Light: Science & Applications

End-stage liver disease (ESLD) is one of the leading causes of death worldwide. Currently, the only curative option for patients with ESLD is liver transplantation. However, the demand for donor livers far exceeds the available supply, partly because many potentially viable livers are discarded following biopsy evaluation. While biopsy is the gold standard for assessing liver histological features related to graft quality and transplant suitability, it often leads to high discard rates due to its susceptibility to sampling errors and limited spatial coverage. Besides, biopsy is invasive, time-consuming, and unavailable in clinical facilities with limited resources. Here, we present an AI-assisted photoacoustic/ultrasound (PA/US) imaging framework for quantitative assessment of human donor liver graft quality and transplant suitablity at the whole-organ scale. With multimodal volumetric PA/US images as the input, our deep-learning (DL) model accurately predicted the risk level of fibrosis and steatosis, which indicate the graft quality and transplant suitability, when comparing with true pathological scores. DL also identified the imaging modes (PAI wavelength and B-mode USI) that correlated the most with prediction accuracy, without relying on ill-posed spectral unmixing. Our method was evaluated in six discarded human donor livers comprising sixty spatially matched regions of interest. Our study will pave the way for a new standard of care in organ graft quality and transplant suitability that is fast, noninvasive, and spatially thorough to prevent unnecessary organ discards in liver transplantation.

Effective predictive biomarkers for immune checkpoint inhibitor (ICI) therapy remain an unmet need across solid tumors. Here, we present an integrated spatial proteomics workflow that combines in situ proximity ligation assay with multiplexed immunofluorescence to directly resolve PD1/PDL1 signaling events at the level of defined cellular phenotypes and their spatial organization within intact tumor tissue. Applied as a proof of concept to tumor samples from patients with metastatic urothelial carcinoma treated with pembrolizumab, this approach reveals that PD1/PDL1 interactions specifically involving cytotoxic CD8CD3 T cells are significantly enriched in complete responders, while such interactions are rare in patients with progressive disease. This interaction defined T cell subset achieves superior discrimination of clinical response compared to single marker PDL1 expression or immune cell abundance alone. By integrating direct detection of protein protein interactions with high dimensional single cell phenotyping, our workflow provides a mechanistically informed, spatially resolved biomarker of functional immune engagement. Beyond urothelial carcinoma, this platform establishes a generalizable framework for translating spatial signaling biology into predictive tools for immunotherapy response across tumor types.

The apolipoprotein E {varepsilon}4 (APOE4) allele is the strongest genetic risk factor for late-onset Alzheimer's disease (AD), the most common form of dementia. APOE4 carriers exhibit cerebrovascular and metabolic dysfunction, structural brain alterations, and gut microbiome changes decades before the onset of clinical symptoms. A better understanding of the early manifestation of these physiological changes is critical for the development of timely AD interventions and risk reduction protocols. Multimodal datasets encompassing a wide range of APOE4- and AD-associated biomarkers provide a valuable opportunity to gain insight into the APOE4 phenotype; however, these datasets often present analytical challenges due to small sample sizes and high heterogeneity. Here, we propose a two-stage multimodal AI model (APOEFormer) that integrates blood metabolites, brain vascular and structural MRI, microbiome profiles, and other clinical and demographic data to predict APOE4 allele status. In the first stage, modality-specific encoders are used to generate initial representations of input data modalities, which are aligned in a shared latent space via self-supervised contrastive learning during pretraining. This objective encourages the learning of informative and consistent representations across modalities by leveraging cross-modality relationships. In the second stage, the pretrained representations are used as inputs to a multimodal transformer that integrates information across modalities to predict a key AD risk genetic variant (APOE4). Across 10 independent experimental runs with different train-validation-test splits, APOEFormer predicts whether an individual carries an APOE4 allele with an average accuracy of 75%, demonstrating robust performance under limited sample sizes. Post hoc perturbation analysis of the predictive model revealed valuable insights into the driving components of the APOE4 phenotype, including key blood biomarkers and brain regions strongly associated with APOE4.

