Machine Learning and Health Science Research: Tutorial

Machine learning (ML) has seen impressive growth in health science research due to its capacity for handling complex data to perform a range of tasks, including unsupervised learning, supervised learning, and reinforcement learning. To aid health science researchers in understanding the strengths and limitations of ML and to facilitate its integration into their studies, we present here a guideline for integrating ML into an analysis through a structured framework, covering steps from framing a research question to study design and analysis techniques for specialized data types.

Comprehensive evaluation of methods for differential expression analysis of metatranscriptomics data

Understanding the function of the human microbiome is important but the development of statistical methods specifically for the microbial gene expression (i.e. metatranscriptomics) is in its infancy. Many currently employed differential expression analysis methods have been designed for different data types and have not been evaluated in metatranscriptomics settings. To address this gap, we undertook a comprehensive evaluation and benchmarking of 10 differential analysis methods for metatranscriptomics data. We used a combination of real and simulated data to evaluate performance (i.e. type I error, false discovery rate and sensitivity) of the following methods: log-normal (LN), logistic-beta (LB), MAST, DESeq2, metagenomeSeq, ANCOM-BC, LEfSe, ALDEx2, Kruskal-Wallis and two-part Kruskal-Wallis. The simulation was informed by supragingival biofilm microbiome data from 300 preschool-age children enrolled in a study of childhood dental disease (early childhood caries, ECC), whereas validations were sought in two additional datasets from the ECC study and an inflammatory bowel disease study. The LB test showed the highest sensitivity in both small and large samples and reasonably controlled type I error. Contrarily, MAST was hampered by inflated type I error. Upon application of the LN and LB tests in the ECC study, we found that genes C8PHV7 and C8PEV7, harbored by the lactate-producing Campylobacter gracilis, had the strongest association with childhood dental disease. This comprehensive model evaluation offers practical guidance for selection of appropriate methods for rigorous analyses of differential expression in metatranscriptomics. Selection of an optimal method increases the possibility of detecting true signals while minimizing the chance of claiming false ones.

A bivariate zero-inflated negative binomial model and its applications to biomedical settings

The zero-inflated negative binomial distribution has been widely used for count data analyses in various biomedical settings due to its capacity of modeling excess zeros and overdispersion. When there are correlated count variables, a bivariate model is essential for understanding their full distributional features. Examples include measuring correlation of two genes in sparse single-cell RNA sequencing data and modeling dental caries count indices on two different tooth surface types. For these purposes, we develop a richly parametrized bivariate zero-inflated negative binomial model that has a simple latent variable framework and eight free parameters with intuitive interpretations. In the scRNA-seq data example, the correlation is estimated after adjusting for the effects of dropout events represented by excess zeros. In the dental caries data, we analyze how the treatment with Xylitol lozenges affects the marginal mean and other patterns of response manifested in the two dental caries traits. An R package "bzinb" is available on Comprehensive R Archive Network.

Selenomonas sputigena acts as a pathobiont mediating spatial structure and biofilm virulence in early childhood caries

Streptococcus mutans has been implicated as the primary pathogen in childhood caries (tooth decay). While the role of polymicrobial communities is appreciated, it remains unclear whether other microorganisms are active contributors or interact with pathogens. Here, we integrate multi-omics of supragingival biofilm (dental plaque) from 416 preschool-age children (208 males and 208 females) in a discovery-validation pipeline to identify disease-relevant inter-species interactions. Sixteen taxa associate with childhood caries in metagenomics-metatranscriptomics analyses. Using multiscale/computational imaging and virulence assays, we examine biofilm formation dynamics, spatial arrangement, and metabolic activity of Selenomonas sputigena, Prevotella salivae and Leptotrichia wadei, either individually or with S. mutans. We show that S. sputigena, a flagellated anaerobe with previously unknown role in supragingival biofilm, becomes trapped in streptococcal exoglucans, loses motility but actively proliferates to build a honeycomb-like multicellular-superstructure encapsulating S. mutans, enhancing acidogenesis. Rodent model experiments reveal an unrecognized ability of S. sputigena to colonize supragingival tooth surfaces. While incapable of causing caries on its own, when co-infected with S. mutans, S. sputigena causes extensive tooth enamel lesions and exacerbates disease severity in vivo. In summary, we discover a pathobiont cooperating with a known pathogen to build a unique spatial structure and heighten biofilm virulence in a prevalent human disease.

