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PubMed2026

The COVID-19 pandemic influenced the temporal dynamics of antimicrobial resistance markers and bacterial community across urban wastewater treatment plants.

Urban wastewater systems represent important interfaces between human activity and the environmental occurrence of antimicrobial resistance (AMR) markers. We assessed the temporal dynamics of intI1, ermB, and the 16 S rRNA gene by quantitative PCR across three wastewater systems (EPC, CJC, and JW) in Fortaleza, Brazil, from November 2021 to November 2023. Bacterial communities were additionally characterized by 16 S rRNA gene metabarcoding in 18 samples collected in December 2021 and January 2022. A synchronized decline in 16 S rRNA gene and intI1 concentrations beginning in late 2022 was observed across all three wastewater systems, suggesting a shift toward lower microbial abundance. The ermB gene showed higher and more variable concentrations during part of the pandemic period, followed by convergence toward lower levels; however, the absence of antimicrobial-consumption data precluded attribution of this pattern to changes in macrolide selective pressure. Normalized antimicrobial resistance marker abundances were comparatively stable at EPC and JW but more variable at CJC. EPC exhibited the highest ASV richness, whereas CJC and JW showed greater diversity according to Shannon and inverse Simpson indices. Beta-diversity analyses identified wastewater system as the principal factor associated with bacterial community structure, while the effect of sampling period was smaller and metric-dependent. Neither ermB nor intI1 was individually associated with community composition, although intI1 showed a limited effect after adjustment for wastewater system in one model. Physicochemical parameters were not significantly associated with normalized marker abundances in the exploratory paired analysis. Arcobacter, Acinetobacter, and other potentially relevant genera were detected, but no direct associations between these taxa and the monitored AMR markers could be established. These findings highlight the value of integrating longitudinal qPCR, microbiome profiling, and environmental characterization to improve the interpretation of targeted AMR markers in One Health wastewater surveillance.

Wastewater↗

PubMed2026

Associations Between Routine Vaccinations and the Risk of New-Onset Idiopathic Uveitis.

OBJECTIVE: To evaluate the association between vaccination and the risk of new-onset idiopathic uveitis (NIU). DESIGN: Retrospective cohort study of aggregated electronic health records from multiple health systems across the United States. SUBJECTS: Subjects who received the coronavirus disease 2019 (COVID-19), human papillomavirus (HPV), varicella, recombinant herpes zoster, or live herpes zoster vaccinations from 2006 to 2025 and propensity-score matched controls. INTERVENTION: Vaccines against COVID-19, HPV, varicella, recombinant herpes zoster, or live herpes zoster. MAIN OUTCOMES AND MEASURES: The main outcome was the incidence of NIU at 3, 6, and 12 months following vaccination. Vaccinated patients were compared with matched controls who did not receive the respective vaccines. Analyses were repeated, excluding patients with previous diagnosis of the respective viral infection. Risk ratios (RR) with 95% confidence intervals (CIs) were calculated for overall NIU and constituent subtypes (anterior, intermediate, posterior, and panuveitis). RESULTS: All tested vaccinations were associated with reduced risk of NIU through 12 months compared with matched controls. Relative risk reductions were 65% for COVID-19 (RR, 0.35; CI, 0.33-0.37), 56% for HPV (RR, 0.44; CI, 0.35-0.54), 71% for varicella (RR, 0.29; CI, 0.25-0.33), 68% for live zoster (RR, 0.32; CI, 0.23-0.43), and 69% for recombinant zoster vaccination (RR, 0.31; CI, 0.26-0.37). Similar reductions were observed after excluding patients with prior diagnoses of the respective viral infections. CONCLUSIONS: Vaccination was associated with a lower risk of idiopathic uveitis, representing the complex interplay between immune modulation and the development of NIU.

Humans↗

PubMed2026

A genome-wide coverage-based pipeline for the identification of host-derived candidate DNA biomarkers from cell-free blood.

