BMC Research Notes

The role of digital media in early childhood development remains highly debated, particularly regarding its impact on language acquisition. While excessive or unsupervised screen exposure has been linked to poorer outcomes, less is known about whether structured and interactive uses of technology can support learning. Building on previous research, we evaluated a brief, educator-supervised tablet-based intervention in 246 children aged 2-5 years from low- to middle-socioeconomic backgrounds attending public early education centers. Using a pre-post design with matched study and control groups, children completed 4-8 short training sessions (15 minutes each) involving interactive word-image associations spanning multiple linguistic categories. Preschoolers additionally engaged in prompted vocalization. Across age groups (2-3, 3-4, and 4-5 years), children in the intervention showed greater gains in language comprehension than controls, including receptive language in toddlers ({beta} = 0.49, p = 0.009), vocabulary and morphology in younger preschoolers ({beta} = 0.59-0.68, all p < 0.05), and grammar comprehension in older preschoolers ({beta} = 0.30, p = 0.038). These effects were consistent after accounting for child and parental characteristics. Together, these findings suggest that the developmental impact of digital media depends less on exposure itself than on how it is used. When embedded in structured, socially guided interactions, even brief tablet-based activities may support early language development

Background: Oral health remains inadequately integrated within the Ayushman Bharat Digital Mission (ABDM), particularly in terms of structured risk assessment and its linkage to insurance-based decision-making. There is a growing need for scalable models that can connect clinical oral health data with digital health systems and support future artificial intelligence (AI)-driven applications. Aim: To develop and pilot test the ABHA-O-SHINE framework for oral health risk prediction and insurance prioritization, with a future scope for AI integration within the Ayushman Bharat Health Account (ABHA) ecosystem. Materials and Methods: A cross-sectional pilot study was conducted among 126 participants attending the outpatient department of Swargiya Dadasaheb Kalmegh Smruti Dental College and Hospital, Nagpur. Participants were selected based on predefined inclusion and exclusion criteria. Data collection included a structured questionnaire and clinical examination using the WHO Oral Health Assessment Form (2013). A composite risk score (0 to 14) was developed incorporating behavioral and clinical parameters. Participants were categorized into low, moderate, and high-risk groups, and corresponding insurance priority levels were assigned. Statistical analysis included descriptive statistics, Chi-square test, Spearman correlation, and binary logistic regression. Results: The majority of participants were categorized under moderate to high-risk groups. Tobacco use showed a statistically significant association with higher risk levels (p less than 0.05). Positive correlations were observed between total risk score and clinical indicators such as DMFT and CPI. Logistic regression analysis identified tobacco use and clinical scores as significant predictors of high-risk categorization. Conclusion: The ABHA-O-SHINE framework demonstrates feasibility in integrating oral health risk assessment with an insurance prioritization model. The framework is designed to be AI-compatible, enabling future automation through machine learning and image-based analysis within the ABDM ecosystem. Keywords: ABHA, ABDM, Oral Health, Risk Assessment, Insurance, Artificial Intelligence.

