A systematic review and data compilation of Chalcididae (Hymenoptera: Chalcidoidea) genetic information

ABSTRACT

Molecular entomology was expected to progress rapidly from basic genetic mapping to comprehensive genome projects. However, for most insect groups, such as parasitic wasps of the family Chalcididae, genetic information remains scarce. There is still an urgent need for identification tools for both taxonomists and applied entomologists to expand the use of biological control agents. This systematic review recovered only 38 papers containing Chalcididae genetic information since 1986, covering 108 species. Most data were published in the last 10 years. Genetic repositories include data for 85 species; in total, information is available for 49 out of 87 genera and 178 out of 1,439 named species. Notably, 141 species have only a single gene sequence available, predominantly the COI gene. Chalcis sispes (Linnaeus, 1761) is the only species with a whole genome sequence available. Available data include nine genes-three mitochondrial (16S, COI, CytB) and six nuclear (18S, 28S, EF-1α, ITS2, RpL27A, mago nashi)-alongside UCEs, exons, karyotypes, mitochondrial genomes, and transcriptomes. Academic journal indexing platforms remain essential for assessing genetic information, as data from older papers may not be available on GenBank or BOLD. Conversely, NCBI is vital for disclosing data when the target group is not mentioned in the main text. Both literature and repositories may contain spurious data, including taxonomic errors, misidentifications, DNA contamination, mixed data, and typos. Taxonomic errors are a universal problem; thus, all downloaded sequences should undergo a “phylogenetic check” alongside known sequences. Such errors may also affect BLAST searches, necessitating caution when evaluating contamination. Authors must carefully verify data quality before publication to ensure this vast global database is populated with trustworthy information.

KEYWORDS:
GenBank; BOLD; DNA barcoding; database; genomics; data quality

INTRODUCTION

“If we successfully combine the tools of molecular biology and genetics with the more traditional disciplines of physiology, behavior, developmental biology, ecology, taxonomy, and evolutionary biology, we can truly come to grips with a range of processes and phenomena concerning insects. The knowledge that will emerge from such initiatives promises to have profound implications for both the cultural and material benefit of mankind”. This optimistic forecast, part of Professor M.J. Whitten’s lecture at Agricultural College, Wageningen University in 1988 (Whitten 1989; see also Whitten 1986), still resonates today. However, nearly 40 years later, the development of genetics in entomology falls considerably short of those expectations.

By October 2025, the Barcode of Life Data System (BOLD) hosted slightly over 20 million COI sequences from insects (Ratnasingham and Hebert 2007). GenBank Release 268.0 includes approximately 5.90 × 10⁹ sequence records across all divisions, but taxonomy-based queries retrieve only about 50 million records associated with Insecta (NCBI 2025). Even in an extreme scenario where all BOLD insect barcodes were unique, of high quality, and added to every GenBank record, the total would reach only approximately 70 million entries. This represents about 1.2% of all GenBank records, indicating that the proportional genetic representation of insects remains very limited. Although some insect-targeted sequencing projects reside in repositories like the Sequence Read Archive (SRA) or bulk-oriented databases (WGS/TSA/TLS) without full annotation, this “hidden” data is unlikely to increase representation by orders of magnitude. Given that insects constitute at least 50% of global described biodiversity (Stork 2018), this imbalance suggests that entomology receives disproportionately low genomic attention relative to its biological relevance.

The high cost of molecular data prospecting was a major obstacle in early years, with resources concentrated on illustrious vertebrates. While molecular entomology was expected to advance rapidly from basic genetic mapping to comprehensive genome projects, most insect groups still lack basic genetic information. Consequently, the expectation that molecular tools would overcome the “taxonomic impediment” has not been fully realized. Although this impediment is largely related to the shortage of specialists, there remains a clear need for reliable identification tools for both taxonomists and applied entomologists. Integrating molecular approaches with traditional taxonomic data could enhance identification accuracy and promote the use of biological control agents (Shimbori et al. 2023).

