Category: Nevin Manimala

PLoS One. 2021 Jun 28;16(6):e0253800. doi: 10.1371/journal.pone.0253800. eCollection 2021.

ABSTRACT

BACKGROUND: The COVID-19 pandemic and control measures adopted by countries globally can lead to stress and anxiety. Investigating the coping strategies to this unprecedented crisis is essential to guide mental health intervention and public health policy. This study examined how people are coping with the COVID-19 crisis in Ghana and identify factors influencing it.

METHODS: This study was part of a multinational online cross-sectional survey on Personal and Family Coping with COVID-19 in the Global South. The study population included adults, ≥18 years and residents in Ghana. Respondents were recruited through different platforms, including social media and phone calls. The questionnaire was composed of different psychometrically validated instruments with coping as the outcome variable measured on the ordinal scale with 3 levels, namely, Not well or worse, Neutral, and Well or better. An ordinal logistic regression model using proportional odds assumption was then applied.

RESULTS: A total of 811 responses were included in the analysis with 45.2% describing their coping level as well/better, 42.4% as neither worse nor better and 12.4% as worse/not well. Many respondents (46.9%) were between 25-34 years, 50.1% were males while 79.2% lived in urban Ghana. Having pre-existing conditions increased the chances of not coping well (aOR = 1.86, 95%CI: 1.15-3.01). Not being concerned about supporting the family financially (aOR = 1.67, 95%CI: 1.06-2.68) or having the feeling that life is better during the pandemic (aOR = 2.37, 95%CI: 1.26-4.62) increased chances of coping well. Praying (aOR: 0.62, 95%CI: 0.43-0.90) or sleeping (aOR: 0.55, 95%CI: 0.34-0.89) more during the pandemic than before reduces coping.

CONCLUSION: In Ghana, during the COVID-19 pandemic, financial security and optimism about the disease increase one’s chances of coping well while having pre-existing medical conditions, praying and sleeping more during the pandemic than before reduces one’s chances of coping well. These findings should be considered in planning mental health and public health intervention/policy.

PMID:34181679 | DOI:10.1371/journal.pone.0253800

PLoS One. 2021 Jun 28;16(6):e0253341. doi: 10.1371/journal.pone.0253341. eCollection 2021.

ABSTRACT

BACKGROUND: The proposed sequential and combinatorial algorithm, suggested as a standard tool for assessing, exploring, and reporting heterogeneity in the meta-analysis, is useful but time-consuming particularly when the number of included studies is large. Metaplot is a novel graphical approach that facilitates performing sensitivity analysis to distinguish the source of substantial heterogeneity across studies with ease and speed.

METHOD: Metaplot is a Stata module based on Stata’s commands, known informally as “ado”. Metaplot presents a two-way (x, y) plot in which the x-axis represents the study codes and the y-axis represents the values of I2 statistics excluding one study at a time (n-1 studies). Metaplot also produces a table in the ‘Results window’ of the Stata software including details such as I2 and χ2 statistics and their P-values omitting one study in each turn.

RESULTS: Metaplot allows rapid identification of studies that have a disproportionate impact on heterogeneity across studies, and communicates to what extent omission of that study may reduce the overall heterogeneity based on the I2 and χ2 statistics. Metaplot has no limitations regarding the number of studies or types of outcome data (binomial or continuous data).

CONCLUSIONS: Metaplot is a simple graphical approach that gives a quick and easy identification of the studies having substantial influences on overall heterogeneity at a glance.

PMID:34181682 | DOI:10.1371/journal.pone.0253341

PLoS One. 2021 Jun 28;16(6):e0253381. doi: 10.1371/journal.pone.0253381. eCollection 2021.

