AIMC Topic: RNA, Ribosomal, 16S
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Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.
Renal failure
Jul 3, 2025
Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk of infection, presenting significant clinical, and economic challenges. Identifying predictive bioma...
Machine learning-based identification of wastewater treatment plant-specific microbial indicators using 16S rRNA gene sequencing.
Scientific reports
Jul 3, 2025
Effluent released from municipal wastewater treatment plants reflects the microbial communities responsible for degrading and removing contaminants within the plants. Monitoring this effluent offers essential insights into its environmental impacts, ...
Machine learning-based mapping of Acidobacteriota and Planctomycetota using 16 S rRNA gene metabarcoding data across soils in Russia.
Scientific reports
Jul 3, 2025
The soil microbiome plays a crucial role in maintaining healthy ecosystems and supporting sustainable agriculture. Studying its biogeographical structure and distribution is essential for understanding the rates and mechanisms of microbially mediated...
TaxaCal: enhancing species-level profiling accuracy of 16S amplicon data.
BMC bioinformatics
May 26, 2025
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
May 15, 2025
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...
Interpretable machine learning algorithms reveal gut microbiome features associated with atopic dermatitis.
Frontiers in immunology
May 1, 2025
BACKGROUND: The "gut-skin axis" has been proposed to play an important role in the development and symptoms of atopic dermatitis. Therefore, we have constructed an interpretable machine learning framework to quantitatively screen key gut flora.
Gut microbiota-derived extracellular vesicles form a distinct entity from gut microbiota.
mSystems
Apr 29, 2025
Extracellular vesicles (EVs), nanoparticles secreted by both gram-negative and gram-positive bacteria, carry various biomolecules and cross biological barriers. Gut microbiota-derived EVs are currently being investigated as a communication mechanism ...
Accurate prediction of absolute prokaryotic abundance from DNA concentration.
Cell reports methods
Apr 28, 2025
Quantification of the absolute microbial abundance in a human stool sample is crucial for a comprehensive understanding of the microbial ecosystem, but this information is lost upon metagenomic sequencing. While several methods exist to measure absol...
The inconsistent pathogenesis of endometriosis and adenomyosis: insights from endometrial metabolome and microbiome.
mSystems
Apr 22, 2025
UNLABELLED: Endometriosis (EM) and adenomyosis (AM) are interrelated gynecological disorders characterized by the aberrant presence of endometrial tissue and are frequently linked with chronic pelvic pain and infertility, yet their pathogenetic mecha...
Unique Microbial Characterisation of Oesophageal Squamous Cell Carcinoma Patients with Different Dietary Habits Based on Light Gradient Boosting Machine Learning Classifier.
Nutrients
Apr 14, 2025
: The microbiome plays an important role in cancer, but the relationship between dietary habits and the microbiota in oesophageal squamous cell carcinoma (ESCC) is not clear. The aim of this study is to explore the complex relationship between the mi...
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