Non-melanoma Skin Cancers in Uranium Miners – Clinical Cases
Bibliographic record
Abstract
Introduction. Using sources of ionizing radiation in various spheres of human life became an integral part of the existence of modern society and the necessary condition for further scientific and technological progress. However, along with the obvious benefits, this increases the radiation burden and number of malignant neoplasms and the related reduction in life expectancy. The various health risks associated with uranium exposure are well known, mainly it concerns the lung cancer, but recently there has been a hypothesis that if uranium is photoactivated by ultraviolet radiation, it may be more dangerous for the skin. This idea is confirmed by statistics on countries with high levels of solar radiation and uranium deposits, which are being actively developed, such as Australia, Kazakhstan, Canada, Russia, etc. The following article provides a brief overview of the literature and presents 2 cases of basal cell carcinoma in the miners of the uranium mine. The aim of the study. To analyze the overview of the accessible literature and the description of two clinical cases of non-melanoma skin cancers in uranium miners from the own practice. Materials and methods. The content analysis, method of systemic comparative analysis, bibliosemantic method of studying of actual scientific researches concerning the practice of non-invasive methods of evaluation were used. The search for sources was carried out in the scientific meteorological databases: PubMed-NCBI, Medline, CochraneLibrary, EMBASE, ResearchGate by keywords: “uranium miners”, “non-melanoma skin cancer”, “UV”, “dermatoscopy”. Digital dermoscopic images were captured using dermatoscope (DermLite DL3 dermatoscope) mounted on a digital camera (Galaxy S-4 Samsung Corporation). Ultrasound gel was also used for immersion dermatoscopy. All images were evaluated using the algorithm of H. Kittler. Conclusions. The overview of the modern literature and clinical cases from our own practice suggest that uranium mining and processing are associated with a wide range of potential adverse human health risks and skin malignancy in this processes in not less important than of respiratory tract, especially since this risk is exacerbated by the exposure to ultraviolet light. The risk of non-melanoma skin cancers in this group requires careful clinical and dermatoscopy monitoring and in the case of suspicious lesions special tests like histology from of the skin biopsy, immunohistochemical stains and/or electron microscopy. The future study of statistical data on skin cancers from similar regions is of no less interest.
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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 | 0.007 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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; both teacher heads agree on what is shown here.
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".