Nevin Manimala

Sleep Breath. 2025 Jun 24;29(4):225. doi: 10.1007/s11325-025-03398-w.

ABSTRACT

PURPOSE: Vitamin D is essential not only for skeletal health but also for regulating sleep. Recent studies suggest a link between vitamin D deficiency and poor sleep quality, including lower sleep efficiency and shorter sleep duration. This study aimed to investigate the relationship between sleep quality and serum vitamin D levels in healthy elderly individuals.

METHODS: This study examined the association between serum vitamin D levels and sleep quality in individuals aged 60 years and above residing in Taipei’s Health & Culture Village and Active Aging Center. A total of 465 participants were enrolled, with those exhibiting specific organ abnormalities or dysfunctions excluded. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), and vitamin D levels were measured using enzyme-linked immunosorbent assay (ELISA). The relationships between vitamin D levels and PSQI scores were analyzed, with a focus on the daytime dysfunction component of the PSQI.

RESULTS: Among the seven domains of the PSQI, the daytime dysfunction domain had the strongest statistical association with serum vitamin D levels (p = 0.044). Participants without daytime dysfunction had significantly higher vitamin D levels than did those with daytime dysfunction (p = 0.04). No significant associations were detected between vitamin D levels and other factors, such as sex or age.

CONCLUSIONS: Higher serum vitamin D levels are associated with better sleep quality in elderly individuals, particularly in those without daytime dysfunction. This finding highlights the potential importance of maintaining adequate vitamin D levels to promote healthy sleep patterns in older adults.

PMID:40555884 | DOI:10.1007/s11325-025-03398-w

Am J Cardiovasc Drugs. 2025 Jun 24. doi: 10.1007/s40256-025-00739-8. Online ahead of print.

ABSTRACT

INTRODUCTION: Whether ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI) carry distinct prognoses after discharge remains a matter of debate. This study aimed to compare 1-year clinical outcomes between patients with STEMI and NSTEMI in a large, real-world cohort.

METHODS: Among 23,270 patients with acute coronary syndrome enrolled in the international PRAISE registry between 2003 and 2019, we included 21,789 patients with a diagnosis of either STEMI or NSTEMI. Clinical characteristics, discharge medications, and outcomes at 1 year were analyzed. The primary outcomes were all-cause mortality, re-infarction, and major bleeding. Multivariable logistic regression and propensity score matching were used to adjust for confounding. Subgroup and interaction analyses were also performed.

RESULTS: The cohort included 12,365 patients with STEMI and 9424 patients with NSTEMI. At baseline, patients with NSTEMI had more comorbidities, cardiovascular risk factors (except diabetes), and prior revascularization. Patients with STEMI were more frequently treated with statins, beta-blockers, and renin-angiotensin-aldosterone system inhibitors at discharge. At 1-year follow-up, overall outcomes were comparable between groups. Nonfatal reinfarction occurred more frequently in patients with NSTEMI (3.4% versus 2.8%, p = 0.022), but this association was not significant after adjustment (odds ratio [OR] 0.90, 95% confidence interval [CI] 0.65-1.24, p = 0.519). Results from propensity score-matched analyses confirmed the absence of prognostic differences. Subgroup analyses revealed significant interactions for diabetes mellitus and completeness of revascularization.

CONCLUSIONS: After accounting for clinical and therapeutic variables, 1-year outcomes were largely similar in patients with STEMI and NSTEMI. Differences in reinfarction risk appear to be driven by baseline characteristics and treatment patterns, rather than infarct type itself.

PMID:40555879 | DOI:10.1007/s40256-025-00739-8

Ann Biomed Eng. 2025 Jun 24. doi: 10.1007/s10439-025-03781-4. Online ahead of print.

