Real-time <i>in vivo</i> imaging of fungal migration to the central nervous system
Why this work is in the frame
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Bibliographic record
Abstract
Recent technical advances have afforded valuable new insights into the pathogenesis of fungal infections in the central nervous system (CNS), which continue to cause devastating complications, particularly in immunocompromised individuals. To cause CNS mycosis, organisms such as Cryptococcus neoformans become blood borne and progress through a series of pathogenic checkpoints that culminate in fungal replication in the brain. Critical steps include fungal arrest in the vasculature of the brain, interaction and signalling of the fungal and endothelial cells leading to transmigration with subsequent parenchymal invasion and fungal replication in the CNS. Previous studies that made use of in vitro and ex vivo approaches contributed greatly to our understanding of brain invasion by fungi. However, the knowledge gained from previous studies relied on in vitro models that did not account for vascular haemodynamics. For this reason, more refined approaches that model blood flow and vascular anatomy are required, andultimately studying fungal invasion and dissemination in vivo. Indeed, in vivo imaging (also known as intravital imaging) has emerged as a valuable technique to probe host-pathogen interactions. In this review, with a focus on C. neoformans, we will provide an overview of the applications of the prior techniques and recent advances, their strengths and limitations in characterizing the migration of fungi into the brain, and unanswered questions that may provide new directions for research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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