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.
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.
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 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.
Objective To explore the effects on quality of life (QOL), the targeted rates of metabolic parameters and cost-effectiveness in newly diagnosed type 2 diabetic patients who underwent multifactorial intensive intervention. Methods One hundred and twenty seven cases in an intensive intervention and 125 cases in a conventional intervention group were investigated by using the SF-36 questionnaire. The comparison of QOL and the targeted rates of metabolic parameters between the two groups were made. We assessed the influence factors of QOL by stepwise regression analysis and evaluated the efficiency by pharmacoeconomic cost-effectiveness analysis. Results The targeted rates of blood glucose, blood lipid and blood pressure with intensive policies were significantly higher than those with conventional policy (P＜0.05). The intensive group’s role limitations due to physical problems (RP), general health (GH), vitality (VT), role limitation due to emotional problems (RE) and total scores after 6 months intervention were significantly higher than those of baseline (P＜0.05). The vitality scores and health transition (HT) of the intensive group were better than those of the conventional group after 6 months intervention. But the QOL scores of the conventional group were not improved after intervention. The difference of QOL’s total scores after intervention was related to that of HbA1c. The total cost-effectiveness rate of blood glucose, blood lipid, blood pressure control and the total cost-effectiveness rate of QOL with intensive policy were higher than those with the conventional policy. Conclusions Quality of life and the targeted rates of blood glucose, blood lipid and blood pressure in newly diagnosed type 2 diabetic patients with multifactorial intensive intervention policy are better and more economic than those with conventional policy.
ObjectiveTo systematically investigate the current status of reporting health economics evidence in clinical practice guidelines and expert consensuses published in China from 2021 to 2023, providing references for the formulation and revision of guidelines and consensuses in our country. MethodsComputer searches were conducted in the CNKI, CBM, WanFang Data, China Academic Journals Full-text Database, PubMed, and Web of Science to collect clinical practice guidelines and expert consensuses published in China from 2021 to 2023. Two researchers independently screened the literature, extracted information on the inclusion of economic evidence in guidelines and consensuses, and then used quantitative analysis methods for description. ResultsA total of 4 236 relevant articles were included, of which 1 066 (25.17%) reported health economics evidence; 120 (11.26%) reported health economics evidence in the formation of recommendation opinions; 109 (10.23%) reported health economics evidence in the grading of evidence quality; 832 (78.05%) reported health economics evidence in the interpretation and explanation of recommendation opinions. ConclusionThe reporting rate of health economics evidence in clinical practice guidelines and expert consensuses published in China is not high. The reporting rate of health economics evidence in consensuses is lower than that in guidelines. It is recommended that during the formulation process of guidelines and consensuses, the application of health economics evidence should be further strengthened in aspects such as the formation of recommendation opinions, the grading of evidence quality, and the interpretation and explanation of recommendation opinions, in order to improve the scientific, rigorous, and applicability of clinical practice guidelines and expert consensuses, and to play the role of guidelines and consensuses in optimizing the allocation of health resources, improving clinical diagnosis and treatment effects, and enhancing the quality of medical care.
Objective To develop a radiomics nomogram based on contrast-enhanced CT (CECT) for preoperative prediction of high-risk and low-risk thymomas. Methods Clinical data of patients with thymoma who underwent surgical resection and pathological confirmation at Northern Jiangsu People's Hospital from January 2018 to February 2023 were retrospectively analyzed. Feature selection was performed using the Pearson correlation coefficient and least absolute shrinkage and selection operator (LASSO) method. An ExtraTrees classifier was used to construct the radiomics signature model and the radiomics signature. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. The radiomics nomogram model was developed by combining the radiomics signature and clinical features. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, and positive predictive value. Calibration curves and decision curves were plotted to assess model accuracy and clinical values. Results A total of 120 patients including 59 females and 61 males with an average age of 56.30±12.10 years. There were 84 patients in the training group and 36 in the validation group, 62 in the low-risk thymoma group and 58 in the high-risk thymoma group. Radiomics features (1 038 in total) were extracted from the arterial phase of CECT scans, among which 6 radiomics features were used to construct the radiomics signature. The radiomics nomogram model, combining clinical-radiological characteristics and the radiomics signature, achieved an AUC of 0.872 in the training group and 0.833 in the validation group. Decision curve analysis demonstrated better clinical efficacy of the radiomics nomogram than the radiomics signature and clinical model. Conclusion The radiomics nomogram based on CECT showed good diagnostic value in distinguishing high-risk and low-risk thymoma, which may provide a noninvasive and efficient method for clinical decision-making.
