BACKGROUND:Exercise as a form of active rehabilitation can improve the dysfunction caused by peripheral nerve injury,and different exercise modalities target different lesion sites and recovery mechanisms. OBJECTIVE:To comprehensively analyze the application and mechanisms of different exercise modalities in functional recovery from peripheral nerve injury. METHODS:A computerized search was conducted in PubMed and CNKI databases for relevant literature published before January 2024.The search terms used were"peripheral nerve injury,spinal cord,exercise,cerebral cortex,muscle atrophy,mirror therapy,blood flow restriction training"in both English and Chinese.Finally,77 articles were included for review. RESULTS AND CONCLUSION:Peripheral nerve injury can cause systemic pathological changes such as skeletal muscle atrophy,corresponding spinal cord segmental lesions,and sensorimotor cortex remodeling.Aerobic exercise can improve dysfunction by enhancing the immune response,promoting glial cell polarization,and promoting the release of nerve growth factor.Blood flow restriction exercise can regulate the secretion of muscle growth factor,promote muscle growth and enhance muscle strength.Mirror movement has a good effect in activating the cerebral cortex and reducing cortical remodeling.Different exercise modalities have potential benefits in functional recovery after peripheral nerve injury;however,there are still some problems and challenges,such as the choice of exercise modalities,the control of exercise intensity and frequency,and the detailed analysis of mechanisms.

Medical artificial intelligence （AI） is a new type of application formed by the combination of machine learning， computer vision， natural language processing， and other technologies with clinical medical treatment. With the continuous iteration and development of relevant technologies， medical AI has shown great potential in improving the efficiency of diagnosis and treatment， and service quality， but it also increases the possibility of triggering ethical issues. Ethical issues resulting from the clinical application of medical AI were analyzed， including the lack of algorithmic interpretability and transparency of medical AI， leading to information asymmetry and cognitive discrepancies； the concerning status of security and privacy protection of medical data； and the complex and unclear division of responsibilities due to the collaborative participation of multiple subjects in the clinical application of medical AI， resulting in increased difficulty in the identification of medical accidents and clarification of responsibilities. The paper proposed the principles of not harming patients’ interests， physician’s subjectivity， fairness and inclusiveness， and rapid response. It also explored the strategies and implementation paths for responding to the ethical issues of medical AI from multiple perspectives， including standardizing the environment and processes， clarifying responsibility attribution， continuously assessing the impact of data protection， guaranteeing data security， ensuring model transparency and interpretability， carrying out multi-subject collaboration， as well as the principles of being driven by ethical values and adhering to the “human health-centeredness.” It aimed to provide guidance for the healthy development of medical AI， ensuring technological progress while effectively managing and mitigating accompanying ethical risks， thereby promoting the benign development of medical AI technology and better serving the healthcare industry and patients.

ObjectiveThe full name of vertebroplasty is percutaneous vertebroplasty (PVP). It is a clinical technique that injects bone cement into the diseased vertebral body to achieve strengthening of the vertebra. The research on the safety and efficacy of bone cement is the basis for clinical application. In this study, vertebroplasty is used to evaluate and compare the safety and efficacy of Tecres and radiopaque bone cement in experimental pigs, and to determine the puncture method suitable for pigs and the pre-clinical evaluation method for the safety and efficacy of bone cement. MethodsTwenty-four experimental pigs (with a body weight of 60-80 kg) were randomly divided into an experimental group (Group A) and a control group (Group B). Group A was the Tecres bone cement group, and Group B was the radiopaque bone cement group, with 12 pigs in each group. Under the monitoring of a C-arm X-ray machine, the materials were implanted into the 1st lumbar vertebra (L1) and 4th lumbar vertebra (L4) of the pigs via percutaneous puncture using the unilateral pedicle approach. The animals were euthanized at 4 weeks and 26 weeks after the operation, respectively. The L4 vertebrae were taken for compressive strength testing, and the L1 vertebrae were taken for hard tissue pathological examination to observe the inflammatory response, bone necrosis, and degree of osseointegration at the implantation site. ResultsThe test results of compressive strength between groups A and B showed no significant difference at 4 weeks and 26 weeks after bone cement implantation (P > 0.05). Observation under an optical microscope (×100) revealed that at 4 weeks postoperatively, both groups A and B showed that the bone cement was surrounded by proliferative fibrous tissue, with lymphocyte infiltration around it. The bone cement was combined with bone tissue, the trabecular arrangement was disordered, and osteoblasts and a small amount of osteoid were formed. At 26 weeks postoperatively, bone cement was visible in both groups A and B. The new bone tissue was mineralized, the trabeculae were fused, the trabecular structure was regular and dense with good continuity, and no obvious inflammatory reaction was observed. ConclusionIn experimental pig vertebrae, there were no significant differences observed in the compressive strength, inflammation response, bone destruction, and integration with the bone between Tecres and non-radiopaque bone cement. Both exhibited good biocompatibility and osteogenic properties. It indicates that using vertebroplasty to evaluate the safety and efficacy of bone cement in pigs is scientifically sound.

BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

This study aims to systematically characterize the targeted effects of Quanduzhong Capsules on cartilage lesions in knee osteoarthritis by integrating spatial transcriptomics data mining and animal experiments validation, thereby elucidating the related molecular mechanisms. A knee osteoarthritis model was established using Sprague-Dawley(SD) rats, via a modified Hulth method. Hematoxylin and eosin(HE) staining was employed to detect knee osteoarthritis-associated pathological changes in knee cartilage. Candidate targets of Quanduzhong Capsules were collected from the HIT 2.0 database, followed by bioinformatics analysis of spatial transcriptomics datasets(GSE254844) from cartilage tissues in clinical knee osteoarthritis patients to identify spatially specific disease genes. Furthermore, a "formula candidate targets-spatially specific genes in cartilage lesions" interaction network was constructed to explore the effects and major mechanisms of Quanduzhong Capsules in distinct cartilage regions. Experimental validation was conducted through immunohistochemistry using animal-derived biospecimens. The results indicated that Quanduzhong Capsules effectively inhibited the degenerative changes in the cartilage of affected joints in rats, which was associated with the regulation of Quanduzhong Capsules on the thioredoxin-interacting protein(TXNIP)-NOD-like receptor family pyrin domain containing 3(NLRP3)-bone morphogenetic protein receptor type 2(BMPR2)-fibronectin 1(FN1)-matrix metallopeptidase 2(MMP2) signal axis in the articular cartilage surface and superficial zones, subsequently inhibiting cartilage matrix degradation leading to oxidative stress and inflammatory diffusion. In summary, this study clarifies the spatially specific targeted effects and protective mechanisms of Quanduzhong Capsules within pathological cartilage regions in knee osteoarthritis, providing theoretical and experimental support for the clinical application of this drug in the targeted therapy on the inflamed cartilage.
Animals ; Osteoarthritis, Knee/metabolism* ; Drugs, Chinese Herbal/administration & dosage* ; Rats, Sprague-Dawley ; Rats ; Male ; Humans ; Capsules ; Female ; Disease Models, Animal

The traditional Chinese medicine(TCM) industry is a crucial part of China's pharmaceutical sector and plays a strategic role in ensuring public health and promoting economic and social development. In response to the practical demand for high-quality development of the TCM industry, this paper focused on the bottlenecks encountered during the digital and intelligent transformation of TCM production systems. Specifically, it explored technical strategies and methodologies for constructing the best TCM production mode. An innovative artificial intelligence(AI)-centered technical architecture for TCM production was proposed, focusing on key aspects of production management including process modeling, state evaluation, and decision optimization. Furthermore, a series of critical technologies were developed to realize the best TCM production mode. Finally, a novel AI-driven TCM production mode characterized by a closed-loop system of "measurement-modeling-decision-execution" was presented through engineering case studies. This study is expected to provide a technological pathway for developing new quality productive forces within the TCM industry.
Artificial Intelligence ; Drugs, Chinese Herbal ; Medicine, Chinese Traditional/methods* ; Humans

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

OBJECTIVE: To identify the gene mutation types of 4 suspected β-thalassemia patients in Yunnan Province, and to analyze the genotypes and hematological phenotypes. METHODS: Whole genome sequencing was performed on the samples of 4 suspected β-thalassemia patients from the Dai ethnic group in a thalassemia endemic area of Yunnan Province, whose hematological phenotypes were not consistent with the results of common thalassemia gene mutations. The mutations of β-globin gene clusters were confirmed by polymerase chain reaction (PCR) and Sanger DNA sequencing technology. RESULTS: The 3.5 kb deletion in β-globin gene cluster (NC_000011.10: g. 5224302-5227791del3490bp) was detected in 4 patients' samples, of which 1 case was also detected with HbE mutation and 1 case with CD17 mutation. These 2 patients displayed moderate anemia phenotype, while the two patients with only the 3.5 kb deletion presented with other mild anemia phenotype. CONCLUSION Heterozygous carriers with rare 3.5 kb deletion of the β-globin gene cluster may develop mild anemia, compound mutations of the 3.5 kb deletion with other mutations may led to intermediate thalasemia with moderate to sever anemia. In areas with a high incidence of thalassemia, suspected patients should undergo genetic testing to avoid missing or misdiagnosing rare mutations.
Humans ; beta-Globins/genetics* ; Multigene Family ; beta-Thalassemia/genetics* ; Mutation ; Genotype ; Sequence Deletion ; Phenotype ; Male ; Female

Intentional tooth replantation (ITR) is an advanced treatment modality and the procedure of last resort for preserving teeth with inaccessible endodontic or resorptive lesions. ITR is defined as the deliberate extraction of a tooth; evaluation of the root surface, endodontic manipulation, and repair; and placement of the tooth back into its original socket. Case reports, case series, cohort studies, and randomized controlled trials have demonstrated the efficacy of ITR in the retention of natural teeth that are untreatable or difficult to manage with root canal treatment or endodontic microsurgery. However, variations in clinical protocols for ITR exist due to the empirical nature of the original protocols and rapid advancements in the field of oral biology and dental materials. This heterogeneity in protocols may cause confusion among dental practitioners; therefore, guidelines and considerations for ITR should be explicated. This expert consensus discusses the biological foundation of ITR, the available clinical protocols and current status of ITR in treating teeth with refractory apical periodontitis or anatomical aberration, and the main complications of this treatment, aiming to refine the clinical management of ITR in accordance with the progress of basic research and clinical studies; the findings suggest that ITR may become a more consistent evidence-based option in dental treatment.
Humans ; Tooth Replantation/methods* ; Consensus ; Periapical Periodontitis/surgery*