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 identify the active components of Desmodium styracifolium （Osb.） Merr. extract （DME） against cholestatic liver injury （CLI）， and to verify its target and molecular mechanism， so as to provide scientific evidence for the clinical application of D.styracifolium （Osb.） Merr. and the development of new hepatoprotective agents.METHODS High-performance liquid chromatography was applied to establish fingerprints of 19 batches of DME， followed by similarity evaluation and identification of common peaks. An in vitro CLI model was constructed by lithocholic acid （LCA）-induced injury in HepG2 cells to evaluate the in vitro protective effect of DME against CLI. Spearman correlation analysis， grey relational analysis and partial least-squares regression analysis were adopted to investigate the spectrum-effect relationship of DME against CLI and preliminarily screen the core active components. Molecular docking and surface plasmon resonance technology were used to predict and verify the binding capacity between core active components and farnesoid X receptor （FXR）. qRT-PCR， Western blot and immunofluorescence staining tests were performed to validate the influences of core active components on the expression of molecules related to FXR/bile salt export pump （BSEP） pathway in CLI model cells.RESULTS A total of 12 common peaks were calibrated in the fingerprints of 19 batches of DME， and the similarity values were all above 0.990. Four common peaks were identified， namely peak 6 （schaftoside）， peak 7 （isoorientin）， peak 11 （isoschaftoside） and peak 12 （isovitexin）. DME at 80 μg/mL exerted the optimal protective effect on CLI-model cells， and the cell viability reached （75.66±4.60）% after 24 h of treatment. Spectrum-effect analysis revealed that schaftoside possessed the strongest correlation with the anti-CLI activity of DME and served as the core active component. Target prediction and validation results showed that the binding energy between schaftoside and FXR was －8.1 kcal/mol， and the equilibrium dissociation constant was 27.3 μmol/L. Mechanistic experiments demonstrated that 100 μmol/L schaftoside significantly elevated the expression levels of FXR and BSEP， and mRNA of their encoded genes Nr1h4 ， Abcb11 in CLI-model cells （ P ＜0.05）.CONCLUSIONS Schaftoside may be the core active component of DME against CLI， and its mechanism may be related to the activation of FXR/BSEP pathway.

This research aims to construct a stable epithelial cell senescence model for screening and e-valuation of senolytics.We explored the optimal conditions for doxorubicin-induced senescence of non-transformed epithelial cells MCF 10A,including the optimal induction concentration,the optimal inter-vention time,and the optimal senescence duration,and confirmed the feasibility of MCF 10A as an epi-thelial senescence model by multiple ways.The optimal condition for Doxorubicin-induced senescence of MCF 10A cells was treatment with 0.6 μmol/L Doxorubicin for 16 h to achieve the best senescence state on the 8th day.Under the optimal induction conditions,the positive rate of senescence-associated β-gal-actosidase(SA-β-gal)staining in the treated group reached 97％.At the same time,biochemical results of detecting the expression of mRNA,proteins,and immunofluorescence demonstrated that the expression levels of senescence-associated secretory phenotype(SASP),p16,p21 and p53 in the treated group were significantly higher than those in the control cells,and Lamin B1 was significantly decreased(P＜0.001),which were consistent with the specific characteristics of senescence.In summary,an epithelial senescence model was successfully induced in MCF 10A cells by Doxorubicin in this study,which will promote the screening of senolytics for senescent epithelial cells.

Objective To investigate the effect of ClearInfinity deep learning reconstruction algorithm on image quality and radiation dose of abdominal computed tomography angiography(CTA)at low kV and low contrast medium.Methods One hundred patients who underwent abdominal CTA were selected and randomly divided into group A and group B.Group A:tube voltage 70 kV,con-trast medium 30-35 mL,divided into A1 and A2 subgroups according to reconstruction algorithm,group A1 50%ClearInfinity,group A2 50%ClearView iterative algorithm;group B:tube voltage 100 kV,contrast medium 60-70 mL,50%ClearView.CT values and standard deviation(SD)values of region of interest(ROI)of abdominal aorta,proper hepatic artery,superior mesenteric artery,renal artery and common iliac artery were evaluated objectively,while signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated;subjective scores were evaluated by two physicians;radiation doses of groups A and B were analyzed.Results Volume CT dose index(CTDIvol),dose length product(DLP)and effective dose(ED)in group A were significantly lower than those in group B(P<0.05),subjective scores in group A1 and group B were higher than those in group A2(P<0.05),and there was no difference between group A1 and group B(P>0.05).Compared with group A1,SNR and CNR of all vessels in group A2 were significantly decreased.CT values of abdominal aorta and common iliac artery,CNR of common iliac artery and supe-rior mesenteric artery in group B were significantly increased,SNR of renal artery was significantly decreased(P<0.05).Conclusion ClearInfinity deep learning reconstruction algorithm combined with 70 kV scanning technology can obtain better abdom-inal CTA image quality,and effectively reduce the radiation dose and contrast medium of patients,which has high clinical application value.

