Nevin Manimala

Comput Biol Med. 2025 Jun 24;195:110577. doi: 10.1016/j.compbiomed.2025.110577. Online ahead of print.

ABSTRACT

BACKGROUND: Precise histopathological assessment of pulmonary lesions in animal models is fundamental to evaluating COVID-19 interventions. The multifocal, heterogeneous distribution of SARS-CoV-2-induced pathology in rhesus macaques presents a critical challenge: balancing comprehensive evaluation against resource efficiency. No statistically-validated sampling optimization exists for this widely-used model. We hypothesized that lobe-specific, statistically-validated sampling thresholds could maintain assessment accuracy while significantly reducing analytical burden.

METHODS: We developed a semi-quantitative scoring system targeting interstitial pneumonia-the predominant histopathological feature in SARS-CoV-2-infected rhesus macaques (n = 12). Two ACVP board-certified pathologists independently evaluated 710-1634 high-power fields (40 × magnification) per animal across seven lung lobes, achieving substantial inter-rater reliability (Cohen’s κ = 0.74). To determine minimum sampling requirements maintaining statistical equivalence with comprehensive assessment, we employed bootstrapping simulation (10,000 iterations) combined with Two One-Sided Tests (TOST) equivalence analysis (bounds: ±0.25 pathology points).

RESULTS: Optimal sampling percentages exhibited significant lobe-specific variability: left caudal (25 %, p = 0.047), right caudal (30 %, p = 0.038), left/right proximal, (50 %, p = 0.044/p = 0.043), right accessory (50 %, p = 0.172), right middle (60 %, p = 0.049), and left middle (75 %, p = 0.084). Power analysis demonstrated robust detection capability (range: 0.45-0.72) at α = 0.05. These optimized parameters reduce required field assessments by 25-75 % while maintaining statistical equivalence.

CONCLUSION: This first anatomically-stratified, statistically-validated methodology significantly enhances histopathological assessment efficiency in the rhesus macaque COVID-19 model. By establishing lobe-specific minimum sampling thresholds that preserve statistical equivalence, our approach optimizes resource utilization while maintaining sensitivity to detect intervention effects, potentially accelerates preclinical therapeutic evaluations.

PMID:40561578 | DOI:10.1016/j.compbiomed.2025.110577

Comput Biol Med. 2025 Jun 24;195:110623. doi: 10.1016/j.compbiomed.2025.110623. Online ahead of print.

ABSTRACT

The PhysioNet/Computing in Cardiology (CinC) Challenge 2016 dataset has driven significant advancements in automated heart sound analysis using machine learning (ML) and deep learning (DL). However, these efforts are constrained by the dataset’s limited size and severe class imbalance, particularly the underrepresentation of coronary artery disease (CAD) cases. This study addresses these limitations by employing generative adversarial networks (GANs) to synthesize realistic CAD-like heart sound segments, augmenting existing datasets to improve classification performance. A Progressive Wasserstein GAN architecture was implemented to generate high-quality audio segments that accurately capture CAD heart sounds’ spectral and temporal characteristics. The quality of synthetic audio was assessed using the Fréchet Audio Distance (FAD), achieving scores of 1.43 and 2.23 when compared to reference CAD and healthy samples, respectively. Novel post-processing steps, including bandpass filtering, further enhanced the fidelity of the synthetic samples. By augmenting the imbalanced heart sound dataset with these samples, we observed substantial improvements in the performance of five classification models. The GAN-augmented training set outperformed traditional augmentation and cost-sensitive learning methods, demonstrating superior sensitivity, specificity, and precision. This study highlights the potential of GAN-based data augmentation to address critical challenges in medical audio datasets. It offers a scalable and cost-effective solution for improving the generalizability and robustness of heart sound classification models, paving the way for enhanced diagnostic tools in biomedical signal processing.

PMID:40561577 | DOI:10.1016/j.compbiomed.2025.110623

Comput Biol Med. 2025 Jun 24;195:110585. doi: 10.1016/j.compbiomed.2025.110585. Online ahead of print.

ABSTRACT

This paper explores the finite-time stable behavior of interacting genetic regulatory networks (GRNs) incorporating spatial diffusion terms under Dirichlet boundary conditions. We propose a coupled model of interacting GRNs and a novel Lyapunov-Krasovskii functional to analyze the dynamic evolution of interactions between the two systems. The lower bound of the time delay can be either zero or non-zero. By employing a delay partitioning approach, we derive less conservative stability criteria for the interacting systems. Finally, numerical simulations are conducted, and the trajectories of mRNA and protein concentrations are provided to validate the accuracy of the proposed criteria.

