Infectious Diseases

Introduction Improved diagnostics are needed for people at risk of tuberculosis, especially adolescents. Tongue swab (TS) molecular testing has emerged as a promising strategy for tuberculosis diagnosis. We evaluated diagnostic accuracy and acceptability of Xpert MTB/RIF Ultra (Xpert) using TS samples for tuberculosis detection among adolescents. Methods We conducted a cross-sectional diagnostic accuracy study with consecutive recruitment in Vietnam. Adolescents aged 10-19 who were recommended to undergo investigation for tuberculosis and had not received tuberculosis treatment in the past years were eligible. Participants provided TS and sputum samples and completed a structured survey regarding sampling experiences. TS was tested on Xpert, with sputum tested on Xpert and liquid culture. We utilised a composite reference standard of a positive result on sputum Xpert or sputum culture to define disease status. Sensitivity, specificity, and diagnostic yield were calculated for TS Xpert. Results From July to December 2025, we enrolled 225 adolescents from Can Tho and An Giang provinces in southern Vietnam. Fewer than half (96/225, 43%) the participants exhibited a tuberculosis -like symptom, and the majority (157/225, 70%) were close contacts of a person recently diagnosed with tuberculosis. TS were collected from all adolescents, while 116 (52%) could provide mucopurulent sputum. Tuberculosis prevalence was relatively low (12/225, 5.3%). TS Xpert sensitivity (90% CI) and specificity (90% CI) were 58.3% (35.6, 78.0) and 99.5% (97.9, 99.9), respectively. Diagnostic yield among all diagnosed was 58.3% (7/12). TS sampling was highly acceptable to adolescents; the short time and simplicity of collecting TS were considered favourably. Conclusions The sensitivity and diagnostic yield of TS Xpert was relatively low among adolescents recommended for tuberculosis investigation, which includes asymptomatic individuals who may not provide high quality sputum. Specificity was excellent, and everyone could provide a TS. TS high acceptability indicates it remains a promising sample for diagnostic algorithms.

Background Tuberculosis (TB) remains a critical public health challenge, with two-thirds of the global TB burden in ten Asian countries. Social vulnerabilities, comorbidities, health inequity, multi-dimensional poverty, malnutrition, and barriers to healthcare access continue to fuel TB epidemic. Inability to detect asymptomatic and sub-clinical TB, combined with passive approach in service delivery and overreliance on smear microscopy, leads to delayed diagnosis, a substantial burden of undetected cases, and continuing TB transmission in the communities. In such a context, the introduction and scale-up of active case-finding approaches - including community-based TB screening using highly sensitive screening tools and novel rapid diagnostics - becomes a strategic priority to interrupt transmission. The growing availability of multiple screening and diagnostic options makes evidence-based decision-making increasingly complex. Methods To estimate the potential epidemiological impact and cost implications of scaling up TB diagnostics and community-based screening in ten high-burden Asian countries, we constructed a mathematical model and evaluated multiple intervention scenarios. We then assessed and compared four service delivery models: 1) digital ultraportable chest x-ray (UPCXR) & Xpert/Truenat in community, 2) digital UPCXR in community and Xpert/Truenat at health facilities, 3) digital UPCXR in community and near point of care (nPOC) at health facilities, 4) nPOC in community & Xpert/Truenat at health facilities - for total investment required and projected health benefits for their cost-effectiveness. Results and conclusions The modelling study indicated that strengthening health facility capacity (with enhanced TB screening, expanded molecular diagnostics, reduced loss to follow-up, private sector standard of care, leading to increased treatment coverage & quality of active disease treatment and reduced post-treatment relapse, scale-up of TB preventive treatment (TPT), and provision of nutritional support to 80% of TB patients and their household contacts) can significantly reduce TB incidence and mortality; however, community-wide mass screening remains essential to achieving TB elimination targets . Targeted screening of vulnerable populations demonstrated greater cost-effectiveness than untargeted screening approaches. Achieving the End TB goals will ultimately require an effective TB vaccine with high population-level coverage. AI-enabled digital UPCXR-based screening combined with Xpert/Truenat testing at the community level demonstrated maximum epidemiological impact potential, while the most cost-efficient model is Digital UPCXR in the community combined with nPOC testing at health facilities. An investment of USD 12.7 billion over the next five years in community-level implementation of digital UPCXR and molecular diagnostics could avert an additional 9.8 million TB cases and 1.9 million deaths across ten Asian countries over a ten-year horizon.

