Quantification of bowel ischaemia using real-time multispectral Single Snapshot Imaging of Optical Properties (SSOP)
Bibliographic record
Abstract
BACKGROUND: Single snapshot imaging of optical properties (SSOP) is a relatively new non-invasive, real-time, contrast-free optical imaging technology, which allows for the real-time quantitative assessment of physiological properties, including tissue oxygenation (StO2). This study evaluates the accuracy of multispectral SSOP in quantifying bowel ischaemia in a preclinical experimental model. METHODS: In six pigs, an ischaemic bowel segment was created by dividing the arcade branches. Five regions of interest (ROIs) were identified on the bowel loop, as follows: ROI 1: central ischaemic; ROI 2: left marginal; ROI 3: left vascularised; ROI 4: right marginal; and ROI 5: right vascularised. The Trident imaging system, specifically developed for real-time tissue oxygenation imaging using SSOP, was used to image before (T0) and after ischaemia induction. Capillary and systemic lactates were measured at each time point (T0, T15, T30, T45, T60), as well as StO2 values acquired by means of SSOP (SSOP-StO2). RESULTS: The mean value of SSOP-StO2 in ROI 1 was 30.08 ± 6.963 and was significantly lower when compared to marginal ROIs (ROI 2 + ROI 4: 45.67 ± 10.02 p = < 0.0001), and to vascularised ROIs (ROI 3 + ROI 5: 48.08 ± 7.083 p = < 0.0001). SSOP-StO2 was significantly correlated with normalised lactates r = - 0.5892 p < 0.0001 and with histology r =- 0.6251 p = 0.0002. CONCLUSION: Multispectral SSOP allows for a contrast-free accurate assessment of small bowel perfusion identifying physiological tissue oxygenation as confirmed with perfusion biomarkers.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".