Parkinson’s disease (PD) is associated with diverse clinical manifestations including motor and non-motor signs and symptoms, and emerging biomarkers. We aimed to reveal the heterogeneity of PD to define subtypes and their progression rates using an automated deep learning algorithm on the top of longitudinal clinical records. This study utilizes the data collected from the Parkinson’s Progression Markers Initiative (PPMI), which is a longitudinal cohort study of patients with newly diagnosed Parkinson’s disease. Clinical information including motor and non-motor assessments, biospecimen examinations, and neuroimaging results were used for identification of PD subtypes. A deep learning algorithm, Long-Short Term Memory (LSTM), was used to represent each patient as a multi-dimensional time series for subtype identification. Both visualization and statistical analysis were performed for analyzing the obtained PD subtypes. As a result, 466 patients with idiopathic PD were investigated and three subtypes were identified. Subtype I (Mild Baseline, Moderate Motor Progression) is comprised of 43.1% of the participants, with average age 58.79 ± 9.53 years, and was characterized by moderate functional decay in motor ability but stable cognitive ability. Subtype II (Moderate Baseline, Mild Progression) is comprised of 22.9% of the participants, with average age 61.93 ± 6.56 years, and was characterized by mild functional decay in both motor and non-motor symptoms. Subtype III (Severe Baseline, Rapid Progression) is comprised 33.9% of the patients, with average age 65.32 ± 8.86 years, and was characterized by rapid progression of both motor and non-motor symptoms. These subtypes suggest that when comprehensive clinical and biomarker data are incorporated into a deep learning algorithm, the disease progression rates do not necessarily associate with baseline severities, and the progression rate of non-motor symptoms is not necessarily correlated with the progression rate of motor symptoms.

Linear mixed models are able to handle an extraordinary range of complications in regression-type analyses. Their most common use is to account for within-subject correlation in longitudinal data analysis. They are also the standard vehicle for smoothing spatial count data. However, when treated in full generality, mixed models can also handle spline-type smoothing and closely approximate kriging. This allows for nonparametric regression models (e.g., additive models and varying coefficient models) to be handled within the mixed model framework. The key is to allow the random effects design matrix to have general structure; hence our label general design. For continuous response data, particularly when Gaussianity of the response is reasonably assumed, computation is now quite mature and supported by the R, SAS and S-PLUS packages. Such is not the case for binary and count responses, where generalized linear mixed models (GLMMs) are required, but are hindered by the presence of intractable multivariate integrals. Software known to us supports special cases of the GLMM (e.g., PROC NLMIXED in SAS or glmmML in R) or relies on the sometimes crude Laplace-type approximation of integrals (e.g., the SAS macro glimmix or glmmPQL in R). This paper describes the fitting of general design generalized linear mixed models. A Bayesian approach is taken and Markov chain Monte Carlo (MCMC) is used for estimation and inference. In this generalized setting, MCMC requires sampling from nonstandard distributions. In this article, we demonstrate that the MCMC package WinBUGS facilitates sound fitting of general design Bayesian generalized linear mixed models in practice.Comment: Published at http://dx.doi.org/10.1214/088342306000000015 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Purpose To perform a systematic review and meta-analysis to determine if there are differences in rates of immediate allergic events between classes of gadolinium-based contrast agents (GBCAs). Materials and Methods PubMed and Google Scholar databases were searched for studies in which rates of immediate adverse events to GBCAs were reported. The American College of Radiology classification system was used to characterize allergic-like events as mild, moderate, or severe, and the total number of administrations of each GBCA was recorded. Where necessary, authors of studies were contacted to clarify data and eliminate physiologic reactions. Relative risks of GBCA types were estimated by using the Mantel-Haenszel type method. Results Nine studies in which immediate reactions to GBCA were recorded from a total of 716 978 administrations of GBCA met the criteria for inclusion and exclusion. The overall rate of patients who had immediate allergic-like reactions was 9.2 per 10 000 administrations and the overall rate of severe immediate allergic-like reactions was 0.52 per 10 000 administrations.. The nonionic linear chelate gadodiamide had the lowest rate of reactions, at 1.5 (95% confidence interval [CI]: 0.74, 2.4) per 10 000 administrations, which was significantly less than that of linear ionic GBCAs at 8.3 (95% CI: 7.5, 9.2) per 10 000 administrations (relative risk, 0.19 [95% CI: 0.099, 0.36]; P< .00001) and less than that for nonionic macrocyclic GBCAs at 16 (95% CI: 14, 19) per 10 000 administrations (relative risk, 0.12 [95% CI: 0.05, 0.31]; P < .001). GBCAs known to be associated with protein binding had a higher rate of reactions, at 17 (95% CI: 15, 20) per 10 000 administrations compared with the same chelate classification without protein binding, at 5.2 (95% CI: 4.5, 6.0) per 10 000 administrations (relative risk, 3.1 [95% CI: 2.4, 3.8]; P < .0001). Conclusion These data show the lowest rate of immediate allergic adverse events with use of the nonionic linear GBCA gadodiamide in comparison with those of ionic linear or nonionic macrocyclic GBCAs. A higher rate of immediate allergic adverse events was associated with ionicity, protein binding, and macrocyclic structure. RSNA, 2017 An earlier incorrect version of this article appeared online. This article was corrected on August 31, 2017.

