This article reviews the benifits and challenges of nano-microspheres (NPs) in the treatment of osteoarthritis (OA). OA is a degenerative disease associated with aging, trauma, and excessive loading, with treatment strategies including basic therapy, drug therapy, reparative therapy, and reconstructive surgery. As emerging nanomaterials, NPs offer unique advantages in promoting cartilage repair due to their high surface area, excellent drug-loading capacity, and good biocompatibility. These advantages include facilitating chondrocyte generation through magnetic-mechanical control of mesenchymal stem cell microspheres and enhancing antioxidant levels using biomimetic liposomal NPs combined with glucosamine. Additionally, NPs can effectively modulate inflammatory responses, such as by inhibiting the formation of M1 macrophages and promoting their polarization to the M2 type to alleviate inflammation. Some NPs also enhance joint lubrication and relieve pain, such as hyaluronic acid-based NPs modified with choline phosphate groups. However, the application of NPs faces challenges such as high production costs, poor biocompatibility for certain types, and unknown long-term safety. Despite these challenges, with advancements in nanotechnology and a deeper understanding of the pathological mechanisms of OA, NPs are expected to provide new therapeutic approaches and more comprehensive and effective treatment options for OA patients in the future.

Efficient drug response prediction is crucial for reducing drug development costs and time, but current computational models struggle with limited experimental data and out-of-distribution issues between in vitro and in vivo settings. To address this, we introduced drug response prediction meta-learner (metaDRP), a novel few-shot learning model designed to enhance predictive accuracy with limited sample sizes across diverse drug-tissue tasks. metaDRP achieves performance comparable to state-of-the-art models in both genomics of drug sensitivity in cancer (GDSC) drug screening and in vivo datasets, while effectively mitigating out-of-distribution problems, making it reliable for translating findings from controlled environments to clinical applications. Additionally, metaDRP's inherent interpretability offers reliable insights into drug mechanisms of action, such as elucidating the pathways and molecular targets of drugs like epothilone B and pemetrexed. This work provides a promising approach to overcoming data scarcity and out-of-distribution challenges in drug response prediction, while promoting the integration of few-shot learning in this field.

OBJECTIVES: The major histocompatibility complex class I chain-related gene A (MICA), a component of the human leukocyte antigen (HLA) gene complex, is involved in the pathogenesis of various diseases including cancers and autoimmune disorders. Rosacea, a chronic inflammatory skin disease with a complex pathogenesis, potentially influenced by genetic and autoimmune factors. This study aims to investigate the relationship among MICA gene polymorphisms, single nucleotide polymorphisms (SNPs), and susceptibility to rosacea, thereby offering new insights into the disease mechanism. METHODS: Peripheral blood DNA samples were collected from 84 patients with rosacea (rosacea group) and 223 healthy volunteers (control group) who visited the Dermatology Outpatient Department of Xiangya Hospital of Central South University between November 2017 and November 2019. MICA genotyping was performed using polymerase chain reaction-sequencing-based typing (PCR-SBT) and the next-generation sequencing (NGS), and the accuracy of the 2 methods was compared. The frequency distributions of MICA alleles between the 2 groups were analyzed. Amino acid clustering and SNP site analyses were conducted to identify haplotype-linked SNPs and to classify MICA polymorphic variants. Distribution differences of these classifications between groups were also examined. RESULTS: Blood tests in rosacea patients showed mildly elevated, with no significant changes in lymphocyte counts. Both PCR-SBT and NGS accurately identified MICA alleles. The most common alleles in the rosacea group were MICA*010:01, MICA*008:04, and MICA*019:01. The frequencies of MICA*002:01 and MICA*027 were significantly lower in the rosacea group compared to controls (6.55% vs 18.16% and 1.19% vs 5.38%, respectively), while and MICA*010:01 were significantly higher (7.74% vs 3.36% and 31.55% vs 18.61%, respectively; all P<0.05). Five short tandem repeat (STR) alleles were identified. Frequencies of MICA-A4 and MICA-A9 were lower in the rosacea group than in the control group (16.07% vs 23.32% and 7.74% vs 17.26%, respectively), whereas MICA-A6 was higher (10.12% vs 4.03%; all P<0.05). Clustering and SNP analysis identified 6 linked SNP sites, classifying MICA variants into Type I (C₃₆+M₁₂₉+K₁₇₃+G₂₀₆+W₂₁₀+S₂₁₅) and Type II (Y₃₆+V₁₂₉+E₁₇₃+S₂₀₆+R₂₁₀+T₂₁₅). Type I MICA variants were significantly associated with rosacea susceptibility. CONCLUSIONS MICA gene polymorphisms are associated with susceptibility to rosacea, and there are 6 linked SNP sites within the MICA gene. Based on this, MICA polymorphic variants are classified into Type I and Type II, with Type I being more closely associated with disease development of rosacea.
Humans ; Polymorphism, Single Nucleotide ; Histocompatibility Antigens Class I/genetics* ; Rosacea/genetics* ; Genetic Predisposition to Disease/genetics* ; Female ; Male ; Adult ; Middle Aged ; Genotype ; Alleles ; Gene Frequency ; Haplotypes ; Case-Control Studies ; Aged ; High-Throughput Nucleotide Sequencing

