Periodontal Status and Quality of Life: Impact of Fear of Pain and Dental Fear

Background. Oral health-related quality of life (OHRQoL) is impacted by periodontal disease and orofacial pain. There is a limited research examining the impact of avoidance of care or physiological arousal related to the fear of pain response on periodontal-related OHRQoL. Methods. Data are from the Center for Oral Health Research in Appalachia family-based study focusing on 1,339 adults. Measures included a modified Periodontal Screening and Recording Index across sextants of dentition, dental fear survey, Fear of Pain Questionnaire-9, and Oral Health Impact Profile-14. Structural equation modeling was used to estimate the effects of periodontal disease screening indicators on OHRQoL including the mediating role of dental fear while accounting for fear of pain. Results. A significant total effect was found for the mandibular anterior sextant, components of dental anxiety/fear, and indicators of OHRQoL (pain and discomfort, , ; psychosocial impact, , ). The maxillary anterior region was significantly associated with pain discomfort (, ) and functionality (, ). Conclusions. Findings provide a granular perspective of periodontal disease indicators and OHRQoL. Dental avoidance/anticipatory fear and physiological arousal mediate OHRQoL in individuals who have indicators of periodontal disease in sextants that may be visible and susceptible to higher pain and psychosocial impact

A Preliminary Genome-Wide Association Study of Pain-Related Fear: Implications for Orofacial Pain

Background. Acute and chronic orofacial pain can significantly impact overall health and functioning. Associations between fear of pain and the experience of orofacial pain are well-documented, and environmental, behavioral, and cognitive components of fear of pain have been elucidated. Little is known, however, regarding the specific genes contributing to fear of pain. Methods. A genome-wide association study (GWAS; ) was performed to identify plausible genes that may predispose individuals to various levels of fear of pain. The total score and three subscales (fear of minor, severe, and medical/dental pain) of the Fear of Pain Questionnaire-9 (FPQ-9) were modeled in a variance components modeling framework to test for genetic association with 8.5 M genetic variants across the genome, while adjusting for sex, age, education, and income. Results. Three genetic loci were significantly associated with fear of minor pain (8q24.13, 8p21.2, and 6q26; for all) near the genes TMEM65, NEFM, NEFL, AGPAT4, and PARK2. Other suggestive loci were found for the fear of pain total score and each of the FPQ-9 subscales. Conclusions. Multiple genes were identified as possible candidates contributing to fear of pain. The findings may have implications for understanding and treating chronic orofacial pain

A Preliminary Genome-Wide Association Study of Pain-Related Fear: Implications for Orofacial Pain

Background. Acute and chronic orofacial pain can significantly impact overall health and functioning. Associations between fear of pain and the experience of orofacial pain are well-documented, and environmental, behavioral, and cognitive components of fear of pain have been elucidated. Little is known, however, regarding the specific genes contributing to fear of pain. Methods. A genome-wide association study (GWAS; N=990) was performed to identify plausible genes that may predispose individuals to various levels of fear of pain. The total score and three subscales (fear of minor, severe, and medical/dental pain) of the Fear of Pain Questionnaire-9 (FPQ-9) were modeled in a variance components modeling framework to test for genetic association with 8.5 M genetic variants across the genome, while adjusting for sex, age, education, and income. Results. Three genetic loci were significantly associated with fear of minor pain (8q24.13, 8p21.2, and 6q26; p<5×10-8 for all) near the genes TMEM65, NEFM, NEFL, AGPAT4, and PARK2. Other suggestive loci were found for the fear of pain total score and each of the FPQ-9 subscales. Conclusions. Multiple genes were identified as possible candidates contributing to fear of pain. The findings may have implications for understanding and treating chronic orofacial pain

Limb development genes underlie variation in human fingerprint patterns

Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized “pattern-block” correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning

