AIMC Topic: Microbiota

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Harnessing vaginal inflammation and microbiome: a machine learning model for predicting IVF success.

NPJ biofilms and microbiomes
Humans are the only species with a commensal Lactobacillus-dominant vaginal microbiota. Reproductive tract microbes have been linked to fertility outcomes, as has intrauterine inflammation, suggesting immune response may mediate adverse outcomes. In ...

Shotgun Metagenomics Identifies in a Cross-Sectional Setting Improved Plaque Microbiome Biomarkers for Peri-Implant Diseases.

Journal of clinical periodontology
AIM: This observational study aimed to verify and improve the predictive value of plaque microbiome of patients with dental implant for peri-implant diseases.

Visualizing fatigue mechanisms in non-communicable diseases: an integrative approach with multi-omics and machine learning.

BMC medical informatics and decision making
BACKGROUND: Fatigue is a prevalent and debilitating symptom of non-communicable diseases (NCDs); however, its biological basis are not well-defined. This exploratory study aimed to identify key biological drivers of fatigue by integrating metabolomic...

Single-tooth resolved, whole-mouth prediction of early childhood caries via spatiotemporal variations of plaque microbiota.

Cell host & microbe
Early childhood caries (ECC) exhibits tooth specificity, highlighting the need for single-tooth-level prevention. We profiled 2,504 dental plaque microbiota samples from 89 preschoolers across two cohorts, tracking compositional changes with imputed ...

TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data.

BMC bioinformatics
BACKGROUND: 16S rRNA amplicon sequencing is a widely used method for microbiome composition analysis due to its cost-effectiveness and lower data requirements compared to metagenomic whole-genome sequencing (WGS). However, inherent limitations in 16S...

Distinguishing critical microbial community shifts from normal temporal variability in human and environmental ecosystems.

Scientific reports
Differentiating significant microbial community changes from normal fluctuations is vital for understanding microbial dynamics in human and environmental ecosystems. This knowledge could enable early warning systems to monitor critical changes affect...

Learning a deep language model for microbiomes: The power of large scale unlabeled microbiome data.

PLoS computational biology
We use open source human gut microbiome data to learn a microbial "language" model by adapting techniques from Natural Language Processing (NLP). Our microbial "language" model is trained in a self-supervised fashion (i.e., without additional externa...

Fluorescence-based spectrometric and imaging methods and machine learning analyses for microbiota analysis.

Mikrochimica acta
Most microbiota determination (skin, gut, soil, etc.) are currently conducted in a laboratory using expensive equipment and lengthy procedures, including culture-dependent methods, nucleic acid amplifications (including quantitative PCR), DNA microar...

Compositional transformations can reasonably introduce phenotype-associated values into sparse features.

mSystems
UNLABELLED: Gihawi et al. (mBio 14:e01607-23, 2023, https://doi.org/10.1128/mbio.01607-23) argued that the analysis of tumor-associated microbiome data by Poore et al. (Nature 579:567-574, 2020, https://doi.org/10.1038/s41586-020-2095-1) is invalid b...

Standardizing a microbiome pipeline for body fluid identification from complex crime scene stains.

Applied and environmental microbiology
Recent advances in next-generation sequencing have opened up new possibilities for applying the human microbiome in various fields, including forensics. Researchers have capitalized on the site-specific microbial communities found in different parts ...