We have created a new data-analysis pipeline for the discovery of host-specific candidate DNA biomarkers derived from sequencing data of cell-free blood. Unlike approaches that rely on specific molecular or genetic signatures, our method leverages the coverage distribution of cell-free DNA sequences mapped to a reference genome, applying statistical analyses to identify informative short genomic regions for biomarker discovery. The pipeline is applicable to diverse diseases and can be used to analyze cell-free DNA sequences from plasma or serum to identify candidate biomarkers that are characteristic of disease states in mammals. Core functionalities were developed in Java and integrated with open-source software tools for the preprocessing of raw sequencing data, complemented by Python scripts for the machine-learning analysis and statistical validation. The pipeline is designed for HPC use and users can access the pipeline through a Galaxy workflow, which offers a user-friendly web interface for input selection prior to execution and analysis progress monitoring. Performance tests, carried out using duplicate sets of COVID-19 samples and controls, showed linear scalability of execution time with an increasing dataset size, as well as a substantial reduction in execution time through parallelized computation, whereby each HPC node is used to process the data of one chromosome. Further statistical tests confirmed the quality of the pipeline's results by showing that the set of identified candidate biomarkers remained stable across varying dataset sizes.

Biomarkers↗

PubMed2026

Cognitive-behavioral therapy for post COVID-19 condition: A pilot randomized controlled trial.

BACKGROUND: The post COVID-19 condition (PCC) is a disabling condition with urgent need for effective treatments. This pilot randomized controlled trial examined the feasibility and efficacy of a cognitive-behavioral therapy (CBT) for PCC in a parallel-group design. METHODS: N = 53 individuals with PCC were randomized to CBT (n = 27) or 16-week wait-list control (WLC) condition (n = 26). One participant allocated to WLC was excluded after randomization, resulting in an intention-to-treat (ITT) sample of N = 52 participants (CBT: n = 27, age: M = 48.59, SD = 11.52, 77.8% female; WLC: n = 25, Age: M = 48.44, SD = 9.81, 60% female). CBT comprised eight group and five individual sessions addressing cognitive and behavioral factors of symptom maintenance (treatment duration if completed: M = 14 weeks, SD = 1 week). Primary outcomes included fatigue (FSS), overall somatic symptom burden (PHQ-15), and respiratory and cardiovascular symptoms (SBQ™-LC "Breathing" and "Circulation"), assessed at pre-, post-assessment, and follow-up. RESULTS: Participants receiving CBT showed a significant medium-sized reduction in fatigue directly after treatment compared to the WLC condition (d = -0.58, p = .045), whereas the WLC group showed a significant increase in overall somatic symptoms (d = 0.60, p = .027). Results for respiratory symptoms were mixed, showing inconsistent patterns across analyses. Retrospectively assessed feasibility and treatment satisfaction were good, with few adverse effects reported. CONCLUSIONS: This pilot trial suggests the feasibility and preliminary efficacy of CBT for PCC. Larger RCTs with active control groups should confirm these findings.

Humans↗

PubMed2026

Timeliness of publication of randomized controlled trials in rheumatology: A systematic review.

OBJECTIVE: To systematically review the (1) timeliness of publication of randomized controlled trials (RCTs) in rheumatology, (2) impact of the COVID-19 pandemic on time to publication, and (3) factors associated with publication delays. METHODS: We searched Medline, Embase, EBM Reviews, Cochrane Central Register of Controlled Trials, and Scopus from January 1, 2018, to June 30, 2023 for Phase 3, superiority, parallel-design RCTs that evaluated any treatment for a rheumatologic illness or a rheumatologic treatment for COVID-19 and reported clinical primary efficacy outcome. Outcomes of interest were time to publication after trial completion and publication delay of >2 years after trial completion. RESULTS: 448 RCTs were included in this systematic review. Median time from completion to publication was 549 days and 65.9 % RCTs were published within 2 years of completion. Compared with RCTs on rheumatologic diseases, RCTs on COVID-19 were published sooner (253 vs. 549 days; p < 0.001) and were more likely to be published within 2 years (adjusted OR 9.49, 95 % CI 2.07 to 43.61). Compared with RCTs completed before March 1, 2020, RCTs completed after March 1, 2020, were published sooner (327 vs. 724 days; p < 0.001) and were more likely to be published within 2 years (adjusted OR 9.40, 95 % CI 5.14 to 17.21). Time from RCT completion to submission accounted for most (67 %) of the time to publication. CONCLUSIONS: Publication delay continues to be an important concern in dissemination of clinical research. Most of the delays in publication were attributable to delays in submission to journals after trial completion.

