Greater trochanteric pain syndrome(GTPS)is a disease caused by structural lesions of the muscles,fascia,ligaments,and bursae near the greater trochanter of the femur.GTPS causes lateral hip joint pain,severely affecting patients' quality of life.Ultrasound has many advantages,such as real-time diagnosis,portable operation,non-radiation,and high resolution,demonstrating a high application value in the diagnosis and interventional therapy of GTPS.This article reviews the current status of ultrasound in the diagnosis and interventional therapy of GTPS and prospects its application.
Humans ; Ultrasonography ; Femur/diagnostic imaging* ; Hip Joint/diagnostic imaging* ; Arthralgia/therapy*

Risks of food safety induced by small molecule drug residues in animal food and environment have become an increasing public concern,so it is necessary to develop highly sensitive and easy-to-operate techniques to detect small molecules.Herein,a metasurface plasma resonance(MetaSPR)sensor chip coupled with gold nanoparticles(AuNPs)was developed for detection of enrofloxacin(ENR)based on competitive immunoassay.The detection range of the sensor for ENR was 0.025-3.2 ng/mL,and the detection limit(3σ)was 20 pg/mL.The biosensor showed excellent performance including high selectivity,good stability,ease to operate and high throughput,etc.The developed method was applied to detection of ENR residues in real samples,with recoveies of 96.0% -105.0%.The proposed sensing strategy provided new technique reference for detection of other small molecules in the field of residue analysis in food safety and environment monitoring.

With the deepening understanding of the pathogenesis of osteoarthritis (OA), therapeutic strategies targeting oxidative stress in chondrocytes have gradually become a research hotspot. This article summarizes the important role of oxidative stress in the development of OA, pointing out that it is closely related to chondrocyte senescence, inflammatory cascade reaction, and cartilage matrix degradation. Given the central role of oxidative stress in the pathological process of OA, inhibiting oxidative stress and the generation of reactive oxygen species (ROS) is considered the key to alleviating chondrocyte damage and senescence, and preventing the progression of OA. Although some progress has been made in current OA research, there are still many challenges, such as the in-depth understanding of the etiology of OA and the limited selection of therapeutic drugs. Future research will focus on a more comprehensive understanding of the mechanism of oxidative stress in OA, exploring new biomarkers and therapeutic targets, and developing new drugs or treatment methods targeting oxidative stress pathways. These efforts are expected to bring more effective treatment options to OA patients, thereby improving their quality of life and prognosis.
Humans ; Oxidative Stress ; Osteoarthritis/pathology* ; Chondrocytes/pathology* ; Animals ; Reactive Oxygen Species/metabolism*

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.