Background Depressive symptoms among reproductive-aged women represent a major public health concern in low- and middle-income countries, yet systematic screening remains limited. In most population survey datasets, the low prevalence of depression results in severe class imbalance, which challenges conventional machine learning models. Therefore, we develop and evaluate a bagging-based ensemble machine learning framework to predict depressive symptoms among reproductive-aged women using highly imbalanced Bangladesh demographic and health survey (BDHS) 2022 data. Methods The sample comprised women aged 15-49 years drawn from BDHS 2022 data. Depressive symptoms were defined using the Patient Health Questionnaire (PHQ-9 [&ge;]10). Candidate predictors were drawn from sociodemographic, reproductive, nutritional, psychosocial, healthcare access, and environmental domains. Feature selection was performed using Elastic Net (EN), Random Forest (RF), and XGBoost model. Five classifiers (EN, RF, Support Vector Machine (SVM), K-nearest neighbors (KNN), and Gradient Boosting Machine (GBM)) were trained using both oversampling-based approaches and the proposed ensemble framework. Model performance was evaluated on an independent test set using accuracy, sensitivity, specificity, F1-score, and the normalized Matthews correlation coefficient (normMCC). Results Approximately 4.8% of women were identified with depressive symptoms. The proposed bagging ensemble framework consistently achieved more balanced predictive performance than oversampling-based models. Average normMCC improved from 0.540 (oversampling) to 0.557 (ensemble). RF and GBM ensembles demonstrated notable improvements in identifying depressive cases, while the EN ensemble achieved the highest overall performance and sensitivity. Threshold optimization yielded stable normMCC across models, indicating robust trade-offs between sensitivity and specificity. Conclusions Bagging-based ensemble learning provides a more robust and balanced approach than synthetic oversampling for predicting depressive symptoms in highly imbalanced population survey data. This approach has important implications for improving early identification and population-level mental health surveillance in resource-constrained settings.

Background Social contact surveys, which measure who-contacts-whom, are widely used to inform infectious disease transmission models and estimate the reproduction number (R), a key metric for assessing epidemic risk. Despite their widespread use, sample size calculations are not routinely performed. Aims To assess the impact of sample size on estimates of R and determine a practical target sample size for social contact surveys used in epidemic modelling. Methods We conducted a review of social contact surveys (2008-2025) to characterise current practice. We characterised the impact of survey size on epidemic metrics using two social contact surveys, the UK Social Contact Survey and POLYMOD (Europe) and two methods. For each dataset and approach, we generated repeated subsamples and calculated the resulting reproduction numbers, characterised their distributions and measured uncertainty. Results We identified 107 unique social contact surveys from 57 studies. Sample sizes ranged from 30 to more than 10,000 participants, with a median of 1,438. One quarter of surveys contained fewer than 1,000 participants. From our simulations, we find that sample sizes below 200 individuals can result in highly variability reproduction numbers. Increasing sample size increases precision, and the most meaningful gains are up to 1,300 individuals. Increasing sample sizes over 3,000 individuals leads to smaller gains. Conclusions A minimum sample size of approximately 1,200-1,300 participants appears sufficient for general-purpose use. These findings support the inclusion of sample size considerations in the design, reporting and interpretation of social contact surveys used for epidemic intelligence and public health decision-making.

Objectives Publicly available datasets dedicated to clinical speech deidentification tasks remain scarce due to privacy constraints and the complexity of speech-level annotation. To address this gap, we compiled the SREDH-AICup sensitive health information (SHI) speech corpus, a time-aligned clinical speech dataset annotated across 38 SHI categories. Methods Two publicly available English medical-domain datasets were adapted to support speech-level de-identification, including script reformulation and controlled re-recorded by 25 participants. Additional Mandarin Chinese clinical-style materials were incorporated to extend linguistic coverage. All audio data were annotated with million-level, time-aligned SHI spans using Label Studio. Inter-annotator agreement was evaluated using Cohen's kappa, following iterative calibration rounds. The resulting corpus supports both automatic speech recognition (ASR) and speech-level recognition of SHIs. Results The final dataset comprises 20 hours of annotated audio, divided into training (10 hours, 1,539 files), validation (5 hours, 775 files), and test (5 hours, 710 files) subsets, totalling 7,830 SHI entities. The language distribution reflects the composition of the selected source materials, with 19.36 hours of English and 0.89 hours of Mandarin Chinese speech. Discussion The corpus exhibits a long-tail distribution consistent with clinical documentation patterns and highlights the limited availability of Chinese medical speech resources. These characteristics underscore both the realism of the dataset and structural challenges associated with multilingual speech de-identification. Conclusion The SREDH-AICup SHI speech corpus provides a clinically grounded, time-aligned speech dataset supporting automated medical speech de-identification research and facilitating future development of multilingual speech-based privacy protection systems.

