Category: Nevin Manimala

Mov Disord. 2026 Mar 2. doi: 10.1002/mds.70234. Online ahead of print.

ABSTRACT

BACKGROUND: Long-term exposure to air pollution has been linked to Parkinson’s disease (PD) incidence, yet evidence is mixed, partly because of challenges with PD diagnosis and definition. We examined this association in a nationwide administrative cohort.

METHODS: We followed 3,280,190 Danish residents ≥30 years old from January 1, 2000 until December 31, 2018 for PD incidence, defined as either first hospital contact for primary PD or redeemed prescription of PD medication, as recorded in the Danish National Patient Registry or Prescription Registry, respectively. We assigned annual mean air pollution exposure concentrations at baseline residential address using the hybrid land-use regression model (fine particulate matter [PM_2.5], nitrogen dioxide [NO₂], ozone [warm-season, O₃w], black carbon [BC]) rendered at 0.1 × 0.1 km. We used Cox proportional hazard models adjusting for age, sex, individual-level, and area-level socioeconomic factors.

RESULTS: During a mean (standard deviation) follow-up of 15.7 (5.6) years, 36,665 participants developed PD. Median (interquartile range [IQR]) exposure levels of PM_2.5, NO₂, O₃w, and BC were 12.4 (2.0), 20.2 (7.9), 80.2 (4.3) μg/m³, and 1.01 (0.4) × 10^-5/m, respectively. Hazard ratios (95% confidence intervals) for associations between air pollutants (per IQR) and PD incidence were: 1.05 (1.03, 1.07) for PM_2.5; 1.03 (1.01, 1.05) for NO₂; 0.98 (0.97, 1.00) for O₃w; and 1.04 (1.02, 1.06) for BC.

CONCLUSIONS: In a representative nationwide cohort, we find that long-term exposure to air pollution is associated with PD incidence. This unique study, with access to incidence data from administrative health registers, provides new evidence supporting air pollution as a PD risk factor. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

PMID:41772748 | DOI:10.1002/mds.70234

Plant Genome. 2026 Mar;19(1):e70208. doi: 10.1002/tpg2.70208.

ABSTRACT

“Cytosine methylation plays an important role in the regulation of gene expression in plants.” Some iteration of this statement can be found in most papers centered on plant epigenetics and has become a widely accepted textbook claim. However, our generalized understanding of how DNA methylation exerts control over transcription is now challenged by observations demonstrating that transcriptional levels of most genes are unresponsive to DNA methylation changes. On a genome-wide scale, associations between DNA methylation and transcription are usually statistically weak. Even when correlations are found, the cause and effect can be difficult to identify, as methylation changes sometimes follow rather than precede transcriptional changes. While a growing number of studies explore a possible connection between differentially expressed genes (DEGs) and differentially methylated genes (DMGs), we demonstrate here that DEG-DMG overlaps are often significantly smaller than what could be expected by chance. This indicates that, contrary to expectations, changes in DNA methylation and changes in transcription sometimes avoid one another. Here, we discuss such observations and their implications for the hypothesis of a widespread control of gene expression directly by DNA methylation. While there are well-documented examples where DNA methylation regulates transcription, we argue that such cases represent a minority of genes, and we opine that approaches of reverse epigenetics are therefore unlikely to find broad application in breeding.

PMID:41772747 | DOI:10.1002/tpg2.70208

Diagn Progn Res. 2026 Mar 3;10(1):5. doi: 10.1186/s41512-026-00220-3.

ABSTRACT

BACKGROUND: Adverse pregnancy outcomes (APOs), such as gestational diabetes, preeclampsia, and placental abruption, are major contributors to maternal and fetal morbidity and mortality, with implications for individual long-term health and health system performance. Existing prediction models for APOs rely primarily on clinical or biomarker data, with few incorporating social, behavioral, or environmental determinants that are critical for shaping perinatal outcomes. This study describes the development and validation protocol for the Adverse Pregnancy Outcomes Population Risk Tool (PregPoRT), a novel, population-based prediction model designed to estimate APO risk using population-based and routinely collected survey and administrative data in Canada.