The A(H1N1)pdm09 virus remains a major global health threat. This study examined the burden of ICU admission, mortality, and associated predictors among patients with A(H1N1)pdm09 pneumonia in a leading center for infectious diseases in Vietnam. Information on demographic, clinical, and laboratory characteristics, and outcomes was retrieved from medical records of adults admitted with influenza A(H1N1)pdm09 during 2009-2019. Among 729 cases, 21.7% (158/729) developed pneumonia. Among 158 pneumonia cases, 36.7% (58/158) developed moderate-to-severe acute respiratory distress syndrome (ARDS), and 15.2% (24/158) received invasive ventilation. ICU admission and mortality rates were 48.7% (77/158, 95%CI 41.1-56.5%) and 8.2% (13/158, 95%CI 4.9-13.6%), respectively. Predictors of ICU admission included being >60 years old (adjusted OR [AOR] 13.864, 95%CI 2.185-87.956, P=0.005), comorbidities (AOR 6.527, 95%CI 1.710-24.915, P=0.006), AST (AOR 1.013, 95%CI 1.001-1.025, P=0.029), and moderate-to-severe ARDS (AOR 14.027, 95%CI 4.220-46.627, P<0.001). Predictors of mortality were invasive ventilation (AOR 55.355, 95%CI 1.486-2062.375, P=0.030) and double-dose oseltamivir or combination therapy (AOR 32.625, 95%CI 1.594-667.661, P=0.024). In conclusion, mortality is not rare in A(H1N1)pdm09 infection. Monitoring of older patients and those with comorbidities, liver enzyme elevation, or moderate-to-severe ARDS is essential for the timely detection of complications requiring intensive care.

BackgroundTuberculosis (TB) remains a leading cause of infectious disease mortality worldwide, and treatment failure contributes to ongoing transmission, drug resistance, and poor clinical outcomes. Artificial intelligence and machine learning approaches have attracted growing interest for predicting tuberculosis treatment outcomes, but the literature is heterogeneous and lacks a comprehensive synthesis. MethodsWe conducted a systematic review and meta-analysis of studies that developed or validated machine learning models to predict TB treatment failure. We searched PubMed/MEDLINE and Embase from January 2000 to October 2025. Studies were eligible if they developed, validated, or implemented an artificial intelligence or machine learning model for the prediction of TB treatment failure or a closely related poor outcome in patients receiving anti-TB treatment. Risk of bias was assessed using the Prediction model Risk Of Bias Assessment Tool. Random-effects meta-analysis was performed to pool area under the curve values, with subgroup analyses and meta-regression to explore heterogeneity. ResultsThirty-four studies were included in the systematic review, of which 19 reported area under the curve values suitable for meta-analysis (total participants, 100,790). Studies were published between 2014 and 2025, with 91% published from 2019 onward. Tree-based methods were the most common algorithm family (52.9%), and multimodal models integrating three or more data types were used in 41.2% of studies. The pooled area under the curve was 0.836 (95% confidence interval 0.799-0.868), with substantial heterogeneity (I{superscript 2} = 97.9%). In subgroup analyses, studies including HIV-positive participants showed lower discrimination (pooled area under the curve 0.748) compared to those excluding them (0.924). Only eight studies (23.5%) performed external validation, and only one study (2.9%) was rated as low risk of bias overall, primarily due to methodological concerns in the analysis domain. Eggers test suggested publication bias (p = 0.024). Major evidence gaps included underrepresentation of high-burden countries, HIV-affected populations, social determinants, pediatric TB, and extrapulmonary disease. ConclusionsMachine learning models for predicting TB treatment failure show promising discrimination but are not yet ready for routine clinical implementation. Performance varies substantially across populations and settings, and methodological limitations, including inadequate validation, poor calibration assessment, and high risk of bias, limit confidence in current estimates. Future research should prioritize rigorous external validation, calibration assessment, and development in underrepresented populations, particularly HIV-affected and high-burden settings. Author SummaryTB kills over a million people annually. While curable, treatment failure remains common and drives ongoing transmission and drug resistance. Researchers increasingly use artificial intelligence and machine learning to predict which patients will fail treatment, but it is unclear if these models are ready for clinical use. We reviewed 34 studies including nearly 1.1 million participants from 22 countries. On average, models correctly distinguished patients who would fail treatment from those who would not 84% of the time, a performance generally considered good. However, this average hid enormous variation. Models developed in populations including HIV-positive people performed substantially worse, suggesting prediction is harder with HIV co-infection. Worryingly, only one study used high-quality methods; 97% had serious flaws in handling missing data, checking calibration, or testing in new populations. Only eight studies validated their models in different settings. To conclude, we found that machine learning is promising in predicting TB treatment failure, but it is not ready for clinical use. Researchers should prioritize validation in high-burden settings, include social determinants, and improve methodological rigor before these tools can help patients.