Intermuscular adipose tissue (IMAT) expansion is closely associated with cardiometabolic disease, yet its cellular organization and regulatory mechanisms remain poorly defined. Here, we define a human IMAT gene signature using bulk transcriptomics and identify candidate regulators for IMAT function including adipogenic transcription factor early B-cell factor 2 (EBF2). To determine how these programs are organized in situ, we mapped this signature in a mouse model of diet-induced CMD using spatial transcriptomics. We found that IMAT expansion occurs within discrete stromal niches surrounding muscle fibers, characterized by coordinated activation of adipogenic, extracellular matrix, inflammatory, and metabolic pathways. Spatial analyses showed that fibro-adipogenic progenitor (FAP) abundance does not predict adipocyte formation, supporting a model of localized and context-dependent lineage transitions. Cross-species comparison revealed partial conservation of human IMAT gene programs, validating the mouse model and highlighting species-specific features. Functional experiments in human primary myoblasts showed that EBF2 is sufficient to induce adipogenic reprogramming. Our findings establish IMAT as an active, spatially organized remodeling niche and identify lineage plasticity as a central mechanism driving its expansion in metabolic disease

Circulating tumor cells (CTCs), and especially CTC-clusters, are linked to poor prognosis and may reveal mechanisms of metastasis and treatment resistance. Therefore, developing unbiased methods for the functional characterization of CTCs in liquid biopsies is an urgent need. Here, we present an evaluation of multiplex imaging mass cytometry (IMC) to analyze CTCs in mice with human xenograft tumors. In a single-step process, IMC uses metal-labeled antibodies to simultaneously detect a large number of proteins/modifications within minimally manipulated small volumes of blood from the tail vein or heart. We used breast cancer cell lines and a patient-derived xenograft (PDX) to assess antibodies for cross-species interpretation. Along with manual verification, HALO-AI-based cell segmentation was used to identify CTCs and quantify markers. Despite some limitations regarding human-specificity, this technology can be used to investigate the effect of genetic and pharmacological interventions on the properties of single and cluster CTCs in tumor-bearing mice.

Background. Federated learning (FL) enables collaborative model training across institutions without sharing patient-level data. However, standard FL algorithms such as FedAvg degrade under non-independently and non-identically distributed (non-IID) data, a prevalent condition when patient demographics, scanner hardware, and disease prevalence differ across hospital sites. Objective. We propose iPS-MFFL (Individualized Per-Site Meta-Federated Feature Learning), a federated framework with a hierarchical local-model architecture that addresses non-IID heterogeneity through (1) a shared feature extractor, (2) multiple weak-learner classification heads that can be trained with heterogeneous training objectives to promote complementary decision boundaries, (3) independent per-learner server aggregation so that each weak learner's parameters are averaged only with its counterparts at other clients, and (4) a lightweight meta-model, itself federated, that adaptively stacks the weak-learner outputs. Methods. We evaluate on the Brain Tumor MRI Classification dataset (7,200 images; 4 classes: glioma, meningioma, pituitary tumor, no tumor) partitioned across K = 5 simulated hospital sites using Dirichlet non-IID sampling (alpha = 0.3). Four baselines are compared: Local-only training, FedAvg, FedProx, and Freeze-FT. All experiments are repeated over three random seeds (13, 42, 2025) and evaluated using paired t-tests, Cohen's d effect sizes, and post-hoc power analysis.

