Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI’s significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI’s utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI’s potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI’s versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

The search for novel bioactive compounds from the natural environment has been rapidly increased with the increase in multi-drug resistant (MDR) pathogens. In the present study, the antimicrobial potential of novel actinomycetes has been evaluated by initial screening of six soil samples. Primary and secondary screening was performed against Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Candida albicans, Candida tropicalis, Trichophyton rubrum, and other MDR bacterial and fungal test strains, and at the end thirteen active isolates were selected for further study. Microbial strains were identified on the basis of growth conditions and other biochemical characters. Five most active microbial strains were identified using 16S rRNA sequence homology and designated as Streptomyces xanthophaeus MTCC 11938, Streptomyces variabilis MTCC 12266, Streptomyces xanthochromogenes MTCC 11937, Streptomyces levis EU 124569 and Streptomyces sp. NCIM 5500. Four antibacterial and three antifungal compounds isolated from the above five isolates were purified and partially characterized using UV absorption and IR spectra. Two antibacterial metabolites, belong to chromone and peptide antibiotic, respectively. The antifungal compounds were found to be of non-polyene nature. In conclusion, we study the isolation of novel bacterial strains of actinomycetes for producing novel compounds having antibacterial and antifungal activities from the unexplored agro-ecological niches of India. Also, this study paves the way for further characterization of these isolates of Streptomyces sp. for their optimum utilization for antimicrobial purposes.

As a replacement to existing fossil fuels, biofuels, have proven their worth; however, their widespread use is limited due to inconsistent yields, higher costs and poor productivity. An oleaginous yeast, Rhodosporidium toruloides has been reported to accumulate substantial amounts of lipids (that can be converted to biofuels) and therefore, it was selected for study and optimization. Apart from lipids, R. toruloides is also reported to produce carotene that can be used as a therapeutic agent. In this study, the culture medium was statistically modelled and optimized for concomitant production of lipids and carotenoids and for improving and maximizing the productivity of lipids as well as carotenes. The two metabolites were expressed differentially in the growth cycle of this organism. Culture medium components were simultaneously varied at 5 different levels using statistical modelling employing RSM. Osmotic stress was introduced in order to simulate saline conditions and optimize the production process to be used in conditions with high salt contents. In this study, we demonstrate 36.2% (w/v) lipid production and 27.2% (w/v) carotenoid production, under osmotic stress with high salt concentrations, for the first time.

The applicability of the statistical tools coupled with artificial intelligence techniques was tested to optimize the critical medium components for the production of extracellular cholesterol oxidase (COD; an enzyme of commercial interest) fromStreptomyces rimosusMTCC 10792. The initial medium component screening was performed using Placket-Burman design with yeast extract, dextrose, starch and ammonium carbonate as significant factors. Response surface methodology (RSM) was attempted to develop a statistical model with a significant coefficient of determination (R² = 0.89847), followed by model optimization using Genetic Algorithm (GA). RSM-GA based optimization approach predicted that the combination of yeast extract, dextrose, starch and ammonium carbonate at concentrations 0.99, 0.8, 0.1, and 0.05 g/100 ml respectively, has resulted in 3.6 folds increase in COD production (5.41 U/ml) in comparison with the un-optimized medium (1.5 U/ml). COD was purified 10.34 folds having specific activity of 12.37 U/mg with molecular mass of 54 kDa. The enzyme was stable at pH 7.0 and 40 °C temperature. The apparent Michaelis constant (K_m) and V_maxvalues of COD were 0.043 mM and 2.21 μmol/min/mg, respectively. This is the first communication reporting RSM-GA based medium optimization, purification and characterization of COD byS. rimosusisolated from the forest soil of eastern India.