Category: Nevin Manimala

BMC Health Serv Res. 2026 May 25. doi: 10.1186/s12913-026-14550-x. Online ahead of print.

ABSTRACT

BACKGROUND: India faces a shortage of skilled health professionals (SHPs). There is limited research measuring SHP deficits in rural public health centres. We estimated the current and future SHP densities and deficits, along with costs for SHP scale-up to achieve Sustainable Development Goal (SDG) 2030 targets.

METHODS: We used the number of SHPs (doctors, nurses, and midwives) at rural primary and community health centres from the Rural Health Statistics reports (2009-2019) to calculate average annual percentage changes (AAPCs) at national and state levels using JoinPoint regression. Using AAPC values, we projected SHP counts and estimated densities (per 10,000 people) for years until 2030. The projected deficits for 2030 were calculated for three target density thresholds, aligning with the Millennium and Sustainable Development Goals. For scale-up costs, a state-wise statistical average of salaries for SHP groups was calculated assuming a 5% annual increase in allowance.

RESULTS: During 2009-19, SHP density grew annually by 2.59% (95% CI: 0.93-4.28). The national SHP density would increase from 8.85 SHPs per 10,000 in 2019 to 11.47 in 2030. In 2030, India is expected to have a deficit of 0.47-1.83 million SHPs. The scale-up costs to cover these deficits would range from INR 1.46-4.96 trillion, about 15% of India’s government health spending.

CONCLUSION: At the current rate, Indian rural public health centres will not achieve the target SHP density by 2030. The costs for scaling up the recruitment and retention of rural SHPs are relatively small, making it feasible.

PMID:42185866 | DOI:10.1186/s12913-026-14550-x

Clin Epigenetics. 2026 May 25. doi: 10.1186/s13148-026-02131-y. Online ahead of print.

ABSTRACT

BACKGROUND: Atrial fibrillation (AF) is a common arrhythmia affecting millions of patients globally. While epigenetic modifications play a significant role in cardiovascular diseases, their contribution to AF remains incompletely understood. Annexin A4 (ANXA4), a calcium-dependent phospholipid-binding protein, is implicated in inflammation and immune-mediated diseases, but its epigenetic regulation and mechanistic role in AF pathogenesis have not been systematically explored.

METHODS: This study employed a multi-omics approach, integrating Mendelian randomization (MR), DNA methylation quantitative trait loci (mQTL)-mediated analysis, bulk and single-cell transcriptomics, and clinical validation. Two-sample MR utilized plasma proteome quantitative trait loci data from 4759 Icelandic individuals and AF genome-wide association study summary statistics from FinnGen and OpenGWAS databases. mQTL-based mediation analysis leveraged data from 27,750 individuals, and ANXA4 promoter methylation in atrial tissue was assessed in the GSE62727 dataset. Clinical validation involved qPCR for ANXA4 mRNA and CpG sites cg11942603 and cg22792910 in peripheral blood from 130 participants (77 AF patients and 53 sinus rhythm controls). Additionally, ANXA4 protein levels in peripheral blood leukocytes were analyzed using Western Blotting. Machine learning models were used to assess diagnostic performance, and logistic regression analyzed clinical associations.

RESULTS: MR analysis provided evidence consistent with genetically predicted higher ANXA4 levels increasing AF risk (odds ratio = 1.17, 95% CI 1.11-1.23, P = 1.73 × 10^-10), with Bayesian colocalization supporting a shared genetic signal at rs17037076. mQTL-based mediation analysis indicated that hypomethylation at cg11942603 and cg22792910 significantly mediated the association between ANXA4 and AF. Peripheral blood and atrial tissue analyses confirmed lower methylation levels at cg11942603 in AF patients. qPCR confirmed significantly elevated ANXA4 mRNA expression in the peripheral blood of AF patients. Western Blot analysis further revealed a significant increase in intracellular ANXA4 protein levels within peripheral blood leukocytes of AF patients (P = 0.01). Single-cell RNA sequencing showed higher ANXA4 expression across various immune cell types in AF. Larger left atrial diameter, log-transformed NT-proBNP, and ANXA4 mRNA were independently associated with AF, while higher ANXA4 methylation was associated with lower odds of AF. Machine learning models demonstrated high discriminative ability for AF diagnosis (AUC 0.949-0.962).

