Bioinformatics analysis on molecular subtypes and clinical characteristics of head and neck squamous cell carcinoma based on genes associated with lactate metabolism / 吉林大学学报(医学版)

Objective:To select the differential prognostic lactic acid metabolism-related genes(LRGs)of the head and neck squamous cell carcinoma(HNSCC)to construct the LRGs prognostic model of HNSCC,and to clarify the potential mechanism.Methods:The HNSCC gene expression and clinical data were obtained from The Cancer Genome Atlas(TCGA)and Gene Expression Omnibus(GEO)Databases,the LRGs were identified through GeneCards Database,and R software was used to screen out the LRGs of HNSCC;univariate Cox regression analysis was used to identify prognosis-related genes;two different subtypes were identified based on the prognostis-related LRGs;Kaplan-Meier(K-M)curve analysis was used to compare the prognosis of the patients between two groups;CIBERSORT algorithm was used to perform the immuno-correlation analysis between two groups;multivariate Cox regression analysis and LASSO regression analysis were used to construct the prognostic model;receiver operating characteristic curve(ROC)and K-M survival curve were used to assess the relationship between LRGs and survival and prognosis of the HNSCC patients.The prognostic model was validated by GSE27020,GSE41613,and GSE65858 datasets.The experiment were grouped based on risk score,and immune-related analysis and tumor score analysis were performed.Results:The TCGA Database differential analysis results showed that 1 196 LRGs were identified from HNSCC samples;univariate Cox regression analysis selected 27 differentially expressed genes(DEGs)associated with the prognosis of the HNSCC patients.Two different LRGs subtypes(Group 1 and Group 2)were identified according to the prognosis-related genes.The K-M survival curves results showed that the overall survival(OS)of the patients in Group 2 was significantly higher than that in Group 1,and the immune cell expression amount of the patients in Group 2 was also higher than that in group 1.The multivariate Cox regression and LASSO regression analysis results screened out 9 LRGs,including hypoxanthine phosphoribosyltransferase 1(HPRT1),amyloid precursor protein(APP),glycogen phosphorylase L(PYGL),urokinase-type plasminogen activator(PLAU),cannabinoid receptor 2(CNR2),stanniocalcin 2(STC2),nucleotide binding oligomerization domain-like receptor protein 1(NLRP1),integrin-linked kinase(ILK),and forkhead box B1(FOXB1);the prognostic model was constructed.The K-M and ROC curve results indicated that the expression levels of above 9 genes were associated with the survival and prognosis of the HNSCC patients,providing good 1-year,2-year,and 3-year survival prediction effect,and the area under ROC curve(AUC)values were all greater than 0.650.Furthermore,the predictive ability of the prognosis model was validated in GSE27020,GSE41613,and GSE65858 datasets.The patients classified based on the risk scores had distinguishable immune statuses.Conclusion:The differentially expressed LRGs of HNSCC screened by bioinformatics methods are related to the survival and prognosis of the HNSCC patients;the prognostic model constructed by 9 LRGs can predict the survival status and treatment response of the HNSCC patients.

Construction and validation of a model for predicting the risk of in-hospital cardiac arrest in emergency rooms / 中华急诊医学杂志

Objective:The predictive model of cardiac arrest in the emergency room was constructed and validated based on Logistic regression.Methods:This study was a retrospective cohort study. Patients admitted to the emergency room of the First Affiliated Hospital of Xinjiang Medical University from January 2020 to July 2021 were included. The general information, vital signs, clinical symptoms, and laboratory examination results of the patients were collected, and the outcome was cardiac arrest within 24 hours. The patients were randomly divided into modeling and validation group at a ratio of 7:3. LASSO regression and multivariable logistic regression were used to select predictive factors and construct a prediction model for cardiac arrest in the emergency room. The value of the prediction model was evaluated using the area under the receiver operator characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).Results:A total of 784 emergency room patients were included in the study, 384 patients occurred cardiac arrest. The 10 variables were ultimately selected to construct a risk prediction model for cardiac arrest: Logit( P)= -4.503+2.159×modified early warning score (MEWS score)+2.095×chest pain+1.670×abdominal pain+ 2.021×hematemesis+2.015×cold extremities+5.521×endotracheal intubation+0.388×venous blood lactate-0.100×albumin+0.768×K ++0.001×D-dimer. The AUC of the model group was 0.984 (95% CI: 0.976-0.993) and that of the validation group was 0.972 (95% CI: 0.951-0.993). This prediction model demonstrates good calibration, discrimination, and clinical applicability. Conclusions:Based on the MEWS score, chest pain, abdominal pain, hematemesis, cold extremities, tracheal intubation, venous blood lactate, albumin, K +, and D-dimer, a predictive model for cardiac arrest in the in-hospital emergency room was constructed to predict the probability of cardiac arrest in emergency room patients and adjust the treatment strategy in time.