Abstract Background Timely diagnosis of tuberculosis and drug resistance remains a cornerstone of effective disease control. Multiplex open molecular platforms capable of simultaneously detecting Mycobacterium tuberculosis complex (MTBc), non-tuberculous mycobacteria (NTM), and resistance to first-line anti-tuberculosis drugs could streamline diagnostic pathways. Methods We conducted a laboratory-based evaluation of two multiplex real-time PCR assays (MTBc/NTM R-Gene and MTB-RIF/INH R-Gene) using 300 well-characterized samples, including 150 MTBc-positive culture isolates (including rifampicin-resistant, isoniazid-resistant, and drug-susceptible strains) and 150 MTBc-negative samples (50 NTM isolates and 100 mycobacteria-negative specimens). Composite reference standards included culture, MPT64 antigen testing, and line probe assay corroborated by phenotypic drug susceptibility testing for resistance profiling, with NTM speciation performed using a dedicated line probe assay. DNA extraction was performed using the QIAamp DNA Mini Kit (QIAGEN, Germany), followed by amplification on a real-time PCR platform according to manufacturer instructions. The diagnostic performance was assessed against composite reference standards. Results The analytical performance for detecting MTBc demonstrated 100% sensitivity and specificity (150/150). NTM detection showed 70.0% sensitivity (35/50) and a specificity of 100%, highlighting limitations in coverage of NTM species. Rifampicin resistance was detected with a sensitivity of 96.0% (48/50) and specificity of 100%, whereas isoniazid resistance detection was 100% sensitive and specific (50/50). Agreement with established reference standards was high ({kappa}=0.76-1.00) within this analytical context. Interpretation This analytical validation demonstrates that multiplex open real-time PCR assays can accurately and simultaneously detect MTBc, NTM, and rifampicin and isoniazid resistance using culture isolates. While these platforms offer potential advantages in flexibility and expanded resistance profiling, additional studies on clinical diagnostic accuracy, cost-effectiveness analyses, and operational feasibility are required to determine their practical utility and programmatic impact in high-burden settings

BackgroundTuberculosis (TB) and human immunodeficiency virus (HIV) are leading causes of infectious disease deaths, with disproportionate impact in low- and middle-income countries (LMICs). Despite well-established biological relationships between these diseases, there is limited information on how TB prevalence differs between people living with and without HIV. MethodsWe conducted a systematic review and meta-analysis of TB prevalence surveys conducted in LMICs and published during January 1st 1993-October 13th 2025 (PROSPERO CRD42024503853). We extracted bacteriologically-confirmed TB prevalence estimates stratified by participant HIV status. Surveys that offered HIV testing to all, sputum-collection-eligible, or TB-positive participants were included in the primary analysis. We applied Bayesian meta-regression to estimate pooled risk ratios (RR) of bacteriologically-confirmed TB prevalence among participants living with versus without HIV. Additionally, we estimated country-level and overall TB notification-to-prevalence (N:P) ratios by HIV status. FindingsOf 10,211 potentially relevant publications, 12 TB prevalence surveys--representing 264,530 participants within nine countries in Southern and Eastern Africa--were used in the primary analysis. Reported TB prevalence was higher among participants living with versus without HIV in 11/12 surveys, with an overall pooled RR of 3{middle dot}86 (95% credible interval: 2{middle dot}41-5{middle dot}53). N:P ratios were higher among participants living with HIV in all examined countries. The overall pooled N:P ratios were 1{middle dot}74 (0{middle dot}59-4{middle dot}56) and 0{middle dot}48 (0{middle dot}17-1{middle dot}20) among participants living with versus without HIV, respectively. InterpretationIn Southern and Eastern Africa, bacteriologically-confirmed TB prevalence is three- to six-times higher among people living with HIV. Comparison of prevalence and notification data suggest higher rates of TB diagnosis for people living with versus without HIV, but also indicates substantial delays in the detection of untreated TB cases for both populations. FundingWellcome Trust, UK National Institute for Health and Care Research, UK Foreign, Commonwealth and Development Office, NIH. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSThere is limited systematic evidence on how the prevalence of TB disease differs between people living with HIV and without HIV. Multiple observational cohorts have described substantially elevated TB incidence among populations with HIV, but disease prevalence will also be affected by differences in mortality and treatment uptake rates. We searched PubMed from inception through January 21, 2026 using the search string ((HIV AND TB) OR HIV/TB) AND (prevalence AND (systematic review OR meta-analysis)) without any restrictions on language. We also reviewed investigators personal libraries. This search yielded 506 publications; however few of these included prevalence data. An analysis conducted in 2020 synthesized HIV status-stratified data from seven national TB prevalence surveys in Africa and found that HIV prevalence was lower among prevalent TB cases than among notified cases. This study did not include subnational surveys and did not distinguish between survey participants with self-reported or test-confirmed HIV status. Added value of this studyThis study synthesized TB prevalence data, stratified by participant HIV status, from national and subnational surveys conducted in LMICs and published between January 1st 1993 and October 13th, 2025. Collated data represented 681,402 survey participants across ten countries. All but one study were conducted in Southern and Eastern Africa. We limited our primary analysis to surveys that systematically tested participants for HIV and bacteriologically-confirmed TB. The prevalence of bacteriologically-confirmed TB was estimated to be three to six times higher than among people living with versus without HIV. Ratios of TB notifications to TB prevalence were higher for people living with HIV compared to people without HIV, suggesting higher rates of TB case detection (and likely shorter duration of disease) for people living with HIV and untreated TB than those without HIV. Implications of all available evidenceFew estimates of community-representative TB prevalence stratified by participant HIV status exist. These surveys have been concentrated in Southern and Eastern Africa, despite TB-HIV burden being distributed globally. Our findings highlight the elevated burden of TB among people living with HIV in these settings, as well as the limited data on the intersection of TB and HIV epidemiology in other world regions. Furthermore, our comparison of notification and prevalence data demonstrate substantial shortfalls in TB case detection, regardless of an individuals HIV status.