ABSTRACT

Functional tricuspid regurgitation (FTR) is closely associated with right ventricular (RV) dysfunction and pulmonary hypertension (PH), both of which contribute to increased morbidity and mortality in patients undergoing tricuspid valve repair or replacement. The biomechanical interplay between these factors remains complex, with conflicting evidence on the effects of edge-to-edge repair (TEER) on RV morphology and function. This study aimed to assess the acute impact of increased pulmonary pressure and RV dilation on TEER performance using an ex vivo pulsatile flow mock loop. A custom-designed clip, replicating state-of-the-art TEER devices, was tested on porcine heart samples under simulated FTR conditions with varying degrees of RV dilation and PH.Results demonstrated that the clip significantly improved valve coaptation, increasing transvalvular systolic pressure and reducing regurgitant flow. However, elevated PH and severe RV dilation compromised its effectiveness, leading to increased regurgitation and a higher risk of pathology recurrence. Statistical analysis identified PH as the primary driver of hemodynamic deterioration, whereas RV dilation predominantly influenced annular morphology. These findings suggest that while TEER provides initial hemodynamic benefits, its efficacy may be limited in advanced FTR cases with progressive RV dysfunction and PH. Further research is needed to evaluate long-term outcomes. Nonetheless, this ex vivo approach allowed for the isolation of key biomechanical mechanisms, offering valuable insights into the structural and functional relationships underlying disease progression.

PMID:40555876 | DOI:10.1007/s10439-025-03781-4

Eur Spine J. 2025 Jun 25. doi: 10.1007/s00586-025-09029-y. Online ahead of print.

ABSTRACT

Spinal tumors represent 15% of all central nervous system malignancies, with intramedullary spinal cord tumors (IMSCTs) being rare. Predominantly ependymomas and astrocytomas, IMSCTs often present late, leading to significant morbidity and mortality. Surgical excision is key but challenging due to the tumors’ complex, invasive nature. Treatment involves a multidisciplinary approach, considering tumor type and patient condition, ranging from subtotal to gross total resection, possibly with adjuvant therapy. This study uses machine learning on National Cancer Database data to predict postoperative outcomes, aiming to develop a risk calculator for clinicians to assess mortality and extended hospital stay risks post-surgery.

OBJECTIVE: This study investigates healthcare outcomes in patients undergoing surgical resection for intradural intramedullary spinal cord tumors (IMSCTs), employing the National Cancer Data Base (NCDB) to identify key variables. We aimed to develop supervised machine learning-based risk calculators to predict high-risk patients for mortality and extended length of stay (eLOS), stratifying IMSCTs by histology to enhance understanding and guide intervention strategies for adverse outcomes.

METHODS: Patients with surgically-treated IMSCTs (2004-2017) was conducted using the NCDB. We extracted demographic and comorbidity data, employing descriptive statistics and supervised machine learning algorithms to predict mortality and eLOS.

RESULTS: The study encompassed 7,243 surgically treated IMSCT cases, including 612 astrocytomas (8.5%), 6,041 ependymomas (83.4%), and 590 hemangioblastomas (8.1%). Mortality and eLOS rates were observed at 10.2% and 27.1%, respectively. Over 12 years (2004-2016), significant management shifts were noted for these spinal tumor types. The predictive models achieved AUCs of 0.721 for mortality and 0.586 for eLOS. Key predictive features for mortality included age, diagnosis year, behavior, histology, radiation, insurance status, patient-hospital distance, tumor grade and size, length of stay, subtotal resection (STR) to gross total resection (GTR), and sex. For eLOS, additional predictors were diagnosis-surgery interval, Charlson/Deyo score, and surgical approach. Web-based tools for both outcomes have been deployed: https://imsct-elos-predict.herokuapp.com/ ; https://imsct-risk-calcualor.herokuapp.com/ .

CONCLUSION: Our nationwide analysis underscores the evolution in IMSCT management and demonstrates the efficacy of machine learning in predicting mortality and eLOS, providing valuable insights for improved patient care.