Key biomarkers for the diagnosis of intervertebral disc degeneration associated with oxidative stress:identification based on bioinformatics and machine learning / 中国组织工程研究

BACKGROUND:Oxidative stress is closely associated with the occurrence and progression of intervertebral disc degeneration,but its underlying mechanisms and effective treatment methods remain unclear. OBJECTIVE:To identify key genes associated with intervertebral disc degeneration accompanied by oxidative stress based on bioinformatics and three machine learning algorithms,as well as to conduct an immune infiltration analysis,followed by experimental validation. METHODS:Gene expression profiles related to intervertebral disc degeneration were obtained from the GEO database and oxidative stress-related genes obtained from the GeneCards database.Differential analysis and weighted gene co-expression networks analysis were performed on the intervertebral disc degeneration dataset.The intersection of the two analyses and the intersection with the oxidative stress-related genes were taken to obtain candidate hub genes.Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses on the candidate hub genes were performed.Machine learning algorithms(LASSO regression,SVM-RFE,and random forest)were used to select the optimal feature genes and perform the receiver operator characteristic curve validation.Simultaneously,immune infiltration analysis was conducted.Nucleus pulposus samples from patients with cervical spondylosis who were treated at the Second Hospital of Shanxi Medical University from July to November 2023 were enrolled as the intervertebral disc degeneration group and nucleus pulposus samples from patients with cervical spinal cord injury as the control group.The relative expression of feature genes in the degenerated intervertebral disc was validated using qPCR method. RESULTS AND CONCLUSION:After differential gene analysis,424 differentially expressed genes were obtained.Weighted gene co-expression networks analysis yielded 5 087 genes,and 1 399 oxidative stress genes were identified,leading to the identification of 23 candidate hub genes.Gene ontology analysis revealed that these candidate hub genes are primarily involved in bacterial defense response,molecular response to bacteria,and other biological processes.In terms of cellular component,they are associated with secretion granule lumen and cytoplasmic vesicle lumen,among others.As for molecular function,they are related to endopeptidase activity and compound binding,including sulfur compounds.Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these candidate hub genes are associated with neutrophil extracellular trap formation and the renin-angiotensin system pathway,among other signaling pathways.By applying three machine learning algorithms and conducting the receiver operator characteristic curve validation,two key genes,HSPA6 and PKD1,were determined.Immune infiltration analysis revealed a strong correlation between HSPA6 and activated dendritic cells(r=0.88,P＜0.001)as well as activated CD4+ T cells(r=-0.72,P＜0.01).Similarly,PKD1 showed close associations with effector memory CD8+ T cells(r=0.55,P＜0.05)and activated dendritic cells(r=-0.56,P＜0.05).qPCR experimental results indicated that the expression level of HSPA6 was lower in the intervertebral disc degeneration group compared with the control group(P＜0.000 1),while the expression level of PKD1 was higher in the intervertebral disc degeneration group(P＜0.000 1).These findings suggest that HSPA6 and PKD1 can serve as biomarkers for intervertebral disc degeneration accompanied by oxidative stress.Interventions targeting HSPA6 and PKD1 may hold promise for improving intervertebral disc degeneration.

A Preliminary Study on the Construction of the CCI Index of the Policy Auxiliary Coefficient for the Payment of Diag-nosis-Intervention Packet / 中国卫生经济

Objective:To explore a solution for the construction of the CCI index with an example of cerebral infarction to provide a guide for adjusting the policy of Diagnosis-Intervention Packet.Methods:Lasso regression was used to screen for comorbidities to construct the model,K-means clustering was used for case severity categorization.CCI indices were calculated as one plus the sum of standardized regression coefficients.Results:According to the analysis,five key variables were found,including other disorders of the lungs,non-insulin-dependent diabetes mellitus with ketoacidosis,systemic inflammatory response syndrome of non-infectious etiology with organ failure,acute duodenal ulcer with bleeding,and chronic obstructive pulmonary disease with acute lower respiratory tract in-fection,with CCI indices of 1,1,1.026,1.034,and 1.101.Simulated calculation's result showed a decrease in medical insurance pay-ment losses after applying CCI indices.Conclusion:The CCI index construction scheme based on Lasso regression and K-means clus-tering is reasonable and effective.

