ObjectiveTo analyze the clinical characteristics of patients with tuberculous pleural effusion and malignant pleural effusion and explore the value of laboratory indexes of pleural effusion in the differential diagnosis of tuberculous pleural effusion and malignant pleural effusion.MethodsThe clinical data and laboratory indexes of pleural effusion of patients with tuberculous pleural effusion and patients with malignant pleural effusion hospitalized in West China Hospital of Sichuan University between January and December 2017 were analyzed retrospectively. Those examinations with statistical significance were selected to establish a binary logistic regression model for diagnosing malignant pleural effusion from tuberculous pleural effusion. Hosmer-Lemeshow test was used to assess the goodness of fit of the logistic model, and a receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model.ResultsThe average age of the 128 patients with tuberculous pleural effusion was (51.60±21.02) years, and the average age of the 164 malignant pleural effusion was (63.52±11.87) years. Patients with tuberculous pleural effusion were prone to getting symptoms of cough, expectoration, fever, chest pain and tightness in breathing, with statistical significance (P<0.05). The level of adenosine deaminase in patients with tuberculous pleural effusion was (23.06±21.29) U/L, higher than that in malignant pleural effusion; the difference was statistically significant (P<0.05). The levels of albumin, glucose, carbohydrate antigen (CA) 125, CA19-9, carcinoembryonic antigen (CEA) and cyto-keratin 19 fragment antigen 21-1 in patients with malignant pleural effusion were higher than those in patients with tuberculous pleural effusion (P<0.05). Logistic regression analysis showed that CA125, CEA and glucose were introduced to model as the main effect. The area under the ROC curve was 0.914 [95% confidence interval (0.864, 0.964)], with an improved diagnostic efficiency.ConclusionsThe clinical manifestations of tuberculous pleural effusion and malignant pleural effusion are multifarious with low specificity. A joint detection of CA125, CEA and glucose in pleural effusion and the joint diagnostic model can identify tuberculous pleural effusion and malignant pleural effusion better.
目的 探討抗環瓜氨酸肽抗體（anti-CCP）與類風濕因子（RF）對類風濕關節炎（RA）的診斷效能，及RF分型檢測在RA活動度判斷中的價值。 方法 選取2012年3月－2013年2月就診的64例RA患者為病例組，103例其他自身免疫性疾病患者為對照組，用酶聯免疫吸附試驗分別檢測anti-CCP和RF-IgM/IgG/IgA，收集數據進行統計分析。 結果 anti-CCP與RF聯合指標對RA的診斷靈敏度最高（92.2%），anti-CCP的特異度最高（95.1%）；RF-IgA的水平與骨關節侵蝕程度呈正相關（rs=0.987，P=0.000）；RF的3個亞型都可反映RA疾病的活動度（P＜0.05）。 結論 anti-CCP與RF聯合診斷RA，可顯著提高診斷靈敏度，RF的分型檢測對于RA患者的活動度監測有重要價值。
ObjectiveTo explore the value of magnetic resonance diffusion weighted imaging (DWI) in preoperative Bismuth-Corlette classification of hilar cholangiocarcinoma (HCCA). MethodsA total of 53 HCCA patients confirmed by postoperative pathology were retrospectively included. The accuracy of two sequence combinations, namely dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) + magnetic resonance cholangiopancreatography (MRCP) and DCE-MRI + MRCP + DWI, in evaluating the longitudinally involved bile duct segments and Bismuth-Corlette classification of HCCA was compared. Additionally, the correlation between apparent diffusion coefficient (ADC) values and tumor Bismuth-Corlette classification as well as degree of differentiation was analyzed. ResultsThere were 318 bile duct segments in 53 HCCA patients. The accuracy rate of DCE-MRI + MRCP was 93.7% (298/318), the sensitivity was 91.5% (161/176), and the specificity was 96.5% (137/142). The accuracy rate of DCE-MRI + MRCP + DWI was 96.5% (307/318), the sensitivity was 96.0% (169/176), and the specificity was 