Tag: nevin manimala

Ann Agric Environ Med. 2025 Sep 18;32(3):383-390. doi: 10.26444/aaem/208382. Epub 2025 Aug 6.

ABSTRACT

INTRODUCTION AND OBJECTIVE: Menstrual poverty lies at the intersection of poverty, sustainability, reproductive rights, and gender inequality. The study investigates menstrual poverty among women affected by the 2023 earthquake in Turkey.

MATERIAL AND METHODS: A descriptive cross-sectional study was conducted between 24 April – 24 May 2023 with 400 women impacted by the earthquake. Data were collected via social media using a survey form. Chi-square tests, Bonferroni test, and binary logistic regression model were used for statistical analysis.

RESULTS: The mean age of participants was 27.27 ± 8.40 years; 69.5% had higher education, 57.0% lived in urban areas, and 90.5% had no chronic disease. A significant relationship was found between access to menstrual products and basic needs (clean water, toilet paper, soap, safe toilet access, and healthcare) during menstruation after the earthquake (p<0.05). A significant correlation was also observed between disruptions to the menstrual cycle and the following variables: lack of privacy, perception that lack of privacy affected menstruation, healthcare access, and difficulty obtaining menstrual products (p<0.05).

CONCLUSIONS: Most participants faced difficulties accessing menstrual products, water, hygiene supplies, privacy, and healthcare. Those living in tents or containers reported greater challenges. These barriers contributed to menstrual poverty and impacted women’s cycles. As menstrual health and hygiene are basic needs and human rights, menstrual poverty must be addressed globally.

PMID:41025184 | DOI:10.26444/aaem/208382

Ann Agric Environ Med. 2025 Sep 18;32(3):377-382. doi: 10.26444/aaem/207636. Epub 2025 Jul 8.

ABSTRACT

INTRODUCTION AND OBJECTIVE: Learning effectiveness is a key element in the educational process that determines how effectively students can assimilate, store, and apply the knowledge acquired. There are many approaches and theories in the research literature exploring the different aspects of this process. Factors influencing learning effectiveness include learning style, motivation, learning techniques, and learning environment. Learning effectiveness also depends on individual student characteristics, including socio-demographic characteristics. The aim of the study is to verify the influence of socio-demographic characteristics on the assessment of the effectiveness of medical simulation as a learning method, using the standardised EPQ tool.

MATERIAL AND METHODS: The study was conducted between 2023-2024 among 306 nursing students by means of a diagnostic survey, using the survey instrument EPQ.

RESULTS: The surveyed students rated the educational techniques best in terms of collaboration. Statistical analysis showed a weak negative correlation between age and the evaluation of active learning, expectations, and the overall evaluation of educational techniques. Statistically significant results were obtained in the correlation of place of residence with the evaluation of educational practices.

CONCLUSIONS: The study showed a general relationship of the influence of selected socio-demographic characteristics on the evaluation of educational practices in medical simulation. Despite the occurrence of a relationship, the age of the subjects did not determine the outcome of the simulation effectiveness evaluation. There is a lack of detailed research in the available literature on the influence of socio-demographic variables on the evaluation of medical simulation educational practices, which allowed the identification of a research gap (white gap).

PMID:41025183 | DOI:10.26444/aaem/207636

Phys Med. 2025 Sep 28;138:105186. doi: 10.1016/j.ejmp.2025.105186. Online ahead of print.

ABSTRACT

INTRODUCTION: Postmortem computed tomography (PMCT) is increasingly used in forensic investigations, offering a non-invasive and objective approach to estimating the postmortem interval (PMI). This study aimed to develop and externally validate radiomic models to distinguish deaths within versus beyond 24 h, using liver radiomic features from PMCT scans..

METHODS: A retrospective analysis was performed on 51 cadavers for model development and validated on 80 independent cases. In the training set, 173 PMCT scans across different PMIs were analyzed. The liver was manually segmented, and 40 radiomic features-statistical, morphological, and fractal-were extracted. Robustness to segmentation variability was assessed with autocontoured segmentations using the Intraclass Correlation Coefficient (ICC). PMI was dichotomized as ≤ 24 versus > 24 h. Univariate analyses identified predictive features, and logistic regression models were built from significant variables. Model performance was evaluated with receiver operating characteristic (ROC) curves, with sensitivity and specificity at the optimal threshold.

RESULTS: Four features were significantly associated with PMI, with liver skewness emerging as the most predictive (p = 9.13 × 10^-4) and robust (ICC = 0.75). A logistic regression model based on skewness achieved an AUC of 0.75 (95 % CI: 0.65-0.86) and 100 % specificity at the optimal threshold, reliably identifying deaths beyond 24 h. Adding a second feature did not improve performance (p = 0.54, DeLong test). External validation confirmed specificity of the skewness model (70 % at the optimal threshold).

