The prescription of Qidong Yixin oral liquid is derived from the experience of national medical master Ren Jixue in treating viral myocarditis （VMC）. It has the functions of tonifying Qi， nourishing the heart，calming the mind， and relieving palpitations. It is used to treat VMC and angina pectoris of coronary heart disease caused by deficiency of both Qi and Yin. However，the understanding of its efficacy evidence， advantageous aspects， dosage and administration， and medication safety remains insufficient in clinical practice. Therefore，the development of the Expert Consensus on the Clinical Application of Qidong Yixin Oral Liquid （hereinafter referred to as consensus） was initiated. Consensus strictly followed the process and methods of the expert consensus on the clinical application of Chinese patent medicines of the China Association of Chinese Medicine，successively completing multiple tasks such as the consensus project initiation，determination of clinical problems，evidence search and evaluation，formation of recommendation opinions and consensus suggestions，solicitation of opinions，peer review， submission for review and release， and so on. Consensus formed a total of 10 recommendation opinions and 12 consensus suggestions，clarifying the clinical positioning，efficacy advantages，syndrome differentiation，dosage and administration，combination therapy，timing of medication，adverse reactions，contraindications， and precautions of Qidong Yixin oral liquid，indicating that it has good clinical advantages and safety in the treatment of VMC and angina pectoris of coronary heart disease，providing norms and references for physicians to safely and rationally apply Qidong Yixin oral liquid. Consensus was reviewed and approved for release by the Standardization Office of the China Association of Chinese Medicine on December 23， 2024. Standard number：GSCACM-376-2024.

OBJECTIVE To investigate the effects and mechanisms of Lycium ruthenicum Murr. in improving sleep. METHODS Network pharmacology was employed to identify the active components of L. ruthenicum and their associated disease targets， followed by enrichment analysis. A caffeine‑induced zebrafish model of sleep deprivation was established ， and the zebrafish were treated with L. ruthenicum Murr. extract （LRME） at concentrations of 0.1， 0.2 and 0.4 mg/mL， respectively； 24 h later， behavioral changes of zebrafish and pathological alterations in brain neurons were subsequently observed. The levels of inflammatory factors [interleukin-6 （IL-6）， IL-1β， IL-10， tumor necrosis factor-α （TNF-α）]， oxidative stress markers [superoxide dismutase （SOD）， malondialdehyde （MDA）， glutathione peroxidase （GSH-Px）， catalase （CAT）]， and neurotransmitters [5- hydroxytryptamine （5-HT）， γ-aminobutyric acid （GABA）， glutamic acid （Glu）， dopamine （DA）， and norepinephrine （NE）] were measured. The protein expression levels of protein kinase B1 （AKT1）， phosphorylated AKT1 （p-AKT1）， epidermal growth factor receptor （EGFR）， B-cell lymphoma 2 （Bcl-2）， sarcoma proto-oncogene，non-receptor tyrosine kinase （SRC）， and heat shock protein 90α family class A member 1 （HSP90AA1） in the zebrafish were also determined. RESULTS A total of 12 active components and 176 intersecting disease targets were identified through network pharmacology analysis. Among these， apigenin， naringenin and others were recognized as core active compounds， while AKT1， EGFR and others served as key targets； EGFR tyrosine kinase inhibitor resistance signaling pathway was identified as the critical pathway. The sleep improvement rates in zebrafish of LRME low-， medium-， and high-dose groups were 54.60%， 69.03% and 77.97%， 开发。E-mail：hjp_yft@163.com respectively， while the inhibition ratios of locomotor distance were 0.57， 0.83 and 0.95， respectively. Compared with the model group， the number of resting counts， resting time and resting distance were significantly increased/extended in LRME medium- and high-dose groups （P＜0.05）. Neuronal damage in the brain was alleviated. Additionally， the levels of IL-6， IL-1β， TNF-α， MDA， Glu， DA and NE， as well as the protein expression levels of AKT1， p-AKT1， EGFR， SRC and HSP90AA1， were markedly reduced （P＜0.05）， while the levels of IL-10， SOD， GSH-Px， CAT， 5-HT and GABA， as well as Bcl-2 protein expression， were significantly elevated （P＜0.05）. CONCLUSIONS L. ruthenicum Murr. demonstrates sleep-improving effects， and its specific mechanism may be related to the regulation of inflammatory responses， oxidative stress， neurotransmitter balance， and the EGFR tyrosine kinase inhibitor resistance signaling pathway.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To develop and validate a nomogram model that integrates systemic inflammatory markers to predict the likelihood of pelvic lymph node metastasis(PLNM)in prostate cancer patients prior to surgery.Methods This study retrospectively analyzed the clinical data and preoperative inflammatory markers—including neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),and monocyte-to-lymphocyte ratio(MLR)—of patients diagnosed with prostate cancer.Univariate and multi-variate logistic regression analyses were conducted to identify markers that were significantly associated with PLNM.Based on the results of the multivariate analysis,a nomogram was developed and its predictive accuracy was assessed using receiver operating characteristic curves(ROC)and calibration plots.Results Among the 334 enrolled patients with prostate cancer,107 were identified with PLNM.Univariate analysis revealed statistically significant differences in free prostate-specific antigen(fPSA),Gleason score,NLR,PLR,MLR,and SII between the PLNM and non-pelvic lymph node metastasis(NPLNM)groups(P<0.05).Multivariate analysis confirmed that fPSA,Gleason score,and SII were independent predictors of PLNM(P<0.05).A nomogram incorporating these predic-tors exhibited strong discriminative ability,with an area under the ROC curve(AUC)of 0.79(95%CI:0.73～0.84).Calibration analysis further demonstrated good consistency between the predicted and observed probabilities of PLNM.Conclusions This study successfully developed a nomogram model based on systemic inflammatory markers for preoperative prediction of pelvic lymph node metastasis in prostate cancer.Owing to its user-friendly design and high predictive accuracy,this tool may serve as a valuable complementary method to conventional imaging techniques,thereby supporting personalized treatment decision-making.