BZINB Model-Based Pathway Analysis and Module Identification Facilitates Integration of Microbiome and Metabolome Data

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental caries, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman's rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath, facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

BZINB model-based pathway analysis and module identification facilitates integration of microbiome and metabolome data

Integration of multi-omics data is a challenging but necessary step to advance our understanding of the biology underlying human health and disease processes. To date, investigations seeking to integrate multi-omics (e.g., microbiome and metabolome) employ simple correlation-based network analyses; however, these methods are not always well-suited for microbiome analyses because they do not accommodate the excess zeros typically present in these data. In this paper, we introduce a bivariate zero-inflated negative binomial (BZINB) model-based network and module analysis method that addresses this limitation and improves microbiome-metabolome correlation-based model fitting by accommodating excess zeros. We use real and simulated data based on a multi-omics study of childhood oral health (ZOE 2.0; investigating early childhood dental disease, ECC) and find that the accuracy of the BZINB model-based correlation method is superior compared to Spearman’s rank and Pearson correlations in terms of approximating the underlying relationships between microbial taxa and metabolites. The new method, BZINB-iMMPath facilitates the construction of metabolite-species and species-species correlation networks using BZINB and identifies modules of (i.e., correlated) species by combining BZINB and similarity-based clustering. Perturbations in correlation networks and modules can be efficiently tested between groups (i.e., healthy and diseased study participants). Upon application of the new method in the ZOE 2.0 study microbiome-metabolome data, we identify that several biologically-relevant correlations of ECC-associated microbial taxa with carbohydrate metabolites differ between healthy and dental caries-affected participants. In sum, we find that the BZINB model is a useful alternative to Spearman or Pearson correlations for estimating the underlying correlation of zero-inflated bivariate count data and thus is suitable for integrative analyses of multi-omics data such as those encountered in microbiome and metabolome studies.

Multi-stage optimal dynamic treatment regimes for survival outcomes with dependent censoring

We propose a reinforcement learning method for estimating an optimal dynamic treatment regime for survival outcomes with dependent censoring. The estimator allows the failure time to be conditionally independent of censoring and dependent on the treatment decision times, supports a flexible number of treatment arms and treatment stages, and can maximize either the mean survival time or the survival probability at a certain time point. The estimator is constructed using generalized random survival forests and can have polynomial rates of convergence. Simulations and data analysis results suggest that the new estimator brings higher expected outcomes than existing methods in various settings.

Healthcare utilisation for elderly people at the onset of the COVID-19 pandemic in South Korea

BACKGROUND

At the onset of the coronavirus disease 2019 (COVID-19) pandemic, health care systems were severely disrupted in many countries and in particular, elderly people vulnerable to COVID-19 may have been reluctant to receive their medical treatment.

METHODS

We conducted interrupted time series analyses (ITSA) using nationwide medical claim data between January 2020 and July 2020, with focus on different disease categories for the patients of 65 to 84-year-olds, i.e., acute upper respiratory infections (AURIs) vs. chronic diseases.

RESULTS

AURIs and chronic diseases showed a sharp contrast with respect to the change in healthcare service utilisation. First, the utilisation rate for chronic diseases changed little whereas for AURIs it dropped by 20.4% year-over-year (yoy) at the onset of the pandemic (week 6, 2020). Second, as social distancing relaxed (week 17, 2020), the AURIs patients trended up and even reached to 7.8% above yoy whereas no significant change found for chronic diseases.

