Tag: nevin manimala

Rev Med Inst Mex Seguro Soc. 2026 Mar 3;64(2):e6877. doi: 10.5281/zenodo.17537377.

ABSTRACT

BACKGROUND: Occupational hazards represent high costs as they have negative consequences not only on the worker who suffers an injury, but also on his or her environment and the employer.

OBJECTIVE: To carry out an analysis on the characterization of occupational accidents associated with the activity or workplace, commuting accidents and occupational diseases, suffered by beneficiaries of the Mexican Social Security Institute registered during the year 2024.

MATERIAL AND METHODS: Descriptive design, cross-sectional with a quantitative approach. The data were obtained through the consultation of the 2024 statistical report generated by the Mexican Social Security Institute.

RESULTS: Of the occupational risks that occurred in 2024, 69% were work accidents, 28% commuting accidents and 3% occupational diseases. Of these, 61% of the risks occurred in men and 39% in women. The age group with the highest frequency of occupational risks was 25 to 29 years old. The most frequent injuries resulting from these accidents were superficial trauma (114,606) and the most frequent occupational diseases were dorsopathies (2940).

CONCLUSIONS: Knowing the characteristics of occupational risks in Mexican workers will facilitate the appropriate focus of efforts for their prevention and therefore the reduction of costs derived from these risks.

PMID:41774846 | DOI:10.5281/zenodo.17537377

J Spec Oper Med. 2026 Mar 3:J.Spec.Oper.Med.2026.1SDN-NWTW. doi: 10.55460/J.Spec.Oper.Med.2026.1SDN-NWTW. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) has the potential to address training limitations and inter-operator variability that constrain the use of lung ultrasound (LUS) in austere and prehospital settings. This pilot study evaluated whether AI-based decision support could improve the diagnostic accuracy and confidence of United States Marine Corps Corpsmen in identifying absent lung sliding, a key indicator of pneumothorax, during LUS interpretation.

METHODS: This pilot-prospective multi-reader, multi-case study involved five military medics, all novices in point-of-care ultrasound, each interpreting 50 de-identified LUS video clips twice, once without AI assistance (control) and once with AI assistance (ATLAS, Deep Breathe Inc., London, Canada), in randomized order with at least a 2-hour washout between sessions. Expert consensus served as a reference standard. Diagnostic performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Differences were analyzed using the Random-Reader Random-Case method. Per-clip reader confidence ratings were compared using the Stuart-Maxwell test.

RESULTS: AI assistance significantly improved diagnostic performance across all measured outcomes. The mean AUROC increased from 0.72 (SD 0.16) without AI to 0.93 (SD 0.04) with AI (P=.03). Sensitivity rose from 0.63 (SD 0.14) to 0.90 (SD 0.09), specificity from 0.70 (SD 0.15) to 0.86 (SD 0.10), and overall accuracy from 0.67 (SD 0.10) to 0.88 (0.06) (McNemar’s test, P<.001). Reader confidence also improved, with high-confidence ratings nearly doubling from 20% to 37%, and low-confidence ratings decreasing from 38% to 33%. These distributional changes were statistically significant (Stuart-Maxwell χ², P<.001).

CONCLUSION: AI support markedly improved the diagnostic accuracy and confidence of novice LUS interpretation for detecting absent lung sliding. These findings suggest that real-time AI-based decision support may help improve access to high-quality LUS in military and other resource-limited care settings.

PMID:41774835 | DOI:10.55460/J.Spec.Oper.Med.2026.1SDN-NWTW

Proc Natl Acad Sci U S A. 2026 Mar 10;123(10):e2514297123. doi: 10.1073/pnas.2514297123. Epub 2026 Mar 3.

ABSTRACT

Liquid crystal mesophases of achiral molecules are normally achiral, yet in a few materials they spontaneously segregate and form right- and left-handed chiral domains. One mechanism that drives chiral segregation is molecular shape fluctuations between axial chiral conformations, where molecular interactions favor matching chirality and promote helical twist. Cooperative chiral ordering may also play a role in chirality amplification, as when a tiny fraction of chiral dopant drives a nematic phase to become cholesteric. We present a model of cooperative chiral ordering in liquid crystals using Maier-Saupe theory, and predict a phase diagram with a segregated cholesteric phase with alternating domains of left- and right-handed chiral twist, with opposite enantiomeric excess, in addition to racemic nematic and isotropic phases. Our model also demonstrates how chiral molecular fluctuations influence the helical twisting power of dopants in the nematic phase, which may be observed even in materials where the segregated cholesteric phase is preempted by a transition to another phase. We compare these results with Monte Carlo simulation studies of the switchable chiral Lebwohl-Lasher model, where each spin switches between right- and left-handed chiral states. Simulation results validate the predicted phase diagram, demonstrate chiral amplification in the racemic nematic phase, and reveal complex coarsening dynamics in the segregated cholesteric phase. These results suggest that molecular fluctuations between degenerate chiral configurations may be a common mechanism to produce cooperative chiral order in achiral liquid crystals.

