Effect of sample preparation techniques on the concentrations and distributions of elements in biological tissues using <i>µ</i>SRXRF: a comparative study
Bibliographic record
Abstract
Routine tissue sample preparation using chemical fixatives is known to preserve the morphology of the tissue being studied. A competitive method, cryofixation followed by freeze drying, involves no chemical agents and maintains the biological function of the tissue. The possible effects of both sample preparation techniques in terms of the distribution of bio-metals (calcium (Ca), copper (Cu) zinc (Zn), and iron (Fe) specifically) in human skin tissue samples was investigated. Micro synchrotron radiation x-ray fluorescence (μSRXRF) was used to map bio-metal distribution in epidermal and dermal layers of human skin samples from various locations of the body that have been prepared using both techniques. For Ca, Cu and Zn, there were statistically significant differences between the epidermis and dermis using the freeze drying technique (p = 0.02, p < 0.01, and p < 0.01, respectively). Also using the formalin fixed, paraffin embedded technique the levels of Ca, Cu and Zn, were significantly different between the epidermis and dermis layers (p = 0.03, p < 0.01, and p < 0.01, respectively). However, the difference in levels of Fe between the epidermis and dermis was unclear and further analysis was required. The epidermis was further divided into two sub-layers, one mainly composed of the stratum corneum and the other deeper layer, the stratum basale. It was found that the difference between the distribution of Fe in the two epidermal layers using the freeze drying technique resulted in a statistically significant difference (p = 0.012). This same region also showed a difference in Fe using the formalin fixed, paraffin embedded technique (p < 0.01). The formalin fixed, paraffin embedded technique also showed a difference between the deeper epidermal layer and the dermis (p < 0.01). It can be concluded that studies involving Ca, Cu and Zn might show similar results using both sample preparation techniques, however studies involving Fe would need more special attention.
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How this classification was reachedexpand
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 | 0.001 | 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) | 0.000 | 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 teacher head, 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".