CONCLUSIONS

The uninterrupted treatment for chronic diseases in contrast to the AURIs implies that the governmental and public responses to the pandemic outbreak worked for efficient healthcare provision to patients in needs of regular check-ups and treatment in the middle of an infectious disease crisis.

Interval censored recursive forests

We propose interval censored recursive forests (ICRF), an iterative tree ensemble method for interval censored survival data. This nonparametric regression estimator addresses the splitting bias problem of existing tree-based methods and iteratively updates survival estimates in a self-consistent manner. Consistent splitting rules are developed for interval censored data, convergence is monitored using out-of-bag samples, and kernel-smoothing is applied. The ICRF is uniformly consistent and displays high prediction accuracy in both simulations and applications to avalanche and national mortality data. An R package icrf is available on CRAN and Supplementary Materials for this article are available online.

Improved Metabolite Prediction Using Microbiome Data-Based Elastic Net Models

Microbiome data are becoming increasingly available in large health cohorts, yet metabolomics data are still scant. While many studies generate microbiome data, they lack matched metabolomics data or have considerable missing proportions of metabolites. Since metabolomics is key to understanding microbial and general biological activities, the possibility of imputing individual metabolites or inferring metabolomics pathways from microbial taxonomy or metagenomics is intriguing. Importantly, current metabolomics profiling methods such as the HMP Unified Metabolic Analysis Network (HUMAnN) have unknown accuracy and are limited in their ability to predict individual metabolites. To address this gap, we developed a novel metabolite prediction method, and we present its application and evaluation in an oral microbiome study. The new method for predicting metabolites using microbiome data (ENVIM) is based on the elastic net model (ENM). ENVIM introduces an extra step to ENM to consider variable importance (VI) scores, and thus, achieves better prediction power. We investigate the metabolite prediction performance of ENVIM using metagenomic and metatranscriptomic data in a supragingival biofilm multi-omics dataset of 289 children ages 3-5 who were participants of a community-based study of early childhood oral health (ZOE 2.0) in North Carolina, United States. We further validate ENVIM in two additional publicly available multi-omics datasets generated from studies of gut health. We select gene family sets based on variable importance scores and modify the existing ENM strategy used in the MelonnPan prediction software to accommodate the unique features of microbiome and metabolome data. We evaluate metagenomic and metatranscriptomic predictors and compare the prediction performance of ENVIM to the standard ENM employed in MelonnPan. The newly developed ENVIM method showed superior metabolite predictive accuracy than MelonnPan when trained with metatranscriptomics data only, metagenomics data only, or both. Better metabolite prediction is achieved in the gut microbiome compared with the oral microbiome setting. We report the best-predictable compounds in all these three datasets from two different body sites. For example, the metabolites trehalose, maltose, stachyose, and ribose are all well predicted by the supragingival microbiome.

Discussion of ‘Estimating time-varying causal excursion effects in mobile health with binary outcomes’

In this discussion, we examine the contributions of Qian et al. (2021) and potential applications of the newly developed estimator for the causal excursion effect in binary outcome data. Specifically, we consider extension of their method to count outcomes and observational data, propose an alternative use of their method for analysing excursion effect trajectories and discuss ways of improving estimator efficiency.

MRI-Visible Perivascular Spaces in the Centrum Semiovale Are Associated with Brain Amyloid Deposition in Patients with Alzheimer Disease-Related Cognitive Impairment

BACKGROUND AND PURPOSE

The association of perivascular spaces in the centrum semiovale with amyloid accumulation among patients with Alzheimer disease-related cognitive impairment is unknown. We evaluated this association in patients with Alzheimer disease-related cognitive impairment and β-amyloid deposition, assessed with [¹⁸F] florbetaben PET/CT.

MATERIALS AND METHODS

MR imaging and [¹⁸F] florbetaben PET/CT images of 144 patients with Alzheimer disease-related cognitive impairment were retrospectively evaluated. MR imaging-visible perivascular spaces were rated on a 4-point visual scale: a score of ≥3 or <3 indicated a high or low degree of MR imaging-visible perivascular spaces, respectively. Amyloid deposition was evaluated using the brain β-amyloid plaque load scoring system.

