Validation of a Bayesian learning model to predict the risk for cannabis use disorder.

BACKGROUND: Cannabis use disorder (CUD) is a growing public health problem. Early identification of adolescents and young adults at risk of developing CUD in the future may help stem this trend. A logistic regression model fitted using a Bayesian learning approach was developed recently to predict the risk of future CUD based on seven risk factors in adolescence and youth. A nationally representative longitudinal dataset, Add Health was used to train the model (henceforth referred as Add Health model). METHODS: We validated the Add Health model on two cohorts, namely, Michigan Longitudinal Study (MLS) and Christchurch Health and Development Study (CHDS) using longitudinal data from participants until they were approximately 30 years old (to be consistent with the training data from Add Health). If a participant was diagnosed with CUD at any age during this period, they were considered a case. We calculated the area under the curve (AUC) and the ratio of expected and observed number of cases (E/O). We also explored recalibrating the model to account for differences in population prevalence. RESULTS: The cohort sizes used for validation were 424 (53 cases) for MLS and 637 (105 cases) for CHDS. AUCs for the two cohorts were 0.66 (MLS) and 0.73 (CHDS) and the corresponding E/O ratios (after recalibration) were 0.995 and 0.999. CONCLUSION: The external validation of the Add Health model on two different cohorts lends confidence to the model's ability to identify adolescent or young adult cannabis users at high risk of developing CUD in later life.

Primary hepatic tuberculosis presenting as fever of unknown origin diagnosed by Positron emission tomography/computed tomography scan (PET/CT).

Tuberculosis (TB), especially extrapulmonary tuberculosis (EPTB), is an important cause of fever of unknown origin (FUO) in areas endemic to TB. Liver involvement in TB in the absence of miliary TB is rare. A definitive diagnosis of primary hepatic TB can be challenging and relies on the histological and/or bacteriological findings of the liver tissue obtained by biopsy. TB should be considered in the differential diagnosis of space-occupying lesions and abscesses of the liver. In our case of a 52-year-old adult male with FUO, a positron emission tomography/computed tomography (PET/CT) scan was used, due to lack of any potential diagnostic clues, and a focal lesion was identified as a potential biopsy site.

Dog tick (Rhipecephalus) causing Lyme disease in an adult human.

Lyme disease is a tick-borne multisystem disorder transmitted by the family of Ixodes and caused by a spirochete, Borrelia. An early manifestation of the disease presents with skin lesions typically called Erythema chronicum migrans (ECM). Doxycycline should be the antibiotic of choice used for its treatment. However, we present the case of Lyme's disease in an adult male with dog tick, Rhipecephalus as a vector.

A Bayesian learning model to predict the risk for cannabis use disorder.

BACKGROUND: The prevalence of cannabis use disorder (CUD) has been increasing recently and is expected to increase further due to the rising trend of cannabis legalization. To help stem this public health concern, a model is needed that predicts for an adolescent or young adult cannabis user their personalized risk of developing CUD in adulthood. However, there exists no such model that is built using nationally representative longitudinal data. METHODS: We use a novel Bayesian learning approach and data from Add Health (n = 8712), a nationally representative longitudinal study, to build logistic regression models using four different regularization priors: lasso, ridge, horseshoe, and t. The models are compared by their prediction performance on unseen data via 5-fold-cross-validation (CV). We assess model discrimination using the area under the curve (AUC) and calibration by comparing the expected (E) and observed (O) number of CUD cases. We also externally validate the final model on independent test data from Add Health (n = 570). RESULTS: Our final model is based on lasso prior and has seven predictors: biological sex; scores on personality traits of neuroticism, openness, and conscientiousness; and measures of adverse childhood experiences, delinquency, and peer cannabis use. It has good discrimination and calibration performance as reflected by its respective AUC and E/O of 0.69 and 0.95 based on 5-fold CV and 0.71 and 1.10 on validation data. CONCLUSION: This externally validated model may help in identifying adolescent or young adult cannabis users at high risk of developing CUD in adulthood.

CBCRisk-Black: a personalized contralateral breast cancer risk prediction model for black women.

