Abnormalities in brain glucose metabolism and accumulation of abnormal protein deposits called plaques and tangles are neuropathological hallmarks of Alzheimer’s disease (AD), but their relationship to disease pathogenesis and to each other remains unclear. Here we show that succinylation, a metabolism-associated post-translational protein modification (PTM), provides a potential link between abnormal metabolism and AD pathology. We quantified the lysine succinylomes and proteomes from brains of individuals with AD, and healthy controls. In AD, succinylation of multiple mitochondrial proteins declined, and succinylation of small number of cytosolic proteins increased. The largest increases occurred at critical sites of amyloid precursor protein (APP) and microtubule-associated tau. We show that in vitro, succinylation of APP disrupted its normal proteolytic processing thereby promoting Aβ accumulation and plaque formation and that succinylation of tau promoted its aggregation to tangles and impaired microtubule assembly. In transgenic mouse models of AD, elevated succinylation associated with soluble and insoluble APP derivatives and tau. These findings indicate that a metabolism-linked PTM may be associated with AD.

Background. The efficacy of robotic‐assisted gait training (RAGT) should be considered versatilely; among which, gait assessment is one of the most important measures; observational gait assessment is the most commonly used method in clinical practice, but it has certain limitations due to the deviation of subjectivity; instrumental assessments such as three‐dimensional gait analysis (3DGA) and surface electromyography (sEMG) can be used to obtain gait data and muscle activation during walking in stroke patients with hemiplegia, so as to better evaluate the rehabilitation effect of RAGT. Objective. This single‐blind randomized controlled trial is aimed at analyzing the impact of RAGT on the 3DGA parameters and muscle activation in patients with subacute stroke and evaluating the clinical effect of improving walking function of RAGT. Methods. This randomized controlled trial evaluated the improvement of 4‐week RAGT on patients with subacute stroke by 3DGA and surface electromyography (sEMG), combined with clinical scales: experimental group (n = 18, 20 sessions of RAGT) or control group (n = 16, 20 sessions of conventional gait training). Gait performance was evaluated by the 3DGA, and clinical evaluations based on Fugl‐Meyer assessment for lower extremity (FMA‐LE), functional ambulation category (FAC), and 6‐minute walk test (6MWT) were used. Of these patients, 30 patients underwent sEMG measurement synchronized with 3DGA; the cocontraction index in swing phase of the knee and ankle of the affected side was calculated. Results. After 4 weeks of intervention, intragroup comparison showed that walking speed, temporal symmetry, bilateral stride length, range of motion (ROM) of the bilateral hip, flexion angle of the affected knee, ROM of the affected ankle, FMA‐LE, FAC, and 6MWT in the experimental group were significantly improved (p < 0.05), and in the control group, significant improvements were observed in walking speed, temporal symmetry, stride length of the affected side, ROM of the affected hip, FMA‐LE, FAC, and 6MWT (p < 0.05). Intergroup comparison showed that the experimental group significantly outperformed the control group in walking speed, temporal symmetry of the spatiotemporal parameters, ROM of the affected hip and peak flexion of the knee in the kinematic parameters, and the FMA‐LE and FAC in the clinical scale (p < 0.05). In patients evaluated by sEMG, the experimental group showed a noticeable improvement in the cocontraction index of the knee (p = 0.042), while no significant improvement was observed in the control group (p = 0.196), and the experimental group was better than the control group (p = 0.020). No noticeable changes were observed in the cocontraction index of the ankle in both groups (p > 0.05). Conclusions. Compared with conventional gait training, RAGT successfully improved part of the spatiotemporal parameters of patients and optimized the motion of the affected lower limb joints and muscle activation patterns during walking, which is crucial for further rehabilitation of walking ability in patients with subacute stroke. This trial is registered with ChiCTR2200066402.

