Automated deep-learning system in the assessment of MRI-visible prostate cancer: comparison of advanced zoomed diffusion-weighted imaging and conventional technique.

BACKGROUND: Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency. METHODS: This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant. RESULTS: DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD. CONCLUSIONS: Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD. TRIAL REGISTRATION: ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.

Application status and views of common bowel cleansing regimens in China / 西安交通大学学报(医学版)

The quality of colonoscopy diagnosis and treatment is closely related to bowel cleansing. At present, polyethylene glycol electrolyte powder is the most widely used bowel cleaning regimen at home and abroad, but its intake is large, which reduces the compliance of patients. In recent years, many reports on the application of new bowel cleansing have emerged abroad. In contrast, the application of bowel cleanser in China is still relatively single, which has large room for improvement. At present, we need to solve the problem of developing a new bowel cleanser suitable for Chinese people to improve patient tolerance and bowel cleaning effect.

Adversarial training for prostate cancer classification using magnetic resonance imaging.

Background: To use adversarial training to increase the generalizability and diagnostic accuracy of deep learning models for prostate cancer diagnosis. Methods: This multicenter study retrospectively included 396 prostate cancer patients who underwent magnetic resonance imaging (development set, 297 patients from Shanghai Jiao Tong University Affiliated Sixth People's Hospital and Eighth People's Hospital; test set, 99 patients from Renmin Hospital of Wuhan University). Two binary classification deep learning models for clinically significant prostate cancer classification [PM1, pretraining Visual Geometry Group network (VGGNet)-16-based model 1; PM2, pretraining residual network (ResNet)-50-based model 2] and two multiclass classification deep learning models for prostate cancer grading (PM3, pretraining VGGNet-16-based model 3; PM4: pretraining ResNet-50-based model 4) were built using apparent diffusion coefficient and T2-weighted images. These models were then retrained with adversarial examples starting from the initial random model parameters (AM1, adversarial training VGGNet-16 model 1; AM2, adversarial training ResNet-50 model 2; AM3, adversarial training VGGNet-16 model 3; AM4, adversarial training ResNet-50 model 4, respectively). To verify whether adversarial training can improve the diagnostic model's effectiveness, we compared the diagnostic performance of the deep learning methods before and after adversarial training. Receiver operating characteristic curve analysis was performed to evaluate significant prostate cancer classification models. Differences in areas under the curve (AUCs) were compared using Delong's tests. The quadratic weighted kappa score was used to verify the PCa grading models. Results: AM1 and AM2 had significantly higher AUCs than PM1 and PM2 in the internal validation dataset (0.84 vs. 0.89 and 0.83 vs. 0.87) and test dataset (0.73 vs. 0.86 and 0.72 vs. 0.82). AM3 and AM4 showed higher κ values than PM3 and PM4 in the internal validation dataset {0.266 [95% confidence interval (CI): 0.152-0.379] vs. 0.292 (95% CI: 0.178-0.405) and 0.254 (95% CI: 0.159-0.390) vs. 0.279 (95% CI: 0.163-0.396)} and test set [0.196 (95% CI: 0.029-0.362) vs. 0.268 (95% CI: 0.109-0.427) and 0.183 (95% CI: 0.015-0.351) vs. 0.228 (95% CI: 0.068-0.389)]. Conclusions: Using adversarial examples to train prostate cancer classification deep learning models can improve their generalizability and classification abilities.

Thin-slab maximum intensity projection of CT angiography for collateral score and clot burden score evaluation: comparison with conventional CT angiography.

BACKGROUND: This study aimed to exam the effects of thin-slab maximum intensity projection (TS-MIP) of computed tomography angiography (CTA) for collateral score (CS) and clot burden score (CBS) evaluation in patients with large-vessel-occlusion (LVO) stroke in the anterior circulation. METHODS: Of 241 consecutive patients with LVO stroke admitted to our center between August 2015 and June 2020, 187 patients were enrolled. CS and CBS were evaluated on conventional CTA and TS-MIP separately. Outcome at 90 days was classified as good if modified Rankin scale (mRS) was ≤2 and as poor if mRS was >2. The correlations between CS and CBS and clinical outcomes were assessed. Receiver operating characteristic (ROC) curve analysis was used to determine the diagnostic values of CS and CBS. Multivariate logistic regression analysis was performed to identify the independent predictors of 90-day good clinical outcomes. RESULTS: The correlation coefficient for clinical outcomes was significantly better for CS based on TS-MIP than that based on conventional CTA (-0.444 vs. -0.285, P=0.039); no significant difference was found in the CBS evaluation (TS-MIP: -0.356 vs. conventional CTA: -0.320, P=0.348). For predicting good clinical outcomes, TS-MIP-based CS was associated with larger area under the curve (AUC) (0.709 vs. 0.609, P=0.004) and higher sensitivity (69.1% vs. 42.0%, P=0.001) than CS based on CTA. In multivariable logistic regression analysis, the factors independently associated with good outcomes were National Institutes of Health Stroke Scale (NIHSS) score at admission (OR =1.147; P<0.001), TS-MIP-based CS (OR =0.326; P<0.001), final modified treatment in cerebral infarction (mTICI) score of 2b/3 (OR =0.098; P<0.001), and hemorrhagic transformation (OR =3.662; P<0.001). CONCLUSIONS: TS-MIP-CTA is superior to conventional CTA for evaluation CS and CBS, and TS-MIP-based CS may be a useful predictor of clinical outcome.

