Tag: nevin manimala

Front Psychol. 2026 Aug 12;17:1861330. doi: 10.3389/fpsyg.2026.1861330. eCollection 2026.

ABSTRACT

Physical education assessments that count toward university admission or course grades typically measure only a student’s fitness at a single time point, overlooking incremental progress and thus providing little incentive for those with lower baseline performance. This study evaluated the feasibility of a multidimensional scoring method that captures changes in standing long jump performance, an indicator of lower-limb explosive power and better reflects student learning. This study had two phases. Phase 1 combined a two-round Delphi process, AHP weighting, and standing long jump data from 211 classes (N = 4,109) to develop the multidimensional evaluation model. Phase 2 is a quasi-experimental study where 4 year 1-2 university classes was randomly selected and assigned to the intervention (IG) and control group (CG). Learning motivation was measured using the college student version of the Work Preference Inventory. Difference-in-difference model was used to analyze the association between motivation and standing long jump distance (post-pre) between IG and CG. Both IG and CG showed significant improvements in both standing long jump and motivation, with IG having significantly better outcomes. The time interaction between the groups confirmed the statistical significance. The OLS regression results showed both internal motivation (p < 0.001) and external motivation (p < 0.001) had significant positive relationship. Both boys’ (p < 0.001) and girls’ (p = 0.029) showed significant improvement. These findings suggest that a multidimensional, progress-sensitive scoring system can enhance students’ motivation and foster positive behavioral change in physical education.

PMID:42656224 | PMC:PMC13506395 | DOI:10.3389/fpsyg.2026.1861330

Front Dent Med. 2026 Aug 12;7:1847615. doi: 10.3389/fdmed.2026.1847615. eCollection 2026.

ABSTRACT

INTRODUCTION: Regenerative endodontic therapy aims to restore the pulp-dentin complex through biological approaches; however, current cell-free techniques often fail to achieve true dental pulp regeneration and may result in fibrous tissue formation or canal calcification. This study investigated dynamic hydrogels as advanced scaffolds for dental pulp stem cell (DPSC)-mediated regeneration, leveraging their viscoelastic properties to regulate cellular behavior and immune responses.

METHODS: Dynamic hydrogels were synthesized using host-guest chemistry (Gel-Mal/HA-Ada-CD-SH) to mimic aspects of the native extracellular matrix and were compared with non-dynamic Gel-Mal hydrogels. DPSCs and macrophages were encapsulated within the hydrogels to evaluate cellular viability, morphology, inflammatory marker expression, and gene expression profiles.

RESULTS: Dynamic hydrogels exhibited a distinct porous microarchitecture and significantly increased NF-κB and VEGF expression while reducing IL-1RA expression compared with non-dynamic hydrogels. Gene expression analysis demonstrated significantly increased TGFβ1, SMAD3, and MAPK expression, together with reduced CD80 expression, in macrophages cultured within dynamic hydrogels. Although iNOS expression was lower in the dynamic hydrogel group, the difference was not statistically significant.

DISCUSSION: These findings suggest that dynamic hydrogels do not simply suppress inflammatory signaling but may modulate inflammatory and tissue remodeling pathways while promoting angiogenesis-associated responses. Limitations of the study include the short-term in vitro design and evaluation of a limited panel of inflammatory and fibrosis-associated markers. Within these limitations, dynamic hydrogels demonstrated favorable cellular and immunomodulatory responses and may provide a biologically active microenvironment that supports cellular activities relevant to regenerative endodontic applications. Further in vivo studies are required to validate their translational potential.

PMID:42656218 | PMC:PMC13506308 | DOI:10.3389/fdmed.2026.1847615

Front Public Health. 2026 Aug 12;14:1878385. doi: 10.3389/fpubh.2026.1878385. eCollection 2026.

ABSTRACT

BACKGROUND: About 52 million residents are insured under the single-payer National Health Insurance (NHI) of South Korea. Twenty-six board-certified clinical specialties practice under this system, yet specialty-level matching between physician manpower and NHI-financed workload has not been reported.

