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Record W2755608235 · doi:10.1093/ofid/ofx163.942

Engraftment and Augmentation of Microbiome Following Fecal Microbiota Transplantation for Recurrent Clostridium difficile Infection

2017· article· en· W2755608235 on OpenAlexaff
Christine Lee, Stephen Rush, J. Scott Weese, Peyman Goldeh, Peter S. Kim

Bibliographic record

VenueOpen Forum Infectious Diseases · 2017
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsIsland HealthUniversity of GuelphMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsClostridium difficileBacteroidetesMicrobiomeTransplantationMedicineFecesMetagenomicsFecal bacteriotherapyC difficileFirmicutesMicrobiologyBiologyBacteriaInternal medicineBioinformaticsGeneticsGene16S ribosomal RNAAntibiotics

Abstract

fetched live from OpenAlex

Abstract Background Recurrent Clostridum diffcile infection (rCDI) poses major challenges to healthcare providers and patients. Fecal Microbiota Transplantation (FMT) is an effective therapy for rCDI, but the exact mechanism of its efficacy is unknown. Current metagenomics literature indicates that abundance of Bacteroidetes and Firmicutes may protect against CD proliferation and recurrence. However, this is too broad to be useful for developing refined and targeted microbial-specific therapy for rCDI, because the long-term safety of FMT remains unknown. We examined the phylogeny of bacteria pre- and post- FMT to determine the key organisms associated with successful FMT to the genera level. Methods A subset of patient stool samples (n = 35) from a phase 2 study comparing fresh vs. frozen FMT for rCDI was sequenced at four time points: pre-FMT; at day 10; at week 5; and at week 13, following the last FMT. The matching donor stool was sequenced simultaneously with the corresponding patients’ pre- and post-FMT samples. Using the binary outcome to a single FMT as the response, we have developed an in-house machine learning algorithm, Φ-LASSO, to isolate key genera using the bacterial phylogenetic structure. Engraftment was defined as: newly detected operational taxonomic unit (OTUs) in the patient post-FMT, which were present in the donor but undetected in the patient pre-FMT. Augmentation was defined as: non-donor OTUs whose levels substantially increased post-FMT. Figure 1 (below) displays the distribution of engrafted and augmented OTUs at varying thresholds. We observed increases over time points within each threshold level. Results Akkermansia, Blautia and Roseburiaappear to be key genera for successful FMT. The Φ-LASSO fits with consistently positive coefficients, see Figure 2. Conclusion In this preliminary study, using Φ-LASSO, we have shown that specific microbes to the genera level are uniformly present in successful FMT. This information may lead to developing refined and targeted microbial-therapy for rCDI. Figure 1 Observed (a) engraftment of distinct donor OTUs on patients and (b) augmentation of distinct OTUs in patients for day 10 (D10), week 5 (W5), and week 13 (W13) post-treatment. Figure 2 Fitted coefficients for donor OTUs selected by Φ-LASSO. Disclosures All authors: No reported disclosures.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.346
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2017
Admission routes1
Has abstractyes

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