Tag: nevin manimala

PLoS One. 2025 Mar 10;20(3):e0319102. doi: 10.1371/journal.pone.0319102. eCollection 2025.

ABSTRACT

INTRODUCTION: This protocol for a systematic review and meta-analysis aims to provide synthesized evidence to determine whether pericapsular nerve group (PENG) block is superior to local anesthetic infiltration in controlling postoperative pain in total hip arthroplasty.

METHODS AND ANALYSIS: PubMed, EMBASE, Web of science, and the Cochrane library will be systematically searched from their inception to December 30, 2024. Randomized controlled trials (RCTs) that compared the analgesic effects of PENG block with local anesthetic infiltration for total hip arthroplasty will be included. The time to first analgesics requirement (analgesia duration) will be the primary outcome. Secondary outcomes will include the postoperative analgesics consumption over 24 hours, visual analog scale (VAS) scores at rest and movement, and the incidence of adverse effects. Statistical analysis will be conducted by RevMan 5.4 software.

ETHICS AND DISSEMINATION: Ethical approval is not applicable. The results of this study will be publicly published.

PROSPERO REGISTRATION NUMBER: CRD42024590888.

PMID:40063895 | DOI:10.1371/journal.pone.0319102

PLOS Digit Health. 2025 Mar 10;4(3):e0000744. doi: 10.1371/journal.pdig.0000744. eCollection 2025 Mar.

ABSTRACT

The lack of sensitive objective outcome measures for hand dexterity is a barrier for clinical assessment of neurological conditions and has negatively affected clinical trials. Here, we clinically validate a new method for measuring hand dexterity, a novel hand worn sensor that digitises the Finger Tapping Test. The device was assessed in a cohort of 180 healthy controls and 51 people with Amyotrophic Lateral Sclerosis (ALS) and compared against rating scales and traditional measures (Nine Hole Peg test and grip dynamometry). 14 features were extracted from the device and using a logistic regression algorithm, a 0-100 dexterity performance score was generated for each participant, which accounted for age/sex differences. The device returned objective ratings of a participant’s hand dexterity (dominant, non-dominant and overall score). The average overall dexterity performance score in all healthy participants was 88 ± 17 (mean ± standard deviation). The overall dexterity score was statistically significantly worse in participants with ALS (age/sex matched healthy subset: 80 ± 20, ALS: 45 ± 32, p-value < 0.0001). The device also had a higher completion rate, (94% dominant hand) compared to the traditional measures (82% dominant hand). This test and scoring system have been validated and the regression model was developed using a framework that is potentially applicable to any relevant condition. This device could act as an objective outcome measure in clinical trials and may be useful in improving patient care.

PMID:40063887 | DOI:10.1371/journal.pdig.0000744

PLoS One. 2025 Mar 10;20(3):e0306266. doi: 10.1371/journal.pone.0306266. eCollection 2025.

ABSTRACT

Climate change impacts require us to reexamine crop growth and yield under increasing temperatures and continuing yearly climate variability. Agronomic and agro-meteorological variables were concorded for a large number of plantings of green bean (Phaseolus vulgaris L.) in three growing seasons over several years from semi-tropical Queensland. Using the Queensland government’s SILO meteorological database matched to sowing dates and crop phenology, we derived planting specific agro-meteorological variables. Linear and nonlinear statistical models were used to predict duration of vegetative and pod filling periods and fresh yield using agro-meteorological variables including thermal time, radiation and days of high temperature stress. High temperatures over 27.5∘C and 30∘C in the pod fill period were associated with a lower fresh bean yield. Differences between specific bean growing sites were examined using our bespoke open source software to derive agro-meteorological variables. Agronomically informed statistical models using production data were useful in predicting time of harvest. These methods can be applied to other commercial crops when crop phenology dates are collected.

PMID:40063884 | DOI:10.1371/journal.pone.0306266

Eur J Prev Cardiol. 2025 Mar 10:zwaf135. doi: 10.1093/eurjpc/zwaf135. Online ahead of print.

ABSTRACT

AIMS: Current risk prediction models, such as the Norwegian NORRISK 2, explain only a modest proportion of cardiovascular disease (CVD) incidence. This study aimed to develop improved sex-specific models for predicting the 10-year CVD risk as well as sex- and age-specific thresholds for intervention.

