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Record W4406716972 · doi:10.3389/fcimb.2025.1490948

Visible and rapid detection of feline chaphamaparvovirus using multienzyme isothermal rapid amplification and lateral flow dipstick assay

2025· article· en· W4406716972 on OpenAlexaboutno aff
Jun Ji, Xinhao Mu, Shunshun Pan, Xin Xu, Shi‐Yuan Zhang, Honghui Huang, Ying Li, Yingzuo Bi, Lunguang Yao

Bibliographic record

VenueFrontiers in Cellular and Infection Microbiology · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Virus Infections Studies
Canadian institutionsnot available
FundersNanyang Normal UniversityNational Natural Science Foundation of China
KeywordsDipstickLoop-mediated isothermal amplificationDetection limitPolymerase chain reactionVirologyBiologyMolecular biologyCATSChromatographyChemistryDNAMedicineGeneticsGeneBiochemistry

Abstract

fetched live from OpenAlex

Feline chaphamaparvovirus (FeChPV) is a novel parvovirus previously reported in Canadian cats and Chinese dogs with diarrhea in 2019 and 2020, respectively. Herein, we aimed to establish a simple detection method for FeChPV in field clinics. The primers and probes for the multienzyme isothermal rapid amplification and lateral flow dipstick (MIRA-LFD) assay were designed to target the conserved regions of the FeChPV genome and determine the optimal reaction temperature and time. Without relying on precision instruments, FeChPV detection using the MIRA-LFD assay was completed within 20 min at 37°C, without any cross-reaction with other reference viruses. The newly established MIRA-LFD assay had a detection limit of 32.3 copies/μL, which was 10-fold lower than that of the nested polymerase chain reaction (PCR) assay. Furthermore, the MIRA-LFD assay detected 29 FeChPV-positive samples among 417 cats with diarrhea, providing a slightly higher positivity rate than the nested PCR assay. These results indicate that the newly developed MIRA-LFD assay for FeChPV detection is an efficient, economical, reliable, and simple method that can help in the early prevention and control of FeChPV infection.

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 score0.381
Threshold uncertainty score0.319

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

Explore more

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