Search Results (530)

Spinal cord injury (SCI) is an acute, devastating neurologic condition that results in permanent progressive motor deficits, sensory disturbances, and autonomic dysfunctions, which limit function, participation, and quality of life. Although substantial progress has been made during the last several decades for both early trauma care and rehabilitation protocols following SCI, long-term neurological recovery remains unpredictable and often incomplete. This manuscript summarizes mechanistic and clinical evidence regarding robotic-assisted rehabilitation (RAR) and spinal neuromodulation (SN), which have been published since 2010 until the present time in a structured narrative review of the literature on these two emerging areas for neurorehabilitation after SCI. RAR provides high-intensity, task-specific training that consistently results in improvements in functional outcomes such as balance, coordination, and independence; however, its impact is limited when it comes to walking speed or voluntary motor control. SN (particularly epidural stimulation) can activate the residual neural pathways to standing up and stepping even after a complete injury but effects are typically stimulus dependent, with heterogeneous clinical results that often lack strong long-term evidence due in part to variability in patient selection, stimulation parameters and rehabilitation protocols. However, there is emerging mechanistic data supporting combining modulation of excitability through SN approaches along with structured sensorimotor training as an approach for enhancing recovery. Collectively, these findings support a shift toward more physiology-driven neurorehabilitation strategies and the need for future research to improve clinical translation and outcome predictability by patient stratification using standardized intervention protocols that include longitudinal evaluation. Full article

Objective: The goal was to investigate the relationship between methodological adherence and clinical outcomes in Progressive Resistance Training (PRT) for Parkinson’s Disease (PD), specifically identifying why findings of “superiority” over active controls remain inconsistent. Methods: This umbrella review utilized a multi-stage process to identify a sample of the primary literature for methodological analysis. An initial search identified 38 systematic reviews published within the specified timeframe. From the reference lists of these reviews, a subset of 34 primary clinical studies was purposefully selected. Inclusion was prioritized for studies providing comprehensive methodological data on PRT protocols and standardized clinical outcomes. Interventions were evaluated using a three-tiered framework: (1) training protocol with specifications of Frequency, Intensity, Time, Type, Volume, and Progression (FITT-VP) (General Exercise), (2) FITT-VP integrated with the American College of Sports Medicine (ACSM) Supplementary Guidelines (Integrated Guidelines), and (3) principles of progression (mechanistic growth). Studies were categorized by control type (active (e.g., aerobic or balance), n = 26; passive (e.g., standard care or no exercise), n = 8). Results: In trials that compared PRT with an active control group, PRT achieved clinical superiority in 57% (n = 15) of trials and 46% (n = 12) when focusing on trials with an effect on specific functional or balance outcomes. Among these successful interventions, 75% maintained high adherence (≥70%) to the Integrated Guidelines, and 58% maintained high adherence to the principles of progression. In the 53% (n = 14) of studies where PRT was found non-superior (equivalent or inferior in functional or balance outcomes) to an active control, 0% met the high adherence threshold for progression. While general FITT-VP compliance remained high (78%), the failure to implement systematic load, specificity, and variation served as a definitive barrier to competitive superiority. In the 100% of studies where PRT outperformed passive controls, high progression was present in 57% of cases. This may suggest that while a baseline resistance stimulus outperforms inactivity, it is fundamentally insufficient to outperform other active clinical therapies. Conclusions: This umbrella review indicates that adherence to the principles of progression may be an important factor influencing the clinical outcomes of PRT in individuals with PD. The variability observed in the current literature suggests that inconsistent application of established exercise frameworks—rather than the failure of the modality itself—could be a contributing element to the reported “inconclusiveness.” To potentially enhance functional outcomes and the comparative effectiveness of PRT, future research should consider prioritizing structured adherence to FITT-VP, Integrated Guidelines, and progression-based frameworks. Establishing a 70% adherence threshold is proposed as a potential benchmark to improve protocol consistency and support rehabilitation efficacy in this population. Full article

