Antimicrobial resistance (AMR) has emerged as a major global public health challenge, with traditional prevention and control methods exhibiting significant limitations in detection efficiency, data processing, and clinical decision-making. Leveraging its robust capabilities in data analysis and pattern recognition, artificial intelligence (AI) technology has been widely applied across multiple critical aspects of AMR containment. Current evidence demonstrates that AI technologies can significantly enhance the efficiency of resistancediagnosis, optimize personalized treatment strategies, and improve real-time monitoring of resistant pathogen transmission. Despite persistent challenges such as data heterogeneity, model interpretability, and ethical compliance in practical applications, AI holds immense promise in supporting precision infection management and addressing the growing crisis of antimicrobial resistance.This article systematically reviews the clinical applications of AI in AMR prevention and control, including resistance detection and prediction based on mass spectrometry and genomic data, the use of clinical decision support systems in anti-infective therapy, as well as the role of AI in epidemiological surveillance, pathogen tracking, early warning systems, and novel antimicrobial drug discovery aiming to provide reference for clinical practice.

Objective:To study the willingness of the medical staff of tertiary hospitals to be dispatched to work for a long time at the district hospitals under the " municipal trusteeship of district-ownership hospitals" mode, and explore the key factors that affect their long-term willingness, for references of optimizing the incentive system of the model.Methods:A questionnaire was designed as guided by the two-factor theory. In May 2019, a typical sample survey was made to investigate 103 medical staff of two tertiary hospitals in Beijing on their willingness to be dispatched to hospitals of district ownership. The survey covered such aspects as their basic personal information, hygiene factors(family responsibility, interpersonal connections expansion, mobilization and encouragement of leaders, etc.), and incentive factors(personal career development, greater potential giving play to their own talents, self-achievement, etc.). Descriptive analysis and univariate hypothesis testing were used to compare the differences of the dispatch willingness of staff of different characteristics. Meanwhile, an ordinal multinomial logistic regression model was built to analyze the influence factors of long-term dispatch willingness of these staff along with individual in-depth interviews.Results:The medical staff when they were dispatched to work at the entrusted hospitals for a long time, were concerned mostly with top six factors. These factors refered to a reasonable incentive(rewards)mechanism, family responsibility, colleague evaluation of the entrusted hospital, interpersonal connections expansion, personal career development, and potential unlocking. Those staff with dispatch experience had higher willingness to be dispatched than those without( P <0.05). The former saw higher of their personal career development resulting from the dispatch(3.91 points), while the latter saw higher their post-dispatch salary incentive mechanism(3.95 points). Compared with common medical staff, medium and high level management staff were more willing to be dispatched for a long time( H=14.536, P<0.001). Leadership mobilization in hygiene factors and personal career development in incentives factors were the key drivers for the long-term willingness of medical staff to be dispatched( P<0.05). Conclusions:The willingness of medical staff to be dispatched for a long time needs to be strengthened in the " municipal trusteeship of district-ownership hospitals" mode. Managers of the district-ownership hospitals should take into full consideration, the needs of different categories and levels of medical staff dispatched, guarantee the remuneration and performance pay of these staff, and enable their career track planning, hence mobilizing their incentives, and increase their willingness to work at such hospitals for a long time.

Objective: To explore the relationship between insulin resistance and plasma hypersensitive reactive protein (hs-CRP) in patients with chronic schizophrenia. Methods: A total of 247 inpatients with chronic schizophrenia (patient group) and 166 cases of normal individuals(control group) were enrolled.Their general demographic and clinical data were collected, fasting blood glucose, hs-CRP, c-peptide and insulin indexes were tested, and insulin resistance index (HOMA-IR) was calculated.The insulin resistance level of the patients group and the control group was compared by Mann-Whitney U test, and the relationship between insulin-resistance and hs-CRP in patients group was analyzed using Spearman correlation analysis. Results: (1)The levels of C-peptide (2.53(2.06, 3.23)ng/ml vs 2.24(1.89, 2.87)ng/ml), insulin (7.68(4.66, 11.97)μIU/ml vs 7.02(4.31, 9.59)μIU/ml) and HOMA-IR (1.75(1.09, 3.07) vs 1.57(0.97, 2.22)) in the patient group were significantly higher than those in the control group(all P<0.05). (2) The levels of HOMA-IR( 1.91(1.21, 3.74) vs 1.70(1.02, 2.72)) in patients with high hs-CRP(≥3 mg/L) was higher than those in the patients with low hs-CRP(<3 mg/L)(P<0.05). (3)Spearman correlation analysis showed that HOMA-IR was positively correlated with plasma hs-CRP level in the patient group (r=0.139, P<0.05). (4)After logarithmic transformation of related variables, multivariate linear regression analysis showed that HOMA-IR was linearly correlated with hs-CRP level and boy weight index. Conclusion The hs-CRP level in chronic schizophrenia has a positive predictive effect on insulin resistance.Detection of hs-CRP level in schizophrenic patients is helpful to assess metabolic risk of insulin.