Interplay between diet, circulating indolepropionate concentrations and cardiometabolic health in US populations.

OBJECTIVES: To identify indolepropionate (IPA)-predicting gut microbiota species, investigate potential diet-microbiota interactions, and examine the prospective associations of circulating IPA concentrations with type 2 diabetes (T2D) and coronary heart disease (CHD) risk in free-living individuals. DESIGN: We included 287 men from the Men's Lifestyle Validation Study, a substudy of the Health Professionals Follow-Up Study (HPFS), who provided up to two pairs of faecal samples and two blood samples. Diet was assessed using 7-day diet records. Associations between plasma concentrations of tryptophan metabolites and T2D CHD risk were examined in 13 032 participants from Nurses' Health Study (NHS), NHSII and HPFS. RESULTS: We identified 17 microbial species whose abundance was significantly associated with plasma IPA concentrations. A significant association between higher tryptophan intake and higher IPA concentrations was only observed among men who had higher fibre intake and a higher microbial species score consisting of the 17 species (p-interaction<0.01). Dietary and plasma concentrations of tryptophan and most kynurenine pathway metabolites were positively associated with T2D risk (HRQ5 vs Q1 ranged from 1.17 to 1.46) while a significant inverse association was found for IPA (HRQ5 vs Q1 (95% CI) 0.70 (0.56 to 0.88)). No associations were found in CHD for any plasma tryptophan metabolites. CONCLUSIONS: Specific microbial species and dietary fibre jointly predicted significantly higher circulating IPA concentrations at higher tryptophan intake. Dietary and plasma tryptophan, as well as its kynurenine pathway metabolites, demonstrated divergent associations from those for IPA, which was significantly predictive of lower risk of T2D.

Interplay between diet and gut microbiome, and circulating concentrations of trimethylamine N-oxide: findings from a longitudinal cohort of US men.

OBJECTIVES: Gut-produced trimethylamine N-oxide (TMAO) is postulated as a possible link between red meat intake and poor cardiometabolic health. We investigated whether gut microbiome could modify associations of dietary precursors with TMAO concentrations and cardiometabolic risk markers among free-living individuals. DESIGN: We collected up to two pairs of faecal samples (n=925) and two blood samples (n=473), 6 months apart, from 307 healthy men in the Men's Lifestyle Validation Study. Diet was assessed repeatedly using food-frequency questionnaires and diet records. We profiled faecal metagenome and metatranscriptome using shotgun sequencing and identified microbial taxonomic and functional features. RESULTS: TMAO concentrations were associated with the overall microbial compositions (permutational analysis of variance (PERMANOVA) test p=0.001). Multivariable taxa-wide association analysis identified 10 bacterial species whose abundance was significantly associated with plasma TMAO concentrations (false discovery rate <0.05). Higher habitual intake of red meat and choline was significantly associated with higher TMAO concentrations among participants who were microbial TMAO-producers (p<0.05), as characterised based on four abundant TMAO-predicting species, but not among other participants (for red meat, P-interaction=0.003; for choline, P-interaction=0.03). Among abundant TMAO-predicting species, Alistipes shahii significantly strengthened the positive association between red meat intake and HbA1c levels (P-interaction=0.01). Secondary analyses revealed that some functional features, including choline trimethylamine-lyase activating enzymes, were associated with TMAO concentrations. CONCLUSION: We identified microbial taxa that were associated with TMAO concentrations and modified the associations of red meat intake with TMAO concentrations and cardiometabolic risk markers. Our data underscore the interplay between diet and gut microbiome in producing potentially bioactive metabolites that may modulate cardiometabolic health.

Bayesian variable selection for multivariate zero-inflated models: Application to microbiome count data.

