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.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective:To develop CNN-Dinov2, a deep learning-based automatic classification model for Gram-stained images, enabling rapid diagnosis of three prevalent Gram-negative bacilli in bloodstream infections: Escherichia coli ( E.coli), Klebsiella pneumoniae ( K.pneumoniae), and Pseudomonas aeruginosa ( P.aeruginosa). Methods:This evaluation study analyzed 1 425 Gram-stained microscopic images from patients with bloodstream infections at Houjie Hospital, in Dongguan City, collected between January 2023 and January 2024. The images, all positive for blood culture and identified as target strains, were categorized into Escherichia coli (419 images), Klebsiella pneumoniae (411 images), Pseudomonas aeruginosa (413 images), and other Gram-negative bacilli (182 images). They were randomly split into a training set (1 141 images), a validation set (141 images), and a test set (143 images) in an 8∶1∶1 ratio. A hybrid CNN-Dinov2 model was developed by integrating ResNet′s local feature extraction with Dinov2′s global pre-trained features, followed by a fully connected layer. The model was optimized by inputting the preprocessed images and adjusting parameters through loss calculation and backpropagation. AlexNet, Dinov2, and ResNet18 served as control models. The models′ classification performance was assessed using accuracy, precision, weighted F1 score, and recall rate, derived from the confusion matrix. The PR curve and AP value further evaluated each model′s classification capability across the four image categories. Results:The CNN-Dinov2 model achieved a training accuracy of 99.74%, a validation accuracy of 98.12%, and a validation loss of 0.070 6, demonstrating robust generalization without overfitting. Validation metrics revealed superior performance with an accuracy of 98.60%, precision of 98.65%, a weighted F1 score of 98.60%, and a recall rate of 98.60%, outperforming other models. The confusion matrix confirmed its strong classification capability, with the highest sum of diagonal values for identifying four types of bacteria. The macro average precision (AP) values under the precision-recall (PR) curves were all 1, indicating excellent discrimination across all categories. Overall, the CNN-Dinov2 model exhibited the best performance among the four models evaluated.Conclusion:This study successfully developed CNN-Dinov2, an automated classification model for Gram staining images. It offers valuable support for the rapid diagnosis of bloodstream infections caused by Escherichia coli, Klebsiella pneumoniae, and Pseudomonas aeruginosa, demonstrating practical utility.

Turning to critical illness is a common stage of various diseases and injuries before death. Patients usually have complex health conditions, while the treatment process involves a wide range of content, along with high requirements for doctor′s professionalism and multi-specialty teamwork, as well as a great demand for time-sensitive treatments. However, this is not matched with critical care professionals and the current state of medical care in China. Telemedicine, which shortens the distance of medical professionals and the gap of disease diagnosis and treatments in various regions through electronic information, can effectively solve the current problem. Therefore, there is an urgent need to develop a standardized, high-quality visualization telemedicine round system .Therefore, experts have been organized to search domestic and foreign literature on telemedicine round for critically ill patients and to form this consensus based on clinical experiences so as to further improve the level of critical care treatments in regions.

Objective: To explore the relationship between sedentary behavior with cardiorespiratory fitness and executive function in adolescents, and to provide some references for sedentary behavior prevention and executive function improvement. Methods: From September to December 2022, a total of 5 018 adolescents aged 13 to 18 years were selected by stratified random sampling method in Shanghai, Suzhou, Taiyuan,Wuyuan, Xingyi, and Urumqi to conduct physical activity survey, as well as cardiorespiratory fitness and executive function assessment. Pearson s correlation was used to analyze the relationship between sedentary behavior, cardiorespiratory fitness and executive function. The mediation effect model was fitted by the bootstrap mediation procedure in the PROCESS (version 3.3 ) SPSS macro compiled by Haves, and the mediation effect of adolescents cardiorespiratory fitness in the relationship between static behavior and executive function was examined using model 4 in the PROCESS SPSS macro, where Boosrap method was used to compute the mediation effect of adolescents cardiorespiratory fitness. where the Boosrap method was used to calculate confidence intervals for the mediating effects. Results: Adolescents daily sedentary time was positively correlated with both the refreshing function (1-back and 2-back) and the switch function reaction time ( r =0.05, 0.07, 0.05, P <0.01). Adolescent VO 2max was negatively correlated with both the refreshing function (1-back,2-back) and the switching function ( r =-0.09, -0.14 , -0.11, P <0.01). Adolescents daily sedentary time was negatively correlated with VO 2max ( r =-0.04, P <0.01); cardiorespiratory fitness mediated effect values between sedentary behavior and refreshing function (1-back and 2-back) and converted function were 0.20(95% CI =0.06-0.36), 0.43(95% CI =0.14-0.74) and 0.13 (95% CI =0.04-0.22), with mediating effect shares of 6.87%, 8.33% and 8.59%, respectively. Conclusion The duration of sedentary behavior in adolescents is related to executive function performance, and cardiorespiratory fitness may serve as a mediator to mediate the association between sedentary behavior and executive function in adolescents.

