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Nevin Manimala Statistics

Push-out bond strength of the calcium silicate-based endodontic cements in the presence of blood: A systematic review and meta-analysis of in vitro studies

Clin Exp Dent Res. 2022 Feb 27. doi: 10.1002/cre2.546. Online ahead of print.

ABSTRACT

OBJECTIVES: The push-out bond strength (POBS) of calcium silicate-based cements (CSCs) to the dentinal wall is considered one of the essential physical properties for clinical success. The presence of blood in the treatment area affects the POBS of these types of cement. This study aimed to evaluate the impact of blood contamination on the bond strength of CSCs and dentinal walls.

MATERIAL AND METHODS: This systematic review was performed by searching electronic databases (MEDLINE-PubMed, Scopus, and EMBASE) to include relevant in vitro studies published between 1992 and April 2020. Two reviewers independently evaluated the selected studies and extracted data on the type of studied CSCs, evaluated area of the teeth, sample size, the dimension of a prepared area, slice thickness, storage duration, the setting of the universal testing machine (UTM), effects of blood contamination on POBS of CSCs and their failure modes. The bond strength of evaluated CSCs in studies was used for network meta-analysis.

RESULTS: Initial searches identified 292 articles, while only 13 articles met the inclusion criteria. Full texts of these articles were evaluated, and data extraction was performed. The effect of blood contamination on bond strength to the dentinal wall was assessed in various CSCs such as PMTA, Biodentine, and AMTA. The network meta-analysis results showed that the bond strength of Biodentine was significantly higher than other types of cement in blood presence (p < .05).

CONCLUSIONS: Based on the current systematic review, despite controversies among the result of the different articles and the lack of data for some CSCs like bioaggregate, it could be concluded that the bond strength of Biodentine to the dentinal wall is better than other evaluated CSCs in the presence of blood.

PMID:35220692 | DOI:10.1002/cre2.546

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Nevin Manimala Statistics

Service evaluation of telehealth in a physiotherapy musculoskeletal setting: Patient outcomes and results from risk stratification

Musculoskeletal Care. 2022 Feb 26. doi: 10.1002/msc.1623. Online ahead of print.

ABSTRACT

INTRODUCTION: Due to COVID-19 the ability to see all patients face-to-face (FTF) was removed. Services implemented telehealth to cater for patients requiring musculoskeletal care. A service evaluation was undertaken to assess the effectiveness of a mixed telehealth/FTF approach and identify if stratifying patients could help tailor intervention.

METHODS: Retrospective analysis of data collected from patients who were assessed by Musculoskeletal Physiotherapists in one Scottish health board was undertaken. Patients were divided into low, medium and high risk sub-groups through the Keele STarT MSK tool. Outcome measures for pain and musculoskeletal health were taken at baseline/discharge along with satisfaction/preference. Descriptive and Inferential statistical analysis was conducted to establish whether changes in the outcome measures within and between risk sub-groups were statistically significant.

RESULTS: Pain level difference from baseline to discharge demonstrated clinically and statistically significant improvements across all risk groups (N = 89). Musculoskeletal health demonstrated clinically significant improvements across all risk groups and statistically significant improvements in the medium/high risk groups but not the low risk. Patients with knee osteoarthritis and low back pain in the medium risk group had fewest appointments while patients with chronic shoulder pain had the most. The majority of patients were satisfied with all mediums but preferred FTF or an option between telehealth/FTF in the future.

CONCLUSION: Telehealth is a promising model of care when utilised in combination with FTF for patients with musculoskeletal conditions. Through stratification, identifying specific conditions and shared decision making it may be possible to treat certain patient groups via telehealth.

PMID:35220671 | DOI:10.1002/msc.1623

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Nevin Manimala Statistics

Ethnicity, disease severity and survival in Canadian patients with Primary Biliary Cholangitis

Hepatology. 2022 Feb 26. doi: 10.1002/hep.32426. Online ahead of print.

ABSTRACT

BACKGROUND & AIMS: We investigate associations between ethnicity, survival, and disease severity in a diverse Canadian cohort of patients with Primary Biliary Cholangitis (PBC).

