Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

This article reports the diagnosis and treatment of a patient with diffuse large B-cell lymphoma (DLBCL) initially manifested as acquired von Willebrand syndrome (AvWS), along with a literature review. The patient, a 22-year-old male, was diagnosed with hereditary von Willebrand disease at the initial stage due to repeated mucosal bleeding, and was later diagnosed with DLBCL (non germinal center type, Ann Arbor stage Ⅳ) due to chest wall mass. Through chemotherapy combined with autologous hematopoietic stem cell transplantation and zebutinib maintenance therapy, the patient achieved sustained complete remission, and the coagulation function returned to normal. This case provides an important reference for the diagnosis and treatment of lymphoma associated AvWS, and highlights the importance of early recognition of basic diseases.

Objective To explore the diagnostic value of the phenomenon of protein-cell separation in cerebrospinal fluid(CSF),as well as the single and combined detections of CSF immunoglobulin G(CSF-IgG),CSF albumin(CSF-ALB)and serum immunoglobu-lin G(S-IgG)for Guillain-Barre syndrome(GBS).Methods A retrospective analysis was conducted on the clinical data of 65 GBS patients(GBS group)and 65 patients with other neurological diseases(non-GBS group).CSF and serum samples were collected from both the groups to analyze the diagnostic efficacy of protein-cell separation phenomenon in CSF,as well as the single and combined de-tections of CSF-IgG,CSF-ALB,and serum-IgG(S-IgG)in GBS.Results CSF protein-cell separation phenomenon was observed in 60.00％(39/65)of GBS patients.Prodromal events were presented in 67.69％(44/65)of GBS patients,which mainly were upper re-spiratory tract infection(43.08％,28/65),digestive tract infection(9.23％,6/65),and herpetic virus infection(7.69％,5/65).The levels of CSF-IgG,CSF-ALB,and S-IgG in the GBS group were significantly higher than those in non-GBS group(P＜0.05).The areas under the ROC curves(AUCROC)for single CSF-IgG,CSF-ALB,S-IgG,protein-cell separation phenomenon IN CSF,and the combination of all the four parameters in diagnosis of GBS were 0.754,0.705,0.682,0.708,and 0.840,respectively.The diagnostic specificities were 87.7％,75.4％,87.7％,81.5％,and 96.9％,respectively.Conclusion Most of the GBS patients were found to have CSF protein-cell separation phenomenon and history of prodromal infection.CSF-IgG,CSF-ALB,S-IgG,and CSF protein-cell separation phenomenon exhibited independent diagnostic value for GBS.The combined detection of the four indicators could improve the diagnostic efficacy for GBS.

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.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

This article reports the diagnosis and treatment of a patient with diffuse large B-cell lymphoma (DLBCL) initially manifested as acquired von Willebrand syndrome (AvWS), along with a literature review. The patient, a 22-year-old male, was diagnosed with hereditary von Willebrand disease at the initial stage due to repeated mucosal bleeding, and was later diagnosed with DLBCL (non germinal center type, Ann Arbor stage Ⅳ) due to chest wall mass. Through chemotherapy combined with autologous hematopoietic stem cell transplantation and zebutinib maintenance therapy, the patient achieved sustained complete remission, and the coagulation function returned to normal. This case provides an important reference for the diagnosis and treatment of lymphoma associated AvWS, and highlights the importance of early recognition of basic diseases.

