Evidence map›Paper›PMID 42625238›Full record

ArticlePorcine health management2026

Detecting swine influenza A virus during fattening and at slaughter: implications for monitoring strategies in fattening herds.

Pauline Deffner, Katrin Jankowitsch, Matthias Eddicks, Susanne Zöls, Mathias Ritzmann, Yury Zablotski, Robert Fux, Timm Harder, Kathrin Lillie-Jaschniski, Julia Stadler

Abstract read
In one paragraph

Article in Porcine health management, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Pauline DeffnerClinic for Swine, Centre for Clinical Veterinary Medicine, Ludwig- Maximilians-Universität München, Oberschleißheim, Germany. pauline.deffner@gmail.com.
Katrin JankowitschClinic for Swine, Centre for Clinical Veterinary Medicine, Ludwig- Maximilians-Universität München, Oberschleißheim, Germany.
Matthias EddicksClinic for Swine, Centre for Clinical Veterinary Medicine, Ludwig- Maximilians-Universität München, Oberschleißheim, Germany.
Susanne ZölsClinic for Swine, Centre for Clinical Veterinary Medicine, Ludwig- Maximilians-Universität München, Oberschleißheim, Germany.
Mathias RitzmannClinic for Swine, Centre for Clinical Veterinary Medicine, Ludwig- Maximilians-Universität München, Oberschleißheim, Germany.
Yury ZablotskiClinic for Swine, Centre for Clinical Veterinary Medicine, Ludwig- Maximilians-Universität München, Oberschleißheim, Germany.
Robert FuxDivision of Virology, Institute for Infectious Diseases and Zoonoses, Department of Veterinary Science, Ludwig-Maximilians-Universität München, Oberschleißheim, Germany.
Timm HarderInstitute of Diagnostic Virology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany.
Kathrin Lillie-JaschniskiCEVA Tiergesundheit, Düsseldorf, Germany.
Julia StadlerClinic for Swine, Centre for Clinical Veterinary Medicine, Ludwig- Maximilians-Universität München, Oberschleißheim, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSwine influenza A virus (swIAV) is a major contributor to respiratory disease in pigs and represents a One-Health concern. This study evaluated whether slaughterhouse sampling can complement or partially substitute on-farm monitoring by comparing slaughterhouse-derived sample specimens for swIAV detection and subtype characterization.

resultsTwenty-one pig farms in Germany were enrolled, and one batch of fatteners per farm was monitored longitudinally using pen-based oral fluids (OFs) at three predefined time points during fattening. In case of acute respiratory distress, tracheobronchial swabs (TBS) were collected from 15 affected pigs. At slaughter, 30 pigs per farm were sampled and tested for swIAV by qPCR, yielding OFs, nasal swabs before and after scalding (NS I/NS II), bronchial swabs (BS), lung tissue (LT), and serum. Overall, 18/21 farms (85.7%) were classified as swIAV-positive, with a seroprevalence of 92.4% in the study population. During fattening, swIAV-RNA was detected in OFs collected on-farm in 7/21 farms (33.3%) at least once, most frequently at the beginning of fattening. Among 15 farms with TBS sampling, swIAV-RNA was detected in 4 farms (26.7%). At slaughter, swIAV-RNA was detected in at least one matrix on 7/21 farms and in 74/540 pigs (13.7%). Detection probability at slaughter differed by specimen: lower respiratory tract samples showed higher detection rates and lower Ct-values than NS (BS: 9.7%, LT: 10.9%; NS: 6.3%). Diagnostic agreement between materials ranged from fair to moderate, highest between BS and LT (κ = 0.58; p < 0.001). Slaughterhouse OFs showed low sensitivity. From selected RT-qPCR-positive samples with Ct < 33, BS yielded the highest proportion of successfully subtyped samples. Subtypes HA-1 C.2.1 (H1avN1EA), HA-1 C.2.4 (H1avN2G), and HA-1B.1 (H1huN2G) were identified in 4/7 RT-qPCR-positive farms.

conclusionDespite longitudinal OF sampling during fattening and additional TBS collection during acute respiratory disease, swIAV-RNA was only detected in a subset of seropositive farms. Molecular detection at slaughter was likewise restricted, whereas serology substantially improved herd-level identification. Among PCR-based sample types in slaughter pigs, BS demonstrated the highest diagnostic yield and suitability for subtype characterization. Even combined, on-farm and slaughterhouse RT-qPCR approaches remained constrained by the transient nature of swIAV shedding. Slaughterhouse sampling therefore complements-but does not replace-structured on-farm investigations for comprehensive surveillance.

Indexed as

MonitoringSampling specimenSlaughterSwine Influenza A virus

Identifiers

PMID42625238
PMCPMC13495275

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.