Objective:Based on the principle that the aggregation-induced emission (AIE) fluorescent probe 6PD-DPAN could bind and aggregate with bacteria, and the fluorescence intensity could reflect the quantity of bacteria, a new method for rapid, convenient, and accurate bacterial drug sensitivity testing was established, which provided a basis for rapid and accurate clinical drug use.Methods:This was a methodological evaluation study. A total of 107 clinical isolates were collected from Houjie Hospital of Dongguan City from January to December 2022, among which 46 isolates were used for the establishment of the new method, and 61 isolates were used for methodological validation. The minimum inhibitory concentration (MIC) determined by broth microdilution method was used as the gold standard, and three antibacterial drugs, gentamicin, levofloxacin, and cefotaxime, were used as experimental drugs. The AIE plate was incubated for 4 hours, and the fluorescence intensity was measured every half an hour to draw a fluorescence change curve. The MIC results were compared with the CLSI breakpoints to determine the bacteria as sensitive, intermediate, or resistant. To simplify the detection process, the ratio of fluorescence intensity at 4 hours(R) was calculated, and the ROC curve was used to analyze the efficacy of R in determining bacterial growth and establish its cutoff value. The new method was used to determine the MIC of 61 clinical isolates, with broth microdilution method as the gold standard. The basic consistency, categorical consistency, very major errors, and major errors of the new method were analyzed, and the consistency between the two methods was determined by the Kappa test.Results:ROC curve analysis of the R after 4 hours of culture: The cut-off value was 3.0, with both sensitivity and specificity for determining bacterial growth being 100%. The median (interquartile) R for bacterial growth inhibition was 11.1 (8.6, 14.4); the median R-value for bacterial growth was 1.1 (1.0, 1.2). Compared to the gold standard, the newly established method showed 100% (61/61) essential agreement in detecting MICs of 61 clinical isolates, with a categorical agreement of 96.7% (59/61). There were no very major or major errors, and the Kappa value was 0.94, indicating good consistency between the newly established method and the microbroth dilution method.Conclusions:This study successfully established a new method for bacterial drug sensitivity testing based on AIE technology, which could obtain satisfactory results within 5 hours, providing a basis for early precision drug treatment in clinical practice.

OBJECTIVE To establish the fingerprint of Shenfukang Ⅱ capsule(SC-Ⅱ), and to screen out its indicative compounds for quality control combined with chemometrics methods and network pharmacology. METHODS The HPLC fingerprint of 10 batches of SC-Ⅱ was established, and similarity evaluation was analyzed by Similarity Evaluation System for Chromatographic Fingerprint of TCM(2012 A Edition) to determine common peaks; common peaks were identified through standard comparison. Chemometrics methods was used to evaluate quality of 10 batches of SC-Ⅱ, and network pharmacology was used to screen out core targets and pathways of SC-Ⅱ. Combined with the above results, indicative compounds for quality control of SC-Ⅱ were screened out. RESULTS A total of 37 common peaks were obtained in the HPLC fingerprint, the similarity of samples was greater than 0.97. Twenty compounds were identified as morroniside, loganin, paeoniflorin and et al. The samples were divided into two categories by chemical pattern recognition, salvianolic acid B, morroniside, salvianolic acid A and paeoniflorin were differential marker compounds for SC-Ⅱ. Network pharmacology predicted that active compounds such as salvianolic acid B, paeoniflorin and morroniside might exert pharmacological effects through 45 core targets and 15 main pathways. The research preliminary preliminarily predicted that morroniside, paeoniflorin and salvianolic acid B were quality control index components for SC-II. CONCLUSION The established HPLC fingerprint method is simple and good repeatability. The quality control indicative compounds of SC-Ⅱ can provide a basis for its quality control.

