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Diabetic foot ulcer (DFU) is a critical complication of diabetes, but the wound microenvironment and its healing process are not completely understood. In this study, we optimized single-cell profiling from sharp debrided ulcers. Our findings demonstrate that healing-DFUs were significantly enriched with distinct fibroblasts expressing genes related to inflammation (CHI3L1, IL6) and extracellular matrix remodeling (ASPN), validating our previous studies on surgically resected ulcers. The race-focused analysis depicted lower expression of key healing-associated genes such as CHIL3L1, MMP11, and SFRP4 in fibroblasts of non-Hispanic Black (NHB) patients compared to White patients. In cellular communication analysis, healing enriched fibroblasts of NHBs exhibited upregulation of signaling pathways such as WNT while those of White showed IGF and MK pathways upregulation. Our findings advocate race as a risk marker of DFU outcomes, likely reflecting underlying disparities in environmental exposures and access to care that profoundly influence healing markers. Using sharp debrided tissues for single-cell assays, this study highlights the need for in-depth investigations into dysregulated wound healing microenvironments of under-represented racial groups., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Purpose: This study aimed to investigate the peak performance characteristics of the world top-8 swimmers and the key factors involved in the journey toward achieving better peak performance., Methods: The results of the world top-8 swimmers from 2001 to 2022 were collected from the World Aquatics performance database. Progression to peak performance was tracked with individual quadratic trajectories (1191 cases). Utilizing k-means clustering to group competitive feature variables, this study investigated key developmental factors through a binary logistic regression model, using the odds ratio (OR) to represent whether a factor was favorable (OR > 1) or unfavorable (OR < 1)., Results: Significant differences (P < .001) in the peak age between men (23.54/3.80) and women (22.31/4.60) were noticed, while no significant differences (P > .05) in the peak-performance window for both sexes appeared. Peak performance occurred at later ages for the sprint for both sexes, and women had a longer duration in peak-performance window for sprint (P < .05). Peak-performance occurred at later ages for the breaststroke and butterfly for both sexes (P < .05). Binary logistic regression revealed that high first-participation performance (OR = 1.502), high major-competition performance (OR = 4.165), early first-major-competition age (OR = 1.441), participation frequency above 4 times/year in both phase 2 (4.3-8.0 times/y, OR = 3.940; 8.1-20.0 times/y, OR = 5.122) and phase 3 (4.1-7.5 times/y: OR = 5.548; 7.7-15.0 times/y: OR = 7.526), and a career length of 10 years or more (10-15 y, OR = 2.102; 16-31 y, OR = 3.480) were favorable factors for achieving better peak performance., Conclusions: Peak performance characteristics varied across sex, swimming stroke, and race distance in the world top-8 swimmers. Meanwhile, the research indicated that certain specific developmental factors were key conditions for the world top-8 swimmers to achieve better peak performance in the future.

In the era of continuous development in Internet of Things (IoT) technology, smart services are penetrating various facets of societal life, leading to a growing demand for interconnected devices. Many contemporary devices are no longer mere data producers but also consumers of data. As a result, massive amounts of data are transmitted to the cloud, but the latency generated in edge-to-cloud communication is unacceptable for many tasks. In response to this, this paper introduces a novel contribution-a layered computing network built on the principles of fog computing, accompanied by a newly devised algorithm designed to optimize user tasks and allocate computing resources within rechargeable networks. The proposed algorithm, a synergy of Lyapunov-based, dynamic Long Short-Term Memory (LSTM) networks, and Particle Swarm Optimization (PSO), allows for predictive task allocation. The fog servers dynamically train LSTM networks to effectively forecast the data features of user tasks, facilitating proper unload decisions based on task priorities. In response to the challenge of slower hardware upgrades in edge devices compared to user demands, the algorithm optimizes the utilization of low-power devices and addresses performance limitations. Additionally, this paper considers the unique characteristics of rechargeable networks, where computing nodes acquire energy through charging. Utilizing Lyapunov functions for dynamic resource control enables nodes with abundant resources to maximize their potential, significantly reducing energy consumption and enhancing overall performance. The simulation results demonstrate that our algorithm surpasses traditional methods in terms of energy efficiency and resource allocation optimization. Despite the limitations of prediction accuracy in Fog Servers (FS), the proposed results significantly promote overall performance. The proposed approach improves the efficiency and the user experience of Internet of Things systems in terms of latency and energy consumption.

Background: Recurrent brain tumors are the leading cause of cancer death in children. Indoleamine 2,3-dioxygenase (IDO) is a targetable metabolic checkpoint that, in preclinical models, inhibits anti-tumor immunity following chemotherapy., Methods: We conducted a phase I trial (NCT02502708) of the oral IDO-pathway inhibitor indoximod in children with recurrent brain tumors or newly diagnosed diffuse intrinsic pontine glioma (DIPG). Separate dose-finding arms were performed for indoximod in combination with oral temozolomide (200 mg/m2/day x 5 days in 28-day cycles), or with palliative conformal radiation. Blood samples were collected at baseline and monthly for single-cell RNA-sequencing with paired single-cell T cell receptor sequencing., Results: Eighty-one patients were treated with indoximod-based combination therapy. Median follow-up was 52 months (range 39-77 months). Maximum tolerated dose was not reached, and the pediatric dose of indoximod was determined as 19.2 mg/kg/dose, twice daily. Median overall survival was 13.3 months (n = 68, range 0.2-62.7) for all patients with recurrent disease and 14.4 months (n = 13, range 4.7-29.7) for DIPG. The subset of n = 26 patients who showed evidence of objective response (even a partial or mixed response) had over 3-fold longer median OS (25.2 months, range 5.4-61.9, p = 0.006) compared to n = 37 nonresponders (7.3 months, range 0.2-62.7). Four patients remain free of active disease longer than 36 months. Single-cell sequencing confirmed emergence of new circulating CD8 T cell clonotypes with late effector phenotype., Conclusions: Indoximod was well tolerated and could be safely combined with chemotherapy and radiation. Encouraging preliminary evidence of efficacy supports advancing to Phase II/III trials for pediatric brain tumors., (© The Author(s) 2023. Published by Oxford University Press on behalf of the Society for Neuro-Oncology.)

Delay-sensitive tasks account for an increasing proportion of all tasks on the Internet of Things (IoT). How to solve such problems has become a hot research topic. Delay-sensitive tasks scenarios include intelligent vehicles, unmanned aerial vehicles, industrial IoT, intelligent transportation, etc. More and more scenarios have delay requirements for tasks and simply reducing the delay of tasks is not enough. However, speeding up the processing speed of a task means increasing energy consumption, so we try to find a way to complete tasks on time with the lowest energy consumption. Hence, we propose a heuristic particle swarm optimization (PSO) algorithm based on a Lyapunov framework (LPSO). Since task duration and queue stability are guaranteed, a balance is achieved between the computational energy consumption of the IoT nodes, the transmission energy consumption and the fog node computing energy consumption, so that tasks can be completed with minimum energy consumption. Compared with the original PSO algorithm and the greedy algorithm, the performance of our LPSO algorithm is significantly improved.