Evidence map›Paper›PMID 41286273›Full record

ArticleEcoHealth2026

Ecological Niche Modeling of Acanthamoeba in Türkiye.

Hakan Kavur, Gülşah Evyapan, Ozan Artun

Abstract read
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In one paragraph

Article in EcoHealth, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Hakan KavurKaraisali Vocational School, Cukurova University, Karaisali, Adana, Turkey. hkavur@cu.edu.tr.ORCID 0000-0002-4188-440X
Gülşah EvyapanFaculty of Medicine, Department of Medical Biology, Yuzuncu Yil University, Van, Turkey.
Ozan ArtunKaraisali Vocational School, Cukurova University, Karaisali, Adana, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Acanthamoeba, is an opportunistic pathogenic organism with a global distribution and the potential to cause severe human infections. This study primarily aimed to identify the environmental factors influencing the distribution of Acanthamoeba by analyzing various bioclimatic and topographic variables, and to predict their potential current and future distribution under 2070 climate change scenarios using ecological niche modeling based on the MaxEnt algorithm. Niche modeling was performed on 20 water and 20 soil samples collected from hot springs, swimming pools, parks, and agricultural areas. The rates of positive water samples in Afyon and Kütahya were 70 and 50%, respectively. We detected 60 and 100% positive rates of soil samples collected in Afyon and Kütahya, respectively. Niche modeling incorporated 19 bioclimatic variables, with BIO3 (Isothermality), BIO4 (Temperature seasonality), BIO13 (Precipitation of the wettest month), and BIO15 (Precipitation seasonality) identified as the most influential predictors. The model showed high predictive performance, with AUC values of 0.991 and 0.977 for current and future projections, respectively. Results suggest a potential increase in Acanthamoeba distribution in future scenarios, especially in the southwestern region of Afyon and southern Kütahya. These findings highlight the importance of environmental monitoring and genotypic characterization of Acanthamoeba for public health risk assessment.

Indexed as

AcanthamoebaEcosystemClimate ChangeEnvironmental MonitoringHumansAcanthamoebaArcGISbioclimatic factorsecological niche modelMaxEnt

Identifiers

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.