Objective: To elucidate the molecular mechanism by which prohibitin 2(PHB2) mediates periodontitis-induced bone tissue inflammation through regulating the nuclear factor kappa B(NF-κB) signaling pathway and its role in irreversible alveolar bone resorption. Methods: Quantitative real-time reverse transcription polymerase chain reaction(qRT-PCR) and immunohistochemistry(IHC) were used to detect the expression differences of inflammatory factors and PHB2 in healthy and inflamed alveolar bone tissues of mice in vivo. In vitro, an inflammatory model was established using lipopolysaccharide(LPS)-induced a mouse calvaria-derived preosteoblastic cell line, subclone E1(MC3T3-E1) cells. Western blot and qRT-PCR were used to clarify the regulatory relationship between PHB2 and inflammatory factors, and immunofluorescence staining was performed to observe changes in PHB2 subcellular localization. PHB2 overexpression plasmids were constructed using molecular cloning, and RNA interference was employed to knock down PHB2 expression to assess its regulatory role in inflammation. Based on RNA-seq data, differential expression analysis based on the negative binomial distribution, version 2(DESeq2) was used for differential expression analysis, and kyoto encyclopedia of genes and genomes(KEGG) pathway enrichment along with gene ontology(GO) functional annotation were performed to identify key signaling pathways and differentially expressed genes. Results: In the mouse periodontitis model, PHB2 expression was significantly upregulated in alveolar bone tissues. In the in vitro inflammatory cell model, PHB2 levels positively correlated with interleukin(IL)-6, IL-1β, and tumor necrosis factor-alpha(TNF-α) levels, and its subcellular localization shifted during inflammation. RNA-seq data and the detection of the level of phosphorylation of p65 protein(p-p65) demonstrated that PHB2 exacerbated inflammatory responses through the NF-κB signaling pathway and was mechanistically linked to upregulation of the upstream chemokine C-X-C motif chemokine ligand 10(CXCL10). Conclusion PHB2 aggravates LPS-induced periodontitis inflammation via the NF-κB signaling pathway, providing new insights into the molecular mechanisms underlying the development of periodontitis.