CONCLUSION: Liver skewness extracted from PMCT shows potential as a biomarker for identifying deaths beyond 24 h, with performance confirmed on an independent cohort.

PMID:41022006 | DOI:10.1016/j.ejmp.2025.105186

Addict Behav. 2025 Sep 26;172:108504. doi: 10.1016/j.addbeh.2025.108504. Online ahead of print.

ABSTRACT

BACKGROUND: E-cigarette use has grown in popularity and is independently associated with alcohol use and mental health (anxiety/depression), but the interactions between alcohol and anxiety/depression with e-cigarette use have not been examined. We examined whether anxiety/depression would influence the association of both alcohol use frequency and heavy episodic drinking (HED) with e-cigarette use frequency, hypothesizing that alcohol use would be more strongly related to e-cigarette use among those with current anxiety/depression.

METHODS: N = 11,006 adults (55 % female; 71 % non-Hispanic White, M age = 42) completed assessments of demographics, past 30-day e-cigarette and alcohol use, and current symptoms of anxiety and depression. Regression models including past 30-day e-cigarette users only (N = 2,395) examined the moderating effects of anxiety/depression (yes/no) on the alcohol-e-cigarette frequency relationship, examining alcohol use frequency and HED separately.

RESULTS: More than one-fifth (21.7 %) of the total sample reported any past 30-day e-cigarette use. Among e-cigarette users, past 30-day alcohol use frequency was associated with e-cigarette use frequency but did not significantly differ by mental health status (IRR = 1.02, 95 % 1.01, 1.02). HED was not associated with e-cigarette use frequency, regardless of mental health status (IRR = 1.02; 95 % CI: 0.93, 1.11).

CONCLUSION: The relationship between current alcohol use and e-cigarette use frequency was not statistically different between individuals who endorsed current anxiety and/or depression vs. those who did not. Findings support the need to consider other substance use within e-cigarette smoking prevention and cessation efforts. Additional longitudinal research is needed to infer directionality and causality.

PMID:41021996 | DOI:10.1016/j.addbeh.2025.108504

Comput Methods Programs Biomed. 2025 Sep 25;273:109081. doi: 10.1016/j.cmpb.2025.109081. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Cutaneous melanoma remains the most lethal form of skin cancer. Although incurable at advanced stages, if diagnosed at an early, localized stage, the five-year survival rate is remarkably high. Recent advancements in artificial intelligence have paved the way for early skin lesion diagnosis, leveraging digital imaging processes into effective solutions. Most of these, however, use Machine Learning and Deep Learning techniques compartmentalized, without combining the produced predictions.

METHODS: This paper introduces MultiExCam, a novel multi approach and explainable architecture for skin cancer detection that integrates both machine and deep learning. Three heterogeneous data from three different techniques are used: dermatoscopic images, features extracted from deep learning techniques, and hand-crafted statistical features. A convolutional neural network is used for both deep feature extraction and initial classification, with the extracted features being combined with handcrafted ones to train four additional machine learning models. An advanced ensemble model, implemented as a Feed Forward Neural Network with gating and attention mechanism, produces the final classification. To enhance interpretability, the architecture employs GradCAM for visualizing critical regions in input images and SHAP for evaluating the contribution of individual features to predictions.

RESULTS: MultiExCam demonstrates robust performance across three diverse datasets (HAM10000, ISIC, MED-NODE), achieving AUC scores of 97%, 91%, and 98% respectively, with corresponding F1-scores of 92%, 87%, and 94%. Comprehensive ablation studies validate the importance of the preprocessing pipeline and ensemble integration, with the hybrid approach consistently outperforming baseline deep learning models by 1-3 percentage points. Unlike existing compartmentalized hybrid solutions, MultiExCam’s adaptive ensemble architecture learns personalized decision strategies for individual lesions, mimicking expert dermatological workflows that integrate multiple evidence sources. The explainability analysis reveals clinically meaningful activation patterns corresponding to established diagnostic criteria including asymmetry, border irregularity, and color variation.

CONCLUSION: MultiExCam establishes a new paradigm for AI-assisted dermatological diagnosis by demonstrating that true hybrid integration of deep learning and machine learning, combined with comprehensive explainability techniques, can achieve both superior diagnostic performance and clinical interpretability. The architecture’s ability to provide accurate classifications while explaining prediction rationale addresses critical requirements for medical AI adoption, offering a promising foundation for clinical decision support systems in melanoma detection.

PMID:41021995 | DOI:10.1016/j.cmpb.2025.109081

JMIR Public Health Surveill. 2025 Sep 29;11:e69655. doi: 10.2196/69655.

