PLoS ONE最新文献

This study evaluated the effectiveness of an app-supported training model versus conventional physical education (PE) on college students' basic athletic abilities and described exercise motivation in the app-supported condition. A quasi-experimental study was conducted with 90 freshman students over a 10-week period. Participants were assigned to a Treatment Group (n = 45), using the "Campus Sports World" app for guided extracurricular training, and a Control Group (n = 45) following traditional instruction. Physical fitness was assessed pre- and post-intervention using standardized tests for speed, endurance, explosive power, and flexibility. Motivation was assessed post-intervention in the Treatment Group via a questionnaire based on Self-Determination Theory. Results indicated that the Treatment Group achieved significantly greater improvements in speed, explosive power, and flexibility compared to the Control Group. However, no significant difference was found in endurance performance. Furthermore, students in the Treatment Group reported high levels of both intrinsic and extrinsic motivation. These findings suggest that integrating mobile applications into college PE can enhance skill-related fitness, and the observed motivation profile provides context for students' engagement with the app-supported model, supporting the adoption of blended learning approaches in higher education.

Terahertz (THz) communication is a promising enabler for next-generation wireless networks because it can support ultra-high data rates. However, severe path loss, molecular absorption, and high sensitivity to blockage significantly limit coverage and reliability. To address these challenges, this work proposes a RIS-assisted UAV positioning (RAVP) framework that integrates reconfigurable intelligent surfaces (RIS) with unmanned aerial vehicles (UAVs) and jointly optimizes RIS configuration and UAV deployment to enhance THz communications. RISs provide controllable reflections to improve propagation conditions, while UAVs enable flexible placement of RISs at advantageous locations. A reinforcement learning (RL)-based strategy that combines modified K-means clustering with gradient-based optimization coordinates user grouping, RIS phase-shift adaptation, and UAV positioning within a unified framework. Simulation results show consistent gains in link robustness, achievable data rate, and user connectivity across different network configurations compared with conventional THz systems without RISs or UAV-assisted optimization. These findings highlight the potential of coordinated RIS-UAV optimization for future 6G-enabled wireless networks, including smart-city and Internet of Things (IoT) applications.

Obesity increases oxidative stress and inflammation and thereby promotes liver damage and metabolic disorders, such as type 2 diabetes. Although exist anti-obesity treatments with employing anorexic drugs, antioxidants, and β3-adrenergic receptor (β3-ADR) agonists, their use adversely effects human health. Herein, the agonistic effect of 5-(((benzo[d]thiazol-2-ilimino)(methylthio)methyl)amino)-2-hydroxybenzoic acid (C1) on β3-ADRs as well as its hepatoprotective and anti-inflammatory properties are investigated in obese Zucker rats. When compared to clobenzorex, C1 (12 mg/Kg bodyweight per day) intragastrically administered for 28 days significantly reduced the bodyweight and daily growth of the animals (p < 0.0001), as it stimulated thermogenesis by activating the uncoupling protein 1 (UCP-1) (p = 0.0090), which was mediated by the positive expression of β3-ADR (p = 0.0149). C1-induced β3-ADR activation was attributed to the formation of conventional hydrogen bonds between the hydroxyl and secondary amine groups of C1 with ASP117, VAL121, and THR122 at the catalytic site on the receptor. In addition, C1 significantly decreased the levels of free fatty acid (p < 0.0001) and tumor necrosis factor alpha (p = 0.0073). Furthermore, C1 increased glutathione levels (p = 0.0245), and showed a tendency to decrease lipid oxidation and interleukin 6 levels. Therefore, these findings nevertheless suggested that C1 acted as a β3-ADR agonist and exerted hepatoprotective and anti-inflammatory effects on obese Zucker rats.

Mobile navigation apps like Google Maps and Apple Maps predominantly use turn-by-turn (TBT) instructions for pedestrians, a paradigm originally designed for vehicular navigation. However, many users actively choose the simpler route preview (RP) mode, which displays only the mapped route without real-time guidance features. This research employs a three-study mixed-methods approach to investigate whether RP could serve as an effective alternative to TBT for pedestrian navigation. Study 1 surveyed user preferences (n = 222), revealing that 44% prefer RP despite its limited features, rising to 76% in familiar environments. Study 2 compared actual navigation performance (n = 195), finding no significant differences between modes across key metrics: navigation errors, phone glances, and spatial learning, with RP on par and even better for some key metrics. Study 3 was a co-design workshop (n = 5) to identify priority enhancements, including landmark integration, intention-based routing, and subtle orientation aids. Overall, these findings challenge the assumption that TBT represents optimal pedestrian navigation design. Instead, they demonstrate that RP, with its cognitive engagement benefits and user autonomy, performs comparably to TBT despite fewer features. When enhanced with targeted improvements, RP could better serve diverse pedestrian needs than current TBT-dominant interfaces. This work provides evidence-based guidance for developing more versatile, cognitively engaging pedestrian navigation systems.