Microorganisms play critical roles in human health and disease. They live in diverse communities in which they interact synergistically or antagonistically. Thus for estimating microbial associations with clinical covariates, such as treatment effects, joint (multivariate) statistical models are preferred. Multivariate models allow one to estimate and exploit complex interdependencies among multiple taxa, yielding more powerful tests of exposure or treatment effects than application of taxon-specific univariate analyses. Analysis of microbial count data also requires special attention because data commonly exhibit zero inflation, i.e., more zeros than expected from a standard count distribution. To meet these needs, we developed a Bayesian variable selection model for multivariate count data with excess zeros that incorporates information on the covariance structure of the outcomes (counts for multiple taxa), while estimating associations with the mean levels of these outcomes. Though there has been much work on zero-inflated models for longitudinal data, little attention has been given to high-dimensional multivariate zero-inflated data modeled via a general correlation structure. Through simulation, we compared performance of the proposed method to that of existing univariate approaches, for both the binary ("excess zero") and count parts of the model. When outcomes were correlated the proposed variable selection method maintained type I error while boosting the ability to identify true associations in the binary component of the model. For the count part of the model, in some scenarios the univariate method had higher power than the multivariate approach. This higher power was at a cost of a highly inflated false discovery rate not observed with the proposed multivariate method. We applied the approach to oral microbiome data from the Pediatric HIV/AIDS Cohort Oral Health Study and identified five (of 44) species associated with HIV infection.

Unjamming and collective migration in MCF10A breast cancer cell lines.

Each cell comprising an intact, healthy, confluent epithelial layer ordinarily remains sedentary, firmly adherent to and caged by its neighbors, and thus defines an elemental constituent of a solid-like cellular collective [1,2]. After malignant transformation, however, the cellular collective can become fluid-like and migratory, as evidenced by collective motions that arise in characteristic swirls, strands, ducts, sheets, or clusters [3,4]. To transition from a solid-like to a fluid-like phase and thereafter to migrate collectively, it has been recently argued that cells comprising the disordered but confluent epithelial collective can undergo changes of cell shape so as to overcome geometric constraints attributable to the newly discovered phenomenon of cell jamming and the associated unjamming transition (UJT) [1,2,5-9]. Relevance of the jamming concept to carcinoma cells lines of graded degrees of invasive potential has never been investigated, however. Using classical in vitro cultures of six breast cancer model systems, here we investigate structural and dynamical signatures of cell jamming, and the relationship between them [1,2,10,11]. In order of roughly increasing invasive potential as previously reported, model systems examined included MCF10A, MCF10A.Vector; MCF10A.14-3-3ζ; MCF10.ErbB2, MCF10AT; and MCF10CA1a [12-15]. Migratory speed depended on the particular cell line. Unsurprisingly, for example, the MCF10CA1a cell line exhibited much faster migratory speed relative to the others. But unexpectedly, across different cell lines higher speeds were associated with enhanced size of cooperative cell packs in a manner reminiscent of a peloton [9]. Nevertheless, within each of the cell lines evaluated, cell shape and shape variability from cell-to-cell conformed with predicted structural signatures of cell layer unjamming [1]. Moreover, both structure and migratory dynamics were compatible with previous theoretical descriptions of the cell jamming mechanism [2,10,11,16,17]. As such, these findings demonstrate the richness of the cell jamming mechanism, which is now seen to apply across these cancer cell lines but remains poorly understood.

Lagged kernel machine regression for identifying time windows of susceptibility to exposures of complex mixtures.

The impact of neurotoxic chemical mixtures on children's health is a critical public health concern. It is well known that during early life, toxic exposures may impact cognitive function during critical time intervals of increased vulnerability, known as windows of susceptibility. Knowledge on time windows of susceptibility can help inform treatment and prevention strategies, as chemical mixtures may affect a developmental process that is operating at a specific life phase. There are several statistical challenges in estimating the health effects of time-varying exposures to multi-pollutant mixtures, such as: multi-collinearity among the exposures both within time points and across time points, and complex exposure-response relationships. To address these concerns, we develop a flexible statistical method, called lagged kernel machine regression (LKMR). LKMR identifies critical exposure windows of chemical mixtures, and accounts for complex non-linear and non-additive effects of the mixture at any given exposure window. Specifically, LKMR estimates how the effects of a mixture of exposures change with the exposure time window using a Bayesian formulation of a grouped, fused lasso penalty within a kernel machine regression (KMR) framework. A simulation study demonstrates the performance of LKMR under realistic exposure-response scenarios, and demonstrates large gains over approaches that consider each time window separately, particularly when serial correlation among the time-varying exposures is high. Furthermore, LKMR demonstrates gains over another approach that inputs all time-specific chemical concentrations together into a single KMR. We apply LKMR to estimate associations between neurodevelopment and metal mixtures in Early Life Exposures in Mexico and Neurotoxicology, a prospective cohort study of child health in Mexico City.