METHODS: PregPoRT will be developed using a retrospective cohort of female-identifying individuals, aged 15-49, who participated in the Canadian Community Health Survey (CCHS) between 2000 and 2017, and had a subsequent delivery hospitalization within two years recorded in the Discharge Abstract Database (DAD). Pre-pregnancy predictors were selected according to a health equity-informed framework by Kramer and colleagues (2019), and include biomedical, behavioral, social, and environmental variables from the CCHS, the Canadian Marginalization Index (CAN-Marg), the Canadian Urban Environmental Health Research Consortium (CANUE), and the Canadian Active Living Environments (Can-ALE) dataset. The primary outcome is a composite measure of APOs (gestational diabetes, preeclampsia, or placental abruption), identified using validated ICD codes. A Weibull accelerated failure time model will be used to estimate the risk of experiencing an APO. Continuous variables will be modeled with restricted cubic splines. Variable selection will be performed using the Least Absolute Shrinkage and Selection Operator (LASSO), and model performance will be assessed via discrimination, calibration, and overall accuracy. Validation strategies include split-sample, bootstrap, and temporal validation using later CCHS cycles. Survey weights will be applied throughout to ensure national representativeness.

DISCUSSION: PregPoRT will be the first Canadian prediction model for APOs that leverages nationally representative, linked survey and administrative data and explicitly integrates social, behavioral, and environmental determinants of health, domains that have been largely absent from prior models. By incorporating modifiable and socially patterned risk factors, the tool is designed to support public health planning, resource allocation, and maternal health equity monitoring.

PMID:41772741 | DOI:10.1186/s41512-026-00220-3

Eur J Med Res. 2026 Mar 2. doi: 10.1186/s40001-026-04125-1. Online ahead of print.

ABSTRACT

BACKGROUND: The mean amplitude of glycemic excursions (MAGE) is considered the “gold standard” for assessing glycemic variability. Surrogate markers for insulin resistance (IR) have been proposed. We aim to explore the associations between surrogate markers of IR and MAGE in type 2 diabetic patients with different body mass indices (BMIs).

METHODS: A retrospective analysis of the clinical data of 309 hospitalized patients with type 2 diabetes was conducted. Patients were divided into two groups on the basis of whether their MAGE level was within the target range (< 3.9 mmol/l). Patients were categorized into two subgroups on the basis of BMI (BMI < 23 nonoverweight people and BMI ≥ 23 overweight people). Each surrogate marker of the IR was divided into three groups according to the interthird spacing of our specific research population and the level and achievement rate of MAGE differences among the three groups were compared. Multivariate logistic regression was used to assess the associations between surrogate markers of IR and MAGE.

RESULTS: (1) Compared with the nontargeted MAGE group, the target group presented statistically significant differences in BMI,2-h postprandial blood glucose (2 h-PG), triglyceride(TG), the triglyceride‒glucose index (TyG), and the homeostasis model assessment of IR on the basis of C-peptide (HOMA2‒IR),(all P < 0.01). (2) In the subgroup with BMI < 23, there were statistically significant differences in MAGE levels among all the IR surrogate marker groups(all p < 0.05), and the triglyceride‒glucose‒body mass index (TyG‒BMI) groups presented differences in the achievement rates of the target MAGE(p < 0.001). In the population with BMI ≥ 23, there were statistically significant differences in both MAGE levels and achievement rates among all the IR surrogate marker groups (all P < 0.05). (3) In the subgroup with BMI < 23, the multivariable logistic regression model revealed that, after adjusting for relevant confounding factors, a high TyG-BMI was a favourable factor for achieving the target MAGE(OR = 16.50, 95% CI 2.44-111.66). In the population with BMI ≥ 23, the multivariable logistic regression model indicated that high TyG (OR = 0.20,95% CI 0.09-0.46), TyG-BMI(OR = 0.27,95% CI:0.13-0.58), the metabolic score for IR (METS‒IR) (OR = 0.36,95% CI 0.17-0.77)and HOMA2-IR(OR = 0.01, 95% CI 0.00-0.02) values were adverse factors for achieving the target.

CONCLUSION: In type 2 diabetic patients with BMI < 23, a high TyG-BMI was a favourable factor for achieving the target MAGE. For BMI ≥ 23, TyG, TyG-BMI, METS-IR and HOMA2-IR were adverse factors for achieving the target MAGE.

PMID:41772737 | DOI:10.1186/s40001-026-04125-1

J Eat Disord. 2026 Mar 2. doi: 10.1186/s40337-026-01561-6. Online ahead of print.

