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Background: Shigella causes severe diarrheal disease, and S. flexneri and S. sonnei are the targets for multivalent vaccine development. Culture-based agglutination has been the gold standard for serotyping, but it is limited by logistics, subjectivity, and the availability of antisera for emerging serotypes. Newer methods, including a qPCR-based approach and whole-genome sequencing offer alternatives, but their performance in Shigella endemic populations are not well documented.

Methods: Shigella isolates obtained from the Enterics for Global Health (EFGH) study in Iquitos, Peru were simultaneously serotyped using four methods: culture-based agglutination, isolate-based qPCR serotyping, stool-based qPCR serotyping and WGS using the in-silico tool ShigaPass. The definitive adjudicated serotype was established by an expert analysis of the WGS data, involving the mapping of sequence reads to known O-antigen biosynthesis and modification genes to identify key mutations.

Results: Results from all four serotyping methods were available for 107/114 isolates. Accuracy for vaccine subtypes S. flexneri 1b, 2a, 3a, 6, and S. sonnei , ranged from 93.3-100% for all methods. Complete concordance between methods was noted in 83/107 isolates, while 24/107 (22.4%) exhibited at least one discrepancy. Most discrepancies derived from S. flexneri serotypes Y, Yv and 1a. Agglutination misclassified eight Y/Yv isolates as 4a, and six isolates correctly classified as 1a by agglutination were classified as 1b by the other methods, a discrepancy associated with a nonsense mutation in the oac gene.

Conclusion: All four serotyping methods achieved acceptable accuracy for Shigella vaccine efficacy evaluation. Although discrepancies are infrequent, WGS provides information of their genomic basis.

Importance: Shigella serotyping is critically important for the evaluation of future multivalent vaccines, of which there are several in advanced stages of development, as well as for monitoring of emerging Shigella serotypes. Culture based agglutination is the most widely used serotyping method, yet its successful implementation is associated with key logistical constraints. This study compares culture-based, qPCR-based, and whole-genome sequencing serotyping methods using isolates from a Shigella -endemic population in Peru. The study demonstrates that molecular and genomic approaches achieve high accuracy for vaccine-relevant serotypes and identifies the genomic basis of serotyping discrepancies. These methods would also reduce variation and improve data quality for future vaccine trials and epidemiologic surveillance. Ultimately, this work informs clinical microbiology laboratories and public health programs that seek a reliable and scalable alternative to traditional serotyping methods.

Background: Neonatal IgM antibodies reflect an in-utero immune response to Treponema pallidum and may offer added diagnostic value. This study evaluated the test performance of treponemal IgM levels measured by the research-use-only (RUO) DPP Syphilis TnT point-of-care (POC) assay for CS risk stratification.

Methods: Conducted from May 2023 to May 2025, this study tested neonatal serum samples from infants born to mothers with syphilis using the DPP Syphilis TnT RUO POC assay, which reports treponemal and nontreponemal IgM levels as relative light units (RLU). Neonates were classified as Confirmed Proven or Highly Probable CS , Possible CS , or CS Less Likely per guidelines; 23 neonates without maternal syphilis served as controls. Treponemal IgM levels were compared across categories using nonparametric tests and ordinal logistic regression. Diagnostic performance used prespecified cutoffs, with agreement assessed against neonatal RPR.

Results: Twenty-two maternal-neonatal dyads were included. Mean treponemal IgM levels rose with CS severity, peaking in the high-risk group ( Possible or Confirmed Proven/Highly Probable CS: 29.9 ± 20.6 RLU) versus CS Less Likely (17.5 ± 20.8 RLU) and controls (3.5 ± 0.8 RLU; p<0.05). Higher IgM levels independently linked to elevated CS risk (OR 1.10 per 1 RLU; p=0.0025). At ≥10 RLU cutoff, treponemal IgM detected 88.9% of high-risk neonates, with 76% agreement to neonatal RPR.

