Current Insights into Diagnosis, Prevention Strategies, Treatment, Therapeutic Targets, and Challenges of Monkeypox (Mpox) Infections in Human Populations
Bibliographic record
Abstract
In the wake of the emergence and worldwide respread of a viral infection called Monkeypox (Mpox), there is a serious threat to the health and safety of the global population. This viral infection was endemic to the western and central parts of Africa, but has recently spread out of this endemic area to various countries, including the United Kingdom (UK), Portugal, Spain, the United States of America (USA), Canada, Sweden, Belgium, Italy, Australia, Germany, France, the Netherlands, Israel, and Mexico. This is a timely review focusing on recent findings and developments in the epidemiology, clinical features, therapeutic targets, diagnosis, prevention mechanisms, research challenges and possible treatment for Mpox. To date (29 November 2022), there have been around 81,225 reported cases of Mpox. In most cases, this illness is mild; however, there is a fatality rate ranging from 1 to 10%, which might be increased due to associated complications and/or secondary infections. There is a real challenge in the diagnosis of Mpox, since its symptoms are very similar to those of other infections, including smallpox and chickenpox. Generally, to prevent/limit the risk and transmission of Mpox, the detection and isolation of infected individuals, as well as hand hygiene and cleanliness, are essential and effective approaches to control/combat this viral infection. Nevertheless, updated information about Mpox from different angles is lacking. Thus, this review provides updated and comprehensive information about the Mpox illness, which should highlight the global burden, pathogenicity, symptoms, diagnosis, prevention measures and possible treatment of this emerging disease.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".