Background: Current deep learning models in computational pathology, radiology, and digital pathology produce opaque predictions that lack the explainable artificial intelligence (xAI) capabilities required for clinical adoption. Despite achieving radiologist-level performance in tasks from whole-slide image (WSI) classification to mammographic screening, these models function as black boxes: clinicians cannot trace predictions to specific biological features, verify outputs against established morphological criteria, or integrate AI reasoning into precision oncology workflows and tumor board decision-making. Methods: We present Virtual Spectral Decomposition (VSD), a modality-agnostic, interpretable-by-design framework that decomposes medical images into six biologically interpretable tissue composition channels using sigmoid threshold functions - the same mathematical structure as CT windowing. Unlike post-hoc xAI methods (Grad-CAM, SHAP, LIME) applied to black-box deep learning models, VSD channels have pre-defined biological meanings derived from tissue physics, providing inherent explainability without sacrificing quantitative rigor. For whole-slide image (WSI) analysis in digital pathology, we introduce the dendritic tile selection algorithm, a biologically-inspired hierarchical architecture achieving 70-80% computational reduction while preferentially sampling the tumor immune microenvironment. VSD is validated across three cancer types and imaging modalities: pancreatic ductal adenocarcinoma (PDAC) on CT imaging, lung adenocarcinoma (LUAD) on H&E-stained pathology slides using TCGA data, and breast cancer on screening mammography. Composition entropy of the six-channel vector is computed as a visual Biological Entropy Index (vBEI) - an imaging biomarker quantifying the diversity of active biological defense systems. Results: In pancreatic cancer, the fat-to-stroma ratio (a novel CT-derived radiomics biomarker) declines from >5.0 (normal) to <0.5 (advanced PDAC), enabling early detection of desmoplastic invasion before mass formation on standard imaging. In lung cancer, composition entropy from H&E whole-slide images correlates with tumor immune microenvironment markers from RNA-seq (CD3: rho=+0.57, p=0.009; CD8: rho=+0.54, p=0.015; PD-1: rho=+0.54, p=0.013) and predicts overall survival (low entropy immune-desert phenotype: 71% mortality vs 29%, p=0.032; n=20 TCGA-LUAD), providing immune phenotyping for checkpoint immunotherapy patient selection from a $5 H&E slide without molecular assays. In breast cancer, each lesion type produces a characteristic six-channel fingerprint functioning as an interpretable computer-aided diagnosis (CAD) system for quantitative BI-RADS assessment and subtype classification (IDC vs ILC vs DCIS vs IBC). A five-level xAI audit trail provides complete traceability from clinical decision support output to specific biological structures visible on the original images. Conclusion: VSD establishes a unified, interpretable-by-design mathematical framework for explainable tissue composition analysis across imaging modalities and cancer types. Unlike black-box deep learning and post-hoc xAI approaches, VSD provides inherently interpretable, clinically verifiable cancer detection and immune phenotyping from standard clinical imaging at existing costs - without requiring foundation model infrastructure, specialized hardware, or molecular assays. The open-source pipeline (Google Colab, Supplementary Material) enables immediate reproducibility and extension to additional cancer types across the pan-cancer TCGA atlas.

Cell-free DNA (cfDNA) profiling enables minimally invasive cancer detection and monitoring. We present SIMMA, a low-input single-molecule sequencing approach that enables multimodal whole-genome and high-depth targeted sequencing of the same cfDNA sample for both tumour-agnostic and tumour-informed liquid biopsy analysis. Across 792 plasma and cerebrospinal fluid cfDNA samples from 277 paediatric patients with diverse brain and extracranial tumours, SIMMA enabled tumour diagnosis, detection of driver mutations, and reconstruction of extrachromosomal DNA (ecDNA) months before clinical relapse. Using conformal prediction trained on genome-wide fragmentomics, genomic and epigenomic data, SIMMA predicts disease burden as a continuous variable and provides well-calibrated uncertainty estimates for each sample, achieving a limit of detection of [~]100 ppm from low-pass whole-genome sequencing data. In summary, SIMMA establishes the clinical utility of multimodal cfDNA profiling with uncertainty quantification for individual patients and unlocks the potential of ecDNA as a liquid biopsy biomarker for disease detection and monitoring across diverse aggressive malignancies.

Human papillomavirus (HPV) testing is the primary method for cervical cancer screening, and a negative HPV test is associated with a very low subsequent risk of invasive cancer. Nevertheless, a small number of cervical cancers are diagnosed following an HPV-negative testing result, posing challenges within HPV-based screening pathways. Using nationwide Swedish registry data of HPV testing, we identified women diagnosed with invasive cervical cancer between 2019 and 2024 and reconstructed HPV testing histories from the National Cervical Screening Registry (NKCx). The most recent HPV test prior to diagnosis was defined as the index test, and longitudinal HPV testing trajectories were classified among women with an HPV-negative index test. Of 3,000 women diagnosed with invasive cancer, 243 (8.1%) had an HPV-negative index test. These women were older at diagnosis and more frequently diagnosed at advanced stages compared with women with an HPV-positive index test. Most HPV-negative index tests (66.3%) were performed in the peri-diagnostic period (+/- 30 days). Among women with an HPV-negative index test, 52.7% (128/243) had no prior HPV testing recorded, while the remainder had consistently HPV-negative histories (33.3%, 83/243) or evidence of prior HPV positivity before the index negative test (14%, 32/243). Possible recurrent HPV positivity following an intervening negative test was rare (0.4%, 1/243). HPV-negative screening results preceding invasive cancer reflect heterogeneous screening histories and cannot be explained solely by test failure. Findings highlighting the importance of reaching women earlier in screening programs and show that fluctuating HPV detectability is rare.