PMID:40555868 | DOI:10.1007/s00586-025-09029-y

Int J Orthop Trauma Nurs. 2025 Jun 16;58:101208. doi: 10.1016/j.ijotn.2025.101208. Online ahead of print.

ABSTRACT

AIM: The research was conducted to identify the discharge education needs of patients undergoing day surgery for hand surgery.

METHOD: This descriptive and cross-sectional study included patients who underwent day surgery for hand conditions, both emergency and elective, over the course of a year at public hospitals under the Istanbul Provincial Health Directorate, specifically in the Department of Hand Surgery on the European side of Istanbul. The total patient count was 1195. The study sample consisted of 100 patients, determined using the G Power analysis method with an effect size of 2.25, a significance level (α) of 0.05, and a power of 90 %. Data were collected using a “Patient Identification Form” and “The Patient Learning Needs Scale,” both developed by the researcher.

RESULTS: The average age of the patients participating in the study was 41.59 ± 15.53, with the majority being male (55.39 %), married (62.31 %), unemployed (53.85 %), and without chronic diseases (75.38 %). The average score of the “Patient Learning Needs Scale” for day surgery hand patients was 156.461 ± 12.133, with the highest average scores in the sub-dimensions “Treatment and Complications” being 32.62 ± 3.75. A significant relationship was found between age and the “Treatment and Complications” sub-dimension of the PLNS, with a correlation of 0.103 (r = 0.103; p < 0.05). It was determined that male patients had greater learning needs regarding skin care (p < 0.05), and statistically significant differences were found among the educational levels in the sub-dimensions of “Activities of Daily Living” “Community and Follow-up,” and “Skin Care” of the PLNS (p < 0.05). The educational needs of employed patients were higher in all sub-dimensions except “Treatment and Complications” and the total score of the scale, with a statistically significant difference found in the “Situation-Related Emotions” sub-dimension. Patients who applied to the emergency department had a higher average total score on the PLNS.

CONCLUSION: It has been determined that patients undergoing day surgery for hand surgery have educational needs regarding treatment and complications, medications, skin care, quality of life, and home care during the discharge process. It is recommended that discharge education programs be increased to consider the needs of the patients.

PMID:40554831 | DOI:10.1016/j.ijotn.2025.101208

Comput Methods Programs Biomed. 2025 Jun 18;269:108898. doi: 10.1016/j.cmpb.2025.108898. Online ahead of print.

ABSTRACT

BACKGROUNDS AND OBJECTIVES: Cardiac arrhythmias, characterized by irregular heartbeats, are difficult to diagnose in real-world scenarios. Machine learning has advanced arrhythmia detection; however, the optimal number of heartbeats for precise classification remains understudied. This study addresses this using machine learning while assessing the performance of arrhythmia detection across inter-patient and intra-patient conditions. Furthermore, the performance-resource trade-offs are evaluated for practical deployment in mobile health (mHealth) applications.

METHODS: Beat-wise segmentation and resampling techniques were utilized for preprocessing electrocardiography (ECG) signals to ensure consistent input lengths. A 1-D convolutional neural network was used to classify the eight multi-labeled arrhythmias. The dataset comprised real-world ECG recordings from the HiCardi wireless device alongside data from the MIT-BIH Arrhythmia database. Model performance was assessed through fivefold cross-validation under both inter-patient and intra-patient conditions.

RESULTS: The proposed model demonstrated peak accuracy at four beats under inter-patient conditions, with minimal improvements beyond this point. This configuration achieved a balance between performance (94.82% accuracy) and resource consumption (training time: 72.27 s per epoch; prediction time: 155 μs per segment). Real-world simulations validated the feasibility of real-time arrhythmia detection for approximately 5000 patients.

CONCLUSION: Utilizing four heartbeats as the input size for arrhythmia classification results in a trade-off between accuracy and computational efficiency. This discovery has significant implications for real-time wearable ECG devices, where both performance and resource constraints are crucial considerations. This insight is expected to serve as a valuable reference for enhancing the design and implementation of arrhythmia detection systems for scalable and efficient mHealth applications.