Objective: To investigate the potential protective effect of Shexiang Tongxin dropping pills (STDP) on ischemia-reperfusion injury and its underlying mechanisms in improving endothelial cell function in coronary microvascular disease (CMVD). Methods: A rat model of myocardial ischemia-reperfusion injury with CMVD was established using ligation and reperfusion of the left anterior descending artery. The effect of STDP (21.6 mg/kg) on cardiac function was evaluated using echocardiography, hematoxylin-eosin staining, and Evans blue staining. The effects of STDP on the microvascular endothelial barrier were assessed based on nitric oxide production, endothelial nitric oxide synthase expression, structural variety of tight junctions (TJs), and the expression of zonula occludens-1 (ZO-1), claudin-5, occludin, and vascular endothelial (VE)-cadherin proteins. The mechanisms of STDP (50 and 100 ng/mL) were evaluated by examining the expression of sphingosine 1-phosphate receptor 2 (S1PR2), Ras Homolog family member A (RhoA), and Rho-associated coiled-coil-containing protein kinase (ROCK) proteins and the distribution of ZO-1, VE-cadherin, and F-actin proteins in an oxygen and glucose deprivation/reoxygenation model. Results: The administration of STDP on CMVD rat model significantly improved cardiac and microvascular endothelial cell barrier functions (all P < .05). STDP enhanced the structural integrity of coronary microvascular positioning and distribution by clarifying and completing TJs and increasing the expression of ZO-1, occludin, claudin-5, and VE-cadherin in vivo (all P < .05). The S1PR2/RhoA/ROCK pathway was inhibited by STDP in vitro, leading to the regulation of endothelial cell TJs, adhesion junctions, and cytoskeletal morphology. Conclusion STDP showed protective effects on cardiac impairment and microvascular endothelial barrier injury in CMVD model rats induced by myocardial ischemia-reperfusion injury through the modulation of the S1PR2/RhoA/ROCK pathway.

This paper aims to discuss the application value and progress of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors and teaching practice of gastrointestinal oncology. Through a comprehensive analysis of the current clinical research status and literature, this paper summarizes the application practice and exploratory thinking of AI and deep learning technologies in gastrointestinal oncology. In diagnosis, AI technologies have improved the early detection and diagnosis efficiency for gastrointestinal tumors by optimizing medical image analysis, especially in the recognition of liver metastases. Applications of AI in pathological diagnosis include automatic recognition of tumor cells and tissue structure, as well as improving diagnostic sensitivity and specificity through feature extraction and pattern recognition. In treatment, the application scenarios of AI include rapid diagnosis, accurate staging, personalized treatment plan formulation, drug development, and surgical assistance. In surgical assistance, AI technology improves the safety and effectiveness of surgery through preoperative evaluation, surgical navigation, and postoperative evaluation. In teaching, AI technology facilitates knowledge acquisition and clinical skill enhancement of medical students by providing a multidisciplinary learning platform, simulating clinical environment and case details, and establishing a remote learning platform. The application of AI technology in teaching also includes deep learning and assessment feedback, providing personalized teaching and real-time assessment for students. This paper discusses the application prospects for AI technology in the teaching practice of gastrointestinal oncology. Although AI technology shows great potential in the diagnosis and treatment of gastrointestinal tumors and teaching gastrointestinal oncology, it also has limitations and needs to be combined with traditional teaching methods to achieve the best teaching results.