ABSTRACT

BACKGROUND: Carers are crucial to the recovery process of people with eating disorders (EDs). The Caregiver Skills (CASK) scale was developed to assess key caregiving skills among carers of individuals with EDs. While psychometric evaluations of the English, German and Spanish versions have supported the original six-factor model, no validated Dutch version exists to date. This study aimed to examine the psychometric properties of a Dutch translation of the CASK. Validated caregiving outcome measures can assist in exploring coping skills in clinical practice and support research evaluating both caregiving interventions and ED treatments.

METHODS: A total of 248 carers (139 female, 109 male; mean age = 48.3 years) completed the CASK prior to assessment at a specialized ED treatment center in the Netherlands. The internal consistency of the total scale and six subscales was assessed using McDonald’s omega. Confirmatory factor analysis (CFA) was used to test the original six-factor structure. Gender differences in subscale and total scores were explored using linear mixed-effects models (LMM).

RESULTS: The original six-factor structure could not be confirmed in CFA, and model fit remained suboptimal after post hoc modifications. However, CFA results for the six subscales separately showed good to acceptable fit, and internal consistency was satisfactory. No gender differences were found in total CASK scores, though male carers reported higher skills on ‘Self-Care’ and ‘Insight and Acceptance’, and female carers reported higher skills on ‘Frustration Tolerance’ and ‘Emotional Intelligence’.

CONCLUSIONS: The Dutch version of the CASK did not replicate the original six-factor structure at the full-scale level. Nonetheless, the six subscales demonstrated adequate psychometric properties and appear particularly suitable for use at the group level, making the instrument relevant for research and care improvement policies. Further studies are needed to determine whether the CASK can also provide added value at the individual level, for example to guide individualized treatment content, and to evaluate its applicability in more diagnostically and culturally diverse samples. While male and female carers reported comparable overall skills, differences in specific subscale scores highlight the importance of considering gender in the context of skill development and support for carers of individuals with EDs.

PMID:41772726 | DOI:10.1186/s40337-026-01561-6

Trop Med Health. 2026 Mar 2. doi: 10.1186/s41182-026-00928-4. Online ahead of print.

ABSTRACT

BACKGROUND: The global tuberculosis (TB) epidemic imposes a substantial burden. As a high-burden country, China faces a significant gap from the World Health Organization (WHO)’s 2025-2030 TB prevention and control targets. This study analyzed the temporal trends of TB epidemiology in mainland China to provide an evidence base for the early achievement of TB control goals.

METHODS: We integrated TB surveillance data (2004-2024) from the National Health Commission of the People’s Republic of China and population data from the National Bureau of Statistics. Joinpoint regression was used to identify trend changes, with the average annual percent change (AAPC) quantifying trend magnitudes. Interrupted time series model was applied to assess intervention effects, and seasonal autoregressive integrated moving average models were employed to predict future incidence and mortality trends.

RESULTS: A total of 19.4854 million cumulative TB cases and 508,000 cumulative deaths were reported during 2004-2024. The incidence rate decreased from 74.644 to 49.888 per 100,000 population (AAPC = – 2.83%, P < 0.001), showing a “winter peak and summer trough” pattern-with a 32.7% higher incidence in winter than in summer. The mortality rate first decreased and then increased: it declined immediately after the full coverage of Directly Observed Treatment, Short-course in 2010 but rose to 0.283 per 100,000 population after 2021. Projections indicate that the achievement rate of the WHO incidence target will be only 43.24% by 2025 (target: 31.708 per 100,000 population) and 39.48% by 2030 (target: 12.683 per 100,000 population). The mortality rate is projected to reach 0.333 per 100,000 population by 2030, compared with the target of 0.013 per 100,000 population.

CONCLUSIONS: Despite notable achievements in TB control in China, significant gaps remain from the WHO’s targets. It is imperative to strengthen precision stratification-based prevention and control, establish a TB diagnosis and treatment guarantee mechanism, and implement remote supervision relying on informatization.

PMID:41772725 | DOI:10.1186/s41182-026-00928-4

Trials. 2026 Mar 3. doi: 10.1186/s13063-026-09577-8. Online ahead of print.

