Targeted elimination of Vancomycin resistance gene vanA by CRISPR-Cas9 system.

OBJECTIVE: The purpose of this study is to reduce the spread of the vanA 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.

Elimination of blaKPC-2-mediated carbapenem resistance in Escherichia coli by CRISPR-Cas9 system.

OBJECTIVE: The purpose of this study is to re-sensitive bacteria to carbapenemases and reduce the transmission of the blaKPC-2 gene by curing the blaKPC-2-harboring plasmid of carbapenem-resistant using the CRISPR-Cas9 system. METHODS: The single guide RNA (sgRNA) specifically targeted to the blaKPC-2 gene was designed and cloned into plasmid pCas9. The recombinant plasmid pCas9-sgRNA(blaKPC-2) was transformed into Escherichia coli (E.coli) carrying pET24-blaKPC-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 blaKPC-2 gene. PCR and qPCR results indicated that prokaryotic CRISPR-Cas9 plasmid transforming drug-resistant bacteria can efficiently clear blaKPC-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 blaKPC-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 blaKPC-2 gene among bacterial pathogens and laid the foundation for subsequent research using the CRISPR-Cas9 system as adjuvant antibiotic therapy.

Thy-Wise: An interpretable machine learning model for the evaluation of thyroid nodules.

Current risk stratification systems for thyroid nodules suffer from low specificity and high biopsy rates. Recently, machine learning (ML) is introduced to assist thyroid nodule diagnosis but lacks interpretability. Here, we developed and validated ML models on 3965 thyroid nodules, as compared to the American College of Radiology Thyroid Imaging, Reporting and Data System (ACR TI-RADS). Subsequently, a SHapley Additive exPlanation (SHAP) algorithm was leveraged to interpret the results of the best-performing ML model. Clinical characteristics including thyroid-function tests were collected from medical records. Five ACR TI-RADS ultrasonography (US) categories plus nodule size were assessed by experienced radiologists. Random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) were used to build US-only and US-clinical ML models. The ML models and ACR TI-RADS were compared in terms of diagnostic performance and unnecessary biopsy rate. Among the ML models, the US-only RF model (hereafter, Thy-Wise) achieved the optimal performance. Compared to ACR TI-RADS, Thy-Wise showed higher accuracy (82.4% vs 74.8% for the internal validation; 82.1% vs 73.4% for external validation) and specificity (78.7% vs 68.3% for internal validation; 78.5% vs 66.9% for external validation) while maintaining sensitivity (91.7% vs 91.2% for internal validation; 91.9% vs 91.1% for external validation), as well as reduced unnecessary biopsies (15.3% vs 32.3% for internal validation; 15.7% vs 47.3% for external validation). The SHAP-based interpretation of Thy-Wise enables clinicians to better understand the reasoning behind the diagnosis, which may facilitate the clinical translation of this model.

Radiomics in precision medicine for gastric cancer: opportunities and challenges.

OBJECTIVES: Radiomic features derived from routine medical images show great potential for personalized medicine in gastric cancer (GC). We aimed to evaluate the current status and quality of radiomic research as well as its potential for identifying biomarkers to predict therapy response and prognosis in patients with GC. METHODS: We performed a systematic search of the PubMed and Embase databases for articles published from inception through July 10, 2021. The phase classification criteria for image mining studies and the radiomics quality scoring (RQS) tool were applied to evaluate scientific and reporting quality. RESULTS: Twenty-five studies consisting of 10,432 patients were included. 96% of studies extracted radiomic features from CT images. Association between radiomic signature and therapy response was evaluated in seven (28%) studies; association with survival was evaluated in 17 (68%) studies; one (4%) study analyzed both. All results of the included studies showed significant associations. Based on the phase classification criteria for image mining studies, 18 (72%) studies were classified as phase II, with two, four, and one studies as discovery science, phase 0 and phase I, respectively. The median RQS score for the radiomic studies was 44.4% (range, 0 to 55.6%). There was extensive heterogeneity in the study population, tumor stage, treatment protocol, and radiomic workflow amongst the studies. CONCLUSIONS: Although radiomic research in GC is highly heterogeneous and of relatively low quality, it holds promise for predicting therapy response and prognosis. Efforts towards standardization and collaboration are needed to utilize radiomics for clinical application. KEY POINTS: • Radiomics application of gastric cancer is increasingly being reported, particularly in predicting therapy response and survival. • Although radiomics research in gastric cancer is highly heterogeneous and relatively low quality, it holds promise for predicting clinical outcomes. • Standardized imaging protocols and radiomic workflow are needed to facilitate radiomics into clinical use.