This is especially relevant for parasitic Hymenoptera, which account for 87.9% of all species used in biological control programs (Narendran 2001). DNA-based identification offers a fast and precise alternative for non-specialists (Hebert et al. 2003) and has proven efficient for economically important Hymenoptera groups (Charul et al. 2023), including the Chalcididae (Cancian de Araujo et al. 2019). This family of parasitoid wasps is associated with at least 140 relevant crop pest species of Lepidoptera and Diptera (Cancian de Araujo, unpublished data). Furthermore, molecular taxonomy often reveals cryptic species (Jörger and Schrödl 2013), a common challenge in applied entomology. However, the accuracy of DNA-based identifications depends on the reliability of reference sequences, which may be compromised by misidentified records in public databases or limited by the costs of sequencing facilities and reagents.

Chalcididae groups approximately 1,439 species and 87 genera (Delvare 2025). Linnaeus (1767) described the first species-Sphex sispes (= Chalcis sispes) and Vespa minuta (= Brachymeria minuta)-over 250 years ago (Narendran and van Achterberg 2016). Despite their importance, the first information on Chalcididae genetic material appeared only in 1986, when Hung described the karyotypes of three Brachymeria Westwood, 1829’ species (Hung 1986). It took another 14 years for the first sequences (28S) to be released (Campbell et al. 2000), and until 2019 for the first article focused primarily on Chalcididae genetics (Cancian de Araujo et al. 2019). Historically, Chalcididae DNA sequences have been released mainly as outgroups in broader phylogenetic studies or as part of faunistic inventories.

Even though available Chalcididae genetic information is limited, it is necessary to survey how much information is public, how it is distributed among taxa, the information sources, and the quality of the associated data. Furthermore, none of the papers that have published Chalcididae genetic information have been dedicated to compiling the knowledge accumulated over the years. In this study, we present the first systematic revision of Chalcididae genetic information. We compare Chalcididae data availability from relevant genetic repositories, genetic data diversity and quality, and data distribution among subordinate taxa. Finally, we present a brief discussion on the implications of data quality associated with genetic information for basic and applied entomology.

MATERIAL AND METHODS

The structure of this study is adapted from Page et al. (2021).

Terminology

Genetic information: Any published information on Chalcididae genetic material, such as karyotypes, genotypes, gene sequences, genetic expression, epigenetics, and genomics. For clarity, data were split into categories (Genes, UCE, Exon, Karyotype, Genome, and Transcriptome). The ‘Genes’ category was subdivided by specific genes.

The National Center for Biotechnology Information (NCBI, https://www.ncbi.nlm.nih.gov/): An institution under the U.S. National Institutes of Health (NIH) that hosts public databases such as GenBank, PubMed, and BLAST. The NCBI stores and enables retrieval of DNA and protein sequences, as well as scientific literature, and offers analytical tools for sequence comparison and genome annotation.

The Barcode of Life Data System (BOLD, boldsystems.org): A freely available informatics workbench for the acquisition, storage, analysis, and publication of specimen data, primarily used for DNA barcode sequences (Cytochrome Oxidase I gene, fragment 5p - COI-5P).

Eligibility criteria

Two data sources were included: scientific articles and genetic information repositories. To be eligible, data from scientific articles had to originate from peer-reviewed publications indexed with a valid DOI and/or ISSN at the time of publication. We also accessed genetic repositories to cross-check information and mine newly published data not recovered by indexing platforms; this step was necessary since papers not mentioning the family in the title, abstract, or keywords are often missed by search engines. When data were available only in repositories, quality was evaluated for contamination and misidentification using BLAST (NCBI sequences) or by inspecting associated images and comparing them with “good sequences” through a genetic distance tree (BOLD sequences, details below).

Information sources

The sources for publications were the academic journals indexing platforms: PubMed (https://pubmed.ncbi.nlm.nih.gov/), Scielo (https://www.scielo.org/), Google Scholar (https://scholar.google.com/), and Periódicos CAPES (https://www.periodicos.capes.gov.br/). Genetic repositories included NCBI’s GenBank (https://www.ncbi.nlm.nih.gov/genbank/) and BOLD (https://boldsystems.org/).

Search strategies

Searches were conducted between October 2024 and January 2025. Given the two main sources, strategies differed accordingly.

In academic journals indexing platforms, consecutive searches were performed using the terms: ‘Chalcididae’; ‘Chalcidoidea’; ‘Chalcididae+gene(s)’; ‘Chalcididae+genome(s)’; ‘Chalcididae+metagenome’; ‘Chalcididae+phylogenomic’; ‘Chalcididae+mitogenomic’; and similar variations for ‘Chalcidoidea’. Searches were made sequentially, starting with PubMed, followed by Google Scholar, Periódicos CAPES, and Scielo.

