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 investigate the expression profile, prognostic value, gene co-expression network, and immunomodulatory role of BRF1 in a pan-cancer context, and to explore its biological functions and molecular regulatory mechanisms in esophageal squamous cell carcinoma (ESCC). Methods The pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized to analyze the differential expression of BRF1 in tumor versus normal tissues, its association with patient survival, pathway enrichment for co-expressed genes, and immune features (including immune checkpoints, cytokines, and immune cell infiltration). The expression profile of BRF1 in ESCC was validated using the Gene Expression Omnibus (GEO) database. In vitro, BRF1 was knocked down in ESCC cells using siRNA. Cell proliferation and migration were assessed by MTT and Transwell assays, respectively. The expression levels of proliferation- and migration-related proteins were detected by Western blotting. The correlation between BRF1 and ferroptosis was analyzed using TCGA data. Results BRF1 was significantly upregulated in over 20 types of cancer, and its high expression was associated with poor prognosis in patients with adrenocortical carcinoma and prostate adenocarcinoma. BRF1 was found to positively regulate the T-cell-mediated cell death pathway in esophageal adenocarcinoma and was associated with the circadian rhythm regulation pathway in pancreatic adenocarcinoma. The correlation of BRF1 with immune checkpoints, cytokine networks, and immune cell infiltration was found to be cancer type-specific. In vitro experiments demonstrated that knocking down BRF1 significantly inhibited the proliferation of ESCC cells, accompanied by the downregulation of the proliferation marker PCNA. Cell migration was also significantly impaired, with decreased expression of Vimentin and MMPs and increased expression of E-cadherin. Furthermore, the expression of BRF1 was positively correlated with that of ferroptosis-antagonizing genes, such as GPX4, HSPA5, and SLC7A11. Conclusion BRF1 plays complex roles in pan-cancer, participating in the regulation of tumorigenesis, progression, and immune infiltration. BRF1 promotes the proliferation and migration of ESCC cells, a mechanism potentially associated with the regulation of ferroptosis resistance. These findings suggest that BRF1 could be a potential therapeutic target for ESCC.

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.

Driven by the widespread adoption of minimally invasive thoracic surgical techniques and the deepening integration of enhanced recovery after surgery (ERAS) protocols, evidence-based perioperative airway management has emerged as a pivotal determinant of surgical safety, pulmonary functional recovery, and long-term prognosis. Currently, substantial regional disparities in thoracic surgical expertise persist across China, manifesting as heterogeneity in preoperative assessment standardization, inconsistent airway preparation strategies, divergent approaches to complication prevention, and limited penetration of advanced anesthetic technologies. To promote the standardization of clinical practices in this domain among hospitals at all levels, a multidisciplinary expert consortium has initiated the development of the Chinese Guidelines for Perioperative Airway Management in Thoracic Surgery (2026 edition). This protocol systematically delineates the methodological framework and structural blueprint underpinning the guideline’s formulation. Compared to the 2020 edition, the updated guideline places specific emphasis on refining stratified criteria for preoperative cardiopulmonary reserve assessment, establishing standardized management pathways for non-intubated spontaneous-ventilation techniques, formulating individualized multimodal perioperative pulmonary rehabilitation regimens, and defining precise intervention workflows for persistent postoperative cough. Collectively, these measures aim to further reduce the baseline incidence of perioperative pulmonary complications and to provide robust support for the clinical translation of enhanced recovery principles within the field of thoracic surgery.

Although video-assisted thoracoscopic surgery (VATS) offers clear advantages, including minimal trauma and rapid recovery, stable intraoperative visualization still depends heavily on the camera assistant. Traditional manual endoscope holding is therefore susceptible to several limitations, including image instability, assistant fatigue, and poor coordination with the primary surgeon. Operating in complex and confined anatomical spaces with a rigid thoracoscope further increases the demands on hand-eye coordination. Thoracoscopic camera-holding technology has progressively evolved from passive support systems to active camera holders and robot-assisted thoracic surgery (RATS) platforms, and is now advancing toward artificial intelligence-driven tracking and intelligent control. Based on this, a series of devices, including multifunctional intelligent assistant arms and active camera-holding arms for thoracic surgery, have been developed and have attracted increasing attention. This article reviews the development of intelligent camera-holding assistant arms and discusses related advances and future directions in the field.

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.