METHODS: This study is an ecological cross-sectional analysis using specialty (n = 26) as the unit of observation. For 2023 and 2024, annual NHI reimbursement per specialty was divided by the mean specialist headcount to yield per-specialist reimbursement. This indicator captures facility-level resource use rather than physician effort in isolation. Specialties were placed on a reimbursement × application-rate plane and sorted into four quadrants. Inter-specialty reallocation was simulated at 100, 70, and 50% efficiency.

RESULTS: Per-specialist reimbursement was 52.5 million KRW (approximately USD 40,000) in radiology (n = 4,247) and 2,668.6 million KRW (approximately USD 2.04 million) in radiation oncology (n = 330). The resulting ratio was 50.8-fold. Coefficient of variation exceeded 90% in both years. Five specialties combined above-median reimbursement with sub-100% application rates; seven showed the reverse pattern. Utilization-volume triangulation confirmed that per-specialist reimbursement was strongly correlated with claims per specialist (Spearman ρ = 0.74) and patients per specialist (ρ = 0.66), supporting its interpretation as a workload-related indicator. The rank-order association between reimbursement and application rate was not statistically detectable (ρ = 0.180; 95% confidence interval, -0.24 to 0.54; p = 0.400), and the study was underpowered for this secondary test. A counterfactual calculation, presented as an upper-bound illustration rather than a policy projection, indicated that relocating 100 specialists from lower-reimbursement to higher-reimbursement quadrants at 70% efficiency corresponded to 68.8 billion KRW in annual NHI reimbursement.

CONCLUSION: Across 2 study years, the 50.8-fold gap between these two specialties did not narrow. This dispersion, which reflects NHI-financed resource consumption rather than physician effort alone, is the principal finding. A statistically detectable link between trainee competition and NHI reimbursement was not observed; the secondary correlation test was underpowered, and its null result is not evidence of no association. These findings provide a specialty-level reimbursement baseline for South Korea’s workforce allocation debate.

PMID:42656209 | PMC:PMC13506305 | DOI:10.3389/fpubh.2026.1878385

Front Med (Lausanne). 2026 Aug 12;13:1891922. doi: 10.3389/fmed.2026.1891922. eCollection 2026.

ABSTRACT

Basal cell carcinoma (BCC) arising in sun-protected genital sites is uncommon and lies outside the typical ultraviolet-driven model of disease. We retrospectively reviewed 41 patients with primary genital BCC treated at a tertiary referral center in China between January 2010 and December 2025, assessing clinicopathologic features, sex-stratified outcomes, p16 expression, and high-risk human papillomavirus (HPV) DNA status. The cohort included 29 males and 12 females. Female patients were older at diagnosis than male patients (74.8 ± 10.8 vs. 65.4 ± 12.0 years; p = 0.019). Nodular BCC was the predominant histologic subtype (29/41, 70.7%), and aggressive histologic components were present in 7 cases (17.1%). p16 immunoreactivity was detected in 4 of 37 successfully tested cases (10.8%), and high-risk HPV DNA was detected in 2 of 27 successfully tested cases (7.4%). Clinical presentation descriptors were retrospectively extracted from routine medical records and were incompletely documented. During a median follow-up of 51 months, five patients developed local recurrence and one male patient developed inguinal lymph node metastasis. In this exploratory cohort, no statistically significant sex-associated difference in recurrence-free survival was detected (log-rank p = 0.65), but the small number of events precluded inference of equivalent outcomes or exclusion of clinically meaningful sex-related differences. In this Asian cohort, genital BCC was usually nodular, only infrequently associated with detectable high-risk HPV in available tested specimens, and generally favorable after surgery. Because genital lesions may be concealed and clinically non-specific, biopsy should be considered for persistent or suspicious genital lesions, with ongoing postoperative surveillance.

