Nevin Manimala

BMC Plant Biol. 2025 Aug 16;25(1):1079. doi: 10.1186/s12870-025-07145-x.

ABSTRACT

BACKGROUND: Biological invasions pose significant ecological and socio-economic threats globally. Heracleum mantegazzianum (giant hogweed) is an invasive plant, extensively invading Europe and North America. It exerts negative impacts on ecosystems, native vegetation, and public health in the invaded range. Although H. mantegazzianum has not been reported from Turkey yet, ecological conditions of the country similar to those prevailing in its native and invaded ranges suggest a high introduction and spread risk for Turkey. Therefore, the current study predicted the introduction and future invasion risk of H. mantegazzianum in Turkey under current and future Coupled Model Intercomparison Project Phase 6 (CMIP6) projections.

METHODS: Maximum Entropy (MaxEnt) model was used to predict introduction and future invasion risk using occurrence data from native and invaded ranges and global environmental data. Only climatic data were used for modeling as future data for soil and socioeconomic attributes are currently unavailable. Multicollinearity among environmental variables was tested and 10 least correlated variables, i.e., bio1 (annual mean temperature), bio2 (mean diurnal range), bio4 (temperature seasonality), bio5 (max temperature of warmest month), bio6 (min temperature of coldest month), bio7 (temperature annual range), bio10 (mean temperature of warmest quarter), bio11 (mean temperature of coldest quarter), bio14 (precipitation of driest month), and bio15 (precipitation seasonality) were used to train and test the model. Furthermore, the model was optimized before training and testing. The model was trained and tested with 18,607 occurrence records of which 75% and 25% were split for training and testing, respectively. Future invasion risk was predicted under two CMIP6 climate change scenarios (SSP1-2.6 and SSP5-8.5). Predictive accuracy of the model was evaluated by area under the receiver operating characteristics curve (AUC), true skill statistics (TSS), sensitivity and specificity.

RESULTS: MaxEnt model predicted introduction and future invasion risk of H. mantegazzianum with high accuracy (AUC = 0.97 ± 0.02; TSS = 0.94 ± 0.04, Kappa = 0.92 ± 0.03, sensitivity = 93.40 ± 2.20, and specificity = 94.80 ± 3.40). The bio14, bio6 and bio1 had the highest permutation importance indicating that temperature and precipitation changes will mediate the introduction and future invasion of H. mantegazzianum. A total 4.2% of Turkey’s land area (31.2 thousand km²) was predicted highly suitable for the introduction of H. mantegazzianum in the Black Sea region under current climate. The CMIP6 climate projections suggest a ~ 50% decline in highly suitable habitats, and aggregation around the Black Sea coast.

CONCLUSION: Climate change is expected to reduce the overall range of H. mantegazzianum in Turkey but may intensify impacts in Black Sea region due to aggregation. Proactive monitoring and management strategies targeting high invasion risk areas guided by invasion risk maps from this study are urgently needed mitigate ecological and socio-economic consequences of H. mantegazzianum in Turkey.

PMID:40817242 | DOI:10.1186/s12870-025-07145-x

BMC Med Inform Decis Mak. 2025 Aug 15;25(1):310. doi: 10.1186/s12911-025-03091-8.

ABSTRACT

BACKGROUND: Cancer patients receiving targeted therapies need to prevent QTc prolongation and life-threatening cardiovascular (CV) events to maintain a balanced benefit-risk ratio. This study aimed to develop an optimal prediction model for QTc prolongation risk and estimate its risk probability in cancer patients treated with oral tyrosine kinase inhibitors (TKIs).

METHODS: This retrospective cohort study analyzed electronic medical records (EMR) of cancer patients newly treated with commonly used oral TKIs at a medical center between January 2016 and December 2020. QTc prolongation was defined as ≥ 450 ms in males and ≥ 470 ms in females using Bazett’s formula. The study followed four key steps: (1) Managing missing data, (2) Identifying important variables, (3) Training and testing the best prediction models, (4). Estimating risk probability and determining cut-off points. Both univariate logistic regression (LR) and supervised machine learning (ML) approaches were used for variable selection. The backward LR method and seven ML algorithms were applied to train and test the prediction models. The best model was identified based on model performance, fitting criteria, area under the receiver operating characteristic curve (AUROC), risk probability cut-off points, and clinical relevance.

