Applied 3D printing for microscopy in health science research
Bibliographic record
Abstract
The rapid prototyping capability offered by 3D printing is considered advantageous for commercial applications. However, the ability to quickly produce precision custom devices is highly beneficial in the research laboratory setting as well. Biological laboratories require the manipulation and analysis of delicate living samples, thus the ability to create custom holders, support equipment, and adapters allow the extension of existing laboratory machines. Applications include camera adapters and stage sample holders for microscopes, surgical guides for tissue preparation, and small precision tools customized to unique specifications. Where high precision is needed, especially the reproduction of fine features, a printer with a high resolution is needed. However, the introduction of cheaper, lower resolution commercial printers have been shown to be more than adequate for less demanding projects. For direct manipulation of delicate samples, biocompatible raw materials are often required, complicating the printing process. This paper will examine some examples of 3D-printed objects for laboratory use, and provide an overview of the requirements for 3D printing for this application. Materials, printing resolution, production, and ease of use will all be reviewed with an eye to producing better printers and techniques for laboratory applications. Specific case studies will highlight applications for 3D-printed devices in live animal imaging for both microscopy and Magnetic Resonance Imaging.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".