Tag: nevin manimala

Vet Rec. 2026 Apr 2. doi: 10.1002/vetr.70554. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) is becoming increasingly prevalent in the modern world, including in veterinary medicine. This cross-sectional study aimed to investigate horse carers’ attitudes towards using AI use in equine care.

METHODS: An online survey was distributed to UK horse owners/carers in 2025, covering participants’ demographics and use of AI and their opinions of AI for equine care. Statistical analysis included descriptive statistics, categorisation of free-text responses and logistic regression to determine factors associated with opinions.

RESULTS: Ninety-seven responses were analysed. Participants had a predominantly positive opinion of AI to automate large datasets for equine care, and a predominantly negative opinion for automating communications and medical decision making. Key categories identified in free-text responses were: AI use in general/equine care, desire for human interaction and AI as a supportive aid only. Positive attitudes towards AI for equine care were significantly associated with participants’ opinions of AI in their own lives (odds ratio [OR]: 3.69, 95% confidence interval [CI]: 3.06‒4.45) and understanding of AI (OR: 1.31, 95% CI 1.03‒1.66).

LIMITATIONS: This is a small exploratory study of horse owners/carers in the UK, and the findings may not be more widely generalisable.

CONCLUSION: Horse owners/carers had mixed opinions on the use of AI in equine care, and their primary concern was around it replacing human decision making.

PMID:41924893 | DOI:10.1002/vetr.70554

Biomed Res Int. 2026;2026(1):e9163340. doi: 10.1155/bmri/9163340.

ABSTRACT

BACKGROUND: World Health Organization reported a lacuna in receiving hepatitis B virus vaccination for a considerable proportion of healthcare workers, with coverage ranging from 18% to 39% in developing countries. In Ethiopia, full immunization coverage ranges from 5.8% to 36.9% among healthcare workers. This study is aimed at assessing the hepatitis B virus vaccination coverage, its determinants and barriers among healthcare workers in Lemi Kura Subcity, Addis Ababa, Ethiopia, 2024.

METHODS: A facility-based convergent parallel mixed design was conducted in a cross-sectional approach among 277 health workers from June 15, 2024 to July 15, 2024 in Health Centers of Lemi Kura Subcity, Addis Ababa. For the quantitative study, the retrospective samples were chosen using simple random sampling. Conversely, purposive sampling was employed for the qualitative study. Multivariable logistic regression was used to identify independent determinants of HBV vaccination. Variables with p value < 0.05 were considered statistically significant.

RESULTS: Hepatitis B vaccination rate was 78.3% (95% CI: 73.6, 83.0). Being female (AOR: 2.50; 95% CI: 1.21, 5.20), having more than 6 years of experience (AOR: 6.35; 95% CI: 2.33, 17.30), history of exposure to a person infected with hepatitis B (AOR: 2.88; 95% CI: 1.33, 6.21), screened for HBV (AOR: 2.72; 95% CI: 1.36, 5.41), and favorable attitude toward hepatitis B (AOR: 7.53; 95% CI: 3.11, 18.23) were statistically significant in multivariable analysis. Moreover, limited resources, misinformation, and low public awareness, workforce challenges, cost and affordability, and stigma and cultural barriers were reported challenges, and improving access, capacity building, and community engagement were reported opportunities of HBV vaccination.

CONCLUSION AND RECOMMENDATIONS: This study found that HBV vaccination coverage among healthcare workers in public health centers of Lemi Kura Subcity, Addis Ababa, Ethiopia, was relatively high compared with several previous reports from similar settings. Vaccination status was independently associated with sex, years of professional experience, history of occupational exposure, prior HBV screening, and attitude toward HBV vaccination.

PMID:41924885 | DOI:10.1155/bmri/9163340

J Sleep Res. 2026 Apr 2:e70336. doi: 10.1111/jsr.70336. Online ahead of print.

