Objective: To analyze the epidemiological characteristics and influencing factors of severe fever with thrombocytopenia syndrome (SFTS) in Zhejiang Province from 2019 to 2023, so as to provide the reference for strengthening SFTS prevention and control. Methods: Data on laboratory-confirmed SFTS cases in Zhejiang Province from 2019 to 2023 were collected through the Infectious Disease Reporting Information System of Chinese Disease Prevention and Control Information System. Meteorological data, geographic environment and socioeconomic factors during the same period were collected from the fifth-generation European Centre for Medium-Range Weather Forecasts, Geospatial Data Cloud, and Zhejiang Statistical Yearbook, respectively. Descriptive epidemiological methods were used to analyze the epidemiological characteristics of SFTS from 2019 to 2023, and a Bayesian spatio-temporal model was constructed to analyze the influencing factors of SFTS incidence. Results: A total of 578 SFTS cases were reported in Zhejiang Province from 2019 to 2023, with an annual average incidence of 0.23/105. The peak period was from May to July, accounting for 52.60%. There were 309 males and 269 females, with a male-to-female ratio of 1.15∶1. The cases were mainly aged 50-<80 years, farmers, and in rural areas, accounting for 82.53%, 77.34%, and 75.43%, respectively. Taizhou City and Shaoxing City reported more SFTS cases, while Shaoxing City and Zhoushan City had higher annual average incidences of SFTS. The Bayesian spatio-temporal interaction model showed good goodness of fit. The results showed that mean temperature (RR=1.626, 95%CI: 1.111-2.378) and mean wind speed (RR=1.814, 95%CI: 1.321-2.492) were positively correlated with SFTS risk, while altitude (RR=0.432, 95%CI: 0.230-0.829) and population density (RR=0.443, 95%CI: 0.207-0.964) were negatively correlated with SFTS risk. Conclusions SFTS in Zhejiang Province peaks from May to July. Middle-aged and elderly people and farmers are high-risk populations. Taizhou City, Shaoxing City, and Zhoushan City are high-incidence areas. Mean temperature, mean wind speed, altitude, and population density can all affect the risk of SFTS incidence.

Objective: To investigate the current status of latent tuberculosis infection (LTBI) among freshmen in junior and senior high schools in Lanzhou, so as to provide scientific basis for improving the tuberculosis prevention and control strategy in schools. Methods: The screening results of 74 516 freshmen in senior and boarding junior high schools in Lanzhou during 2023 and 2024 were collected. The Chi square test and multivariate Logistic regression model were applied to analyze LTBI level, strongly positive risk for tuberculin skin test (TST) and related factors of the freshmen. Results: During 2023 and 2024, the screening rate of tuberculosis among freshmen in senior and boarding junior high schools in Lanzhou was 93.45%, of which the positive rate for TST was 5.71%, the infection rate for LTBI was 3.80%, and the strongly positive rate for TST was 1.24%. There were statistically significant differences in the screening rate of tuberculosis among freshmen in different years, grades, regions, school types and districts ( χ 2=5.34, 2 463.88, 3 516.13, 132.34, 4 436.56, all P <0.05). Multivariate Logistic regression analysis showed that senior high schools ( OR =1.62, 2.18) and urban areas ( OR =2.08, 3.07 ) were all related factors for LTBI and strong positivity for TST among freshmen; schools located in Xigu District, Honggu District, Yongdeng County, Yuzhong County, and Lanzhou New Area ( OR =3.57, 5.67, 9.12, 3.70, 3.64) were related factors of strong positivity for TST among freshmen (all P <0.05). Conclusions The LTBI level among freshmen in senior and boarding junior high schools in Lanzhou is relatively low. Grades and regions are related factors for LTBI and strong positivity for TST.