BackgroundPrecise control of DYRK1A dosage is essential for embryonic development, including craniofacial morphogenesis. While LZTS2 is among the most consistently identified DYRK1A-interacting proteins, its roles in embryonic development remain incompletely understood, and its potential contribution to craniofacial development has not been examined. Xenopus laevis was used to test the role of LZTS2 in craniofacial development and its functional relationship with DYRK1A. ResultsLzts2 and Dyrk1a showed overlapping expression during craniofacial development, with both proteins present in developing facial tissues. Knockdown of Lzts2 disrupted craniofacial morphogenesis and reduced expression of the neural crest-associated genes sox9 and pax3. These phenotypes closely resembled those caused by decreasing Dyrk1a function. Sub-phenotypic reductions of Lzts2 and Dyrk1a synergized to produce craniofacial defects, while partial reduction of Lzts2 attenuated aspects of the phenotype caused by Dyrk1a overexpression. Comparative analysis of human phenotypes associated with copy number gains of LZTS2 and DYRK1A revealed striking overlap, consistent with a potential functional interaction between these genes in humans. ConclusionsThese findings identify Lzts2 as a previously unrecognized regulator of craniofacial development and support a functional interaction with Dyrk1a during embryogenesis. Modulating LZTS2 or related regulatory partners may provide a strategy to selectively tune DYRK1A-dependent developmental pathways

Background While a mobile lung cancer screening (mLCS) program can mitigate barriers to access, this study conducted a survey study to assess barriers and facilitators to mLCS which could inform the implementation of new mLCS programs or inform modifications to existing programs. Methods Patient eligibility included current age of 50 to 80 and had undergone any cancer screening at Moffitt Cancer Center (MCC) between January 1, 2023 and December 1, 2024. A web-based survey was administered from May 2025 to June 2025 which collected data on health behaviors, barriers, facilitators, screening preferences, and demographics. Descriptive statistics were used to quantify survey responses. Results Among participants who completed the survey, 73.4% reported no concerns about getting screened in a mobile screening unit, 67.9% reported concerned about the cost or if insurance covered mobile lung cancer screening, and 82.4% reported they would be screened if a voucher or insurance would pay for it. For preferences, 54.1% reported no preference for the time of year for a mobile screening event, 59.6% reported they will be willing to wait up to 30 minutes to get screened, and 44% would travel more than 20 minutes to get screened. There were no statistically significant differences in barriers and facilitators when the analyses were stratified by LCS eligibility. Conclusions We found acceptability of mobile lung cancer screening and preferences that are actionable including daytime weekday events, indoor waiting, short waits, proximity to home, clear cost coverage, and streamlined clinician recommendation.

Background: Sibling-matched designs control for shared familial confounding but remain vulnerable to non-shared confounders. Bi-directional sensitivity analyses, which stratify families by whether the older or younger sibling was exposed, are commonly used to assess carryover effects. We aimed to demonstrate how this methodological approach can introduce severe confounding by parity. Methods: We conducted simulations motivated by a recent epidemiological study. The true causal effect of a hypothetical exposure (prenatal acetaminophen) on neurodevelopmental outcomes was set to strictly null. To introduce parity-related confounding, baseline exposure and outcome probabilities were varied slightly by birth order. We compared conditional logistic regression effect estimates from total sibling models against bi-directional stratified models. Results: In the total simulated sibling cohort, models yielded the true null effect (odds ratio = 1.00) when adjusting for parity. However, the bi-directional analyses exhibited divergent artifactual signals. Because parity is perfectly collinear with exposure in these stratified subsets, it cannot be adjusted for. For example, when the older sibling was exposed, the odds ratio for autism spectrum disorder was 1.68; when the younger was exposed, the odds ratio was 0.60. Conclusions: Divergent estimates in bi-directional sibling analyses can be a predictable artifact of parity confounding rather than evidence of carryover effects or invalidating unmeasured bias. Overall sibling models adjusting for parity may remain robust despite divergent stratified sensitivity results.