Humans↗

PubMed2026

2024-2025 BNT162b2 KP.2 COVID-19 full season vaccine effectiveness from vaccine registries linked to administrative claims in two states: A cohort study in non-immunocompromised adults.

BACKGROUND: Data on effectiveness of COVID-19 vaccinations during the 2024-2025 respiratory season are limited, particularly among those with underlying medical conditions (UMC). We estimated BNT162b2 KP.2 vaccine effectiveness (VE) against COVID-19-associated hospital admission, emergency department (ED), and urgent care (UC) visits in two U.S. states. METHODS: Retrospective cohort study of non-immunocompromised adults living in Louisiana or California, with ≥1 year prior continuous enrollment in insurance plans contributing to the HealthVerity claims database beginning August 22, 2024. The effectiveness of BNT162b2 KP.2 vaccine (2024-2025 formulation, hereafter referred to as BNT162b2), measured as a time-varying exposure against hospital admission, ED, or UC encounters with International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) code U07.1 was calculated as 1 - adjusted hazard ratio using Cox proportional hazard models adjusted for age group, sex, state, insurance payor, presence or absence of UMCs, and pre-index healthcare utilization. Stratifications included those aged 65 years and older, those aged 18-64 years with UMCs, and those aged 18-64 years without UMCs. RESULTS: The cohort included 6,256,421 individuals (93% California, 7% Louisiana); 330,565 (5%) received the BNT162b2 vaccine. Vaccinated individuals were older and had more comorbidities, wellness visits, and prior influenza vaccination. Overall, 66% of the study population had ≥1 UMC; the most prevalent conditions were obesity (25%), history of immunocompromised conditions (23%), and mental health conditions (19%). COVID-19-related encounter rates for ED, UC or hospitalization were lower among vaccinated compared to unvaccinated persons (25.1 vs 36.3 per 100,000 person-months). Among all adults, VE was 37% against hospitalization, 12% against ED/UC encounters, and 16% against ED/UC/hospitalization encounters. Results were similar across age groups and UMCs. CONCLUSIONS: BNT162b2 provided protection against COVID-19-associated outcomes of ED, UC or hospitalization among non-immunocompromised U.S. adults, including those with UMCs, over the course of the 2024-2025 respiratory virus season, supporting continued vaccine recommendations. REGISTRATION: This study was posted on clinicaltrials.gov prior to analyses (NCT06923137).

Adolescent↗

PubMed2026

Serum Proteomic Profiling Reveals Renin-Associated Immune and Cytoskeletal Dysregulation in Post-COVID-19 Condition Patients with Secondary Adrenal Insufficiency.

Post-COVID-19 condition (PCC) with secondary adrenal insufficiency (SAI) involves multiorgan dysfunction, potentially linked to renin-angiotensin-aldosterone system dysregulation. The molecular basis of renin-associated pathology remains unclear. Here, PCC+SAI patients were stratified by upright renin into low- (<38.8 pg/mL) and high-renin (≥38.8 pg/mL) groups. Clinical, endocrine, and proteomic analyses were performed. We found that high-renin patients showed increased BMI, lipids, renin, and aldosterone, but reduced aldosterone-to-renin ratio. Proteomic annalysis identified 20 differentially expressed proteins (DEPs), including 17 upregulated and 3 downregulated proteins in Ren-H patients. Functional annotation revealed that 15 DEPs were immune-related (e.g., APOC4, APOE, C4BPA, CFAH, CFHR3, PF4V, PLF4), while FLNA and COF1 represented cytoskeletal proteins. These DEPs were primarily involved in immune response, complement and coagulation cascades, and MAPK signaling pathways. Correlation analyses indicated that upright renin was positively correlated with complement-related proteins and platelet-derived immune factors, while cytoskeletal proteins (FLNA, COF1) showed positive associations with serum Na+ levels. Additionally, white blood cell and platelet counts were positively correlated with the majority of DEPs. In conclusion, exploratory proteomic analyses suggest that elevated upright renin in PCC+SAI may be associated with immune dysregulation, complement activation, and cytoskeletal remodeling, offering novel insights into the endocrine-immune interactions driving postviral sequelae.

