Effect of Silver Diamine Fluoride Treatment on Microbial Profiles of Plaque Biofilms from Root/Cervical Caries Lesions

2019 ◽  
Vol 53 (5) ◽  
pp. 555-566 ◽  
Author(s):  
Heba Mitwalli ◽  
Marcio D.A. Mourao ◽  
Joseph Dennison ◽  
Peter Yaman ◽  
Bruce J. Paster ◽  
...  
Keyword(s):  
Linear Models ◽  
Negative Binomial ◽  
Principal Component ◽  
Shannon Index ◽  
Ratio Test ◽  
Silver Diamine Fluoride ◽  
Fluoride Treatment ◽  
Supragingival Plaque ◽  
Rdna Sequencing ◽  
Miseq Platform

Purpose: To assess the effect of silver diamine fluoride (SDF) on microbial profiles present in plaque from root/cervical carious lesions, and its association with caries lesion arrest. Materials and Methods: Twenty patients with at least one soft cavitated root/cervical carious lesion were included. One lesion/patient was randomly selected and treated with 38% SDF. Supragingival plaque samples were harvested at preintervention and 1 month postintervention. Using an MiSeq platform, 16S rDNA sequencing of the V3-V4 regions was used to determine bacterial profiles. Clinical evaluation of lesion hardness was used to evaluate arrest. t tests, principal component analysis (PCA), multidimensional scaling (MDS), and generalized linear models (GLMs) tests were used for statistical comparisons. Results: From a total of 40 plaque samples, 468 probe targets were observed. Although 60% of lesions became hard postintervention, PCA and MDS tests showed no distinct pre- and postintervention groups. In addition, pre- and postintervention differences in diversity (Shannon index) of microbial profiles between patients with and without lesion arrest were not statistically different. A likelihood ratio test for pre- versus postintervention differences within patients, i.e., adjusting for differences between patients using negative binomial GLMs, showed 17 bacterial taxa with significant differences (FDR <0.05). Conclusion: Although 60% of lesions hardened after SDF treatment, this was not directly due to either overall statistically significant differences in microbial profiles or differences in microbial diversity. Nevertheless, there was a trend with some acid-producing species in that their relative abundance was reduced postintervention. The negative binomial GLMs showed 17 bacterial taxa that were significantly different after SDF treatment.

scholarly journals The bias generated by sequencing platforms in analyzing of maternal-neonate gut microbiota profiles and diversity

2019 ◽  
Author(s):  
Jie Wei ◽  
Yue Cheng ◽  
Xin Cao ◽  
Xiaohong Tuo ◽  
Kaichong Jiang ◽  
...  
Keyword(s):  
Relative Abundance ◽  
Compliance Rate ◽  
Principal Component ◽  
Amplicon Sequencing ◽  
Shannon Index ◽  
Culture Methods ◽  
Illumina Hiseq ◽  
Rdna Sequencing ◽  
Group A ◽  
Sequencing Platforms

Abstract Background: Microbiome is an important internal ecosystem closely related to host health. Most of the bacteria existed in the internal ecosystem cannot be isolated with laboratory bacteriological culture methods, while 16S rDNA sequencing is considered and used for the bacterial identification by through the high-throughput platforms. The aim of this study was to compare the microbiota analysis result using two next-generation sequencing platforms and bioinformatics pipelines. Results: 56 maternal-neonate fecal samples were sequenced and analyzed by 16S rRNA amplicon sequencing both by Ion Torrent S5-xl and Illumina Hiseq 2500 with standard protocols at same lab. For the richness and diversity of microbiota, index of chao1, observed_specise, PD_whole_tree, simpson and good_converage varied significantly except Shannon index at two platforms (P<0.05). The relative abundance of bacteria at different taxonomy levels is checked from phylum to species level, the more species of bacteria sequenced and annotated, the lower the correlation of the relative abundance of the bacteria founded between two platforms. The sequencing results are consistent between two platforms. Principal component analysis (PCA) results showed that more than 87% of samples were concentrated. According to principal coordinate analysis (PCoA), 56 samples of the two platforms were divided into two clusters, and the compliance rate of the two platforms is 71.43%. The differences between microbial community structures generated from two platforms were tested by multi-response permutation procedure (MRPP), which showed significant differences at family and genus levels separately (A=0.094, P=0.001; A=0.085, P=0.002). When maternal and neonate samples were considered, at family level, there was no difference in microbiota composition between two platform for maternal group (A=0.006, P=0.149), while in the neonate group, it showed significant differences (A=0.035, P=0.006). At the genus level, there existed significant differences in microbiota both in maternal and neonate group (A=0.0216, P=0.004; A=0.098, P=0.001). Conclusion: Although the relative abundance of microbiota sequenced at two different platforms is basically similar, the diversity and correlation coefficient are still quite different. To increase reproducibility and reliability in cohort studies, it is important to use the same sequencing platforms and the corresponding pipeline to reduce the systematic error in microbiome analysis.

