This study aims to explore the pharmacodynamic material basis of Jinbei Oral Liquid(JBOL) against idiopathic pulmonary fibrosis(IPF) based on serum pharmacochemistry and network pharmacology. The ultra-high performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry(UHPLC-Q-TOF-MS/MS) technology was employed to analyze and identify the components absorbed into rat blood after oral administration of JBOL. Combined with network pharmacology, the study explored the pharmacodynamic material basis and potential mechanism of JBOL against IPF through protein-protein interaction(PPI) network construction, "component-target-pathway" analysis, Gene Ontology(GO) functional enrichment, and Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway enrichment analysis. First, a total of 114 compounds were rapidly identified in JBOL extract according to the exact relative molecular mass, fragment ions, and other information of the compounds with the use of reference substances and a self-built compound database. Second, on this basis, 70 prototype components in blood were recognized by comparing blank serum with drug-containing serum samples, including 28 flavonoids, 25 organic acids, 4 saponins, 4 alkaloids, and 9 others. Finally, using these components absorbed into blood as candidates, the study obtained 212 potential targets of JBOL against IPF. The anti-IPF mechanism might involve the action of active ingredients such as glycyrrhetinic acid, cryptotanshinone, salvianolic acid B, and forsythoside A on core targets like AKT1, TNF, and ALB and thereby the regulation of multiple signaling pathways including PI3K/AKT, HIF-1, and TNF. In conclusion, JBOL exerts the anti-IPF effect through multiple components, targets, and pathways. The results would provide a reference for further study on pharmacodynamic material basis and pharmacological mechanism of JBOL.
Drugs, Chinese Herbal/pharmacokinetics* ; Animals ; Tandem Mass Spectrometry ; Network Pharmacology ; Rats ; Chromatography, High Pressure Liquid ; Rats, Sprague-Dawley ; Male ; Idiopathic Pulmonary Fibrosis/metabolism* ; Humans ; Administration, Oral ; Protein Interaction Maps/drug effects* ; Signal Transduction/drug effects*

OBJECTIVE: This study examines utilizes the advantages of machine learning algorithms to discern key determinants in prognosticate postoperative circulatory complications (PCCs) for older patients. METHODS: This secondary analysis of data from a randomized controlled trial involved 1,720 elderly participants in five tertiary hospitals in Beijing, China. Participants aged 60-90 years undergoing major non-cardiac surgery under general anesthesia. The primary outcome metric of the study was the occurrence of PCCs, according to the European Society of Cardiology and the European Society of Anaesthesiology diagnostic criteria. The analysis metrics contained 67 candidate variables, including baseline characteristics, laboratory tests, and scale assessments. RESULTS: Our feature selection process identified key variables that significantly impact patient outcomes, including the duration of ICU stay, surgery, and anesthesia; APACHE-II score; intraoperative average heart rate and blood loss; cumulative opioid use during surgery; patient age; VAS-Move-Median score on the 1st to 3rd day; Charlson comorbidity score; volumes of intraoperative plasma, crystalloid, and colloid fluids; cumulative red blood cell transfusion during surgery; and endotracheal intubation duration. Notably, our Random Forest model demonstrated exceptional performance with an accuracy of 0.9872. CONCLUSION We have developed and validated an algorithm for predicting PCCs in elderly patients by identifying key risk factors.
Aged ; Aged, 80 and over ; Female ; Humans ; Male ; Middle Aged ; Cardiovascular Diseases/etiology* ; Machine Learning ; Postoperative Complications/etiology* ; Risk Factors ; Randomized Controlled Trials as Topic ; Secondary Data Analysis

The stability of ammonium ion selective electrode is an important indicator to ensure accurate monitoring of ammonia nitrogen concentration in drinking water.However,in long-term monitoring process,interfering ions and water molecules in water samples may penetrate into the interior of the ammonium ion selective electrode to form a water layer,which affects the potential response and stability of the electrode.Perfluorooctanoic acid is a low surface energy material,and doping it in polyaniline can reduce surface energy of the composite and improve surface roughness.In this work,five ammonium ion selective electrodes were prepared by doping polyaniline with different concentrations of perfluorooctanoic acid as a solid contact layer,which made the solid contact layer of electrode had hydrophobic properties,thereby improving stability of the ammonium ion selective electrode.The stability of the ion-selective electrode was evaluated by potential drift experiment,and the optimal doping concentration of perfluorooctanoic acid in the sediment solution was determined to be 5 mmol/L.The experiment results showed that the solid contact layer had a water contact angle of 132o under the doping concentration,the potential drift rate was 41.66 μV/h,and potential drift rate in the aqueous layer test was 1.31 mV/h,which were all better than those of the unmodified electrode.The standard deviation of the electrode potential was 1.42 mV,which was obviously superior to that of the unmodified electrode.The characteristics of high stability of the electrode made it suitable for long-term monitoring of ammonia nitrogen content in water samples.

