Latent inhibition (LI) is demonstrated when a previously unattended/inconsequential stimulus is less effective in a new learning situation than a novel stimulus. In rats and humans, LI is reduced by dopamine agonists and increased by dopamine antagonists. In addition, LI is attenuated in actively psychotic schizophrenia patients, thus conferring strong predictive validity to the animal LI preparation for schizophrenia. However, the validity of the attentional construct in the LI model of schizophrenia dysfunction depends on confirming two assumptions: that animal and human LI share a common process, and that the process is related to selective attention. Evidence to support both assumptions is presented, followed by a description of a conditioned attention theory that emphasizes the role of initial levels of attention elicited by repeated relevant and irrelevant stimuli, and the differences between these levels in schizophrenia and normal groups.
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Database (Oxford)
January 2025
Department of In Vitro Toxicology and Dermato-Cosmetology (IVTD), Vrije Universiteit Brussel, Laarbeeklaan 103, Brussels 1090, Belgium.
The European Union's ban on animal testing for cosmetic products and their ingredients, combined with the lack of validated animal-free methods, poses challenges in evaluating their potential repeated-dose organ toxicity. To address this, innovative strategies like Next-Generation Risk Assessment (NGRA) are being explored, integrating historical animal data with new mechanistic insights from non-animal New Approach Methodologies (NAMs). This paper introduces the TOXIN knowledge graph (TOXIN KG), a tool designed to retrieve toxicological information on cosmetic ingredients, with a focus on liver-related data.
View Article and Find Full Text PDFIntegr Environ Assess Manag
January 2025
Office of Chemical Safety Office Pollution Prevention, Office of Pollution Prevention and Toxics, US Environmental Protection Agency, Washington, DC, United States.
The U.S. Environmental Protection Agency is committed to the implementation of new approach methodologies (NAMs) to enhance the scientific basis for chemical hazard assessments.
View Article and Find Full Text PDFDrug Deliv Transl Res
January 2025
Kinimmune, Inc. St. Louis, 63141, Missouri, USA.
PD-L1/PD-1 checkpoint inhibitors (CPIs) are mainstream agents for cancer immunotherapy, but the prognosis is unsatisfactory in solid tumor patients lacking preexisting T-cell reactivity. Adjunct therapy strategies including the intratumoral administration of immunostimulants aim to address this limitation. CpG oligodeoxynucleotides (ODNs), TLR9 agonists that can potentiate adaptive immunity, have been widely investigated to tackle PD-L1/PD-1 resistance, but clinical success has been hindered by inconsistent efficacy and immune-related toxicities caused by systemic exposure.
View Article and Find Full Text PDFNucleic Acids Res
January 2025
CAS Key Laboratory of Pathogen Microbiology and Immunology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, 100101, China.
The heterotrimeric RNA-dependent RNA polymerase (RdRp) of influenza A virus catalyzes viral RNA transcription (vRNA→mRNA) and replication (vRNA→cRNA→vRNA) by adopting different conformations. A switch from transcription to replication occurs at a relatively late stage of infection. We recently reported that the viral NS2 protein, expressed at later stages from a spliced transcript of the NS segment messenger RNA (mRNA), inhibits transcription, promotes replication and plays a key role in the transcription-to-replication switch.
View Article and Find Full Text PDFCureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
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