For NCBI repositories, we searched for ‘Chalcididae’, retaining all results for the selection process, including all availa ble databases, followed by a screening through each returned record. No specific domain or complex query was built, as the number of returned records was suitable for direct screening.

For BOLD, we used the taxonomy search tool for ‘Chalcididae’. All other searchable fields were left empty, and the search included public records. Contaminants and records without genetic information were excluded, and extant records were inspected for misidentification. Since BOLD relies on user-introduced data, it is susceptible to Putative Misidentified Records (PMR). To address this, a Kimura 2-Parameter distance tree was produced using BOLD V4.0 tools with the following parameters:Search: Tax(Chalcididae); Include public records (3,489 records returned) (3,489 records selected); Data Type: Nucleotide; Distance Model: Kimura 2 Parameter; Marker: COI-5P; Colourization: Barcode; Cluster (BIN); Label: Process ID; Label: Taxon; Label: Barcode Cluster (BIN); Filter: length > 100bp only; Filter: exclude records flagged as misidentifications; Filter: exclude records with stop codons; Filter: exclude contaminants.

The tree is provided as Supplementary Material S1. All clades were mapped, with special attention to long branches. PMR were individually inspected via associated images. We rejected all PMR lacking images (including those mined from GenBank) and those with images of other families (OTUs highlighted in yellow in the tree). In cases where a record was associated with a BIN known to belong to Chalcididae, it was treated as an image misassociation and included in the final database (OTUs highlighted in green in the tree). Records sharing BINs with verified Chalcididae but lacking images were also retained.

Journal articles selection process

To be selected for this study, an article should provide any information related to Chalcididae genetic material. The selection process was performed in three steps, from more inclusive to more exclusive, to avoid excluding papers containing Chalcididae genetic information. The first step was searching in the indexing platforms with the targeted terms. The second step was evaluating the title and summary to exclude spurious matches; the exclusion criteria were the absence of information about Chalcididae or genetics. Papers that might contain any information with the potential to be of interest were retained. The third step consisted of manual screening through the papers searching for the term Chalcididae. When the term Chalcididae was found, we performed a subsequent screening through the paper looking for any genetic information related to this family.

Data collection and synthesis methods

All identified Chalcididae genetic information was included in the study database. Data were summarized and categorized by type and taxon (Table 1).

Table 1
Summary of the Chalcididae genetic information published in scientific journals until April 2024.

Paper	Type of data	Taxon coverage with molecular information	Valid species included
Amalin et al. (1988)*	karyotype	One genus (1 sp.)	1
Ashfaq et al. (2022)	COI-5P	Chalcididae (86 spp.), one genus (1 sp.)	0
Bukowski et al. (2022)	COI-5P	Chalcididae (8 spp.)	0
Campbell et al. (2000)	28S	Seven genera (11 sp.)	0
Cancian de Araujo et al. (2018a)	COI-5P	Haltichellinae (1 sp.)	0
Cancian de Araujo et al. (2018b)	COI-5P	Haltichellinae (1 sp.), two genera (2 sp.)	0
Cancian de Araujo et al. (2019)	COI-5P	Eight genera (141 sp.)	32
Carpenter et al. (1999)	18S, 28S, COI	One genus (1 sp.)	0
Chen et al. (2004)	28S, 18S, 16S, and COI	One genus (1 sp.)	1
Cruaud et al. (2013)	COI, CytB, EF-1α, ITS2, RpL27A, mago nashi	One genus (1 sp.)	1
Cruaud et al. (2021)	UCEs	62 genera (116 sp.)	48
Cruaud et al. (2024)	exon, UCEs	Brachymeriinae (2 sp.), Chalcidinae (4 sp.), Cratocentrinae (1 sp.), Dirhininae (1 sp.), Epitraninae (2 sp.), Haltichellinae (6 sp.), Phasgonophorinae (2 sp.), Smicromorphinae (1 sp.)	11
deWaard et al. (2019)	COI-5P	Chalcididae (18 spp.), two genera (4 sp.)	3
Dowton and Austin (2001)	16S, 28S, COI	One genus (1 sp.)	1
Forbes and Funk (2013)	COI-5P	One genus (1 sp.)	1
Gillespie et al. (2005)	28S	One genus (1 sp.)	1
Gokhman and Quicke (1995)*	karyotype	Two genera (4 sp.)	4
Halim et al. (2018)	COI-5P	One genus (2 sp.)	2
Hebert et al. (2016)	COI-5P	Chalcididae (11 spp.)	0
Heraty et al. (2011)	18S, 28S, COI, EF-1α-F2	One genus (1 sp.)	1
Heraty et al. (2013)	18S, 28S	19 genera (23 sp.)	12
Hung (1986)*	karyotype	One genus (3 sp.)	3
Jiang et al. (2022)	mitogenome	Two genera (4 sp.)	2
Johnson et al. (2001)*	karyotype	One genus (1 sp.)	1
Kinyanjui et al. (2021)	COI-5P	Two genera (2 sp.)	0
Klopfstein and Ronquist (2013)	elongation factor-1α	One genus (1 sp.)	0
Larinfeli et al. (2019)	COI-5P	One genus (1 sp.)	1
Liu et al. (2019)	transcriptome	One genus (1 sp.)	1
Munro et al. (2011)	18S, 28S	18 genera (41 sp.)	9
Peters et al. (2017)*	transcriptome	One genus (1 sp.)	1
Ramage et al. (2017)	COI-5P	Chalcididae (2 sp.)	0
Schulmeister et al. (2002)	16S,18S, 28S, COI	One genus (1 sp.)	0
Zohng-Li et al. (2006)	18S	One genus (2 sp.)	1
Shashank et al. (2022)	COI-5P	Five genera (7 sp.)	3
Smith et al. (2011)	COI-5P	Two genera (2 sp.)	2
Tang et al. (2019)	mitogenome	One genus (1 sp.)	0
Telfer et al. (2015)	COI-5P	One genus (1 sp.)	1
Zhao et al. (2021)	mitogenome	Two genera (3 sp.)	2

