PurposePersistent deficits in muscle strength and physical function remain common after total knee arthroplasty (TKA). This study investigated whether a bilateral prehabilitation program combining low-load blood flow restriction (BFR) resistance training of the operative limb with conventional high-load resistance training of the contralateral limb improves preoperative muscular reserve and postoperative recovery.MethodsIn this prospective RCT, 19 patients scheduled for primary TKA completed a six-week bilateral prehabilitation program. Participants were randomized to either low-load resistance training with BFR (60% limb occlusion pressure) or a sham-BFR control intervention for the operative limb, while both groups performed conventional resistance training of the contralateral limb. Outcomes included lower-limb strength, functional performance (6-minute walk test [6MWT], chair-rising test [CRT]), pain (VAS), and KOOS assessed preoperatively and up to three months postoperatively.ResultsCompared with controls, the INT achieved greater preoperative improvements in leg press and extension strength and maintained superior strength at 6 weeks and 3 months postoperatively (p < 0.05). Functional performance was superior, with better CRT and 6MWT performance preoperatively and at 3 months. Additionally, INT showed significantly lower pain values and greater KOOS improvements in Symptoms and Quality of Life before and three months postoperatively in comparison to CON (p < 0.05). Despite higher perceived exertion during exercise sessions, no adverse events occurred.ConclusionsBilateral prehabilitation combining low-load BFR training of the operative limb with high-load resistance training of the contralateral limb enhanced preoperative muscular and functional reserve, attenuated postoperative decline, and improved early recovery following TKA.Clinical trial registrationhttps://clinicaltrials.gov/study/NCT06964022, identifier NCT06964022.
ObjectiveTo evaluate offline binary classification of recorded fatigue conditions using inertial measurement unit (IMU) acceleration features and five machine-learning algorithms.MethodsThe author-confirmed performance dataset contained 32 observations per condition. The revised primary IMU dataset comprised 70 recordings from 25 roster-linked athletes, with 22 non-fatigue and 48 fatigue labels after correction of four misclassified recordings. Seven-sensor and right-forearm configurations provided 231 and 33 candidate features. A newly reconstructed analysis retained five documented outer athlete-grouped folds and used three grouped inner folds for training-only preprocessing, sparse logistic feature selection and hyperparameter tuning.ResultsCMJ was 40.01 ± 8.51 and 30.27 ± 7.29 in the non-fatigue and fatigue conditions, respectively. Across classifiers, fold-mean AUC ranged from 0.655 to 0.846 for seven sensors and from 0.715 to 0.846 for the right forearm. Macro F1 ranged from 0.600 to 0.775 and from 0.655 to 0.775, respectively. Specificity ranged from 0.483 to 0.700 and from 0.550 to 0.650. Code, inclusion decisions, actual inner and outer assignments and held-out predictions are supplied for this new run.ConclusionThe results support further investigation of IMU-based condition classification, but do not establish algorithm superiority, sensor equivalence, physiological validity of every recording label, or real-time monitoring performance.
