Age-related macular degeneration(ARMD)is a progressive visual impairment fundus disease that frequently occurs in individuals aged >55 years. The main risk factors are aging, long-term smoking, genetics, and racial differences. Pathogenesis includes abnormal function of the retinal pigment epithelium, damaged blood-retinal barrier, and abnormal immune function. Currently, intravitreal injection(IVI)of anti-vascular endothelial growth factor(VEGF)drugs is the preferred treatment option for ARMD in clinical practice. However, it also faces challenges such as repeated treatments, high medical costs, and poor patient compliance. The predicament in the treatment of ARMD has given rise to several new treatment options. This article aims to review the treatment methods and progress of dry ARMD and wet ARMD, providing new ideas for addressing the limitations of the current clinical anti-VEGF treatment.

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.

ObjectiveColorectal cancer (CRC) is a highly heterogeneous malignancy, and traditional Chinese medicine (TCM) syndrome differentiation is widely used in its clinical management. However, the biological basis underlying different TCM syndromes remains insufficiently understood. This study aimed to investigate the proteomic differences between qi-blood deficiency syndrome (QBDS) and damp-heat syndrome (DHS) in CRC patients and to establish a “syndrome-differential protein-herbal component” network for exploring potential molecular mechanisms and therapeutic targets associated with syndrome-specific treatment strategies. MethodsTumor tissue samples were collected from 10 patients with pathologically confirmed CRC, including 5 patients diagnosed with QBDS and 5 with DHS according to standardized TCM syndrome differentiation criteria. Tandem mass tag (TMT)-based quantitative proteomics was employed to identify differentially expressed proteins (DEPs) between the two syndrome groups. Functional enrichment analyses, including Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were performed to characterize the biological functions and signaling pathways associated with the identified DEPs. Least absolute shrinkage and selection operator (LASSO) regression analysis was further applied to screen key syndrome-related proteins and construct a core protein prediction model. Subsequently, molecular docking was conducted to evaluate the binding affinities between representative herbal compounds, astragaloside IV and berberine, and the identified core proteins. Finally, cell counting kit-8 (CCK-8) assays were performed in HCT 116 and HT-29 colorectal cancer cell lines to validate the anti-proliferative activities of these compounds in vitro. ResultsA total of 44 DEPs were identified between QBDS and DHS tissues, including 21 upregulated and 23 downregulated proteins in the QBDS group relative to the DHS group. Functional enrichment analyses revealed that these proteins were primarily associated with antiviral immune responses, regulation of inflammatory signaling, NF‑κB signaling pathways, cholesterol metabolism, and other biological processes relevant to tumor progression and host immune regulation. LASSO regression analysis identified eight core proteins with potential syndrome-specific significance. Among them, ST7, GPCPD1, and PODXL were closely associated with QBDS, whereas FAT1, GRB7, KRT7, MAFF, and MMP8 were associated with DHS. Molecular docking demonstrated favorable interactions between astragaloside IV and QBDS-related proteins, with binding energies lower than -5 kcal/mol, indicating stable binding activity. Berberine exhibited even lower binding energies and stronger binding specificity toward DHS-related core proteins. In vitro experiments further confirmed that both compounds inhibited CRC cell proliferation, while berberine displayed significantly greater anti-proliferative efficacy. The lowest 48 h IC₅₀ value of berberine reached 15.21 mg/L in CRC cells, indicating a stronger growth-inhibitory effect than astragaloside IV. ConclusionCRC patients with QBDS and DHS exhibit distinct proteomic characteristics, suggesting that different TCM syndromes possess unique molecular signatures. The identified core proteins may serve as potential biomarkers for syndrome differentiation, while astragaloside IV and berberine may exert therapeutic effects through targeting syndrome-specific molecular networks. These findings provide preliminary proteomic evidence supporting syndrome-guided precision integrative medicine in CRC and offer new insights into the biological basis of TCM syndrome differentiation and individualized therapeutic strategies.

