Purpose We used high-resolution spectral-domain optical coherence tomography (SD-OCT) with retinal segmentation to determine how ganglion cell loss relates to history of acute optic neuritis (ON), retinal nerve fiber layer (RNFL) thinning, visual function, and vision-related quality of life (QOL) in multiple sclerosis (MS). Design Cross-sectional study. Participants A convenience sample of patients with MS (n = 122; 239 eyes) and disease-free controls (n = 31; 61 eyes). Among MS eyes, 87 had a history of ON before enrollment. Methods The SD-OCT images were captured using Macular Cube (200×200 or 512×128) and ONH Cube 200×200 protocols. Retinal layer segmentation was performed using algorithms established for glaucoma studies. Thicknesses of the ganglion cell layer/inner plexiform layer (GCL+IPL), RNFL, outer plexiform/inner nuclear layers (OPL+INL), and outer nuclear/photoreceptor layers (ONL+PRL) were measured and compared in MS versus control eyes and MS ON versus non-ON eyes. The relation between changes in macular thickness and visual disability was also examined. Main Outcome Measures The OCT measurements of GCL+IPL and RNFL thickness; high contrast visual acuity (VA); low-contrast letter acuity (LCLA) at 2.5% and 1.25% contrast; on the 25-item National Eye Institute Visual Function Questionnaire (NEI-VFQ-25) and 10-Item Neuro-Ophthalmic Supplement composite score. Results Macular RNFL and GCL+IPL were significantly decreased in MS versus control eyes (P<0.001 and P = 0.001) and in MS ON versus non-ON eyes (P<0.001 for both measures). Peripapillary RNFL, macular RNFL, GCL+IPL, and the combination of macular RNFL+GCL+IPL were significantly correlated with VA (P≤0.001), 2.5% LCLA (P<0.001), and 1.25% LCLA (P≤0.001). Among OCT measurements, reductions in GCL+IPL (P<0.001), macular RNFL (P = 0.006), and the combination (macular RNFL+GCL+IPL; P<0.001) were most strongly associated with lower (worse) NEI-VFQ-25 and 10-Item Supplement QOL scores; GCL+IPL thinning was significant even accounting for macular RNFL thickness (P = 0.03 for GCL+IPL, P = 0.39 for macular RNFL). Conclusions We demonstrated that GCL+IPL thinning is most significantly correlated with both visual function and vision-specific QOL in MS, and may serve as a useful structural marker of disease. Our findings parallel those of magnetic resonance imaging studies that show gray matter disease is a marker of neurologic disability in MS.

Substantial evidence links alcohol drinking and serotonin (5-HT) functioning in animals. Lowered central 5-HT neurotransmission has been found in a subgroup of alcoholics, possibly those with more aggressive, assaultive tendencies. Several rodent studies have also suggested that intact 5-HT systems are important determinants of sensitivity and/or tolerance to ethanol-induced ataxia and hypothermia. Null mutant mice lacking the 5-HT1B receptor gene (5-HT1B-/-) have been developed that display enhanced aggression and altered 5-HT release in slice preparations from some, but not all, brain areas. We characterized these mice for sensitivity to several effects of ethanol. Mutant mice drank twice as much ethanol as wild-type mice, and voluntarily ingested solutions containing up to 20% ethanol in water. Their intake of food and water, and of sucrose, saccharin and quinine solutions, was normal. Mutants were less sensitive than wild-types on a test of ethanol-induced ataxia and, with repeated drug administration, tended to develop tolerance more slowly. In tests of ethanol withdrawal and metabolism, mutants and wild-type mice showed equivalent responses. Our results suggest that the 5-HT1B receptor participates in the regulation of ethanol drinking, and demonstrate that serotonergic manipulations lead to reduced responsiveness to certain ataxic effects of ethanol without affecting dependence.

Article impact statement: Data sharing and coordinated monitoring are needed to assess species' response to threats to inform conservation planning at relevant scales.

NLP improved the predictive performance of automated HIV risk assessment by extracting terms in clinical text indicative of high-risk behavior. Future studies should explore more advanced techniques for extracting social and behavioral determinants from clinical text.

BackgroundDiabetes case finding based on structured medical records does not fully identify diabetic patients whose medical histories related to diabetes are available in the form of free text. Manual chart reviews have been used but involve high labor costs and long latency.ObjectiveThis study developed and tested a Web-based diabetes case finding algorithm using both structured and unstructured electronic medical records (EMRs).MethodsThis study was based on the health information exchange (HIE) EMR database that covers almost all health facilities in the state of Maine, United States. Using narrative clinical notes, a Web-based natural language processing (NLP) case finding algorithm was retrospectively (July 1, 2012, to June 30, 2013) developed with a random subset of HIE-associated facilities, which was then blind tested with the remaining facilities. The NLP-based algorithm was subsequently integrated into the HIE database and validated prospectively (July 1, 2013, to June 30, 2014).ResultsOf the 935,891 patients in the prospective cohort, 64,168 diabetes cases were identified using diagnosis codes alone. Our NLP-based case finding algorithm prospectively found an additional 5756 uncodified cases (5756/64,168, 8.97% increase) with a positive predictive value of .90. Of the 21,720 diabetic patients identified by both methods, 6616 patients (6616/21,720, 30.46%) were identified by the NLP-based algorithm before a diabetes diagnosis was noted in the structured EMR (mean time difference = 48 days).ConclusionsThe online NLP algorithm was effective in identifying uncodified diabetes cases in real time, leading to a significant improvement in diabetes case finding. The successful integration of the NLP-based case finding algorithm into the Maine HIE database indicates a strong potential for application of this novel method to achieve a more complete ascertainment of diagnoses of diabetes mellitus.