Cutaneous leishmaniasis in Iran: A review of epidemiological aspects, with emphasis on molecular findings
Bibliographic record
Abstract
Leishmania parasites can cause zoonotic cutaneous leishmaniasis (CL) by circulating between humans, rodents, and sandflies in Iran. In this study, published data were collected from scientific sources such as Web of Science, Scopus, PubMed, Springer, ResearchGate, Wiley Online, Ovid, Ebsco, Cochrane Library, Google scholar, and SID. Keywords searched in the articles, theses, and abstracts from 1983 to 2021 were cutaneous leishmaniasis, epidemiology, reservoir, vector, climatic factors, identification, and Iran. This review revealed that CL was prevalent in the west of Iran, while the center and south of Iran were also involved in recent years. The lack of facilities in suburban regions was an aggravating factor in the human community. Some parts of southern Iran were prominent foci of CL due the presence of potential rodent hosts in these regions. Rhombomys opimus, Meriones lybicus, and Tatera indica were well-documented species for hosting the Leishmania species in Iran. Moreover, R. opimus has been found with a coinfection of Leishmania major and L. turanica from the northeast and center of Iran. Mashhad, Kerman, Yazd, and sometimes Shiraz and Tehran foci were distinct areas for L. tropica. Molecular identifications using genomic diagnosis of kDNA and ITS1 fragments of the parasite indicated that there is heterogeneity in leishmaniasis in different parts of the country. Although cutaneous leishmaniasis has been a predicament for the health system, it is relatively under control in Iran.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".