Search

OBJECTIVE To investigate whether early neurochemical abnormalities are detectable by high-field magnetic resonance spectroscopy (MRS) in individuals with spinocerebellar ataxias (SCAs) 1, 2, 3, and 6, including patients without manifestation of ataxia. METHODS A cohort of 100 subjects (N = 18-21 in each SCA group, including premanifest mutation carriers; mean score on the Scale for the Assessment and Rating of Ataxia [SARA]

BACKGROUND: Spinocerebellar ataxia type 1 (SCA1) causes progressive degeneration of the cerebellum and brainstem. Volumetric magnetic resonance imaging (MRI) was shown to be more sensitive to disease progression than the most sensitive clinical measure, the Scale for the Assessment and Rating of Ataxia (SARA), in longitudinal studies, and magnetic resonance spectroscopy (MRS) was shown to detect neurochemical abnormalities with high sensitivity cross‐sectionally in SCA1. OBJECTIVES: The objectives of this study were to compare the sensitivities to change of volumetric MRI, MRS, and SARA in a 3‐year longitudinal study in SCA1. METHODS: A total of 16 early‐to‐moderate stage patients with SCA1 (SARA 0‐14) and 21 matched healthy participants were scanned up to 3 times with 1.5‐year intervals. Ataxia severity was assessed with SARA. T(1)‐weighted images and magnetic resonance spectra from the cerebellar vermis, cerebellar white matter, and pons were acquired at 3T. RESULTS: The pontine total N‐acetylaspartate‐to‐myo‐inositol ratio was the most sensitive MRS measure to change (−3.9 ± 4.6%/yr in SCA1 vs. −0.3 ± 3.5%/yr in controls; P

Purpose To determine the test-retest reproducibility of neurochemical concentrations obtained with a highly optimized, short-echo, single-voxel proton MR spectroscopy (MRS) pulse sequence at 3T and 7T using state-of-the-art hardware. Methods A semi-LASER sequence (echo time = 26–28 ms) was used to acquire spectra from the posterior cingulate and cerebellum at 3T and 7T from six healthy volunteers who were scanned four times weekly on both scanners. Spectra were quantified with LCModel. Results More neurochemicals were quantified with mean Cramer-Rao lower bounds (CRLBs) ≤20% at 7T than at 3T despite comparable frequency-domain signal-to-noise ratio. Whereas CRLBs were lower at 7T (P

Background Oral taxa are often found in the chronic obstructive pulmonary disease (COPD) lung microbiota, but it is not clear if this is due to a physiologic process such as aspiration or experimental contamination at the time of specimen collection. Methods Microbiota samples were obtained from nine subjects with mild or moderate COPD by swabbing lung tissue and upper airway sites during lung lobectomy. Lung specimens were not contaminated with upper airway taxa since they were obtained surgically. The microbiota were analyzed with 16S rRNA gene qPCR and 16S rRNA gene hypervariable region 3 (V3) sequencing. Data analyses were performed using QIIME, SourceTracker, and R. Results Streptococcus was the most common genus in the oral, bronchial, and lung tissue samples, and multiple other taxa were present in both the upper and lower airways. Each subject’s own bronchial and lung tissue microbiota were more similar to each other than were the bronchial and lung tissue microbiota of two different subjects (permutation test, p = 0.0139), indicating more within-subject similarity than between-subject similarity at these two lung sites. Principal coordinate analysis of all subject samples revealed clustering by anatomic sampling site (PERMANOVA, p = 0.001), but not by subject. SourceTracker analysis found that the sources of the lung tissue microbiota were 21.1% (mean) oral microbiota, 8.7% nasal microbiota, and 70.1% unknown. An analysis using the neutral theory of community ecology revealed that the lung tissue microbiota closely reflects the bronchial, oral, and nasal microbiota (immigration parameter estimates 0.69, 0.62, and 0.74, respectively), with some evidence of ecologic drift occurring in the lung tissue. Conclusion This is the first study to evaluate the mild-moderate COPD lung tissue microbiota without potential for upper airway contamination of the lung samples. In our small study of subjects with COPD, we found oral and nasal bacteria in the lung tissue microbiota, confirming that aspiration is a source of the COPD lung microbiota.

For clinical trials with time-to-event as the primary endpoint, the clinical cutoff is often event-driven and the log-rank test is the most commonly used statistical method for evaluating treatment effect. However, this method relies on the proportional hazards assumption in that it has the maximal power in this circumstance. In certain disease areas or populations, some patients can be curable and never experience the events despite a long follow-up. The event accumulation may dry out after a certain period of follow-up and the treatment effect could be reflected as the combination of improvement of cure rate and the delay of events for those uncurable patients. Study power depends on both cure rate improvement and hazard reduction. In this paper, we illustrate these practical issues using simulation studies and explore sample size recommendations, alternative ways for clinical cutoffs, and efficient testing methods with the highest study power possible.

High-dimensional linear classifiers, such as the support vector machine (SVM) and distance weighted discrimination (DWD), are commonly used in biomedical research to distinguish groups of subjects based on a large number of features. However, their use is limited to applications where a single vector of features is measured for each subject. In practice data are often multi-way, or measured over multiple dimensions. For example, metabolite abundance may be measured over multiple regions or tissues, or gene expression may be measured over multiple time points, for the same subjects. We propose a framework for linear classification of high-dimensional multi-way data, in which coefficients can be factorized into weights that are specific to each dimension. More generally, the coefficients for each measurement in a multi-way dataset are assumed to have low-rank structure. This framework extends existing classification techniques, and we have implemented multi-way versions of SVM and DWD. We describe informative simulation results, and apply multi-way DWD to data for two very different clinical research studies. The first study uses metabolite magnetic resonance spectroscopy data over multiple brain regions to compare patients with and without spinocerebellar ataxia, the second uses publicly available gene expression time-course data to compare treatment responses for patients with multiple sclerosis. Our method improves performance and simplifies interpretation over naive applications of full rank linear classification to multi-way data. An R package is available at https://github.com/lockEF/MultiwayClassification ., Comment: 25 pages, 5 figures, 2 tables