Increased expression of Rho-associated protein kinase 2 confers astroglial Stat3 pathway activation during epileptogenesis.

Patients with TLE are prone to tolerance to antiepileptic drugs. Based on the perspective of molecular targets for drug resistance, it is necessary to explore effective drug resistant genes and signaling pathways for the treatment of TLE. We performed gene expression profiles in hippocampus of patients with drug-resistant TLE and identified ROCK2 as one of the 20 most significantly increased genes in hippocampus. In vitro and in vivo experiments were performed to identify the potential role of ROCK2 in epileptogenesis. In addition, the activity of Stat3 pathway was tested in rat hippocampal tissues and primary cultured astrocytes. The expression levels of ROCK2 in the hippocampus of TLE patients were significantly increased compared with the control group, which was due to the hypomethylation of ROCK2 promoter. Fasudil, a specific Rho-kinase inhibitor, alleviated epileptic seizures in the pilocarpine rat model of TLE. Furthermore, ROCK2 activated the Stat3 pathway in pilocarpine-treated epilepsy rats, and the spearman correlation method confirmed that ROCK2 is associated with Stat3 activation in TLE patients. In addition, ROCK2 was predominantly expressed in astrocytes during epileptogenesis, and induced epileptogenesis by activating astrocyte cell cycle progression via Stat3 pathway. The overexpressed ROCK2 plays an important role in the pathogenesis of drug-resistant epilepsy. ROCK2 accelerates astrocytes cell cycle progression via the activation of Stat3 pathway likely provides the key to explaining the process of epileptogenesis.

Influencing factors of iron metabolism assessment in patients with myelodysplastic syndrome: A retrospective study / 中华血液学杂志

Objective: To analyze the influencing factors of iron metabolism assessment in patients with myelodysplastic syndrome. Methods: MRI and/or DECT were used to detect liver and cardiac iron content in 181 patients with MDS, among whom, 41 received regular iron chelation therapy during two examinations. The adjusted ferritin (ASF) , erythropoietin (EPO) , cardiac function, liver transaminase, hepatitis antibody, and peripheral blood T cell polarization were detected and the results of myelofibrosis, splenomegaly, and cyclosporine were collected and comparative analyzed in patients. Results: We observed a positive correlation between liver iron concentration and ASF both in the MRI group and DECT groups (r=0.512 and 0.606, respectively, P<0.001) , only a weak correlation between the heart iron concentration and ASF in the MRI group (r=0.303, P<0.001) , and no significant correlation between cardiac iron concentration and ASF in the DECT group (r=0.231, P=0.053) . Moreover, transfusion dependence in liver and cardiac [MRI group was significantly associated with the concentration of iron in: LIC: (28.370±10.706) mg/g vs (7.593±3.508) mg/g, t=24.30, P<0.001; MIC: 1.81 vs 0.95, z=2.625, P<0.05; DECT group: liver VIC: (4.269±1.258) g/L vs (1.078±0.383) g/L, t=23.14, P<0.001: cardiac VIC: 1.69 vs 0.68, z=3.142, P<0.05]. The concentration of EPO in the severe iron overload group was significantly higher than that in the mild to moderate iron overload group and normal group (P<0.001) . Compared to the low-risk MDS group, the liver iron concentration in patients with MDS with cyclic sideroblasts (MDS-RS) was significantly elevated [DECT group: 3.80 (1.97, 5.51) g/L vs 1.66 (0.67, 2.94) g/L, P=0.004; MRI group: 13.7 (8.1,29.1) mg/g vs 11.6 (7.1,21.1) mg/g, P=0.032]. Factors including age, bone marrow fibrosis, splenomegaly, T cell polarization, use of cyclosporine A, liver aminotransferase, and hepatitis antibody positive had no obvious effect on iron metabolism. Conclusion: There was a positive correlation between liver iron concentration and ASF in patients with MDS, whereas there was no significant correlation between cardiac iron concentration and ASF. Iron metabolism was affected by transfusion dependence, EPO concentration, and RS.