ABSTRACT

BACKGROUND: Effective disease surveillance is essential for understanding pathogens’ epidemiology, detecting outbreaks, and enabling timely public health responses. In the United Kingdom, large-scale studies, such as the Office for National Statistics COVID-19 Infection Survey (CIS), have monitored SARS-CoV-2 transmission but required significant resources, making them challenging to sustain when pandemic-specific funding ends and also in resource-limited settings. In contrast, the Virus Watch study, at lower cost, relied on self-reported and linked national testing data as well as symptomatic testing, while Severe Acute Respiratory Infections Watch (SARI) leveraged hospital data for cost-effective surveillance.

OBJECTIVE: This study aimed to evaluate the effectiveness of Virus Watch as a surveillance system in monitoring COVID-19 positivity, incidence, and hospitalization rates in England and Wales, using data from the CIS and SARI as benchmarks for comparison, while considering the key differences in the study designs, including recruitment strategies, incentives, and testing criteria.

METHODS: We used the Virus Watch prospective community cohort study to estimate COVID-19 positivity, incidence, and hospitalization rates in England and Wales from June 2020 to March 2023. Rate estimates were compared with CIS modeled positivity and incidence rates, and with SARI COVID-19 hospitalization rates. Global synchrony between datasets was measured using overall Spearman ⍴ and local synchrony using 9-week rolling Spearman ⍴. For England, comparisons with CIS estimates used Virus Watch rates calculated with and without linked national testing data. Positivity rates were also assessed overall and separately before and after the end of free national testing.

RESULTS: A total of 58,628 participants were recruited into the Virus Watch study, of whom 52,526 (89.6%) were resident in England and 1532 (2.6%) in Wales; region was missing for the remainder. Virus Watch-estimated COVID-19 positivity and incidence rates in England, calculated with and without linked testing data, showed strong global synchrony with CIS estimates (positivity ⍴: 0.91 and 0.90; both P<.001 and incidence ⍴: 0.92 and 0.90; both P<.001) and strong local synchrony (positivity ⍴: median 0.75, IQR 0.53-0.85 and median 0.67, IQR 0.47-0.83, and incidence ⍴: median 0.76, IQR 0.49-0.88 and median 0.66, IQR 0.45-0.82), despite having lower absolute values. Global and local synchrony of positivity rates were similar for periods before and after the end of free national testing, although the difference between Virus Watch and CIS estimates was greater post-free testing. COVID-19 hospitalization rates were also lower and less synchronized with SARI estimates. In Wales, Virus Watch estimates exhibited greater variability (positivity ρ: 0.75, P<.001; incidence rate ρ: 0.85, P<.001) and lower local synchrony (positivity ρ: median 0.61, IQR 0.34-0.74, and incidence ρ: median 0.52, IQR 0.38-0.71) compared to England.

CONCLUSIONS: Our results highlight the effectiveness of the Virus Watch approach in providing accurate estimates of COVID-19 positivity and incidence rates, even in the absence of national surveillance systems. This low-cost method can be adapted to various settings, particularly low-resource ones, to strengthen public health surveillance and inform timely interventions.

PMID:41021969 | DOI:10.2196/69655

IEEE Trans Biomed Eng. 2025 Sep 29;PP. doi: 10.1109/TBME.2025.3615514. Online ahead of print.

ABSTRACT

Scaling neural recording systems to thousands of channels creates extreme bandwidth demands, posing a challenge for resource-constrained, implantable devices. This work introduces an adaptive, multi-stage compression framework for high-bandwidth neural interfaces. The system combines a Wired-OR analog-to-digital compressive readout with a digital core that adaptively requantizes, selectively samples, and encodes the neural signals. Although prior work suggests that action potential recordings can be re-quantized to approximately the signal-to-noise (SNR) number of bits without significantly degrading decoding performance, our results show that the required resolution can often be reduced even further. By matching the number of quantization levels to the electrode’s maximum SNR ($bm {lceil log _{2} rm{SNR} rceil }$ number of bits), we retain waveform fidelity while eliminating unnecessary precision that primarily captures noise. Recorded spike samples are selected using a mutual information-based criterion to preserve both spatial and temporal discriminative waveform features. A static entropy coder completes the pipeline with low computation overhead compression optimized for neural signal statistics. Evaluated on 512-channel macaque retina ex vivo data, the system preserves 90% of spikes while achieving a 1098× total compression over baseline.

PMID:41021967 | DOI:10.1109/TBME.2025.3615514

IEEE J Biomed Health Inform. 2025 Sep 29;PP. doi: 10.1109/JBHI.2025.3614093. Online ahead of print.