Pancreatic cancer is a kind of highly malignant tumor with a low survival rate and poor prognosis. The effectiveness of gemcitabine as a first-line chemotherapy drug is limited; however, it can activate dendritic cells and improve antigen presentation which increase the sensitivity of tumor cell to immunotherapy. Although immunotherapy has made some advancements in cancer treatment, the therapeutic benefit of programmed cell death receptor 1/programmed death receptor-ligand 1 (PD-1/PD-L1) blockade therapy remains relatively low. The chemokine C-X-C chemokine ligand 12 (CXCL12) contributes to an immunosuppressive tumor microenvironment by recruiting immunosuppressive cells. The receptor C-X-C motif chemokine receptor 4 (CXCR4), highly expressed in various tumors including pancreatic cancer, plays a crucial role in tumor development and progression. In this study, the anti-tumor immune response of human peripheral blood mononuclear cell (hPBMC) was enhanced using the combination of BsNb PX4 (anti-PD-L1&CXCR4 bispecific nanobody) and gemcitabine. In a co-culture system of gemcitabine-pretreated hPBMCs with tumor cells, the BsNb PX4 synergized gemcitabine to improve the cytotoxic activity of hPBMCs against tumor cells. Flow cytometry analysis confirmed increased ratio of CD8⁺ to CD4⁺ T cells in combination treatment. In NOD/SCID mice bearing pancreatic cancer, the combination treatment exhibited more infiltration of CD8⁺ T cells into tumor tissues, contributing to an effective anti-tumor response. This study presents potential new therapies for the treatment of pancreatic cancer. Ethical approval was obtained for collection of hPBMC samples from the Local Ethics Committee of Shanghai Jiao Tong University. All animal experiments were approved by the Animal Ethic Committee of Shanghai Jiao Tong University (authorizing number: A2024246).

OBJECTIVE To provide medication reference for duloxetine use in clinical settings， particularly for patients with depression in primary medical institutions in Xinjiang that lack therapeutic drug monitoring conditions. METHODS The medical records of 281 depression inpatients taking duloxetine in the First Affiliated Hospital of Xinjiang Medical University from January 2022 to December 2023 were retrospectively collected. They were divided into training set （196 cases） and test set （85 cases） in the ratio of 7∶3. Feature selection was performed by encapsulating random forests （RF） with recursive feature elimination. Four machine learning algorithms， namely support vector machine， RF， extreme gradient boosting （XGBoost） and artificial neural network， were used to construct duloxetine blood concentration prediction model. The prediction performance of the models was evaluated and compared by coefficient of determination （R2）， mean absolute error （MAE） and root mean squared error （RMSE）. The feature of the selected optimal model was explained by Shapley additive explanation method， and the importance ranking of the features and the influence on the prediction results of duloxetine blood concentration were determined. RESULTS A total of 29 characteristic variables were selected， including age， ethnicity， body mass index（BMI）， etc. XGBoost showed the highest R2 （0.808）， and the lowest MAE （7.644） and RMSE （10.808）. The ranking of feature importance for predicting the blood concentration of duloxetine was as follows： BMI＞age＞other 20 feature sets （including liver and kidney function and biochemical indicators）＞daily dosage＞comorbidities＞combination therapy＞ethnicity＞white blood cell count＞hemoglobin＞height. CONCLUSIONS XGBoost model possesses the best prediction performance of duloxetine blood concentration； BMI and age have a greater impact on the prediction of duloxetine blood concentration.

In July 2024,the Diagnosis Related Groups(DRG)2.0 is released based on the Notice from the National Healthcare Security Administration on Issuing the DRG 2.0 and Deepening the Relevant Work.Compared with DRG 1.1,version 2.0 was established based on a wider range of suggestions regarding the Adjacent Diagnosis Related Groups(ADRG),Major Comorbidity or Complication(MCC),and Comorbidity or Complication(CC)from various institutions.A list of disease diagnoses and surgical operations that are not used as grouping rules was compiled,and grouping efficacy was further improved by upgrading the algorithms for MCC and CC with the help of AI.Meanwhile,it is necessary to pay more attention to the number of cases of ADRG,the better methods to list the MCC/CC,the suggestions of various doctors and continuously standardize the data and update the grouping scheme of DRG.

