Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Metagenomic next-generation sequencing, as an unbiased detection technology, demonstrates higher diagnostic efficacy than traditional methods. Gut microorganisms are important flora for safeguarding health and have become a hot research topic. Modeling and analyzing the genomic data of intestinal flora using machine learning is very important in disease prediction and diagnosis. This paper briefly introduces the characteristics of metagenomic next-generation sequencing, key algorithms and evaluation indexes of machine learning, outlines the main steps of combining machine learning with metagenomic next-generation sequencing, and summarizes the application of the combination of machine learning and metagenomic next-generation sequencing technology in the study of intestinal flora, which will provide a more accurate method for diagnosis and prediction of the related diseases, and give more ideas for the future research and clinical practice.

With the rapid development of computer vision technology, deep learning models have demonstrated new research area and potential value in intelligent microbiological detection. By utilizing multilayer neural networks and large amounts of training data, these models are capable of automated extraction and analysis for complex features, thereby improving the efficiency and accuracy of detection. This paper introduces the research background of deep learning in microbiological image detection, and elaborates on the methods for constructing microbiological image datasets, including data collection, preprocessing, annotation, and partitioning, and introduces typical deep learning models as well as their application examples in various microbiological detection. Deep learning in microbiological image analysis faces numerous challenges which needs further development.

Objective:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.

Objective To analyze the procurement data of lipid-lowering drugs in hospitals at different levels in Jiangsu Province from October 2019 to September 2023,to evaluate the impact of the volume-based procurement(VBP)policy,and to provide references for clinical rational drug use and healthcare policy optimization.Methods Based on procurement data from the Jiangsu Provincial Health Information Center,statistical analyses of procurement expenditures,defined daily doses(DDDs),and defined daily cost(DDC)were conducted.Mixed-effects models were applied to assess changes in procurement expenditures,DDDs,and DDC before and after VBP implementation.Results From 2019 to 2023,statins dominated the market in Jiangsu Province,with rosuvastatin recording the highest DDDs(748 million).Statins,traditional Chinese medicines,and cholesterol absorption inhibitors ranked highest in procurement expenditures.Tertiary hospitals accounted for the largest share of usage(47.6%)and expenditures(55.8%),while secondary hospitals had the lowest DDC(1.22 yuan)and tertiary hospitals the highest(1.89 yuan).Post-VBP,procurement expenditures and DDC decreased by 53.9%and 35.4%,respectively.Primary hospitals showed the largest expenditure reduction(61.6%),and secondary hospitals exhibited the greatest DDC decline(53.9%).DDDs increased significantly in primary care settings(e.g.,pitavastatin surged by 239.79%in secondary hospitals),while tertiary hospitals saw reduced usage of some drugs(e.g.,amlodipine/atorvastatin decreased by 7.34%).Mixed-effects models confirmed that VBP significantly reduced expenditures(OR=-1.07,P<0.01)and DDC(OR=-2.70,P<0.01)while indirectly lowering prices of non-VBP drugs.After covariate adjustment,expenditure reductions for rosuvastatin and atorvastatin narrowed,ezetimibe expenditures increased(OR=0.13,P<0.01),and pitavastatin usage declined(OR=-0.10,P<0.01).Changes in amlodipine/atorvastatin and ezetimibe lacked statistical significance due to short VBP implementation periods.Tertiary hospitals demonstrated the strictest policy adherence,with the largest expenditure and DDC reductions(P<0.01).Subgroup analysis revealed that the policy did not significantly affect clinical demand(DDDs)in hospitals at different levels,though it was considered to have triggered adjustments in medication structure.Conclusion Jiangsu's lipid-lowering drug structure aligns with guidelines(statin-based,moderate-intensity preference).VBP effectively reduced costs,with tertiary hospitals prioritizing originator-to-generic substitution and primary hospitals reflecting cost-control and demand variations.Confounding factors influenced policy evaluation.The study recommends continuous monitoring and policy optimization to enhance procurement efficiency,ensure rational clinical use,and sustain cost savings,providing insights for further healthcare reform.

