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 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.

OBJECTIVES: To analyze the early clinical outcomes of the Xcor^TM transcatheter aortic valve replacement (TAVR) system in treating severe aortic stenosis. This study has been registered at Chinese Clinical Trial Registry (ChiCTR2200065593). METHODS: This single-arm, prospective clinical trial enrolled patients with severe aortic stenosis treated with the Xcor^TM TAVR system at the Section of Heart Valve & Coronary Artery Surgery, Guangdong Provincial People's Hospital. Perioperative and follow-up parameters were compared to evaluate differences in hemodynamic outcomes. All-cause mortality, aortic regurgitation, paravalvular leakage, cerebrovascular events, and reoperation were analyzed. RESULTS: Thirty-two patients with severe aortic stenosis were included (20 males, 12 females), with (70.9±4.3) years old and a Society of Thoracic Surgeons (STS) score of 6.45% (6.07%, 7.28%). Notably, 87.5% of patients had New York Heart Association (NYHA) class≥Ⅲ. All patients underwent successful Xcor^TM bioprosthesis implantation, achieving an immediate technical success rate of 100.0% and device success rate of 96.9%. Mean aortic valve gradient decreased from (55.21±23.17) mmHg (1 mmHg=0.133 kPa) to (8.45±5.30) mmHg, peak aortic jet velocity decreased from (4.66±0.85) m/s to (1.99±0.48) m/s, aortic valve area increased from (0.66±0.21) cm² to (2.09±0.67) cm² (all P<0.01). Intraoperative ventricular fibrillation occurred in one patient, while one case exhibited moderate prosthetic valve regurgitation and paravalvular leakage post-procedure. At 12-month follow-up, sustained improvements were observed in cardiac function, left ventricular ejection fraction, hemodynamic parameters, and SF-12 quality-of-life scores (all P<0.01). All-cause mortality was 12.5% (4/32), with 13.8% (4/29) developing moderate paravalvular leakage. CONCLUSIONS The Xcor^TM TAVR system demonstrated favorable early outcomes in severe aortic stenosis patients, significantly improving symptoms and hemodynamics while exhibiting excellent performance in preventing malignant arrhythmias and coronary obstruction.
Humans ; Male ; Female ; Aortic Valve Stenosis/surgery* ; Transcatheter Aortic Valve Replacement/methods* ; Aged ; Follow-Up Studies ; Prospective Studies ; Treatment Outcome ; Aged, 80 and over ; Heart Valve Prosthesis ; Middle Aged

Fractional flow reserve (FFR), an established modality for functionally assessing coronary artery disease, is increasingly applied to diagnose and manage lower extremity arterial disease. By incorporating functional parameters, FFR enhances revascularization precision by quantifying the hemodynamic impact of stenotic lesions, thereby overcoming limitations of conventional imaging. Key clinical applications in lower extremity disease include functional assessment in moderate intermittent claudication, post-vascular preparation strategy optimization, and predicting revascularization outcomes and complications. Advances in pressure wire and microcatheter systems, alongside non-invasive imaging-derived FFR techniques, are improving its feasibility and applicability. However, widespread adoption is challenged by the complex anatomy of the lower extremity arterial system, frequent severe calcification and diffuse disease, and a current lack of standardized FFR cutoff values. Promoting the standardized use of FFR is crucial for shifting the clinical management paradigm from anatomy-based repair toward functional reconstruction.
Humans ; Lower Extremity/blood supply* ; Peripheral Arterial Disease/diagnosis* ; Fractional Flow Reserve, Myocardial ; Endovascular Procedures/methods* ; Intermittent Claudication/physiopathology*

This consensus will introduce the characteristics of fillers used in the surgical cavities of domestic nasal surgery patients based on relevant literature and expert opinions. It will also provide recommendations for the selection of cavity fillers for different nasal diseases, with chronic sinusitis as a representative example.
Humans ; Nasal Cavity/surgery* ; Nasal Surgical Procedures ; China ; Consensus ; Sinusitis/surgery* ; Dermal Fillers