Background: Mucormycosis is a rapidly progressive and often fatal invasive fungal infection caused by moulds in the order, Mucorales. Early diagnosis is essential for effective clinical management; however, conventional diagnostic approaches such as culture and histopathology are slow, insensitive, and require specialist mycological expertise. Although molecular methods are available for disease detection, they are not widely accessible. At present, no enzyme immunoassay (EIA) exists for the detection of mucormycosis. Methods: A murine IgG1 monoclonal antibody (mAb), FH12, was generated against extracellular polysaccharides (EPSs) produced by Mucorales pathogens during active growth. The antibody was characterised for specificity, epitope stability, and antigen localisation using ELISA, immunoblotting, and immunofluorescence techniques. The mAb was incorporated into a Sandwich-ELISA and evaluated using culture filtrates, purified EPSs spiked into human serum, and tissue homogenates from a patient with cutaneous mucormycosis caused by Lichtheimia ramosa. Results: mAb FH12 demonstrated pan-Mucorales specificity and no cross-reactivity with other clinically relevant yeasts and moulds. The epitope recognised by FH12 is periodate-insensitive and moderately heat-stable. The Sandwich-ELISA detected EPS antigens in human serum with limits of detection ranging from pg/mL to low ng/mL levels, and successfully identified the EPS biomarker in patient tissue homogenates. Conclusion: The FH12-based Sandwich-ELISA shows high sensitivity and specificity, and has the potential to be used as a laboratory-based adjunct diagnostic test for the detection of mucormycosis in humans.

Socioeconomic inequalities in health and well-being are a major public health concern, particularly in ageing populations. Education is a key determinant shaping multiple aspects of health outcomes. We used cross-sectional data from wave 9 of the German sample (n=4,148) of the Survey of Health, Ageing and Retirement in Europe (SHARE) to test whether formal education is associated with well-being in later adulthood, with health literacy, self-rated health, and preventive health behaviours as possible mediators. Our results showed that education was positively associated with greater well-being, but only via indirect pathways. Specifically, self-rated health, health literacy, and fruit and vegetable consumption mediated the relationship between education and well-being accounting for 54.7, 24.7, and 12.6 percent of the total effect, respectively. In addition, there were significant positive correlations between education and health literacy, as well as high-intensity physical activity, daily fruit and vegetable consumption, more preventive health check-ups, and less smoking. In contrast, alcohol consumption was more common among those with higher levels of education. All health behaviours and health literacy were correlated directly or indirectly (i.e., mediated by health) with well-being. These findings highlight the importance of examining indirect pathways linking education to well-being in later life. Interventions aimed at improving health literacy and promoting healthy behaviours may help reduce educational inequalities in quality of life among older adults.

Background: Although the hemagglutination inhibition (HAI) titer remains the gold standard correlate of protection against influenza, it does not fully capture the broader antibody responses that contribute to immunity. Methods: We analyzed immune responses in paired pre-infection and convalescent sera from 306 RT-PCR-confirmed A/H3N2 infections from two household studies (2014-18) in Managua, Nicaragua. Antibody responses were measured by HAI and enzyme-linked immunosorbent assays (ELISAs) against full-length hemagglutinin (HA), the HA stalk, and neuraminidase (NA). Participants were classified as HAI responders ([&ge;]4-fold HAI rise), alternate responders (no HAI rise but [&ge;]4-fold boost in [&ge;]1 ELISA), or no-response individuals (no [&ge;]4-fold rise in any assay). We compared demographic, clinical, and pre-infection antibody characteristics across these groups. We also analyzed predictors of an NA response. Results: Overall, 77% of participants had HAI seroconversion or a 4-fold rise. Among the 23% HAI non-responders, 62% had alternate antibody responses. No-response individuals had the highest pre-infection HAI and full-length HA titers (p < 0.0001), the lowest viral loads, and the fewest fever or influenza like illness (ILI) symptoms (p < 0.01). An NA response was more common among symptomatic individuals (p = 0.0483) and those with low or high baseline NA titers. Conclusions: High baseline HAI titers can limit detectable 4-fold rises and are associated with milder illness. Evaluating additional immune responses may capture a more complete picture of the host response to infection, thereby improving surveillance and informing vaccine development. Keywords: Influenza A/H3N2; Hemagglutination inhibition (HAI); Neuraminidase antibodies; symptomatic vs asymptomatic infection; correlates of protection.