Identification and analysis of 2 Corynebacterium diphtheria strains in Guangdong Province / 中华预防医学杂志

Objective: To identify and analyze two strains of C. diphtheriae in Guangdong Province by combining whole genome sequencing with traditional detection methods. Methods: The C. diphtheriae was isolated from Guangzhou in 2010 and Zhuhai in 2020 respectively. Isolates were identified by API Coryne strips and MALDI-TOF-MS. Genomic DNA was sequenced by using Illumina. The assembly was performed for each strain using CLC software. J Species WS online tool was used for average nucleoside homology identification, then narKGHIJ and tox gene were detected by NCBI online analysis tool BLSATN. MEGA-X was used to build a wgSNP phylogenetic tree. Results: GD-Guangzhou-2010 was Belfanti and GD-Zuhai-2020 was Gravis. ANIb between GD-Guangzhou-2010 and C. belfantii was 99.61%. ANI between GD-Zhuhai-2020 and C. diphtheriae was 97.64%. BLASTN results showed that the nitrate reduction gene narKGHIJ and tox gene of GD-Guangzhou-2010 was negative, while GD-Zhuhai-2020 nitrate reduction gene narKGHIJ was positive. There were two obvious clades in wgSNP phylogenetic tree. The first clades included all Mitis and Gravis types strains as well as GD-Zhuhai-2020. The second clades contained all isolates of C.belfantii, C.diphtheriae subsp. lausannense and GD-guangzhou-2010. Conclusion: Two non-toxic C. diphtheriae strains are successfully isolated and identified. The phylogenetic tree suggests that GD-Guangzhou-2010 and GD-Zhuhai-2020 are located in two different evolutionary branches.

Synthesizing High-b-Value Diffusion-weighted Imaging of the Prostate Using Generative Adversarial Networks.

PURPOSE: To develop and evaluate a diffusion-weighted imaging (DWI) deep learning framework based on the generative adversarial network (GAN) to generate synthetic high-b-value (b =1500 sec/mm2) DWI (SYNb1500) sets from acquired standard-b-value (b = 800 sec/mm2) DWI (ACQb800) and acquired standard-b-value (b = 1000 sec/mm2) DWI (ACQb1000) sets. MATERIALS AND METHODS: This retrospective multicenter study included 395 patients who underwent prostate multiparametric MRI. This cohort was split into internal training (96 patients) and external testing (299 patients) datasets. To create SYNb1500 sets from ACQb800 and ACQb1000 sets, a deep learning model based on GAN (M0) was developed by using the internal dataset. M0 was trained and compared with a conventional model based on the cycle GAN (Mcyc). M0 was further optimized by using denoising and edge-enhancement techniques (optimized version of the M0 [Opt-M0]). The SYNb1500 sets were synthesized by using the M0 and the Opt-M0 were synthesized by using ACQb800 and ACQb1000 sets from the external testing dataset. For comparison, traditional calculated (b =1500 sec/mm2) DWI (CALb1500) sets were also obtained. Reader ratings for image quality and prostate cancer detection were performed on the acquired high-b-value (b = 1500 sec/mm2) DWI (ACQb1500), CALb1500, and SYNb1500 sets and the SYNb1500 set generated by the Opt-M0 (Opt-SYNb1500). Wilcoxon signed rank tests were used to compare the readers' scores. A multiple-reader multiple-case receiver operating characteristic curve was used to compare the diagnostic utility of each DWI set. RESULTS: When compared with the Mcyc, the M0 yielded a lower mean squared difference and higher mean scores for the peak signal-to-noise ratio, structural similarity, and feature similarity (P < .001 for all). Opt-SYNb1500 resulted in significantly better image quality (P ≤ .001 for all) and a higher mean area under the curve than ACQb1500 and CALb1500 (P ≤ .042 for all). CONCLUSION: A deep learning framework based on GAN is a promising method to synthesize realistic high-b-value DWI sets with good image quality and accuracy in prostate cancer detection.Keywords: Prostate Cancer, Abdomen/GI, Diffusion-weighted Imaging, Deep Learning Framework, High b Value, Generative Adversarial Networks© RSNA, 2021 Supplemental material is available for this article.