This paper aims to discuss the application value and progress of artificial intelligence (AI) in the diagnosis and treatment of gastrointestinal tumors and teaching practice of gastrointestinal oncology. Through a comprehensive analysis of the current clinical research status and literature, this paper summarizes the application practice and exploratory thinking of AI and deep learning technologies in gastrointestinal oncology. In diagnosis, AI technologies have improved the early detection and diagnosis efficiency for gastrointestinal tumors by optimizing medical image analysis, especially in the recognition of liver metastases. Applications of AI in pathological diagnosis include automatic recognition of tumor cells and tissue structure, as well as improving diagnostic sensitivity and specificity through feature extraction and pattern recognition. In treatment, the application scenarios of AI include rapid diagnosis, accurate staging, personalized treatment plan formulation, drug development, and surgical assistance. In surgical assistance, AI technology improves the safety and effectiveness of surgery through preoperative evaluation, surgical navigation, and postoperative evaluation. In teaching, AI technology facilitates knowledge acquisition and clinical skill enhancement of medical students by providing a multidisciplinary learning platform, simulating clinical environment and case details, and establishing a remote learning platform. The application of AI technology in teaching also includes deep learning and assessment feedback, providing personalized teaching and real-time assessment for students. This paper discusses the application prospects for AI technology in the teaching practice of gastrointestinal oncology. Although AI technology shows great potential in the diagnosis and treatment of gastrointestinal tumors and teaching gastrointestinal oncology, it also has limitations and needs to be combined with traditional teaching methods to achieve the best teaching results.

This article reviews the benifits and challenges of nano-microspheres (NPs) in the treatment of osteoarthritis (OA). OA is a degenerative disease associated with aging, trauma, and excessive loading, with treatment strategies including basic therapy, drug therapy, reparative therapy, and reconstructive surgery. As emerging nanomaterials, NPs offer unique advantages in promoting cartilage repair due to their high surface area, excellent drug-loading capacity, and good biocompatibility. These advantages include facilitating chondrocyte generation through magnetic-mechanical control of mesenchymal stem cell microspheres and enhancing antioxidant levels using biomimetic liposomal NPs combined with glucosamine. Additionally, NPs can effectively modulate inflammatory responses, such as by inhibiting the formation of M1 macrophages and promoting their polarization to the M2 type to alleviate inflammation. Some NPs also enhance joint lubrication and relieve pain, such as hyaluronic acid-based NPs modified with choline phosphate groups. However, the application of NPs faces challenges such as high production costs, poor biocompatibility for certain types, and unknown long-term safety. Despite these challenges, with advancements in nanotechnology and a deeper understanding of the pathological mechanisms of OA, NPs are expected to provide new therapeutic approaches and more comprehensive and effective treatment options for OA patients in the future.

To explore the application value of endoscopic ultrasound fine-needle aspiration (EUS-FNA) and endoscopic retrograde cholangiopancreatography (ERCP) in the diagnosis and treatment ofpatients with pancreatic cancer combined with obstructive jaundice.

