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PubMed2026

Evaluation of three Aspergillus antibody assays for screening of chronic pulmonary aspergillosis: prospective diagnostic accuracy study.

OBJECTIVES: Chronic pulmonary aspergillosis (CPA) is a frequent complication of pulmonary tuberculosis (PTB), particularly in high-burden settings where access to reliable serological diagnostics remains limited. We evaluated the diagnostic performance of two immunochromatographic technology (ICT) lateral flow assays (LFAs) and an ELISA for CPA screening among patients with active or previously treated PTB. METHODS: In this two-year prospective multicentre diagnostic evaluation, serum from adults with prior or active PTB was tested using the Era Biology Aspergillus IgG ICT LFA, LDBio Aspergillus IgG/IgM ICT LFA, and Bordier Aspergillus fumigatus IgG ELISA. CPA diagnosis was established using a consensus composite reference standard incorporating clinical, immunological, radiological, and microbiological criteria. The Bordier ELISA was used as part of the immunological component of the consensus CPA diagnosis, with a cutoff optical density of ≥1.0. Diagnostic accuracy, agreement statistics, receiver operating characteristic analysis, and latent class analysis (LCA) were performed. RESULTS: Among 340 participants, 24 (7.06%) had CPA. Proportion of participants with positive antibody tests among all tested individuals were 6.76% for LDBio ICT LFA, 20.0% for Era Biology ICT LFA, and 11.47% for Bordier ELISA. Against consensus CPA diagnosis, Bordier ELISA showed 87.50% sensitivity and 94.30% specificity, LDBio ICT LFA 58.33% sensitivity and 97.15% specificity, and Era Biology LFA 66.67% sensitivity and 83.54% specificity. LCA estimated CPA prevalence at 7.72%. LCA-derived sensitivities and specificities were 86.58% and 99.92% for LDBio ICT LFA, 83.39% and 85.31% for Era Biology LFA, and 79.10% and 94.19% for Bordier ELISA. CONCLUSIONS: The Bordier ELISA showed high sensitivity and specificity, while the LDBio ICT LFA demonstrated very high specificity with strong LCA-derived performance. These findings support the use of ELISA for laboratory diagnosis and ICT as a point-of-care screening tool for CPA in resource-limited settings. Era Biology Aspergillus IgG LFA demonstrated moderate sensitivity and acceptable diagnostic performance, indicating its potential utility as a supplementary screening assay for CPA in settings where rapid, point-of-care testing is required.

Humans↗
PubMed2026

Minimizing Off-Target Effects of CRISPR-Cas9 With Optimized sgRNA: Evaluation of Efficiency and Specificity in the Tumor Protein 53 (TP53) Region.

CRISPR-Cas9 is a widely used genetic tool with therapeutic potential in molecular biology. CRISPR-Cas9 enables precise genome editing by its ability to target specific DNA sequence. After off-target and on-target regions are identified, CRISPR-Cas9 is applied to these regions based on the match between the guide RNA (gRNA) and target DNA sequence. This study points to the off-target impact of mismatches between the gRNA and target DNA on exon regions of the TP53 gene, which are involved in regulating multiple genes and cellular functions. Off-target positions are typically evaluated using scoring methods. In this study, we have used latent class analysis to reveal subclasses of off-target positions. Thus, we have created the levels of off-target positions and evaluated the effects of mismatching positions within these classes using machine learning classifiers. The results revealed that mismatching positions could be categorized into three levels: low, middle, and high off-target positions. We have improved a computational framework to minimize off-target effects and to identify the PAM sequences in the gRNA design. Thus, carefully designed gRNAs will ensure that desired genetic edits are performed and target variants are achieved. This work will avail the future research aimed at optimizing genome editing by customizing CRISPR-Cas9 to target specific protospacer DNA through gRNA.

CRISPR-Cas Systems↗
PubMed2026

Infant Dietary Patterns and Early Childhood Weight Outcomes: A Secondary Analysis from the Starting Early Program Trial.

