Category: Nevin Manimala

Sci Rep. 2026 May 26. doi: 10.1038/s41598-026-50687-9. Online ahead of print.

ABSTRACT

Accurate prediction of soil surface wetness (SSW) is vital for effective land management and resource optimization, particularly in sensitive ecosystems like the Western Himalayas. The main objective of the present study is to improve the accuracy of SSW prediction using hybrid and bagging models in the selected sites in Punjab, north-western India. The study utilizes ten machine-learning models comprising five base learners-random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), logistic model tree (LMT), and classification and regression tree (CART) and their corresponding AdaBoost-based hybrid variants: AdaBoost-RF, AdaBoost-XGBoost, AdaBoost-LightGBM, AdaBoost-LMT, and AdaBoost-CART. The SSW data set was collected from NASA POWER platform and Goddard Earth Observing System (GEOS) derived data which covers the period from 1986 to 2021. For model development, we applied a monthly data-lagging technique to generate different model scenarios. For feature selection, we applied greedy stepwise and best-first algorithms to identify the most effective predictors and improve model efficiency. Evaluations of the models were based on a variety of statistical indices. The results show that the RF model achieved the highest correlation coefficients (CC) across the study areas, ranging from 0.64 to 0.76 in Moga, 0.61 to 0.82 (XGBoost) in Hoshiarpur, and 0.41 to 0.64 (LMT) in Firozpur during the testing period. Accordingly, the hybrid AdaBoost-LightGBM model was the best, with CC values of 0.61-0.76, 0.63-0.82, and 0.49-0.60 for Moga, Hoshiarpur, and Firozpur, respectively. Overall model performance was limited to moderate and varied across locations and scenarios; although AdaBoost-LMT showed the best relative performance, the results primarily support its use for reproducing temporal variability in GEOS-derived SSW rather than precise wetness estimation. The findings contribute to improving SSW estimation and support data-driven decision-making for sustainable land and water management in the selected sites in Punjab, north-western India.

PMID:42192144 | DOI:10.1038/s41598-026-50687-9

NPJ Parkinsons Dis. 2026 May 26. doi: 10.1038/s41531-026-01409-5. Online ahead of print.

ABSTRACT

Motor impairment in Parkinson’s disease (PD) is classically attributed to striatal dopaminergic degeneration, yet dopamine loss alone does not fully explain symptom severity, suggesting a key role for circuit-level mechanisms. The cortico-basal ganglia-thalamo-cortical (CBGTC) system is central to dopaminergic motor modulation, but whether dysfunction within individualized CBGTC circuits mediates motor severity across disease stages remains unclear. Here, we studied 76 PD patients (40 mild, 36 moderate-to-severe) who underwent ¹⁸F-FP-CIT PET and multimodal MRI. Individualized long and short CBGTC loops were reconstructed using connectivity profile-based segmentation and probabilistic tractography, with functional connectivity (FC) quantified alongside striatal dopaminergic integrity. Compared with mild PD, moderate-to-severe PD showed a global reduction in striatal dopamine binding, most pronounced in the bilateral caudate. Critically, FC between the caudate and premotor cortex in the more-affected hemisphere was selectively reduced with increasing disease severity in both CBGTC loops (p-FDR = 0.027). The cross-sectional mediation models demonstrated that caudate-premotor FC statistically accounted for the association between caudate dopaminergic loss and motor symptom severity. These findings position individualized cortico-basal ganglia circuit connectivity as a potential mechanistically grounded biomarker linking molecular pathology to motor impairment in PD, with potential relevance for future circuit-guided therapeutic strategies.

PMID:42192116 | DOI:10.1038/s41531-026-01409-5

Nat Commun. 2026 May 26. doi: 10.1038/s41467-026-73397-2. Online ahead of print.

ABSTRACT

Accurate determination of enantiomeric purity is essential for advancing chiral materials in nanotechnology, optoelectronics, and quantum information science. Chiroptical spectroscopic techniques provide rapid, non-destructive measurements of enantiomeric excess (ee), but their use for complex systems like single-walled carbon nanotubes (SWCNTs) is limited by the lack of enantiopure references for calibration. Here we demonstrate an absolute approach combining hyperspectral imaging (HSI) with single-nanotube counting statistics and ensemble chiroptical spectroscopy such as electronic circular dichroism (ECD) and Raman optical activity (ROA) to quantify ee without requiring such standards. Analysis of thousands of individual nanotubes reveals sensitivity of HSI and chiroptical responses to synthesis, purification, and SWCNT concentration, highlighting pronounced source-dependent inhomogeneity. Nevertheless, universal calibration curves for ECD and ROA intensities are established from the purest, most uniform enantiomer-sorted samples. This methodology is extendable to other SWCNT chiralities and chiroptical techniques, enabling quantitative enantiomer sorting and systematic investigations of chirality-dependent properties and applications.

