[Objective] To explore the effectiveness of applying the PDCA (Plan-Do-Check-Act) cycle to enhance the compatibility rate of five Rh blood group antigen phenotypes between donors and recipients across multiple hospital campuses. [Methods] Clinical blood transfusion data from May to July 2022 were selected. Specific improvement measures were formulated based on the survey results, and the PDCA cycle management model was implemented from August 2022. The post-intervention phase spanned from August 2022 to October 2023. The Rh phenotype compatibility rate, the detection rate of Rh system antibodies, and the proportion of Rh system antibodies among unexpected antibodies were compared between the pre-intervention phase (May to July 2022) and the post-intervention phase. [Results] After the continuous improvement with the PDCA cycle, the compatibility rate for the five Rh blood group antigen phenotypes between donors and recipients from August to October 2023 reached 81.90%, significantly higher than the 70.54% recorded during the pre-intervention phase (May to July 2022, P<0.01), and displayed a quarterly upward trend (β=0.028, P<0.05). The detection rate of Rh blood group system antibodies (β=-9.839×10^-5, P<0.05) and its proportion among all detected antibodies (β=-0.022, P<0.05) showed a quarterly decreasing trend, both demonstrating a negative correlation with the enhanced compatibility rate (r values of -0.981 and -0.911, respectively; P<0.05). [Conclusion] The implementation of targeted measures through the PDCA cycle can effectively increase the compatibility rate of five Rh blood group antigen phenotypes between donors and recipients, reduce the occurrence of unexpected Rh blood group antibodies, thereby lowering the risk of transfusion and enhancing the quality and safety of medical care.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.

Objective:To investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

[Objective]To propose syndrome differentiation and treatment approach of acupuncture for migraine based on the relationship between"spleen and stomach disorders"and the etiology and pathogenesis of migraine,providing theoretical and practical references for the clinical management.[Methods]The connotation of"spleen and stomach disorders"and its association with migraine were systematically reviewed.Based on this perspective,treatment strategies and acupuncture prescriptions for migraine were formulated,and supported by a representative clinical case study.[Results]"Spleen and stomach disorders"serves as a critical pathological foundation for migraine.Its pathogenesis can be summarized into four patterns"impaired spleen transport leading to malnourishment of the brain""phlegm-dampness obstructing the orifices and impairing mental clarity""spleen-stomach disharmony causing Qi stagnation"and"spleen deficiency with blood stasis resulting in orifice obstruction".Acupuncture treatment should prioritize regulating spleen-stomach function,supplemented by dredging local meridians and harmonizing Qi and blood.Key acupoints include Zhongwan(CV12),Xiawan(CV10),Qihai(CV6),and Guanyuan(CV4)for reinforcing primordial Qi,consolidating the foundation,and harmonizing Qi and blood;Zusanli(ST36),Shangjuxu(ST37)and Xiajuxu(ST39)for regulating Qi movement and balancing Yin and Yang;Sizhukong(TE23)and Fengchi(GB20)for clearing the head,improving vision,and relieving pain.In the presented case,the patient was diagnosed as dual deficiency of Qi and blood,acupuncture therapy focusing on replenishing Qi,nourishing blood and unblocking collaterals achieved significant clinical efficacy.[Conclusion]"Spleen and stomach disorders"is a pivotal mechanism underlying recurrent migraine.Acupuncture treatment for migraine should emphasize restoring spleen-stomach function,prioritizing the reinforcement of primordial Qi and harmonization of Qi and blood,thereby addressing the root cause to reduce migraine recurrence.

Objective To investigate dynamic changes in myocardial protein phosphorylation during the acute phase of myocardial infarction (MI) in mice. Methods Six 8-week-old C57BL/6J mice were randomly assigned to MI model (n＝3) or sham-operated control (n＝3) groups. Cardiac tissues were harvested 72 hours post-intervention for proteomic analysis. Phosphorylation modifications were systematically characterized using liquid chromatography-mass spectrometry (LC-MS). Bioinformatics analyses included differential phosphorylation screening, functional enrichment, hierarchical clustering, and protein-protein interaction network. Results LC-MS identified 1 921 differentially phosphorylated sites (20 tyrosine and 1 901 serine/threonine sites) across 851 proteins. Compared with controls, MI hearts exhibited significant phosphorylation upregulation at 1 545 sites and downregulation at 376 sites (P＜0.05). Conclusions This study delineates MI-associated phosphorylation dynamics, providing mechanistic insights and potential therapeutic targets for acute MI intervention.

