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Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analyses of disease data sets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and mechanobiology. Our 3D convolutional neural network recognized TMJ disorder patients through participant-specific morphological features in condylar, ramus, and chin. Driven by deep learning model outputs, biomechanical modeling revealed that small mandibular size and flat condylar shape were associated with increased TMJ disorder risk through increased joint force, decreased tissue nutrient availability and cell ATP production, and increased TMJ disc strain energy density. Combining explainable deep learning and multiscale biomechanical modeling addresses the “mechanism unknown” limitation undermining translational confidence in clinical applications of deep learning and increases methodological accessibility for smaller clinical data sets by providing the crucial biomechanical context.

An easy-to-implement global procedure for testing the four assumptions of the linear model is proposed. The test can be viewed as a Neyman smooth test and relies only on the standardized residual vector. If the global procedure indicates a violation of at least one of the assumptions, then the components of the global test statistic can be used to gain insight into which assumptions have been violated. The procedure can also be used in conjunction with associated deletion statistics to detect unusual observations. Simulation results are presented indicating the sensitivity of the procedure in detecting model violations under a variety of situations, and its performance is compared with three potential competitors, including a procedure based on the Box-Cox power transformation. The procedure is demonstrated by applying it to a new car mileage dataset and a water salinity dataset that has been used earlier to illustrate model diagnostics.

Many human diseases are attributable to complex interactions among genetic and environmental factors. Statistical tools capable of modeling such complex interactions are necessary to improve identification of genetic factors that increase a patient's risk of disease. Logic Forest (LF), a bagging ensemble algorithm based on logic regression (LR), is able to discover interactions among binary variables predictive of response such as the biologic interactions that predispose individuals to disease. However, LF's ability to recover interactions degrades for more infrequently occurring interactions. A rare genetic interaction may occur if, for example, the interaction increases disease risk in a patient subpopulation that represents only a small proportion of the overall patient population. We present an alternative ensemble adaptation of LR based on boosting rather than bagging called LBoost. We compare the ability of LBoost and LF to identify variable interactions in simulation studies. Results indicate that LBoost is superior to LF for identifying genetic interactions associated with disease that are infrequent in the population. We apply LBoost to a subset of single nucleotide polymorphisms on the PRDX genes from the Cancer Genetic Markers of Susceptibility Breast Cancer Scan to investigate genetic risk for breast cancer. LBoost is publicly available on CRAN as part of the LogicForest package, http://cran.r-project.org/.

Background To utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets. Results We combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as AMIGO2 , Gem , and CXCL11 that have not been shown to associate with, but may play roles in, metastasis. Conclusions CDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments. Availability : CDEP is implemented in R and freely available at: http://genomebioinfo.musc.edu/CDEP/ Contact : zhengw@musc.edu

Temporomandibular joint (TMJ) disorders disproportionally affect females, with female to male prevalence varying from 3:1 to 8:1. Sexual dimorphisms in masticatory muscle attachment morphometry and association with craniofacial size, critical for understanding sex-differences in TMJ function, have not been reported. The objective of this study was to determine sex-specific differences in three-dimensional (3D) TMJ muscle attachment morphometry and craniofacial sizes and their impact on TMJ mechanics. Human cadaveric TMJ muscle attachment morphometry and craniofacial anthropometry (10Males; 11Females) were determined by previously developed 3D digitization and imaging-based methods. Sex-differences in muscle attachment morphometry and craniofacial anthropometry, and their correlation were determined, respectively using multivariate general linear and linear regression statistical models. Subject-specific musculoskeletal models of the mandible were developed to determine effects of sexual dimorphisms in mandibular size and TMJ muscle attachment morphometry on joint loading during static biting. There were significant sex-differences in craniofacial size (p = 0.024) and TMJ muscle attachment morphometry (p < 0.001). TMJ muscle attachment morphometry was significantly correlated with craniofacial size. TMJ contact forces estimated from biomechanical models were significantly, 23% on average (p < 0.001), greater for females compared to those for males when generating the same bite forces. There were significant linear correlations between TMJ contact force and both 3D mandibular length (R2 = 0.48, p < 0.001) and muscle force moment arm ratio (R2 = 0.68, p < 0.001). Sexual dimorphisms in masticatory muscle morphology and craniofacial sizes play critical roles in subject-specific TMJ biomechanics. Sex-specific differences in the TMJ mechanical environment should be further investigated concerning mechanical fatigue of TMJ discs associated with TMJ disorders.

