Conclusion: MEO relieved DSS-induced colitis by modulating the MAPK pathway. The experimental results indicate that the MAPK pathway might be inhibited by the synergistic effect of gamma-Muurolene, Curzerene, beta-Elemene, and Furanoeudesma 1.3-diene in MEO, which provides a novel idea for subsequent research and development of new anti-colitis drugs.

A python library for the fast and scalable computation of biologically meaningful individual specific networks.

Individual Specific Networks (ISNs) are a tool used in computational biology to infer Individual Specific relationships between biological entities from omics data. ISNs provide insights into how the interactions among these entities affect their respective functions. To address the scarcity of solutions for efficiently computing ISNs on large biological datasets, we present ISN-tractor, a data-agnostic, highly optimized Python library to build and analyse ISNs. ISN-tractor demonstrates superior scalability and efficiency in generating Individual Specific Networks (ISNs) when compared to existing methods such as LionessR, both in terms of time and memory usage, allowing ISNs to be used on large datasets. We show how ISN-tractor can be applied to real-life datasets, including The Cancer Genome Atlas (TCGA) and HapMap, showcasing its versatility. ISN-tractor can be used to build ISNs from various -omics data types, including transcriptomics, proteomics, and genotype arrays, and can detect distinct patterns of gene interactions within and across cancer types. We also show how Filtration Curves provided valuable insights into ISN characteristics, revealing topological distinctions among individuals with different clinical outcomes. Additionally, ISN-tractor can effectively cluster populations based on genetic relationships, as demonstrated with Principal Component Analysis on HapMap data.

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.

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.

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.

Inhibitory effect of human interleukin-24 on the proliferation, migration, and invasion of cervical cancer cells.

OBJECTIVE: This study aimed to identify significantly differentially expressed genes (DEGs) related to cervical cancer by exploring extensive gene expression datasets to unveil new therapeutic targets. METHODS: Gene expression profiles were extracted from the Gene Expression Omnibus, The Cancer Genome Atlas, and the Genotype-Tissue Expression platforms. A differential expression analysis identified DEGs in cervical cancer cases. Weighted gene co-expression network analysis (WGCNA) was implemented to locate genes closely linked to the clinical traits of diseases. Machine learning algorithms, including LASSO regression and the random forest algorithm, were applied to pinpoint key genes. RESULTS: The investigation successfully isolated DEGs pertinent to cervical cancer. Interleukin-24 was recognized as a pivotal gene via WGCNA and machine learning techniques. Experimental validations demonstrated that human interleukin (hIL)-24 inhibited proliferation, migration, and invasion, while promoting apoptosis, in SiHa and HeLa cervical cancer cells, affirming its role as a therapeutic target. CONCLUSION: The multi-database analysis strategy employed herein emphasized hIL-24 as a principal gene in cervical cancer pathogenesis. The findings suggest hIL-24 as a promising candidate for targeted therapy, offering a potential avenue for innovative treatment modalities. This study enhances the understanding of molecular mechanisms of cervical cancer and aids in the pursuit of novel oncological therapies.

Co-Expression Network Analysis and Molecular Docking Demonstrate That Diosgenin Inhibits Gastric Cancer Progression via SLC1A5/mTORC1 Pathway.

Background: Tumor-Node-Metastasis (TNM) stage of gastric cancer (GC) is one of the main factors affecting clinical outcome. The aim of this study was to explore the targets related to TNM stage of GC, and screening natural bioactive drug. Methods: RNA sequencing data of the TCGA-STAD cohort were downloaded from UCSC database. Genes associated with TNM staging were identified by weighted gene co-expression network analysis (WGCNA). Univariate Cox regression, least absolute shrinkage and selection operator (LASSO), extreme gradient boosting (Xgboost), random forest (RF) and cytohubba plug-in of cytoscope were applied to screen hub genes. Natural bioactive ingredients were available from the HERB database. Molecular docking was used to evaluate the binding activity of active ingredients to the hub protein. CCK-8, flow cytometry, transwell and Western blot assays were used to analyze the effects of diosgenin on GC cells. Results: 898 TNM-related genes were screened out through WGCNA. Three genes associated with GC progression/prognosis were identified, including nuclear receptor subfamily 3 group C member 2 (NR3C2), solute carrier family 1 member 5 (SLC1A5) and FAT atypical cadherin 1 (FAT1) based on the machine learning algorithms and hub co-expression network analysis. Diosgenin had good binding activity with SLC1A5. SLC1A5 was highly expressed in GC and was closely associated with tumor stage, overall survival and immune infiltration of GC patients. Diosgenin could inhibit cell viability and invasive ability, promote apoptosis and induce cell cycle arrest in G0/G1 phase. In addition, diosgenin promoted cleaved caspase 3 expression and inhibited Ki67, cyclin D1, p-S6K1, and SLC1A5 expression levels, while the mTORC1 activator (MHY1485) reversed this phenomenon. Conclusion: For the first time, this work reports diosgenin may inhibit the activation of mTORC1 signaling through targeting SLC1A5, thereby inhibiting the malignant behaviors of GC cells.