Radiogenomic association between the T2-FLAIR mismatch sign and IDH mutation status in adult patients with lower-grade gliomas: an updated systematic review and meta-analysis.

OBJECTIVES: To reveal a radiogenomic correlation between the presence of the T2-fluid-attenuated inversion recovery resection (T2-FLAIR) mismatch sign on MR images and isocitrate dehydrogenase (IDH) mutation status in adult patients with lower-grade gliomas (LGGs). METHODS: A web-based systemic search for eligible literature up to April 13, 2021, was conducted on PubMed, Embase, and the Cochrane Library databases by two independent reviewers. This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We included studies evaluating the accuracy of the T2-FLAIR mismatch sign in diagnosing the IDH mutation in adult patients with LGGs. The T2-FLAIR mismatch sign was defined as a T2-hyperintense lesion that is hypointense on FLAIR except for a hyperintense rim. RESULTS: Fourteen studies (n = 1986) were finally identified. The mean age of patients in the included studies ranged from 38.5 to 56 years. The pooled area under the curve (AUC), sensitivity, and specificity were obtained for each molecular profile: IDHmut-Codel: 0.46 (95% confidence interval [CI]: 0.42-0.50), 1% (95%CI: 0-7%), and 69% (95%CI: 62-75%), respectively; IDHmut-Noncodel: 0.75 (95%CI: 0.71-0.79), 42% (95%CI: 34-50%), and 99% (95%CI: 96-100%), respectively; IDH-Mutation regardless of 1p/19q codeletion status: 0.77 (95%CI: 0.73-0.80), 29% (95%CI: 21-40%), and 99% (95%CI: 92-100%), respectively. CONCLUSIONS: The T2-FLAIR mismatch sign was an insensitive but highly specific marker for IDHmut-Noncodel and IDH-Mutation LGGs, whereas it was not a useful marker for IDHmut-Codel LGGs. The findings might identify the T2-FLAIR mismatch sign as a non-invasive imaging biomarker for the selection of patients with IDH-mutant LGGs. KEY POINTS: • The T2-FLAIR mismatch sign was not a sensitive sign for IDH mutation in LGGs. • The T2-FLAIR mismatch sign was related to IDHmut-Noncodel with a specificity of 99%. • The pooled specificity (69%) of the T2-FLAIR mismatch sign for IDHmut-Codel was low.

Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis.

PURPOSE: Prediction 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. METHODS: We 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. RESULTS: Fifteen 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). CONCLUSIONS: Radiomics 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.

Deep Learning With 3D Convolutional Neural Network for Noninvasive Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

