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.

To summarize the clinical characteristics, antibody spectrum and neuroelectrophysiological features of autoimmune nodopathy(AN), and to explore the phenotypic differences among different antibody-positive subgroups.

Chemical constituents of n-butanol fraction of ethanol extract from the leaves of Cyclocarya paliurus (Batalin) Iljinskaja were studied. Ten compounds were purified by silica gel, MCI, ODS, Sephadex LH-20 column chromatography and semi-preparative high-performance liquid chromatography. Based on the physicochemical properties and spectroscopic data, these compounds were identified as (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(3′′,5′′-dimethoxy-(E)-p-coumaroyl)]-β-D-glucopyranoside (1), (4S)-4,8-dihydroxy-α-tetralone-5-[6′-(E)-feruloyl]-β-D-glucopyranoside (2), (1S,3R,4S,5R)-4,5-di-O-caffeoyl quinic acid methyl ester (3), (1R,3R,4S,5R)-3,4-di-O-caffeoyl quinic acid methyl ester (4), (3R,5R)-3,4,5-tri-O-caffeoylquinic acid methyl ester (5), (1R,3R,4S,5R)-3-O-caffeoyl-5-O-p-coumaroyl quinic acid methyl ester (6), (1R,3R,4S,5R)-3-O-p-coumaroy-4-O-caffeoyl quinic acid methyl ester (7), (1R,3R,4S,5R)-3-O-caffeoyl-4-O-p-coumaroy quinic acid methyl ester (8), (3R,5R)-3,5-di-O-caffeoyl quinic acid methyl ester (9) and (1S,3R,4R,5R)-4-O-caffeoyl-5-O-feruloyl quinic acid methyl ester (10). Among them, compound 1 was a new compound, and compounds 3–10 were isolated from the genus Cyclocarya for the first time.

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.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Objective To investigate the efficacy and safety of conventional open Latarjet surgery,arthroscopic Latarjet surgery,arthroscopic Bankart repair combined with Remplissage surgery in the treatment of recurrent anterior shoulder dislocation(RASD)with scapula glenoid bone defect>15%and meshing Hill-Sachs lesion.Methods The clinical data of 65 patients with RASD with 15%～25%scapula glenoid bone defect and meshing Hill-Sachs lesion admitted to our hospital from January 2022 to December 2024 were retrospectively analyzed.They were divided into group A,group B,and group C according to different surgical methods.Among them,group A underwent conventional open Latarjet surgery(n=18),group B underwent arthroscopic Latarjet surgery(n=21),and group C underwent arthroscopic Bankart repair combined with Remplissage surgery(n=26).The surgical conditions,preoperative and postoperative scores of related scales[visual analogue scale(VAS),Constant-Murley shoulder score,the University of California Los Angeles(UCLA)shoulder score],shoulder range of motion,postoperative complications and recurrence were compared among the three groups.Results Operation time:group B was longer than group A and group C(P<0.05),and group A was longer than group C(P<0.05).Intraoperative blood loss and hospital stay:group A was more or longer than group B and group C(P<0.05),group B was more or longer than group C(P<0.05).The VAS scores of the three groups at 1,6 and 12 months after operation were lower than those before operation(P<0.05).Pain VAS score at 1 and 6 months after operation:group A was higher than group B and group C(P<0.05).Pain VAS score at 1 month after operation:group B was higher than group C.Pain VAS score at 12 months after operation:there was no significant difference among the three groups(P>0.05).The UCLA shoulder scores of group A at 6 and 12 months after operation and group B and group C at 1,6 and 12 months after operation were higher than those before operation(P<0.05).UCLA score at 1 month after operation:group A0.05).The Constant-Murley scores of group A at 6 and 12 months after operation and group B and group C at 1,6 and 12 months after operation were higher than those before operation(P<0.05).Constant-Murley score at 1 month after operation:group A was lower than group B and group C(P<0.05).Constant-Murley score at 6 and 12 months after operation:three groups were equivalent(P>0.05).Forward flexion and lifting range of motion in the three groups:at 12 months after operation>before operation(P<0.05).Forward flexion and lifting range of motion was evaluated at 12 months after operation:there was no significant difference among the three groups(P>0.05).The range of motion of external rotation at body side and external rotation at 90° abduction before operation and at 12 months after operation:there was no significant difference between group A and group B(P>0.05).External rotation at body side and external rotation at 90° abduction of group C:at 12 months after operation of it was less than that before operation(P<0.05).The range of motion of external rotation at body side and external rotation at 90° abduction was obtained at 12 months after operation:group A and group B were greater than group C(P<0.05).Postoperative complication rate and recurrence rate:three groups were equivalent(P>0.05).Conclusion Conventional open Latarjet surgery,arthroscopic Latarjet surgery,arthroscopic Bankart repair combined with Remplissage surgery can effectively improve shoulder joint function and relieve pain in the treatment of RASD with 15%～25%scapula glenoid bone defect and meshing Hill-Sachs lesion.However,the two shoulder arthroscopic procedures are less traumatic and have faster postoperative recovery.