APPROACH & RESULTS: Patients with PBC were included from the Canadian Network for Autoimmune Liver disease (CaNAL). Ethnicity was defined using a modified list adopted from Statistics Canada and ethnicities with small sample were grouped. Clinical events were defined as liver decompensation, hepatocellular carcinoma, liver transplantation (LTx), or death. Clinical event-free and LTx-free survival were analyzed using Cox regression. Trajectories of serum liver function tests were assessed over time using mixed-effects regression. HRQOL was assessed using the Short Form 36 (SF-36), PBC-40 questionnaire, and 5-D Itch scale and analyzed using mixed-effects regression. The cohort included 1538 patients with PBC from six sites and was comprised of 82% White patients, 4.7% Indigenous, 5.5% East Asian, 2.6% South Asian, and 5.1% Miscellaneous ethnicities. Indigenous patients were the only ethnic group with impaired liver transplant-free and event-free survival compared to White patients (HR 3.66, 95%CI 2.23-6.01; HR 3.09, 95%CI 1.94-4.92). Indigenous patients were more likely to have a clinical event before diagnosis (10%) than all other ethnicity groups despite similar age at diagnosis. Indigenous patients presented with higher alkaline phosphatase, total bilirubin, and GLOBE scores than White patients and these relative elevations persisted during follow-up.

CONCLUSIONS: Indigenous Canadians with PBC present with advanced disease and have worse long-term outcomes compared to White patients.

PMID:35220609 | DOI:10.1002/hep.32426

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Nevin Manimala Statistics

Association of smoking, lung function, and COPD in COVID-19 risk: A 2 step Mendelian randomization study

Addiction. 2022 Feb 27. doi: 10.1111/add.15852. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Smoking increases the risk of severe COVID-19, but whether lung function or chronic obstructive pulmonary disease (COPD) mediate the underlying associations is unclear. We conducted the largest Mendelian randomization study, to date, to address these questions.

DESIGN: Mendelian randomization study using summary statistics from genome wide association studies (GWAS), FinnGen, and UK Biobank. The main analysis was inverse variance weighted method, and we included a range of sensitivity analyses to assess robustness of findings.

SETTING: GWAS which included international consortia, FinnGen, and UK Biobank PARTICIPANTS: Sample size ranged from 193,638 to 2,586,691.

MEASUREMENTS: Genetic determinants of lifetime smoking index, lung function (e.g forced expiratory volume in 1 second (FEV1 )), COPD and different severities of COVID-19.

RESULTS: Smoking increased the risk of COVID-19 compared with population controls, for overall COVID-19 (odds ratio (OR) 1.19 per standard deviation (SD) of lifetime smoking index, 95% confidence interval (CI) 1.11 to 1.27), hospitalized COVID-19 (OR 1.67, 95% CI 1.42 to 1.97) or severe COVID-19 (OR 1.48, 95% CI 1.11 to 1.98), with directionally consistent effects from sensitivity analyses. Lung function and COPD liability did not appear to mediate these associations.

CONCLUSION: There is genetic evidence that smoking likely increases the risk of severe COVID-19 and possibly also milder forms of COVID-19. Decreased lung function and increased risk of chronic obstructive pulmonary disease do not seem to mediate the effect of smoking on COVID-19 risk.

PMID:35220625 | DOI:10.1111/add.15852

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Nevin Manimala Statistics

Correlation between knee anatomy and joint laxity using principal component analysis

J Orthop Res. 2022 Feb 26. doi: 10.1002/jor.25294. Online ahead of print.

ABSTRACT

Knee articular geometry and surface morphology greatly affect knee joint mechanics. Intra-subject variations in bone morphology and the passive range of motion have been well documented in the literature; however, the relationship between these two characteristics is not well understood. The objective of this study was to describe the correlation between knee joint anatomical features and passive range of motion using a statistical model. A principal component model was developed using femoral and tibial articular geometry, knee joint initial stance position, and the passive laxity envelope obtained from 27 cadaveric knees. The results from the principal component analysis showed high correlation between the anatomical features and the tibiofemoral passive envelope; an increase in the average femoral condyle radii, an increase in slope of the tibial spine, and a higher tibial plateau concavity correlated with a decrease in varus-valgus and internal-external range of motion. Understanding the correlation between anatomical features and tibiofemoral laxity could aid in the development of orthopedic implant designs by quantifying the effect of perturbing specific anatomical features on knee laxity and identifying specific implant femoral and tibial articular geometry necessary to obtain a targeted passive range of motion.

