INTRODUCTION:To evaluate different factors affecting two different cohorts of dental students' perceptions with the flipped classroom (FC) model in a preclinical dental course over six years. MATERIALS AND METHODS:In 2016 (cohort #1) and 2022 (cohort #2), second-year dental students participated in FC model in a preclinical dental course consisting of pre-class recorded videos and lectures along with classroom interactive exercises. Students then completed an anonymous pen-and-paper survey evaluating three factors influencing students' overall study experience with the FC model. Survey responses from cohort #1 and cohort #2 were compared to assess the effect of generation, student demographics and COVID-19 pandemic on students' perceptions. RESULTS:When asked to rate the FC experience out of 5, the mean ratings of cohort #1 and cohort #2 were 4.3 (±0.9) and 4.0 (±0.8), respectively. Cohort #2 preferred viewing slides and videos prior to class and had a higher agreement that pre-class course materials were beneficial to class preparation. Conversely, cohort #1 had a higher agreement that in-class discussion and exercises helped them to learn course content. From cohort #2, 88.6% of males and 71.6% of females preferred the FC approach over traditional lecture. CONCLUSION:Both cohorts reported positively with the FC model of teaching. Cohort #2 had higher positive reports with the pre-class work and cohort #1 had a higher positive report with in-class work. Course design should reflect the students' points of view and learning perspectives with careful consideration of the student population in the classroom.

ABSTRACT Objectives To assess the infrazygomatic crest (IZC) and palatal bone width, height, and angulation in patients with different vertical facial growth types as potential miniscrew insertion sites. Materials and Methods In this retrospective cone-beam computed tomography study, 162 subjects (81 males and 81 females, mean age 16.05 ± 0.65 years) were included. They were divided into three groups (hypodivergent, normodivergent, and hyperdivergent) based on the Frankfort mandibular plane angle. Ten buccal bone measurements were made at two different coronal sections: maxillary first molar mesiobuccal and distobuccal roots (bilaterally). Six palatal bone measurements were made on a sagittal section at the maxillary central incisors (bilaterally). A total of 32 measurements per subject were considered in the study. Results No significant difference was observed for the IZC (width and angle) at the maxillary first molar mesiobuccal root. A comparison of normodivergent and hyperdivergent groups for buccal width at the distobuccal root of the first molar showed significant differences. Palatal bone thickness at the level of 2 mm distal to the apex of the central incisor was significantly higher for the hyperdivergent group (10.43 mm) compared with the normodivergent (7.58 mm) and hypodivergent groups (7.83 mm). Conclusions Hyperdivergent subjects tend to present a longer and deeper IZC and increased palatal bone thickness compared with other groups. The recommended insertion angle for the IZC mini-implant at 3 mm from the alveolar crest should be between 75.5° and 77°.

INTRODUCTION:This study evaluated the effectiveness of a video presentation instruction compared to the prevailing traditional lecture provided in the preclinical classroom when introducing computer-aided design/computer-aided manufacturing (CAD/CAM) technology.MATERIALS AND METHODS:This study was conducted as a controlled educational trial (single-blind) on 120 first-year dental students. Students were assigned to two learning groups: the control (n = 60, traditional lecture) and the intervention (n = 60, video presentation). Both groups completed a self-efficacy scale (SES) survey before instruction. Then, the intervention group actively participated in a one-session video presentation while the control group was given a traditional lecture. After the presentations, student knowledge was assessed with a hands-on simulated exercise that was scored out of 60 points. At last, students completed a post-instruction student perception and attitude (SPA) survey. Inferential hypothesis testing was conducted with two-sample t-tests, and significance was defined as p ≤ .05.RESULTS:There were no significant differences in the student pre-instruction SES scores between the control and intervention groups. After the video learning, CAD/CAM knowledge application during the hands-on simulated exercise showed significantly higher results in the intervention group as the assessment scores from the intervention group averaged 13.6 ± 2.0 points higher than the control group (p < .001). The results from the post-instruction SPA survey showed no statistically significant difference between groups.CONCLUSION:With the video presentation technique, our first-year dental students were able to greatly enhance their CAD/CAM application skills. The results from this study could be applied at other dental educational institutions with potential similar outcomes.

