<p>Investigation of psoriasis skin tissue by label-free multi-modal imaging: a case study on a phototherapy-treated patient</p>
Bibliographic record
Abstract
Background: Psoriasis is a systemic inflammatory disease characterized by epidermal proliferation in the skin. Altered lipid metabolism is considered to be a central factor in the psoriatic etiopathogenesis. Thus, it is necessary to visualize chemical specificity of the samples for better medical diagnosis and treatment. Here, we investigate its role in the development of psoriatic lesions, before and after ultraviolet phototherapy, in a case study. Methods: The distribution and morphology of different lipids and fibrous proteins in psoriatic (lesional) tissues were visualized by two complementary label-free imaging techniques: 1) non-linear microscopy (NLM), providing images of lipids/proteins throughout the skin layers at submicrometer resolution; and 2) mass spectrometry imaging (MSI), offering high chemical specificity and hence the detection of different lipid species in the epidermal and dermal regions. A conventional method of histological evaluation was performed on the tissues, with no direct comparison with NLM and MSI. Results: Psoriatic tissues had a higher lipid content, mainly in cholesterol, in both the epidermal and dermal regions, compared to healthy tissues. Moreover, the collagen and elastin fibers in the psoriatic tissues had a tendency to assemble as larger bundles, while healthy tissues showed smaller fibers more homogeneously spread. Although phototherapy significantly reduced the cholesterol content, it also increased the amounts of collagen in both lesional and non-lesional tissues. Conclusion: This study introduces NLM and MSI as two complementary techniques which are chemical specific and can be used to assess and visualize the distribution of lipids, collagen, and elastin in a non-invasive and label-free manner. Keywords: psoriasis skin tissue, non-linear microscopy, lipids, collagen, time-of-flight secondary ion mass spectrometry (ToF-SIMS)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".