摘要:目的: 金黄色葡萄球菌(金葡菌)的感染近年来已成为医院内的主要致病菌,而其耐药性也呈逐渐升高的趋势,为了解该菌在我院的感染和耐药情况,为临床合理使用抗生素提供科学依据。 方法 : 用经典生理生化鉴定方法,对各种临床标本主要来源于痰液和各种伤口脓液标本分离到的102株金葡菌进行生物学特性及药敏试验。 结果 : 从我们医院2007年5月至2009年8月所分离出来的102株金葡菌中青霉素耐药性8923%,氨苄青霉素耐药率为9385%,没有发现万古霉素耐药菌。 结论 : 除万古霉素外,耐药率较低的依次是利福平、苯唑青霉素、环丙沙星、呋喃妥因、阿米卡星、磺胺甲基异恶唑、红霉素,而青霉素G、氨苄青霉素、四环素耐药性情况非常严重,并且多重耐药,耐药性强,应引起临床的高度重视。Abstract: Objective: To analyze the bionomics and antimicrobial susceptibility of staphylococcus aureus, which was the main pathogenic bacterium with high drug tolerance in our hospital, in order to provide the rational use of antibiotics. Methods : Samples of one hundred and two staphylococcus aureus cases from sputamentum and pus were evaluated by classic physiology and biochemistry methods to test the bionomics and antimicrobial susceptibility. Results : The drug resistance rate to penicillin, penbritin and vancomycin was 8923%, 9385% and 0, separately. Conclusion : Besides vancomycin, the drug resistance rate of rifampicin, oxazocilline, ciprofloxacin, furadantin, amikacin, sulfamethoxazole and sulfamethoxazole increased one by one. The resistance to penicillin G, penbritin and tetracycline was serious, including multidrug resistant, which should be paid highly attention.
Objective To systematically review the current situation of health economics evaluation of gastric cancer screening. Methods The PubMed, EMbase, The Cochrane Library, Web of Science, CNKI, WanFang Data and VIP databases were electronically searched to collect the health economics evaluation studies on gastric cancer screening from January 1st, 1975 to September 30th, 2021. Two reviewers independently screened the literature, extracted data and assessed the risk of bias of the included studies. Then, qualitative analysis was performed. Results A total of 44 studies were included. Most of the targeted populations of the study were high-risk groups in areas with a high incidence of gastric cancer. Screening methods such as endoscopy and Helicobacter pylori infection detection were mainly evaluated in those studies. According to the results, about 47% of the studies evaluated a single screening method. A total of 35 studies showed that they established models, however, only a few calibrated the models. Conclusion Most studies of gastric cancer screening reviews neither calibrate the results nor consider the effect of smoking on the progression of gastric cancer. Those evaluated screening programs are limited.
Objective For potential patients with better prognosis of non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations, a simpler and more effective model with easy-to-obtain histopathological parameters was established. MethodsThe computed tomography (CT) images of 158 patients with EGFR-mutant NSCLC who were first diagnosed in West China Hospital of Sichuan University were retrospectively analyzed, and the target areas of the lesions were described. Patients were randomly assigned to either a model training group or a test group.The radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) regression method was used to screen out the valuable radiomics features. The logistic regression method was used to establish a radiomic model, and the nomogram was used to evaluate the discrimination ability. Finally, the calibration curve, receiver characteristic curve (ROC), Kaplan-Meier curve and decision curve analysis (DCA) were employed to assess model efficacy. ResultsA nomogram combining three important clinical factors : gender, lesion location, treatment, and imaging risk score was established to predict the 3-year, 5-year, and 8-year survival rates of NSCLC patients with EGFR mutation. The calibration curve demonstrated highly consistent between model-predicted survival probabilities and observed overall survival (OS). The area under the curve (AUC) -ROC of the predicted 3-year, 5-year and 8-year OS was 0.70, 0.79 and 0.68, respectively. The Kaplan-Meier curve revealed significant OS disparities when comparing high- and low-risk patient subgroups. The DCA curve showed that the predicted 3-year and 5-year OS increased more clinical benefits than the treatment of all patients or no treatment.ConclusionThe nomogram for predicting the survival prognosis of NSCLC patients with EGFR mutation was constructed and verified, which can effectively predict the survival time range of NSCLC patients, and provide a reference for more individualized treatment decisions for such patients in clinical practice.
In recent years, the computer science represented by artificial intelligence and high-throughput sequencing technology represented by omics play a significant role in the medical field. This paper reviews the research progress of the application of artificial intelligence combined with omics data analysis in the diagnosis and treatment of non-small cell lung cancer (NSCLC), aiming to provide ideas for the development of a more effective artificial intelligence algorithm, and improve the diagnosis rate and prognosis of patients with early NSCLC through a non-invasive way.
