Correlation of idiopathic benign paroxysmal positional vertigo with cerebral small vessel disease.

BACKGROUND: Benign paroxysmal positional vertigo (BPPV) is the most prevalent form of peripheral vertigo, with vascular lesions being one of its suspected causes. The older adults are particularly vulnerable to BPPV. Cerebral small vessel disease (CSVD), on the other hand, is a clinical condition that results from damage of cerebral small vessels. Vascular involvement resulting from age-related risk factors and proinflammatory state may act as the underlying factor linking both BPPV and CSVD. AIM: The objective of this study is to explore the potential correlation between BPPV and CSVD by examining whether individuals aged 50 and older with BPPV exhibit a greater burden of CSVD. MATERIALS AND METHODS: This retrospective study included patients aged 50 years and older who had been diagnosed with BPPV. A control group consisting of patients diagnosed with idiopathic facial neuritis (IFN) during the same time period was also included. The burden of cerebral white matter hyperintensities (WMHs) was evaluated using the Fazekas scale. An ordinal regression analysis was conducted to investigate the potential correlation between BPPV and WMHs. RESULTS: The study included a total of 101 patients diagnosed with BPPV and 116 patients with IFN. Patients with BPPV were found to be significantly more likely (OR = 2.37, 95% CI 1.40-4.03, p = 0.001) to have a higher Fazekas score compared to the control group. Brain infarctions, hypertension, and age were all identified as significant predictors of white matter hyperplasia on MRI, with OR of 9.9 (95% CI 4.21-24.84, P<0.001), 2.86 (95% CI 1.67-5.0, P<0.001), and 1.18 (95% CI 1.13-1.22, P<0.001) respectively. CONCLUSION: Our findings suggest that vascular impairment caused by age-related risk factors and proinflammatory status may be contributing factors to the development of BPPV in individuals aged 50 and above, as we observed a correlation between the suffering of BPPV and the severity of WMHs.

Real-World Performance of the Afirma Genomic Sequencing Classifier (GSC)-A Meta-analysis.

CONTEXT: The Afirma® GSC aids in risk stratifying indeterminate thyroid nodule cytology (ITN). The 2018 GSC validation study (VS) reported a sensitivity (SN) of 91%, specificity (SP) of 68%, positive predictive value (PPV) of 47%, and negative predictive value (NPV) of 96%. Since then, 13 independent real-world (RW) postvalidation studies have been published. OBJECTIVE: This study's objective is to compare the RW GSC performance to the VS metrics. METHODS: Rules and assumptions applying to this analysis include: (1) At least 1 patient with molecular benign results must have surgery for that study to be included in SN, SP, and NPV analyses. (2) Molecular benign results without surgical histology are considered true negatives (TN) (as are molecular benign results with benign surgical histology). (3) Unoperated patients with suspicious results are either excluded from analysis (observed PPV [oPPV] and observed SP [oSP]) or assumed histology negatives (false positives; conservative PPV [cPPV] and conservative SP [cSP]) 4. Noninvasive follicular thyroid neoplasm with papillary-like nuclear features is considered malignant. RESULTS: In RW studies, the GSC demonstrates a SN, oSP, oPPV, and NPV of 97%, 88%, 65%, 99% respectively, and conservative RW performance showed cSP of 80% and cPPV of 49%, all significantly higher than the VS except for SN and cPPV. There was also a higher benign call rate (BCR) of 67% in RW studies compared to 54% in the VS (P < 0.05). CONCLUSION: RW data for the Afirma GSC demonstrates significantly better oSP and oPPV performance than the VS, indicating an increased yield of cancers for resected GSC suspicious nodules. The higher BCR likely increases the overall rate of clinical observation in lieu of surgery.

Housing market capitalization of environmental risk: evidence from the Tianjin explosion.

Do environmental risks affect housing prices in the vicinity of industrial plants? Prior studies concentrated on the capitalization effect of infrequent natural disasters instead of industry-related environmental risks caused by production accidents in our everyday lives. In this paper, we first use the 2015 Tianjin explosion in China as an exogenous shock, which led to serious casualties and property damage with nationwide concern, to estimate the effects of environmental risk on housing prices far away from the explosion site. Based on the housing transaction units and chemical plant datasets in Beijing with the difference-in-differences hedonic model, the results indicate that first, housing prices near chemical plants decreased significantly after the explosion and lasted for at least 2 years. Second, the mitigation effects of production and consumption amenities and regional demographic features were examined. The conclusions of this paper provide a policy basis for constantly implementing public sentiment management and safety supervision, balancing industrial and residential land layouts, and improving local amenities and demographics to ensure the stability of the land market.

Statistics or biology: the zero-inflation controversy about scRNA-seq data.

Researchers view vast zeros in single-cell RNA-seq data differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as missing data to be corrected. To help address the controversy, here we discuss the sources of biological and non-biological zeros; introduce five mechanisms of adding non-biological zeros in computational benchmarking; evaluate the impacts of non-biological zeros on data analysis; benchmark three input data types: observed counts, imputed counts, and binarized counts; discuss the open questions regarding non-biological zeros; and advocate the importance of transparent analysis.

Maximizing Small Biopsy Patient Samples: Unified RNA-Seq Platform Assessment of over 120,000 Patient Biopsies.

Despite its wide-ranging benefits, whole-transcriptome or RNA exome profiling is challenging to implement in a clinical diagnostic setting. The Unified Assay is a comprehensive workflow wherein exome-enriched RNA-sequencing (RNA-Seq) assays are performed on clinical samples and analyzed by a series of advanced machine learning-based classifiers. Gene expression signatures and rare and/or novel genomic events, including fusions, mitochondrial variants, and loss of heterozygosity were assessed using RNA-Seq data generated from 120,313 clinical samples across three clinical indications (thyroid cancer, lung cancer, and interstitial lung disease). Since its implementation, the data derived from the Unified Assay have allowed significantly more patients to avoid unnecessary diagnostic surgery and have played an important role in guiding follow-up decisions regarding treatment. Collectively, data from the Unified Assay show the utility of RNA-Seq and RNA expression signatures in the clinical laboratory, and their importance to the future of precision medicine.

mbImpute: an accurate and robust imputation method for microbiome data.

A critical challenge in microbiome data analysis is the existence of many non-biological zeros, which distort taxon abundance distributions, complicate data analysis, and jeopardize the reliability of scientific discoveries. To address this issue, we propose the first imputation method for microbiome data-mbImpute-to identify and recover likely non-biological zeros by borrowing information jointly from similar samples, similar taxa, and optional metadata including sample covariates and taxon phylogeny. We demonstrate that mbImpute improves the power of identifying disease-related taxa from microbiome data of type 2 diabetes and colorectal cancer, and mbImpute preserves non-zero distributions of taxa abundances.