97.2% (138/142). Receiver operating characteristic (ROC) curve analysis showed that the area under the ROC curve (AUC) of DCE-MRI + MRCP + DWI was 0.966 [95%CI (0.940, 0.983), P＜0.001], and its diagnostic efficacy was superior to that of DCE-MRI + MRCP [AUC=0.940, 95%CI (0.908, 0.963), P＜0.001]. The DeLong test indicated a statistically significant difference in AUC between the two sequences (Z=2.633, P＜0.01). The accuracy rates of preoperative Bismuth-Corlette classification of HCCA evaluated by DCE-MRI + MRCP and DCE-MRI + MRCP + DWI were 86.8% (46/53) and 94.3% (50/53), respectively. After adding the DWI sequence, the consistency between Bismuth-Corlette classification results and surgical pathological classification results (Kappa=0.922, P<0.001) was higher than that of DCE-MRI + MRCP sequence (Kappa=0.820, P<0.001), with a statistically significant difference (χ2=160.370, P<0.001). In addition, the ADC value of HCCA was negatively correlated with tumordegree of differentiation (rs=–0.524, P<0.001), but had no significant correlation with its Bismuth-Corlette classification (rs=–0.058, P=0.682). ConclusionsDCE-MRI + MRCP + DWI sequence can effectively improve the accuracy in preoperative evaluation of the involvement of bile duct segments and Bismuth-Corlette classification of HCCA, which provides guidance for precise preoperative surgical planning in clinical practice. In addition, the ADC value can provide additional information required for non-invasive preoperative prediction of the prognosis of HCCA patients.
The SAS is considered as internationally-known standard software in the field of data processing and statistics, which is also excellent in conducting meta-analysis; however, it require users to have higher technical expertise due to its complex and difficult program coding. Assessing statistical power calculation of significance tests is one of important steps in meta-analysis. Guy Cafri et al., developed a macro (%metapower) for well implement this calculation in SAS. This macro is specifically designed to implement the statistical power calculation of overall results of meta-analysis, heterogenity, and subgroup analysis, which is easy to operate. This article introduces%metapower based on examples.
Objective To explore the relationship of self-efficacy and coping styles with parenting styles in patients with schizophrenia, and provide the theory and practical basis for family-interventions of rehabilitation of patients with schizophrenia. Methods From January to June 2015, General Self- Efficacy Scale, Simplified Coping Style Questionnaire and Egma Minnen av Bardndosnauppforstran were used to evaluate 60 inpatients with schizophrenia and in good rehabilitation in a grade A tertiary general hospital. Results The scores of self-efficacy, parental emotional warmth and father’s over protection were lower in patients with schizophrenia than the norms (P<0.01). The scores of parental punishment and rejection and father’s over intervention were higher in patients with schizophrenia than the norms (P<0.01). In patients with schizophrenia, the active coping domain was positively correlated to parental emotion warmth (P<0.05); the negative coping domain was positively correlated to parental rejection, father’s over protection and mother’s over intervention (P<0.05); self-efficacy was positively correlated to father’s emotion warmth and preference of parents (P<0.05). Conclusions Active family-interventions is important in the rehabilitation of patients with schizophrenia. The parents should be instructed to correctly educate the children, to improve the patients’ general self-efficacy, and help the patients successfully solve the problem with good coping style.