There is an ongoing Oropouche Fever (OF) outbreak in Brazil since 2024. There are dengue and chikungunya prediction models available, but none to help discriminate dengue, chikungunya, and OF. Objective: This study aims to develop and validate clinical prediction models for dengue, chikungunya, OF. Methods: This study uses surveillance data from Espirito Santo state / Brazil, from 2023-2025. Epidemiological investigations and biological samples were used to conclude cases as either (a) clinical-epidemiologically confirmed, (b) laboratory confirmed, or (c) discarded. The predictors were all data related to signs, symptoms, and comorbidities available in the notification forms. The analysis was performed using random forest regression models, one for each outcome, in development and validation datasets. Results: A total of 465,280 observations were analyzed, 261,691 dengue cases (56.6%), 18,676 chikungunya cases (4.0%), 12,174 OF cases (2.6%), and 179,115 discarded cases (38.6%). All three models had good discrimination and moderate to good calibration after scaling prediction. The models retained from 26 to 16 predictors each. Leukopenia and vomiting were the most discriminatory predictors for dengue, arthritis, arthralgia, and rash were the most discriminatory for chikungunya, and epidemiological features were the most relevant for OF. The dengue, chikungunya, and OF models had ROC AUC of 0.726, 0.851, and 0.896 in the validation set, respectively. Conclusion: This research identified predictors most discriminative between dengue, chikungunya, and OF. We developed and validated predictive models, one for each condition, with moderate to very good performance available at https://pedrobrasil.shinyapps.io/INDWELL/. One may use them in diagnostic work-up and arbovirus surveillance.

Background Nepal has set a goal to eliminate lymphatic filariasis (LF) by 2030. As of 2024, Nepal has stopped the mass drug administration (MDA) in 56 of the 64 endemic districts and completed two rounds of MDA in six districts with persistent LF ([&ge;]2% antigen prevalence) using the three-drug regimen of Ivermectin, Diethylcarbamazine, and Albendazole (IDA), exceeding 65% coverage. We subsequently conducted an Epidemiological Monitoring Survey (EMS) to assess the impact of the MDA in reduction of LF infection prevalence below the transmission threshold and examine the factors associated with it. Methods We conducted a cross-sectional EMS nine months after MDA in 12 evaluation units (EUs) across six districts, with two sites per EU. We recruited a total of 7,343 individuals aged [&ge;]20 years, sampled using multi-stage sampling, ensuring at least 300 blood samples collected per site. We collected data on demographics and MDA participation. We performed the LF antigen testing for all participants, followed by night blood microfilariae testing in antigen-positive individuals. Statistical analyses included non-parametric tests, Chi-square and Fishers Exact tests, and multivariable logistic regression to assess outcomes after adjusting for potential confounders. Results Nine of 12 evaluation units (EUs) recorded <1% microfilaremia, meeting the WHO threshold for passing EMS, while three EUs failed with [&ge;]1% prevalence in at least one site. Antigen and MF prevalence were 4.47% and 0.34%, respectively (ratio 13:1). Both Antigen and MF prevalences were significantly associated with female sex (AOR= 0.564, 95% CI: 0.441-0.721 and AOR = 0.326, 95% CI: 0.129-0.826 respectively) and participation in the most recent MDA round (AOR = 0.477; 95% CI: 0.385-0.591 and AOR = 0.089; 95% CI: 0.017-0.464 respectively). MDA uptake was influenced by age (<40 years, AOR = 0.72; 95% CI: 0.653-0.793), sex (female, AOR = 1.438; 95% CI: 1.29-1.603), cross-border residence (AOR = 0.616; 95% CI: 0.558-0.681), and occupation (agriculture and housewife, AOR = 1.144; 95% CI: 1.008-1.298). MF prevalence was also associated with younger age (<40 years, AOR = 0.211; 95% CI: 0.071-0.626). Conclusion The survey indicates progress toward LF elimination, with nine of twelve EUs achieving WHOs <1% microfilaremia threshold after two rounds of IDA-MDA. However, transmission persists in three sites, likely linked to poor MDA participation among specific subgroups--particularly males, younger adults, and cross-border populations. Strengthening MDA coverage and compliance across all demographic and occupational groups, with special focus on border areas, is essential to achieve LF elimination in Nepal.