Type 2 diabetes mellitus (T2DM) is frequently complicated by dyslipidemia, which accelerates insulin resistance and the progression of cardiovascular and hepatic diseases. While exercise intervention is a cornerstone of T2DM management, a systems-level understanding of its underlying molecular mechanisms remains incomplete. This article summarizes current evidence to propose that exercise functions as a signaling network regulator, concurrently modulating critical lipid metabolism-related signaling pathways: cyclic adenosine monophosphate (cAMP), phosphatidylinositol 3-kinase–protein kinase B (PI3K–AKT), forkhead box O (FOXO), and mitogen-activated protein kinase (MAPK) signaling pathways. We delineate how dysregulation of these signaling pathways contributes to lipid disorders in T2DM, highlighting their tissue-specific and often bidirectional roles. Subsequently, we detail the molecular adaptations induced by various exercise modalities—from aerobic training to high-intensity intervals—that restore homeostasis of this signaling network. By integrating these findings, we present a novel framework for precision exercise—defined as the tailoring of exercise modality, intensity, and volume based on an individual’s predominant signaling pathway disturbance, assessed via circulating or tissue-specific biomarkers. This framework advocates for future exercise prescriptions to be guided by molecular profiling alongside traditional physiological indicators. This mechanistic insight not only deepens our comprehension of exercise physiology but also paves the way for more effective, personalized strategies to combat T2DM and its metabolic complications. Full article

Background: Regular aerobic exercise during childhood promotes critical physiological adaptations in the cardiovascular and respiratory system. Finswimming, a unique aquatic sport, requires high-intensity demands and specific breathing patterns. The present study aimed to compare respiratory function and cardiorespiratory responses between young male finswimmers and sedentary age-matched non-athletes. Methods: Thirty-two boys aged 8 to 12 years old were stratified into the finswimmers group (FSG, n = 16) and the non-athletes group (NAG, n = 16). Assessments included pulmonary function (spirometry) and respiratory muscle strength (Maximum Inspiratory Pressure, MIP/Maximum Expiratory Pressure, MEP). Exercise capacity was evaluated using the modified shuttle walk test (MSWT). Results: The FSG exhibited significantly higher pulmonary function (Forced Vital Capacity, Forced Expiratory Volume in 1 s, Maximum Voluntary Ventilation; p < 0.05) and superior MIP compared to the NAG (105.3 ± 24.8 versus 87.3 ± 24.7 cmH₂O; p = 0.022). During the MSWT, FSG covered substantially greater distances (746.6 ± 97.2 versus 591.1 ± 86.4 m; p < 0.001) with lower levels of leg fatigue (Borg 0–10) (0.53 ± 0.39 versus 2.13 ± 1.93; p = 0.004) and demonstrated lower heart rate recovery time (4.47 ± 0.68 versus 5.75 ± 0.68 min; p < 0.001) compared to NAG. At the iso-level (8th level of MSWT), FSG scored lower levels of leg fatigue (0.13 ± 0.12 versus 2.02 ± 2.0; p = 0.001) compared to NAG, indicating better peripheral oxygen % saturation (100 ± 0.0 versus 98.14 ± 1.16; p < 0.001). Conclusions: Systematic exercise training enhances profound cardiorespiratory and peripheral muscle adaptations in children. Enhanced cardiorespiratory function allows young athletes to achieve higher workloads and recover faster than sedentary peers, highlighting the sport’s role in establishing a robust cardiorespiratory fitness. Full article

Background: Patients with pancreatic cancer (PC) commonly present with reduced aerobic fitness, sarcopenia, and malnutrition, which may increase perioperative risk and compromise access to chemotherapy treatments. Although exercise-based prehabilitation can improve physical fitness, its implementation is often limited by short diagnostic-to-surgery intervals and treatment-related toxicity. Methods: We conducted a pilot prospective pretest–posttest feasibility study in Torino, Italy. Patients with PC undergoing neoadjuvant chemotherapy prior to surgery were offered a 4-week, partially supervised, home-based bimodal exercise prehabilitation program (single-arm design) combining remotely monitored high-intensity interval training (HIIT) on a cycle ergometer with functional and resistance exercises. The primary outcome was adherence to prescribed exercise frequency, intensity, and duration, objectively assessed via remote monitoring. Secondary outcomes included cardiorespiratory fitness (CPET), muscle function, body composition, fatigue, quality of life, and circulating inflammatory markers. Results: From July 2022 to February 2024, 23 patients were screened; 15 were eligible and 10 enrolled. Four participants discontinued the intervention (two due to asthenia/fatigue, one due to chemotherapy-related adverse events, and one for organizational reasons), leaving six participants who completed the program. Among completers, fatigue and quality of life did not change meaningfully. Aerobic capacity and muscle function outcomes were generally stable, with few pre–post changes exceeding the minimum clinically important difference (MCID) thresholds used. Body composition markers and the assessed circulating cytokines/chemokines remained unchanged except for IL-6 levels, which decreased significantly (p < 0.05). Conclusions: A partially supervised, home-based HIIT-based prehabilitation program is feasible for a subset of PC patients undergoing neoadjuvant therapy, but a substantial attrition rate suggests the need for more flexible symptom-adapted prescriptions and enhanced supportive strategies. Full article