Objective To explore the risk factors of nosocomial pulmonary infection in acute pesticide poisoning. Methods The clinical data of patients with acute pesticide poisoning hospitalized in the Emergency Department of the First Affiliated Hospital of Wannan Medical College and the Second Affiliated Hospital of Wannan Medical College between January 1, 2021 and September 30, 2023 were retrospectively analyzed. Patients were divided into pulmonary infection group and non-pulmonary infection group according to whether they had pulmonary infection during hospital. Multiple logistic regression was used to analyze the independent risk factors of nosocomial pulmonary infection in patients with acute pesticide poisoning, and a risk prediction model (nomogram) was constructed. The predictive efficacy of nomogram and independent predictors in nosocomial pulmonary infection were analyzed by using the receiver operating characteristic curve. Calibration curve and decision curve were used to evaluate the differentiation and clinical application value of the model. Results A total of 189 patients with acute pesticide poisoning were included in the study, with an average age of (58.12±18.45) years old, 98 males (51.85%) and 91 females (48.15%). There were 36 cases (19.05%) of pulmonary infection. Multiple logistic regression analysis showed that age [odds ratio (OR)=1.030, 95% confidence interval (CI) (1.001, 1.060), P=0.040], type 2 diabetes mellitus [OR=2.770, 95%CI (1.038, 7.393), P=0.042], ischemic cerebrovascular disease [OR=3.213, 95%CI (1.101, 9.376), P=0.033], white blood cell count [OR=1.080, 95%CI (1.013, 1.152), P=0.019], activities of daily living score [OR=0.981, 95%CI (0.965, 0.998), P=0.024] were independent predicting factors for nosocomial pulmonary infection in acute pesticide poisoning. The area under the curve of nosocomial pulmonary infection in patients with acute pesticide poisoning predicted by nomogram based on the above factors was 0.813 (P<0.001). The calibration curve showed that the prediction probability was consistent with the actual occurrence probability (P=0.912), and the decision curve showed that the nomogram had good clinical application value. Conclusions Age, activities of daily living score, type 2 diabetes mellitus, ischemic cerebrovascular disease, and white blood cell count are independent predictors of nosocomial pulmonary infection in acute pesticide poisoning. The nomogram constructed based on them has good differentiation and consistency, which can provide basis for early identification and intervention of clinical staff.
To accurately capture and effectively integrate the spatiotemporal features of electroencephalogram (EEG) signals for the purpose of improving the accuracy of EEG-based emotion recognition, this paper proposes a new method combining independent component analysis-recurrence plot with an improved EfficientNet version 2 (EfficientNetV2). First, independent component analysis is used to extract independent components containing spatial information from key channels of the EEG signals. These components are then converted into two-dimensional images using recurrence plot to better extract emotional features from the temporal information. Finally, the two-dimensional images are input into an improved EfficientNetV2, which incorporates a global attention mechanism and a triplet attention mechanism, and the emotion classification is output by the fully connected layer. To validate the effectiveness of the proposed method, this study conducts comparative experiments, channel selection experiments and ablation experiments based on the Shanghai Jiao Tong University Emotion Electroencephalogram Dataset (SEED). The results demonstrate that the average recognition accuracy of our method is 96.77%, which is significantly superior to existing methods, offering a novel perspective for research on EEG-based emotion recognition.
Objective To explore the change of serum levels of neutrophil gelatinase-associated lipocalin (NGAL), tissue inhibitor of metalloproteinases-2 (TIMP-2), and insulin-like growth factor-binding protein 7 (IGFBP-7) in the early stage of multiple trauma, and their predictive efficacy for acute kidney injury (AKI). Methods The multiple trauma patients admitted between February 2020 and July 2021 were prospectively selected, and they were divided into AKI group and non-AKI group according to whether they developed AKI within 72 h after injury. The serum levels of NGAL, TIMP-2, and IGFBP-7 measured at admission and 12, 24, and 48 h after injury, the Acute Pathophysiology and Chronic Health Evaluation Ⅱ(APACHE Ⅱ) score, intensive care unit duration, rate of renal replacement therapy, and 28-day mortality rate were compared between the two groups. Results A total of 51 patients were included, including 20 in the AKI group and 31 in the non-AKI group. The APACHE Ⅱ at admission (20.60±3.57 vs. 11.61±3.44), intensive care unit duration [(16.75±2.71) vs. (11.13±3.41) d], rate of renal replacement therapy (35.0% vs. 0.0%), and 28-day mortality rate (25.0% vs. 3.2%) in the AKI group were higher than those in the non-AKI group (P<0.05). The serum levels of NGAL and IGFBP-7 at admission and 12, 24, and 48 h after injury in the AKI group were all higher than those in the non-AKI group (P<0.05). For the prediction of AKI, the areas under receiver operating characteristic curves and 95% confidence intervals of serum NGAL, TIMP-2 and IGFBP-7 12 h after injury were 0.98 (0.96, 1.00), 0.92 (0.83, 1.00), and 0.87 (0.78, 0.97), respectively. Conclusion Serum NGAL, TIMP-2, and IGFBP-7 have high predictive efficacy for AKI secondary to multiple trauma, and continuous monitoring of serum NGAL can be used for early prediction of AKI secondary to multiple trauma.