Accelerated failure time models for semi-competing risks data in the presence of complex censoring.

Statistical analyses that investigate risk factors for Alzheimer's disease (AD) are often subject to a number of challenges. Some of these challenges arise due to practical considerations regarding data collection such that the observation of AD events is subject to complex censoring including left-truncation and either interval or right-censoring. Additional challenges arise due to the fact that study participants under investigation are often subject to competing forces, most notably death, that may not be independent of AD. Towards resolving the latter, researchers may choose to embed the study of AD within the "semi-competing risks" framework for which the recent statistical literature has seen a number of advances including for the so-called illness-death model. To the best of our knowledge, however, the semi-competing risks literature has not fully considered analyses in contexts with complex censoring, as in studies of AD. This is particularly the case when interest lies with the accelerated failure time (AFT) model, an alternative to the traditional multiplicative Cox model that places emphasis away from the hazard function. In this article, we outline a new Bayesian framework for estimation/inference of an AFT illness-death model for semi-competing risks data subject to complex censoring. An efficient computational algorithm that gives researchers the flexibility to adopt either a fully parametric or a semi-parametric model specification is developed and implemented. The proposed methods are motivated by and illustrated with an analysis of data from the Adult Changes in Thought study, an on-going community-based prospective study of incident AD in western Washington State.

Multivariate Bayesian variable selection exploiting dependence structure among outcomes: Application to air pollution effects on DNA methylation.

The analysis of multiple outcomes is becoming increasingly common in modern biomedical studies. It is well-known that joint statistical models for multiple outcomes are more flexible and more powerful than fitting a separate model for each outcome; they yield more powerful tests of exposure or treatment effects by taking into account the dependence among outcomes and pooling evidence across outcomes. It is, however, unlikely that all outcomes are related to the same subset of covariates. Therefore, there is interest in identifying exposures or treatments associated with particular outcomes, which we term outcome-specific variable selection. In this work, we propose a variable selection approach for multivariate normal responses that incorporates not only information on the mean model, but also information on the variance-covariance structure of the outcomes. The approach effectively leverages evidence from all correlated outcomes to estimate the effect of a particular covariate on a given outcome. To implement this strategy, we develop a Bayesian method that builds a multivariate prior for the variable selection indicators based on the variance-covariance of the outcomes. We show via simulation that the proposed variable selection strategy can boost power to detect subtle effects without increasing the probability of false discoveries. We apply the approach to the Normative Aging Study (NAS) epigenetic data and identify a subset of five genes in the asthma pathway for which gene-specific DNA methylations are associated with exposures to either black carbon, a marker of traffic pollution, or sulfate, a marker of particles generated by power plants.

Temporal trend in the reported birth prevalence of cleft lip and/or cleft palate in Brazil, 2000 to 2013.

BACKGROUND: The birth prevalence of cleft lip with or without cleft palate (CL/P) in Brazil increased between the years from 1975 to 1994 but has not been evaluated for temporal trend since then. METHODS: We used data from the Brazilian National Health Information System for the years 2000 through 2013. We calculated the reported CL/P birth prevalence each year per 10,000 live births and estimated the average increase in reported prevalence per year (and 95% confidence interval [CI]) by fitting a negative binomial regression model. We also estimated the temporal trend in each of the five Brazilian regions for this time period. RESULTS: The overall reported birth prevalence was 4.85 (95% CI, 4.78-4.91) per 10,000 live births. The reported birth prevalence of CL/P increased over this time period, from 3.94 (95% CI, 3.73-4.17) per 10,000 in 2000 to 5.46 (95% CI, 5.20-5.74) per 10,000 in 2013. The temporal trend differed for different Brazilian geographic regions, being confined primarily to the Northeast (4.7% per year; 95% CI, 4.0%-5.5%), North (3.3% per year; 95% CI, 1.8%-4.7%), and Central (2.9% per year; 95% CI, 0.9%-4.9%) regions. CONCLUSION: In recent years, there appears to be an upward trend in the reported prevalence of CL/P in Brazil, confined to the less developed regions of the country. The increase likely reflects improved surveillance; whether it also reflects etiologic differences is unknown. Birth Defects Research (Part A) 106:789-792, 2016. © 2016 Wiley Periodicals, Inc.