Background: Artificial intelligence (AI) has the potential to improve the efficiency of evidence synthesis and reduce human error. However, robust methods for evaluating rapidly evolving AI tools within the practical workflows of evidence synthesis remain underdeveloped. This protocol describes a study design for assessing the effectiveness, efficiency, and usability of AI tools in comparison to traditional human-only workflows in the context of Cochrane systematic reviews. Methods: Members of the Cochrane Evaluation of (Semi-) Automated Review (CESAR) Methods Project developed an adaptive platform study-within-a-review (SWAR) design, modeled after clinical platform trials. This design employs a master protocol to concurrently evaluate multiple AI tools (interventions) against a standard human-only process (control) across three key review tasks: title and abstract screening, full-text screening, and data extraction. The adaptive framework allows for the addition or removal of AI tools based on interim performance analyses without necessitating a restart of the study. Performance will be assessed using metrics such as accuracy (sensitivity, specificity, precision), efficiency (time on task), response stability, impact of errors, and usability, in alignment with Responsible use of AI in evidence SynthEsis (RAISE) principles. Results: The study will generate comparative data about the performance and usability of specific AI tools employed in a semi- or fully automated manner relative to standard human effort. The protocol provides a flexible framework for the assessment of AI tools in evidence synthesis, addressing the limitations of static, one-time evaluations. Discussion: This study protocol presents a novel methodological approach to addressing the challenges of evaluating AI tools for evidence syntheses. By validating entire workflows rather than individual technologies, the findings will establish an evidence base for determining the viability of integrating AI into evidence-synthesis workflows. The adaptive design of this study is flexible and can be adopted by other investigators, ensuring that the evaluation framework remains relevant as new tools emerge.

Hemoglobinopathies are inherited disorders of hemoglobin, most notably sickle cell anemia and thalassemia. These conditions result from mutations in the globin genes, leading either to structural abnormalities in the globin chains or to reduced synthesis of normal globin chains. Hemoglobinopathies is a worldwide health problem according to the World Health Organization; it affects mostly the indigenous Tharu groups in Nepal. Both the global and local rates of illness and death associated with these diseases are on the rise. The objective of this study was to assess the presence of hemoglobinopathies and common mutations of the beta-globin gene within the Tharu population in western Nepal. A cross-sectional study of 1,400 Tharu individuals was conducted among individuals obtained through hospitals within the Banke district, Bardiya district, and Kailali district in western Nepal. A thorough hematological analysis was done with the use of a Sysmex XN-350 analyzer. Hemoglobin variants were detected via high-performance liquid chromatography (HPLC). The molecular characterization of the seven most common mutations of {beta}-thalassemia was performed on a subset of 20 confirmed cases by using a real-time PCR kit.The total number of cases diagnosed with hemoglobinopathies was 14.43% (n=202 out of 1,400). Sickle cell trait (HbAS) was reported as the most prevalent type of Hemoglobinopathies (8.50% of population), followed by {beta}-thalassemia trait (4.00%). In addition to these disorders were sickle cell disease (HbSS), HbE trait, and compound heterozygous states. Hematological parameters differed significantly across types of hemoglobinopathies, and the patterns of microcytic, hypochromic, and hemolytic anemia were also distinct. Commonly documented symptoms included fatigue and joint pain (42.5% and 23.1%, respectively). Molecular characterization of {beta}-thalassemia cases demonstrated that most individuals were compound heterozygotes with IVS1-6 (T>C) as the most prevalent variant. The research identified that the Tharu population in western Nepal has a significant burden of hemoglobinopathies (especially sickle cell trait and {beta}-thalassemia), highlighting the requirement for appropriate screening programs, genetic counseling and public health strategies to help manage and prevent these conditions within this particular region.

