Characterization and predictive functional profiles on metagenomic 16S rRNA data of liver transplant recipients: A longitudinal study
Bibliographic record
Abstract
Long-term survival after Liver Transplantation (LT) is often compromised by infectious and metabolic complications. We aimed to delineate alterations in intestinal microbiome (IM) over time that could contribute to medical complications compromising long-term survival following LT. Fecal samples from LT recipients were collected at 3 months (3 M) and 6 months (6 M) post-LT. The bacterial DNA was extracted using E.Z.N.A. Stool DNA Kit and 16S rRNA gene sequencing at V4 hypervariable region was performed. DADA2 and Phyloseq was implemented to analyze the taxonomic composition. Differentially abundant taxa were identified by metagenomeSeq and LEfSe. Piphillin, an Inferred functional metagenomic analysis tool was used to study the bacterial functional content. For comparison, healthy samples were extracted from NCBI and analyzed similarly. The taxonomic & functional profiles of LT recipients were validated with metagenomic sequencing data from animals exposed to immunosuppressants using Venny. Our findings provide a new perspective on longitudinal increase in specific IM communities post-LT along with an increase in bacterial genes associated with metabolic and infectious disease.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".