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Abstract Soil-transmitted helminth (STH) infections account for a significant global health burden, necessitating mass drug administration with benzimidazole-class anthelmintics, such as albendazole (ALB), for morbidity control. However, ALB efficacy shows substantial variability, presenting challenges for achieving consistent treatment outcomes. We have explored the potential impact of the baseline gut microbiota on ALB efficacy in hookworm-infected individuals through microbiota profiling and machine learning (ML) techniques. Our investigation included 89 stool samples collected from hookworm-infected individuals that were analyzed by microscopy and quantitative PCR (qPCR). Of these, 44 were negative by microscopy for STH infection using the Kato-Katz method and qPCR 21 days after treatment, which entails a cure rate of 49.4%. Microbiota characterization was based on amplicon sequencing of the V3–V4 16S ribosomal RNA gene region. Alpha and beta diversity analyses revealed no significant differences between participants who were cured and those who were not cured, suggesting that baseline microbiota diversity does not influence ALB treatment outcomes. Furthermore, differential abundance analysis at the phylum, family and genus levels yielded no statistically significant associations between bacterial communities and ALB efficacy. Utilizing supervised ML models failed to predict treatment response accurately. Our investigation did not provide conclusive insights into the relationship between gut microbiota and ALB efficacy. However, the results highlight the need for future research to incorporate longitudinal studies that monitor changes in the gut microbiota related to the infection and the cure with ALB, as well as functional metagenomics to better understand the interaction of the microbiome with the drug, and its role, if there is any, in modulating anthelmintic treatment outcomes in STH infections. Interdisciplinary approaches integrating microbiology, pharmacology, genetics and data science will be pivotal in advancing our understanding of STH infections and optimizing treatment strategies globally. Graphical Abstract

Background: Pre-travel consultation and chemoprophylaxis measures for malaria are a key component in the prevention of imported malaria in travelers. In this study we report a predictive tool for assessing personalized malaria risk in travelers based on the analysis of electronic medical records from travel consultations. The tool aims to guide physicians in the recommendation of appropriate prophylaxis prior to their trip. We also provide best-practice recommendations for pre-processing noisy and highly sparse real world evidence data. Methods: We leveraged a large EMR dataset, containing demographic information about travelers and their destination. The data has been previously preprocessed using various strategies to handle missing and unbalanced data. We compared multiple machine learning approaches to assess the risk of malaria acquisition in travelers during their travels. Additionally, a feature importance analysis was performed using SHAP (SHapley Additive Explanations) values to identify patterns associated with malaria risk. Results: Our study revealed that our XGB models achieved high predictive capacity (AUC >0.80). The most significant features predicting malaria infection during travel included travel destinations with low malaria risk, vaccination history, number of countries visited, age, and trip duration. Remarkably, we were able to obtain a reduced model with only five features. When comparing this model with a population of travelers recommended for malaria chemoprophylaxis, we observed that it was deemed necessary in only 40% of these travelers. This suggests that 60% received chemoprophylaxis despite having a low personalized risk of malaria. Conclusion: We have developed an algorithmic tool that utilizes a concise survey to generate a personalized travel risk assessment, effectively minimizing the prescription of unnecessary malaria chemoprophylaxis. Through the identification of patterns linked to predictions, our model significantly enhances the efficacy of pre-travel consultations.

IntroductionWe examined the gut microbiota of travellers returning from tropical areas with and without traveller’s diarrhoea (TD) and its association with faecal lipocalin-2 (LCN2) levels.MethodsParticipants were recruited at the Hospital Clinic of Barcelona, Spain, and a single stool sample was collected from each individual to perform the diagnostic of the etiological agent causing gastrointestinal symptoms as well as to measure levels of faecal LCN2 as a biomarker of gut inflammation. We also characterised the composition of the gut microbiota by sequencing the region V3-V4 from the 16S rRNA gene, and assessed its relation with the clinical presentation of TD and LCN2 levels using a combination of conventional statistical tests and unsupervised machine learning approaches.ResultsAmong 61 participants, 45 had TD, with 40% having identifiable etiological agents. Surprisingly, LCN2 levels were similar across groups, suggesting gut inflammation occurs without clinical TD symptoms. Differential abundance (DA) testing highlighted a microbial profile tied to high LCN2 levels, marked by increased Proteobacteria and Escherichia-Shigella, and decreased Firmicutes, notably Oscillospiraceae. UMAP analysis confirmed this profile’s association, revealing distinct clusters based on LCN2 levels. The study underscores the discriminatory power of UMAP in capturing meaningful microbial patterns related to clinical variables. No relevant differences in the gut microbiota composition were found between travellers with or without TD.DiscussionThe findings suggest a correlation between gut microbiome and LCN2 levels during travel, emphasising the need for further research to discern the nature of this relationship.

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The RTS,S/AS01E vaccine targets the circumsporozoite protein (CSP) of the Plasmodium falciparum (P. falciparum) parasite. Protein microarrays were used to measure levels of IgG against 1000 P. falciparum antigens in 2138 infants (age 6–12 weeks) and children (age 5–17 months) from 6 African sites of the phase III trial, sampled before and at 4 longitudinal visits after vaccination. One month postvaccination, IgG responses to 17% of all probed antigens showed differences between RTS,S/AS01E and comparator vaccination groups, whereas no prevaccination differences were found. A small subset of antigens presented IgG levels reaching 4- to 8-fold increases in the RTS,S/AS01E group, comparable in magnitude to anti-CSP IgG levels (~11-fold increase). They were strongly cross-correlated and correlated with anti-CSP levels, waning similarly over time and reincreasing with the booster dose. Such an intriguing phenomenon may be due to cross-reactivity of anti-CSP antibodies with these antigens. RTS,S/AS01E vaccinees with strong off-target IgG responses had an estimated lower clinical malaria incidence after adjusting for age group, site, and postvaccination anti-CSP levels. RTS,S/AS01E-induced IgG may bind strongly not only to CSP, but also to unrelated malaria antigens, and this seems to either confer, or at least be a marker of, increased protection from clinical malaria.

Alzheimer's disease (AD) continuum is defined as a cascade of several neuropathological processes that can be measured using biomarkers, such as cerebrospinal fluid (CSF) levels of Aβ, p-tau, and t-tau. In parallel, brain anatomy can be characterized through imaging techniques, such as magnetic resonance imaging (MRI). In this work we relate both sets of measurements and seek associations between biomarkers and the brain structure that can be indicative of AD progression. The goal is to uncover underlying multivariate effects of AD pathology on regional brain morphological information. For this purpose, we used the projection to latent structures (PLS) method. Using PLS, we found a low dimensional latent space that best describes the covariance between both sets of measurements on the same subjects. Possible confounder effects (age and sex) on brain morphology are included in the model and regressed out using an orthogonal PLS model. We looked for statistically significant correlations between brain morphology and CSF biomarkers that explain part of the volumetric variance at each region-of-interest (ROI). Furthermore, we used a clustering technique to discover a small set of CSF-related patterns describing the AD continuum. We applied this technique to the study of subjects in the whole AD continuum, from the pre-clinical asymptomatic stages all the way through to the symptomatic groups. Subsequent analyses involved splitting the course of the disease into diagnostic categories: cognitively unimpaired subjects (CU), mild cognitively impaired subjects (MCI), and subjects with dementia (AD-dementia), where all symptoms were due to AD.