PMID:41774802 | DOI:10.1073/pnas.2514297123

PLoS One. 2026 Mar 3;21(3):e0343981. doi: 10.1371/journal.pone.0343981. eCollection 2026.

ABSTRACT

BACKGROUND: The National Institute for Health and Care Research accepts applications for pilot and feasibility studies to their Research for Patient Benefit (RfPB) programme. There has been limited work describing the design practices of these applications and funding status. Knowing some of the qualities which may contribute towards a pilot or feasibility study application successfully gaining funding could help researchers improve the quality of their applications. Therefore, this study describes the protocol for a review looking at the characteristics of funded and non-funded external pilot trial applications. In particular, the primary objective is to describe the planned sample size and sample size justifications.

METHODS: The study will be conducted on 100 applications from Competition 31-37 with a randomised feasibility design, identified and given access to us by RfPB where the lead applicant has consented. We will screen these applications to identify the external pilot trials, first looking through the titles and then the full text. Following this, we will extract data on information such as medical area, study design, objective(s), sample size, sample size justification, and funding outcome stage one and two. Validation will be performed on 20% of the data extracted; discrepancies will be resolved by discussion or a third reviewer will decide if there is no consensus. We will use descriptive statistics to summarise quantitative data, and will analyse qualitative data using thematic analysis. Findings will be summarised through discussion with the project contributors to produce a reader-friendly guidance document.

DISCUSSION: This work will provide a more complete picture of RfPB external randomised pilot and feasibility trials. The findings will assist researchers when planning their pilot trials, and could help improve the quality of submitted applications.

PROTOCOL REGISTRATION: Open Science Framework protocol registration DOI: https://doi.org/10.17605/OSF.IO/PYKVG.

PMID:41774693 | DOI:10.1371/journal.pone.0343981

J Nephrol. 2026 Feb 3:aajaf013. doi: 10.1093/joneph/aajaf013. Online ahead of print.

ABSTRACT

BACKGROUND: Symptom burden in chronic kidney disease (CKD) patients has a negative impact on functional status and quality of life. Despite the high prevalence among hemodialysis (HD) patients, symptoms are often underestimated. We investigated the determinants of symptoms and their impact on mortality in a large cohort of HD patients.

METHODS: We analyzed 1825 HD patients for whom at least one questionnaire about symptoms was available. Machine learning-based algorithms were used to identify longitudinal variables associated with symptoms. Univariate and multivariate Cox regression analyses were used to investigate the association between symptoms and mortality. The additional prognostic value of symptoms was explored by using C-statistics and machine learning classification analyses.

RESULTS: Appetite Rating, Non-vascular Nervous System Score and Difficulty Following Diet were the variables most associated with symptoms, both at baseline and in longitudinal analyses. Survival analyses showed an independent association between symptoms and all the considered outcomes (Death, Hazard Ratio [HR]fper 10-unit increase: 0.91, 95% confidence interval [CI] 0.87-0.96, P < 0.001; Cardiovascular hospitalizations, HRper 10-unit increase: 0.89, 95% CI 0.84-0.94, P < 0.001; infectious hospitalizations, HRper 10-unit increase: 0.91, 95% CI 0.87-0.96, P < 0.001). However, classification analyses performed by machine learning showed that adding a symptom score to a base model did not significantly improve the performance of the models. Similarly, the improvement in R2, in discrimination power, and in reclassification capability was almost null.

DISCUSSION: In the large dataset of the Hemodialysis (HEMO) trial, we found that, among the analyzed variables, those related to eating habits and previous comorbidities were the most closely associated with the symptoms score, both at baseline and during follow-up. Although symptoms were associated with severe clinical outcomes, their prognostic power was limited. However, considering symptom burden in dialysis patients, strict monitoring of eating habits may help improve quality of life in these patients.

PMID:41774670 | DOI:10.1093/joneph/aajaf013

IEEE Trans Biomed Eng. 2026 Mar 3;PP. doi: 10.1109/TBME.2026.3669561. Online ahead of print.

ABSTRACT

OBJECTIVE: To enable reliable, noise-robust monitoring of lesion formation during radiofrequency (RF) ablation with intracardiac echocardiography (ICE), this study introduces Dynamic Distribution Entropy (DDE), which quantifies temporal-spatial changes in backscatter statistics while suppressing ablation-induced interference.

