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Motivation in general, and social motivation in particular are important for interpersonal functioning in individuals with schizophrenia. Still, their roles after accounting for social cognition, are not well understood. The sample consisted of 147 patients with schizophrenia. General motivation was measured using the Behavioral inhibition/activation scale (BIS/BAS). Social motivation was measured by Passive social withdrawal and Active social avoidance items from PANSS. Interpersonal functioning was evaluated with Birchwoods Social Functioning Scale (SFS). We used Exploratory Graph Analysis for network estimation and community detection. Active social avoidance, passive social withdrawal, and social withdrawal/engagement (from SFS) were the most important nodes. In addition, three distinct communities were identified: Social cognition, Social motivation, and Interpersonal functioning. Notably, the BIS and BAS measures of general motivation were not part of any community. BAS showed stronger links to functioning than BIS. Passive social withdrawal was more strongly linked to interpersonal functioning than social cognitive abilities. Results suggest that social motivation, especially social approach, is more closely related to interpersonal functioning in schizophrenia than general motivation. In contrast, we found that general motivation was largely unrelated to social motivation. This pattern highlights the importance of type of motivation for understanding variability in interpersonal difficulties in schizophrenia.

BACKGROUND: People with schizophrenia on average are more socially isolated, lonelier, have more social cognitive impairment, and are less socially motivated than healthy individuals. People with bipolar disorder also have social isolation, though typically less than that seen in schizophrenia. We aimed to disentangle whether the social cognitive and social motivation impairments observed in schizophrenia are a specific feature of the clinical condition v. social isolation generally. METHODS: We compared four groups (clinically stable patients with schizophrenia or bipolar disorder, individuals drawn from the community with self-described social isolation, and a socially connected community control group) on loneliness, social cognition, and approach and avoidance social motivation. RESULTS: Individuals with schizophrenia (n = 72) showed intermediate levels of social isolation, loneliness, and social approach motivation between the isolated (n = 96) and connected control (n = 55) groups. However, they showed significant deficits in social cognition compared to both community groups. Individuals with bipolar disorder (n = 48) were intermediate between isolated and control groups for loneliness and social approach. They did not show deficits on social cognition tasks. Both clinical groups had higher social avoidance than both community groups. CONCLUSIONS: The results suggest that social cognitive deficits in schizophrenia, and high social avoidance motivation in both schizophrenia and bipolar disorder, are distinct features of the clinical conditions and not byproducts of social isolation. In contrast, differences between clinical and control groups on levels of loneliness and social approach motivation were congruent with the groups degree of social isolation.

PurposeReal world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.MethodsWe performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.ResultsArtificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping.ConclusionsDespite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

The African claw-toed frog, Xenopus laevis, is a well-established laboratory model for the biology of vertebrate oogenesis, fertilisation, and development at embryonic, larval, and metamorphic stages. For ovulation, X. laevis females are usually injected with chorionic gonadotropin, whereupon they lay typically hundreds to thousands of eggs in a day. After being rested for a minimum of three months, animals are re-used. The literature suggests that adult females can lay much larger numbers of eggs in a short period. Here, we compared the standard single ovulation protocol with a double ovulation protocol, in which females were ovulated, then re-ovulated after seven days and then rested for three months before re-use. We quantified egg number, fertilisation rate (development to cleavage stage), and corticosterone secretion rate as a measure of stress response for the two protocol groups over seven 3-month cycles. We found no differences in egg number-per-ovulation or egg quality between the groups and no long-term changes in any measures over the 21-month trial period. Corticosterone secretion was elevated by ovulation, similarly for the single ovulation as for the first ovulation in the double-ovulation protocol, but more highly for the second ovulation (to a level comparable to that seen following shipment) in the latter. However, both groups exhibited the same baseline secretion rates by the time of the subsequent cycle. Double ovulation is thus transiently more stressful/demanding than single ovulation but within the levels routinely experienced by laboratory X. laevis. Noting that stress hormone corticosterone/cortisol secretion is linked to physiological processes, such as ovulation, that are not necessarily harmful to the individual, we suggest that the benefits of a doubling in egg yield-per-cycle per animal without loss of egg quality or signs of acute or long-term harm may outweigh the relatively modest and transient corticosterone elevation we observed. The double ovulation protocol therefore represents a potential new standard practice for promoting the 3Rs (animal use reduction, refinement and replacement) mission for Xenopus research.