Objectives: Longer lifespans lead to longer time on retirement, despite the efforts to raise the retirement age. Therefore, it is important to study how the retirement years can be spent without diseases. This study examined socioeconomic and sociodemographic differences in healthy years spent on retirement. Methods: We followed a cohort of retired Finnish municipal employees (N=4231, average follow-up 15.4 years) on national administrative registers for major chronic diseases: cancer, coronary heart disease, cerebrovascular disease, diabetes, asthma or chronic obstructive pulmonary disease, dementia, mental disorders, and alcohol-related disorders. Median healthy years on retirement and age at first occurrence of illness (ICD-10 and ATC-based) in each combination of sex, occupational class, and age of retirement were predicted using Royston-Parmar models. Prevalence rates for each diagnostic group were calculated. Results: Most healthy years on retirement were spent by women having worked in semi-professional jobs who retired at age 60-62 (median predicted healthy years 11.6, 95% CI 10.4-12.7). The least healthy years on retirement were spent by men having worked in routine non-manual jobs who retired after age 62 (median predicted healthy years 6.5, 95% CI 4.4-9.5). Diabetes was slightly more common among lower occupational class women, and dementia among manual working women having retired at age 60-62. Discussion: Healthy years on retirement are not enjoyed equally by women and men and those who retire early or later. Policies aiming to increase the retirement age should consider the effects of these gaps on retirees and the equitability of those effects.

Background Higher levels of sedentary behaviour, such as leisure screen time (LST), and lower levels of physical activity are associated with diseases across multiple body systems which contribute to a large global health burden. Whether these associations are causal is unclear. The primary aim of this study is to investigate the causal effects of higher LST (given greater power) and, secondarily, lower moderate-to-vigorous intensity physical activity (MVPA), on a wide range of diseases in a hypothesis-free approach. Methods A two-sample Mendelian randomisation phenome-wide association study was conducted for the main analyses. Genetic single nucleotide polymorphisms (SNPs) were first selected as exposure genetic instruments for LST (hours of television watched per day; 117 SNPs) and MVPA (higher vs. lower; 18 SNPs) based on the genome-wide significant threshold (p < 5*10-8) from the largest relevant genome-wide association study (GWAS). For disease outcomes, we used summary results from FinnGen GWAS, including 1,719 diseases defined by hospital discharge International Classification of Diseases (ICD) codes in 453,733 European participants. For the main analyses, we used the inverse-variance weighting method with a Bonferroni corrected p-value of p [&le;] 3.47*10-4. Sensitivity analyses included Steiger filtering, MR-Egger and weighted median analyses, and data from UK Biobank were used to explore replication. Findings Genetically predicted higher LST was associated with increased risk of 87 (5.1% of the 1,719) diseases. Most of these diseases were in musculoskeletal and connective tissue (n=37), genitourinary (n=12) and respiratory (n=8) systems. Genetic liability to lower MVPA was associated with six diseases: three in musculoskeletal and connective tissue and genitourinary systems (with greater risk of these diseases also identified with higher LST), and three in respiratory and genitourinary systems. Sensitivity analyses largely supported the main analyses. Results replicated in UK Biobank, where data available. Conclusions Higher levels of sedentary behaviour, and lower levels of physical activity, causally increase the risk of diseases across multiple body systems, making them promising targets for reducing multimorbidity.

BackgroundNegative symptoms are core features of schizophrenia that relate strongly to functional impairment, yet interventions targeting these symptoms remain largely ineffective. Emerging theoretical work highlights how environmental factors may shape and maintain negative symptoms. Although racial disparities in schizophrenia diagnosis among Black Americans are well documented and linked to racial stress and psychosis, the impact of racial stress on negative symptoms has not been examined. This study provides an initial test of a novel theory proposing that racial stress - here measured by racial discrimination - influences negative symptom severity through exacerbation of negative cognitions about the self, particularly defeatist performance beliefs (DPB). Study DesignParticipants diagnosed with schizophrenia-spectrum disorder (SSD) (N = 208; 80 Black, 128 White) completed the Positive and Negative Syndrome Scale (PANSS), the Defeatist Beliefs Scale, and self-report measures of subjective racial and ethnic discrimination (Racial and Ethnic Minority Scale and General Ethnic Discrimination Scale). Relationships among variables were tested using linear regression and mediation analysis. Study ResultsBlack participants exhibited significantly greater total and experiential negative symptoms than White participants with no group difference in DPB. Racial discrimination explained 46% of the relationship between race and negative symptoms. Among Black participants, higher DPB were associated with greater negative symptom severity. Discrimination was positively related to both DPB and negative symptoms. DPB partially mediated the relationship between discrimination and negative symptoms. ConclusionsFindings suggest that racial stress contributes to negative symptom severity via defeatist beliefs among Black individuals, highlighting potential targets for culturally informed interventions.