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 To explore the diagnostic value of the phenomenon of protein-cell separation in cerebrospinal fluid(CSF),as well as the single and combined detections of CSF immunoglobulin G(CSF-IgG),CSF albumin(CSF-ALB)and serum immunoglobu-lin G(S-IgG)for Guillain-Barre syndrome(GBS).Methods A retrospective analysis was conducted on the clinical data of 65 GBS patients(GBS group)and 65 patients with other neurological diseases(non-GBS group).CSF and serum samples were collected from both the groups to analyze the diagnostic efficacy of protein-cell separation phenomenon in CSF,as well as the single and combined de-tections of CSF-IgG,CSF-ALB,and serum-IgG(S-IgG)in GBS.Results CSF protein-cell separation phenomenon was observed in 60.00％(39/65)of GBS patients.Prodromal events were presented in 67.69％(44/65)of GBS patients,which mainly were upper re-spiratory tract infection(43.08％,28/65),digestive tract infection(9.23％,6/65),and herpetic virus infection(7.69％,5/65).The levels of CSF-IgG,CSF-ALB,and S-IgG in the GBS group were significantly higher than those in non-GBS group(P＜0.05).The areas under the ROC curves(AUCROC)for single CSF-IgG,CSF-ALB,S-IgG,protein-cell separation phenomenon IN CSF,and the combination of all the four parameters in diagnosis of GBS were 0.754,0.705,0.682,0.708,and 0.840,respectively.The diagnostic specificities were 87.7％,75.4％,87.7％,81.5％,and 96.9％,respectively.Conclusion Most of the GBS patients were found to have CSF protein-cell separation phenomenon and history of prodromal infection.CSF-IgG,CSF-ALB,S-IgG,and CSF protein-cell separation phenomenon exhibited independent diagnostic value for GBS.The combined detection of the four indicators could improve the diagnostic efficacy for GBS.

Objective: To analyze the strengths, weaknesses, opportunities and threats of the construction on intelligent vaccination clinics in Zhejiang Province, so as to provide countermeasures for promoting the construction of intelligent vaccination clinics in Zhejiang Province. Methods: By reviewing the annual reports of Zhejiang immunization planning, survey data from Zhejiang Centers for Disease Control and Prevention and Immunization Intelligent Service System, data of human resources of immunization planning, vaccine procurement, construction progress of intelligent vaccination clinics and vaccination were collected. The relevant literature was searched to gather information on the construction standards and norms of intelligent vaccination clinics. The analysis of the strengths, weaknesses, opportunities and threats (SWOT) of the construction of intelligent vaccination clinics was conducted, and corresponding countermeasures and suggestions were proposed. Results: The National Immunization Program reported vaccine rate in Zhejiang Province is more than 99%, and standardized vaccination clinics have been popularized throughout the province. The vaccination staff are professional, and a province-wide intelligent immunization service information system has been established, providing the resources and conditions for the construction of intelligent vaccination clinics. However, there are problems such as low data quality and matching efficiency in vaccination, insufficient data interoperability and sharing, unbalanced regional capabilities in intelligent transformation, and uneven distribution of talent and resources. It is crucial to seize the opportunities presented by the development of big data and artificial intelligence, rely on the regional development of the Internet and health industry, seize the opportunity of rapid growth in demand for intelligent vaccination services and high public acceptance, accelerate the construction of intelligent vaccination clinics, and establish intelligent vaccination service standards as soon as possible. Conclusion We should seize the opportunities presented by the digital reform and development, fully utilize the existing vaccination resources and strengths, address the shortcomings, and accelerate the construction of intelligent vaccination clinics in Zhejiang Province.

OBJECTIVES: Several countries have successfully eliminated measles, and China is making significant strides toward achieving this goal. This study focused on investigating the patterns of measles infections in Zhejiang Province, China, as well as control measures. The objective was to provide valuable insights that could contribute to the development of nationwide elimination strategies. METHODS: We analyzed measles surveillance data from 2005 to 2022 in Zhejiang Province. We utilized a joinpoint regression model to examine trends in measles. Additionally, we employed SaTScan version 9.5 to identify spatial-temporal clusters. Finally, we used an age-period-cohort model to assess the effects of age, period, and cohort. RESULTS: The age-standardized incidence rate (ASIR) of measles infection in Zhejiang Province from 2005 to 2022 was 5.24 per 100,000, showing a consistent and significant downward trend with an annual percentage change of -24.93% (p<0.05). After 2020, the ASIR for measles infection fell to below 0.1 per 100,000. The majority of measles cases occurred in individuals either without an immunization history or with an unknown immunization status, representing 41.06% and 41.40% of the cases from 2010 to 2022, respectively. According to data from the National Measles Surveillance System, the annual rate of discarded measles cases from 2009 to 2014, and the annual rate of discarded measles and rubella cases from 2015 to 2022, were both above 2 per 100,000, indicating the high sensitivity of the measles surveillance system. CONCLUSIONS The significant reduction in measles incidence from 2005 to 2022 demonstrates substantial progress in Zhejiang Province towards the elimination of measles.