METHODS: DDE integrates ultrafast plane-wave acquisition with singular value decomposition clutter filtering to stabilize entropy estimates. We evaluated DDE in (i) simulations modeling ablation-driven scatterer-size changes and (ii) ex-vivo porcine hearts imaged by ICE during RF ablation. DDE was compared with typical Shannon entropy, k-nearest neighbor entropy, cumulative residual entropy, and horizontally normalized Shannon entropy. Metrics included structural similarity (SSIM), intersection-over-union (IoU), and lesion-size agreement versus optical ground truth.

RESULTS: In simulations, DDE and CRE closely tracked dynamic scatterer evolution, yielding highest SSIM over frames (0.96). In ex-vivo experiments, DDE demonstrated the most accurate lesion-size estimation under RF-ON conditions, exhibiting the lowest bias (4.85 mm²), standard deviation (1.53 mm²), and root mean square error (5.08 mm²) among all evaluated entropy-based methods. Lesion sizes derived from DDE exhibited the best agreement with optical measurements.

CONCLUSION: DDE provides robust and accurate intraoperative monitoring of lesion formation in ICE guided RF ablation, outperforming conventional entropy imaging under ablation-related noise.

SIGNIFICANCE: DDE offers a practical, noise-resistant quantitative ultrasound biomarker for real-time lesion assessment, supporting decision-making during ICE-guided cardiac ablation without requiring changes to clinical workflow.

PMID:41774665 | DOI:10.1109/TBME.2026.3669561

IEEE Trans Pattern Anal Mach Intell. 2026 Mar 3;PP. doi: 10.1109/TPAMI.2026.3669664. Online ahead of print.

ABSTRACT

Charts are common in literature across various scientific fields, conveying rich information easily accessible to readers. Current chart-related tasks focus on either chart perception that extracts information from the visual charts, or chart reasoning given the extracted data, e.g. in a tabular form. In this paper, we introduce StructChart, a novel framework that leverages Structured Triplet Representations (STR) to achieve a unified and label-efficient approach to chart perception and reasoning tasks, which is generally applicable to different downstream tasks, beyond the question-answering task as specifically studied in peer works. Specifically, StructChart first reformulates the chart data from the tubular form (linearized CSV) to STR, which can friendlily reduce the task gap between chart perception and reasoning. We then propose a Structuring Chart-oriented Representation Metric (SCRM) to quantitatively evaluate the chart perception task performance. To augment the training, we further explore the potential of Large Language Models (LLMs) to enhance the diversity in both chart visual style and statistical information. Extensive experiments on various chart-related tasks demonstrate the effectiveness and potential of a unified chart perception-reasoning paradigm to push the frontier of chart understanding.

PMID:41774644 | DOI:10.1109/TPAMI.2026.3669664

Otolaryngol Head Neck Surg. 2026 Mar 3. doi: 10.1002/ohn.70162. Online ahead of print.

ABSTRACT

OBJECTIVE: Determine whether shipboard motion variability relates to simulator‑sickness symptoms, Mal de Débarquement syndrome (MdDS) features, and cognition, and whether migraine or motion‑sickness history modify these vestibular effects.

STUDY DESIGN: Prospective observational cohort.

SETTING: USNS Mercy (T‑AH 19) during Pacific Partnership 2024.

METHODS: 38 Active‑Duty personnel were tested at baseline (land), after California-Hawaii (CA-HI; rougher), and after Chuuk-Hawaii (CHUUK-HI; calmer). A centrally mounted, inertial measurement unit (IMU) yielded per‑minute standard deviation of linear‑acceleration magnitude (IMU SD).

OUTCOMES: Simulator Sickness Questionnaire (SSQ); MdDS calculator mapped to Bárány criteria; Rey-Osterrieth Complex Figure (ROCF); Symbol Digit Modalities Test (SDMT); Stroop Test; linear mixed-effects model using voyage leg and IMU SD as predictors of symptom outcomes and adjusted for migraine and motion‑sickness history.

RESULTS: IMU SD was higher on CA-HI. SSQ totals were higher on CA-HI and increased with IMU SD. MdDS criteria counts, and cases were similar between legs; Migraine Disability Assessment Score (MIDAS) was positively associated with MdDS criterion burden, but not with SSQ. ROCF showed slower copy/recall and lower recall accuracy on CA-HI; SDMT and Stroop errors were largely unchanged. There were no statistical differences based on migraine or motion‑sickness history, but analyses were limited by small subgroup sizes.

