Identification of Two Novel Carbapenemase-Encoding Hybrid Plasmids Harboring blaNDM-5 and blaKPC-2 in a Clinical ST11-KL47 Klebsiella pneumoniae.

Background: Emergence of blaKPC and blaNDM co-harboring Klebsiella pneumoniae has escalated the threat of Carbapenem-resistant Klebsiella pneumoniae (CRKP) to healthcare. It remains unknown the prevalence and molecular characteristics of CRKP co-producing KPC and NDMs carbapenemases in Henan. Methods and Results: Twenty-seven CRKP strains isolated from different times were selected randomly in affiliated cancer hospital of Zhengzhou University from January 2019 to January 2021, among which one KPC-2 and NDM-5 positive CRKP named K9 was isolated from an abdominal pus sample of a 63-year-old male patient with leukemia. Sequencing of K9 determined that K9 belonged to ST11-KL47, which is resistant to antibiotics such as meropenem, ceftazidime-avibactam and tetracycline. K9 carried two different plasmids that contained blaNDM-5 and blaKPC-2. Both plasmids were shown to be novel hybrid plasmids and IS26 played an important role in generation of two plasmids. Gene blaKPC-2 was flanked by the NTEKPC-Ib-like genetic structure (IS26-ΔTn3-ISKpn8-blaKPC-2-ISKpn6-IS26) and was located on a conjugative IncFII/R/N type hybrid plasmid. Conclusion: The resistance gene blaNDM-5 located on a region organized as IS26-blaNDM-5-ble-trpF-dsbD-ISCR1-sul1-aadA2-dfrA12-IntI1-IS26 was carried by a phage-plasmid. We described a clinical CRKP co-producing KPC-2 and NDM-5 and emphasized an urgent need to control their further spread.

Establishment and validation of a novel prognostic model for non-virus-related hepatocellular carcinoma.

OBJECTIVE: The incidence of non-virus-related hepatocellular carcinoma (NV-HCC) in hepatocellular carcinoma (HCC) is steadily increasing. The aim of this study was to establish a prognostic model to evaluate the overall survival (OS) of NV-HCC patients. METHODS: Overall, 261 patients with NV-HCC were enrolled in this study. A prognostic model was developed by using LASSO-Cox regression analysis. The prognostic power was appraised by the concordance index (C-index), and the time-dependent receiver operating characteristic curve (TD-ROC). Kaplan-Meier (K-M) survival analysis was used to evaluate the predictive ability in the respective subgroups stratified by the prognostic model risk score. A nomogram for survival prediction was established by integrating the prognostic model, TNM stage, and treatment. RESULTS: According to the LASSO-Cox regression results, the number of nodules, lymphocyte-to-monocyte ratio (LMR), prognostic nutritional index (PNI), alkaline phosphatase (ALP), aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio (SLR) and C-reactive protein (CRP) were included for prognostic model construction. The C-index of the prognostic model was 0.759 (95% CI 0.723-0.797) in the development cohort and 0.796 (95% CI 0.737-0.855) in the validation cohort, and its predictive ability was better than TNM stage and treatment. The TD-ROC showed similar results. K-M survival analysis showed that NV-HCC patients with low risk scores had a better prognosis (P < 0.05). A nomogram based on the prognostic model, TNM stage, and treatment was constructed with sufficient discriminatory power with C-indexes of 0.78 and 0.85 in the development and validation cohort, respectively. CONCLUSION: For NV-HCC, this prognostic model could predict an OS benefit for patients, which may assist clinicians in designing individualized therapeutic strategies.

A novel clinical model for predicting malignancy of solitary pulmonary nodules: a multicenter study in chinese population.

BACKGROUND: This study aimed to establish and validate a novel clinical model to differentiate between benign and malignant solitary pulmonary nodules (SPNs). METHODS: Records from 295 patients with SPNs in Sun Yat-sen University Cancer Center were retrospectively reviewed. The novel prediction model was established using LASSO logistic regression analysis by integrating clinical features, radiologic characteristics and laboratory test data, the calibration of model was analyzed using the Hosmer-Lemeshow test (HL test). Subsequently, the model was compared with PKUPH, Shanghai and Mayo models using receiver-operating characteristics curve (ROC), decision curve analysis (DCA), net reclassification improvement index (NRI), and integrated discrimination improvement index (IDI) with the same data. Other 101 SPNs patients in Henan Tumor Hospital were used for external validation cohort. RESULTS: A total of 11 variables were screened out and then aggregated to generate new prediction model. The model showed good calibration with the HL test (P = 0.964). The AUC for our model was 0.768, which was higher than other three reported models. DCA also showed our model was superior to the other three reported models. In our model, sensitivity = 78.84%, specificity = 61.32%. Compared with the PKUPH, Shanghai and Mayo models, the NRI of our model increased by 0.177, 0.127, and 0.396 respectively, and the IDI changed - 0.019, -0.076, and 0.112, respectively. Furthermore, the model was significant positive correlation with PKUPH, Shanghai and Mayo models. CONCLUSIONS: The novel model in our study had a high clinical value in diagnose of MSPNs.

Establishment and Validation of Nomogram Model Integrated With Inflammation-Based Factors for the Prognosis of Advanced Non-Small Cell Lung Cancer.

OBJECTS: Inflammation is one of the hallmarks of cancer. Tumor-associated inflammatory response plays a crucial role in enhancing tumorigenesis. This study aimed to establish an effective predictive nomogram based on inflammation factors in patients with advanced non-small cell lung cancer (NSCLC). METHODS: We retrospectively evaluated 887 patients with advanced NSCLC between November 2004 and December 2015 and randomly divided them into primary (n = 520) and validation cohorts (n = 367). Cox regression analysis was used to identify prognostic factors for building the nomogram. The predictive accuracy and discriminative ability of the nomogram were determined using a concordance index (C-index), calibration plot, and decision curve analysis and were compared to the TNM staging system. RESULTS: The nomogram was established using independent risk factors (P < 0.05): age, TNM stage, C reaction protein-to-albumin ratio (CAR), and neutrophils (NEU). The C-index of the model for predicting OS had a superior discrimination power compared to that of the TNM staging system both in the primary [0.711 (95% CI: 0.675-0.747) vs 0.531 (95% CI: 0.488-0.574), P < 0.01] and validation cohorts [0.703, 95% CI: 0.671 -0.735 vs 0.582, 95% CI: 0.545-0.619, P < 0.01]. Decision curves also demonstrated that the nomogram had higher overall net benefits than that of the TNM staging system. Subgroup analyses revealed that the nomogram was a favorable prognostic parameter in advanced NSCLC (P < 0.05). The results were internally validated using the validation cohorts. CONCLUSIONS: The proposed nomogram with inflammatory factors resulted in an accurate prognostic prediction in patients with advanced NSCLC.