This study addresses the critical need for sustainable energy by optimizing diaphragm-type piezoelectric elements for efficient waste vibration energy harvesting. Traditional experimental optimization of complex, non-linear design parameters including applied load, tapper diameter, and support structures is often resource-intensive and time-consuming. To overcome these limitations, we developed a novel hybrid machine learning framework that seamlessly integrates an Artificial Neural Network (ANN) with a Modified Grey Wolf Optimization (mGWO) algorithm. The ANN was rigorously trained on experimental data using Bayesian Regularization, establishing itself as a robust and high-fidelity surrogate model capable of accurately predicting voltage output based on diverse input parameters, evidenced by an R-value close to 1. This predictive model subsequently served as the fitness function for the mGWO algorithm, which incorporated a non-linear control parameter to efficiently explore the multi-dimensional design space and effectively balance exploration with exploitation. The framework successfully identified the optimal configuration for maximizing voltage output, predicting a theoretical maximum of approximately 70.67 V. This optimal setup notably involved a high applied load of 100 N, the 6CA multi-pointed tapper configuration, and the three-support boundary condition, which is consistent with the experimentally validated results. The computational findings demonstrated excellent agreement with empirical results while providing significantly higher resolution for design insights. This validated, predictive tool offers a substantial advancement for the future scaling and design optimization of piezoelectric energy harvesters, minimizing the need for extensive physical prototyping and ensuring efficient stress transfer without mechanical failure. Full article

High-intensity interval training (HIIT) has emerged as a time-efficient exercise strategy with potential benefits for older adults. However, evidence regarding its effects on functional fitness, body composition, and quality of life in older women remains limited. This randomized controlled trial included community-dwelling older women allocated to a HIIT group or a control group. The intervention consisted of a 65-week HIIT program (3 sessions/week), while the control group maintained usual activities. Functional fitness was assessed using standardized field-based tests, body composition was evaluated by bioelectrical impedance analysis, and quality of life was measured using the WHOQOL-BREF questionnaire. Pre- and post-intervention assessments were performed under standardized conditions. Data were analyzed using mixed ANOVA models, with significance set at p < 0.05. Compared with the control group, the HIIT group significantly improved aerobic capacity (2MST: +25.4 vs. −19.6 repetitions; p < 0.001), lower-limb strength (30s CST: +4.8 vs. −2.6 repetitions; p < 0.001), and mobility (TUG: −0.3 vs. +0.4 s; p < 0.001). Body composition improved with reductions in body fat percentage (−1.8% vs. +1.9%; p < 0.001) and visceral fat index (−0.6 vs. +0.3; p < 0.001), alongside increased total body water (+2.3% vs. −1.8%; p < 0.001). Quality of life improved significantly in physical, psychological, and environmental domains (p < 0.001). HIIT was associated with improvements in functional fitness, body composition, and quality of life, with no major adverse events reported. These findings support the use of HIIT as a practical intervention to enhance health and functional independence in aging populations. Full article

