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Enregistrement W1985977177 · doi:10.3201/eid1408.080083

Rarity of Influenza A Virus in Spring Shorebirds, Southern Alaska

2008· letter· en· W1985977177 sur OpenAlexaboutno aff
Kevin Winker, Erica Spackman, David E. Swayne

Notice bibliographique

RevueEmerging infectious diseases · 2008
Typeletter
Langueen
DomaineMedicine
ThématiqueInfluenza Virus Research Studies
Établissements canadiensnon disponible
Organismes subventionnairesJapan Science and Technology AgencyU.S. Department of Agriculture
Mots-clésSandpiperCalidrisBayGeographySpring (device)WaterfowlFisheryEcologyBiologyPredationHabitat

Résumé

récupéré en direct d'OpenAlex

To the Editor: Knowledge of avian influenza (AI) virus and its host epidemiology and ecology is essential for effective monitoring and mitigation (1). Applicability of global and continental-scale models will be key for expanding this knowledge base. Research in the Delaware Bay area, eastern United States, suggests an ecologic and epidemiologic viewpoint of AI virus in wild birds in which shorebirds (family Scolopacidae) are predominant hosts in spring; however, research in Alberta, Canada, suggests that waterfowl are such in autumn (2,3). AI virus surveillance in Europe (4) suggests that the spring aspect of this scenario does not apply there. To increase knowledge of AI transport among shorebirds in spring in the North Pacific, we conducted AI virus surveillance during the springs of 2006 and 2007 at the Copper River Delta area of Alaska. Millions of birds congregate at this location in the spring, resulting in the highest spring shorebird concentrations in the New World (5). We also sampled gulls (Laridae), which are common and heretofore unsurveyed for AI in this ecosystem. In 2006 and 2007, 1,050 shorebirds (Western Sandpiper, Calidris mauri, and Least Sandpiper, C. minutilla) and 770 Glaucous-winged Gulls (Larus glaucescens) were sampled during peak spring migration at Hartney Bay, Cordova, Alaska (60°28′N 146°8′W; Table). Fresh fecal samples were obtained from tidal flats within <1 to 90 min after identified flocks were dispersed, and samples were placed in sterile medium (brain heart infusion buffer with 10,000 U/mL penicillin G, 1 mg/mL gentamicin, and 20 μg/mL amphotericin B) and either kept cool (<1 week) before transport to Fairbanks (2006) or placed into liquid nitrogen within 2 h of collection (2007). Samples were stored at –70° C; shipped frozen overnight to Athens, Georgia; and maintained frozen until analyzed. Samples were screened by real-time reverse transcriptase–PCR (RT-PCR) for influenza A virus, and virus isolation was performed on samples that were positive. RNA was extracted by adding 250 μL of sample to 750 μL Trizol LS reagent (Invitrogen, Inc., Carlsbad, CA, USA). Samples were mixed and incubated at room temperature for 10 min. A total of 200 μL of chloroform was then added, incubation was continued for 5 min, and samples were centrifuged for 15 min at 12,000 × g at 4° C. Supernatant was removed, and 50 μL was extracted with the MagMax AI/ND viral RNA extraction kit (Ambion, Inc. Austin, TX, USA). RNA was tested for AI virus matrix (M) gene. A positive test result for this gene indicates the presence of any influenza viruses (6) when an internal positive control is used (7). Positive samples were processed for virus isolation in embryonated chicken eggs by standard methods (8). Real-time RT-PCR results were corroborated by processing 50 randomly selected negative samples for virus isolation with 3 egg passages. Screening for AI virus was conducted on 1,820 samples (Table). Among these, 1 AI virus was identified (A/Glaucous-wingedGull/AK/4906A/2006; H16N?), reflecting an overall prevalence of 0.055% (0% in shorebirds and 0.13% in gulls). Table Species and sample sizes of wild bird hosts screened for avian influenza virus, Cordova, Alaska, May 2006 and May 2007 Results of power analysis (9) suggested that our shorebird samples would detect infection rates >0.9% with 99% probability (95% probability of detecting rates 1%–2% or higher in each year). In gulls, probability of detecting infection rates >1% across both years of the study (>6% in 2006 and >1%–2% in 2007) was 95%. Virus prevalence in spring shorebirds in Alaska was substantially lower than prevalence in spring shorebirds in the Delaware Bay area (3) and more similar to prevalence in spring shorebirds in Europe (4). Our shorebird samples (1,050) were fewer than those in other studies (3; 4,266 samples from 4 species over 16 years, and 4; 3,159 samples from 47 species over 8 years, with 35% from spring), representing 25% and 33% of those studies, respectively. Our study covered only 2 years, but it would detect AI virus infections in shorebirds at rates >1%–2% within each year with 95% probability and at rates >0.9% across years with 99% probability. Thus, the prevalence rate among Copper River Delta shorebirds in our study is lower than that found in the 16-year Delaware Bay study (3). In the Delaware Bay area, 4 shorebird species were sampled: 3 Calidris and 1 Arenaria (3). Precise statistics are unavailable, but the average 16-year prevalence rate was 14.2%, fluctuating annually from ≈2% to ≈38% (3). In Europe AI viruses were absent among spring shorebirds (4). Differences in prevalence rates found among studies may be influenced by species sampled, sampling procedures, and seasonal timing (4). However, with >1,000 spring shorebirds sampled, results suggest that differences might exist between the world’s major migration systems (3,4). Our results corroborate other recent results (10) suggesting that AI prevalence rates among shorebirds at Delaware Bay are not typical within North America. Present evidence indicates (this study; 3,10) that the role of shorebirds in AI virus ecology and epidemiology is heterogeneous within North America and within a genus (Calidris). These findings confirm that knowledge of how AI viruses cycle in wild bird hosts remains incomplete at continental and family-level taxonomic scales. Only further surveillance can fill these knowledge gaps.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,016

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,000
Communication savante0,0010,001
Science ouverte0,0010,000
Intégrité de la recherche0,0020,001
Charge utile insuffisante (le modèle a refusé de juger)0,0050,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,047
Tête enseignante GPT0,336
Écart entre enseignants0,290 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations37
Publié2008
Routes d'admission1
Résumé présentoui

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