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 evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective To explore medical institution-led exercise training interventions,prelimina-ry preventive measures,and comprehensive health service strategies for elderly adults.Methods Sixty elderly adults from communities under the jurisdiction of primary hospitals who voluntarily participated in the training and met the inclusion criteria were divided into 75 to 79 years old group and 80 to 84 years old group.Elastic bands exercise were selected as the method for physical training for elderly community members.A total of three sets(9 items)of exercise,including upper and lower limb mus-cle strength exercises and balance as well as coordination training,were designed.The training period lasted for 12 weeks,with each session lasting 1 hour,three times a week.The medical team partici-pated in training supervision and follow-up evaluations throughout the process,dynamically adjusting the training intensity based on individual needs.Results Comprehensive safety and support meas-ures significantly improved training participation and completion rates.No sports injuries or adverse events occurred throughout the process,and all participants completed the training plan.After train-ing,statistically significant differences were observed in grip strength,five-times sit-to-stand test duration,Berg Balance Scale(BBS)scores,and Timed Up and Go(TUG)test results(P＜0.05),indicating effective physical fitness improvement.Conclusion Elastic band exercises are a suitable mode for physical improvement training among elderly adults,significantly enhancing muscle strength in the limbs and trunk and improving balance function.The positive communication and in-teraction in group activities significantly boost elderly adults'confidence in healthy living,leading to notable improvements in their mental state.

Objective To explore the application of plasma 7 microRNA (miR7) testing in the differential diagnosis of very early-stage hepatocellular carcinoma (HCC). Methods This study is a multicenter real-world study. Patients with single hepatic lesion (maximum diameter≤2 cm) who underwent plasma miR7 testing at Zhongshan Hospital, Fudan University, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Anhui Provincial Hospital, and Peking University People’s Hospital between January 2019 and December 2024 were retrospectively enrolled. Patients were divided into very early-stage HCC group and non-HCC group, and the clinical pathological characteristics of the two groups were compared. The value of plasma miR7 levels, alpha-fetoprotein (AFP), and des-gamma-carboxy prothrombin (DCP) in the differential diagnosis of very early-stage HCC was evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC). In patients with both negative AFP and DCP (AFP＜20 ng/mL, DCP＜40 mAU/mL), the diagnostic value of plasma miR7 for very early-stage HCC was analyzed. Results A total of 64 528 patients from 4 hospitals underwent miR7 testing, and 1 682 were finally included, of which 1 073 were diagnosed with very early-stage HCC and 609 were diagnosed with non-HCC. The positive rate of miR7 in HCC patients was significantly higher than that in non-HCC patients (67.9% vs 24.3%, P＜0.001). ROC curves showed that the AUCs for miR7, AFP, and DCP in distinguishing HCC patients from the non-HCC individuals were 0.718, 0.682, and 0.642, respectively. The sensitivities were 67.85%, 43.71%, and 44.45%, and the specificities were 75.70%, 92.78%, and 83.91%, respectively. The pairwise comparison of AUCs showed that the diagnostic efficacy of plasma miR7 detection was significantly better than that of AFP or DCP (P＜0.05). Although its specificity was slightly lower than AFP and DCP, the sensitivity was significantly higher. Among patients negative for both AFP and DCP, miR7 maintained an AUC of 0.728 for diagnosing very early-stage HCC, with 67.82% sensitivity and 77.73% specificity. Conclusions Plasma miR7 testing is a potential molecular marker with high sensitivity and specificity for the differential diagnosis of small hepatic nodules. In patients with very early-stage HCC lacking effective molecular markers (negative for both AFP and DCP), miR7 can serve as a novel and effective molecular marker to assist diagnosis.

