Deep learning enhanced Rydberg multifrequency microwave recognition.

Recognition of multifrequency microwave (MW) electric fields is challenging because of the complex interference of multifrequency fields in practical applications. Rydberg atom-based measurements for multifrequency MW electric fields is promising in MW radar and MW communications. However, Rydberg atoms are sensitive not only to the MW signal but also to noise from atomic collisions and the environment, meaning that solution of the governing Lindblad master equation of light-atom interactions is complicated by the inclusion of noise and high-order terms. Here, we solve these problems by combining Rydberg atoms with deep learning model, demonstrating that this model uses the sensitivity of the Rydberg atoms while also reducing the impact of noise without solving the master equation. As a proof-of-principle demonstration, the deep learning enhanced Rydberg receiver allows direct decoding of the frequency-division multiplexed signal. This type of sensing technology is expected to benefit Rydberg-based MW fields sensing and communication.

Nano-realgar suppresses lung cancer stem cell growth by repressing metabolic reprogramming.

BACKGROUND: Recent studies in cancer biology suggest that metabolic glucose reprogramming is a potential target for cancer treatment. However, little is known about drug intervention in the glucose metabolism of cancer stem cells (CSCs) and its related underlying mechanisms. METHODS: The crude realgar powder was Nano-grinded to meets the requirements of Nano-pharmaceutical preparations, and Nano-realgar solution (NRS) was prepared for subsequent experiments. Isolation and characterization of lung cancer stem cells (LCSCs) was performed by magnetic cell sorting (MACS) and immunocytochemistry, respectively. Cell viability and intracellular glucose concentration were detected by MTT assay and glucose oxidase (GOD) kit. Protein expressions related to metabolic reprogramming was detected by ELISA assay. Determination of the expression of HIF-1α and PI3K/Akt/mTOR pathways was carried out by RT-PCR and western blotting analysis. A subcutaneous tumor model in BALB/c-nu mice was successfully established to evaluate the effects of Nano-realgar on tumor growth and histological structure, and the expression of HIF-1α in tumor tissues was measured by immunofluorescence. RESULTS: Nano-realgar inhibits cell viability and induces glucose metabolism in LCSCs, and inhibits protein expression related to metabolic reprogramming in a time- and dose-dependent manner. Nano-realgar downregulated the expression of HIF-1α and PI3K/Akt/mTOR pathways in vitro and in vivo. Nano-realgar inhibits tumor growth and changes the histological structure of tumors through in vivo experiments and consequently inhibits the constitutive activation of HIF-1α signaling. CONCLUSIONS: These results reveal that Nano-realgar inhibits tumor growth in vitro and in vivo by repressing metabolic reprogramming. This inhibitory effect potentially related to the downregulation HIF-1α expression via PI3K/Akt/mTOR pathway.

Prognostic Value of MicroRNA-15a in Human Cancers: A Meta-Analysis and Bioinformatics.

BACKGROUND: Although several studies have proved the relationship between the prognostic value of miRNA-15a and different types of cancer, the result remains controversial. Thus, a meta-analysis was conducted to clarify the prognostic value of miRNA-15a expression level in human cancers. METHODS: We enrolled appropriate literature by searching the databases of PubMed, Embase, and Web of Science. Subsequently, we extracted HRs and their 95% CIs and calculated pooled results of miRNA-15a for overall survival (OS) and disease-free survival (DFS). Besides, subgroup analysis, sensitivity analysis, and publication bias were also revealed in this study. We also further validated this meta-analysis using the Kaplan-Meier plotter database. RESULT: 10 studies, including 1616 patients, were embraced in our meta-analysis. The result showed the lower expression of miRNA-15a significantly predicted adverse OS (HR=2.17, 95% CI: 1.41-3.34), but there is no significant association between the expressing level and DFS in cancer patient (HR=2.04, 95% CI: 0.60-6.88). Based on Kaplan-Meier plotter database, we found the same results in bladder Carcinoma, head-neck squamous cell carcinoma, liver hepatocellular carcinoma, lung squamous cell carcinoma, pancreatic ductal adenocarcinoma, rectum adenocarcinoma, stomach adenocarcinoma, and uterine corpus endometrial carcinoma, but opposite results were found in cervical squamous cell carcinoma and esophageal carcinoma. CONCLUSION: Low expressing levels of miRNA-15a indicated poor OS, while miRNA-15a can be used as a prediction biomarker in different cancer types.