From the perspective of the new institutional economics, the institutional change of hospital accreditation & evaluation in China was summarized and the experiences of hospital accreditation & evaluation from international organizations and other countries were refined to put forward the counter-measures for institutional innovations of accreditation & evaluation in China. First, it’s urgent for the government to issue the standards of hospital accreditation and discriminating hospital evaluation; second, these standards should pass the certification by the International Society for Quality in Health Care External Evaluation Association; finally, China should construct the commission on certification and accreditation administration for healthcare to supervise the social or third part organizations.
Biliary tract cancer is characterized by occult onset, highly malignancy and poor prognosis. Traditional medical imaging is an important tool for surgical strategies and prognostic assessment, but it can no longer meet the urgent need for accurate and individualized treatment in patients with biliary tract cancer. With the advent of the digital imaging era, the advancement of artificial intelligence technology has given a new vitality to digital imaging, and provided more possibilities for the development of medical imaging in clinical applications. The application of radiomics in the diagnosis and differential diagnosis of benign and malignant tumors of biliary tract, assessment of lymph node status, early recurrence and prognosis assessment provides new means for the diagnosis and treatment of patients with biliary tract cancer.
ObjectiveTo establish a machine learning model based on computed tomography (CT) radiomics for preoperatively predicting invasive degree of lung ground-glass nodules (GGNs). MethodsWe retrospectively analyzed the clinical data of GGNs patients whose solid component less than 3 cm in the Department of Thoracic Surgery of Shanghai Pulmonary Hospital from March 2021 to July 2021 and the First Hospital of Lanzhou University from January 2019 to May 2022. The lesions were divided into pre-invasiveness and invasiveness according to postoperative pathological results, and the patients were randomly divided into a training set and a test set in a ratio of 7∶3. Radiomic features (1 317) were extracted from CT images of each patient, the max-relevance and min-redundancy (mRMR) was used to screen the top 100 features with the most relevant categories, least absolute shrinkage and selection operator (LASSO) was used to select radiomic features, and the support vector machine (SVM) classifier was used to establish the prediction model. We calculated the area under the curve (AUC), sensitivity, specificity, accuracy, negative predictive value, positive predictive value to evaluate the performance of the model, drawing calibration and decision curves of the prediction model to evaluate the accuracy and clinical benefit of the model, analyzed the performance in the training set and subgroups with different nodule diameters, and compared the prediction performance of this model with Mayo and Brock models. Two primary thoracic surgeons were required to evaluate the invasiveness of GGNs to investigate the clinical utility of the model. ResultsA total of 400 patients were divided into the training set (n=280) and the test set (n=120) according to the admission criteria. There were 267 females and 133 males with an average age of 52.4±12.7 years. Finally, 8 radiomic features were screened out from the training set data to build SVM model. The AUC, sensitivity and specificity of the model in the training and test sets were 0.91, 0.89, 0.75 and 0.86, 0.92, 0.60, respectively. The model showed good prediction performance in the training set 0-10 mm, 10-20 mm and the test set 0-10 mm, 10-20 mm subgroups, with AUC values of 0.82, 0.88, 0.84, 0.72, respectively. The AUC of SVM model was significantly better than that of Mayo model (0.73) and Brock model (0.73). With the help of this model, the AUC value, sensitivity, specificity and accuracy of thoracic surgeons A and B in distinguishing invasive or non-invasive adenocarcinoma were significantly improved. ConclusionThe SVM model based on radiomics is helpful to distinguish non-invasive lesions from invasive lesions, and has stable predictive performance for GGNs of different sizes and has better prediction performance than Mayo and Brock models. It can help clinicians to more accurately judge the invasiveness of GGNs, to make more appropriate diagnosis and treatment decisions, and achieve accurate treatment.
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.
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.
Diabetic kidney disease, as a common complication of diabetes, is one of the main causes of end-stage renal disease. Because of the rapid progress of its course and the limited means of treatment, it is of great clinical significance to seek biomarkers from early diagnosis for the treatment of diabetic kidney disease. At present, there are limited methods for early diagnosis of diabetic kidney disease. As a widely used research method, metabonomics can detect metabolites in diseases and provide biomarkers for disease diagnosis and prognosis. This article summarizes the changes of amino acids, lipids, organic acids and other metabolites in blood or urine of patients with diabetic kidney disease.
Abstract： Objective To study the molecular mechanism of pathologic states related to cardiopulmonary bypass (CPB) and screen the differential proteins from the serum before and after CPB in the open heart surgery patients. Methods By the twodimensional gel electrophoresis (2DE), we took the blood samples from each of the sixteen open heart surgery patients 30 minutes before CPB, 1 hour after CPB, and 24 hours after CPB. The protein spots were analyzed by the PDQuest image analysis software and the differential protein spots were identified by matrixassisted laser desorption/ionizationtime of flightmass spectrometry (MALDITOF-MS). Then, enzymelinked immunosorbent assay (ELISA) was used to determine the expression level of serum amyloid A protein (SAA) in the serum of healthy people and the enrolled patients before and after CPB. Results Through 2DE in combination with massspectrometry, 7 proteins altered in expression were identified, including SAA, haptoglobin (HPT), leucinerich alpha2-glycoprotein (A2GL), hemoglobin subunit beta (HBB), serine/threonineprotein phosphatase 2A -regulatory subunit B″ subunit gamma (P2R3C), transthyretin (TTHY), and T-complex protein 11-like protein2 (T11L2). ELISA analysis showed that SAA levels in healthy people and the open heart surgery patients 30 minutes before CPB were not statistically different (t=-1.955, P=0.056), while the SAA level rose from 54.47±48.32 μg/ml 30 min before CPB to 1 017.78±189.92 μg/ml 24 hours after CPB in the serum of open heart surgery patients． Conclusion The results of this pilot study illustrate that SAA, HPT, A2GL, HBB, P2R3C, TTHY and T11L2 may be the molecule markers of pathologic state related to CPB. Acute phase reaction happens intensively after CPB in human body.
