Neurocritical care involves complex pathophysiological mechanisms, and its incidence is higher, injuries are more severe, and treatment is more challenging in high-altitude environments. This consensus, based on the latest domestic and international evidence-based medical data, establishes a standardized, goal-oriented framework for neurocritical care management applicable in high-altitude regions and nationwide. The consensus was developed following international standards for evidence quality assessment and underwent two rounds of Delphi expert consultation, resulting in 32 recommendation statements covering three parts: management systems, monitoring and assessment, and core strategies. Key updates include: advocating for the establishment of independent neurocritical care units and implementing precise tiered diagnosis and treatment based on the "Five Differences in Critical Care" concept; constructing a "trinity" multimodal brain monitoring system centered on cerebral blood flow, cerebral oxygenation, and brain function, emphasizing routine bedside transcranial Doppler ultrasound, cerebral oximetry, and continuous electroencephalography monitoring; shifting management strategies from mild hypothermia therapy to targeted temperature management, and defining the "446" target management pathway for the supercritical stage; emphasizing the assessment of static and dynamic cerebrovascular autoregulation functions through multimodal methods to achieve individualized optimal mean arterial pressure management; elevating cerebrospinal fluid management goals to the level of "glymphatic system" function maintenance; implementing a multidisciplinary collaborative, whole-process management model focusing on patients' long-term neurological functional outcomes; de-escalation criteria include multidimensional indicators such as recovery of brain structure, restoration of cerebrovascular autoregulation, improvement in cerebrospinal fluid dynamics, and reduction in biomarker levels; and integrating cutting-edge technologies like artificial intelligence into post-critical care management and rehabilitation planning. This consensus systematically integrates the entire process of neurocritical care management, reflecting the modern connotation of goal-oriented, dynamic, and multimodal integration in neurocritical care medicine. It aims to adapt to new trends such as deepening understanding of pathophysiological mechanisms, the integration of medicine and engineering, and the empowerment of artificial intelligence, thereby further advancing the discipline of critical care medicine.

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.

ObjectiveTo investigate the current awareness and optimization recommendations on pneumonia with unknown etiology (PUE) surveillance among Shanghai healthcare professionals, and to provide evidence for the improvement of the surveillance system. MethodsIn December 2024, healthcare professional participants from diverse medical and health institutions were randomly selected within each stratum using a stratified cluster sampling method in all 16 districts of Shanghai for questionnaire surveys on their knowledge and suggestions for optimization of the PUE surveillance system. Descriptive statistics analyses, multivariate logistic regressions, and content analyses were used for data analyses. ResultsA total of 385 survey subjects were involved in the study, including 119 professional staffs from disease prevention and control centers and 266 from medical institutions. Those who understood the case definition for PUE correctly accounted for 67.01% (258/385). Multivariate regression model analyses showed that female gender (aOR=2.22, 95%CI: 1.36‒3.66), professionals from disease control institutions (aOR=2.02, 95%CI: 1.12‒3.72), and those who self-assessed as being familiar with the monitoring system (aOR=2.18, 95%CI:1.23‒3.89) had better understanding of the definition. Overall, 89.61% (345/385) of the survey subjects acknowledged the necessity of the surveillance system. Small number of cases meeting the case definition (54.46%, 214/379), insufficient diagnostic awareness (39.84%, 151/379), and lack of incentives for case reporting (29.29%, 111/379) were major problems for the current surveillance system, with variations observed across different types of institutions. Suggested segments for optimizing the surveillance system included monitoring purpose, case definition, form of monitoring and reporting procedure, organization mechanism, and epidemiological investigation and response. ConclusionThe necessity of PUE surveillance is commonly recognized among healthcare workers. However, the existing system falls short in terms of mastery of case definition and overall surveillance adaptability. There is an urgent need for optimization and improvement to enhance the capacity for preventing and controlling emerging and sudden respiratory infectious diseases.