Construction and validation of rectal cancer prognostic model by LASSO-Cox regression / 安徽医科大学学报

Objective To construct and appraise a new model for predicting the prognosis of rectal cancer patients using the Lasso-Cox strategy.Methods The clinical pathological data of 599 rectal cancer patients who underwent radical resection were analyzed.Comparison between groups,Lasso and Cox regression were used to select varia-bles and construct a model,and its discrimination,consistency,and clinical benefits were appraised by the receiv-er operating characteristic(ROC),calibration curve,and decision curve analysis.Results Comparison between groups showed that age,body mass index(BMI),preoperational nutrition status,carbohydrate antigen199(CA199),preoperative chemotherapy,intraoperative blood transfusion,vascular or nerve invasion,cancer nod-ules,pathologic T,N,and TNM stages,tumor recurrence or metastasis,radiotherapy and postoperative survival time were associated with grouping of death or survival in rectal cancer patients.Among them,8 variables were se-lected by lasso and contained into the Cox regression model.Age(HR=1.04,P＜0.05),BMI(HR=0.89,P＜0.05),blood transfusion(HR=2.29,P＜0.05),postoperative chemotherapy(HR=0.16,P＜0.01),recur-rence(HR=43.67,P＜0.01),and metastasis(HR=2.75,P＜0.05)were identified as independent prognostic factors,which were used to construct a nomogram model.The area under the curve(AUC)and the 95％confi-dence interval of the receiver operating characteristic(ROC)curve of the predictive model was 0.95(0.91-0.99),P＜0.01.The predicted probability of 1-year and 3-year survival was close to the actual probability.The DCA curve of the model was far away from a decision line parallel to the X-axis and another line with a negative slope.Conclusion The newly established nomogram has good discrimination,consistency and clinical benefits,which help predict the prognosis of rectal cancer after surgery.

Study on LASSO regression prediction of lymph nodes around hepatic artery metastasis based on blood routine index in patients with distant gastric cancer / 中国医师进修杂志

Objective:To investigate the value of LASSO regression prediction of lymph nodes around hepatic artery metastasis based on blood routine index in patients with distant gastric cancer.Methods:The clinical data of 110 patients with distant gastric cancer from June 2018 to February 2022 in Jieshou People′s Hospital were retrospective analyzed. Among them, 43 patients had lymph nodes around hepatic artery metastases (metastasis group), and 67 patients have not lymph nodes around hepatic artery metastases (non-metastasis group). The basic clinical data were recorded; the routine blood test was detected, the indexes including white blood cell count, neutrophil percentage, lymphocyte count, platelet count, lymphocyte percentage, acidophil count, basophils count, hemoglobin, red blood cell distribution width (RDW), platelet distribution width (PDW) and neutrophil to lymphocyte ratio (NLR). The R language 4.1.0 software "grpreg" package was used to establish a Group LASSO Logistic regression analysis model to finally select the factors predicting lymph node around hepatic arterial metastasis in patients with distal gastric cancer. Nomogram were made using R language 3.5.3 software package and rms program package, calculated the consistency index (C-index), and the accuracy of the model was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve.Results:The white blood cell count, neutrophil percentage, NLR and lymphocyte percentage in metastasis group were significantly higher than those in non-metastasis group: (12.16 ± 1.62) × 10 9/L vs. (9.38 ± 2.64) × 10 9/L, (73.36 ± 6.94)% vs. (52.21 ± 6.23)%, 3.23 ± 0.63 vs. 2.35 ± 0.13 and (48.62 ± 3.64)% vs. (31.02 ± 2.94)%, the acidophil count and basophils count were significantly lower than those in non-metastasis group: (0.31 ± 0.03) × 10 9/L vs. (0.36 ± 0.04) × 10 9/L and (0.08 ± 0.01) × 10 9/L vs. (0.09 ± 0.02) × 10 9/L, the degree of differentiation and TNM stage were also worse than those in non-metastasis group, and there were statistical differences ( P<0.01). The nomogram was constructed to predict lymph nodes around hepatic artery metastasis based on the degree of differentiation, TNM stage, white blood cell count, neutrophil percentage, NLR, lymphocyte percentage, acidophil count and basophils count in patients with distant gastric cancer, the scores of each indexes were 20.64, 26.42, 32.83, 25.78, 38.46, 35.65, 29.06 and 18.26 scores, the total score (227.10 scores) was the incidence of the nomogram model (29.82%). The validation result showed C-index of 0.819 and 0.806 (95% CI 0.785 to 0.864 and 0.779 to 0.816), and the correction curves for both sets were fitted well to the ideal curve with AUC of 0.801 and 0.810 (95% CI 0.784 to 0.826 and 0.795 to 0.852), and the decision curve showed high net benefit value with threshold probability from 1% to 9%. Conclusions:LASSO regression model combined with white blood cell count, neutrophil percentage, NLR, lymphocyte percentage, acidophil count and basophils count is ideal to predict lymph nodes around hepatic artery metastasis in patients with distant gastric cancer.

