Lactic acid improves Treg manufacturing and in vivo function
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adoptive cell therapy using regulatory T cells (Tregs) is a promising approach to suppress immune responses in autoimmunity and transplantation, but it is challenging to expand pure and optimally suppressive cells. Lactic acid (LA) is associated with enhanced Treg function in tumors so we hypothesized that it may be beneficial during Treg expansion. We found that addition of LA at day 3 post-stimulation onwards improved viability and purity, increased glycolysis upon re-stimulation, and led to superior suppressive function. In Tregs expressing chimeric antigen receptors (CARs) specific for HLA-A2, LA not only enhanced viability and purity but also significantly reduced tonic signaling-associated expression of exhaustion-associated markers (PD-1, TIM-3, LAG-3, TOX, and BLIMP-1). The effects of LA were not fully recapitulated by either pH-neutral lactate or low pH. In immunodeficient mouse models of chronic stimulation and xenogeneic graft-versus-host disease, LA-conditioned human Tregs demonstrated enhanced stability, reduced exhaustion marker expression, and improved efficacy. Thus, LA has a multimodal effect on human polyclonal and CAR Treg purity, viability, and function, representing a method to generate an optimal Treg product for cell therapy.
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.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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 it