PMID:42192104 | DOI:10.1038/s41467-026-73397-2

Cardiovasc Interv Ther. 2026 May 26. doi: 10.1007/s12928-026-01304-z. Online ahead of print.

ABSTRACT

In patients with ACS undergoing PCI, de-escalation to P2Y12-inhibitor monotherapy reduces bleeding, but the optimal timing of aspirin withdrawal is uncertain. We conducted pairwise and network meta-analyses of randomized trials comparing P2Y12-inhibitor monotherapy after aspirin discontinuation versus dual antiplatelet therapy (DAPT) in ACS post-PCI. Random-effects models were used. The network meta-analysis (NMA) compared four strategies (12-month DAPT [central comparator]; aspirin stop < 1 month, 1-2 months, or 3 months) and ranked treatments using SUCRA. (PROSPERO ID: CRD420251151605). A total of 33,292 participants from 10 RCTs were included. In the pairwise meta-analysis, monotherapy reduced net adverse clinical events (NACE) (5.1% vs. 6.7%; RR: 0.75; 95% CI: 0.65-0.86) and bleeding, including clinically relevant bleeding (RR: 0.45; 95% CI: 0.39-0.53) and major bleeding (RR: 0.47; 95% CI: 0.37-0.60). In the NMA, aspirin discontinuation at 3 months showed a trend toward lower NACE versus 12-month DAPT (RR 0.66; 95% CI 0.42-1.04) and ranked most favorable for net and ischemic outcomes (NACE, MI, MACCE, and all-cause mortality), although estimates for individual ischemic endpoints were imprecise and not statistically different from 12-month DAPT. Aspirin discontinuation at < 1 month showed an unfavorable mortality ranking, with a concordant meta-regression signal suggesting higher mortality with earlier aspirin withdrawal. De-escalation to P2Y₁₂ inhibitor monotherapy after PCI in ACS reduced NACE, mainly by lowering bleeding. In network analyses, aspirin discontinuation at 3 months ranked most favorable for net and ischemic outcomes, whereas discontinuation at < 1 month showed an unfavorable mortality signal.

PMID:42192090 | DOI:10.1007/s12928-026-01304-z

J Assoc Res Otolaryngol. 2026 May 26. doi: 10.1007/s10162-026-01054-y. Online ahead of print.

ABSTRACT

PURPOSE: Investigate the genetic components of sensorineural hearing loss (SNHL) by performing genome-wide meta-analyses using the data from FinnGen and Estonian Biobank.

METHODS: We studied genome-wide associations of SNHL in FinnGen and the Estonian Biobank in the general population and in sex- and age-of-onset stratified subgroups. The study-specific GWASs were combined through inverse variance-weighted genome-wide meta-analyses, encompassing a total of 531,059 individuals (Ncases = 35,960). Age-stratified meta-analyses included 28,198 individuals diagnosed at the age of 55 years or after and 7762 individuals diagnosed before the age of 55 years, with 495,099 controls. Sex-stratified meta-analyses included 313,501 females (Ncases = 17,761) and 217,558 males (Ncases = 18,199).

RESULTS: In the meta-analysis focusing on the general population, 22 SNHL-associated loci (±1 Mb window) were observed, three of which were previously unreported. In the sex-stratified analysis, two previously unreported SNHL loci were observed in the female subgroup and one locus in the male subgroup. Additionally, in the age-stratified analysis, two previously unreported SNHL loci were observed in the subgroup of those that were diagnosed at the age of 55 years or after. In those diagnosed before the age of 55 years, one previously unreported locus was observed. Overall, 32 loci were associated with SNHL at p < 5 × 10^-8 in at least one of the study groups. Of these, nine have not been previously reported in association with SNHL. We also estimated if there were significant differences in the effect sizes of the lead variants at each locus between the analytical subgroups and observed differences for 14 variants.

CONCLUSIONS: Previously unreported SNHL risk loci and differences in effect sizes found in this study provide additional insight into the genetic underpinnings of SNHL. Our results validate the role of mechano-transduction and genetic components affecting the structure of the inner ear in the background of SNHL. Our study contributes to our understanding of the genetic causes of SNHL and may open the door for further translational research.

PMID:42192083 | DOI:10.1007/s10162-026-01054-y

J Racial Ethn Health Disparities. 2026 May 26. doi: 10.1007/s40615-026-03024-9. Online ahead of print.