Humans↗

PubMed2026

Plasma Proteomic Profiling of Comorbid and Noncomorbid COVID-19 Patients in ICU.

Type 2 Diabetes (T2D) and hypertension (HTN) are common comorbidities in severe COVID-19, yet their specific impact on proteomic recovery remains unclear. This study analyzed plasma protein signatures of critical COVID-19 patients with and without these comorbidities (COVID-only group [COG] and COVID comorbid group [CTHG]) on the first and last days of ICU stay. Proteomic analysis revealed a systemic shift characterized by upregulated immune responses and downregulated metabolic processes at admission across all patients. Survival was fundamentally defined by the restoration of homeostasis; liver-derived proteins─including LPA, TTR, and AHSG─were initially suppressed but rebounded significantly in survivors. This homeostatic recovery was impaired in CTHG compared to COG, with CTHG survivors showing attenuated recovery of metabolic markers. Distinct mortality-associated signatures also emerged between groups. COG nonsurvivors exhibited liver failure and severe hemolysis marked by persistent suppression of haptoglobin (HP). In contrast, CTHG mortality was driven by lipid metabolism dysregulation, with CD5L and APOA2 levels dropping specifically in comorbid nonsurvivors, often accompanied by a paradoxical elevation in APOA4─likely reflecting impaired renal clearance rather than restored lipid homeostasis. These findings indicate that preexisting T2D and HTN hinder physiological resolution of metabolic and lipid dysregulation, providing proteomic evidence for distinct mortality risks associated with failure to restore metabolic homeostasis in comorbid COVID-19 patients.

Humans↗

PubMed2025

Implementation of a Face-To-Face Vs Virtual Peer-Integrated Collaborative Care Intervention for Mental Health Treatment of Physical Trauma Survivors: A Qualitative Study of Lessons from the COVID-19 Pandemic.

OBJECTIVE: We assessed the impact of the COVID-19 pandemic on the implementation of a peer-integrated enhancement of integrated clinical care intervention to address the mental health needs of 450 patients undergoing treatment for a physical injury. METHODS: Qualitative data were collected by 7 clinician investigators of a randomized controlled trial acting as participant observers in a trauma care setting of a major U.S. metropolitan hospital and analyzed in collaboration with an external mixed methods specialist. RESULTS: The pandemic created or exacerbated several implementation barriers, including increased risk of infection, homelessness, hospitalizations and comorbid conditions such as fentanyl overdoses that increased demand on emergency department and Trauma Center services, imposition of safety measures to reduce risk of infection in clinical settings, transition from face-to-face to virtual interactions with study patients, shortages of specialty mental health providers, suspension of recruitment of patients into the study, scheduling calls with patients, and an increased workload for the study clinical interventionists. Peer specialists perceived the transition to virtual interactions with patients reduced their effectiveness; however, this was not reflected in assessments of patient satisfaction with services received and may have inadvertently increased adoption by Trauma Center staff. Reduction in reach of the intervention to target population was temporary. CONCLUSIONS: The COVID-19 pandemic exacerbated existing barriers and created new barriers to successfully implementing evidence-based practices in trauma care settings, resulting in an attenuation of their effectiveness. However, the shift from face-to-face to virtual services delivery may have actually led to improved implementation outcomes. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT03569878. Registered June 15, 2018.

Humans↗

PubMed2025

The impact of the COVID-19 pandemic on osteoporotic fractures: a systematic review and meta-analysis.