ObjectiveTo investigate the radiomics features to distinguish invasive lung adenocarcinoma with micropapillary or solid structure. MethodsA retrospective analysis was conducted on patients who received surgeries and pathologically confirmed invasive lung adenocarcinoma in our hospital from April 2016 to August 2019. The dataset was randomly divided into a training set [including a micropapillary/solid structure positive group (positive group) and a micropapillary/solid structure negative group (negative group)] and a testing set (including a positive group and a negative group) with a ratio of 7∶3. Two radiologists drew regions of interest on preoperative high-resolution CT images to extract radiomics features. Before analysis, the intraclass correlation coefficient was used to determine the stable features, and the training set data were balanced using synthetic minority oversampling technique. After mean normalization processing, further radiomics features selection was conducted using the least absolute shrinkage and selection operator algorithm, and a 5-fold cross validation was performed. Receiver operating characteristic (ROC) curves were depicted on the training and testing sets to evaluate the diagnostic performance of the radiomics model. ResultsA total of 340 patients were enrolled, including 178 males and 162 females with an average age of 60.31±6.69 years. There were 238 patients in the training set, including 120 patients in the positive group and 118 patients in the negative group. There were 102 patients in the testing set, including 52 patients in the positive group and 50 patients in the negative group. The radiomics model contained 107 features, with the final 2 features selected for the radiomics model, that is, Original_ glszm_ SizeZoneNonUniformityNormalized and Original_ shape_ SurfaceVolumeRatio. The areas under the ROC curve of the training and the testing sets of the radiomics model were 0.863 (95%CI 0.815-0.912) and 0.857 (95%CI 0.783-0.932), respectively. The sensitivity was 91.7% and 73.7%, the specificity was 78.8% and 84.0%, and the accuracy was 85.3% and 78.4%, respectively. ConclusionThere are differences in radiomics features between invasive pulmonary adenocarcinoma with or without micropapillary and solid structures, and the radiomics model is demonstrated to be with good diagnostic value.
The widespread application of low-dose computed tomography (LDCT) has significantly increased the detection of pulmonary small nodules, while accurate prediction of their growth patterns is crucial to avoid overdiagnosis or underdiagnosis. This article reviews recent research advances in predicting pulmonary nodule growth based on CT imaging, with a focus on summarizing key factors influencing nodule growth, such as baseline morphological parameters, dynamic indicators, and clinical characteristics, traditional prediction models (exponential and Gompertzian models), and the applications and limitations of radiomics-based and deep learning models. Although existing studies have achieved certain progress in predicting nodule growth, challenges such as small sample sizes and lack of external validation persist. Future research should prioritize the development of personalized and visualized prediction models integrated with larger-scale datasets to enhance predictive accuracy and clinical applicability.
Objective To review the progress of artificial intelligence (AI) and radiomics in the study of abdominal aortic aneurysm (AAA). Method The literatures related to AI, radiomics and AAA research in recent years were collected and summarized in detail. Results AI and radiomics influenced AAA research and clinical decisions in terms of feature extraction, risk prediction, patient management, simulation of stent-graft deployment, and data mining. Conclusion The application of AI and radiomics provides new ideas for AAA research and clinical decisions, and is expected to suggest personalized treatment and follow-up protocols to guide clinical practice, aiming to achieve precision medicine of AAA.
ObjectiveTo analyze the protein expression changes in the retina of non-arteritic anterior ischemic optic neuropathy (NAION) in rats.MethodsThe rat NAION (rNAION) model was established by Rose Bengal and laser. Twenty Sprague-Dawley rats were randomly divided into 4 groups, the normal control group, the laser control group, the RB injection control group, and the rNAION model group, with 5 rats in each group. The right eye was used as the experimental eye. The retina was dissected at the third day after modeling. Enzyme digestion method was used for sample preparation and data collection was performed in a non-dependent collection mode. The data were quantitatively analyzed by SWATH quantitative mass spectrometry, searching for differential proteins and performing function and pathway analysis.ResultsCompared with the other three control groups, a total of 184 differential proteins were detected in the rNAION group (expression fold greater than 1.5 times and P＜0.05), including 99 up-regulated proteins and 85 down-regulated proteins. The expressions of glial fibrillary acidic protein, guanine nucleotide binding protein 4, laminin 1, 14-3-3γ protein YWHAG were increased. Whereas the expressions of Leucine-rich glioma-inactivated protein 1, secretory carrier-associated membrane protein 5, and Clathrin coat assembly protein AP180 were decreased. The differential proteins are mainly involved in biological processes such as nerve growth, energy metabolism, vesicle-mediated transport, the regulation of synaptic plasticity, apoptosis and inflammation. Pathway enrichment analysis showed that PI3K-Akt signaling pathway and complement and thrombin reaction pathway was related to the disease.ConclusionThe protein expressions of energy metabolism, nerve growth, synaptic vesicle transport and PI3K-Akt signaling pathway can regulate the neuronal regeneration and apoptosis in NAION.