Reduced Lentivirus Susceptibility in Sheep with TMEM154 Mutations

Visna/Maedi, or ovine progressive pneumonia (OPP) as it is known in the United States, is an incurable slow-acting disease of sheep caused by persistent lentivirus infection. This disease affects multiple tissues, including those of the respiratory and central nervous systems. Our aim was to identify ovine genetic risk factors for lentivirus infection. Sixty-nine matched pairs of infected cases and uninfected controls were identified among 736 naturally exposed sheep older than five years of age. These pairs were used in a genome-wide association study with 50,614 markers. A single SNP was identified in the ovine transmembrane protein (TMEM154) that exceeded genome-wide significance (unadjusted p-value 3×10−9). Sanger sequencing of the ovine TMEM154 coding region identified six missense and two frameshift deletion mutations in the predicted signal peptide and extracellular domain. Two TMEM154 haplotypes encoding glutamate (E) at position 35 were associated with infection while a third haplotype with lysine (K) at position 35 was not. Haplotypes encoding full-length E35 isoforms were analyzed together as genetic risk factors in a multi-breed, matched case-control design, with 61 pairs of 4-year-old ewes. The odds of infection for ewes with one copy of a full-length TMEM154 E35 allele were 28 times greater than the odds for those without (p-value<0.0001, 95% CI 5–1,100). In a combined analysis of nine cohorts with 2,705 sheep from Nebraska, Idaho, and Iowa, the relative risk of infection was 2.85 times greater for sheep with a full-length TMEM154 E35 allele (p-value<0.0001, 95% CI 2.36–3.43). Although rare, some sheep were homozygous for TMEM154 deletion mutations and remained uninfected despite a lifetime of significant exposure. Together, these findings indicate that TMEM154 may play a central role in ovine lentivirus infection and removing sheep with the most susceptible genotypes may help eradicate OPP and protect flocks from reinfection

Integrative analysis of T cell motility from multi-channel microscopy data using TIAM.

Integrative analytical approaches are needed to study and understand T cell motility as it is a highly coordinated and complex process. Several computational algorithms and tools are available to track motile cells in time-lapse microscopy images. In contrast, there has only been limited effort towards the development of tools that take advantage of multi-channel microscopy data and facilitate integrative analysis of cell-motility. We have implemented algorithms for detecting, tracking, and analyzing cell motility from multi-channel time-lapse microscopy data. We have integrated these into a MATLAB-based toolset we call TIAM (Tool for Integrative Analysis of Motility). The cells are detected by a hybrid approach involving edge detection and Hough transforms from transmitted light images. Cells are tracked using a modified nearest-neighbor association followed by an optimization routine to join shorter segments. Cell positions are used to perform local segmentation for extracting features from transmitted light, reflection and fluorescence channels and associating them with cells and cell-tracks to facilitate integrative analysis. We found that TIAM accurately captures the motility behavior of T cells and performed better than DYNAMIK, Icy, Imaris, and Volocity in detecting and tracking motile T cells. Extraction of cell-associated features from reflection and fluorescence channels was also accurate with less than 10% median error in measurements. Finally, we obtained novel insights into T cell motility that were critically dependent on the unique capabilities of TIAM. We found that 1) the CD45RO subset of human CD8 T cells moved faster and exhibited an increased propensity to attach to the substratum during CCL21-driven chemokinesis when compared to the CD45RA subset; and 2) attachment area and arrest coefficient during antigen-induced motility of the CD45A subset is correlated with surface density of integrin LFA1 at the contact

Integrative analysis of T cell motility from multi-channel microscopy data using TIAM.

Integrative analytical approaches are needed to study and understand T cell motility as it is a highly coordinated and complex process. Several computational algorithms and tools are available to track motile cells in time-lapse microscopy images. In contrast, there has only been limited effort towards the development of tools that take advantage of multi-channel microscopy data and facilitate integrative analysis of cell-motility. We have implemented algorithms for detecting, tracking, and analyzing cell motility from multi-channel time-lapse microscopy data. We have integrated these into a MATLAB-based toolset we call TIAM (Tool for Integrative Analysis of Motility). The cells are detected by a hybrid approach involving edge detection and Hough transforms from transmitted light images. Cells are tracked using a modified nearest-neighbor association followed by an optimization routine to join shorter segments. Cell positions are used to perform local segmentation for extracting features from transmitted light, reflection and fluorescence channels and associating them with cells and cell-tracks to facilitate integrative analysis. We found that TIAM accurately captures the motility behavior of T cells and performed better than DYNAMIK, Icy, Imaris, and Volocity in detecting and tracking motile T cells. Extraction of cell-associated features from reflection and fluorescence channels was also accurate with less than 10% median error in measurements. Finally, we obtained novel insights into T cell motility that were critically dependent on the unique capabilities of TIAM. We found that 1) the CD45RO subset of human CD8 T cells moved faster and exhibited an increased propensity to attach to the substratum during CCL21-driven chemokinesis when compared to the CD45RA subset; and 2) attachment area and arrest coefficient during antigen-induced motility of the CD45A subset is correlated with surface density of integrin LFA1 at the contact

Generalized Polya Urn for Time-Varying Pitman-Yor Processes

This article introduces a class of first-order stationary time-varying Pitman-Yor processes. Subsuming our construction of time-varying Dirichlet processes presented in (Caron et al., 2007), these models can be used for time-dynamic density estimation and clustering. Our intuitive and simple construction relies on a generalized Polya urn scheme. Significantly, this construction yields ´ marginal distributions at each time point that can be explicitly characterized and easily controlled. Inference is performed using Markov chain Monte Carlo and sequential Monte Carlo methods. We demonstrate our models and algorithms on epidemiological and video tracking data