Characterization of a novel circular bacteriocin from Bacillus velezensis 1-3, and its mode of action against Listeria monocytogenes.

In this study, isolate Bacillus velezensis1-3 was selected out for its anti- Listeria potency, from which a novel circular bacteriocin, velezin, was purified out of the fermentate, and then characterized. Facilitated with a broad antibacterial spectrum, velezin has demonstrated decent inhibitive activity against of foodborne pathogen L. monocytogenes ATCC 19115. It exerted the antibacterial activity through damaging the membrane integrity of targeted cell and causing leakage of vital elements, including K+ ion. It was noteworthy that velezin also inhibited the biofilm formation by L. monocytogenes ATCC 19115. At the challenge of velezin, L. monocytogenes ATCC 19115 up-regulated expression of genes associated with membrane, ion transporters, stressing-related proteins as well as the genes responsible for the synthesis of small molecule. Taken together, velezin may have potential to be a candidate as natural additive used in food/feed in the future.

Multimodal data integration for predicting progression risk in castration-resistant prostate cancer using deep learning: a multicenter retrospective study.

Purpose: Patients with advanced prostate cancer (PCa) often develop castration-resistant PCa (CRPC) with poor prognosis. Prognostic information obtained from multiparametric magnetic resonance imaging (mpMRI) and histopathology specimens can be effectively utilized through artificial intelligence (AI) techniques. The objective of this study is to construct an AI-based CRPC progress prediction model by integrating multimodal data. Methods and materials: Data from 399 patients diagnosed with PCa at three medical centers between January 2018 and January 2021 were collected retrospectively. We delineated regions of interest (ROIs) from 3 MRI sequences viz, T2WI, DWI, and ADC and utilized a cropping tool to extract the largest section of each ROI. We selected representative pathological hematoxylin and eosin (H&E) slides for deep-learning model training. A joint combined model nomogram was constructed. ROC curves and calibration curves were plotted to assess the predictive performance and goodness of fit of the model. We generated decision curve analysis (DCA) curves and Kaplan-Meier (KM) survival curves to evaluate the clinical net benefit of the model and its association with progression-free survival (PFS). Results: The AUC of the machine learning (ML) model was 0.755. The best deep learning (DL) model for radiomics and pathomics was the ResNet-50 model, with an AUC of 0.768 and 0.752, respectively. The nomogram graph showed that DL model contributed the most, and the AUC for the combined model was 0.86. The calibration curves and DCA indicate that the combined model had a good calibration ability and net clinical benefit. The KM curve indicated that the model integrating multimodal data can guide patient prognosis and management strategies. Conclusion: The integration of multimodal data effectively improves the prediction of risk for the progression of PCa to CRPC.

Multiparametric MRI radiomics in prostate cancer for predicting Ki-67 expression and Gleason score: a multicenter retrospective study.

PURPOSE: Prostate cancer (PCa) with high Ki-67 expression and high Gleason Scores (GS) tends to have aggressive clinicopathological characteristics and a dismal prognosis. In order to predict the Ki-67 expression status and the GS in PCa, we sought to construct and verify MRI-based radiomics signatures. METHODS AND MATERIALS: We collected T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC) images from 170 PCa patients at three institutions and extracted 321 original radiomic features from each image modality. We used support vector machine (SVM) and least absolute shrinkage and selection operator (LASSO) logistic regression to select the most informative radiomic features and built predictive models using up sampling and feature selection techniques. Using receiver operating characteristic (ROC) analysis, the discriminating power of this feature was determined. Subsequent decision curve analysis (DCA) assessed the clinical utility of the radiomic features. The Kaplan-Meier (KM) test revealed that the radiomics-predicted Ki-67 expression status and GS were prognostic factors for PCa survival. RESULT: The hypothesized radiomics signature, which included 15 and 9 selected radiomics features, respectively, was significantly correlated with pathological Ki-67 and GS outcomes in both the training and validation datasets. Areas under the curve (AUC) for the developed model were 0.813 (95% CI 0.681,0.930) and 0.793 (95% CI 0.621, 0.929) for the training and validation datasets, respectively, demonstrating discrimination and calibration performance. The model's clinical usefulness was verified using DCA. In both the training and validation sets, high Ki-67 expression and high GS predicted by radiomics using SVM models were substantially linked with poor overall survival (OS). CONCLUSIONS: Both Ki-67 expression status and high GS correlate with PCa patient survival outcomes; therefore, the ability of the SVM classifier-based model to estimate Ki-67 expression status and the Lasso classifier-based model to assess high GS may enhance clinical decision-making.