Background: Sub-Saharan Africa (SSA) still experiences a high burden of micronutrient deficiencies. For monitoring of micronutrient status among young children in SSA, non-invasive alternatives to blood-based biomarkers are desirable. Handheld Raman spectrophotometry appears to offer this alternative to quantify intracellular stores of micronutrients. In rural Burkina Faso and Kenya, we validated the Cell-/SO-Check device (ZellCheck(R)) against conventional laboratory-based methods. Methods: For this validation study, we recruited children aged [&ge;]24 months attending routine clinics within the Health and Demographic Surveillance Systems (HDSS) in Siaya and Nouna. Anthropometric measurements and venous blood samples were taken. Plasma ferritin, soluble transferrin receptor (sTfR) and C-reactive protein (CRP) were measured by ELISA, and plasma zinc by atom absorption. The spectrometer was used to quantify zinc and iron. For continuous outcomes, we generated Bland Altman plots and calculated bias and limits of agreement (LoA). For binary outcomes, we produced Receiver Operator Characteristic (ROC) areas under the curve (AUC), and estimated sensitivity, specificity and predictive values. Results: We analysed data of 48 children from Burkina Faso and 54 children from Kenya (male: 53%; age range: 24-66 months). According to spectrophotometry, the proportions of iron deficiency and zinc deficiency were 16.7% and 25.5%, respectively. The median concentrations were for ferritin 24.0 {micro}g/L (range: 2.0-330.0), for sTfR 5.7 mg/L (2.8-51.0), and for zinc 9.9 {micro}mol/L (5.2-25.0). The corresponding bias for iron levels by spectrophotometry was 42.4 with LoA: -18.7, 103.6. The bias for zinc levels was 7.5 with LoA: -49.3, 64.2. For the classification of deficiency, the ROC-AUC, sensitivity, and specificity for spectrophotometry vs. biomarker-based diagnosis were for iron deficiency 0.62, 68% and 55%, respectively, and for zinc deficiency 0.55, 33% and 91%, respectively. Conclusions: The Cell-/SO-Check device may be used to rank children in population-based studies in SSA according to their zinc status, but not iron status. The method should not replace the standard laboratory measurements for clinical diagnoses of zinc and iron deficiencies.

Transcriptomic analyses are widely used to elucidate the molecular mechanisms driving gametogenesis and reproduction in fish, yet their accuracy depends heavily on appropriate normalization of gene expression data. Conventional approaches that rely on single or multiple reference genes are problematic during teleost oogenesis, as profound structural and physiological remodeling of the ovary challenges the assumption that commonly used reference transcripts remain stable. In this study, we assessed by qPCR the transcriptional variability of four widely used reference genes (actb, ef-1, gapdh, and 18S rRNA) throughout the oogenic cycle of the thicklip grey mullet (Chelon labrosus), using geNorm and NormFinder analyses, and we additionally evaluated total cDNA concentration as an alternative normalization factor. To examine the performance and interpretive consequences of each normalization strategy, we compared expression patterns of key steroidogenic genes (star, cyp19a1a, and cyp11b) normalized by individual reference genes, combinations of reference genes, or total cDNA concentration. All evaluated reference genes displayed notable transcriptional variability across oogenesis, confirming their limited suitability as sole internal controls. In contrast, normalization approaches integrating multiple reference genes and/or total cDNA concentration consistently provided greater stability and more reliable biological interpretation. These results support a refined and more robust normalization framework for transcriptional analyses in fish ovaries, particularly during stages of extensive tissue remodeling. Our findings demonstrate cDNA-based normalization is straightforward, rapid, and easy to implement across laboratories, providing a practical alternative for achieving accurate, reproducible transcript quantification in fish ovary studies.