Objective: To establish a real-time quality control scheme based on the median (MD-IC) of internal control cycle threshold value in negative samples (NEG-IC-CT), so as to monitor anomalies such as progressive drift in nucleic acid testing system not covered by conventional internal quality control (IQC) in blood center nucleic acid laboratories, and to verify its feasibility. Methods: The internal control CT values of 54 426 negative samples were retrospectively collected. These samples were from four reagent batches of the two new and old equipment sets during the operation of the Wantai nucleic acid testing system in our blood center. The daily median of NEG-IC-CT values was used as the research indicator. Control limits were calculated using median absolute deviation (MAD) to construct the Median-MAD quality control chart. The monitoring performance of this scheme for the operation status of the testing system was simultaneously evaluated. Results: Statistical analysis showed significant differences in NEG-IC-CT value distribution between the new and old equipment sets, as well as between the two different reagent batches of the old equipment (P<0.000 1). The NEG-IC-CT value performance of the two different reagent batches of the new equipment was no significant difference in distribution (P>0.05). This scheme identified three typies of distinct anomalies. The out-of-control events observed with the old equipment in both the O1 and O2 reagent batches suggested potential performance decay due to equipment aging. The unreported change of reagent batch in time of Phase B with new equipment caused a stepwise drift on the quality control chart. In the later stage of Phase A with the new equipment, an alert was triggered, indicating potential quality risks associated with practices such as the mixed use of the remaining reagents and extremely long operator working hours. Conclusion: The realtime quality control scheme based on NEG-IC-CT value established in this study has been preliminarily validated for its monitoring effectiveness in nucleic acid testing in our blood center. This scheme performed well in detecting differences among testing systems and reagent batches, serving as an effective supplement to routine internal quality control. It can provide an intuitive and effective evaluation method for monitoring the performance of the nucleic acid testing process at blood center.

Background: Disorders of the blood-brain barrier (BBB) arising from diabetes mellitus are closely related to diabetic encephalopathy. Previous research has suggested that neuron-glia antigen 2 (NG2)-glia plays a key role in maintaining the integrity of the BBB. However, the mechanism by which NG2-glia regulates the diabetic BBB remains unclear. Methods: Type 2 diabetes mellitus (T2DM) db/db mice and db/m mice were used. Evans-Blue BBB permeability tests and transmission electron microscopy techniques were applied. Tight junction proteins were assessed by immunofluorescence and transmission electron microscopy. NG2-glia number and signaling pathways were evaluated by immunofluorescence. Detection of matrix metalloproteinase-9 (MMP-9) in serum was performed using enzyme-linked immunosorbent assay (ELISA). Results: In T2DM db/db mice, BBB permeability in the hippocampus significantly increased from 16 weeks of age, and the structure of tight junction proteins changed. The number of NG2-glia in the hippocampus of db/db mice increased around microvessels from 12 weeks of age. Concurrently, the expression of MMP-9 increased in the hippocampus with no change in serum. Sixteen- week-old db/db mice showed activation of the Wnt/β-catenin signaling in hippocampal NG2-glia. Treatment with XAV-939 improved structural and functional changes in the hippocampal BBB and reduced MMP-9 secretion by hippocampal NG2-glia in db/db mice. It was also found that the upregulation of β-catenin protein in NG2-glia in the hippocampus of 16-week-old db/db mice was significantly alleviated by treatment with XAV-939. Conclusion The results indicate that NG2-glia can lead to structural and functional disruption of the diabetic BBB by activating Wnt/β-catenin signaling, upregulating MMP-9, and degrading tight junction proteins.

Background: and Purpose Live cell-based assays (LCBA) are increasingly used for serological antibody detection due to their ability to preserve antigen conformation, offering moderately higher sensitivity than fixed cell-based assays. However, the clinical necessity of prioritizing LCBA for the detection of aquaporin-4 immunoglobulin G (AQP4-IgG) in neuromyelitis optica spectrum disorder (NMOSD) remains unclear, especially when compared to its established role in diagnosing myelin oligodendrocyte glycoprotein antibody-associated disease. Methods: We compared the performance of live cell-based assays using immunofluorescence (LCBA-IF) and fixed cell-based assays using immunofluorescence (FCBA-IF) in detecting AQP4-IgG in 90 cases of NMOSD meeting 2015 International Panel for Neuromyelitis Optica Diagnosis criteria, alongside 40 controls. Additionally, we further investigated the relationship between AQP4-IgG titers as measured by LCBA-IF and FCBA-IF and clinical parameters in NMOSD patients. Results: Results showed 96.9% agreement between LCBA-IF and FCBA-IF (Cohen’s κ=0.935), with a strong Spearman correlation (0.977, p<0.001). Both methods demonstrated 100% specificity, with LCBA-IF showing slightly higher sensitivity compared to FCBA-IF. Within LCBAIF-tested groups, statistically significant differences in annualized relapse rates were observed across all pairwise comparisons (low-titer vs. moderate-titer, low-titer vs. high-titer, and moderate-titer vs. high-titer; all p<0.050). However, this association reached statistical significance in some FCBA-IF-tested groups. Conclusions Overall, there is a strong concordance between LCBA-IF and FCBA-IF in detecting AQP4-IgG, where LCBA-IF shows slightly higher sensitivity. Furthermore, there is a potential link between elevated AQP4-IgG titers and an increased risk of relapse, and this correlation may appear more pronounced when using LCBA-IF.

