From the Cover: Tributyltin Alters the Bone Marrow Microenvironment and Suppresses B Cell Development
Bibliographic record
Abstract
Organotins are industrial chemicals and agricultural pesticides, and they contaminate both outdoor and indoor environments. Organotins are detectable in human sera at biologically active concentrations and are immuno-and neuro-toxicants. Triphenyltin, tributyltin (TBT) and dibutyltin activate peroxisome proliferator-activated receptor γ in bone marrow multipotent mesenchymal stromal cells and promote adipogenesis. TBT also has been shown to suppress osteogenesis; osteoblasts not only support bone homeostasis but also support B lymphopoiesis. In addition, developing B cells are highly sensitive to exogenous insults. Thus, we hypothesized that bone marrow B cells may be negatively affected by TBT exposure both directly, through activation of apoptosis, and indirectly, through alterations of the bone marrow microenvironment. TBT activated apoptosis in developing B cells at environmentally relevant concentrations (as low as 80 nM) in vitro, via a mechanism that is distinct from that induced by high dose (μM) TBT and that requires p53. TBT suppressed the proliferation of hematopoietic cells in an ex vivo bone marrow model. Concurrent treatment of stromal cells and B cells or pretreatment of stromal cells with TBT induced adipogenesis in the stromal cells and reduced the progression of B cells from the early pro B (Hardy fraction B) to the pre B stage (Hardy fraction D). In vivo, TBT induced adipogenesis in bone marrow, reduced "aging-sensitive" AA4+CD19+ B cells in bone marrow, and reduced splenic B cell numbers. Immunosenescence and osteoporosis are adverse health effects of aging, we postulate that TBT exposure may mimic, and possibly intensify, these pathologies.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.182 | 0.065 |
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".