The aim of this updated guideline is to provide comprehensive recommendations for the management of gout in patients with common comorbidities, such as chronic kidney disease(CKD), cardiovascular disease(CVD), diabetes, osteoarthritis(OA), and gastrointestinal disorders. This guideline was developed by a multidisciplinary expert panel consisting of specialists in endocrinology, rheumatology, nephrology, cardiology, gastroenterology, and methodology. The development process adhered to standard methodologies, including PICO(population, intervention, comparator, and outcomes) question deconstruction, systematic literature review, the Grading of Recommendations Assessment, Development and Evaluation(GRADE) for evidence and recommendation evaluation, Delphi voting, and expert consensus. The guideline presents 26 evidence-based recommendations addressing 7 clinical questions for patients with hyperuricemia and gout in the context of comorbidities. Key recommendations include the maintenance of strict serum urate targets, particularly for patients with CKD stage≥3, chronic gouty arthritis, and OA, in order to prevent disease progression. In patients with CVD or diabetes, intra-articular triamcinolone is preferred over systemic glucocorticoids. Prioritized anti-inflammatory treatments for patients with CKD, gastrointestinal diseases and OA are recommended. The guideline also introduces emerging therapies, such as interleukin-1 inhibitors and selective urate transport inhibitors, as potential treatment options for refractory cases. The update offers a comprehensive, patient-centered approach to managing gout, particularly in individuals with associated comorbidities. Multidisciplinary collaboration and emerging new treatments and evidence ensure the optimization of the recommendations.

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 an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective To explore the predictive factors of recurrence of chronic suppurative otitis media(CSOM)after otoendoscopic surgery,and construct a risk quantified nomogram model.Methods The clinical data of 485 patients with CSOM who underwent otoendoscopic surgery in our hospital from February 2021 to February 2023 were retrospectively analyzed.The patients were divided into the recurrence group and the non-recurrence group according to the recurrence situation during the follow-up period.The clinical data of patients between the two groups were compared,and the predictive factors of postoperative recurrence were analyzed by Cox regression method.A risk quantified nomogram model was constructed based on these predictive factors.The predictive efficiency of the model was verified by receiver operating characteristic(ROC)curve,and the calibration degree of the model was verified by Hosmer-Lemeshow goodness of fit analysis,and the clinical net benefit of the model was evaluated by decision curve analysis(DCA)method.Results Follow-up ranged from 6 to 39 months,with a median of 21(11,32)months,the postoperative recurrence rate was 14.64%(71/485).Combined with recurrent upper respiratory tract infections(HR=3.019,95%CI:1.609 to 5.664),combined with chronic rhinosinusitis(HR=2.428,95%CI:1.529 to 3.856),combined with adenoid hypertrophy(HR=2.214,95%CI:1.381 to 3.551),middle ear risk index(MERI)score(HR=2.863,95%CI:1.445 to 5.675),the seven-item eustachian tube dysfunction questionnaire(ETDQ-7)score(HR=2.679,95%CI:1.505 to 4.765)and inadequate drainage of the surgical cavity(HR=2.373,95%CI:1.503 to 3.746)were the risk factors for postoperative recurrence(P<0.05).A risk quantified nomogram model of recurrence after otoendoscopic surgery in CSOM patients was constructed based on the predictive factors of Cox regression analysis,among them the risk value ranges of patients who combined with recurrent upper respiratory tract infections,combined with chronic rhinosinusitis,combined with adenoid hypertrophy,MERI score,ETDQ-7 score,and inadequate drainage of the surgical cavity were 0 to 65 points,0 to 56 points,0 to 52 points,0 to 76 points,0 to 64 points,and 0 to 76 points,respectively.The area under the curve of ROC,sensitivity and specificity of the model for predicting postoperative recurrence were 0.928(95%CI:0.875 to 0.948),85.92%and 88.89%,respectively.There was no significant difference between the predicted probability of postoperative recurrence and the actual probability(P>0.05).The threshold probability of postoperative recurrence predicted by the model in the range of 4%to 92%could obtain clinical net benefit.Conclusion The predictive factors of recurrence after otoendoscopic surgery in CSOM patients include combined with recurrent upper respiratory tract infections,combined with chronic rhinosinusitis,combined with adenoid hypertrophy,MERI score,ETDQ-7 score and inadequate drainage of the surgical cavity,the risk quantified nomogram model based on these factors is highly effective in predicting postoperative recurrence.

