Colorectal cancer （CRC） is one of the most common cancers， with its incidence ranking high among cancers. It stands as the second leading cause of cancer-related death worldwide. In the early stages， CRC lacks specific symptoms， and most patients are diagnosed at advanced stages， making it a major research focus in the field of gastrointestinal tumors. Currently， clinical CRC treatments face several common challenges， including high surgical risks， frequent metastasis and recurrence， drug resistance， and significant side effects from chemotherapy and radiation therapy. With the development and application of traditional Chinese medicine （TCM）， it has been found that TCM and its active ingredients can effectively inhibit CRC cell proliferation， invasion， migration， and angiogenesis， and promote apoptosis and autophagy， thereby slowing the progression of CRC. This has become a key focus of CRC treatment research. Salvia Miltiorrhiza has multiple pharmacological effects， including activating blood circulation to dispel blood stasis， unlocking meridians to relieve pain， clearing heat to calm irritability， and cooling blood to reduce abscesses. It contains a variety of chemical components， including diterpenoids， phenolic acids， flavonoids， polysaccharides， nitrogen-containing compounds， steroids， and lactone compounds. This review summarized the molecular mechanisms of Salvia miltiorrhiza and its active ingredients in the treatment of CRC. It is found that these ingredients exert anti-CRC effects through various molecular mechanisms， including cell cycle arrest， promotion of apoptosis， inhibition of cell invasion and migration， induction of autophagy， suppression of tumor angiogenesis， and remodeling of the tumor microenvironment. The review aims to provide new insights for the drug development and clinical application of Salvia miltiorrhiza in CRC treatment.

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.

ObjectiveTo identify potential influencing factors of urate crystal deposition at ankle/foot in patients with hyperuricemia （HUA）， and to analyze the predictive value of inflammatory indicators for urate crystal deposition in patients with different traditional Chinese medicine （TCM） syndromes， so as to provide potential reference for clinical risk assessment and individualized TCM intervention. MethodsA retrospective study was carried out with the enrollment of 231 HUA patients from The Third Affiliated People's Hospital of Fujian University of Traditional Chinese Medicine between January 2021 and December 2024. The enrolled patients were further divided into a crystal deposition-positive group （143 cases） and a crystal deposition-negative group （88 cases） according to the results of dual-energy computed tomography （CT）. Sociodemographic data， living habits， serum uric acid levels， and inflammatory indicators of the enrolled patients were collcted， and TCM syndrome differentiation was performed. Furthermore， univariate analysis was used to compare inter-group differences in clinical characteristics. MMultivariate Logistic regression was applied to identify the influencing factors of urate crystal deposition. In addition， the receiver operating characteristic （ROC） curves were plotted to evaluate the predictive efficacy of inflammatory indicators for crystal deposition across different TCM syndromes. ResultsThere were statistically significant inter-group differences in the proportion of males， age， body mass index， proportion of mental labor， rate of low water intake， and rate of high-sugar beverage consumption （P<0.05），whereas no significant difference in low exercise intensity was found between the two groups. Furthermore， compared with the negative group， the positive group had higher serum uric acid level， neutrophil-to-lymphocyte ratio （NLR）， and platelet-to-lymphocyte ratio （PLR）， but lower systemic immune-inflammation index （SIRI） （P<0.05）. Regarding the distribution of TCM syndromes， the positive group was dominated by the dampness-heat accumulation syndrome （55/143，38.46%）， while the negative group was mainly characterized by the phlegm-turbidity obstruction syndrome （44/88，50.00%）. Multivariate Logistic regression analysis revealed that high-sugar beverage consumption， elevated NLR， and elevated PLR were risk factors for urate crystal deposition ［odd ratio （OR） = 8.002， 5.377， 1.034， respectively； 95% CI 1.572-40.732， 2.179-13.270， 1.013-1.054，all P<0.05］， while SIRI was a protective factor （OR = 0.869， 95% CI 0.778-0.971， P<0.05）. In the positive group， patients with the dampness-heat accumulation syndrome exhibited the highest NLR， while the lowest PLR and SIRI， showing statistically significant differences with those of other syndromes （all P<0.05）. In addition， ROC curve analysis indicated that for the dampness-heat accumulation syndrome， the combined "NLR + PLR" model had an area under the curve （AUC） of 0.901 （95% CI 0.850-0.951， P<0.01）， with a sensitivity of 89.1% and a specificity of 79.5%； for the blood stasis-heat obstruction syndrome， the combined "NLR + PLR" model had an AUC of 0.880 （95% CI 0.825-0.934， P<0.01）， with a sensitivity of 100.0% and a specificity of 67.3%； for the liver-kidney Yin-deficiency syndrome， the single PLR model had an AUC of 0.842 （95% CI 0.731-0.952， P<0.01）， with a sensitivity of 83.3% and a specificity of 84.0%. ConclusionUrate crystal deposition in HUA patients exhibits intimate associations with high-sugar beverage consumption as well as elevated NLR and PLR levels. Meanwhile， TCM syndrome differentiation has potential correlation with inflammatory characteristics. The inflammatory indicator-based prediction model constructed based on TCM syndromes exhibits good predictive value.

Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.

GPR126, also known as ADGRG6, is one of the most deeply studied aGPCRs. Initially, GPR126 was thought to be a receptor associated with muscle development and was primarily expressed in the muscular and skeletal systems. With the deepening of research, it was found that GPR126 is expressed in multiple mammalian tissues and organs, and is involved in many biological processes such as embryonic development, nervous system development, and extracellular matrix interactions. Compared with other aGPCRs proteins, GPR126 has a longer N-terminal domain, which can bind to ligands one-to-one and one-to-many. Its N-terminus contains five domains, a CUB (complement C1r/C1s, Uegf, Bmp1) domain, a PTX (Pentraxin) domain, a SEA (Sperm protein, Enterokinase, and Agrin) domain, a hormone binding (HormR) domain, and a conserved GAIN domain. The GAIN domain has a self-shearing function, which is essential for the maturation, stability, transport and function of aGPCRs. Different SEA domains constitute different GPR126 isomers, which can regulate the activation and closure of downstream signaling pathways through conformational changes. GPR126 has a typical aGPCRs seven-transmembrane helical structure, which can be coupled to Gs and Gi, causing cAMP to up- or down-regulation, mediating transmembrane signaling and participating in the regulation of cell proliferation, differentiation and migration. GPR126 is activated in a tethered-stalk peptide agonism or orthosteric agonism, which is mainly manifested by self-proteolysis or conformational changes in the GAIN domain, which mediates the rapid activation or closure of downstream pathways by tethered agonists. In addition to the tethered short stem peptide activation mode, GPR126 also has another allosteric agonism or tunable agonism mode, which is specifically expressed as the GAIN domain does not have self-shearing function in the physiological state, NTF and CTF always maintain the binding state, and the NTF binds to the ligand to cause conformational changes of the receptor, which somehow transmits signals to the GAIN domain in a spatial structure. The GAIN domain can cause the 7TM domain to produce an activated or inhibited signal for signal transduction, For example, type IV collagen interacts with the CUB and PTX domains of GPR126 to activate GPR126 downstream signal transduction. GPR126 has homology of 51.6%-86.9% among different species, with 10 conserved regions between different species, which can be traced back to the oldest metazoans as well as unicellular animals.In terms of diseases, GPR126 dysfunction involves the pathological process of bone, myelin, embryo and other related diseases, and is also closely related to the occurrence and development of malignant tumors such as breast cancer and colon cancer. However, the biological function of GPR126 in various diseases and its potential as a therapeutic target still needs further research. This paper focuses on the structure, interspecies differences and conservatism, signal transduction and biological functions of GPR126, which provides ideas and references for future research on GPR126.