Objective To establish an ultra-high performance liquid phase tandem triple quadrupole mass spectrometry method for the determination of L-synephrine in rat plasma and to study its pharmacokinetics.Methods The plasma samples were precipitated by acetonitrile vortex,centrifuged,dried by nitrogen,dissolved by mobile phase,and then analyzed.Chromatographic column:Shiseido CAPCELL PAK CR(1∶4)column(2.0 mm × 150.0 mm,5 μm),temperature:25 ℃.Mobile phase:Acetonitrile:0.1％formic acid solution(containing 10 mmoL·L-1 ammonium formate)(73∶27)with flow rate:0.3 mL·min-1 and injection volume was 2 μL.The electrospray ion source ionization and positive ion multiple reaction monitoring mode were used for mass spectrometry detection,and biological sample methodology verification was carried out according to requirements.After intragastric administration with 5 mg·kg-1 dose of L-synephrine,blood samples were collected at different time points to investigate the pharmacokinetic characteristics of L-synephrine in rats.Results L-synephrine showed a good linear relationship in the range of 2-800 ng·mL-1,the lowest limit of quantification of L-synephrine was 2 ng·mL-1.Precision,extraction recovery,matrix effect,accuracy,dilution effect and residual effect all meet the requirements all met the requirements.The main pharmacokinetic parameters:Cmax were(464.83±76.68)ng·L-1,tmax were(0.67±0.26)h,t1/2z were(1.89±0.48)h,AUC0-t were(602.26±42.25)ng·mL-1·h-1,AUC0-∞ were(612.28±41.18)ng·mL-1·h-1.Conclusion The established ultra high performance liquid chromatography-tandem triple quadrupole mass spectrometry method for the determination of L-synephrine in rat plasma is simple and rapid,and can be used for the determination of L-synephrine in plasma.

Objectives: . Our study aimed to explore the role of the potassium channel KCNK1 in head and neck squamous cell carcinoma, focusing on its impact on tumor growth, invasion, and metastasis. We also investigated the therapeutic potential of quinidine, a known KCNK1 inhibitor, in both in vitro cell lines and a zebrafish patient-derived xenograft (PDX) model. Methods: . We established primary cell cultures from head and neck cancer tissues and employed the FaDu cell line for in vitro studies, modulating KCNK1 expression through overexpression and knockdown techniques. We evaluated cell migration, invasion, and proliferation. Additionally, we developed a zebrafish PDX model to assess the impact of quinidine on tumor growth and metastasis in vivo. RNA sequencing and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to elucidate the molecular mechanisms underlying the role of KCNK1 in cancer progression. Results: . Overexpression of KCNK1 in FaDu cells resulted in enhanced cell migration and invasion, whereas its knockdown diminished these processes. In the zebrafish PDX model, quinidine markedly inhibited tumor growth and metastasis, demonstrating a significant reduction in tumor volume and micrometastasis rates compared to the control groups. The molecular analyses indicated that KCNK1 plays a role in critical signaling pathways associated with tumor growth, such as the Ras and MAPK pathways. Conclusion . Our findings highlight the critical role of KCNK1 in promoting tumor growth and metastasis in head and neck cancer. The inhibitory effect of quinidine on tumor progression in the zebrafish PDX model highlights the therapeutic potential of targeting KCNK1. These results suggest that KCNK1 could serve as a valuable therapeutic target for head and neck cancer, warranting further investigation into treatments that target KCNK1.

