The classic formula Mulisan is the 45th of the 93 formulas in the Catalogue of Ancient Classic Formulas （second batch） of Han medicine published by the National Administration of Traditional Chinese Medicine. It consists of Ostreae Concha， Astragali Radix， Ephedrae Radix et Rhizoma， and wheat， with the effect of replenishing qi and stopping sweating. It is a common formula in the clinical treatment with traditional Chinese medicine. This study analyzes the historical evolution， composition， dosage， original plants and their processing methods， decocting method， efficacy， indications， and modern clinical application of Mulisan by tracing， comparative analysis， and bibliometric methods. The results showed that Mulisan firstly appeared in the Pulse Classic written by WANG Shuhe in the Western Jin Dynasty. The formulation idea can be traced back to the Important Prescriptions Worth a Thousand Gold for Emergency in the Tang Dynasty. The herb composition， dosage， efficacy， and indications of Mulisan were first recorded in the Treatise on Diseases， Patterns， and formulas Related to Unification of the Three Etiologies in the Southern Song dynasty. In terms of original plants and their processing methods， Ostreae Concha is the shell of Ostrea rivularis， which should be calcined before use. Astragali Radix and Ephedrae Radix et Rhizoma are the dried roots of Astragalus membranaceus var. mongholicus and Ephedra sinica， respectively， the raw material of which should be used. Wheat is the dried mature fruit of T. aestivum， which can be used without processing， while the stir-fried fruit， being thin and deflated， demonstrates better effect. The composition of Mulisan is Ostreae Concha 8.26 g， Astragali Radix 8.26 g， Ephedrae Radix et Rhizoma 8.26 g， and wheat 7.92 g. The medicinal materials should be ground into coarse powder and decocted with 450 mL water to reach a volume of 240 mL， and the decoction should be taken warm. In modern clinical practice， Mulisan has a wide range of indications， including spontaneous sweating and night sweating caused by Yang deficiency or Qi deficiency. The clinical disease spectrum treated by Mulisan involves endocrine system diseases， neurological diseases， respiratory system diseases， and cancer. This formula plays a significant role in the treatment of internal medicine diseases in traditional Chinese medicine. This study aims to provide a scientific basis for the subsequent research， development， and clinical application of Mulisan.

The classic formula Mulisan is the 45th of the 93 formulas in the Catalogue of Ancient Classic Formulas （second batch） of Han medicine published by the National Administration of Traditional Chinese Medicine. It consists of Ostreae Concha， Astragali Radix， Ephedrae Radix et Rhizoma， and wheat， with the effect of replenishing qi and stopping sweating. It is a common formula in the clinical treatment with traditional Chinese medicine. This study analyzes the historical evolution， composition， dosage， original plants and their processing methods， decocting method， efficacy， indications， and modern clinical application of Mulisan by tracing， comparative analysis， and bibliometric methods. The results showed that Mulisan firstly appeared in the Pulse Classic written by WANG Shuhe in the Western Jin Dynasty. The formulation idea can be traced back to the Important Prescriptions Worth a Thousand Gold for Emergency in the Tang Dynasty. The herb composition， dosage， efficacy， and indications of Mulisan were first recorded in the Treatise on Diseases， Patterns， and formulas Related to Unification of the Three Etiologies in the Southern Song dynasty. In terms of original plants and their processing methods， Ostreae Concha is the shell of Ostrea rivularis， which should be calcined before use. Astragali Radix and Ephedrae Radix et Rhizoma are the dried roots of Astragalus membranaceus var. mongholicus and Ephedra sinica， respectively， the raw material of which should be used. Wheat is the dried mature fruit of T. aestivum， which can be used without processing， while the stir-fried fruit， being thin and deflated， demonstrates better effect. The composition of Mulisan is Ostreae Concha 8.26 g， Astragali Radix 8.26 g， Ephedrae Radix et Rhizoma 8.26 g， and wheat 7.92 g. The medicinal materials should be ground into coarse powder and decocted with 450 mL water to reach a volume of 240 mL， and the decoction should be taken warm. In modern clinical practice， Mulisan has a wide range of indications， including spontaneous sweating and night sweating caused by Yang deficiency or Qi deficiency. The clinical disease spectrum treated by Mulisan involves endocrine system diseases， neurological diseases， respiratory system diseases， and cancer. This formula plays a significant role in the treatment of internal medicine diseases in traditional Chinese medicine. This study aims to provide a scientific basis for the subsequent research， development， and clinical application of Mulisan.

