Advantages of Low-Voltage Electron Microscopy (LVEM) for Clinical Nephropathology
Bibliographic record
Abstract
Background: Transmission electron microscopy (TEM) is a crucial tool for the investigation of nephropathologies. This technique plays a critical role in the identification of ultrastructural abnormalities with nanometer-scale resolution. TEM can confidently identify ~90% of cases where light microscopy is limited due to minimal pathological abnormalities. Standard high-voltage TEM systems (HV-TEM) operate at accelerating voltages of ≥80 kV and are routinely used for diagnosing kidney disease through production of detailed images of renal cells. However, HV-TEM is limited by its complexity, high costs, and low image contrast unless specimens are stained with heavy metals. This has led many clinics to discontinue the use of TEM, concentrating it in a few major centers. Low voltage electron microscopy (LVEM) is introduced as an alternative to overcome these challenges. LVEM systems are compact, simple to operate and maintain, afordable and produce higher image contrast, making the post-staining step optional. Methods: In this work, TEM images of unhealthy renal cells were collected using LVEM at accelerating voltages of 15 kV and 25 kV (Delong Instruments LVEM 25) and compared to images collected from HV-TEM. Thin sections (~80 nm) were prepared following standard fixation, sectioning and post-staining protocols (0.5% uranyl acetate). Results: LVEM images provided directly comparable image data with enhanced contrast. As expected, the presence of abnormal ultrastructure was recognized in different unhealthy cells (Figure 1), as such as, (a) fingerprint-like deposits in renal cells typically observed in Lupus glomerulonephritis disease; (b) Irregular glomerular basement membranes in cells contaminated with Alport syndrome, and (c) the presence of zebra bodies in renal cells characteristic of Fabry’s disease. Conclusion: LVEM showed as an alternative to HV-TEM systems for investigation of nephropathology. LVEM allows for a simple, rapid, high-contrast and high-quality imaging of renal cells. In addition, the proposed technique offers a compact and more cost-effective solution, making EM once again widely accessible for clinical nephropathology.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".