Objective To establish a noninvasive diagnostic model for liver fibrosis in chronic hepatitis B(CHB)patients with normal alanine aminotransferase(ALT)and an age of≤30 years by selecting specific indicators from the commonly used noninvasive indicators such as clinical,biochemical,and imaging indicators,to avoid invasive liver biopsy in such patients to some extent,and to guide the timing of antiviral therapy.Methods A total of 251 CHB patients with normal ALT and an age of≤30 years who underwent liver biopsy in Shenzhen Third People's Hospital and The First Hospital of Changsha from January 2019 to January 2022 were enrolled,with 175 patients in the model group and 76 patients in the validation group,and commonly used clinical indicators were obtained based on clinical experience and related articles.The two-independent-samples t test or the two-independent-samples Mann-Whitney U rank sum test was used for comparison of continuous data between groups,and the chi-square test was used for comparison of categorical data between groups.A Spearman rank correlation analysis was used to investigate the correlation between each indicator and liver fibrosis and identify the indicators with correlation(P<0.01,r>0.200);a Logistic regression analysis was used to establish a noninvasive diagnostic model,and the receiver operating characteristic(ROC)curve was used to evaluate its performance and perform validation of the model;this model was then compared with the widely used models of aspartate aminotransferase-to-platelet ratio index(APRI)and fibrosis-4(FIB-4).The Kappa consistency test was used to investigate the consistency of pathological results.Results A total of 17 commonly used clinical indicators were obtained,among which 9 indicators(ALT,aspartate aminotransferase[AST],gamma-glutamyl transpeptidase[GGT],ferritin[FERR],platelet count[PLT],procollagen type Ⅲ amino-terminal peptide[PⅢP],collagen Ⅳ[CⅣ],HBV DNA,and spleen thickness)were correlated with liver fibrosis(P<0.01,r>0.232).Based on the above indicators,the predictive model was established as P=1/(1+e-γ),γ=-1.902+0.106×AST-0.011×PLT-0.265×HBV DNA+0.059×PⅢP,in which P was the probability for predicting≥S2 liver fibrosis and γ was the predictive index.The comparison between each indicator and the model showed that the model had the largest area under the ROC curve of 0.852,with a sensitivity of 92.7%and a specificity of 76.9%.The model was validated in 76 patients and showed an accuracy of 77.600%.The model was compared with APRI and FIB-4,and the results showed that the model has good accuracy.Conclusion Compared with the models of APRI and FIB-4 commonly used in the world,this model can more accurately judge the degree of liver fibrosis in such patients,thereby replacing liver biopsy to some extent and guiding the timing of antiviral therapy.

Objective: To evaluate the anti-fatigue effects of different extracts from Cistanche tubulosa (Schenk) Wight (C. tubulosa, Rou Cong Rong), focusing on central and exercise-induced fatigue in mice. This study investigated the pharmacological effects of the total oligosaccharides, polysaccharides, and phenylethanoid glycosides (CPhGs) extracted from C. tubulosa. Methods: Models of sleep deprivation and forced swimming fatigue were established to simulate central and exercise-induced fatigue. The mice were treated with different extracts of C. tubulosa, and their effects were assessed using behavioral tests to measure exercise capacity, learning, and memory function. Biochemical analyses were performed to evaluate the changes in serum and brain neurotransmitter levels, liver and muscle glycogen storage, and various fatigue-related biomarkers. Results: This study found that treatment with C. tubulosa extract improved exercise capacity, learning, and memory in mice. Total oligosaccharides from C. tubulosa enhanced adrenocorticotropic hormone, cholinesterase, and thyroid-stimulating hormone levels, reduced cortisol levels in central fatigue models, and ameliorated biochemical markers of exercise-induced fatigue, including lowering lactic acid, blood urea nitrogen, and malondialdehyde levels. Among the tested extracts, the total oligosaccharides showed the most comprehensive anti-fatigue effects. Conclusion The anti-fatigue effects of C. tubulosa, particularly those of its total oligosaccharides, are pronounced in both central and exercise-induced fatigue. These effects are mediated by the regulation of neurotransmitter levels, enhancement of glycogen storage, and improvement of antioxidant enzyme activity, suggesting potential therapeutic benefits in fatigue-related conditions.