Background: The accuracy of microbial community detection in 16S rRNA marker-gene and metagenomic studies suffers from contamination and sequencing errors that lead to either falsely identifying microbial taxa that were not in the sample or misclassifying the taxa of DNA fragment reads. Removing contaminants and filtering rare features are two common approaches to deal with this problem. While contaminant detection methods use auxiliary sequencing process information to identify known contaminants, filtering methods remove taxa that are present in a small number of samples and have small counts in the samples where they are observed. The latter approach reduces the extreme sparsity of microbiome data and has been shown to correctly remove contaminant taxa in cultured “mock” datasets, where the true taxa compositions are known. Although filtering is frequently used, careful evaluation of its effect on the data analysis and scientific conclusions remains unreported. Here, we assess the effect of filtering on the alpha and beta diversity estimation as well as its impact on identifying taxa that discriminate between disease states.Results: The effect of filtering on microbiome data analysis is illustrated on four datasets: two mock quality control datasets where the same cultured samples with known microbial composition are processed at different labs and two disease study datasets. Results show that in microbiome quality control datasets, filtering reduces the magnitude of differences in alpha diversity and alleviates technical variability between labs while preserving the between samples similarity (beta diversity). In the disease study datasets, DESeq2 and linear discriminant analysis Effect Size (LEfSe) methods were used to identify taxa that are differentially abundant across groups of samples, and random forest models were used to rank features with the largest contribution toward disease classification. Results reveal that filtering retains significant taxa and preserves the model classification ability measured by the area under the receiver operating characteristic curve (AUC). The comparison between the filtering and the contaminant removal method shows that they have complementary effects and are advised to be used in conjunction.Conclusions: Filtering reduces the complexity of microbiome data while preserving their integrity in downstream analysis. This leads to mitigation of the classification methods' sensitivity and reduction of technical variability, allowing researchers to generate more reproducible and comparable results in microbiome data analysis.

BACKGROUND: A modified computed tomography angiography (CTA)–based Carotid Plaque Reporting and Data System (Plaque-RADS) classification was applied to a cohort of patients with embolic stroke of undetermined source to test whether high-risk Plaque-RADS subtypes are more prevalent on the ipsilateral side of stroke. With the widespread use of CTA for stroke evaluation, a CTA-based Plaque-RADS would be valuable for generalizability. METHODS: A retrospective observational cross-sectional study was conducted at a single integrated health system comprised of 3 hospitals with a comprehensive stroke center between October 1, 2015, and April 1, 2017. Patients with unilateral anterior circulation stroke and <50% carotid stenosis on CTA were retrospectively identified. Maximum plaque thickness and ulceration were assessed by a neuroradiologist blinded to the stroke side. A semiautomated segmentation software measured intraplaque hemorrhage volumes. Modified CTA-based Plaque-RADS classification was defined as (1) no plaque, (2) plaque thickness 50 mm ³ irrespective of plaque thickness. High-risk plaque subtypes (Plaque-RADS 3 and 4) were compared with low-risk subtypes (Plaque-RADS 1 and 2). RESULTS: Ninety-four patients (55% women; median age, 66 years) were included. CTA-based Plaque-RADS categories for plaques ipsilateral to the stroke side were as follows: (1) 14.9%, (2) 42.6%, (3) 41.5%, and (4) 1.1%. Carotid plaques contralateral to stroke side were Plaque-RADS: (1) 21.3%, (2) 46.8%, (3) 31.9%, and (4) 0%. When compared with the contralateral side, plaques ipsilateral to the stroke side were significantly associated with high-risk Plaque-RADS subtypes in a mixed-effects logistic model adjusting for age and sex (adjusted odds ratio, 2.10 [95% CI, 1.20–3.71]; P =0.01). CONCLUSIONS: Carotid plaque ipsilateral to the stroke side was significantly associated with CTA-based high-risk Plaque-RADS subtypes in an embolic stroke of undetermined source cohort. A CTA-based Plaque-RADS classification may be useful for identifying potentially causative carotid plaque phenotypes in patients with embolic stroke of undetermined source.