scholarly journals 508. Biomarker elevation during COVID-19: Differences between ambulatory and hospitalized individuals

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S319-S320
Author(s):  
Paul W Blair ◽  
Charlotte Lanteri ◽  
Deborah Striegel ◽  
Brian Agan ◽  
Ryan C Maves ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Negative Binomial ◽  
Principal Component ◽  
Component Analysis ◽  
Ambulatory Setting ◽  
Level Of Care ◽  
Symptom Duration ◽  
Duration Of Symptoms ◽  
Course Of Illness ◽  
D Dimer

Abstract Background While the majority of illness due to COVID-19 does not require hospitalization, little has been described about the host inflammatory response in the ambulatory setting. Differences in the levels of inflammatory signaling proteins between outpatient and hospitalized populations could identify key maladaptive immune responses during COVID-19. Methods Samples were collected from 76 participants (41% female, mean 46.8 years of age) enrolled at five military treatment facilities between March 20, 2020 and June 17, 2020 in an ongoing prospective COVID-19 cohort. This analysis was restricted to those with positive SARS-CoV-2 (severe acute respiratory syndrome–coronavirus 2) RT-PCR testing and included hospitalized (N=29; 10 requiring an ICU stay) and non-hospitalized (N=43) participants. Severity markers (IL6, D-dimer, procalcitonin, ferritin, ICAM-1, IL5, lipocalin, RAGE, TNFR, VEGFA, IFNγ, IL1β) were measured in plasma (mg/dL) using the Ella immunoassay and natural log transformed. Univariate negative binomial regression was performed to determine relative risk of hospitalization. Using the full marker panel, we performed a Principal Component Analysis (PCA) to determine directions of maximal variance in the data. Pearson’s correlation coefficient was determined between analytes and each axis. Results Participants requiring ambulatory-, hospital-, and ICU-level care had samples collected at 44.0 (IQR: 35.0–51.0), 40.0 (13.0–51.0), and 47.5 (21.0–54.0) days, respectively. Higher unadjusted levels of IL6, D-dimer, procalcitonin, or ferritin were each associated with hospitalization (Table 1). The PCA showed a separation along axes between level of care and duration of symptoms (Fig 1). While significant correlations were noted with a number of biomarkers, PC1 most correlated with TNFR1 (r=0.88) and PC2 most correlated with IL6Ra (r=0.95). PC1 axis variation accounted for 36.5% of variance and the PC2 axis accounted for 20.0% of variance. Figure 1. Principal Component Analysis (PCA) of biomarkers by level of care and symptom duration. Conclusion TNFR1 and IL6Ra levels correlated with differences in the proinflammatory states between hospitalized and non-hospitalized individuals including time points late in the course of illness. Further analysis of these preliminary findings is needed to evaluate for differences by stages of illness. Disclosures All Authors: No reported disclosures

scholarly journals The relationship between empathy and altruistic motivations in nursing studies: a multi-method study

BMC Nursing ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Linda Messineo ◽  
Luciano Seta ◽  
Mario Allegra
Keyword(s):  
Nursing Education ◽  
Nursing Students ◽  
Health Professionals ◽  
Linear Models ◽  
Principal Component ◽  
Motivational Factors ◽  
Care Process ◽  
First Year ◽  
Young Males ◽  
Young Females