RESULTS: The statistical 12-parameter model demonstrated excellent performance (AUROC = 0.89, sensitivity = 0.91, specificity = 0.75) and strong discrimination ability for risk probability prediction (AUROC = 0.78, cut-off = 0.46), outperforming other ML models. In the final best model: the baseline risk probability of QTc prolongation was 0.13, even in the absence of other contributing factors. Baseline QTc prolongation and a history of cardiovascular disease (excluding arrhythmia, cardiomyopathy, etc.) contributed the most to incremental risk probability (0.471 and 0.282, respectively), after controlling for other factors. The remaining 10 factors each contributed to an increased probability of QTc prolongation for more than 0.14 probability.

CONCLUSIONS: A logistic regression model utilizing 12 easily accessible variables from EMRs outperformed ML models in predicting the risk probability of QTc prolongation in cancer patients newly treated with five oral TKIs. These findings serve as a valuable clinical reference for integrating digital monitoring into cardiovascular care for cancer survivors undergoing targeted therapy with TKIs. They also underscore the importance of screening baseline ECG before initiating TKIs to assess the risk of QTc prolongation, facilitating early intervention and prevention in the future.

PMID:40817221 | DOI:10.1186/s12911-025-03091-8

BMC Ophthalmol. 2025 Aug 15;25(1):460. doi: 10.1186/s12886-025-04283-6.

ABSTRACT

BACKGROUND: The therapeutic outcome for Type 2 Diabetic Retinopathy (T2DR) following vitrectomy has been unsatisfactory, with no definitive biomarker available to predict treatment response. Identifying a biomarker correlated with treatment efficacy is crucial, as the vitreous-situated between the lens and retina-may influence retinal metabolic perturbations.

METHODS: Vitreous samples were collected during vitrectomy, and their metabolic profiles were analyzed using Ultraperformance Liquid Chromatography coupled with Tandem Mass Spectrometry (UPLC-MS/MS). Statistical analyses were conducted to identify metabolites and metabolic pathways correlated with therapeutic outcomes.

RESULTS: Patients demonstrating poor therapeutic responses exhibited elevated levels of specific metabolites, including Dodecanoylcarnitine, Linoleylcarnitine, Stearylcarnitine, Decanoic acid, and Proline. Perturbed metabolic pathways included Fatty Acid Biosynthesis, Beta Oxidation of Very Long Chain Fatty Acids, and Mitochondrial Beta-Oxidation of Short Chain Saturated Fatty Acids. These metabolites showed strong discriminatory power for predicting positive outcomes, with Area Under the Curve (AUC) values of 0.925, 0.885, 0.864, 0.811, and 0.808, respectively.

CONCLUSIONS: This study highlights the potential of Dodecanoylcarnitine, Linoleylcarnitine, Stearylcarnitine, Decanoic acid, and Proline as biomarkers for predicting therapeutic outcomes following vitrectomy for T2DR. These findings provide novel insights into the metabolic factors influencing treatment response variability and suggest pathways for future therapeutic interventions.

PMID:40817201 | DOI:10.1186/s12886-025-04283-6

Emerg Radiol. 2025 Aug 16. doi: 10.1007/s10140-025-02378-7. Online ahead of print.

ABSTRACT

PURPOSE: The incidence of subdural hematomas (SDHs) is increasing due to the aging population, frequent use of anticoagulants/antiplatelets, and fall-related trauma. While some acute SDHs remain stable and require no intervention, others expand, necessitating neurosurgical management. Our study objective was to better identify predictors of acute SDH enlargement to guide clinical management.

METHODS: This retrospective study analyzed 32,401 noncontrast CT brain scans over six years. We identified 262 patients with acute SDHs and evaluated demographic, clinical, and radiologic factors associated with hematoma enlargement and the need for surgical intervention. Statistical analyses, including univariate analyses, logistic regression and receiver operating characteristic curves, were performed to determine predictors of SDH growth and surgery.

RESULTS: SDH enlargement occurred in 58/232 (25%) of patients with follow-up imaging. Larger initial SDH size, concurrent subarachnoid hemorrhage, hypertension, convexity location, and midline shift were significantly associated with hematoma expansion (p < 0.05). No patient with an initial SDH ≤ 3 mm required surgery initially or in follow-up, although 8/72 (11.1%) enlarged (maximum width 10 mm). An 8.5-mm initial SDH size threshold best predicted the need for surgical intervention (AUC 0.81).

CONCLUSIONS: Initial SDH size, hypertension, SAH, initial midline shift, and convexity location are key predictors of hematoma expansion. Although patients’ with SDHs ≤ 3 mm rarely expanded, they never required surgery. A prospective study to determine a more judicious use of hospital-based resources, especially for those patients with initial SDH size > 3 mm who have risk factors for expansion, would be an important step in the management of SDHs.