Objective To investigate the feasibility of fluorescence analysis method for determining the encapsulation efficiency(EE)of PEGylated liposomes(PEG-L)and their application in studying the accelerated blood clearance(ABC)phenomenon.Meth-ods DiR-labeled PEGylated liposomes(PEG-DiR-L)were prepared via ethanol injection.Free DiR and total DiR samples were ob-tained using ultrafiltration centrifugation and ultrasonic demulsification,respectively.The EE of PEG-DiR-L was calculated by quantif-ying DiR levels in both samples using a multifunctional microplate reader.Male Sprague-Dawley(SD)rats were divided into two groups:the single injection group[first injected with phosphate-buffered saline(PBS),followed by PEG-DiR-L 7days later]and the repeated injection group(first injected with empty PEG-L,followed by PEG-DiR-L 7days later).Blood samples were collected from the eye canthus of rats,and the plasma concentrations of PEG-DiR-L were measured to construct pharmacokinetic profiles and calculate key pharmacokinetic parameters.Tissue distribution was assessed by ex vivo imaging of heart,liver,spleen,lung,and kidney samples u-sing the IVIS spectroscopy.Results The EE of PEG-DiR-L determined by ultrafiltration centrifugation was 97.68％±0.23％.Com-pared to the single-dose group,the repeated-dose group exhibited significantly accelerated plasma clearance,with reduced AUC(0→t)、AUC(0→∞)、MRT(0→t)、MRT(0→∞)and t1/2z,alongside increased CLz and Vz(P＜0.05).Hepatic accumulation of PEG-DiR-L was markedly enhanced in the repeated-dose group.Conclusion The results of plasma pharmacokinetics and tissue distribution indicated that the ABC phenomenon occurred in the repeated injection group,suggesting that the fluorescence analysis method is able to determine the encapsulation rate of PEG-L and be used to study the ABC phenomenon.

Objective:To evaluate the effect of dexmedetomidine on the viability of dopaminergic neurons in the ventral tegmental area (VTA) of morphine-addicted mice.Methods:Experiment Ⅰ Thirty SPF healthy adult male C57BL/6 mice, aged 8 weeks, weighing 20-25 g, were divided into 3 groups ( n=10 each) using the random number table method: normal saline group (NS group), dexmedetomidine 50 μg/kg group (DEX50 group), and dexmedetomidine 100 μg/kg group (DEX100 group). A morphine addiction model was established by intraperitoneal injection of increasing doses of morphine (10, 20, 30, 40, 50 and 50 mg/kg) for 6 consecutive days in mice. After the successful establishment of the model, dexmedetomidine 50 and 100 μg/kg were intraperitoneally injected for 14 consecutive days in group DEX50 and group DEX100 respectively, while normal saline was given instead in group C. The conditioned place preference (CPP) experiment was conducted every other day. Experiment Ⅱ Thirty SPF healthy adult male C57BL/6 mice, aged 8 weeks, weighing 20-25 g, were divided into 3 groups ( n=10 each) by the random number table method: control group (C group), morphine group (Mor group) and dexmedetomidine 50 μg/kg group (DEX50 group). Normal saline was intraperitoneally injected for 10 consecutive days in group C. Morphine with increasing doses was intraperitoneally injected for 6 days, and then normal saline was intraperitoneally injected for 4 consecutive days in group Mor. Morphine with increasing doses was intraperitoneally injected for 6 days, and then dexmedetomidine 50 μg/kg was intraperitoneally injected for 4 consecutive days in group DEX50. The mice were anesthetized at 90 min after the last intraperitoneal injection, brain tissues were harvested, and the corresponding brain slices of the VTA were selected for c-Fos immunofluorescence staining. Experiment Ⅲ Ten dopamine transporter-Cre recombinase mice were divided into 2 groups ( n=5 each) by the random number table method: morphine group (Mor group) and morphine+ dexmedetomidine 50 μg/kg group (Mor+ DEX group). Stereotaxic viral injection was performed in the brain. rAAV-EF1α-DIO-GCaMP6s was injected into the VTA and an optical fiber was implanted. Three weeks later, a morphine addiction model was established based on Experiment Ⅰ for the CPP experiment, morphine was intraperitoneally injected in group Mor, and morphine and dexmedetomidine were intraperitoneally injected in group Mor+ DEX. The viral fluorescence signals were recorded at 5 min before and 20 min after the drug administration in the three groups. Results:Experiment Ⅰ There was no statistically significant difference in the CPP scores after developing the morphine addiction model among the three groups ( P>0.05). Compared with group NS, the CPP scores were significantly decreased at 4-14 days of the continuous administration in group DEX50 and group DEX100 ( P<0.05). Experiment Ⅱ Compared with group C, the number of c-Fos positive cells in the VTA was significantly increased in group Mor ( P<0.05). Compared with group Mor, the number of c-Fos positive cells in the VTA was significantly decreased in group DEX ( P<0.05). Experiment Ⅲ Compared with that before administration, the calcium signals of dopaminergic neurons in the VTA were significantly enhanced in group Mor ( P<0.05), and no statistically significant difference was found in the calcium signals of dopaminergic neurons in the VTA in group Mor+ DEX ( P>0.05). Compared with group Mor, no statistically significant difference was found in the calcium signals of dopaminergic neurons in the VTA before drug administration ( P>0.05), and the calcium signals of dopaminergic neurons in the VTA were significantly weakened after administration in group Mor+ DEX ( P<0.05). Conclusions:The mechanism by which dexmedetomidine promotes the extinction of morphine addiction is related to the inhibition of the viability of dopaminergic neurons in the VTA of mice.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.