BackgroundEarly-life adversity (ELA) is a risk factor for enduring pain in youth and is associated with alterations in brain morphology and function. However, it remains unclear whether ELA-related neurobiological changes contribute to the development of enduring pain in early adolescence. MethodsUsing data from the Adolescent Brain Cognitive Development (ABCD) Study, we examined multimodal magnetic resonance imaging (MRI) markers in children assessed at baseline (ages 9-11 years) and at 2-year follow-up (ages 11-13 years). ELA exposure was defined at baseline to maximise temporal separation between early adversity and later enduring pain. Participants with enduring pain at follow-up (n = 322) were compared to matched pain-free controls (n = 644). Structural MRI, diffusion MRI (fractional anisotropy, mean diffusivity), and resting-state functional connectivity data were analysed. Linear models tested main effects of enduring pain, ELA, and their interaction on brain metrics, controlling for relevant covariates. ResultsELA exposure was associated with smaller caudate and nucleus accumbens volumes, and reduced surface area of the left rostral middle frontal gyrus. No significant effects of enduring pain or ELA-by-enduring pain interaction were observed across grey matter, white matter, or functional connectivity measures. ConclusionsELA was associated with alterations in fronto-striatal regions in late childhood, but these changes were not linked to enduring pain in early adolescence. These findings suggest that ELA-related neurobiological alterations may represent early markers of vulnerability rather than concurrent correlates of enduring pain. Longitudinal follow-up is needed to determine whether these alterations contribute to later chronic pain risk.

Background: Gamma oscillation dysfunction has been implicated in neuropsychiatric disorders. Restoring gamma oscillations via brain stimulation represents an emerging therapeutic approach. However, the strength of its clinical effects and treatment moderators remain unclear. Method: We conducted a systematic review and meta-analysis to examine the clinical effects of gamma neuromodulation in neuropsychiatric disorders. A literature search for controlled trials using gamma stimulation was performed across five databases up until April 2025. Effect sizes were calculated using Hedge's g. Separate analyses using the random-effects model examined the clinical effects in schizophrenia (SZ), major depressive disorder (MDD), bipolar disorder, and autism spectrum disorder. For SZ and MDD, subgroup analyses evaluated the effects of stimulation modality, stimulation frequency, treatment duration, and pulses per session. Result: Fifty-six studies met the inclusion criteria (NSZ = 943, NMDD = 916, NBD = 175, NASD = 232). In SZ, gamma stimulation was associated with improvements in positive (k = 10, g = -0.60, p < 0.001), negative (k = 12, g = -0.37, p = 0.03), depressive (k = 8, g = -0.39, p < 0.001), anxious symptoms (k = 5, g = -0.59, p < 0.001), and overall cognitive function (k = 7, g = 0.55, p < 0.001). Stimulation frequency and treatment duration moderated therapeutic effects. In MDD, reductions in depressive symptoms were observed (k = 23, g = -0.34, p = 0.007). Conclusion: Gamma neuromodulation showed moderate therapeutic benefits in SZ and MDD. Substantial heterogeneity likely reflects protocol differences, highlighting the need for well-powered future trials.

Single photon emission computed tomography (SPECT) is a highly specialized imaging modality that enables measurement of regional cerebral perfusion and, in particular, resting cerebral blood flow (rCBF). Recent technological advances have improved SPECT quantification and reliability, making it increasingly useful for studying rCBF abnormalities and perfusion network alterations in psychiatric and neurological disorders. To characterize large scale functional organization in SPECT data, data driven decomposition methods such as independent component analysis (ICA) have been used to extract covarying perfusion patterns that map onto interpretable brain networks. Blind ICA provides a data driven approach to estimate these networks without strong prior assumptions. More recently, a hybrid approach that leverages spatial priors to guide a spatially constrained ICA (sc ICA) have been used to fully automate the ICA analysis while also providing participant-specific network estimates. While this has been reliably demonstrated in fMRI with the NeuroMark template, there is currently no comparable SPECT template. A SPECT template would enable automatic estimation of functional SPECT networks with participant-specific expressions that correspond across participants and studies. The current study introduces a new replicable NeuroMark SPECT template for estimating canonical perfusion covariance patterns (networks). We first identify replicable SPECT networks using blind ICA applied to two large sample SPECT datasets. We then demonstrate the use of the resulting template by applying sc-ICA to an independent schizophrenia dataset. In sum, this work presents and shares the first NeuroMark SPECT template and demonstrating its utility in an independent cohort, providing a scalable and robust framework for network-based analyses.