PMID:40561573 | DOI:10.1016/j.compbiomed.2025.110585

Vaccine. 2025 Jun 24;61:127408. doi: 10.1016/j.vaccine.2025.127408. Online ahead of print.

ABSTRACT

BACKGROUND: People without effective immunization are vulnerable to infection with hepatitis B virus (HBV). At present, there is no appropriate hepatitis B vaccination strategy for HBV-susceptible adults. We aim to assess the long-term effect of neonatal hepatitis B immunization and HBV markers among college students, so as to explore hepatitis B vaccination strategies suitable for high-risk group.

METHODS: The enrolled freshmen from four universities were initially tested for hepatitis B screening using colloidal gold test strips. Subjects with positive hepatitis B surface antigen (HBsAg) or negative hepatitis B surface antibody (anti-HBs) were further confirmed using Abbott reagents. HBsAg and anti-HBs double negative individuals were administered hepatitis B vaccination.

RESULTS: Using Abbott reagents, we confirmed that among 3242 enrolled freshmen, 1604 (49.5 %) were negative for both HBsAg and anti-HBs, and 27 (0.8 %) were HBsAg-positive. Among the double negative freshmen, 1263 received hepatitis B vaccination. After the first and second dose of hepatitis B vaccine, the protective anti-HBs seroconversion rates reached 91.4 % and 98.5 %, respectively. Only one (0.1 %) freshman was still negative for anti-HBs after the third dose of hepatitis B vaccine. In addition, 96.3 % (104/108) of the fresmen who failed to achieve protective anti-HBs seroconversion after the first dose of hepatitis B vaccine had a baseline anti-HBs level < 2 mIU/mL.

CONCLUSION: The HBsAg prevalence among college students has been significantly reduced after the integration of hepatitis B vaccine into Expanded Program on Immunization, but the rate of seroprotective anti-HBs among these students remains low. Hepatitis B vaccination or booster dose is advised for a high-risk group who have negative anti-HBs, and two doses of hepatitis B vaccine are advised for those with anti-HBs < 2 mIU/mL.

PMID:40561569 | DOI:10.1016/j.vaccine.2025.127408

Nurse Educ Pract. 2025 Jun 20;86:104436. doi: 10.1016/j.nepr.2025.104436. Online ahead of print.

ABSTRACT

BACKGROUND: Accidental injuries, especially thoracic trauma, are a leading cause of death worldwide. Nurses need systematic training to improve trauma care competency. Traditional approaches limit instructor involvement and learner participation. Integrating online simulation with decision-branch may enhance clinical reasoning and improve learning outcomes.

OBJECTIVE: This study aimed to evaluate the effectiveness of an online simulation-based learning approach with decision-branching videos (Online SimuBranch) in enhancing nurses’ knowledge of thoracic trauma care and clinical reasoning ability.

DESIGN: Randomized controlled trial with two-group repeated measures design was used.

METHODS: A convenience sample of 95 nurses from a regional hospital in Taiwan was randomly assigned to either experimental (n = 49). or control group (n = 46). The experimental group received a thoracic trauma care course using Online SimuBranch, while the control group received traditional lecture-based instruction. Data were collected at pre-intervention, one-week post-intervention and twelve-week post-intervention. Instruments used were a demographic information sheet, Thoracic Trauma Knowledge Scale and Clinical Reasoning Ability Scale. Results were analyzed using Generalized Estimating Equations (GEE).

RESULTS: The experimental group showed a significant improvement in thoracic trauma knowledge (p < .001), sustained for twelve weeks. Their clinical reasoning scores were higher than the control group but not statistically significant (p > .05).

CONCLUSION: This study found that the Online SimuBranch enhances thoracic trauma knowledge. Although not statistically significant, the intervention enhanced nurses’ reasoning ability and confidence in trauma care. Future initiatives are recommended to incorporate Online SimuBranch into continuous education to strengthen the connection between knowledge and practice.

PMID:40561561 | DOI:10.1016/j.nepr.2025.104436

JMIR Form Res. 2025 Jun 25;9:e60859. doi: 10.2196/60859.

ABSTRACT

BACKGROUND: Given the recent evolution and achievements in brain-computer interface (BCI) technologies, understanding public perception and sentiments toward such novel technologies is important for guiding their communication strategies in marketing and education.