BACKGROUND: Microvascular invasion (MVI) is a critical prognostic factor of hepatocellular carcinoma (HCC). However, it could only be obtained by postoperative histological examination. PURPOSE: To develop an end-to-end deep-learning models based on MRI images for preoperative prediction of MVI in HCC patients who underwent surgical resection. STUDY TYPE: Retrospective. POPULATION: Two hundred and thirty-seven patients with histologically confirmed HCC. FIELD STRENGTH: 1.5 T and 3.0 T. SEQUENCE: Axial T2 -weighted (T2 -w) with turbo spin echo sequence, T2 -Spectral Presaturation with Inversion Recovery (T2 -SPIR), and dynamic contrast-enhanced (DCE) imaging with fat suppressed enhanced T1 high-resolution isotropic volume examination. ASSESSMENT: The patients were randomly divided into training (N = 158) and validation (N = 79) sets. Data augmentation by random rotation was performed on the training set and the sample size increased to 1940 for each MR sequence. A three-dimensional convolutional neural network (3D CNN) was used to develop four deep-learning models, including three single-layer models based on single-sequence, and fusion model combining three sequences. MVI status was obtained from the postoperative pathology reports. STATISTICAL TESTS: The dice similarity coefficient (DSC) and Hausdorff distance (HD) were applied to assess the similarity and reproducibility between the manual segmentations of tumor from two radiologists. Receiver operating characteristic curve analysis was used to evaluate model performance. MVI was identified in 92 (38.8%) patients. Good reproducibility with interobserver DSCs of 0.90, 0.89, and 0.89 and HDs of 4.09, 3.67, and 3.60 was observed for PVP, T2 WI, and T2 -SPIR, respectively. The fusion model achieved an area under the curve (AUC) of 0.81, sensitivity of 69%, and specificity of 79% in the training set and 0.72, sensitivity of 55%, and specificity of 81% in the validation set. DATA CONCLUSION: 3D CNN model may serve as a noninvasive tool to predict MVI in HCC, whereas its accuracy needs to be enhanced with larger cohort. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC). PURPOSE: To develop a model to evaluate distant metastasis-free survival (DMFS) in LANPC and to explore the value of additional chemotherapy to concurrent chemoradiotherapy (CCRT) for different risk groups. STUDY TYPE: Retrospective. POPULATION: In all, 233 patients with biopsy-confirmed nasopharyngeal carcinoma (NPC) from two hospitals. FIELD STRENGTH: 1.5T and 3T. SEQUENCE: Axial T2 -weighted (T2 -w) and contrast-enhanced T1 -weighted (CET1 -w) images. ASSESSMENT: Deep learning was used to build a model based on MRI images (including axial T2 -w and CET1 -w images) and clinical variables. Hospital 1 patients were randomly divided into training (n = 169) and validation (n = 19) cohorts; Hospital 2 patients were assigned to a testing cohort (n = 45). LANPC patients were divided into low- and high-risk groups according to their DMFS (P < 0.05). Kaplan-Meier survival analysis was performed to compare the DMFS of different risk groups and subgroup analysis was performed to compare patients treated with CCRT alone and treated with additional chemotherapy to CCRT in different risk groups, respectively. STATISTICAL TESTS: Univariate analysis was performed to identify significant clinical variables. The area under the receiver operating characteristic (ROC) curve (AUC) was used to assess the model performance. RESULTS: Our deep-learning model integrating the deep-learning signature, node (N) stage (from TNM staging), plasma Epstein-Barr virus (EBV)-DNA, and treatment regimens yielded an AUC of 0.796 (95% confidence interval [CI]: 0.729-0.863), 0.795 (95% CI: 0.540-1.000), and 0.808 (95% CI: 0.654-0.962) in the training, internal validation, and external testing cohorts, respectively. Low-risk patients treated with CCRT alone had longer DMFS than patients treated with additional chemotherapy to CCRT (P < 0.05). DATA CONCLUSION: The proposed deep-learning model, based on MRI features and clinical variates, facilitated the prediction of DMFS in LANPC patients. LEVEL OF EVIDENCE: 3. TECHNICAL EFFICACY STAGE: 4.

Resolution of serologic problems due to cold agglutinin mediated autoimmune hemolytic anemia and its transfusion decision.

BACKGROUND: Autoimmune hemolytic anemia (AIHA) is a rare disease characterized by hemolysis caused by autoantibodies against erythrocyte surface antigen. These antibodies can be classified as warm, cold, or mixed types. METHODS: We report two cases of cold agglutinin disease (CAD), which were eventually diagnosed owing to blood group discrepancy. Resolution was achieved after washing the red blood cells (RBCs) with warm saline and absorbing the autoantibodies at 4°C with the washed RBCs. We also assessed the patient's condition and discussed the strategy of blood transfusion. RESULTS: The first case occurred after postoperative chemotherapy for rectal cancer, and the other manifested with anemia from the outset. Direct antiglobulin tests were positive and revealed autoantibodies against C3d only. Cold agglutinin titration was performed, and the titers of both were 1:1024. Eventually, the patient's condition stabilized without blood transfusion. CONCLUSION: The serological discrepancies observed in the blood transfusion department can successfully guide blood transfusion decisions in cases of CAD.

Novel coronavirus disease 2019 (COVID-19): relationship between chest CT scores and laboratory parameters.

PURPOSE: To quantify the severity of 2019 novel coronavirus disease (COVID-19) on chest CT and to determine its relationship with laboratory parameters. METHODS: Patients 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. RESULTS: A 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). CONCLUSION: Severity of lung abnormalities quantified on chest CT might correlate with laboratory parameters in the early and progressive stages. However, larger cohort studies are necessary.

Evaluation of a Commercial Colloidal Gold Assay for Detection of Influenza A and B Virus in Children's Respiratory Specimens.

