ArticlebioRxiv : the preprint server for biology2023
Looking at the Full Picture: Utilizing Topic Modeling to Determine Disease-Associated Microbiome Communities.
Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed, 1 citations in OpenAlex.
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
The microbiome is a complex micro-ecosystem that provides the host with pathogen defense, food metabolism, and other vital processes. Alterations of the microbiome (dysbiosis) have been linked with a number of diseases such as cancers, multiple sclerosis (MS), Alzheimer's disease, etc. Generally, differential abundance testing between the healthy and patient groups is performed to identify important bacteria (enriched or depleted in one group). However, simply providing a singular species of bacteria to an individual lacking that species for health improvement has not been as successful as fecal matter transplant (FMT) therapy. Interestingly, FMT therapy transfers the entire gut microbiome of a healthy (or mixture of) individual to an individual with a disease. FMTs do, however, have limited success, possibly due to concerns that not all bacteria in the community may be responsible for the healthy phenotype. Therefore, it is important to identify the
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.