PMID:35220608 | DOI:10.1002/jor.25294

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Nevin Manimala Statistics

Group and individual social network metrics are robust to changes in resource distribution in experimental populations of forked fungus beetles

J Anim Ecol. 2022 Feb 27. doi: 10.1111/1365-2656.13684. Online ahead of print.

ABSTRACT

Social interactions drive many important ecological and evolutionary processes. It is therefore essential to understand the intrinsic and extrinsic factors that underlie social patterns. A central tenet of the field of behavioral ecology is the expectation that the distribution of resources shapes patterns of social interactions. We combined experimental manipulations with social network analyses to ask how patterns of resource distribution influence complex social interactions. We experimentally manipulated the distribution of an essential food and reproductive resource in semi-natural populations of forked fungus beetles (Bolitotherus cornutus). We aggregated resources into discrete clumps in half of the populations and evenly dispersed resources in the other half. We then observed social interactions between individually marked beetles. Half-way through the experiment, we reversed the resource distribution in each population, allowing us to control any demographic or behavioral differences between our experimental populations. At the end of the experiment, we compared individual and group social network characteristics between the two resource distribution treatments. We found a statistically significant but quantitatively small effect of resource distribution on individual social network position and detected no effect on group social network structure. Individual connectivity (individual strength) and individual cliquishness (local clustering coefficient) increased in environments with clumped resources, but this difference explained very little of the variance in individual social network position. Individual centrality (individual betweenness) and measures of overall social structure (network density, average shortest path length, and global clustering coefficient) did not differ between environments with dramatically different distributions of resources. Our results illustrate that the resource environment, despite being fundamental to our understanding of social systems, does not always play a central role in shaping social interactions. Instead, our results suggests that sex differences and temporally fluctuating environmental conditions may be more important in determining patterns of social interactions.

PMID:35220593 | DOI:10.1111/1365-2656.13684

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Nevin Manimala Statistics

A discriminational attitude and behavior in the healthcare field: Homophobia level in healthcare professionals working in primary health services and the affecting factors

Perspect Psychiatr Care. 2022 Feb 27. doi: 10.1111/ppc.13059. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the homophobia level among the healthcare professionals working in primary healthcare services and the affecting factors.

DESIGN AND METHODS: This descriptive and cross-sectional study conducted between November 2018 and April 2019, included 184 healthcare professionals.

FINDINGS: The mean total score of the Hudson and Ricketts Homophobia Scale was 103.55 ± 30.47. There was a statistically significant difference between the marital status of the healthcare professionals, what they felt during the care/treatment of lesbian, gay, bisexual, transgender, and intersex (LGBTI) individuals, level of knowledge about such individuals, willingness to know more about them, and the status of having LGBTI acquaintances and the median homophobia score.

PRACTICE IMPLICATIONS: The results will be beneficial for LGBTI individuals, who have problems in communicating with primary healthcare personnel, to benefit from the services effectively.

PMID:35220597 | DOI:10.1111/ppc.13059

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Nevin Manimala Statistics

Evaluation of the Efficacy of Dexmedetomidine as A Local Anesthetics Adjuvant in Children: A Meta Analysis of Randomized Controlled Trials

J Clin Pharmacol. 2022 Feb 27. doi: 10.1002/jcph.2039. Online ahead of print.