PMID:40554829 | DOI:10.1016/j.cmpb.2025.108898

Comput Biol Chem. 2025 Jun 16;119:108550. doi: 10.1016/j.compbiolchem.2025.108550. Online ahead of print.

ABSTRACT

BACKGROUND: In contemporary context, epileptic seizure stands as the most prevalent neurological disorder arising from the sudden atypical release of brain neurons and after effect of stress. The electroencephalogram (EEG) has been widely employed for epilepsy detection. Noteworthy are the Deep Learning methods and signal processing techniques utilized for seizures classification and detection. Through advancements in Deep Learning within the biomedical domain, several methodologies have been applied to identify and forecast seizure occurrences based on EEG data collected from individuals with epilepsy, typically confined from temporary in medical screening with standard scalp-EEG or intra-cerebral electrodes.

PURPOSE: This work aims to generate a mechanism for seizure recognition from EEG signals using a classification technique based on Deep Learning. The initial phase involves pre-processing, where denoising of input EEG signals is performed by employing the Short-Time Fourier Transform (STFT). Consequently, time-domain, spectral, and statistical features were extracted from pre-processed signals.

METHODS: Then, feature selection is performed utilizing Fractional Adadelta Chameleon Swarm Algorithm (FrAdadelta-CSA), a method that integrates the notion of fractional calculus into Adadelta Chameleon Swarm Algorithm (Adadelta-CSA). Finally, seizure prediction is conducted based on the selected features using SpikeGoogle-DenseNet, a hybrid model of SpikeGoogle and DenseNet.

RESULTS AND CONCLUSION: Experimental outcomes reveal that the proposed method achieved an accuracy of 96.2 %, sensitivity of 97.3 %, and specificity of 94.5 %.

PMID:40554820 | DOI:10.1016/j.compbiolchem.2025.108550

Res Vet Sci. 2025 Jun 19;193:105777. doi: 10.1016/j.rvsc.2025.105777. Online ahead of print.

ABSTRACT

This study investigated the presence of δ bovine papillomaviruses (BPV-1, BPV-2, BPV-13, BPV-14) in equine skin lesions from Southern Italy, focusing on equine sarcoids and their histopathological correlations. 63 equine skin samples were analysed using PCR and sequencing for BPV detection, and their histopathological features were assessed. BPV DNA was detected in 69.84% of the samples, with BPV-1 and BPV-2 being the most prevalent genotype, followed by BPV-13, while BPV-14 was not detected. BPV DNA was also found in non-sarcoid tumours and non-neoplastic conditions. Histopathological analysis revealed in 41 samples typical sarcoid features: fibroblastic atypia and extracellular matrix deposition. Despite no statistical correlation was found between BPV genotypes and histopathological features, BPV-1 infection was associated with more severe fibroblastic atypia and abundant extracellular matrix. This study provides insights into the prevalence and potential pathogenic roles of different BPV genotypes in equine sarcoids and other skin lesions, underscoring the critical need for further research to develop targeted therapies.

PMID:40554817 | DOI:10.1016/j.rvsc.2025.105777

JMIR Form Res. 2025 Jun 24;9:e70853. doi: 10.2196/70853.

ABSTRACT

BACKGROUND: The common phrase “representation matters” asserts that media has a measurable and important impact on civic society’s perception of self and others. The representation of health in media, in particular, may reflect and perpetuate a society’s disease burden.

OBJECTIVE: In this study, for the top 10 major causes of death in the United States, we aimed to examine how cinematic representation overall and by-gender mortality diverges from reality.

METHODS: Using crowd-sourced data on over 68,000 film deaths from Cinemorgue Wiki, we employ natural language processing techniques to analyze shifts in representation of deaths in movies versus the 2021 National Vital Statistics Survey top 10 mortality causes. We parsed, stemmed, and classified each film death database entry, and then categorized film deaths by gender using a specifically trained gender text classifier.