Hilar cholangiocarcinoma is insidious in onset and often causes obstructive jaundice due to bile stasis,leading to impaired liver function.For tumors with malignant obstructive jaundice,biliary drainage is often performed before surgery in clinical practice.Currently,the commonly used drainage methods are percutaneous transhepatic biliary drainage(PTBD)and endoscopic retrograde biliary drain-age(ERBD),but there are controversies over the advantages and disadvantages of the two drainage methods.PTBD drainage can often lead to tumor implantation metastasis,but the underlying mechanism remains unclear.We detected tumor cells in PTBD and ERBD bile samples from hilar cholangiocarcino-ma patients,subsequently explored their tumorigenicity and mechanisms through tumorsphere assay in vitro and xenograft tumor models in vivo.The experiments included benign gallstones group(30 cases)as a negative control,PTBD group(14 cases)and ERBD group(13 cases).Tumorsphere formation was i-dentified in 3 cases(23％)among the 13 cases of ERBD group,in 6 cases(42％)among the 14 cases of PTBD group,but there were no tumor cells or formed tumorspheres in the 30 cases of benign gallstone group.The tumor sphere formation ability of cells in the PTBD group was significantly higher than that in ERBD group.Subcutaneous xenograft tumor assays showed that tumor growth in the PTBD group was sig-nificantly higher than that in the ERBD group.Tumor cells in PTBD bile possessed stronger tumorigenici-ty compared with the ERBD group.Mechanically,stem cell transcription factor Nanog mRNA levels were significantly higher in the PTBD group compared to the ERBD group.Both tumorsphere formation and xenograft tumor growth were reduced by Nanog knockdown in three cases of the PTBD group,indicating the important roles of Nanog in tumorigenicity of PTBD group tumor cells.The half-life of Nanog mRNA was longer in PTBD group cells than in ERBD group cells,suggesting potential post-transcriptional regu-lation on Nanog mRNA.The Nanog m6A level was higher in PTBD group tumor cells compared to the ERBD group.Analysis of methyltransferases and demethylases,ALKBH5(α-ketoglutarate dependent dioxygenase alkb family homolog 5)mRNA levels were lower in the PTBD group than in the ERBD group and significantly correlated with the m6A level of Nanog.ALKBH5 knockdown led to an increase in the m6A level of Nanog,while ALKBH5 overexpression decreased the m6A level of Nanog.Dual-luciferase activity assays demonstrated that ALKBH5 knockdown significantly enhanced luciferase activity,whereas ALKBH5 overexpression reduced it.Further studies confirmed that ALKBH5 knockdown upregulated both the mRNA and protein levels of Nanog,whereas overexpressing ALKBH5 downregulated them.ALKBH5 mediated the demethylation modification of Nanog mRNA,and the lower levels of ALKBH5 expression in the PTBD group promoted Nanog's m6A modification.Overexpressing ALKBH5 decreased tumorsphere growth,while ALKBH5 knockdown increased it,which was subsequently reduced by the simultaneous Nanog knockdown again.In sum,tumor cells in PTBD and ERBD drainage bile from hilar cholangiocar-cinoma patients exhibited tumorigenicity.Compared to the ERBD group,tumor cells in PTBD bile with lower ALKBH5 expression levels enhanced Nanog's m6A modification to upregulate Nanog expression levels,resulting in stronger tumorigenicity.These findings are significant for elucidating propensity to tumor implantation metastasis from PTBD drainage.