Objective To observe the safety and effectiveness of single dose intravenous infusion of tranexamic acid(TX-A)in dual level posterior lumbar interbody fusion(PLIF),and to explore the changes and trends in perioperative white blood cell(WBC),erythrocyte sedimentation rate(ESR),and C-reactive protein(CRP).Methods Between October 2020 and September 2022,46 patients with lumbar degenerative disease were treated with dual level PLIF,including 18 males and 28 females,with an average age of(60.24±10.68)years old,from 34 to 80 years old.They were divided into observation group and control group according to different treatment methods.There were 28 patients in the observation group,including 12 males and 16 females,with an average age of(61.04±9.03)years old.There were 3 cases with lumbar disc herniation(LDH),lumbar spinal stenosis(LSS)18 cases,lumbar spondylolisthesis(LS)7 cases.TXA(1 g/100 ml)was administered intravenously 15 min before skin incision after general anesthesia.The control group consisted of 18 patients,including 6 males and 12 females,with an average age of(59.00±13.04)years old.There were 5 cases with LDH,LSS 9 cases,LS 4 cases,and TXA was not used.The operation time,intraoperative bleeding volume,postoperative drainage volume,postoperative deep vein thrombosis(DVT),postoperative hospital stay,postoperative activated partial thromboplastin time(APTT),prothrombin time(PT),thrombin time(TT),fibrinogen(FIB),platelet(PLT),red blood cell(RBC),hemoglobin(HB),hematocrit(HCT),the first day,the fourth day,the seventh day and the last tested after operation WBC,ESR and CRP were recorded.Results The postop-erative wounds of the patients healed well and there was no DVT.46 patients were followed up from 3 to 6 months.The intraop-erative blood loss was 400.0(300.0,500.0)ml and the postoperative drainage was 260.0(220.0,450.0)ml in the observation group,which were lower than the control group[600.0(400.0,1000.0)ml,395.0(300.0,450.0)ml],P＜0.05.There was no significant difference between the two groups in operation time,postoperative hospital stay,postoperative APTT,PT,TT,FIB,PLT,RBC,HB,HCT,and postoperative WBC,ESR and CRP at different times(P＞0.05).Conclusion Single dose intravenous infusion of TXA can reduce the blood loss of bi-segmental PLIF,and has no significant effect on WBC,ESR and CRP after op-eration.

Objectives: . Our study aimed to explore the role of the potassium channel KCNK1 in head and neck squamous cell carcinoma, focusing on its impact on tumor growth, invasion, and metastasis. We also investigated the therapeutic potential of quinidine, a known KCNK1 inhibitor, in both in vitro cell lines and a zebrafish patient-derived xenograft (PDX) model. Methods: . We established primary cell cultures from head and neck cancer tissues and employed the FaDu cell line for in vitro studies, modulating KCNK1 expression through overexpression and knockdown techniques. We evaluated cell migration, invasion, and proliferation. Additionally, we developed a zebrafish PDX model to assess the impact of quinidine on tumor growth and metastasis in vivo. RNA sequencing and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to elucidate the molecular mechanisms underlying the role of KCNK1 in cancer progression. Results: . Overexpression of KCNK1 in FaDu cells resulted in enhanced cell migration and invasion, whereas its knockdown diminished these processes. In the zebrafish PDX model, quinidine markedly inhibited tumor growth and metastasis, demonstrating a significant reduction in tumor volume and micrometastasis rates compared to the control groups. The molecular analyses indicated that KCNK1 plays a role in critical signaling pathways associated with tumor growth, such as the Ras and MAPK pathways. Conclusion . Our findings highlight the critical role of KCNK1 in promoting tumor growth and metastasis in head and neck cancer. The inhibitory effect of quinidine on tumor progression in the zebrafish PDX model highlights the therapeutic potential of targeting KCNK1. These results suggest that KCNK1 could serve as a valuable therapeutic target for head and neck cancer, warranting further investigation into treatments that target KCNK1.