Background: Population-based cancer registers (PBCR) are important for monitoring trends in cancer epidemiology, facilitating the implementation of effective cancer services. Adolescents and Young Adult (AYA) with cancer are a patient group with a unique set of needs. The utility of PBCR in AYA is limited by the lack of AYA-specific data items. STRONG AYA, an international multidisciplinary consortium is addressing this through federated learning (FL) methodology and novel data visualisation concepts. A Core Outcome Set (COS) has been developed to measure outcomes of importance through clinical data and Patient Reported Outcomes (PROs). We describe how data from the Yorkshire Specialist Register of Cancer in Children and Young People (YSRCCYP), a PBCR in the UK is being used within STRONG AYA and how the subsequent analyses can guide patient consultations. Methods: Data from the YSRCCYP were imported into a Vantage 6 node, from which FL analyses are performed along with data provided by other consortium members. The results are extracted into the PROMPT software and integrated into patient electronic healthcare records. Results: Healthcare professionals can view the results of individual PROs at various time points and in comparison, to summary analyses carried out within the STRONG AYA infrastructure. Results can be filtered by age, disease, country and stage. Conclusion: We have demonstrated how a regional PBCR can contribute to a pan-European infrastructure and analyses viewed to enhance patient consultations. Such analyses have the potential to be used for research and policy-making, improving outcomes for AYA.

Abstract Background: To influence malaria-related behaviours, it is important to understand key behavioural drivers, encourage enablers and address barriers to individuals and communities adoption of interventions to prevent malaria. The capability(C), opportunity(O), and motivation(M) Behaviour(B) model (COM-B model) was used to inform development of perennial malaria chemoprevention (PMC) social, and behaviour change (SBC) message delivered through routine immunization (RI) platform. This paper presents how the COM-B model was used for designing the SBC messages for PMC using the findings from a qualitative study. Methodology The COM-B model provided the theoretical framework for designing the PMC SBC intervention by identifying, capability, opportunity motivation for PMC as well as the barriers, and possible enablers for PMC uptake. A qualitative study was conducted as key source of information. Twelve focus group discussions (FGDs) were conducted with the target audience comprising of mothers of children under two years, pregnant women, men, ward development committee members, community mobilizers and health workers. A total of 120 people participated in the study. An SBC workshop was conducted to develop key messages and content for a community dialogue flipbook and facilitators' guide. Results Knowledge of malaria signs that prompt mothers to seek health care for their children as well as awareness about malaria prevalence and severity, were identified as capabilities that could drive behaviour change, while forgetting the time to visit the health facility was noted as a hindrance. Opportunities and social influencers included spousal support, the positive influence of health workers, accessibility and affordability of the intervention, and the availability of transportation. Motivation was shaped by the perceived seriousness of malaria as a health problem that could lead to the death of children. Fathers were motivated when they observed reduced malaria burden and improved child health, although a lack of perceived urgency remained a demotivating factor for seeking care. Mothers' motivation was strengthened by encouragement from husbands, community mobilisers and health workers. Conclusion The COM-B model provided an effective framework for identifying and developing key messages that informed changes needed to improve capability, opportunities, motivation of individuals and communities towards increased uptake of PMC during PMC pilot study in Osun state Nigeria.

Hormone replacement therapy (HRT) with oestrogen and progestogen is a common medical treatment for alleviating symptoms of menopause. Since 2015, its use has been increasing in the UK. Unscheduled bleeding can be a symptom of endometrial cancer, and guidelines state that women experiencing this should have an urgent referral for suspected endometrial cancer. However, unscheduled bleeding is also common in women taking HRT, particularly in the first few months after starting HRT or if there is a change in regimen. Current guidelines may result in women on HRT receiving referrals that are not necessary and undergoing unpleasant and invasive tests such as hysteroscopy. However, there is a lack of current information to guide recommendations. This protocol describes a cohort study in the ORCHID-e database of anonymised patient records from English primary care. We will use a cohort of women aged over 40 years starting on HRT with oestrogen and progestogen, age matched to women who have not started HRT. Exposure will be a prescription for oestrogen containing HRT with no previous prescription for oestrogen containing HRT in the previous year. Index date in each matched set will be the date of this prescription. Prescriptions for progestogen containing drugs will not be used to define the exposure, but this information will be extracted to describe the study population and for sensitivity analyses. Outcomes will be consultations for unscheduled bleeding, urgent referrals for suspected endometrial cancer, and diagnosis of endometrial cancer. Women will be followed up until they change exposure status or are otherwise censored. Women who start taking HRT in follow-up will re-enter the cohort in the exposed group. We will describe proportions of women with a code for consulting with unscheduled bleeding, proportions of those women referred for further investigation on the pathway for suspected endometrial cancer, and proportions diagnosed with endometrial cancer within one year of referral. We will investigate the diagnostic accuracy of unscheduled bleeding for endometrial cancer separately for women on HRT and those not on HRT. Analyses will be done by 6-month categories of time since index, age, calendar year, sociodemographic variables, risk factors for endometrial cancer, type of HRT.

