Host Cell Geometry and Cytoskeletal Organization Governs <i>Candida</i>–Host Cell Interactions at the Nanoscale
Bibliographic record
Abstract
Candida is one of the most common opportunistic fungal pathogens in humans. Its adhesion to the host cell is required in parasitic states and is important for pathogenesis. Many studies have shown that there is an increased risk of developing candidiasis when normal tissue barriers are weakened or when immune defenses are compromised, for example, during cancer treatment that induces immunosuppression. The mechanical properties of malignant cells, such as adhesiveness and viscoelasticity, which contribute to cellular invasion and migration are different from those of noncancerous cells. To understand host invasion and its relationship with host cell health, we probed the interaction of Candida spp. with cancerous and noncancerous human cell lines using atomic force microscopy in the single-cell force spectroscopy mode. There was significant adhesion between Candida and human cells, with more adhesion to cancerous versus noncancerous cell lines. This increase in adhesion is related to the mechanobiological properties of cancer cells, which have a disorganized cytoskeleton and lower rigidity. Altered geometry and cytoskeletal disruption of the human cells impacted adhesion parameters, underscoring the role of cytoskeletal organization in Candida –human cell adhesion and implicating the manipulation of cell properties as a potential future therapeutic strategy.
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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 | 0.000 |
| 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 | 0.001 | 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".