OBJECTIVE: This study aims to explore the public perception of BCI technology by examining posts on X (formerly known as Twitter) using natural language processing (NLP) methods.

METHODS: A mixed methods study was conducted on BCI-related posts from January 2010 to December 2021. The dataset included 65,340 posts from 38,962 unique users. This dataset was subject to a detailed NLP analysis including VADER, TextBlob, and NRCLex libraries, focusing on quantifying the sentiment (positive, neutral, and negative), the degree of subjectivity, and the range of emotions expressed in the posts. The temporal dynamics of sentiments were examined using the Mann-Kendall trend test to identify significant trends or shifts in public interest over time, based on monthly incidence. We used the Sentiment.ai tool to infer users’ demographics by matching predefined attributes in users’ profile biographies to certain demographic groups. We used the BERTopic tool for semantic understanding of discussions related to BCI.

RESULTS: The analysis showed a significant rise in BCI discussions in 2017, coinciding with Elon Musk’s announcement of Neuralink. Sentiment analysis revealed that 59.38% (38,804/65,340) of posts were neutral, 32.75% (21,404/65,340) were positive, and 7.85% (5132/65,340) were negative. The average polarity score demonstrated a generally positive trend over the course of the study (Mann-Kendall Statistic=0.266; τ=0.266; P<.001). Most posts were objective (50,847/65,340, 77.81%), with a smaller proportion being subjective (14,393/65,340, 22.02%). Biographic analysis showed that the “broadcasting” group contributed the most to BCI discussions (17,803/58,030, 30.67%), while the “scientific” group, contributing 27.58% (n=16,005), had the highest overall engagement metrics. The emotional analysis identified anticipation (score = 10,802/52,618, 20.52%), trust (score=9244/52,618, 17.56%), and fear (score=7344/52,618, 13.95%) as the most prominent emotions in BCI discussions. Key topics included Neuralink and Elon Musk, practical applications of BCIs, and the potential for gamification.

CONCLUSIONS: This NLP-assisted study provides a decade-long analysis of public perception of BCI technology based on data from X. Overall, sentiments were neutral yet cautiously apprehensive, with anticipation, trust, and fear as the dominant emotions. The presence of fear underscores the need to address ethical concerns, particularly around data privacy, safety, and transparency. Transparent communication and ethical considerations are essential for building public trust and reducing apprehension. Influential figures and positive clinical outcomes, such as advancements in neuroprosthetics, could enhance favorable perceptions. The gamification of BCI, particularly in gaming and entertainment, also offers potential for wider public engagement and adoption. However, public perceptions on X may differ from other platforms, affecting the broader interpretation of results. Despite these limitations, the findings provide valuable insights for guiding future BCI developments, policy making, and communication strategies.

PMID:40561510 | DOI:10.2196/60859

JMIR Public Health Surveill. 2025 Jun 25;11:e65573. doi: 10.2196/65573.

ABSTRACT

BACKGROUND: To complete the Ending the HIV Epidemic initiative in areas with high HIV incidence, there needs to be a greater understanding of the demographic, behavioral, and geographic factors that influence the rate of new HIV diagnoses. This information will aid the creation of targeted prevention and intervention efforts.

OBJECTIVE: This study aims to identify the geographic distribution of risk groups and their role within potential transmission networks in Florida.

METHODS: Public data from the Florida Department of Health and behavioral data from the Surveillance Tools and Reporting System between 2012 and 2022 were used in these analyses. We analyzed records as a combination of variables of interest (gender, age, race or ethnicity, and HIV risk group) to create demographic-behavioral profiles (DBPs) that represent the profiles of people newly diagnosed with HIV. We then used the resulting DBPs to characterize Florida counties and HIV coordination areas and calculated the county-to-county and area-to-area rank (Spearman) correlation. We then drew a dendrogram based on the correlation matrix and identified clusters of similar counties and areas. Lastly, network analysis used HIV contact tracing data from the Surveillance Tools and Reporting System to identify HIV transmission and exposure contact networks and characterized large networks by DBPs and geolocation.