Upregulation of lncRNA LANCL1-AS1 inhibits the progression of non-small-cell lung cancer via the miR-3680-3p/GMFG axis.

Patients with non-small-cell lung cancer (NSCLC) have a low survival rate. Long non-coding RNA (LncRNA) LANCL1 antisense RNA 1 (LANCL1-AS1) was indicated to be downregulated in NSCLC; however, its detailed function in NSCLC is unanswered. Real-time quantitative polymerase chain reaction revealed the downregulation of LANCL1-AS1 in NSCLC cell lines and subcellular fractionation assay showed that LANCL1-AS1 was mainly located in the cytoplasm of NSCLC cells. Cell counting kit-8, Transwell, and tube formation assays displayed that overexpression of LANCL1-AS1 suppressed NSCLC cell proliferation, migration, invasiveness, and angiogenesis in vitro. Animal experiments validated the tumor-suppressive role of LANCL1-AS1 in tumor-bearing mice. Mechanistically, LANCL1-AS1 upregulated glia maturation factor gamma (GMFG) expression by competitively binding to miR-3680-3p. GMFG knockdown reversed LANCL1-AS1 overexpression-mediated inhibitory impact on NSCLC malignant behaviors. Collectively, LANCL1-AS1 upregulation inhibits the progression of NSCLC by modulating the miR-3680-3p/GMFG axis.

Agarose oligosaccharide- silver nanoparticle- antimicrobial peptide- composite for wound dressing.

Marine polysaccharides or oligosaccharides have potential to promote wound healing due to their biocompatibility and physicochemical properties. However, microbial infection delays wound healing process, and novel antimicrobial wound dressings are urgently needed. Here, agarose oligosaccharides (AGO) obtained from marine red algae were used as a reducing and stabilizer for green synthesis of silver nanoparticles (AgNPs), and further successfully connected with odorranain A (OA), one of antimicrobial peptides (AMPs), to obtain a novel composite nanomaterial (AGO-AgNPs-OA). Transmission electron microscopy (TEM) and Malvern particle size analyzer showed that AGO-AgNPs-OA was spherical or elliptic with average size of about 100 nm. Circular dichroism (CD) spectroscopy showed that AGO-AgNPs stabilized the α-helical structure of OA. AGO-AgNPs-OA showed stronger anti-bacterial activities than AGO-AgNPs, and had good biocompatibility and significant promoting effect on wound healing. Our data suggest that AMPs conjugated marine oligosaccharides and AgNPs may be effective and safe antibacterial materials for wound therapy.

Alginate-chitosan oligosaccharide-ZnO composite hydrogel for accelerating wound healing.

Moist, breathable and antibacterial microenvironment can promote cell proliferation and migration, which is beneficial to wound healing. Here, we fabricated a novel sodium alginate-chitosan oligosaccharide­zinc oxide (SA-COS-ZnO) composite hydrogel by spontaneous Schiff base reaction, using aldehydated sodium alginate (SA), chitosan oligosaccharide (COS), and zinc oxide (ZnO) nanoparticles, which can provide a moist and antibacterial environment for wound healing. The porosity and swelling degree of SA-COS-ZnO hydrogel are 80% and 150%, respectively, and its water vapor permeability is 682 g/m2/24h. The composite hydrogel showed good biocompatibility to blood cells, 3T3 cells, and 293T cells, and significant antibacterial activity against Escherichia coli, Staphylococcus aureus, Candida albicans, and Bacillus subtilis. Moreover, the hydrogel showed a promoting effect on wound healing in a rat scald model. The present study suggests that marine carbohydrates composite hydrogels are promising in wound care management.