LASSO regression based risk prediction model establishment for Klebsiella pneumonia infection in patients with severe acute pancreatitis / 中华胰腺病杂志

Objective:To construct a risk prediction model for infection with Klebsiella pneumonia (KP) for patients with severe acute pancreatitis (SAP).Methods:Retrospective analysis was done on the clinical data of 109 SAP patients who were admitted to Shanghai General Hospital, between March 2016 and December 2021. Patients were classified into infection group ( n=25) and non-infection group ( n=84) based on the presence or absence of KP infection, and the clinical characteristics of the two groups were compared. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension of the variables with statistical significance in univariate analysis. A nomogram prediction model was created by incorporating the optimized features from the LASSO regression model into the multivariate logistic regression analysis. Receiver operating characteristic curve (ROC) was drawn and the area under curve (AUC) was calculated; and consistency index (C-index) were used to assess the prediction model's diagnostic ability. Results:A total of 25 strains of KP were isolated from 109 patients with SAP, of which 21(84.0%) had multi-drug resistance. 20 risk factors (SOFA score, APACHEⅡ score, Ranson score, MCTSI score, mechanical ventilation time, fasting time, duration of indwelling of the peritoneal drainage tube, duration of deep vein indwelling, number of invasive procedures, without or with surgical intervention, without or with endoscopic retrograde cholangiopancreatography (ERCP), types of high-level antibiotics used, digestion disorders, abnormalities in blood coagulation, metabolic acidosis, pancreatic necrosis, intra-abdominal hemorrhage, intra-abdominal hypertension, length of ICU stay and total length of hospital stay) were found to be associated with KP infection in SAP patients by univariate analysis. The four variables (APACHEⅡ score, duration of indwelling of the peritoneal drainage tube, types of high-level antibiotics used, and total length of hospital stay) were extracted after reduced by LASSO regression. These four variables were found to be risk factors for KP infection in SAP patients by multiple logistic regression analysis (all P value <0.05). Nomogram prediction model for KP infection in SAP was established based on the four variables above. The verification results of the model showed that the C-index of the model was 0.939, and the AUC was 0.939 (95% CI 0.888-0.991), indicating that the nomogram model had relatively accurate prediction ability. Conclusions:This prediction model establishes integrated the basic clinical data of patients, which could facilitate the risk prediction for KP infection in patients with SAP and thus help to formulate better therapeutic plans for patients.

Development and validation of a prognostic model for patients with sepsis in intensive care unit / 中华危重病急救医学

