Amid the global wave of digital economy, China's medical artificial intelligence applications are rapidly advancing through technological innovation and policy support, while facing multifaceted evaluation and regulatory challenges. The dynamic algorithm evolution undermines the consistency of assessment criteria, multimodal systems lack unified evaluation metrics, and conflicts persist between data sharing and privacy protection. To address these issues, the China National Health Development Research Center has established a value assessment framework for artificial intelligence medical technologies, formulated the country's first technical guideline for clinical evaluation, and validated their practicality through scenario-based pilot studies. Furthermore, this paper proposes introducing a "regulatory sandbox" model to test technical compliance in controlled environments, thereby balancing innovation incentives with risk governance.
Artificial Intelligence/legislation & jurisprudence* ; China ; Humans ; Algorithms

OBJECTIVE: To explore the accuracy of human-computer interaction software in identifying and locating type C1 distal radius fractures. METHODS: Based on relevant inclusion and exclusion criteria, 14 cases of type C1 distal radius fractures between September 2023 and March 2024 were retrospectively analyzed, comprising 3 males and 11 females(aged from 27 to 82 years). The data were assigned randomized identifiers. A senior orthopedic physician reviewed the films and measured the ulnar deviation angle, radial height, palmar inclination angle, intra-articular step, and intra-articular gap for each case on the hospital's imaging system. Based on the reduction standard for distal radius fractures, cases were divided into reduction group and non-reduction group. Then, the data were sequentially imported into a human-computer interaction intelligent software, where a junior orthopedic physician analyzed the same radiological parameters, categorized cases, and measured fracture details. The categorization results from the software were consistent with manual classifications (6 reduction cases and 8 non-reduction cases). For non-reduction cases, the software performed further analyses, including bone segmentation and fracture recognition, generating 8 diagnostic reports containing fracture recognition information. For the 6 reduction cases, the senior and junior orthopedic physicians independently analyzed the data on the hospital's imaging system and the AI software, respectively. Bone segments requiring reduction were identified, verified by two senior physicians, and measured for displacement and rotation along the X (inward and outward), Z (front and back), and Y (up and down) axes. The AI software generated comprehensive diagnostic reports for these cases, which included all measurements and fracture recognition details. RESULTS: Both the manual and AI software methods consistently categorized the 14 cases into 6 reduction and 8 non-reduction groups, with identical data distributions. A paired sample t-test revealed no statistically significant differences (P>0.05) between the manual and software-based measurements for ulnar deviation angle, radial ulnar bone height, palmar inclination angle, intra-articular step, and joint space. In fracture recognition, the AI software correctly identified 10 C-type fractures and 4 B-type fractures. For the 6 reduction cases, a total of 24 bone fragments were analyzed across both methods. After verification, it was found that the bone fragments identified by the two methods were consistent. A paired sample t-tests revealed that the identified bone fragments and measured displacement and rotation angles along the X, Y, and Z axes were consistent between the two methods. No statistically significant differences(P>0.05) were found between manual and software measurements for these parameters. CONCLUSION Human-computer interaction software employing AI technology demonstrated comparable accuracy to manual measurement in identifying and locating type C1 distal radius fractures on CT imaging.
Humans ; Male ; Female ; Radius Fractures/surgery* ; Middle Aged ; Adult ; Aged ; Aged, 80 and over ; Tomography, X-Ray Computed/methods* ; Retrospective Studies ; Software ; Wrist Fractures

Neuropathic pain, a debilitating condition caused by dysfunction of the somatosensory nervous system, remains difficult to treat due to limited understanding of its molecular mechanisms. Bioinformatics analysis identified cerebellin 2 (CBLN2) as highly enriched in human and murine proprioceptive and nociceptive neurons. We found that CBLN2 expression is persistently upregulated in dorsal root ganglia (DRG) following spinal nerve ligation (SNL) in mice. In addition, transcription factor SOX11 binds to 12 cis-regulatory elements within the Cbln2 promoter to enhance its transcription. SNL also induced SOX11 upregulation, with SOX11 and CBLN2 co-localized in nociceptive neurons. The siRNA-mediated knockdown of Sox11 or Cbln2 attenuated SNL-induced mechanical allodynia and thermal hyperalgesia. High-throughput sequencing of DRG following intrathecal injection of CBLN2 revealed widespread gene expression changes, including upregulation of numerous NF-κB downstream targets. Consistently, CBLN2 activated NF-κB signaling, and inhibition with pyrrolidine dithiocarbamate reduced CBLN2-induced pain hypersensitivity, proinflammatory cytokines and chemokines production, and neuronal hyperexcitability. Together, these findings identified the SOX11/CBLN2/NF-κB axis as a critical mediator of neuropathic pain and a promising target for therapeutic intervention.
Animals ; Neuralgia/metabolism* ; Ganglia, Spinal/metabolism* ; Up-Regulation ; Mice ; NF-kappa B/metabolism* ; SOXC Transcription Factors/genetics* ; Male ; Neuroinflammatory Diseases/metabolism* ; Mice, Inbred C57BL ; Nerve Tissue Proteins/genetics* ; Hyperalgesia/metabolism* ; Signal Transduction ; Spinal Nerves