BACKGROUND: The Starting Early Program (StEP) promotes healthy nutrition during early life and leads to healthier child weight, but whether dietary patterns contribute to weight or mediate StEP weight outcomes has not been studied. OBJECTIVES: This secondary analysis identified infant dietary patterns in StEP, determined associations between dietary patterns and child weight outcomes, and examined whether dietary patterns mediated the relationship between StEP and child weight. METHODS: Data were from 377 mother-infant dyads in a randomized trial testing the efficacy of StEP. Dietary patterns at 10 months were identified using latent class analysis. Child weights were abstracted from medical records at 12, 24, and 36 months. Associations between infant dietary patterns and weight-for-age z-score (WFAz) and likelihood of being classified as overweight (WFA ≥85th percentile) were assessed using linear and logistic multivariable regression models. Mediation was used to assess intervention effects on WFAz via impacts on infant dietary patterns. RESULTS: Four classes of infant dietary patterns were identified: Breastfed-High variety, Formula fed-High variety, Formula fed-Low variety, and Mixed fed-Low variety. Compared to the Breastfed-High variety class, infants in the Formula fed-Low variety class had higher WFAz and were more likely to be classified as overweight at 24 and 36 months. Participation in StEP increased membership in Breastfed-High variety, which mediated the association between StEP and lower WFAz at 24 months. CONCLUSIONS: Infant dietary patterns were identified, and some were associated with child overweight. StEP was associated with a dietary pattern most consistent with guidelines, which mediated intervention effects on child weight.

Humans↗
PubMed2025

Genomic structural equation modeling elucidates the shared genetic architecture of allergic disorders.

BACKGROUND: The intricate shared genetic architecture underlying allergic disorders-including allergic asthma, atopic dermatitis, contact dermatitis, allergic rhinitis, allergic conjunctivitis, allergic urticaria, anaphylaxis, and eosinophilic esophagitis-remains incompletely characterized. METHODS: Our study employed genomic structural equation modeling (Genomic SEM) to define the common factor representing the shared genetic architecture of allergic disorders. Coupled with diverse post-GWAS analytical methods, we aimed to discover susceptible loci and investigate genetic associations with external traits. Furthermore, we explored enriched genetic pathways, cellular layers, and genomic elements, and investigated putative plasma protein biomarkers. Polygenic risk score (PRS) analyses, leveraging our integrated GWAS data, were conducted to assess chromosomal-level risk associations for allergic disorders. RESULTS: A well-fitted genomic SEM integrated GWAS data, revealing the shared genetic architecture of allergic disorders. We identified a total of 2038 genome-wide significant SNP loci (p&#x2009;<&#x2009;5e-8), including 31 previously unreported loci. Fine-mapping of variants and gene sets pinpointed 2 causal variants and 31 candidate susceptible genes. Genetic correlation analyses further illuminated the shared genetic architecture underlying multiple traits, notably psychiatric disorders. Preliminary findings identified four putative causal plasma protein biomarkers. CONCLUSION: Notably, this study presents the first comprehensive genetic characterization of allergic disorders through a GWAS analysis of an unmeasured composite phenotype, providing novel insights into shared etiological pathways across these conditions.

Humans↗
PubMed2026

Beyond multidimensionality: a systematic review of recurrent frailty archetypes in community-dwelling older adults.

