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.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

AIM:To analyze the efficacy of modified Zhujing formula combined with intravitreal ranibizumab(IVR)injection in the treatment of wet age-related macular degeneration(wARMD).METHODS: A prospective study was conducted on wARMD patients at the Ophthalmology Department of Yulin Hospital of Traditional Chinese Medicine from September 2022 to October 2024. The study subjects were divided into the experimental group and the control group according to the random number table method. The control group received IVR treatment, while the experimental group was treated with modified Zhujing formula in addition to IVR injection. The clinical efficacy, TCM symptom scores, central retinal thickness(CRT), best corrected visual acuity(BCVA), macular hemorrhage area, choroidal neovascularization area(CNV), ocular hemodynamic parameters [resistance index(RI), maximum diastolic blood flow(EDV), maximum systolic blood flow(PSV)], and 1-year recurrence rate were compared between the two groups.RESULTS: This study included 60 eyes from 60 wARMD patients. Among them, the control group consisted of 30 patients and 30 eyes, while the experimental group consisted of 30 patients and 30 eyes. The age of the control group was 67.52±3.12 y, with 17 males and 13 females. The age of the experimental group was 67.62±3.04 y, with 18 males and 12 females.The clinical efficacy of the experimental group(97%)was higher than that of the control group(73%)(P<0.05). After treatment, the scores of blurred vision, soreness and weakness of the waist and knees, restlessness and insomnia, dizziness and tinnitus in the experimental group were all lower than those in the control group(all P<0.05); the EDV and PSV in the experimental group were both higher than those in the control group(both P<0.05); the BCVA, CRT, macular hemorrhage area, CNV and RI of the experimental group were all lower than those of the control group(all P<0.05), and the 1-year recurrence rate in the experimental group(3%)was lower than that in the control group(27%)(P<0.05). CONCLUSION:The combined use of modified Zhujing formula and IVR can effectively alleviate symptoms such as blurred vision and retinal hemorrhage in wARMD patients, improve vision and ocular hemodynamic conditions, and reduce the recurrence rate. This suggests that there may be a synergistic enhancing effect.

Objective To develop and validate a prediction model for in-hospital recurrent intensive care unit (ICU) admission in critically ill patients with ischemic stroke (IS) based on machine learning (ML) algorithms. Methods Clinical data from 2 929 IS patients were included from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Least absolute shrinkage and selection operator (LASSO) regression was used to identify predictive factors, and the synthetic minority over-sampling technique (SMOTE) was employed to create a derivation cohort comprising 2 583 patients. These patients were randomly divided into a training set (n=2 066) and a test set (n=517) at an 8:2 ratio. Five ML algorithms, including decision tree, random forest, adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and support vector machine (SVM), were performed to construct prediction models. Five-fold cross-validation was used to evaluate the performance of the model in the training set. The area under the receiver operating characteristic curve (ROC-AUC) and decision curve analysis (DCA) were used to assess and compare the models in the testing set. The best-performing model was interpreted by shapley additive explanations (SHAP). Results Among the 2 929 patients included, 704 (24.0%) experienced in-hospital recurrent ICU admission. Among the five ML models, the random forest model demonstrated the best predictive performance, with an AUC of 0.839 (95%CI 0.801–0.877). Feature importance analysis identified five most significant features affecting model prediction, including APS Ⅲ score, albumin, age, heart rate, and SOFA score. Conclusions ML-based models can effectively predict the risk of in-hospital recurrent ICU admission in critically ill patients with IS. The random forest model showed superior predictive performance, which may have potential applications in early clinical risk stratification and intervention.

