Emergence of an Extensive Drug Resistant Citrobacter portucalensis Clinical Strain Harboring bla SFO-1, bla KPC-2, and bla NDM-1.

Background: To explore the plasmid characteristics and transfer mechanisms of an extensive drug resistant (XDR) clinical isolate, Citrobacter portucalensis L2724hy, co-producing bla SFO-1, bla NDM-1, and bla KPC-2. Methods: Species confirmation of L2724hy was achieved through 16S rRNA sequencing and Average Nucleotide Identity (ANI) analysis. Antimicrobial susceptibility testing (AST) employed the agar dilution and micro broth dilution methods. Identification of resistance genes was carried out by PCR and whole-genome sequencing (WGS). Essential resistance gene locations were verified by S1 nuclease pulsed-field gel electrophoresis (S1-PFGE) and southern hybridization experiments. Subsequent WGS data analysis delved into drug resistance genes and plasmids. Results: The confirmation of the strain L2724hy as an extensive drug-resistant Citrobacter portucalensis, resistant to almost all antibiotics tested except polymyxin B and tigecycline, was achieved through 16S rRNA sequencing, ANI analysis and AST results. WGS and subsequent analysis revealed L2724hy carrying bla SFO-1, bla NDM-1, and bla KPC-2 on plasmids of various sizes. The uncommon ESBL gene bla SFO-1 coexists with the fosA3 gene on an IncFII plasmid, featuring the genetic environment IS26-fosA3-IS26-ampR-bla SFO-1-IS26. The bla NDM-1 was found on an IncX3 plasmid, coexisting with bla SHV-12, displaying the sequence IS5-IS3000-IS3000-Tn2-bla NDM-1-ble-trpF-dsbD-cutA-gros-groL, lacking ISAa125. The bla KPC-2 is located on an unclassified plasmid, exhibiting the sequence Tn2-tnpR-ISKpn27-bla KPC-2-ISKpn6-korC. Conjugation assays confirmed the transferability of both bla NDM-1 and bla KPC-2. Conclusion: We discovered the coexistence of bla SFO-1, bla NDM-1, and bla KPC-2 in C. portucalensis for the first time, delving into plasmid characteristics and transfer mechanisms. Our finding highlights the importance of vigilant monitoring of drug-resistance genes and insertion elements in uncommon strains.

Antioxidant peptides from edible aquatic animals: Preparation method, mechanism of action, and structure-activity relationships.

Peptides with strong antioxidant activity have been increasingly extracted from various edible aquatic animals, as natural substitutes for synthetic antioxidants. In this paper, a systematic review of the research progress on the enzymatic hydrolysis strategy and structure-activity relationship of antioxidant peptides from edible aquatic animals, especially marine animals, over the last decade was presented. The selection of enzymes varied markedly among organs and tissues. Tools and indicators used in the purification and identification process were clarified. The similarity and the difference in structure and antioxidant activity between vertebrate-derived peptides and invertebrate-derived peptides were discussed. The stability of antioxidant peptides was reviewed. Most peptides could maintain activity under mild conditions, but they hardly resisted gastrointestinal digestion. The poor ability of peptides to cross the small intestinal epithelium in prototype form brought a challenge for food and pharmaceutical applications.

Classification of Early and Late Mild Cognitive Impairment Using Functional Brain Network of Resting-State fMRI.

Using the Pearson correlation coefficient to constructing functional brain network has been evidenced to be an effective means to diagnose different stages of mild cognitive impairment (MCI) disease. In this study, we investigated the efficacy of a classification framework to distinguish early mild cognitive impairment (EMCI) from late mild cognitive impairment (LMCI) by using the effective features derived from functional brain network of three frequency bands (full-band: 0.01-0.08 Hz; slow-4: 0.027-0.08 Hz; slow-5: 0.01-0.027 Hz) at Rest. Graphic theory was performed to calculate and analyze the relationship between changes in network connectivity. Subsequently, three different algorithms [minimal redundancy maximal relevance (mRMR), sparse linear regression feature selection algorithm based on stationary selection (SS-LR), and Fisher Score (FS)] were applied to select the features of network attributes, respectively. Finally, we used the support vector machine (SVM) with nested cross validation to classify the samples into two categories to obtain unbiased results. Our results showed that the global efficiency, the local efficiency, and the average clustering coefficient were significantly higher in the slow-5 band for the LMCI-EMCI comparison, while the characteristic path length was significantly longer under most threshold values. The classification results showed that the features selected by the mRMR algorithm have higher classification performance than those selected by the SS-LR and FS algorithms. The classification results obtained by using mRMR algorithm in slow-5 band are the best, with 83.87% accuracy (ACC), 86.21% sensitivity (SEN), 81.21% specificity (SPE), and the area under receiver operating characteristic curve (AUC) of 0.905. The present results suggest that the method we proposed could effectively help diagnose MCI disease in clinic and predict its conversion to Alzheimer's disease at an early stage.