IntroductionCognitive load detection is essential for understanding brain responses to mental effort and cognitive demands. Electroencephalogram (EEG) signals provide valuable information about neural activity and offer a non-invasive approach to identifying cognitive load. However, accurate detection and the identification of brain regions with superior discriminative capabilities remain important challenges.MethodsThis study proposes an EEG-based cognitive load detection framework using empirical Fourier decomposition (EPFD), entropy-based feature extraction, and the hyper-learning binary dragonfly feature selection algorithm (HLBDA). EEG signals recorded from each brain lobe over four-second intervals were decomposed into 10 intrinsic mode functions (IMFs). Entropy-based features were extracted from the IMFs, and the most informative features were selected using HLBDA. The selected features were classified using machine learning (ML) classifiers. The proposed framework was evaluated on the MAT, STEW, and MLR datasets. Additionally, lobe-wise cognitive load detection was performed on the MAT and STEW datasets to investigate the discriminative capabilities of five individual brain lobes. Subject-wise leave-one-out cross-validation (LOOCV) was employed to prevent data leakage.ResultsThe proposed framework achieved classification accuracies of 97.13%, 96.48%, and 98.2% on the MAT, STEW, and MLR datasets, respectively, using the K-nearest neighbors (KNN) classifier for MAT and STEW and the ensemble subspace KNN (ESSK) classifier for MLR. In the lobe-wise analysis, the frontal lobe demonstrated superior discriminative capability for cognitive load detection, achieving accuracies of 96.7% and 96.6% on the MAT and STEW datasets, respectively, using the ESSK classifier.DiscussionThe results demonstrate the effectiveness of the proposed EPFD–HLBDA-based framework for EEG-based cognitive load detection across multiple datasets. The superior performance of the frontal lobe suggests its potential as a particularly informative region for distinguishing cognitive states. Furthermore, the use of subject-wise LOOCV strengthens the evaluation by reducing the risk of data leakage. Compared with recently reported methods, the proposed framework demonstrates competitive classification performance and highlights the potential of lobe-specific EEG analysis for reliable cognitive load assessment.
IntroductionThis study introduces an innovative computational framework for modeling and recognizing physiological characteristics linked to emotional responses induced by music. Traditional approaches to emotion recognition often rely on static feature extraction and linear classification methods, which fail to capture the temporal and dynamic nature of emotional states.MethodsTo address these limitations, the proposed MemorySparseRule framework integrates temporal dependency modeling with sparse representation learning, enabling a more nuanced analysis of the complex interactions between music and emotional responses. The framework is composed of three core modules: the Memory Stabilization Adapter, which ensures robust temporal data processing; the Sparse Interaction Filter, which isolates significant physiological patterns; and the Rule Grounded State Binder, which consolidates these patterns into interpretable emotional states.Results and DiscussionExperimental evaluations demonstrate that the MemorySparseRule model achieves superior performance in emotion recognition tasks, with an accuracy improvement of 15% compared to existing methods. These findings underscore the potential of the framework to advance affective computing and deepen the understanding of music psychology, offering a computational lens through which the intricate relationship between auditory stimuli and human emotions can be explored. This research not only enhances the precision of emotion recognition systems but also contributes to the development of applications in therapeutic music interventions and personalized emotional analysis.
IntroductionNecrotizing enterocolitis (NEC) is characterized by exaggerated inflammation and epithelial barrier injury, but the relationship between inflammatory signaling and intestinal lipid-metabolic dysfunction remains incompletely defined.MethodsWe integrated analysis of the human intestinal transcriptomic dataset GSE46619 with experiments in rat IEC-6 cells and a neonatal mouse NEC model. IEC-6 cells were challenged with LPS or recombinant IL-1β and treated with Il1b siRNA or fenofibrate. In vivo, male and female C57BL/6 neonatal mice underwent formula-feeding/hypoxia exposure from postnatal day 7 to 10 after AAV9-shIl1b or control-vector administration on postnatal day 2; a separate NEC group received fenofibrate.ResultsHuman NEC samples showed increased inflammatory signatures and lower fatty-acid-metabolism GSVA scores, with an inverse association between IL1B expression and the fatty-acid-metabolism score. In IEC-6 cells and mouse ileum, inflammatory or NEC-like injury was accompanied by reduced tight-junction and PPARα/CPT1A-associated markers and increased lipid-peroxidation/lipid-content indices. Il1b knockdown and fenofibrate each partially attenuated these abnormalities and improved histological injury in vivo.DiscussionThese findings support a working model linking IL-1β-associated inflammation, lipid metabolic alterations, and epithelial barrier injury in experimental NEC. Because fatty-acid oxidation flux and PPARα loss-of-function were not tested, the data do not establish a direct IL-1β-PPARα causal pathway or strict PPARα dependency.