ABSTRACT

Previous studies have examined how the COVID-19 pandemic affected dream recall, dream content, and nightmares. However, relatively little attention has been devoted to the individual and situational factors associated with pandemic-induced changes in dreams. The threat simulation theory of dreaming predicts that threatening situations in our waking life (situational factors) influence threatening dream content. At the same time, individual differences predispose some people to be more prone to experiencing threatening dreams more frequently than others. Using a large Finnish sample of prospective dream diaries, we analysed the relative importance of individual (e.g., belonging to a COVID-19 risk group, life satisfaction, depression and anxiety symptoms) and situational factors (e.g., daily COVID-19 worry, COVID-19 media consumption, negative and positive emotions) to determine the best predictors for threatening events and COVID-19-related threatening events in dreams. Random forest analyses revealed that individual factors were consistently better predictors than situational factors for both threatening events and pandemic-related threatening events in dreams. Lower life satisfaction was the only statistically significant predictor of threatening events and experiencing fewer positive emotions in the past 2 weeks was the only statistically significant predictor of pandemic-related threatening events in dreams. These findings suggest that the propensity to experience threatening dream content, including pandemic-related threatening events, is more of a stable trait rather than a daily fluctuating feature of dreams. In light of the threat simulation theory, it could be argued that individual variation in the proneness to simulate threatening events adaptively interacts with daily experiences to modulate threatening dream content.

PMID:41924860 | DOI:10.1111/jsr.70336

Int J Artif Organs. 2026 Apr 2:3913988261435494. doi: 10.1177/03913988261435494. Online ahead of print.

ABSTRACT

BACKGROUND: Type 1 diabetes (T1D) management remains particularly challenging in the presence of external disturbances such as stress or physical activity (PA). The glycemic impact of PA is still not fully understood and lacks standardized modeling approaches. This work seeks to describe the relationship between PA, time-in-range and glucose predictability in people with T1D (PwT1D).

METHODS: This study uses data from the Type 1 Diabetes and Exercise Initiative (T1DEXI) clinical trial, the largest trial to date in PwT1D undergoing both free-living and structured exercise. A characterization pipeline extracts summary statistics including glucose, carbohydrate intake and PA signals. These features are used to perform unsupervised clustering of subjects using various techniques. The relevance of the cluster-defining variables is then assessed, and their relationship to the performance of a long short-term memory (LSTM) neural network trained to forecast glucose 1 h into the future is analyzed.

RESULTS: The spectral clustering algorithm successfully separates individuals into three groups based on glycemic control metrics. Results indicate that subjects with higher levels of weekly PA exhibit lower prediction errors. This suggests that regular PA enhances the predictability of glucose trends, enabling more accurate forecasting models.

CONCLUSION: Since fear of hypoglycemia remains one of the main barriers to PA in PwT1D, these findings are particularly relevant: regular exercise not only promotes better glycemic regulation but also improves the performance of predictive models, which could strengthen automated insulin delivery systems, support more reliable decision-support tools and contribute to safer and more confident engagement in PA.

PMID:41924857 | DOI:10.1177/03913988261435494

Int J Artif Organs. 2026 Apr 2:3913988261429899. doi: 10.1177/03913988261429899. Online ahead of print.

ABSTRACT

BACKGROUND: Elderly patients requiring emergency intubation and invasive mechanical ventilation (IMV) in the emergency department (ED) face substantial mortality risk. However, prediction models utilizing only ED-obtainable variables for early prognostication remain limited.

OBJECTIVE: To develop and internally validate a prognostic model using variables available at ED intubation to predict in-hospital mortality in elderly patients.

METHODS: This retrospective cohort study included 273 patients aged ⩾ 65 years who underwent emergency intubation and IMV initiation in the ED of a tertiary hospital between June 2019 and June 2024. Candidate predictors included demographics, comorbidities, Glasgow Coma Scale (GCS), vasopressor use, admission lactate, PaO2/FiO2 ratio, shock index, and baseline laboratory values. Multiple imputation addressed missing data. Least absolute shrinkage and selection operator (LASSO) logistic regression identified optimal predictors, followed by multivariate logistic regression. Internal validation employed bootstrap resampling (1000 iterations). Model performance was assessed via discrimination (C-statistic), calibration (calibration plot, Brier score), and clinical utility (decision curve analysis).

RESULTS: The overall in-hospital mortality rate was 72.2% (197/273). After LASSO selection and clinical adjudication, the final model included age, GCS score, admission lactate, and vasopressor requirement as independent predictors. The model demonstrated excellent discrimination (C-statistic 0.834, 95% CI: 0.784-0.884), good calibration, and superior net benefit across threshold probabilities of 0.3-0.8 compared to default strategies. Bootstrap-corrected optimism was minimal (optimism-corrected C-statistic 0.827). Sensitivity analyses confirmed model robustness.