BACKGROUND: Frailty is a clinically heterogeneous geriatric syndrome commonly summarised using physical or multidomain severity scores. Whether person-centred analyses identify recurring within-frailty configurations has not been systematically examined in community-dwelling older adults. METHODS: We searched PubMed, Embase, MEDLINE, and CINAHL (January 2000-November 2025) for cross-sectional studies using latent class, latent profile, or analogous clustering methods to derive frailty subgroups. Quality was assessed using the AHRQ checklist and a purpose-built appraisal of person-centred model reporting. Study-derived classes were mapped in duplicate to a structured archetype framework developed through comparison of class-defining features across studies. RESULTS: Fourteen reports representing 12 independent datasets from eight countries were included. Six configurations were identified: minimally impaired reference, mobility-physical, nutritional-metabolic, cognitive-predominant, combined cognitive-physical, and psychosocial/mood-predominant. Convergence was measurement-dependent. The reference and mobility-physical configurations recurred across physical-only and multidomain indicator sets, while the combined cognitive-physical configuration appeared across several multidomain frameworks but required cognition to be measured. The remaining configurations emerged only when their defining domains were included. Evidence of prognostic value beyond aggregate frailty severity came from one deficit-index study. Collapsing shared-provenance reports and excluding the boundary-eligible study did not alter recurrence; excluding the Croatian dataset left five configurations recurrent, with the cognitive-predominant configuration supported by one independent dataset. CONCLUSIONS: Person-centred analyses identify recurring within-frailty configurations, but their apparent stability is partly measurement-dependent. A five-configuration core persisted after exclusion of the Croatian dataset, whereas the cognitive-predominant configuration remained weakly replicated. Harmonised indicators and rigorous external validation are needed before clinical application.

Humans↗
PubMed2026

Symptom Burden After Dialysis Initiation and Its Association With Hospitalization.

RATIONALE & OBJECTIVE: Symptom burden is distressing for patients living with kidney failure, but there is limited information about the combination of symptoms and individual symptoms that most strongly predict health care use in this group. We classified and summarized patients' symptom burden levels and changes over time and estimated associations with hospitalizations among patients receiving incident hemodialysis. STUDY DESIGN: Longitudinal, observational. SETTING & PARTICIPANTS: Individuals initiating dialysis in the United States. EXPOSURE: Kidney Disease Quality of Life-36 (KDQOL-36) measure. OUTCOME: First hospitalization after dialysis initiation. ANALYTICAL APPROACH: Latent transition analysis was used to identify symptom burden classes using the KDQOL-36. Cox regression models were used to assess whether individual KDQOL-36 symptoms and symptom burden groups were associated with hospitalization risk after dialysis initiation, independent of demographics and comorbid conditions. RESULTS: 1,818 participants were Black (29%), were aged >65 years (59%), were women (42%), had diabetes (49%), and had hypertension (74%). Latent transition analysis identified the following 3 symptom burden groups: (1) low (low severity of all symptoms and kidney disease impacts), (2) moderate (high physical health impact and overall burden of kidney disease), and (3) high (high levels of all symptoms and kidney disease impact). After adjusting for patient characteristics, all KDQOL-36 scales except the Effects of Kidney Disease scale were associated with a higher hazard of hospitalization. Using the symptom burden groups, a high symptom burden was associated with a 20% increase in the hazard of hospitalization. A 1-category worsening in pain interference and in fatigue was associated with a 12% and an 8% increased hazard of hospitalization, respectively. LIMITATIONS: Findings may not generalize outside the United States. CONCLUSIONS: Pain interference and fatigue, as well as an overall symptom burden, are useful prognostic indicators in patients receiving in-center hemodialysis. Symptom burden should remain a treatment target in hemodialysis.

Hemodialysis↗
PubMed2025

Characterization of gut microbiota and metabolites in renal transplant recipients during COVID-19 and prediction of one-year allograft function.

BACKGROUND: The gut-lung-kidney axis is pivotal in immune-related kidney diseases, with gut dysbiosis potentially exacerbating the severity of Coronavirus disease 2019 (COVID-19) in recipients of kidney transplant. This study aimed to characterize the gut microbiome and metabolome in renal transplant recipients with COVID-19 pneumonia over a one-year follow-up period. METHODS: A total of 30 renal transplant recipients were enrolled, comprising 17 with COVID-19 pneumonia, six with mild COVID-19, and seven without COVID-19. Fecal samples were collected at the onset of infection for gut microbiome and metabolome analysis. Generalized Estimating Equations (GEE) model and Latent Class Growth Mixed Model (LCGMM) were employed to dissect the relationships among clinical characteristics, laboratory tests, and gut microbiota and metabolites. RESULTS: Four microbial phyla (Deferribacteres, TM7, Fusobacteria, and Gemmatimonadetes) and 13 genera were significantly enriched across three recipients groups, correlating with baseline inflammatory response and allograft function. Additionally, 52 differentially expressed metabolites were identified, with seven significantly correlating with eight altered microbiota genera. LCGMM revealed two distinct classes of recipients, with those suffering from COVID-19 pneumonia exhibiting significantly elevated serum creatinine (Scr) trajectories over the one-year period. GEE further identified 12 genera and 181 metabolites closely associated with these trajectories; a multivariable model incorporating gut metabolites of 1-Caffeoylquinic Acid and PMK was found to effectively predict one-year allograft function. CONCLUSIONS: Our study indicates a possible interaction between the composition of the gut microbiota and metabolites community and COVID-19 in renal transplant recipients, particularly in relation to disease severity and the prediction of one-year allograft function.

