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Nevin Manimala Statistics

Health effects of air pollutant mixtures on overall mortality among the elderly population using Bayesian Kernel machine regression (BKMR)

Chemosphere. 2021 Jul 17;286(Pt 1):131566. doi: 10.1016/j.chemosphere.2021.131566. Online ahead of print.

ABSTRACT

It is well documented that fine particles matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are associated with a range of adverse health outcomes. However, most epidemiologic studies have focused on understanding their additive effects, despite that individuals are exposed to multiple air pollutants simultaneously that are likely correlated with each other. Therefore, we applied a novel method – Bayesian Kernel machine regression (BKMR) and conducted a population-based cohort study to assess the individual and joint effect of air pollutant mixtures (PM2.5, O3, and NO2) on all-cause mortality among the Medicare population in 15 cities with 656 different ZIP codes in the southeastern US. The results suggest a strong association between pollutant mixture and all-cause mortality, mainly driven by PM2.5. The positive association of PM2.5 with mortality appears stronger at lower percentiles of other pollutants. An interquartile range change in PM2.5 concentration was associated with a significant increase in mortality of 1.7 (95% CI: 0.5, 2.9), 1.6 (95% CI: 0.4, 2.7) and 1.4 (95% CI: 0.1, 2.6) standard deviations (SD) when O3 and NO2 were set at the 25th, 50th, and 75th percentiles, respectively. BKMR analysis did not identify statistically significant interactions among PM2.5, O3, and NO2. However, since the small sub-population might weaken the study power, additional studies (in larger sample size and other regions in the US) are in need to reinforce the current finding.

PMID:34293557 | DOI:10.1016/j.chemosphere.2021.131566

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Nevin Manimala Statistics

The effect of violence in childhood on school success factors in US children

Child Abuse Negl. 2021 Jul 19;120:105217. doi: 10.1016/j.chiabu.2021.105217. Online ahead of print.

ABSTRACT

BACKGROUND: A robust literature-base on adverse childhood experiences (ACEs) provides strong evidence on the relationships between social adversity in childhood and the health and well-being of individuals across the lifespan. One form of social adversity, exposure to violence in childhood, is not only harmful to a child’s health and well-being, but detrimental to their performance in school. Poor performance in school may affect educational attainment later in life and hinder a child’s upward social mobility. We focus on the impact of violence-related ACEs on school success factors to add new evidence on how violence in childhood affects a child’s educational progress.

OBJECTIVE: To examine the impact of violence-related ACEs on school success factors, including grade repetition, school absence, and school-home contact.

PARTICIPANTS AND SETTINGS: This study uses secondary data analysis of a nationally representative survey, the National Survey of Children’s Health (NSCH), to study a sample of non-institutionalized children aged 6-17 in the US (n = 35,122).

METHODS: We employed binary logistic regression and multinomial logistic regression using 95% confidence intervals to analyze the effect of violence in childhood on three school success factors, controlling for socio-demographic and health status characteristics.

RESULTS: We found that violence in childhood increases the likelihood of grade repetition (OR = 1.47, 95% CI, 1.12-1.92), school-home contact (OR = 2.20, 95% CI, 1.86-2.60), and school absence greater than one week (OR=1.4, 95%CI,1.08-2.00; OR=1.86, 95%CI, 1.36-2.60), controlling for socio-demographic and health status characteristics.

CONCLUSIONS: Violence in childhood has a statistically significant negative impact on each of the school success factors included in this study.

PMID:34293551 | DOI:10.1016/j.chiabu.2021.105217

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Nevin Manimala Statistics

Leveraging unsupervised image registration for discovery of landmark shape descriptor

Med Image Anal. 2021 Jul 9;73:102157. doi: 10.1016/j.media.2021.102157. Online ahead of print.

ABSTRACT

In current biological and medical research, statistical shape modeling (SSM) provides an essential framework for the characterization of anatomy/morphology. Such analysis is often driven by the identification of a relatively small number of geometrically consistent features found across the samples of a population. These features can subsequently provide information about the population shape variation. Dense correspondence models can provide ease of computation and yield an interpretable low-dimensional shape descriptor when followed by dimensionality reduction. However, automatic methods for obtaining such correspondences usually require image segmentation followed by significant preprocessing, which is taxing in terms of both computation as well as human resources. In many cases, the segmentation and subsequent processing require manual guidance and anatomy specific domain expertise. This paper proposes a self-supervised deep learning approach for discovering landmarks from images that can directly be used as a shape descriptor for subsequent analysis. We use landmark-driven image registration as the primary task to force the neural network to discover landmarks that register the images well. We also propose a regularization term that allows for robust optimization of the neural network and ensures that the landmarks uniformly span the image domain. The proposed method circumvents segmentation and preprocessing and directly produces a usable shape descriptor using just 2D or 3D images. In addition, we also propose two variants on the training loss function that allows for prior shape information to be integrated into the model. We apply this framework on several 2D and 3D datasets to obtain their shape descriptors. We analyze these shape descriptors in their efficacy of capturing shape information by performing different shape-driven applications depending on the data ranging from shape clustering to severity prediction to outcome diagnosis.

