ObjectiveTo investigate the protective effect and mechanism of action of flavonoid combination of Alpiniae Officinarum Rhizoma （A. officinarum） against Helicobacter pylori （H. pylori） gastritis in mice. MethodsAfter acclimatization for one week， 56 SPF-grade healthy C57BL/6J mice were gavaged with mixed antibiotics for three consecutive days. They were randomly divided into a normal group， model group， positive drug group （triple therapy group）， and low- and high-dose groups （100， 200 mg·kg^-1） of flavonoid combination of A. officinarum. The H. pylori gastritis mice model was established by gavage with H. pylori bacterial suspension in each group except for the normal group. After successful modeling， mice were administrated with corresponding drugs once a day for two weeks. Hematoxylin-eosin （HE） staining was used to observe the pathological changes in gastric tissue. Rapid urease test paper was used to detect the positive rate of H. pylori. Silver staining was used to observe the H. pylori adherence on the surface of gastric tissue. Immunohistochemistry was used to detect the protein expression of interleukin-8 （IL）-8 and myeloid differentiation factor （MyD88） in gastric tissue. The serum levels of IL-6， tumor necrosis factor-α （TNF-α）， IL-8， and IL-1β were detected by enzyme-linked immunosorbent assay （ELISA）. The expressions of phosphatidylinositol 3-kinase/protein kinase B （PI3K/Akt） protein were detected by Western blot. ResultsCompared with those in the normal group， mice in the model group had lower gastric weight coefficients， higher pH of gastric juice， 100% H. pylori infection rate， and significantly changed gastric histopathology. The expressions of IL-8 and MyD88 proteins in the gastric tissue of mice in the model group were significantly elevated， and the serum levels of inflammatory factors IL-6， TNF-α， IL-8， and IL-1β were significantly up-regulated in mice. Compared with that in the model group， the gastric weight coefficient of mice in each treatment group of the flavonoid combinations of A. officinarum was elevated （P<0.01）， and the pH of gastric juice was reduced （P<0.01）. The infection rate of H. pylori was reduced. The expressions of IL-8 and MyD88 proteins in the gastric tissue of mice in the treatment groups were significantly reduced （P<0.01）， and the serum levels of inflammatory factors IL-6， TNF-α， IL-8， and IL-1β were significantly reduced in a dose-dependent manner （P<0.01）. The flavonoid combinations of A. officinarum down-regulated the expression of PI3K and Akt proteins in H. pylori gastritis-infected cells （P<0.01）. ConclusionThe protective effect of flavonoid combinations of A. officinarum against H. pylori gastritis is associated with the inhibition of H. pylori infection rate and regulation of PI3K/Akt signaling pathway， resulting in inhibiting the release of inflammatory factors.

Natural products (NPs) have long held a significant position in various fields such as medicine, food, agriculture, and materials. The chemical space covered by NPs is extensive but often underexplored. Therefore, high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems. Mass spectrometry (MS) has emerged as a powerful platform for the annotation and discovery of NPs. MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information. Additionally, the released annotation methodologies, based on a variety of informatics tools, continuously improve the ability to annotate the structure and properties of compounds. This review examines the current mainstream databases and annotation methodologies, focusing on their advantages and limitations. Prospects for future technological advancements are then discussed in terms of novel applications and research objectives. Through a systematic overview, this review aims to provide valuable insights and a reference for MS-based NPs annotation, thereby promoting the discovery of novel natural entities.
Biological Products/chemistry* ; Mass Spectrometry/methods* ; Databases, Factual ; Drug Discovery/methods* ; Humans

Objective:To investigate the implementation effect of the Diagnosis-Related Group(DRG)payment reform.Methods:Taking Panzhihua City in Sichuan Province,which implemented the DRG payment reform in 2018,as the treatment group,and the remaining 20 non-pilot prefecture-level administrative regions within the province as the control group,the panel data was constructed.The Synthetic Control Method(SCM)was employed to evaluate the effects of the DRG payment reform.Results:Four years after the implementation,the inpatient diagnosis and treatment costs in Panzhihua City were approximately 1200 yuan lower,and drug costs were about 500 yuan lower than the synthetic control group.Conclusion:The DRG payment reform has led to a significant reduction in both inpatient diagnosis and treatment costs and drug expenses,effectively mitigating the rapid growth of medical insurance fund expenditures.It is recommended to leverage artificial intelligence to optimize the precision of DRG grouping,utilize the technological outreach of national medical centers,and promote synergy between the DRG payment reform and public hospital performance reform,construct a medical service payment model that"controls costs,improves quality,and promotes efficiency",thereby enhancing the effectiveness of the DRG payment reform.

Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Objective: To evaluate the effectiveness of "Internet plus" continuous intervention on psychological status and nursing satisfaction of patients with depression after hospital discharge, so as to provide the reference for reducing the recurrence risk of patients with depression and improving the quality of life. Methods: From January to December 2024, patients with mild to moderate depression who were hospitalized in a tertiary grade-a mental health specialized hospital in Wenzhou City and met the discharge criteria were selected as the research objects. The patients were divided into the control group and the intervention group according to a ratio of 1∶1 by the random number table method. Hamilton Depression Scale, Hamilton Anxiety Scale, and Nursing Satisfaction Questionnaire were used to evaluate depressive symptoms, anxiety symptoms, and nursing satisfaction before and after intervention. Covariance analysis was used to compare the differences between the two groups before and after the intervention. Results: A total of 62 patients with mild to moderate depression were enrolled, with 31 patients in the intervention group and 31 patients in the control group. Before the intervention, there were no statistically significant differences in gender, age, course of disease, educational level, marital status, depression symptoms score, anxiety symptoms score, and nursing satisfaction score between the two groups (all P>0.05). After the intervention, the scores of depression and anxiety symptoms in the intervention group decreased by 8.87 and 5.01 points, respectively, compared with those before the intervention, and the scores of depression and anxiety symptoms in the control group decreased by 2.52 and 1.16 points, respectively (all P<0.05). After the intervention, the scores of depression and anxiety symptoms in the intervention group decreased more than those in the control group (both P<0.05). The nursing satisfaction score of the intervention group increased by 6.57 points on average compared with that before the intervention, and that of the control group increased by 4.23 points on average (both P<0.05). There was no statistically significant difference in the increase of nursing satisfaction scores between the two groups before and after intervention (P>0.05). Conclusion The "Internet plus" continuous intervention has a good effect on improving the depressive symptoms and anxiety symptoms of patients with depression after haspital discharge, which can consolidate the treatment effect and improve nursing satisfaction.

Objective:To investigate the implementation effect of the Diagnosis-Related Group(DRG)payment reform.Methods:Taking Panzhihua City in Sichuan Province,which implemented the DRG payment reform in 2018,as the treatment group,and the remaining 20 non-pilot prefecture-level administrative regions within the province as the control group,the panel data was constructed.The Synthetic Control Method(SCM)was employed to evaluate the effects of the DRG payment reform.Results:Four years after the implementation,the inpatient diagnosis and treatment costs in Panzhihua City were approximately 1200 yuan lower,and drug costs were about 500 yuan lower than the synthetic control group.Conclusion:The DRG payment reform has led to a significant reduction in both inpatient diagnosis and treatment costs and drug expenses,effectively mitigating the rapid growth of medical insurance fund expenditures.It is recommended to leverage artificial intelligence to optimize the precision of DRG grouping,utilize the technological outreach of national medical centers,and promote synergy between the DRG payment reform and public hospital performance reform,construct a medical service payment model that"controls costs,improves quality,and promotes efficiency",thereby enhancing the effectiveness of the DRG payment reform.

Objective To establish the therapeutic effect prediction model of platelet transfusion in hematological patients,and receiver operating characteristic(ROC)curve and clinical cases are used to evaluate the clinical application value of the predic-tion model.Methods A total of 485 patients with hematological diseases who received platelet transfusion therapy in Kailuan General Hospital from January 2020 to December 2023 were selected,corrected count increment(CCI)was used to divide the patients into platelet transfusion effective group(n=340)and transfusion ineffective group(n=145).Multivariate Logistic regres-sion analysis was used to establish the prediction model of platelet infusion efficacy,and ROC curve was used to evaluate the application effect of the forcasting model.109 clinical cases were used to verify the practical application effect of the model,and the sensitivity,specificity and accuracy were calculated.Results Among 485 patients with hematological diseases,the incidence of ineffective platelet transfusion was 29.90%(145/485).Compated with the effective group,the ineffective group had more previous platelet transfusions was higher,and the difference was statistically significant(t=-4.435,P<0.05).In the ineffective group,there were more cases of hyperplenism,aplastic anemia and lymphoma,higher infection rate and higher positive rate of platelet antibody,and the differences were statistically significant(χ2=6.301～37.522,all P<0.05).Multivariate Logistic regres-sion analysis found that previous platelet infusion times,infection,leukemia,aplastic anemia and platelet antibodies were risk factors for ineffective platelet transfusion in patients with hematological diseases(Wald χ2=5.224～21.548,all P<0.05).Based on these risk factors,platelet infusion effect prediction models 1 and 2 were constructed.ROC curve was used to evaluate the application effect of the prediction model.The area under the curve(AUC),cut-offpoint,sensitivity and specificity of model 1 were 0.884,0.042,82.35%,88.89%.The AUC,cut-offpoint,corresponding sensitivity and specificity of prediction model 2 were 0.910,59.784,81.18%,94.44%,respectively.The Z values of model 1 and model 2 were 12.159 and 13.151,respectively.The prediction effect of model 2 was better than that of model 1.The actual application results showed that the sensitivity,specificity and accuracy of prediction model 1,2 were 85.71%,92.05%,90.89%and 90.48%,93.18%,92.66%,respectively.Conclusion The ineffective rate of platelet transfusion in hematological patients is relatively high.The prediction models 1 and 2 for platelet transfusion effectiveness have good results in predicting ineffective platelet transfusion,and prediction model 2 is better than pre-diction model 1,which can provide reliable basis for hematological patients on accurate platelet transfusion.

