Advanced Biology

Background: Generative artificial intelligence (GenAI) chatbots have shown utility in assisting with various research tasks. Traditional, complementary, and integrative medicine (TCIM) is a patient-centric approach that emphasizes holistic well-being. The integration of TCIM and GenAI presents numerous key opportunities. However, TCIM researchers' attitudes toward GenAI tools remain less understood. This large-scale, international cross-sectional survey aimed to elucidate the attitudes and perceptions of TCIM researchers regarding the use of GenAI chatbots in the scientific process. Methods: A search strategy in Ovid MEDLINE identified corresponding authors who were TCIM researchers. Eligible authors were invited to complete an anonymous online survey administered via SurveyMonkey. The survey included questions on socio-demographic characteristics, familiarity with GenAI chatbots, and perceived benefits and challenges of using GenAI chatbots. Results were analysed using descriptive statistics and thematic content analysis. Results: The survey received 716 responses. Most respondents reported familiarity with GenAI chatbots (58.08%) and viewed them as very important to the future of scientific research (54.37%). The most acknowledged benefits included workload reduction (74.07%) and increased efficiency in data analysis/experimentation (71.14%). The most frequently reported challenges involved bias, errors, and limitations. More than half of the respondents (57.02%) expressed a need for training to use GenAI chatbots in the scientific process, alongside an interest in receiving training (72.07%). However, 43.67% indicated that their institutions did not offer these programs. Discussion: By developing a deeper understanding of TCIM researchers' perspectives, future AI applications in this field can be more informed, and guide future policies and collaboration among researchers.

Altered iron homeostasis has long been implicated in Parkinson's Disease (PD), although the mechanisms have not been clear. Given the critical role of PD-related activating mutations in LRRK2 (leucine-rich repeat protein kinase 2) within membrane trafficking pathways we examined the impact of a homozygous mutant LRRK2G2019S on iron homeostasis within the RAW macrophage cell line with high iron capacity. Proteomics analysis revealed a dysregulation of iron-related proteins in steady state with highly elevated levels of ferritin light chain and a reduction of ferritin heavy chain. LRRK2G2019S mutant cells showed efficient ferritinophagy upon iron chelation, but upon iron overload there was a near complete block in the degradation of the ferritinophagy adaptor NCOA4. These conditions lead to an accumulation of phosphorylated Rab8 at the plasma membrane, which is selectively inhibited by LRRK type II kinase inhibitors. Iron overload then leads to increased oxidative stress and ferroptotic cell death. These data implicate LRRK2 as a key regulator of iron homeostasis and point to the need for an increased focus on the mechanisms of iron dysregulation in PD.

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.

Study ObjectivesTo evaluate wearable sleep staging across sleep apnea severity, including very severe sleep apnea defined as an apnea-hypopnea index (AHI)[≥] 50 events/h, and to assess how training-set composition affects performance in this subgroup. MethodsWe analyzed 552 overnight recordings, 318 from the Sleep Lab Dataset and 234 from the Hospital Dataset. In the Hospital Dataset, 26.5% had very severe sleep apnea. We developed a deep learning model for sleep staging using RR intervals from wrist-worn photoplethysmography and three-axis accelerometry. Baseline performance was assessed by cross-validation under 5-stage and 4-stage staging. We examined night-level associations with AHI severity. We also compared the baseline model with an ablation model trained on the same number of recordings but with more Sleep Lab Dataset and lower-AHI Hospital Dataset recordings, evaluating both models in the very severe subgroup. ResultsIn 5-stage classification, Cohens kappa was 0.586 in the Sleep Lab Dataset and 0.446 in the Hospital Dataset. Under 4-stage staging, the gap narrowed, with kappa values of 0.632 and 0.525, respectively. In the Hospital Dataset, performance declined with increasing AHI severity. Among 62 recordings with very severe sleep apnea, reducing high-AHI representation in training lowered kappa from 0.365 to 0.303. ConclusionsWearable sleep staging performance declined across greater sleep apnea severity in this clinical cohort. Clinical utility may benefit from training data that better represent the target severity spectrum and from selecting staging granularity to match the intended use case. Statement of SignificanceRepeated laboratory polysomnography is impractical for long-term sleep apnea management. Wearable sleep staging could support scalable monitoring, yet its reliability in clinically severe sleep apnea has remained unclear. This study developed and evaluated a wearable sleep staging approach in both sleep-laboratory and hospital cohorts. The hospital cohort included many severe and very severe cases. Performance was lower in the hospital cohort and declined with greater sleep apnea severity. A coarser staging scheme reduced the gap between cohorts, and models trained without representative very severe cases performed worse in this target population. These findings highlight the value of severity-aware model development and motivate future multi-night home validation with reliability cues.

