Category: Nevin Manimala

BMC Cardiovasc Disord. 2025 Aug 7;25(1):585. doi: 10.1186/s12872-025-05035-6.

ABSTRACT

BACKGROUND: Hypertension is a major public health issue globally, affecting hundreds of millions of people. Although the association between alcohol consumption and hypertension has been widely demonstrated, uncertainties remain regarding the dose-response relationship, potential confounding factors, and population-specific differences.

METHODS: Data from NHANES participants aged 20 and above from 2007 to 2018 were analyzed. A weighted multivariate logistic regression model assessed the association between daily alcohol intake and hypertension prevalence, calculating adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Restricted cubic splines (RCS) examined the potential non-linear association. Subgroup analyses evaluated interaction effects across demographic variables to identify differences in the alcohol-hypertension association among populations.

RESULTS: This study included 30,127 eligible participants, representing 228,198,440 individuals in the United States population after weighting. In the fully adjusted Model 3, a significant positive association was observed between daily alcohol intake and hypertension prevalence (OR: 1.007, 95% CI: 1.005-1.009; P < 0.001). Compared to the abstainers group (0 g/d), participants in the very heavy consumers group (> 40 g/d) had a significantly higher prevalence of hypertension (OR: 1.873, 95% CI: 1.585-2.213; P < 0.001). RCS analysis further confirmed the positive association between daily alcohol intake and hypertension prevalence, with no significant non-linearity detected. In the subgroup analysis, a significant interaction was detected in the racial/ethnic subgroup (P for interaction < 0.05).

CONCLUSION: A significant association exists between very heavy alcohol consumers (> 40 g/d) and an increased prevalence of hypertension. These findings underscore the importance of including alcohol intake assessments in hypertension management and prevention strategies. Further research is needed to explore the causal association between alcohol intake and hypertension and to investigate its potential biological mechanisms.

PMID:40770292 | DOI:10.1186/s12872-025-05035-6

Sci Rep. 2025 Aug 6;15(1):28710. doi: 10.1038/s41598-025-09972-2.

ABSTRACT

Mammography is important for breast cancer detection, and calcifications and masses are the most common mammographic features. In this exploratory study, we evaluated the association between these findings and immune gene expression to determine the role that inflammation may play in mass- or calcification-associated breast cancers. This study included 205 breast cancer patients in the Carolina Breast Cancer Study and the Carolina Mammography Registry. Hierarchical clustering and logistic regression were used to examine associations between radiologist-reported imaging features and tumor RNA expression of 48 immune-related genes. The Bonferroni correction adjusted for multiple comparisons. There were statistically significant associations between CCL7 and calcifications (odd ratio [OR] = 0.52, 95% confidence interval [CI] = 0.35, 0.75) and between CCL7 and the presence of a mass (OR = 1.48 95% CI = 1.18, 1.88). Furthermore, lower expression of genes with anti-inflammatory function was associated with calcifications (p = 0.04), but not masses. However, neither mammographic masses nor calcifications were associated with global gene expression patterns using hierarchical clustering. This analysis of bulk immune markers suggests plausible associations between immune alterations and mammographic findings. Larger studies and immunoprofiling approaches sensitive to subtle variation should be conducted to identify targetable imaging characteristics, which alongside immunotherapeutic advances, may facilitate targeted breast cancer treatment.

PMID:40770282 | DOI:10.1038/s41598-025-09972-2

Sci Data. 2025 Aug 6;12(1):1371. doi: 10.1038/s41597-025-05723-0.

ABSTRACT

Changing crop patterns are primary driver of land use change and can impact global atmospheric cycles. While existing studies have mapped the distribution of several crops in China, harvest area maps for a complete set of crops over the past decades are lacking. This study pioneered the development of a spatiotemporal dataset of harvest area maps for 16 crop types in China at a 1-km resolution from 1990 to 2020 with 5-year intervals. Prefecture-level crop statistics were allocated to grids based on synthetical crop suitability score, which is evaluated by natural and socioeconomic factors. County-level validations demonstrated the built dataset is highly consistent with statistics, especially for primary grains and oilseed. Moreover, crop harvest area at sub-pixel level can better represent gradient changes within urban-rural zones. The built crop maps revealed the harvest zones for maize, rice and soybeans in Northern China have steadily expanded since 1990. This dataset fully supports identification of spatiotemporal changes in China’s crop patterns and can serve as critical input for biogeochemical and agricultural models.

PMID:40770232 | DOI:10.1038/s41597-025-05723-0

Sci Rep. 2025 Aug 6;15(1):28698. doi: 10.1038/s41598-025-14698-2.

