Rationale: Nicotine can enhance attention and attribution of incentive salience to nicotine-associated stimuli. However, it is not clear whether inter-individual differences in attentional capacities prior to any exposure could play a role in vulnerability to nicotine self-administration. We further explored this vulnerability through pre-existing inter-individual differences in attention to a reward-predictive cue in drug-free animals.
Methods: A cued version of the Fixed Consecutive Number schedule (FCN16) of reinforcement task was used to assess attention. This task consists in completing a long chain of sequential lever presses to obtain a reward, and examines the rats' ability to pay attention to a cue light that signals its availability. Rats were then trained to self-administer nicotine intravenously (30 μg/kg/0.1 mL). Drug-taking and seeking behaviors were investigated.
Results: Our results showed important inter-individual differences in response for nicotine during the progressive ratio schedule of reinforcement. By comparing rats in the lower and upper quartiles of the mean breaking point, we showed that high-motivated rats were also more sensitive to the reinforcing properties of nicotine than low-motivated ones. We found that while both groups did not differ in premature responding in the FCN16 task, high-motivated rats were more efficient in taking the cue light into account than low-motivated rats as shown by a higher proportion of optimal chains, indicating a higher level of attention to the reward-predictive cue. Moreover, it was positively correlated with higher motivation for nicotine, a hallmark of nicotine addiction.
Conclusions: These results suggest that higher attention to reward-associated cues prior to drug taking predicts vulnerability to nicotine-reinforcing properties.
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http://dx.doi.org/10.1007/s00213-018-4901-0 | DOI Listing |
J Neurosci Methods
January 2025
Dept. of Physiology and Pharmacology, Sapienza University of Rome, 00185 Rome, Italy; Neuropharmacology Unit, IRCCS Santa Lucia Foundation, 00143 Rome, Italy. Electronic address:
Background: Only a small percentage of trauma-exposed subjects develop PTSD, with females being twice as likely. Most rodent models focus on males and fail to account for inter-individual variability in females.
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View Article and Find Full Text PDFExpert Opin Drug Discov
January 2025
Centro de Investigación en Reproducción Animal, Universidad Autónoma de Tlaxcala - CINVESTAV Tlaxcala, Tlaxcala, México.
Introduction: Existing pharmacotherapies for schizophrenia have not progressed beyond targeting dopamine and serotonin neurotransmission. Rodent models of schizophrenia are a necessary tool for elucidating neuropathological processes and testing potential pharmacotherapies, but positive preclinical results in rodent models often do not translate to positive results in the clinic.
Areas Covered: The authors reviewed PubMed for studies that applied rodent behavioral models of schizophrenia to assess the antipsychotic potential of several novel pharmacotherapies currently under investigation.
Geroscience
January 2025
AgelessRx, Ann Arbor, MI, USA.
Rapamycin, also known as sirolimus, has demonstrated great potential for application in longevity medicine. However, the dynamics of low-dose rapamycin bioavailability, and any differences in bioavailability for different formulations (e.g.
View Article and Find Full Text PDFGlob Epidemiol
June 2025
Business Analytics (BANA) Program, Business School, University of Colorado, 1475 Lawrence St. Denver, CO 80217-3364, USA.
AI-assisted data analysis can help risk analysts better understand exposure-response relationships by making it relatively easy to apply advanced statistical and machine learning methods, check their assumptions, and interpret their results. This paper demonstrates the potential of large language models (LLMs), such as ChatGPT, to facilitate statistical analyses, including survival data analyses, for health risk assessments. Through AI-guided analyses using relatively recent and advanced methods such as Individual Conditional Expectation (ICE) plots using Random Survival Forests and Heterogeneous Treatment Effects (HTEs) estimated using Causal Survival Forests, population-level exposure-response functions can be disaggregated into individual-level exposure-response functions.
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