Improving diversity in U.S. Alzheimers disease (AD) research is a pressing need. By 2050, Hispanic and Latino Americans will comprise 30% of the population. Hispanics are 1.5 times more likely and Blacks are twice as likely to develop AD compared to Whites, yet both remain vastly underrepresented in clinical trials research. Aging and AD research mentorship of underrepresented STEM undergraduates is designed to promote entry into related professions by students committed to decreasing disparities in AD research participation and clinical care. The NIA-funded MADURA program recruited 93 students from backgrounds historically underrepresented in STEM majors and/or from NIH-defined disadvantaged backgrounds. Trainees were placed in aging/AD research labs and received weekly training and mentorship from faculty research PIs and other types of supervisors (postdoctoral researchers, graduate students, research assistant staff...) Our study examined student ratings of the program and mentor behaviors, using a program-specific survey and the Mentoring Competency Assessment-21 (MCA-21). Trainees were highly satisfied with both mentoring relationships and the overall program. Student rated MCA-21 competency areas were quite high for both P.I.s and other types of research mentors. However, there were striking differences in associations between competencies and relationship and program satisfaction, by mentor type. For PI mentors, no MCA-21 competencies were associated with relationship satisfaction, but five of six competencies were associated with relationship satisfaction for other mentor types. Similarly, no PI mentor competencies were significantly correlated with overall placement satisfaction, but all six competencies were correlated with overall placement satisfaction for other mentor types. The authors discuss the likelihood of differing student expectations of faculty PI versus other types of research mentors, recommendations for assessing role-specific student expectations (including functions primarily possible only for senior faculty PIs), and utilizing nearer-peer plus PI faculty mentors to comprehensively address the gamut of mentee needs.

Objective: Skin sympathetic nerve activity (SKNA) has emerged as a promising non-invasive surrogate measure of sympathetic drive, but its relevant physiological characteristics remain ill-defined. This observational study aims to investigate its regulatory patterns during rest and Valsalva maneuver (VM) in healthy participants. Method: Using a two-layer strategy integrating signal analysis and physiological modelling, we analyzed data recorded from 41 subjects performing repeated VMs. The observational layer includes time-domain feature comparisons using linear mixed-effect models, and time-varying spectral coherence analysis. The mechanistic layer proposes a mathematical model to investigate whether baroreflex and respiratory modulation are sufficient to reproduce the observed HR and average SKNA (aSKNA) dynamics. Main Results: Mean integrated SKNA (iSKNA) showed more significant change than HRV for VM induced effects. We also found mean iSKNA increase during VM varies with BMI and sex. The coherence analysis indicated that iSKNA strongly synchronized with EDR under resting conditions. The proposed model successfully reproduced main characteristics of aSKNA dynamics, yielding a high median Pearson correlation coefficient of 0.80 ([Q1, Q3] = [0.60, 0.91]). In contrast, HR dynamics were only partially captured, with a median PCC of 0.37 ([Q1, Q3] = [0.16, 0.55]). These results likely suggest SKNA provides a more direct representation of sympathetic burst dynamics during VM in healthy subjects. Significance: This study provides convergent evidence that SKNA reflects known autonomic regulatory influences in healthy subjects. These findings strengthen the physiological interpretability of SKNA while clarifying its appropriate use as a practical biomarker of sympathetic function.