Purpose: PARP inhibitors have become the standard-of-care treatment for homologous recombination deficient (HRD) high-grade serous ovarian cancer (HGSOC). However, not all HRD tumors respond to PARPi. Biomarkers to predict response are needed. [18F]FluorThanatrace ([18F]FTT) is a PARPi-analog PET radiotracer that noninvasively measures PARP-1 expression. Herein, we evaluate [18F]FTT as a biomarker to predict response to PARPi in patient-derived xenograft (PDX) models and subjects with HRD HGSOC. Experimental Design: In PDX models, [18F]FTT-PET was performed before and after PARPi (olaparib), ataxia-telangiectasia inhibitor (ATRi), or both (PARPi-ATRi). Changes in [18F]FTT were correlated with tumor volume changes. Subjects were imaged with [18F]FTT-PET at baseline and after ∼1 week of PARPi. Changes in [18F]FTT-PET uptake were compared with changes in tumor size (RECISTv1.1), CA-125, and progression-free survival (PFS). Results: A decrease in [18F]FTT tumor uptake after PARPi correlated with response to PARPi, or PARPi-ATRi treatment in PARPi-resistant PDX models (r = 0.77–0.81). In subjects (n = 11), percent difference in [18F]FTT-PET after ∼7 days of PARPi compared with baseline correlated with best RECIST response (P = 0.01), best CA-125 response (P = 0.033), and PFS (P = 0.027). All subjects with >50% reduction in [18F]FTT uptake had >6-month PFS and >50% reduction in CA-125. Utilizing only baseline [18F]FTT uptake did not predict such responses. Conclusions: The decline in [18F]FTT uptake shortly after PARPi initiation provides a measure of drug-target engagement and shows promise as a biomarker to guide PARPi therapies in this pilot study. These results support additional preclinical mechanistic and clinical studies in subjects receiving PARPi ± combination therapy. See related commentary by Liu and Zamarin, p. 1384

Background: Cerebrospinal fluid (CSF) oligoclonal bands (OCB) are a diagnostic biomarker in multiple sclerosis (MS). The central vein sign (CVS) is an imaging biomarker for MS that may improve diagnostic accuracy. Objectives: The objective of the study is to examine the diagnostic performance of simplified CVS methods in comparison to OCB in participants with clinical or radiological suspicion for MS. Methods: Participants from the CentrAl Vein Sign in MS (CAVS-MS) pilot study with CSF testing were included. Select-3 and Select-6 (counting up to three or six CVS+ lesions per scan) were rated on post-gadolinium FLAIR* images. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value for Select-3, Select-6, OCB, and combinations thereof were calculated for MS diagnosis at baseline and at 12 months. Results: Of 53 participants, 25 were OCB+. At baseline, sensitivity for MS diagnosis was 0.75 for OCB, 0.83 for Select-3, and 0.71 for Select-6. Specificity for MS diagnosis was 0.76 for OCB, 0.48 for Select-3, and 0.86 for Select-6. At 12 months, PPV for MS diagnosis was 0.95 for Select-6 and 1.00 for Select-6 with OCB+ status. Discussion: Results suggest similar diagnostic performance of simplified CVS methods and OCB. Ongoing studies will refine whether CVS could be used in replacement or in conjunction with OCB.

EEG plays an integral part in the diagnosis and management of children with genetic epilepsies. Nevertheless, how quantitative EEG features differ between genetic epilepsies and neurologic outcomes remains largely unknown. In this study, we aimed to identify quantitative EEG biomarkers in STXBP1-related, SCN1A-related, and SYNGAP1-related childhood epilepsy and associated neurologic outcomes. We retrospectively collected clinical scalp EEGs from the Children's Hospital of Philadelphia. After removing artifacts and epochs with excess noise or altered state from EEGs, we extracted spectral features. We validated our preprocessing pipeline by comparing automatically detected posterior dominant rhythm (PDR) with annotations from clinical EEG reports. Next, as a coarse measure of pathologic slowing, we compared the alpha-delta bandpower ratio between controls and patients with different genetic epilepsies. We then trained random forest models with localized spectral features to predict diagnoses of STXBP1, SCN1A, and SYNGAP1 and estimate seizure frequency and motor function across a broader cohort. We evaluated EEGs from individuals with pathogenic variants in STXBP1 (95 EEGs, n = 20; 40% female; mean age 3.98 years), SCN1A (154 EEGs, n = 68; 51% female; mean age 6.24 years), and SYNGAP1 (46 EEGs, n = 21; 57% female; mean age 6.97 years) and neurotypical controls (847 EEGs, n = 806; 55% female; mean age 7.08 years). There was strong agreement between the automatically calculated PDR and annotations from clinical EEG reports (R² = 0.75). Individuals with STXBP1-related epilepsy had a significantly lower alpha-delta ratio than controls (median Cohen d = -0.95, p < 0.001) across all ages. Models accurately predicted a diagnosis of STXBP1 (area under the curve [AUC] = 0.92), SYNGAP1 (AUC = 0.86), and SCN1A (AUC = 0.85) against controls and each other (accuracy = 0.74). From these models, we isolated highly correlated biomarkers, including the alpha-theta ratio in frontal, occipital, and parietal electrodes associated with STXBP1, SYNGAP1, and SCN1A, respectively. Models were unable to predict seizure frequency (AUC = 0.53). Models predicted motor scores significantly better than age-based null models (p < 0.001). These results suggest that some genetic epilepsies and functional outcome measures have distinct quantitative EEG signatures. Furthermore, EEG spectral features are predictive of some functional outcome measures. Large-scale retrospective quantitative analysis of clinical EEGs has the potential to discover novel biomarkers and to quantify and track individuals' disease progression across development.

LowGAN, a generative model trained on paired high-resolution 3-T and lower-resolution portable 64-mT MRI data, improved low-field-strength image quality and brain morphometry and white matter lesion conspicuity in multiple sclerosis.