ABSTRACT

BACKGROUND: Current intraoperative margin assessment techniques lack the accuracy needed for clinical practice. Indocyanine green fluorescence imaging (ICG-FI) offers a useful technique for detection of tumoral tissue, including breast cancer (BC). There is a great inconsistency in the literature regarding the use of ICG for BC fluorescence imaging mainly concerning ICG dosing and the timing of administration. This study aims to determine the dose and timing of intravenous (IV) ICG administration that provides the optimal diagnostic accuracy for ICG-FI margin assessment during breast-conserving surgery (BCS).

METHODS: This study (BREASTIFLU-1) is a phase II cross-sectional diagnostic accuracy study. It is a five-arm, single-center, prospective, randomized, observational-interventional, open-label study that will include patients with histologically proven early-stage (cT1-T2, cN0-N1) BC who are planned to undergo BCS. Two preoperative timeframes will be used for the administration of five different IV ICG doses in single-dose patient arms. In the intraoperative arms, patients will receive 0.125 mg/kg or 0.25 mg/kg ICG administered IV at induction anesthesia. In the preoperative arms, patients will receive 0.5 mg/kg, 1 mg/kg, or 2 mg/kg ICG administered 24 h before surgery. The primary endpoint is accuracy of the ICG-FI technique for the detection of positive surgical margins. Secondary endpoints include the following: comparison of ICG-FI technique accuracy at different doses and timings, characterization of breast tumor fluorescence, and evaluation of fluorescence intensity of axillary lymph nodes. The trial aims to include 227 patients. Participant recruitment is expected to be complete at the beginning of 2026, and results for the primary outcome are expected to be available in 2026.

DISCUSSION: BREASTIFLU-1 is the first initiative to compare an intraoperative dose of ICG to preoperative ICG administration and is aligned with efforts to standardize the ICG-FI technique. Determining the best dose and timing for IV ICG injection is a crucial step toward using ICG-FI in practice. ICG-FI may provide a relatively easy and quick method to use for reducing the rate of positive margins after BCS.

TRIAL REGISTRATION: This trial was registered on February 15, 2023 at Clinical Trials Information System EU2023-504024-26-00 and on September 10, 2023 at ClinicalTrials.gov NCT06227338.

PMID:41772708 | DOI:10.1186/s13063-026-09577-8

J Cheminform. 2026 Mar 2. doi: 10.1186/s13321-026-01174-w. Online ahead of print.

ABSTRACT

Data curation is a fundamental yet often underappreciated aspect of cheminformatics and computational drug discovery. Large public and proprietary databases now provide vast amounts of chemical structure, physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET), and bioactivity data. However, these resources contain structural inconsistencies, annotation errors, and heterogeneous experimental conditions that can limit model performance and reproducibility. This narrative review summarizes why and how data should be curated before use in cheminformatics workflows. We frame chemical data curation around two complementary pillars: structural curation and curation of experimental conditions. On the structural side, we review existing standardization and quantitative structure-activity relationship (QSAR)-ready workflows, including handling of salts and mixtures, parent-child policies, aromatization, tautomer handling, stereochemistry validation, and duplicate detection with conflict resolution. On the experimental side, we synthesize evidence that assay protocols, measurement methods, and reporting practices introduce substantial uncertainty and bias in physicochemical and ADMET endpoints as well as bioactivity data, and we outline practical strategies for assembling condition-aware datasets from the literature and public databases. Across case studies, we highlight how curated structure-condition pairs yield more accurate, robust, and reproducible models than raw, unfiltered collections. Rather than introducing a new predictive method or performing a formal statistical meta-analysis, we provide a structured narrative synthesis of current best practices, tools, and decision points for data curation in cheminformatics. This review offers practical, evidence-based guidance on the structural and experimental-condition curation required to build reliable cheminformatics models.Scientific Contribution: This article does not introduce a new algorithm but provides a practice-oriented, structured synthesis of data curation in cheminformatics. We (i) formulate a two-pillar framework that treats structural curation and experimental-condition curation as equally important components of cheminformatics workflows; (ii) consolidate scattered best practices into concrete workflows, checklists, and decision maps for building “QSAR-ready” and condition-aware datasets; and (iii) integrate endpoint-specific case studies showing that rigorous curation materially improves predictive performance and reproducibility. We also identify open challenges and research directions for scaling and automating curation, including the use of workflow technologies and large language models, and for establishing community standards for condition metadata.

PMID:41772705 | DOI:10.1186/s13321-026-01174-w

Radiat Oncol. 2026 Mar 2. doi: 10.1186/s13014-026-02811-2. Online ahead of print.