Particle radiotherapy is a new type of radiotherapy with superior targeting and precision for more accurate radiotherapy.Considering the complexity of the particle beams and the different lesion sites and physique of patients,a high-precision beam monitoring system which enables the monitoring of the spatial and energy distributions of the beams,such as position,dose and other important parameters,is needed to ensure the efficacy of particle radiotherapy.The beam monitoring system plays a crucial role in the safety and accuracy of particle therapy.By monitoring vital parameters such as the position and dose of the beams,the treatment plan can be adjusted in real-time,thus ensuring accurate lesion irradiation while minimizing the effects on the surrounding healthy tissues.Consequently,the development and validation of such a beam monitoring system are essential in particle radiotherapy.Herein the advances in beam monitoring for quality assurance for particle therapy are summarized.It gives a brief description of particle radiotherapy,emphasizing the significance of beam monitoring systems for particle radiotherapy,focuses on the development of particle beam monitoring and introduces different types of detectors,along with their applications and research advancements,and explores the future development trend of beam monitoring system and potential challenges it may face.

Da Chaihutang from the Treatise on Febrile Disease （Shanghanlun） has the function of harmonizing lesser Yang and discharging internal heat. It is formulated by ZHANG Zhongjing for the concurrent diseases of lesser Yang and Yang brightness and has been widely used in the treatment of digestive system diseases， especially malignant tumors. By review of the articles published in the last 20 years， this paper summarizes the application of Da Chaihutang in treating digestive system tumors from syndrome analysis， clinical research， and mechanism research. Da Chaihutang can treat the syndrome involving lesser Yang and Yang brightness in the digestive system， release interior and exterior to expel pathogen， and remove obstruction by conforming to the descending nature of the six fu-organs. In clinical practice， Da Chaihutang can directly treat digestive system malignant tumors such as liver cancer， pancreatic cancer， intestinal cancer， gastric cancer， and gallbladder cancer. In addition， it can relieve common complications of digestive system malignant tumors， such as cancerous fever， malignant obstructive jaundice， and constipation. Moreover， it can alleviate the adverse reactions caused by Western medical treatment， such as post-embolization syndrome， side effects of chemotherapy， and incomplete postoperative obstruction. Da Chaihutang is effective when used alone as it can relieve clinical symptoms， improve prognosis， and prolong survival of advanced patients and is safe and non-toxic， suitable for long-term use by tumor patients. Regarding the mechanism， Da Chaihutang can promote the apoptosis and inhibit the proliferation of tumor cells， reduce inflammation and inflammatory injury， and improve the liver function. The clear effect and mechanism confirms the anti-tumor effect of Da Chaihutang. This paper comprehensively describes the current research status of Da Chaihutang in the treatment of digestive system tumors and puts forward the deficiencies and improvement measures for the current research， aiming to provide reference for the application of this formula in treating digestive system tumors， the establishment of Chinese and Western medicine treatment schemes of tumors， and the research and development of anti-tumor drugs.

Objective To evaluate the acute exacerbation of chronic obstructive pulmonary disease(COPD)(AECOPD)status via combining clinical data,lung function parameters with CT radiomic features based on machine learning method.Methods A total of 343 COPD patients,including 158 AECOPD patients and 185 non-AECOPD patients were retrospectively selected and randomly divided into training and testing sets at a ratio of 7∶3.The radiomics features were calculated after automatically delineating the whole lung volume of interest(VOI).Five machine learning methods were used to construct the AECOPD diagnostic model,then the corresponding Radiomics score(Rad-score)was calculated in the training set and was validated in the testing set.The logistic-combined model was established after integrating age,Global Initiative for Chronic Obstructive Lung Disease(GOLD)classification,vital capacity(VC),forced vital capacity(FVC),forced expiratory volume in one second(FEV1),FEV1％pred,FEV1/FVC％,peak expiratory flow(PEF),maximum ventilatory volume(MVV),and Rad-score value.The area under the curve(AUC)of receiver operating characteristic(ROC)curve was calculated to evaluate the evaluated performance of all models.Results The logistic regression model had the best diagnostic performance,with AUC of 0.724 and 0.758 in the training and testing sets,respectively.The performance of the logistic-combined model to diagnose AECOPD was superior to that of the single logistic regression model,with the AUC of 0.777 and 0.760 in the training and testing sets,respectively.Conclusion A combination strategy including clinical data,lung function parameters,and CT radiomics may be helpful to diagnose AECOPD status,with moderate diagnostic performance.