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

Staphylococcus aureus is a Gram-positive bacterium with strong pathogenicity. With the widespread use of antibiotics， its multi-drug resistance has gradually increased. Among them， methicillin-resistant S. aureus （MRSA） is one of the main pathogens of hospital and community infections. Antimicrobial peptides are short-chain peptides with good antibacterial effects and low drug resistance， which have been widely studied in recent years. This study summarizes the mechanism of action of antimicrobial peptides and related study on antimicrobial peptides against MRSA from different sources. It is found that the mechanisms of action of antimicrobial peptides include targeting bacterial cell membranes， bacterial cells， and bacterial cell walls， etc. Besides isolating antimicrobial peptides with anti-MRSA activity from animals， plants， and microorganisms， antimicrobial peptides can also be obtained through synthetic methods. Among them， GHa-derived peptides from animal sources， Ib-AMP4 from plant sources， Ph-SA from microbial sources， the synthetic peptide LLKLLLKLL-NH2， and so on， due to their effective antibacterial activity， rapid bactericidal speed， and low toxicity， are promising candidates for anti-MRSA drugs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Background: This study aimed to estimate temporal trends in metabolically unhealthy obesity (MUO) among United States (US) adults by age, sex, race/ethnicity, and income from 1999 to 2018. Methods: We included 17,230 non-pregnant adults from a nationally representative cross-sectional study, the National Health and Nutrition Examination Survey (NHANES). MUO was defined as body mass index ≥30 kg/m2 with any metabolic disorders in blood pressure, blood glucose, and blood lipids. The age-adjusted percentage of MUO was calculated, and linear regression models estimated trends in MUO. Results: The weighted mean age of adults was 47.28 years; 51.02% were male, 74.64% were non-Hispanic White. The age-adjusted percentage of MUO continuously increased in adults across all subgroups during 1999–2018, although with different magnitudes (all P<0.05 for linear trend). Adults aged 45 to 64 years consistently had higher percentages of MUO from 1999–2000 (34.25%; 95% confidence interval [CI], 25.85% to 42.66%) to 2017–2018 (42.03%; 95% CI, 35.09% to 48.97%) than the other two age subgroups (P<0.05 for group differences). The age-adjusted percentage of MUO was the highest among non-Hispanic Blacks while the lowest among non-Hispanic Whites in most cycles. Adults with high-income levels generally had lower MUO percentages from 1999–2000 (22.63%; 95% CI, 17.00% to 28.26%) to 2017–2018 (32.36%; 95% CI, 23.87% to 40.85%) compared with the other two subgroups. Conclusion This study detected a continuous linear increasing trend in MUO among US adults from 1999 to 2018. The persistence of disparities by age, race/ethnicity, and income is a cause for concern. This calls for implementing evidence-based, structural, and effective MUO prevention programs.

Objective To evaluate the surgical efficacy of unilateral pneumonectomy for the treatment of tuberculous destroyed lung, analyze the causes of severe postoperative complications, and explore clinical management strategies. Methods A retrospective analysis was conducted on the clinical data of patients with tuberculous destroyed lung who underwent unilateral pneumonectomy at the Public Health Clinical Center of Chengdu from 2017 to 2023. Postoperative severe complications were statistically analyzed. Patients were divided into a non-severe complication group and a severe-complication group, and the causes, management, and outcomes of complications were analyzed. Results A total of 134 patients were included, comprising 69 males and 65 females, with a mean age of 17-73 (40.43±12.69) years. There were 93 patients undergoing left pneumonectomy and 41 patients undergoing right pneumonectomy. Preoperative sputum smear was positive in 35 patients, all of which converted to negative postoperatively. There were 58 patients with hemoptysis preoperatively, and none experienced hemoptysis postoperatively. Postoperative incisional infection occurred in 8 (5.97%) patients, and postoperative pulmonary infection in 26 (19.40%) patients. Severe postoperative complications occurred in 17 (12.69%) patients, including empyema in 9 (6.72%) patients, bronchopleural fistula with empyema in 1 (0.75%) patient, severe pneumonia in 3 (2.24%) patients, postpneumonectomy syndrome in 1 (0.75%) patient, chylothorax in 1 (0.75%) patient, ketoacidosis in 1 (0.75%) patient, and heart failure with severe pneumonia in 1 (0.75%) patient. Perioperative mortality occurred in 2 (1.49%) patients, both of whom underwent right pneumonectomy. Multivariate logistic regression analysis revealed that a history of ipsilateral thoracic surgery, concomitant Aspergillus infection, and greater blood loss were independent risk factors for severe complications following unilateral pneumonectomy for tuberculous destroyed lung (P<0.05). Conclusion Unilateral pneumonectomy for patients with tuberculous destroyed lung can significantly improve the clinical cure rate, sputum conversion rate, and hemoptysis cessation rate. However, there is a certain risk of severe perioperative complications and mortality, requiring thorough perioperative management and appropriate management of postoperative complications.