Chloroquine enhances the efficacy of chemotherapy drugs against acute myeloid leukemia by inactivating the autophagy pathway.

Current therapy for acute myeloid leukemia (AML) is largely hindered by the development of drug resistance of commonly used chemotherapy drugs, including cytarabine, daunorubicin, and idarubicin. In this study, we investigated the molecular mechanisms underlying the chemotherapy drug resistance and potential strategy to improve the efficacy of these drugs against AML. By analyzing data from ex vivo drug-response and multi-omics profiling public data for AML, we identified autophagy activation as a potential target in chemotherapy-resistant patients. In THP-1 and MV-4-11 cell lines, knockdown of autophagy-regulated genes ATG5 or MAP1LC3B significantly enhanced AML cell sensitivity to the chemotherapy drugs cytarabine, daunorubicin, and idarubicin. In silico screening, we found that chloroquine phosphate mimicked autophagy inactivation. We showed that chloroquine phosphate dose-dependently down-regulated the autophagy pathway in MV-4-11 cells. Furthermore, chloroquine phosphate exerted a synergistic antitumor effect with the chemotherapy drugs in vitro and in vivo. These results highlight autophagy activation as a drug resistance mechanism and the combination therapy of chloroquine phosphate and chemotherapy drugs can enhance anti-AML efficacy.

Design of the sparrow search algorithm (SSA) for airborne radioactive hotspot detection.

In the context of using aircraft as a pivotal tool for detecting radioactive hotspots, the acquisition of radioactivity data was conducted through a CeBr3 scintillation crystal detector mounted on a helicopter. However, challenges arose, including managing extensive data volumes, computationally demanding tasks, and susceptibility to local optima issues. To address these challenges and leverage the benefits of the Sparrow Search Algorithm (SSA) in global optimization and convergence speed, an improved SSA was devised. This improved version integrated SSA principles with the intricacies of searching for radioactive hotspots. The algorithm employed a matrix segmentation method to process data matrices derived from measured data, aiming to enhance efficiency and accuracy. An empirical analysis was conducted, performing 100 iterations on an experimental matrix to scrutinize the impact of matrix segmentation. Computation times and results were compared across different segmentation levels, confirming the favorable algorithmic outcomes of the method. The practical viability and convergence stability of the algorithm were further assessed using genuine measured data, with segmented matrices generated for evaluation. Remarkably, a comparison between computational outcomes and manually identified data reaffirmed the algorithm's reliability in effectively detecting radioactive hotspots.