PMID:34293535 | DOI:10.1016/j.media.2021.102157

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Nevin Manimala Statistics

Envelope following response measurements in young veterans are consistent with noise-induced cochlear synaptopathy

Hear Res. 2021 Jul 10;408:108310. doi: 10.1016/j.heares.2021.108310. Online ahead of print.

ABSTRACT

Animal studies have demonstrated that noise exposure can lead to the loss of the synapses between the inner hair cells and their afferent auditory nerve fiber targets without impacting auditory thresholds. Although several non-invasive physiological measures appear to be sensitive to cochlear synaptopathy in animal models, including auditory brainstem response (ABR) wave I amplitude, the envelope following response (EFR), and the middle ear muscle reflex (MEMR), human studies of these measures in samples that are expected to vary in terms of the degree of noise-induced synaptopathy have resulted in mixed findings. One possible explanation for the differing results is that synaptopathy risk is lower for recreational noise exposure than for occupational or military noise exposure. The goal of this analysis was to determine if EFR magnitude and ABR wave I amplitude are reduced among young Veterans with a history of military noise exposure compared with non-Veteran controls with minimal noise exposure. EFRs and ABRs were obtained in a sample of young (19-35 years) Veterans and non-Veterans with normal audiograms and robust distortion product otoacoustic emissions (DPOAEs). The statistical analysis is consistent with a reduction in mean EFR magnitude and ABR wave I amplitude (at 90 dB peSPL) for Veterans with a significant history of noise exposure compared with non-Veteran controls. These findings are in agreement with previous ABR wave I amplitude findings in young Veterans and are consistent with animal models of noise-induced cochlear synaptopathy.

PMID:34293505 | DOI:10.1016/j.heares.2021.108310

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Nevin Manimala Statistics

The effect of esketamine in patients with treatment-resistant depression with and without comorbid anxiety symptoms or disorder

Depress Anxiety. 2021 Jul 22. doi: 10.1002/da.23193. Online ahead of print.

ABSTRACT

BACKGROUND: Comorbid anxiety is generally associated with poorer response to antidepressant treatment. This post hoc analysis explored the efficacy of esketamine plus an antidepressant in patients with treatment-resistant depression (TRD) with or without comorbid anxiety.

METHODS: TRANSFORM-2, a double-blind, flexible-dose, 4-week study (NCT02418585), randomized adults with TRD to placebo or esketamine nasal spray, each with a newly-initiated oral antidepressant. Comorbid anxiety was defined as clinically noteworthy anxiety symptoms (7-item Generalized Anxiety Disorder scale [GAD-7] score ≥10) at screening and baseline or comorbid anxiety disorder diagnosis at screening. Treatment effect based on change in Montgomery-Åsberg Depression Rating Scale (MADRS) total score, and response and remission were examined by presence/absence of comorbid anxiety using analysis of covariance and logistic regression models.

RESULTS: Approximately 72% (162/223) of patients had baseline comorbid anxiety. Esketamine-treated patients with and without anxiety demonstrated significant reductions in MADRS (mean [SD] change from baseline at day 28: -21.0 [12.51] and -22.7 [11.98], respectively). Higher rates of response and remission, and a significantly greater decrease in MADRS score at day 28 were observed compared to antidepressant/placebo, regardless of comorbid anxiety (with anxiety: difference in LS means [95% CI] -4.2 [-8.1, -0.3]; without anxiety: -7.5 [-13.7, -1.3]). There was no significant interaction of treatment and comorbid anxiety (p = .371). Notably, in the antidepressant/placebo group improvement was similar in those with and without comorbid anxiety.

CONCLUSION: Post hoc data support efficacy of esketamine plus an oral antidepressant in patients with TRD, regardless of comorbid anxiety.

PMID:34293233 | DOI:10.1002/da.23193

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Nevin Manimala Statistics

Associations of genetically predicted circulating insulin-like growth factor-1 and insulin-like growth factor binding protein-3 with bladder cancer risk

Mol Carcinog. 2021 Jul 22. doi: 10.1002/mc.23334. Online ahead of print.