PURPOSE: Black breast cancer (BC) survivors have a higher risk of developing contralateral breast cancer (CBC) than Whites. Existing CBC risk prediction tools are developed based on mostly White women. To address this racial disparity, it is crucial to develop tools tailored for Black women to help them inform about their actual risk of CBC. METHODS: We propose an absolute risk prediction model, CBCRisk-Black, specifically for Black BC patients. It uses data on Black women from two sources: Breast Cancer Surveillance Consortium (BCSC) and Surveillance, Epidemiology, and End Results (SEER). First, a matched lasso logistic regression model for estimating relative risks (RR) is developed. Then, it is combined with relevant hazard rates and attributable risks to obtain absolute risks. Six-fold cross-validation is used to internally validate CBCRisk-Black. We also compare CBCRisk-Black with CBCRisk, an existing CBC risk prediction model. RESULTS: The RR model uses data from BCSC on 744 Black women (186 cases). CBCRisk-Black has four risk factors (RR compared to baseline): breast density (2.13 for heterogeneous/extremely dense), family history of BC (2.28 for yes), first BC tumor size (2.14 for T3/T4, 1.56 for TIS), and age at first diagnosis of BC (1.41 for < 40). The area under the receiver operating characteristic curve (AUC) for 3- and 5-year predictions are 0.72 and 0.65 for CBCRisk-Black while those are 0.65 and 0.60 for CBCRisk. CONCLUSION: CBCRisk-Black may serve as a useful tool to clinicians in counseling Black BC patients by providing a more accurate and personalized CBC risk estimate.

Joint risk prediction for hazardous use of alcohol, cannabis, and tobacco among adolescents: A preliminary study using statistical and machine learning.

For some, substance use during adolescence may be a stepping stone on the way to substance use disorders in adulthood. Risk prediction models may help identify adolescent users at elevated risk for hazardous substance use. This preliminary analysis used cross-sectional data (n = 270, ages 13-18) from the baseline dataset of a randomized controlled trial intervening with adolescent alcohol and/or cannabis use. Models were developed for jointly predicting quantitative scores on three measures of hazardous substance use (Rutgers Alcohol Problems Index, Adolescent Cannabis Problem Questionnaire, and Hooked on Nicotine Checklist) based on personal risk factors using two statistical and machine learning methods: multivariate covariance generalized linear models (MCGLM) and penalized multivariate regression with a lasso penalty. The predictive accuracy of a model was evaluated using root mean squared error computed via leave-one-out cross-validation. The final proposed model was an MCGLM model. It has eleven risk factors: age, early life stress, age of first tobacco use, age of first cannabis use, lifetime use of other substances, age of first use of other substances, maternal education, parental attachment, family cigarette use, family history of hazardous alcohol use, and family history of hazardous cannabis use. Different subsets of these risk factors feature in the three outcome-specific components of this joint model. The quantitative risk estimate provided by the proposed model may help identify adolescent substance users of cannabis, alcohol, and tobacco who may be at an elevated risk of developing hazardous substance use.

A preliminary risk prediction model for cannabis use disorder.

The ongoing trend toward legalization of cannabis for medicinal/recreational purposes is expected to increase the prevalence of cannabis use disorder (CUD). Thus, it is imperative to be able to predict the quantitative risk of developing CUD for a cannabis user based on their personal risk factors. Yet no such model currently exists. In this study, we perform preliminary analysis toward building such a model. The data come from n = 94 regular cannabis users recruited from Albuquerque, New Mexico during 2007-2010. As the data are cross-sectional, we only consider risk factors that remain relatively stable over time. We apply statistical and machine learning classification techniques that allow n to be small relative to the number of predictors. We use predictive accuracy estimated using leave-one-out-cross-validation to evaluate model performance. The final model is a LASSO logistic regression model consisting of the following seven risk factors: age; level of enjoyment from initial cigarette smoking; total score on Impulsive Sensation-Seeking Scale questionnaire; score on cognitive instability factor of Barratt Impulsivity Scale questionnaire; and scores on neuroticism, openness, and conscientiousness personality traits of Neuroticism, Extraversion, and Openness inventory. This model has an overall accuracy of 0.66 and the area under its receiver operating characteristic curve is 0.65. In summary, a preliminary relative risk model for predicting the quantitative risk of CUD is developed. It can be employed to identify users at high risk of CUD who may be provided with early intervention.