Paraplegia caused by spinal cord injury is a serious neurological complication, for which surgery is currently the main treatment method. Due to different surgical approaches, patients are usually expected to maintain a passive prone position for a long time or switch between the supine and prone positions. Affected by multiple factors such as neurogenic sensory disorders, pathological changes in muscle tone and operative duration, the risk of intraoperative acquired pressure injury (IAPI) is significantly increased. Current clinical prevention strategies for IAPI in these patients predominantly focus on localized pressure relief during positioning, lacking systematic, standardized comprehensive prevention protocols or evidence-based guidelines. To address it, Department of Nursing, Orthopedics Branch, China International Exchange and Promotive Association for Medical and Health Care, Spinal Trauma Professional Committee, Orthopedics Branch, Chinese Medical Doctor Association, Nursing Group of Spine and Spinal Cord Professional Committee of Chinese Association of Rehabilitation Medicine organized experts in relevant fields to formulate Guideline for the prevention of intraoperative acquired pressure injury in paraplegic patients with spinal cord injury ( version 2025), based on evidence-based medical evidence and latest research results and clinical practice at home and abroad. Eleven recommendations were put forward from the aspects of preoperative risk assessment, intraoperative prevention strategies, postoperative handover and monitoring, and supportive mechanisms for IAPI prevention, aiming to standardize the prevention measures and management strategies of IAPI in paraplegic patients with spinal cord injury and accelerate the recovery of patients and improve the therapeutic effect.

Metagenomic next-generation sequencing, as an unbiased detection technology, demonstrates higher diagnostic efficacy than traditional methods. Gut microorganisms are important flora for safeguarding health and have become a hot research topic. Modeling and analyzing the genomic data of intestinal flora using machine learning is very important in disease prediction and diagnosis. This paper briefly introduces the characteristics of metagenomic next-generation sequencing, key algorithms and evaluation indexes of machine learning, outlines the main steps of combining machine learning with metagenomic next-generation sequencing, and summarizes the application of the combination of machine learning and metagenomic next-generation sequencing technology in the study of intestinal flora, which will provide a more accurate method for diagnosis and prediction of the related diseases, and give more ideas for the future research and clinical practice.

With the rapid development of computer vision technology, deep learning models have demonstrated new research area and potential value in intelligent microbiological detection. By utilizing multilayer neural networks and large amounts of training data, these models are capable of automated extraction and analysis for complex features, thereby improving the efficiency and accuracy of detection. This paper introduces the research background of deep learning in microbiological image detection, and elaborates on the methods for constructing microbiological image datasets, including data collection, preprocessing, annotation, and partitioning, and introduces typical deep learning models as well as their application examples in various microbiological detection. Deep learning in microbiological image analysis faces numerous challenges which needs further development.

