Optical Microscopy and the Extracellular Matrix Structure: A Review
Bibliographic record
Abstract
Biological tissues are not uniquely composed of cells. A substantial part of their volume is extracellular space, which is primarily filled by an intricate network of macromolecules constituting the extracellular matrix (ECM). The ECM serves as the scaffolding for tissues and organs throughout the body, playing an essential role in their structural and functional integrity. Understanding the intimate interaction between the cells and their structural microenvironment is central to our understanding of the factors driving the formation of normal versus remodelled tissue, including the processes involved in chronic fibrotic diseases. The visualization of the ECM is a key factor to track such changes successfully. This review is focused on presenting several optical imaging microscopy modalities used to characterize different ECM components. In this review, we describe and provide examples of applications of a vast gamut of microscopy techniques, such as widefield fluorescence, total internal reflection fluorescence, laser scanning confocal microscopy, multipoint/slit confocal microscopy, two-photon excited fluorescence (TPEF), second and third harmonic generation (SHG, THG), coherent anti-Stokes Raman scattering (CARS), fluorescence lifetime imaging microscopy (FLIM), structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), ground-state depletion microscopy (GSD), and photoactivated localization microscopy (PALM/fPALM), as well as their main advantages, limitations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".