METHODS: Data from 31,946 participants (40-79 years) without prior CVD were analyzed. Data were randomly split into a training set (for estimation) and a test set (for model evaluation). An extreme gradient boosting (XGBoost) model was used to identify the most important predictive variables. Next, prediction models were developed on the training set for each sex separately using XGBoost and logistic regression. The models were evaluated on the test set using receiver-operating characteristic (ROC) and precision recall (PR) curves. Finally, age- and sex-specific thresholds for intervention were explored.

RESULTS: All traditional risk factors included in NORRISK 2 and the European SCORE2 model were important predictors for males, but not for females. Potential new risk predictors were identified. The XGBoost model improved CVD risk prediction for males: 0.013- and 0.012-unit increase in ROC-AUC compared to NORRISK 2 and SCORE2 respectively, and 12% and 11% increase in PR-AUC respectively. For females, neither the XGBoost nor logistic regression model performed significantly better than NORRISK 2 and SCORE2. Age- and sex-specific thresholds showed an improvement in sensitivity compared with NORRISK 2-suggested thresholds.

CONCLUSIONS: By employing statistical learning and incorporating sex-specific risk factors, we propose improved risk prediction models for CVD in males. Introducing sex-specific thresholds for intervention could enhance CVD prevention for both sexes.

PMID:40063873 | DOI:10.1093/eurjpc/zwaf135

PLoS Negl Trop Dis. 2025 Mar 10;19(3):e0012918. doi: 10.1371/journal.pntd.0012918. Online ahead of print.

ABSTRACT

BACKGROUND: COVID-19 pandemic had unprecedented global impact on health and society, highlighting the need for a detailed understanding of SARS-CoV-2 evolution in response to host and environmental factors. This study investigates the evolution of SARS-CoV-2 via mutation dynamics, focusing on distinct age cohorts, geographical location, and vaccination status within the Indian population, one of the nations most affected by COVID-19.

METHODOLOGY: Comprehensive dataset, across diverse time points during the Alpha, Delta, and Omicron variant waves, captured essential phases of the pandemic’s footprint in India. By leveraging genomic data from Global Initiative on Sharing Avian Influenza Data (GISAID), we examined the substitution mutation landscape of SARS-CoV-2 in three demographic segments: children (1-17 years), working-age adults (18-64 years), and elderly individuals (65+ years). A balanced dataset of 69,975 samples was used for the study, comprising 23,325 samples from each group. This design ensured high statistical power, as confirmed by power analysis. We employed bioinformatics and statistical analyses, to explore genetic diversity patterns and substitution frequencies across the age groups.

PRINCIPAL FINDINGS: The working-age group exhibited a notably high frequency of unique substitutions, suggesting that immune pressures within highly interactive populations may accelerate viral adaptation. Geographic analysis emphasizes notable regional variation in substitution rates, potentially driven by population density and local transmission dynamics, while regions with more homogeneous strain circulation show relatively lower substitution rates. The analysis also revealed a significant surge in unique substitutions across all age groups during the vaccination period, with substitution rates remaining elevated even after widespread vaccination, compared to pre-vaccination levels. This trend supports the virus’s adaptive response to heightened immune pressures from vaccination, as observed through the increased prevalence of substitutions in important regions of SARS-CoV-2 genome like ORF1ab and Spike, potentially contributing to immune escape and transmissibility.

CONCLUSION: Our findings affirm the importance of continuous surveillance on viral evolution, particularly in countries with high transmission rates. This research provides insights for anticipating future viral outbreaks and refining pandemic preparedness strategies, thus enhancing our capacity for proactive global health responses.

PMID:40063870 | DOI:10.1371/journal.pntd.0012918

Eur Radiol. 2025 Mar 10. doi: 10.1007/s00330-025-11449-9. Online ahead of print.

ABSTRACT

BACKGROUND: Recognising bone injuries in children is a critical part of children’s imaging, and, recently, several AI algorithms have been developed for this purpose, both in research and commercial settings. We present an updated systematic review of the literature, including the latest developments.

METHODS/MATERIALS: Scopus, Web of Science, Pubmed, Embase, and Cochrane Library databases were queried for studies published between 1 January 2011 and 6 September 2024 matching search terms ‘child’, ‘AI’, ‘fracture,’ and ‘imaging’. Retrieved studies were evaluated, and descriptive statistics were collated for diagnostic performance.