ObjectiveLong term monitoring of obstructive sleep apnea (OSA) severity may be relevant for several clinical applications. We developed a method for estimating the apnea-hypopnea index (AHI) using wrist-worn, reflective photoplethysmography (PPG). ApproachA neural network was developed to detect respiratory events using PPG and PPG-derived sleep stages as input. The development database encompassed retrospective data from three polysomnographic datasets (N=3111), including a dataset with concurrent reflective PPG recordings from a wrist-worn device (N=969). The model was pre-trained with (transmissive) finger-PPG signals from all overnight recordings and then fine-tuned to wrist-PPG characteristics using transfer learning. Validation was performed on the test portion of the development set and on a fourth, external hold-out dataset containing both wrist-PPG and PSG data (N=171). Performance was evaluated in terms of AHI estimation accuracy and OSA severity classification. Main ResultsThe fine-tuned wrist-PPG model demonstrated strong agreement with the PSG-derived gold-standard AHI, achieving intra-class correlation coefficients of 0.87 in the test portion of the development set and 0.91 in the external hold-out validation set. Diagnostic performance was high, with accuracies above 80% for all severity thresholds. SignificanceThe study highlights the potential of reflective PPG-based AHI estimation, achieving high estimation performance in comparison with PSG. These measurements can be performed with relatively comfortable sensors integrated in convenient wrist-worn wearables, enabling long-term assessment of sleep disordered breathing, both in a diagnostic phase, and during therapy follow-up.

Introduction: Solitary plasmacytomas (SP) are rare neoplasm of localised proliferation of clonal plasma cells. It can be classified based on site of involvement and bone marrow involvement. It is an indolent disease in the majority of patients. Primary modality of treatment is radiotherapy and surgical excision. Materials and methods: This was a retrospective audit of SP who were treated and followed up at a tertiary care center in eastern India from January 2012 to December 2025. Patients who has solitary plasma cytoma with more than 10% plasma cells, POEMS syndrome, have been excluded from analysis. Results: We identified 46 patients of SP. The median age of the studied population was 53 years (23-75 years). Males were more commonly affected than females (M:F=2.2:1). Most common chief complaints were bony pain (67.4%). SBP was seen in 39 (84.8%) cases whereas SEP was seen in 7 (15.2%) cases. Vertebra was the most common site of involvement (61.4%). Median M band concentration 0.24 g/dL (0.1 to 1.95 gm/dL). IgG was the most common isotype accounting for 60.6% cases. Six cases (13%) had minimal bone marrow involvement. The majority of the patients received local radiotherapy (89.1%). With a median follow up of 5.4 years (95% CI: 1.8 - 9.0), median OS was not reached, median PFS was 9.22 years (95% CI: 5.8-12.6), median time to next treatment (TTNT) was 9.86 years (95% CI: 6.8 - 12.9). Conclusion: Solitary plasmacytoma commonly affects young males. Bones are more commonly affected than extramedullary sites. SP has a lower rate of progression and excellent prognosis when treated with local radiotherapy.

IntroductionIndividuals with COPD can be classified according to their levels of physical activity (PA) and physical capacity (PC). The relationship between nutrition and body composition within these classifications remains unclear. ObjectivesTo compare the body composition and food intake of people with COPD and verify the associations. MethodsCross-sectional exploratory analysis study in which body composition and food intake were assessed in individuals with COPD. Classification was based on six-minute walk test (PC) and accelerometry(PA): Quadrant "can do, dont do" (I-preserved PC, low PA); quadrant "can do, do do" (II-preserved PC, preserved PA). Results72 individuals with COPD, 39 in quadrant I and 33 in quadrant II, with mean ages of (69 {+/-} 6) (67 {+/-} 7), respectively. Group I had a higher proportion of males, whereas group II had a higher proportion of females. A positive trend in skeletal muscle mass (p=0.011) (B= 2.883) and a negative trend in basal metabolic rate (p=0.010) (B=-0.092) for group I. ConclusionBrazilians with COPD classified in quadrants I and II showed similar results in terms of body composition and food intake. A positive trend in skeletal muscle mass was observed for the group I. These findings align with the pathophysiological model of COPD, in which the preservation of muscle mass and adequate protein intake support functional capacity and the maintenance of higher physical activity levels.

Patients who participate in intracranial neuroscience research make invaluable contributions to our understanding of the brain, accelerating the development of neurotechnological interventions. Engagement of patients as part of this research presents unique challenges, where study goals can be distant from immediate clinical applications and require specialized domain knowledge. Yet methods for meaningfully integrating patient communities as part of these research efforts is essential, as intracranial neuroscience guides the application of artificial intelligence for understanding and enhancing human cognition. In order to identify what patients consider meaningful research engagement we interviewed individuals who participated in a study during their Deep Brain Stimulation (DBS) surgery and attended a group event where they interacted with our research team. Analysis of semi-structured interviews identified four main themes: interest in science and the future of clinical care, contributing to science to improve lives, connecting with others, and accessibility considerations. Based on these insights, we propose strategies for transformational participation of patient communities in intracranial neuroscience research with respect to engagement objectives, communication and scope. This approach offers a foundation for sustaining relationships between scientists and communities rooted in trust and transparency, to ensure that impacts of neurotechnology on human health and cognition are aligned with patient needs as well as desired public values.