CONCLUSIONS: Our multi-omics approach provides evidence that hypomethylation at specific ANXA4 CpG sites is associated with increased gene expression and elevated AF risk. Increased ANXA4 mRNA and protein levels are associated with an enhanced immune response, particularly in myeloid cells, suggesting its potential as a diagnostic candidate to differentiate AF from sinus rhythm. Machine learning models further support the predictive value of ANXA4. Furthermore, decreased ANXA4 methylation levels were associated with TET2 upregulation. ANXA4 expression and promoter methylation may serve as potential diagnostic indicators and candidate targets for future precision medicine strategies in AF.

PMID:42185861 | DOI:10.1186/s13148-026-02131-y

BMC Public Health. 2026 May 25. doi: 10.1186/s12889-026-27762-x. Online ahead of print.

ABSTRACT

BACKGROUND: Among the key strategies for protecting the environment are education, modifying people’s behavior, fostering pro-environmental values, and promoting active citizenship. With this in mind, the present study sought to investigate the effect of an educational intervention based on the Theory of Planned Behavior (TPB) on environmentally responsible behaviors (ERBs) among lower secondary students.

METHODS: This quasi-experimental study was conducted among 100 lower secondary school students in Piranshahr, Iran, selected using a multistage cluster sampling method. Schools were randomly allocated to the intervention or control groups, resulting in 50 students in each group. A demographic form and a researcher-developed questionnaire, grounded in the constructs of the TPB and centered on environmental issues as well as ERBs, served as the data collection instruments. The educational program, also designed according to TPB constructs, focused on three key aspects of ERBs. This program was delivered to intervention group students over three 45-minute sessions, along with a separate 60-minute session for parents and school staff. Data gathering took place at two time points: prior to the educational intervention and again three months later.

RESULTS: In the intervention group, following the educational intervention, there was a statistically significant increase in the mean scores of TPB constructs-including attitude, subjective norms, perceived behavioral control, intention, and behavior-as well as in knowledge about the environment and ERBs, compared with the pre-test and the control group (p < 0.05).

CONCLUSION: The educational intervention grounded in the TPB was found to be effective in enhancing ERBs among lower secondary school students. Given that students are the future builders of society, environmental education holds particular importance for this group. Therefore, the intervention developed in this study is proposed as a straightforward, low-cost strategy that can complement existing school programs. The adoption of such interventions has the potential to significantly reinforce ERBs and, over time, lead to better environmental outcomes.

PMID:42185857 | DOI:10.1186/s12889-026-27762-x

BMC Public Health. 2026 May 25. doi: 10.1186/s12889-026-26939-8. Online ahead of print.

ABSTRACT

BACKGROUND: Volatile organic compounds (VOCs), particularly toluene, are major contributors to indoor air quality degradation in residential settings, yet comprehensive assessment of global residential toluene concentrations remains limited. This systematic review and meta-analysis aimed to: (1) quantify pooled toluene concentrations in residential indoor air, (2) examine temporal trends, and (3) identify factors influencing concentrations.

METHODS: Following PRISMA 2020 guidelines, we systematically searched PubMed, ScienceDirect, and SpringerLink databases through April 13, 2023. Studies quantitatively reporting toluene concentrations in residential indoor air were included. We conducted random-effects meta-analysis, temporal trend analysis, and moderator analyses for geographical region, smoking status, and season. Health risk was assessed using hazard quotient (HQ) calculations.

RESULTS: Twenty-three studies met inclusion criteria (from 444 initially identified). The pooled toluene concentration was 27.13 µg/m³ [95% CI: 20.05 – 34.20 µg/m³], showing a statistically significant decreasing temporal trend (β = -0.034, p = 0.004). Rural areas exhibiting significantly higher levels than urban areas (p < 0.001). Smoking status and seasonal variations showed no significant impact. The non-cancer health risk (HQ) values were low (< 1) for all of the studies (mean HQ = 3.10 × 10⁻⁴), and publication bias was detected.

CONCLUSIONS: Residential toluene concentrations have declined over the past two decades but vary significantly by geographical region. Current exposure levels appear to pose minimal acute health risks, though publication bias warrants cautious interpretation. Standardized sampling methodologies and larger sample sizes are needed for improved risk assessment.