BackgroundThe most prevalent kind of oral cancer is oral squamous cell carcinoma (OSCC), which has a poor prognosis because of delayed detection and a lack of molecular indicators. MethodsTranscriptomic data from TCGA were analyzed to identify differentially expressed genes between OSCC and normal samples. Functional enrichment analysis was performed to determine biological pathways. A protein-protein interaction network was constructed using STRING and visualized in Cytoscape to identify hub genes. ResultsA total of 5732 differentially expressed genes were identified, including 2459 upregulated and 3273 downregulated genes. Network analysis revealed several highly connected hub genes such as CDK1, CCNB1, TOP2A, BUB1, and MMP9. Functional enrichment indicated significant involvement of cell cycle regulation and cancer-associated pathways. ConclusionThis integrative analysis identified key regulatory hub genes that may be involved in OSCC progression. These genes may serve as promising biomarkers and therapeutic targets for future studies.

Objectives: To examine the pattern, magnitude, and demographic distribution of measurable improvements across five outcome parameters following three monthly pharmacist-led nutritional counselling sessions delivered to community-dwelling participants in semi-urban India. Design: Secondary analysis of interventional follow-up data from a prospective community-based study. Setting: Schools and colleges in Narasaraopeta, Andhra Pradesh, India, from September 2021 to March 2022. Participants: Of 1,200 participants assessed at baseline, 1,135 (94.6%) completed at least one counselling session and formed the analysis cohort. The age range was 10 to 60 years. The majority of participants, 92.4%, were aged between 11 and 20 years. All 1,135 were anaemic at baseline. Interventions: Three structured monthly counselling sessions were delivered by pharmacy students under qualified faculty pharmacist supervision. Each session included individualised dietary guidance, lifestyle modification advice, and culturally adapted written health education materials. Primary and secondary outcome measures: Cumulative proportion of participants achieving measurable improvement in body mass index (BMI), waist circumference (WC), hip circumference (HC), waist to hip ratio (WHR), and haemoglobin (Hb) concentration at each session, stratified by age group and sex. Results: All five parameters showed progressive cumulative improvement across sessions. By session three, 44 participants (3.6%) showed improved BMI, 39 (3.25%) achieved reduced WC, 34 (2.8%) reduced HC, 33 (2.75%) improved WHR, and 115 (9.5%) demonstrated improved Hb. Adolescents aged 11 to 20 years were consistently the most responsive subgroup. Haemoglobin showed the steepest improvement trajectory, rising from 1.75% at session one to 9.5% at session three, representing a 5.4 fold increase achieved through dietary counselling alone without pharmacological supplementation. Conclusions: Three monthly pharmacist led nutritional counselling sessions produce measurable and progressive improvements in both anthropometric and haematological outcomes in community settings. Adolescents are the most responsive population. These findings support the integration of pharmacists into community non communicable disease prevention programmes in India and provide a replicable low resource model applicable to comparable global settings.