Guided by the cancer toxin theory of TCM master Professor Zhou Zhongying,and absorbing the thoughts of academician Tong Xiaolin's theory of state-target,the pathogenesis of multiple myeloma is summarized as deficiency of healthy qi,bone erosion and marrow damage,cancer toxin accumulation,phlegm and stasis mingling,the combat between healthy qi and evil qi,and dynamic evo-lution;a full-cycle anti-cancer and detoxification prevention and treatment strategy for multiple myeloma is proposed that incorporates the ideas of nourishing deficiency and strengthening healthy qi,preventing cancer and detoxification,resolving phlegm and removing stasis throughout the entire treatment process.The scientific connotation of the pathogenesis theory of"healthy qi deficiency and cancer toxin"in multiple myeloma is explained from multiple aspects such as protein overload and tumor microenvironment,and a prescrip-tion-drug medicine system with Xuanbi Xiaoliu Formula as the core is constructed,which provides new ideas and scientific basis for the clinical diagnosis and treatment plan and full-cycle prevention and control model of multiple myeloma combining Chinese and West-ern medicine.

Objective·To determine hyaluronan(HA)expression in the endocrine-resistant microenvironment of estrogen receptor-positive(ER+)breast cancer and elucidate its impact on the acquired resistance.Methods·Chemiluminescent immunoassay was used to quantify HA levels in the culture supernatants of fulvestrant-resistant breast cancer cells.An immunofluorescence(IF)assay was performed to visualize the colocalization of CD44 and HA in MCF7/FulR cells.Using an established adaptive endocrine-resistant breast cancer mouse model,HA expression in resistant breast cancer tissues was assessed by immunohistochemistry(IHC)assay.Single-cell RNA sequencing(scRNA-seq)and RNA sequencing(RNA-seq)were conducted to examine transcriptomic profiles and alterations in HA-related genes in resistant breast cancer cells.Flow cytometry(FCM)was utilized to measure the proportion of CD44+CD24-cells in MCF7/FulR.The correlation between HA synthesis genes and cell stemness was investigated in clinical ER+breast cancers from GEO data sets.Hyaluronidase(HAase)treatment was applied to remove the"HA coat",and RT-qPCR and Western blotting analysis were carried out to monitor changes in stemness-related molecules.CCK-8 assays,flow cytometry(FCM),and Hoechst 33258 staining were performed to determine changes in apoptosis and fulvestrant efficiency after HAase treatment.Results·IF results revealed that compared with MCF7 cells,the"HA coat"on the surface of MCF7/FulR cells was significantly thickened.IHC demonstrated markedly increased HA retention in fulvestrant-resistant mouse breast cancer tissues.ScRNA-seq and RNA-seq analyses indicated elevated expression of stemness-related genes and HA synthesis-associated genes in fulvestrant-resistant breast cancer cells.Correlation analysis revealed a positive association between HA synthesis and cancer stemness in ER+breast cancer.IF and RT-qPCR results demonstrated that removing the HA coating from the surface of MCF7/FulR cells led to a significant reduction in the expression of stemness-related molecules;concurrently,CCK-8 assays,FCM analysis,and Hoechst 33258 staining revealed that"HA coat"clearance reduced MCF7/FulR'tolerance to fulvestrant and increased apoptosis.Conclusion·Endocrine-resistant breast cancer cells develop an enriched"HA coat",which promotes stemness in fulvestrant-resistant tumors.Disruption of this HA coat through HAase treatment effectively reduces cell stemness,induces apoptosis,and re-sensitizes breast cancer cells to fulvestrant.

Objective·To determine hyaluronan(HA)expression in the endocrine-resistant microenvironment of estrogen receptor-positive(ER+)breast cancer and elucidate its impact on the acquired resistance.Methods·Chemiluminescent immunoassay was used to quantify HA levels in the culture supernatants of fulvestrant-resistant breast cancer cells.An immunofluorescence(IF)assay was performed to visualize the colocalization of CD44 and HA in MCF7/FulR cells.Using an established adaptive endocrine-resistant breast cancer mouse model,HA expression in resistant breast cancer tissues was assessed by immunohistochemistry(IHC)assay.Single-cell RNA sequencing(scRNA-seq)and RNA sequencing(RNA-seq)were conducted to examine transcriptomic profiles and alterations in HA-related genes in resistant breast cancer cells.Flow cytometry(FCM)was utilized to measure the proportion of CD44+CD24-cells in MCF7/FulR.The correlation between HA synthesis genes and cell stemness was investigated in clinical ER+breast cancers from GEO data sets.Hyaluronidase(HAase)treatment was applied to remove the"HA coat",and RT-qPCR and Western blotting analysis were carried out to monitor changes in stemness-related molecules.CCK-8 assays,flow cytometry(FCM),and Hoechst 33258 staining were performed to determine changes in apoptosis and fulvestrant efficiency after HAase treatment.Results·IF results revealed that compared with MCF7 cells,the"HA coat"on the surface of MCF7/FulR cells was significantly thickened.IHC demonstrated markedly increased HA retention in fulvestrant-resistant mouse breast cancer tissues.ScRNA-seq and RNA-seq analyses indicated elevated expression of stemness-related genes and HA synthesis-associated genes in fulvestrant-resistant breast cancer cells.Correlation analysis revealed a positive association between HA synthesis and cancer stemness in ER+breast cancer.IF and RT-qPCR results demonstrated that removing the HA coating from the surface of MCF7/FulR cells led to a significant reduction in the expression of stemness-related molecules;concurrently,CCK-8 assays,FCM analysis,and Hoechst 33258 staining revealed that"HA coat"clearance reduced MCF7/FulR'tolerance to fulvestrant and increased apoptosis.Conclusion·Endocrine-resistant breast cancer cells develop an enriched"HA coat",which promotes stemness in fulvestrant-resistant tumors.Disruption of this HA coat through HAase treatment effectively reduces cell stemness,induces apoptosis,and re-sensitizes breast cancer cells to fulvestrant.

