Chiropractic rehabilitation in accelerated rehabilitation after total hip arthroplasty for Crowe type IV hip dysplasia.

To investigate the efficacy of chiropractic rehabilitation therapy in Crowe IV developmental dysplasia of the hip (DDH) patients after total hip arthroplasty. Seventy-two patients with Crowe IV type DDH hospitalized in the Department of Orthopedics I of Ya'an Hospital of Traditional Chinese Medicine from January 2021 to June 2023 were selected for the study, and they were divided into 36 cases in the chiropractic rehabilitation therapy group (the treatment group) and 36 cases in the traditional rehabilitation therapy group (the control group) according to the method of randomized grouping. All patients were evaluated at preoperative, 1, 3, and 6 months postoperatively for follow-up, and the muscle strength of the affected limb, the patient's walking gait, the shortened length of the affected limb, the visual analog scale score (VAS score), the Oswestry Dysfunction Index Score (ODI score), the Harris Hip Score, and the degree of pelvic tilt were recorded to evaluate the results of the study. A total of 4 subjects withdrew from the study, 2 in the treatment group, and 2 in the control group. The muscle strength of the affected limb, walking gait, shortened length of the affected limb, VAS score, ODI score, Harris score, and pelvic tilt in the treatment and control groups improved significantly compared with the preoperative period. Comparisons between the 2 groups revealed that at the final follow-up visit, the limp gait of the patients in the treatment group was significantly reduced, the shortened length of the affected limb was significantly reduced, the VAS score was significantly reduced, and the ODI score was significantly reduced, in the treatment group relative to that of the control group, Harris Hip Score was significantly improved, and the degree of pelvic tilt was significantly reduced, but the improvement in muscle strength of the affected limb was not statistically significant. In future clinical practice, we recommend that chiropractic rehabilitation be used as a routine adjunctive treatment after TKA in patients with Crowe IV DDH to optimize outcomes and improve patients' quality of life.

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning.

Introduction: This study examines the state and trait effects of short-term mindfulness-based stress reduction (MBSR) training using convolutional neural networks (CNN) based deep learning methods and traditional machine learning methods, including shallow and deep ConvNets as well as support vector machine (SVM) with features extracted from common spatial pattern (CSP) and filter bank CSP (FBCSP). Methods: We investigated the electroencephalogram (EEG) measurements of 11 novice MBSR practitioners (6 males, 5 females; mean age 35.7 years; 7 Asians and 4 Caucasians) during resting and meditation at early and late training stages. The classifiers are trained and evaluated using inter-subject, mix-subject, intra-subject, and subject-transfer classification strategies, each according to a specific application scenario. Results: For MBSR state effect recognition, trait effect recognition using meditation EEG, and trait effect recognition using resting EEG, from shallow ConvNet classifier we get mix-subject/intra-subject classification accuracies superior to related previous studies for both novice and expert meditators with a variety of meditation types including yoga, Tibetan, and mindfulness, whereas from FBSCP + SVM classifier we get inter-subject classification accuracies of 68.50, 85.00, and 78.96%, respectively. Conclusion: Deep learning is superior for state effect recognition of novice meditators and slightly inferior but still comparable for both state and trait effects recognition of expert meditators when compared to the literatures. This study supports previous findings that short-term meditation training has EEG-recognizable state and trait effects.

Decoding Coordinated Directions of Bimanual Movements From EEG Signals.

Bimanual coordination is common in human daily life, whereas current research focused mainly on decoding unimanual movement from electroencephalogram (EEG) signals. Here we developed a brain-computer interface (BCI) paradigm of task-oriented bimanual movements to decode coordinated directions from movement-related cortical potentials (MRCPs) of EEG. Eight healthy subjects participated in the target-reaching task, including (1) performing leftward, midward, and rightward bimanual movements, and (2) performing leftward and rightward unimanual movements. A combined deep learning model of convolution neural network and bidirectional long short-term memory network was proposed to classify movement directions from EEG. Results showed that the average peak classification accuracy for three coordinated directions of bimanual movements reached 73.39 ± 6.35 %. The binary classification accuracies achieved 80.24 ± 6.25 , 82.62 ± 7.82 , and 86.28 ± 5.50 % for leftward versus midward, rightward versus midward and leftward versus rightward, respectively. We also compared the binary classification (leftward versus rightward) of bimanual, left-hand, and right-hand movements, and accuracies achieved 86.28 ± 5.50 %, 75.67 ± 7.18 %, and 77.79 ± 5.65 %, respectively. The results indicated the feasibility of decoding human coordinated directions of task-oriented bimanual movements from EEG.

