Correlation between <i>SAP2</i> and <i>CAP1</i> in clinical strains of <i>Candida albicans</i> at planktonic and biofilm states
Bibliographic record
Abstract
This study aimed to explore the influences of SAP2 and CAP1 on itraconazole (ITR) resistance of Candida albicans at different states. A total of 10 ITR-resistant strains and 10 ITR-sensitive strains were used for SAP2 sequencing and CAP1 sequencing. SAP2 sequencing showed no missense mutation, and three synonymous mutations. CAP1 gene sequencing identified two missense mutations M140I (8) and K191Q (4), and 14 synonymous mutations G201A (1), A246C (5), C282T (6), G288A (6), C321T (7), A399C (16), C432T (16), C465T (11), G552A (16), G669T (1), G672A (1), G681T (2), T783C (1), and T819A (2). The biofilm formation capacity of resistant C. albicans strains, including the CAP1∆/∆ strain, was stronger. Afterward, real-time quantitative PCR was used to analyze the expression of SAP2 and CAP1. Compared with the sensitive strains, SAP2 and CAP1 expressions were both significantly upregulated in resistant strains at planktonic and biofilm states ( P < 0.05). Compared with the strains at planktonic state, SAP2 was significantly upregulated, while CAP1 was significantly downregulated at biofilm states ( P < 0.05). Additionally, SAP2 expression in the CAP1 knocked down strain of C. albicans was significantly upregulated, and SAP2 expression was evidently downregulated in the CAP1∆/∆ strain at biofilm states compared with that at planktonic states ( P < 0.05). Loss of CAP1 can increase SAP2 level and may influence the biofilm formation of C. albicans, thus increasing ITR resistance of C. albicans.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".