Mechanical pain is one of the most common causes of clinical pain, but there remains a lack of effective treatment for debilitating mechanical and chronic forms of neuropathic pain. Recently, neurotoxin GsMTx4, a selective mechanosensitive (MS) channel inhibitor, has been found to be effective, while the underlying mechanism remains elusive. Here, with multiple rodent pain models, we demonstrated that a GsMTx4-based 17-residue peptide, which we call P10581, was able to reduce mechanical hyperalgesia and neuropathic pain. The analgesic effects of P10581 can be as strong as morphine but is not toxic in animal models. The anti-hyperalgesic effect of the peptide was resistant to naloxone (an μ-opioid receptor antagonist) and showed no side effects of morphine, including tolerance, motor impairment, and conditioned place preference. Pharmacological inhibition of TRPV4 by P10581 in a heterogeneous expression system, combined with the use of Trpv4 knockout mice indicates that TRPV4 channels may act as the potential target for the analgesic effect of P10581. Our study identified a potential drug for curing mechanical pain and exposed its mechanism.

In the era of intelligence and individualization, gastric cancer surgery is under-going multidimensional advancements. The authors focus on the cutting-edge progress and future challenges of artificial intelligence (AI) in the diagnosis and decision-making, treatment and drug development, as well as postoperative rehabilitation in gastric cancer surgery. In terms of diagnosis, AI integrates imaging, liquid biopsy, pathology, and multimodal technologies to enhance diagnostic comprehensiveness and accuracy. Regarding decision-making, AI assists in formulating personalized treatment plans, conducting risk assessments, and predicting prognoses. In the treatment domain, AI facilitates the advancement of individualized surgical approaches, supports postoperative follow-up, and aids in physician education and training. In drug development, the introduction of virtual cell models and AlphaFold has improved the efficiency and accuracy of mechanistic and clinical research. For postoperative rehabilitation guidance, AI provides personalized recommendations to optimize treatment outcomes.AI holds great promise in gastric cancer surgery across diagnosis and decision-making, treatment and drug development, and postoperative rehabilitation. However, current AI technologies face challenges such as data sharing and privacy protection, multicenter research and model generalization, human-machine collaboration, interpretability, ethical considerations, sustaina-bility, and widespread adoption. Addressing these challenges will require collective efforts to fully leverage AI′s advantages in gastric cancer diagnosis and treatment.

In the era of intelligence and individualization, gastric cancer surgery is under-going multidimensional advancements. The authors focus on the cutting-edge progress and future challenges of artificial intelligence (AI) in the diagnosis and decision-making, treatment and drug development, as well as postoperative rehabilitation in gastric cancer surgery. In terms of diagnosis, AI integrates imaging, liquid biopsy, pathology, and multimodal technologies to enhance diagnostic comprehensiveness and accuracy. Regarding decision-making, AI assists in formulating personalized treatment plans, conducting risk assessments, and predicting prognoses. In the treatment domain, AI facilitates the advancement of individualized surgical approaches, supports postoperative follow-up, and aids in physician education and training. In drug development, the introduction of virtual cell models and AlphaFold has improved the efficiency and accuracy of mechanistic and clinical research. For postoperative rehabilitation guidance, AI provides personalized recommendations to optimize treatment outcomes.AI holds great promise in gastric cancer surgery across diagnosis and decision-making, treatment and drug development, and postoperative rehabilitation. However, current AI technologies face challenges such as data sharing and privacy protection, multicenter research and model generalization, human-machine collaboration, interpretability, ethical considerations, sustaina-bility, and widespread adoption. Addressing these challenges will require collective efforts to fully leverage AI′s advantages in gastric cancer diagnosis and treatment.

Objective:To investigate the application effect of case-based collaborative learning (CBCL) based on data-information-knowledge-wisdom (DKIW) model in the training of the informatization teaching ability of clinical teachers.Methods:From March to August in 2022, 71 clinical teachers from four grade A tertiary hospitals in Tianjin, China, were selected as subjects and were randomly divided into control group with 35 patients and experimental group with 36 patients using a random number table. The teachers in the control group received blended teaching online and offline, and those in the experimental group received CBCL teaching based on DIKW model. The two groups were compared in terms of theoretical assessment score, informatization teaching demonstration score, and informatization teaching ability score before and after intervention. SPSS 27.0 was used for the t-test and the Mann-Whitney U rank sum test. Results:Compared with the control group after intervention, the experimental group had significantly higher scores of theoretical assessment (83.50±3.11) and informatization teaching demonstration (84.19±1.89) ( P<0.05). After intervention, the control group had significant increases in the total score of informatization teaching ability (74.34±4.08) and the scores of each dimension (15.40±1.19, 19.29±1.62, 28.54±1.67, and 11.11±1.79), and the experimental group also had significant increases in the total score of informatization teaching ability (83.64±5.25) and the scores of each dimension (16.53±1.21, 20.94±1.98, 33.03±2.10, and 13.14±1.48); the experimental group had significantly higher scores than the control group ( P<0.05). Conclusions:The CBCL teaching model based on DIKW model can help to improve the comprehensive informatization teaching ability of clinical teachers.