PMID:42656207 | PMC:PMC13506306 | DOI:10.3389/fmed.2026.1891922

Cureus. 2026 Jul 26;18(7):e113419. doi: 10.7759/cureus.113419. eCollection 2026 Jul.

ABSTRACT

Cardiac telemetry monitoring is an essential tool for assessing cardiac function in hospitalized patients; however, its overuse in low-risk populations can increase healthcare costs, alarm fatigue, and unnecessary resource utilization. This study aimed to assess the use of cardiac telemetry at a tertiary care center in Puerto Rico. Using a retrospective cross-sectional review of medical records, we evaluated whether telemetry was used appropriately according to established clinical criteria and identified patterns of overuse. The findings provide a foundation for developing an institutional telemetry protocol to optimize telemetry utilization, enhance patient safety, and improve cost-effectiveness. A total of 822 hospitalized patients receiving cardiac telemetry were included. Variables such as history of cardiac disease, comorbidities, length of stay, indication for telemetry, and duration of monitoring were analyzed. Our findings revealed that 476 (57.9%) patients received telemetry for non-evidence-based reasons, while 346 (42.1%) had clear clinical indications. Patients with non-evidence-based telemetry had longer monitoring durations, averaging 5.68 ± 5.90 days compared to 2.98 ± 2.94 days for those with evidence-based indications. They also spent slightly less time in the emergency department, with a mean of 7.18 ± 4.73 hours, compared to 7.98 ± 6.44 hours for the evidence-based group. Although not statistically significant (p = 0.137), the mean length of stay was higher among non-evidence-based cases (6.73 ± 8.25 days) than among evidence-based cases (4.46 ± 7.38 days). These differences contributed to an estimated cost of $4,711 per patient for non-evidence-based telemetry compared to $3,122 per patient for evidence-based monitoring. This study suggests that implementing standardized, evidence-based guidelines for telemetry initiation and discontinuation could reduce overuse, lower costs, decrease alarm fatigue, and maintain patient safety while showcasing the potential benefits of a hospital-wide protocol to optimize telemetry practices.

PMID:42656202 | PMC:PMC13506248 | DOI:10.7759/cureus.113419

Philos Trans A Math Phys Eng Sci. 2026 Aug 27;384(2327):20250073. doi: 10.1098/rsta.2025.0073.

ABSTRACT

Conformal prediction (CP) has emerged as a cutting-edge methodology in statistics and machine learning, providing prediction intervals with finite-sample frequentist coverage guarantees. Yet, its interplay with Bayesian statistics-often criticized for lacking frequentist guarantees-remains underexplored. Recent work has suggested that CP can ‘calibrate’ Bayesian prediction regions, thereby imparting frequentist validity and motivating deeper investigation into frequentist-Bayesian hybrids. On the other side, Bayesian procedures have the potential to enhance CP with more informative intervals, towards nearly optimal solutions under a decision-theoretic framework. Thus, the two paradigms can be jointly used for a principled balance between validity and efficiency. This work provides a unified treatment of this emerging interface with open directions. After surveying existing ideas, we consolidate the literature with a Bayesian version of split CP and present a simple analysis of the binomial model, investigating priors’ role, efficiency and computational complexity. This article is part of the theme issue ‘Advancing uncertainty quantification in AI systems’.

PMID:42656165 | DOI:10.1098/rsta.2025.0073

Philos Trans A Math Phys Eng Sci. 2026 Aug 27;384(2327):20250067. doi: 10.1098/rsta.2025.0067.

ABSTRACT

As artificial intelligence (AI) systems are being widely deployed in safety-critical and high-stakes applications (e.g. medical diagnosis, autonomous vehicles, financial risk assessment), there is a growing demand for providing reliable and trustworthy machine predictions. However, since AI models become more complex in structure (a prominent example being deep neural networks) and bigger in size (e.g. large language model systems such as ChatGPT and Gemini), being able to understand, explain and quantify confidence in their predictions are ongoing challenges. Therefore, this special issue is dedicated to exploring the forefront of reliable uncertainty quantification in AI systems, using conformal prediction (CP), a leading statistical framework that offers predictions with valid coverage guarantees under minimal assumptions. The issue comprises the most recent, most novel and most practical developments of CP-based methods in cutting-edge AI applications, highlighting improvements over traditional methods.

