University of Iowa

About: University of Iowa is a education organization based out in Iowa City, Iowa, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 49229 authors who have published 109171 publications receiving 5021465 citations. The organization is also known as: UI & The University of Iowa.

Language dominance in neurologically normal and epilepsy subjects: a functional MRI study.

Abstract: Language dominance and factors that influence language lateralization were investigated in right-handed, neurologically normal subjects (n = 100) and right-handed epilepsy patients (n = 50) using functional MRI. Increases in blood oxygenation-dependent signal during a semantic language activation task relative to a non-linguistic, auditory discrimination task provided an index of language system lateralization. As expected, the majority of both groups showed left hemisphere dominance, although a continuum of activation asymmetry was evident, with nearly all subjects showing some degree of right hemisphere activation. Using a categorical dominance classification, 94% of the normal subjects were considered left hemisphere dominant and 6% had bilateral, roughly symmetric language representation. None of the normal subjects had rightward dominance. There was greater variability of language dominance in the epilepsy group, with 78% showing left hemisphere dominance, 16% showing a symmetric pattern and 6% showing right hemisphere dominance. Atypical language dominance in the epilepsy group was associated with an earlier age of brain injury and with weaker right hand dominance. Language lateralization in the normal group was weakly related to age, but was not significantly related to sex, education, task performance or familial left-handedness.

Genome‐wide search for genes affecting the risk for alcohol dependence

Abstract: Alcohol dependence is a leading cause of morbidity and premature death. Several lines of evidence suggest a substantial genetic component to the risk for alcoholism: sibs of alcoholic probands have a 3-8 fold increased risk of also developing alcoholism, and twin heritability estimates of 50-60% are reported by contemporary studies of twins. We report on the results of a six-center collaborative study to identify susceptibility loci for alcohol dependence. A genome-wide screen examined 291 markers in 987 individuals from 105 families. Two-point and multipoint nonparametric linkage analyses were performed to detect susceptibility loci for alcohol dependence. Multipoint methods provided the strongest suggestions of linkage with susceptibility loci for alcohol dependence on chromosomes 1 and 7, and more modest evidence for a locus on chromosome 2. In addition, there was suggestive evidence for a protective locus on chromosome 4 near the alcohol dehydrogenase genes, for which protective effects have been reported in Asian populations.

Patient-related barriers to management of cancer pain

Abstract: Patients' reluctance to report pain and to use analgesics are considered major barriers to pain management. To explore this problem, 270 patients with cancer completed a 27-item self-report questionnaire (BQ) that assessed the extent to which they have concerns about reporting pain and using pain medication. The 8 specific concerns included fear of addiction, beliefs that 'good' patients do not complain about pain, and concern about side effects. Patients also completed a measure of pain severity and pain interference (the BPI). The percentages of patients having concerns assessed by the BQ ranged from 37% to 85%. Those who were older, less educated, or had lower incomes were more likely to have concerns. Higher levels of concern were correlated with higher levels of pain. Based on their reports of pain medications used in the past week and on their reports of pain severity, patients were categorized as under-medicated versus adequately medicated. Those who were under-medicated reported significantly higher levels of concern. The data are discussed in terms of implications for research and practice.

Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

Abstract: Purpose To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-on the same publicly available set of fundus images and previously reported consensus reference standard set, by three US Board certified retinal specialists. Methods We used the previously reported consensus reference standard of referable DR (rDR), defined as International Clinical Classification of Diabetic Retinopathy moderate, severe nonproliferative (NPDR), proliferative DR, and/or macular edema (ME). Neither Messidor-2 images, nor the three retinal specialists setting the Messidor-2 reference standard were used for training IDx-DR version X2.1. Sensitivity, specificity, negative predictive value, area under the curve (AUC), and their confidence intervals (CIs) were calculated. Results Sensitivity was 96.8% (95% CI: 93.3%-98.8%), specificity was 87.0% (95% CI: 84.2%-89.4%), with 6/874 false negatives, resulting in a negative predictive value of 99.0% (95% CI: 97.8%-99.6%). No cases of severe NPDR, PDR, or ME were missed. The AUC was 0.980 (95% CI: 0.968-0.992). Sensitivity was not statistically different from published IDP sensitivity, which had a CI of 94.4% to 99.3%, but specificity was significantly better than the published IDP specificity CI of 55.7% to 63.0%. Conclusions A deep-learning enhanced algorithm for the automated detection of DR, achieves significantly better performance than a previously reported, otherwise essentially identical, algorithm that does not employ deep learning. Deep learning enhanced algorithms have the potential to improve the efficiency of DR screening, and thereby to prevent visual loss and blindness from this devastating disease.