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PurposePrediction of immunotherapy response and outcome in patients with non-small cell lung cancer (NSCLC) is challenging due to intratumoral heterogeneity and lack of robust biomarkers. The aim of this study was to systematically evaluate the methodological quality of radiomic studies for predicting immunotherapy response or outcome in patients with NSCLC.MethodsWe systematically searched for eligible studies in the PubMed and Web of Science datasets up to April 1, 2021. The methodological quality of included studies was evaluated using the phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool. A meta-analysis of studies regarding the prediction of immunotherapy response and outcome in patients with NSCLC was performed.ResultsFifteen studies were identified with sample sizes ranging from 30 to 228. Seven studies were classified as phase II, and the remaining as discovery science (n = 2), phase 0 (n = 4), phase I (n = 1), and phase III (n = 1). The mean RQS score of all studies was 29.6%, varying from 0 to 68.1%. The pooled diagnostic odds ratio for predicting immunotherapy response in NSCLC using radiomics was 14.99 (95% confidence interval [CI] 8.66–25.95). In addition, radiomics could divide patients into high- and low-risk group with significantly different overall survival (pooled hazard ratio [HR]: 1.96, 95%CI 1.61–2.40, p < 0.001) and progression-free survival (pooled HR: 2.39, 95%CI 1.69–3.38, p < 0.001).ConclusionsRadiomics has potential to noninvasively predict immunotherapy response and outcome in patients with NSCLC. However, it has not yet been implemented as a clinical decision-making tool. Further external validation and evaluation within clinical pathway can facilitate personalized treatment for patients with NSCLC.

PurposeTo quantify the severity of 2019 novel coronavirus disease (COVID-19) on chest CT and to determine its relationship with laboratory parameters.MethodsPatients with real-time fluorescence polymerase chain reaction (RT-PCR)–confirmed COVID-19 between January 01 and February 18, 2020, were included in this study. Laboratory parameters were retrospectively collected from medical records. Severity of lung changes on chest CT of early, progressive, peak, and absorption stages was scored according to the percentage of lung involvement (5 lobes, scores 1–5 for each lobe, range 0–20). Relationship between CT scores and laboratory parameters was evaluated by the Spearman rank correlation. The Bonferroni correction adjusted significance level was at 0.05/4 = 0.0125.ResultsA total of 84 patients (mean age, 47.8 ± 12.0 years [standard deviation]; age range, 24–80 years) were evaluated. The patients underwent a total of 339 chest CT scans with a median interval of 4 days (interquartile range, 3–5 days). Median chest CT scores peaked at 4 days after the beginning of treatment and then declined. CT score of the early stage was correlated with neutrophil count (r = 0.531, P = 0.011). CT score of the progressive stage was correlated with neutrophil count (r = 0.502, P < 0.001), white blood cell count (r = 0.414, P = 0.001), C-reactive protein (r = 0.511, P < 0.001), procalcitonin (r = 0.423, P = 0.004), and lactose dehydrogenase (r = 0.369, P = 0.010). However, CT scores of the peak and absorption stages were not correlated with any parameter (P > 0.0125). No sex difference occurred regarding CT score (P > 0.05).ConclusionSeverity of lung abnormalities quantified on chest CT might correlate with laboratory parameters in the early and progressive stages. However, larger cohort studies are necessary.

Abstract Objective The purpose of this study is to re-sensitive bacteria to carbapenemases and reduce the transmission of the bla KPC−2 gene by curing the bla KPC−2-harboring plasmid of carbapenem-resistant using the CRISPR-Cas9 system. Methods The single guide RNA (sgRNA) specifically targeted to the bla KPC−2 gene was designed and cloned into plasmid pCas9. The recombinant plasmid pCas9-sgRNA(bla KPC−2) was transformed into Escherichia coli (E.coli) carrying pET24-bla KPC−2. The elimination efficiency in strains was evaluated by polymerase chain reaction (PCR) and quantitative real-time PCR (qPCR). Susceptibility testing was performed by broth microdilution assay and by E-test strips (bioMérieux, France) to detect changes in bacterial drug resistance phenotype after drug resistance plasmid clearance. Results In the present study, we constructed a specific prokaryotic CRISPR-Cas9 system plasmid targeting cleavage of the bla KPC−2 gene. PCR and qPCR results indicated that prokaryotic CRISPR-Cas9 plasmid transforming drug-resistant bacteria can efficiently clear bla KPC−2-harboring plasmids. In addition, the drug susceptibility test results showed that the bacterial resistance to imipenem was significantly reduced and allowed the resistant model bacteria to restore susceptibility to antibiotics after the bla KPC−2-containing drug-resistant plasmid was specifically cleaved by the CRISPR-Cas system. Conclusion In conclusion, our study demonstrated that the one plasmid-mediated CRISPR-Cas9 system can be used as a novel tool to remove resistance plasmids and re-sensitize the recipient bacteria to antibiotics. This strategy provided a great potential to counteract the ever-worsening spread of the bla KPC−2 gene among bacterial pathogens and laid the foundation for subsequent research using the CRISPR-Cas9 system as adjuvant antibiotic therapy.