Calculation of Apparent Diffusion Coefficients in Prostate Cancer Using Deep Learning Algorithms: A Pilot Study.

BACKGROUND: Apparent diffusion coefficients (ADCs) obtained with diffusion-weighted imaging (DWI) are highly valuable for the detection and staging of prostate cancer and for assessing the response to treatment. However, DWI suffers from significant anatomic distortions and susceptibility artifacts, resulting in reduced accuracy and reproducibility of the ADC calculations. The current methods for improving the DWI quality are heavily dependent on software, hardware, and additional scan time. Therefore, their clinical application is limited. An accelerated ADC generation method that maintains calculation accuracy and repeatability without heavy dependence on magnetic resonance imaging scanners is of great clinical value. OBJECTIVES: We aimed to establish and evaluate a supervised learning framework for synthesizing ADC images using generative adversarial networks. METHODS: This prospective study included 200 patients with suspected prostate cancer (training set: 150 patients; test set #1: 50 patients) and 10 healthy volunteers (test set #2) who underwent both full field-of-view (FOV) diffusion-weighted imaging (f-DWI) and zoomed-FOV DWI (z-DWI) with b-values of 50, 1,000, and 1,500 s/mm2. ADC values based on f-DWI and z-DWI (f-ADC and z-ADC) were calculated. Herein we propose an ADC synthesis method based on generative adversarial networks that uses f-DWI with a single b-value to generate synthesized ADC (s-ADC) values using z-ADC as a reference. The image quality of the s-ADC sets was evaluated using the peak signal-to-noise ratio (PSNR), root mean squared error (RMSE), structural similarity (SSIM), and feature similarity (FSIM). The distortions of each ADC set were evaluated using the T2-weighted image reference. The calculation reproducibility of the different ADC sets was compared using the intraclass correlation coefficient. The tumor detection and classification abilities of each ADC set were evaluated using a receiver operating characteristic curve analysis and a Spearman correlation coefficient. RESULTS: The s-ADCb1000 had a significantly lower RMSE score and higher PSNR, SSIM, and FSIM scores than the s-ADCb50 and s-ADCb1500 (all P < 0.001). Both z-ADC and s-ADCb1000 had less distortion and better quantitative ADC value reproducibility for all the evaluated tissues, and they demonstrated better tumor detection and classification performance than f-ADC. CONCLUSION: The deep learning algorithm might be a feasible method for generating ADC maps, as an alternative to z-ADC maps, without depending on hardware systems and additional scan time requirements.

Comparative Study on Iron Content Detection by Energy Spectral CT and MRI in MDS Patients.

Objective: The purpose of this study was to identify the difference between dual energy spectral computed tomography (DECT) and magnetic resonance imaging (MRI) used to detect liver/cardiac iron content in Myelodysplastic syndrome (MDS) patients with differently adjusted serum ferritin (ASF) levels. Method: Liver and cardiac iron content were detected by DECT and MRI. Patients were divided into different subgroups according to the level of ASF. The receiver operating characteristic curve (ROC) analysis was applied in each subgroup. The correlation between iron content detected by DECT/MRI and ASF was analyzed in each subgroup. Result: ROC curves showed that liver virtual iron content (LVIC) Az was significantly less than liver iron concentration (LIC) Az in the subgroup with ASF < 1,000 ng/ml. There was no significant difference between LVIC Az and LIC Az in the subgroup with 1,000 ≤ ASF < 2,500 ng/ml and 2,500 ≤ ASF < 5,000 ng/ml. LVIC Az was significantly higher than LIC Az in the subgroup with ASF <5,000 and 5,000 ≤ ASF ng/ml. In patients undergoing DECT and MRI examination on the same day, ASF was significantly correlated with LVIC, whereas no significant correlation was observed between ASF and LIC. After removing the data of ASF > 5,000 mg/L in LIC, LIC became correlated with ASF. There was no significant difference between the subgroup with 2,500 ≤ ASF < 5,000 ng/ml and 5,000 ng/ml ≤ ASF in LIC expression. Furthermore, both LIC and liver VIC had significant correlations with ASF in patients with ASF < 2,500 ng/ml, while LVIC was still correlated with ASF, LIC was not correlated with ASF in patients with 2,500 ng/ml ≤ ASF. Moreover, neither cardiac VIC nor myocardial iron content (MIC) were correlated with ASF in these subgroups. Conclusion: MRI and DECT were complementary to each other in liver iron detection. In MDS patients with high iron content, such as ASF ≥ 5,000 ng/ml, DECT was more reliable than the MRI in the assessment of iron content. But in patients with low iron content, such as ASF < 1,000 ng/ml, MRI is more reliable than DECT. Therefore, for the sake of more accurately evaluating the iron content, the appropriate detection method can be selected according to ASF.