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

Transformer models have emerged as pivotal tools within the realm of drug discovery, distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes. Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data, these models showcase remarkable efficacy across various tasks, including new drug design and drug target identification. The adaptability of pre-trained transformer-based models renders them indispensable assets for driving data-centric advancements in drug discovery, chemistry, and biology, furnishing a robust framework that expedites innovation and discovery within these domains. Beyond their technical prowess, the success of transformer-based models in drug discovery, chemistry, and biology extends to their interdisciplinary potential, seamlessly combining biological, physical, chemical, and pharmacological insights to bridge gaps across diverse disciplines. This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields. In our review, we elucidate the myriad applications of transformers in drug discovery, as well as chemistry and biology, spanning from protein design and protein engineering, to molecular dynamics (MD), drug target identification, transformer-enabled drug virtual screening (VS), drug lead optimization, drug addiction, small data set challenges, chemical and biological image analysis, chemical language understanding, and single cell data. Finally, we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.

Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. To realize inductive learning, this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights, enabling inductive predictions for the unseen DGIs. Consequently, the final model was superior to existing models, with significant improvement in predicting both external unknown and unseen DGIs. The practical feasibility of our model was further confirmed through case study and molecular docking. In summary, this study establishes an efficient data-driven approach through the proposed modeling, suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Computational analysis can accurately detect drug-gene interactions(DGIs)cost-effectively.However,transductive learning models are the hotspot to reveal the promising performance for unknown DGIs(both drugs and genes are present in the training model),without special attention to the unseen DGIs(both drugs and genes are absent in the training model).In view of this,this study,for the first time,proposed an inductive learning-based model for the precise identification of unseen DGIs.In our study,by integrating disease nodes to avoid data sparsity,a multi-relational drug-disease-gene(DDG)graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions.Following the extraction of graph features by utilizing graph embedding algorithms,our next step was the retrieval of the attributes of individual gene and drug nodes.In this way,a hybrid feature charac-terization was represented by integrating graph features and node attributes.Machine learning(ML)models were built,enabling the fulfillment of transductive predictions of unknown DGIs.To realize inductive learning,this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights,enabling inductive predictions for the unseen DGIs.Consequently,the final model was superior to existing models,with significant improvement in predicting both external unknown and unseen DGIs.The practical feasibility of our model was further confirmed through case study and molecular docking.In summary,this study establishes an efficient data-driven approach through the proposed modeling,suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Transformer models have emerged as pivotal tools within the realm of drug discovery,distinguished by their unique architectural features and exceptional performance in managing intricate data landscapes.Leveraging the innate capabilities of transformer architectures to comprehend intricate hierarchical dependencies inherent in sequential data,these models showcase remarkable efficacy across various tasks,including new drug design and drug target identification.The adaptability of pre-trained trans-former-based models renders them indispensable assets for driving data-centric advancements in drug discovery,chemistry,and biology,furnishing a robust framework that expedites innovation and dis-covery within these domains.Beyond their technical prowess,the success of transformer-based models in drug discovery,chemistry,and biology extends to their interdisciplinary potential,seamlessly combining biological,physical,chemical,and pharmacological insights to bridge gaps across diverse disciplines.This integrative approach not only enhances the depth and breadth of research endeavors but also fosters synergistic collaborations and exchange of ideas among disparate fields.In our review,we elucidate the myriad applications of transformers in drug discovery,as well as chemistry and biology,spanning from protein design and protein engineering,to molecular dynamics(MD),drug target iden-tification,transformer-enabled drug virtual screening(VS),drug lead optimization,drug addiction,small data set challenges,chemical and biological image analysis,chemical language understanding,and single cell data.Finally,we conclude the survey by deliberating on promising trends in transformer models within the context of drug discovery and other sciences.