RESULTS

Compared with patients negative for β-amyloid, those positive for it were older and more likely to have lower cognitive function, a diagnosis of Alzheimer disease, white matter hyperintensity, the Apolipoprotein E ε4 allele, and a high degree of MR imaging-visible perivascular spaces in the centrum semiovale. Multivariable analysis, adjusted for age and Apolipoprotein E status, revealed that a high degree of MR imaging-visible perivascular spaces in the centrum semiovale was independently associated with β-amyloid positivity (odds ratio, 2.307; 95% CI, 1.036-5.136; P = .041).

CONCLUSIONS

A high degree of MR imaging-visible perivascular spaces in the centrum semiovale independently predicted β-amyloid positivity in patients with Alzheimer disease-related cognitive impairment. Thus, MR imaging-visible perivascular spaces in the centrum semiovale are associated with amyloid pathology of the brain and could be an indirect imaging marker of amyloid burden in patients with Alzheimer disease-related cognitive impairment.

The bacterial microbiome and metabolome in caries progression and arrest

Aim: This in vivo experimental study investigated bacterial microbiome and metabolome longitudinal changes associated with enamel caries lesion progression and arrest. Methods: We induced natural caries activity in three caries-free volunteers prior to four premolar extractions for orthodontic reasons. The experimental model included placement of a modified orthodontic band on smooth surfaces and a mesh on occlusal surfaces. We applied the caries-inducing protocol for 4- and 6-weeks, and subsequently promoted caries lesion arrest via a 2-week toothbrushing period. Lesions were verified clinically and quantitated via micro-CT enamel density measurements. The biofilm microbial composition was determined via 16S rRNA gene Illumina sequencing and NMR spectrometry was used for metabolomics. Results: Biofilm maturation and caries lesion progression were characterized by an increase in Gram-negative anaerobes, including Veillonella and Prevotella. Streptococcus was associated caries lesion progression, while a more equal distribution of Streptococcus, Bifidobacterium, Atopobium, Prevotella, Veillonella, and Saccharibacteria (TM7) characterized arrest. Lactate, acetate, pyruvate, alanine, valine, and sugars were more abundant in mature biofilms compared to newly formed biofilms. Conclusions: These longitudinal bacterial microbiome and metabolome results provide novel mechanistic insights into the role of the biofilm in caries progression and arrest and offer promising candidate biomarkers for validation in future studies.

An Automated Machine Learning Classifier for Early Childhood Caries

Purpose: The purpose of the study was to develop and evaluate an automated machine learning algorithm (AutoML) for children's classification according to early childhood caries (ECC) status. Methods: Clinical, demographic, behavioral, and parent-reported oral health status information for a sample of 6,404 three- to five-year-old children (mean age equals 54 months) participating in an epidemiologic study of early childhood oral health in North Carolina was used. ECC prevalence (decayed, missing, and filled primary teeth surfaces [dmfs] score greater than zero, using an International Caries Detection and Assessment System score greater than or equal to three caries lesion detection threshold) was 54 percent. Ten sets of ECC predictors were evaluated for ECC classification accuracy (i.e., area under the ROC curve [AUC], sensitivity [Se], and positive predictive value [PPV]) using an AutoML deployment on Google Cloud, followed by internal validation and external replication. Results: A parsimonious model including two terms (i.e., children's age and parent-reported child oral health status: excellent/very good/good/fair/poor) had the highest AUC (0.74), Se (0.67), and PPV (0.64) scores and similar performance using an external National Health and Nutrition Examination Survey (NHANES) dataset (AUC equals 0.80, Se equals 0.73, PPV equals 0.49). Contrarily, a comprehensive model with 12 variables covering demographics (e.g., race/ethnicity, parental education), oral health behaviors, fluoride exposure, and dental home had worse performance (AUC equals 0.66, Se equals 0.54, PPV equals 0.61). Conclusions: Parsimonious automated machine learning early childhood caries classifiers, including single-item self-reports, can be valuable for ECC screening. The classifier can accommodate biological information that can help improve its performance in the future.