Purpose: Gamma-glutamyl transferase (GGT) has been reported as being involved in tumor progression. Previous studies documented a potential relationship between serum GGT level and survival outcome in several types of human malignancies. However, the association between serum GGT levels and response to neoadjuvant chemotherapy (NAC) has not yet been reported. The present study aimed to evaluate the association between pre-therapeutic serum GGT level and the efficacy, long-term survival, and adverse reactions of NAC and to investigate its role in predicting NAC sensitivity in patients with breast cancer. Methods: A total of 129 patients were recruited and stratified into 2 groups according to serum GGT level (< 29 U/L and ≥ 29 U/L). The association between pre-therapeutic serum GGT levels and clinicopathological parameters was examined. The correlation between pre-therapeutic serum GGT levels and pathological complete response (pCR) was analyzed using univariate and multivariate logistic regression. Survival analyses of relapse-free survival (RFS) and disease-free survival (DFS) were performed. Pearson's χ 2 test and multivariate logistic regression model were used to analyze the correlation between pre-therapeutic serum GGT levels and adverse reactions. Results: Pre-therapeutic serum GGT levels were associated with pCR among breast cancer patients treated with NAC. Multivariate analysis showed that low-level GGT significantly increased pCR rate. Patients in the high-level GGT group had poorer survival than those in the low-level GGT group. Subgroup analysis demonstrated that serum GGT level was potentially related to RFS and DFS in the hormone receptor-positive group. Low levels of GGT are significantly associated with a higher incidence of neutropenia. Conclusion Pre-therapeutic serum GGT level is an independent and novel biomarker for predicting the efficiency, prognosis, and adverse reactions to NAC in breast cancer patients.Patients with low pre-therapeutic serum GGT levels are more likely to have higher pCR rates, better RFS and DFS, and higher hematologic toxicity.

To establish the experts consensus on the management of delirium in critically ill patients.A special committee was set up by 15 experts from the Chinese Critical Hypothermia-Sedation Therapy Study Group.Each statement was assessed based on the GRADE (Grading of Recommendations Assessment,Development,and Evaluation) principle.Then the Delphi method was adopted by 36 experts to reassess all the statements.(1) Delirium is not only a mental change,but also a clinical syndrome with multiple pathophysiological changes.(2) Delirium is a form of disturbance of consciousness and a manifestation of abnormal brain function.(3) Pain is a common cause of delirium in critically ill patients.Analgesia can reduce the occurrence and development of delirium.(4) Anxiety or depression are important factors for delirium in critically ill patients.(5) The correlation between sedative and analgesic drugs and delirium is uncertain.(6) Pay attention to the relationship between delirium and withdrawal reactions.(7) Pay attention to the relationship between delirium and drug dependence/ withdrawal reactions.(8) Sleep disruption can induce delirium.(9) We should be vigilant against potential risk factors for persistent or recurrent delirium.(10) Critically illness related delirium can affect the diagnosis and treatment of primary diseases,and can also be alleviated with the improvement of primary diseases.(11) Acute change of consciousness and attention deficit are necessary for delirium diagnosis.(12) The combined assessment of confusion assessment method for the intensive care unit and intensive care delirium screening checklist can improve the sensitivity of delirium,especially subclinical delirium.(13) Early identification and intervention of subclinical delirium can reduce its risk of clinical delirium.(14) Daily assessment is helpful for early detection of delirium.(15) Hopoactive delirium and mixed delirium are common and should be emphasized.(16) Delirium may be accompanied by changes in electroencephalogram.Bedside electroencephalogram monitoring should be used in the ICU if conditions warrant.(17) Pay attention to differential diagnosis of delirium and dementia/depression.(18) Pay attention to the role of rapid delirium screening method in delirium management.(19) Assessment of the severity of delirium is an essential part of the diagnosis of delirium.(20) The key to the management of delirium is etiological treatment.(21) Improving environmental factors and making patient comfort can help reduce delirium.(22) Early exercise can reduce the incidence of delirium and shorten the duration of delirium.(23) Communication with patients should be emphasized and strengthened.Family members participation can help reduce the incidence of delirium and promote the recovery of delirium.(24) Pay attention to the role of sleep management in the prevention and treatment of delirium.(25) Dexmedetomidine can shorten the duration of hyperactive delirium or prevent delirium.(26) When using antipsychotics to treat delirium,we should be alert to its effect on the heart rhythm.(27) Delirium management should pay attention to brain functional exercise.(28) Compared with non-critically illness related delirium,the relief of critically illness related delirium will not accomplished at one stroke.(29) Multiple management strategies such as ABCDEF,eCASH and ESCAPE are helpful to prevent and treat delirium and improve the prognosis of critically ill patients.(30) Shortening the duration of delirium can reduce the occurrence of long-term cognitive impairment.(31) Multidisciplinary cooperation and continuous quality improvement can improve delirium management.Consensus can promote delirium management in critically ill patients,optimize analgesia and sedation therapy,and even affect prognosis.