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 investigate the availability and use of child safety seats among children aged 0-3 years, so as to provide the basis for improving riding safety for children. Methods: Parents of children aged 0-3 years in Huangpu District, Shanghai Municipality, were recruited using the stratified multistage random sampling method from May to July 2024. Demographic information, family travel patterns, the use of child safety seat and related health beliefs were collected using questionnaire surveys. Factors affecting the use of child safety seats were identified using a multivariable logistic regression model. Results: Totally 514 valid questionnaires were recovered, with an effective rate of 96.98%. The respondents included 122 fathers (23.74%) and 392 mothers (76.26%), with a median age of 34.00 (interquartile range, 5.00) years. There were 446 families equipping with child safety seats, accounting for 86.77%; and 169 families using child safety seats, accounting for 32.88%. Multivariable logistic regression analysis showed that the parents who had children aged >1-2 years (OR=0.597, 95%CI: 0.366-0.973), travelled 2-4 times per month (OR=0.359, 95%CI: 0.213-0.607) or once per month or less (OR=0.384, 95%CI: 0.202-0.729), and scored high in perceived barrier (OR=0.634, 95%CI: 0.486-0.827) were less likely to use child safety seats; the parents who had children with local household registration (OR=2.506, 95%CI: 1.356-4.633), travelled 5-<10 km (OR=1.887, 95%CI: 1.148-3.101) or ≥10 km (OR=2.319, 95%CI: 1.355-3.967), always wore seat belts (OR=2.342, 95%CI: 1.212-4.524), scored high in perceived susceptibility (OR=1.392, 95%CI: 1.091-1.778) and self-efficacy (OR=1.413, 95%CI: 1.156-1.727) were more likely to use child safety seats. Conclusions Equipping family cars with child safety seats and using them can prevent and reduce traffic injuries among children aged 0-3 years. It is recommended to strengthen publicity to promote the use of child safety seats.

Objective To analyze the characteristics of postoperative hospital-acquired infections and drug sensitivity in lung transplant recipients over the past 5 years in a single center. Methods A total of 724 lung transplant recipients at Wuxi People's Hospital from January 2019 to December 2023 were selected. Based on the principles of hospital-acquired infection diagnosis, a retrospective analysis was conducted on the hospital infection situation and infection sites of lung transplant recipients, and an analysis of the distribution of hospital-acquired infection pathogens and their antimicrobial susceptibility test status was performed. Results Among the 724 lung transplant recipients, 275 cases of hospital-acquired infection occurred, with an infection rate of 38.0%. The case-time infection rate decreased from 54.2% in 2019 to 22.8% in 2023, showing a downward trend year by year (Z=30.98, P<0.001). The main infection site was the lower respiratory tract, accounting for 73.6%. The pathogens were mainly Gram-negative bacteria, with the top four being Acinetobacter baumannii (37.1%), Pseudomonas aeruginosa (17.3%), Klebsiella pneumoniae (13.7%), and Stenotrophomonas maltophilia (13.4%), with imipenem resistance rates of 89%, 53%, 58% and 100%, respectively. Gram-positive bacteria were mainly Staphylococcus aureus (3.6%), with a methicillin resistance rate of 67%. Conclusions Over the past 5 years, the hospital-acquired infections in lung transplant recipients have shown a downward trend, mainly involving lower respiratory tract infections, with the main pathogens being Acinetobacter baumannii, Pseudomonas aeruginosa and Klebsiella pneumoniae, all of which have high resistance rates to imipenem.

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.

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.

The 2024 National Education Work Conference pointed out that at the current juncture of the critical period for achieving the goals and tasks of the 14th Five-Year Plan,the implementation of the Education Powerhouse Construction Plan Outline should be taken as the main line of work,and building first-class disciplines is an crucial task for a higher education powerhouse.In 2022,forensic medicine was officially listed as a first-level discipline under the medical category,presenting an un-precedented historical opportunity for the development of forensic medicine.The forensic medicine dis-cipline of Southern Medical University comprehensively improves the quality of talent cultivation and facilitates the construction of first-class disciplines as its main direction.It aims to initiate and imple-ment a high-level faculty team building plan featuring"combining recruitment and cultivation,inter-disciplinary integration";make vigorous efforts to establish a first-level doctoral program,refine advan-tageous second-level disciplines and research directions;and establish an innovative research platform from a high starting point with deep integration.The discipline adheres to moral cultivation and the Five Domains of Education simultaneous development,to build a high-quality talent joint training model.Guided by the construction of the national legal system and industry needs,the discipline will enhance social service capabilities.The forensic medicine construction in our university will continue to contribute to the rule of law in China and educational power.

Nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) is a cytosolic pattern recognition receptor that recognizes multiple pathogen-associated molecular patterns and damage-associated molecular patterns. It is a cytoplasmic immune factor that responds to cellular stress signals, and it is usually activated after infection or inflammation, forming an NLRP3 inflammasome to protect the body. Aberrant NLRP3 inflammasome activation is reportedly associated with some inflammatory diseases and metabolic diseases. Recently, there have been mounting indications that NLRP3 inflammasomes play an important role in liver injuries caused by a variety of diseases, specifically hepatic ischemia/reperfusion injury, hepatitis, and liver failure. Herein, we summarize new research pertaining to NLRP3 inflammasomes in hepatic injury, hepatitis, and liver failure. The review addresses the potential mechanisms of action of the NLRP3 inflammasome, and its regulation in these liver diseases.
Humans ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/physiology* ; Animals ; Liver Diseases/metabolism* ; Liver/metabolism* ; Reperfusion Injury/metabolism*

Objective:To present a set of clinically representative quality assurance (QA) test cases for stereotactic radiosurgery (SRT) plans of multiple intracranial metastases, in order to assess the plan quality and machine execution capabilities.Methods:Based on the clinical characteristics of multiple brain metastases, four groups of test cases with three target volumes (TVs), six TVs, nine TVs, and TVs near organs at risk (OARs) were designed. For these cases, SRT plans were developed, and plan quality was assessed using metrics including the Radiation Therapy Oncology Group conformality index (RTOG CI), gradient index (GI), homogeneity index (HI), and the volume of normal brain tissue receiving a dose of 24 Gy ( V24 Gy), which was defined as the volume enclosed by the 24 Gy isodose line around the Brain-PTV ( V24 Gy of Brain-PTV). Verification plans were generated for each test case, including the verification of point doses, planar doses (PD), and SRS MapCHECK (SMC) semiconductor matrix planar doses. Compared with the calculated result of the treatment planning system (TPS), the criteria for the γ analysis of planar doses were set at 1 mm/2% and 2 mm/2%. Results:For the four groups of test cases, the mean CI, GI, HI, and V24 Gy of Brain-PTV were 1.04±0.03, 3.79±0.40, 0.73±0.01 and (7.46±3.80) cm 3, respectively. The mean deviations of the point doses were 0.88%±0.98%, 1.47%±0.79%, 1.52%± 0.76%, and 1.17% ± 0.38%, respectively. The mean γ passing rates of the single fields for PDs were greater than 98% at 2 mm/2% and exceeding 96% at 1 mm/2%, and the mean γ pass rates of the SMC semiconductor matrix for PDs were 97.75% ± 2.31% and 99.33% ± 0.62%, at 1 mm/2% and 2 mm/2% respectively. Conclusions:The proposed QA test cases for SRT of multiple intracranial metastases allow for the effective assessments of the plan quality and machine execution capabilities and, thus, can assist various centers in clinical applications.