The high tumor uptake achieved with these trifunctional ligands predicts larger (up to 4×) doses delivered to the tumor than can be achieved with Lu-PSMA-617. Although PSMA-mediated kidney uptake was also observed, the exceptional area under the curve (AUC) in the tumor warrants further investigation of these novel ligands as candidates for RLT.

Background: Although nephrogenic systemic fibrosis (NSF) affects the use of gadolinium-based contrast agents (GBCAs) in MRI, there continues to be limited knowledge because of the small number of patients with NSF. Purpose: To perform a systematic review of NSF. Materials and Methods: PubMed database was searched by using the term "Nephrogenic systemic fibrosis" from January 2000 to February 2019. Articles reporting details on individual patients with NSF diagnosis on the basis of both clinical presentations and biopsy confirmation were included. Data were pooled and authors were contacted for clarifications. Rates of NSF were compared through 2008 versus after 2008 and for group I versus group II GBCAs, assuming equal market share. Results: Included were 639 patients from 173 articles. Data regarding sex were found for 295 men and 254 women. Age at NSF symptom onset was reported for 177 patients (mean, 49 years 6 16 [standard deviation]; age range, 6-87 years). There were 529 patients with documented exposure to GBCAs including gadodiamide (n = 307), gadopentetate dimeglumine (n = 49), gadoversetamide (n = 6), gadobutrol (n = 1), gadobenate dimeglumine (n = 1), multiple (n = 41), and unknown (n = 120). Among patients with previous exposure, only seven patients were administered GBCA after 2008, yielding a lower rate of NSF after 2008 (P , .001). There were motion limitations in 70.8% (296 of 418) of patients, indicating a more serious debilitation. Associated factors reported for NSF included exposure to GBCA group I (P , .001), dialysis, proinflammatory conditions, hyperphosphatemia, bblockers, and epoetin. For 341 patients with follow-up, 12 patients were cured and 72 patients partially improved including one during pregnancy. Among those 84 patients reported as cured or improved, in 34 patients cure or improvement occurred after renal function restoration. Four deaths were attributed to NSF. Conclusion: Although 639 patients with biopsy-confirmed nephrogenic systemic fibrosis were reported, only seven were after gadolinium-based contrast agent exposure after 2008, indicating that regulatory actions and practice changes have been effective preventive measures. Improvement and sometimes cure with renal function restoration are now possible.

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Missing data are frequently encountered in biomedical, epidemiologic and social research. It is well known that a naive analysis without adequate handling of missing data may lead to bias and/or loss of efficiency. Partly due to its ease of use, multiple imputation has become increasingly popular in practice for handling missing data. However, it is unclear what is the best strategy to conduct multiple imputation in the presence of high-dimensional data. To answer this question, we investigate several approaches of using regularized regression and Bayesian lasso regression to impute missing values in the presence of high-dimensional data. We compare the performance of these methods through numerical studies, in which we also evaluate the impact of the dimension of the data, the size of the true active set for imputation, and the strength of correlation. Our numerical studies show that in the presence of high-dimensional data the standard multiple imputation approach performs poorly and the imputation approach using Bayesian lasso regression achieves, in most cases, better performance than the other imputation methods including the standard imputation approach using the correctly specified imputation model. Our results suggest that Bayesian lasso regression and its extensions are better suited for multiple imputation in the presence of high-dimensional data than the other regression methods.

Multiple genes have been implicated in Parkinson disease pathogenesis, but the relationship between regional expression of these genes and regional dysfunction across the brain is unknown. We address this question by joint analysis of high resolution magnetic resonance imaging data from the Parkinson's Progression Markers Initiative and regional genetic microarray expression data from the Allen Brain Atlas. Regional brain atrophy and genetic expression was co-registered to a common 86 region brain atlas and robust multivariable regression analysis was performed to identify genetic predictors of regional brain atrophy. Top candidate genes from GWAS analysis, as well as genes implicated in trans-synaptic alpha-synuclein transfer and autosomal recessive PD were included in our analysis. We identify three genes with expression patterns that are highly significant predictors of regional brain atrophy. The two most significant predictors are LAG3 and RAB5A, genes implicated in trans-synaptic synuclein transfer. Other well-validated PD-related genes do not have expression patterns that predict regional atrophy, suggesting that they may serve other roles such as disease initiation factors.