Objective: To evaluate the clinical diagnostic value of colloidal gold assay for detection of influenza A and B virus. Methods: Results were compared for colloidal gold assay for influenza antigen detection and real-time RT-PCR for RNA detection. Results: By performing the colloidal gold assay, the positive rate was 25.67% (105/409) for influenza A virus and 8.56% (35/409) for influenza B virus. While tested by real-time RT-PCR, 107(26.2%) were positive for influenza A virus and 35(8.6%) for influenza B virus. Using real-time RT-PCR as the gold standard, the sensitivity and specificity of the colloidal gold assay were 84.1% and 95.0%, respectively, for influenza A virus and 85.7% and 98.7% for influenza B virus. Conclusion: This colloidal gold assay may be one of the viable tools for the rapid screening for influenza viruses in an outpatient clinical practice.

Evaluation of renal function in chronic kidney disease using histogram analysis based on multiple diffusion models.

OBJECTIVES: To compare the diagnostic value of histogram features of multiple diffusion metrics in predicting early renal impairment in chronic kidney disease (CKD). METHODS: A total of 77 patients with CKD (mild group, estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2) and 30 healthy controls (HCs) were enrolled. Diffusion-weighted imaging was performed by using single-shot echo planar sequence with 13 b values (0, 20, 50, 80, 100, 150, 200, 500, 800, 1000, 1500, 2000, and 2500 s/mm2). Diffusion models including mono-exponential (Mono), intravoxel incoherent motion (IVIM), stretched-exponential (SEM), and kurtosis (DKI) were calculated, and their histogram features were analysed. All diffusion models for predicting early renal impairment in CKD were established using logistic regression analysis, and diagnostic efficiency was compared among the models. RESULTS: All diffusion models had high differential diagnosis efficiency between the mild group and HCs. The areas under the curve (AUCs) of Mono, IVIM, SEM, DKI, and the combined diffusion model for predicting early renal impairment in CKD were 0.829, 0.809, 0.760, 0.825, and 0.861, respectively. There were no significant differences in AUCs except SEM and combined model, SEM, and DKI model. There were significant correlations between eGFR/serum creatinine and some of histogram features. CONCLUSIONS: Histogram analysis based on multiple diffusion metrics was practicable for the non-invasive assessment of early renal impairment in CKD. ADVANCES IN KNOWLEDGE: Advanced diffusion models provided microstructural information. Histogram analysis further reflected histological characteristics and heterogeneity. Histogram analysis based on multiple diffusion models could provide an accurate and non-invasive method to evaluate the early renal damage of CKD.

The biological function of the type II toxin-antitoxin system ccdAB in recurrent urinary tract infections.

Urinary tract infections (UTIs) represent a significant challenge in clinical practice, with recurrent forms (rUTIs) posing a continual threat to patient health. Escherichia coli (E. coli) is the primary culprit in a vast majority of UTIs, both community-acquired and hospital-acquired, underscoring its clinical importance. Among different mediators of pathogenesis, toxin-antitoxin (TA) systems are emerging as the most prominent. The type II TA system, prevalent in prokaryotes, emerges as a critical player in stress response, biofilm formation, and cell dormancy. ccdAB, the first identified type II TA module, is renowned for maintaining plasmid stability. This paper aims to unravel the physiological role of the ccdAB in rUTIs caused by E. coli, delving into bacterial characteristics crucial for understanding and managing this disease. We investigated UPEC-induced rUTIs, examining changes in type II TA distribution and number, phylogenetic distribution, and Multi-Locus Sequence Typing (MLST) using polymerase chain reaction (PCR). Furthermore, our findings revealed that the induction of ccdB expression in E. coli BL21 (DE3) inhibited bacterial growth, observed that the expression of both ccdAB and ccdB in E. coli BL21 (DE3) led to an increase in biofilm formation, and confirmed that ccdAB plays a role in the development of persistent bacteria in urinary tract infections. Our findings could pave the way for novel therapeutic approaches targeting these systems, potentially reducing the prevalence of rUTIs. Through this investigation, we hope to contribute significantly to the global effort to combat the persistent challenge of rUTIs.

Peritonitis-associated with peritoneal dialysis following Ureaplasma parvum infection: A case report and literature review.

We report a patient diagnosed with peritonitis due to a rare infection of Ureaplasma parvum after receiving peritoneal dialysis for two years. This microorganism rarely causes peritoneal dialysis-associated peritonitis (PDAP). This is the first case of PDAP caused by Ureaplasma parvum. In the present case, the pathogen was identified through next-generation sequencing of PD fluid samples. The patient was treated with intraperitoneal (IP) levofloxacin combined with vancomycin and oral clarithromycin which effectively improved her symptoms. This case creates awareness that Ureaplasma parvum can cause PDAP and can be diagnosed using next-generation sequencing(NGS).