CONCLUSIONS: A parsimonious model incorporating age, GCS score, admission lactate, and vasopressor use accurately predicts in-hospital mortality in elderly patients undergoing emergency intubation and IMV, using only variables readily available at the time of ED presentation. This tool has the potential to facilitate early, evidence-informed shared decision-making.

PMID:41924851 | DOI:10.1177/03913988261429899

Ann Med. 2026 Dec;58(1):2647569. doi: 10.1080/07853890.2026.2647569. Epub 2026 Apr 2.

ABSTRACT

BACKGROUND: Frailty is a significant health concern associated with diminished physiological reserves and increased healthcare burdens. Currently, effective models for predicting frailty risk are lacking. This study utilizes machine learning-based models to early identify community-dwelling older adults, enhancing risk assessment accuracy and guiding targeted interventions to slow frailty progression.

METHODS: A cross-sectional analysis of data from 1,156 older adults across 31 community health centers in Nanjing, conducted between January and October 2024, was performed. Independent predictors of frailty were identified using univariate analysis and the least absolute shrinkage and selection operator. The dataset was divided into 70% training and 30% testing subsets. Six machine learning (ML) models were developed and their performances compared. The SHapley Additive exPlanations (SHAP) method was applied to interpret the models, and a web-based risk calculator was created.

RESULTS: Our dataset showed that 22.3% of older adults were frail. Significant predictors of frailty were identified as age, education, medicine, vegetable, cognitive status, number of diseases, hemoglobin, total cholesterol, and neutrophil-to-lymphocyte ratio. Among the six ML models, Categorical Boosting (CatBoost) exhibited the highest performance, attaining an AUROC of 0.886 in the training set and 0.831 in the testing set.

CONCLUSIONS: The developed CatBoost model and web calculator can be employed by general practitioners to proactively identify high-risk community-dwelling older adults, thereby enabling timely interventions to mitigate the progression of frailty. The tool’s simplicity and replicability effectively facilitate the promotion and management of frailty prevention within the community.

PMID:41924849 | DOI:10.1080/07853890.2026.2647569

Nurs Outlook. 2026 Mar 31;74(3):102749. doi: 10.1016/j.outlook.2026.102749. Online ahead of print.

ABSTRACT

BACKGROUND: National nursing organizations and policy frameworks call for incorporating social determinants of health (SDOH) into nursing curricula to improve workforce readiness and promote health equity. Although providers acknowledge the importance of SDOH, gaps remain in preparing prelicensure nursing students to address systemic and community health factors.

PURPOSE: This project assessed the effectiveness of the Toward Health Equity and Literacy (2HEAL) program’s online modules in improving BSN students’ perceived competence in addressing SDOH.

METHODS: The 2HEAL team created a series of asynchronous, interactive modules covering various SDOH topics. BSN students (n= 346) completed the modules between August 2023 and March 2025. A retrospective self-assessment survey measured changes in students’ perceived competence. Data from 1,131 surveys compared pre- and post-module scores. Net Promoter Scores (NPS) evaluated module acceptability.

FINDINGS: Students reported a statistically significant increase in perceived competency across all SDOH topics appearing in more than one module, with an overall average increase of +0.50 points. Post-module scores showed students self-reported proficiency in 68% of assessed SDOH domains. The greatest gains were in civic engagement (+0.63), while environmental health showed the smallest increase (+0.26). Modules achieved an average NPS of +36, indicating high learner satisfaction.

DISCUSSION: This project demonstrates that structured, competency-based digital modules can improve BSN students’ perceived competence in addressing SDOH. Results highlight improvement in addressing civic engagement, immigrant health, and health literacy, and reveal ongoing gaps in climate and environmental health education. The 2HEAL model provides a scalable way to incorporate SDOH training into pre-licensure programs.

PMID:41924837 | DOI:10.1016/j.outlook.2026.102749

Clin Breast Cancer. 2026 Mar 10;26(5):11-18. doi: 10.1016/j.clbc.2026.03.006. Online ahead of print.

ABSTRACT

BACKGROUND: Preoperative MRI is used for staging breast cancer and is considered particularly useful in invasive lobular carcinoma (ILC).