ImportanceBlood neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are entering clinical use in neurology as markers of neuroaxonal and astrocytic injury, but their utility in psychiatry is unclear. ObjectiveTo determine whether psychiatric diagnoses are associated with altered plasma NfL and GFAP levels. Design, Setting, and ParticipantsThis population-based study examined plasma NfL and GFAP among 47,495 participants from the UK Biobank (54.0% female; 93.5% White; mean [SD] age 56.8 [8.2] years) who provided blood samples and sociodemographic and clinical data between 2006 and 2010. Normative modeling was applied to assess associations between 7 lifetime psychiatric diagnostic categories and deviations from expected NfL and GFAP levels, while accounting for neurological diagnoses, cardiometabolic burden, and substance use. Data were analyzed between July 2025 and March 2026. Main Outcomes and MeasuresDeviations in plasma NfL and GFAP levels from normative predictions. ResultsRelative to the reference population, plasma NfL levels were higher among individuals with bipolar disorder (d=0.20; 95% CI, 0.03-0.37; p=0.03), recurrent depressive disorder (d=0.23; 95% CI, 0.07-0.38; p=0.009), and depressive episodes (d=0.06; 95% CI, 0.02-0.10; p=0.01), lower among individuals with anxiety disorders (d=-0.07; 95% CI, -0.12 to -0.02; p=0.008), but did not differ in schizophrenia spectrum, stress-related, or other psychiatric disorders. Plasma GFAP levels were not elevated in any psychiatric disorders. Variability in NfL levels was greater among individuals with schizophrenia spectrum disorders (variance ratio [VR]=1.30; p=0.005), depressive episodes (VR=1.06; p=0.006), and anxiety disorders (VR=1.08; p=0.005). Variability in GFAP levels was increased only in anxiety disorders (VR=1.08; p=0.01). Plasma NfL levels exceeding percentile-based normative thresholds were more common among individuals with schizophrenia spectrum disorders, bipolar disorder, recurrent depressive disorder, and depressive episodes. Neurological diagnoses, cardiometabolic burden, and substance use were associated with plasma NfL and GFAP levels. Conclusions and RelevanceThis study provides population-level evidence of plasma NfL elevation in bipolar and depressive disorders and increased variability in schizophrenia spectrum, bipolar and depressive disorders, supporting its potential as a biomarker in psychiatry and informing its ongoing neurological applications. Plasma GFAP levels, in contrast, were largely unaltered across psychiatric disorders. Key PointsO_ST_ABSQuestionC_ST_ABSAre plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) levels altered in psychiatric disorders? FindingsIn this cohort study including 47,495 individuals, normative modeling revealed that plasma NfL levels were elevated in bipolar and depressive disorders, whereas plasma GFAP levels were not elevated in any psychiatric disorder. Plasma NfL levels also showed higher variability in schizophrenia spectrum, bipolar, and depressive disorders. MeaningPlasma NfL shows distinct alterations in schizophrenia spectrum and affective disorders, supporting its further investigation as a biomarker in clinical psychiatry and highlighting the need to consider psychiatric comorbidity in neurological applications.

The global ambition to end the human immunodeficiency virus (HIV) epidemic requires understanding which system-level policy levers, enacted under the framework of Universal Health Coverage (UHC), are most effective in achieving both transmission reduction and diagnostic coverage. This study addresses an important evidence gap by quantifying the within-country association between measurable UHC policy indicators and the estimated rate of new HIV infections across nine Southeast Asian countries between 2013 and 2022. Employing a Fixed-Effects panel data methodology, the analysis controls for time-invariant national heterogeneity, ensuring reliable estimates of policy impact. We found that marginal changes in total current health expenditure (CHE) as a percentage of gross domestic product (GDP) were not statistically significantly associated with changes in HIV incidence. However, increases in the UHC Infectious Disease Service Coverage Index were statistically significantly associated with concurrent reductions in HIV incidence (p < 0.001), suggesting the efficacy of targeted service implementation as the principal driver of curbing new HIV infections. In addition, the UHC Reproductive, Maternal, Newborn, and Child Health Service Coverage Index exhibited a statistically significant positive association with changes in HIV incidence (p < 0.01), which is interpreted as a vital surveillance artefact resulting from expanded detection and reporting of previously undiagnosed HIV cases. Furthermore, out-of-pocket (OOP) health expenditure as a percentage of CHE showed a counter-intuitive negative association with changes in HIV incidence (p < 0.01), suggesting this metric primarily shows ongoing indirect cost burdens on the established patient cohort, or, alternatively, presents a diagnostic access barrier that results in lower case finding. These findings suggest that policymakers should prioritise investment in targeted infectious disease service efficacy over aggregate fiscal commitment and utilise integrated sexual health platforms for strengthened HIV surveillance and case identification.

