Refinement of a chronic cranial window implant in the rat for longitudinal in vivo two–photon fluorescence microscopy of neurovascular function
Bibliographic record
Abstract
Longitudinal studies using two-photon fluorescence microscopy (TPFM) are critical for facilitating cellular scale imaging of brain morphology and function. Studies have been conducted in the mouse due to their relatively higher transparency and long term patency of a chronic cranial window. Increasing availability of transgenic rat models, and the range of established behavioural paradigms, necessitates development of a chronic preparation for the rat. However, surgical craniotomies in the rat present challenges due to craniotomy closure by wound healing and diminished image quality due to inflammation, restricting most rat TPFM experiments to acute preparations. Long-term patency is enabled by employing sterile surgical technique, minimization of trauma with precise tissue handling during surgery, judicious selection of the size and placement of the craniotomy, diligent monitoring of animal physiology and support throughout the surgery, and modification of the home cage for long-term preservation of cranial implants. Immunohistochemical analysis employing the glial fibrillary acidic protein (GFAP) and ionized calcium-binding adaptor molecule-1 (Iba-1) showed activation and recruitment of astrocytes and microglia/macrophages directly inferior to the cranial window at one week after surgery, with more diffuse response in deeper cortical layers at two weeks, and amelioration around four weeks post craniotomy. TPFM was conducted up to 14 weeks post craniotomy, reaching cortical depths of 400 µm to 600 µm at most time-points. The rate of signal decay with increasing depth and maximum cortical depth attained had greater variation between individual rats at a single time-point than within a rat across time.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".