Objective:To analyze the risk factors related to the prognosis of patients with sepsis in intensive care unit (ICU), construct a nomogram model, and verify its predictive efficacy.Methods:A retrospective cohort study was conducted using data from Medical Information Mart for Intensive Care-Ⅳ 0.4 [MIMIC-Ⅳ (version 2.0)]. The information of 6 500 patients with sepsis who meet the diagnostic criteria of Sepsis-3 were collected, including demography characteristics, complications, laboratory indicators within 24 hours after ICU admission, and final outcome. Using a simple random sampling method, the patients were divided into a training set and a validation set at a ratio of 7∶3. The restricted cubic spline (RCS) was used to explore whether there was a linear relationship between each variable and the prognosis, and the nonlinear variables were truncated into categorical variables. All variables were screened by LASSO regression and included in multivariate Cox regression analysis to analyze the death risk factors in ICU patients with sepsis, and construct a nomograph. The consistency index, calibration curve and receiver operator characteristic curve (ROC curve) were used to evaluate the prediction efficiency of nomogram model. The decision curve analysis (DCA) was used to validate the clinical value of the model and its impact on actual decision-making.Results:Among 6 500 patients with sepsis, 4 551 were in the training set and 1 949 were in the validation set. The 28-day, 90-day and 1-year mortality in the training set were 27.73% (1?262/4?551), 34.76% (1?582/4?551), and 42.98% (1?956/4?551), respectively, those in the validation set were 27.24% (531/1?949), 33.91% (661/1?949), and 42.23% (823/1?949), respectively. Both in training set and the validation set, compared with the final survival patients, the death patients were older, and had higher sequential organ failure assessment (SOFA) score and simplified acute physiology scoreⅡ (SAPSⅡ), more comorbidities, less urine output, and more use of vasoactive drugs, kidney replacement therapy, and mechanical ventilation. By RCS analysis, the variables with potential nonlinear correlation with the prognosis risk of septic patients were transformed into categorical variable. The variables screened by LASSO regression were enrolled in the multivariate Cox regression model. The results showed that age [hazard ratio ( HR) = 1.021, 95% confidence interval (95% CI) was 1.018-1.024], SOFA score ( HR = 1.020, 95% CI was 1.000-1.040), SAPSⅡ score > 44 ( HR = 1.480, 95% CI was 1.340-1.634), mean arterial pressure (MAP) ≤ 75 mmHg (1 mmHg ≈ 0.133 kPa; HR = 1.120, 95% CI was 1.026-1.222), respiratory rate (RR; HR = 1.044, 95% CI was 1.034-1.055), cerebrovascular disease ( HR = 1.620, 95% CI was 1.443-1.818), malignant tumor ( HR = 1.604, 95% CI was 1.447-1.778), severe liver disease ( HR = 1.330, 95% CI was 1.157-1.530), use of vasoactive drugs within 24 hours ( HR = 1.213, 95% CI was 1.101-1.336), arterial partial pressure of oxygen (PaO 2; HR = 0.999, 95% CI was 0.998-1.000), blood lactic acid (Lac; HR = 1.066, 95% CI was 1.053-1.079), blood urea nitrogen (BUN) > 8.9 mmol/L ( HR = 1.257, 95% CI was 1.144-1.381), total bilirubin (TBil; HR = 1.023, 95% CI was 1.015-1.031), and prothrombin time (PT) > 14.5 s ( HR = 1.232, 95% CI was 1.127-1.347) were associated with the death of ICU patients with sepsis (all P < 0.05). Based on the above factors, a nomogram model was constructed, and the model validation results showed that the consistency index was 0.730. The calibration curve showed a good consistency between the predicted results of the nomogram model and observed results in the training and validation sets. ROC curve analysis showed that the area under the ROC curve (AUC) predicted by the nomogram model in the training set and the validation set for 28-day, 90-day and 1-year death risk was 0.771 (95% CI was 0.756-0.786) and 0.761 (95% CI was 0.738-0.784), 0.777 (95% CI was 0.763-0.791) and 0.765 (95% CI was 0.744-0.787), 0.677 (95% CI was 0.648-0.707) and 0.685 (95% CI was 0.641-0.728), respectively. DCA analysis showed that the nomogram model had significant net benefits in predicting 28-day, 90-day, and 1-year death risk, verifying the clinical value of the model and its good impact on actual decision-making. Conclusions:The death risk factors related to ICU patients with sepsis include age, SOFA score, SAPSⅡ score > 44, MAP ≤ 75 mmHg, RR, cerebrovascular disease, malignant tumors, severe liver disease, use of vasoactive drugs within 24 hours, PaO 2, Lac, BUN, TBil, PT > 14.5 s. The nomogram model constructed based on this can predict the death risk of ICU patients with sepsis.