Quantitative analysis of marker compounds in Angelica gigas, Angelica sinensis, and Angelica acutiloba by HPLC/DAD.

Although Danggui is the root of Angelica gigas NAKAI in the Korean Pharmacopoeia, it is determined that Danggui is also the root of Angelica sinensis (OLIV.) DIELS in China and Hong Kong, as well as the root of Angelica acutiloba KITAGAWA in Japan. Accordingly, we tried to develop an identification method using the main compounds in A. gigas, A. sinensis, and A. acutiloba through HPLC/diode-array detector (DAD). This method was fully validated for linearity, accuracy, precision, recovery, and robustness. Multivariate analysis was also implemented after pattern analysis and monitoring. As a result, each compound pattern of A. gigas, A. sinensis, and A. acutiloba was identified, making it possible to distinguish them from each other.

Planetary Health Diet Index and risk of total and cause-specific mortality in three prospective cohorts.

BACKGROUND: In 2019, the EAT-Lancet Commission proposed a healthy dietary pattern that, along with reductions in food waste and improved agricultural practices, could feed the increasing global population sustainably. We developed a Planetary Health Diet Index (PHDI) to quantify adherence to the EAT-Lancet reference diet. OBJECTIVES: We aimed to assess associations between PHDI and total and cause-specific mortality in 3 prospective cohorts of males and females in the United States. METHODS: We followed 66,692 females from the Nurses' Health Study (1986-2019), 92,438 females from the Nurses' Health Study II (1989-2019), and 47,274 males from the Health Professionals Follow-up Study (1986-2018) who were free of cancer, diabetes, and major cardiovascular diseases at baseline. The PHDI was calculated every 4 y using a semiquantitative food frequency questionnaire. Hazard ratios (HRs) were calculated using multivariable proportional-hazards models. RESULTS: During follow-up, we documented 31,330 deaths among females and 23,206 among males. When comparing the highest with the lowest quintile of PHDI, the pooled multivariable-adjusted HRs were 0.77 [95% confidence interval (CI): 0.75, 0.80] for all-cause mortality (P-trend < 0.0001). The PHDI was associated with lower risk of deaths from cardiovascular diseases (HR: 0.86; 95% CI: 0.81, 0.91), cancer (HR: 0.90; 95% CI: 0.85, 0.95), respiratory diseases (HR: 0.53; 95% CI: 0.48, 0.59), and neurodegenerative diseases (HR: 0.72; 95% CI: 0.67, 0.78). In females, but not males, the PHDI was also significantly associated with a lower risk of deaths from infectious diseases (HR: 0.62; 95% CI: 0.51, 0.76). PHDI scores were also associated inversely with greenhouse gas emissions and other environmental impacts. CONCLUSIONS: In 3 large United States-based prospective cohorts of males and females with up to 34 y of follow-up, a higher PHDI was associated with lower risk of total and cause-specific mortality and environment impacts.

Plasma metabolomic profiles associated with mortality and longevity in a prospective analysis of 13,512 individuals.

Experimental studies reported biochemical actions underpinning aging processes and mortality, but the relevant metabolic alterations in humans are not well understood. Here we examine the associations of 243 plasma metabolites with mortality and longevity (attaining age 85 years) in 11,634 US (median follow-up of 22.6 years, with 4288 deaths) and 1878 Spanish participants (median follow-up of 14.5 years, with 525 deaths). We find that, higher levels of N2,N2-dimethylguanosine, pseudouridine, N4-acetylcytidine, 4-acetamidobutanoic acid, N1-acetylspermidine, and lipids with fewer double bonds are associated with increased risk of all-cause mortality and reduced odds of longevity; whereas L-serine and lipids with more double bonds are associated with lower mortality risk and a higher likelihood of longevity. We further develop a multi-metabolite profile score that is associated with higher mortality risk. Our findings suggest that differences in levels of nucleosides, amino acids, and several lipid subclasses can predict mortality. The underlying mechanisms remain to be determined.

MEASURING PERFORMANCE FOR END-OF-LIFE CARE.