PMID:42656163 | DOI:10.1098/rsta.2025.0067

Philos Trans A Math Phys Eng Sci. 2026 Aug 27;384(2327):20250080. doi: 10.1098/rsta.2025.0080.

ABSTRACT

Conformal predictive systems are sets of predictive distributions with theoretical out-of-sample calibration guarantees. The calibration guarantees are typically that the set contains a forecast distribution whose prediction intervals exhibit the correct marginal coverage at all levels. Conformal predictive systems are constructed using conformity measures that quantify how well possible outcomes conform with historical data. However, alternative methods have been proposed to construct predictive systems with more appealing theoretical properties. We study an approach to construct predictive systems that we term residual distribution predictive systems (RDPSs). In the split conformal setting, this approach nests conformal predictive systems with a popular class of conformity measures, providing an alternative perspective on the classical approach. In the full conformal setting, the two approaches differ, and the new approach has the advantage that it does not rely on a conformity measure satisfying fairly stringent requirements to ensure that the predictive system is well-defined; it can readily be implemented alongside any point-valued regression method to yield predictive systems with out-of-sample calibration guarantees. The empirical performance of this approach is assessed using simulated data, where it is found to perform competitively with conformal predictive systems. However, the new approach offers considerable scope for implementation with alternative regression methods. This article is part of the theme issue ‘Advancing uncertainty quantification in AI systems’.

PMID:42656161 | DOI:10.1098/rsta.2025.0080

Philos Trans A Math Phys Eng Sci. 2026 Aug 27;384(2327):20250069. doi: 10.1098/rsta.2025.0069.

ABSTRACT

Current experimental scientists have been increasingly relying on simulation-based inference (SBI) to invert complex nonlinear models with intractable likelihoods. However, posterior approximations obtained with SBI are often miscalibrated, causing credible regions to undercover true parameters. We develop CP4SBI, a model-agnostic conformal calibration framework that constructs credible sets with local Bayesian coverage. Our two proposed variants, namely local calibration via regression trees and cumulative distribution function CDF-based calibration, enable finite-sample local coverage guarantees for any scoring function, including highest posterior density (HPD), symmetric and quantile-based regions. Experiments on widely used SBI benchmarks demonstrate that our approach improves the quality of uncertainty quantification for neural posterior estimators (NPEs) using both normalizing flows and score-diffusion modelling. This article is part of the theme issue ‘Advancing uncertainty quantification in AI systems’.

PMID:42656156 | DOI:10.1098/rsta.2025.0069

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2026 Aug 25;43(4):845-852. doi: 10.7507/1001-5515.202502034.

ABSTRACT

Systemic lupus erythematosus (SLE) frequently involves the central nervous system, inducing abnormal alterations in brain functional and structural networks and resulting in cognitive and psychological dysfunction in patients. To identify abnormal brain functional network patterns associated with SLE, this study adopted a dynamic threshold strategy to detect key functional connections and construct sparse brain functional networks. A graph transformation network (GTNet) was utilized to model the optimized networks, capturing local topological features and global dependencies to effectively identify abnormal patterns of SLE-related brain functional networks. Experimental results showed that the proposed model achieved an average classification accuracy of (87.48 ± 6.77)% on the resting-state functional magnetic resonance imaging dataset consisting of 107 SLE patients and 107 healthy controls. Further analysis showed that the difference in small-world properties between the two groups was statistically significant ( t = -2.96, P < 0.01). In conclusion, the model constructed in this study provides a new scheme for the auxiliary diagnosis of SLE, and its automated classification architecture has potential application value.

PMID:42656117 | DOI:10.7507/1001-5515.202502034