RESULTS: Twenty-six eligible articles were included; seventeen (17/26, 65.%) of these were published within the last two years. Six studies (6/26, 23.1%) used open-source datasets to train their algorithm, the remainder used local data. Sixteen studies (16/26, 61.5%) evaluated a single joint (wrist, elbow, or ankle); multiple bones within the appendicular skeleton were assessed in the other ten studies. Seven articles (7/26, 26.9%) related to the performance of a commercial AI tool. Accuracy of AI models ranged from 85.0 to 100.0%. Six studies (6/26, 23.1%) evaluated the accuracy of human readers with and without AI assistance, of which two studies found a statistically significant improvement when humans were assisted by AI. The largest pool of human readers in any paper consisted of 11 readers of varying experience.

CONCLUSION: The pace of research in AI fracture detection in children’s imaging has increased. Studies show high accuracy of AI models, but proof of clinical impact, cost-effectiveness, and any socioeconomic or ethical bias are still lacking.

KEY POINTS: Question AI model development has rapidly increased in recent years. We present the latest developments in AI model diagnostic accuracy for paediatric fracture detection. Findings Studies now demonstrate performance improvement when AI is used to assist human interpretation of paediatric fractures, especially when aiding junior radiologists. Clinical relevance Studies show high accuracy for AI models; however, further research is needed to evaluate AI across diverse age groups, bone diseases, and fracture types. Evidence of real-world patient benefit for AI and any socioeconomic or ethical bias are still lacking.

PMID:40063108 | DOI:10.1007/s00330-025-11449-9

J Orofac Orthop. 2025 Mar 10. doi: 10.1007/s00056-025-00576-z. Online ahead of print.

ABSTRACT

OBJECTIVES: The primary objective of this study was to investigate the feasibility of magnetic resonance imaging (MRI) usage over computed tomography (CT) to perform three-dimensional (3D) cephalometric analyses. The secondary objective is to find intra- and interobserver reliability of manual cephalometric landmarks identification in both CT and MRI scan data.

METHODS: Data from 40 patients were used in this study, with orthodontists manually identifying 37 landmarks on both CT and MRI scans. The interclass correlation coefficient (ICC) was calculated individually for both CT and MRI scan data to find intra- and interobserver reliability. In addition to ICC, paired t‑test and mean error were also calculated. Ground truth landmarks were calculated by considering the mean values of manually located 37 landmarks by observers for both CT and MRI. Thirty-seven cephalometric measurements (29 linear, 6 angular, and 2 ratios) were measured using 37 ground truth landmarks. Mean error (ME) between CT and MRI measurements was calculated and paired t‑test was performed to find the reliability of MRI usage over CT. Bland-Altman analysis was also performed on the measurements to check the agreement between CT and MRI.

RESULTS: The intra- and interobserver reliability was found to be reliable (ICC > 0.98, and P > 0.05) for all 37 landmarks in both CT and MRI. The ME for linear measurements was found to be 1.81 mm for hard tissue, 1.72 mm for soft tissue, and 1.53° for hard tissue angular measurements between CT and MRI. The paired t‑test performed on measurements between CT and MRI proved to be statistically insignificant (p > 0.05). The Bland-Altman analysis also showed strong agreement and low systemic bias between CT and MRI data.

CONCLUSIONS: The strong ICC and P values shows the high reliability and reproducibility of manual landmark identification on both CT and MRI. The ME for the linear and angular measurements between CT and MRI was found to be well within acceptable limits. The results of paired t‑test and Bland-Altman analyses for cephalometric measurements between CT and MRI has shown strong evidence supporting the use of MRI as a substitute for CT.

PMID:40063106 | DOI:10.1007/s00056-025-00576-z

Int J Biometeorol. 2025 Mar 10. doi: 10.1007/s00484-025-02886-8. Online ahead of print.