In recent years, the application of artificial intelligence (AI) in the field of biology has witnessed remarkable advancements. Among these, the most notable achievements have emerged in the domain of protein structure prediction and design, with AlphaFold and related innovations earning the 2024 Nobel Prize in Chemistry. These breakthroughs have transformed our ability to understand protein folding and molecular interactions, marking a pivotal milestone in computational biology. Looking ahead, it is foreseeable that the accurate prediction of various physicochemical properties of proteins—beyond static structure—will become the next critical frontier in this rapidly evolving field. One of the most important protein properties is thermodynamic stability, which refers to a protein’s ability to maintain its native conformation under physiological or stress conditions. Accurate prediction of protein stability, especially upon single-point mutations, plays a vital role in numerous scientific and industrial domains. These include understanding the molecular basis of disease, rational drug design, development of therapeutic proteins, design of more robust industrial enzymes, and engineering of biosensors. Consequently, the ability to reliably forecast the stability changes caused by mutations has broad and transformative implications across biomedical and biotechnological applications. Historically, protein stability was assessed via experimental methods such as differential scanning calorimetry (DSC) and circular dichroism (CD), which, while precise, are time-consuming and resource-intensive. This prompted the development of computational approaches, including empirical energy functions and physics-based simulations. However, these traditional models often fall short in capturing the complex, high-dimensional nature of protein conformational landscapes and mutational effects. Recent advances in machine learning (ML) have significantly improved predictive performance in this area. Early ML models used handcrafted features derived from sequence and structure, whereas modern deep learning models leverage massive datasets and learn representations directly from data. Deep neural networks (DNNs), graph neural networks (GNNs), and attention-based architectures such as transformers have shown particular promise. GNNs, in particular, excel at modeling spatial and topological relationships in molecular structures, making them well-suited for protein modeling tasks. Furthermore, attention mechanisms enable models to dynamically weigh the contribution of specific residues or regions, capturing long-range interactions and allosteric effects. Nevertheless, several key challenges remain. These include the imbalance and scarcity of high-quality experimental datasets, particularly for rare or functionally significant mutations, which can lead to biased or overfitted models. Additionally, the inherently dynamic nature of proteins—their conformational flexibility and context-dependent behavior—is difficult to encode in static structural representations. Current models often rely on a single structure or average conformation, which may overlook important aspects of stability modulation. Efforts are ongoing to incorporate multi-conformational ensembles, molecular dynamics simulations, and physics-informed learning frameworks into predictive models. This paper presents a comprehensive review of the evolution of protein thermodynamic stability prediction techniques, with emphasis on the recent progress enabled by machine learning. It highlights representative datasets, modeling strategies, evaluation benchmarks, and the integration of structural and biochemical features. The aim is to provide researchers with a structured and up-to-date reference, guiding the development of more robust, generalizable, and interpretable models for predicting protein stability changes upon mutation. As the field moves forward, the synergy between data-driven AI methods and domain-specific biological knowledge will be key to unlocking deeper understanding and broader applications of protein engineering.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE: To reveal the effects and potential mechanisms by which synaptic vesicle glycoprotein 2A (SV2A) influences the distribution of amyloid precursor protein (APP) in the trans-Golgi network (TGN), endolysosomal system, and cell membranes and to reveal the effects of SV2A on APP amyloid degradation. METHODS: Colocalization analysis of APP with specific tagged proteins in the TGN, ensolysosomal system, and cell membrane was performed to explore the effects of SV2A on the intracellular transport of APP. APP, β-site amyloid precursor protein cleaving enzyme 1 (BACE1) expressions, and APP cleavage products levels were investigated to observe the effects of SV2A on APP amyloidogenic processing. RESULTS: APP localization was reduced in the TGN, early endosomes, late endosomes, and lysosomes, whereas it was increased in the recycling endosomes and cell membrane of SV2A-overexpressed neurons. Moreover, Arl5b (ADP-ribosylation factor 5b), a protein responsible for transporting APP from the TGN to early endosomes, was upregulated by SV2A. SV2A overexpression also decreased APP transport from the cell membrane to early endosomes by downregulating APP endocytosis. In addition, products of APP amyloid degradation, including sAPPβ, Aβ _1-42, and Aβ _1-40, were decreased in SV2A-overexpressed cells. CONCLUSION These results demonstrated that SV2A promotes APP transport from the TGN to early endosomes by upregulating Arl5b and promoting APP transport from early endosomes to recycling endosomes-cell membrane pathway, which slows APP amyloid degradation.
Amyloid beta-Protein Precursor/genetics* ; Membrane Glycoproteins/genetics* ; Animals ; Protein Transport ; Nerve Tissue Proteins/genetics* ; Humans ; Mice ; Endosomes/metabolism* ; trans-Golgi Network/metabolism*

