Optimizing the extraction of active components from Salvia miltiorrhiza by combination of machine learning models and intelligent optimization algorithms and its correlation analysis of antioxidant activity.

We extracted Sal B and TIIA from Salvia miltiorrhiza using enzymatic-assisted ethanol extraction. ACONN predicted optimal process conditions. Enzymolysis and alcohol extraction were used, optimizing conditions and evaluating antioxidant activity. ACONN analyzed data and ACO optimized conditions. Lab verification comprehensively evaluated the conditions. The correlation between Sal B, TIIA, and their antioxidant activities was examined. Weights of 0.5739 and 0.4260 evaluated Sal B and TIIA. ACONN had a 97.46% fitting degree. Optimized extraction conditions improved yield and quality, yielding a comprehensive evaluation value of 27.69 with 4.46% average errors. This approach enhances extraction and compound quality. Antioxidant activity strongly correlated with component yield, influenced by extraction conditions. ACONN-optimized extraction improved Sal B and TIIA yield and quality, with potential as natural antioxidants. Integrating machine learning and optimization algorithms in industrial extraction enhances efficiency and environmental preservation.

Nano-Diamond-Enhanced Integrated Response of a Surface Plasmon Resonance Biosensor.

Surface plasmon resonance (SPR) sensing is a real-time detection technique for measuring biomolecular interactions on gold surfaces. This study presents a novel approach using nano-diamonds (NDs) on a gold nano-slit array to obtain an extraordinary transmission (EOT) spectrum for SPR biosensing. We used anti-bovine serum albumin (anti-BSA) to bind NDs for chemical attachment to a gold nano-slit array. The covalently bound NDs shifted the EOT response depending on their concentration. The number of ND-labeled molecules attached to the gold nano-slit array was quantified from the change in the EOT spectrum. The concentration of anti-BSA in the 35 nm ND solution sample was much lower than that in the anti-BSA-only sample (approximately 1/100). With the help of 35 nm NDs, we were able to use a lower concentration of analyte in this system and obtained better signal responses. The responses of anti-BSA-linked NDs had approximately a 10-fold signal enhancement compared to anti-BSA alone. This approach has the advantage of a simple setup and microscale detection area, which makes it suitable for applications in biochip technology.

A comprehensive review of stroke-related signaling pathways and treatment in western medicine and traditional Chinese medicine.

This review provides insight into the complex network of signaling pathways and mechanisms involved in stroke pathophysiology. It summarizes the historical progress of stroke-related signaling pathways, identifying potential interactions between them and emphasizing that stroke is a complex network disease. Of particular interest are the Hippo signaling pathway and ferroptosis signaling pathway, which remain understudied areas of research, and are therefore a focus of the review. The involvement of multiple signaling pathways, including Sonic Hedgehog (SHH), nuclear factor erythroid 2-related factor 2 (Nrf2)/antioxidant response element (ARE), hypoxia-inducible factor-1α (HIF-1α), PI3K/AKT, JAK/STAT, and AMPK in pathophysiological mechanisms such as oxidative stress and apoptosis, highlights the complexity of stroke. The review also delves into the details of traditional Chinese medicine (TCM) therapies such as Rehmanniae and Astragalus, providing an analysis of the recent status of western medicine in the treatment of stroke and the advantages and disadvantages of TCM and western medicine in stroke treatment. The review proposes that since stroke is a network disease, TCM has the potential and advantages of a multi-target and multi-pathway mechanism of action in the treatment of stroke. Therefore, it is suggested that future research should explore more treasures of TCM and develop new therapies from the perspective of stroke as a network disease.

Motion Similarity Evaluation between Human and a Tri-Co Robot during Real-Time Imitation with a Trajectory Dynamic Time Warping Model.

Precisely imitating human motions in real-time poses a challenge for the robots due to difference in their physical structures. This paper proposes a human-computer interaction method for remotely manipulating life-size humanoid robots with a new metrics for evaluating motion similarity. First, we establish a motion capture system to acquire the operator's motion data and retarget it to the standard bone model. Secondly, we develop a fast mapping algorithm, by mapping the BVH (BioVision Hierarchy) data collected by the motion capture system to each joint motion angle of the robot to realize the imitated motion control of the humanoid robot. Thirdly, a DTW (Dynamic Time Warping)-based trajectory evaluation method is proposed to quantitatively evaluate the difference between robot trajectory and human motion, and meanwhile, visualization terminals render it more convenient to make comparisons between two different but simultaneous motion systems. We design a complex gesture simulation experiment to verify the feasibility and real-time performance of the control method. The proposed human-in-the-loop imitation control method addresses a prominent non-isostructural retargeting problem between human and robot, enhances robot interaction capability in a more natural way, and improves robot adaptability to uncertain and dynamic environments.