Objectives: To identifiy risk factors for antimicrobial resistance (AMR) in seven pathogen-antimicrobial combinations in patients with cancer and cancer survivors. Methods: Using data from patients with recent or past cancer diagnostic codes in Oxfordshire, UK, we examined associations between 22 potential risk-factors and AMR in blood culture isolates, collected between 1-April-2015 and 31-March-2025. Results: Among 5,975 bacteraemias in 4,365 adults, we analysed 3,141 (52.6%) due to Enterobacterales and 620 (10.4%) due to Enterococcus faecalis/faecium in 2,752 patients. Fourteen risk-factors for antimicrobial-resistant bacteraemia were identified, varying across pathogen-antimicrobial combinations. Compared with no previous antimicrobial susceptibility test result, prior resistance to the same antibiotic in any culture in the last year was strongly associated with AMR across all pathogen-antimicrobial combinations (all p<=0.001). Prior antibiotic exposure and younger age were also positively associated with AMR in four and five combinations, respectively. Cancer type showed modest effects; lymphoid/haematopoietic malignancies were associated with higher odds (vs colorectal cancer) of trimethoprim-sulfamethoxazole-resistant Enterobacterales (aOR=2.07 95%CI 1.40-3.06) and vancomycin-resistant Enterococcus bacteraemia (aOR=6.68, 1.21-36.91). Conclusions: Previous resistance was the greatest risk factor for bacteraemia with AMR in cancer patients and survivors, with prior antibiotic exposure and age also contributing. Lymphoid/haematopoietic malignancies increased risk of resistance to specific antimicrobials. Keywords: antimicrobial resistance, bacteraemia, cancer, risk factors

While vaccination conflicts have become apparent, physicians' attitudes toward those with differing views remain unclear. Through an online survey of 492 physicians and 5,252 members of the general public in Japan in February 2026, we investigated attitudes toward four vaccines (influenza, measles, HPV, and COVID-19). Intergroup bias was assessed as ingroup minus outgroup attitudes using a feeling thermometer. Multilevel regression examined associations with agreement group and physician status. Intergroup bias was significantly positive in both agreement and disagreement groups across all vaccine types, and was higher in the agreement group. Physicians exhibited higher intergroup bias than the general public. These findings indicate that vaccination conflict is bidirectional: physicians, often viewed as targets of hostility from vaccine-hesitant individuals, themselves exhibit greater intergroup bias toward those with opposing views. Interventions to raise physicians' awareness of their own bias, alongside communication strategies for vaccine-hesitant individuals, are needed.

A multiplex diagnostic test is evaluated for self-reported long COVID associated persistent symptoms and a poor recovery from a SARS-CoV-2 infection. A mass-standardised concentration of total antibodies (AC), high-quality (HQ) antibodies and percentage of HQ antibodies (HQ%) is assessed against a spectrum of spike proteins to the SARS-CoV-2 variants: Wuhan, , {delta}, and the Omicron variants BA.1, BA.2, BA.2.12.1, BA.2.75, BA.5, CH.1.1, BQ.1.1 and XBB.1.5 in three cohorts. A cohort of control patients (n = 46) recovered (CC) and a cohort of self-declared long COVID patients (n = 113) (LCC). A nested Receiver Operating Characteristic (ROC) analysis, performed for the variant with lowest HQ concentration in the spectrum, produced an area under the curve and AUC = 0.61 (0.53-0.70) for the CC vs LCC cohorts. For the LCC cohort, the cut-off thresholds for AC = 0.8 mg/L, HQ = 1.5 mg/L and HQ% of 34% were determined, leading to a 71% sensitivity and 66% specificity derived by the Youden metric. The cohorts may be fully classified based on ROC and outlier analysis to give an incidence of persistent virus 62% (95% CI 52% - 71%), hyperimmune 12% (95% CI 7% - 20%) and unclassified, 26% (95% CI 18% - 35%). The overall diagnostic accuracy for both the hyper and hypo immune is 69%. All clinical interventions can now be tailored for the heterogenous long COVID patient cohort.