Collisions between motorcycles and car doors that are being opened are common, preventable accidents that can result in fatalities. A critical limitation of safety advancements in both cars and motorcycles is high cost associated with the use of radar sensors. In this study, a deep learning model was integrated into an inexpensive and camera-utilizing automatic braking assistance system for motorcycles to enhance braking performance and alert motorcyclists to avoid collisions. This research involved two stages: (1) the training of a deep learning model for detecting car door states and (2) the design of safety mechanisms for selecting appropriate braking intensity and front braking ratio values on the basis of the model’s output, time-to-collision, the rider’s braking action, and the initial braking speed, in order to achieve optimal braking performance. Specifically, the YOLOv12s object detection model showed high performance in predicting the states of car doors, exhibiting precision, recall, and mean average precision values of 90.5%, 80.6%, and 87.8%, respectively. The braking intensity of the system was set to 0%, 25%, 50%, or 100% in scenarios involving opening states of the car door (closed, small, medium, or large opening), time-to-collision values, and the rider’s braking action. The optimal front braking ratio function was determined based on the initial braking speed to achieve the optimal braking performance. At an initial braking speed of 60 km/h, the braking stroke under a front braking ratio of 45% was 35.61% and 13.37% shorter than those under front braking ratios of 20% and 60%, respectively. The proposed braking assistance system can feasibly be deployed in the real world because it can respond within a safe time window under the conditions studied, which is approximately 0.5 s. However, further refinement is required, including improvement of the robustness of the object detection model through the collection of a larger and more diverse dataset, experimental measurement of front braking ratios to determine the optimal braking performance in real scenarios, and design of a physical actuator to control braking intensity and the front braking ratio in real time. Full article

High-stakes logistics, defined as supply chains where delays, quality loss, or noncompliance have serious human, safety, financial, or geopolitical consequences, are a prominent case of a broader reality: global supply chains are safety-, cost-, and time-critical socio-technical systems where forecasting quality, vendor coordination, and operational decisions shape service levels and stakeholder welfare. At the same time, decarbonization pressures and the growing use of AI for planning and control introduce new risks and trade-offs across energy, computation, and physical logistics. We develop a multi-agent framework that models supply chain system-of-systems dynamics drawing on (1) supply chain decision functions (shipment planning, sourcing and vendor management), (2) national energy-transition conditions that determine grid carbon intensity, and (3) carbon-aware computation accounting for AI-enabled decision support. Methodologically, we combine predictive analytics, unsupervised segmentation, and a carbon-cost-of-intelligence layer in a scenario-based assessment of how national energy-transition profiles–from Norway to India–affect the intensity of AI compute carbon, meaning the carbon emissions generated by the hardware and data centers required to train and run AI models. We introduce the carbon-adjusted supply chain performance (CASP) metric that integrates physical transport carbon, cold-chain overhead where applicable, and AI compute carbon into a per-package-type performance measure. Our analysis yields three actionable outputs for systems engineering and environmental management: carbon, service, and cost trade-off frontiers; governance levers (sourcing portfolio rules, buffers, and compute policies); and system-level early-warning indicators for disruption amplification. This study implements a tool-augmented multi-agent system (orchestrator, risk, and sourcing agents) using AWS bedrock and strands agents, where LLM-based agents orchestrate deterministic analytical engines through structured tool interfaces with adaptive query generation. Theoretically, we extend previous systems-of-systems and sustainable supply chain findings by formalizing package-type-specific carbon–service frontiers and by embedding AI compute carbon into a socio-technical resilience framework. Practically, the CASP benchmark, governance lever analysis, and multi-agent implementation provide decision-makers with concrete tools to compare carriers, routes, and compute strategies across countries while making transparent the trade-offs between service reliability and total carbon. Full article

Chronic kidney disease (CKD) leads to metabolic and cardiovascular complications, and the dysregulation of key biomolecules, namely fibroblast growth factor 23 (FGF23) and Klotho, plays a central role. This study investigated the effects of high-intensity interval training (HIIT) and moderate aerobic training (AT) on FGF23, Klotho, mineral metabolism, apoptosis markers (BAX, Bcl2), and atrial fibrillation (AF) in a rat CKD model. The study used 35 Wistar rats randomly assigned to control (CTL), sham (SH), CKD, CKD + HIIT, and CKD + AT groups. CKD was induced by 5/6 nephrectomy surgery. Exercise interventions consisted of eight weeks of HIIT (80–100% of maximum speed, 24–54 min/week) or AT (45–55% of maximum speed, 40–60 min/week), conducted three times weekly on a treadmill. We measured heart weight, blood levels of FGF23, Klotho, and mineral metabolism markers, as well as the heart expression of apoptosis proteins (i.e., BAX, Bcl2) and atrial fibrillation (AF). Both exercise types reduced the heart weight and heart/body weight ratio; attenuated CKD-induced elevations in FGF23 and reductions in Klotho; improved blood levels of phosphate, PTH, and vitamin D; and modulated apoptotic markers by decreasing BAX and increasing Bcl2 levels. Exercise improved cardiac function and reduced the AF duration. These findings emphasize that exercise could be a helpful non-pharmacological intervention to ameliorate CKD-induced cardiovascular and metabolic disturbances through the modulation of the FGF23 and Klotho pathways. Full article

