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.

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.

ObjectiveTo explore the establishment of performance assessment indicators for the classification and grading of public health staff in district-level Centers for Disease Control and Prevention (CDCs), and to provide a basis for such evaluations. MethodsThrough literature review and group interviews, performance evaluation indicators were developed based on competency evaluation. Experts were invited to evaluate the weight of performance evaluation indicators for public health staff from different categories, with the average value used to represent the weight of each indicator. ResultsTwenty-nine experts from universities in Shanghai, municipal CDCs, and district CDCs participated, yielding an expert authority coefficient of 0.86. The performance evaluation indicators for department managers were categorized into three levels, with 4 indicators at the primary level, 16 indicators at the secondary level, and 42 indicators at the tertiary level, while those for general staff included 4 primary indicators, 15 secondary indicators, and 36 tertiary indicators. Significant differences were observed in the weight coefficients of the primary indicators (internal operations, professional work, and learning and growth) between department managers and general staff. The top three secondary indicators for department managers were department management, monitoring and prevention, and level of expertise. For mid-level and senior staff, the top three secondary indicators were monitoring and prevention, level of expertise, and research work. The top three secondary indicators for junior staff were monitoring and prevention, professional expertise, and professional attitude. No significant statistical differences were found among tertiary indicators. ConclusionThe developed performance evaluation indicators are reliable. Staff at different levels and classifications should be evaluated using different performance evaluation standards to accurately reflect individual performance and contributions.

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 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.

OBJECTIVE To investigate the variations in taste， aroma and volatile organic compounds of Rhei Radix et Rhizoma decoction pieces derived from different sources， and to identify their origins. METHODS The flavor， odor and volatile organic compounds of Rhei Radix et Rhizoma decoction pieces from different sources were compared and analyzed by using electronic tongue， electronic nose， and gas chromatography-ion mobility spectrometry （GC-IMS）. Principal component analysis （PCA）， partial least squares-discriminant analysis （PLS-DA）， orthogonal partial least squares discriminant analysis （OPLS-DA） and Fisher discriminant analysis were employed to identify the origins of Rhei Radix et Rhizoma decoction pieces and establish the basis discrimination criteria. RESULTS The differences in taste of Rhei Radix et Rhizoma decoction pieces from 3 origins were primarily characterized by bitterness， astringency， and bitter-astringent aftertaste. In terms of smell， variations were mainly observed in inorganic sulfides， organic sulfides containing aromatic components， methane and other short-chain alkanes， alcohols， ethers， aldehydes and ketones， as well as nitrogen oxides. Differentially volatile organic compounds mainly consisted of alcohols， aldehydes and ketones. Furthermore， the samples from 8 batches could be effectively classified into 3 categories.Three types of Rhei Radix et Rhizoma decoction pieces can be effectivily identified based on the peak intensity ratio between volatile substances. For example， when the peak intensity of 2-acetylfuran was 3-19 times that of isobutyric acid [dimer]， it was considered as Rheum officinale Baill. CONCLUSIONS The discriminant models established in this study， along with the criteria for determining the origins based on the peak intensity ofcharacteristic volatile compounds， can be utilized for the identification of Rhei Radix et Rhizoma decoction pieces.

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 summarize the clinical experience of the treatment for complex aortic coarctation with extra anatomic bypass and anatomic correction techniques. Methods The clinical data of patients with complex aortic coarctation treated in the First Affiliated Hospital of Nanjing Medical University and Friendship Hospital of Ili Kazakh Autonomous Prefecture between April 2012 and November 2020 were retrospectively reviewed. Results A total of 12 patients were enrolled, including 5 males and 7 females aged 11-54 (34.3±16.2) years. Extra anatomic bypass grafting was performed in 8 patients and anatomic correction was performed in 4 patients. The operations were successful in all patients. There was no perioperative death. The average cardiopulmonary bypass time was 203.0±46.0 min (7 median incision patients), and the average intraoperative blood loss was 665.0±102.0 mL. The average postoperative ventilator support time was 32.3±7.5 h, and the average postoperative hospital stay time was 10.2±4.3 d. The mean drainage volume of median incision was 1 580.0±360.0 mL, and the mean drainage time was 9.3±2.7 d. The mean drainage volume of left thoracotomy was 890.0±235.0 mL, and the mean drainage time was 4.8±2.5 d. One patient had a transient hoarse after operation and recovered 6 months later. The follow-up period ranged from 2 to 10 years with an average time of 81.0±27.0 months. All patients had a recovery of hypertension, cardiac afterload after 2 years postoperatively. One patient who received an artificial blood vessel replacement in situ was examined stenosis recurrence at the third year after discharge. Others were asymptomatic during the follow-up period. There were no death or other complications. Conclusion The treatment strategy for complex aortic coarctation should be individualized according to the anatomical features and concomitant heart diseases. Extra anatomic bypass technique is a safe and feasible choice.