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Record W4414642633 · doi:10.1007/s00248-025-02597-y

Seasonal Variations in the Microbiome of Hyalomma excavatum Ticks in Algeria

2025· article· en· W4414642633 on OpenAlexaff
Salma Kaoutar Abdelali, Lynda Aissaoui, Ana Laura Cano‐Argüelles, Elianne Piloto‐Sardiñas, Lianet Abuin‐Denis, Apolline Maître, Angélique Foucault‐Simonin, Lourdes Mateos‐Hernández, Myriam Kratou, Alejandra Wu‐Chuang, Dasiel Obregón, Alejandro Cabezas‐Cruz

Bibliographic record

VenueMicrobial Ecology · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsUniversity of Guelph
FundersEuropean Commission
KeywordsMicrobiomeHyalommaMicrobial ecologyPathogenTickMetagenomicsVector (molecular biology)Taxon

Abstract

fetched live from OpenAlex

Ticks are key vectors of zoonotic diseases, with their microbiomes playing a critical role in tick physiology, survival, and vector competence. This study presents the first investigation of the microbiome in Hyalomma excavatum ticks from Algeria, focusing on seasonal variations in bacterial diversity, community composition, and pathogen interactions. Using next-generation sequencing (NGS), the microbiome of 21 female ticks collected during spring, summer, and autumn was analyzed. Beta diversity analysis revealed significant seasonal shifts in microbial community structure, while alpha diversity metrics showed no significant differences in richness and evenness. Co-occurrence network analysis demonstrated seasonal shifts in microbial interactions, particularly between symbionts and pathogens, highlighting Francisella as a key taxon in tick survival and pathogen dynamics. Rickettsia presence varied by season, influencing microbial network stability. These findings underscore the ecological determinants shaping the microbiome and its potential role in pathogen transmission. Understanding seasonal microbiome shifts provides valuable insights for managing tick-borne diseases and could inform the development of targeted, season-specific vector control strategies.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.006
GPT teacher head0.233
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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