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Record W1999463367 · doi:10.1556/avet.2011.029

Postmortem small babesia-like morphology of Babesia canis — Short communication

2011· article· en· W1999463367 on OpenAlexaboutno aff
Zoltán Demeter, Elena Alina Palade, Éva Balogh, Csaba Jakab, Róbert Farkas, Balázs Tánczos, Sándor Hornok

Bibliographic record

VenueActa Veterinaria Hungarica · 2011
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsnot available
FundersMagyar Tudományos Akadémia
KeywordsBabesia canisBiologyCanisBabesiaParasitemiaPathologyBabesiosisPuppyHistologyParasite hostingSpleenVirologyMedicineImmunologyMalaria

Abstract

fetched live from OpenAlex

Here we report a case of canine babesiosis with unusual morphology of the causative agent. A male, seven-week-old Labrador retriever puppy, exhibiting severe anaemia and haemoglobinuria, was presented at the Clinic of Internal Medicine in February 2011. The puppy was euthanised. The most relevant pathological changes were icterus, severe splenomegaly, generalised lymphadenopathy and haemoglobin nephrosis. Samples were collected from various organs for histology within one hour post mortem. Impression smears were also prepared from the spleen after overnight storage at 4 °C. Tissue sections and smears showed the presence of multiple, coccoid intraerythrocytic bodies that measured 1-2 μm and resembled small babesiae. No large piroplasms were seen. DNA was extracted from the spleen, and a conventional PCR was performed for the amplification of a 450-bp region of the 18S rRNA gene of piroplasms. The causative agent was identified as Babesia canis canis, with 99% sequence identity to other European isolates. Sequence identity to B. gibsoni was only 91%. This is the first account to verify that the morphology of the large canine piroplasm, B. canis, can be uniformly small babesia-like post mortem or following the storage of tissue samples.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.867
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.040
GPT teacher head0.244
Teacher spread0.204 · 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 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

Citations13
Published2011
Admission routes1
Has abstractyes

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