The functional role of cellular senescence during vascular calcification in chronic kidney disease.

Vascular calcification (VC) has emerged as a key predictor of cardiovascular events in patients with chronic kidney disease (CKD). In recent years, an expanding body of research has put forth the concept of accelerated vascular aging among CKD patients, highlighting the significance of vascular cells senescence in the process of VC. Within the milieu of uremia, senescent vascular endothelial cells (VECs) release extracellular microvesicles (MV) that promote vascular smooth muscle cells (VSMCs) senescence, thereby triggering the subsequent osteogenic phenotypic switch and ultimately contributing to the VC process. In addition, senescent vascular progenitor or stem cells with diminished ability to differentiate into VECs and VSMCS, compromise the repair of vascular integrity, on the other hand, release a cascade of molecules associated with senescence, collectively known as the senescence-associated secretory phenotype (SASP), perpetuating the senescence phenomenon. Furthermore, SASP triggers the recruitment of monocytes and macrophages, as well as adjacent VECs and VSMCs into a pro-adhesive and pro-inflammatory senescent state. This pro-inflammatory microenvironment niche not only impacts the functionality of immune cells but also influences the differentiation of myeloid immune cells, thereby amplifying the reduced ability to effectively clear senescent cells of senescent macrophages, promoted calcification of VSMCs. The objective of this paper is to provide a comprehensive review of the contribution of vascular cell senescence to the emergence and advancement of VC. Gaining a comprehensive understanding of the involvement of cellular senescence within the vessel wall is pivotal, especially when it comes to its intersection with VC. This knowledge is essential for advancing groundbreaking anti-aging therapies, aiming to effectively mitigate cardiovascular diseases.

Rapid, Label-Free Prediction of Antibiotic Resistance in Salmonella typhimurium by Surface-Enhanced Raman Spectroscopy.

The rapid identification of bacterial antibiotic susceptibility is pivotal to the rational administration of antibacterial drugs. In this study, cefotaxime (CTX)-derived resistance in Salmonella typhimurium (abbr. CTXr-S. typhimurium) during 3 months of exposure was rapidly recorded using a portable Raman spectrometer. The molecular changes that occurred in the drug-resistant strains were sensitively monitored in whole cells by label-free surface-enhanced Raman scattering (SERS). Various degrees of resistant strains could be accurately discriminated by applying multivariate statistical analyses to bacterial SERS profiles. Minimum inhibitory concentration (MIC) values showed a positive linear correlation with the relative Raman intensities of I990/I1348, and the R2 reached 0.9962. The SERS results were consistent with the data obtained by MIC assays, mutant prevention concentration (MPC) determinations, and Kirby-Bauer antibiotic susceptibility tests (K-B tests). This preliminary proof-of-concept study indicates the high potential of the SERS method to supplement the time-consuming conventional method and help alleviate the challenges of antibiotic resistance in clinical therapy.

The Diagnostic Value of Procalcitonin Versus Other Biomarkers in Prediction of Bloodstream Infection.

BACKGROUND: We compared the diagnostic utility of procalcitonin (PCT), C-reactive protein (CRP), and hematological markers, including white blood cell count (WBC), neutrophils (NEU), percentage of neutrophils (NEU%), lymphocytes (LYM), neutrophil-lymphocyte count ratio (NLCR), and platelet count (PLT) for predicting bloodstream infection (BSI), which was confirmed by blood culture (BC). METHODS: A retrospective analysis was conducted for 1807 inpatients. The level of PCT, CRP, blood cells, and blood culture results were compared between the positive blood culture group and negative blood culture group; each indicator was analyzed in the performance of bacterial BSI diagnosis by drawing ROC curves. RESULTS: Blood cultures were positive in 230 patients; hence, the prevalence of bacteremia was 12.7%. There were significant differences in the median value for each marker between positive group BCs and negative group BCs (p < 0.05). The areas under the receiver operating characteristic curves (ROC-AUCs) of PCT, CRP, WBC, NEU, NUE%, LYM, NLCR, and PLT for discriminating positive BCs from negative BCs were 0.811, 0.654, 0.612, 0.634, 0.684, 0.595, 0.682, and 0.633 respectively. PCT concentrations of gram-negative (14.94 ng/mL, IQR 2.93  48.76) were significantly higher than gram-positive (4.74 ng/mL, IQR 1.22  17.5) and fungal (1.47 ng/mL, IQR 0.66  35.34). CONCLUSIONS: PCT proved to be the most reliable predictor of BSI, second were NEU% and NLCR. A higher PCT level was found in patients with a gram-negative BSI compared to gram-positive BSI and fungal BSI.

