Effects of Amphetamine on Striatal Dopamine Release, Open-Field Activity, and Play in Fischer 344 and Sprague–Dawley Rats

Previous work from our laboratories has shown that juvenile Fischer 344 (F344) rats are less playful than other strains and also appear to be compromised in dopamine (DA) functioning. To determine whether the dysfunctional play in this strain is associated with deficits in the handling and delivery of vesicular DA, the following experiments assessed the extent to which F344 rats are differentially sensitive to the effects of amphetamine. When exposed to amphetamine, striatal slices obtained from F344 rats showed a small increase in unstimulated DA release when compared with slices from Sprague–Dawley rats; they also showed a more rapid high K+-mediated release of DA. These data provide tentative support for the hypothesis that F344 rats have a higher concentration of cytoplasmic DA than Sprague–Dawley rats. When rats were tested for activity in an open field, F344 rats presented a pattern of results that was consistent with either an enhanced response to amphetamine (3 mg/kg) or a more rapid release of DA (10 mg/kg). Although there was some indication that amphetamine had a dose-dependent differential effect on play in the two strains, play in F344 rats was not enhanced to any degree by amphetamine. Although these results are not consistent with our working hypothesis that F344 rats are less playful because of a deficit in vesicular release of DA, they still suggest that this strain may be a useful model for better understanding the role of DA in social behavior during the juvenile period

Heterogeneity and Breadth of Host Antibody Response to KSHV Infection Demonstrated by Systematic Analysis of the KSHV Proteome

<div><p>The Kaposi sarcoma associated herpesvirus (KSHV) genome encodes more than 85 open reading frames (ORFs). Serological evaluation of KSHV infection now generally relies on reactivity to just one latent and/or one lytic protein (commonly ORF73 and K8.1). Most of the other polypeptides encoded by the virus have unknown antigenic profiles. We have systematically expressed and purified products from 72 KSHV ORFs in recombinant systems and analyzed seroreactivity in US patients with KSHV-associated malignancies, and US blood donors (low KSHV seroprevalence population). We identified several KSHV proteins (ORF38, ORF61, ORF59 and K5) that elicited significant responses in individuals with KSHV-associated diseases. In these patients, patterns of reactivity were heterogeneous; however, HIV infection appeared to be associated with breadth and intensity of serological responses. Improved antigenic characterization of additional ORFs may increase the sensitivity of serologic assays, lead to more rapid progresses in understanding immune responses to KSHV, and allow for better comprehension of the natural history of KSHV infection. To this end, we have developed a bead-based multiplex assay detecting antibodies to six KSHV antigens.</p></div

Bead-based assay.

<p><b>A</b>. Comparison of reactivity to individual antigens in RDP and HAMB subjects (Mann-Whitney test) <b>B</b>. Receiver operating characteristics. <b>C</b>. Assessment of signal specificity by antigen pre-adsorption. MFI, Median Fluorescence Intensity.</p

A framework for validating AI in precision medicine: considerations from the European ITFoC consortium

International audienceBackground: Artificial intelligence (AI) has the potential to transform our healthcare systems significantly. New AI technologies based on machine learning approaches should play a key role in clinical decision-making in the future. However, their implementation in health care settings remains limited, mostly due to a lack of robust validation procedures. There is a need to develop reliable assessment frameworks for the clinical validation of AI. We present here an approach for assessing AI for predicting treatment response in triple-negative breast cancer (TNBC), using real-world data and molecular-omics data from clinical data warehouses and biobanks. Methods: The European "ITFoC (Information Technology for the Future Of Cancer)" consortium designed a framework for the clinical validation of AI technologies for predicting treatment response in oncology. Results: This framework is based on seven key steps specifying: (1) the intended use of AI, (2) the target population, (3) the timing of AI evaluation, (4) the datasets used for evaluation, (5) the procedures used for ensuring data safety (including data quality, privacy and security), (6) the metrics used for measuring performance, and (7) the procedures used to ensure that the AI is explainable. This framework forms the basis of a validation platform that we are building for the "ITFoC Challenge". This community-wide competition will make it possible to assess and compare AI algorithms for predicting the response to TNBC treatments with external real-world datasets. Conclusions: The predictive performance and safety of AI technologies must be assessed in a robust, unbiased and transparent manner before their implementation in healthcare settings. We believe that the consideration of the ITFoC consortium will contribute to the safe transfer and implementation of AI in clinical settings, in the context of precision oncology and personalized care