Chemotherapy-Induced Neuropathy and Drug Discovery Platform Using Human Sensory Neurons Converted Directly from Adult Peripheral Blood
Bibliographic record
Abstract
Abstract Chemotherapy-induced peripheral neuropathy (PN) is a disorder damaging the peripheral nervous system (PNS) and represents one of the most common side effects of chemotherapy, negatively impacting the quality of life of patients to the extent of withdrawing life-saving chemotherapy dose or duration. Unfortunately, the pathophysiological effects of PN are poorly understood, in part due to the lack of availability of large numbers of human sensory neurons (SNs) for study. Previous reports have demonstrated that human SNs can be directly converted from primitive CD34+ hematopoietic cells, but was limited to a small-scale product of SNs and derived exclusively from less abundant allogenic sources of cord or drug mobilized peripheral blood (PB). To address this shortcoming, we have developed and report detailed procedures toward the generation of human SN directly converted from conventionally drawn PB of adults that can be used in a high-content screening platform for discovery-based studies of chemotherapy agents on neuronal biology. In the absence of mobilization drugs, cryogenically preserved adult human PB could be induced to (i)SN via development through expandable neural precursor differentiation. iSNs could be transferable to high-throughput procedures suitable for high-content screening applicable to neuropathy for example, alterations in neurite morphology in response to chemotherapeutics. Our study provides the first reported platform using adult PB-derived iSNs to study peripheral nervous system-related neuropathies as well as target and drug screening potential for the ability to prevent, block, or repair chemotherapy-induced PN damage. Stem Cells Translational Medicine 2019;8:1180–1191
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".