Objective: To address the lack of fine-grained clinical recognition for specific Yang deficiency syndrome subtypes and the limitations of conventional object detection models in extracting irregular, low-contrast tongue phenotypes. This study aims to develop an objective subtype recognition framework based on an improved You Only Look Once nano (YOLO11n) architecture, using a standardized visual phenotype matrix to translate macroscopic traditional Chinese medicine (TCM) descriptions into quantifiable clinical targets. Methods: This cross-sectional diagnostic study consecutively enrolled adult inpatients admitted to the Department of Thyroid and Breast Surgery, The First Affiliated Hospital of Wannan Medical University (Yijishan Hospital), between September 1, 2024 and June 1, 2025, who were suspected of having Yang deficiency constitution based on initial TCM consultation. Clinical tongue image data were collected for analysis. Based on an Expert Visual Phenotype Annotation Matrix, a five-category recognition system was established, including the following TCM syndrome subtypes: spleen-dampness exuberance syndrome, mild kidney Yang deficiency syndrome, upper heat and lower cold syndrome, simultaneous Yin-Yang deficiency syndrome, and Yin deficiency and fluid depletion syndrome (negative control). The proposed Yang deficiency YOLO (YD-YOLO) model, built upon the YOLO11n baseline, integrates the Cross Stage Partial with kernel size 2 (C3k2)-GhostBottleneck-Dynamic Convolution (GBDC) module into the backbone to adaptively extract low-contrast features, and embeds the multipath aggregation coordinate attention (MACA) mechanism into the neck to suppress background interference through multi-scale spatial coordination. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize feature attribution and evaluate the biological plausibility of the model’s focus. Model performance was evaluated through ablation and comparative experiments using mean average precision (mAP), precision, recall, F1 score, inference speed (frames per second, FPS), overall accuracy, Cohen’s kappa, and the area under the receiver operating characteristic (ROC) curve (AUC). Results: Based on the final inclusion of 1 186 clinical cases, the YD-YOLO model had an overall accuracy of 91.5%, a Cohen’s kappa of 0.912, and an mAP@50 of 0.731 [higher than the YOLO11n baseline (0.681)], with AUC ranging from 0.91 to 0.97 across all TCM syndrome subtypes. Among the TCM syndrome subtypes, the mild kidney Yang deficiency syndrome had the highest mAP@50 (0.900), and the inference speed reached 89.00 FPS. Grad-CAM analysis showed that the model localized activation to key TCM pathological features, such as marginal tooth marks and focal root coatings, while suppressing non-diagnostic oral background noise. Conclusion The YD-YOLO model demonstrates the feasibility of deep learning for the fine-grained classification of TCM Yang deficiency subtypes. By integrating visual phenotype quantification with model interpretability, the proposed framework provides an objective basis for syndrome differentiation, supporting the development of standardized digital diagnostic systems and the provision of clinical decision support in TCM practice.

Armeniacae Semen Amarum， the dried mature seeds of plants such as Prunus armeniaca var. ansu （Rosaceae）， have a long history for medicinal use. Modern research indicates that the core active compounds of Armeniacae Semen Amarum， such as amygdalin， fatty acids， and volatile oils， exhibit multiple pharmacological effects including antitussive， bronchodilatory， anti-inflammatory， antioxidant， antitumor， antifibrotic， and immunomodulatory effects. Consequently， their potential applications in treating respiratory diseases， tumors， tissue injury， and metabolic disorders have garnered significant attention. However， a systematic integrated analysis of the relationship between the traditional efficacy， modern pharmacology， key material basis， and multi-pathway mechanisms of Armeniacae Semen Amarum remains lacking. Using bibliometric methods combined with CiteSpace visualization technology， this study systematically summarizes the research hotspots on Armeniacae Semen Amarum and its representative component amygdalin， including the key roles in inflammation regulation， apoptosis induction， cell cycle arrest， oxidative stress balance， and fibrosis modulation. This article reviews the research progress in Armeniacae Semen Amarum across from four major dimensions—traditional applications， visualization analysis， material basis， and modern pharmacological studies， providing a reference for the further basic and clinical studies of Armeniacae Semen Amarum in respiratory diseases， oncology， and immune-related disorders.

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.

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 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.

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.

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 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 construct a diagnostic model for the phlegm-dampness obstruction syndrome in patients with coronary heart disease stable angina pectoris(CSAP);To provide a reference for clinical syndrome differentiation.Methods Totally 305 patients'clinical data were collected from the Department of Cardiology,Dongying Hospital Affiliated to Shandong University of Traditional Chinese Medicine,from May 2022 to January 2024.The least absolute shrinkage and selection operator(LASSO)was used to select features,and multiple models were constructed and compared using machine learning(ML)algorithms.The optimal ML model was selected for training,validation,and testing.Finally,the operational logic of the optimal model was explained using Shapley Additive Explanations(SHAP),and two typical examples were provided to help users understand the model's operational logic.Results LASSO regression identified chest pain,body mass index(BMI),limb heaviness,drinking history,age,triglycerides(TG),total cholesterol(TC),and low-density lipoprotein cholesterol(LDL-C)as features included in the model.After comparing multiple models,the Gaussian Naive Bayes(GNB)model demonstrated the best performance.The final constructed GNB model achieved an average AUC of 0.938(95%CI:0.903-0.972)in the training set,an average AUC of 0.927(95%CI:0.851-0.992)in the validation set,and an AUC of 0.856(95%CI:0.751-0.961)in the test set.The learning curve showed that the error between the training and validation sets in the model converged as the number of training samples increased.The calibration curve showed that the model had good consistency in predicting the probability of observed phlegm-dampness obstruction syndrome patients.The clinical decision curve(DCA)showed that the model could provide clinical benefits for patients at a decision threshold below 0.7.The features ranked by SHAP importance in order were chest pain,BMI,LDL-C,TG,limb heaviness,TC,drinking history and age.Conclusion The diagnostic model for CSAP phlegm-dampness obstruction syndrome constructed in this study can assist physicians in the syndrome differentiation of patients,thereby enabling the formulation of integrated clinical treatment plans combining traditional Chinese and Western medicine,and aiding patients in achieving better clinical therapeutic outcomes.