Erratum.

Glioma is the most common and lethal malignant intracranial tumor. Long noncoding RNAs (lncRNAs) have been identified as pivotal regulators in the tumorigenesis of glioma. However, the role of lncRNA urothelial carcinoma-associated 1 (UCA1) in glioma genesis is still unknown. The purpose of this study was to investigate the underlying function of UCA1 on glioma genesis. The results demonstrated that UCA1 was upregulated in glioma tissue and indicated a poor prognosis. UCA1 knockdown induced by si-UCA1 significantly suppressed the proliferative, migrative, and invasive activities of glioma cell lines (U87 and U251). Bioinformatics analysis and luciferase reporter assay verified the complementary binding within UCA1 and miR-122 at the 3-UTR. Functional experiments revealed that UCA1 acted as an miR-122 sponge to modulate glioma cell proliferation, migration, and invasion via downregulation of miR-122. Overall, the present study demonstrated that lncRNA UCA1 acts as an endogenous sponge of miR-122 to promote glioma cell proliferation, migration, and invasion, which provides a novel insight and therapeutic target in the tumorigenesis of glioma.

Analysis of the influencing factors related to liver and cardiac iron overload in MDS patients detected by MRI in the real world.

OBJECTIVES: We aim to explore and analyze the related influencing factors of liver and cardiac iron overload in MDS patients detected by magnetic resonance imaging (MRI). METHODS: We have detected cardiac T2* and liver T2* by MRI in 105 MDS patients. Among them, 20 patients accepted MRI examination before and after iron chelation therapy (ICT). Results: We found that adjusted ferritin (ASF) was significantly correlated with liver T2* and cardiac T2*. RBC transfusion volume, brain natriuretic peptide (BNP) and age were the related factors of cardiac T2*, while RBC transfusion volume and erythropoietin (EPO) were related factors of liver T2*. After ICT, the changes of ASF and liver T2* were earlier than cardiac T2*. Chronic hepatitis but virus copy normal's has no significant effect on liver iron deposition. CONCLUSION: These results showed special attention should be paid to these related influencing factors of liver and cardiac T2* expression when we evaluated iron overload and detected the efficacy of ICT in MDS patients.

Advanced zoomed diffusion-weighted imaging vs. full-field-of-view diffusion-weighted imaging in prostate cancer detection: a radiomic features study.

OBJECTIVES: We aimed to compare the efficiency of prostate cancer (PCa) detection using a radiomics signature based on advanced zoomed diffusion-weighted imaging and conventional full-field-of-view DWI. METHODS: A total of 136 patients, including 73 patients with PCa and 63 without PCa, underwent multi-parametric magnetic resonance imaging (mp-MRI). Radiomic features were extracted from prostate lesion areas segmented on full-field-of-view DWI with b-value = 1500 s/mm2 (f-DWIb1500), advanced zoomed DWI images with b-value = 1500 s/mm2 (z-DWIb1500), calculated zoomed DWI with b-value = 2000 s/mm2 (z-calDWIb2000), and apparent diffusion coefficient (ADC) maps derived from both sequences (f-ADC and z-ADC). Single-imaging modality radiomics signature, mp-MRI radiomics signature, and a mixed model based on mp-MRI and clinically independent risk factors were built to predict PCa probability. The diagnostic efficacy and the potential net benefits of each model were evaluated. RESULTS: Both z-DWIb1500 and z-calDWIb2000 had significantly better predictive performance than f-DWIb1500 (z-DWIb1500 vs. f-DWIb1500: p = 0.048; z-calDWIb2000 vs. f-DWIb1500: p = 0.014). z-ADC had a slightly higher area under the curve (AUC) value compared with f-ADC value but was not significantly different (p = 0.127). For predicting the presence of PCa, the AUCs of clinical independent risk factors model, mp-MRI model, and mixed model were 0.81, 0.93, and 0.94 in training sets, and 0.74, 0.92, and 0.93 in validation sets, respectively. CONCLUSION: Radiomics signatures based on the z-DWI technology had better diagnostic accuracy for PCa than that based on the f-DWI technology. The mixed model was better at diagnosing PCa and guiding clinical interventions for patients with suspected PCa compared with mp-MRI signatures and clinically independent risk factors. KEY POINTS: • Advanced zoomed DWI technology can improve the diagnostic accuracy of radiomics signatures for PCa. • Radiomics signatures based on z-calDWIb2000 have the best diagnostic performance among individual imaging modalities. • Compared with the independent clinical risk factors and the mp-MRI model, the mixed model has the best diagnostic efficiency.

