Can a DNA vaccine protect against Chagas disease? A systematic review of preclinical studies.

The causal inference problem in multisensory perception poses a fundamental challenge to our brains in a multisensory environment: how to decide whether sensory stimuli originate from a common source and should be integrated, or from distinct sources and should be segregated. The brain addresses this problem by inferring causal structure from the spatiotemporal disparity of multisensory stimuli. However, it remains unclear whether the brain handles causal inference implicitly, or whether it requires effortful and explicit cognitive processing. This study investigated how human observers (N=47) implicitly infer causal structure when judging the auditory distance of two sequential audiovisual stimuli. In this distance task, we combined representational similarity analysis and multidimensional scaling to retrieve participants' auditory spatial representations. We then compared visual biases on auditory representations (i.e., the ventriloquist effect) to visual biases in three classical auditory localisation and causal judgment tasks. We found that visual biases in the distance task were less influenced by the spatial disparity of the audiovisual stimuli compared to the classical tasks. This pattern was best fitted by a computational stochastic-fusion model. Only in the joint localisation and causal task, small spatial disparity increased the visual bias as predicted by a computational Bayesian causal inference model. Our results suggest that causal inference requires explicit cognitive processing that observers only apply if the causal structure of stimuli is directly relevant to the task. Otherwise, the brain relies on simpler automatic decision strategies such as stochastic fusion.

• Surveyed 1,000 rural households in Mahendragarh (Haryana) and Jhunjhunu (Rajasthan) to assess health impacts of biomass fuel use. • Identified high prevalence of respiratory symptoms, especially among women and children exposed to smoke from wood, dung cakes, and crop residues. • Developed a comprehensive analytical system integrating PM monitoring, weather data, and health surveys to model exposure-risk relationships. • Applied machine learning (Random Forest, XGBoost, LSTM) and causal inference methods (Granger causality, Causal Impact) to predict pollution and health outcomes. • Designed real-time API and dashboard tools for public health alerts and policy support in rural air quality management. This study investigates the spatiotemporal dynamics of ambient air pollution and its health impacts across two semi-urban districts in India- Jhunjhunu and Mahendragarh, using a multidisciplinary approach combining statistical analysis, machine learning, and causal inference. A one-year high-resolution monitoring dataset of PM₁, PM₂.₅, PM₄, and PM₁₀ was integrated with structured household health surveys covering over 1,000 households. High-resolution monitoring of PM₁, PM 2.5 , PM₄, and PM₁₀, along with survey-based health data, was analyzed to explore pollutant behavior, exposure-response relationships, and symptom prevalence. Linear regression models effectively predicted PM 2.5 trends in Jhunjhunu, while advanced models such as Random Forest, XGBoost, and Long Short-Term Memory (LSTM) captured complex variability in Mahendragarh. Models were trained using a 70:30 train–test split with k-fold cross-validation and evaluated using RMSE, MAE, and R² metrics. LSTM and XGBoost achieved the best performance (R² up to 0.87; RMSE reduced by approximately 30% compared to linear regression). SHAP analysis highlighted PM₁ and PM₄ as critical predictors, underscoring the need to expand national air quality standards beyond PM 2.5 and PM₁₀. Explainable machine learning using SHAP identified PM₁ and PM₄ as influential predictors of health-related outcomes, underscoring the need to expand national air quality standards beyond PM2.5 and PM₁₀. Granger-causal links, residual diagnostics, and health symptom anomalies revealed significant associations between particulate pollution and respiratory, cardiovascular, and visual symptoms, particularly in Mahendragarh. Policy insights emphasize cleaner fuel adoption, improved ventilation, and awareness campaigns to mitigate risk among vulnerable, low-income households. By integrating machine learning with epidemiological modeling, this study provides robust, location-specific evidence to support targeted environmental health interventions in under-monitored regions. A key innovation of this study lies in the joint monitoring and modeling of PM₁ and PM₄ alongside conventional PM₂.₅ and PM₁₀ using explainable ML and causal inference. This framework captures nonlinear exposure–response patterns and improves predictive accuracy while providing mechanistic insight into particle-size-specific health risks. The results offer actionable evidence for clean fuel transition, household ventilation improvements, and community-level air quality management in semi-urban and rural settings. Integration of high-resolution particulate monitoring, machine learning, and causal inference reveals strong links between PM₁-PM₁₀ exposure and cardiopulmonary and ocular symptoms in semi-urban India, highlighting PM₁ and PM₄ as key predictors for targeted interventions.