ObjectiveTo investigate the value of different noninvasive diagnostic models in the diagnosis of esophageal and gastric varices since there is a high risk of esophageal and gastric varices in patients with compensated hepatitis B cirrhosis and significant portal hypertension， and to provide a basis for the early diagnosis of esophageal and gastric varices. MethodsA total of 108 patients with significant portal hypertension due to compensated hepatitis B cirrhosis who attended Guangdong Provincial Hospital of Traditional Chinese Medicine from November 2017 to November 2023 were enrolled， and according to the presence or absence of esophageal and gastric varices under gastroscopy， they were divided into esophageal and gastric varices group （GOV group） and non-esophageal and gastric varices group （NGOV group）. Related data were collected， including age， sex， imaging findings， and laboratory markers. The chi-square test was used for comparison of categorical data between groups； the least significant difference t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups. The receiver operating characteristic （ROC） curve was plotted to evaluate the diagnostic value of five scoring models， i.e.， fibrosis-4 （FIB-4）， LOK index， LPRI， aspartate aminotransferase-to-platelet ratio index （APRI）， and aspartate aminotransferase/alanine aminotransferase ratio （AAR）. The binary logistic regression method was used to establish a combined model， and the area under the ROC curve （AUC） was compared between the combined model and each scoring model used alone. The Delong test was used to compare the AUC value between any two noninvasive diagnostic models. ResultsThere were 55 patients in the GOV group and 53 patients in the NGOV group. Compared with the NGOV group， the GOV group had a significantly higher age （52.64±1.44 years vs 47.96±1.68 years， t=0.453， P<0.05） and significantly lower levels of alanine aminotransferase ［42.00 （24.00 — 17.00） U/L vs 82.00 （46.00 — 271.00） U/L， Z=-3.065， P<0.05］， aspartate aminotransferase ［44.00 （32.00 — 96.00） U/L vs 62.00 （42.50 — 154.50） U/L，Z=-2.351， P<0.05］， and platelet count ［100.00 （69.00 — 120.00）×10⁹/L vs 119.00 （108.50 — 140.50）×10⁹/L， Z=-3.667， P<0.05］. The ROC curve analysis showed that FIB-4， LOK index， LPRI， and AAR used alone had an accuracy of 0.667， 0.681， 0.730， and 0.639， respectively， in the diagnosis of esophageal and gastric varices （all P<0.05）， and the positive diagnostic rates of GOV were 69.97%， 65.28%， 67.33%， and 58.86%， respectively， with no significant differences in AUC values （all P>0.05）， while APRI used alone had no diagnostic value （P>0.05）. A combined model （LAF） was established based on the binary logistic regression analysis and had an AUC of 0.805 and a positive diagnostic rate of GOV of 75.80%， with a significantly higher AUC than FIB-4， LOK index， LPRI， and AAR used alone （Z=-2.773，-2.479，-2.206， and-2.672， all P<0.05）. ConclusionFIB-4， LOK index， LPRI， and AAR have a similar diagnostic value for esophageal and gastric varices in patients with compensated hepatitis B cirrhosis and significant portal hypertension， and APRI alone has no diagnostic value. The combined model LAF had the best diagnostic efficacy， which provides a certain reference for clinical promotion and application.

