Nevin Manimala

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

BMC Med Educ. 2026 May 25. doi: 10.1186/s12909-026-09503-1. Online ahead of print.

ABSTRACT

BACKGROUND: Palliative Care focuses on improving quality of life and preventing suffering, especially near the end of life. With an aging population that frequently seeks care in the Emergency Department (ED), it is crucial for Emergency Medicine (EM) physicians to be well versed in the skills needed for palliative care. However, many EM residency programs lack formal training in this area. This study aimed to develop, implement, and assess a year-long, simulation-based palliative care curriculum.

METHODS: This IRB-exempt observational cohort study was conducted from July 2023 to June 2024 at a large urban EM residency program. A group of content experts in palliative care and simulation-based education designed the curriculum utilizing the Kern model of curriculum development, integrating simulation, small group discussion, and didactic lectures. The curriculum was delivered episodically throughout the academic year during regular residency didactics. The curriculum’s impact on knowledge, attitudes, and practice patterns regarding palliative care was assessed using pre- and post- intervention surveys.

RESULTS: Fourteen EM residents completed both pre- and post-intervention surveys. There was a statistically significant improvement in self-reported practice patterns (35.7% to 60.7% p = 0.0055), but no significant change in attitudes (73.4% to 73.4% p = 0.5020) or knowledge (48.9% to 57.1% p = 0.0758).

CONCLUSIONS: A year-long simulation-based palliative care curriculum significantly improved EM residents’ practice patterns in regard to palliative care, with a positive trend in knowledge retention. Feasibility of implementation was demonstrated. Findings suggest that simulation-based training effectively teaches key palliative care skills. This curriculum may serve as a model for integrating palliative care education into other EM residency programs and may improve resident preparedness in caring for critically ill patients near the end of life.

PMID:42185816 | DOI:10.1186/s12909-026-09503-1

BMC Nephrol. 2026 May 25. doi: 10.1186/s12882-026-05066-x. Online ahead of print.

ABSTRACT

BACKGROUND: Patients with end-stage kidney disease (ESKD) and acute kidney injury on chronic kidney disease (AKI-on-CKD) experience significant symptom burden and high mortality, yet evidence for structured palliative integration within nephrology remains limited in low- and middle-income countries.

AIM: To describe the timing and reasons for referrals from Nephrology to Palliative Medicine, the interventions delivered, and clinical outcomes following referral.

METHODS: A retrospective cohort study was conducted of all adult patients (n = 325) referred from Nephrology to Palliative Medicine. Data included demographics, renal diagnosis, dialysis modality, comorbidities, treatment intent, and documented palliative interventions, and were analyzed using descriptive statistics.

RESULTS: Mean age was 63.6 ± 14.1 years; 229 (70.5%) were male. Renal diagnoses comprised ESKD (215; 66.2%), AKI-on-CKD (86; 26.5%), and AKI (16; 4.9%). Dialysis modalities included maintenance hemodialysis (194, 59.7%), dialysis for AKI (63, 19.4%), and conservative kidney management (21, 6.5%). Referrals were primarily for GOC discussions (41.5%), followed by pain and symptom management (29.2%) and end-of-life (EOL) care (27.4%). Referrals specifically for conservative kidney management were uncommon (1.8%). Among deaths, 154 (75%) occurred in the hospital. Common triggers for referral included sepsis, multiorgan failure, and decisions to stop dialysis.

CONCLUSION: Palliative care involvement predominantly occurred during acute decline-often related to sepsis, multiorgan failure, or dialysis withdrawal. Although most patients died in the hospital, the high frequency of GOC and EOL discussions highlights the need for earlier, proactive palliative care engagement to support informed decision-making, symptom relief, and care aligned with patient preferences.

PMID:42185815 | DOI:10.1186/s12882-026-05066-x

BMC Nephrol. 2026 May 25. doi: 10.1186/s12882-025-04723-x. Online ahead of print.

ABSTRACT

BACKGROUND: CKD-related anemia remains a major complication in patients receiving peritoneal dialysis (PD), with limited treatment options beyond erythropoiesis-stimulating agents. Roxadustat, an oral hypoxia-inducible factor prolyl hydroxylase inhibitor, has shown promise in correcting CKD-related anemia and modulating iron and metabolic parameters. However, its evidence in PD remains limited.

METHODS: We conducted a systematic search of PubMed, Scopus, and Web of Science from inception to July 20, 2025. We included studies reporting the efficacy and safety of roxadustat in PD. Statistical analysis was performed using Review Manager (RevMan 5.4 for Windows) and R Studio.

RESULTS: Eight studies were included in the meta-analysis (n = 607). Roxadustat significantly increased hemoglobin at all time points (MD 0.35 g/dL, 95% CI 0.28-0.41; p < 0.00001), serum iron (MD 0.95 µmol/L, 95% CI 0.02-1.89; p = 0.05), and total iron-binding capacity (MD 6.25 µmol/L, 95% CI 3.95-8.55; p < 0.00001), and reduced hepcidin (MD – 12.28 ng/mL, 95% CI – 21.06 to – 3.50; p = 0.006). No significant effects were observed for ferritin (p = 0.49), transferrin saturation (p = 0.45), cholesterol (p = 0.07), LDL (p = 0.14), HDL (p = 0.27), triglycerides (p = 0.26), CRP (p = 0.75), systolic blood pressure (p = 0.10), or diastolic blood pressure (p = 0.08). Heterogeneity was low to moderate for most outcomes.

CONCLUSION: Roxadustat is effective in improving hemoglobin and iron metabolism in patients with PD, while exerting neutral effects on lipids, inflammation, and blood pressure. These findings support its role as a promising therapy for CKD-related anemia in PD.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:42185813 | DOI:10.1186/s12882-025-04723-x