Objective To explore the effect of corn oligopeptide (COP) on dexamethasone-induced muscle atrophy. Methods Forty-nine male Sprague-Dawley rats aged 8 weeks were divided into blank group (n=10) and model group (n=39). The rats in the model group were intraperitoneally injected with dexamethasone (1.0 mg/kg), and the rats in the blank group were injected with normal saline. After 19 days, one rat in the blank group and three rats in the model group were taken to observe whether the model was successfully constructed. After successful modeling, the rats in the model group were randomly divided into model control group, COP low-dose group (COP-L group, 0.5 g/kg), COP medium-dose group (COP-M group, 1.0 g/kg) and COP high-dose group (COP-H group, 2.0 g/kg), with 9 rats in each group. After 33 days, the grip strength of the rats was measured, and then the gastrocnemius, soleus, tibialis anterior and metatarsal muscles were separated and weighed, and muscle fiber diameter, relative expression of Atrogin-1 and MuRF-1 mRNA were measured. Non-targeted metabolomics of gastrocnemius muscle were measured. Results Compared with that in the blank group, the body weight of rats in the model group reduced (P<0.05), and myofibril rupture was observed, indicating that the model was successful. Compared with those in the model control group, the grip strength increased in the COP-L and COP-M groups (P<0.05); the muscle coefficients of gastrocnemius and soleus in the COP-L and COP-H groups increased (P<0.05), and the muscle coefficients of plantaris in the COP-L and COP-M groups increased (P<0.05); the muscle fiber diameter of the tibial anterior muscle increased in the three doses of COP groups (P<0.05), and the muscle fiber diameter of the plantaris muscle increased in the COP-M and COP-H groups (P<0.05); the relative expression of Atrogin-1 mRNA decreased in the three doses of COP groups (P<0.05), while the relative expression of MurF-1 mRNA in the COP-L and COP-H groups decreased (P<0.05). The amino acid synthesis pathway, glycolysis pathway, and acid metabolism pathway were activated in gastrocnemius muscle. Conclusions COP can significantly improve the muscle atrophy induced by dexamethasone. The mechanism may be related to the decrease of Atrogin-1 and MuRF-1 expression in ubiquitin-proteasome pathway and the increase of amino acid biosynthesis.
ObjectiveTo explore the utility of machine learning-based radiomics models for risk stratification of severe asymptomatic carotid stenosis (ACS). MethodsThe clinical data and head and neck CT angiography images of 188 patients with severe carotid artery stenosis at the Department of Cardiovascular Surgery, China-Japan Friendship Hospital from 2017 to 2021 were retrospectively collected. The patients were randomly divided into a training set (n=131, including 107 males and 24 females aged 68±8 years), and a validation set (n=57, including 50 males and 7 females aged 67±8 years). The volume of interest was manually outlined layer by layer along the edge of the carotid plaque on cross-section. Radiomics features were extracted using the Pyradiomics package of Python software. Intraclass and interclass correlation coefficient analysis, redundancy analysis, and least absolute shrinkage and selection operator regression analysis were used for feature selection. The selected radiomics features were constructed into a predictive model using 6 different supervised machine learning algorithms: logistic regression, decision tree, random forest, support vector machine, naive Bayes, and K nearest neighbor. The diagnostic efficacy of each prediction model was compared using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), which were validated in the validation set. Calibration and clinical usefulness of the prediction model were evaluated using calibration curve and decision curve analysis (DCA). ResultsFour radiomics features were finally selected based on the training set for the construction of a predictive model. Among the 6 machine learning models, the logistic regression model exhibited higher and more stable diagnostic efficacy, with an AUC of 0.872, a sensitivity of 100.0%, and a specificity of 66.2% in the training set; the AUC, sensitivity and specificity in the validation set were 0.867, 83.3% and 78.8%, respectively. The calibration curve and DCA showed that the logistic regression model had good calibration and clinical usefulness. ConclusionThe machine learning-based radiomics model shows application value in the risk stratification of patients with severe ACS.
ObjectiveTo explore the value of a radiomics model based on ultrasound imaging in predicting the HER-2 status of breast cancer prior to surgery.MethodsA total of 230 patients with invasive breast cancer were retrospectively analyzed, all the patients underwent preoperative breast ultrasound examination. According to the order of examination time, the patients were categorized into training group (n=115) and validation group (n=115). Image J software was used to manually delineate the lesion area in the ultrasound image along the tumor boundary. Pyradiomics was used to extract 1 820 features from each lesion area, and three statistical methods were used to screen features. A logistic regression model was used to construct ultrasound imaging radiomics model. The receive operating characteristic curve (ROC), calibration curve and decision curve were used to evaluate the performance and value of ultrasound imaging radiomics model in predicting HER-2 status.ResultsNine key image features were identified to construct ultrasound imaging radiomics model. The area of under the ROC curve of the model in the training group and the validation group were 0.82 (95%CI 0.74 to 0.90) and 0.81 (95%CI 0.72 to 0.89), respectively. The calibration curve showed that the model had a good calibration in both the training and validation groups.ConclusionsUltrasound-based imaging radiomics model is of significant value in predicting the HER-2 status of breast cancer prior to surgery.