Objective:This study aims to develop a rapid identification technique for various genotypes of extended-spectrum β-lactamase (ESBL) producing Escherichia coli using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS) in conjunction with machine learning algorithms. Methods:A total of 158 Escherichia coli strains were isolated from the clinical laboratory of the First Hospital of Jilin University from August 2018 to December 2022. Polymerase chain reaction (PCR) was employed to detect the CTX-M-1, CTX-M-8, CTX-M-9, and SHV genes. Mass spectral data of the bacterial strains were acquired by MALDI-TOF MS with a cooperative matrix of (E)-propyl α-cyano-4-hydroxycinnamate (CHCA-C3). Models based on random forest (RF), logistic regression (LR), and support vector machine (SVM) algorithms were constructed. The performance of the constructed models was evaluated using metrics including accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Mass spectral peaks exhibiting sensitivity and specificity exceeding 80% in the models were designated as characteristic peaks. To validate the efficacy of the cooperative matrix of CHCA-C3, clinical isolates of ESBL-producing Escherichia coli were analyzed by MALDI-TOF MS using the conventional CHCA matrix for comparative purposes. Results:Among the 158 strains of Escherichia coli, 91 strains produced ESBL, all of which were CTX-M genotype. The AUC values for the respective models were as follows: CTX-M-1 genotype exhibited AUC values of 0.98 for LR, 1.00 for RF, and 0.73 for SVM; CTX-M-9 genotype exhibited AUC values of 0.93 for LR, 0.99 for RF, and 0.76 for SVM; for CTX-M-8, all models achieved an AUC of 1.00, indicating excellent classification performance with respect to accuracy, specificity, and sensitivity. The characteristic mass spectral peaks associated with each genotype included: CTX-M-1 genotype at m/z 6 390; CTX-M-8 genotype at m/z 5 224, m/z 5 393, and m/z 9 021; CTX-M-9 genotype at m/z 5 161 and m/z 5 273. In the MALDI-TOF MS analysis conducted with the conventional CHCA matrix, the characteristic peak at m/z 9 021 for CTX-M-8 was the only one detected, with the characteristic peaks for CTX-M-1 and CTX-M-9 remaining undetected. Conclusion:The application of cooperative matrix of CHCA-C3 in conjunction with MALDI-TOF MS and machine learning algorithms facilitates the rapid and precise identification of extended-spectrum β-lactamase (ESBL)-producing Escherichia coli. This approach offers a feasible solution for evidence-based clinical therapy and the control of healthcare-associated infections.

Objective To analyze the procurement data of lipid-lowering drugs in hospitals at different levels in Jiangsu Province from October 2019 to September 2023,to evaluate the impact of the volume-based procurement(VBP)policy,and to provide references for clinical rational drug use and healthcare policy optimization.Methods Based on procurement data from the Jiangsu Provincial Health Information Center,statistical analyses of procurement expenditures,defined daily doses(DDDs),and defined daily cost(DDC)were conducted.Mixed-effects models were applied to assess changes in procurement expenditures,DDDs,and DDC before and after VBP implementation.Results From 2019 to 2023,statins dominated the market in Jiangsu Province,with rosuvastatin recording the highest DDDs(748 million).Statins,traditional Chinese medicines,and cholesterol absorption inhibitors ranked highest in procurement expenditures.Tertiary hospitals accounted for the largest share of usage(47.6%)and expenditures(55.8%),while secondary hospitals had the lowest DDC(1.22 yuan)and tertiary hospitals the highest(1.89 yuan).Post-VBP,procurement expenditures and DDC decreased by 53.9%and 35.4%,respectively.Primary hospitals showed the largest expenditure reduction(61.6%),and secondary hospitals exhibited the greatest DDC decline(53.9%).DDDs increased significantly in primary care settings(e.g.,pitavastatin surged by 239.79%in secondary hospitals),while tertiary hospitals saw reduced usage of some drugs(e.g.,amlodipine/atorvastatin decreased by 7.34%).Mixed-effects models confirmed that VBP significantly reduced expenditures(OR=-1.07,P<0.01)and DDC(OR=-2.70,P<0.01)while indirectly lowering prices of non-VBP drugs.After covariate adjustment,expenditure reductions for rosuvastatin and atorvastatin narrowed,ezetimibe expenditures increased(OR=0.13,P<0.01),and pitavastatin usage declined(OR=-0.10,P<0.01).Changes in amlodipine/atorvastatin and ezetimibe lacked statistical significance due to short VBP implementation periods.Tertiary hospitals demonstrated the strictest policy adherence,with the largest expenditure and DDC reductions(P<0.01).Subgroup analysis revealed that the policy did not significantly affect clinical demand(DDDs)in hospitals at different levels,though it was considered to have triggered adjustments in medication structure.Conclusion Jiangsu's lipid-lowering drug structure aligns with guidelines(statin-based,moderate-intensity preference).VBP effectively reduced costs,with tertiary hospitals prioritizing originator-to-generic substitution and primary hospitals reflecting cost-control and demand variations.Confounding factors influenced policy evaluation.The study recommends continuous monitoring and policy optimization to enhance procurement efficiency,ensure rational clinical use,and sustain cost savings,providing insights for further healthcare reform.