Conclusion: The DPP Syphilis TnT RUO POC assay's treponemal IgM levels discriminated CS risk categories effectively and may supplement current algorithms to improve neonatal CS stratification.

Isolating specific cognitive effects of antidepressant drugs is crucial to develop targeted and individualized treatment selection in psychiatry. In this double-blind, placebo-controlled study in healthy controls, we used computational modeling to characterize the cognitive effects of two classes of drugs for depression, escitalopram, a typical SSRI which increases serotonergic transmission, and agomelatine, which activates melatonin receptors and antagonizes 5-HT _2C serotonergic receptors. 128 healthy participants were randomized to receive either escitalopram (20 mg), agomelatine (25 mg or 50 mg) or placebo for 8 weeks and performed two complementary learning tasks at three time-points allowing to measure early (3 days), intermediate (2 weeks) and delayed (8 weeks) treatment effects. The first task was a simple probabilistic instrumental learning task evaluating how participants learned from positive and negative feedback. The second task was a more complex reversal learning task devised to assess learning from positive and negative feedback in an unstable environment. At 8 weeks, both drugs improved accuracy in task 1 and decreased choice stochasticity in task 2 compared to placebo. Agomelatine 25 and 50 mg had an additional early beneficial effect on reward processing at 3 days whereas agomelatine 50 mg showed maximal effects at 2 weeks. Our study provides one of the very first cognitive evaluations of the delayed effects of antidepressant drugs in healthy volunteers. It reveals that they share common beneficial effect on learning along with pharmacological-specific effects. All observed effects varied highly over time, highlighting the non-linearity of the cognitive impact.

Background: People living with HIV (PLWH) are more susceptible to persistent human papilloma virus (HPV) infection; however, data regarding oral HPV burden among youth with or without perinatal HIV exposure or infection in sub-Saharan Africa remain scarce. This study characterized how dental, immune and behavioral factors contribute to oral HPV susceptibility among youth and mothers across varying HIV exposure groups.

Methods: This baseline analysis leveraged data from a prospective cohort in Nigeria. Participants were categorized at recruitment as HIV-infected (HI), HIV-uninfected (HU), HIV-exposed uninfected (HEU), or HIV-unexposed uninfected (HUU). Standardized questionnaires captured behavioral data, and comprehensive dental examinations assessed DMFT (Decayed, Missing, Filled Teeth), OHIS (Oral Hygiene Index Simplified), and GIS (Gingival Inflammation Score). Oral rinse specimens were tested for oral HPV DNA (Seegene assay). Blood samples were collected from all participants for immune parameters (CD4/CD8). Multivariable regression and machine-learning approaches were used to identify key predictors across immunologic, behavioral, and oral-health domains.

Results: Although overall oral HPV prevalence was low, detection significantly differed across study groups. Oral HPV DNA was exclusively detected in mothers living with HIV (N=8/404) and 7 youth (N=7/671; HI = 4, HEU = 2, HUU = 1). Among youth, HPV correlated with lower CD4/CD8 ratios and poorer oral health In mothers, HPV positivity was linked to earlier sexual debut and lower CD4 counts. Machine learning models revealed distinct age-specific patterns; dental metrics and immune measures were the primary predictors in youth, outranking traditional behavioral factors whereas immune features and dental indices dominated in mothers.

Conclusions: Despite low prevalence, oral HPV clustered among PLWH and was strongly associated with modifiable dental indices. These findings identify oral health as a potential determinant of HPV susceptibility and underscore the importance of integrating oral health promotion within HIV care to elucidate and mitigate pathways linking oral health, immunity, and viral persistence.

Background: Failure of rapid diagnostic tests (RDTs) to detect Plasmodium parasites in peripheral blood of individuals is a major barrier to successful case management and control of malaria in Ethiopia. Characterizing factors contributing to RDT failure is essential if malaria control and elimination strategies are to succeed.