PurposeAntibiotic use (ABU) is a major driver of antimicrobial resistance (AMR), but ABU patterns are poorly understood in low-income countries where the burden of AMR is great and ABU is insufficiently regulated. Here, we report ABU from ten sites ranging from rural villages to small cities in Madagascar, a country with high AMR levels, and present results from modeling to identify factors that may be associated with ABU in this setting. MethodsWe conducted surveys of 290 individuals from ten sites in the SAVA Region of northeast Madagascar to gather data on sociodemographic characteristics, agricultural and animal husbandry practices, recent antibiotic use, the antibiotics that participants recalled using in their lifetimes, and the sources of their antibiotics. Using these data, we conducted statistical analyses with a mixed-effects logistic model to determine which characteristics were associated with recent antibiotic use. ResultsNearly all respondents (N=283, 97.6%) reported ABU in their lifetimes, with amoxicillin being the most widely reported antibiotic (N=255, 90.1% of those reporting ABU). All recalled antibiotics were classified as frontline drugs except for ciprofloxacin. Most respondents who reported antibiotic use also reported obtaining antibiotics without prescriptions from local stores (N=273, 96.5%), while only 52.3% (N=148) reported obtaining antibiotics through a prescriptive route, such as from a health clinic or private doctor. Of the 127 individuals (44.9%) who reported recent ABU, men were found to be significantly less likely to have recently taken antibiotics than women. ConclusionsOur findings provide new insights into ABU in agricultural settings in low-income countries, which have historically been understudied in AMR and pharmacoepidemiologic research. Knowledge of ABU patterns supports understanding of AMR dynamics and AMR control efforts in these contexts, such as interventions on inappropriate antibiotic dispensing. Key pointsO_LIAntibiotic use (ABU) in Madagascar is largely unstudied despite its role in antimicrobial resistance (AMR), which Madagascar faces a high burden of. C_LIO_LIABU was widespread among livestock owners in northeast Madagascar, with the majority of study participants reporting ABU in their lifetimes and most people reporting ABU also having taken antibiotics in the previous three months. C_LIO_LIMost respondents reported obtaining their antibiotics from non-pharmaceutical stores, indicating high levels of unregulated ABU, though more than half also reported sourcing their antibiotics through prescriptive means (like doctors and health clinics). C_LIO_LIMen were less likely than women to have taken antibiotics in the previous three months. C_LIO_LIThese findings support the development of interventions to mitigate the burden of AMR in Madagascar and similar contexts while underscoring the need for more comprehensive research on the drivers and patterns of ABU. C_LI Plain language summaryIn this study, we provide basic information on antibiotic use (ABU) patterns in Madagascar, a country that experiences high levels of resistance but has been particularly understudied in AMR and pharmacological research. We surveyed 290 farmers with livestock from ten sites across northeast Madagascar about their ABU and found that nearly all study participants (N=283, 97.6%) have used antibiotics in their lifetimes, while a little under half of those who reported ABU also reported using antibiotics in the previous three months (N=127, 44.9%). The most used antibiotic was amoxicillin (N=255, 90.1%). Most people obtained their antibiotics from sources that do not require prescriptions, like general stores, indicating that most ABU is unregulated. Through modeling, we also found that men were less likely than women to have taken antibiotics in the previous three months (OR=0.50, CI 0.30-0.82). These findings help us better understand the dynamics of ABU in low-income countries, which have historically been understudied in AMR and pharmacological research. They also support efforts to mitigate the burden of AMR by revealing ABU dynamics that may contribute to the emergence and spread of AMR, as well as identifying targets for intervention to curb inappropriate ABU.