ObjectiveTo investigate the current awareness and optimization recommendations on pneumonia with unknown etiology (PUE) surveillance among Shanghai healthcare professionals, and to provide evidence for the improvement of the surveillance system. MethodsIn December 2024, healthcare professional participants from diverse medical and health institutions were randomly selected within each stratum using a stratified cluster sampling method in all 16 districts of Shanghai for questionnaire surveys on their knowledge and suggestions for optimization of the PUE surveillance system. Descriptive statistics analyses, multivariate logistic regressions, and content analyses were used for data analyses. ResultsA total of 385 survey subjects were involved in the study, including 119 professional staffs from disease prevention and control centers and 266 from medical institutions. Those who understood the case definition for PUE correctly accounted for 67.01% (258/385). Multivariate regression model analyses showed that female gender (aOR=2.22, 95%CI: 1.36‒3.66), professionals from disease control institutions (aOR=2.02, 95%CI: 1.12‒3.72), and those who self-assessed as being familiar with the monitoring system (aOR=2.18, 95%CI:1.23‒3.89) had better understanding of the definition. Overall, 89.61% (345/385) of the survey subjects acknowledged the necessity of the surveillance system. Small number of cases meeting the case definition (54.46%, 214/379), insufficient diagnostic awareness (39.84%, 151/379), and lack of incentives for case reporting (29.29%, 111/379) were major problems for the current surveillance system, with variations observed across different types of institutions. Suggested segments for optimizing the surveillance system included monitoring purpose, case definition, form of monitoring and reporting procedure, organization mechanism, and epidemiological investigation and response. ConclusionThe necessity of PUE surveillance is commonly recognized among healthcare workers. However, the existing system falls short in terms of mastery of case definition and overall surveillance adaptability. There is an urgent need for optimization and improvement to enhance the capacity for preventing and controlling emerging and sudden respiratory infectious diseases.

Patients with lung cancer experience a heavy symptom burden during the perioperative period, which seriously affects their recovery and quality of life. Traditional symptom management models rely mainly on scheduled ward rounds during hospitalization and outpatient follow-up after discharge. However, they have limitations such as delayed symptom recognition, lack of post-discharge monitoring, and non-quantitative symptom assessment, which often lead to delayed interventions and low patient satisfaction. In recent years, the symptom management model based on electronic patient-reported outcomes (ePRO) has been increasingly valued in clinical practice. Existing high-level evidence from both domestic and international studies indicates that, through proactive monitoring, real-time alerts, and remote interventions, this model enables dynamic and continuous symptom management and helps improve patient recovery and healthcare experience. As a supplement to routine medical care, the ePRO-based symptom management model aims to enhance the quality of care rather than replace existing medical processes. To promote the standardized application of this model in perioperative lung cancer care, this consensus integrates domestic and international evidence. After multiple rounds of voting by more than 50 experts, it formulates 12 consensus statements covering the three core components, symptom monitoring, alerting, and intervention, to provide scientific and practical recommendations for clinical practice.

The co-occurrence of diabetes mellitus complicated with depression （DD） is a clinically prevalent comorbidity characterized by complex pathogenesis and challenging therapeutic management， severely impairing patients' quality of life. Recent research indicates that dysregulation in microglia-neuron interaction plays a pivotal role in the pathological progression of DD. Sustained hyperglycemia can activate central microglia， promoting the activation of NOD-like receptor protein 3（NLRP3） inflammasome and the release of abundant pro-inflammatory cytokines， such as interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， which trigger neuroinflammation. This cascade leads to hippocampal neuronal damage， impaired synaptic plasticity， and neurotransmitter imbalances， ultimately exacerbating depressive-like behaviors. Traditional Chinese medicine （TCM） offers advantages in preventing and treating DD through its multi-component， multi-target， and holistic regulatory approach. TCM can intervene in excessive microglial activation and their aberrant interactions with neurons by modulating neurotrophic signaling pathways such as brain-derived neurotrophic factor （BDNF）/tyrosine receptor kinase B （TrkB）， suppressing neuroinflammation， preserving neuronal structure and function， regulating synaptic plasticity， and improving glucose and lipid metabolism， thereby exerting synergistic therapeutic effects. Focusing on the role of microglia-neuron interaction in the pathological progression of DD， this review systematically elaborates on the research progress of TCM in regulating this interactive process， providing new perspectives and potential targets for the clinical prevention and treatment of DD.

The co-occurrence of diabetes mellitus complicated with depression （DD） is a clinically prevalent comorbidity characterized by complex pathogenesis and challenging therapeutic management， severely impairing patients' quality of life. Recent research indicates that dysregulation in microglia-neuron interaction plays a pivotal role in the pathological progression of DD. Sustained hyperglycemia can activate central microglia， promoting the activation of NOD-like receptor protein 3（NLRP3） inflammasome and the release of abundant pro-inflammatory cytokines， such as interleukin-1β （IL-1β） and tumor necrosis factor-α （TNF-α）， which trigger neuroinflammation. This cascade leads to hippocampal neuronal damage， impaired synaptic plasticity， and neurotransmitter imbalances， ultimately exacerbating depressive-like behaviors. Traditional Chinese medicine （TCM） offers advantages in preventing and treating DD through its multi-component， multi-target， and holistic regulatory approach. TCM can intervene in excessive microglial activation and their aberrant interactions with neurons by modulating neurotrophic signaling pathways such as brain-derived neurotrophic factor （BDNF）/tyrosine receptor kinase B （TrkB）， suppressing neuroinflammation， preserving neuronal structure and function， regulating synaptic plasticity， and improving glucose and lipid metabolism， thereby exerting synergistic therapeutic effects. Focusing on the role of microglia-neuron interaction in the pathological progression of DD， this review systematically elaborates on the research progress of TCM in regulating this interactive process， providing new perspectives and potential targets for the clinical prevention and treatment of DD.