RefLaTEA: a robust visualization and analysis framework leveraging background data for enhanced insight.

A regional classification framework integrating AI and causal inference revealing the drivers of lake eutrophication in China.

Learning fair representation for fine-tuning pre-trained language models.

This study aims to use routinely collected health data and trial emulation methodology to inform the design of a pragmatic randomized controlled trial (RCT) in people requiring multivessel coronary revascularization with severe symptomatic multivessel disease and high-risk characteristics, typically underrepresented in previous RCTs. Hospital episode statistics (HES) linked to Office for National Statistics will be the main data source. The study population is patients who require multivessel myocardial revascularization with at least one of the following high-risk characteristics: age >75 years, female, diagnosed with acute coronary syndrome, heart failure, chronic kidney disease, peripheral vascular disease, or intermediate frailty risk. The intervention procedure is coronary artery bypass grafting (CABG) and the control (reference) is percutaneous coronary intervention (PCI). Outcomes include all-cause and cardiovascular (CV) death, CV hospitalization, major adverse cardiovascular events, and major vascular complications or bleeding within 5 years of the index procedure. This study includes 3 stages of statistical analyses: (1) latent class analysis (LCA) to identify mutually exclusive patient clusters (latent classes) representing different clinical phenotypes, (2) instrumental variable analysis (IVA) to estimate the average treatment effect (ATE) in the whole population and each patient cluster; and (3) repeating stage 2 in an emulated trial population obtained by matching the HES population with individual participant data from an RCT. We will then co-design the protocol for a definitive clinical trial in partnership with patients, public, and stakeholders. This study introduces a novel, stepwise data science framework that integrates machine learning (unsupervised learning through LCA), causal inference, and trial emulation methods applied in big data, to design a future stratified and adaptive RCT of CABG versus PCI in high-risk patients. Our proposed approach fosters new collaborations among data scientists, trial methodologists, clinicians, and patient and public representatives in complex trial designs for diverse, high-risk populations. This study represents a new framework for co-production in trials of cardiovascular interventions, which offers a scalable model and has the potential to transfer to other disease areas. URL: https://www. gov/study/NCT05853536. Unique identifier: NCT05853536.

Reliable paratransit services are essential for urban mobility, particularly for individuals with disabilities who depend on demand-responsive transportation. However, service inefficiencies in urban paratransit systems, such as travel time variability, congestion impacts, and scheduling constraints, continue to pose significant challenges. This study introduces a multitier analytical framework that integrates spatial–temporal modeling, machine learning–based outlier detection, and causal inference to systematically evaluate the reliability of paratransit services in an urban context. Leveraging trip transaction data from a metropolitan paratransit system, we develop the paratransit efficiency index (PEI) to assess travel time reliability at both system-wide and individual trip levels. We then analyze PEI's spatial–temporal variability using geographically and temporally weighted regression and identify outlier trips with high PEI using XGBoost to pinpoint service unreliability and systemic inefficiencies. Later, we utilize causal inference techniques to show that peak-hour pick-ups causally contribute to travel time inefficiency (a 19.4% relative increase in outlier probability), whereas subscription-based bookings causally improve service consistency (a 33.9% relative decrease). The findings provide actionable insights for urban planners and transit agencies to optimize scheduling, mitigate congestion effects, and explore innovative strategies such as integrating transportation network companies for high-cost or unreliable trips. By addressing critical urban transportation equity issues, this study offers data-driven solutions to enhance the efficiency and resilience of paratransit services in growing metropolitan areas.

Indirect relationship between alexithymia and binge eating disorder in the context of obesity.

SPICE-GRADE: simultaneous processing of indirect causal evidence in complex pathways using GRADE - an exploratory case study.