BackgroundSynthetic data have emerged as a complementary strategy for secondary use of clinical registries, enabling data sharing without patient-level exposure. In spine surgery, multicenter data sharing is constrained by institutional governance and patient privacy regulations. Validated synthetic data generation may enable broader access to surgical outcomes data for artificial intelligence development without compromising patient confidentiality. ObjectiveTo describe and benchmark a three-domain validated synthetic data pipeline applied to a multicenter, tokenized spine surgery registry (SpineBase), and to establish a reproducible certification framework for synthetic spine surgery datasets. MethodsWe extracted 125 sacroiliac joint fusion cases from the SpineBase registry (SIBONE study, IRB-SOFCOT approval Ref. 14-2025; CNIL MR-004 Ref. 2234503 v 0). A GaussianCopula generative model was trained on 52 structured variables spanning demographics, preoperative assessments, operative details, and longitudinal outcomes at 3, 6, 12, and 24 months. Synthetic datasets of 100, 1,000, and 10,000 patients were generated. Validation followed a three-domain framework: (1) fidelity, assessed by Kolmogorov-Smirnov tests and Jensen-Shannon divergence; (2) utility, assessed by train-on-synthetic, test-on-real (TSTR) methodology; and (3) privacy, assessed by nearest-neighbor distance ratio (NNDR), membership inference attack, and k-anonymity proxy. ResultsAll three validation gates passed. Fidelity: mean KS p-value 0.52 (threshold >0.05). Privacy: NNDR >1.0 in 98.9% of synthetic records; membership inference AUROC 0.57. Utility: 12-month Oswestry Disability Index prediction yielded Pearson r = 0.29, consistent with expected attenuation at N = 125. A SHA-256 cryptographic hash of each certified dataset was anchored on the Solana blockchain for immutable provenance. ConclusionsA validated, blockchain-anchored synthetic data pipeline for spine surgery registries is technically feasible and meets current publication-standard criteria for fidelity and privacy. Utility metrics scale with registry size, creating a direct incentive for multicenter data contribution. This framework provides a reproducible methodology for synthetic data certification in spine surgery research, and establishes certified synthetic datasets as a privacy-native substrate for expert-annotation pipelines -- as demonstrated in the companion Spine Reviews study.

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.

Purpose: To determine whether spatial decomposition of longitudinal retinal nerve fiber layer (RNFL) change maps reveals distinct modes of glaucomatous progression masked by conventional averaging, and to validate these modes through structure function mapping and genetic association analysis. Methods: Pixel wise RNFL rates of change were computed from longitudinal optic disc OCT scans of 15,242 eyes (8,419 adults with primary open angle glaucoma [POAG]; Massachusetts Eye and Ear, 1998 to 2023). A loss only constraint zeroed all thickening values, reflecting the biological prior that adult RNFL does not regenerate. Nonnegative matrix factorization decomposed these maps into spatial progression components (80% training set). Components were evaluated in a heldout set (20%) for retinotopic structure function concordance, visual field (VF) progressor classification against global and quadrant RNFL rates, and enrichment of genetic association signals at established POAG loci. Results: Six anatomically distinct progression patterns emerged, including diffuse circumferential loss, focal peripapillary defects, and arcuate bundle degeneration. Pattern based models significantly outperformed global RNFL rate for classifying VF progressors (area under the curve, 0.750 [95% CI, 0.709 to 0.790] vs. 0.702; P = .0096) and explained additional variance in functional decline (Nagelkerke pseudoR2, 0.301 vs. 0.198; P = .0011). Structure function mapping confirmed retinotopic coherence. Spatial phenotypes recovered stronger genetic signals than global rates at 85.3% of established POAG loci, suggesting they capture more biologically homogeneous endophenotypes of progression. Conclusions: Glaucomatous structural progression occurs through spatially distinct modes with independent structure function and genetic signatures that conventional RNFL averaging obscures.