Objective:To evaluate the clinical outcomes of combined complete preservation of chordal structure mitral valve replacement (C-MVR) with total anatomical arterial myocardial revascularization (TACR) in coronary patients with moderate-to-severe or severe ischemic mitral regurgitation (IMR).Methods:This is a retrospective multi-center case series study. Data were retrospectively collected from 127 patients with coronary artery disease with moderate to severe or severe IMR who received TACR with C-MVR from July 2015 to April 2024 in 13 hospitals in China. There were 90 males and 37 females, aged (56.5±10.7) years (range: 33 to 74 years). Perioperative data and follow-up data including left ventricular ejection fraction, left ventricular end-diastolic diameter, and patency rate of arterial grafts of patients were collected. Comparisons were made using paired sample t-test or χ2 test. Results:In this cohort of 127 patients, 67 underwent concurrent tricuspid valve repair. During surgery, 113 grafts of the left internal mammary artery (LIMA), 127 grafts of the left radial artery, 80 grafts of the right radial artery, and 110 grafts of the right internal mammary artery (RIMA) were harvested. The number of the distal anastomosis was 4.2±0.4 (range: 3 to 5). The aortic cross-clamp time and cardiopulmonary bypass time were (97.5±23.4) minutes (range: 90 to 161 minutes) and (145.4±19.2) minutes (range: 101 to 210 minutes), respectively. There was one operative death. Intraoperative placement of an intra-aortic balloon pump was performed in 21 patients to improve the left ventricular ejection. No sternal ischemic occurred. All patients completed follow-up, with a mean follow-up period of (64.3±7.5) months (range: 4 to 110 months). No major cerebrovascular events occurred during the follow-up period, and all patients survived. Left ventricular ejection fraction improved postoperatively (55.0%±5.3% vs. 41.0%±15.3%, t=17.23, P<0.01). The proportion of patients with New York Heart Association functional class ≤2 increased postoperatively (23.6% (30/127) vs. 87.3% (110/126), χ2=103.77, P<0.01). The proportion of patients with Canadian Cardiovascular Society Angina Classification ≤3 decreased postoperatively (4.8% (6/126) vs. 78.7% (100/127), χ2=142.19, P<0.01). The left ventricular end-diastolic diameter decreased postoperatively ((5.70±4.50) cm vs. (6.10±0.23) cm, t=12.15, P<0.01). Coronary multi-detector computed tomography angiography (MDCTA) follow-up was conducted for (60.5±11.7) months (range: 6 to 109 months) postoperatively. MDCTA confirmed the patency rates of the grafts: 96.4% (108/112) for the LIMA grafts, 88.9% (112/126) for the left radial artery grafts, 93.7% (74/79) for the right radial artery grafts, and 90.9% (100/110) for the free RIMA grafts. No significant differences in graft patency rates were observed between the arterial grafts ( χ2=5.24, P=0.155). Conclusion:The results of this multi-centre study demonstrate satisfactory mid-term results of C-MVR with TACR for the treatment of coronary artery disease with moderate to severe or severe IMR.

Objective To propose a lung artery segmentation method that integrates shape and position prior knowledge, aiming to solve the issues of inaccurate segmentation caused by the high similarity and small size differences between the lung arteries and surrounding tissues in CT images. Methods Based on the three-dimensional U-Net network architecture and relying on the PARSE 2022 database image data, shape and position prior knowledge was introduced to design feature extraction and fusion strategies to enhance the ability of lung artery segmentation. The data of the patients were divided into three groups: a training set, a validation set, and a test set. The performance metrics for evaluating the model included Dice Similarity Coefficient (DSC), sensitivity, accuracy, and Hausdorff distance (HD95). Results The study included lung artery imaging data from 203 patients, including 100 patients in the training set, 30 patients in the validation set, and 73 patients in the test set. Through the backbone network, a rough segmentation of the lung arteries was performed to obtain a complete vascular structure; the branch network integrating shape and position information was used to extract features of small pulmonary arteries, reducing interference from the pulmonary artery trunk and left and right pulmonary arteries. Experimental results showed that the segmentation model based on shape and position prior knowledge had a higher DSC (82.81%±3.20% vs. 80.47%±3.17% vs. 80.36%±3.43%), sensitivity (85.30%±8.04% vs. 80.95%±6.89% vs. 82.82%±7.29%), and accuracy (81.63%±7.53% vs. 81.19%±8.35% vs. 79.36%±8.98%) compared to traditional three-dimensional U-Net and V-Net methods. HD95 could reach (9.52±4.29) mm, which was 6.05 mm shorter than traditional methods, showing excellent performance in segmentation boundaries. Conclusion The lung artery segmentation method based on shape and position prior knowledge can achieve precise segmentation of lung artery vessels and has potential application value in tasks such as bronchoscopy or percutaneous puncture surgery navigation.

