Effect of a maldistribution of microvascular blood flow on capillary O<sub>2</sub> extraction in sepsis
Bibliographic record
Abstract
Inherent in the remote organ injury caused by sepsis is a profound maldistribution of microvascular blood flow. Using a 24-h rat cecal ligation and perforation model of sepsis, we studied O(2) transport in individual capillaries of the extensor digitorum longus (EDL) skeletal muscle. We hypothesized that erythrocyte O(2) saturation (SO(2)) levels within normally flowing capillaries would provide evidence of either a mitochondrial failure (increased SO(2)) or an O(2) transport derangement (decreased SO(2)). Using a spectrophotometric functional imaging system, we found that sepsis caused 1) an increase in stopped flow capillaries (from 10 to 38%, P < 0.05), 2) an increase in the proportion of fast-flow to normal-flow capillaries (P < 0.05), and 3) a decrease in capillary venular-end SO(2) levels from 58.4 +/- 20.0 to 38.5 +/- 21.2%, whereas capillary arteriolar-end SO(2) levels remained unchanged compared with the sham group. Capillary O(2) extraction increased threefold (P < 0.05) and was directly related to the degree of stopped flow in the EDL. Thus impaired O(2) transport in early stage sepsis is likely the result of a microcirculatory dysfunction.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".