Probiotics are non-pathogenic microorganisms that can interact with the gastrointestinal microbiota. They have numerous beneficial health effects that include enhancement of the host immune response, antiallergic, antimicrobial, anti-cancer, and anti-inflammatory properties. Probiotics are capable of restoring the impaired microbiome of a dysbiotic gut. They can be isolated from different environments. However, it is frequently suggested that probiotics for human use should come from human sources. The objective of this study was to isolate and characterize novel probiotic strains from the saliva and feces of healthy human individuals. To meet the criteria for probiotic attributes, the isolates were subjected to numerous standard morphological and biochemical tests. These tests included Gram staining, catalase tests, antibiotic susceptibility testing, hemolytic and antagonistic evaluation, tolerance tests involving temperature, NaCl levels, pH and bile salts, adherence ability assays, and genotypic characterization involving 16S rRNA gene sequencing. From 26 saliva and 11 stool samples, 185 microbial strains were isolated. Based on morphological and biochemical characteristics, 14 potential probiotic candidates were selected and identified genotypically. The new strains belonged to Lactobacillus fermentum, Enterococcus faecium, and Enterococcus hire. The selected strains were non-hemolytic, showed high tolerance to low pH and bile salts, and strong adherence abilities. Furthermore, the strains displayed a wide range of antimicrobial activities, particularly against antibiotic-resistant pathogens such as methicillin resistant Staphylococcus aureus (MRSA). Moreover, five of the selected isolates demonstrated antiproliferative features against human colon cancer cell line (Caco-2). The results of this investigation confirm the diversity of microbial populations in the human gut and saliva, and since these strains are of human origin, they will highly likely display maximal activities in food and drugs set for human use. Hence, the new strains of this study require additional in vivo experiments to assess their health-promoting effects.

Electro-biodegradation is a novel technique for cyanide degradation in aqueous solutions. Many physical, chemical, and biological methods have been developed and used to treat cyanide degradation. The biological methods are more environmentally-friendly and economically cost-effective when compared to other techniques, however, the process reaction time period is much longer and the efficiency is lower. In this research, the bacterial strain, Bacillus pumilus ATCC 7061, was tested for the first time to introduce the Cyanide Electro-biodegradation technique. By using a direct current power supply, electrons were generated in an electro-biodegradation cell containing culture media at free cyanide concentrations of 100 to 500 mg/l, under alkaline conditions. Experimental tests showed that when electrons were added and bacteria were inoculated into the aqueous media containing 100, 200, 300, 400 and 500 mg/l of free cyanide, the cyanide degradation efficiency increased from 16.2, 21.6, 29.5, 38.7 and 44.5% to 98.6, 99.3, 99.7, 99.8 and 99.7%, in 36, 72, 137, 233 and 301 h, respectively. The results show that by adding electrons, the process reaction time decreases and cyanide degradation efficiency increases significantly. The results presented here demonstrate for the first time the importance and the significance of the electro-biodegradation technique in the efficient degradation and removal of cyanide present in aqueous solutions.

A pan-cancer analysis summarizing the overall changes in mRNA and protein stability of ADM9, as well as its oncogenic function on immune cell line modulation and checkpoints within the tumor microenvironment (TME), is lacking, despite the fact that ADM9 up-regulation is correlated with the progression of many cancers. Therefore, in this study, we comprehensively analyzed the role of ADAM9 expression and its prognostic value in different cancers to fill this gap. Multiple bioinformatics databases such as Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) were used to evaluate the ADAM9 genetic alternation, phosphorylation, and methylation, and indicated highly positive correlated genes that might play a critical interaction with ADAM9 and their molecular function with GO analysis. We also evaluate the effect of higher ADAM9 with prominent immune modulatory genes and immune infiltration especially in liver cancer pathogenesis stimulates lower NK cell effector functions based on its role in MICA shedding and increasing the Tregs infiltration. Immunohistochemistry (IHC) staining from 90 pathologically verified samples proved the positive correlation between ADAM9 and tumor stages and proved the higher expression of ADAM9 correlated genes (SNX9, APP, TNF, CDH1, ITGAV, MAD2L2) in HCC pathogenesis. In conclusion, this pan-cancer study provides a comprehensive understanding of the prognostic value of ADAM9 in various tumors emphasizing its importance to be considered as an innovative treatment approach, especially in tumor immunity shortly.

The microbiome, as a vast and dynamic community of microbes, is now recognized as a key regulator of host physiology, profoundly influencing health and susceptibility to disease. Accordingly, probiotics are a mainstay of prevention and treatment. But the individual complexity and dynamic specificity of an individual’s microbiome make the previous “one-size-fits-all” research model completely invalid. This review systematically analyzes the applications of artificial intelligence (AI) and machine learning (ML) as transformative computational tools necessary to surmount these challenges. In this article, we detail how these computational methods have been applied throughout the entire research and development pathway of probiotics, including novel strain identification (through multi-omics analysis), formulation and production optimization, and elucidation of complex mechanisms of action in the host. Furthermore, we highlight the emerging frontier of personalized probiotic therapy, demonstrating how AI/ML can be utilized to predict treatment efficacy based on individual host data. The objective of this article is to provide a detailed discourse on the actual and prospective applications of AI and ML in this process, ultimately delineating their revolutionary potential to inform the design of the next generation of probiotics with unprecedented precision, efficacy, and sustainability.