The blood level of transforming growth factor-β rises in the early stages of acute protein and energy deficit in the weanling mouse
Bibliographic record
Abstract
Plasma transforming growth factor (TGF)-beta levels are high in the advanced stages of acute (wasting) pre-pubescent deficits of protein and energy. Consequently, this potently anti-inflammatory cytokine may help to sustain the depression of inflammatory immune competence in acute malnutrition. Our objective was to determine if plasma TGF-beta levels rise during the early stages of acute malnutrition and, secondarily, to confirm the elevation reported previously in advanced weight loss. In two experiments, male and female C57BL/6J mice, initially 19 d old, consumed ad libitum a complete purified diet (group C), or in restricted daily quantities (group R) or had free access to an isoenergetic low-protein diet (group LP). TGF-beta bioactivity in platelet-poor plasma was determined via inhibition of Mv1Lu mink lung cell proliferation after 3 d (Expt 1, early stage) or 14 d (Expt 2, advanced stage) of dietary intervention. At 3 d, mean plasma TGF-beta bioactivities were 802 (C), 2952 (R) and 4678 (LP) pg/ml, and after 14 d mean bioactivities were 1786 (C), 5360 (R) and 5735 (LP) pg/ml. At both time points, the malnourished groups differed from age-matched controls (P </= 0.05). Thus, metabolically distinct weanling systems mimicking paediatric marasmus (group R) and kwashiorkor (group LP) exhibit an early rise in blood TGF-beta concentration, and this cytokine joins corticosterone and IL-10 as a third anti-inflammatory hormone temporally positioned to contribute to the initiation (and maintenance) of malnutrition-associated immune depression. This investigation contributes new insight into the active anti-inflammatory form of immune competence that appears to prevail in acute pre-pubescent malnutrition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".