Screening the Structure and Binding Affinity of Ochratoxin A Aptamers
Notice bibliographique
Résumé
Mycotoxins are fungal secondary metabolites that contaminate a wide range of agricultural commodities worldwide.Ochratoxin A (OTA) is of particular interest as it is one of the most abundant food-contaminating mycotoxins.Produced by numerous fungal species from the Aspergillus and Penicillium genera, research suggests that OTA may be teratogenic as well as immunotoxic to humans and carcinogenic to rodents.In addition to its toxicity, OTA is a stable molecule that can resist most food processing and does not result in any visible damage to crops, therefore careful testing is required of all crop samples prior to processing.Aptamers are single-stranded oligonucleotides, typically DNA or RNA, that are capable of specifically interacting with high affinity to a desired target.Recently, several groups have developed aptamers for OTA.We have aimed to understand the secondary structures of these aptamers using a DNase I assay.It is also useful for future applications of these aptamers, to compare their affinities using the same analytical techniques and conditions.The affinity of each aptamer to OTA has been tested using a DNase I assay, as well as a magnetic bead affinity assay.We found that using K d affinity methods to compare aptamers was unreliable and instead developed several direct competitive affinity tests.Initial results appear promising, however further testing is required.Once the optimal conditions are found, the optimal aptamer can be chosen for use in existing antibody technology.This can be done to develop useful sensors and detection methods, such as the preparation of a lateral flow assay for cost efficient, onsite detection of OTA.The production of these novel detection platforms will allow rapid detection of this problematic mycotoxin, iii reducing the amount of grain waste and the cost of detection as well as ultimately reducing the exposure of mycotoxins to humans.v Kerry, you have been my biggest supporter throughout all of my years at Carleton.You have always believed in me no matter what, and it has really helped me get through rough times and that means so much!Carleton will always remind me of you.Emily, you are such a kind and caring person and you have been there for me so many times over the past two years.Thank you for always listening when I needed it most.You are going to do great things, and I can't wait to see that happen.Erin, girl...I can't even ha.Thank you for all of the help you have given me, both in the lab, and out of it!You are so brilliant, I swear you sparkle... with glitter!Thank you for having faith in me when I didn't have faith in myself.I will miss our late night science dates, trips to St. Hubert's, and our photo shoots (but basically I will miss your hair).Daffy, thank you for always engaging me in my endless cat loving conversations (and only "cageing me a few times ha")!Monica, thank you for your help on so many occasions!Your love of organic chemistry, while it is still unthinkable to me, is something I admire!Nadine, your optimism and outlook on life truly inspire me!Thank you for all of your encouragement and help over the last year!McKenzie, my lab baby
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».