Purpose The abductor hallucis (ABDH) contributes to foot function and mobility, which can influence quality of life, making accurate assessment of muscle volume important. While magnetic resonance imaging (MRI) is often considered to be the reference standard of medical imaging, ultrasound (US) provides a cost-effective, rapid and dynamic way of assessing muscle size and function. This study aimed to evaluate the criterion validity and intrarater reliability of US-derived ABDH muscle volume measurements compared with MRI when image analysis was performed by assessors with limited experience. Methods Us And MRI were employed to measure the right and left ABDH muscle volume involving 22 participants (females = 12; males = 10). Following exclusions, 37 feet were available for analysis, with 36 complete MRI-US observations. Muscle volume of the ABDH was calculated across the muscle belly every 0.5 cm both manually by utilizing a truncated cone (TC) formula and with a semi-automated tool in the OsiriX (OX) software. Intrarater reliability was assessed using intraclass correlation coefficients (ICC), standard error of measurement (SEm), and minimal detectable change (MDC). Criterion validity was evaluated using Pearson correlations, Bland-Altman analyses, and two one-sided tests (TOST) with a determined ±10% equivalence margin. Results Side-specific intrarater reliability was high across methods (ICC(3,1) = 0.932–0.997), with SEm values of 0.251–0.709 cm³ and relative MDC values of 4.48%−12.95%. Very strong associations were observed among methods (r = 0.906–0.995). Bland-Altman limits-of-agreement half-widths were 6.52%−9.84% for MRI-MRI and 17.86%−27.35% for MRI-US comparisons. All three method comparisons met the ± 10% equivalence criterion. Conclusion US-derived ABDH volume measurements demonstrated high intrarater reliability and statistical equivalence to MRI within the ± 10% margin. Although MRI-US comparisons showed greater individual-level variability than MRI-MRI comparisons, US may provide a practical alternative for assessing ABDH muscle volume when MRI is impractical.
Sepsis is heterogeneous, and transcripts that distinguish sepsis from health may lack clinical specificity. We tested whether S100A8 is reproducibly associated with sepsis and maps to a donor-aware myeloid state defined independently of S100A8 . A discovery whole-blood cohort was analyzed on the log2 robust multi-array average scale using maximum-probe aggregation, limma, weighted gene co-expression network analysis (WGCNA), and leakage-controlled feature prioritization. The analysis identified 1,508 differentially expressed genes; 265 overlapped the strongest sepsis-positive WGCNA module. The network retained all 802 samples and 11,719 genes; the black-module eigengene correlated with sepsis status (r = 0.409; false-discovery rate 3.72 × 10 − 33), and S100A8 belonged to this module (module membership 0.700). S100A8 was upregulated (log2 fold change 1.87; false-discovery rate 2.15 × 10 − 171). Discrimination against clinical comparators was contrast dependent, and outcome analyses did not support a stable prognostic association. Donor-level monocyte pseudo-bulk analysis supported increased S100A8 and decreased HLA-DRA but included only two control and four sepsis donors. Across single-cell datasets, S100A8 tracked an emergency-myeloid program and inversely tracked antigen presentation. In THP-1 cells, S100A8 knockdown attenuated lipopolysaccharide-associated reductions in CD74 and HLA-DRα. Triptolide retained a similar NF-κB phosphorylation effect after S100A8 knockdown, providing no evidence that this effect required S100A8 . S100A8 is therefore prioritized as a marker of a sepsis-associated emergency-myeloid, HLA-DR-low state, not as an established diagnostic, prognostic, direct drug-binding, or therapeutic target.
Researchers have long studied allostatic (over)load as an estimated measure of individual cumulative stress over a lifetime. Often called the overall ‘wear and tear’ from social and environmental stressors, allostatic (over)load shows promise as a practical indicator of general health trends in community settings. This data processing workflow aims to document our overall approach and reasoning when calculating allostatic (over)load for data analysis and knowledge sharing. The included repository features an R script for generating datasets using this workflow from the following data sources: All of Us Research Program data repository. Health and Retirement Study (HRS). National Health and Nutrition Examination Survey (NHANES). Our allostatic (over)load measurement process, along with the linked repository, provides a reproducible workflow to process secondary data and offers insights into protocol-driven measurement practices in community environments.
Early identification of patients at risk for lower-extremity deep vein thrombosis (LEDVT) after severe trauma remains difficult. This retrospective cohort study evaluated whether admission aggregate index of systemic inflammation (AISI), derived from platelet, neutrophil, monocyte, and lymphocyte counts, was associated with ultrasound-confirmed in-hospital LEDVT detected before the earlier of discharge or post-injury day 30 and whether it improved risk stratification. Among 223 adults with an Injury Severity Score of 16 or higher and evaluable bilateral lower-extremity ultrasonography, 93 (41.7%) had LEDVT. Each 1-SD increase in log-transformed AISI was associated with LEDVT after adjustment for age, sex, Injury Severity Score, Glasgow Coma Scale score, and shock index (odds ratio [OR], 2.07; 95% CI, 1.47–2.91; P < 0.001) and after additional adjustment for D-dimer, fibrin/fibrinogen degradation products, prothrombin time, activated partial thromboplastin time, and fibrinogen (OR, 1.86; 95% CI, 1.26–2.73; P = 0.002). Adding AISI to clinical variables increased the area under the receiver operating characteristic curve (AUC) from 0.619 to 0.714 (AUC difference, 0.095; bootstrap 95% CI, 0.023 to 0.168; P = 0.009). After coagulation markers were included, the AUC difference was smaller and uncertain (0.018; 95% CI, −0.010 to 0.046; P = 0.166), although the Brier score improved by 0.014 (P = 0.020). For Model 4, bootstrap-corrected and repeated-cross-validation AUCs were 0.797 and 0.790, and calibration slopes were 0.831 and 0.773, respectively. With 93 events and 11 candidate parameters, these internally validated results indicate shrinkage and require prospective external validation; AISI should be interpreted as a complementary admission marker.
The Artificial Potential Field (APF) method is widely used in robot path planning due to its simplicity and computational efficiency. However, it is well known to suffer from the local minima problem, where the resultant force acting on the robot becomes zero at non-goal configurations, leading to motion stagnation in cluttered environments. This paper proposes an optimization-based waypoint generation framework to address the local minima issue in APF navigation while ensuring explicit collision avoidance. When the direct path between the robot and the target is infeasible due to obstacle interference, intermediate waypoints are generated by solving a constrained optimization problem that guides the robot along a locally defined reference direction while enforcing obstacle clearance constraints for each path segment. Unlike conventional APF modifications that rely on artificial perturbation forces or random escape strategies, the proposed method constructs collision-free intermediate waypoints through finite candidate evaluation and explicit segment-wise collision checking, enabling the robot to avoid obstacles and overcome local-minima while preserving goal-directed motion and ensuring convergence to the target. The effectiveness of the proposed approach is validated through numerical simulations on two robotic platforms, namely a point-mass robot and a wheelchair-type robotic model. The results demonstrate that the method successfully resolves local minima situations, ensures collision-free motion, and consistently guides the robots to their target locations in complex environments. Furthermore, the framework is general and can be applied to different robotic systems.