ABSTRACT

Dexmedetomidine has been identified as a useful adjunct to improve the effect of nerve blocks in adults, however, its effect for children is not fully investigated. This meta-analysis is aimed to evaluate the reliability and efficacy of dexmedetomidine as a local anesthetics adjunct for pediatric surgeries. Eligible studies were searched in Cochrane, Embase, PubMed and CBM. The RevMan 5.4 was used to assess the risk of bias of each study and perform statistical analysis. Stata 15.0 was used to evaluate the publication bias of primary outcomes. Thirteen RCTs involving 722 patients aged 6 months to 12 years were harvested Statistical analysis showed that dexmedetomidine resulted in significantly longer duration of analgesia (standardized mean difference [SMD], 7.16; 95% confidence interval [CI], 4.88 to 9.43; P < 0.001, I2 = 98%); reducing the 1-h pain score (mean difference [MD], -0.27; 95% CI, -0.47 to -0.06; P = 0.01; I2 = 28%), the cumulative doses of rescue analgesic requirements: 2 doses (risk ratio [RR], 0.26; 95% CI, 0.14 to 0.49; P < 0.001, I2 = 0), 3 doses (RR, 0.04; 95% CI, 0.01 to 0.16; P < 0.001, I2 = 4%), and the frequency of emergence agitation (RR, 0.44; 95% CI, 0.22 to 0.91; P = 0.03, I2 = 0); shortening the onset time of blocks (MD, -3.56; 95% CI, -6.39 to -0.74; P = 0.01; I2 = 90%). However, the incidence of some side effects, including hypotension, bradycardia, nausea and vomiting, pruritis, urinary retention, and respiratory depression, was not significantly different between dexmedetomidine group and placebo. Therefore, dexmedetomidine is a reliable and efficient adjunct to local anesthetics in children. This article is protected by copyright. All rights reserved.

PMID:35220587 | DOI:10.1002/jcph.2039

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Nevin Manimala Statistics

Bayesian interaction selection model for multi-modal neuroimaging data analysis

Biometrics. 2022 Feb 27. doi: 10.1111/biom.13648. Online ahead of print.

ABSTRACT

Multi-modality or multi-construct data arise increasingly in functional neuroimaging studies to characterize brain activity under different cognitive states. Relying on those high-resolution imaging collections, it is of great interest to identify predictive imaging markers and inter-modality interactions with respect to behavior outcomes. Currently, most of the existing variable selection models do not consider predictive effects from interactions, and the desired higher-order terms can only be included in the predictive mechanism following a two-step procedure, suffering from potential mis-specification. In this paper, we propose a unified Bayesian prior model to simultaneously identify main effect features and inter-modality interactions within the same inference platform in the presence of high dimensional data. To accommodate the brain topological information and correlation between modalities, our prior is designed by compiling the intermediate selection status of sequential partitions in light of the data structure and brain anatomical architecture, so that we can improve posterior inference and enhance biological plausibility. Through extensive simulations, we show the superiority of our approach in main and interaction effects selection, and prediction under multi-modality data. Applying the method to the Adolescent Brain Cognitive Development (ABCD) study, we characterize the brain functional underpinnings with respect to general cognitive ability under different memory load conditions. This article is protected by copyright. All rights reserved.

PMID:35220581 | DOI:10.1111/biom.13648

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Nevin Manimala Statistics

Multi-source single-cell data integration by MAW barycenter for gaussian mixture models

Biometrics. 2022 Feb 27. doi: 10.1111/biom.13630. Online ahead of print.

ABSTRACT

One key challenge encountered in single-cell-data clustering is to combine clustering results of datasets acquired from multiple sources. We propose to represent the clustering result of each dataset by a Gaussian mixture model (GMM) and produce an integrated result based on the notion of Wasserstein barycenter. However, the precise barycenter of GMMs, a distribution on the same sample space, is computationally infeasible to solve. Importantly, the barycenter of GMMs may not be a GMM containing a reasonable number of components. We thus propose to use the Minimized Aggregated Wasserstein (MAW) distance to approximate the Wasserstein metric and develop a new algorithm for computing the barycenter of GMMs under MAW. Recent theoretical advances further justify using the MAW distance as an approximation for the Wasserstein metric between GMMs. We also prove that the MAW barycenter of GMMs has the same expectation as the Wasserstein barycenter. Our proposed algorithm for clustering integration scales well with the data dimension and the number of mixture components, with complexity independent of data size. We demonstrate that the new method achieves better clustering results on several single-cell RNA-seq datasets than some other popular methods. This article is protected by copyright. All rights reserved.

PMID:35220585 | DOI:10.1111/biom.13630