Continuous Bimanual Trajectory Decoding of Coordinated Movement From EEG Signals.

While many voluntary movements involve bimanual coordination, few attempts have been made to simultaneously decode the trajectory of bimanual movements from electroencephalogram (EEG) signals. In this study, we proposed a novel bimanual brain-computer interface (BCI) paradigm to reconstruct the continuous trajectory of both hands during coordinated movements from EEG. The protocol required human subjects to complete a bimanual reaching task to the left, middle, or right target while EEG data were collected. A multi-task deep learning model combining the EEGNet and long short-term memory network (LSTM) was proposed to decode bimanual trajectories, including position and velocity. Decoding performance was evaluated in terms of the correlation coefficient (CC) and normalized root mean square error (NRMSE) between decoded and real trajectories. Experimental results from 13 human subjects showed that the grand-averaged combined CC values achieved 0.54 and 0.42 for position and velocity decoding, respectively. The corresponding combined NRMSE values were 0.22 and 0.23. Both CC and NRMSE were significantly superior to the chance level (p<0.05). Comparative experiments also indicated that the proposed model significantly outperformed some other commonly-used methods in terms of CC and NRMSE for continuous trajectory decoding. These findings demonstrated the feasibility of simultaneously decoding bimanual trajectory from EEG, indicating the potential of bimanual control for coordinated tasks.

Symmetric Convolutional and Adversarial Neural Network Enables Improved Mental Stress Classification From EEG.

Electroencephalography (EEG) is widely used for mental stress classification, but effective feature extraction and transfer across subjects remain challenging due to its variability. In this paper, a novel deep neural network combining convolutional neural network (CNN) and adversarial theory, named symmetric deep convolutional adversarial network (SDCAN), is proposed for stress classification based on EEG. The adversarial inference is introduced to automatically capture invariant and discriminative features from raw EEG, which aims to improve the classification accuracy and generalization ability across subjects. Experiments were conducted with 22 human subjects, where each participant's stress was induced by the Trier Social Stress Test paradigm while EEG was collected. Stress states were then calibrated into four or five stages according to the changing trend of salivary cortisol concentration. The results show that the proposed network achieves improved accuracies of 87.62% and 81.45% on the classification of four and five stages, respectively, compared to conventional CNN methods. Euclidean space data alignment approach (EA) was applied and the improved generalization ability of EA-SDCAN across subjects was also validated via the leave-one-subject-out-cross-validation, with the accuracies of four and five stages being 60.52% and 48.17%, respectively. These findings indicate that the proposed SDCAN network is more feasible and effective for classifying the stages of mental stress based on EEG compared with other conventional methods.

Adsorption behavior and mechanism of Pb(II) and complex Cu(II) species by biowaste-derived char with amino functionalization.

This work aimed to develop a feasible technology to reuse dead pig biowaste for heavy metals removal, which was first carbonized into pig bone char (PBC) after pyrolysis and further functionalized with amino (NH2) to improve its affinity with heavy metals. The application of PBC after NH2-functionalization for aqueous heavy metals removal was explored, including Pb2+, Cu2+, and different complex Cu(II) species. PBC@SiO2-NH2 showed comparable performance with AC@SiO2-NH2. The adsorption capacity of Pb2+ and Cu2+ by PBC@SiO2-NH2 was 120 and 30 mg g-1, respectively. The NH2 functionalization enhanced the adsorption of complex Cu(II) species, and PBC@SiO2-NH2 exhibited good performance under a wide pH range and coexisting ions. The adsorption of Cu(II)EDTA species with an EDTA/Cu(II) molar ratio of 1.0 by PBC would be enhanced ~3 times and ~6 times after NH2 functionalization and further addition of Fe3+, respectively. The results of EDX, elemental mapping, and XPS confirmed the adsorption of Cu(II) species. The adsorption kinetics, isotherm, and thermodynamics of different Cu(II) species by PBC@SiO2-NH2 were investigated. The regeneration of PBC@SiO2-NH2 was easily performed via acidic wash. These results suggested the application potential of the reuse of biowaste, which could be a promising adsorbent for aqueous heavy metals after specific functionalization.

The regulation of IMF deposition in pectoralis major of fast- and slow- growing chickens at hatching.

