Bibliographic record
Abstract
With an aging population the number of patients receiving renal replacement therapy in the United Kingdom is rising rapidly and is unlikely to reach steady state for another 25 years (1). Although end-stage renal failure is relatively rare, treatment with dialysis or transplantation is very expensive, and presently accounts for more than 2% of the total NHS budget. The importance of anemia in chronic kidney disease (CKD) has been increasingly recognized since the introduction of erythropoietin therapy in the 1980s. The Kidney Disease Outcomes Quality Initiative guidelines of the National Kidney Foundation define anemia in CKD as a hemoglobin concentration of less than 11 g/dL in premenopausal females and prepubertal patients, and less than 12 g/dL in adult males and postmenopausal females. Data from a Canadian cohort demonstrate that anemia is common in this cohort, being present in approximately 25% of patients with CKD whose glomerular filtration rate is more than 50 mL/min/1.73 m2, 44% between 35 and 49 mL/min/1.73 m2, 51% between 25 and 34 mL/min/1.73 m2, and 87% below 25 mL/min/1.73 m2 (2). The importance of treating anemia is highlighted by the adverse consequences both for the individual patient and for the healthcare system ranging from effects on quality of life, cognitive function and libido through to increased mortality and morbidity with its associated economic burden. There is a strong association between anemia and cardiovascular disease, with one of the earliest manifestations of heart disease in anemic CKD patients being left ventricular hypertrophy. Analysis of patients in the Studies Of Left Ventricular Dysfunction trial found that anemia and CKD were independent risk factors for mortality among patients with heart failure caused by left ventricular dysfunction (3). In the United Kingdom, there are 6000 deaths per year caused by heart failure associated with cardiovascular disease and the annual mortality for those with heart failure ranges from 10% to more than 50%. Anemia in CKD is caused by a relative deficiency of erythropoietin (EPO), although it is important to exclude other treatable causes of anemia. Although there are several commercially available EPOs: epoetin alfa, epoetin beta, and darbepoetin alfa, they are costly and associated with possible complications such as pure red cell aplasia caused by anti-EPO antibodies, which can result from exogenous administration. In this edition of Transplantation, Yokoo et al. have demonstrated that they are able to transplant human mesenchymal stem cells (hMSC)-derived organoids, with a morphology and function similar to kidney, into rodents with renal injury which are then able to respond to appropriate physiological cues (anemia) and produce EPO. Specifically, these organoids respond to the induction of anemia by producing appropriate additional EPO which they show shortens the time to restoration of hemoglobin. Mesenchymal stem cells are a poorly defined population of cells most commonly found in bone marrow, although they also reside in adipose tissue. They are not readily isolated on the basis of cell surface expression, and are usually isolated by their ability to adhere to plastic (4). Their identity is subsequently confirmed by their ability to differentiate down chondrocytic and adipocytic lineages. Prolonged passaging of MSCs in culture can result in a change phenotypically and potentially also functionally, and it would be important therefore to specify a maximal passage number within which MSCs will be used. MSCs can be seen therefore to comprise a fairly heterogeneous population, and although they may offer exciting therapeutic possibilities there are significant issues to be overcome in terms of defining and isolating a more homogeneous population of cells. In this study the hMSCs require adenoviral transfection with Glial cell-derived neurotrophic factor to ensure they develop into kidney-like organoids, and so there is still an element of manipulation which may raise concerns about translation into clinical practice. Ideally, the adoption of a kidney phenotype should be performed without a need for transfection. Nevertheless, once transplanted they seem to retain the ability to function physiologically, only releasing EPO when required rather than producing it constitutively. Of note, the model used is an acute model rather than the chronic variety seen in clinical practice, and it would be important to ascertain that the organoids functioned in a similar fashion in that setting. As with many small animal models the challenge will be to successfully scale it up in larger animal models, before human use could be contemplated. It is interesting that the hMSC derived organoids are tolerated by the immune system after omental transplantation, and it would be important to explore the mechanisms underpinning this, and assessing how durable this immunotolerance is. Will episodes such as sepsis or administration of tumor necrosis factor α/interferon which result in immune activation trigger rejection of the organoids? Of note, MSCs have well reported immunomodulatory functions which may in part account for their immune tolerance (5). It would be worthwhile establishing if there is a population of undifferentiated hMSCs within the organoids which are facilitating this immune privilege. The approach by Yokoo et al. offers hope for the future, and once issues relating to longevity of efficacy and immunotolerance in larger models are resolved we can look forward to novel therapeutic approaches for patients with CRF.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".