Improper gait patterns, impaired balance and foot drop consistently plague stroke survivors, preventing them from walking independently and safely. Neuromuscular electrical stimulation (NMES) technology can help patients reactivate their muscles and regain motor coordination. This study aims to systematically review and summarize the evidence for the potential benefits of NMES on the improvement of gait patterns after stroke. PubMed, Cochrane Library, Embase, Science Direct and Web of Science were systematically searched until April 2024, to identify randomized controlled trials with the following criteria: stroke survivors as participants; NMES as intervention; conventional rehabilitation as a comparator; and gait assessment, through scales or quantitative parameters, as outcome measures. 29 publications involving 1711 patients met the inclusion criteria. Meta-analysis showed no significant differences in Ten-meter walk test, Fugl-Meyer assessment lower extremity, Modified Ashworth Assessment and asymmetry between the NMES group and the control group. Besides, NMES was associated with changes in outcome indicators such as quantitative gait analysis speed [SMD = 0.53, 95% CI (0.20, 0.85), P = 0.001], cadence [SMD = 0.76, 95% CI (0.32, 1.20), P = 0.0008], affected side step length [SMD = 0.73, 95% CI (0.16, 1.31), P = 0.01], angle of ankle dorsiflexion [WMD = 1.57, 95% CI (0.80, 2.33), P < 0.0001], Six-Minute Walk Test [WMD = 14.83, 95% CI (13.55, 16.11), P<0.00001]. According to the PEDro scale, 21 (72.4%) studies were of high quality and 8 were of moderate quality (27.6%). Taken together, the review synthesis indicated that NMES might play a potential role in stroke-induced walking dysfunction. And NMES may be superior for survivors in the chronic phase than the acute and subacute phases, and the efficacy of short sessions received by patients was greater than that of those who participated in a longer session. Additionally, further comparisons of the effects of NMES with different types or stimulation frequencies may provide unexpected benefits.

The existing surface electromyography-based pattern recognition system (sEMG-PRS) exhibits limited generalizability in practical applications. In this paper, we propose a stacked weighted random forest (SWRF) algorithm to enhance the long-term usability and user adaptability of sEMG-PRS. First, the weighted random forest (WRF) is proposed to address the issue of imbalanced performance in standard random forests (RF) caused by randomness in sampling and feature selection. Then, the stacking is employed to further enhance the generalizability of WRF. Specifically, RF is utilized as the base learner, while WRF serves as the meta-leaning layer algorithm. The SWRF is evaluated against classical classification algorithms in both online experiments and offline datasets. The offline experiments indicate that the SWRF achieves an average classification accuracy of 89.06%, outperforming RF, WRF, long short-term memory (LSTM), and support vector machine (SVM). The online experiments indicate that SWRF outperforms the aforementioned algorithms regarding long-term usability and user adaptability. We believe that our method has significant potential for practical application in sEMG-PRS.

To improve human–machine interactions, many studies have replicated the human motor nervous system to control lower limb exoskeletons. However, this approach is hindered by its intricacy and the disparities between human and machine capabilities, leading to suboptimal adaptability and constrained practicality. This article presents a gait-generation strategy for lower limb exoskeletons utilizing central pattern generators (CPGs). This method offers a simplified model compared with the traditional CPG methods, significantly easing the parameter optimization process. The strategy is bifurcated into two segments. The initial segment encapsulates the human motor nervous system, constructs a musculoskeletal structure, and generates a basic bionic gait. The subsequent segment employs adaptive oscillators to assimilate and refine this basic gait through weak coupling theory. It further broadens the application range by integrating variational controllers and state observers. We implemented this strategy in a bench-type lower limb exoskeleton, allowing adjustments in walking speed and integrating a preference control algorithm for personalized exoskeleton management. Experimental evaluations confirmed the efficacy of this control system. The results indicated that our system can overcome the limitations of fixed training modes in bench-type lower limb exoskeletons and can adapt to the unique gait characteristics of various users, thereby facilitating gait customization.