Neuropilin-1 (NRP-1) functions as an essential co-receptor for SARS-CoV-2, facilitating viral entry by binding the spike protein's C-end rule (CendR) motif in its b1 domain, yet it has received less attention than ACE2 in therapeutic development. This in silico study evaluates plant-derived polyphenols as potential selective inhibitors of the NRP-1 CendR pocket to disrupt SARS-CoV-2 engagement, addressing limitations of synthetic inhibitors like EG01377, which exhibit modest affinity (-5.83 kcal/mol) and potential off-target risks. High-throughput molecular docking of 10,000 phytochemicals using AutoDock Vina identified 10 polyphenols with binding affinities ranging from -9.87 to -6.63 kcal/mol, led by 6"-O-acetyldaidzin (-9.87 kcal/mol) and phloretin (-8.64 kcal/mol), forming stable hydrogen bonds and π-cation interactions with critical residues (e.g., THR-401, GLU-367 for 6"-O-acetyldaidzin; PRO-311, ILE-400 for phloretin), as visualized in Discovery Studio. Notably, four of the top inhibitors are isoflavonoid derivatives, highlighting a chemical class enrichment. Molecular dynamics simulations over 100 ns using Desmond indicated moderate complex stability (RMSD: 0.6-3.8 Å; RMSF <0.5 Å at binding site). ADMET-Tox profiling via SwissADME and ProTox-II revealed drug-like properties, including high gastrointestinal absorption (>70% for leads) and low toxicity (classes 4-5), though 6"-O-acetyldaidzin shows limited bioavailability due to its high H-bond acceptor count (10) and large size, suggesting need for formulation optimization. The NRP-1 b1 homology model, built with SWISS-MODEL, exhibited high fidelity (GMQE: 0.79; Ramachandran favored regions: 90.3%). This focused computational screening of polyphenols against NRP-1 complements prior studies and identifies candidates for experimental validation as potential SARS-CoV-2 inhibitors. Limitations include the in silico nature, and lack of membrane/sialic acid models, necessitating in vitro and in vivo testing against SARS-CoV-2 variants.

Objectives: Women, Indigenous peoples, racialized individuals, and persons with disabilities remain underrepresented in the electrical industry. This study explored the lived experience of underrepresented electrical workers related to their mental health and workplace integration.

Methods: A qualitative narrative design was employed. One-on-one interviews were conducted with eleven participants who self-identified as women, Indigenous peoples, racialized individuals, and/or persons with disabilities. Interview data were first analyzed using a narrative thematic approach and informed the development of creative non-fictional stories.

Results: Three stories were developed. Story 1 - "Asking for a ride: being a women electrician" illustrated the experiences of a woman apprentice who faced inadequate job site accommodations and sexism challenges in the workplace. Story 2 - "The lunch talk: Indigenous people and racialized individuals in the trade" highlighted the experiences of Indigenous and racialized participants who encountered language barriers and discriminatory comments. Story 3 - "Luke's notes: living and working with disability" demonstrated the impacts of physical disability on the daily work of electrical workers, particularly in managing the physical demands and mental health strains.

Conclusions: Electrical workers from underrepresented groups experience persistent barriers to mental health and workplace integration, including a lack of accommodations, limited social support, and experiences of discrimination at the workplace. These individuals also reported challenges in seeking workplace support due to a "toughness" culture within the industry. Electrical employers should foster an inclusive organizational culture that prioritizes the health and psychosocial well-being of underrepresented workers.