Objective:To investigate the efficacy of radiotherapy in patients with advanced esophageal cancer receiving first-line chemoimmunotherapy.Methods:A retrospective analysis was conducted on the data of 137 patients with Stage Ⅳ esophageal squamous cell carcinoma (ESCC) treated at our hospital from January 2018 to May 2023. These patients were divided into two groups: a group treated with first-line chemoimmunotherapy combined with radiotherapy (chemoimmunotherapy + radiotherapy group, n = 43) and a group treated with only chemoimmunotherapy ( n = 94). Inverse probability of treatment weighting (IPTW) was applied to balance baseline characteristics between the groups. With overall survival (OS) and progression-free survival (PFS) as study endpoints, the survival data were analyzed using the Kaplan-Meier method, the log-rank test, and the Cox regression method. Results:Before calibration, the chemoimmunotherapy + radiotherapy group significantly outperformed the sole chemoimmunotherapy group in median PFS (13.6 months vs. 7.0 months; HR: 0.501, 95% CI: 0.309-0.811, P = 0.005). After calibration using the COX proportional-hazards model for age, gender, Eastern Cooperative Oncology Group (ECOG) performance status, smoking history, T/N/M stage, and tumor location, the chemoimmunotherapy + radiotherapy group still had significant advantages in PFS (14.7 months vs. 7.0 months; HR: 0.441, 95% CI: 0.261-0.745, P = 0.002). IPTW analysis further confirmed this trend (13.9 months vs. 7.0 months; HR: 0.492, 95% CI: 0.304-0.795, P < 0.001). Specifically, the median OS of the chemoimmunotherapy + radiotherapy group demonstrated significant improvement in all analyses: pre-calibration (29.5 months vs. 18.0 months; HR: 0.507, 95% CI: 0.297-0.867, P = 0.013), after calibration using the Cox model (27.5 months vs. 16.7 months; HR: 0.470, 95% CI: 0.266-0.830, P = 0.009), and after calibration using IPTW (29.5 months vs. 16.9 months; HR: 0.448, 95% CI: 0.262-0.764, P < 0.001). Conclusions:The combination of radiotherapy and first-line chemoimmunotherapy can significantly improve survival outcomes of patients with advanced ESCC, suggesting its potential as a standard treatment strategy.

Objective:To analyze the prognostic factors of patients with liver cirrhosis and portal vein thrombosis (PVT), and to construct a prognostic prediction model based on machine learning methods.Methods:The clinical data of 388 patients with liver cirrhosis and PVT admitted to the Fifth Medical Center of PLA General Hospital from January 2022 to April 2024 were retrospectively collected and analyzed, including 243 males and 145 females, aged (56.9±10.9) years. A total of 388 patients were randomly divided into the training set ( n=310) and the testing set ( n=78) in a 4∶1 ratio. The Boruta algorithm was used to screen the key features in the training set, and then four machine learning algorithms, including random forest, support vector machine, generalized linear model and Bayesian, were used to establish a survival prediction model. Model performance was evaluated by the receiver operating characteristic (ROC) curves of the test set and the training set. The patients were followed up for 1 year for survival. Sort the importance of features based on the SHAP value. Results:There were 250 patients (80.6%) who survived and 60 (19.4%) who died. The model for end-stage liver disease score, total bilirubin, serum creatinine, prothrombin time, international normalized ratio, D-dimer, white blood cell count, severe ascites ratio, and Child-Pugh grade C ratio of liver function in the death group were higher than those in the survival group, and the red blood cell count and hematocrit were lower than those in the survival group, and the differences were statistically significant (all P<0.05). The areas under the ROC curve for predicting survival by random forest, support vector machine, generalized linear model and Bayesian model were 0.92, 0.78, 0.81 and 0.71 in the training set, and the area under the ROC curve in the testing set were 0.81, 0.72, 0.67 and 0.68, respectively. Random forest had the best prediction performance, with an accuracy of 81.7%, a sensitivity of 84.6%, and a specificity of 76.9% in the testing set. In the analysis of the importance of characteristic parameters of the random forest model, total bilirubin, red blood cells, hematocrit, serum creatinine, ascites classification, etc. had a relatively high contribution to the model. Conclusion:In the survival prediction model of patients with liver cirrhosis and PVT based on machine learning algorithm, the random forest model had high prediction performance, and total bilirubin may be the most important factor affecting the survival prognosis of patients.