Insulin-like growth factor 1 (IGF-1) expression is up-regulated in lymphoblastoid cell lines of lithium responsive bipolar disorder patients
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
Bipolar disorder (BD) is a debilitating psychiatric disease characterized by alternating episodes of mania and depression. Among mood stabilizers, lithium is the mainstay for the treatment of BD, with approximately one-third of patients showing remission from episode recurrence. While there is evidence suggesting genetic load for lithium response in BD, its molecular underpinnings are still not completely understood. To identify genes potentially involved in (or correlated with) lithium response, we carried out a genome-wide expression analysis on lymphoblastoid cell lines (LCLs) from 10 BD patients responders (R) and 10 non-responders (NR) to lithium. We compared expression levels of the two groups and tested whether in vitro lithium treatment had different effects in LCLs of R compared to NR. At basal, 2060 genes were differentially expressed between R and NR while no genes were differentially regulated by lithium in the two groups. After pathway analysis based on the 2060 genes, 9 genes were selected for validation with qRT-PCR. Eight genes were validated in the same sample of LCLs while only insulin-like growth factor 1 (IGF-1) was significantly over-expressed in R compared to NR in the same sample as well as in an independent sample comprised of 6 R and 6 NR (sample 1, fold change=1.94; p=0.005; sample 2, fold change=2.21; p=0.005). IGF-1 was also significantly over-expressed in R but not in NR when compared to a sample of non-psychiatric controls. Our findings suggest that IGF-1 may be involved in lithium response, supporting further investigation on its potential as a biomarker.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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