ABSTRACT

Insulin-like growth factors (IGF) play important roles in carcinogenesis. The associations of circulating IGF-1 and insulin-like growth factor-binding protein-3 (IGFBP-3) with the risks of bladder cancer remain unclear. In this large case control study of 2011 bladder cancer cases and 2369 heathy controls, we assessed the associations of circulating IGF-1 and IGFBP-3 with bladder cancer risks using a Mendelian randomization approach, which uses genetic variants as instruments to study causal relationship between risk factors and diseases. We first constructed a weighted genetic risk score (GRS) predictive of circulating IGF-1 and IGFBP-3 using 413 genome-wide association study-identified single nucleotide polymorphisms (SNPs) associated with IGF-1 and four SNPs with IGFBP-3, respectively. We found that higher GRS for IGF-1 was associated with a significantly reduced bladder cancer risk (odds ratio [OR] = 0.66 per SD increase, 95% confidence interval [CI], 0.54-0.82, p < 0.001). We then used a summary statistics-based MR method, inverse-variance weighting (IVW), and found a similar risk estimate (OR = 0.67 per SD increase, 95% CI = 0.54-0.83, p < 0.001). When we categorized individuals into high and low IGF-1 groups using the median GRS value in the controls, the high GRS group had a 21% reduced bladder cancer risk (OR = 0.79, 95% CI = 0.70-0.89) compared to the low GRS group. Genetically predicted circulating IGFBP-3 was not associated with bladder cancer risk. In conclusion, our data demonstrated for the first time a strong inverse relationship between circulating IGF-1 level and bladder cancer risk.

PMID:34293213 | DOI:10.1002/mc.23334

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Nevin Manimala Statistics

Predicting mutant outcome by combining deep mutational scanning and machine learning

Proteins. 2021 Jul 22. doi: 10.1002/prot.26184. Online ahead of print.

ABSTRACT

MOTIVATION: Deep mutational scanning provides unprecedented wealth of quantitative data regarding the functional outcome of mutations in proteins. A single experiment may measure properties (e.g., structural stability) of numerous protein variants. Leveraging the experimental data to gain insights about unexplored regions of the mutational landscape is a major computational challenge. Such insights may facilitate further experimental work and accelerate the development of novel protein variants with beneficial therapeutic or industrially relevant properties. Here we present a novel, machine learning approach for the prediction of functional mutation outcome in the context of deep mutational screens.

RESULTS: Using sequence (one-hot) features of variants with known properties, as well as structural features derived from models thereof, we train predictive statistical models to estimate the unknown properties of other variants. The utility of the new computational scheme is demonstrated using five sets of mutational scanning data, denoted “targets”: (a) protease specificity of APPI (amyloid precursor protein inhibitor) variants; (b – d) three stability related properties of IGBPG (immunoglobulin G-binding β1 domain of streptococcal protein G) variants; and (e) fluorescence of GFP (green fluorescent protein) variants. Performance is measured by the overall correlation of the predicted and observed properties, and enrichment – the ability to predict the most potent variants and presumably guide further experiments. Despite the diversity of the targets the statistical models can generalize variant examples thereof and predict the properties of test variants with both single and multiple mutations. This article is protected by copyright. All rights reserved.

PMID:34293212 | DOI:10.1002/prot.26184

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Nevin Manimala Statistics

cVEMP and VAT for the diagnosis of vestibular migraine

Eur J Clin Invest. 2021 Jul 22:e13657. doi: 10.1111/eci.13657. Online ahead of print.

ABSTRACT

BACKGROUND: Although the diagnostic criteria of vestibular migraine (VM) have already been defined, various clinical manifestations of VM and the lack of pathognomonic biomarker result in high rate of misdiagnosis and mismanagement. A timely and accurate diagnosis tool for evaluation of VM is highly needed.

OBJECTIVE: The current study aims to investigate the potential feasibility of cervical vestibular evoked myogenic potential (cVEMP) and vestibular autorotation test (VAT) as a diagnosis tool for VM.

METHODS: A total of 211 subjects were recruited into the current study with all subjects meeting the inclusion and exclusion criteria. The subjects were divided into 3 groups: healthy control group, general migraine group, and VM group. Test of cVEMP and VAT were conducted in all the groups, and the generated data were statistically compared.

RESULTS: Compared with the other two groups, cVEMP P13-N23 amplitudes of VM patients showed a significant decline. Mean latency values of the VM group had no significant difference in comparison with other groups. Asymmetry ratios showed increased level in VM patients compared to the control groups, without significant difference. VAT results showed that all the horizontal gain, horizontal phase, vertical gain, and vertical phase differ from the other two groups to varying degrees at higher frequency.

CONCLUSION: cVEMP and VAT have potential usage in the assessment of VM and can serve as powerful tool in diagnosis of VM.