Although not without controversy, readmission is entrenched as a hospital quality metric with statistical analyses generally based on fitting a logistic-Normal generalized linear mixed model. Such analyses, however, ignore death as a competing risk, although doing so for clinical conditions with high mortality can have profound effects; a hospital's seemingly good performance for readmission may be an artifact of it having poor performance for mortality. in this paper we propose novel multivariate hospital-level performance measures for readmission and mortality that derive from framing the analysis as one of cluster-correlated semi-competing risks data. We also consider a number of profiling-related goals, including the identification of extreme performers and a bivariate classification of whether the hospital has higher-/lower-than-expected readmission and mortality rates via a Bayesian decision-theoretic approach that characterizes hospitals on the basis of minimizing the posterior expected loss for an appropriate loss function. in some settings, particularly if the number of hospitals is large, the computational burden may be prohibitive. To resolve this, we propose a series of analysis strategies that will be useful in practice. Throughout, the methods are illustrated with data from CMS on N = 17,685 patients diagnosed with pancreatic cancer between 2000-2012 at one of J = 264 hospitals in California.

Local heat application to the leg reduces muscle sympathetic nerve activity in human.

The study was designed to assess the effects of local heat (LH) application on postganglionic muscle sympathetic nerve activity (MSNA) measured by microneurography in healthy men. In the first protocol, MSNA of the left peroneal nerve, blood pressure (BP), heart rate (HR), and skin temperature of the shin (TSK) were recorded in nine men. In the second protocol, leg blood flow (LBF) was measured in the same subjects by strain-gauge plethysmography. In both protocols, after 10 min of rest in the supine position, a heated hydrocollator pack was applied to the shin and anterior foot for 15 min and recovery was monitored over a period of 20 min. TSK gradually increased from 31.7 ± 0.1 to 41.9 ± 0.5°C (mean ± SEM) during LH. No subject complained of pain, and BP and HR remained constant. The MSNA burst rate (16.1 ± 2.1 beats/min) during the control period decreased significantly (P < 0.05) to 72.0 ± 2.3% during LH. Total MSNA also decreased to 59.2 ± 2.6% (P < 0.05) during LH, but both immediately returned to baseline at recovery. In contrast, LBF in the left leg significantly and immediately increased (P < 0.05) after LH application and remained significantly elevated until the end of the recovery period. These results suggest that: (1) LH application significantly attenuates MSNA without any changes in HR and BP. (2) Other factors in addition to MSNA seem to control regional blood flow in the lower extremity during LH.

Enhancing Physicochemical Properties and Single Cell Performance of Sulfonated Poly(arylene ether) (SPAE) Membrane by Incorporation of Phosphotungstic Acid and Graphene Oxide: A Potential Electrolyte for Proton Exchange Membrane Fuel Cells.

The development of potential and novel proton exchange membranes (PEMs) is imperative for the further commercialization of PEM fuel cells (PEMFCs). In this work, phosphotungstic acid (PWA) and graphene oxide (GO) were integrated into sulfonated poly(arylene ether) (SPAE) through a solution casting approach to create a potential composite membrane for PEMFC applications. Thermal stability of membranes was observed using thermogravimetric analysis (TGA), and the SPAE/GO/PWA membranes exhibited high thermal stability compared to pristine SPAE membranes, owing to the interaction between SPAEK, GO, and PWA. By using a scanning electron microscope (SEM) and atomic force microscope (AFM), we observed that GO and PWA were evenly distributed throughout the SPAE matrix. The SPAE/GO/PWA composite membrane comprising 0.7 wt% GO and 36 wt% PWA exhibited a maximum proton conductivity of 186.3 mS cm-1 at 90 °C under 100% relative humidity (RH). As a result, SPAE/GO/PWA composite membrane exhibited 193.3 mW cm-2 of the maximum power density at 70 °C under 100% RH in PEMFCs.

Enhanced Hydroxide Conductivity and Dimensional Stability with Blended Membranes Containing Hyperbranched PAES/Linear PPO as Anion Exchange Membranes.