Point-of-care testing for the detection of SARS-CoV-2: a systematic review and meta-analysis

OBJECTIVE

To evaluate the diagnostic accuracy of the Food and Drug Administration Emergency Use Authorization (FDA-EUA) authorized point-of-care tests (POCTs) for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

MATERIALS AND METHODS

A systematic literature search was conducted using the PubMed, Embase, and Web of Science databases for articles published till August 10, 2020. We included studies providing information regarding diagnostic test accuracy of FDA-EUA POCTs for SARS-CoV-2 detection. The methodologic quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The review protocol is registered in the International Prospective Register of Systematic Reviews (protocol number CRD42020202248).

RESULTS

We included 26 studies describing a total of 3242 samples. The summary sensitivity and specificity were 0.94 [95% confidence interval (CI): 0.88-0.97] and 1.00 (95% CI: 0.99-1.00), respectively. The area under the summary receiver operating characteristic curve was 1.00 (95% CI: 0.99-1.00). A pooled analysis based on the index test revealed a summary sensitivity and specificity of Cepheid Xpert Xpress SARS-CoV-2 [0.99 (95% CI: 0.97-1.00) and 0.99 (95% CI: 0.94-1.00, respectively)] and ID NOW COVID-19 [0.78 (95% CI: 0.74-0.82) and 1.00 (95% CI: 0.98-1.00), respectively].

CONCLUSIONS

FDA-EUA POCTs, especially molecular assays, have high sensitivity, specificity, and overall diagnostic accuracy for detecting SARS-CoV-2. If approved, FDA-EUA POCTs can provide a rapid and practical way to identify infected individuals early on and help to limit the strain on the healthcare system. However, more high-quality clinical data are required to support our results.

Metabolomics Insights in Early Childhood Caries

Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. The study's analytical sample comprised 289 children ages 3 to 5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using International Caries Detection and Classification System (ICDAS) criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant was analyzed using ultra-performance liquid chromatography-tandem mass spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log₂-transformed values, applying a false discovery rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. There were 503 named metabolites identified, including microbial, host, and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS ≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor P = 8 × 10^-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including fucose (P = 3.0 × 10^-6) and N-acetylneuraminate (p = 6.8 × 10^-6) with higher ECC prevalence, as well as catechin (P = 4.7 × 10^-6) and epicatechin (P = 2.9 × 10^-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.

Cohort Profile: ZOE 2.0-A Community-Based Genetic Epidemiologic Study of Early Childhood Oral Health

Early childhood caries (ECC) is an aggressive form of dental caries occurring in the first five years of life. Despite its prevalence and consequences, little progress has been made in its prevention and even less is known about individuals' susceptibility or genomic risk factors. The genome-wide association study (GWAS) of ECC ("ZOE 2.0") is a community-based, multi-ethnic, cross-sectional, genetic epidemiologic study seeking to address this knowledge gap. This paper describes the study's design, the cohort's demographic profile, data domains, and key oral health outcomes. Between 2016 and 2019, the study enrolled 8059 3-5-year-old children attending public preschools in North Carolina, United States. Participants resided in 86 of the state's 100 counties and racial/ethnic minorities predominated-for example, 48% (n = 3872) were African American, 22% white, and 20% (n = 1611) were Hispanic/Latino. Seventy-nine percent (n = 6404) of participants underwent clinical dental examinations yielding ECC outcome measures-ECC (defined at the established caries lesion threshold) prevalence was 54% and the mean number of decayed, missing, filled surfaces due to caries was eight. Nearly all (98%) examined children provided sufficient DNA from saliva for genotyping. The cohort's community-based nature and rich data offer excellent opportunities for addressing important clinical, epidemiologic, and biological questions in early childhood.