A segmented measurement error model for modeling and analysis of method comparison data.

Method comparison studies are concerned with estimating relationship between two clinical measurement methods. The methods often exhibit a structural change in the relationship over the measurement range. Ignoring this change would lead to an inaccurate estimate of the relationship. Motivated by a study of two digoxin assays where such a change occurs, this article develops a statistical methodology for appropriately analyzing such studies. Specifically, it proposes a segmented extension of the classical measurement error model to allow a piecewise linear relationship between the methods. The changepoint at which the transition takes place is treated as an unknown parameter in the model. An expectation-maximization-type algorithm is developed to fit the model and appropriate extensions of the existing measures are proposed for segment-specific evaluation of similarity and agreement. Bootstrapping and large-sample theory of maximum likelihood estimators are employed to perform the relevant inferences. The proposed methodology is evaluated by simulation and is illustrated by analyzing the digoxin data.

A Bayesian Hierarchical Framework for Pathway Analysis in Genome-Wide Association Studies.

BACKGROUND: Pathway analysis allows joint consideration of multiple SNPs belonging to multiple genes, which in turn belong to a biologically defined pathway. This type of analysis is usually more powerful than single-SNP analyses for detecting joint effects of variants in a pathway. METHODS: We develop a Bayesian hierarchical model by fully modeling the 3-level hierarchy, namely, SNP-gene-pathway that is naturally inherent in the structure of the pathways, unlike the currently used ad hoc ways of combining such information. We model the effects at each level conditional on the effects of the levels preceding them within the generalized linear model framework. To deal with the high dimensionality, we regularize the regression coefficients through an appropriate choice of priors. The model is fit using a combination of iteratively weighted least squares and expectation-maximization algorithms to estimate the posterior modes and their standard errors. A normal approximation is used for inference. RESULTS: We conduct simulations to study the proposed method and find that our method has higher power than some standard approaches in several settings for identifying pathways with multiple modest-sized variants. We illustrate the method by analyzing data from two genome-wide association studies on breast and renal cancers. CONCLUSION: Our method can be helpful in detecting pathway association.

A Bayesian latent variable approach to aggregation of partial and top-ranked lists in genomic studies.

In genomic research, it is becoming increasingly popular to perform meta-analysis, the practice of combining results from multiple studies that target a common essential biological problem. Rank aggregation, a robust meta-analytic approach, consolidates such studies at the rank level. There exists extensive research on this topic, and various methods have been developed in the past. However, these methods have two major limitations when they are applied in the genomic context. First, they are mainly designed to work with full lists, whereas partial and/or top-ranked lists prevail in genomic studies. Second, the component studies are often clustered, and the existing methods fail to utilize such information. To address the above concerns, a Bayesian latent variable approach, called BiG, is proposed to formally deal with partial and top-ranked lists and incorporate the effect of clustering. Various reasonable prior specifications for variance parameters in hierarchical models are carefully studied and compared. Simulation results demonstrate the superior performance of BiG compared with other popular rank aggregation methods under various practical settings. A non-small-cell lung cancer data example is analyzed for illustration.

Effect of exogenous melatonin and different photoperiods on oxidative status and antioxidant enzyme activity in Chhotanagpuri ewe.