Background/Aims: Lenvatinib resistance remains a critical barrier in advanced hepatocellular carcinoma (HCC) therapy. However, the underlying mechanisms and strategies for reversing resistance remain incompletely understood. Methods: Integrated transcriptomics of lenvatinib-resistant patient tumors and an acquired-resistance murine model identified a novel macrophage subpopulation. Functional validation employed CRISPR-SAM screening, conditioned medium (CM) assays, subcutaneous/orthotopic xenografts, patient-derived organoids (PDOs), and patient-derived xenografts (PDXs). Mechanistic studies included ChIP-qPCR, co-immunoprecipitation, and pharmacologic targeting. Clinical relevance was assessed in a retrospective cohort. Results: Resistant HCC exhibited significant enrichment of a COLEC12high TAM subset , which correlated with poor survival and treatment response. These TAMs secreted neuregulin-1 (NRG1) , activating HER2/HER3-AKT signaling in tumor cells to drive cancer stemness and lenvatinib resistance. Mechanistically, in TAMs COLEC12 sequestered STAT1 in the cytoplasm, preventing its phosphorylation, and thereby derepressing STAT3-mediated NRG1 transcription. Depletion of NRG1 reversed the stemness phenotypes and resensitized tumors to lenvatinib both in vitro and in vivo. Clinically, high NRG1 expression predicted an inferior lenvatinib response and shorter survival. Crucially, the bispecific anti-HER2/HER3 antibody zenocutuzumab restored lenvatinib efficacy in PDOs, PDXs, and murine models. Conclusions Our work establishes the COLEC12high TAM/NRG1 axis as a master regulator of therapeutic resistance and identifies NRG1 as a predictive biomarker, providing a clinically actionable strategy to overcome lenvatinib resistance in HCC.

Objective To analyze the factors affecting the prognosis of patients with locally advanced or metastatic non-small cell lung cancer (NSCLC) undergoing immunotherapy and construct an individualized prognostic nomogram prediction model. Methods A retrospective analysis was conducted on the clinical data of 385 patients with driver gene-negative, locally advanced or metastatic NSCLC who received first-line immune checkpoint inhibitors. Univariate and multivariate Cox regression analyses were used to identify prognostic risk factors, and a prognostic nomogram model was established. The predictive performance of the model was evaluated using the concordance index (C-index), time-dependent receiver operating characteristic (ROC) curves and area under the curve (AUC), and calibration curves. The cutoff value of the nomogram was calculated to stratify patients by risk. Survival curves were calculated by Kaplan-Meier analysis. Results Age (HR=1.775, 95%CI: 1.265-2.490), degree of differentiation (HR=0.365, 95%CI: 0.257-0.519), low PD-L1 expression (HR=0.661, 95%CI: 0.455-0.960), high PD-L1 expression (HR=0.423, 95%CI: 0.297-0.603), SCC-Ag (HR=1.549, 95%CI: 1.109-2.163), and CONUT score (HR=2.527, 95%CI: 1.797-3.554) were independent risk factors affecting overall survival (OS) of patients with NSCLC undergoing immunotherapy. The nomogram prediction model constructed on the basis of these factors had a C-index of 0.767. Time-dependent ROC curves for survival showed that the AUCs for 1-, 2-, and 3-year OS were 0.830, 0.853, and 0.886, respectively. Calibration curves indicated that the nomogram-predicted survival rates were in good agreement with the actual outcomes. The cutoff value for the study’s nomogram prediction model was 136.60 points, and survival curves showed statistically significant differences between different risk groups (P<0.05). Conclusion The nomogram model established in this study can effectively predict the prognosis of patients with driver gene-negative locally advanced or metastatic NSCLC treated with first-line immunosuppressive therapy. It provides a new tool for assessing prognosis and aids clinicians in formulating individualized treatment plans.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