Humans↗
PubMed2026

Genotype-structure-phenotype correlations define divergent natural history in early-onset spastic paraplegia type 4.

Hereditary spastic paraplegia type 4 (SPG4), caused by variants in SPAST, is the most common form of HSP and exhibits a remarkable phenotypic heterogeneity ranging from late-onset pure presentations to severe, early-onset complex disease. Robust genotype-phenotype correlations and detailed natural history data are lacking, limiting clinical trial readiness. We analyzed 206 patients with genetically confirmed SPG4 enrolled across seven international centers, complemented by high-quality literature-derived cases. Deep phenotyping included standardized motor scales, spasticity ratings, developmental milestones, and patient-reported outcomes. We developed an extended essentiality-mapping framework to classify SPAST missense variants by integrating in silico pathogenicity predictions, evolutionary constraint, physicochemical residue connectivity, and variant enrichment within the human spastin hexamer structure. Plasma neurofilament light chain (pNfL) using was quantified using Simoa in 26 patients and 101 controls. We identified 136 distinct SPAST variants, including 10 novel variants. Variant class segregated strongly by inheritance, with de novo cases enriched for missense variants and inherited cases showing a variety of variant classes with enrichment for truncating variants. Longitudinal analysis revealed two latent trajectories: a rapidly progressive severe subgroup enriched for de novo missense variants, and a biphasic moderate subgroup enriched for inherited truncating variants. Patient stratification integrating spastin essentiality mapping (missense variants affecting essential, neutral, or context-dependent residues) with established genetic modifiers (biallelic pathogenic variants or modifier variants in trans) classified patients into predicted severe and moderate subgroups with divergent age at onset and clinical disease progression. The severe subgroup showed early developmental delays, rapid loss of ambulation, and declining quality of life, while the moderate subgroup displayed delayed but accelerating disease progression. pNfL levels were elevated in both subgroups, most pronounced in severe early disease. This study provides the most detailed natural history of SPG4 to date and introduces a biologically informed stratification framework that links variant class and location to divergent clinical trajectories. These data establish clinically meaningful benchmarks and offer a genotype-based framework to improve anticipatory care and optimize trial design for SPG4.

SPAST↗
PubMed1979

Ideology, class and the National Health Service.

Since the start of the British National Health Service, disputes between the government and the medical profession have become formalized battles with well-recognized rules. But between 1974 and 1976 the consensus underlying the conflict was challenged by the Labour Government's policy on private practice and pay beds. This paper examines the course of the conflict and analyzes the factors underlying the eruption of this issue. It draws attention to the role of the trade-unions in activating the Labour Party's latent ideological commitment on private practice. Although the issue appears to conform to a class-conflict model, this simple symmetry becomes blurred on closer analysis. In conclusion the paper argues that while socio-structural factors extrinsic to the health service explain the appearance of private practice on the political agenda, it is factors endogenous to the NHS which explain the outcome of the dispute. In turn, however, these endogenous factors have little to do with the fact that the NHS is delivering a commodity called "health." Instead, what is important is that the NHS is a complex organization and, as such, depends on the co-operation of a variety of groups--ranging from the medical profession to laundry workers. The analysis, therefore, concludes that the power of the medical profession derives not from its elite status but from its position as an organized group in a complex industry.

