ObjectiveThis study aimed to establish a monocrotaline （MCT）-induced pulmonary arterial hypertension （PAH） rat model to systematically evaluate the protective effect of Xiao Qinglongtang （XQLT） on right cardiac function in model rats and further elucidate the underlying regulatory mechanism. MethodsSixty male SD rats were randomly assigned to the normal group， model group， XQLT low-， medium-， and high-dose groups （XQLT-L/M/H）， and the beraprost sodium tablet group （BST）. Except for the normal group， rats in all other groups were given a single subcutaneous injection of MCT （60 mg·kg^-1） to induce PAH. Three weeks after injection， rats in the XQLT-L/M/H groups were administered XQLT intragastrically at 3.07， 6.14， 12.28 g·kg^-1·d^-1， respectively. Rats in the BST group received beraprost sodium at 12.6 μg·kg^-1·d^-1， and rats in the model group received an equal volume of saline. All treatments lasted for 3 weeks. Right ventricular systolic pressure （RVSP） was measured by right ventricular catheterization. Cardiac function was assessed by echocardiography. The right ventricle was weighed to calculate the right ventricular hypertrophy index （RVHI）. Hematoxylin-eosin （HE） staining， Masson staining， and transmission electron microscopy were used to observe myocardial morphology. Serum metabolomic changes were analyzed using ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS）. Data-independent acquisition （DIA） proteomics was used to detect differentially expressed （DE） proteins in the right ventricle， and Western blot was used to measure the expression of uncoupling protein 3 （UCP3）， phosphatidylinositol 3-kinase catalytic subunit p110α （PIK3CA）， L1 cell adhesion molecule （L1CAM）， and quinone oxidoreductase （CRYZ）. UPLC-MS/MS was used to analyze the chemical components of XQLT. ResultsCompared with the normal group， the model group showed significantly increased RVSP and RVHI （P<0.05）， along with pathological changes in myocardial morphology. Compared with the model group， all XQLT-treated groups exhibited reductions in RVSP and RVHI as well as significant improvements in cardiac function and myocardial morphology. Among the XQLT groups， XQLT-M showed the most pronounced effects （P<0.05）， comparable to the BST group. Serum metabolomics revealed 105 differential metabolites in the XQLT groups versus the model group ［variable importance in projection （VIP） >1， P<0.05］， including 58 upregulated and 47 downregulated metabolites. KEGG enrichment analysis indicated that XQLT intervention downregulated phenylalanine metabolism （P<0.01） and upregulated unsaturated fatty acid biosynthesis （P<0.05）. Proteomics analysis showed that 982 DE proteins were identified in the MCT groups versus the normal group， including 455 upregulated and 527 downregulated proteins （|fold change （FC）| >1.3， P<0.05）. Compared with the model group， 237 DE proteins were identified in the XQLT groups， including 124 upregulated and 113 downregulated proteins （|FC| >1.3， P<0.05）， with 57 overlapping DE proteins. KEGG enrichment suggested that XQLT mainly modulated pathways related to mineral absorption， ribosomal biogenesis， peroxisomes， glycolysis/gluconeogenesis， spliceosomes， and thyroid hormone signaling. Western blot analysis showed that， compared with the model group， XQLT increased the expression of UCP3， PIK3CA， and L1CAM， while decreasing the expression of CRYZ （P<0.05）. ConclusionXQLT exerts a protective effect on right heart function in MCT-induced PAH rats， and its mechanism is associated with maintaining myocardial homeostasis and alleviating right ventricular remodeling.

To systematically analyzed the reporting status of core elements in publicly available clinical practice guideline(hereafter referred to as "guideline") protocols published domestically and internationally over the past decade, identified existing problems, and provided evidence to inform the standardized writing and publication of future guideline protocols.

Objective To explore the effectiveness of the cumulative sum (CUSUM) and the exponentially weighted moving average (EWMA) for early warning of influenza epidemic using two datasets of reported influenza cases and influenza-like illness (ILI) cases. Methods Using the reported cases of influenza and ILI in Daxing District, Beijing, from week 23 of 2018 to week 22 of 2024 as data sets, the CUSUM and EWMA models were established, respectively. The positive rate of influenza etiology was used as the ^“gold standard^”, and the Youden index was used as the evaluation index to compare the early warning effect of the two models under different data sets and different parameters. Results In CUSUM, the optimal Youden indices of the reported influenza cases set and the ILI cases set were 0.751 and 0.635, respectively. In EWMA, the optimal Youden indices of the reported influenza cases set and the ILI cases set were 0.544 and 0.464, respectively. The optimal EWMA and CUSUM models could both issue early warning signals in advance of the ^“gold standard^”. Conclusion In the influenza epidemic early warning in Daxing District, Beijing, the CUSUM model established with the reported cases of influenza can achieve good early warning effects, but the model parameters need to be dynamically adjusted according to the local epidemic characteristics.

Objective To analyze the clinical and therapeutic characteristics of Epstein-Barr virus (EBV)-negative posttransplant lymphoproliferative disease (PTLD) with diffuse large B-cell lymphoma (DLBCL) in the context of specific cases and literature. Methods A case of EBV-negative DLBCL (GCB type) after kidney transplantation is reported. The patient was a 45-year-old male who underwent living-related kidney transplantation in 2016 and has been receiving triple immunosuppressive therapy with tacrolimus, mycophenolate mofetil and methylprednisolone since then. In 2024, the patient presented with intermittent fever, night sweats and gastrointestinal symptoms. The diagnosis was confirmed by endoscopic pathology, immunohistochemical staining and positron emission tomography/computed tomography. The R-CDOP regimen (rituximab + cyclophosphamide + liposomal doxorubicin + vincristine + dexamethasone) was used for treatment. Results The patient was diagnosed with EBV-negative DLBCL (GCB type, Ann Arbor stage Ⅳ B). After 4 cycles of R-CDOP chemotherapy, the efficacy assessment was partial remission, and the transplant kidney function remained stable. Conclusions For EBV-negative PTLD after kidney transplantation, it is necessary to break through the "virus-dependent" diagnostic thinking. In clinical practice, the focus should be on protecting the transplant kidney, and individualized treatment plans should be developed for patients.