Objective The article introduces the present status of the application of comparative proteomics in study of tumor marker. Methods This essay review the present status and advances of the application of comparative proteomics in study of tumor marker through refer considerable literatures about proteome, proteomics and tumor marker. Results Follow the study of human genome deepening; the paradox between the finiteness of genes’ number and stability of genes’ structure and the variety of the life phenomena is more conspicuous. Then, the study of proteomics was pushed to the advancing front of life science research. The application of comparative proteomics to tumor research becomes a hot spot nowadays. Conclusion Screening tumor marker via comparative proteomics is an extremely promising research.
ObjectiveTo summarize the research results of metabolites of breast cancer based on metabonomics technology, and systematically reviews them in order to provide a new direction for the research of metabolism of breast cancer.MethodBy searching the relevant literatures in recent years, the application of metabonomics in identifying high-risk breast cancer population, monitoring the progress of tumor and evaluating the response of radiotherapy and chemotherapy were analyzed and summarized.ResultsWith the development of high-resolution, high-sensitivity and high-throughput bioanalysis platform technology, metabolomics had been widely used in breast cancer research field by virtue of its unique perspective and technical advantages to more accurately, systematically and dynamically monitor the changes of host metabolites.ConclusionMetabolomics technology provides a new research direction for primary prevention, early screening and diagnosis of breast cancer and optimal treatment strategy selection.
ObjectiveTo establish a classification model based on knee MRI radiomics, realize automatic identification of meniscus tear, and provide reference for accurate diagnosis of meniscus injury. Methods A total of 228 patients (246 knees) with meniscus injury who were admitted between July 2018 and March 2021 were selected as the research objects. There were 146 males and 82 females; the age ranged from 9 to 76 years, with a median age of 53 years. There were 210 cases of meniscus injury in one knee and 18 cases in both knees. All the patients were confirmed by arthroscopy, among which 117 knees with meniscus tear and 129 knees with meniscus non-tear injury. The proton density weighted-spectral attenuated inversion recovery (PDW-SPAIR) sequence images of sagittal MRI were collected, and two doctors performed radiomics studies. The 246 knees were randomly divided into training group and testing group according to the ratio of 7∶3. First, ITK-SNAP3.6.0 software was used to extract the region of interest (ROI) of the meniscus and radiomic features. After retaining the radiomic features with intraclass correlation coefficient (ICC)>0.8, the max-relevance and min-redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO) were used for filtering the features to establish an automatic identification model of meniscus tear. The receiver operator characteristic curve (ROC) and the corresponding area under the ROC curve (AUC) was obtained; the model performance was comprehensively evaluated by calculating the accuracy, sensitivity, and specificity. Results A total of 1 316-dimensional radiomic features were extracted from the meniscus ROI, and the ICC within the group and ICC between the groups of the 981-dimensional radiomic features were both greater than 0.80. The redundant information in the 981-dimensional radiomic features was eliminated by mRMR, and the 20-dimensional radiomic features were retained. The optimal feature subset was further selected by LASSO, and 8-dimensional radiomic features were selected. The average ICC within the group and the average ICC between the groups were 0.942 and 0.920, respectively. The AUC of the training group was 0.889±0.036 [95%CI (0.845, 0.942), P<0.001], and the accuracy, sensitivity, and specificity were 0.873, 0.869, and 0.842, respectively; the AUC of the testing group was 0.876±0.036 [95%CI (0.875, 0.984), P<0.001], and the accuracy, sensitivity, and specificity were 0.862, 0.851, and 0.845, respectively. ConclusionThe model established by the radiomics method has good automatic identification performance of meniscus tear.