Cellular communication through biochemical signaling is fundamental to every biological activity. Investigating cell signaling diffusions across cell types can further help understand biological mechanisms. In recent years, this has become an important research topic as single-cell sequencing technologies have matured. However, cell signaling activities are spatially constrained, and single-cell data cannot provide spatial information for each cell. This issue may cause a high false discovery rate, and using spatially resolved transcriptomics data is necessary. On the other hand, as far as we know, most existing methods focus on providing an ad hoc measurement to estimate intercellular communication instead of relying on a statistical model. It is undeniable that descriptive statistics are straightforward and accessible, but a suitable statistical model can provide more accurate and reliable inference. In this way, we propose a generalized linear regression model to infer cellular communications from spatially resolved transcriptomics data, especially spot-based data. Our BAyesian Tweedie modeling of COMmunications (BATCOM) method estimates the communication scores between cell types with the consideration of their corresponding distances. Due to the properties of the regression model, BATCOM naturally provides the direction of the communication between cell types and the interaction of ligands and receptors that other approaches cannot offer. We conduct simulation studies to assess the performance under different scenarios. We also employ BATCOM in a real-data application and compare it with other existing algorithms. In summary, our innovative model can fill gaps in the inference of cell–cell communication and provide a robust and straightforward result.

AIM:The use of cannabis, which contains multiple antimicrobials, may be a risk factor for periodontitis. We hypothesized that multiple oral spirochetes would be phytocannabinoid-resistant and that cannabidiol (CBD) would act as an environmental stressor to which Treponema denticola would respond transcriptionally, thereby providing first insights into spirochetal survival strategies.MATERIALS AND METHODS:Oral spirochete growth was monitored spectrophotometrically in the presence and absence of physiologically relevant phytocannabinoid doses, the transcriptional response to phytocannabinoid exposure determined by RNAseq, specific gene activity fluxes verified using qRT-PCR and orthologues among fully sequenced oral spirochetes identified.RESULTS:Multiple strains of oral treponemes were resistant to CBD (0.1-10 μg/mL), while T. denticola ATCC 35405 was resistant to all phytocannabinoids tested (CBD, cannabinol [CBN], tetrahydrocannabinol [THC]). A total of 392 T. denticola ATCC 35405 genes were found to be CBD-responsive by RNAseq. A selected subset of these genes was independently verified by qRT-PCR. Genes found to be differentially activated by both methods included several involved in transcriptional regulation and toxin control. Suppressed genes included several involved in chemotaxis and proteolysis.CONCLUSIONS:Oral spirochetes, unlike some other periodontal bacteria, are resistant to physiological doses of phytocannabinoids. Investigation of CBD-induced transcriptomic changes provided insight into the resistance mechanisms of this important periodontal pathogen. These findings should be considered in the context of the reported enhanced susceptibility to periodontitis in cannabis users.

BackgroundEvidence on the etiology behind bone loss around submerged, prosthetically nonloaded implants is still limited. The long-term stability and success of implants with early crestal bone loss (ECBL), especially when placed as two-stage implants, is uncertain. Hence, the aim of this retrospective study is to analyze the potential patient-level, tooth- and implant-related factors for ECBL around osseointegrated, submerged implants, before restoration as compared with healthy implants with no bone loss. MethodsRetrospective data were collected from patient electronic health records between 2015 and 2022. Control sites included healthy implants with no bone loss and test sites included implants with ECBL, both of which were submerged. Patient, tooth and implant level data were collected. ECBL was assessed using periapical radiographs obtained during implant placement and second-stage surgeries. Generalized estimating equation logistic regression models were used to account for multiple implants within patients. ResultsThe total number of implants included in the study was 200 from 120 patients. Lack of supportive periodontal therapy (SPT) was shown to have nearly five-times higher risk of developing ECBL and was statistically significant (p < 0.05). Guided bone regeneration (GBR) procedures before implant placement had a protective effect with an odds ratio of 0.29 (p < 0.05). ConclusionsLack of SPT was significantly associated with ECBL, while sites that received GBR procedures prior to implant placement were less likely to exhibit ECBL. Our results underscore the importance of periodontal treatment and SPT for peri-implant health, even when the implants are submerged and unrestored.

Objectives: To assess the effect of standard filtered back projection (FBP) and iterative reconstruction (IR) methods on CBCT image noise and processing time (PT), acquired with various acquisition parameters with and without metal artefact reduction (MAR). Methods: CBCT scans using the Midmark EIOS unit of a human mandible embedded in soft tissue equivalent material with and without the presence of an implant at mandibular first molar region were acquired at various acquisition settings (milliamperages [4mA-14mA], FOV [5 × 5, 6 × 8, 9 × 10 cm], and resolutions [low, standard, high] and reconstructed using standard FBP and IR, and with and without MAR. The processing time was recorded for each reconstruction. ImageJ was used to analyze specific axial images. Radial transaxial fiducial lines were created relative to the implant site. Standard deviations of the gray density values (image noise) were calculated at fixed distances on the fiducial lines on the buccal and lingual aspects at specific axial levels, and mean values for FBP and IR were compared using paired t-tests. Significance was defined as p < 0.05. Results: The overall mean for image noise (± SD) for FBP was 198.65 ± 55.58 and 99.84 ± 16.28 for IR. IR significantly decreased image noise compared to FBP at all acquisition parameters (p < 0.05). Noise reduction among different scanning protocols ranged between 29.7% (5 × 5 cm FOV) and 58.1% (5mA). IR increased processing time by an average of 35.1 s. Conclusions: IR significantly reduces CBCT image noise compared to standard FBP without substantially increasing processing time.

Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing provides new opportunities to explore these changing gene-gene interactions. Here, we present a sparse hierarchical Bayesian factor model to identify differences across network structures from different biological conditions in scRNA-seq data. Our methodology utilizes latent factors to impact gene expression values for each cell to help account for zero-inflation, increased cell-to-cell variability, and overdispersion that are unique characteristics of scRNA-seq data. Condition-dependent parameters determine which latent factors are activated in a gene, which allows for not only the calculation of gene-gene co-expression within each group but also the calculation of the co-expression differences between groups. We highlight our methodology's performance in detecting differential gene-gene associations across groups by analyzing simulated datasets and a SARS-CoV-2 case study dataset.

Background Gene co-expression networks (GCNs) are powerful tools that enable biologists to examine associations between genes during different biological processes. With the advancement of new technologies, such as single-cell RNA sequencing (scRNA-seq), there is a need for developing novel network methods appropriate for new types of data. Results We present a novel sparse Bayesian factor model to explore the network structure associated with genes in scRNA-seq data. Latent factors impact the gene expression values for each cell and provide flexibility to account for common features of scRNA-seq: high proportions of zero values, increased cell-to-cell variability, and overdispersion due to abnormally large expression counts. From our model, we construct a GCN by analyzing the positive and negative associations of the factors that are shared between each pair of genes. Conclusions Simulation studies demonstrate that our methodology has high power in identifying gene-gene associations while maintaining a nominal false discovery rate. In real data analyses, our model identifies more known and predicted protein-protein interactions than other competing network models.

Single-cell RNA sequencing (scRNA-seq) technologies are revolutionary tools allowing researchers to examine gene expression at the level of a single cell. Traditionally, transcriptomic data have been analyzed from bulk samples, masking the heterogeneity now seen across individual cells. Even within the same cellular population, genes can be highly expressed in some cells but not expressed (or lowly expressed) in others. Therefore, the computational approaches used to analyze bulk RNA sequencing data are not appropriate for the analysis of scRNA-seq data. Here, we present a novel statistical model for high dimensional and zero-inflated scRNA-seq count data to identify differentially expressed (DE) genes across cell types. Correlated random effects are employed based on an initial clustering of cells to capture the cell-to-cell variability within treatment groups. Moreover, this model is flexible and can be easily adapted to an independent random effect structure if needed. We apply our proposed methodology to both simulated and real data and compare results to other popular methods designed for detecting DE genes. Due to the hurdle model's ability to detect differences in the proportion of cells expressed and the average expression level (among the expressed cells), our methods naturally identify some genes as DE that other methods do not, and we demonstrate with real data that these uniquely detected genes are associated with similar biological processes and functions.

There exist numerous programs and packages that perform validation for a given clustering solution; however, clustering algorithms fare differently as judged by different validation measures. If more than one performance measure is used to evaluate multiple clustering partitions, an optimal result is often difficult to determine by visual inspection alone. This paper introduces optCluster, an R package that uses a single function to simultaneously compare numerous clustering partitions (created by different algorithms and/or numbers of clusters) and obtain a “best” option for a given dataset. The method of weighted rank aggregation is utilized by this package to objectively aggregate various performance measure scores, thereby taking away the guesswork that often follows a visual inspection of cluster results. The optCluster package contains biological validation measures as well as clustering algorithms developed specifically for RNA sequencing data, making it a useful tool for clustering genomic data. Availability: This package is available for free through the Comprehensive R Archive Network (CRAN) at http://cran.rproject.org/web/packages/optCluster/

Determining the best clustering algorithm and ideal number of clusters for a particular dataset is a fundamental difficulty in unsupervised clustering analysis. In biological research, data generated from Next Generation Sequencing technology and microarray gene expression data are becoming more and more common, so new tools and resources are needed to group such high dimensional data using clustering analysis. Different clustering algorithms can group data very differently. Therefore, there is a need to determine the best groupings in a given dataset using the most suitable clustering algorithm for that data. This paper presents the R package optCluster as an efficient way for users to evaluate up to ten clustering algorithms, ultimately determining the optimal algorithm and optimal number of clusters for a given set of data. The selected clustering algorithms are evaluated by as many as nine validation measures classified as “biological”, “internal”, or “stability”, and the final result is obtained through a weighted rank aggregation algorithm based on the calculated validation scores. Two examples using this package are presented, one with a microarray dataset and the other with an RNA-Seq dataset. These two examples highlight the capabilities the optCluster package and demonstrate its usefulness as a tool in cluster analysis.