HSDB is a toxicology data file on the National Library of Medicine's (NLM) Toxicology Data Network (TOXNET®). It focuses on the toxicology of potentially hazardous chemicals. It is enhanced with information on human exposure, industrial hygiene, emergency handling procedures, environmental fate, regulatory requirements, and related areas. All data are referenced and derived from a core set of books, government documents, technical reports and selected primary journal literature. HSDB is peer-reviewed by the Scientific Review Panel (SRP), a committee of experts in the major subject areas within the data bank's scope. HSDB is organized into individual chemical records, and contains over 5000 such records. ... [Information of the supplier]
The potential of a pesticide or biocide to cause adverse effects in the developing embryo or fetus is an important consideration in any health risk assessment for humans and wildlife. Such information is usually derived from experimental studies in which pregnant laboratory animals are exposed to various concentrations of compounds during critical stages of fetal development. The terms and diagnostic criteria used to describe fetal anomalies need to be consistent from one laboratory to another. Consequently, the DevTox Project has three main objectives: To harmonize the nomenclature used to describe developmental anomalies in laboratory animals, to assist in the visual recognition of developmental anomalies with the aid of photographs, and to provide a historical control database of developmental effects in laboratory animals. ... [Information of the supplier]
Database of Bacterial ExoToxins for Human (DBETH) is a database of sequences, structures, interaction networks and analytical results for 229 exotoxins, from 26 different human pathogenic bacterial genus. All toxins are classified into 24 different Toxin classes. The aim of DBETH is to provide a comprehensive database for human pathogenic bacterial exotoxins. DBETH also provides a platform to its users to identify potential exotoxin like sequences through Homology based as well as Non-homology based methods. In homology based approach the users can identify potential exotoxin like sequences either running BLASTp against the toxin sequences or by running HMMER against toxin domains identified by DBETH from human pathogenic bacterial exotoxins. In Non-homology based part DBETH uses a machine learning approach to identify potential exotoxins (Toxin Prediction by Support Vector Machine based approach). ... [Information of the supplier]
This database is primary aimed at providing an exhaustive and updated registry of sequence variants identified in auto-inflammatory disorder related genes. Since we believe that an attempt to retrieve phenotypical information from all patients identified throughout the world would be an impossible task, we chose to allow only one inclusion per variant (duplicates are automatically rejected), although we allocated a short space for clinical information on the initial patient. The relatively high number of variants with unknown associated phenotype likely stems from the fact that most data are submitted by laboratories performing genetic diagnosis, which do not always have relevant clinical information about the patients. Conversely, a number of apparently simple polymorphisms, i.e. intronic variants not located in splice acceptor or donor sites and silent mutations, were found in symptomatic individuals during the diagnostic test. Since functional experiments are generally lacking, we cannot rule out that these variants do not alter regulatory splice elements, thus acting as true mutations. For all these reasons, we recommend that this database should not be used as a reference for phenotype-genotype correlation. ... [Information of the supplier]