Objective To compare the dosimetric differences, plan complexity, verification pass rate, and execution efficiency of volumetric modulated arc therapy (VMAT) plans designed for post-mastectomy radiotherapy of breast cancer using Halcyon 2.0 and VitalBeam accelerators in flattening filter-free mode. Methods A retrospective analysis was conducted on imaging data from 87 female breast cancer patients aged 29 to 75 (49.9 ± 9.9) years who underwent modified radical mastectomy followed by radiotherapy. VMAT plans were designed using both VitalBeam and Halcyon accelerators in flattening filter-free mode. The dosimetric parameters, plan complexity, verification pass rate, and execution efficiency were compared between the plans designed for both accelerators, with a significance level of P<0.05. Results In terms of target volume parameters, Halcyon 2.0 plans demonstrated superior homogeneity index and conformity index. For organs at risk, the Halcyon 2.0 plans showed lower doses for the lungs (D_20% and D_mean) and heart (D_mean) compared to the VitalBeam plans (all P<0.05). Regarding plan complexity, Halcyon 2.0 plans exhibited better median average aperture area variation (0.35 ± 0.02), smaller aperture score, and larger median plan aperture area than VitalBeam plans (all P<0.05). However, VitalBeam plans had a superior median modulation complexity score (0.28 ± 0.01) compared to Halcyon 2.0 plans (P<0.05). Patient plans were assessed using the ArcCHECK phantom, revealing higher pass rates and significantly shorter execution time for Halcyon plans compared to VitalBeam plans (P<0.05). Conclusion Both Halcyon 2.0 and VitalBeam accelerators can provide high-quality VMAT plans for postoperative radiotherapy of breast cancer, with excellent verification pass rates ensuring treatment accuracy and efficacy. However, Halcyon 2.0 offers lower plan complexity, simplifies the treatment planning process, and reduces execution time, thereby achieving higher treatment efficiency and demonstrating greater advantages in clinical applications.

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

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.

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 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.

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.

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.

Ceria nanoparticles(CeO2 NPs)have become popular materials in biomedical and industrial fields due to theirpotential applications in anti-oxidation,cancer therapy,photocatalytic degradation of pollutants,sensors,etc.Many methods,including gas phase,solid phase,liquid phase,and the newly proposed green synthesis method,have been reported for the synthesis of CeO2 NPs.Due to the wide application of CeO2 NPs,concerns about their adverse impacts on human health have been raised.This review covers recent studies on the biomedical applications of CeO2 NPs,including their use in the treatment of various diseases(e.g.,Alzheimer's disease,ischemic stroke,retinal damage,chronic inflammation,and cancer).CeO2 NP toxicity is discussed in terms of the different systems of the human body(e.g.,cytotoxicity,genotoxicity,respiratory toxicity,neurotoxicity,and hepatotoxicity).This comprehensive review covers both fundamental discoveries and exploratory progress in CeO2 NP research that may lead to practical developments in the future.

Objective:To analyze the bubble dynamic characteristics of shock wave enhanced emission photoacoustic streaming(SWEEPS)technique in free water region under different temporay delays.Methods:The Er∶YAG laser tip was activated in free water model with SWEEPS mode at 150-600 μs pulse delay.The bubble dynamic process during irrigation was recorded by a high-speed camera(200 000 Hz).Matlab was used to analyze the interaction between bubble made by the dual pulses frame by frame.The distance between bubble remnants and laser tip was measured before the bubble disappeared.The experimental data were statisti-cally analyzed by SPSS 19.0.Results:In free water region,different temporal delays caused different interaction between the bub-bles activated by the SWEEPS technique with a dual pulse modality.The interactions include bubble fusion,bubble collision and bubble separation.When the temporal delay between 360-440 μs,bubble collision was the most violent,and the farthest distance between bubble remnants and laser tip was reached.Conclusion:In free water region,the dual pulse of SWEEPS technique can lead to bubble interaction which may enhance the cavitation effect of Er∶YAG laser irrigation,and improve the debridement in clinic ap-plication.