[Objective] To explore the incidence and risk factors of blood transfusion undergoing total knee revision (TKR) using a nationwide database. [Methods] A retrospective data analysis was conducted based on the Nationwide Inpatient Sample (NIS), enrolling patients who underwent TKR from 2015 to 2019 with complete information. Patients under 18 years old and those using anticoagulants, antiplatelets, antithrombotic and non-steroidal were excluded. The patients were divided into two groups based on whether they received blood transfusion or not. The demographic characteristics, length of stay (LOS), total charge of hospitalization, hospital characteristics, hospital mortality, comorbidities and perioperative complications by Wilcoxon rank test for continuous data and chi-square test for categorical data. Logistic regression was performed to identify risk factors of blood transfusion undergoing TKR. [Results] The NIS database included 63 359 patients who underwent TKR. Among them, 5 271 patients received blood transfusion, with an incidence of blood transfusion of 7.8%. There was a decrease in the incidence over the years from 2015 to 2019, dropping from 10.2% to 6.5%. TKR patients requiring transfusions had experienced longer LOS, incurred higher total medical expenses, utilized Medicare more frequently, and had increased in-hospital mortality rates (all P<0.001). Independent risk factors for blood transfusion included female gender, iron-deficiency anemia, rheumatoid disease, collagen vascular disease, chronic blood loss anemia, congestive heart failure, coagulopathy, diabetes with chronic complications, lymphoma, fluid and electrolyte disorders, peripheral vascular disorders, renal failure, valvular disease and weight loss (malnutrition). In addition, risk factors for transfusion in TKR surgery included sepsis, acute myocardial infarction, deep vein thrombosis, gastrointestinal bleeding, heart failure, pneumonia, urinary tract infection, acute renal failure, postoperative delirium, wound infection, lower limb nerve injury, hemorrhage, seroma, hematoma, wound rupture and non healing. [Conclusion] Our findings highlight the importance of recognizing the risk factors of blood transfusion in TKR and establishing corresponding clinical pathways and intervention measures to reduce the occurrence of adverse events.

To investigate the incidence of postoperative neuropsychic dysfunction (PND) in Parkinson's disease (PD) patients undergoing deep brain stimulation (DBS) and to analyze its influencing factors.

ObjectiveTo investigate the expression of serum Aldo-keto reductase family 1 member B10 （AKR1B10） in patients with hepatocellular carcinoma （HCC） in northern China， and to provide a new and valuable biomarker for the clinical diagnosis of HCC. MethodsThis study was conducted among 102 patients with HCC， 119 patients with benign liver disease， and 132 patients with other malignant tumors who attended The Affiliated Hospital of Chengde Medical University and 148 healthy individuals who underwent physical examination from May 2020 to May 2024. ELISA and chemiluminescence were used to measure the serum levels of AKR1B10 and alpha-fetoprotein （AFP）. The Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups， and the Kruskal-Wallis H test was used for comparison between three groups and further comparison between two groups； the chi-square test was used for comparison of categorical data between groups. The area under the ROC curve （AUC） was used to assess diagnostic efficiency. ResultsThe expression level of AKR1B10 was 3 053.79 （1 475.67‍ ‍—‍ ‍4 605.86） pg/mL in the HCC group， 1 324.42 （659.68‍ ‍—‍ ‍2 023.88） pg/mL in the benign liver disease group， 660.68 （377.56‍ ‍—‍ ‍2 087.77） pg/mL in the other malignant tumor group， and 318.30 （82.73‍ ‍—‍ ‍478.82） pg/mL in the healthy group， with a significant difference between the four groups （H=240.86， P<0.001）， and further comparison between two groups showed that the HCC group had a significantly higher level than the other three groups （all P<0.001）. The ROC curve analysis of the HCC group and the other three groups showed that serum AKR1B10 had an optimal cut-off value of 1 584.97 pg/mL in the diagnosis of HCC， with an AUC of 0.86 （95% confidence interval ［CI］： 0.82‍ ‍—‍ ‍0.90）， a sensitivity of 74.3%， and a specificity of 85.2%. Compared with each indicator alone， a combination of AKR1B10 and AFP could improve the sensitivity （81.8%） and specificity （91.4%） of HCC diagnosis. AKR1B10 had an AUC of 0.84 （95%CI： 0.78‍ ‍—‍ ‍0.90） in the diagnosis of patients with early- or middle-stage HCC， with a sensitivity of 76.2% and a specificity of 81.2%. AKR1B10 had an AUC of 0.85 （95%CI： 0.77‍ ‍—‍ ‍0.92） in the diagnosis of patients with AFP-negative HCC， with a sensitivity of 81.6% and a specificity of 79.9%. ConclusionAKR1B10 is a promising serological marker for the diagnosis of HCC， and a combination of AKR1B10 and AFP can improve the detection rate of HCC patients in northern China， especially those with early- or middle-stage HCC and AFP-negative HCC.