BackgroundIn England, the burden of respiratory infections varies by ethnicity, contributing to health inequalities, but the role of additional demographic factors remains underexplored. We quantified how differences in social mixing and demographic characteristics between ethnic groups cause inequalities in transmission dynamics. MethodsWe analysed the association between the ethnicity and the number of contacts of 12,484 participants in the 2024-2025 Reconnect social contact survey, using a negative binomial regression model. We simulated respiratory pathogen epidemics using a compartmental model stratified by age, ethnicity, and contact levels, at a national level and in major cities in England. FindingsAfter adjusting for demographic variables, participants of Black and Mixed ethnicities had more contacts than those of White ethnicity (rate ratios (RR): 1.18 [95% Credible Interval (CI): 1.11-1.26], and 1.31 [95% CI: 1.14-1.52]). Participants of Asian ethnicity had fewer contacts (RR: 0.85 [95% CI: 0.79-0.91]). In national-level simulations, individuals of White ethnicity had the lowest attack rates due to demographic differences and mixing patterns. Local demographic structures changed simulated dynamics: attack rates in individuals of Black and Mixed ethnicities were approximately double those of White ethnicity in Birmingham, but less than 60% higher in Liverpool. InterpretationDemographic characteristics and mixing patterns create inequalities in transmission dynamics between ethnicities, while local demographic characteristics and pathogen infectiousness change the expected relative burden. To ensure mitigation strategies are effective and equitable, their evaluation must explicitly account for inequalities arising from local context. FundingMedical Research Council, National Institute for Health and Care Research, Wellcome Trust Research in context Evidence before this studyWe searched PubMed for population-based studies quantifying differences in respiratory infections between ethnic groups, up to 1 April 2026, with no language restrictions. Keywords included: (respiratory pathogens OR influenza OR COVID-19) AND (ethnic* OR race) AND (inequ*) AND (compartmental model OR incidence rate ratio OR hazard ratio). We excluded studies that focused on non-respiratory pathogens (e.g. looking at consequences of COVID-19 on incidence of other pathogens). A population-based cohort study showed that influenza infection risk was higher in South Asian, Black, and Mixed ethnic groups compared to White ethnicity in England. Another population-based cohort study highlighted that during the first wave of COVID-19 in England, the South Asian, Black, and Mixed ethnic groups were more likely to test positive and to be hospitalised than the White ethnic group. Census data in England showed that the distributions of age, household size, household income and employment status differed between ethnic groups, and the recent Reconnect social contact surveys highlighted the impact of each demographic factor on the participants number of contacts. Added value of this studyOur study shows that social contact patterns, mixing, and demographic structure all lead to unequal infection risk between ethnic groups in respiratory pathogen epidemics. Using the largest available social contact survey in England, we show that both the average number of contacts and the proportion of high-contact individuals varied by ethnic group, even after adjusting for participants demographics. These differences, together with mixing patterns and age structure, led to lower expected incidence among individuals of White ethnicity than in all other ethnic groups in simulated outbreaks. The level of inequality between ethnic groups changed when we used different values of pathogen transmissibility. Finally, as ethnic composition and population structure differ between cities in England, our results show differences in expected inequalities at a local level. Implications of all the available evidenceInequalities in infection risk between ethnic groups are context- and pathogen-dependent. They arise from both local population structure and contact patterns. Detailed information on mixing between groups and population structure is needed to accurately measure group-specific infection risk. These findings indicate that public health interventions based only on national-level estimates conceal regional variation in risk and may ultimately increase inequalities. Public health interventions need to be tailored to local contexts to be equitable and effective. Finally, our findings provide a foundation for understanding the progression from infection-risk inequalities to disparities in disease presentation and clinical outcomes.

Despite the evolution of epidemiological analysis and modeling tools, difficulties still remain, especially in developing countries, regarding the availability and use of these tools. Often expensive, requiring high technical expertise, demanding constant connectivity of several or sometimes even significant resources, these tools, although efficient, present a major gap with the operational realities of health districts. It is in this context that we introduce Episia, an open-source Python library designed and conceived to provide a framework to facilitate epidemiological analysis and modeling. It integrates a suite of compartmental epidemic models (SIR, SEIR, SEIRD) with a sensitivity analysis using the Monte Carlo method, a complete biostatistics suite validated against the OpenEpi reference standard, as well as a native DHIS2 client for automated data ingestion. Developed in Burkina Faso, it is optimized and aims not only to address these health challenges encountered in Africa but also remains a versatile tool for global health informatics.