To estimate the prevalence, frequency, and severity of musculoskeletal symptoms (MSS), and associations with preclinical history, loupe practices, and ergonomic exposures across a dental workforce and training pipeline over a 12-month period. A cross-sectional online survey was administered to dental students and clinicians. The survey assessed 12-month MSS prevalence by body region, symptom frequency and severity, care-seeking behaviours, loupe use and fitting practices, and exposure to posture or ergonomic training. Descriptive statistics were reported by cohort. Musculoskeletal symptom prevalence was assessed using a 12-month retrospective recall window administered at a single time point, consistent with standardised Nordic Questionnaire methodology. Associations between categorical variables were assessed using χ² tests, and differences in pain severity were examined using non-parametric Mann-Whitney U tests. A total of 308 respondents completed the survey. Musculoskeletal symptoms were highly prevalent, with practising clinicians reporting greater symptom frequency and severity than students. The neck, upper back, and lower back were the most commonly affected regions. A subset of participants reported musculoskeletal disorder (MSD) conditions prior to commencing dental training. Loupe uptake varied across cohorts, and post-purchase fitting or magnification review was undertaken infrequently among loupe users. When stratified by professional status, neck pain prevalence among practising clinicians was virtually identical regardless of loupe use (75.7% vs 76.0%). An adjusted logistic regression identified an association between loupe use and neck pain in the overall sample (adjusted OR = 2.06, 95% CI 1.25-3.41, p = 0.005). This most plausibly reflects confounding by clinical exposure in the student subgroup rather than a direct effect of loupe use. Musculoskeletal symptom burden was evident across all career stages surveyed. Practising clinicians reported greater symptom frequency and severity than students at the time of survey; however, the cross-sectional design precludes temporal or causal inference. These findings support a prevention-oriented approach across the dental training-practice continuum that prioritises early risk identification, appropriate visual ergonomics practices with verification of use, and sustained, skills-based ergonomic training with ongoing reinforcement. This study highlights the critical role of ergonomics in preventing MSD among dental professionals. MSD are common among dental professionals and are evident from early in dental training. Early ergonomic education, proper loupe fitting, and reinforcement of correct posture are essential to reduce risk. Preventive strategies can improve practitioner health, career longevity, and quality of patient care.

Causal-Nest: A framework for automated causal discovery and inference

Quadriceps fat pad impingement: where patellofemoral maltracking meets the metabolic-inflammatory axis.

From association to intervention: Semantic trajectories and knowledge frontiers in epilepsy-gut microbiota research revealed by bibliometrics and NLP.

Placebo analysis for causal inference in tourism

Debates about whether development projects improve living conditions persist, partly because observational estimates can be biased by incomplete adjustment and because reliable outcome data are scarce at the neighborhood level. We address both issues in a continent-scale, sector-specific evaluation of Chinese and World Bank projects across 9899 neighborhoods in 36 African countries (2002-2013), representative of ∼ 88% of the population. First, we use a recent dataset that measures living conditions with a machine-learned wealth index derived from contemporaneous satellite imagery, yielding a consistent panel of 6.7 km square mosaics. Second, to strengthen identification, we proxy officials’ map-based placement criteria using pre-treatment daytime satellite images and fuse these with tabular covariates to estimate funder- and sector-specific ATEs via inverse-probability weighting. Incorporating imagery often shrinks effects relative to tabular-only models. On average, both donors raise wealth, with larger and more consistent gains for China; sector extremes in our sample include Trade and Tourism (330) for the World Bank (+12.29 IWI points), and Emergency Response (700) for China (+15.15). Assignment-mechanism analyses also show World Bank placement is often more predictable from imagery alone (as well as from tabular covariates). This suggests that Chinese project placements are more driven by non-visible, political, or event-driven factors than World Bank placements. To probe residual concerns about selection on observables, we also estimate within-neighborhood (unit) fixed-effects models at a spatial resolution about 67 times finer than prior fixed-effects analyses, leveraging the computer-vision-imputed IWI panels; these deliver smaller but, for Chinese projects, directionally consistent effects. Methodologically, we extend recent EO–ML causal inference frameworks by fusing pre-treatment satellite imagery with tabular covariates to estimate treatment propensities, and by systematically benchmarking image-augmented estimators against tabular-only and unit fixed-effects designs using new assignment-mechanism diagnostics. Empirically, we provide a continent-wide, sector-specific comparison of the neighborhood-level wealth effects of Chinese and World Bank projects across 9899 African neighborhoods. • Satellite imagery controls reveal that prior studies likely overstated aid benefits in Africa. • On average, Chinese development projects generate larger wealth gains than World Bank projects. • Earth Observation and machine learning improve causal estimates by proxying for planners’ maps. • China’s project placement is less predictable, suggesting unique political or event-driven logics. • Both donors raise wealth, but impacts vary widely by sector, with Emergency Response having the largest effect.