Objective To investigate the effect of sodium-glucose cotransporter 2 inhibitor (empagliflozin, EMPA) on myocardial remodeling in a mouse uremic cardiomyopathy (UCM) model induced by 5/6 nephrectomy, through the phosphatidylinositol 3 kinase (PI3K)/protein kinase B (PKB/AKT)/p65 signaling pathway. Methods The animals were divided into three groups: Sham group (n=6), UCM group (n=8), and UCM＋EMPA group (n=8). A UCM model was established in C57BL/6N mice using the 5/6 nephrectomy. Starting from 5 weeks post-surgery, EMPA or a placebo was administered. After 16 weeks, blood pressure, serum creatinine, blood urea nitrogen, 24-hour urine glucose and urine sodium were measured. Cardiac structure and function were assessed by echocardiography. Hematoxylin-eosin (HE) staining and Masson trichrome staining were used to observe pathological changes in the heart and kidneys. Wheat germ agglutinin (WGA) staining was used to evaluate myocardial hypertrophy. The real-time quantitative PCR (RT-qPCR) was used to detect the expression levels of myocardial hypertrophy- and fibrosis-related mRNAs. Western blotting was used to detect the expression levels of PI3K, AKT and p65 in myocardial tissues. Results After 16 weeks, UCM group exhibited significantly higher blood pressure, serum creatinine, blood urea nitrogen than sham group (P＜0.01); UCM＋EMPA group exhibited lower blood pressure, serum creatinine, blood urea nitrogen, and higher 24 h urine sodium and glucose than UCM group (P＜0.05). Echocardiographic results showed ventricular remodeling in the UCM group, evidenced by left ventricular wall thickening, left ventricular enlargement, increased left ventricular mass, and decreased systolic function (P＜0.05); ventricular remodeling was alleviated (P＜0.05), though there was no significant improvement in systolic function in UCM＋EMPA group. HE and Masson stainings revealed myocardial degeneration, necrosis, and interstitial fibrosis in UCM group (P＜0.01); the myocardial pathology improved with reduced collagen deposition in UCM＋EMPA group (P＜0.01). WGA staining confirmed myocardial hypertrophy in UCM group (P＜0.01), while myocardial hypertrophy was alleviated in UCM＋EMPA group (P＜0.01). RT-qPCR results showed myocardial hypertrophy- and fibrosis-related genes (NPPA, NPPB, MYH7, COL1A1, COL3A1, TGF-β1) were upregulated in UCM group (P＜0.05), but downregulated in UCM＋EMPA group. Western blotting showed PI3K, p-AKT/AKT ratio, and p-p65/p65 ratio were increased in UCM group, but decreased in UCM＋EMPA group (P＜0.05). Conclusion EMPA can improve myocardial hypertrophy and fibrosis in the UCM mouse model, and it may play the role through inhibiting the PI3K/AKT/p65 signaling pathway.

Objective To evaluate the environmental radioactive safety level in China, monitor the radioactivity of strontium-90 (⁹⁰Sr) in seafood from selected marine regions of China, and optimize the di-(2-ethylhexyl)phosphoric acid (HDEHP) extraction chromatography method for determining Sr-90 in seafood. Methods In 2023, seafoods of fish, shrimp, shellfish, and seaweed were collected from the Shandong Province (Bohai Sea and Yellow Sea) and Hainan Province (South China Sea). The levels of Sr in the samples were determined by inductively coupled plasma atomic emission spectrometer (ICP-AES). The ⁹⁰Sr separation were performed using HDEHP extraction chromatography, while the recovery of ⁹⁰Sr were determined by the gravitmetry with the assistant of ICP-AES. Results The content of strontium in seafoods varies greatly, and excessive strontium and calcium in seafood may lead to overestimated recovery due to insufficient leaching during chromatographic separation by HDEHP extraction. Therefore, the yttrium content in the eluent should be analyzed by ICP . The radioactivity of ⁹⁰Sr in seafood from the sea areas in Shandong Province was 0.22-1.85 Bq/kg (dry weight), and that of seafood from Hainan Province was 0.19-1.82 Bq/kg (dry weight). Conclusion For the analysis of shirmp and seaweed samples, the recovery rate of ⁹⁰Sr should be analyzed using both gravimetry and ICP-AES. There is no significant linear correlation between total Sr and ⁹⁰Sr in seafood. There is no significant difference in ⁹⁰Sr radioactivity between the seafood samples collected from Shandong and Hainan. The ⁹⁰Sr radioactivity levels of all 28 samples are below the limit specified in the Limited concentrations of radioactive materials in foods (GB 14882—1994) and are within the range of environmental background fluctuations.