ABSTRACT

Visceral adipose tissue (VAT) is a key marker of both metabolic health and habitual physical activity (PA). Excess VAT is highly correlated with type 2 diabetes and insulin resistance. The mechanistic basis for this pathophysiology relates to overloading the liver with fatty acids. VAT is also a highly labile fat depot, with increased turnover stimulated by catecholamines during exercise. VAT can be measured with sophisticated imaging technologies, but can also be inferred directly from PA. We tested this relationship using National Health and Nutrition Examination Survey (NHANES) data from 2011-2014, for individuals aged 20-60 years with 7 days of accelerometry data (n=2,456 men; 2,427 women) [1]. Two approaches were used for estimating VAT from activity. The first used engineered features based on movements during gait and sleep, and then ridge regression to map summary statistics of these features into a VAT estimate. The second approach used deep neural networks trained on 24 hours of continuous accelerometry. A foundation model first mapped each 10 s frame into a high-dimensional feature vector. A transformer model then mapped each day’s feature vector time series into a VAT estimate, which were averaged over multiple days. For both approaches, the most accurate estimates were obtained with the addition of covariate information about subject demographics and body measurements. The best performance was obtained by combining the two approaches, resulting in VAT estimates with correlations of r=0.86. These findings demonstrate a strong relationship between PA and VAT and, by extension, between PA and metabolic health risks.

PMID:41021956 | DOI:10.1109/JBHI.2025.3614093

IEEE Trans Neural Syst Rehabil Eng. 2025 Sep 29;PP. doi: 10.1109/TNSRE.2025.3615417. Online ahead of print.

ABSTRACT

Chronic low back pain (LBP) significantly impairs daily functional movements. Persistent pain may trigger alterations in neuromuscular control, particularly muscle coordination. However, muscle synergy patterns during specific functional tasks in patients with LBP remain unclear. We recruited 36 participants, including 18 patients with chronic LBP (the LBP group) and 18 healthy participants (the control group). Surface electromyography signals were recorded from ten trunk and lower-limb muscles during three common functional tasks: sit-to-stand, trunk flexion, and lifting. Muscle synergies were extracted via non-negative matrix factorization. Cosine similarity analysis and statistical parametric mapping were applied to evaluate spatial (motor modules) and temporal (motor primitives) differences between groups. Compared to the control group, participants with chronic LBP exhibited a reduced number of muscle synergies during sit-to-stand, indicating adaptive reorganization of motor modules. Although spatial muscle synergy structures were largely conserved between groups, significant temporal differences emerged in trunk flexion, particularly during eccentric phases. Patients with LBP showed prolonged and temporally shifted activation of spinal extensors and hip-pelvic stabilizers, suggesting compensatory mechanisms to mitigate spinal loading. Muscle contribution patterns during lifting tasks also differed significantly between groups, despite similar temporal activation. In conclusion, patients with chronic LBP demonstrate distinct muscle synergy adaptations characterized by reduced complexity, altered timing during eccentric trunk movements, and modified lower-limb recruitment strategies. Trunk flexion emerged as a particularly sensitive task for identifying neuromuscular deficits in LBP. These findings provide targeted insights for clinical rehabilitation, emphasizing eccentric trunk control, motor control timing, and lower-limb muscle retraining.

PMID:41021946 | DOI:10.1109/TNSRE.2025.3615417

Med Sci Sports Exerc. 2025 Sep 25. doi: 10.1249/MSS.0000000000003856. Online ahead of print.

ABSTRACT

OBJECTIVES: To compare brain MRI outcomes between children who play American football vs non-contact sport controls testing the hypotheses that history (primary) and duration (secondary) of football participation would be associated with differences in cortical thickness, subcortical volume, resting state functional connectivity, and white matter diffusivity.

METHODS: This secondary analysis of cross-sectional baseline data from the Adolescent Brain Cognitive Development (ABCD) Study compared brain MRI outcomes between 9-10 year-old children who play American football (n=1194) vs. non-contact sport controls (n=807). Outcomes included 74 bilateral cortical thickness regions; 10 gray matter subcortical volumes, with a priori focus on the hippocampi; resting-state functional connectivity (169 network-network correlations and 247 network-region correlations across 13 resting-state functional networks and 19 regions); and 21 diffusion tensor measures.

RESULTS: Football participation was associated with global effects on cortical thickness (p=0.017), network-to-network resting state connectivity (p=0.010), and fiber tract volume (FDR-adjusted p=0.015) in primary analysis, but the only significant post-hoc finding after FDR correction was smaller cortical thickness adjacent to the left anterior transverse collateral sulcus in the football group (Cohen D=-0.258, FDR-adjusted p=0.017). There were no significant duration of football play effects in secondary analyses (all p>0.05). Targeted analysis of hippocampal volumes yielded no significant football or duration of play results (both p>0.05), but suggested a potential trend of lower hippocampal volumes with increasing duration of play.

CONCLUSIONS: At ages 9-10, participation in American football was associated with minimal differences across a large array of structural, functional, and diffusion tensor MRI outcomes. While the clinical implications of these cross-sectional results are unknown, they merit additional investigation and can contribute to the ongoing discussion surrounding contact sport participation in children.

PMID:41021925 | DOI:10.1249/MSS.0000000000003856