Objective:To evaluate the clinical outcomes of combined complete preservation of chordal structure mitral valve replacement (C-MVR) with total anatomical arterial myocardial revascularization (TACR) in coronary patients with moderate-to-severe or severe ischemic mitral regurgitation (IMR).Methods:This is a retrospective multi-center case series study. Data were retrospectively collected from 127 patients with coronary artery disease with moderate to severe or severe IMR who received TACR with C-MVR from July 2015 to April 2024 in 13 hospitals in China. There were 90 males and 37 females, aged (56.5±10.7) years (range: 33 to 74 years). Perioperative data and follow-up data including left ventricular ejection fraction, left ventricular end-diastolic diameter, and patency rate of arterial grafts of patients were collected. Comparisons were made using paired sample t-test or χ2 test. Results:In this cohort of 127 patients, 67 underwent concurrent tricuspid valve repair. During surgery, 113 grafts of the left internal mammary artery (LIMA), 127 grafts of the left radial artery, 80 grafts of the right radial artery, and 110 grafts of the right internal mammary artery (RIMA) were harvested. The number of the distal anastomosis was 4.2±0.4 (range: 3 to 5). The aortic cross-clamp time and cardiopulmonary bypass time were (97.5±23.4) minutes (range: 90 to 161 minutes) and (145.4±19.2) minutes (range: 101 to 210 minutes), respectively. There was one operative death. Intraoperative placement of an intra-aortic balloon pump was performed in 21 patients to improve the left ventricular ejection. No sternal ischemic occurred. All patients completed follow-up, with a mean follow-up period of (64.3±7.5) months (range: 4 to 110 months). No major cerebrovascular events occurred during the follow-up period, and all patients survived. Left ventricular ejection fraction improved postoperatively (55.0%±5.3% vs. 41.0%±15.3%, t=17.23, P<0.01). The proportion of patients with New York Heart Association functional class ≤2 increased postoperatively (23.6% (30/127) vs. 87.3% (110/126), χ2=103.77, P<0.01). The proportion of patients with Canadian Cardiovascular Society Angina Classification ≤3 decreased postoperatively (4.8% (6/126) vs. 78.7% (100/127), χ2=142.19, P<0.01). The left ventricular end-diastolic diameter decreased postoperatively ((5.70±4.50) cm vs. (6.10±0.23) cm, t=12.15, P<0.01). Coronary multi-detector computed tomography angiography (MDCTA) follow-up was conducted for (60.5±11.7) months (range: 6 to 109 months) postoperatively. MDCTA confirmed the patency rates of the grafts: 96.4% (108/112) for the LIMA grafts, 88.9% (112/126) for the left radial artery grafts, 93.7% (74/79) for the right radial artery grafts, and 90.9% (100/110) for the free RIMA grafts. No significant differences in graft patency rates were observed between the arterial grafts ( χ2=5.24, P=0.155). Conclusion:The results of this multi-centre study demonstrate satisfactory mid-term results of C-MVR with TACR for the treatment of coronary artery disease with moderate to severe or severe IMR.

BACKGROUND:Rheumatoid arthritis is a chronic autoimmune disease.Early diagnosis is crucial for preventing disease progression and for effective treatment.Therefore,it is of significance to investigate the diagnostic characteristics and immune cell infiltration of rheumatoid arthritis. OBJECTIVE:Based on the Gene Expression Omnibus(GEO)database,to screen potential diagnostic markers of rheumatoid arthritis using machine learning algorithms and to investigate the relationship between the diagnostic characteristics of rheumatoid arthritis and immune cell infiltration in this pathology. METHODS:The gene expression datasets of synovial tissues related to rheumatoid arthritis were obtained from the GEO database.The data sets were merged using a batch effect removal method.Differential expression analysis and functional correlation analysis of genes were performed using R software.Bioinformatics analysis and three machine learning algorithms were used for the extraction of disease signature genes,and key genes related to rheumatoid arthritis were screened.Furthermore,we analyzed immune cell infiltration on all differentially expressed genes to examine the inflammatory state of rheumatoid arthritis and investigate the correlation between their diagnostic characteristics and infiltrating immune cells. RESULTS AND CONCLUSION:In both rheumatoid arthritis and normal synovial tissues,we identified 179 differentially expressed genes,with 124 genes up-regulated and 55 genes down-regulated.Enrichment analysis revealed a significant correlation between rheumatoid arthritis and immune response.Three machine learning algorithms identified LRRC15 and MICB as potential biomarkers of rheumatoid arthritis.LRRC15(area under the curve=0.964,95%confidence interval:0.924-0.992)and MICB(area under the curve=0.961,95%confidence interval:0.923-0.990)demonstrated strong diagnostic performance on the validation dataset.The infiltration of 13 types of immune cells was altered,with macrophages being the most affected.In rheumatoid arthritis,the majority of proinflammatory pathways in immune cell function were activated.Immunocorrelation analysis revealed that LRRC15 and MICB had the strongest correlation with M1 macrophages.To conclude,this study identified LRRC15 and MICB as potential diagnostic markers for rheumatoid arthritis,with strong diagnostic performance and significant correlation with immune cell infiltration.Machine learning and bioinformatics analysis deepened the understanding of immune infiltration in rheumatoid arthritis and provided new ideas for the diagnosis and treatment of rheumatoid arthritis.