The cartilage endplate (CEP) is implicated as the main pathway of nutrient supply to the healthy human intervertebral disc (IVD). In this study, the diffusivities of nutrient/metabolite solutes in healthy CEP were assessed, and further correlated with tissue biochemical composition and structure. The CEPs from non-degenerated human IVD were divided into four regions: central, lateral, anterior, and posterior. The diffusivities of glucose and lactate were measured with a custom diffusion cell apparatus under 0%, 10%, and 20% compressive strains. Biochemical assays were conducted to quantify the water and glycosaminoglycan (GAG) contents. The Safranin-O and Ehrlich׳s hematoxylin and eosin staining and scanning electron microscopy (SEM) were performed to reveal the tissue structure of the CEP. Average diffusivities of glucose and lactate in healthy CEP were 2.68±0.93×10−7cm2/s and 4.52±1.47×10−7cm2/s, respectively. Solute diffusivities were region-dependent (p<0.0001) with the highest values in the central region, and mechanical strains impeded solute diffusion in the CEP (p<0.0001). The solute diffusivities were significantly correlated with the tissue porosities (glucose: p<0.0001, r=0.581; lactate: p<0.0001, r=0.534). Histological and SEM studies further revealed that the collagen fibers in healthy CEP are more compacted than those in the nucleus pulposus (NP) and annulus fibrosus (AF) and show no clear orientation. Compared to human AF and NP, much smaller solute diffusivities in human CEP suggested that it acts as a gateway for solute diffusion through the disc, maintaining the balance of nutritional environment in healthy human disc under mechanical loading and preventing the progression of disc degeneration.

This study examined the interactive effects of acculturation (host culture acquisition) and enculturation (heritage culture retention) on Latina/o caregivers’ beliefs about their child completing the human papillomavirus vaccine series. Participants were 161 caregiver-child dyads from Florida. Using multiple regression, caregiver knowledge and health beliefs (perceived threat, benefits, barriers, subjective norms, and self-efficacy) about series completion were predicted from caregivers’ scores on acculturation, enculturation, and their interaction, controlling for sociodemographics. Acculturation and enculturation interacted to predict knowledge, benefits, barriers, and self-efficacy. Caregivers with high acculturation scores generally supported series completion, regardless of their enculturation score. However, when acculturation was low, caregivers who retained more (vs. less) of their heritage culture were more knowledgeable and held more favorable beliefs about series completion. Findings highlight the importance of independently assessing acculturation and enculturation in Latina/o immigrant populations. Overlooking enculturation may lead to incomplete conclusions about acculturation and health.

Approximately 30% of temporomandibular joint (TMJ) disorders include degenerative changes to the articular disc, with sex-specific differences in prevalence and severity. Limited tensile biomechanical properties of human TMJ discs have been reported. Stress relaxation tests were conducted on TMJ disc specimens harvested bilaterally from six males and six females (68.9±7.9 years), with step-strain increments of 5%, 10%, 15%, 20% and 30%, at 1% strain-per-second. Stress versus strain plots were constructed, and Young׳s Modulus, Instantaneous Modulus and Relaxed Modulus were determined. The effects of direction, region, and sex were examined. Regional effects were significant (p<0.01) for Young׳s Modulus and Instantaneous Modulus. Anteroposteriorly, the central region was significantly stiffer than medial and lateral regions. Mediolaterally, the posterior region was significantly stiffer than central and anterior regions. In the central region, anteroposteriorly directed specimens were significantly stiffer compared to mediolateral specimens (p<0.04). TMJ disc stiffness, indicated by Young׳s Modulus and Instantaneous Modulus, was higher in directions corresponding to high fiber alignment. Additionally, human TMJ discs were stiffer for females compared to males, with higher Young׳s Modulus and Instantaneous Modulus, and female TMJ discs relaxed less. However, sex effects were not statistically significant. Using second-harmonic generation microscopy, regional collagen fiber organization was identified as a potentially significant factor in determining the biomechanical properties for any combination of direction and region. These findings establish structure–function relationships between collagen fiber direction and organization with biomechanical response to tensile loading, and may provide insights into the prevalence of TMJ disorders among women.

Using multiplex bead assays to measure urine proteins has a great potential for biomarker discovery, but substances in urine (the matrix) can interfere with assay measurements. By comparing the recovery of urine spiked with known quantities of several common analytes, this study demonstrated that the urine matrix variably interfered with the accurate measurement of low abundance proteins. Dilution of the urine permitted a more accurate measure of these proteins, equivalent to the standard dilution technique when the diluted analytes were above the limits of detection of the assay. Therefore, dilution can be used as an effective technique for overcoming urine matrix effects in urine immunoassays. These results may be applicable to other biological fluids in which matrix components interfere with assay performance.