Abstract Background The efficient management of relational competences in healthcare professionals is crucial to ensuring that a patient’s treatment and care process is conducted positively. Empathy is a major component of the relational skills expected of health professionals. Knowledge of undergraduate healthcare students’ empathic abilities is important for educators in designing specific and efficient educational programmes aimed at supporting or enhancing such competences. In this study, we measured first-year undergraduate nursing students’ attitudes towards professional empathy in clinical encounters. The students’ motivations for entering nursing education were also evaluated. This study takes a multi-method approach based on the use of qualitative and quantitative tools to examine the association between students’ positive attitudes towards the value of empathy in health professionals and their prosocial and altruistic motivations in choosing to engage in nursing studies. Methods A multi-method study was performed with 77 first-year nursing students. The Jefferson Scale of Empathy (JSE) – Health Professions Student Version was administered. Students’ motivations for choosing nursing studies were detected through an open question and thematically analysed. Using explorative factor analysis and principal component analysis, a dimensional reduction was conducted to identify subjects with prosocial and altruistic motivations. Finally, linear models were tested to examine specific associations between motivation and empathy. Results Seven distinct themes distinguishing internal and external motivational factors were identified through a thematic analysis of students’ answers regarding their decision to enter a nursing degree course. Female students gained higher scores on the empathy scale than male ones. When students’ age was considered, this difference was only observed for younger students, with young females’ total scores being higher than young males'. High empathy scores were positively associated with altruistic motivational factors. A negative correlation was found between external motivational factors and the scores of the Compassionate Care subscale of the JSE. Conclusions Knowing the level of nursing students’ empathy and their motivational factors for entering nursing studies is important for educators to implement training paths that enhance students’ relational attitudes and skills and promote the positive motivational aspects that are central to this profession.

scholarly journals Electrical Capacitance Characteristics of Wood Chips at Low Frequency Ranges: A Cheap Tool for Quality Assessment

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3494
Author(s):  
Jakub Lev ◽  
Václav Křepčík ◽  
Egidijus Šarauskis ◽  
František Kumhála
Keyword(s):  
Moisture Content ◽  
Linear Models ◽  
Economic Value ◽  
Wood Chip ◽  
Principal Component ◽  
Dissipation Factor ◽  
Industrial Applications ◽  
Wood Chips ◽  
Electrical Capacitance ◽  
Low Frequencies

Moisture content is one of the most important parameters related to the quality of wood chips that affects both the calorific and economic value of fuel chips. For industrial applications, moisture content needs to be detected quickly. For this purpose, various indirect moisture content measurement methods (e.g., capacitance, NIR, microwave, ECT, X-ray CT, and nuclear MR) have been investigated with different results in the past. Nevertheless, determining wood chip moisture content in real time is still a challenge. The main aim of this article was therefore to analyze the dielectric properties of wood chips at low frequencies (10 kHz–5 MHz) and to examine the possibility of using these properties to predict wood chip moisture content and porosity. A container-type probe was developed for this purpose. The electrical capacitance and dissipation factor of wood chips with different moisture content was measured by an LCR meter at 10 kHz, 50 kHz, 100 kHz, 500 kHz, 1 MHz, and 5 MHz frequencies. Wood chip porosity was also measured using a gas displacement method. Linear models for moisture content and porosity prediction were determined by backward stepwise linear regression. Mathematical model was developed to better understand the physical relationships between moisture content, porosity, and electrical capacitance. These models were able to predict the moisture content of observed quantities of wood chips with the required accuracy (R2 = 0.9−0.99). This finding opens another path to measuring the moisture content and porosity of wood chips in a relatively cheap and fast way and with adequate precision. In addition, principal component analysis showed that it is also possible to distinguish between individual wood chip fraction sizes from the information obtained.

Functional Linear Regression

2018 ◽  
Author(s):  
Hervé Cardot ◽  
Pascal Sarda
Keyword(s):  
Linear Regression ◽  
Least Squares ◽  
Linear Models ◽  
Estimation Error ◽  
Asymptotic Properties ◽  
Principal Component Regression ◽  
Principal Component ◽  
Penalized Least Squares ◽  
Open Problems ◽  
Functional Linear Regression

This article presents a selected bibliography on functional linear regression (FLR) and highlights the key contributions from both applied and theoretical points of view. It first defines FLR in the case of a scalar response and shows how its modelization can also be extended to the case of a functional response. It then considers two kinds of estimation procedures for this slope parameter: projection-based estimators in which regularization is performed through dimension reduction, such as functional principal component regression, and penalized least squares estimators that take into account a penalized least squares minimization problem. The article proceeds by discussing the main asymptotic properties separating results on mean square prediction error and results on L2 estimation error. It also describes some related models, including generalized functional linear models and FLR on quantiles, and concludes with a complementary bibliography and some open problems.