CONCLUSION: Rougher sea states (greater IMU variability) were linked to higher acute symptom burden and specific visuospatial memory impairments. The incidence of persistent MdDS did not differ by leg. IMU‑informed monitoring with brief, targeted cognitive tests may support future planning and post‑voyage screening to identify at-risk individuals.

PMID:41774616 | DOI:10.1002/ohn.70162

Gerontology. 2026 Mar 3:1-18. doi: 10.1159/000550778. Online ahead of print.

ABSTRACT

BACKGROUND: Walking speed is widely recognised as an informative indicator of physical capability in later life, and is frequently used to evaluate mobility and functional health in older adults. Although the measurement of community walking speed (CWS) has been made possible using accelerometers, it is unclear whether discrepancies between laboratory walking speed (LWS) and CWS makes a difference in predicting the incidence of future disabilities in older adults.

OBJECTIVE: Using data from the National Center for Geriatrics and Gerontology-Study of Geriatric Syndromes (NCGG-SGS), we examined whether LWS and CWS each independently predict incident disability. The discrepancy was defined as the difference between LWS and CWS, and we further analyzed the characteristics of older adults who exhibited such discrepancies.

METHODS: Participants comprised 1,631 older adults (mean age = 70 years; 62.7% women). LWS was measured using a WalkWay device that measures the distribution of foot pressure during walking. CWS was derived using a model that estimated gait speed based on composite acceleration calculated from the mean triaxial acceleration within a single gait cycle. Participants were instructed to wear a triaxial accelerometer for at least 14 days. The incidence of disability was prospectively determined for five years.

RESULTS: The Cox proportional hazards regression models revealed a statistically significant association between the z-scores of LWS (hazard ratio [HR], 0.54; 95% confidence interval [CI], 0.41-0.72) and CWS (HR, 0.59; 95% CI, 0.41-0.83), and disability incidence. There were no statistically significant interactions between LWS and CWS for disability (HR, 1.00; 95% CI, 0.76-1.31). The area under the receiver operating characteristics curve (ROC AUC) of the LWS and CWS were 0.752 and 0.709, respectively. Regarding discrepancies between the fast LWS group and the fast CWS group, residual analysis showed that the fast LWS group had a higher proportion of women than the fast CWS group.

CONCLUSION: LWS and CWS were found to be independently associated with disability. Further research must determine how CWS should be interpreted, because participants who showed discrepancies between CWS and LWS did not exhibit a significant association with incident disability. Healthcare providers could use either CWS and LWS as a significant indicator in healthcare practice for older populations.

PMID:41774602 | DOI:10.1159/000550778

J Nephrol. 2026 Feb 24:aajaf015. doi: 10.1093/joneph/aajaf015. Online ahead of print.

ABSTRACT

BACKGROUND: Kidney transplant recipients with pre-existing atherosclerosis or coronary artery disease (CAD) have an increased risk of adverse post-transplant outcomes. However, the extent to which pre-transplant CAD influences mortality, cardiovascular events, and graft function remains unclear. This systematic review and meta-analysis aims to evaluate the impact of pre-existing CAD on all-cause mortality, post-transplant cardiovascular events, and graft failure in kidney transplant recipients.

METHODS: A systematic literature search was conducted using PubMed, Scopus, Web of Science, Cochrane Library, and Ovid MEDLINE. Studies reporting outcomes in kidney transplant recipients with and without pre-existing CAD were included. The primary outcomes were all-cause mortality, major cardiovascular events, and graft failure post-transplantation. Risk estimates were pooled using a random-effects model, with heterogeneity assessed using the I² statistic. The study protocol was registered with PROSPERO (CRD42024600751).

RESULTS: A total of 16 studies involving 112,416 kidney transplant recipients were included. Patients with pre-transplant CAD had a significantly higher risk of all-cause mortality compared to those without CAD (hazard ratio [HR] = 1.68, 95% confidence Interval [CI]: 1.38-2.06, P < .01), with high heterogeneity (I² = 60.0%). The risk of post-transplant cardiovascular events was also significantly increased in patients with CAD (HR = 2.78, 95% CI: 2.00-3.86, P < .01), with moderate heterogeneity (I² = 36.1%). Graft failure was more common in recipients with pre-transplant CAD, although the effect size was smaller (HR = 1.09, 95% CI: 1.03-1.16, P < .01), with no observed heterogeneity (I² = 0%).

CONCLUSIONS: Pre-existing CAD in kidney transplant recipients is associated with a significantly increased risk of all-cause mortality and post-transplant cardiovascular events, as well as a modestly but significantly increased risk of graft failure. These findings stress the need for enhanced cardiovascular risk assessment and management strategies in transplant candidates with CAD to improve long-term outcomes.

PMID:41774596 | DOI:10.1093/joneph/aajaf015