Objective The purpose of this study is to reduce the spread of the v anA gene by curing the vanA -harboring plasmid of vancomycin-resistant using the CRISPR-Cas9 system. Methods Two specific spacer sequence (sgRNAs) specific was designed to target the vanA gene and cloned into plasmid CRISPR-Cas9. The role of the CRISPR-Cas system in the plasmid elimination of drug-resistance genes was verified by chemically transformation and conjugation delivery methods. Moreover, the elimination efficiency in strains was evaluated by plate counting, PCR, and quantitative real-time PCR (qPCR). Susceptibility testing was performed by broth microdilution assay and by Etest strips (bioMérieux, France) to detect changes in bacterial drug resistance phenotype after drug resistance plasmid clearance. Results In the study, we constructed a specific prokaryotic CRISPR-Cas9 system plasmid targeting cleavage of the vanA gene. PCR and qPCR results indicated that recombinant pCas9-sgRNA plasmid can efficiently clear vanA -harboring plasmids. There was no significant correlation between sgRNA lengths and curing efficiency. In addition, the drug susceptibility test results showed that the bacterial resistance to vancomycin was significantly reduced after the vanA -containing drug-resistant plasmid was specifically cleaved by the CRISPR-Cas system. The CRISPR-Cas9 system can block the horizontal transfer of the conjugated plasmid pUC19- vanA . Conclusion In conclusion, our study demonstrated that CRISPR-Cas9 achieved plasmid clearance and reduced antimicrobial resistance. The CRISPR-Cas9 system could block the horizontal transfer of plasmid carrying vanA . This strategy provided a great potential to counteract the ever-worsening spread of the vanA gene among bacterial pathogens and laid the foundation for subsequent research using the CRISPR-Cas9 system as adjuvant antibiotic therapy.

Although KIF4A has been found to play an important role in a variety of tumors and is closely associated with the activation of immunocytes, its role in bladder cancer (BC) remains unclear. Here, we report increased expression of KIF4A in both lymph node-positive and high grade BC tissues. High expression of KIF4A has been significantly correlated with fewer CD8 + tumor-infiltrating lymphocytes (TILs) and a much worse prognosis in patients with BC. With respect to promoting tumor growth, the expression of KIF4A in promoting tumor growth was more pronounced in immune-competent mice (C57BL/6) than in immunodeficient mice (BALB/C). In addition, the more increased accumulation of myeloid-derived suppressor cells (MDSCs) was observed in tumor-bearing mice with KIF4A overexpression than in the control group. Transwell chemotaxis assays revealed that KIF4A overexpression in T24 cells increased MDSC recruitment. Furthermore, according to ELISA results, CXCL5 was the most noticeably increased cytokine in the KIF4A-transduced BC cells. Additional studies in vitro and in vivo showed that the capability of KIF4A to promote BC cells to recruit MDSCs could be significantly inhibited by anti-CXCL5 antibody. Therefore, our results demonstrated that KIF4A-mediated BC production of CXCL5 led to an increase in MDSC recruitment, which contributed to tumor progression.

Cancer remains a leading cause of death worldwide and total number of cases globally is increasing. Novel treatment strategies are therefore desperately required for radical treatment of cancers and long survival of patients. A new technology using high pulsed electric field has emerged from military application into biology and medicine by applying nsPEF as a means to inhibit cancer. However, molecular mechanisms of nsPEF on tumors or cancers are still unclear. In this paper, we found that nsPEF had extensive biological effects in cancers, and clarified its possible molecular mechanisms in vitro and in vivo. It could not only induce cell apoptosis via dependent-mitochondria intrinsic apoptosis pathway that was triggered by imbalance of anti- or pro-apoptosis Bcl-2 family proteins, but also inhibit cell proliferation through repressing NF-κB signaling pathway to reduce expressions of cyclin proteins. Moreover, nsPEF could also inactivate metastasis and invasion in cancer cells by suppressing Wnt/β-Catenin signaling pathway to down-regulating expressions of VEGF and MMPs family proteins. More importantly, nsPEF could function safely and effectively as an anti-cancer therapy through inducing tumor cell apoptosis, destroying tumor microenvironment, and depressing angiogenesis in tumor tissue in vivo. These findings may provide a creative and effective therapeutic strategy for cancers.

