Cognitive-exercise dual-task promotes cognitive function recovery in chronic cerebral ischemia male rats through regulating PI3K/Akt signaling pathway via inhibition of EphrinA3/EphA4.

Chronic cerebral ischemia (CCI) can lead to vascular cognitive impairment, but therapeutic options are limited. Cognitive-exercise dual-task (CEDT), as a potential rehabilitation intervention, can attenuate cognitive impairment. However, the related mechanisms remain unclear. In this study, 2-vessel occlusion (2-VO) in male SD rats was performed to establish the CCI model. The rats were treated with cognitive, exercise, or CEDT intervention for 21 days. The Morris water maze (MWM) test was used to assess cognitive ability. TUNEL staining was used to detect the neuronal apoptosis. Immunofluorescence, RT-qPCR and Western blot were used to detect the protein or mRNA levels of EphrinA3, EphA4, p-PI3K, and p-Akt. The results showed that CEDT could improve performance in the MWM test, reverse the increased expression of EphrinA3 and EphA4, and the reduced expression of p-PI3K and p-Akt in CCI rats, which was superior to exercise and cognitive interventions. In vitro, oxygenglucose deprivation (OGD) challenge of astrocytes and neuronal cells were used to mimic cerebral ischemia. Immunofluorescence assay revealed that the levels of MAP-2, p-PI3K, and p-Akt were reduced in EphrinA3 overexpressed cells after OGD stimulation. Finally, the knock-down of EphrinA3 by shRNA significantly promoted the recovery of cognitive function and activation of PI3K/Akt after CEDT treatment in CCI rats. In conclusion, our study suggests that CEDT promotes cognitive function recovery after CCI by regulating the signaling axis of EphrinA3/EphA4/PI3K/Akt.

TXNIP Regulates NLRP3 Inflammasome-Induced Pyroptosis Related to Aging via cAMP/PKA and PI3K/Akt Signaling Pathways.

Aging is an inevitable natural process with time-dependent dysfunction and the occurrence of various diseases, which impose heavy burdens on individuals, families, and society. It has been reported that NLRP3 inflammasome-induced pyroptosis contributes significantly to age-related diseases and aging, while TXNIP is suggested to be involved in regulating pyroptosis mediated by NLRP3. However, the mechanism between TXNIP and NLRP3 inflammasome is still unclear. In this study, we used HT-22 cells to explore the effect of TXNIP on pyroptosis and its potential association with the aging. Also, we delved into the underlying mechanisms. Our findings revealed that TXNIP significantly augmented pyroptosis in HT-22 cells, primarily by enhancing the activation of the NLRP3 inflammasome and promoting the release of proinflammatory cytokines. Remarkably, as TXNIP levels increased, we observed a corresponding rise in the number of p16-positive cells, which is indicative of aging. Furthermore, we conducted experiments to modulate the improvement of TXNIP on NLRP3 inflammasome-induced pyroptosis, that is, the PI3K activator 740 Y-P and the PKA activator DC2797 inhibited the effect, while the PI3K inhibitor LY294002 and the PKA inhibitor H89 enhanced the effect. In conclusion, our study demonstrated that TXNIP regulates NLRP3 inflammasome-induced pyroptosis in HT-22 cells related to aging via the PI3K/Akt and cAMP/PKA pathways.

Multimodal fusion and human-robot interaction control of an intelligent robot.

Introduction: Small-scaled robotic walkers play an increasingly important role in Activity of Daily Living (ADL) assistance in the face of ever-increasing rehab requirements and existing equipment drawbacks. This paper proposes a Rehabilitation Robotic Walker (RRW) for walking assistance and body weight support (BWS) during gait rehabilitation. Methods: The walker provides the patients with weight offloading and guiding force to mimic a series of the physiotherapist's (PT's) movements, and creates a natural, comfortable, and safe environment. This system consists of an omnidirectional mobile platform, a BWS mechanism, and a pelvic brace to smooth the motions of the pelvis. To recognize the human intentions, four force sensors, two joysticks, and one depth-sensing camera were used to monitor the human-machine information, and a multimodal fusion algorithm for intention recognition was proposed to improve the accuracy. Then the system obtained the heading angle E, the pelvic pose F, and the motion vector H via the camera, the force sensors, and the joysticks respectively, classified the intentions with feature extraction and information fusion, and finally outputted the motor speed control through the robot's kinematics. Results: To validate the validity of the algorithm above, a preliminary test with three volunteers was conducted to study the motion control. The results showed that the average error of the integral square error (ISE) was 2.90 and the minimum error was 1.96. Discussion: The results demonstrated the efficiency of the proposed method, and that the system is capable of providing walking assistance.