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Abstract The resistance to antibiotics in Gram-negative bacilli causing sepsis is a warning sign of failure of therapy. Klebsiella pneumoniae (K. pneumoniae) and Escherichia coli (E. coli) represent major Gram-negative bacilli associated with sepsis. Quinolone resistance is an emerging resistance among E. coli and K. pneumoniae. Therefore, the present study aimed to study the presence of plasmid-mediated quinolone resistance (PMQR) genes qnrA, qnrB, and qnrS by polymerase chain reaction (PCR) in E. coli and K. pneumoniae isolated from pediatric patients with sepsis. This was a retrospective cross-sectional study that included pediatric patients with healthcare-associated sepsis. The E. coli and K. pneumoniae isolates were identified by microbiological methods. PMQR genes namely qnrA, qnrB, and qnrS were detected in E. coli and K. pneumoniae isolates by PCR. The results were analyzed by SPPS24, and the qualitative data was analyzed as numbers and percentages and comparison was performed by Chi-square test, P was significant if

Multiple myeloma (MM) is a prevalent yet incurable hematologic malignancy. Despite the proven efficacy of proteasome inhibitors in treating MM, resistance to Bortezomib-based treatments persists in a subset of patients. This case control study explores the potential of circulating endothelial progenitor cells (EPCs) as biomarkers for predicting response to Proteasome Inhibitor based therapy combined with Dexamethasone in MM patients. This study was conducted on 105 MM patients receiving bortezomib plus dexamethasone therapy and 90 healthy individuals as a control group. Utilizing 8-color multi-parameter flow cytometry, we assessed the levels of circulating EPCs, identified through CD34 FITC and CD309 PE markers at diagnosis and after one treatment cycle (4 weeks). Our findings revealed that patients exhibiting poor response to therapy showed significantly higher CD34/CD309 values than those with a good response (p < 0.001). The delineation of response based on CD34/CD309 expression was established with a cutoff ≤ 0.9 for percentage (yielding 100% sensitivity and 94.1% specificity) and ≤ 12.5 for absolute value (also with 100% sensitivity and 94.1% specificity). These results underscore the potential of EPC population levels, as quantified by CD34/CD309, to serve as a predictive biomarker for immunomodulatory treatment in MM patients undergoing Proteasome Inhibitor and Dexamethasone therapy. [ABSTRACT FROM AUTHOR]

Recently, the design and implementation of new healthcare systems have gained an interest in both industry and academia. The amalgamation between the Internet of Things, cloud, edge computing, and big data helps the proliferation of new scenarios for smart medical services and applications. Deep learning is currently paying a lot of attention for its utilization with big healthcare data. To this end, the main objective of this study is to propose a Deep Learning H2O (DLH2O) framework for improving the performance and selection of the optimal features to predict emergency cases. The proposed DLH2O framework consists of data preprocessing layer, feature selection layer and deep learning layer. The DLH2O framework aims to find the optimal subset of features and minimize the error of the classification through a proposed new variant of the Whale Optimization Algorithm (WOA) called ACP-WOA. The proposed changes have been done on the following parameters a, a2, A, and C which should affect both exploration and exploitation of WOA. The experiments conducted in order to test the validity of DLH2O Framework. In regard to the datasets, five experiments are performed for this purpose. The results demonstrate the superiority of ACP-WOA compared to the other state-of-the-art meta-heuristic algorithms in terms of time, error, and scalability. The proposed ACP-WOA is also tested on CEC2017 benchmark functions and proves its superiority over WOA in terms of accuracy.

In machine learning, datastream prediction is a challenging issue, particularly when dealing with enormous amounts of continuous data. The dynamic nature of data makes it difficult for traditional models to handle and sustain real-time prediction accuracy. This research uses a multi-processor long short-term memory (MPLSTM) architecture to present a unique framework for datastream regression. By employing several central processing units (CPUs) to divide the datastream into multiple parallel chunks, the MPLSTM framework illustrates the intrinsic parallelism of long short-term memory (LSTM) networks. The MPLSTM framework ensures accurate predictions by skillfully learning and adapting to changing data distributions. Extensive experimental assessments on real-world datasets have demonstrated the clear superiority of the MPLSTM architecture over previous methods. This study uses the transformer, the most recent deep learning breakthrough technology, to demonstrate how well it can handle challenging tasks and emphasizes its critical role as a cutting-edge approach to raising the bar for machine learning. [ABSTRACT FROM AUTHOR]