ABSTRACT

Acculturation has been shown to be relevant to health outcomes. However, methodological limitations remain in the existing literature, particularly the lack of psychometric validation of acculturation measures for Asian American population. As a result, little is known about distinct acculturation profiles within this population and how these profiles relate to perceived stress and health. This study employs latent profile analysis (LPA) to identify distinct acculturation profiles among 337 Asian Americans and to examine their associations with perceived health and perceived stress. LPA were used based on acculturated-related variables, including perceived acculturation, immigration generation, associated ethnic group, heritage language competency pressure, English competency pressure, pressure to acculturation, pressure against acculturation. We detected a 3-class solution: acculturated, bicultural, and enculturated. Compared to bicultural group, the acculturated group has statistically significant lower perceived stress (p < .001), higher perceived mental health (p < .001) and physical health (p < .001); the enculturated group reported significantly lower perceived mental health than the bicultural group (p <.001). These findings highlight nuanced differences in health perceptions across acculturation profiles. Notably, the enculturated group may be at elevated risk for poorer mental health outcomes, underscoring the need for culturally tailored mental health interventions. By elucidating how acculturation experiences impact perceived health and stress, this study offers practical insights for mental health professionals and healthcare providers, encouraging the development of tailored, culturally sensitive interventions for Asian American subgroups based on their acculturation experiences.

PMID:42192078 | DOI:10.1007/s40615-026-03024-9

Reprod Sci. 2026 May 26. doi: 10.1007/s43032-026-02125-4. Online ahead of print.

ABSTRACT

This study aims to further clarify the correlation between the quantity of natural killer (NK) cells in the endometrium and the successful implantation of embryos during the specific implantation window. Patients were stratified into four distinct groups according to the quantity of natural killer (NK) cells detected during the implantation window. The groups were defined as follows: Group A (NK cell percentage ≥ 10%, n = 62), Group B (NK cell percentage: 5%-9.99%, n = 128), Group C (NK cell percentage: 1%-4.99%, n = 106), and Group D (NK cell percentage ≤ 0.5%, n = 30). Among Group A, 32 patients received intrauterine infusion of dexamethasone (designated as Subgroup A1), while the remaining 30 patients did not undergo this treatment (designated as Subgroup A2). For Groups B and D, immunohistochemistry was performed to detect the expression of IGFBP1 (an endometrial decidualization marker) and HBP1 (a decidualization-related transcription factor). The live birth rate in Group D was statistically significantly lower than that in the other three study groups. For Group A, after dexamethasone administration, a notable reduction in NK cell quantity was observed in Subgroup A1 (patients who received intrauterine dexamethasone infusion) during the implantation window; however, the pregnancy rates of Subgroup A1 and Subgroup A2 (patients without dexamethasone treatment) were comparable. Furthermore, in Group D patients, the expression levels of the decidualization markers IGFBP1 and HBP1 were significantly lower than those in the reference group (Group B, as specified in Methods). Insufficient natural killer (NK) cells during the embryo implantation window significantly impede the embryo implantation process. This shortage of NK cells may be attributed to inadequate endometrial decidualization. Notably, although intrauterine infusion of dexamethasone effectively reduces NK cell counts, it does not exert a significant impact on clinical outcomes.

PMID:42192073 | DOI:10.1007/s43032-026-02125-4

Environ Monit Assess. 2026 May 27;198(6):657. doi: 10.1007/s10661-026-15505-9.

ABSTRACT

Continuous and uninterrupted air quality monitoring is essential for environmental management and public policy formulation, which requires the absence of missing data and good quality measurements. However, due to a variety of factors (local power outages, data transmission, instrument calibration, preventive maintenance, weather conditions, etc.), measurement gaps with different time windows frequently occur in historical air quality data. This work addresses the problem of missing data in air quality monitoring time series, which compromises the quality of information and hinders decision-making related to air pollution. Carbon monoxide (CO) data were imputed in artificially generated gaps (from 24 to 72 h) for a monitoring station located in Salvador, Bahia (Brazil). Three dynamic modeling strategies with different architectures and learning algorithms were applied: XGboost and two recurrent neural networks (LSTM and RNN). The results showed that, although XGboost presented the lowest medians associated with RMSE and MAE distributions (0.1028 and 0.1266 ppm, respectively), the difference compared to the neural networks was not statistically significant. The statistical analysis of the predictions showed that the mean of the residuals does not differ significantly from zero, indicating an absence of systematic bias and suggesting that the imputed values preserve the dominant dynamics and seasonal patterns of the original series. The percentages of gaps consistently described by the models were 82.0% (XGboost) and 91.3% (LSTM and RNN recurrent neural networks). The results demonstrate that the adopted model structures (decision tree and recurrent neural networks), along with a systematic approach involving the analysis and preparation of the training sample (identification of input variables, mapping of existing gaps in the historical data of the measurement station, and generation of artificial gaps, among others), enabled the imputation of dynamic CO data, preserving the dominant behavior of the time series and ensuring the validity of environmental monitoring.