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.

Targeted protein degradation (TPD) is a therapeutic strategy to remove disease-causing proteins by routing them to the ubiquitin-proteasome, autophagy, or lysosme machineries. For instance, proteolysis-targeting chimeras (PROTACs) are synthetic hetero-bifunctional small molecules that simultaneously bind the target and an E3 ubiquitin ligase to drive ubiquitination and degradation by the proteasome. Despite considerable success, designing such molecules is challenging and the number of currently addressable ubiquitin E3 ligases is limited. Here we demonstrate hetero-bifunctional de novo designed proteins as alternatives for TPD to access more targets and ligases. First, we develop a stable and highly adaptable helix-turn-helix scaffold for presenting different binding sites. Next, we use computational protein design to incorporate and embellish hot-spot- binding sites to target BCL-xL, plus short linear motifs (SLiMs) for KLHL20 ligase recruitment. The resulting mono- and bi-functionalised proteins bind the targets in vitro, and the latter degrade BCL-xL in cells leading to apoptosis.

HighlightsO_LIHealth inequalities have widened over 15 years, favouring high-income groups C_LIO_LIInequality in physical activity & mental health widened the most pre-intervention C_LIO_LIPost-intervention, inequalities persisted but stayed relatively unchanged. C_LIO_LILong-term illness and unemployment were key drivers of inequality C_LIO_LIThe greenway may have slowed down the inequality widening but the impact is limited C_LI BackgroundEvidence concerning health inequalities following urban green and blue space UGBS) interventions is limited. This study examined the changes in health inequalities after a major urban regeneration project, the Connswater Community Greenway (CCG), in Belfast, UK. MethodCross-sectional household surveys were conducted in 2010/11 (baseline), 2017/18 (immediately after completion), and 2023/24 (long-term follow-up) with a sample of approximately 1,000 adults each wave. Using concentration indices (CI), income-related health inequalities for three outcomes (physical activity, mental wellbeing and quality of life) were measured. A regression-based decomposition of concentration index examined the contribution of sociodemographic factors to the observed inequalities underpinning each outcome over time. ResultsAcross three waves, there was widening of inequalities over the 15-year period across all three health outcomes, with those from high-income groups reported higher levels of physical activity (CI=0.33, SE=0.026), better mental wellbeing (CI=0.03, SE=0.003), and better quality of life (CI=0.09, SE=0.008). The widening inequalities mainly occurred during the construction phase of CCG (2010-2017) and remained stable post-intervention (2017-2023). Decomposition analysis revealed that the pro-poor concentration of long-term illness and unemployment was the key driver that together explained approximately 51%-76% of the inequalities. ConclusionThe CCG was limited in reducing health inequalities which were mainly driven by long-term illness and unemployment - factors beyond the direct scope of the UGBS intervention - resulting in low-income groups likely to fall further behind the wealthier groups. The widening of inequality is consistent with findings from other public interventions that did not have a primary equity focus.

This study evaluates the feasibility of implementing artificial intelligence (AI)-driven disease surveillance systems at Julius Nyerere International Airport (JNIA) in Tanzania, a key hub for regional and international travel. Through a mixed-methods approach combining qualitative interviews and quantitative surveys, the research assesses the infrastructure, human resource capacity, and regulatory frameworks necessary for AI integration. Findings indicate that while Port Health Officers are strongly optimistic about AIs potential to enhance disease detection, the airport faces significant barriers, including outdated infrastructure, insufficient technical resources, and a lack of trained personnel. Ethical and privacy concerns, particularly surrounding data security, also emerged as key challenges, compounded by limited public awareness and the socio-cultural acceptability of AI systems. Furthermore, the study identifies gaps in national policies and inter-agency coordination that hinder the effective implementation of AI technologies. The research concludes that while current conditions render AI adoption infeasible, strategic investments in infrastructure, workforce training, and policy development could pave the way for future integration, enhancing public health surveillance at JNIA and potentially other airports in low- and middle-income countries. This study contributes critical insights into the barriers and opportunities for AI-driven disease surveillance in low-resource settings, specifically focusing on a high-priority transit point, international airports. It emphasizes the importance of region-specific solutions to enhance health security in East Africa and supports the broader global health agenda by advocating for international collaboration and the development of scalable disease surveillance systems. Future research should explore pilot AI implementations at other airports to evaluate real-world challenges and refine AI systems for broader applicability, including cost-effectiveness analyses and integration of public perspectives on AI.