Beds↗
PubMed2026

Understanding psychosocial adjustment in military-to-civilian transition: A latent profile analysis of ex-serving Australian Defence Force members.

Military-to-civilian transition is a critical life stage that can expose veterans to elevated risks of psychological distress, social difficulties, and reduced wellbeing. Although psychosocial factors are central to successful reintegration, little is known about distinct patterns of needs among ex-serving Australian Defence Force (ADF) members. This study used latent profile analysis (LPA) to identify psychosocial needs profiles across five domains of the Military-Civilian Adjustment and Reintegration Measure (M-CARM) in a sample of 725 ex-serving ADF members. The optimal three-class solution identified: a Low Adjustment Need (LAN) group (20.7%) reporting minimal reintegration challenges; a Cultural Adjustment Need (CAN) group (42.9%) characterized by cultural adaptation difficulties, particularly beliefs about civilians and regimentation; and a Cultural and Psychological Need (CPN) group (36.4%) showing broader challenges across beliefs about civilians, purpose and connection, regimentation, and resentment and regret. The CAN group was more likely to be male, have lower educational attainment, and have no combat deployment history. The CPN group was similarly male dominated with lower education, and was additionally characterized by Navy service, unemployment or not being in the labor force, and medical discharge. Compared with the LAN group, both CAN and CPN groups reported higher levels of depression, anxiety, posttraumatic stress, and nightmare distress, as well as poorer quality of life and greater functional impairment. These findings highlight persistent reintegration challenges among veterans and support the need for stratified support models, ranging from psychoeducation to intensive multidisciplinary care, to better address diverse psychosocial needs of ex-serving ADF members.

Australian defense force↗
PubMed2026

Isolation, identification, and genomic characterization of Staphylococcus aureus phage vB_SauL_202595 and its bacteriostatic application in dairy products.

Staphylococcus aureus is an important pathogen associated with bovine mastitis and dairy product contamination, posing economic and public health risks through the food chain. In this study, a temperate phage, vB_SauL_202595, was isolated from a dairy farm environmental sample using S. aureus SHZ-0127 as the host, and its biological characteristics, genomic features, and antibacterial activity in dairy matrices were evaluated. vB_SauL_202595 lysed 18 of 66 tested S. aureus strains, with a lysis susceptibility rate of 27.3%, including 5 highly susceptible strains, indicating a relatively limited host range. The optimal multiplicity of infection was 0.01, the latent period was approximately 30 min, and the burst size was approximately 316 PFU/cell. The phage remained stable at 4&#xb0;C-37&#xb0;C and pH 6-10. Genome analysis showed that vB_SauL_202595 belongs to the class Caudoviricetes, has a genome of 44,503 bp with 33.59% GC content, and encodes 63 predicted proteins. No typical antibiotic resistance genes or major virulence factors were detected; however, integrase and repressor genes were identified, supporting its temperate nature. vB_SauL_202595 inhibited S. aureus SHZ-0127 growth, reduced mature biofilm biomass, and decreased viable bacterial counts in milk and yogurt, with reductions of 1.23 and 1.42 log10 CFU/mL under representative conditions, respectively. From a One Health perspective, these findings provide foundational evidence for reducing S. aureus contamination and related antimicrobial resistance risks along the dairy chain. Overall, vB_SauL_202595 represents a candidate phage resource for dairy-associated S. aureus biocontrol research, but its limited host range and lysogeny-related genes require further safety assessment before food-related applications.IMPORTANCEStaphylococcus aureus is a major pathogen associated with bovine mastitis and a common contaminant in dairy products, causing economic losses and public health risks through the food chain. Although phage-based biocontrol has emerged as a promising strategy for controlling S. aureus contamination in dairy products, systematic evidence regarding phage activity in actual dairy matrices remains limited. In this study, we isolated and characterized a dairy farm environment-derived temperate phage, vB_SauL_202595, and evaluated its biological characteristics, genomic features, host range, stability, biofilm removal ability, and antibacterial performance in milk and yogurt. These findings provide foundational experimental evidence for phage-based dairy biocontrol against S. aureus. However, due to its limited host range and lysogeny-related genomic features, vB_SauL_202595 should be considered a candidate phage resource for further study. Broader validation, including phage-cocktail testing, long-term storage assays, product quality assessment, and regulatory safety evaluation, is needed before practical application.