Unveiling the multi-dimensional frailty network among older cancer survivors in China: A network analysis study.

INTRODUCTION: Frailty has a significant impact on the overall quality of life of older cancer survivors, but the relationships among frailty symptoms are not well understood. This study aims to explore the specific associations among multidimensional symptoms of frailty among older cancer survivors by employing network analysis to provide supportive evidence for targeted interventions in the future. MATERIALS AND METHODS: Data were obtained by cluster sampling from three large Grade-A tertiary hospitals in Shandong Province, China, and collected through face-to-face interviews by trained investigators. We included patients who were diagnosed with a solid malignant tumor at the age of 60 years or older. Frailty indicators were measured by the Groningen Frailty Indicator (GFI) and analyzed primarily through network analysis, including network estimation, centrality, and stability analysis. The relative importance of a node in a network was tested by centrality analyses, and Spearman correlations were applied to estimate the relationships between symptom pairs (symptom score) and symptom clusters (standardized symptom score) in the symptom network. In terms of centrality, the indexes of strength, closeness, and betweenness were adopted to measure the importance of nodes. RESULTS: Five hundred and eight older cancer survivors were included, with an average age of 68.4 years (standard deviation [SD] = 5.4), and a higher proportion were male (n = 307[60.4%]). The prevalence of frailty among older cancer survivors was 58.9% (n = 299), with a mean GFI score of 4.46 (SD = 2.87). The strongest edge was between "dressing and undressing" and "going to the toilet" (r = 0.58). The nodes with the higher strength centrality were "going to the toilet" (rS=1.09), "walking around outside" (rS=0.97), and "part of social network" (rS=0.96); and the nodes with the higher closeness centrality were "mark physical fitness" (rC=0.005), "calm and relaxed" (rC=0.005), and "nervous or downhearted" (rC=0.005). DISCUSSION: This study demonstrated that older cancer survivors in China have a high prevalence of frailty, with self-care and social participation-related symptoms playing a key role in the multidimensional network of frailty symptoms. Psychological symptoms can rapidly influence other symptoms within this network. Therefore, prioritizing psychological symptoms in the assessment of older adults with cancer is essential for effective frailty management.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022.

BACKGROUND: Gastrointestinal neoplasm (GN) significantly impact the global cancer burden and mortality, necessitating early detection and treatment. Understanding the evolution and current state of research in this field is vital. AIM: To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research, focusing on key contributors, institutions, and thematic evolution. METHODS: This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the "bibliometrix" R package, VOSviewer, and CiteSpace. The analysis focused on the distribution of publications, contributions by institutions and countries, and trends in keywords. The methods included data synthesis, network analysis, and visualization of international collaboration networks. RESULTS: This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration. It highlights the United States' critical role in advancing this field, with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute. The last five years, substantial advancements have been made, representing nearly 45% of the examined literature. Publication rates have dramatically increased, from 20 articles in 2002 to 112 in 2022, reflecting intensified research efforts. This study underscores a growing trend toward interdisciplinary and international collaboration, with the Journal of Clinical Oncology standing out as a key publication outlet. This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks. CONCLUSION: This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis. This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy, ultimately enhancing worldwide patient care.

Constructing networks for comparison of collagen types.

Collagens are structural proteins that are predominantly found in the extracellular matrix of multicellular animals, where they are mainly responsible for the stability and structural integrity of various tissues. All collagens contain polypeptide strands (α-chains). There are several types of collagens, some of which differ significantly in form, function, and tissue specificity. Because of their importance in clinical research, they are grouped into subdivisions, the so-called collagen families, and their sequences are often analysed. However, problems arise with highly homologous sequence segments. To increase the accuracy of collagen classification and prediction of their functions, the structure of these collagens and their expression in different tissues could result in a better focus on sequence segments of interest. Here, we analyse collagen families with different levels of conservation. As a result, clusters with high interconnectivity can be found, such as the fibrillar collagens, the COL4 network-forming collagens, and the COL9 FACITs. Furthermore, a large cluster between network-forming, FACIT, and COL28a1 α-chains is formed with COL6a3 as a major hub node. The formation of clusters also signifies, why it is important to always analyse the α-chains and why structural changes can have a wide range of effects on the body.

Estimation of Personal Symptom Networks Using the Ising Model for Adult Survivors of Childhood Cancer: A Simulation Study with Real-World Data Application.