Construction and validation of a Nomogram-based prediction model for the risk of gout in men / 中华内分泌代谢杂志

Objective:To investigate the risk factors of gout and establish a columnar graph model to predict the risk of gout development.Methods:A total of 1 032 Han Chinese men attending the Affiliated Hospital of Traditional Chinese Medicine of Xinjiang Medical University, People′s Hospital of Xinjiang Uygur Autonomous Region, and the First Affiliated Hospital of Xinjiang Medical University from 2018 to 2020 were selected as study subjects and divided into training set(722 cases)and validation set(310 cases)by simple random sampling method in the ratio of 7∶3. General information and biochemical indices of the subjects were collected. The collected information was used to assess the risk of gout prevalence. LASSO regression analysis of R Studio software was used to screen the best predictors, and was introduced to construct a column line graph model for predicting gout risk using receiver operating characteristic(ROC)curves, and the Hosmer-Lemeshow test was used to assess the discrimination and calibration of the column line graph model. Finally, decision curve analysis(DCA)was performed using the rmda program package to assess the clinical utility of the model in validation data.Results:Age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio were risk factors for gout( P<0.05). The column line graph prediction model based on the above five independent risk factors had good discrimination(AUC value: 0.923 for training set validation and 0.922 for validation set validation)and accuracy(Hosmer-Lemeshow test: P>0.05 for validation set validation); decision curve analysis showed that the prediction model curve had clinical practical value. Conclusion:The nomogram model established by combining age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio indicators can predict the risk of gout more accurately.

LncRNA Prognostic Risk Scoring Model for Gastrointestinal Tumors Based on TCGA Database / 肿瘤防治研究

Objective To establish a lncRNA prognostic risk model for gastrointestinal tumors based on the TCGA database and evaluate the prognosis of patients. Methods We collected the data of patients with esophageal cancer, gastric cancer, colon cancer and rectal cancer in the TCGA database. Univariate Cox analysis, Lasso and multivariate Cox analysis were performed to construct the prognostic risk scoring model. The model was validated and tested for independence. Time-dependent ROC curve analysis was performed to evaluate the clinical application value of the model. Results We established a prognostic risk model based on 13 lncRNAs. The three-year AUC of the training set and the validation set were 0.746 and 0.704, respectively. The pan-cancer data set was divided into high- and low-risk groups for survival analysis. The 5-year survival rate of the low-risk group was significantly higher than that of the high-risk group; among all cancer types, the five-year survival rates of the low-risk group were higher than those of the high-risk group. Multivariate Cox analysis showed that the risk score could be an independent indicator of prognosis. Conclusion The 13-gene prognostic risk score model is constructed successfully. The risk score obtained by this model can be used as an independent prognostic predictor of the patients with gastrointestinal cancer.

A cohort study of developing a death risk prediction model in patients with septic shock based on MIMIC-Ⅲ database / 中华危重病急救医学

Objective:To develop and validate a model for predicting death risk in septic shock patients using LASSO-Logistic methods.Methods:A retrospective cohort study was conducted. Based on the open-source database Medical Information Mart for Intensive Care-Ⅲ v1.4 (MIMIC-Ⅲ v1.4), the septic shock patients meeting the Sepsis-3 criteria were included, and the data on demographic characteristics, major signs, laboratory examinations, hospitalization, and outcomes were extracted. Predictive variables were selected by LASSO regression and predictive models were derived using Logistic regression. The calibration of the model was evaluated using the Hosmer-Lemeshow test and discrimination was evaluated using the receiver operator characteristic curve (ROC curve).Results:A total of 693 patients with septic shock were enrolled, in which 445 patients survived and 248 patients dead within 30 days and the mortality was 35.8%. Logistic regression model was constructed according to nine predictive variables and outcome variables screened by LASSO regression method, which showed that advanced age, Elixhauser index, blood lactic acid (Lac), K + level and mechanical ventilation were associated with increased 30-day mortality [odds ratio ( OR) and 95% confidence interval (95% CI) was 1.023 (1.010-1.037), 1.047 (1.022-1.074), 1.213 (1.133-1.305), 2.241 (1.664-3.057), 2.165 (1.433-3.301), respectively, all P < 0.01], and reduced systolic blood pressure (SBP), diastolic blood pressure (DBP), body temperature, and pulse oxygen saturation (SpO 2) were also associated with increased 30-day mortality [ OR (95% CI) was 0.974 (0.957-0.990), 0.972 (0.950-0.994), 0.693 (0.556-0.857), 0.971 (0.949-0.992), respectively, all P < 0.05]. The calibration curve showed that the predicted risk of septic shock death risk prediction model had good agreement with the real situation. ROC curve analysis showed that the area under the ROC curve (AUC) of the prediction model was 0.839 (95% CI was 0.803-0.876), which could distinguish patients at risk of death from those at risk of survival. Conclusions:The septic shock death risk prediction model has a good ability to identify the 30-day mortality risk of septic shock patients, including nine hospital readily variables (age, Elixhauser index, mechanical ventilation, Lac, K +, SBP, DBP, body temperature and SpO 2). The model could be used by clinicians to calculate the risk of death in septic shock individuals.