ABSTRACT

OBJECTIVE: To minimize the radiation injury for white matter (WM) pathways during brain arteriovenous malformation (bAVM) stereotactic radiosurgery (SRS), the WM tractography is integrated into treatment planning to identify WM pathways and restrict receiving dose. Manual segmentation of eloquent bAVM adjacent to WM pathways is time-consuming and prone to substantial inter-practitioner variability due to intricate entanglement within eloquent brain areas. The objective of this study is to develop and evaluate a deep learning (DL) system for the segmentation of eloquent bAVM in a clinical setting.

METHODS: A total of 191 eloquent bAVM patients who underwent WM tractography and 3D time-of-flight magnetic resonance angiography (TOF-MRA) images were enrolled. 153 patients were used to construct a two-stage DL bAVM segmentation ensemble (TBASE) consisting of 2D detection and 3D segmentation models to segment the bAVM, the other 38 to test performance. Comparative experiments with ResNet and U-Net were conducted to validate the effectiveness of the proposed network. A randomized multi-reader evaluation was then conducted to assess the impact of TBASE assistance for bAVM segmentation using ten algorithm-unseen cases. Six medical professionals contoured the same series of cases in both assisted and unassisted modes, with a 6-week memory washout period between each session. The aided and unaided Dice Similarity Coefficients (DSC), Hausdorff Distance (HD), along with contouring times were compared.

RESULTS: The mean values and standard deviations for DSC and HD of TBASE are 0.87 ± 0.03 and 3.51 ± 0.26, respectively, while Res-Net and U-Net results are 0.75 ± 0.12 and 4.14 ± 0.99, 0.77 ± 0.09 and 3.94 ± 0.82, respectively. The average volume difference across all patients in test dataset is 0.25 ± 1.39 cc, with no statistically significant variation observed. With TBASE assistance, the mean DSC of readers improved from 0.76 ± 0.07 to 0.86 ± 0.05 (P < 0.001), with corresponding values of mean HD reducing from 4.31 ± 0.68 to 3.35 ± 0.17 (P < 0.001) and a mean time saving of 52.15% ± 13.85% per patient. Less-experienced readers achieved greater improvements in contouring accuracy compared to specialists (DSC increase: 0.15 ± 0.11 vs. 0.06 ± 0.09; P < 0.001), while demonstrating similar reductions in contouring time as specialists (50.68% ± 14.62% vs. 55.1% ± 11.98%; P = 0.217).

CONCLUSIONS: The reliable eloquent bAVM automated-segmentation method has been validated in clinical workflow. The TBASE assistance improved the accuracy and efficiency of the eloquent bAVM manual delineation while considering WM pathway protection.

CLINICAL TRIAL NUMBER: Not applicable.

PMID:41772699 | DOI:10.1186/s13014-026-02811-2

Arch Public Health. 2026 Mar 3. doi: 10.1186/s13690-026-01873-8. Online ahead of print.

ABSTRACT

BACKGROUND: Long-term spatio-temporal analyses of influenza in coastal mega-cities remain limited. This study explores influenza dynamics in Tianjin, China (2010-2023) to inform prevention strategies.

METHODS: The data was based on case data from Tianjin (2010-2023) and the Tianjin Statistical Yearbook. Temporal trends, spatial auto-correlation (global and local Moran’s I), and spatio-temporal clusters (scan statistics) were assessed.

RESULTS: From 2010 to 2023, Tianjin reported a cumulative total of 195,426 influenza cases. The eastern region displayed the top three prevalence indices among the sixteen districts. Spatial auto-correlation analyses indicated that the evolution of influenza incidence rates in Tianjin follows a pattern of being “high along the eastern coast”. The analysis of spatio-temporal scanning identified the most significant cluster in eastern Tianjin, occurring from December 2017 to May 2019. This cluster covered four districts with a relative risk (RR) of 7.73 and log likelihood ratio (LLR) of 6597.49 (P < 0.001). Additionally, a similar cluster emerged in the eastern region of Tianjin from December 2019 to December 2023, covering three districts (RR: 27.41; LLR: 74011.43; P < 0.001).

CONCLUSIONS: Mega-city influenza prevention prioritizes high-economic-activity zones. Coastal mega-cities target influenza spread in old-new economic transition areas.

PMID:41772678 | DOI:10.1186/s13690-026-01873-8