While controlled interrupted time series (CITS) are commonly used to evaluate public health policies, how to incorporate control(s) into their statistical modelling has received limited attention. We aimed to compare the statistical performance of different model formulations for including control groups in various segmented regression model specifications (with a particular focus on CITS and Difference-in-Difference [DiD] designs) under conditions where their assumptions are met, as well as when they are violated. Based on a real-world dataset, we simulated and compared the statistical performance of four model formulations grounded on segmented regressions for including control groups in a pre- and post-evaluation. The compared model formulations were: (1) CITS segmented regression, (2) DiD segmented regression, (3) single ITS of the difference between control and intervention series, and (4) incorporating the control as a covariate in a single ITS. Models were tested across scenarios challenging assumptions around the control group (e.g., non-parallel trends -challenging DiD assumptions-, or inconsistent trend difference over time between groups -challenging CITS assumption-) or regression errors (e.g., heteroscedasticity or autocorrelation). We also included models, including restricted cubic splines of time, which may mitigate distortions from assumption violations. Additionally, we tested for detecting non-parallel trends. Standard DiD, CITS, and the ITS of the difference between series yielded the lowest bias whenever their design assumptions were satisfied. Overall, including time splines as covariates into ITS of the difference between series achieved the lowest bias and highest coverage also when design assumptions were violated. This makes it a valuable tool for causal inference in settings with parallel, non-parallel or inconsistent trend patterns between groups. Since violations of the trends assumption are often undetectable, methods robust to such violations are extremely valuable. Modelling CITS as an ITS of the difference between series is among the most robust methods to embed control series into model specifications. Incorporating time splines as model covariates within an ITS of the difference has the potential of reducing bias from assumption violations (including parallel trends) without negative impacts when assumptions hold.

The association between liver disease and stroke risk: A cross-sectional study with machine learning in a large-scale Chinese cohort.

Leveraging real-world data (RWD) from dental records offers significant potential to overcome logistical and financial barriers in population-based caries research; however, comprehensive evaluations of such studies remain limited. This meta-epidemiological study systematically characterized the temporal and geographical distribution, study characteristics, and methodological quality of RWD-based caries research. A systematic literature search across MEDLINE, EMBASE, and Web of Science databases through December 2024 identified 230 eligible studies. Publication increased exponentially, with 57.8% published between 2015 and 2024, predominantly from Nordic countries (38.3%) and the United States (22.0%). Three-quarters were published in dental journals. About half used a cohort study design. Most studies (80.0%) used dental records from various sources. Over 25% had a sample size exceeding 10,000. Nordic studies frequently used public/school dental service records and, with larger samples, whereas U.S. studies primarily used university dental school records and focused more on adults and older persons. More association-based studies were published after 2015, with larger sample sizes. Nevertheless, 47.4% of studies inadequately reported caries examination methods, and only 8.7% reported agreement test results. Although use of multivariable regression for confounding adjustment increased markedly after 2015 (38.6%-60.5%), causal inference frameworks and advanced analytic methods for confounding adjustment remained rare (4.4%) even among studies investigating causal relationships, and only 7.0% addressed missing data. RWD-based caries research has grown rapidly, but with notable methodological limitations, highlighting the urgent need for enhanced reporting standards, analytical rigor, and practical strategies to improve quality across diverse clinical settings and resource contexts.

Assessing the causal effect of tobacco retail density on cardiovascular and pulmonary disease hospitalizations in the United States.