Membrane proteins are integral components of cellular membranes, accounting for approximately 30% of the mammalian proteome and serving as targets for 60% of FDA-approved drugs. They are critical to both physiological functions and disease mechanisms. Their functional protein-protein interactions form the basis for many physiological processes, such as signal transduction, material transport, and cell communication. Membrane protein interactions are characterized by membrane environment dependence, spatial asymmetry, weak interaction strength, high dynamics, and a variety of interaction sites. Therefore, in situ analysis is essential for revealing the structural basis and kinetics of these proteins. This paper introduces currently available in situ analytical techniques for studying membrane protein interactions and evaluates the characteristics of each. These techniques are divided into two categories: label-based techniques (e.g., co-immunoprecipitation, proximity ligation assay, bimolecular fluorescence complementation, resonance energy transfer, and proximity labeling) and label-free techniques (e.g., cryo-electron tomography, in situ cross-linking mass spectrometry, Raman spectroscopy, electron paramagnetic resonance, nuclear magnetic resonance, and structure prediction tools). Each technique is critically assessed in terms of its historical development, strengths, and limitations. Based on the authors’ relevant research, the paper further discusses the key issues and trends in the application of these techniques, providing valuable references for the field of membrane protein research. Label-based techniques rely on molecular tags or antibodies to detect proximity or interactions, offering high specificity and adaptability for dynamic studies. For instance, proximity ligation assay combines the specificity of antibodies with the sensitivity of PCR amplification, while proximity labeling enables spatial mapping of interactomes. Conversely, label-free techniques, such as cryo-electron tomography, provide near-native structural insights, and Raman spectroscopy directly probes molecular interactions without perturbing the membrane environment. Despite advancements, these methods face several universal challenges: (1) indirect detection, relying on proximity or tagged proxies rather than direct interaction measurement; (2) limited capacity for continuous dynamic monitoring in live cells; and (3) potential artificial influences introduced by labeling or sample preparation, which may alter native conformations. Emerging trends emphasize the multimodal integration of complementary techniques to overcome individual limitations. For example, combining in situ cross-linking mass spectrometry with proximity labeling enhances both spatial resolution and interaction coverage, enabling high-throughput subcellular interactome mapping. Similarly, coupling fluorescence resonance energy transfer with nuclear magnetic resonance and artificial intelligence (AI) simulations integrates dynamic structural data, atomic-level details, and predictive modeling for holistic insights. Advances in AI, exemplified by AlphaFold’s ability to predict interaction interfaces, further augment experimental data, accelerating structure-function analyses. Future developments in cryo-electron microscopy, super-resolution imaging, and machine learning are poised to refine spatiotemporal resolution and scalability. In conclusion, in situ analysis of membrane protein interactions remains indispensable for deciphering their roles in health and disease. While current technologies have significantly advanced our understanding, persistent gaps highlight the need for innovative, integrative approaches. By synergizing experimental and computational tools, researchers can achieve multiscale, real-time, and perturbation-free analyses, ultimately unraveling the dynamic complexity of membrane protein networks and driving therapeutic discovery.