ObjectiveTo evaluate the predictive ability and clinical application value of artificial intelligence (AI) systems in the benign and malignant differentiation and pathological type of pulmonary nodules, and to summarize clinical application experience. MethodsA retrospective analysis was conducted on the clinical data of patients with pulmonary nodules admitted to the Department of Thoracic Surgery, Second Hospital of Lanzhou University, from February 2016 to February 2025. Firstly, pulmonary nodules were divided into benign and non-benign groups, and the discriminative abilities of AI systems and clinicians were compared. Subsequently, lung nodules reported as precursor glandular lesions (PGL), microinvasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) in postoperative pathological results were analyzed, comparing the efficacy of AI systems and clinicians in predicting the pathological type of pulmonary nodules. ResultsIn the analysis of benign/non-benign pulmonary nodules, clinical data from a total of 638 patients with pulmonary nodules were included, of which there were 257 males (10 patients and 1 patient of double and triple primary lesions, respectively) and 381 females (18 patients and 1 patient of double and triple primary lesions, respectively), with a median age of 55.0 (47.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis of the two groups of variables showed that, except for nodule location, the differences in the remaining variables were statistically significant (P<0.05). Multivariate logistic regression analysis showed that age, nodule type (subsolid pulmonary nodule), average density, spicule sign, and vascular convergence sign were independent influencing factors for non-benign pulmonary nodules, among which age, nodule type (subsolid pulmonary nodule), spicule sign, and vascular convergence sign were positively correlated with non-benign pulmonary nodules, while average density was negatively correlated with the occurrence of non-benign pulmonary nodules. The area under the receiver operating characteristic curve (AUC) of the malignancy risk value given by the AI system in predicting non-benign pulmonary nodules was 0.811, slightly lower than the 0.898 predicted by clinicians. In the PGL/MIA/IAC analysis, clinical data from a total of 411 patients with pulmonary nodules were included, of which there were 149 males (8 patients of double primary lesions) and 262 females (17 patients of double primary lesions), with a median age of 56.0 (50.0, 61.0) years. Different lesions in the same patient were analyzed as independent samples. Univariate analysis results showed that, except for gender, nodule location, and vascular convergence sign, the differences in the remaining variables among the three groups of PGL, MIA, and IAC patients were statistically significant (P<0.05). Multinomial multivariate logistic regression analysis showed that the differences between the parameters in the PGL group and the MIA group were not statistically significant (P>0.05), and the maximum diameter and average density of the nodules were statistically different between the PGL and IAC groups (P<0.05), and were positively correlated with the occurrence of IAC as independent risk factors. The average AUC value, accuracy, recall rate, and F1 score of the AI system in predicting lung nodule pathological type were 0.807, 74.3%, 73.2%, and 68.5%, respectively, all better than the clinical physicians’ prediction of lung nodule pathological type indicators (0.782, 70.9%, 66.2%, and 63.7% respectively). The AUC value of the AI system in predicting IAC was 0.853, and the sensitivity, specificity, and optimal cutoff value were 0.643, 0.943, and 50.0%, respectively. ConclusionThis AI system has demonstrated high clinical value in predicting the benign and malignant nature and pathological type of lung nodules, especially in predicting lung nodule pathological type, its ability has surpassed that of clinical physicians. With the optimization of algorithms and the adequate integration of multimodal data, it can better assist clinical physicians in formulating individualized diagnostic and treatment plans for patients with lung nodules.
ObjectiveTo systematically review the models for predicting coronary artery disease (CAD) and demonstrate their predictive efficacy. MethodsPubMed, EMbase and China National Knowledge Internet were searched comprehensively by computer. We included studies which were designed to develop and validate predictive models of CAD. The studies published from inception to September 30, 2020 were searched. Two reviewers independently evaluated the studies according to the inclusion and exclusion criteria and extracted the baseline characteristics and metrics of model performance.ResultsA total of 30 studies were identified, and 19 diagnostic predictive models were for CAD. Seventeen models had external validation group with area under curve (AUC)>0.7. The AUC for the external validation of the traditional models, including Diamond-Forrester model, updated Diamond-Forrester model, Duke Clinical Score, CAD consortium clinical score, ranged from 0.49 to 0.87.ConclusionMost models have modest discriminative ability. The predictive efficacy of traditional models varies greatly among different populations.