Objective The method of metabonomics based on nuclear magnetic resonance (NMR) imaging was used to explore the difference in metabolites of serum and bile, and to analyze the metabolic variation related to the pathogenesis of gallbladder stones between normal people/liver transplantation donors and patients with gallbladder stones. Methods Prospectively collected the serum samples (17 cases) and bile samples (19 cases) in 19 patients with gallbladder stones who underwent surgery in West China Hospital form March 2016 to December 2016, as well as the serum samples of 10 healthy persons and the bile samples of 15 liver transplantation donors at the same time period. The differences of metabolites in the blood and bile in these 3 groups were compared by using 1H-NMR metabonomics technology and chemometric methods. Results The concentrations of valine, alanine, lysine, glutamine, glutamate, pyruvate, creatinine, choline, alpha-glucose, beta-glucose, tyrosine, histidine, and hypoxanthine in serum of patients with gallbladder stones decreased significantly, comparing with those of healthy people without gallbladder stones (P<0.05), while 1, 2-propanediol, acetoacetate, and lactate increased significantly in the serum of patients with gallbladder stones (P<0.05). The concentrations of taurine conjugated bile acids, glycine conjugated bile acids, choline, and phosphatidylcholine decreased significantly in the bile of patients with gallbladder stones when compared with those of liver transplantation donors (P<0.05), while cholesterol increased significantly in the bile of patients with gallbladder stones (P<0.05). Conclusions There are significant differences of the serum and bile metabolites between patients with gallbladder stones and healthy men without gallbladder stones/liver transplantation donors. 1H-NMR metabonomics is helpful to investigate the pathogenesis of gallbladder stones.
ObjectiveTo construct a multimodal imaging radiomics model based on enhanced CT features to predict tumor regression grade (TRG) in patients with locally advanced rectal cancer (LARC) following neoadjuvant chemoradiotherapy (NCRT). MethodsA retrospective analysis was conducted on the Database from Colorectal Cancer (DACCA) at West China Hospital of Sichuan University, including 199 LARC patients treated from October 2016 to October 2023. All patients underwent total mesorectal excision after NCRT. Clinical pathological information was collected, and radiomics features were extracted from CT images prior to NCRT. Python 3.13.0 was used for feature dimension reduction, and univariate logistic regression (LR) along with Lasso regression with 5-fold cross-validation were applied to select radiomics features. Patients were randomly divided into training and testing sets at a ratio of 7∶3 for machine learning and joint model construction. The model’s performance was evaluated using accuracy, sensitivity, specificity, and the area under the curve (AUC). Receiver operating characteristic curve (ROC), confusion matrices, and clinical decision curves (DCA) were plotted to assess the model’s performance. ResultsAmong the 199 patients, 155 (77.89%) had poor therapeutic outcomes, while 44 (22.11%) had good outcomes. Univariate LR and Lasso regression identified 8 clinical pathological features and 5 radiomic features, including 1 shape feature, 2 first-order statistical features, and 2 texture features. LR, support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost) models were established. In the training set, the AUC values of LR, SVM, RF, XGBoost models were 0.99, 0.98, 1.00, and 1.00, respectively, with accuracy rates of 0.94, 0.93, 1.00, and 1.00, sensitivity rates of 0.98, 1.00, 1.00, and 1.00, and specificity rates of 0.80, 0.67, 1.00, and 1.00, respectively. In the testing set, the AUC values of 4 models were 0.97, 0.92, 0.96, and 0.95, with accuracy rates of 0.87, 0.87, 0.88, and 0.90, sensitivity rates of 1.00, 1.00, 1.00, and 0.95, and specificity rates of 0.50, 0.50, 0.56, and 0.75. Among the models, the XGBoost model had the best performance, with the highest accuracy and specificity rates. DCA indicated clinical benefits for all 4 models. ConclusionsThe multimodal imaging radiomics model based on enhanced CT has good clinical application value in predicting the efficacy of NCRT in LARC. It can accurately predict good and poor therapeutic outcomes, providing personalized clinical surgical interventions.