Functional microbial diversity dynamics in common effluent treatment plants of South Gujarat and hydrocarbon degradation

2015 ◽  
Vol 61 (6) ◽  
pp. 389-397 ◽  
Author(s):  
Purvi Zaveri ◽  
Nasreen Munshi ◽  
Alok Vaidya ◽  
Sanjay Jha ◽  
G. Naresh Kumar
Keyword(s):  
Microbial Diversity ◽  
Restriction Analysis ◽  
Principal Component ◽  
Hydrocarbon Degradation ◽  
Effluent Treatment ◽  
Organic Load ◽  
Aeration Tank ◽  
Conventional Procedure ◽  
Rdna Sequencing ◽  
Functional Microbial Diversity

Common effluent treatment plants (CETPs) of South Gujarat region, India, process wastewater generated by more than 2500 industries because of the nonfeasibility of processing at the individual industrial unit. This study assessed functional microbial diversity in wastewater samples of CETPs over a geological belt using Ecoplate®, isolation of the most abundant bacteria, and screening for hydrocarbon degradation. The high evenness (EPielou) values (0.9) in almost all samples indicated a highly even community structure. Principal component analysis of carbon source utilization showed a cluster of all inlet samples except E1 and another cluster of all outlet samples; aeration tank community samples were dispersed. In spite of the high richness found in microbial communities, 60 morphologically similar organisms were observed and isolated; 46 out of them were subjected to amplified ribosomal DNA restriction analysis with MboI, HaeIII, and TaqI enzyme, followed by UPGMA clustering. In screening the most abundant bacteria from each cluster, one of the cultures showed a high potential for hydrocarbon degradation and was identified as Pseudomonas citronellolis by 16S rDNA sequencing. Because of its highly adapted inherent nature, this bacterium may help augment the conventional procedure in wastewater treatment and efficiently decrease the organic load.

scholarly journals Metabonomic-Transcriptome Integration Analysis on Osteoarthritis and Rheumatoid Arthritis

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Ningyang Gao ◽  
Li Ding ◽  
Jian Pang ◽  
Yuxin Zheng ◽  
Yuelong Cao ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Gap Junction ◽  
Molecular Mechanisms ◽  
Linear Models ◽  
Principal Component ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Nf Kappa B ◽  
Linear Module ◽  
Hedgehog Acyltransferase

Purpose. This study is aimed at exploring the potential metabolite/gene biomarkers, as well as the differences between the molecular mechanisms, of osteoarthritis (OA) and rheumatoid arthritis (RA). Methods. Transcriptome dataset GSE100786 was downloaded to explore the differentially expressed genes (DEGs) between OA samples and RA samples. Meanwhile, metabolomic dataset MTBLS564 was downloaded and preprocessed to obtain metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEG-metabolite relations. Finally, metabolic pathway enrichment analysis was performed to investigate the differences between the molecular mechanisms of OA and RA. Results. A total of 976 DEGs and 171 metabolites were explored between OA samples and RA samples. The PCA and linear module analysis investigated 186 DEG-metabolite interactions including Glycogenin 1- (GYG1-) asparagine_54, hedgehog acyltransferase- (HHAT-) glucose_70, and TNF receptor-associated factor 3- (TRAF3-) acetoacetate_35. Finally, the KEGG pathway analysis showed that these metabolites were mainly enriched in pathways like gap junction, phagosome, NF-kappa B, and IL-17 pathway. Conclusions. Genes such as HHAT, GYG1, and TRAF3, as well as metabolites including glucose, asparagine, and acetoacetate, might be implicated in the pathogenesis of OA and RA. Metabolites like ethanol and tyrosine might participate differentially in OA and RA progression via the gap junction pathway and phagosome pathway, respectively. TRAF3-acetoacetate interaction may be involved in regulating inflammation in OA and RA by the NF-kappa B and IL-17 pathway.

scholarly journals Temporal analysis of the relationship between dengue and meteorological variables in the city of Rio de Janeiro, Brazil, 2001-2009

2012 ◽  
Vol 28 (11) ◽  
pp. 2189-2197 ◽  
Author(s):  
Adriana Fagundes Gomes ◽  
Aline Araújo Nobre ◽  
Oswaldo Gonçalves Cruz
Keyword(s):  
Minimum Temperature ◽  
Rio De Janeiro ◽  
Linear Models ◽  
Negative Binomial ◽  
Critical Factor ◽  
Temporal Analysis ◽  
Temporal And Spatial Distribution ◽  
Temperature And Precipitation ◽  
The Relationship ◽  
The City