Staphylococcus aureus↗
PubMed2026

Symptom Networks and Core Symptoms in Patients with Solid Tumors Undergoing Chemotherapy: A Systematic Review.

OBJECTIVES: To summarize symptom network characteristics in patients with solid tumors undergoing chemotherapy and synthesize evidence on core symptoms, bridge symptoms, and temporal associations. METHODS: We systematically searched eight databases through October 2025 to identify studies that applied symptom network analysis to adults with solid tumors receiving chemotherapy. Eligible studies assessed symptoms using cross-sectional, longitudinal, or interventional designs. Two reviewers independently screened articles and extracted data on study characteristics, symptom assessment, and network outcomes. Methodological quality was assessed using the National Institutes of Health Study Quality Assessment Tool. RESULTS: Twenty-seven studies involving 13,452 participants were included, yielding 79 symptom networks. Fatigue was the most frequently identified core symptom (10/20, 50%), whereas sadness, lack of appetite, and nausea each occurred in 10% of studies, with variation across cancer types, treatment phases, and latent classes. Bridge symptoms included disturbed sleep, lack of appetite, and dry mouth (2/7, 28.6%). Studies evaluating temporal associations found that symptoms such as sadness, dyspnea, somnolence, and dry mouth predicted subsequent changes in appetite, distress, nausea, and other outcomes. Strength metrics showed acceptable stability (correlation stability coefficients: 0.28-0.83). CONCLUSIONS: Fatigue was frequently identified as a central symptom across studies, largely reflecting evidence from breast cancer studies. Core symptoms varied across cancer types, treatment phases, and latent classes, suggesting heterogeneity. IMPLICATIONS FOR NURSING PRACTICE: These findings highlight the importance of considering relationships among symptoms in clinical care. Focusing on key symptoms such as fatigue, while tailoring management strategies to cancer-specific symptom patterns, may support more effective symptom management.

Humans↗
PubMed2026

Deep generative models in biological sequence and structure analysis and design.

Deep generative models have transformed biological sequence modeling from predictive analysis toward increasingly controllable design. Early biological applications of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) established latent representation learning and sequence synthesis, while recent advances in transformer-based language models, discrete diffusion, flow-matching, and multimodal generative frameworks have substantially expanded the scope of biological design. This review examines generative models for DNA, RNA, and protein sequence design, emphasizing how different model classes represent biological constraints, operate over discrete and continuous spaces, and integrate sequence, structure, and function. We compare VAEs, GANs, autoregressive and masked language models, diffusion models, and flow-based approaches across genomics, transcriptomics, and proteomics, with particular attention to controllability, long-range dependency modeling, structural grounding, generalization, and experimental utility. We further examine evaluation strategies, out-of-distribution generalization, and closed-loop design-build-test-learn workflows that connect in silico generation with empirical validation. We distinguish fundamental modality-dependent constraints including sequence discreteness, context length, structural coupling, and physical or thermodynamic requirements from architecture-dependent advantages that reflect the current state of the field. Current studies suggest that long-context models are particularly useful for genome-scale representation and sequence modeling, whereas structure-aware diffusion, flow-based, and inverse-folding approaches provide better frameworks for geometry-constrained RNA and protein design. This perspective provides a critical framework for understanding the present capabilities, limitations, and convergence of generative approaches toward reliable and experimentally grounded biological design.

Biological sequence analysis↗
PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗
PubMed2026

Persistent trauma three decades after the Halabja massacre: psychological symptom profiles and hair cortisol levels in gas attack survivors.