Objective To investigate the epidemiological characteristics of knee osteoarthritis(KOA)among military personnel in plateau regions and to explore its risk factors.Methods From July 2023 to July 2024,a multi-stage stratified cluster random sampling method was employed to survey the prevalence of KOA and related risk factors among military personnel in the northwest plateau regions of China,covering different altitudes(1500-4500 m)and geographical areas(Gansu,Qinghai,Tibet,and Xinjiang).All study subjects were divided into KOA and non-KOA groups based on the presence or absence of KOA.Variables including age,gender,body mass index(BMI),education level,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury were selected for univariate analyses between groups.Variables with P<0.05 in the univariate analyses were included in the binary multifactor logistic regression to identify risk factors for KOA.Results A total of 3000 questionnaires were distributed,and 2854 valid questionnaires were collected,with a response rate of 95.13%.The sample included 2584 males and 270 females,with 510 cases of KOA,resulting in a prevalence rate of 17.9%.Univariate analysis showed that there were statistically significant differences between KOA and non-KOA groups in terms of age,BMI,smoking status,military rank,military branch,service duration,regional altitude,annual average temperature,training duration,perceived training intensity,and history of knee injury(P<0.05).However,no significant differences were found in gender and education level(P>0.05).Binary multivariate logistic regression analysis revealed that older age(OR=1.382,P=0.017),higher BMI(P<0.01),smoking(OR=1.929,P<0.01),higher military rank(OR=1.485,P=0.007),being a member of the Armed Police(P<0.01),longer service duration(P<0.01),higher regional altitude(OR=1.459,P<0.01),lower annual average temperature(OR=1.188,P=0.001),longer training duration(P<0.01),higher perceived training intensity(OR=2.450,P<0.01),and history of knee injury(OR=2.768,P=0.002)were independent risk factors for KOA.Conclusions Older age,overweight/obesity,smoking,higher military rank,being a member of the Armed Police,longer service duration,higher altitude,cold climate,longer training duration,higher training intensity,and history of knee injury are independent risk factors for KOA among military personnel in the northwest plateau regions of China.

A primary hallmark of pathological cardiac hypertrophy is excess protein synthesis due to enhanced translational activity. However, regulatory mechanisms at the translational level under cardiac stress remain poorly understood. Here we examined the translational regulations in a mouse cardiac hypertrophy model induced by transaortic constriction (TAC) and explored the conservative networks versus the translatome pattern in human dilated cardiomyopathy (DCM). The results showed that the heart weight to body weight ratio was significantly elevated, and the ejection fraction and fractional shortening significantly decreased 8 weeks after TAC. Puromycin incorporation assay showed that TAC significantly increased protein synthesis rate in the left ventricle. RNA-seq revealed 1,632 differentially expressed genes showing functional enrichment in pathways including extracellular matrix remodeling, metabolic processes, and signaling cascades associated with pathological cardiomyocyte growth. When combined with ribosome profiling analysis, we revealed that translation efficiency (TE) of 1,495 genes was enhanced, while the TE of 933 genes was inhibited following TAC. In DCM patients, 1,354 genes were upregulated versus 1,213 genes were downregulated at the translation level. Although the majority of the genes were not shared between mouse and human, we identified 93 genes, including Nos3, Kcnj8, Adcy4, Itpr1, Fasn, Scd1, etc., with highly conserved translational regulations. These genes were remarkably associated with myocardial function, signal transduction, and energy metabolism, particularly related to cGMP-PKG signaling and fatty acid metabolism. Motif analysis revealed enriched regulatory elements in the 5' untranslated regions (5'UTRs) of transcripts with differential TE, which exhibited strong cross-species sequence conservation. Our study revealed novel regulatory mechanisms at the translational level in cardiac hypertrophy and identified conserved translation-sensitive targets with potential applications to treat cardiac hypertrophy and heart failure in the clinic.
Animals ; Humans ; Cardiomegaly/physiopathology* ; Ribosomes/physiology* ; Protein Biosynthesis/physiology* ; Mice ; Cardiomyopathy, Dilated/genetics* ; Ribosome Profiling

According to the principle and current domestic and international construction processes of core outcome set(COS) and the characteristics of post-stroke aphasia, this study built COS with evidence-based support for traditional Chinese medicine(TCM) treatment of post-stroke aphasia. Firstly, a comprehensive review was conducted on the articles about the TCM treatment of post-stroke aphasia that were published in the four major Chinese databases, three major English databases, and three clinical registration centers over the past five years. The articles were analyzed and summarized, on the basis of which the main part of the COS for clinical research on the TCM treatment of post-stroke aphasia was formed. Secondly, clinical doctors and related nursing personnel were interviewed, and important outcome indicators in the clinical diagnosis and treatment process were supplemented to form a pool of core outcome indicators. Two rounds of Delphi surveys were carried out to score the importance of the core outcome indicators in the pool. Finally, a consensus meeting of experts was held to establish the COS for clinical research on the TCM treatment of post-stroke aphasia. The final COS included a total of 268 studies [236 randomized controlled trials(RCTs), 21 Meta-analysis, and 11 clinical registration protocols] and 20 open questionnaire survey results. After two rounds of Delphi surveys, a total of 14 outcome indicators and their corresponding measurement tools were included in the expert consensus meeting. The final expert consensus meeting determined the COS for post-stroke aphasia, which included 9 indicator domains and 12 outcome indicators.
Humans ; Aphasia/therapy* ; Stroke/complications* ; Medicine, Chinese Traditional ; Drugs, Chinese Herbal/therapeutic use* ; Treatment Outcome