• *Papers not recovered by indexing platforms.

Bias of assessment

Molecular biology is a dynamic field where new equipment accelerates scientific discovery and the publication of data. This increases the chance that data published between our search and this article’s publication were not included. Furthermore, since the selection process requires screening thousands of results, there is a risk of excluding articles due to human error. There is also a risk that older articles may not be indexed by the chosen platforms. However, since PubMed is part of NCBI and GenBank often associates genetic information with its source, we expect these to serve as a backup for papers not recovered via indexing platforms because they lacked targeted keywords.

RESULTS

Academic journals indexing platforms

Although genetic repositories are the primary source of Chalcididae data, academic journal indexing platforms remain essential for recovering older information. The primary search across these platforms returned 34,186 records, of which only 170 contained information on Chalcidoidea or Chalcididae. After a second screening to retain only publications presenting Chalcididae genetic data, 33 articles were recovered (up to April 2024). Five additional papers, not found by the indexing platforms but known to the authors, were accessed directly. One preprint and one Ph.D. thesis were identified but discarded according to the eligibility criteria. A summary of the results is presented in Table 1, with complete details (access, data type, taxa coverage, and valid species) provided in Supplementary Material S2.

Overall, molecular data for Chalcididae have been released sporadically since Hung (1986). While publications have become more frequent in the last decade, only 11 articles were published between 1986 and 2011. Recent data are typically part of studies not primarily focused on the group, such as faunistic inventories (e.g., Cancian de Araujo et al. 2018b, Shashank et al. 2022), crop pest studies (Halim et al. 2018, Kinyanjui et al. 2021), research on other taxa (e.g., Cruaud et al. 2013), or higher-level phylogenetic studies (e.g., Munro et al. 2011, Heraty et al. 2013, Cruaud et al. 2024 among others, see Supplementary Material S2). The first study focused primarily on Chalcididae using molecular data was Cancian de Araujo et al. (2019), which presented COI-5P sequences for 141 species across eight genera from the Peruvian Amazon. Subsequently, Cruaud et al. (2021) conducted the first genomic study focused on the family, using 538 Ultra-Conserved Elements (UCEs) to assess generic and suprageneric phylogenetic relationships. Cruaud et al. (2024) published a large molecular phylogeny of Chalcidoidea based on UCEs and exons from 433 taxa, including 20 Chalcididae. Finally, Sivell et al. (2024) released the genome of Chalcis sispes, currently the only complete genome available for the family and the most recent study providing genetic data for this group.