Objective:To construct a model of mitochondria-related genes (Mito-RGs) in hepatocellular carcinoma (HCC), and predict the immune, prognostic and therapeutic characteristics of HCC based on the model, so as to provide a new idea for the diagnosis and treatment of HCC.Methods:The expression profiles of HCC and corresponding clinical information were obtained from the Cancer Genome Atlas (TCGA) database. Univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression were used to construct a prognostic model of HCC based on Mito-RGs, and the International Cancer Genome Consortium-Liver Cancer-RIKEN-Japan ICGC-LIRI-JP dataset were used for validation. GO and KEGG analyses were performed to investigate the signaling pathways enriched for differentially expressed genes in the high- and low-risk groups. Immune infiltration was assessed using CIBERSORT. Single-cell data were used to study the proportion of immune cells in high- and low-risk groups of HCC samples and the relationship with cell proliferation. Cellminer was used to assess the relationship between risk score models and drug sensitivity.Results:A risk-prognostic model of HCC containing seven Mito-RGs ( DTYMK, ACADS, HMGCS2, CYP27A1, TOMM40L, STOM, and AKR1B10) was constructed. High-risk HCC patients had a worse prognosis. Genes upregulated in the high- and low-risk groups of differentially expressed genes were enriched in cell cycle and metabolism-related pathways. Single-cell data showed higher proportions of CD8 + T cells, macrophages and monocytes, and proliferating cells in the high-risk group. CIBERSORT analysis suggested that Treg cells and M0 macrophages were more abundant in the high-risk group, whereas CD8 + T cells and CD4 + memory T cells were less abundant. Patients in the high-risk group were more sensitive to myeloid cell leukemia sequence 1 inhibitor, vincristine, phosphatidylinositol kinase beta subunit inhibitor, and aurora kinase A, while trametinib, selumetinib, extracellular regulated protein kinase, and mitogen-activated extracellular signal-regulated kinase were more effective in the low-risk group. Conclusion:The constructed Mito-RGs model is capable of providing a more accurate assessment of the prognosis and the degree of immune cell infiltration in HCC patients.

Objective:To construct a model of mitochondria-related genes (Mito-RGs) in hepatocellular carcinoma (HCC), and predict the immune, prognostic and therapeutic characteristics of HCC based on the model, so as to provide a new idea for the diagnosis and treatment of HCC.Methods:The expression profiles of HCC and corresponding clinical information were obtained from the Cancer Genome Atlas (TCGA) database. Univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate Cox regression were used to construct a prognostic model of HCC based on Mito-RGs, and the International Cancer Genome Consortium-Liver Cancer-RIKEN-Japan ICGC-LIRI-JP dataset were used for validation. GO and KEGG analyses were performed to investigate the signaling pathways enriched for differentially expressed genes in the high- and low-risk groups. Immune infiltration was assessed using CIBERSORT. Single-cell data were used to study the proportion of immune cells in high- and low-risk groups of HCC samples and the relationship with cell proliferation. Cellminer was used to assess the relationship between risk score models and drug sensitivity.Results:A risk-prognostic model of HCC containing seven Mito-RGs ( DTYMK, ACADS, HMGCS2, CYP27A1, TOMM40L, STOM, and AKR1B10) was constructed. High-risk HCC patients had a worse prognosis. Genes upregulated in the high- and low-risk groups of differentially expressed genes were enriched in cell cycle and metabolism-related pathways. Single-cell data showed higher proportions of CD8 + T cells, macrophages and monocytes, and proliferating cells in the high-risk group. CIBERSORT analysis suggested that Treg cells and M0 macrophages were more abundant in the high-risk group, whereas CD8 + T cells and CD4 + memory T cells were less abundant. Patients in the high-risk group were more sensitive to myeloid cell leukemia sequence 1 inhibitor, vincristine, phosphatidylinositol kinase beta subunit inhibitor, and aurora kinase A, while trametinib, selumetinib, extracellular regulated protein kinase, and mitogen-activated extracellular signal-regulated kinase were more effective in the low-risk group. Conclusion:The constructed Mito-RGs model is capable of providing a more accurate assessment of the prognosis and the degree of immune cell infiltration in HCC patients.