Background Transcatheter arterial chemoembolization (TACE) is the mainstay of therapy for intermediate-stage hepatocellular carcinoma (HCC); yet its efficacy varies between patients with the same tumor stage. Accurate prediction of TACE response remains a major concern to avoid overtreatment. Thus, we aimed to develop and validate an artificial intelligence system for real-time automatic prediction of TACE response in HCC patients based on digital subtraction angiography (DSA) videos via a deep learning approach. Methods This retrospective cohort study included a total of 605 patients with intermediate-stage HCC who received TACE as their initial therapy. A fully automated framework (i.e., DSA-Net) contained a U-net model for automatic tumor segmentation (Model 1) and a ResNet model for the prediction of treatment response to the first TACE (Model 2). The two models were trained in 360 patients, internally validated in 124 patients, and externally validated in 121 patients. Dice coefficient and receiver operating characteristic curves were used to evaluate the performance of Models 1 and 2, respectively. Results Model 1 yielded a Dice coefficient of 0.75 (95% confidence interval [CI]: 0.73–0.78) and 0.73 (95% CI: 0.71–0.75) for the internal validation and external validation cohorts, respectively. Integrating the DSA videos, segmentation results, and clinical variables (mainly demographics and liver function parameters), Model 2 predicted treatment response to first TACE with an accuracy of 78.2% (95%CI: 74.2–82.3), sensitivity of 77.6% (95%CI: 70.7–84.0), and specificity of 78.7% (95%CI: 72.9–84.1) for the internal validation cohort, and accuracy of 75.1% (95% CI: 73.1–81.7), sensitivity of 50.5% (95%CI: 40.0–61.5), and specificity of 83.5% (95%CI: 79.2–87.7) for the external validation cohort. Kaplan-Meier curves showed a significant difference in progression-free survival between the responders and non-responders divided by Model 2 ( p  = 0.002). Conclusions Our multi-task deep learning framework provided a real-time effective approach for decoding DSA videos and can offer clinical-decision support for TACE treatment in intermediate-stage HCC patients in real-world settings.

BackgroundTo develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function.MethodsA retrospective study of 174 diabetic patients (training cohort, n = 123; validation cohort, n = 51) who underwent renal MRI scans was included. They were assigned to normal function (n = 71), mild or moderate impairment (n = 69), and severe impairment groups (n = 34) according to renal function. Four methods of kidney segmentation on T2-weighted images (T2WI) were compared, including regions of interest covering all coronal slices (All-K), the largest coronal slices (LC-K), and subregions of the largest coronal slices (TLCO-K and PIZZA-K). The speeded-up robust features (SURF) and support vector machine (SVM) algorithms were used for texture feature extraction and model construction, respectively. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of models.ResultsThe models based on LC-K and All-K achieved the nonsignificantly highest accuracy in the classification of renal function (all p values > 0.05). The optimal model yielded high performance in classifying the normal function, mild or moderate impairment, and severe impairment, with an area under the curve of 0.938 (95% confidence interval [CI] 0.935–0.940), 0.919 (95%CI 0.916–0.922), and 0.959 (95%CI 0.956–0.962) in the training cohorts, respectively, as well as 0.802 (95%CI 0.800–0.807), 0.852 (95%CI 0.846–0.857), and 0.863 (95%CI 0.857–0.887) in the validation cohorts, respectively.ConclusionWe developed and internally validated an MRI-based machine-learning model that can accurately evaluate renal function. Once externally validated, this model has the potential to facilitate the monitoring of patients with impaired renal function.Key pointsTexture analysis based on coronal T2-weighted MR images could evaluate the renal function in patients with diabetes.The All-K and LC-K outperformed other segmentation methods in the evaluation of renal function impairment.The segmentation methods could affect the results of renal function evaluation and the integrity of the coronal slices was crucial for renal imaging texture analysis.

Purpose: The objectives of this study are to determine the differences in clonality, virulence gene (VG) content and phylogenetic group between non extended-spectrum beta-lactamase-producing E. coli (non-ESBL- EC) and ESBL-EC isolates from urine. Patients and Methods: This study characterized a total of 100 clinical E. coli isolates consecutively obtained from the inpatients hospitalized in The First Affiliated Hospital of Ningbo University in China by polymerase-chain reaction (PCR). Results: Phylogenetic group B2 was found to be the most prevalent in both ESBL- EC and non-ESBL-EC group. Among 100 clinical isolates, the count of acquired virulence genes in group B2 was found to be significantly higher than that in group A, B1, and D (p <0.001). Additionally, the presence of content within virulence genes (the total number of virulence genes detected per isolate) in B2 of non-ESBL-EC and ESBL-EC showed a significant difference (p <0.001). ST131 was detected exclusively in ESBL- EC, while ST95 and ST73 were the main sequence types in non-ESBL-EC. Conclusion: Our study demonstrated the different distribution of MLST, phylogenetic group in ESBL-EC and non-ESBL- EC group. The inverse association between beta-lactamase resistance and VG content performed in this study should get a lot more attention. At the same time, we should also be wary of the appearance of non-ESBL-EC isolates of group B2 harboring more virulence genes which will lead to high pathogenicity. Keywords: E. coli, clonal structure, ST73, ST95, ST131, ST1193, virulence genes