Construction and Validation of Prognostic Risk Score Model of Autophagy Related Genes in Lung Adenocarcinoma / 中国肺癌杂志

BACKGROUND@#Autophagy related genes (ARGs) regulate lysosomal degradation to induce autophagy, and are involved in the occurrence and development of a variety of cancers. The expression of ARGs in tumor tissues has a great prospect in predicting the survival of patients. The aim of this study was to construct a prognostic risk score model for lung adenocarcinoma (LUAD) based on ARGs.@*METHODS@#5,786 ARGs were obtained from GeneCards database. Gene expression profiles and clinical data of 395 LUAD patients were collected from The Cancer Genome Atlas (TCGA) database. All ARGs expression data were extracted, and The ARGs differentially expressed were identified by R software. Survival analysis of differentially expressed ARGs was performed to screen for ARGs with prognostic value, and functional enrichment analysis was performed. The least absolute selection operator (LASSO) regression and Cox regression model were used to construct a prognostic risk scoring model for ARGs. The receiver operating characteristic (ROC) curve was drawn to obtain the optimal cut-off value of risk score. According to the cut-off value, the patients were divided into high-risk group and low-risk group. The area under curve (AUC) and the Kaplan-Meier survival curve was plotted to evaluate the model performance, which was verified in external data sets. Finally, univariate and multivariate Cox regression analysis was applied to evaluate the independent prognostic value of the model, and its clinical relevance was analyzed.@*RESULTS@#Survival analysis, Lasso regression and Cox regression analysis were used to construct a LUAD prognostic risk score model with five ARGs (ADAM12, CAMP, DKK1, STRIP2 and TFAP2A). The survival time of patients with low-risk score in this model was significantly better than that of patients with high-risk score (P<0.001). The model showed good prediction performance for LUAD in both the training set (AUCmax=0.78) and two external validation sets (AUCmax=0.88). Risk score was significantly associated with the prognosis of LUAD patients in univariate and multivariate Cox regression analyses, suggested that risk score could be a potential independent prognostic factor for LUAD. Correlation analysis of clinical characteristic showed that high risk score was closely associated with high T stage, high tumor stage and poor prognosis.@*CONCLUSIONS@#We constructed a LUAD risk score model consisting of five ARGs, which can provide a reference for predicting the prognosis of LUAD patients, and may be used in combination with tumor node metastasis (TNM) staging for prognosis prediction of LUAD patients in the future.

Application of immune cell infiltration in the diagnosis and prognosis of non-small cell lung cancer / 生物工程学报

Immune cell infiltration is of great significance for the diagnosis and prognosis of cancer. In this study, we collected gene expression data of non-small cell lung cancer (NSCLC) and normal tissues included in TCGA database, obtained the proportion of 22 immune cells by CIBERSORT tool, and then evaluated the infiltration of immune cells. Subsequently, based on the proportion of 22 immune cells, a classification model of NSCLC tissues and normal tissues was constructed using machine learning methods. The AUC, sensitivity and specificity of classification model built by random forest algorithm reached 0.987, 0.98 and 0.84, respectively. In addition, the AUC, sensitivity and specificity of classification model of lung adenocarcinoma and lung squamous carcinoma tissues constructed by random forest method 0.827, 0.75 and 0.77, respectively. Finally, we constructed a prognosis model of NSCLC by combining the immunocyte score composed of 8 strongly correlated features of 22 immunocyte features screened by LASSO regression with clinical features. After evaluation and verification, C-index reached 0.71 and the calibration curves of three years and five years were well fitted in the prognosis model, which could accurately predict the degree of prognostic risk. This study aims to provide a new strategy for the diagnosis and prognosis of NSCLC based on the classification model and prognosis model established by immune cell infiltration.

