Exploring the Nature of the Antimicrobial Metabolites Produced by Paenibacillus ehimensis Soil Isolate MZ921932 Using a Metagenomic Nanopore Sequencing Coupled with LC-Mass Analysis

The continuous emergence of multidrug-resistant (MDR) pathogens poses a global threat to public health. Accordingly, global efforts are continuously conducted to find new approaches to infection control by rapidly discovering antibiotics, particularly those that retain activities against MDR pathogens. In this study, metagenomic nanopore sequence analysis coupled with spectroscopic methods has been conducted for rapid exploring of the various active metabolites produced by Paenibacillus ehimensis soil isolate. Preliminary soil screening resulted in selection of a Gram-positive isolate identified via 16S ribosomal RNA gene sequencing as Paenibacillus ehimensis MZ921932. The isolate showed a broad range of activity against MDR Gram-positive, Gram-negative, and Candida spp. A metagenomics sequence analysis of the soil sample harboring Paenibacillus ehimensis isolate MZ921932 (NCBI GenBank accession PRJNA785410) revealed the presence of conserved biosynthetic gene clusters of petrobactin, tridecaptin, locillomycin (β-lactone), polymyxin, and macrobrevin (polyketides). The liquid chromatography/mass (LC/MS) analysis of the Paenibacillus ehimensis metabolites confirmed the presence of petrobactin, locillomycin, and macrobrevin. In conclusion, Paenibacillus ehimensis isolate MZ921932 is a promising rich source for broad spectrum antimicrobial metabolites. The metagenomic nanopore sequence analysis was a rapid, easy, and efficient method for the preliminary detection of the nature of the expected active metabolites. LC/MS spectral analysis was employed for further confirmation of the nature of the respective active metabolites

Enhanced Multiple Speakers’ Separation and Identification for VOIP Applications Using Deep Learning

Institutions have been adopting work/study-from-home programs since the pandemic began. They primarily utilise Voice over Internet Protocol (VoIP) software to perform online meetings. This research introduces a new method to enhance VoIP calls experience using deep learning. In this paper, integration between two existing techniques, Speaker Separation and Speaker Identification (SSI), is performed using deep learning methods with effective results as introduced by state-of-the-art research. This integration is applied to VoIP system application. The voice signal is introduced to the speaker separation and identification system to be separated; then, the “main speaker voice” is identified and verified rather than any other human or non-human voices around the main speaker. Then, only this main speaker voice is sent over IP to continue the call process. Currently, the online call system depends on noise cancellation and call quality enhancement. However, this does not address multiple human voices over the call. Filters used in the call process only remove the noise and the interference (de-noising speech) from the speech signal. The presented system is tested with up to four mixed human voices. This system separates only the main speaker voice and processes it prior to the transmission over VoIP call. This paper illustrates the algorithm technologies integration using DNN, and voice signal processing advantages and challenges, in addition to the importance of computing power for real-time applications

Exploring the Nature of the Antimicrobial Metabolites Produced by <i>Paenibacillus ehimensis</i> Soil Isolate MZ921932 Using a Metagenomic Nanopore Sequencing Coupled with LC-Mass Analysis

The continuous emergence of multidrug-resistant (MDR) pathogens poses a global threat to public health. Accordingly, global efforts are continuously conducted to find new approaches to infection control by rapidly discovering antibiotics, particularly those that retain activities against MDR pathogens. In this study, metagenomic nanopore sequence analysis coupled with spectroscopic methods has been conducted for rapid exploring of the various active metabolites produced by Paenibacillus ehimensis soil isolate. Preliminary soil screening resulted in selection of a Gram-positive isolate identified via 16S ribosomal RNA gene sequencing as Paenibacillus ehimensis MZ921932. The isolate showed a broad range of activity against MDR Gram-positive, Gram-negative, and Candida spp. A metagenomics sequence analysis of the soil sample harboring Paenibacillus ehimensis isolate MZ921932 (NCBI GenBank accession PRJNA785410) revealed the presence of conserved biosynthetic gene clusters of petrobactin, tridecaptin, locillomycin (β-lactone), polymyxin, and macrobrevin (polyketides). The liquid chromatography/mass (LC/MS) analysis of the Paenibacillus ehimensis metabolites confirmed the presence of petrobactin, locillomycin, and macrobrevin. In conclusion, Paenibacillus ehimensis isolate MZ921932 is a promising rich source for broad spectrum antimicrobial metabolites. The metagenomic nanopore sequence analysis was a rapid, easy, and efficient method for the preliminary detection of the nature of the expected active metabolites. LC/MS spectral analysis was employed for further confirmation of the nature of the respective active metabolites

Novel insights into the synergistic effects of selenium nanoparticles and metformin treatment of letrozole - induced polycystic ovarian syndrome: targeting PI3K/Akt signalling pathway, redox status and mitochondrial dysfunction in ovarian tissue

ABSTRACTPurpose Polycystic ovary syndrome (PCOS) has a series of reproductive and metabolic consequences. Although the link between PCOS, IR, and obesity, their impact on the pathogenesis of PCOS has yet to be determined. Dysfunction of PI3K/AKT pathway has been reported as the main cause of IR in PCOS. This study purposed to explore the effects of selenium nanoparticles (SeNPs) alone and combined with metformin (MET) in a PCOS-IR rat model.Methods After 3 weeks of treatment with SeNPs and/or MET, biochemical analysis of glycemic & lipid profiles, and serum reproductive hormones was performed. Inflammatory, oxidative stress, and mitochondrial dysfunction markers were determined colormetrically. The expression of PI3K and Akt genes were evaluated by Real-time PCR. Histopathological examination and Immunohistochemical analysis of Ki-67 expression were performed.Results The results showed that treatment with SeNPs and/or MET significantly attenuated insulin sensitivity, lipid profile, sex hormones levels, inflammatory, oxidative stress and mitochondrial functions markers. Additionally, PI3K and Akt genes expression were significantly upregulated with improved ovarian histopathological changes.Conclusion Combined SeNPs and MET therapy could be potential therapeutic agent for PCOS-IR model via modulation of the PI3K/Akt pathway, enhancing anti-inflammatory and anti-oxidant properties and altered mitochondrial functions.HighlightsThe strong relationship between obesity, insulin resistance, and polycystic ovarian syndrome.Disturbance of the PI3K/Akt signaling pathway is involved in the progression of polycystic ovary syndrome-insulin resistance (PCOS-IR).In PCOS-IR rats, combined SeNPs and metformin therapy considerably alleviated IR by acting on the PI3K/Akt signaling pathway.The combination of SeNPs and metformin clearly repaired ovarian polycystic pathogenesis and improved hormonal imbalance in PCOS-IR rats