Mining and Evaluation of Post-marketing Safe Alert Signals of Pegaspargase and L-asparaginase Based on BCPNN Method / 中国药房

OBJECTIVE： To mine and evaluate the post-marketing safety alert signals of pegaspargase （PEG-ASP） and L-asparaginase （L-ASP），and compare the safety differences between them ，so as to provide reference for clinical safe and rational drug use. METHODS ： The adverse drug event （ADE） reports of PEG-ASP and L-ASP issued by FDA adverse event reporting system from Jan. 1st，2004-Jun. 30th，2020 were retrieved. BCPNN method was used to mine the safety signals of these two drugs under the condition that the lower limit of information component （IC－2SD）＞0 and the number of events ≥3. The medium and strong signals of two drugs with IC －2SD≥1.5 were evaluated and compared in 8 system organ class，such as gastrointestinal system ，hepatobiliary system ，blood and lymphatic system ，blood vessels and lymphatic vessels ， nervous system ，immune system ，metabolism and nutrition ，various examinations. IC value of specific ADE signal and its 95％ confidence interval were analyzed by time scanning spectrum. RESULTS & CONCLUSIONS ：The reports of PEG-ASP and L-ASP as suspected drugs were 2 324 and 3 824；67 and 68 medium and strong signals were included ，respectively. In gastrointestinal system，the common strong signal of PEG-ASP and L-ASP was necrotic pancreatitis. In hepatobiliary system ，both of them showed strong signal in venoocclusive liver disease ，and this ADE was not included in the drug instruction. In blood and lymphatic system ， common strong signals of the two drugs were febrile neutropenia ，coagulation disorder ，neutropenia and febrile bone marrow regeneration disorder ；in blood vessels and lymphatic vessels ，in addition to haemodynamic instability ，IC values of other signals of L-ASP were higher than those of PEG-ASP. In nervous system ，IC values of other signals of L-ASP were higher than those of PEG-ASP except for intracranial haemorrhage. In immune system ，anaphylactic reaction was a medium signal for L-ASP but was a strong signal for PEG-ASP. In metabolism and nutritional diseases ，except for tumor lysis syndrome ，IC values of other signals of L-ASP were higher than those of PEG-ASP. The results of time scanning spectrum showed that the signals of necrotic pancreatitis and coagulation disorder of PEG-ASP were stable ，while the signals of veno occlusive liver disease and hypersensitivity were unstable and needed to be observed ；above four signals of L-ASP were stable signals. When using PEG-ASP or L-ASP clinically ， close attention should be paid to the safety problems such as hypersensitivity ，coagulation disorder ，thrombosis，necrotic pancreatitis，venoocclusive liver disease and hypoproteinemia.

Correlation between hepatitis B virus infection and deep infection after spinal internal fixation surgery and analysis of pathogenic bacteria / 中国医师进修杂志

