Line-field dynamic optical coherence tomography platform for volumetric assessment of biological tissues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Dynamic optical coherence tomography (dOCT) utilizes time-dependent signal intensity fluctuations to enhance contrast in OCT images and indirectly probe physiological processes in cells. Majority of the dOCT studies published so far are based on acquisition of 2D images (B-scans or C-scans) by utilizing point-scanning Fourier domain (spectral or swept-source) OCT or full-field OCT respectively, primarily due to limitations in the image acquisition rate. Here we introduce a novel, high-speed spectral domain line-field dOCT (SD-LF-dOCT) system and image acquisition protocols designed for fast, volumetric dOCT imaging of biological tissues. The imaging probe is based on an exchangeable afocal lens pair that enables selection of combinations of transverse resolution (from 1.1 µm to 6.4 µm) and FOV (from 250 × 250 µm 2 to 1.4 × 1.4 mm 2 ), suitable for different biomedical applications. The system offers axial resolution of ∼ 1.9 µm in biological tissue, assuming an average refractive index of 1.38. Maximum sensitivity of 90.5 dB is achieved for 3.5 mW optical imaging power at the tissue surface and maximum camera acquisition rate of 2,000 fps. Volumetric dOCT images acquired with the SD-LF-dOCT system from plant tissue (cucumber), animal tissue (mouse liver) and human prostate carcinoma spheroids allow for volumetric visualization of the tissues’ cellular and sub-cellular structures and assessment of cellular motility.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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