BackgroundCurable sexually transmitted infections (STIs), including Chlamydia trachomatis, Neisseria gonorrhoeae, and Trichomonas vaginalis, remain highly prevalent among pregnant women in South Africa. Despite poor diagnostic performance in pregnancy, syndromic management remains standard care. Point-of-care (POC) screening enables aetiological diagnosis and same-visit treatment but is not yet included in national guidelines. We conducted a mixed-methods process evaluation to examine determinants of antenatal POC STI screening implementation in public facilities. MethodsThis evaluation was embedded within the three-arm Philani Ndiphile randomized trial (March 2021-February 2025) across four public clinics in the Eastern Cape. Screening used a near-POC, electricity-dependent nucleic acid amplification test with a 90-minute turnaround time. Reach, Adoption, Implementation, and Maintenance were assessed using the RE-AIM framework. Quantitative indicators included uptake of screening, treatment, and follow-up attendance. Qualitative data included in-depth interviews with 20 pregnant women and five focus group discussions with 21 research staff and government healthcare workers. The Consolidated Framework for Implementation Research guided qualitative analysis. Findings were integrated using narrative weaving. ResultsScreening uptake was high (99.0%), with treatment coverage of 95.2% at baseline and 93.5% at repeat screening. Same-day treatment was lower (50.7% and 69.8%) and varied substantially by facility, reflecting operational constraints including turnaround time, patient volume, infrastructure, and electricity. Attendance was higher when screening was integrated into routine ANC. Women valued screening for infant health, while providers recognised advantages over syndromic management but highlighted workforce, resource, and maintenance constraints. Socioeconomic factors, including transport costs, hunger, and work commitments, influenced retention and waiting. ConclusionsAntenatal POC STI screening was acceptable and achieved high treatment coverage in a research setting. However, same-day treatment was constrained by operational requirements of the testing platform. Scale-up will require workflow integration, strengthened health system capacity, and faster diagnostics suited to routine antenatal care. Key MessagesO_ST_ABSWhat is already known on this topicC_ST_ABSSyndromic management remains standard antenatal care in many low-resource settings despite failing to capture up to 89% of infections that remain asymptomatic. Point-of-care aetiological screening has demonstrated feasibility, acceptability, and potential clinical benefit in research settings, yet has not been widely adopted into national policy. Limited evidence exists on the health system requirements and contextual determinants influencing scale-up within routine public facilities. What this study addsThis mixed-methods process evaluation demonstrates high uptake and treatment coverage of antenatal POC STI screening in a trial setting, while identifying facility-level, structural, and socioeconomic factors shaping same-day treatment and retention. We show that implementation success varies substantially across clinics and depends on assay characteristics, workflow integration, human resources, infrastructure reliability, and follow-up capacity. How this study might affect research, practice or policyThese findings provide implementation-relevant evidence to inform national policy deliberations on integrating POC STI screening into antenatal care. Sustainable scale-up will require context-adapted delivery models, strengthened workforce and supply systems, faster diagnostics, and alignment with existing ANC workflows to ensure equitable and durable impact.

BackgroundInsecticides remain the cornerstone of mosquito vector control for malaria, dengue, and other mosquito-borne diseases, yet global patterns of deployment and their socioeconomic and environmental drivers are poorly characterized. Understanding where and why insecticides are used is essential for better targeting control efforts and ensuring they are effective, equitable, and efficient. MethodsWe analyzed annual country-level insecticide-use data from 122 countries (1990-2019), reported as standard spray coverage for insecticide-treated nets (ITNs), residual spraying (RS), spatial spraying (SS), and larviciding (LA). Generalized linear mixed models and hurdle models quantified associations between deployment and disease incidence, human development index (HDI), human population density, temperature, and precipitation. Models were evaluated using repeated cross-validation and applied to generate downscaled predictions of insecticide use at subnational administrative region level 2 (ADM2) globally. FindingsInsecticide deployment increased with malaria and dengue incidence, but this response was substantially stronger in higher-HDI countries, indicating that deployment depends on socioeconomic capacity as well as disease burden that leads to weaker scaling in lower-resource settings. Intervention types exhibited distinct patterns; ITN use tracked malaria burden, whereas infrastructure-intensive approaches (e.g., RS and SS) were concentrated in higher-HDI settings and increased with Aedes-borne disease incidence. Downscaled ADM2-level maps uncovered substantial within-country heterogeneity that is obscured at the national scale, highlighting regions where predicted deployment remains low relative to disease risk across sub-Saharan Africa, South Asia, and parts of Latin America. InterpretationGlobal insecticide deployment reflects not only epidemiological need but also economic and logistical capacity, creating mismatches between risk and control. High-resolution mapping can support more equitable allocation of interventions, guide insecticide resistance stewardship, and improve strategic planning as climate and urbanization reshape mosquito-borne disease risk.