Microscopie à deux photons pour l’imagerie cellulaire fonctionnelle : avantages et enjeux ou Un photon c’est bien… mais deux c’est mieux !
Bibliographic record
Abstract
One of the main challenges of modern biochemistry and cell biology is to be able to observe molecular dynamics in their functional context, i.e. in live cells in situ. Thus, being able to track ongoing molecular events with maximal spatial and temporal resolution (within subcellular compartments), while minimizing interference with tissue biology, is key to future developments for in situ imaging. The recent use of non-linear optics approaches in tissue microscopy, made possible in large part by the availability of femtosecond pulse lasers, has allowed major advances on this front that would not have been possible with conventional linear microscopy techniques. Of these approaches, the one that has generated most advances to date is two-photon laser scanning fluorescence microscopy. While this approach does not really provide improved resolution over linear microscopy in non absorbing media, it allows us to exploit a window of low absorbance in live tissue in the near infrared range. The end result is much improved tissue penetration, minimizing unwanted excitation outside the focal area, which yields an effective improvement in resolution and sensitivity. The optical system is also simplified and, more importantly, phototoxicity is reduced. These advantages are at the source of the success of two-photon microscopy for functional cellular imaging in situ. Yet, we still face further challenges, reaching the limits of resolution that conventional optics can offer. Here we review some recent advances in optics/photonics approaches that hold promises to improve our ability to probe the tissue in finer areas, at faster speed, and deeper into the tissue. These include super-resolution techniques, introduction of non paraxial optics in microscopy and use of amplified femtosecond lasers, yielding enhanced spatial and temporal resolution as well as tissue penetration.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".