BACKGROUND: Recent reports suggest that the COVID-19 pandemic and associated lockdowns may have influenced the epidemiology of osteoporotic fractures, but results vary across regions and fracture types. The aim of this study was to provide evidence-based insights into the impact of the pandemic on osteoporotic fracture incidence. METHODS: We searched four databases (PubMed, Embase, Cochrane Library, and Web of Science) up to August 2025 for observational or retrospective studies comparing osteoporotic fracture incidence during the COVID-19 pandemic (2020) with the pre-pandemic period (2019). The primary outcome of interest was the change in fracture incidence, analysed using risk ratios (RR) with 95% confidence intervals (CI) in Review Manager 5.4. Subgroup analyses were performed by sex, geographic region, and fracture type. RESULTS: Nine studies meeting the inclusion criteria were analysed. Overall, "all types" of osteoporotic fractures showed a significant decrease during the pandemic (RR = 0.85, 95% CI 0.80-0.91, p < 0.0001). Specifically, forearm fractures decreased significantly (RR = 0.87, 95% CI 0.79-0.96, p = 0.002). However, for the most clinically significant fractures, no statistically significant global change was found for hip fractures (RR = 0.93, 95% CI 0.76-1.15, p = 0.14) or vertebral fractures (RR = 1.35, 95% CI 0.85-2.15, p = 0.20). In regional subgroup analysis, hip fracture incidence decreased significantly in South America (RR = 0.79, p = 0.0004) and in both males and females, but no significant change was observed in Europe (RR = 0.92, 95% CI 0.81-1.04, p = 0.17). CONCLUSION: During the COVID-19 pandemic, there was a decrease in the incidence of minor fractures, such as those of the forearm, likely due to reduced outdoor activity. However, the incidence of major osteoporotic fractures (hip and vertebral) remained stable globally, with significant reductions observed only in specific regions like South America.

Humans↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

Wounds that echo: community perceptions of the socio-structural determinants of community violence in post-apartheid South Africa in the context of COVID-19.

The COVID-19 pandemic and its associated public health measures significantly altered the social, economic, and psychological landscape of communities worldwide. In South Africa, the post-COVID-19 period has been marked by a notable surge in homicide rates and interpersonal and community violence. Using a combined structural and social disorganisation framework, this qualitative study critically explores community members' perceptions of the socio-structural factors contributing to community violence, in the context of COVID-19. Utilising data from in-depth interviews and focus group discussions, this study examines the lived experiences of residents in a marginalised high-risk South African community, unpacking the interplay between structural inequities, social disintegration, and community violence. Community violence emerged not as periodic or individual, but as structurally generated, geographically concentrated, and socially normalised. The findings demonstrate that community violence is perceived as being embedded in cycles of survival, where long-standing systemic inequality, economic precarity, spatial disadvantage, and institutional neglect and inequity generate contexts in which community violence becomes normalised and self-reinforcing. The study findings advocate for interventions that not only address immediate catalysts of violence but also the deeper historical and structural determinants of violence in the post-pandemic era, while ensuring preparedness for effective violence prevention during future pandemics.

Humans↗

PubMed2026

The impact of alcohol sale restrictions on unnatural deaths during the COVID-19 pandemic in Johannesburg, South Africa.

The COVID-19-related restrictions on the sale of alcohol in South Africa presented a unique opportunity to examine the association between alcohol availability and the prevalence of unnatural deaths. The study sample included all unnatural deaths investigated by the Johannesburg Forensic Pathology Services Medico-Legal Laboratory during the four COVID-19 alcohol restriction periods compared to the same time periods in the previous year when there were no restrictions. When alcohol sales were initially prohibited, there was a 54.2% decrease in cases of unnatural deaths (p < 0.05), suggesting a link between alcohol use and the occurrence of such deaths. Over all four periods of alcohol sale bans, there was a total reduction of unnatural deaths by 26.4% with declining frequencies in all demographics. There were significant decreases (p < 0.05) in the frequency of deaths in males, Black and Coloured individuals, and the 21-40 years age group. Deaths whose circumstances were a result of motor vehicle accidents, pedestrian vehicle accidents, and firearm discharges decreased significantly (p < 0.05). The cause of death due to blunt forces trauma and gunshot wounds also significantly decreased (p < 0.05). This study highlights the significant impact alcohol consumption has on mortality in South Africa.

Humans↗

PubMed2026

Suicide in rural South Africa before and during COVID-19: evidence from forensic mortuary data in Limpopo Province.