RESULTS: Overall, movies strongly overrepresent suicide and, to a lesser degree, accidents. In terms of gender, movies overrepresent men and underrepresent women for nearly every major mortality cause, including heart disease and cerebrovascular disease (chi-square test, P<.001); 73.6% (477/648) of film deaths from heart disease were men (vs 384,866/695,547, 55.4% in real life) and 69.4% (50/72) of film deaths from cerebrovascular disease were men (vs 70,852/162,890, 43.5% in real life). The 2 exceptions for which women were overrepresented are suicide and accidents (chi-square test, P<.001), with 39.7% (945/2382) deaths from suicide in film being women (vs 9825/48,183, 20.4% in real life) and 38.8% (485/1250) deaths from accidents in film being women (vs 75,333/225,935, 33.5% in real life).

CONCLUSIONS: We discuss the implications of under- and overrepresenting causes of death overall and by gender, as well as areas of future research.

PMID:40554798 | DOI:10.2196/70853

JMIR Res Protoc. 2025 Jun 24;14:e64405. doi: 10.2196/64405.

ABSTRACT

BACKGROUND: Black and Latino men are at increased risk for poor diabetes health outcomes but are underrepresented in lifestyle interventions for weight loss and diabetes prevention. Although relatively few men participate in the National Diabetes Prevention Program (NDPP), it remains the most widely available evidence-based approach to type 2 diabetes prevention in the United States. Thus, an NDPP tailored to Black and Latino men has the potential to address prior limitations of NDPP implementation and reduce gender, racial, and ethnic diabetes disparities. It also provides an opportunity to define a population for targeted outreach and evaluate the reach of our recruitment methods and interventions.

OBJECTIVE: We tailored the US Centers for Disease Control and Prevention Prevent T2 curriculum for the NDPP for Black and Latino men, called Power-Up, and will evaluate its effects in comparison to standard mixed-gender NDPP groups via virtual delivery. The primary aim of the project is to assess the effect of Power-Up versus NDPP on weight loss among men with prediabetes. The secondary aim is to compare the engagement and retention of men with prediabetes in Power-Up versus NDPP. We will also examine the reach of our recruitment methods and engagement in our screening, consenting, and assessment procedures prior to the point of randomization. We hypothesized that men randomized to Power-Up would achieve greater percent weight loss from baseline at 16 weeks (end of Core sessions) and 1 year (end of Maintenance sessions) than men randomized to standard, mixed-gender NDPP. Power-Up is also expected to have better engagement and retention.

METHODS: Using the electronic health record (EHR) systems of a large academic medical center and a network of small to medium independent primary care practices throughout New York City, we identified Black and Latino men who met eligibility criteria for NDPP and enrolled them in a randomized controlled trial in which they were assigned 1:1 to receive Power-Up or the standard, mixed-gender NDPP over 1 year via online videoconferencing. Coaches delivering these interventions were trained according to the standards for the NDPP. Power-Up will be delivered by men coaches. Weight will be collected with home-based electronic scales for primary outcome analyses. Engagement will be assessed by session attendance logs.

RESULTS: We identified 11,052 men for outreach based on EHR data, successfully screened 26% of them, consented and enrolled 22% of these, and randomly assigned 48% of consented participants. Primary and secondary outcome analyses will be assessed among randomized men.

CONCLUSIONS: This study highlights the effort required to reach and engage Black and Latino men for virtually delivered diabetes prevention programs. Forthcoming trial results for weight loss and engagement will further inform efforts to address disparities in diabetes prevention through tailored programming for Black and Latino men.

TRIAL REGISTRATION: ClinicalTrials.gov NCT04104243; https://clinicaltrials.gov/study/NCT04104243.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64405.

PMID:40554781 | DOI:10.2196/64405