Evidence map›Paper›PMID 41627445›Full record

ArticleArchives of microbiology2026

Viability and prediction of encapsulated Trichoderma spp. isolates using machine learning: a multi-layer perceptron-based approach.

Thalesram Izidoro Pinotti, Fábio Sandro Dos Santos, Yanka Manoelly Dos Santos Gaspar, Tiago de Oliveira Sousa, Thiago Pajeú Nascimento, Alice Maria Gonçalves Santos

Abstract read
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In one paragraph

Article in Archives of microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

6 authors.

Thalesram Izidoro PinottiCampus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus, 64900-000, PI, Brazil.ORCID http://orcid.org/0009-0004-5063-0606
Fábio Sandro Dos SantosCampus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus, 64900-000, PI, Brazil.ORCID http://orcid.org/0000-0002-0135-4981
Yanka Manoelly Dos Santos GasparCampus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus, 64900-000, PI, Brazil.ORCID http://orcid.org/0009-0000-5855-3183
Tiago de Oliveira SousaCampus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus, 64900-000, PI, Brazil.ORCID http://orcid.org/0000-0001-7855-348X
Thiago Pajeú NascimentoCampus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus, 64900-000, PI, Brazil.ORCID http://orcid.org/0000-0003-3480-6734
Alice Maria Gonçalves SantosCampus Professora Cinobelina Elvas, Federal University of Piauí, Bom Jesus, 64900-000, PI, Brazil. alicemgsantos@ufpi.edu.br.ORCID http://orcid.org/0000-0003-0672-1709

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The development of microbial bioproducts requires understanding the stability and viability of cells during storage. Accordingly, this study evaluated the viability of capsules containing different strains of Trichoderma spp. isolated from native plants of the Piauí Cerrado, using an integrated approach combining microencapsulation and computational modeling. Capsules were produced by ionic gelation in a sodium alginate matrix, and fungal viability was monitored by plating and conidial counting over 60 days of storage. The resulting data were used to train a Multilayer Perceptron (MLP) Artificial Neural Network, structured with an ELU activation function, L2 regularization, and dropout to prevent overfitting. The model was also able to generate projections beyond the experimental period; however, these estimates are strictly exploratory in nature and serve only as a proof of concept for the application of MLPs to describe potential trends beyond the 60 days observed. Within the experimental window, strains UFPI07, UFPI10, UFPI11, and UFPI16 maintained populations above 7 log CFU mL

Indexed as

Machine LearningMicrobial ViabilityTrichodermaCapsulesMultilayer PerceptronsNeural Networks, ComputerPrediction AlgorithmsPredictive Learning ModelsCapsulesAgricultural sustainabilityArtificial neural networksBiofungicidesFungal viabilityPredictive modeling

Identifiers

PMID41627445

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.