PMID:40817177 | DOI:10.1007/s10140-025-02378-7

Sci Rep. 2025 Aug 15;15(1):29928. doi: 10.1038/s41598-025-15901-0.

ABSTRACT

Due to high variability, potato virus Y (PVY) is an excellent model for developing new virus detection and strain differentiation methods. We present a pioneering assessment using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) to identify three predominant strains of PVY: PVY^O, PVY^NTN, and PVY^N-Wi. We prepared and characterized the genomic, protein, and whole-virus samples. MALDI analysis yielded distinct spectral signatures for each strain, enabling identification. The protein extracts analyzed in the LP 2-20 kDa mode showed the highest spectral richness for N-Wi and O strains, with significant statistical differentiation (p < 0.05) across specific m/z values. RT-qPCR linear detection ranged from 6000 to 0.6 pg of viral RNA. MALDI-TOF MS identified the PVY strains down to 0.001 mg/mL. The Principal Component Analysis outcomes highlighted the distinct clustering of PVY strains based on their MALDI-TOF spectra, with a 45% laser power setting emerging as optimal for balancing the spectral quality across strains. Mann-Whitney U-test comparisons of ion intensity distributions reinforced MALDI’s discriminative accuracy, revealing statistically significant (p < 0.05) ion signals unique to each strain. Our study steps forward in applying MALDI-TOF MS for simultaneous viral strain detection and identification.

PMID:40817166 | DOI:10.1038/s41598-025-15901-0

J Med Syst. 2025 Aug 16;49(1):105. doi: 10.1007/s10916-025-02239-3.

ABSTRACT

The early prediction of sudden cardiac death (SCD) has garnered considerable global attention as a potentially life-saving intervention for at-risk individuals. While various strategies have been proposed, many are constrained by prediction time resolution (typically analyzing 1- to 2-min ECG segments) and early prediction time windows not exceeding 20 min. In this study, we propose a novel yet straightforward methodology that combines locality preserving projection (LPP) features and fuzzy entropy (FuEn) based on empirical mode decomposition (EMD) from individual ECG beats containing 1000 data points. Specifically, 15 features were extracted: 14 discriminative LPP features selected from the training data using the feature ranking method, along with one FuEn feature calculated from the first intrinsic mode function (IMF1) of the EMD. These selected features are applied to test data to differentiate between normal subjects and those at risk of SCD. A distinguishing aspect of our approach is that it analyzes each single ECG beat for SCD prediction, rather than relying on 1- or 2-min segments. Additionally, we incorporate group-based fivefold cross-validation to ensure a robust evaluation of prediction performance. Our method successfully predicts SCD 30 min in advance with an accuracy of 97.6%. In principle, the features extracted from this methodology can be integrated into portable medical sensors for real-time SCD risk assessment, suitable for use both in medical facilities and at home under the supervision of healthcare providers.

PMID:40817165 | DOI:10.1007/s10916-025-02239-3

Eur Radiol. 2025 Aug 15. doi: 10.1007/s00330-025-11934-1. Online ahead of print.

NO ABSTRACT

PMID:40817141 | DOI:10.1007/s00330-025-11934-1

Sci Data. 2025 Aug 15;12(1):1435. doi: 10.1038/s41597-025-05782-3.

ABSTRACT

As the world’s largest CO₂ emitter, China needs accurate city-level CO₂ emission accounts to formulate effective low-carbon policies. However, previous studies mainly accounted for emissions from fossil fuel combustion and overlooked process-related CO₂ emissions from industrial production (e.g., mineral, chemical, metal products), which account for approximately 13% of China’s total emissions. In this study, we built the first time-series dataset of process-related CO₂ emissions for 289 Chinese cities from 2000 to 2021. The dataset covers 11 industrial products and adheres to the methodology recommended by the Intergovernmental Panel on Climate Change (IPCC). We applied China-specific emission factors and compiled industrial output data from city statistical yearbooks and bulletins. Missing output data were imputed using missForest models. The estimated uncertainty of the process-related emissions in our dataset ranges from 3.87% to 3.91%. Our dataset provides a robust foundation for analyzing emission patterns at the city level and for designing targeted low-carbon policies.

PMID:40817113 | DOI:10.1038/s41597-025-05782-3

BMC Public Health. 2025 Aug 15;25(1):2790. doi: 10.1186/s12889-025-23898-4.

