Patients with Takayasu arteritis combined with aortic valve disease often have a poor prognosis following surgical valve replacement, frequently encountering complications such as perivalvular leakage, valve detachment, and anastomotic aneurysm. This article presents a high-risk case wherein severe aortic valve insufficiency associated with Takayasu arteritis was successfully managed through transcatheter aortic valve implantation via the transapical approach. The patient had satisfactory valve function with no complications observed during the six-month postoperative follow-up. This case provides a minimally invasive and feasible alternative for the clinical management of such high-risk patients.

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 To explore the clinical value of albumin-globulin ratio(AGR)combined with prostate-specific antigen(PSA)and Gleason score in predicting biochemical recurrence(BCR)after radical resection in patients with localized prostate cancer, and to provide reference for early identification of BCR high-risk patients and individualized treatment. Methods The clinical data of 114 patients with localized prostate cancer treated during Sep.2019 and Sep.2023 were retrospectively analyzed.According to postoperative BCR status, patients were divided into the BCR group(n=43)and non-BCR group(n=71).Preoperative clinical and pathological data were compared between the two groups.Univariate and multivariate Cox regression analyses were used to assess the correlation between these indicators and BCR.The predictive performance of the combined model was evaluated using receiver operating characteristic(ROC)curve.The Kaplan-Meier method was used to compare biochemical recurrence-free survival(BFS)among patients with different AGR, PSA levels and Gleason scores.Results Compared with the non-BCR group, the BCR group had significantly higher PSA level and ISUP grading, but significantly lower AGR level(P＜0.001).Multivariate Cox regression analyses showed that AGR was negatively correlated with BCR(HR=0.031, 95%CI:0.008-0.119, P＜0.001), PSA was positively correlated with BCR(HR=1.009, 95%CI:1.002-1.016, P=0.017), and Gleason score was positively correlated with BCR(HR=1.551, 95%CI:1.199-2.005, P＜0.001).The area under the ROC curve(AUC)for the combined prediction model(AGR + PSA + Gleason score)was 0.886, with a sensitivity of 90.7% and a specificity of 78.9%.Kaplan-Meier analyses showed that patients in the low AGR group, high PSA group, and high Gleason score group had significantly lower BFS than those in the high AGR group, low PSA group and low Gleason score group, respectively(all P＜0.0001).Conclusion The combination of AGR, PSA and Gleason score is closely associated with postoperative BCR in patients with localized prostate cancer.The combined prediction model demonstrates high predictive efficacy and can provide an important reference for the early identification of high-risk BCR patients and for guiding individualized postoperative treatment.

Objective To analyze the multi-target mechanism of Paeoniflorin in the intervention of oral submucosal fibrosis(OSF)systematically,based on molecular dynamics simulation and network pharmacology.Methods Identify potential targets of Paeoniflorin were predicted by using database.OSF-related disease targets and identified drug-disease intersecting targets were screened.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were conducted to validate the molecular binding capabilities between Paeoniflorin and core targets.Finally,molecular dynamics simulations were performed to verify binding stability.Results A total of 20 overlapping targets were identified,including key genes such as transforming growth factor(TGF)-β1,interleukin(IL)-6,and tumor necrosis factor(TNF)-α.TGF-β1,IL-6,and TNF formed the core hub.The enrichment analysis revealed that the target molecules were significantly enriched in the TGF-β1,phosphatidylinositol 3-kinease-actin(PI3K-Akt),and nuclear factor κB(NF-κB)signaling pathways.Molecular docking confirmed high affinity binding of Paeoniflorin to targets including TGF-β1,while molecular dynamics simulations verified stable interactions between Paeoniflorin and both TGF-β1 and B-cell lymphoma-2 targets.Conclusion This study revealed that Paeoniflorin inhibits the inflammatory-fibrotic cascade of OSF through synergistic regulation of TGF-β1/Smad,PI3K-Akt and NF-κB pathways.