Objective:To explore the correlation between hepatitis B virus (HBV) infection and deep infection after spinal internal fixation surgery and analysis of pathogenic bacteria.Methods:One hundred and eighty-four patients who underwent spinal internal fixation with HBV infection in Xiaogan First People′s Hospital of Hubei Province from January 2013 to January 2019 were selected as the HBV infection group, and 184 patients who underwent spinal internal fixation with non-HBV infection were selected as the non-HBV infection group. The incidence of deep infection and the distribution of pathogenic bacteria were compared between 2 groups. The influencing factors of postoperative deep infection and HBV reactivation in patients with HBV infection were analyzed by single factor analysis and multi-factor Logistics regression analysis.Results:The incidence of deep infection after spinal internal fixation surgery in HBV infection group was significantly higher than that in non-HBV infection group: 19.57% (36/184) vs. 9.24% (17/184), and there was statistical difference ( P<0.01). The pathogenic bacteria of deep infection in both groups were mainly acinetobacter bausinensis, klebsiella pneumoniae, staphylococcus aureus, staphylococcus epidermidis. There was no statistically significant difference in the distribution of pathogenic bacteria between 2 groups ( P>0.05). The deep infection incidences in age ≥ 65 years, operation time ≥ 3 h, intraoperative blood loss ≥ 1000 ml, CD 4+/CD 8+<1.4, total lymphocyte count<0.7 × 10 9/L, liver function abnormalities (AST>40 U/L or ALT>50 U/L), HBV-DNA (+) patients with HBV infection were significantly higher: 27.16%(22/81) vs. 13.59%(14/103), 28.77%(21/73) vs. 13.51%(15/111), 31.15%(19/61) vs. 13.82%(17/123), 29.69%(19/64) vs. 14.17%(17/120), 27.78% (20/72) vs. 14.29%(16/112), 7/18 vs. 17.47%(29/166), 30.43%(21/69) vs. 13.04%(15/115), and there were statistical differences ( P<0.05 or <0.01). Multivariate Logistic regression analysis showed that intraoperative blood loss (≥ 1 000 ml), CD 4+/CD 8+(<1.4), total lymphocyte count (<0.7 × 10 9/L), and HBV-DNA (+) were independent risk factors for deep infection after spinal internal fixation in patients with HBV infection ( P<0.01 or <0.05). The HBV reactivation incidence in age ≥ 65 years, operation time ≥ 3 h, intraoperative blood loss ≥ 1 000 ml, liver function abnormalities, HBV-DNA (+), postoperative deep infection patients with HBV infection were significantly increased: 33.33% (27/81) vs. 18.45% (19/103), 34.25% (25/73) vs. 18.92% (21/111), 34.43% (21/61) vs. 20.33% (25/123), 8/18 vs. 22.89% (38/166), 34.78% (24/69) vs. 19.13% (22/115), 41.67% (15/36) vs. 20.95% (31/148), and there were statistical differences ( P<0.05). Multivariate Logistic regression analysis showed that intraoperative blood loss (≥ 1 000 ml), HBV-DNA (+) and postoperative deep infection were independent risk factors for HBV reactivation after spinal internal fixation in patients with HBV infection ( P<0.05 or <0.01). Conclusions:HBV infection significantly increases the incidence of deep infection after spinal internal fixation surgery, and the independent risk factors are intraoperative blood loss (≥1 000 ml), CD 4+/CD 8+ (<1.4), total lymphocyte count (<0.7 × 10 9/L), and HBV-DNA (+). Spinal internal fixation surgery can cause HBV reactivation, and its independent risk factors are intraoperative blood loss (≥ 1 000 ml), HBV-DNA (+) and postoperative deep infection.

Researches on the illness risk of essential hypertension complicated with coronary heart disease based on machine learning algorithm / 解放军医学杂志

[Abstract] Objective To study a model of screening the risk factors of essential hypertension complicated with coronary heart disease and establishing the individual risk classification, and provide a computer-aided diagnostic methods for disease occurrence. Methods To collect 70 clinical information including 2791 patients with essential hypertension complicated with coronary heart disease and 2135 patients with simple essential hypertension diagnosed from January 1, 2014 to May 31, 2019 in Chongqing Medical University medical big data platform, screen out the indicators with statistical differences in single factor analysis. With R3.6.1 to construct logistic regression classification model and 3 machine learning models of BP neural network, random forest and extreme gradient rise (XGBoost), then compare the relevant parameters of various models and select the optimal classification model. Results According to the univariate analysis, 44 indexes with statistical difference were selected and included in logistic regression classification model and machine learning model. The classification accuracy in test set of logistic regression classification model, BP neural network model, random forest model, XGBoost model was 0.852, 0.968, 0.966 and 0.976, respectively, and the area under the work characteristic curve (AUC) of the subjects was 0.853, 0.970, 0.967 and 0.977, respectively. Applying XGBoost model with optimal performance to clinical practice of cardiology in the University Town Hospital of Chongqing Medical University. The diagnostic sensitivity was 1.000, specificity was 0.912, accuracy was 0.926, and AUC was 0.956. Conclusion Establishment of XGBoost model has a good auxiliary diagnostic function for essential hypertension complicated with coronary heart disease, and has achieved good results in clinical practice.

Rehabilitation Approaches from European Physiotherapy Guideline for Parkinson's Disease: A Comment / 中国康复理论与实践

This paper introduced European Guidelines for Physiotherapy of Parkinson's Disease, especially the form, content and selection, to give a reference of evidence-based practice in China.