AIM: The present study was conducted to evaluate the effect of exogenous melatonin under different photoperiods on oxidative status in Chhotanagpuri ewe. MATERIALS AND METHODS: A total of 42 non-pregnant, non-lactating Chhotanagpuri ewe, having body weight ranging between 14.11±0.09 and 15.38±0.06 kg, were selected and were isolated from rams 2 months before melatonin administration. The selected animals were allocated randomly into seven groups, namely, Group I (normal control), Group II (long day [LD] control), Group III (LD+melatonin administration orally, 3 mg/day), Group IV (LD+melatonin administration subcutaneously, 1 mg/day), Group V (short day [SD] control), Group VI (SD+melatonin administration orally, 3 mg/day), and Group VII (SD+melatonin administration subcutaneously, 1 mg/day) comprising six animals in each group. Rams were then introduced into each group after completion of exogenous administration of melatonin. Blood samples with anticoagulant in vials were collected from each animal day before the start of the experiment and thereafter every month up to 5th month. Hemolysate was prepared for estimation of oxidative stress parameters such as malondialdehyde (MDA), superoxide dismutase (SOD), and catalase (CAT). RESULTS: It was observed that the level of MDA was significantly (p<0.05) higher in LD groups (Group II, III and IV) in comparison to control and SD groups (VI and VII) at 1st month. MDA concentration after exogenous administration of melatonin was significantly (p<0.05) decreased in Group IV and VI in comparison to 1st month. SOD was significantly (p<0.05) higher in SD groups (V, VI, and VII) at the 1st month in comparison to 0 day. After exogenous administration of melatonin, SOD concentration was significantly (p<0.05) higher in Groups III and IV in comparison to 1st month. CAT was significantly (p<0.05) higher in SD groups (V, VI, and VII) in comparison to control and LD groups. After exogenous administration of melatonin, CAT concentration was significantly (p<0.05) higher in Groups III, IV, VI, and VIII in comparison to Groups I, II, and V. At the 3rd month, CAT concentration significantly (p<0.05) decreased in Groups III, IV, VI, and VII in comparison to 2nd month of experiment. However, a decreasing trend of CAT was observed in all the groups from 3rd to 5th month. CONCLUSION: The present experiment revealed that exogenous melatonin was able to reduce significantly the level of MDA and increased the activity of SOD and CAT in Chhotanagpuri ewe.

Dose-dependent effect of mammographic breast density on the risk of contralateral breast cancer.

PURPOSE: Increased mammographic breast density is a significant risk factor for breast cancer. It is not clear if it is also a risk factor for the development of contralateral breast cancer. METHODS: The data were obtained from Breast Cancer Surveillance Consortium and included women diagnosed with invasive breast cancer or ductal carcinoma in situ between ages 18 and 88 and years 1995 and 2009. Each case of contralateral breast cancer was matched with three controls based on year of first breast cancer diagnosis, race, and length of follow-up. A total of 847 cases and 2541 controls were included. The risk factors included in the study were mammographic breast density, age of first breast cancer diagnosis, family history of breast cancer, anti-estrogen treatment, hormone replacement therapy, menopausal status, and estrogen receptor status, all from the time of first breast cancer diagnosis. Both univariate analysis and multivariate conditional logistic regression analysis were performed. RESULTS: In the final multivariate model, breast density, family history of breast cancer, and anti-estrogen treatment remained significant with p values less than 0.01. Increasing breast density had a dose-dependent effect on the risk of contralateral breast cancer. Relative to 'almost entirely fat' category of breast density, the adjusted odds ratios (and p values) in the multivariate analysis for 'scattered density,' 'heterogeneously dense,' and 'extremely dense' categories were 1.65 (0.036), 2.10 (0.002), and 2.32 (0.001), respectively. CONCLUSION: Breast density is an independent and significant risk factor for development of contralateral breast cancer. This risk factor should contribute to clinical decision making.

Validation of a personalized risk prediction model for contralateral breast cancer.

PURPOSE: Women diagnosed with unilateral breast cancer are increasingly choosing to remove their other unaffected breast through contralateral prophylactic mastectomy (CPM) to reduce the risk of contralateral breast cancer (CBC). Yet a large proportion of CPMs are believed to be medically unnecessary. Thus, there is a pressing need to educate patients effectively on their CBC risk. We had earlier developed a CBC risk prediction model called CBCRisk based on eight personal risk factors. METHODS: In this study, we validate CBCRisk on independent clinical data from the Johns Hopkins University (JH) and MD Anderson Cancer Center (MDA). Women whose first breast cancer diagnosis was either invasive and/or ductal carcinoma in situ and whose age at first diagnosis was between 18 and 88 years were included in the cohorts because CBCRisk was developed specifically for these women. A woman who develops CBC is called a case whereas a woman who does not is called a control. The cohort sizes are 6035 (with 117 CBC cases) for JH and 5185 (with 111 CBC cases) for MDA. We computed the relevant calibration and validation measures for 3- and 5-year risk predictions. RESULTS: We found that the model performs reasonably well for both cohorts. In particular, area under the receiver-operating characteristic curve for the two cohorts range from 0.61 to 0.65. CONCLUSIONS: With this independent validation, CBCRisk can be used confidently in clinical settings for counseling BC patients by providing their individualized CBC risk. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

Semiparametric modeling and analysis of longitudinal method comparison data.

