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Development of the Intestinal Microbiota of Dairy Calves and Changes Associated with Cryptosporidium spp. Infection in Brazil

2024· article· en· W4401807712 on OpenAlexaff
José Antônio Bessegatto, Júlio Augusto Naylor Lisbôa, Felippe Danyel Cardoso Martins, Roberta Lemos Freire, Elias Jorge Facury Filho, Amauri Alcindo Alfieri, Márcio Costa

Bibliographic record

VenueMicroorganisms · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicParasitic Infections and Diagnostics
Canadian institutionsUniversité de Montréal
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsCryptosporidiumBiologyMicrobiologyDairy cattleFecesImmunologyAnimal science

Abstract

fetched live from OpenAlex

Cryptosporidium spp. is one of the most important pathogens infecting nursing calves worldwide. This study aimed to investigate the intestinal microbiota of dairy calves during the first month of life and the impact of diarrhea caused by Cryptosporidium on a Brazilian farm. Fecal samples from 30 calves were collected during the first month of life, and fecal scores were recorded. Samples from the second, third, and fourth days of life were analyzed by DNA sequencing of the 16S rRNA gene. In addition, samples of sixteen calves positive for Cryptosporidium spp. were retrospectively chosen according to the development of diarrhea: four and two days before diarrhea, at the onset of diarrhea, after four days of diarrhea, at the end of diarrhea, and after six days of diarrhea resolution. Diarrhea was observed in all calves (100%), starting at day 5 of life, and all calves tested positive for Cryptosporidium in at least one sample. The microbiota richness increased with age but was retarded by diarrhea. Compositional changes associated with Cryptosporidium infection included increases in Fusobacterium, Prevotella, and Peptostreptococcus, as well as decreases in Collinsella and Lachnospiraceae. In conclusion, Cryptosporidium infection has the potential to decrease richness and change the composition of the intestinal microbiota of dairy calves.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.220
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations4
Published2024
Admission routes1
Has abstractyes

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