Methods: We consented and enrolled 148 individuals with suspected malaria presenting to health clinics in Mizan Aman, Ethiopia. We administered a clinical questionnaire, diagnosed the presence of malaria parasites via RDT, and collected venous blood. Samples were assayed using molecular methods to detect parasite DNA, Plasmodium species, parasite load, and pfhrp2 and pfhrp3 gene deletions. RNA-seq libraries and LC-MS proteomics data were generated from all molecularly confirmed P. falciparum -infected individuals.

Results: We identified 29/148 (27.9%) individuals as P. falciparum PCR positive with 26/29 (89.7%) false negative by a P.f/Pan RDT. RDT+ P. falciparum and P. vivax infections had higher parasite densities than RDT- infections. Of the 29 P. falciparum infections, 27 (93.1%) had deletions in both pfhrp2 and pfhrp3 genes, and 22 (75.9%) had negligible pfhrp2 transcripts. Ten P. falciparum samples had detectable PfLDH peptides, but no samples had PfHRP2 or PfHRP3 peptides detectable by LC-MS.

Conclusions: Our molecular, transcriptomic, and proteomic characterization of P. falciparum infections that fail detection by PfHRP2/pLDH-based RDTs in Mizan Aman, Ethiopia, revealed a heterogeneous array of factors that could be responsible for the observed RDT failure.

Aims: Despite the availability of clinical risk scores for atherosclerotic cardiovascular disease (ASCVD), their use is limited because the required predictor data are often missing. We developed and validated ECG-ASCVD, a scalable risk prediction paradigm that utilizes ECGs to target ASCVD risk factor assessment.

Methods: Adults aged 30-79 who had undergone a clinical ECG were identified in the Yale New Haven Health System (YNNHS) and a state death index. We developed ECG-ASCVD-12, ECG-ASCVD-IMAGE, and ECG-ASCVD-1 to predict time-to-ASCVD from 12-lead ECG signals, ECG images, and lead-1 signals, respectively. Model performance was assessed in held-out individuals without prior ASCVD and in two external prospective cohorts, ELSA-Brasil (ELSA) and the UK Biobank (UKB). We then simulated the deployment of ECG-ASCVD in a random sample of 100,000 adults at YNHHS.

Results: The development cohort included 363,788 individuals (median age, 57.1 [45.5-67.2] years; 48% Women). The YNHHS, ELSA, and UKB test cohorts included 83,917, 10,934, and 54,166 individuals, respectively. ECG-ASCVD-12 demonstrated generalizable discrimination (C-index: 0.684 to 0.746) and remained independently associated with ASCVD (adjusted hazard ratio: 1.23-1.34 per SD) after adjusting for PREVENT scores (C-index: 0.696-0.782) across the validation cohorts. ECG-ASCVD-IMAGE performed similarly (C-index: 0.673-0.748) while ECG-ASCVD-1 had modestly lower performance (C-index: 0.671-0.735). Simulated deployment suggested that ECG-ASCVD could enable the detection of high ASCVD risk patients who lack the data required for PREVENT.

Conclusion: We developed an ECG-ASCVD toolkit and validated it across diverse multinational cohorts. These results highlight the potential utility of resting ECG information for predicting ASCVD risk, enabling targeted screening.

Ebola virus disease (EVD) survivors often present with clinical sequelae after acute disease resolution, called post-Ebola syndrome (PES). Why some survivors develop these sequelae and others do not is poorly defined. Altered metabolism has been noted in acute EVD but not studied in PES. We identified differential expression of metabolites involved in multiple metabolic pathways in EVD survivors with PES. This included the tricarboxylic acid cycle, amino acid, nucleotide, and short chain fatty acid metabolism. Several of these pathways are associated with immune dysfunction. The identified metabolites have potential use as biomarkers of post-Ebola syndrome.

Background: The predictive power of polygenic scores (PGSs) for lithium treatment response in bipolar disorder (BD) remains limited.

