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Bacterial DNA Induced TNF-alpha Expression in Buffaloes (Murrah) in Comparison to that of Cross Breed Cattle

2013· article· en· W3144640256 on OpenAlexvenueno aff
P. Nisha, J. Thanislass, P. X. Antony, H. K. Mukhopadhyay, K. V. Subba Reddy

Bibliographic record

VenueJournal of Buffalo Science · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune Response and Inflammation
Canadian institutionsnot available
Fundersnot available
KeywordsBreedBiologyImmune systemPeripheral blood mononuclear cellIncubation periodReal-time polymerase chain reactionVeterinary medicineAnimal scienceGene expressionAndrologyIncubationImmunologyGeneGeneticsIn vitroBiochemistryMedicine

Abstract

fetched live from OpenAlex

Buffaloes are generally considered to be disease resistance. But systematic studies to understand the underlying mechanism of disease resistance in buffaloes in comparison to that of cattle are scanty. Therefore, the present study was undertaken to study the immune response in terms of TNF-α expression in PBMCS isolated from buffaloes in comparison to that of cattle. PBMCs were isolated from blood collected from healthy buffaloes and cross breed cattle and incubated with bacterial (E.coli) DNA at different concentration for a different period of time. Total RNA was isolated and mRNA expression of TLR9 and TNF-α was studied. Expression of actin gen was studied as positive control. Incubation of PBMCs with bacterial DNA resulted in the expression of TLR9 in both, buffaloes and cattle. But, the expression of TNF-α was seen only in the case of buffaloes and the level was found to increase with the increase in bacterial DNA concentration and time. Thus this study reports the inherent difference in the immune response of buffaloes in comparison to that of cattle.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0030.000

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 head0.027
GPT teacher head0.303
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2013
Admission routes1
Has abstractyes

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