The National Center for Biotechnology Information (NCBI)

Searching the NCBI database for “Chalcididae” returned 590 genomic sequences. This search was particularly important for identifying UCE sequences of nine species associated with Cruaud et al. (2019) that were not released in the original paper. The NCBI also provided 23 COI-5P sequences unavailable on BOLD and one unpublished CytB sequence. In total, 27 species have genetic information available exclusively through NCBI. These results, including molecular markers, taxa coverage, and valid species, are summarized in Supplementary Material S3.

The Barcoding of Life Data System (BOLD)

The BOLD search recovered 3,489 records (Supplementary Material S4), including 2,491 sequences and 2,153 Barcode Index Numbers (BINs), which correspond to 556 individual BINs. Except for two records containing COI-3P sequences, all records consist of COI-5P. Of the total recovered, 3,224 records are public, containing 2,432 sequences (2,105 BINs) and 538 individual BINs. The 265 non-public records belong to unpublished projects from the Zoologische Staatssammlung München (ZSM) and the Universidade Federal do Espírito Santo (UFES), corresponding to 59 sequences and 18 individual BINs. Specimen data for these ongoing projects were omitted from the present publication.

As previously noted, BOLD relies on user-provided data and is susceptible to Putative Misidentified Records (PMR). Consequently, a Kimura 2-Parameter distance tree was produced to track misidentifications (parameters described in the Methods; tree provided in Supplementary Material S1). A total of 146 records, corresponding to 42 clusters and 32 individual BINs, were identified as misidentifications and did not belong to Chalcididae. After excluding these problematic records, we retained 2,345 COI-5P sequences (2,016 with BINs) and 523 individual BINs for Chalcididae, encompassing both public and unpublished records from ZSM and UFES. In total, 41 species have genetic information available exclusively at BOLD.

Taxonomic and genetic information summary

When summarizing the taxonomic and genetic data (Supplementary Material S3), we included only information associated with valid species to avoid redundancy, with the expectation that material currently identified at generic or supra-generic levels will be updated in the future.

At least one type of genetic information is available for every valid subfamily of Chalcididae. In total, there is data for 49 genera and 178 named species. One hundred forty-one species have only a single sequence available-predominantly the COI gene-while 22 species have two sequences available. Chalcis sispes is the only species with a whole genome sequence (Sivell et al. 2024). Ninety-six species have their data published primarily in scientific articles, and 70 species have information available exclusively in genetic repositories.

We identified information for nine genes: three mitochondrial (16S, COI, CytB) and six nuclear (18S, 28S, EF-1α, ITS2, RpL27A, mago nashi), alongside data on UCEs, exons, karyotypes, mitochondrial genomes, and transcriptomes. The availability of genetic information is heterogeneous; for instance, only H. rufipes has sequences for the ITS2, RpL27A, and mago nashi genes. Only two species have data for 16S, CytB, and EF-1α, and another two have mitochondrial genomes and transcriptomes available. After COI, the genes with the best coverage are 18S (18 species) and 28S (19 species). There is also significant coverage for UCEs (62 species) and exons (12 species), largely due to the work of Cruaud et al. (2021, 2024).

DISCUSSION

Academic journal indexing platforms remain essential for accessing genetic information, as many older publications contain sequences not available in NCBI databases; in the past, most journals did not require sequence data deposition prior to publication. Conversely, searching NCBI was vital to locate unpublished and published Chalcididae molecular sequences missed by indexing platforms, particularly when the family was used as an outgroup in phylogenetic studies focused on other groups, without any mention of the term Chalcididae in the text. The same happens when the data is part of a DNA barcoding surveys where lower-ranking taxa were restricted to supplementary material. These factors must be considered collectively when aggregating data for new projects.

Some published data may appear as unpublished in NCBI, leading to citation errors. Furthermore, certain NCBI records may lack a clear relation to the publication associated with them in the database. We also recommend caution for non-specialists when using the NCBI Taxonomy Browser due to potential taxonomic errors-e.g., Diapria conica (Fabricius, 1775), a Diapriidae wasp, is incorrectly recorded as a Chalcididae taxon. Like BOLD, NCBI is susceptible to spurious data, including obsolete taxonomic combinations, misidentifications, DNA contamination (which is not automatically flagged), data management errors (upload of genetic information of one specimen associated with another specimen collecting data, etc.), typos, among others. Therefore, we recommend that all downloaded sequences undergo a “phylogenetic check” alongside known sequences to verify their placement. In this context, BOLD’s “one-specimen-one-page” arrangement stands out, as associated images allow specialists to verify taxonomic data-a process not straightforward in INSDC repositories. BOLD also maintains a continuous taxonomic curatorial pipeline, allowing for taxonomic updates. This is especially useful for Chalcididae, as DNA barcoding has proven congruent with robust morphological analysis and helps reveal identification errors and cryptic species (Cancian de Araujo et al. 2019). Overall, we encourage authors to double-check taxonomic information before uploading it to any repository.

