Fecal Microbiome and Resistome Profiling of Healthy and Diseased Pakistani Individuals Using Next-Generation Sequencing
Bibliographic record
Abstract
In this paper, we aimed to characterize the fecal microbiome and its resistomes of healthy and diseased subjects infected with multidrug-resistant Escherichia coli using next-generation sequencing (NGS). After initial screening, 26 stools samples belonging to healthy (n = 13) and diseased subjects (n = 13) were selected and subjected to NGS. A total of 23 and 42 antibiotic-resistant genes (ARGs) conferring resistance to 6 and 9 classes of antibiotics were identified in the resistomes of healthy and diseased subjects, respectively. Bacteroidetes were found to be the major phylum in both healthy and diseased subjects; however, Proteobacteria was predominantly present in the diseased subjects only. Microbial dysbiosis and predominance of various ARGs in the resistome of diseased subjects reflect the excessive usage of antibiotics in Pakistan and warrants immediate attention to regulate the use of various antimicrobials.
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".