Objective: To explain the healthcare industry standard (WS/T 865-2025) "Basic dataset of blood collection and supply in blood establishments" from the perspectives of project initiation, purpose and significance, compilation methods, and standard content, so as to help blood establishment personnel better understand the standard and facilitate its correct implementation and enforcement. Methods: Starting from the actual work needs of blood establishments, a technical route combining top-down and bottom-up approaches was designed. Object-oriented modeling technology was adopted to establish an information model. Datasets from existing information systems and blood information standards were collected. After comprehensive evaluation, data subsets were formed and data elements were extracted. Subsequently, the attributes of the data elements were standardized in accordance with published health information data standards. Results: Eleven data subsets, 170 data elements, 35 allowed values of data elements, and 15 value domain code tables were established. Each subset corresponds to a business process, thus forming a complete data chain. Conclusion: This standard provides a set of data standards for blood establishment information systems with standardized terms, clear definitions, and unambiguous semantic contexts. It enables the standardization of blood establishment business data collection and thereby lays the foundation for data sharing.

Objective: To assess the current status of bacterial detection capabilities in various blood collection and supply and related laboratories across China, and to identify key factors affecting the consistency of test results. Methods: A pooled analysis was conducted on data from 21 external quality assessment (EQA) rounds for blood bacterial detection carried out by the China International Transfusion Infection Prevention and Control (CITIC) project from 2019 to 2025. The study covered three types of laboratories-blood centers, central blood stations, and commercial laboratories—across 23 provincial-level administrative regions in China. Statistics were compiled on the number of participating laboratories, and the concordance rates were calculated for different institution types, different automated blood culture systems, different bacterial concentrations, and different strains. Non-standard reporting issues were also reviewed, and statistical methods were applied for analysis. Results: Over the seven-year period, the number of participating laboratories increased from 161 to 236, with an average annual growth rate of 6.58%, and the annual concordance rates ranged from 82.61% to 94.92%. The overall laboratory concordance rates for the three types of participating laboratories were all>89.0% (P>0.05), while commercial laboratories showed a significant year-by-year increase (70.97%-100%, P<0.05). The concordance rates of automated blood culture systems from eight manufacturers showed no significant differences (97.98%-100%, P>0.05), indicating stable performance across all devices. The overall sample concordance rate was 98.88%, with negative samples showing a lower rate than positive samples (97.97% vs 99.14%); positive sample rates increased with higher concentrations; and low-concentration Gram-negative bacterial samples had a lower concordance rate than Gram-positive ones (93.15% vs 100%). A total of 50 instances of non-standard result reporting were identified, with incorrect time units accounting for the highest proportion (52.0%). Conclusion The participation scale and overall testing proficiency of blood collection and supply and related laboratories nationwide have improved year by year, and all types of automated blood culture devices can meet the requirements for blood bacterial screening. Detection of negative and low-concentration positive samples remains a weak link in current bacterial EQA, and the standardization of laboratory data reporting requires further strengthening. Continuous: external quality assessment, targeted training, and standardized reporting procedures can effectively enhance the homogenization level of blood bacterial testing across the country and ensure the safety of clinical transfusion.

Objective: To investigate the relationship between physical development indicators and the onset and progression of myopia among children and adolescents, so as to provide theoretical support for coordinated vision and physical health management. Methods: A prospective cohort study was conducted. In September 2022, 3 102 students from grade one in primary school to grade three in junior high school (five year primary school and four year junior high school) from six schools in Huantai County, Shandong Province, were selected using multistage cluster random sampling method to participate in an epidemiological survey on myopia, with follow up completed in September 2023. Follow up value minus baseline value( d ) was used to assess changes in physical development and vision indicators. Multivariate Logistic regression analysis was used to examine the relationship between physical development indicators and the incidence of myopia. Generalized linear models were established to analyze the relationship between physical development indicators and changes in d spherical equivalent (SE) and d axial length (AL) . Results: Multivariate Logistic regression analysis showed that, after adjusting for factors such as gender, age, and baseline body mass index, the third ( Q 3)and fourth ( Q 4)quartiles of d height showed increased risks of myopia onset within one year compared to the first quartile( Q 1) ( OR =1.85,95% CI =1.18-2.88; OR =1.74,95% CI =1.09-2.78,both P ＜0.05). Results from the generalized linear model indicated that, after adjusting for confounding factors such as gender, age, and baseline SE, d SE was negatively correlated with d height and d weight in children and adolescents ( β =-0.024, 95% CI =-0.031 to -0.018; β =-0.006, 95% CI =-0.011 to -0.001), d AL was positively correlated with d height and d weight in children and adolescents ( β =0.011, 95% CI =0.008-0.013; β =0.005, 95% CI =0.003-0.007) (all P ＜0.05). Conclusions Physical development indicators in children and adolescents were associated with the onset and progression of myopia. Dynamic monitoring and early intervention of myopia should be strengthened for children and adolescents with rapid height and weight gain to achieve coordinated management of myopia and physical development.