The effects of brain radiotherapy selection on the survival of driver gene-positive non-small cell lung cancer patients with brain metastases / 中华放射医学与防护杂志

Objective:To explore the appropriate radiotherapy time and method in the treatment of patients with brain metastases (BM) due to from non-small cell lung cancer (NSCLC) harboring epidermal growth factor receptor (EGFR) mutation.Methods:Totally 69 EGFR-mutant NSCLC patients with BM treated in Zhongnan Hospital of Wuhan University from January 2014 to September 2018 were retrospectively reviewed. The patients were divided into two groups according to the time of brain radiotherapy, including the upfront radiotherapy group ( n=45) who received concurrent brain radiotherapy and EGFR-tyrosine kinase inhibitors(TKI)treatments and deferred radiotherapy group ( n=24) who received brain radiotherapy after intracranial progression during EGFR-TKI treatment. The upfront radiotherapy group was further divided into two groups, the group treated with WBRT concurrent with EGFR-TKI ( n=20) and the group treated with SRS concurrent with EGFR-TKI ( n=25). Overall survival (OS), progression-free survival (PFS) and intracranial progression-free survival (iPFS) time were evaluated. Results:The median OS of 69 patients was 31.2 months. For the upfront and deferred radiotherapy groups, the 1-, 2- year OS were 95%, 64% and 80%, 35%, the difference between the two groups was statistically significant. On subgroup analysis, the upfront WBRT, upfront SRS and deferred radiotherapy groups 1-, 2- year OS were 95%, 96%, 80% and 42%, 88%, 35%. Moreover, the upfront SRS group was associated with improved OS relative to the deferred radiotherapy group ( HR: 0.10, 95% CI: 0.23-0.46, P=0.003), but the upfront WBRT and deferred radiotherapy groups shared similar OS ( HR: 0.54, 95% CI: 0.21-1.32, P=0.180). There were no significant difference in iPFS and PFS between the upfront and deferred radiotherapy groups( P>0.05). Conclusions:Upfront brain radiotherapy prolonged the survival of BM patients metastasized from EGFR-mutant NSCLC. SRS concurrent with EGFR-TKI may be superior to WBRT concurrent with EGFR-TKI in the treatment of BM metastasized from EGFR-mutant NSCLC.

Enzymatic Degradation of Graphene Quantum Dots by Human Peroxidases.

Carbon-based nanomaterials have demonstrated to be potent candidates for biomedical applications. Recently, graphene quantum dots (GQDs) have emerged as an attractive tool for bioimaging, biosensing, and therapy. Hence, studying their biodegradability in living systems is essential to speed up the translation toward real clinical innovations. Here, the enzymatic degradation of GQDs using human myeloperoxidase and eosinophil peroxidase is investigated. Transmission electron microscopy, fluorescence, and Raman spectroscopy are used to evaluate the biodegradation of GQDs. Signs of degradation by both enzymes are observed already after a few hours of incubation with each enzyme, being more evident after a couple of days of treatment. Molecular dynamics simulations show intimate interactions between the enzymes and the GQDs. The conformation of both peroxidases is slightly altered to favor the interactions, while the GQD sheets distort a little to adapt to the surface of the enzymes. The biodegradability of the GQDs ensures their real potential in the practical biomedical applications.

Atherosclerosis in intracranial or extracranial vessels in diabetic patients and the association with stroke subtype.

BACKGROUND: Diabetes mellitus (DM) is associated with increased prevalence and severity of atherosclerosis. This study aimed to assess the prevalence and location of atherosclerosis in intracranial and extracranial vessels in diabetic patients and to investigate their association with ischemic stroke subtype. METHODS: Diabetes patients (n=128) and nondiabetic patients (n=195) were enrolled. Brain MRI, MR angiography, and digital subtraction angiography (DSA) imaging findings in the two groups were retrospectively compared. The characteristics of atherosclerosis (prevalence, location, severity) and collateral flow in diabetic and nondiabetic patients and their association with stroke subtype were analyzed. RESULTS: Atherosclerosis in extracranial vessels was more common in diabetes patients than in nondiabetic patients (43.8% vs. 23.1%; P<0.001). Symptomatic stenoses were commonly in the proximal internal carotid artery (ICA) and proximal vertebral artery (pVA). Diabetes patients were more likely to have lacunar infarction (49.2% vs. 32.3%; P=0.002) and less likely to have large artery infarct (36.7% vs. 48.2%; P=0.042). DM (OR, 2.03; 95% CI, 1.96-4.30; P=0.006) and age >65 years (OR, 2.55; 95% CI, 1.24-5.22; P=0.011) were independent risk factors for lacunar infarct. Diabetes patients with symptomatic extracranial stenosis or occlusion, combined with good collateral circulation, had significantly higher risk of lacunar infarction than nondiabetic patients (47.8% vs. 30.5%; P=0.045). CONCLUSIONS: DM aggravates the severity of extracranial atherosclerosis. Lacunar stroke is relatively common in diabetic patients and could even be due to large artery disease (LAD).