ABSTRACT

The adoption of Artificial Intelligence (AI) is reshaping the labor market; however, individuals’ perceptions of its impact remain inconsistent. This study investigates the presence of the Invulnerability Bias (IB), where workers perceive that AI will have a greater impact on others’ jobs than on their own, and Optimism Bias by Type of Impact (OBTI), where individuals perceive AI’s future impact on their own job as more positive than on others’. The study analyzes survey data collected from 201 participants, recruited through social media using convenience sampling. The data were analyzed using a combination of statistical and machine learning methods, including the Wilcoxon test, ordinary least squares regression, clustering, random forests, and decision trees. Results confirm a significant IB, but not OBTI; only 31.8% perceived AI’s future impact on their own job as more positive than on others’. Analysis shows that greater knowledge of AI correlates with lower IB, suggesting that familiarity with AI reduces the tendency to externalize perceived risk. Furthermore, bias levels vary across professional sectors: healthcare, law, and public administration exhibit the highest IB, while technology-related professions show lower levels. These findings highlight the need for interventions to improve workers’ awareness of AI’s potential future impact on employment.

PMID:40770226 | DOI:10.1038/s41598-025-14698-2

Sci Rep. 2025 Aug 6;15(1):28723. doi: 10.1038/s41598-025-14406-0.

ABSTRACT

Alzheimer’s disease (AD) presents a pressing global health challenge, demanding improved strategies for early detection and understanding its progression. In this study, we address this need by employing survival analysis techniques to predict transition time from Cognitive Normal (CN) to Mild Cognitive Impairment (MCI) in elderly individuals, considering the predictive value of baseline comorbidities. Leveraging data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker & Lifestyle Flagship Study of Ageing (AIBL) databases, we construct feature sets encompassing demographics, cognitive scores, and comorbidities. Various machine learning and deep learning methods for survival analysis are employed. Our top-performing model, fast random forest, achieves a concordance index of 0.84 when considering all feature modalities, with comorbidity data emerging as a significant predictor. The top features identified by the best-performing model include one demographic feature (age), seven cognitive scores (ADAS13, RAVLT learning, FAQ, ADAS11, RAVLT immediate, CDRSB, ADASQ4), and two comorbidities (Endocrine & Metabolic, Renal & Genitourinary). Age is highlighted as the most influential predictor, while cognitive scores are crucial indicators of Alzheimer’s disease. External validation against the AIBL dataset affirms the robustness of our approach. Overall, our study contributes to a deeper understanding of the role of baseline comorbidities in AD risk prediction and emphasizes the importance of incorporating comprehensive feature assessment in clinical practice for early diagnosis and personalized treatment planning.

PMID:40770222 | DOI:10.1038/s41598-025-14406-0

Sci Rep. 2025 Aug 6;15(1):28719. doi: 10.1038/s41598-025-09508-8.

ABSTRACT

With the instantaneous economic development, air quality keeps on dwindling. Some key factors for the emergence and evolution of air pollution are high-intensity pollution emissions and adverse weather circumstances. In air pollutants, Particulate Matter (PM) possessing less than 2.5Mu is considered the most severe health issue, resulting in respiratory tract illness and cardiovascular disease. Therefore, it is mandatory to predict PM 2.5 concentrations accurately to ward off the general public from the desperate influence of air pollution in advance owing to its complex nature. Aiming at the complexity of air quality prediction, a new method called Hebbian Concordance and Attention-based Long Short-Term Memory (HC-ALSTM) is proposed. The HC-ALSTM method is split into four sections. They are preprocessing using the Statistical Normalization-based Preprocessing model, feature extraction employing the Generalised Hebbian Spatio Temporal Feature extraction model, feature selection using Concordance Correlation function, and Attention-based Long Short-Term Memory for air quality prediction. First, the Statistical Normalization-based Preprocessing model is applied to the raw dataset to normalize the impact of distinct air pollutants on the bordering factor. Second, with the Generalised Hebbian Spatio Temporal Feature extraction algorithm, processed samples are applied to extract the dimensionality-reduced spatio-temporal feature. Third, with the extracted features, essential or significant features are selected using Concordance Correlation analysis that determines the impact of pollutant concentration of bordering places for predicting air quality index involving both city and state, daily and hourly. Finally, Attention-based Long Short-Term Memory is applied to the extracted and selected features to predict air quality accurately. Through evaluation and analysis using two other evaluation methods, the proposed HC-ALSTM method performs better in error and time. Our method has dramatically improved air quality prediction accuracy and overhead compared with other methods.

PMID:40770205 | DOI:10.1038/s41598-025-09508-8

Sci Rep. 2025 Aug 6;15(1):28802. doi: 10.1038/s41598-025-13041-z.