RESULTS: We identified 37 DBPs. The largest DBPs were Hispanic and non-Hispanic Black males aged 25-49 reporting male-to-male sexual contact (n=7539 and n=4329, respectively), non-Hispanic White males aged 25-49 reporting male-to-male sexual contact (n=4221), and non-Hispanic Black females aged 25-49 reporting heterosexual contact (n=3371). The state could be broken up generally into 2 transmission and exposure clusters by region: Northwestern or Northern and Central or Southern. We identified several counties with similar DBPs that were not in the same HIV coordination area. A total of 3097 contact networks were identified among 7944 people with HIV contact tracing data. Most (n=2508, 81%) networks involve only 2 people, 11% (n=349) involve 3 people, 7% (n=224) involve 4 to 19 people, and 6 networks involve 20 or more people. As network size increases, the proportion of people within the network who identify as female, non-Hispanic Black, aged older than 50 years, and exposed to HIV via heterosexual contact decreases.

CONCLUSIONS: We identified distinct risk groups and clusters of transmission and exposure throughout Florida. These results can help regions identify health disparities and allocate their HIV prevention and intervention resources accordingly. The goal of this work was to highlight areas of need in a high-incidence setting, not to contribute to existing stigma against vulnerable groups, and it is important to consider the ethics and possible harm of advanced methodologies such as contact network analysis when addressing public health problems.

PMID:40561506 | DOI:10.2196/65573

JMIR Mhealth Uhealth. 2025 Jun 25;13:e59926. doi: 10.2196/59926.

ABSTRACT

BACKGROUND: With a focus on postoperative dysfunctions that may occur after maxillofacial tumor resection and the difficulties faced during home rehabilitation, we developed a mobile health app based on nurse-patient cooperation. The app extends rehabilitation care from hospital to home with the help of artificial intelligence, Internet of Things, and other technologies, thus helping patients to better carry out their home functional rehabilitation and meet their health needs.

OBJECTIVE: The primary objective of this quasi-experimental study is to evaluate the impact of the Intelligent Home Rehabilitation Care Platform on the quality of life, self-management, and functional impairment in patients with oral and maxillofacial head and neck tumors. We aim to determine whether the intervention through this platform can lead to significant improvements in these areas compared with traditional postdischarge care methods.

METHODS: In this study, patients with oral and maxillofacial head and neck tumors who had undergone surgery were recruited from allied hospitals in the Yangtze River Delta region, divided into an experimental group (n=138) and a control group (n=123), and received either the Intelligent Home Rehabilitation Care Platform intervention or the conventional health care and guidance, respectively. The intervention lasted 3 months, and the patients’ quality of life, self-management efficacy, and improvement in dysfunction were assessed at 1 week (baseline), 1 month (T1), and 3 months (T2) postoperatively. SPSS software (IBM Corp) was used to perform the chi-square test, rank sum test, t test, repeated-measures ANOVA, and generalized estimation equation for data analysis.

RESULTS: We analyzed the effects of the quality of life and self-management using the generalized estimating equation method. The generalized estimating equation results showed that after adjusting for age, sex, pathological histology, cancer stage, and primary site, the intervention group had a significantly higher improvement in quality of life than the control group at the T2 (regression coefficient, β=-68.020, 95 % CI -116.639 to -19.412; P=.006) stage. The degree of improvement in self-management efficacy was significantly higher in the T1 (regression coefficient, β =-7.030, 95 % CI -9.540 to -4.520; P<.001) and T2 (regression coefficient, β =-13.245, 95 % CI -16.923 to -9.566; P<.001) stages than in the control group. The results of repeated-measures ANOVA and rank sum test showed that the experimental group showed improvements in shoulder function, dysphagia, and trismus after mHealth intervention; however, the differences were not significant.

CONCLUSIONS: The Intelligent Home Rehabilitation Care Platform interventions can effectively enhance patients’ self-management efficacy, improving quality of life and facilitate recovery from dysfunctions. Therefore, mHealth may be used in oncology care to provide smarter home self-care for patients.

PMID:40561502 | DOI:10.2196/59926

JMIR Cardio. 2025 Jun 25;9:e71366. doi: 10.2196/71366.

ABSTRACT

BACKGROUND: Advances in digital technology, such as health apps and telerehabilitation systems, offer promising treatment modalities in the secondary prevention of cardiovascular disease. However, the successful adoption of digital technology in clinical practice depends on a variety of factors. A comprehensive understanding of the influencing factors on digital technology usage in health care can support the complex implementation process of digital technology in clinical practice.

OBJECTIVE: The aim of this study was to identify barriers and facilitators of digital technology usage in cardiovascular disease secondary prevention from the perspective of health care professionals, and to explore whether certain characteristics of health care professionals are related to the current usage of digital technology in clinical practice.