BACKGROUND: The lipid from egg yolk is largely consumed in supplying the energy for embryonic growth until hatching. The remaining lipid in the yolk sac is transported into the hatchling's tissues. The gene expression profiles of fast- and slow-growing chickens, Arbor Acres (AA) and Beijing-You (BJY), were determined to identify global differentially expressed genes and enriched pathways related to lipid metabolism in the pectoralis major at hatching. RESULTS: Between these two breeds, the absolute and weight-specific amounts of total yolk energy (TYE) and intramuscular fat (IMF) content in pectoralis major of fast-growing chickens were significantly higher (P < 0.01, P < 0.01, P < 0.05, respectively) than those of the slow-growing breed. IMF content and u-TYE were significantly related (r = 0.9047, P < 0.01). Microarray analysis revealed that gene transcripts related to lipogenesis, including PPARG, RBP7, LPL, FABP4, THRSP, ACACA, ACSS1, DGAT2, and GK, were significantly more abundant in breast muscle of fast-growing chickens than in slow-growing chickens. Conversely, the abundance of transcripts of genes involved in fatty acid degradation and glycometabolism, including ACAT1, ACOX2, ACOX3, CPT1A, CPT2, DAK, APOO, FUT9, GCNT1, and B4GALT3, was significantly lower in fast-growing chickens. The results further indicated that the PPAR signaling pathway was directly involved in fat deposition in pectoralis major, and other upstream pathways (Hedgehog, TGF-beta, and cytokine-cytokine receptor interaction signaling pathways) play roles in its regulation of the expression of related genes. CONCLUSIONS: Additional energy from the yolk sac is transported and deposited as IMF in the pectoralis major of chickens at hatching. Genes and pathways related to lipid metabolism (such as PPAR, Hedgehog, TGF-beta, and cytokine-cytokine receptor interaction signaling pathways) promote the deposition of IMF in the pectoralis major of fast-growing chickens compared with those that grow more slowly. These findings provide new insights into the molecular mechanisms underlying lipid metabolism and deposition in hatchling chickens.

Protein Profiles for Muscle Development and Intramuscular Fat Accumulation at Different Post-Hatching Ages in Chickens.

Muscle development and growth influences the efficiency of poultry meat production, and is closely related to deposition of intramuscular fat (IMF), which is crucial in meat quality. To clarify the molecular mechanisms underlying muscle development and IMF deposition in chickens, protein expression profiles were examined in the breast muscle of Beijing-You chickens at ages 1, 56, 98 and 140 days, using isobaric tags for relative and absolute quantification (iTRAQ). Two hundred and four of 494 proteins were expressed differentially. The expression profile at day 1 differed greatly from those at day 56, 98 and 140. KEGG pathway analysis of differential protein expression from pair-wise comparisons (day 1 vs. 56; 56 vs. 98; 98 vs. 140), showed that the fatty acid degradation pathway was more active during the stage from day 1 to 56 than at other periods. This was consistent with the change in IMF content, which was highest at day 1 and declined dramatically thereafter. When muscle growth was most rapid (days 56-98), pathways involved in muscle development were dominant, including hypertrophic cardiomyopathy, dilated cardiomyopathy, cardiac muscle contraction, tight junctions and focal adhesion. In contrast with hatchlings, the fatty acid degradation pathway was downregulated from day 98 to 140, which was consistent with the period for IMF deposition following rapid muscle growth. Changes in some key specific proteins, including fast skeletal muscle troponin T isoform, aldehyde dehydrogenase 1A1 and apolipoprotein A1, were verified by Western blotting, and could be potential biomarkers for IMF deposition in chickens. Protein-protein interaction networks showed that ribosome-related functional modules were clustered in all three stages. However, the functional module involved in the metabolic pathway was only clustered in the first stage (day 1 vs. 56). This study improves our understanding of the molecular mechanisms underlying muscle development and IMF deposition in chickens.

[Compositional Variation of Spent Mushroom Substrate During Cyclic Utilization and Its Environmental Impact].

Nutrition components and elements analysis of spent mushroom substrates/composts (SMS/SMC) during a cyclic utilization were performed to state the compositional variation during reutilization and composting process. Environmental risk assessment of heavy metals and other pollutants were also taken into consideration. The results showed that the water consumption during reutilization reached 13.8%; while the protein and polysaccharide contents increased by 32.9% and 20.4%, respectively, suggesting that SMS still had a lot of nutrients. After composting disposal, however, the protein and polysaccharide contents decreased by 50% and 79%, respectively, while the lignin, cellulose and hemicellulose contents didn't show a significant difference; the C/N ratio decreased; the total humic acid content increased by 18.6%, all of which means that the composting process made great contributions to organic degradation. The heavy metal analysis showed that As, Hg, Pb, Cd, Cr concentrations in organic compost met the requirement of limit standard (NY525-2012). In addition, the results of column leaching test showed that N, P and organics in both SMS and SMC had a possibility of leaching loss, and the accumulation of TN and COD in SMC leachate decreased by 15.0% and 62.8%, respectively, compared to SMS group.