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.