[摘 要] 目的：旨在构建Trop2-ECD与幽门螺杆菌铁蛋白（HPF）融合纳米颗粒疫苗，并评价其免疫原性与抗三阴性乳腺癌（TNBC）活性。方法：构建Trop2-ECD_HPF纳米颗粒疫苗，并在293F细胞中表达和纯化。通过尺寸排阻色谱和透射电子显微镜验证疫苗的纯度和均一性。使用铝佐剂配制疫苗对雌性BALB/c小鼠进行免疫，通过酶联免疫吸附法（ELISA）检测血清中Trop2特异性抗体滴度，并评估其对4T1-Trop2肿瘤模型的抑制效果。结果：Trop2-ECD_HPF纳米颗粒粒径约20 nm，颗粒形成率较高，纯度较高，能够显著诱导小鼠产生高滴度的Trop2特异性抗体，疫苗组第6周抗体滴度达106，显著高于佐剂组（P < 0.000 1）。在动物实验中，疫苗组小鼠的肿瘤体积显著减小（抑瘤率为38.6%），肿瘤增长速度显著减缓（P < 0.001），显示出较强的抗肿瘤效果。结论：Trop2-ECD_HPF融合纳米颗粒疫苗可有效诱导特异性体液免疫应答，显著抑制TNBC生长，为靶向Trop2的纳米疫苗开发提供了实验依据。

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.

Objective: To explore glymphatic impairment in pediatric refractory epilepsy (RE) using multi-parameter magnetic resonance imaging (MRI), assess its relationship with white-matter (WM) abnormalities and clinical indicators, and preliminarily evaluate the performance of multi-parameter MRI in discriminating RE from drug-sensitive epilepsy (DSE). Materials and Methods: We retrospectively included 70 patients with DSE (mean age, 9.7 ± 3.5 years; male:female, 37:33) and 26 patients with RE (9.0 ± 2.9 years; male:female, 12:14). The diffusion tensor imaging analysis along the perivascular space (DTI-ALPS) index as well as fractional anisotropy (FA), mean diffusivity (MD), and nodal efficiency values were measured and compared between patients with RE and DSE. With sex and age as covariables, differences in the FA and MD values were analyzed using tract-based spatial statistics, and nodal efficiency was analyzed using a linear model. Pearson’s partial correlation was analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the discrimination performance of the MRI-based machine-learning models through five-fold cross-validation. Results: In the RE group, FA decreased and MD increased in comparison with the corresponding values in the DSE group, and these differences mainly involved the callosum, right and left corona radiata, inferior and superior longitudinal fasciculus, and posterior thalamic radiation (threshold-free cluster enhancement, P < 0.05). The RE group also showed reduced nodal efficiency, which mainly involved the limbic system, default mode network, and visual network (false discovery rate, P < 0.05), and significantly lower DTI-ALPS index (F = 2.0, P = 0.049). The DTI-ALPS index was positively correlated with FA (0.25 ≤ r ≤ 0.32) and nodal efficiency (0.22 ≤ r ≤ 0.37), and was negatively correlated with the MD (-0.24 ≤ r≤ -0.34) and seizure frequency (r = -0.47). A machine-learning model combining DTI-ALPS, FA, MD, and nodal efficiency achieved a cross-validated ROC curve area of 0.83 (sensitivity, 78.2%; specificity, 84.8%). Conclusion Pediatric patients with RE showed impaired glymphatic function in comparison with patients with DSE, which was correlated with WM abnormalities and seizure frequency. Multi-parameter MRI may be feasible for distinguishing RE from DSE.