Salient safety conditioning improves novel discrimination learning

Generalized fear is one purported mechanism of anxiety that is a target of clinical and basic research. Impaired fear discrimination has been primarily examined from the perspective of increased fear learning, rather than how learning about non-threatening stimuli affects fear discrimination. To address this question, we tested how three Safety Conditioning protocols with varied levels of salience allocated to the safety cue compared to classic Fear Conditioning in their impact on subsequent innate anxiety, and differential fear learning of new aversive and neutral cues. Using a high anxiety strain of mice (129SvEv, Taconic), we show that Fear Conditioned animals show little exploration of the anxiogenic center of an open field 24 h later, and poor discrimination during new differential conditioning 7 days later. Three groups of mice underwent Safety Conditioning, (i) the safety tone was unpaired with a shock, (ii) the safety tone was unpaired with the shock and co-terminated with a house light signaling the end of the safety period, and (iii) the safety tone was unpaired with the shock and its beginning co-occurred with a house light, signaling the start of a safety period. Mice from all Safety Conditioning groups showed higher levels of open field exploration than the Fear Conditioned mice 24 h after training. Furthermore, Safety Conditioned animals showed improved discrimination learning of a novel non-threat, with the Salient Beginning safety conditioned group performing best. These findings indicate that high anxiety animals benefit from salient safety training to improve exploration and discrimination of new non-threating stimuli.

MRI size assessment of cerebral microvasculature using diffusion-time-dependent stimulated-echo acquisition: A feasibility study in rodent

In this study, a stimulated-echo (STE) method was employed to robustify the cerebral vessel size estimation near air-tissue, bone-tissue interfaces, and large vessels. The proposed solution is to replace the relaxation rate change from gradient-echo (GRE) with that from STE with long diffusion time after the injection of an intravascular contrast agent, superparamagnetic iron oxide nanoparticles. The corresponding diffusion length of STE is shorter than the length over which the unwanted macroscopic field inhomogeneities but is still longer than the correlation length of the fields induced by small vessels. Therefore, the unwanted field inhomogeneities are refocused, while preserving microscopic susceptibility contrast from cerebral vessels. The mean vessel diameter (dimensionless) derived from the diffusion-time-varying STE method was compared to the mean vessel diameter obtained by a conventional spin-echo (SE) and GRE combination based on Monte-Carlo proton diffusion simulations and in vivo rat experiments at 7 ​T. The in vivo mean vessel diameter from the MRI experiments was directly compared to available reference mouse brain vasculature obtained by a knife-edge scanning microscope (KESM), which is considered to be the gold standard. Monte-Carlo simulation revealed that SE and GRE-based MR relaxation rate changes (ΔR₂ and ΔR₂∗, respectively) can be enhanced using single STE-based MR relaxation rate change (ΔR_STE) by regulating diffusion time, especially for small vessels. The in vivo mean vessel diameter from the STE method demonstrated a closer agreement with that from the KESM compared to the combined SE and GRE method, especially in the olfactory bulb and cortex. This study demonstrates that STE relaxation rate changes can be used as consistent measures for assessing small cerebral microvasculature, where macroscopic field inhomogeneity is severe and signal contamination from adjacent large vessels is significant.

Prevalence of human papillomavirus in oral gargles and tonsillar washings

OBJECTIVES

Human papillomavirus (HPV) infection drives carcinogenesis in the oropharynx. No standard sampling or HPV detection methods for evaluating oropharyngeal HPV infection exist. The prevalence of oral HPV infection in Japan is unknown.

MATERIALS AND METHODS

We examined 435 healthy Japanese individuals to address whether adding tonsillar washing to oral gargling would improve HPV detection. We compared HPV assessment using GENOSEARCH HPV31 versus nested PCR and direct sequencing. Associations between HPV infection and demographic and behavioral characteristics were examined.