Hilar cholangiocarcinoma is insidious in onset and often causes obstructive jaundice due to bile stasis,leading to impaired liver function.For tumors with malignant obstructive jaundice,biliary drainage is often performed before surgery in clinical practice.Currently,the commonly used drainage methods are percutaneous transhepatic biliary drainage(PTBD)and endoscopic retrograde biliary drain-age(ERBD),but there are controversies over the advantages and disadvantages of the two drainage methods.PTBD drainage can often lead to tumor implantation metastasis,but the underlying mechanism remains unclear.We detected tumor cells in PTBD and ERBD bile samples from hilar cholangiocarcino-ma patients,subsequently explored their tumorigenicity and mechanisms through tumorsphere assay in vitro and xenograft tumor models in vivo.The experiments included benign gallstones group(30 cases)as a negative control,PTBD group(14 cases)and ERBD group(13 cases).Tumorsphere formation was i-dentified in 3 cases(23％)among the 13 cases of ERBD group,in 6 cases(42％)among the 14 cases of PTBD group,but there were no tumor cells or formed tumorspheres in the 30 cases of benign gallstone group.The tumor sphere formation ability of cells in the PTBD group was significantly higher than that in ERBD group.Subcutaneous xenograft tumor assays showed that tumor growth in the PTBD group was sig-nificantly higher than that in the ERBD group.Tumor cells in PTBD bile possessed stronger tumorigenici-ty compared with the ERBD group.Mechanically,stem cell transcription factor Nanog mRNA levels were significantly higher in the PTBD group compared to the ERBD group.Both tumorsphere formation and xenograft tumor growth were reduced by Nanog knockdown in three cases of the PTBD group,indicating the important roles of Nanog in tumorigenicity of PTBD group tumor cells.The half-life of Nanog mRNA was longer in PTBD group cells than in ERBD group cells,suggesting potential post-transcriptional regu-lation on Nanog mRNA.The Nanog m6A level was higher in PTBD group tumor cells compared to the ERBD group.Analysis of methyltransferases and demethylases,ALKBH5(α-ketoglutarate dependent dioxygenase alkb family homolog 5)mRNA levels were lower in the PTBD group than in the ERBD group and significantly correlated with the m6A level of Nanog.ALKBH5 knockdown led to an increase in the m6A level of Nanog,while ALKBH5 overexpression decreased the m6A level of Nanog.Dual-luciferase activity assays demonstrated that ALKBH5 knockdown significantly enhanced luciferase activity,whereas ALKBH5 overexpression reduced it.Further studies confirmed that ALKBH5 knockdown upregulated both the mRNA and protein levels of Nanog,whereas overexpressing ALKBH5 downregulated them.ALKBH5 mediated the demethylation modification of Nanog mRNA,and the lower levels of ALKBH5 expression in the PTBD group promoted Nanog's m6A modification.Overexpressing ALKBH5 decreased tumorsphere growth,while ALKBH5 knockdown increased it,which was subsequently reduced by the simultaneous Nanog knockdown again.In sum,tumor cells in PTBD and ERBD drainage bile from hilar cholangiocar-cinoma patients exhibited tumorigenicity.Compared to the ERBD group,tumor cells in PTBD bile with lower ALKBH5 expression levels enhanced Nanog's m6A modification to upregulate Nanog expression levels,resulting in stronger tumorigenicity.These findings are significant for elucidating propensity to tumor implantation metastasis from PTBD drainage.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Viral myocarditis(VMC)is the leading cause of dilated cardiomyopathy,which can lead to heart failure and sudden cardiac death.With the development of high-throughput sequencing technology,non-coding RNA(ncRNA)plays an important role in the occurrence and development of VMC.ncRNA promotes the occurrence and development of VMC by regulating viral replication,immune cell function,myocardial cell death,myocardial interstitial fibrosis,and other pathological processes.This article reviews the research progress of ncRNA in VMC and provides new ideas for the pathogenesis,diagnosis,and treatment of VMC.

The traditional commodity specifications of Chinese medicinal materials are mainly divided into different grades based on macroscopic characteristics. As the basis for high quality and good price, there is still a lack of systematic evaluation on whether they are consistent with the current standards and whether they can reflect the internal quality of medicinal material. Panax notoginseng is a commonly used, large consumption of Chinese medicinal material. At present, it is divided into 8 grades in the market based on "Tou" (the number of crude drug /500 g), but it is not related to the standard of total saponins of Panax notoginseng (the sum of three saponins) in Chinese Pharmacopoeia. In this study, ultra-performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF-MS/MS) coupled with mass spectrometry molecular network were used for the rapid identification of saponins of Panax notoginseng with different "Tou" and a total of 64 saponins were identified. Seventeen saponins related to "Tou" were screened by orthogonal partial least squares discriminant analysis (OPLS-DA). The content of five saponins R₁, Rb₁, Rg₁, Rd, and Re in Panax notoginseng with different "Tou" was determined by high performance liquid chromatography (HPLC). The results of correlation analysis showed that Rd and R₁ with the largest VIP values among the differential saponins, which significantly negatively correlated with "Tou" (P < 0.05). Based on the determination results of 36 batches of samples, using Rd/Total Panax notoginseng saponins (TPNS) ratio ( > 0.08) as the index, Panax notoginseng can be divided into two grades: 20-60 "Tou" (superior) and 80-200 "Tou" (qualified). Based on the concept of "macroscopic characteristics and chemical profiling", this study integrates the non-targeted analysis and quantitative determination methods to provide a new strategy for quality evaluation of Panax notoginseng.