Studies comparing two or more methods of measuring a continuous variable are routinely conducted in biomedical disciplines with the primary goal of measuring agreement between the methods. Often, the data are collected by following a cohort of subjects over a period of time. This gives rise to longitudinal method comparison data where there is one observation trajectory for each method on every subject. It is not required that observations from all methods be available at each observation time. The multiple trajectories on the same subjects are dependent. We propose modeling the trajectories nonparametrically through penalized regression splines within the framework of mixed-effects models. The model also uses random effects of subjects and their interactions to capture dependence in observations from the same subjects. It additionally allows the within-subject errors of each method to be correlated. It is fit using the method of maximum likelihood. Agreement between the methods is evaluated by performing inference on measures of agreement, such as concordance correlation coefficient and total deviation index, which are functions of parameters of the assumed model. Simulations indicate that the proposed methodology performs reasonably well for 30 or more subjects. Its application is illustrated by analyzing a dataset of percentage body fat measurements. Copyright © 2017 John Wiley & Sons, Ltd.

A model for individualized risk prediction of contralateral breast cancer.

PURPOSE: Patients diagnosed with invasive breast cancer (BC) or ductal carcinoma in situ are increasingly choosing to undergo contralateral prophylactic mastectomy (CPM) to reduce their risk of contralateral BC (CBC). This is a particularly disturbing trend as a large proportion of these CPMs are believed to be medically unnecessary. Many BC patients tend to substantially overestimate their CBC risk. Thus, there is a pressing need to educate patients effectively on their CBC risk. We develop a CBC risk prediction model to aid physicians in this task. METHODS: We used data from two sources: Breast Cancer Surveillance Consortium and Surveillance, Epidemiology, and End Results to build the model. The model building steps are similar to those used in developing the BC risk assessment tool (popularly known as Gail model) for counseling women on their BC risk. Our model, named CBCRisk, is exclusively designed for counseling women diagnosed with unilateral BC on the risk of developing CBC. RESULTS: We identified eight factors to be significantly associated with CBC-age at first BC diagnosis, anti-estrogen therapy, family history of BC, high-risk pre-neoplasia status, estrogen receptor status, breast density, type of first BC, and age at first birth. Combining the relative risk estimates with the relevant hazard rates, CBCRisk projects absolute risk of developing CBC over a given period. CONCLUSIONS: By providing individualized CBC risk estimates, CBCRisk may help in counseling of BC patients. In turn, this may potentially help alleviate the rate of medically unnecessary CPMs.

Comparative Morphologic Evaluation and Occluding Effectiveness of Nd: YAG, CO2 and Diode Lasers on Exposed Human Dentinal Tubules: An Invitro SEM Study.

INTRODUCTION: Dentinal hypersensitivity is one of the most common problem, encountered in dental practice but has least predictable treatment outcome. The advent of lasers in dentistry has provided an additional therapeutic option for treating dentinal hypersensitivity. Although various lasers have been tried over a period of time to treat dentinal hypersensitivity, but still the doubt persist as to which laser leads to maximum dentinal tubular occlusion and is most suitable with minimal hazardous effects. AIM: To compare the effects of Nd: YAG, CO2 and 810-nm diode lasers on width of exposed dentinal tubule orifices and to evaluate the morphologic changes on dentinal surface of human tooth after laser irradiation by scanning electron microscope (SEM). MATERIALS AND METHODS: Forty root specimens were obtained from ten freshly extracted human premolars, which were randomly divided into four groups of ten each. Group I: control group treated with only saline, Group II: Nd:YAG laser, Group III: CO2 laser and Group IV: 810-nm diode laser. The specimens were examined using SEM. After calculating mean tubular diameter for each group, the values were compared statistically using parametric one-way ANOVA test and Turkey's post hoc multiple comparison test. RESULTS: All the three lased groups showed a highly statistical significant result with p-value of <0.001 as compared to non-lased group. On intergroup comparison within the lased groups, all the three groups showed statistically significant difference in the reduction of dentinal tubular diameter (p-value < 0.001). CONCLUSION: Nd: YAG laser was found to be most effective, followed by the CO2 laser and 810-nm diode laser was found to be least effective. The morphologic changes like craters, cracks and charring effect of the dentine were seen maximum by the use of CO2 laser.