With the widespread adoption of low-dose CT screening and the extensive application of high-resolution CT, the detection rate of sub-centimeter lung nodules has significantly increased. How to scientifically manage these nodules while avoiding overtreatment and diagnostic delays has become an important clinical issue. Among them, lung nodules with a consolidation tumor ratio less than 0.25, dominated by ground-glass shadows, are particularly worthy of attention. The therapeutic challenge for this group is how to achieve precise and complete resection of nodules during surgery while maximizing the preservation of the patient's lung function. The "watershed topography map" is a new technology based on big data and artificial intelligence algorithms. This method uses Dicom data from conventional dose CT scans, combined with microscopic (22-24 levels) capillary network anatomical watershed features, to generate high-precision simulated natural segmentation planes of lung sub-segments through specific textures and forms. This technology forms fluorescent watershed boundaries on the lung surface, which highly fit the actual lung anatomical structure. By analyzing the adjacent relationship between the nodule and the watershed boundary, real-time, visually accurate positioning of the nodule can be achieved. This innovative technology provides a new solution for the intraoperative positioning and resection of lung nodules. This consensus was led by four major domestic societies, jointly with expert teams in related fields, oriented to clinical practical needs, referring to domestic and foreign guidelines and consensus, and finally formed after multiple rounds of consultation, discussion, and voting. The main content covers the theoretical basis of the "watershed topography map" technology, indications, operation procedures, surgical planning details, and postoperative evaluation standards, aiming to provide scientific guidance and exploration directions for clinical peers who are currently or plan to carry out lung nodule resection using the fluorescent microscope watershed analysis method.

Objective To explore the influencing factors of acute kidney injury in severe burn patients,and to construct a visual risk nomogram model.Methods A total of 390 patients with severe burn admitted to the Institute of Burn Frostbite and Tissue Function Reconstruction of Chinese People's Armed Police Force Specialty Medical Center from January 2018 to January 2022 were collected as an internal training data set,and 50 patients with severe burn admitted from February to December 2022 were collected as an external validation data set.The 390 patients of the internal training data set were divided into the acute kidney injury group and the non-acute kidney injury group according to the occurrence of acute kidney injury,and the baseline data of patients in the two groups were compared.Univariate and multivariate Logistic regression were used to analyze the risk factors of acute kidney injury in severe burn patients of the internal training data set,and a nomogram model was drawn.Subsequently,the model was verified both internally and externally.Kaplan-Meier analysis and Log-rank test were used to compare the 90-day survival rate of patients between the acute kidney injury group and the non-acute kidney injury group.Results The burn area(OR=1.18,95%CI:1.06 to 2.36,P=0.004),sequential organ failure assessment(SOFA)score(OR=1.81,95%CI:1.21 to 5.92,P<0.001),inhalation injury(OR=3.21,95%CI:1.23 to 6.35,P<0.001),neutrophil to lymphocyte ratio(NLR)(OR=1.22,95%CI:1.05 to 3.65,P<0.001)and albumin(ALB)(OR=0.78,95%CI:0.57 to 0.92,P=0.011)were the independent risk factors for the development of acute kidney injury in severe burn patients.The nomogram model was established by the above factors.The area under the receiver operating characteristic curve(AUC)of the internal training data set was 0.833(95%CI:0.752 to 0.935),the sensitivity was 81.2%,and the specificity was 83.2%.The AUC of the external validation data set was 0.842(95%CI:0.762 to 0.912),the sensitivity 87.2%,and the specificity was 78.7%.The 90-day survival rate of patients in the acute kidney injury group after burns was significantly lower than that in the non-acute kidney injury group(P<0.001).Conclusion Larger burn area,higher SOFA score,combined inhalation injury,increased NLR,and decreased ALB level are the risk factors for the occurrence of acute kidney injury in severe burn patients,which are related to the 90-day survival rate of patients after burns.The nomogram model based on the risk factors can provide certain reference for clinical individualized prevention and treatment of acute kidney injury in severe burn patients.