Overexpression of MexAB-OprM efflux pump in carbapenem-resistant Pseudomonas aeruginosa.

Efflux pump systems are one of the most important mechanisms conferring multidrug resistance in Pseudomonas aeruginosa. MexAB-OprM efflux pump is one of the largest multi-drug resistant efflux pumps with high-level expression, which is controlled by regulatory genes mexR, nalC, and nalD. This study investigated the role of efflux pump MexAB-OprM in 75 strains of carbapenem-resistant P. aeruginosa and evaluated the influence of point mutation of the regulatory genes. The minimum inhibitory concentrations of imipenem and meropenem, with or without MC207110, an efflux pump inhibitor, were determined by agar dilution method to select the positive strains for an overexpressed active efflux pump. Carba NP test and EDTA-disk synergy test were used for the detection of carbapenemase and metallo-ß-lactamases, respectively. The gene mexA, responsible for the fusion protein structure, and the reference gene rpoD of the MexAB-OprM pump were amplified by real-time PCR. The quantity of relative mRNA expression was determined simultaneously. By PCR method, the efflux regulatory genes mexR, nalC, and nalD and outer membrane protein OprD2 were amplified for the strains showing overexpression of MexAB-OprM and subsequently analyzed by BLAST. Among the 75 P. aeruginosa strains, the prevalence of efflux pump-positive phenotype was 17.3 % (13/75). Carba NP test and EDTA-disk synergy test were all negative in the 13 strains. PCR assay results showed that ten strains overexpressed the MexAB-OprM efflux pump and were all positive for the regulatory genes mexR, nalC, and nalD. Sequence analysis indicated that of the ten isolates, nine had a mutation (Gly â†’ Glu) at 71st amino acid position in NalC, and eight also had a mutation (Ser â†’ Arg) at 209th position in NalC. Only one strain had a mutation (Thr â†’ Ile) at the 158th amino acid position in NalD, whereas eight isolates had mutations in MexR. In conclusion, overexpression of efflux pump MexAB-OprM plays an important role in carbapenem-resistant P. aeruginosa. The mutations of regulatory genes may be a main factor contributing to overexpression of MexAB-OprM.

A semi-supervised learning based method: Laplacian support vector machine used in diabetes disease diagnosis.

Pattern recognition methods could be of great help to disease diagnosis. In this study, a semi-supervised learning based method, Laplacian support vector machine (LapSVM), was used in diabetes diseases prediction. The diabetes disease dataset used in this article is Pima Indians diabetes dataset obtained from the UCI Repository of Machine Learning Databases and all patients in the dataset are females at least 21 years old of Pima Indian heritage. Firstly, LapSVM was trained as a fully-supervised learning classifier to predict diabetes dataset and 79.17% accuracy was obtained. Then, it was trained as a semi-supervised learning classifier and we got the prediction accuracy 82.29%. The obtained accuracy 82.29% is higher than other previous reports. The experiments led to the finding that LapSVM offers a very promising application, i.e., LapSVM can be used to solve a fully-supervised learning problem by solving a semi-supervised learning problem. The result suggests that LapSVM can be of great help to physicians in the process of diagnosing diabetes disease and it could be a very promising method in the situations where a lot of data are not class-labeled.