PMID:42192051 | DOI:10.1007/s10661-026-15505-9

Support Care Cancer. 2026 May 27;34(6):582. doi: 10.1007/s00520-026-10816-6.

ABSTRACT

PURPOSE: As the number of cancer survivors increases, psychological distress has become an important issue. Using nationally representative data, we evaluated mental health outcomes among Korean cancer survivors compared with cancer-free controls and developed models to identify individuals at risk of psychological distress.

METHODS: We analyzed data from the Korea National Health and Nutrition Examination Survey (KNHANES) from 2007 to 2021. Psychological outcomes were assessed using standardized questionnaires, and a composite distress outcome was constructed. Risk stratification models were developed among cancer survivors using logistic regression and machine learning algorithms, including random forest, XGBoost, LightGBM, support vector machines, k-nearest neighbors, and naïve Bayes.

RESULTS: A total of 88,061 participants were included, comprising 3733 cancer survivors and 84,328 cancer-free controls. Compared with cancer-free controls, cancer survivors had higher odds of depressed mood (OR 1.33; 95% CI 1.18-1.51), suicidal ideation (OR 1.14; 95% CI 1.00-1.31), suicide planning (OR 1.91; 95% CI 1.37-2.65), and mental health counseling (OR 1.36; 95% CI 1.08-1.71). Among cancer survivors, multiple models were evaluated, with logistic regression showing the highest performance (AUROC 0.689), followed by XGBoost (0.686). In logistic regression, longer working hours, depression history, activity limitation, female sex, smoking, employment, low income, and distorted body image were independently associated with distress. SHAP analysis identified activity limitation, sex, and depression history as key factors.

CONCLUSIONS: Cancer survivors experience increased psychological distress across multiple outcomes. Machine learning-based models may help identify individuals at higher risk of psychological distress, supporting risk-based assessment in survivorship care.

PMID:42192026 | DOI:10.1007/s00520-026-10816-6

Eur J Nutr. 2026 May 26;65(4):140. doi: 10.1007/s00394-026-04002-x.

ABSTRACT

PURPOSE: To examine the associations of planetary health diet (PHD) with all-cause and cause specific mortality, alongside heart age based on the Guangzhou Biobank Cohort Study (GBCS) and conduct mediation analysis.

METHODS: Participants were recruited from the GBCS and were aged ≥ 50 years. Dietary information was collected using a validated Food Frequency Questionnaire. Participants were assigned PHD scores between 0 (no adherence to PHD) and 140 (complete adherence to PHD). Primary outcomes were all-cause, cardiovascular disease (CVD) and cancer mortality. Causes of death were identified through death registry. Secondary outcome, heart age, was calculated using sex-specific 10-year CVD risk prediction models previously developed and validated in the GBCS. Cox proportional hazards regression and linear regression were used to analyze the associations of PHD scores with mortality and heart age. Mediation analyses were conducted using the difference method implemented by the “mediate” SAS macro.

RESULTS: Of 25,550 participants aged 50+ years, during 417,590 person-years of follow-up, higher PHD scores was linearly associated with lower all-cause and CVD but not cancer mortality (hazard ratio (HR) (95% confidence interval (CI)) per 10-point increment: 0.94 (0.92-0.97), 0.92 (0.89-0.95) and 0.97 (0.93-1.01)). The association with all-cause mortality was mediated by white blood cell count (WBC), waist-to-hip ratio and waist-to-hip-to-height ratio (mediation proportion (95% CI): 6.2% (3.2-11.7%), 2.6% (0.9-7.2%) and 5.4% (2.8-9.9%)), whereas the association with CVD mortality was mediated by WBC and waist-to-hip-to-height ratio (7.9% (4.1-14.9%) and 7.4% (3.0-17.0%)). A negative association between PHD scores and heart age was observed in women but not in men (β (95% CI) per 10-point increment: – 0.13 (- 0.24, -0.01) and 0.05 (- 0.15, 0.25) years, P_interaction < 0.001).

CONCLUSION: Higher adherence to PHD was linearly associated with lower all-cause and CVD but not cancer mortality in Chinese aged 50+ years, and with lower heart age in women only. Our findings advocate for PHD in middle-aged to older Chinese, particularly women to improve cardiovascular health.

PMID:42192022 | DOI:10.1007/s00394-026-04002-x