Misunderstandings and general strategies of statistical modeling in medical research papers / 中华医学科研管理杂志

Objective To summarize and analyze the Logistic and linear regression modeling of medical research papers in 2018,to propose a general modeling strategy.Methods Search 2018 China Knowledge Network medical research related papers,extract some papers for evaluation,identify and analyze possible modeling defects in the paper writing process,provide the general method of modeling.Results In the China Knowledge Network database,1 319 medical research papers were detected in 2018,and 125 papers were randomly selected for evaluation.Identified issues include no data cleaning before modeling,insufficient attention to modeling,and model evaluation after modeling.Conclusions There are defects in the modeling process of medical research papers,and further attention and enhancement are needed in the writing process.

Quantitative analysis of hepatocellular carcinomas pathological grading in non-contrast magnetic resonance images / 生物医学工程学杂志

In order to solve the pathological grading of hepatocellular carcinomas (HCC) which depends on biopsy or surgical pathology invasively, a quantitative analysis method based on radiomics signature was proposed for pathological grading of HCC in non-contrast magnetic resonance imaging (MRI) images. The MRI images were integrated to predict clinical outcomes using 328 radiomics features, quantifying tumour image intensity, shape and text, which are extracted from lesion by manual segmentation. Least absolute shrinkage and selection operator (LASSO) were used to select the most-predictive radiomics features for the pathological grading. A radiomics signature, a clinical model, and a combined model were built. The association between the radiomics signature and HCC grading was explored. This quantitative analysis method was validated in 170 consecutive patients (training dataset: = 125; validation dataset, = 45), and cross-validation with receiver operating characteristic (ROC) analysis was performed and the area under the ROC curve (AUC) was employed as the prediction metric. Through the proposed method, AUC was 0.909 in training dataset and 0.800 in validation dataset, respectively. Overall, the prediction performances by radiomics features showed statistically significant correlations with pathological grading. The results showed that radiomics signature was developed to be a significant predictor for HCC pathological grading, which may serve as a noninvasive complementary tool for clinical doctors in determining the prognosis and therapeutic strategy for HCC.

Cytokine analysis of Zhuangguguanjie wan-induced idiosyncratic liver injury based on mathematical modeling / 药学学报

In this study, we used a mathematic-based modeling system to screen the cytokines that are sensitive to Zhuangguguanjie wan (ZGW)-induced idiosyncratic liver injury. The values of 27 cytokines were used as the data source in rat liver of lipopolysaccharide (LPS) + ZGW group. The alanine aminotransferase (ALT) activity value of liver function indexes was used as the outcome evaluation index of liver injury. Cytokines of ZGW-induced idiosyncratic liver injury were screened using Logistic regression, random forest method, LASSO Logistics regression and method of combining rule discovery algorithm with LASSO, and cytokines filtered out were revalued in THP1 macrophage. Susceptible cytokine combinations:interleukin-1β (IL-1β), epidermal growth factor (EGF) and interleukin-18 (IL-18) closely related to ZGW-induced idiosyncratic liver injury were obtained after preliminary screening analysis. The result of revalued in THP1 showed that the ethanolic extract of ZGW (EtZ) combined with IL-1β or IL-18 synergistically enhanced tumor necrosis factor-α (TNF-α) secretion in THP1 macrophage, and EtZ combined with IL-1β significantly enhanced interleukin-6 (IL-6) secretion in THP1 macrophage, but EtZ combined with EGF markedly inhibited IL-6 secretion in THP1 macrophage. The results suggest that the sensitive cytokines that can be characterized in the ZGW-induced idiosyncratic liver injury are IL-1β and IL-18, which provides a basis for screening the ZGW-induced idiosyncratic liver injury patients, and a new experimental evidence for clinical safety medication and risk prevention of ZGW.

Prognostic factors of colorectal cancer and establishment of colorectal cancer prognosis model / 中华医学图书情报杂志

The factors influencing the prognosis of colorectal cancer were studied after its characteristic variables were screened by stepwise logistic regression analysis, Bayesian model averaging analysis, and LASSO regression a-nalysis respectively. A model of colorectal cancer prognosis was established according to the artificial neural net-work classification algorithm for the assessment of colorectal cancer. The highest accuracy was detected in the model of colorectal cancer prognosis established by Bayesian model averaging analysis combined with artificial neural net-work classification algorithm.