ArticleScientific reports2021
Wolbachia detection in Aedes aegypti using MALDI-TOF MS coupled to artificial intelligence.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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.
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.
Who cites it
10 citing papers in PubMed.
- The Effect of Increased Temperature on Dengue Virus in the VectorTropical medicine and infectious disease · 2026Article
- Proof-of-Concept Machine Learning Framework for Arboviral Disease Classification Using Literature-Derived Synthetic Data: Methodological Development Preceding Clinical Validation.Healthcare (Basel, Switzerland) · 2026Article
- A Scoping Review of Machine Learning Applications Across Epidemiological Stages of Zoonotic Disease.Transboundary and emerging diseases · 2026Article
- Making MALDI-TOF MS for entomological parameters accessible: A practical guide for in-house library creation.PloS one · 2025Article
- Review
- MALDI-TOF MS Profiling and Its Contribution to Mosquito-Borne Diseases: A Systematic Review.Insects · 2024Review
- Challenges for ticks and tick-borne diseases research in Southeast Asia: Insight from the first international symposium in Cambodia.PLoS neglected tropical diseases · 2024Review
- Applications of MALDI-TOF Mass Spectrometry to the Identification of Parasites and Arthropod Vectors of Human Diseases.Microorganisms · 2022Review
- Sex-specific distribution and classification of Wolbachia infections and mitochondrial DNA haplogroups in Aedes albopictus from the Indo-Pacific.PLoS neglected tropical diseases · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The mosquito Aedes aegypti is the major vector of arboviruses like dengue, Zika and chikungunya viruses. Attempts to reduce arboviruses emergence focusing on Ae. aegypti control has proven challenging due to the increase of insecticide resistances. An emerging strategy which consists of releasing Ae. aegypti artificially infected with Wolbachia in natural mosquito populations is currently being developed. The monitoring of Wolbachia-positive Ae. aegypti in the field is performed in order to ensure the program effectiveness. Here, the reliability of the Matrix‑Assisted Laser Desorption Ionization‑Time Of Flight (MALDI‑TOF) coupled with the machine learning methods like Convolutional Neural Network (CNN) to detect Wolbachia in field Ae. aegypti was assessed for the first time. For this purpose, laboratory reared and field Ae. aegypti were analyzed. The results showed that the CNN recognized Ae. aegypti spectral patterns associated with Wolbachia-infection. The MALDI-TOF coupled with the CNN (sensitivity = 93%, specificity = 99%, accuracy = 97%) was more efficient than the loop-mediated isothermal amplification (LAMP), and as efficient as qPCR for Wolbachia detection. It therefore represents an interesting method to evaluate the prevalence of Wolbachia in field Ae. aegypti mosquitoes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.