PMID:42185855 | DOI:10.1186/s12889-026-26939-8

BMC Cancer. 2026 May 26. doi: 10.1186/s12885-026-16244-3. Online ahead of print.

ABSTRACT

BACKGROUND: Angiogenesis plays a crucial role in the pathophysiology of multiple myeloma (MM). Tyrosine kinase with immunoglobulin-like and EGF-like domains 2 (TIE-2) is an important regulator of angiogenesis; however, its diagnostic value in MM has not been fully clarified. This study aimed to evaluate the diagnostic significance of serum TIE-2 levels in newly diagnosed, treatment-naïve patients with MM compared with healthy individuals.

METHODS: In this prospective case-control study conducted at Adana City Training and Research Hospital between September 2017 and June 2021, 38 patients diagnosed with MM according to International Myeloma Working Group (IMWG) criteria and 37 healthy volunteers with similar age/gender characteristics were included. Serum TIE-2 levels were measured by ELISA method. Data were statistically analyzed using SPSS 20.0 software.

RESULTS: Serum TIE-2 levels were found to be significantly higher in MM patients compared to the control group (1.4 vs. 0.9 ng/ml; p < 0.001). ROC analysis demonstrated an area under the curve (AUC) of 0.756 (95% CI: 0.643-0.848; p < 0.001). At a cut-off value of > 0.99 ng/mL, the sensitivity was 89.5% and the specificity was 59.5%.

CONCLUSIONS: Elevated serum TIE-2 levels in newly diagnosed MM patients suggest that this marker may reflect angiogenesis-related activation associated with MM biology. However, due to its moderate specificity, TIE-2 should be considered a complementary exploratory biomarker rather than a stand-alone diagnostic tool.

PMID:42185854 | DOI:10.1186/s12885-026-16244-3

BMC Pregnancy Childbirth. 2026 May 25. doi: 10.1186/s12884-026-08916-6. Online ahead of print.

ABSTRACT

BACKGROUND/AIM: Neonatal respiratory distress syndrome (RDS) remains a significant cause of morbidity, particularly in infants of diabetic mothers. This study aimed to evaluate the predictive accuracy of non-invasive prenatal ultrasound parameters-Fetal Lung Volume (FLV) and Pulmonary Artery Resistance Index (PA-RI)-for anticipating RDS in this high-risk population.

PATIENTS AND METHODS: This prospective cohort study was conducted at Ain Shams University Maternity Hospital over 18 months. 123 pregnant women with Diabetes scheduled for elective cesarean delivery were enrolled. Within 72 h of pre-delivery, FLV was measured by 3D ultrasonography with VOCAL analysis, and PA-RI was assessed by pulsed-wave Doppler. Neonatal RDS was diagnosed by a blinded pediatrician according to the Vermont Oxford Network criteria. Statistical analysis included ROC curves to determine optimal cut-offs and predictive values.

RESULTS: The incidence of RDS was 13.0% (n = 16). Affected neonates had significantly lower median FLV (29.8 vs. 38.2 cm³, p < 0.001) and higher median PA-RI (0.88 vs. 0.68, p < 0.001). ROC analysis revealed that both parameters were excellent predictors. FLV ≤ 33.5 cm³ showed 93.8% Sensitivity, 87.9% Specificity, 53.6% Positive Predictive Value, and 99.0% Negative Predictive Value (AUC 0.941). PA-RI > 0.75 demonstrated 100% Sensitivity, 90.7% Specificity, 64.0% Positive Predictive Value, and 100% Negative Predictive Value (AUC 0.963). The combined model achieved the highest accuracy (AUC 0.981). Both parameters showed strong correlations with RDS severity (FLV: P = -0.80; PA-RI: P = + 0.77) and NICU stay duration (FLV: P = -0.77; PA-RI: P = + 0.75).

CONCLUSION: FLV and PA-RI are highly accurate, non-invasive tools for predicting neonatal RDS in Diabetic pregnancies. PA-RI, with its perfect Sensitivity and NPV in our cohort, is particularly effective for ruling out the condition, enabling improved perinatal risk stratification and management.

PMID:42185847 | DOI:10.1186/s12884-026-08916-6

J Transl Med. 2026 May 25. doi: 10.1186/s12967-026-08033-0. Online ahead of print.

