Bibliographic record
Abstract
Clinicians often struggle with patients in intensive care suffering from sepsis. Aside from the complex problems of septicaemia itself there a whole host of secondary problems linked to loss of muscle mass and metabolic disturbances. Hypercytokinaemia is a hallmark of sepsis and a number of other inflammatory conditions and we have an increasing appreciation of the effects that these endocrine molecules have on numerous processes (Pedersen, 2009). Two cytokines that are strongly implicated are tumour necrosis factor-α (TNF-α) and interleukin-6 (IL-6), both of which have been shown to be elevated in inflammation and play a role in the aetiology of muscle wasting (Frost & Lang, 2007). It is increasingly clear that blunting the appearance of TNF-α and IL-6 may be beneficial and help abate muscle wasting. The anti-inflammatory effects of glucocorticoids are widely exploited in various clinical scenarios and yet their use in sepsis, even after more than 50 years, is contentious (Annane et al. 2009). Presumably, it would make sense to use glucocorticoids to suppress inflammation during highly pro-inflammatory states, but we also know that glucocorticoids can be in and of themselves activators of proteolysis (Tisdale, 2007). So the answer to the question of glucorticoid efficacy in the treatment of sepsis, at least insofar as muscle wasting is concerned, is a tricky one that appears to be dose dependent (Annane et al. 2009). An intriguing question is still how glucocorticoids would exert their benefit in terms of preserving muscle mass, if indeed they do, during endotoxaemia? A mechanistic light has been shone on this question and some praiseworthy insight is provided in this issue of The Journal of Physiology by Crossland et al. (2010). What the authors found was that a low dose infusion of dexamethasone during lipopolysaccharide (LPS)-induced endotoxaemia in rodents blunted a rise in muscle muscle proteolysis and expression of IL-6 and TNF-α mRNA. Interestingly, the normal endotoxaemia-induced rise in muscle atrophy F-box (MAFbx) and muscle RING finger 1 (MuRF1) mRNA expression, two prototypical proteolytic proteins both implicated in muscle wasting in a variety of conditions (Lecker et al. 2004), remained unchanged with DEX. How then is dexamethasone (DEX) blocking proteolysis? The answer to this question is not forthcoming from Crossland's data, but there was DEX-induced suppression of the rise in cathepsin-L expression and activity as well as metallothionein-1A expression, the significance of which are not known, but both or either could have had an impact on proteolysis. Interestingly, and following up on their previous work (Crossland et al. 2008), the authors showed that DEX also resulted in a reduced rise or the normal LPS triggered rise in pyruvate dehydrogenase kinase 4 (PDK4) mRNA as well as a upregulation of glycogenolysis and lactate accumulation. The metabolic crossover of DEX is intriguing and raises the question of just how the glucocortoids are exerting both anti-proteolytic and metabolic effects? The authors put their money on a cytokine-mediated suppression of Akt (protein kinase B) and forkhead box O1 (FOXO1); the phosphorylation of both proteins was enhanced with low dose DEX compared to the LPL-induced septic condition. The authors propose a scheme whereby cytokine inhibition is the top of the cascade and the nexus is Akt and FOXO; the missing link is still how proteolysis is down-regulated with DEX. Attractive candidates might include the calpains (Tidball & Spencer, 2002) or caspase-3 (Supinski et al. 2009), both of which have been hypothesized to play an initiatory role in mediating further proteolysis by the ubiquitin proteasomal pathway. More definitive experiments on whether the two hypothesized cytokines, TNF-α and IL-6, are actually causative in this process are needed. Aside from proteolytic activation, LPS-induced endotoxaemia also resulted in elevated expression of pyruvate dehydrogenase kinase (PDK) mRNA and protein. Again, this finding is consistent with the previous work from this group (Crossland et al. 2008) and highlights an interesting and important point of convergence for FOXO and its role as an integrative sensor of cellular stress. The administration of DEX markedly reduced PDK mRNA expression and also resulted in less glycogen degradation and lactate accumulation, which is most likely due to less PDK-induced suppression of pyruvate dehydrogenase activity. An important next step to gain a better understanding of how Akt and FOXO are involved in this pathway will be to somehow manipulate, perhaps through receptor blockade, TNF-α, which the authors speculated was the main reason for why DEX inhibited the rise in PDK mRNA and protein. Crossland and her colleagues are to be congratulated for their work in this area. Clearly future work will need to focus back in on the exact role of TNF-α and IL-6 in this process. The authors’ data represents not only an exciting mechanistic advance but one that advances our knowledge potentially leading to new ideas on how sepsis-induced complications might be mitigated in humans.
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 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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.008 |
| 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 teacher head, 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".