Background Comprehensive sexuality education (CSE) is essential to the health and well-being of young people. In the Democratic Republic of Congo (DRC), where more than 65% of the population is under the age of 25, access to interpersonal CSE remains limited owing to sociocultural and structural barriers. This exposes young people to persistent socio-sanitary vulnerabilities. In this context, mobile health apps (MHAs) constitute a promising solution, supported by the growing use of smartphones among young Congolese. However, this group's intention to use MHAs for CSE has been the subject of little research to date. Objective The aim of this study was to identify predictors of intention to use MHAs among young Congolese, based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). Methods A predictive correlational study was conducted in eight public secondary schools in Bukavu (DRC) with a stratified random sample of 859 students. Predictors of intention to use--performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and perceived risk (PR)--and moderators--age, gender, and past MHA experience--were measured from data collected through a self-administered UTAUT questionnaire. Descriptive and multivariate analyses were run on SPSS version 28. Results Mean age of participants was 16.3 years (SD = 1.5). Boys made up 55.1% of the sample. Overall, 51.0% of the sample owned a smartphone, of which 62.3% reported having easy access to mobile data and 16.2% were already using MHAs to learn about sexual health. Intention to use MHAs was positively influenced by PE ({beta} = 0.523, p < 0.001), EE ({beta} = 0.115, p < 0.001), and SI ({beta} = 0.113, p < 0.001). FC (p = 0.260) and PR (p = 0.631), however, had no significant influence. Age moderated all of the relationships tested (F (1, 849-854) = 9.97-20.82; p [&le;] 0.002), with more marked effects observed among younger participants 14-15 years old. The final model explained 44% of the variance, indicating good predictive power. Conclusion Intention to use digital CSE was explained primarily by PE, EE, and SI and moderated by age. To strengthen this intention, stakeholders will need to promote e-interventions that are pertinent, easy to use, socially valorized, and tailored to young people's needs and to the local context.

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.

BackgroundThis study aimed to assess caregiver knowledge of Infant and Young Child Feeding (IYCF), child health, severe acute malnutrition (SAM) screening, and Community-Based Management of Acute Malnutrition (CMAM), its determinants, and associations with IYCF/ WaSH (water, sanitation, and hygiene) practices among caregivers of children 6-59 months with SAM in Ethiopian agrarian and pastoralist settings. MethodData were from the baseline survey of the R-SWITCH Ethiopia cluster-randomized controlled trial (cRCT), which screened [~]28,000 children aged 6-59 months and identified 686 SAM cases. Caregiver knowledge was evaluated using a validated 32-item questionnaire (Cronbachs for internal reliability) and analyzed via linear mixed-effects and Poisson regression models in Stata 17. ResultsCaregiver knowledge was positively associated with improved IYCF/WaSH practices among children aged 6-23 months with SAM, including higher minimum dietary diversity (MDD: IRR=1.50), minimum acceptable diet (MAD: IRR=1.63), and reduced zero vegetable/fruit intake (IRR=0.77), as well as MDD in children aged 24-59 months, improved water access (IRR=1.19), water treatment (IRR=2.02), and handwashing stations (IRR=1.41). Literate ({beta} = 4.1; 95% CI:1.5-6.6, p= 0.016), pregnant({beta} = 4.4; 95% CI:0.9-7.8, 0.018), having child weighing at a health post/ health center ({beta} = 8.9;95% CI:3.5-14.2,p [&le;] 0.001), and higher household wealth index ({beta} = 11.8;95% CI:3.6-20.1,p= 0.005) were associated with higher knowledge, while possible depression ({beta} = -0.3;95% CI: -0.5 to 0.0, p= 0.015) was associated with lower knowledge. ConclusionCaregiver knowledge determines better IYCF/WaSH practices among children aged 6-59 months with SAM. Literacy, pregnancy, having child weighing at a health post or health center, and greater household wealth were associated with caregivers knowledge, whereas possible depression was associated with lower knowledge. Integrating context-specific caregiver education and mental health support into CMAM, GMP(Growth monitoring and promotion), and primary care services could enhance feeding/WaSH practices in Ethiopia.