Aim: To enhance prediction of lithium responsiveness by developing a multi-trait PGS (mt-PGS) combining genetic information from multiple phenotypes implicated in lithium response and/or BD aetiology.

Methods: We analysed data collected from BD patients who had received lithium treatment for at least six months and participated in the International Consortium on Lithium Genetics (ConLi+Gen, N=2,367) study. The ALDA scale was used to assess lithium responsiveness, and treatment outcome was defined as continuous ALDA score (0-10) and categorical outcome (favourable ≥7 vs unfavourable response). PGSs were calculated for 59 phenotypes grouped into five clinical-biological clusters: clinical lithium exemplar (#22 phenotypes), cardiometabolic (#17), autoimmune/inflammatory (#5), neurocognitive (#8) and renal function (#7). We applied cross-validated machine learning regression approaches in both outcomes within each cluster, and the selected features from each cluster were subsequently combined to construct the final mt-PGS models. Model performance was assessed using explained variance (R2) for the continuous outcome, and McFaddens pseudo-R2 as well as standard classification model parameters for the categorical outcomes.

Results: The mt-PGS explained 5.07% (continuous outcome) to 9.02% (categorical outcome) of the interindividual variability in lithium responsiveness. Classification accuracy (AUC) for the categorical outcome was 68.13% (95% CI: 64.86, 71.77). Of the five clusters, the PGSs for clinical lithium exemplar phenotypes were most strongly associated with lithium responsiveness, accounting for 2.97%-6.20% of its variability.

Conclusions: By integrating polygenic scores for multiple relevant phenotypes, predictive accuracy for lithium response improved up to nine-fold compared to single-trait methods. Future research incorporating larger, more diverse populations and combining genetic scores with clinical data holds promise for further enhancing prediction and advancing clinical implementation.

Large language models (LLMs) increasingly operate in high-stakes settings including healthcare and medicine, where demographic attributes such as race and ethnicity may be explicitly stated or implicitly inferred from text. However, existing studies primarily document outcome-level disparities, offering limited insight into internal mechanisms underlying these effects. We present a mechanistic study of how race and ethnicity are represented and operationalized within LLMs. Using two publicly available datasets spanning toxicity-related generation and clinical narrative understanding tasks, we analyze three open-source models with a re-producible interpretability pipeline combining probing, neuron-level attribution, and targeted intervention. We find that demographic information is distributed across internal units with substantial cross-model variation. Although some units encode sensitive or stereotype-related associations from pretraining, identical demographic cues can induce qualitatively different behaviors. Interventions suppressing such neurons reduce bias but leave substantial residual effects, suggesting behavioral rather than representational change and motivating more systematic mitigation.

Over the time of the COVID-19 pandemic, many school systems started to utilize educational software to identify students actively planning suicide and other acts of violence. This study examines associations between county-level youth suicide rates and the implementation of GoGuardian Beacon, a school-based software using machine learning methods for identifying students at risk for suicide. Using difference-in-differences and event study methods, we analyzed 2018-2022 suicide data comparing 70 counties with sustained Beacon implementation to 1,215 matched comparison counties that never implemented Beacon. In our primary analysis, counties that maintained consistent Beacon use had 24.4% lower youth suicide rates during 2021-2022 (p < 0.05). In sensitivity analyses defining implementation based on initial adoption regardless of subsequent use, the association was attenuated and not statistically significant. Taken together, these findings indicate that counties with sustained use of Beacon had lower youth suicide rates in our primary analyses, while also highlighting the possibility that broader contextual factors (e.g., local mental health infrastructure and school system characteristics) contribute to the observed differences. Randomized trials with prospective follow-up, more information on school and community resources, and quality of Beacon response pathways after identification are needed to understand the effect of Beacon and clarify the independent contribution of digital monitoring tools within comprehensive youth suicide prevention strategies.