Concise Review: Increasing the Validity of Cerebrovascular Disease Models and Experimental Methods for Translational Stem Cell Research
Bibliographic record
Abstract
Interspecies differences, anatomical and physiological aspects, as wells as simplified study designs contribute to an overestimation of treatment effects and limit the transferability of experimental results into clinical applications. Confounders of cell therapies for cerebrovascular disorders (CVD) include common CVD comorbidities, frequent medications potentially affecting endogenous and transplanted stem cells, as well as age- and immune-system-related effects. All those can contribute to a substantial modeling bias, ultimately limiting the prospective quality of preclinical research programs regarding the clinical value of a particular cell therapy. In this review, we discuss the nature and impact of most relevant confounders. We provide suggestions on how they can be considered to enhance the validity of CVD models in stem cell research. Acknowledging substantial and sometimes surprising effects of housing conditions, chronobiology, and intersex differences will further augment the translational value of animal models. We finally discuss options for the implementation of high-quality functional and imaging readout protocols. Altogether, this might help to gain a more holistic picture about the therapeutic impact of a particular cell therapy for CVD, but also on potential side and off-site effects of the intervention. Stem Cells 2017;35:1141-1153.
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 | 0.017 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".