ABSTRACT

BACKGROUND: Cell-cell communication (CCC) mediated by ligand-receptor (L-R) interactions is fundamental to deciphering tissue development and disease mechanisms. While single-cell RNA sequencing (scRNA-seq) has advanced this field, existing computational methods for inferring CCC often suffer from limitations such as dependence on static databases and a failure to capture the sequential dependency of amino acids within proteins, which restricts their generalizability and predictive accuracy. Therefore, the primary objective of this study was to develop a robust computational framework capable of identifying potential L-R interactions directly from protein sequence data, thereby overcoming the reliance on static databases and enabling the discovery of novel signaling pairs.

METHODS: To achieve this objective, we introduce CellAL, a deep learning-based framework for predicting potential interacting L-R pairs and decoding cellular communication. The CellAL pipeline consists of two main stages: (1) L-R Pair Identification, which extracts sequence features using BioTriangle, selects informative features via XGBoost to reduce dimensionality, and classifies interactions using a Long Short-Term Memory (LSTM) network integrated with an attention mechanism specifically designed to capture long-range sequence dependencies that characterize structural binding affinities; and (2) CCC Inference, which filters identified pairs using scRNA-seq data and quantifies crosstalk intensity through a comprehensive scoring strategy that combines expression thresholding, expression product, and specific expression metrics.

RESULTS: Performance evaluations on four standard L-R interaction datasets demonstrated that CellAL significantly surpassed classical protein-protein interaction prediction methods and achieved competitive performance against state-of-the-art ensemble models, achieving the highest AUPR values on three datasets. The identified To achieve L-R pairs showed a high degree of overlap with existing databases such as CellChat and Connectome. Furthermore, when applied to human melanoma scRNA-seq data, CellAL successfully inferred critical signaling networks, revealing strong bidirectional crosstalk between melanoma cells and cancer-associated fibroblasts (CAFs), macrophages, and endothelial cells. These findings were consistent with results from three other representative CCC prediction tools.

CONCLUSIONS: CellAL effectively overcomes the limitations of database dependence by leveraging sequence-level biochemical modeling to predict structural L-R interactions. By integrating deep learning predictions with transcriptomic data, CellAL provides a robust and valuable tool for dissecting complex CCC networks at single-cell resolution, particularly within the tumor microenvironment.

PMID:42185831 | DOI:10.1186/s12967-026-08033-0

BMC Infect Dis. 2026 May 25. doi: 10.1186/s12879-026-13648-1. Online ahead of print.

ABSTRACT

BACKGROUND: Blood transfusion is an essential component of healthcare delivery but poses a risk of transfusion-transmissible infections (TTIs), particularly in low- and middle-income countries where donor screening systems may be inadequate. This study aimed to determine the prevalence of major TTIs and assess their association with sociodemographic characteristics among blood donors at a tertiary care hospital in Lahore, Pakistan.

METHODS: A retrospective cross-sectional study was conducted using blood donor records of 550 individuals. All donations were routinely screened for hepatitis B virus (HBV), hepatitis C virus (HCV), human immunodeficiency virus (HIV), syphilis, and malaria. Prevalence rates were calculated using descriptive statistics. Associations between TTIs and donor characteristics were evaluated using chi-square tests. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of infection.

RESULTS: The overall prevalence of TTIs was 12.18%. HBV was the most prevalent infection (4.73%), followed by HCV (4.18%), syphilis (2.55%), and HIV (0.73%). No cases of malaria were detected. Significant associations were observed between selected TTIs and donor characteristics, particularly age and donor status. Multivariate logistic regression analysis confirmed that these sociodemographic factors remained independently associated with infection status.

CONCLUSIONS: A substantial burden of transfusion-transmissible infections persists among blood donors in this setting. Strengthening donor selection, improving screening strategies, and promoting voluntary non-remunerated blood donation are essential to enhance transfusion safety and reduce the risk of TTIs in resource-limited settings.

PMID:42185824 | DOI:10.1186/s12879-026-13648-1

BMC Med Inform Decis Mak. 2026 May 25. doi: 10.1186/s12911-026-03584-0. Online ahead of print.