RESULTS

Most participants who were HPV-positive based on oral gargles were also HPV-positive based on tonsillar washings: 11 (64.7%) of 17 on nested PCR and 12 (70.6%) of 17 on GENOSEARCH HPV31. Although HPV infection was more prevalent in oral gargles followed by tonsillar washings than in oral gargles alone, the difference was not statistically significant (nested PCR, 4.8% vs. 3.9%, P = 0.46; GENOSEARCH HPV31, 5.3% vs. 3.9%, P = 0.33). The overall agreement between nested PCR and GENOSEARCH HPV31 was 98.6%, with 76.0% positive agreement. The overall prevalence of oral HPV infection in Japan was 5.7% (95% confidence interval, 3.9-8.3%). Men had a significantly higher prevalence of oral HPV infection than women (8.3% vs. 2.6%, P = 0.02). Infection increased with number of lifetime sexual partners (P < 0.001 for trend).

CONCLUSION

The oropharynx is probably the major source of HPV-infected cells in oral gargles. Oral gargling could be a standard sampling method for evaluating oropharyngeal HPV infection. GENOSEARCH HPV31 could be an option for oral HPV detection.

Radiomics Study of Thyroid Ultrasound for Predicting BRAF Mutation in Papillary Thyroid Carcinoma: Preliminary Results

BACKGROUND AND PURPOSE

It is not known how radiomics using ultrasound images contribute to the detection of BRAF mutation. This study aimed to evaluate whether a radiomics study of gray-scale ultrasound can predict the presence or absence of B-Raf proto-oncogene, serine/threonine kinase (BRAF) mutation in papillary thyroid cancer.

MATERIALS AND METHODS

The study retrospectively included 96 thyroid nodules that were surgically confirmed papillary thyroid cancers between January 2012 and June 2013. BRAF mutation was positive in 48 nodules and negative in 48 nodules. For analysis, ROIs from the nodules were demarcated manually on both longitudinal and transverse sonographic images. We extracted a total of 86 radiomics features derived from histogram parameters, gray-level co-occurrence matrix, intensity size zone matrix, and shape features. These features were used to build 3 different classifier models, including logistic regression, support vector machine, and random forest using 5-fold cross-validation. The performance including accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve, of the different models was evaluated.

RESULTS

The incidence of high-suspicion nodules diagnosed on ultrasound was higher in the BRAF mutation-positive group than in the mutation-negative group (P  =  .004). The radiomics approach demonstrated that all classification models showed moderate performance for predicting the presence of BRAF mutation in papillary thyroid cancers with an area under the curve value of 0.651, accuracy of 64.3%, sensitivity of 66.8%, and specificity of 61.8%, on average, for the 3 models.

CONCLUSIONS

Radiomics study using thyroid sonography is limited in predicting the BRAF mutation status of papillary thyroid carcinoma. Further studies will be needed to validate our results using various diagnostic methods.

Confidence Intervals for the Area Under the Receiver Operating Characteristic Curve in the Presence of Ignorable Missing Data

Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this article, we compare different methods of constructing Wald-type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find that constructing confidence intervals using multiple imputation based on logistic regression gives the most robust coverage probability and the choice of confidence interval method is less important. However, when missingness rate is less severe (e.g. less than 70%), we recommend using Newcombe's Wald method for constructing confidence intervals along with multiple imputation using predictive mean matching.

Structural and molecular modelling studies of antimelanogenic piper-amide TRPM1 antagonists

Piper-amides exhibit diverse biological activities, including antimelanogenic effects. In our previous studies, we identified a potent piper-amide derivative that inhibited melanogenesis via the TRPM1 calcium channel. Despite its potential as a therapeutic target, the three-dimensional structure of TRPM1 is still not available. Thus, structure-guided compound design and the discovery of novel inhibitors of melanogenesis have been limited. In the present study, a series of computational methods, including homology modelling, docking, molecular dynamics simulation and field-based pharmacophore modelling, were integrated to explore the structural features of natural piper-amide-like compounds related to the TRPM1 target. These studies suggested the binding mode and provided a 3D pharmacophore model of the ligands, which can be helpful in understanding the TRPM1-ligand interactions at the molecular level and in designing potent antagonists of TRPM1.