Objective To organize a nationwide interlaboratory comparison for measurement of gross α and gross β radioactivity in water, and improve the laboratory analysis of gross α and gross β radioactivity in water. Methods A unified comparison protocol was developed by the organizers. The groundwater with high natural radioactivity was used as water sample and distributed randomly to the participating laboratories. The participating laboratories used routine analytical methods to measure the samples and provided information such as analytical results, original records, and test reports. The results were evaluated using z-score. Results A total of 76 laboratories participated in the comparison, all employing the evaporation concentration-α/β counting method. Among them, 69 laboratories achieved |z| ≤ 2 for both gross α and gross β radioactivity measurements, and 32 laboratories achieved |z| ≤ 0.50 for both gross α and gross β radioactivity measurements. There were 69 laboratories with qualified results and 30 laboratories with excellent results, yielding a qualified rate of 90.8% and an excellent rate of 39.5%. Seven laboratories showed unqualified results and the unqualified rate was 9.2%. Conclusion Most laboratories have the ability to analyze gross α and gross β radioactivity in water. The main reasons for the deviation in comparison results are calibration efficiency, errors in the total residue mass caused by improper water sample processing operations. By analyzing the main technical problems existed in unqualified laboratories, their ability for measurement of gross α and gross β radioactivity in water has been improved.

BACKGROUND:Rheumatoid arthritis is a condition that affects the entire immune system in the body and is known for causing inflammatory hyperplasia in the joints and destruction of articular cartilage.The pathogenesis of rheumatoid arthritis is still unclear;therefore,there is an urgent need to discover new highly sensitive and specific diagnostic biomarkers. OBJECTIVE:To identify and screen key genes in the synovium of rheumatoid arthritis patients using bioinformatics techniques and machine learning algorithms and to construct and validate a rheumatoid arthritis prediction model. METHODS:Three datasets containing synovial tissue samples from rheumatoid arthritis patients(GSE77298,GSE55235,GSE55457)were downloaded from the Gene Expression Omnibus(GEO)database.GSE77298 and GSE55235 were used as the training set,while GSE55457 served as the test set,with a total of 66 samples,including 39 samples from rheumatoid arthritis patients and 27 normal synovial samples.Differentially expressed genes in the training set were selected using R language,and then the weighted gene co-expression network analysis was used to modularize the genes in the training set.The most relevant module was selected,and feature genes within this module were identified.Differentially expressed genes and the feature genes from the module were intersected for the subsequent machine learning analysis.Three machine learning methods,namely the least absolute shrinkage and selection operator algorithm,support vector machine with recursive feature elimination,and random forest algorithm,were employed to further analyze the intersected genes and identify the hub genes.The hub genes obtained from these three machine learning algorithms were intersected again to obtain the key genes in the synovium of rheumatoid arthritis.A predictive rheumatoid arthritis model was constructed using these key genes as variables,and the risk of developing rheumatoid arthritis in patients was inferred based on the model.The receiver operating characteristic curve was used to determine the diagnostic value of the rheumatoid arthritis prediction model and its key genes. RESULTS AND CONCLUSION:Through the differential analysis,a total of 730 differentially expressed genes were identified in the training set,and 185 feature genes were identified in the weighted gene co-expression network analysis feature modules.There were 159 intersected genes obtained.There were 4 hub genes identified by the least absolute shrinkage and selection operator algorithm,11 hub genes by the support vector machine with recursive feature elimination algorithm,and 5 hub genes by the random forest algorithm.After intersection,2 key genes(TNS3 and SDC1)were obtained.Based on the two key genes,a nomogram model was constructed in the training and test sets,with good fit between the calibration prediction curve and the standard curve,and good clinical efficacy in predicting the onset of rheumatoid arthritis.These findings indicate that TNS3 and SDC1,obtained based on bioinformatics and machine learning algorithms,may become key targets for the diagnosis and treatment of rheumatoid arthritis.