ABSTRACT

BACKGROUND: A significant challenge in blood donation is the occurrence of adverse donor reactions (ADRs) and their subsequent negative impact on the blood supply and public health. A promising strategy to mitigate these events is the deployment of non-invasive, cost-effective artificial intelligence (AI) models for donor screening and monitoring. This study aims to systematically review the AI models utilized in the identification and prediction of ADRs.

METHODS: This study used a systematic review approach in line with the PRISMA 2020 guidelines. A search was performed across various databases, including Web of Science, PubMed, Embase, Google Scholar, and Scopus. The results were combined narratively and presented using descriptive statistics. The quality of eligible studies was assessed using the Newcastle-Ottawa Scale (NOS) tool.

RESULTS: Among the 13 studies, nine were classified as immediate reactions and four as delayed reactions. The commonly used models included regression models, classical statistical models, and machine learning algorithms such as Random Forests, Gradient Boosting Machines (GBMs), XGBoost, and Artificial Neural Networks (ANNs). The main standard evaluation metrics for the models included Odds Ratio, Precision-Recall Area Under the Curve (PR-AUC), F1 Score, Precision, and Recall.

CONCLUSIONS: Adverse reactions among blood donors negatively impact donor retention and, by extension, the stability of the blood supply for patient care. In this context, AI models may offer a promising tool for supporting the prediction and monitoring of ADRs. However, the available studies are not sufficient to support the widespread adoption of these models in clinical or operational decision-making. Heterogeneity in study design, outcomes, and evaluation metrics together with limitations such as limited implementation, risk of bias, unclear reference standards, and a lack of external validation has constrained the interpretability and generalizability of the findings. Therefore, future research with more rigorous study designs, standardized reporting, harmonized evaluations, and external validation is essential to establish the effectiveness and reliability of these models.

PMID:42185820 | DOI:10.1186/s12911-026-03584-0

BMC Cancer. 2026 May 25. doi: 10.1186/s12885-026-16154-4. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is increasingly recognized as a valuable tool for the early detection and prognosis of oral cancer, addressing the challenge of high mortality due to late diagnosis. Artificial intelligence based diagnostic models have the potential to improve accuracy in differentiating between malignant, premalignant and benign oral lesions. This systematic review and meta-analysis evaluated the diagnostic performance of non-imaging and image-based artificial intelligence models and narratively synthesized evidence on prognostic and risk stratification applications in oral cancer.

METHODS: This study follows PRISMA guidelines to ensure quality and reproducibility. A systematic search across PubMed, Embase, Web of Science, Google Scholar and Scopus identified studies from 2010 to 2024 on artificial intelligence applications in oral cancer diagnosis. Sixteen eligible studies met predefined inclusion criteria, including AI-based screening compared to histology. Data extraction and bias assessment were conducted independently using QUADAS-2. The findings highlight AI’s potential in early detection and prognosis, emphasizing the need for further validation and clinical integration to enhance diagnostic accuracy.

RESULTS: A total of 801 studies were initially identified, with 53 undergoing further review, ultimately selecting 16 studies. Sample sizes varied from 70 to 44,000, allowing a broad evaluation of AI’s diagnostic performance. Artificial intelligence models showed wide range of sensitivity (42%-100%), specificity (63%-100%), and accuracy (63%-100%). Meta-analysis revealed a pooled sensitivity of 0.90 (95% CI: 0.81-0.98), specificity of 0.89 (95% CI: 0.84-0.95), and accuracy of 0.89 (95% CI: 0.83-0.95), with substantial heterogeneity (I² = 100%). Image-based models had higher pooled sensitivity (0.94 vs. 0.76, P = 0.320), specificity (0.93 vs. 0.79, P = 0.025), and accuracy (0.93 vs. 0.81, P = 0.042).

CONCLUSIONS: Artificial intelligence models show promising diagnostic performance for oral cancer based on retrospective clinical data. Although image-based models, particularly convolutional neural networks, demonstrated higher pooled sensitivity and specificity than non-imaging models, these differences were not statistically significant. Results should be interpreted with caution due to substantial heterogeneity. Advances reported in the literature, such as multimodal approaches and data augmentation, may improve non-imaging model performance and help narrow the gap between methodologies. These developments highlight AI’s potential in enhancing early detection and prognosis of oral cancer.

PMID:42185818 | DOI:10.1186/s12885-026-16154-4