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 understand mosquito density, species composition, and seasonal variations around the Beijing Capital International Airport(BCIA)and provide scientific evidence for mosquito-borne disease prevention and control. Methods Meteorological data were collected from 2016 to 2022, and mosquito density was monitored at seven surveillance sites around the BCIA from May to October each year using CO2-baited mosquito traps to analyze the relationship between mosquito density and meteorological factors. Results In total,68 518 female mosquitoes were captured, with a mosquito density of 10.20 per light · hour. The dominant species was Culex pipiens pallens(73.90%), followed by Aedes albopictus(9.71%)and Ae. vexans(9.29%);the mosquito density was highest in 2016(14.8 per light·hour)and lowest in 2022(4.46 per light·hour). Significant statistical differences were observed in mosquito density among different species(F=18.118, P<0.05). The density of Cx. pipiens pallens and other mosquito species showed a mutually exclusive trend; the peak of mosquito density varied based on years and habitats, with significant statistical differences in mosquito density among different habitats(F=8.504, P<0.05). The highest mosquito density was observed near the Wenyu River(21.50 per light·hour), whereas the lowest was recorded at the BCIA construction site(2.44 per light· hour). The monthly average temperature(r=0.595), monthly average highest temperature(r=0.575), and monthly average lowest temperature(r=0.624)showed moderate positive correlations, whereas the monthly average air pressure(r=-0.484)showed a moderate negative correlation. The monthly average minimum temperature was included in the regression model(F=25.575, P<0.000), and the equation Y=1.029X4-8.181 was used. The monthly average air pressure 2 months prior(b=-2.418, β=-1.619, P<0.05)and the monthly average relative humidity(b=-0.739, β=-1.201, P<0.05)significantly negatively predicted the mosquito density. Notably, the regression equation used was y=2526.170+(-2.418)X9+(-0.739)X13. The exposure-response analysis revealed that the density of mosquitoes was not linearly related to the average monthly air pressure, rainfall, and duration of sunshine. Conclusions The dominant mosquito species around the BCIA was Cx. pipiens pallens, Ae. albopictus, and Ae. vexans. During the control of Cx. pipiens pallens, the impact on other mosquito species must also be considered. Environmental management, breeding sites, and scientific use of pesticides should be prioritized based on the activity periods of mosquitoes. Further studies on mosquitoes and meteorological factors should be conducted to provide references and novel avenues for mosquito control.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Metabolic dysfunction-associated steatotic liver disease (MASLD), the most prevalent chronic liver condition globally, lacks adequate and effective therapeutic remedies in clinical practice. Recent studies have increasingly highlighted the close connection between the ubiquitin-proteasome system (UPS) and the progression of MASLD. This relationship is crucial for understanding the disease's underlying mechanism. As a sophisticated process, the UPS govern protein stability and function, maintaining protein homeostasis, thus influencing a multitude of elements and biological events of eukaryotic cells. It comprises four enzyme families, namely, ubiquitin-activating enzymes (E1), ubiquitin-conjugating enzymes (E2), ubiquitin-protein ligases (E3), and deubiquitinating enzymes (DUBs). This review aims to delve into the array of pathways and therapeutic targets implicated in the ubiquitination within the pathogenesis of MASLD. Therefore, this review unveils the role of ubiquitination in MASLD while spotlighting potential therapeutic targets within the context of this disease.

Image 1.