A series of novel blended anion exchange membranes (AEMs) were prepared with hyperbranched brominated poly(arylene ether sulfone) (Br-HB-PAES) and linear chloromethylated poly(phenylene oxide) (CM-PPO). The as-prepared blended membranes were fabricated with different weight ratios of Br-HB-PAES to CM-PPO, and the quaternization reaction for introducing the ionic functional group was performed by triethylamine. The Q-PAES/PPO-XY (quaternized-PAES/PPO-XY) blended membranes promoted the ion channel formation as the strong hydrogen bonds interconnecting the two polymers were maintained, and showed an improved hydroxide conductivity with excellent thermal behavior. In particular, the Q-PAES/PPO-55 membrane showed a very high hydroxide ion conductivity (90.9 mS cm-1) compared to the pristine Q-HB-PAES membrane (32.8 mS cm-1), a result supported by the morphology of the membrane as determined by the AFM analysis. In addition, the rigid hyperbranched structure showed a suppressed swelling ratio of 17.9-24.9% despite an excessive water uptake of 33.2-50.3% at 90 °C, and demonstrated a remarkable alkaline stability under 2.0 M KOH conditions over 1000 h.

Ameliorated Performance of Sulfonated Poly(Arylene Ether Sulfone) Block Copolymers with Increased Hydrophilic Oligomer Ratio in Proton-Exchange Membrane Fuel Cells Operating at 80% Relative Humidity.

We designed and synthesized a series of sulfonated poly(arylene ether sulfone) (SPES) with different hydrophilic or hydrophobic oligomer ratios using poly-condensation strategy. Afterward, we fabricated the corresponding membranes via a solution-casting approach. We verified the SPES membrane chemical structure using nuclear magnetic resonance (1H NMR) and confirmed the resulting oligomer ratio. Field-emission scanning electron microscope (FE-SEM) and atomic force microscope (AFM) results revealed that we effectively attained phase separation of the SPES membrane along with an increased hydrophilic oligomer ratio. Thermal stability, glass transition temperature (Tg) and membrane elongation increased with the ratio of hydrophilic oligomers. SPES membranes with higher hydrophilic oligomer ratios exhibited superior water uptake, ion-exchange capacity, contact angle and water sorption, while retaining reasonable swelling degree. The proton conductivity results showed that SPES containing higher amounts of hydrophilic oligomers provided a 74.7 mS cm-1 proton conductivity at 90 °C, which is better than other SPES membranes, but slightly lower than that of Nafion-117 membrane. When integrating SPES membranes with proton-exchange membrane fuel cells (PEMFCs) at 60 °C and 80% relative humidity (RH), the PEMFC power density exhibited a similar increment-pattern like proton conductivity pattern.

SemiCompRisks: An R Package for the Analysis of Independent and Cluster-correlated Semi-competing Risks Data.

Semi-competing risks refer to the setting where primary scientific interest lies in estimation and inference with respect to a non-terminal event, the occurrence of which is subject to a terminal event. In this paper, we present the R package SemiCompRisks that provides functions to perform the analysis of independent/clustered semi-competing risks data under the illness-death multi-state model. The package allows the user to choose the specification for model components from a range of options giving users substantial flexibility, including: accelerated failure time or proportional hazards regression models; parametric or non-parametric specifications for baseline survival functions; parametric or non-parametric specifications for random effects distributions when the data are cluster-correlated; and, a Markov or semi-Markov specification for terminal event following non-terminal event. While estimation is mainly performed within the Bayesian paradigm, the package also provides the maximum likelihood estimation for select parametric models. The package also includes functions for univariate survival analysis as complementary analysis tools.

Outcome of early dental implant placement versus other dental implant placement protocols: A systematic review and meta-analysis.

BACKGROUND: The aim of this systematic review and meta-analysis was to compare the clinical efficacy of the early dental implant placement protocol with immediate and delayed dental implant placement protocols. METHODS: An electronic and manual search of literature was made to identify clinical studies comparing early implant placement with immediate or delayed placement. Data from the included studies were pooled and quantitative analyses were performed for the implant outcomes reported as the number of failed implants (primary outcome variable) and for changes in peri-implant marginal bone level, peri-implant probing depth, and peri-implant soft tissue level (secondary outcome variables). RESULTS: Twelve studies met the inclusion criteria. Significant difference in risk of implant failure was found neither between the early and immediate placement protocols (risk difference = -0.018; 95% confidence interval [CI] = -0.06, 0.025; P = 0.416) nor between early and delayed placement protocols (risk difference = -0.008; 95% CI = -0.044, 0.028; P = 0.670). Pooled data of changes in peri-implant marginal bone level demonstrated significantly less marginal bone loss for implants placed using the early placement protocol compared with those placed in fresh extraction sockets (P = 0.001; weighted mean difference = -0.14 mm; 95% CI = -0.22, -0.05). No significant differences were found between the protocols for the other variables. CONCLUSIONS: The available evidence supports the clinical efficacy of the early implant placement protocol. Present findings indicate that the early implant placement protocol results in implant outcomes similar to immediate and delayed placement protocols and a superior stability of peri-implant hard tissue compared with immediate implant placement.