Tolerance bands for functional data.

Often the object of inference in biomedical applications is a range that brackets a given fraction of individual observations in a population. A classical estimate of this range for univariate measurements is a "tolerance interval." This article develops its natural extension for functional measurements, a "tolerance band," and proposes a methodology for constructing its pointwise and simultaneous versions that incorporates both sparse and dense functional data. Assuming that the measurements are observed with noise, the methodology uses functional principal component analysis in a mixed model framework to represent the measurements and employs bootstrapping to approximate the tolerance factors needed for the bands. The proposed bands also account for uncertainty in the principal components decomposition. Simulations show that the methodology has, generally, acceptable performance unless the data are quite sparse and unbalanced, in which case the bands may be somewhat liberal. The methodology is illustrated using two real datasets, a sparse dataset involving CD4 cell counts and a dense dataset involving core body temperatures.

Characterization of Cement Particles Found in Peri-implantitis-Affected Human Biopsy Specimens.

PURPOSE: Peri-implantitis is a disease characterized by soft tissue inflammation and continued loss of supporting bone, which can result in implant failure. Peri-implantitis is a multifactorial disease, and one of its triggering factors may be the presence of excess cement in the soft tissues surrounding an implant. This descriptive study evaluated the composition of foreign particles from 36 human biopsy specimens with 19 specimens selected for analysis. The biopsy specimens were obtained from soft tissues affected by peri-implantitis around cement-retained implant crowns and compared with the elemental composition of commercial luting cement. MATERIALS AND METHODS: Nineteen biopsy specimens were chosen for the comparison, and five test cements (TempBond, Telio, Premier Implant Cement, Intermediate Restorative Material, and Relyx) were analyzed using scanning electron microscopy equipped with energy dispersive x-ray spectroscopy. This enabled the identification of the chemical composition of foreign particles embedded in the tissue specimens and the composition of the five cements. Statistical analysis was conducted using classification trees to pair the particles present in each specimen with the known cements. RESULTS: The particles in each biopsy specimen could be associated with one of the commercial cements with a level of probability ranging between .79 and 1. TempBond particles were found in one biopsy specimen, Telio particles in seven, Premier Implant Cement particles in four, Relyx particles in four, and Intermediate Restorative Material particles in three. CONCLUSION: Particles found in human soft tissue biopsy specimens around implants affected by peri-implant disease were associated with five commercially available dental cements.

Golden half ring sign for identification of pseudophacocele.

Dislocation of intraocular lens (IOL) is a serious complication of blunt ocular trauma in pseudophakic eyes. Here, a 72-year-old male patient with subconjunctival dislocation of an IOL (pseudophacocele) secondary to bull horn injury was reported. In this case report, a new sign named as "golden half ring sign" was described for easy identification and localization of subconjunctival dislocation of IOL in patient with open globe injury (surgical wound dehiscence) associated dense subconjunctival hemorrhage.

A heteroscedastic measurement error model for method comparison data with replicate measurements.

Measurement error models offer a flexible framework for modeling data collected in studies comparing methods of quantitative measurement. These models generally make two simplifying assumptions: (i) the measurements are homoscedastic, and (ii) the unobservable true values of the methods are linearly related. One or both of these assumptions may be violated in practice. In particular, error variabilities of the methods may depend on the magnitude of measurement, or the true values may be nonlinearly related. Data with these features call for a heteroscedastic measurement error model that allows nonlinear relationships in the true values. We present such a model for the case when the measurements are replicated, discuss its fitting, and explain how to evaluate similarity of measurement methods and agreement between them, which are two common goals of data analysis, under this model. Model fitting involves dealing with lack of a closed form for the likelihood function. We consider estimation methods that approximate either the likelihood or the model to yield approximate maximum likelihood estimates. The fitting methods are evaluated in a simulation study. The proposed methodology is used to analyze a cholesterol dataset.