Purpose: To test whether histology-derived gene-expression signatures from routine hematoxylin and eosin slides are prognostic for recurrence and predictive of chemotherapy benefit in early breast cancer. Methods: We conducted a multi-cohort study including CALGB 9344 (anthracycline +/- paclitaxel), CALGB 9741 (standard vs dose-dense chemotherapy), a pooled Chicago real-world cohort, and the American Cancer Society (ACS) Cancer Prevention Studies-II and -3. Whole-slide images were processed with a previously described pipeline to generate 61 histology-derived signatures per patient. The primary endpoint was distant recurrence-free interval (DRFI), except in ACS, where breast cancer-specific survival was used. Secondary endpoints include distant recurrence-free survival (DRFS) and overall survival. The most prognostic signature in CALGB 9344, selected by Harrell's C-index, was evaluated in additional cohorts. Signature-treatment interaction was assessed by likelihood-ratio tests. Multivariable Cox models incorporating age, tumor size, nodal status, estrogen/progesterone receptor status, and signature were fit in CALGB 9344 to improve risk stratification. Results: A total of 7,170 patients were included across four cohorts. The top histology-derived signature in CALGB 9344 showed strong prognostic performance for 5-year DRFI (C-index 0.63) and performed well across validation cohorts (C-index 0.60, 0.70, and 0.62 in CALGB 9741, Chicago, and ACS, respectively). The strongest predictive signal for treatment benefit was observed for DRFS. High-risk cases identified by the signature demonstrated greater benefit from taxane in CALGB 9344 (adjusted hazard ratio [aHR] 0.76 for DRFS, 95% CI 0.66-0.88; interaction p=0.028), from dose-dense chemotherapy in CALGB 9741 (aHR 0.69, 95% CI 0.56-0.85; interaction p=0.039), and differential chemotherapy benefit in the Chicago cohort (aHR 0.84, 95% CI 0.59-1.21; interaction p=0.009). Combined clinical-histology models improved risk stratification and identified low-risk groups with a 2%-10% risk of distant recurrence or breast cancer death. Conclusion: Histology-derived signatures from H&E images are broadly prognostic and, unlike clinical factors, may predict chemotherapy benefit.

Abstract Background Spontaneous coronary artery dissection (SCAD) is a well-recognised cause of acute coronary syndrome particularly among women without conventional cardiovascular risk factors. Increasing evidence indicates a genetic contribution; however, the underlying genetic architecture of SCAD remains insufficiently understood. Objective The aim of this study was to assess the prevalence of rare variants in previously reported SCAD associated genes and to explore the potential presence of novel genetic alterations in well-characterised Swedish patients with SCAD. Methods The study comprised 201 patients enrolled in SweSCAD, a national project examining the clinical characteristics, aetiology, and outcomes of SCAD. All individuals had a confirmed diagnosis based on invasive coronary angiography. Comprehensive exome sequencing was performed to identify rare variants contributing to disease susceptibility. Results Genetic variants that have been associated with SCAD according to current clinical genetics practice for variant reporting were identified in approximately 4 % of patients. In addition, rare potentially relevant variants were detected in almost 60 % of patients in genes associated with vascular integrity and vascular remodelling. Conclusion This study supports SCAD as a genetically complex arteriopathy, driven by rare high?impact variants together with broader polygenic susceptibility. Variants in collagen, vascular extracellular matrix, and oestrogen?responsive pathways provide biologically plausible links to female?predominant disease. Although the diagnostic yield of clearly actionable variants is modest, these findings support broader genomic evaluation beyond overt syndromic presentations and highlight the need for larger integrative genomic and functional studies to refine risk stratification and management.

Drug targets supported by human genetic evidence have significantly higher approval rates, making genome-wide association studies a valuable resource for drug candidate prioritisation. Transcriptome-wide association study signature-matching is an emerging in silico approach that integrates GWAS data with expression quantitative trait loci to generate a disease gene expression signature, which is then compared against drug perturbation databases such as the Connectivity Map. Despite recent adoption, there is no consensus on optimal methodology. Here, we systematically benchmark key parameters, including TWAS method, eQTL tissue model, similarity metric, gene set size, and CMap cell line, using LDL cholesterol, familial combined hyperlipidemia, and asthma as proof-of-concept traits. We demonstrate that while TWAS signature-matching can successfully prioritise known first-line treatments, performance is highly sensitive to parameter choice; for instance, the selection of the cell line used for drug signatures alone can dramatically alter drug prioritisation. Based on these findings, we propose a best-practice framework for robust, genetically-informed drug prioritisation using TWAS signature-matching.

Earlier menopause is a risk factor for several age-related diseases, including dementia. The biological pathways linking menopause timing to later-life brain aging are not understood. Leveraging large-scale plasma proteomics in postmenopausal women from the UK Biobank (N=15,012), earlier menopause was associated with upregulation of pro-inflammatory and extracellular matrix degradation pathways, plus accelerated aging across proteomic clocks of organ and cellular aging, including brain and oligodendrocyte aging. Elevated GDF15, a canonical aging marker, was the top protein correlate of earlier menopause. We observed robust replication of menopause timing proteomic shifts in the Women's Health Initiative Long Life Study (N=1,210). In UKB, proteins associated with earlier menopause, including GDF15, exhibited concordant associations with incident dementia risk and brain atrophy, cerebral small vessel disease burden, and white matter microstructural integrity. Collectively, our findings identify proteomic signatures linking ovarian aging to brain aging, providing a framework to inform interventions to reduce dementia risk.