With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To analyze the incidence rate and risk factors of atrial fibrillation (AF) in elderly patients with chronic obstructive pulmonary disease (COPD). Methods A total of 392 patients with COPD in the hospital were selected from December 2020 to December 2024. Patients' data (medical history, echocardiographic data, etc.) were recorded. The incidence rate of AF in patients was recorded and the patients were grouped according to whether or not they developed AF. The differences were compared between the two groups, and the risk factors of AF occurrence in elderly patients with COPD were analyzed. Results Among the 392 patients with COPD, 127 cases (32.40%) met the diagnostic criteria of AF. There were statistically significant differences in age, pulmonary infection, heart failure, diabetes, smoking history, disease duration, LAD, severe renal insufficiency and coronary heart disease between the AF group and the non-AF group (P<0.05). Multivariate logistic regression analysis suggested that age, pulmonary infection, heart failure, diabetes mellitus, smoking history, disease course, LAD, severe renal insufficiency and coronary heart disease were all risk factors for elderly patients with COPD (OR=1.129, 4.246, 3.955, 4.125, 2.038, 1.112, 1.102, 4.397, and 3.865, P<0.05). Conclusion The incidence rate of AF is high in elderly patients with COPD. Age, pulmonary infection, heart failure, diabetes mellitus, smoking history, course of disease, LAD, severe renal insufficiency, and coronary heart disease are all risk factors for AF.

T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.

OBJECTIVES: To explore the efficacy of DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy for management of cancer pain and provide reference for its standardized clinical application. Methods and. RESULTS: Recommendations were formulated based on literature review and expert group discussion, and consensus was reached following expert consultation. The consensus recommendations are comprehensive, covering the entire treatment procedures from preoperative assessment and preparation, surgical operation process, postoperative management and traditional Chinese medicine treatment to individualized treatment planning. The study results showed that the treatment plans combining traditional Chinese with Western medicine effectively alleviated cancer pain, reduced the use of opioid drugs, and significantly improved the quality of life and enhanced immune function of the patients. Postoperative follow-up suggested good treatment tolerance among the patients without serious complications. CONCLUSIONS The formulated consensus is comprehensive and can provide reference for clinicians to use DSA-guided intrathecal drug delivery system combined with Zi Wu Liu Zhu Acupoint Therapy. The combined treatment has a high clinical value with a good safety profile for management of cancer pain.
Humans ; Medicine, Chinese Traditional ; Cancer Pain/therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Drug Delivery Systems ; Pain Management/methods* ; China

Apical microsurgery is accurate and minimally invasive, produces few complications, and has a success rate of more than 90%. However, due to the lack of awareness and understanding of apical microsurgery by dental general practitioners and even endodontists, many clinical problems remain to be overcome. The consensus has gathered well-known domestic experts to hold a series of special discussions and reached the consensus. This document specifies the indications, contraindications, preoperative preparations, operational procedures, complication prevention measures, and efficacy evaluation of apical microsurgery and is applicable to dentists who perform apical microsurgery after systematic training.
Microsurgery/standards* ; Humans ; Apicoectomy ; Contraindications, Procedure ; Tooth Apex/diagnostic imaging* ; Postoperative Complications/prevention & control* ; Consensus ; Treatment Outcome

OBJECTIVE: To establish a genotyping method for Diego, MNS and Kell blood groups based on quantitative real-time PCR (qPCR) technology, and preliminarily apply it to the screening of rare blood groups in blood donors. METHODS: Blood group gene standards containing heterozygous and homozygous alleles were prepared by blood group serological and PCR-SBT methods. Specific amplification primers and hybridization probes were designed, and explore to establish the qPCR method for detecting Diego, MNS, and Kell blood group genotypes. Then the established qPCR method was used to identify blood group genotypes of 186 blood donor samples. RESULTS: A method based on qPCR technology was established to identify Dia/Dib, S/s and K/k blood group antigens. The genotyping results of the gene standard samples were consistent with the serological testing results and genotypes detected by PCR-SBT. qPCR testing of 186 samples identified 11 cases of DI*A/B heterozygosity and 19 cases of GYPB*S/s heterozygosity, and the rest were DI*B/B, GYPB*s/s, KEL*02/02 homozygosity. No rare blood group genotypes of DI*A/A, GYPB*S/S, KEL*01.01/01.01 were found. CONCLUSION The established qPCR method is suitable for genotyping on Diego, MNS and Kell blood group, and it can be used for batch screening of blood donors and the establishment of rare blood group bank.
Humans ; Genotype ; Genotyping Techniques/methods* ; Real-Time Polymerase Chain Reaction/methods* ; Blood Group Antigens/genetics* ; Kell Blood-Group System/genetics* ; Blood Donors ; Blood Grouping and Crossmatching/methods* ; Erythrocytes ; MNSs Blood-Group System/genetics*