Identification of Medically Important <i>Candida</i> and Non- <i>Candida</i> Yeast Species by an Oligonucleotide Array
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The incidence of yeast infections has increased in the recent decades, with Candida albicans still being the most common cause of infections. However, infections caused by less common yeasts have been widely reported in recent years. Based on the internal transcribed spacer 1 (ITS 1) and ITS 2 sequences of the rRNA genes, an oligonucleotide array was developed to identify 77 species of clinically relevant yeasts belonging to 16 genera. The ITS regions were amplified by PCR with a pair of fungus-specific primers, followed by hybridization of the digoxigenin-labeled PCR product to a panel of oligonucleotide probes immobilized on a nylon membrane for species identification. A collection of 452 yeast strains (419 target and 33 nontarget strains) was tested, and a sensitivity of 100% and a specificity of 97% were obtained by the array. The detection limit of the array was 10 pg of yeast genomic DNA per assay. In conclusion, yeast identification by the present method is highly reliable and can be used as an alternative to the conventional identification methods. The whole procedure can be finished within 24 h, starting from isolated colonies.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 it