OBJECTIVE: Data on homocysteine (Hcy) status and its determinants are limited among women during pregnancy and postpartum. This cross-sectional study aimed to investigate Hcy levels during pregnancy and postpartum, and to explore the determinants like geographic factor. METHODS: This study was conducted in women at mid-pregnancy, late-pregnancy and postpartum from southern, central and northern China. Approximately 132 women were included in each stratum by the three phases and regions. Plasma Hcy concentrations were assessed using high-performance liquid chromatography (HPLC), with hyperhomocysteinemia defined as > 10.0 µmol/L. Quantile regression was to estimate medians and interquartile ranges ( IQRs), and logistic regression to examine the determinants of hyperhomocysteinemia. RESULTS: For 1,190 women included, the median (IQR) Hcy concentration was 5.66 (4.62, 7.37) μmol/L. The adjusted median in mid-pregnancy, late-pregnancy and postpartum women was 4.75 (4.13, 5.54), 5.72 (4.81, 6.85) and 7.09 (5.65, 8.75) μmol/L, respectively, showing an increasing trend ( P < 0.001). This increasing trend persisted across the three regions. Higher Hcy concentrations were observed in women residing in northern region and those with younger age or lower economic status. A total of 106 (8.9%) women had hyperhomocysteinemia, with a higher prevalence in those residing in northern region (16.0%), or in postpartum women (16.5%). CONCLUSION Hcy levels, varying with geographic region, maternal age and economic status, are increased from mid-pregnancy to late-pregnancy and postpartum, indicating a need to monitor Hcy levels in pregnant and postpartum women to control potential risks related to elevated Hcy levels.
Humans ; Female ; Pregnancy ; Homocysteine/blood* ; China/epidemiology* ; Adult ; Postpartum Period/blood* ; Cross-Sectional Studies ; Hyperhomocysteinemia/blood* ; Young Adult ; Pregnancy Trimester, Third/blood* ; Pregnancy Trimester, Second ; East Asian People

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

OBJECTIVE To investigate the impact of Netupitant and palonosetron hydrochloride capsules （NEPA） on the pharmacokinetics of Paclitaxel for injection （albumin bound） （i. e. albumin-bound paclitaxel） under different intestinal microenvironment conditions. METHODS Male SD rats were divided into a normal group and a model group （n＝16）. Rats in the model group were intragastrically administered vancomycin solution to establish an intestinal disorder model. The next day after modeling， intestinal microbiota diversity was analyzed， and the mRNA expressions of cytochrome P450 3A1 （CYP3A1） and CYP2C11 in small intestine and liver tissues as well as those protein expressions in liver tissue were measured. Male SD rats were grouped as described above （n＝16）. The normal group was subdivided into the TP chemotherapy group （TP-1 group） and the TP chemotherapy+NEPA group （TP+NEPA-1 group）； the model group was subdivided into the TP chemotherapy group （TP-2 group） and the TP chemotherapy+NEPA group （TP+NEPA-2 group） （n＝8）. Rats in the TP+NEPA-1 and TP+NEPA-2 groups received a single intragastric dose of NEPA suspension （25.8 mg/kg， calculated by netupitant）. One hour later， all four groups received a single tail vein injection of albumin-bound paclitaxel and cisplatin. Blood samples were collected at different time points after the last administration. Using azithromycin as the internal standard， plasma paclitaxel concentrations were determined by liquid chromatography-tandem mass spectrometry. The main pharmacokinetic parameters were calculated using DAS 2.0 software and compared between groups. RESULTS Compared with the normal group， the model group showed significantly decreased Chao1 and Shannon indexes （P＜0.05）， significant alterations in microbiota composition and relative abundance， and significantly downregulated expressions of CYP3A1 mRNA in liver tissue and CYP2C11 mRNA in both small intestine and liver tissues （P＜0.05）. Compared with the TP-1 group， the AUC0－t， AUC0－∞， MRT0－t of paclitaxel in the TP-2 group， the cmax， AUC0－t， AUC0－∞ of paclitaxel in the TP+NEPA-1 group and TP+NEPA-2 group were significantly increased or prolonged； CL of paclitaxel in the TP-2 group， Vd and CL of paclitaxel in the TP+NEPA-1 group and the TP+NEPA-2 group were significantly decreased or shortened （P＜0.05）. Compared with the TP-2 group， cmax of paclitaxel in the TP+NEPA-2 group was significantly increased， and Vd and MRT0－t were significantly decreased or shortened （P＜0.05）. CONCLUSIONS Intestinal microbiota disorder affects the mRNA expressions of CYP3A1 and CYP2C11， leading to decreased clearance and increased systemic exposure of paclitaxel. Concomitant administration of NEPA under normal intestinal microbiota condition increases paclitaxel exposure. However， under conditions of intestinal microbiota disorder， concomitant administration of NEPA has a limited impact on paclitaxel systemic exposure.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.