BACKGROUND: Survivors of the 1988 Halabja chemical attack have experienced severe, chronic trauma, yet limited research has examined the long-term psychological and biological consequences of chemical warfare exposure more than three decades later. AIMS: To examine long-term post-traumatic stress disorder (PTSD) symptoms, depression/anxiety symptoms, somatic symptoms and perceived stress among survivors of the Halabja chemical attack, and to investigate the associations of trauma exposure, sociodemographic factors and hair cortisol concentrations (HCCs) with psychological symptom severity and symptom profiles. METHOD: A total of 209 survivors of the Halabja chemical attack participated in structured interviews using culturally adapted and field-tested instruments, including the Post-Traumatic Stress Disorder Checklist for DSM-5, Hopkins Symptom Checklist-25, the Patient Health Questionnaire-13, the Perceived Stress Scale-14 and an adapted War and Adversity Exposure Checklist-26. Hair cortisol and cortisone concentrations were assessed as biomarkers of chronic stress. Data were analysed using correlations, independent-samples t-tests, multiple linear regression analyses, multinomial logistic regression and latent profile analysis (LPA). RESULTS: Survivors demonstrated a substantial long-term psychological symptom burden. Overall, 87.6% of participants exceeded the cut-off for probable PTSD symptoms, and 75.1% exceeded the cut-off for elevated depression/anxiety symptoms. Most participants (81.8%) reported exposure to 11 or more lifetime traumatic events. Men reported significantly greater cumulative trauma exposure, whereas women reported significantly greater somatic symptom severity. Higher educational attainment and employment were consistently associated with lower psychological symptom severity across multiple outcomes. HCCs were not significantly associated with PTSD symptoms or latent symptom-profile membership after adjustment for trauma exposure and psychosocial variables. The LPA identified four symptom-severity classes characterised by differing levels of PTSD and depression/anxiety symptoms. Greater cumulative trauma exposure and lower social support were associated with membership in more severe symptom classes. CONCLUSIONS: Survivors of the Halabja chemical attack continue to experience substantial psychological distress more than three decades after exposure. Long-term symptom severity appears to be more strongly associated with cumulative trauma exposure and psychosocial adversity than with HCCs. The findings highlight substantial heterogeneity in symptom presentation and support the need for long-term, culturally sensitive and trauma-informed mental health interventions for survivors of chemical warfare and mass violence.

Halabja chemical attack↗
PubMed2026

Early-stage trajectories of social-occupational functioning and long-term functional outcome prediction in early psychosis: A 12-year follow-up of the randomized controlled trial on extended early intervention.

BACKGROUND: Functional impairment in psychosis often persists despite symptomatic remission. There is a paucity of research examining early-course psychosocial functioning trajectories, and none has been conducted to examine relationship between the trajectories and prospective long-term functional outcomes in early psychosis sample. METHODS: We conducted 12-year follow-up of a randomized controlled trial on extended early intervention for first-episode psychosis to identify early-course social-occupational functioning trajectories and their baseline predictors and associations with 12-year outcomes. Participants who completed Social and Occupational Functioning Scale (SOFAS) scores at three or more timepoints between baseline and 3-year follow-up were included in the study. Premorbid adjustment, illness characteristics, symptom severity, functioning, and treatment profiles were assessed. Latent growth mixture modeling was employed to derive early-course social-occupational functioning trajectories based on SOFAS scores over 3-year follow-up. RESULTS: A total of 148 participants were included in this study, with 106 patients having completed the 12-year follow-up. Our results identified four distinct trajectories, including persistently-good class, gradually-improved class, suboptimal-stable class, and persistently-poor class. Patients in persistently-poor class had more severe negative symptoms at baseline compared to patients in persistently-good class. Patients with persistently-poor trajectory had worse long-term outcomes than those with other classes in the majority of functional measures at 12-year follow-up. CONCLUSIONS: The majority of patients were classified in early-stage suboptimal or poor functional trajectories. Above one-fourth of the participants exhibited persistently-poor social-occupational functioning trajectory, which predicted worse functional outcomes at 12-year follow-up. These findings highlighted the importance of tracking functional changes during the initial years of illness.

Humans↗