Background: Artificial intelligence chatbots (AICs), as a form of generative artificial intelligence (AI), are increasingly being considered for use in scholarly peer review to assist with tasks such as identifying methodological issues, verifying references, and improving language clarity. Despite these potential benefits, concerns remain regarding their reliability, ethical implications, and transparency. Evidence on how medical journal peer reviewers perceive the role and impact of AICs is limited. This study explored reviewers' familiarity with AICs, perceived benefits and challenges, ethical concerns, and anticipated future roles in peer review. Methods: We conducted a cross-sectional online survey of medical journal peer reviewers. Corresponding author information was extracted from MEDLINE-indexed articles added to PubMed within a two-month period using an R-based approach. A total of 72,851 authors were invited via email to participate; those who self-identified as peer reviewers were eligible. The 29-item survey assessed familiarity with AICs and perceptions of their benefits and limitations in peer review. The survey was administered via SurveyMonkey from April 28 to June 16, 2025, with two reminder emails sent during the data collection period. Results: A total of 1,260 respondents completed the survey. Most participants were familiar with AICs (86.2%) and had used tools such as ChatGPT for general purposes (87.7%), but the majority had not used AICs for peer review (70.3%). Most respondents reported that their institutions do not provide training on AIC use in peer review (69.5%), although many expressed interest in such training (60.7%). Perceptions of AIC benefits were mixed, while concerns were widely shared, particularly regarding potential algorithmic bias (80.3%) and issues related to trust and user acceptance (73.3%). Conclusions: While familiarity with AICs is high among medical journal peer reviewers, their use in peer review remains limited. There is clear interest in training and guidance, however, concerns related to ethics, data privacy, and research integrity persist and should be addressed before broader implementation.

Mediation analysis quantifies how an exposure affects an outcome through an intermediate variable. We extend mediation analysis to capture the cumulative effects of longitudinal predictors on longitudinal outcomes. Our proposed model examines how mediators transmit the effects of the current and previous exposure on the current outcome. We construct a least-squared estimator for cumulative indirect effect (CIE) and used three approaches (exact form, delta method, and bootstrap procedure) to estimate its standard error (SE). The estimator of CIE is unbiased with no unmeasured confounding and independent model errors between mediator model and outcome model at all time points, as shown in statistical inference and in simulations. While three SE estimates are numerically similar, bootstrap procedure is recommended due to its simplicity in implementation. We apply this method to Framingham Heart Study offspring cohort to assess if DNA methylation mediates the association of alcohol consumption with systolic blood pressure over two time points. We identify two CpGs (cg05130679 and cg05465916) as mediators and construct a composite DNA methylation score from 11 CpGs, which mediates for 39% of the cumulative effect. In conclusion, we propose an unbiased estimator for CIE. Future studies will investigate the missingness in mediators and outcomes.

Background: Population-based cohort studies are increasingly expected to demonstrate benefits for public health and wider society. However, there is limited systematic evidence on what such impact entails or how it is generated and sustained. To address this gap, we examined researcher perspectives on the impact of cohort studies. Methods: We conducted, to our knowledge, the first quantitative study of researcher views on cohort impact, recruiting active cohort researchers through national and international networks between August and December 2025. The anonymous cross-sectional survey captured researcher characteristics, perceived contributions, impact processes, challenges, and open-ended reflections. Results: A total of 163 cohort researchers participated, primarily from Australia (42%) and the UK (23%). Participants perceived their work as informing a wide range of societal issues and reported investing an average of 24% of their work time in impact-related activities. While most respondents (73%) believed their research leads to tangible policy or practice change, two thirds indicated that impact is rarely or never demonstrable shortly after study completion (67%) and seldom attributable to a single study (67%). Key concerns included pressure to overstate contributions (80%), perceived disadvantages for cohort studies in impact assessments (78%), and inadequate skills or resources to achieve impact (65%). Conclusions: Cohort researchers perceive their work as generating broad societal contributions and invest substantial effort in supporting impact. However, they face systemic challenges in both achieving and demonstrating impact. These findings highlight the need for impact frameworks that better capture complexity, long-term influence, and cumulative contributions, while mitigating unintended consequences.