Suicide is a growing public health concern in South Africa, with rural provinces such as Limpopo facing heightened vulnerability due to limited mental health services and socio-economic inequalities. Evidence on the impact of COVID-19 on suicide in rural contexts remains limited. This study examined suicide trends in Limpopo Province and changes associated with the COVID-19 period. A retrospective interrupted time series analysis was conducted using forensic mortality data from 1 January 2019 to 31 December 2021, allowing assessment of pre-existing trends and changes following COVID-19 lockdowns. Among 5770 unnatural deaths, 957 were suicides. The proportion of suicides increased from 29.5% in 2019 to 36.9% in 2021. Suicides predominantly occurred among males and young adults, with hanging accounting for over 90% of deaths throughout. Interrupted time series analysis revealed a significant downward trend in suicide cases during the strict national lockdown (Alert Level 5), with a 31% reduction in incidence (IRR = 0.69, 95% CI: 0.49-0.98). Less restrictive lockdown levels showed no significant effects. Suicide mortality increased prior to COVID-19, with a subsequent decline during the strictest lockdown period. Stable demographic patterns and methods highlight persistent vulnerabilities and the need for sustained suicide-prevention strategies beyond pandemic.

Humans↗

PubMed2026

DIVAS: an R package for identifying shared and individual variations of multiomics data.

MOTIVATION: Multiomics data integration aims to identify biological patterns shared across molecular modalities. Most existing methods detect either jointly shared variation, across all modalities, or individual variation, unique to a single modality, but overlook partially shared variation, shared by only a subset of modalities. This is a critical limitation, because many biological mechanisms manifest in some but not all molecular modalities. RESULTS: We present an open-source R package implementing data integration via analysis of subspaces (DIVAS), a framework for systematically identifying jointly shared, partially shared and individual variations across multiple data types. DIVAS combines angle-based subspace analysis with inference through rotational bootstrap, hierarchically searching all combinations of modalities to decompose multiomics data into interpretable components with scores and loadings. In simulations with a known sharing structure, DIVAS recovered every component across a wide range of noise levels, whereas existing methods did not. Applied to multi-modal COVID-19 data, it reveals partially shared immune and metabolic dysregulation patterns underpinning disease severity that conventional approaches would miss. AVAILABILITY AND IMPLEMENTATION: DIVAS is available at https://github.com/ByronSyun/DIVAS, with documentation and vignettes. The COVID-19 case study vignette is available at https://byronsyun.github.io/DIVAS_COVID19_CaseStudy/.

Multiomics↗

PubMed2026

Knowledge-driven interpretable neural networks provide mechanistic insight.

Analyzing omics data in the context of pathway knowledge is critical for understanding the molecular mechanisms underlying pathological changes. However, current pathway analysis methods do not model the detailed mechanistic nature of biological interactions, limiting the understanding of pathway behavior to a relatively shallow level. To address this issue, we present a knowledge-driven machine learning framework that embeds features into pathway graphs and models reactions analytically, producing interpretable feature hierarchies and subnetworks in which functional associations are estimated to model biological interactions. The approach is agnostic to feature selection, enabling the use of full omics data sets without discarding weak signals. Applications to breast cancer microRNA-gene regulation data and COVID-19 metabolomic data highlight immune and metabolic pathways relevant to disease progression. This framework bridges predictive modeling with mechanistic interpretation and offers a foundation for integrative pathway analysis.

Humans↗

PubMed2026

Escalation of CTX-M-producing extensively drug-resistant Shigella spp. in Kolkata, India, following the COVID-19 pandemic.