ESBL-Producing and Non-ESBL-Producing Escherichia coli Isolates from Urinary Tract Differ in Clonal Distribution, Virulence Gene Content and Phylogenetic Group.

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.

Nanosecond Pulsed Electric Field Induces an Antitumor Effect in Triple-Negative Breast Cancer via CXCL9 Axis Dependence in Mice.

Triple-negative breast cancer (TNBC) is a refractory tumor, and therapeutic options are very limited. Local ablation has been applied recently. Chemokines play a critical role in the recruitment of immune cells into ablative tumors. Nanosecond pulsed electric field (nsPEF) shows potential anti-tumor efficacy, but the mechanism for maintaining the immune effect is not very clear. Here, we applied nsPEF for treating 4T1 breast cancer cells in vitro. RNA sequencing (RNA-seq) was applied. Anti-CXCL9 was used alone or combined with nsPEF to treat triple-negative breast cancer in mice. We demonstrated that nsPEF effectively induced cell apoptosis and inhibited the growth and metastasis of triple-negative breast cancer. An immune effect, especially chemotaxis, was activated by nsPEF. The number of infiltrated CD8+ T cells was increased significantly. We found that the inhibition of residual breast cancer growth by nsPEF was dependent on the CXCL9 axis. In conclusion, our work demonstrated that nsPEF effectively ablated the tumor, aroused an immune response, and inhibited residual breast cancer growth via CXCL9 axis dependence in mice.

MRI texture-based machine learning models for the evaluation of renal function on different segmentations: a proof-of-concept study.

BACKGROUND: To develop and validate an MRI texture-based machine learning model for the noninvasive assessment of renal function. METHODS: A 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. RESULTS: The 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. CONCLUSION: We 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.

Non-classical MHC-Ι genes in chronic hepatitis B and hepatocellular carcinoma.

The non-classical human leukocyte antigens (HLA)-E, HLA-G, and HLA-F have been shown to modulate immune responses. We examined whether non-classical HLA polymorphisms are associated with hepatitis B virus (HBV) infection and hepatocellular carcinoma (HCC). Fifteen Single-nucleotide polymorphisms (SNPs) in these non-classical class I alleles were investigated by ligase detection reaction. A fragment of 650 bp located in the 3' untranslated region of HLA-G was investigated. Four SNPs (rs17875380, rs41557518, rs114465251, and rs115492845) were associated with altered susceptibility to HBV or HCC, and HLA-F*01:04, HLA-G*01:05N, and HLA-E*01:01 were associated with hepatitis B or hepatitis B complicated with HCC. Six of 16 designated HLA-E, -G, and -F haplotypes were associated with risk of hepatitis B or HCC. Our study provides healthy reference and detailed analyses of non-classical HLA class Ι polymorphisms that provide insight into immune mechanisms involved in susceptibility to hepatitis B and HCC.

Research on correlations of miR-585 expression with progression and prognosis of triple-negative breast cancer.

Triple-negative breast cancer is a special type of breast cancer, characterized by younger onset age, shorter survival period, higher malignant degree, higher mortality, recurrence and metastasis. Triple-negative breast cancer is more harmful to women's life and health, compared with other types of breast cancer. This paper mainly studied the role of miR-585 in triple-negative breast cancer. Real-time quantitative PCR was used to detect the expression of miR-585 in triple-negative breast cancer cell lines and tissues. Kaplan-Meier curve and Cox proportional hazards model analysis were used to investigate the prognostic value of miR-585 in triple-negative breast cancer. CCK-8 and Transwell assays were used to detect cell proliferation, invasion and migration. miR-585 was significantly down-regulated in triple-negative breast cancer cells and tissues. The low expression of miR-585 has been shown to be significantly associated with poor prognosis in triple-negative breast cancer patients. Abnormally low expression of miR-585 can promote cell proliferation, migration and invasion. Overall, abnormally low expression of miR-585 is associated with prognosis and progression of triple-negative breast cancer. miR-585 may serve as a prognostic biomarker for patients with triple-negative breast cancer and it is expected to be a new method and strategy for the treatment of triple-negative breast cancer.

KIF4A promotes tumor progression of bladder cancer via CXCL5 dependent myeloid-derived suppressor cells recruitment.

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.