Rational strategy for studying microbiome of the ocular surface of people using hard contact lenses by method of 16S rRNA gene metabarcoding
Bibliographic record
Abstract
The study is based on the hypothesis that high taxonomic diversity of bacteria detectable on the eye surface by molecular genetic methods is attributed to the high level of its contamination by skin microflora. Such contamination would make it problematic to identify the fractions of real ocular surface microbiome, which remains behind the one-percent cut-off threshold adopted in the metagenomic analysis. Hard contact lenses for long-wearing act as a physical filter preventing DNA contamination from random microorganisms, and at the same time providing adhesion to the living cells of bacteria and fungi. To confirm this assumption, a detailed analysis of references was carried out, supplemented by original laboratory research. MATERIAL AND METHODS: rRNA gene sequencing using 454 GS Junior (Ion Torrent) and Illumina MiSeq platforms. RESULTS: (2.1%). Analysis of the samples obtained directly from the mucosa revealed dominance of typical skin-associated microorganisms. CONCLUSION: The present study proposes a contamination-reduction algorithm for microbiological testing of the ocular surface using hard contact lenses for prolonged wearing as a carrier for microbial DNA.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".