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SLC35F1 is a member of the sugar-like carrier (SLC) superfamily that is expressed in the mammalian brain. Malfunction of SLC35F1 in humans is associated with neurodevelopmental disorders. To get insight into the possible roles of Slc35f1 in the brain, we generated Slc35f1-deficient mice. The Slc35f1-deficient mice are viable and survive into adulthood, which allowed examining adult Slc35f1-deficient mice on the anatomical as well as behavioral level. In humans, mutation in the SLC35F1 gene can induce a Rett syndrome-like phenotype accompanied by intellectual disability (Fede et al. Am J Med Genet A 185:2238–2240, 2021). The Slc35f1-deficient mice, however, display only a very mild phenotype and no obvious deficits in learning and memory as, e.g., monitored with the novel object recognition test or the Morris water maze test. Moreover, neuroanatomical parameters of neuronal plasticity (as dendritic spines and adult hippocampal neurogenesis) are also unaltered. Thus, Slc35f1-deficient mice display no major alterations that resemble a neurodevelopmental phenotype.
Background: Highly accurate detection of intracranial hemorrhages (ICH) on head computed tomography (HCT) scans can prove challenging at high-volume centers. This study aimed to determine the number of additional ICHs detected by an artificial intelligence (AI) algorithm and to evaluate reasons for erroneous results at a level I trauma center with teleradiology services.
Methods: In a retrospective multi-center cohort study, consecutive emergency non-contrast HCT scans were analyzed by a commercially available ICH detection software (AIDOC, Tel Aviv, Israel). Discrepancies between AI analysis and initial radiology report (RR) were reviewed by a blinded neuroradiologist to determine the number of additional ICHs detected and evaluate reasons leading to errors.
Results: 4946 HCT (05/2020-09/2020) from 18 hospitals were included in the analysis. 205 reports (4.1%) were classified as hemorrhages by both radiology report and AI. Out of a total of 162 (3.3%) discrepant reports, 62 were confirmed as hemorrhages by the reference neuroradiologist. 33 ICHs were identified exclusively via RRs. The AI algorithm detected an additional 29 instances of ICH, missed 12.4% of ICH and overcalled 1.9%; RRs missed 10.9% of ICHs and overcalled 0.2%. Many of the ICHs missed by the AI algorithm were located in the subarachnoid space (42.4%) and under the calvaria (48.5%). 85% of ICHs missed by RRs occurred outside of regular working-hours. Calcifications (39.3%), beam-hardening artifacts (18%), tumors (15.7%), and blood vessels (7.9%) were the most common reasons for AI overcalls. ICH size, image quality, and primary examiner experience were not found to be significantly associated with likelihood of incorrect AI results.
Conclusion: Complementing human expertise with AI resulted in a 12.2% increase in ICH detection. The AI algorithm overcalled 1.9% HCT.
Trial registration: German Clinical Trials Register (DRKS-ID: DRKS00023593).
The thesis is about ideological change of political parties and the way parties gather information, learn by updating their beliefs and ultimately make "rational choices". Analyzing 1451 policy moves of 137 parties in 22 OECD-countries from 1950 to 2013 it is a story about rational learning, about emulating other parties abroad and chasing public opinion. Yet, the "internal life" of a party conditions the effects when activists have some influence over the formation of party policy. As volunteers facing a scarcity of time and resources, members of the party on the ground have a different information horizon, and may arrive at the opposite decision where to move than party elites which (can) rest their decision on a broader set of information resources. In some parties the party on the ground thus constitutes an "internal wall of resistance" to the strategy party elites would choose, if they were free from constraints.