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Compilation of Historical Local Lymph Node Data for Evaluation of Skin Sensitization Alternative Methods

2005· article· en· 408 citations· W1993658135 on OpenAlex· 10.2310/6620.2005.05040

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian venueIt was published in a Canadian venue.

No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

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.

Opus teacher head0.074
GPT teacher head0.381
Teacher spread
0.307 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

BACKGROUND: Within the toxicology community, considerable effort is directed toward the development of alternative methods for skin sensitization testing. The availability of high-quality, relevant, and reliable in vivo data regarding skin sensitization is essential for the effective evaluation of alternative methodologies. Ideally, data derived from humans would be the most appropriate source because the test methods are attempting to predict a toxicologic effect in humans. Unfortunately, insufficient human data of the necessary quality are available, so it is necessary to rely on the best available animal data. In recent years, the local lymph node assay (LLNA) has emerged as a practical option for assessing the skin sensitization potential of chemicals. In addition to accurately identifying skin sensitizers, the LLNA can also provide a reliable measure of relative sensitization potency, information that is pivotal to the successful management of human health risks. OBJECTIVE: To provide a database of robust in vivo data to calibrate, evaluate, and eventually validate new approaches for skin sensitization testing. METHODS: LLNA data derived from previously conducted studies were compiled from the published literature and unpublished sources. RESULTS: We provide a database that comprises LLNA data on 211 individual chemicals. This extensive chemical data set encompasses both the chemical and biologic diversity of known chemical allergens. To cover the range of relative allergenic potencies, the data set includes data on 13 extreme, 21 strong, 69 moderate, and 66 weak contact allergens, classified according to each allergen's mathematically estimated concentration of chemical required to induce a threefold stimulation index. In addition, there are also 42 chemicals that are considered to be nonsensitizers. In terms of chemical diversity, the database contains data pertaining to the chemical classes represented by aldehydes, ketones, aromatic amines, quinones, and acrylates, as well as compounds that have different reactivity mechanisms. In addition to two-dimensional chemical structures, the physicochemical parameters included are log Kp, log K(o/w), and molecular weight. CONCLUSIONS: The list of chemicals contained in the data set represents both the chemical and biologic diversity that is known to exist for chemical allergens and non-allergens. It is anticipated that this database will help accelerate the development, evaluation, and eventual validation of new approaches to skin sensitization assessment.

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.

The record

Venue
Dermatitis
Topic
Contact Dermatitis and Allergies
Field
Medicine
Canadian institutions
Funders
Keywords
Local lymph node assaySkin sensitizationSensitizationMedicineSet (abstract data type)ToxicologyComputer scienceImmunologyBiology
Has abstract in OpenAlex
yes