Objective:To establish an automatic classification approach for bone marrow cells based on microscopic hyperspectral imaging and three-dimensional spectral convolutional neural network (Spec-CNN).Methods:The research type is establishment of methodology. The study included 306 newly diagnosed patients' bone marrow smears under Wright's staining from the Department of Hematology of the First Medical Center of the PLA General Hospital from November 1st, 2013 to April 30th, 2024. The high-spectrum data and 4k image data of bone marrow cells were simultaneously collected using a microscopic hyperspectral-4k optical path integrated imaging system (with a spectral resolution of 400—1 000 nm). The high-spectrum data was used for model training, while the 4k image data recognized by morphologists was only used as a reference for labeling the high-spectrum data. The high-spectrum data set was divided into training set, validation set and test set in a ratio of 14∶6∶5. The training set and validation set were used to train and fine-tune the Spec-CNN model, and the test set was used to evaluate the model performance. The sensitivity, specificity ,accuracy ,and Kappa coefficient were calculated for comparing the manual annotation results as gold standard with the intelligent identification results of the Spec-CNN model. Five non-data set samples were used for external validation.Results:The acquired hyperspectral data and 4k imaging dataset comprised of 32 categories and 64 800 bone marrow cells. In the test set, the Spec-CNN model demonstrated weighted-average indicators on classification metrics across 32 cell types: sensitivity 87.79%, specificity 99.31%, and accuracy 98.78%, and Kappa coefficient 0.869. For external validation, the mean correct identification rate of bone marrow cells reached 83.28%.Conclusion:We successfully established an automatic classification method of bone marrow cells based on microscopic hyperspectral imaging and three-dimensional Spec-CNN. This method has a good automatic classification ability for 32 types of bone marrow nucleated cells, which has a certain auxiliary effect on improving the diagnosis efficiency of blood diseases for bone marrow morphologists.

Poly(lactic-co-glycolide)(PLGA)is a key excipient in long-acting sustained-release preparations,and its degradation properties directly affect the drug release behavior.In this study,PLGA microspheres were prepared by microfluidic techniques,and the morphology changes of the microspheres were observed by scanning electron microscopy(SEM).In alkaline environment,due to the accelerated hydrolysis of ester bonds,the surface of the microspheres was rapidly dissolved and eroded,and the degradation rate was significantly higher than that in acidic environment.High temperature accelerated the degradation of PLGA microspheres.Under neutral and alkaline conditions,the microspheres showed aggregation and adhesion.Under acidic conditions,the microspheres gradually decomposed into irregular fragments.The high ionic strength further promoted the surface corrosion of the microspheres,especially under extreme pH conditions.Simultaneously,PLGA microspheres encapsulating coumarin were prepared to simulate the microsphere formulation.The release rate of coumarin after degradation of the microspheres under different conditions was observed by measuring the absorbance with ultraviolet-visible spectrophotometry.The results were consistent with those of the blank microspheres.This study revealed that the degradation of PLGA microspheres was significantly pH-dependent,temperature sensitive and ion strength responsive.These findings not only helped to understand and optimize the long-term stability and controlled release performance of drug-carrying microspheres,but also provided a theoretical basis for further improvement of PLGA-based drug carrier design.

Objective To analyze the risk factors of critically ill patients infected with Acinetobacter bau-mannii(AB)and carbapenem resistant Acinetobacter baumannii(CRAB).Methods From January 2022 to June 2023,the data of Intensive Care Unit(ICU)patients admitted to Second Hospital of Shanxi Medical Uni-versity in Shanxi Province were collected.According to whether they were infected with AB,the patients were divided into an observation group and a control group(98 cases each).The observation group was further di-vided into a carbapenem sensitive Acinetobacter baumannii(CSAB)group(72 cases)and a CRAB group(26 cases).Mann-Whitney U test,chi-square test and other univariate and multivariate binary Logistic regression were used to analyze the risk factors of AB and CRAB infection for critically ill patients.The prognosis was analyzed by Kaplan Meier survival analysis.Results Long stay in ICU,previous use of carbapenem drugs and high Acute Physiology and Health Evaluation(APACHE Ⅱ)score were independent risk factors for AB sus-ceptibility(P＜0.05),while the independent risk factors for CRAB susceptibility were invasive ventilation and delayed surgery(P＜0.01).In addition,CRAB infection,COVID-19 and shock was risk factors for death in critically ill patients,and invasive ventilation,indwelling drainage tube and operation could reduce the risk of death in critically ill patients(P＜0.05).Conclusion ICU stay time,APACHE Ⅱ score,previous use of car-bapenem drugs and invasive ventilation increase the risk of AB and CRAB infection in critically ill patients.In-vasive ventilation,indwelling drainage and early surgery could reduce the risk of death from AB and CRAB in-fection in critically ill patients.

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.