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Objective To analyze the routine test parameter levels of patients with colorectal adenoma and colorectal cancer, and develop a prediction model. Methods A total of 580 patients diagnosed with colorectal adenoma (117 patients) and colorectal cancer (463 patients) were included in the retrospective study. The patients were randomly divided into two groups according to a 7:3 ratio: a training set with 406 cases and a validation set with 174 cases. Logistic regression analysis was used to establish a prediction model, and a nomogram was drawn. The model′s discrimination, calibration, and clinical applicability were evaluated using receiver operating characteristic curve (ROC), calibration plot, and decision curve analysis (DCA). Results Univariate logistic regression analysis identified 13 potential predictors: age, fecal occult blood test (FOBT), fibrinogen (FIB), thrombin time (TT), albumin (ALB), white blood cell value (WBC), neutrophil count (NEUT#), hematocrit value (HCT), mean corpuscular hemoglobin (MCH), red cell distribution width (RDW), platelet count (PLT), mean platelet volume (MPV), and activated partial thromboplastin time (APTT). Multivariate logistic regression analysis showed MPV, FIB, ALB, FOBT, TT, and HCT were risk factors for colorectal cancer in patients with colorectal adenoma (P<0.05). A nomogram was constructed based on these predictors to build a prediction model. The AUC of the ROC curve was 0.915 for colorectal cancer in the training set and 0.836 in the validation set. Calibration plots demonstrated high prediction accuracy and good model calibration. DCA results indicated the prediction model provided greater net benefit compared with the extreme models at threshold probabilities of approximately 55%-95%. Conclusion The developed prediction model exhibits satisfactory discrimination, calibration, and clinical applicability. The model can serve as an auxiliary tool in distinguishing between colorectal adenoma and colorectal cancer in patients.
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Objective:To explore the correlation between serum protein factor level and clinical symptoms and cognitive impairment in patients with schizophrenia and to predict the degree of cognitive impairment, so as to provide an auxiliary method for clinical evaluation of cognitive impairment severity and prognosis of schizophrenia.Methods:From September 2017 to April 2019, 71 schizophrenic patients diagnosed in the First Affiliated Hospital of Kunming Medical University were selected as the patient group, and 65 healthy volunteers from the physical examination center of the same hospital were selected as the control group.The concentrations of tumor necrosis factor α(TNF-α), brain-derived neurotrophic factor(BDNF) and calcium-binding protein β(S100β) in peripheral blood were detected by ELISA method.Cognitive function was evaluated by MATRICS consensus cognitive battery(MCCB) cognitive assessment.The clinical symptoms of patients were evaluated by positive and negative syndrome scale(PANSS) scale.SPSS 20.0 software was used for statistical analysis, independent sample t-test was used for comparison between groups, and Pearson correlation analysis was used for the relationship between serum protein factor level and cognitive function and clinical symptoms.In order to objectively predict, evaluate and verify the severity of cognitive impairment in schizophrenia, Bayes discriminant function was established with serum protein factor concentration and PANSS total score as independent variables and the defect degree of cognitive factors in MCCB as dependent variables. Results:The serum TNF-α((63.2±25.2)pg/L vs (31.4±14.3)pg/L) and S100β((68.0±26.4)pg/L vs (47.3±20.2)pg/L) concentrations in the patient group were higher than those in the control group.The concentration of serum BDNF in the patient group was lower than that in the control group ((2 517.8±1 140.2)pg/L vs (5 202.2±447.2)pg/L), and the difference was statistically significant ( P=0.000). In the retrospective test of cognitive impairment severity in Bayes discriminant function model, the correct discrimination rates of four cognitive factors were speed of processing(SoP) 69.0%, Verbal learning(VeL) 63.4%, reasoning and problem solving(RPS) 76.1% and visual learning(ViL) 73.2%.The correct discrimination rates of cross-examination were SoP 66.2%, VeL 60.6%, RPS 73.2%, ViL 66.2. Conclusion:The levels of serum protein factors TNF-α, BDNF and S100β and clinical symptom scores of schizophrenia have different degrees of correlation with the severity score of cognitive impairment.Bayes discriminant function model has higher correct discrimination rate for the severity of cognitive impairment of schizophrenia.It is found that the levels of schizophrenia-related protein factors and clinical symptom scores may have predictive effect on the severity of cognitive impairment, providing a more objective basis for the clinical efficacy evaluation of schizophrenia patients.
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In this paper， the types and expression characteristics of microRNAs related to cognitive impairment in schizophrenia patients were reviewed， so as to provide a reference for further research on the characteristics of cognitive impairment in schizophrenia patients， and to open up new ideas for further research on the molecular mechanism of cognitive impairment as well as subsequent precise treatment and prognosis evaluation.
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Objective To investigate the serum levels of neuregulin-1 (NRG-1) and the gamma activity of the prefrontal cortex of electroencephalogram (EEG) in the resting state in first-episode schizophrenia patients and exam﹣ine their correlation with clinical symptoms and cognitive function. Methods The serum levels of NRG-1 were mea﹣sured in 53 patients and 58 controls. The gamma activity was first collected from the lead of FP1 and FP2 of the pre﹣frontal cortex of EEG and was then measured by using time-frequency analysis. The psychotic symptoms were as﹣sessed by positive and negative syndrome scale (PANSS). The MATRICS consensus cognitive battery (MCCB) was used to assess the cognitive function. Results The serum levels of NRG-1 was significantly lower in the case group than in the control [(7.36±3.96) pg/mL vs. (11.02±8.78) pg/mL, P=0.006]. The gamma activity was significantly different be﹣tween the case group and the control group [39(73.6%) vs. 14(26.4%), P<0.001]. The scores of TMT in MCCB was significantly higher while the scores of BACS SC, HVLT-R, NAB, BVMT-R and CF scores were significantly lower in the case group than the control group (P<0.01). There was a negative correlation between the serum NRG-1 level and the gamma activity in the case group (r=-0.542, P<0.001). There was a negative correlation between the serum NRG-1 level with PANSS (r=-0.360, P=0.009), while the gamma activity was positively correlated with PANSS (r=0.278, P=0.046) in the case group. The serum NRG-1 level was significantly positively correlated with the scores of HVLT-R in the case group (r=0.332, P=0.016), and the gamma activity was significantly negatively correlated with the scores of HVLT-R (r=-0.442, P=0.001) and NAB (r=-0.307, P=0.027). Conclusion The serum NRG-1 level and the gamma activity are correlated with the clinical symptoms and cognitive impairment of patients with first-episode schizophrenia to some degree, suggesting that abnormal neurobiochemical and neuroelectrophysiological reactions exist and interact with each other in the early stage of schizophrenia.