Physical exercise can influence cognitive performance and neurobiological processes, but evidence spans diverse modalities, intensities, and adult populations. Acute exercise represents a state of transient skeletal muscle activation that induces systemic signaling through metabolic, endocrine, and myokine-mediated pathways, which may contribute to neurocognitive modulation. To map the breadth of acute exercise–cognition research, characterize cognitive and biological outcomes, and identify consistent patterns and gaps. Studies of adults (≥18 years) involving a single exercise session or short microcycle (≤7 days) with pre–post assessment of cognition and/or neurobiological markers across any exercise modality (aerobic, resistance, high-intensity interval training/HIIT, combined, vibration, mind–body) were included. PubMed and CENTRAL were systematically searched, yielding 101 studies. Data were extracted using a structured framework capturing exercise modality, dose, cognitive domains, biomarkers, neuroimaging outcomes, population characteristics, and study design features. Most studies examined young adults (53%) or older adults (32%). Aerobic exercise predominated (62%), followed by resistance (18%) and combined modalities (12%). Moderate-to-vigorous aerobic exercise consistently improved executive function, processing speed, and working memory. Resistance exercise also enhanced executive function in several trials (31 studies). Neurobiological correlates included increases in Brain-Derived Neurotrophic Factor (BDNF), lactate, catecholamines, and prefrontal activation, though variability in sampling limited mechanistic conclusions. Acute exercise is consistently associated with improvements in executive function and processing speed across modalities. Standardized exercise protocols, biomarker timing, and cognitive assessments are needed to strengthen mechanistic synthesis. Full article

Objectives: Myofascial pain syndrome (MPS) is a common musculoskeletal condition, and while percutaneous needle electrolysis (PNE) and intramuscular electrical stimulation (IMES) are emerging therapies for myofascial pain syndrome and tendinopathies, their effects remain unclear. This systematic review aimed to characterize the methodological features and synthesize the evidence on the clinical improvement and adverse events rates of PNE and IMES in treating MPS and tendinopathies. Data Sources: PubMed, Scopus, Web of Science, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform, Google Scholar, and reference lists. Searches were carried out on 10 July 2025 and repeated on 16 March 2026, just before final analysis. New results found during final searches were screened for inclusion to ensure currency of the review. Methods: We selected studies based on the PICOS framework and predefined selection criteria: Population: adults with MPS or active myofascial trigger points (TrPs), or tendinopathies; Intervention: PNE or IMES; Comparator: sham procedures, other interventions, or no intervention; Outcomes: pain intensity (e.g., Visual Analogue Scale or Numeric Pain Rating Scale), pressure pain threshold (PPT), and functional measures; and Study Design: experimental studies. Studies focused exclusively on post-surgical or neuropathic pain, studies without a relevant comparator, and studies not reporting clinically meaningful outcomes were excluded. We assessed the risk of bias of included studies and performed a narrative synthesis. Results: From 737 identified records, 30 studies met the selection criteria. PNE was generally effective in reducing pain and improving function in tendinopathies and MPS, although results varied across outcomes and follow-ups. IMES showed moderate evidence for reducing pain and enhancing function, particularly cervical range of motion and PPT. However, both interventions had inconsistent clinical improvement and adverse events rates on disability indices and quality of life. Most studies had a high risk of bias due to challenges in blinding. Reported adverse events were minor and self-limiting, indicating that both therapies are generally safe when performed by trained clinicians. Conclusions: PNE and IMES may improve pain and some functional outcomes in MPS and tendinopathies; however, these findings should be interpreted cautiously because most included studies had a high risk of bias. Full article