ABSTRACT

Due to the uncertainty ​​of geomechanical parameters, it is necessary to investigate the risks arising from geotechnical hazards in tunnel design from a statistical perspective. In this study, a hybrid quantitative approach incorporating uncertainty in geomechanical parameters, the finite element method (FEM), and the variation coefficient method (VCM) was employed to investigate geotechnical hazards in the Alborz tunnel. At first, by considering five statistical intervals [µ, µ + 0.5(SD), µ-0.5(SD), µ + SD, and µ-SD], different values ​​of geomechanical parameters including uniaxial compressive strength of intact rock, density, depth, Young’s modulus and tensile strength of rock mass, cohesion, friction angle, and in situ stress ratio were generated for three formations of the tunnel. Next, five numerical simulations were performed for each formation using the finite element method. The outputs of the FEM analysis, including the maximum displacement, the maximum axial force in the support systems, and the major principal stress around the tunnel, were used to calculate the three geotechnical hazards namely squeezing potential, seismic activity effects, and stress concentration around the tunnel. The results showed that the vulnerability index for squeezing potential has the highest value in each geological formation. The results of this study and the presented approach can be used as a hybrid model for investigating and predicting hazards in rock tunnels. Additionally, this approach reduces the negative impact of uncertainty in geomechanical parameters on safe and economical design in underground spaces.

PMID:40770202 | DOI:10.1038/s41598-025-13041-z

Sci Rep. 2025 Aug 6;15(1):28693. doi: 10.1038/s41598-025-10837-x.

ABSTRACT

In the current industrial scenario, permanent magnet synchronous motors are widely employed for drive based applications and many other robotics and machine tool applications. With a simple structure and high torque-to-inertia ratio, PMSM are able to be operated even in medical industry and laboratory experimentation set ups. The main limitation of PMSM is the presence of inherent coupled flux and torque which makes it very difficult to control. This paper focuses on fuzzy based PI controllers along with novel neural based controller for speed control of PMSM. A novel slime mould algorithm based Elman spiking neural network (ESNN) model hybridized with fuzzy inference proportional-integral controller is designed in this paper to regulate the speed and torque of permanent magnet synchronous motor drive. Due to the existence of randomness in the proposed soft computing controller, it is tested for its validity and suitability by performing statistical analysis and is observed to be valid to act as a controller model for PMSM drive mechanism. In this paper, this soft computing controller possess randomness during first phase of weight update and during optimal gain value determination Simulation process for the designed new soft computing controller was done in MATLAB.

PMID:40770200 | DOI:10.1038/s41598-025-10837-x

NPJ Breast Cancer. 2025 Aug 6;11(1):85. doi: 10.1038/s41523-025-00804-0.

ABSTRACT

U.S. Black women have disproportionately high breast cancer mortality, partly due to later-stage diagnoses. We examined how social drivers of health (SDOH) relate to stage at diagnosis by analyzing data from 4,995 breast cancer survivors in the Black Women’s Health Study, Carolina Breast Cancer Study, and Women’s Circle of Health Studies. SDOH were self-reported and stage was ascertained from medical records. We used polytomous logistic regression to estimate odds ratios (ORs) for diagnosis at stages III/IV or II versus stage I (referent), adjusting for age, insurance status, and income. Meta-analyzed results indicated that underutilization of screening mammography (OR = 3.21, 95% CI 1.90-5.43) and income below the federal poverty line (OR = 1.91, 95% CI 1.17-3.10) were significantly associated with later stage diagnosis (III/IV). ORs for lack of insurance and lower education were above 1.0, but not consistently statistically significant. These findings substantiate the importance of the affordability and utilization of breast cancer screening.

PMID:40770199 | DOI:10.1038/s41523-025-00804-0

Neuropsychol Rev. 2025 Aug 7. doi: 10.1007/s11065-025-09671-9. Online ahead of print.

ABSTRACT

Dynamic functional connectivity (dFC) methods could shift understandings about brain-behavior relationships. Information processing speed (IPS) may be of particular interest to dFC analyses as dFC is able to capture time-sensitive FC changes. The present systematic review aims to explore the association between IPS and dFC of resting-state functional magnetic resonance imaging (rsfMRI) data in healthy individuals. Included papers were published through July 24, 2023. Searches conducted on ProQuest and ScienceDirect used the search terms processing speed AND fMRI AND resting state AND dynamic functional connectivity OR dynamic functional network connectivity. Studies were eligible based on the following inclusion criteria: empirical research, published in English, use of a well-characterized healthy population (n > 30), use of rsfMRI, calculation of dFC, measurement of IPS, and a statistical test between dFC and IPS. Results reveal mixed findings. Five studies report no relationship between dFC and IPS, whereas eight report mixed or positive findings. We noted several trends in findings that may be driving inconsistencies. Over half of the reviewed studies used the Human Connectome Project data. Second, IPS was more likely to be related to dFC if images were acquired using an eyes open procedure with fixation on a crosshair. As all included IPS measures involved a visual component, IPS and dFC measurement might both be capturing information about visuoperceptual connections. Future work that addresses these biases and trends may illuminate the nature of the relationship between dFC and IPS.

PMID:40770165 | DOI:10.1007/s11065-025-09671-9