PMID:34293195 | DOI:10.1111/eci.13657

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Nevin Manimala Statistics

Clinical safety evaluation of contrast agents based on real-world evidence

J Clin Pharm Ther. 2021 Jul 22. doi: 10.1111/jcpt.13485. Online ahead of print.

ABSTRACT

WHAT IS KNOWN AND OBJECTIVE: This study was aimed at comparing the adverse drug reactions (ADRs) arising from the use of iodinated contrast medium (ICM) and gadolinium-based contrast media (GBCM), and to provide a basis for the clinical selection of contrast media.

METHODS: Retrospective data for ADR cases occurring from the use of ICM or GBCM during enhanced scanning in computed tomography and magnetic resonance imaging were collected between June/2013 and May/2020 from Wenling Hospital of Traditional Chinese Medicine. Chi-square tests were performed based on the characteristics of patients and the classification of contrast medium. Bonferroni correction was applied to the statistical analyses with multiple comparisons of proportions.

RESULTS: Among 27,328 patients who were subjected to enhanced CT scanning, 207 cases (0.76%) showed ICM-related ADRs. Among 16,381 patients who were subjected to enhanced MRI scanning, 25 cases (0.15%) showed ADRs related to GBCM. The incidence of ADR induced by GBCM was significantly lower than ICM-induced ADR (p < 0.01). There were no significant differences in the incidence among different types of ICM, including ioversol and iodixanol, as well as iodixanol from different manufacturers (p > 0.05). Interestingly, the ADR incidence of ICM seemed to be associated with gender, with a significantly higher incidence in females than in male patients, and it was also associated with the age, with a lower occurrence in older (>44 years) compared to younger patients.

WHAT IS NEW AND CONCLUSION: With respect to ADR incidence, the safety profile of ICM of different types and different manufacturers was found to be similar in clinical use, warranting no need of specifically choosing imported or more expensive products. While choosing contrast medium type for clinical use, attention should be paid to certain populations, especially to younger and female patients when the patients are about to undergo a contrast-enhanced examination.

PMID:34293194 | DOI:10.1111/jcpt.13485

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Nevin Manimala Statistics

Antibody status and cumulative incidence of SARS-CoV-2 infection among adults in three regions of France following the first lockdown and associated risk factors: a multicohort study

Int J Epidemiol. 2021 Jul 19:dyab110. doi: 10.1093/ije/dyab110. Online ahead of print.

ABSTRACT

BACKGROUND: We aimed to estimate the seropositivity to anti-SARS-CoV-2 antibodies in May-June 2020 after the first lockdown period in adults living in three regions in France and to identify the associated risk factors.

METHODS: Between 4 May 2020 and 23 June 2020, 16 000 participants in a survey on COVID-19 from an existing consortium of three general adult population cohorts living in the Ile-de-France (IDF) or Grand Est (GE) (two regions with high rate of COVID-19) or in the Nouvelle-Aquitaine (NA) (with a low rate) were randomly selected to take a dried-blood spot for anti-SARS-CoV-2 antibodies assessment with three different serological methods (ClinicalTrial Identifier #NCT04392388). The primary outcome was a positive anti-SARS-CoV-2 ELISA IgG result against the spike protein of the virus (ELISA-S). Estimates were adjusted using sampling weights and post-stratification methods. Multiple imputation was used to infer the cumulative incidence of SARS-CoV-2 infection with adjustments for imperfect tests accuracies.

RESULTS: The analysis included 14 628 participants, 983 with a positive ELISA-S. The weighted estimates of seropositivity and cumulative incidence were 10.0% [95% confidence interval (CI): 9.1%, 10.9%] and 11.4% (95% CI: 10.1%, 12.8%) in IDF, 9.0% (95% CI: 7.7%, 10.2%) and 9.8% (95% CI: 8.1%, 11.8%) in GE and 3.1% (95% CI: 2.4%, 3.7%) and 2.9% (95% CI: 2.1%, 3.8%) in NA, respectively. Seropositivity was higher in younger participants [odds ratio (OR) = 1.84 (95% CI: 1.79, 6.09) in <40 vs 50-60 years old and OR = 0.56 (95% CI: 0.42, 0.74) in ≥70 vs 50-60 years old)] and when at least one child or adolescent lived in the same household [OR = 1.30 (95% CI: 1.11, 1.53)] and was lower in smokers compared with non-smokers [OR = 0.71 (95% CI: 0.57, 0.49)].

CONCLUSIONS: Seropositivity to anti-SARS-CoV-2 antibodies in the French adult population was ≤10% after the first wave. Modifiable and non-modifiable risk factors were identified.

PMID:34293141 | DOI:10.1093/ije/dyab110