Exploration of the red blood cell biomechanics with digital holographic microscopy: Towards a methodology to identify cellular phenotypes related to major psychiatric disorders
Bibliographic record
Abstract
Major psychiatric diseases (MPDs), including schizophrenia, are diagnosed very late, due to the lack of effective biomarkers. Post-mortem analyzes show that the lipid composition of neuronal membranes differs in people with schizophrenia. This difference is also reflected in several types of cells, including red blood cells (RBCs). RBCs are known to exhibit spectacular biomechanical properties (BPs) resulting in unique deformability capacity and spontaneous membrane vibrations at the nanoscale . It has been reported that the lipid composition of the RBC membranes affects these BPs. An accurate biomechanical characterization of RBCs could thus reveal MPD-related phenotypes. Quantitative Phase Digital Holography Microscopy (QP-DHM), providing images with a nonmetric axial sensitivity, represents a highly relevant technique to quantitatively study the RBC biomechanics. As a first step to identify MPD-related RBC phenotypes, we have started to develop a methodology based on QP-DHM to characterize RBC biomechanics. Studies are conducted in different conditions known to specifically impact RBC BPs. Concretely membrane vibrations and RBC deformations are monitored in environments controlled for temperature, ph, O2 and CO2 partial pressures. We are also going to start a biomechanical study of RBCs obtained from mice, having benefited from a strict diet allowing a control of their membrane lipid composition. Then, aiming at identifying MPD-related RBC phenotypes, we will apply this methodology on our clinical data set, composed or RBC samples collected from patients suffering for MPDs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".