Myasthenia gravis is an autoimmune disorder characterized by impaired neuromuscular transmission. Thymectomy is one of the therapeutic options for acetylcholine receptor antibody-positive myasthenia gravis patients. The quality of perioperative care is directly associated with surgical safety and patient outcomes. However, there is currently a lack of specialized nursing consensus or guidelines specifically addressing the care of these patients domestically or internationally. To promote the standardization and normalization of perioperative nursing care for myasthenia gravis patients undergoing thymectomy and to ensure treatment efficacy, a panel of 57 experts from relevant fields was convened. Based on evidence-based medicine and clinical practice experience, discussions were held on various aspects including condition assessment, nutritional support, medication management, and airway care, resulting in a consensus with 18 final recommendations by using the Delphi method through two rounds of expert consultation. This consensus aims to provide a scientific reference for the perioperative nursing care of myasthenia gravis patients undergoing thymectomy.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

Chronic pancreatitis （CP） is a common disease in clinical practice characterized by progressive inflammatory fibrosis of the pancreas. Gut microbiota， known as the “second genome” of humans， bidirectionally modulates the progression of fibrosis in CP via the gut-pancreas axis. This article systematically elaborates on the characteristics of gut microbiota during the progression of CP and its molecular mechanism in mediating pancreatic fibrosis through bacterial translocation， metabolites， immune regulatory networks， and microbe-pancreatic stellate cell interactions， with a focus on the pivotal role of short-chain fatty acids and inflammatory cytokine networks in pancreatic stellate cell activation and extracellular matrix deposition. In addition， this article explores the potential value of gut microbiota-targeted interventions in the prevention and treatment of CP fibrosis， such as probiotics， prebiotics， and fecal microbiota transplantation， and discusses the translational potential of using multi-omics technologies to identify diagnostic biomarkers and novel therapeutic targets for CP， in order to provide new ideas for the precise diagnosis and treatment of CP.

OBJECTIVE To introduce the development of an intelligent prescription review decision-support system for anti-tumor drugs and assess its clinical application outcomes. METHODS Relevant data sources， including national and local pharmaceutical administration policies， clinical practice guidelines/consensus， hospital information systems data， and genetic testing results， were integrated. Adhering to the principles of structure， standardization and dynamic updating， a knowledge base covering chemotherapeutic， targeted and immunotherapeutic agents was constructed using a dual-dimensional modeling approach that combined “drug attributes” and “clinical contexts”. This knowledge base was then embedded into the hospital’s electronic medical order system to establish the prescription review decision-support system. The application and performance of the system were evaluated at Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine. RESULTS A knowledge base containing 18 318 prescription review rules for anti-tumor drugs was constructed， and a closed-loop prescription review system was successfully established， encompassing pre-prescription real-time intervention， in-process interactive review， and post-prescription evaluation and analysis. From 2021 to 2024， the system generated a total of 57 879 alerts for prescriptions of five typical categories of anti-tumor drugs. For platinum-containing prescriptions， 22 577 alerts were generated， with Cisplatin for injection （lyophilized） being the most frequently alerted drug （13 445 alerts）， and “ototoxicity risk due to combined use” alerts remained high （7 682 alerts）. For methotrexate-containing prescriptions， 3 721 alerts were recorded， primarily related to “precaution-related issues” （76.4%， 2 843/3 721）. For doxorubicin-containing prescriptions， 17 301 alerts were triggered， primarily related to “dosage and administration” （14 315 alerts）. For human epidermal growth factor receptor 2-targeted agents-containing prescriptions， 1 007 alerts were issued， mostly related to “reimbursement restrictions” （956 alerts）. For programmed death-1/programmed death-ligand 1 inhibitors-containing prescriptions， the alerts increased year by year， totaling 13 273 alerts， primarily related to “inappropriate indication” （9 118 alerts）. Over the 4 years， the physician response rates to system alerts were 21.4%， 27.1%， 33.5% and 51.6%， respectively. CONCLUSIONS An intelligent decision-support system for anti-tumor drug prescription review， encompassing a closed-loop process of “real-time pre-event intervention， interactive in-event prescription review， post-event evaluation and analysis”， has been successfully constructed and implemented throughout the entire workflow. There is a discernible trend in this hospital， where the focus on monitoring anti-tumor drugs is shifting towards immunotherapy drugs. Additionally， the acceptance rate of physicians regarding prescription review opinions has been steadily increasing year by year.

The importance of consensus research in medical decision-making has become increasinglyprominent. However, this field has long lacked unified terminology definitions and reporting standards, leading to significant heterogeneity in study design, implementation, and result presentation that affects the credibility and reproducibility of outcomes. The ACCurate COnsensus Reporting Document (ACCORD) in the field of biomedical research provides a structured writing framework for various consensus methods such as the Delphi method and nominal group technique, aiming to enhance the completeness and transparency of study reports. Combined with specific cases, this article interprets the core items of ACCORD, offering references for the design, implementation, and reporting of high-quality consensus research in China.