Taxonomic errors are not exclusive to genetic repositories. For instance, the supplementary material of Smith et al. (2011) incorrectly ranked 445 pteromalids as Chalcididae, and typos exist in other works, such as Notaspidium braziliense in Cancian de Araujo et al. (2019) and Chalcis myrifex in Cruaud et al. (2024). While easily detected by taxonomists, these errors can mask the true number of records during automatic data summarization and confuse non-taxonomists.

Although BOLD data is expected to be public in NCBI, 41 species have COI-5P sequences available only on BOLD, highlighting the importance of multi-source searches. Without consulting older articles and BOLD, 49 species would have been omitted from the present study.

Finally, misidentifications in NCBI may lead to misinterpretations when using the Basic Local Alignment Search Tool (BLAST). Inexperienced users might mistake a strong but incorrect taxonomic match for contamination. We recommend triple-checking suspicious BLAST results by repeating the search limited to target taxa and comparing score values. If suspicion persists, sequences should be downloaded for a “phylogenetic check” to confirm their placement within the expected cluster.

ACKNOWLEDGEMENTS

This publication is dedicated to our dear friend and colleague, Stefan Schmidt, who passed away in March 2024. Stefan was a steadfast supporter of Chalcidoidea genetic studies, and his absence is a profound loss to the chalcidology community.

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ADDITIONAL NOTES

• ZooBank register
  https://zoobank.org/466D012E-3BA3-45FD-BD53-5CE00FB108A3
• Data Availability Statement
  The data supporting the findings of this study are available within the article and its supplementary materials.
• Funding
  This work was supported by the Fundação de Amparo à Pesquisa e Inovação do Espírito Santo (PRONEM 0657/2022, 2022-PTJH4 to BCS; PROFIX 1018/2022, 2022-KX881 to BCA) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES-DS, 88887.913225/2023-00 to BCS)
• Ethical Statement
  This study is based exclusively on previously published data of invertebrate animals.
• AI Statement
  No artificial intelligence (AI) tools were used in the conception, analysis, or writing of this study.
• How to cite this article
  Araujo BC, Simoneli BC, Sossai JM, Meira DD, Louro ID, Tavares MT (2026) A systematic review and data compilation of Chalcididae (Hymenoptera: Chalcidoidea) genetic information. Zoologia 43: e25062. https://doi.org/10.1590/S1984-4689.v43.e25062
• Published by
  Sociedade Brasileira de Zoologia at Scientific Electronic Library Online - https://www.scielo.br/zool

Supplementary Material

Supplementary S1.

BOLD distance tree with all Chalcididae sequences longer than 100bp.

Authors: BCA, BCS, JMS, MTT.

Data type: K2P distance tree.

Supplementary S2.

Data compilation of all publications containing Chalcididae genetic information.

Authors: BCA, BCS, JMS, MTT

Data type: Access, data type, taxa coverage, and valid species

Supplementary S3.

Chalcididae genetic information available per taxon.

Authors: BCA, BCS, JMS, MTT

Data type: Molecular markers, taxa coverage, and valid species.

Supplementary S4.

Chalcididae records available at BOLD.

Authors: BCA, BCS, JMS, MTT

Data type: BOLD record database.

Copyright notice: This dataset is made available under the Open Database License - ODBbL (https://opendatacommons.org/licenses/odbl/1.0/). The ODbL is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited. Link: https://doi.org/10.1590/S1984-4689.v43.e25062

Edited by

• Editorial responsibility
  Gabriel L.F. Mejdalani

Data availability

The data supporting the findings of this study are available within the article and its supplementary materials.

Publication Dates

• Publication in this collection
  12 June 2026
• Date of issue
  2026

History

• Received
  13 Aug 2025
• Accepted
  07 Nov 2025