Nevin Manimala

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

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

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

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

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

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

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

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

Folia Microbiol (Praha). 2022 Feb 26. doi: 10.1007/s12223-021-00934-5. Online ahead of print.

ABSTRACT

Mucormycosis is a deadly opportunistic disease caused by a group of fungus named mucormycetes. Fungal spores are normally present in the environment and the immune system of the body prevents them from causing disease in a healthy immunocompetent individual. But when the defense mechanism of the body is compromised such as in the patients of diabetes mellites, neustropenia, organ transplantation recipients, and other immune-compromised states, these fungal spores invade our defense mechanism easily causing a severe systemic infection with approximately 45-80% of case fatality. In the present scenario, during the COVID-19 pandemic, patients are on immunosuppressive drugs, glucocorticoids, thus are at high risk of mucormycosis. Patients with diabetes mellitus are further getting a high chance of infection. Usually, the spores gain entry through our respiratory tract affecting the lungs and paranasal sinuses. Besides, they can also enter through damage into the skin or through the gastrointestinal route. This review article presents the current statistics, the causes of this infection in the human body, and its diagnosis with available recent therapies through recent databases collected from several clinics and agencies. The diagnosis and identification of the infection were made possible through various latest medical techniques such as computed tomography scans, direct microscopic observations, MALDI-TOF mass spectrometry, serology, molecular assay, and histopathology. Mucormycosis is so uncommon, no randomized controlled treatment studies have been conducted. The newer triazoles, posaconazole (POSA) and isavuconazole (ISAV) (the active component of the prodrug isavuconazonium sulfate) may be beneficial in patients who are refractory to or intolerant of Liposomal Amphotericin B. but due to lack of early diagnosis and aggressive surgical debridement or excision, the mortality rate remains high. In the course of COVID-19 treatments, there must be more vigilance and alertness are required from clinicians to evaluate these invasive fungal infections.

PMID:35220559 | DOI:10.1007/s12223-021-00934-5

Int J Cardiovasc Imaging. 2022 Feb 27. doi: 10.1007/s10554-022-02574-3. Online ahead of print.

ABSTRACT

Pulse wave velocity (PWV) can evaluate potential atherosclerosis (AS) and ultrafast pulse wave velocity (ufPWV) is a new technique to accurately assess PWV. However, few studies have examined the predictive value of ufPWV for AS risk. We aimed to establish a classification model for AS risk diagnosis based on ufPWV, so that AS can be diagnosed and prevented in advance. We collected imaging data, as well as clinical and laboratory data. A total of 613 patients with 20 attributes were admitted in this study. There were 392 patients with hyperlipidemia (AS risk group) and 221 healthy adults as the control group. In order to build AS risk prediction models, we considered decision tree, five different ensemble learning (EL) models [random forest (RF), adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost) and light gradient boosting machine (LGBM)] and two different feature selection methods [statistical analysis and RF]. Accuracy and the area under the ROC curve (AUC) were used as the main criterion for model evaluation. In the prediction of AS risk with statistical analysis as the feature selection method, the performances of XGBoost (accuracy: 0.851; AUC: 0.884) and RF (accuracy: 0.844; AUC: 0.889) were better than other models. Besides, in the prediction of AS risk with RF as the feature selection method, the performances of LGBM (accuracy: 0.870; AUC: 0.903) and XGBoost (accuracy: 0.857; AUC: 0.903) were better than other models. In conclusions, EL models with RF as the feature selection method might provide accurate results in predicting AS risk. Besides, ufPWV, especially PWV of left common carotid artery at the end of systole, was an important feature in the AS risk prediction models.

PMID:35220527 | DOI:10.1007/s10554-022-02574-3