Objectives: . Our study aimed to explore the role of the potassium channel KCNK1 in head and neck squamous cell carcinoma, focusing on its impact on tumor growth, invasion, and metastasis. We also investigated the therapeutic potential of quinidine, a known KCNK1 inhibitor, in both in vitro cell lines and a zebrafish patient-derived xenograft (PDX) model. Methods: . We established primary cell cultures from head and neck cancer tissues and employed the FaDu cell line for in vitro studies, modulating KCNK1 expression through overexpression and knockdown techniques. We evaluated cell migration, invasion, and proliferation. Additionally, we developed a zebrafish PDX model to assess the impact of quinidine on tumor growth and metastasis in vivo. RNA sequencing and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to elucidate the molecular mechanisms underlying the role of KCNK1 in cancer progression. Results: . Overexpression of KCNK1 in FaDu cells resulted in enhanced cell migration and invasion, whereas its knockdown diminished these processes. In the zebrafish PDX model, quinidine markedly inhibited tumor growth and metastasis, demonstrating a significant reduction in tumor volume and micrometastasis rates compared to the control groups. The molecular analyses indicated that KCNK1 plays a role in critical signaling pathways associated with tumor growth, such as the Ras and MAPK pathways. Conclusion . Our findings highlight the critical role of KCNK1 in promoting tumor growth and metastasis in head and neck cancer. The inhibitory effect of quinidine on tumor progression in the zebrafish PDX model highlights the therapeutic potential of targeting KCNK1. These results suggest that KCNK1 could serve as a valuable therapeutic target for head and neck cancer, warranting further investigation into treatments that target KCNK1.

Objective To propose a neural network-based ventilator fault prediction method with a self-developed underlying data preprocessing method for ventilator IoT data.Methods Firstly the ventilator IoT data were sorted,categorized and cleaned.Secondly nondimensionalization and encoding of all the data were implemented via feature engineering methods based on data distribution.Thirdly data dimensionality reduction was carried out with a self-encoder.Finally,an artificial neural network was used for training with the abnormal data as the training label and predicting the abnormal data faults as the training objective,and the prediction performance of the neural network model was evaluated by calculating the accuracy,precision,recall rate,negative prediction value and specificity.Results The neural network model behaved well in learning with the accuracy being 99.68％,the precision being 99.66％,the recall rate being 99.99％,the negative prediction value being 99.95％and the specificity being 96.52％.Conclusion The proposed ventilator fault prediction model based on underlying data preprocessing and neural network can be used for the prediction of specific faults,and references are provided for IoT data-based medical equipment operation and maintenance management.[Chinese Medical Equipment Journal,2023,44(9):8-13]

OBJECTIVE: To develop and validate a three-year risk prediction model for new-onset cardiovascular diseases (CVD) among female patients with breast cancer. METHODS: Based on the data from Inner Mongolia Regional Healthcare Information Platform, female breast cancer patients over 18 years old who had received anti-tumor treatments were included. The candidate predictors were selected by Lasso regression after being included according to the results of the multivariate Fine & Gray model. Cox proportional hazard model, Logistic regression model, Fine & Gray model, random forest model, and XGBoost model were trained on the training set, and the model performance was evaluated on the testing set. The discrimination was evaluated by the area under the curve (AUC) of the receiver operator characteristic curve (ROC), and the calibration was evaluated by the calibration curve. RESULTS: A total of 19 325 breast cancer patients were identified, with an average age of (52.76±10.44) years. The median follow-up was 1.18 [interquartile range (IQR): 2.71] years. In the study, 7 856 patients (40.65%) developed CVD within 3 years after the diagnosis of breast cancer. The final selected variables included age at diagnosis of breast cancer, gross domestic product (GDP) of residence, tumor stage, history of hypertension, ischemic heart disease, and cerebrovascular disease, type of surgery, type of chemotherapy and radiotherapy. In terms of model discrimination, when not considering survival time, the AUC of the XGBoost model was significantly higher than that of the random forest model [0.660 (95%CI: 0.644-0.675) vs. 0.608 (95%CI: 0.591-0.624), P < 0.001] and Logistic regression model [0.609 (95%CI: 0.593-0.625), P < 0.001]. The Logistic regression model and the XGBoost model showed better calibration. When considering survival time, Cox proportional hazard model and Fine & Gray model showed no significant difference for AUC [0.600 (95%CI: 0.584-0.616) vs. 0.615 (95%CI: 0.599-0.631), P=0.188], but Fine & Gray model showed better calibration. CONCLUSION It is feasible to develop a risk prediction model for new-onset CVD of breast cancer based on regional medical data in China. When not considering survival time, the XGBoost model and the Logistic regression model both showed better performance; Fine & Gray model showed better performance in consideration of survival time.
Humans ; Female ; Adult ; Middle Aged ; Adolescent ; Breast Neoplasms/epidemiology* ; Cardiovascular Diseases/etiology* ; Proportional Hazards Models ; Logistic Models ; China/epidemiology*