METHODS: We conducted an exploratory online survey, inquiring about the perspectives and uses of digital technologies in cardiovascular disease secondary prevention. We developed an original questionnaire to address the study aim. The survey invitation was distributed among health care professionals from November 2021 to February 2022, via all cardiac rehabilitation centers, all community-based disease management services for patients with chronic heart failure, and all relevant national health care professional associations in Austria. Qualitative survey data were analyzed using thematic content analysis. Quantitative survey data were analyzed using descriptive statistics, group comparison tests, and association statistics.

RESULTS: Overall, 125 health care professionals (mean age 41, SD 11 y; n=80, 64% females) across different professions and settings, including cardiac rehabilitation phases I through IV, were recruited. General readiness for using digital technologies in the care of cardiac patients was high, but only 65 (52%) respondents reported doing so. The top 3 rated barriers to digital technology use were poor user-experience of devices and apps, lack of cost coverage, and low digital competence of patients. The top 3 rated potential application areas for digital technology were organization and appointment planning, documenting treatments, and creating personalized treatment plans. The top 3 rated facilitators for digital technology use were assurance of patient safety, assurance of patients’ privacy, and availability of technical support. Greater personal use of digital technology, younger age, and higher technology affinity of health care professionals was associated with higher readiness to use digital technology with cardiac patients.

CONCLUSIONS: While there is interest in digital technology for the secondary prevention of cardiovascular disease in Austria, barriers to uptake need to be addressed. Our findings may inform the design and implementation of future digitalization projects.

PMID:40561497 | DOI:10.2196/71366

JMIR Form Res. 2025 Jun 25;9:e74043. doi: 10.2196/74043.

ABSTRACT

BACKGROUND: Despite national asthma care guidelines, care gaps persist between best-practice and clinical practice, contributing to poor health outcomes. The Provider Asthma Assessment Form (PAAF) is an electronic asthma management and Knowledge Translation tool with an embedded decision support algorithm for severe and/or uncontrolled asthma, designed to support evidence-based asthma management.

OBJECTIVE: In this study, we aimed to document baseline asthma practice patterns and determine whether the broader intervention of PAAF integration into a primary care electronic medical record (EMR) improves evidence-based asthma diagnosis and management.

METHODS: We performed a single-center pre- and postobservational study at an academic Family Health Team in Kingston, Ontario, Canada. Retrospective baseline data were collected for 2 years prior to PAAF implementation from January 2018 to December 2019. Prospective postintervention data were collected from October 2022 to July 2024. A validated adult asthma EMR case definition was applied to EMR data to identify suspected or objectively confirmed asthma cases for both datasets, on which detailed manual chart abstractions were performed. A data extraction was performed for completed PAAFs.

RESULTS: There were 230 patients in the retrospective baseline and 143 patients in the postimplementation cohort. Overall, 31.3% (n=72) of patients at baseline versus 23.8% (n=34) at postimplementation had confirmed asthma. There were significantly more pulmonary function tests requested after the implementation of the PAAF (postimplementation: n=70, 49%; baseline: n=71, 30.9%; P<.001). A significantly higher percent of postimplementation patients were on single inhaler controller and reliever therapy (postimplementation: n=31, 21.7%; baseline: n=2, 0.9%; P<.001), inhaled corticosteroid/long-acting β-2 agonist therapy (postimplementation: n=36, 25.2%; baseline: n=34, 14.8%; P=.01), and inhaled corticosteroid if their asthma was uncontrolled (postimplementation: n=69, 62.2%; baseline: n=100, 43.5%; P=.002). Barriers were significantly more commonly addressed after implementation (postimplementation: n=24, 16.8%; baseline: n=11, 4.8%; P<.001). A significantly higher average number of asthma control parameters was documented when the PAAF was used (PAAF: mean 5.4, SD 1.9; manual chart abstraction: mean 2.3, SD 1.2; P<.001). Care as assessed by key Primary Care-Asthma Performance Indicators showed improvement in the postimplementation cohort, which did not reach statistical significance.

CONCLUSIONS: The multifaceted intervention of implementing the PAAF in this primary care practice was associated with improved documentation of diagnosis status and asthma control parameters and improved adherence with evidence-based recommendations for care, such as the use of pulmonary function tests and addressing barriers to effective asthma management. However, uptake was low, and key asthma care gaps were still common. Future directions should involve evaluating the impact of the PAAF on care and outcomes after widespread implementation in primary care settings and investigating methods to increase user uptake of the PAAF.

PMID:40561494 | DOI:10.2196/74043