Mapping the Tooth Enamel Proteome and Amelogenin Phosphorylation Onto Mineralizing Porcine Tooth Crowns.

Tooth enamel forms in an ephemeral protein matrix where changes in protein abundance, composition and posttranslational modifications are critical to achieve healthy enamel properties. Amelogenin (AMELX) with its splice variants is the most abundant enamel matrix protein, with only one known phosphorylation site at serine 16 shown in vitro to be critical for regulating mineralization. The phosphorylated form of AMELX stabilizes amorphous calcium phosphate, while crystalline hydroxyapatite forms in the presence of the unphosphorylated protein. While AMELX regulates mineral transitions over space and time, it is unknown whether and when un-phosphorylated amelogenin occurs during enamel mineralization. This study aims to reveal the spatiotemporal distribution of the cleavage products of the most abundant AMLEX splice variants including the full length P173, the shorter leucine-rich amelogenin protein (LRAP), and the exon 4-containing P190 in forming enamel, all within the context of the changing enamel matrix proteome during mineralization. We microsampled permanent pig molars, capturing known stages of enamel formation from both crown surface and inner enamel. Nano-LC-MS/MS proteomic analyses after tryptic digestion rendered more than 500 unique protein identifications in enamel, dentin, and bone. We mapped collagens, keratins, and proteolytic enzymes (CTSL, MMP2, MMP10) and determined distributions of P173, LRAP, and P190 products, the enamel proteins enamelin (ENAM) and ameloblastin (AMBN), and matrix-metalloprotease-20 (MMP20) and kallikrein-4 (KLK4). All enamel proteins and KLK4 were near-exclusive to enamel and in excellent agreement with published abundance levels. Phosphorylated P173 and LRAP products decreased in abundance from recently deposited matrix toward older enamel, mirrored by increasing abundances of testicular acid phosphatase (ACPT). Our results showed that hierarchical clustering analysis of secretory enamel links closely matching distributions of unphosphorylated P173 and LRAP products with ACPT and non-traditional amelogenesis proteins, many associated with enamel defects. We report higher protein diversity than previously published and Gene Ontology (GO)-defined protein functions related to the regulation of mineral formation in secretory enamel (e.g., casein α-S1, CSN1S1), immune response in erupted enamel (e.g., peptidoglycan recognition protein, PGRP), and phosphorylation. This study presents a novel approach to characterize and study functional relationships through spatiotemporal mapping of the ephemeral extracellular matrix proteome.

Dimensional accuracy of cone beam CT with varying angulation of the jaw to the X-ray beam.

OBJECTIVES: Cone beam CT (CBCT) machines do not always allow for patients to be scanned in the ideal position for image acquisition. This study aimed to investigate the influence of the position/angulation of the mandible relative to the X-ray beam of a CBCT machine. METHODS: Five sequential CBCT scans were captured of a human mandible at each angulation of 10°, 20°, 30°, and 40° using a coronal and sagittal positioning. Inspection software utilized a best-fit alignment to automatically calculate the three-dimensional variation at 15 standardized points of interest. RESULTS: Statistically significant differences were found between the dimensional accuracy of CBCT scans taken at 10° (26.3 µm) of coronal angulation, as well as those taken at 20° (-17.3 mm) and 30° (35.2 mm) of sagittal angulations (p < 0.001, 0.016, and <0.001, respectively). The largest deviations in accuracy included an overall maximum deviation of 490 mm. CONCLUSIONS: The position of the mandible with respect to the X-ray beam has a clinically insignificant effect on dimensional accuracy, up to the maximum angle of 40° assessed.