Purpose: Childhood cancer survivors experience interconnected symptoms, patterns of which can be elucidated by network analysis. However, current symptom networks are constructed based on the average survivors without considering individual heterogeneities. We propose to evaluate personal symptom network estimation using the Ising model with covariates through simulations and estimate personal symptom network for adult childhood cancer survivors. Patients and Methods: We adopted the Ising model with covariates to construct networks by employing logistic regressions for estimating associations between binary symptoms. Simulation experiments assessed the robustness of this method in constructing personal symptom network. Real-world data illustration included 1708 adult childhood cancer survivors from the St. Jude Lifetime Cohort Study (SJLIFE), a retrospective cohort study with prospective follow-up to characterize the etiology and late effects for childhood cancer survivors. Patients' baseline symptoms in 10 domains (cardiac, pulmonary, sensation, nausea, movement, pain, memory, fatigue, anxiety, depression) and individual characteristics (age, sex, race/ethnicity, attained education, personal income, and marital status) were self-reported using survey. Treatment variables (any chemo or radiation therapy) were obtained from medical records. Personal symptom network of 10 domains was estimated using the Ising model, incorporating individual characteristics and treatment data. Results: Simulations confirmed the robustness of the Ising model with covariates in constructing personal symptom networks. Real-world data analysis identified age, sex, race/ethnicity, education, marital status, and treatment (any chemo and radiation therapy) as major factors influencing symptom co-occurrence. Older childhood cancer survivors showed stronger cardiac-fatigue associations. Survivors of racial/ethnic minorities had stronger pain-fatigue associations. Female survivors with above-college education demonstrated stronger pain-anxiety associations. Unmarried survivors who received radiation had stronger association between movement and memory problems. Conclusion: The Ising model with covariates accurately estimates personal symptom networks. Individual heterogeneities exist in symptom co-occurrence patterns for childhood cancer survivors. The estimated personal symptom network offers insights into interconnected symptom experiences.

Exploring potential ion channel targets for rheumatoid arthritis: combination of network analysis and gene expression analysis.

Rheumatoid arthritis (RA) is a systemic autoimmune disease characterized by chronic inflammation of the synovial membrane that leads to the destruction of cartilage and bone. Currently, pharmacological targeting of ion channels is being increasingly recognized as an attractive and feasible strategy for the treatment of RA. The present work employs a network analysis approach to predict the most promising ion channel target for potential RA-treating drugs. A protein-protein interaction map was generated for 343 genes associated with inflammation in RA and ion channel genes using Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape. Based on the betweenness centrality and traffic values as key topological parameters, 17 hub nodes were identified, including FOS (9800.85), tumor necrosis factor (3654.60), TGFB1 (3305.75), and VEGFA (3052.88). The backbone network constructed with these 17 hub genes was intensely analyzed to identify the most promising ion channel target using network analyzer. Calcium permeating ion channels, especially store-operated calcium entry channels, and their associated regulatory proteins were found to highly interact with RA inflammatory hub genes. This significant ion channel target for RA identified by theoretical and statistical studies was further validated by a pilot case-control gene expression study. Experimental verification of the above findings in 75 RA cases and 25 controls showed increased ORAI1 expression. Thus, with a combination of network analysis approach and gene expression studies, we have explored potential targets for RA treatment.

Assessment of Recovery Time Effects on Human Primary Neonatal Dermal Fibroblasts After Exposure to Solar-Simulated Ultraviolet Radiation.

Introduction: Photoaging that is accompanied by gene expression alteration is known as early aging of the skin due to overexposure to natural and/or artificial ultraviolet radiation (UVR). The assessment of gene expression alteration in human primary neonatal dermal fibroblasts depending on recovery time after exposure to solar simulated ultraviolet radiation (ssUVR) is the main aim of this bioinformatic study. Methods: Data are extracted from Gene Expression Omnibus (GEO). The pre-evaluation is done via the GEO2R program. The Significant differentially expressed genes (DEGs) were assessed via protein-protein interaction (PPI) network analysis, and the central genes were identified. The central genes were enriched via gene ontology assessment. Results: Among 224 significant DEGs, 20 central genes including TOP2A, MKI67, BRCA1, HELLS, MAD2L1, ANLN, KIF11, MSH2, KRAS, NCAPG, RFC3, PLK4, WDHD1, BLM, CDKN3, KIF15, SMARCA5, and ATAD2 as hub genes and TOP2A, MKI67, BRCA1, ANLN, KRAS, PLK4, SMARCA5, MMP2, and TLR4 as bottleneck genes were determined. Eight central genes were associated with 16 biological terms. Conclusion: In conclusion, significant differences appeared between gene expression conditions of the cells after 1-day and 5-day recovery. Molecular events include the repair and continuation of photodamages. It is possible to introduce drug targets to prevent the progress of induced damages.