Objective To investigate the clinical prognosis value of the positivity rate of oligoclonal bands(OCB)and immunoglobulin G(IgG)level of cerebrospinal fluid(CSF)in severe encephalitis.Methods A total of 699 cases of encephalitis patients admitted to the Department of Neurology of the First Affiliated Hospital of Air Force Military Medical University,and Xijing 986 Hospital from January 2016 to October 2020 were enrolled.According to the severity of their diseases,these patients were divided into a mild(n=360)group and a severe(n=339)group.CSF and serum samples were collected from the patient at the time of admission,and the differences in cerebrocyte count,glucose contem,glucose content,chlorine content,IgG of CSF and OCB of CSF and serum were compared.According to the GOS score of patients with severe encephalitis at discharge,the patients were divided into good prognosis group(n=259)and poor prognosis group(n=80),and multivariate Logistic regression analysis was used to analyze factors that affected the prognosis of severe encephalitis patients,and the correlation between the OCB and IgG of CSF and prognosis of patients with severe encephalitis.The predictive value of CSF IgG for the prognosis of patients with severe encephalitis was tested,and receiver operating characteristic(ROC)curve was plotted.Results Compared to patients with mild encephalitis,patients with severe encephalitis had a higher proportion of fever,pulmonary infection,status epilepticus,and mechanical ventilation,and were more likely to be complicated by stroke and hydrocephalus,and the differences were statistically significant(χ2=5.319～245.179,all P<0.05).There were significant differences in the positive rate of cerebrocyte count,chlorine content,IgG content and OCB in cerebrospinal fluid between the two groups(Z=-3.623,-4.875,-3.518,χ2=6.581,all P<0.05).CSF OCB and CSF IgG were independent risk factors for poor prognosis in patients with severe encephalitis(Wald χ2=7.295,0.001,all P<0.05).A restrictive cubic spline plot showed a linear relationship between CSF IgG and poor prognosis.The AUC(95%CI)of CSF IgG was 0.754(0.632～0.876).Conclusion The CSF IgG content and positive rate of CSF OCB in patients with severe encephalitis with poor prognosis are higher than those in patients with good prognosis,and detecting these two indicators has certain reference value for the prognosis prediction of patients with severe encephalitis.

Objective To investigate the effect of ClearInfinity deep learning reconstruction algorithm on image quality and radiation dose of abdominal computed tomography angiography(CTA)at low kV and low contrast medium.Methods One hundred patients who underwent abdominal CTA were selected and randomly divided into group A and group B.Group A:tube voltage 70 kV,con-trast medium 30-35 mL,divided into A1 and A2 subgroups according to reconstruction algorithm,group A1 50%ClearInfinity,group A2 50%ClearView iterative algorithm;group B:tube voltage 100 kV,contrast medium 60-70 mL,50%ClearView.CT values and standard deviation(SD)values of region of interest(ROI)of abdominal aorta,proper hepatic artery,superior mesenteric artery,renal artery and common iliac artery were evaluated objectively,while signal-to-noise ratio(SNR)and contrast-to-noise ratio(CNR)were calculated;subjective scores were evaluated by two physicians;radiation doses of groups A and B were analyzed.Results Volume CT dose index(CTDIvol),dose length product(DLP)and effective dose(ED)in group A were significantly lower than those in group B(P<0.05),subjective scores in group A1 and group B were higher than those in group A2(P<0.05),and there was no difference between group A1 and group B(P>0.05).Compared with group A1,SNR and CNR of all vessels in group A2 were significantly decreased.CT values of abdominal aorta and common iliac artery,CNR of common iliac artery and supe-rior mesenteric artery in group B were significantly increased,SNR of renal artery was significantly decreased(P<0.05).Conclusion ClearInfinity deep learning reconstruction algorithm combined with 70 kV scanning technology can obtain better abdom-inal CTA image quality,and effectively reduce the radiation dose and contrast medium of patients,which has high clinical application value.