The LINE-1 and Alu retrotransposon elements, with more than 90% of their sequences being methylated, contribute to 30% of the human genome. Their hypomethylation profile, representing global methylation in cellular and cell-free DNA (cfDNA) from cancer, has been considered an attractive noninvasive biomarker of cancer. LINE-1 and Alu methylation profiling has preferentially been performed by real-time methylation-specific PCR (qMSP), pyrosequencing, and methylation-sensitive high-resolution melting (MS-HRM), which are bisulfite-based PCR approaches that require reference sequences amplified by the Methylation Independent PCR (MIP) primers to normalize the quantification data. A technical weakness of MIP primers is unequal amplification, termed PCR amplification bias, leading to an under- or overestimation of expected methylation levels, and thus, hindering the effectiveness of DNA methylation-based biomarkers. To date, the PCR amplification bias of MIP primers that may affect the methylation analysis of repeat sequences such as LINE-1 and Alu has not yet been described. Our study demonstrated for the first time the detrimental impact of biased MIP primers on LINE-1 and Alu methylation profiles, causing a significant shift from the hypomethylated status to hypermethylated in cancer tissues and in cfDNA from cancer patients. Unexpectedly, this shift was also observed in cfDNA, even when quantified by the unbiased MIP primers, depending on the reference sequences. Our results suggest that an impartial reference for the methylation quantitation of repetitive elements, most importantly in cfDNA, should be further established to ensure cross-platform consistencies in DNA methylation profiling through bisulfite-based PCR techniques.

[This corrects the article DOI: 10.1371/journal.pone.0339567.].

Introduction: Periprosthetic joint and implant-associated infections remain serious complications despite highly standardized diagnostic and therapeutic protocols. Repeated revision procedures substantially increase morbidity and the risk of therapeutically uncontrollable infection scenarios. This pilot study, we evaluated the effect of HMO (human milk oligosaccharides)-containing carbohydrates on S. aureus biofilm formation using crystal violet assays and implant models.

Methods: S. aureus EDCC 5055 biofilm formation was quantified in the presence of HMO-containing carbohydrates (HMO-C) at concentrations of 5%, 7%, 9%, and 11% and lactose-alone in 96-well plates. After processing, the plate contents were read out using a 595nm Phomo plate reader (Anthos Mikrosystems, Krefeld, Germany). Biofilm activity was further evaluated on titanium disks pre-incubated with HMO-C solutions. Bacterial growth kinetics were also analyzed in TSB with the 5% to 11% HMO-C solutions.

Results: The results demonstrated a significant reduction in S. aureus biofilm formation with the addition of HMO-C (HMO-C-5%: mean value ([Formula: see text])=0.271nm; p = 0.021; HMO-C groups 7%-11%: [Formula: see text] = 0.211nm, 0.179nm, 0.147nm; All p = 0.001) against the positive control EDCC 5055 (PC) ([Formula: see text] = 0.335nm). Lactose (L) alone did not significantly affect biofilm formation (L5%-11%: p = 1.0). No significant biofilm reduction was observed for titanium implants, though medium changes indicated bacterial inhibition at higher HMO-C solutions (9%-11%: [Formula: see text] = 0.844nm, 0.940nm; Both p = 0.001).

Conclusion: Media supplemented with HMOS-C significantly reduced S. aureus ED CC 5055 biofilm formation in vitro in the crystal violet microtiter plate assay. Bacterial invasion on titanium could not be demonstrably changed, but S. aureus growth curve was significantly reduced. Further studies with optimized implant models and standardized HMO formulations are warranted to clarify the translational potential of HMO-C for the prevention of implant-associated infections.

Accurate segmentation of lesions in prostate magnetic resonance images (MRI) is important for assessing patient health and personalized treatment in the clinic. However, the traditional UNet segmentation network has low segmentation accuracy because of the fuzzy boundary and low contrast. Therefore, we propose a Lightweight Mamba-UNet (LM-UNet) prostate MRI image segmentation method. Initially, the encoder-decoder backbone structure consists of parallel vision mamba (PV-Mamba) and efficient multi-scale attention (EMA). The number of model parameters is reduced by constructing PV-Mamba while extracting the correlation between features over long distances. The EMA is then used to learn different spatial features in groups and construct cross-spatial information aggregation methods for richer feature aggregation. Subsequently, we construct the edge feature extraction (EFE) and the edge feature fusion (EFF) to achieve different levels of feature fusion in the encoder. Ultimately, we suggest a multi-stage and multi-level skip connections (MMSC) to achieve multi-level fusion between the encoder and decoder, there reducing semantic discrepancies between contextual features and improving segmentation accuracy. Experimental results demonstrate that on the PROMISE12 dataset, LM-UNet outperforms seven comparative segmentation methods in terms of parameter count, computational memory requirements, and precise segmentation of lesion margins.