The increasing popularity of carnivore and animal-based diets among athletes has generated substantial interest, despite limited direct scientific evidence supporting their efficacy and safety in sport-specific contexts. This narrative review critically evaluates the current evidence and examines the physiological, performance, and health-related implications of these dietary models in athletic populations. These dietary models, characterized by the partial or complete exclusion of plant-derived foods, are often promoted on the basis of mechanistic arguments, anecdotal reports, and extrapolations from research on ketogenic and very low-carbohydrate diets. However, their physiological relevance, long-term health implications, and compatibility with the demands of athletic training remain poorly defined. This narrative review provides a critical perspective on the current evidence related to carnivore and animal-based diets in sport, integrating findings from studies on low-carbohydrate, ketogenic, high-protein, and elimination-based dietary patterns. The analysis focuses on metabolic adaptations, body composition, exercise performance, gastrointestinal function, micronutrient adequacy, hormonal responses, and potential long-term health risks. Particular attention is given to the distinction between metabolic adaptations and functional performance outcomes, as well as to the high interindividual variability in dietary responses. The available evidence suggests that while carbohydrate restriction may induce specific metabolic adaptations, such as increased fat oxidation, these changes do not consistently translate into improved performance, particularly in high-intensity or high-volume training contexts. Moreover, the highly restrictive nature of carnivore and animal-based diets raises concerns about micronutrient deficiencies, alterations in the gut microbiota, changes in the lipid profile, and potential effects on eating behaviours, particularly in competitive athletic populations. Given the absence of well-controlled, long-term intervention studies in athletes, carnivore and animal-based diets cannot currently be recommended as safe or optimal nutritional strategies for sports performance. Rather than representing viable alternatives to established sports nutrition guidelines, these dietary models may be better understood as experimental or short-term tools within highly controlled research or diagnostic frameworks. Future research should prioritize rigorous, sport-specific study designs, long-term safety outcomes, and personalized approaches that account for individual metabolic and physiological variability. Full article

Accurate and stable acquisition of the spatial distribution of soybean planting areas is essential for supporting precision agricultural monitoring and ensuring food security. However, crop remote-sensing mapping for specific regions still faces critical data bottlenecks: high-precision, large-scale pixel-level annotation is costly, resulting in scarce available labeled samples that make it difficult to construct large-scale training datasets. Although parameter-intensive models such as FCN and SegNet can achieve sufficient end-to-end training on large-scale public remote sensing datasets like LoveDA, when directly applied to the data-limited dataset in this study area, the models are prone to overfitting, leading to a significant decline in generalization ability. To address these issues, this study proposes a lightweight U-shaped semantic segmentation model, SimSDRU-Net. The model utilizes a pre-trained VGG-16 backbone to extract shallow texture and deep semantic features. The pre-trained weights mitigate the impact of overfitting in data-limited settings. In the decoding stage, a parameter-free lightweight SimAM attention module enhances effective soybean features and suppresses soil background redundancy, while an embedded S-DRU unit fuses multi-scale features for deep complementary reconstruction to improve edge detail capture. A label dataset was constructed using Sentinel-2 images as the data source and Menard County (USA) as the study area. The USDA CDL was used as a foundation for the dataset, with Google high-resolution images serving as visual interpretation aids. In the context of the experiment, Deeplabv3+ and U-Net++ were compared with U-Net under identical conditions. The results demonstrated that SimSDRU-Net exhibited optimal performance, with MIoU of 89.03%, MPA of 93.81%, and OA of 95.96%. Specifically, SimSDRU-Net uses the SimAM attention module to generate spatial attention weights by analyzing feature statistical differences through an energy function, so as to adaptively enhance soybean texture features. Meanwhile, the S-DRU unit groups, dynamically weights, and cross-branch reconstructs multi-scale convolutional features to preserve fine boundary details and achieve accurate segmentation of soybean plots. The present study demonstrates that SimSDRU-Net integrates lightweight design and high precision in data-limited scenarios, thereby providing effective technical support for the rapid extraction of soybean planting areas in North America. Full article

High-intensity interval training (HIIT) is presented as a safe, effective, and time-efficient strategy for individuals with type 1 diabetes, offering benefits for glycemic control, cardiovascular function, and physical fitness, with a lower risk of hypoglycemia than other exercise modalities. However, substantial variability exists among protocols, and there is no consensus on optimal dosage. This study reviewed 18 investigations to identify key parameters for safe and effective implementation. Results emphasize the importance of individualized programming, adherence to protocols, frequent glucose monitoring, and professional supervision. A preliminary framework is proposed to guide personalized HIIT programs for people with type 1 diabetes. Full article