PURPOSE: The aim of this retrospective study was to compare diagnostic, operative and long-term outcomes in ILC patients who underwent preoperative breast MRI to those who did not.

MATERIAL AND METHODS: Between 2010 and 2012, 361 patients with postoperative diagnosis of ILC were enrolled in this study.

RESULTS: Preoperative MRI was performed for 245 (67.9%) women. MRI identified 21 additional cancers and resulted in 62 benign findings. Reoperation rate due to insufficient margins was lower in the MRI group 11 (8.8%) compared to no-MRI group 14 (21.9%), P = .014. Mastectomy rates were equal, 131 (53.5%) and 62 (53.4%), P = 1.000. No statistical difference was found in local recurrences; 2 (1.8%) versus 2 (3.8%), P = .596, nor in 10-year disease-free survival, 88.5% compared to 84.5%, P = .295. The 10-year overall survival was 79.0% and 74.5%, P = .351. Preoperative MRI did not decrease the risk of breast cancer recurrence nor increase survival in ILC patients. The reoperation rate was lower in the MRI-group, but the preoperative MRI had a large proportion of false positive findings.

CONCLUSION: Preoperative MRI did not affect long-term outcomes in ILC patients; however, recurrence rates were low in both groups. ILC patients may benefit from preoperative MRI, as the reoperation rate was lower.

PMID:41924820 | DOI:10.1016/j.clbc.2026.03.006

Epidemics. 2026 Mar 28;55:100902. doi: 10.1016/j.epidem.2026.100902. Online ahead of print.

ABSTRACT

Since the COVID-19 pandemic, many jurisdictions have adopted wastewater-based surveillance for various pathogens. Indeed, monitoring pathogen concentration in wastewater, usually measured in RNA or DNA copies per milliliter, can efficiently assess the prevalence of infections in entire communities. However, wastewater-based surveillance does not provide a directly interpretable and actionable metric for public health. Here, we propose a statistical framework that assesses the relationship between COVID-19 hospital admissions and SARS-CoV-2 concentrations in wastewater for several large urban centres in Canada between 2021 and 2024. We also use this analysis to categorize early into an infection wave the clinical severity of future SARS-CoV-2 epidemics.

PMID:41924781 | DOI:10.1016/j.epidem.2026.100902

Poult Sci. 2026 Mar 3;105(6):106734. doi: 10.1016/j.psj.2026.106734. Online ahead of print.

ABSTRACT

Causes of normal mortality in laying hens have received relatively little scientific attention, despite health, welfare, economic and environmental implications. Targeting control and preventative measures to currently relevant flock-specific mortality causes is essential for successful outcomes in commercial flocks. Monitoring of mortality causes is a prerequisite. However, diagnostic criteria are not consistently reported and terminology varied between previous studies. The objective of this study was to investigate associations between pathological findings in dead laying hens with an explorative approach applied to a previously obtained dataset on pathological findings in Danish laying hens from seven non-caged commercial flocks. This approach may provide new insights compared to descriptive analysis of predefined diagnoses. A dataset with pathological findings across 49 pathological variables in 1,648 laying hens was analyzed with multiple correspondence analysis to explore multidimensional associations between pathological findings in laying hens and how patterns in combinations of findings of individual hens may cluster as diagnoses or tentative causes of death. Unlike descriptive statistics, the multivariate approach enabled us to illustrate the complexity of pathological processes. The first four dimensions of the multiple correspondence analysis were retained, accounting for 20.2% of the variance. The results indicated clustering of hens suggesting diagnoses of similar expected chronicity and general etiology (infectious versus non-infectious). The most dominant clusters corresponded to the two most common causes of mortality diagnosed on the same hens and reported in previous studies: salpingitis-peritonitis and cannibalism. In addition, the results suggested at least two different clusters of hens that died due to cannibalism (acute or prolonged course with concurrent pathologies). These may point to relevant differences in etiology and pathogenesis that should be explored in future studies. We suggested recommendations for time-efficient field necropsies and preventative and control measures to target the most common causes of mortality in laying hens. The results may be used by farmers and their advisors to improve monitoring of health and welfare of laying hen flocks in non-cage housing systems.

PMID:41924760 | DOI:10.1016/j.psj.2026.106734