Probiotic Fermentation Prediction Using AI

A method for screening personalized probiotics, prebiotics, foods, health functional foods, and drugs using fecal samples and a proprietary medium composition. The method involves treating fecal samples with candidate materials, culturing, and analyzing the resulting microbiota and metabolites to identify effective personalized microbiota-improving agents. The composition includes L-cysteine, sodium chloride, sodium carbonate, potassium chloride, and hemin, which are combined with fecal samples to create a controlled in vitro environment that mimics the human gut. The method enables rapid screening of personalized microbiota-improving agents using fecal samples and special media, and can be used to diagnose diseases caused by intestinal disorders.

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Using machine learning to design bacterial communities for specific tasks like suppressing pathogens or degrading contaminants. The method involves training a machine learning model to predict community performance based on bacterial strain composition. The training uses real experimental data on actual communities. The model is then used to identify communities with high predicted performance for a task. This allows efficient screening of large numbers of possible communities. The training strategy involves generating data with strains and task scores, and prioritizing genetically diverse communities to reduce redundancy.

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As a promising probiotic strain,

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Predicting environmental preferences of microorganisms through genomic analysis. The method leverages genomic data to infer optimal growth conditions for microorganisms that are difficult to culture in vitro. By analyzing genomic features associated with environmental adaptations, the system identifies genes and proteins that correlate with specific growth preferences across environmental gradients. This enables the prediction of optimal growth conditions for both cultivated and uncultivated microorganisms, expanding our understanding of microbial environmental preferences beyond cultivation-based methods.

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Using machine learning models to predict unmeasured cellular quantitative measures like viability to reduce lab experimentation burden in drug discovery. The models are trained on measured data and then used to generate predictions for untested conditions. The predictions can fill in gaps in dose-response matrices, identify synergistic effects, and reduce the number of experiments needed to find drugs with synergy. The models can handle sparse input data and process images as well as numerical values. They can also augment training data with external sources like genomic data.

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A method for identifying high-performing microorganisms for bioreactor production by correlating genotype with bioreactor fitness. The method involves culturing a library of genetically modified microorganisms in parallel bioreactors, tracking the frequency of each strain, and assigning a quantitative fitness score based on its relative abundance. This score is then used to select strains for further evaluation in high-throughput screening, enabling the identification of strains with superior bioreactor performance and improved genotypes.

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A method for rapidly determining the viability of individual spores using an α-glucosidase test. The method involves adding a spore suspension to a solid medium containing 4-methylumbelliferyl-α-D-glucoside (4-MUG), allowing viable spores to germinate and synthesize α-glucosidase, and then detecting the resulting 4-methylumbelliferone (4-MU) fluorescence under an excitation light source. The fluorescence pattern reveals the presence and viability of individual spores.

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Probiotics, such as

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The gut microbiota plays a crucial role in maintaining human health and influencing disease states. Recent advancements in artificial intelligence (AI) have opened new avenues for exploring the intricate functionalities of the gut microbiota. This article aims to provide an overview of the current state-of-the-art applications of AI in microbiome analysis, with examples related to metabolomics, transcriptomics, proteomics, and genomics. It also offers a perspective on the use of such AI solutions in probiotic interventions for various clinical settings. This comprehensive understanding can lead to the development of targeted therapies that modulate the gut microbiota to improve health outcomes. This article explores the innovative application of AI in understanding the complex interactions within the gut microbiota. By leveraging AI, researchers aim to uncover the microbiotas role in human health and disease, particularly focusing on CIDs and probiotic interventions.

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The probiotic properties of

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A method for determining optimal combinations of microbial strains for industrial applications, such as biotechnology and environmental engineering, using machine learning and genome analysis. The method integrates genomic, metabolic, and growth data to predict strain interactions and optimize productivity, efficiency, or production of specific metabolites. It employs reinforcement learning and collaborative filtering algorithms to identify strain combinations that maximize growth and metabolic interactions.

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Lactic acid bacteria have a long and extensive application record as probiotic strains. Lately, due to wide-spread use of molecular-genetic, transcirptomic, proteomic, metabolomic etc. studies and the massive accumulation of data on the structure and functions of symbiotic intestinal microbiota, the interest in probiotic microorganisms tends to expand year-by-year. Lactic acid bacteria were the first used probiotic species, are still of sharp market demand and have been recognized as the most thoroughly studied microbes among functional relatives. However, characteristics inherent to probiotic bacteria are strain-specific, as a rule, and for preparations with defined purpose it is essential to select special strains showing a complex of appropriate properties. It accentuates the top relevance of research aimed at selection of lactic acid bacterial strains suitable for diverse probiotic preparations. For several decades we curried out investigations to isolate strains of lactic acid bacteria from various natural sources, to characterize their properties and to derive technologies of p... Read More

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The probiotic potential of

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A computer-implemented method for real-time prediction of microorganism growth or inhibition in antimicrobial susceptibility testing (AST) using artificial intelligence. The method involves training a deep learning neural network to classify sequences of images of incubated microorganisms based on pixel intensity changes over time. The network extracts spatio-temporal features from the image sequences and predicts growth or inhibition based on an output score. The method enables rapid AST results without requiring high-resolution images, making it suitable for high-throughput testing systems.

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As a famous probiotic,

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This study evaluated the characteristics of lactic acid bacteria (LAB) isolated from kkakdugi for its use as a probiotic.In addition, the possibility of using it as a material for promoting antioxidant activity and skin functionality was evaluated.To verify the feasibility of LAB as probiotics, their survival rates in artificial gastric juice and artificial bile were evaluated.In artificial gastric juice, the average number of probiotics was maintained at 5.310 9 colony-forming units (CFU)/mL, showing a survival rate of about 99%.In artificial bile, the average number of probiotics was maintained at 1.210 9 CFU/mL, showing a survival rate of about 95%.The survival rate indicated their ability to reach the target site to exert their effects.In addition, autoaggregation and cell surface hydrophobicity experiments were conducted to indirectly confirm their ability to adhere to the gastrointestinal tract surface.The autoaggregation rate of all LAB strains increased over time.Specifically, L. plantarum K1-9 and L. brevis K2-9 strains showed high hydrophobicity.LAB culture supernatants w... Read More

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A method for predicting cell culture results that iteratively refines culture conditions through machine learning-based modeling of biological behavior. The method receives initial culture conditions, predicts biological behavior based on a trained model, updates the culture conditions based on the predicted behavior, and repeats the process until convergence. The trained model can be based on machine learning or biological mechanism-of-action models, including genome-scale metabolic models and cell signaling models.

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Predicting cell viability in bioreactors during biomolecule manufacturing using machine learning models trained on process parameters such as time elapsed, base addition, and volume, with higher importance assigned to parameters like time elapsed and base addition compared to parameters like pH and dissolved oxygen.

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The selection of appropriate probiotic strains is vital for their successful inclusion in foods. These strains must withstand processing to reach consumers with 10

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The gut microbiota is a potentially modifiable risk factor for various health complications. Therefore, advanced techniques are warranted to understand the relationship between gut microbiota, disease, and clinical relevance. This chapter focuses on the emerging application of artificial intelligence (AI) techniques in the studies of gut microbiota. It opens with a discussion on the role of gut microbiota in health, mentions an overview of AI techniques, and provides information on the application of AI in the study of the gut microbiome and its role in the diagnosis and treatment of diseases. It also gives a glimpse into the challenges and future direction of artificial intelligence in gut microbiome research. The chapter provides new insights into the extraordinary applications of AI in the study of the gut microbiome.

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