Shigella spp. is recognized by the World Health Organization as a high-priority pathogen due to its global prevalence, unique pathogenic mechanisms, and growing antimicrobial resistance (AMR). Nearly half of all Shigella strains worldwide are now multidrug-resistant (MDR), and the emergence of extensively drug-resistant (XDR) variants-resistant to ciprofloxacin, ceftriaxone, and azithromycin-has severely limited effective treatment options. The present study is based on prospective laboratory surveillance involving 323 Shigella isolates collected during 2021-2023, with pre-COVID-19 pandemic data included from a previously published study solely for historical comparison. The presence of antibiotic resistance genes (ARGs) was investigated, and whole-genome sequencing (WGS) was performed on representative isolates to assess phylogenetic relatedness with global isolates. Approximately 10% of isolates exhibited resistance to third-generation cephalosporins. While only 3% of Shigella isolates carried the blaCTX-M-15 gene from 2013 to 2019, its prevalence increased to 26% by 2022-2023. Among 38 ceftriaxone-resistant S. sonnei isolates, 33 were also resistant to azithromycin, categorizing them as XDR. These isolates showed 48% clonal similarity and high phylogenetic resemblance to the isolates reported from England. Hybrid genome assembly revealed a plasmid harboring both the blaCTX-M-15 and mphA ARGs. Conjugation experiments and plasmid profiling confirmed the plasmid's transferability. We report a rising trend in third-generation cephalosporin resistance among Shigella spp., primarily driven by the spread of extended-spectrum β-lactamase-producing S. flexneri and the emergence of XDR S. sonnei. These findings underscore the urgent need for strengthened national AMR containment strategies and enhanced international surveillance of cephalosporin-resistant Shigella to mitigate this growing public health threat.IMPORTANCEShigella is a leading cause of diarrheal disease globally and has been prioritized by the World Health Organization due to its rapid acquisition of antimicrobial resistance. Our prospective surveillance in Kolkata, India, reveals a worrisome escalation of third-generation cephalosporin resistance over the past decade, primarily associated with the spread of blaCTX-M-15 and the emergence of extensively drug-resistant (XDR) S. sonnei. The detection of plasmids carrying both blaCTX-M-15 and mphA, coupled with evidence of their transferability, highlights the potential for accelerated dissemination of multidrug resistance. When compared with a global data set of international genomes, the Kolkata XDR isolates were found to cluster closely with isolates reported from England. By linking local surveillance with global genomic context, our findings provide critical insights for treatment guidelines, antimicrobial stewardship, and the design of international containment strategies aimed at curbing the rise of cephalosporin- and azithromycin-resistant Shigella.

India↗

PubMed2026

Beyond genes: EpiSwitch® and Orion platform-powered 3D genome architecture biomarkers reveal shared biology across ME/CFS, long COVID, PTSD, rheumatoid arthritis, and multiple sclerosis.

BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), Long COVID (LC19), post-traumatic stress disorder (PTSD), rheumatoid arthritis (RA), and multiple sclerosis (MS) are clinically distinct disorders that share substantial symptom overlap, including persistent fatigue, cognitive impairment, autonomic dysfunction, and immune dysregulation. Although these conditions differ in diagnosis and clinical presentation, their underlying biological mechanisms remain poorly understood and may involve convergent regulatory pathways. METHODS: The EpiSwitch® 3D genomics platform and Orion knowledgebase were used to integrate chromosome conformation signatures with genome-wide association study (GWAS)-derived datasets across ME/CFS, LC19, PTSD, RA, and MS. Three-dimensional genomic anchors were mapped to coding genes and analysed using STRING protein-protein interaction networks and Cytoscape-based systems biology approaches. Disease-specific anchor datasets were generated and compared at both gene and network levels to identify shared biological processes and regulatory mechanisms. RESULTS: Analysis of the ME/CFS dataset identified 552 unique 3D genomic anchors mapped to 567 genes, with analogous disease-specific anchor sets generated for LC19, PTSD, RA, and MS. Direct overlap between disease-associated genes was limited; however, higher-order network analyses revealed substantial interconnectivity and convergence across conditions. Shared biological pathways included immune and cytokine signalling, interferon responses, mitochondrial function, metabolic regulation, and neuroendocrine processes. Highly connected hub genes included immune regulatory nodes such as LAG3 and components of the mTOR signalling pathway, implicating T-cell exhaustion, chronic immune activation, and immunometabolic dysregulation as common mechanisms underlying these disorders. CONCLUSIONS: These findings support a systems-level model in which clinically overlapping fatigue-associated syndromes arise from perturbations of interconnected regulatory networks rather than discrete disease-specific pathways. Despite limited genetic overlap, substantial convergence at the network level suggests shared biological architecture across ME/CFS, LC19, PTSD, RA, and MS. The identification of common regulatory pathways provides a mechanistic framework for the development of cross-disease diagnostic and therapeutic strategies. By capturing dynamic regulatory states, 3D genomic biomarkers offer significant potential for objective blood-based diagnostics, patient stratification, and the identification of shared therapeutic targets across complex chronic disorders. These findings support the application of precision medicine approaches and may accelerate the development of novel interventions for fatigue-associated multisystem diseases.

Humans↗