Introduction:Cerebellar Ataxia, Neuropathy and Vestibular Areflexia Syndrome (CANVAS) is an autosomal recessive neurodegenerative disease characterized by adult onset and slowly progressive sensory neuropathy, cerebellar dysfunction, and vestibular impairment. In most cases, the disease is caused by biallelic (AAGGG)nrepeat expansions in the second intron of the Replication Factor Complex subunit 1 (RFC1). However, a small number of cases with typical CANVAS do not carry the common biallelic repeat expansion. The objective of this study was to expands the genotypic spectrum of CANVAS by identifying point mutations inRFC1coding region associated with this condition.Methods:Fifteen individuals diagnosed with CANVAS and carrying only one heterozygous (AAGGG)nexpansion inRFC1underwent WGS or WES to test for the presence of a second variant inRFC1or other unrelated gene. To assess the impact of truncating variants onRFC1expression we tested the level of RFC1 transcript and protein on patients’ derived cell lines.Results:We identified seven patients from five unrelated families with clinically defined CANVAS carrying a heterozygous (AAGGG)nexpansion together with a second truncating variantin transinRFC1, which included: c.1267C>T (p.Arg423Ter), c.1739_1740del (p.Lys580SerfsTer9), c.2191del (p.Gly731GlufsTer6) and c.2876del (p.Pro959GlnfsTer24). Patient fibroblasts containing the c.1267C>T (p.Arg423Ter) or c.2876del (p.Pro959GlnfsTer24) variants demonstrated nonsense-mediated mRNA decay and reduced RFC1 transcript and protein.Discussion:Our report expands the genotype spectrum of RFC1 disease. FullRFC1sequencing is recommended in cases affected by typical CANVAS and carrying monoallelic (AAGGG)nexpansions. Also, it sheds further light on the pathogenesis of RFC1 CANVAS as it supports the existence of a loss of function mechanism underlying this complex neurodegenerative condition.

Background: A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods: The study included consecutively discharged patients between 1st of January 2017 and 31st of December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models' performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion: The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.

To describe the clinical characteristics and long-term outcome of Escherichia coli-associated granulomatous ileocolitis in dogs.MethOds: Retrospective review of medical records from dogs with periodic acid-Schiff positive (PAS+) granulomatous ileocolitis and mucosally invasive E. coli in the ileum and colon. Initial bacterial colonisation was evaluated using fluorescence in situ hybridization (FISH) in all dogs and corroborated with colonic and/or ileal culture, when performed.Results: Four boxer dogs and 1 French Bulldog with PAS+ granulomatous ileocolitis (GIC) were evaluated. All dogs had chronic diarrhoea refractory to empirical therapy. Ileocolonoscopy revealed mucosal haemorrhage and ulceration in the ileum (3/4) and colon (5/5). E. coli were visualised as clusters within the ileal and colonic mucosa. Complete (CR, 4/5) or partial (PR, 1/5) clinical response to fluoroquinolones was noted in all dogs within 30 days. CR was sustained in three of four dogs (median disease-free interval 40 months, range 16 to 60). Two dogs relapsed while receiving fluoroquinolones.Repeat biopsy isolated multidrug-resistant, mucosally invasive E. coli in the ileum (1/2) and colon (2/2). Targeted antimicrobial therapy was associated with long-term PR (78 months) in both dogs. clinical significance: Concurrent E. coli-associated granulomatous inflammation in the ileum and colon did not impart a poor clinical outcome or lack of response to the conventional standard of care for granulomatous colitis in dogs that were aggressively diagnosed and treated. Clinical outcome was influenced by antimicrobial resistance, with response dependent upon antimicrobial therapy informed by susceptibility testing.

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Background:A common problem in machine learning applications is availability of data at the point of decision making. The aim of the present study was to use routine data readily available at admission to predict aspects relevant to the organization of psychiatric hospital care. A further aim was to compare the results of a machine learning approach with those obtained through a traditional method and those obtained through a naive baseline classifier. Methods:The study included consecutively discharged patients between 1stof January 2017 and 31stof December 2018 from nine psychiatric hospitals in Hesse, Germany. We compared the predictive performance achieved by stochastic gradient boosting (GBM) with multiple logistic regression and a naive baseline classifier. We tested the performance of our final models on unseen patients from another calendar year and from different hospitals. Results: The study included 45,388 inpatient episodes. The models’ performance, as measured by the area under the Receiver Operating Characteristic curve, varied strongly between the predicted outcomes, with relatively high performance in the prediction of coercive treatment (area under the curve: 0.83) and 1:1 observations (0.80) and relatively poor performance in the prediction of short length of stay (0.69) and non-response to treatment (0.65). The GBM performed slightly better than logistic regression. Both approaches were substantially better than a naive prediction based solely on basic diagnostic grouping. Conclusion:The present study has shown that administrative routine data can be used to predict aspects relevant to the organisation of psychiatric hospital care. Future research should investigate the predictive performance that is necessary to provide effective assistance in clinical practice for the benefit of both staff and patients.