Dengue, a reemerging disease, is one of the most important viral diseases transmitted by mosquitoes. Climate is considered an important factor in the temporal and spatial distribution of vector-transmitted diseases. This study examined the effect of seasonal factors and the relationship between climatic variables and dengue risk in the city of Rio de Janeiro, Brazil, from 2001 to 2009. Generalized linear models were used, with Poisson and negative binomial distributions. The best fitted model was the one with "minimum temperature" and "precipitation", both lagged by one month, controlled for "year". In that model, a 1°C increase in a month's minimum temperature led to a 45% increase in dengue cases in the following month, while a 10-millimeter rise in precipitation led to a 6% increase in dengue cases in the following month. Dengue transmission involves many factors: although still not fully understood, climate is a critical factor, since it facilitates analysis of the risk of epidemics.

scholarly journals An Ethology of Adaptation: Dolphins Stop Feeding but Continue Socializing in Construction-Degraded Habitat

2021 ◽  
Vol 8 ◽  
Author(s):  
Ann Weaver
Keyword(s):  
Linear Models ◽  
Negative Binomial ◽  
Environmental Changes ◽  
Bottlenose Dolphins ◽  
Observation Time ◽  
Mixed States ◽  
The West ◽  
Behavioral Adaptations ◽  
Construction Zone ◽  
Free Ranging

Adaptation is a biological mechanism by which organisms adjust physically or behaviorally to changes in their environment to become more suited to it. This is a report of free-ranging bottlenose dolphins’ behavioral adaptations to environmental changes from coastal construction in prime habitat. Construction was a 5-year bridge removal and replacement project in a tidal inlet along west central Florida’s Gulf of Mexico coastline. It occurred in two consecutive 2.5-year phases to replace the west and east lanes, respectively. Lane phases involved demolition/removal of above-water cement structures, below-water cement structures, and reinstallation of below + above water cement structures (N = 2,098 photos). Data were longitudinal (11 years: 2005–2016, N = 1,219 surveys 2–4 times/week/11 years, N = 4,753 dolphins, 591.95 h of observation in the construction zone, 126 before-construction surveys, 568 during-construction surveys, 525 after-construction surveys). The dependent variable was numbers of dolphins (count) in the immediate construction zone. Three analyses examined presence/absence, total numbers of dolphins, and numbers of dolphins engaged in five behavior states (forage-feeding, socializing, direct travel, meandering travel, and mixed states) across construction. Analyses were GLIMMIX generalized linear models for logistic and negative binomial regressions to account for observation time differences as an exposure (offset) variable. Results showed a higher probability of dolphin presence than absence before construction began, more total dolphins before construction, and significant decreases in the numbers of feeding but not socializing dolphins. Significant changes in temporal rhythms also revealed finer-grained adaptations. Conclusions were that the dolphins adapted to construction in two ways, by establishing feeding locations beyond the disturbed construction zone and shifting temporal rhythms of behaviors that they continued to exhibit in the construction zone to later in the day when construction activities were minimized. This is the first study to suggest that the dolphins learned to cope with coastal construction with variable adjustments.

scholarly journals Generalized linear models with random effects for the description of data with excess zeros

2011 ◽  
Vol 54 (6) ◽  
pp. 661-675
Author(s):  
N. Mielenz ◽  
K. Thamm ◽  
M. Bulang ◽  
J. Spilke
Keyword(s):  
Random Effects ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Linear Models ◽  
Negative Binomial ◽  
Detailed Comparison ◽  
Response Scale ◽  
Explanatory Variables ◽  
Excess Zeros ◽  
Explanatory Factors

Abstract. In this paper count data with excess zeros and repeated observations per subject are evaluated. If the number of values observed for the zero event in the trial substantially exceeds the expected number (derived from the Poisson or from the negative binomial distribution), then there is an excess of zeros. Hurdle and zero-inflated models with random effects are available in order to evaluate this type of data. In this paper both model approaches are presented and are used for the evaluation of the number of visits to the feeder per cow per hour. Finally, for the analysis of the target trait a hurdle model with random effects based on a negative binomial distribution was used. This analysis was derived from a detailed comparison of models and was needed because of a simpler computer implementation. For improved interpretation of the results, the levels of the explanatory factors (for example, the classes of lactation) were not averaged in the link scale, but rather in the response scale. The deciding explanatory variables for the pattern of visiting activities in the 24-hour cycle are the milking and cleaning times at hours 4, 7, 12 and 20. The highly significant differences in the visiting frequencies of cows of the first lactation and those of higher lactations were explained by competition for access to the feeder and thus to the feed.

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