AIM To study the chemical constituents from the stems and barks of Maytenus variabilis(Hemsl.)C.Y.Cheng.METHODS The 95％ethanol extract from the stems and barks of M.variabilis was isolated and purified by silica gel,Sephadex LH-20 and semi preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.RESULTS Twenty-three compounds were isolated and identified as β-amyrin(1),3β-acetoxyolean-12-en-11-one(2),ursa-12-ene-11-one-3-ol octocosate(3),friedelin(4),canophyllol(5),pinoresinol(6),medioresinol(7),isolariciresinol(8),dihydrodehydrodiconiferyl alcohol(9),vanillic acid(10),7R,8S-5-methoxydihydrodehydroconiferyl alcohol(11),β-hydroxypropiovanillone(12),triptregeline B(13),triptregeline E(14),(+)-evofolin B(15),2,5-dimethoxybenzoquinone(16),olean-12-ene-3,11-dione(17),β-sitosterol(18),(-)-(7R,7'R,7"S,8S,8'S,8"S)-4',4"-dihydroxy-3,3',3",5-tetramethoxy-7,9',7',9-diepoxy-4,8"-oxy-8,8'-sesquineolignan-7",9"-diol(19),phyllostadimer B(20),rayalinol(21),lyoniresinol(22),dihydrobuddlenol B(23).CONCLUSION Compounds 3,9-11,13-14,16,19-21,23 are isolated from genus Maytenus for the first time,and compounds 2,4-5,7-8,12,15,17,22 are first found from this plant.

AIM To study the chemical constituents of Anaphalis margaritacea(L.)Benth.& Hook.f.and their antioxidant activities.METHODS Separation and purification were performed using silica gel,Sephadex LH-20 and semi-preparative HPLC,then the structures of obtained compounds were identified by physicochemical properties and spectral data.The antioxidant activity was determined by DPPH method and ABTS method.RESULTS Twenty-three compounds were isolated and identified as trans-tilidroside(1),4'-hydroxydehydrokawain(2),apigenin(3),3-O-kaempferol-3-O-acetyl-6-O-(p-coumamoyl)-α-D-glucopyranoside(4),kaempferol(5),quercetin-3-O-β-D-(6-O-Z-p-coumamoyl)-glucopyranoside(6),tiliroside(7),kaempferol-3-O-β-D-glucoside(8),3,5-dihydroxy-7,8-dimethoxyflavone(9),bis(2-ethylhexyl)adipate(10),3,5-dihydroxy-6,7,8-trimethoxyflavone(11),stigmasterol(12),myriophylloside B(13),1-hexadecanol(14),chlorogenic acid(15),4-hydroxy-N-{ 4-[3-(4-hydroxyphenyl)-E-acryloylamino]-butyl}-benzamide(16),3,6-dimethylpiperazine-2,5-dione(17),β-adenosine(18),5,6-dehydrokawain(19),kaempferol-3-O-(2",6"-di-O-E-p-coumaroyl)-β-D-glucopyranoside(20),kaempferol-3-O-(3"-O-E-p-coumaroyl)-(6"-O-E-feruloyl)-β-D-glucopyranoside(21),4,5-di-caffeoylquinic acid butyl ester(22),3,4-di-caffeoylquinic acid butyl ester(23).The IC50 values of compounds 1,7,22-23 against DPPH free radicals were(24.67±1.63)-(53.41±1.61)μmol/L,and the IC50 values of compounds 8,21-23 against ABTS+free radicals were(15.22±0.89)-(41.66±6.29)μmol/L.CONCLUSION Compounds 9,19-23 are isolated from genus Anaphalis for the first time,and 2,10,13,14,16,17,19-23 are first isolated from this plant.Compounds 1,7-8,21-23 have strong antioxidant activity.

Cervical spinal cord injury without fracture-dislocation (CSCIWFD) is referred to as a special type of cervical spinal cord injury characterized by traumatic spinal cord dysfunction and no significant bony structural abnormalities on imagines. Duo to the high risk of missed diagnosis during the initial consultation, CSCIWFD may lead to progressive neurological deterioration or even complete paralysis, severely impacting patients′ prognosis. Currently, there are no established consensuses over the diagnosis and treatment of CSCIWFD, such as the lack of evidence-based standards for indications of non-surgical treatment and risk of secondary neurological injury, as well as debates over the optimal timing for surgical intervention and indications for different surgical approaches. To address these issues, the Spine Trauma Group of the Orthopedic Branch of the Chinese Medical Doctor Association organized experts in the relevant fields to formulate Diagnosis and treatment guideline for acute cervical spinal cord injury without fracture- dislocation in adults ( version 2025) . Based on evidence-based medicine and the principles of scientific rigor and clinical applicability, the guidelines proposed 11 recommendations covering terminology, diagnosis, evaluation treatment, and rehabilitation, etc., aiming to standardize the management of CSCIWFD.