ABSTRACT

The association of low-level ozone (O₃) exposure with the mortality risk of ischemic heart disease (IHD) and stroke remains to be investigated. This study aimed to investigate the relationship between low-level O₃ exposure and mortality risk of IHD and stroke in Yibin, a city in southwestern China. A Poisson distribution lagged nonlinear model was used to assess the effect of O₃ exposure on IHD and stroke mortality and to explore the susceptible population according to gender and age subgroups and the susceptible season according to seasonal subgroups and to analyse the health effects under low O₃ exposure compared with high O₃ exposure. The mean O₃ exposure concentration from 2014 to 2020 was approximately 48.3 μg/m3. There was a major lagged effect of O₃ exposure on IHD and stroke. For every 10.0 μg/m³ increase in O₃ concentration, the cumulative risks of death for the two diseases were 1.0211 (95% Confidence Interval [CI]: 1.0064, 1.0358) and 1.0211 (95% CI: 1.0064, 1.0357), respectively. The mortality risks of IHD and stroke for women were 1.0064 (95% CI: 1.0016, 1.0113) and 1.0030 (95% CI: 1.0008, 1.0051), and for those aged > 65 years, they were 1.0082 (95% CI: 1.0026, 1.0139) and 1.0018 (95% CI: 1.0002, 1.0034), and the mortality risks in the warm season were 1.0043 (95% CI: 1.0007, 1.0080) and 1.0038 (95% CI: 1.0005, 1.0072), respectively. The introduction of other pollutants (PM_2.5, PM₁₀, NO₂, CO) to construct a dual-pollutant model showed that the effect of O₃ on the mortality risk of IHD and stroke remained statistically significant. This study consolidates the evidence for a positive correlation between low-level O₃ exposure and the mortality risk of IHD and stroke. The findings provide preliminary exploratory insights into the potential impact of air pollution on these diseases, offering a valuable reference for future research.

PMID:40063105 | DOI:10.1007/s00484-025-02886-8

J Acoust Soc Am. 2025 Mar 1;157(3):1650-1664. doi: 10.1121/10.0036053.

ABSTRACT

In a recent research paper, we introduced the statistical wave field theory, which establishes the statistical laws of waves propagating in a bounded volume. These laws hold after many reflections on the boundary surface and at high frequency. The statistical wave field theory is the first statistical theory of reverberation that provides the closed-form expression of the power distribution and the correlations of the wave field jointly over time, frequency and space, in terms of the geometry and the specific admittance of the boundary surface. In this paper, we refine the theory predictions, by investigating the impact of a curved boundary surface on the wave field statistics. In particular, we provide an improved closed-form expression of the reverberation time in room acoustics that holds at lower frequency.

PMID:40063083 | DOI:10.1121/10.0036053

J Am Med Inform Assoc. 2025 Mar 10:ocaf023. doi: 10.1093/jamia/ocaf023. Online ahead of print.

ABSTRACT

OBJECTIVES: Large language models (LLMs) are increasingly utilized in healthcare, transforming medical practice through advanced language processing capabilities. However, the evaluation of LLMs predominantly relies on human qualitative assessment, which is time-consuming, resource-intensive, and may be subject to variability and bias. There is a pressing need for quantitative metrics to enable scalable, objective, and efficient evaluation.

MATERIALS AND METHODS: We propose a unified evaluation framework that bridges qualitative and quantitative methods to assess LLM performance in healthcare settings. This framework maps evaluation aspects-such as linguistic quality, efficiency, content integrity, trustworthiness, and usefulness-to both qualitative assessments and quantitative metrics. We apply our approach to empirically evaluate the Epic In-Basket feature, which uses LLM to generate patient message replies.

RESULTS: The empirical evaluation demonstrates that while Artificial Intelligence (AI)-generated replies exhibit high fluency, clarity, and minimal toxicity, they face challenges with coherence and completeness. Clinicians’ manual decision to use AI-generated drafts correlates strongly with quantitative metrics, suggesting that quantitative metrics have the potential to reduce human effort in the evaluation process and make it more scalable.

DISCUSSION: Our study highlights the potential of a unified evaluation framework that integrates qualitative and quantitative methods, enabling scalable and systematic assessments of LLMs in healthcare. Automated metrics streamline evaluation and monitoring processes, but their effective use depends on alignment with human judgment, particularly for aspects requiring contextual interpretation. As LLM applications expand, refining evaluation strategies and fostering interdisciplinary collaboration will be critical to maintaining high standards of accuracy, ethics, and regulatory compliance.

CONCLUSION: Our unified evaluation framework bridges the gap between qualitative human assessments and automated quantitative metrics, enhancing the reliability and scalability of LLM evaluations in healthcare. While automated quantitative evaluations are not ready to fully replace qualitative human evaluations, they can be used to enhance the process and, with relevant benchmarks derived from the unified framework proposed here, they can be applied to LLM monitoring and evaluation of updated versions of the original technology evaluated using qualitative human standards.

PMID:40063081 | DOI:10.1093/jamia/ocaf023