Climate Change, Natural Disasters, and Cutaneous Fungal Infections
Bibliographic record
Abstract
Fungal infections are an important source of morbidity and mortality that can manifest as superficial or invasive diseases. Diagnostic techniques for human fungal pathogens remain problematic, and multi-drug resistance is emerging. This review addresses the potential emergence of new fungal pathogens in changing environments and reported instances of cutaneous fungal infections after natural disasters. Global warming does more than increase the mean global temperature; it is associated with changing precipitation patterns and major climatic events. With natural disasters, niches are created for the proliferation of fungal pathogens affecting humans across previously existing geographical boundaries. Here, we reviewed reports of cutaneous fungal infections after natural disasters, including earthquakes, floods, tsunamis, hurricanes, and tornadoes. Of importance is the potential for thermal adaptation leading to the evolution of new human pathogens, exacerbated by the elevated environmental fungal levels in disaster situations. Studies have documented higher risks of contracting typical tinea infections, as well as opportunistic, trauma-related infections by environmental fungi. The latter is especially concerning due to atypical clinical presentations that could lead to treatment delays, antifungal resistance, and systemic complications. These support the importance of considering climate change as affecting the adaptation of these pathogens and the consequences of this change for human populations. A One Health framework should be advocated to address the impact of climate change on dermatological care.
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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 | 0.001 | 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".