ABSTRACT

BACKGROUND: Cervical cancer is the fourth most common cancer in women worldwide. Effective primary prevention with human papillomavirus vaccination and secondary prevention with screening can prevent most cervical cancer cases. Cervical cancer screening uptake varies among women in underserved populations. Research that adds to the understanding of the individual and geographic area-level characteristics of women and their screening status is valuable for public health intervention planning. This study aimed to identify these characteristics related to cervical cancer screening status.

METHODS: The study population included women between the ages of 28 to 69 years in Alberta. Data was extracted from administrative health data sources and linked to the Alberta Cervical Cancer Screening Program database to determine screening status. Descriptive bivariate analysis was conducted to describe variations in cervical cancer screening statuses and individual-level sociodemographic, health system factors, and geographic characteristics. Multinomial logistic regression analysis was conducted to investigate the relationship between these characteristics and screening participation. Geospatial analyses including heat maps were used to visualize variation in screening participation across the province. Getis-Ord Gi* hot-spot analysis was used to determine the location and magnitude of spatial autocorrelation.

RESULTS: The study included 933,965 eligible women. Compared with those who are currently up-to-date for screening, those who have no record of screening tend to be older (OR: 3.63; 95% CI: 3.57 to 3.70), reside in the South Zone (OR: 1.51; 95% CI: 1.47 to 1.55), were health system non-users (OR: 2.95: 95% CI: 2.86 to 3.04), did not see a general practitioner (OR: 13.86; 95% CI: 13.32 to 14.43), or had no usual provider of care (OR: 3.227; 95% CI: 3.141 to 3.315). There are statistically significant hot spots of women who are overdue or have no record of cervical cancer screening in the North, Central, and Calgary Zones.

CONCLUSIONS: This study found that cervical cancer screening participation varied across geographical, health system and sociodemographic characteristics and identified clusters of regions with higher proportions of women who are under-screened in Alberta, Canada. Overall, these findings will help inform the design of interventions that aims to improve cervical cancer screening participation among underserved groups.

PMID:40817111 | DOI:10.1186/s12889-025-23898-4

Sci Rep. 2025 Aug 15;15(1):29948. doi: 10.1038/s41598-025-03971-z.

ABSTRACT

Healthcare-associated urinary tract infections (HAUTIs) represent a significant challenge to healthcare systems worldwide, contributing to patient morbidity, mortality, and increased healthcare costs. Understanding the epidemiology and risk factors associated with HAUTIs is crucial for implementing targeted prevention strategies and optimizing patient care. This study aimed to assess the incidence and characteristics of HAUTIs, as well as their associated factors, at the University Hospital Center Sahloul in Sousse, Tunisia. We conducted a longitudinal study over a three-month period at the University Hospital Center Sahloul in Sousse, Tunisia, over a 3-month period (September-November 2022). Patient data were collected daily, and HAUTIs were defined according to standardized criteria. Statistical analysis was performed to identify risk factors associated with HAUTIs. A total of 1947 patients were included, with an age median of 55 years and a male predominance. Patients were mainly hospitalized in medical and surgical departments with a median length of stay of seven days. Among our patients, 23.1% had been hospitalized in the past 6 months and 33% had a urinary catheter at the time of HAUTI diagnosis. The incidence of HAUTIs was 2.8% and 3.75% among catheterized patients, with an incidence density of 3.08 cases per 1000 hospitalization days. HAUTIs were more frequent in medical (3.9%) and surgical (1.9%) departments. The majority of HAUTIs were symptomatic, with fever being the most common sign. Urinary cultures were positive in all cases with the majority of pathogens isolated being Gram-negative bacteria (56.6%). Pathogens were resistant to antibiotics in 37.5% of the cases. Univariate analysis showed several risk factors associated with HAUTIs, while multivariate analysis showed independent risk factors including increased length of stay (p < 0.001), advanced age (p = 0.012), hospitalization method (p = 0.005), and history of neurogenic bladder (p = 0.019). Clinical improvement was observed in 72.2% of cases, and mortality was low (1.7%). HAUTIs remain a big concern, particularly in medical and surgical departments. Our study shows that prolonged hospitalization, advanced age, hospitalization method and history of neurogenic bladder are significant risk factors for HAUTIs. The high prevalence of antibiotic resistance highlights the need for antimicrobial stewardship programs in local hospitals. In order to reduce HAUTI incidence and improve quality of care, strict infection control measures must implemented, patients at risk must be detected early and catheter management must be optimized.

PMID:40817091 | DOI:10.1038/s41598-025-03971-z