This paper explored the specific mechanism of ginsenoside Rg_1 in regulating mitochondrial fusion through the neurogenic gene Notch homologous protein 1(Notch1) pathway to alleviate hypoxia/reoxygenation(H/R) injury in HL-1 cells. The relative viability of HL-1 cells after six hours of hypoxia and two hours of reoxygenation was detected by cell counting kit-8(CCK-8). The lactate dehydrogenase(LDH) activity in the cell supernatant was detected by the lactate substrate method. The content of adenosine triphosphate(ATP) was detected by the luciferin method. Fluorescence probes were used to detect intracellular reactive oxygen species(Cyto-ROS) levels and mitochondrial membrane potential(ΔΨ_m). Mito-Tracker and Actin were co-imaged to detect the number of mitochondria in cells. Fluorescence quantitative polymerase chain reaction and Western blot were used to detect the mRNA and protein expression levels of Notch1, mitochondrial fusion protein 2(Mfn2), and mitochondrial fusion protein 1(Mfn1). The results showed that compared with that of the control group, the cell activity of the model group decreased, and the LDH released into the cell culture supernatant increased. The level of Cyto-ROS increased, and the content of ATP decreased. Compared with that of the model group, the cell activity of the ginsenoside Rg_1 group increased, and the LDH released into the cell culture supernatant decreased. The level of Cyto-ROS decreased, and the ATP content increased. Ginsenoside Rg_1 elevated ΔΨ_m and increased mitochondrial quantity in HL-1 cells with H/R injury and had good protection for mitochondria. After H/R injury, the mRNA and protein expression levels of Notch1 and Mfn1 decreased, while the mRNA and protein expression levels of Mfn2 increased. Ginsenoside Rg_1 increased the mRNA and protein levels of Notch1 and Mfn1, and decreased the mRNA and protein levels of Mfn2. Silencing Notch1 inhibited the action of ginsenoside Rg_1, decreased the mRNA and protein levels of Notch1 and Mfn1, and increased the mRNA and protein levels of Mfn2. In summary, ginsenoside Rg_1 regulated mitochondrial fusion through the Notch1 pathway to alleviate H/R injury in HL-1 cells.
Ginsenosides/pharmacology* ; Receptor, Notch1/genetics* ; Signal Transduction/drug effects* ; Mice ; Animals ; Mitochondrial Dynamics/drug effects* ; Mitochondria/metabolism* ; Cell Line ; Reactive Oxygen Species/metabolism* ; Oxygen/metabolism* ; Cell Hypoxia/drug effects* ; Cell Survival/drug effects* ; Membrane Potential, Mitochondrial/drug effects* ; Humans

This study aimed to characterize and identify the non-volatile components in aqueous and ethanolic extracts of the stems and leaves of Rhododendron tomentosum by using sensitive and efficient ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry(UHPLC-Q-TOF-MS) combined with a self-built information database. By comparing with reference compounds, analyzing fragment ion information, searching relevant literature, and using a self-built information database, 118 compounds were identified from the aqueous and ethanolic extracts of R. tomentosum, including 35 flavonoid glycosides, 15 phenolic glycosides, 12 flavonoids, 7 phenolic acids, 7 phenylethanol glycosides, 6 tannins, 6 phospholipids, 5 coumarins, 5 monoterpene glycosides, 6 triterpenes, 3 fatty acids, and 11 other types of compounds. Among them, 102 compounds were reported in R. tomentosum for the first time, and 36 compounds were identified by comparing them with reference compounds. The chemical components in the ethanolic and aqueous extracts of R. tomentosum leaves and stems showed slight differences, with 84 common chemical components accounting for 71.2% of the total 118 compounds. This study systematically characterized and identified the non-volatile chemical components in the ethanolic and aqueous extracts of R. tomentosum for the first time. The findings provide a reference for active ingredient research, quality control, and product development of R. tomentosum.
Rhododendron/chemistry* ; Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Mass Spectrometry/methods* ; Plant Leaves/chemistry*

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 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.