ABSTRACT

During the COVID-19 pandemic, governments globally had to impose severe contact restriction measures and social mobility limitations in order to limit the exposure of the population to COVID-19. These public health policy decisions were informed by statistical models for infection rates in national populations. In this work, we are interested in modelling the temporal evolution of national-level infection counts for the United Kingdom (UK-Wales, England, Scotland), Germany (GM), Italy (IT), Spain (SP), Japan (JP), Australia (AU) and the United States (US). We model the national-level infection counts for the period January 2020 to January 2021, thus covering both the pre- and post-vaccine roll-out periods, in order to better understand the most reliable model structure for the COVID-19 epidemic growth curve. We achieve this by exploring a variety of stochastic population growth models and comparing their calibration, with respect to in-sample fitting and out-of-sample forecasting, both with and without exposure adjustment, to the most widely used and reported growth model, the Gompertz population model, often referred to in the public health policy discourse during the COVID-19 pandemic. Model risk as we explore it in this work manifests in the inability to adequately capture the behaviour of the disease progression growth rate curve. Therefore, our concept of model risk is formed relative to the standard reference Gompertz model used by decision-makers, and then we can characterise model risk mathematically as having two components: the dispersion of the observation distribution, and the structure of the intensity function over time for cumulative counts of new infections daily (i.e. the force of infection) attributed directly to the COVID-19 pandemic. We also explore how to incorporate in these population models the effect that governmental interventions have had on the number of infected cases. This is achieved through the development of an exposure adjustment to the force of infection comprised of a purpose-built sentiment index, which we construct from various authoritative public health news reporting. The news reporting media we employed were the New York Times, the Guardian, the Telegraph, Reuters global blog, as well as national and international health authorities: the European Centre for Disease Prevention and Control, the United Nations Economic Commission for Europe, the United States Centres for Disease Control and Prevention, and the World Health Organisation. We find that exposure adjustments that incorporate sentiment are better able to calibrate to early stages of infection spread in all countries under study.

PMID:34181686 | DOI:10.1371/journal.pone.0253381

PLoS One. 2021 Jun 28;16(6):e0253117. doi: 10.1371/journal.pone.0253117. eCollection 2021.

ABSTRACT

The substantial improvement in the efficiency of switching filters, intended for the removal of impulsive noise within color images is described. Numerous noisy pixel detection and replacement techniques are evaluated, where the filtering performance for color images and subsequent results are assessed using statistical reasoning. Denoising efficiency for the applied detection and interpolation techniques are assessed when the location of corrupted pixels are identified by noisy pixel detection algorithms and also in the scenario when they are already known. The results show that improvement in objective quality measures can be achieved by using more robust detection techniques, combined with novel methods of corrupted pixel restoration. A significant increase in the image denoising performance is achieved for both pixel detection and interpolation, surpassing current filtering methods especially via the application of a convolutional network. The interpolation techniques used in the image inpainting methods also significantly increased the efficiency of impulsive noise removal.

PMID:34181667 | DOI:10.1371/journal.pone.0253117

PLoS One. 2021 Jun 28;16(6):e0253150. doi: 10.1371/journal.pone.0253150. eCollection 2021.

ABSTRACT

BACKGROUND: To investigate the application of ultrasound elastography in monitoring the effects of the transforming growth factor (TGF)-β1 signaling pathway-targeted combination therapy for hepatic fibrosis.

METHODS: 1. Short hairpin RNA (shRNA) constructs targeted towards TβR1 were designed, synthesized, and packaged using an adeno-associated virus (AAV), and the effective target shRNA was selected based on transfection results. 2. Fifty rats were randomly allocated (n = 10 per group) to the (A) control group, (B) model group, (C) 0-week therapy group, (D) 4-week therapy group, and (E) combination therapy group. At weeks 2, 4, 6, 8, 10, and 12, acoustic radiation force impulse (ARFI) elastography was used to measure the liver stiffness, inner diameter of the portal vein diameter, and blood velocity; radio frequency ultrasound imaging was used to measure the abdominal aortic elasticity parameter and pulse wave velocity (PWV) of the rats. 3. At week 12, portal vein puncture was performed to measure the portal venous pressure, and rat liver specimens were obtained for the pathological measurement of the degree of hepatic fibrosis.

RESULTS: 1. An shRNA interference sequence targeted towards TβR1 was successfully designed, screened, and packaged using an AAV, and small-animal imaging results indicated expression of the specific shRNA in the liver. 2. At week 12, the ultrasound elastography results were significantly different between the experimental groups and the control group (p < 0.01); among the experimental groups, differences were significant between the therapy groups and the model group (p < 0.01). For groups C and E, the therapeutic effects on hepatic fibrosis in rats were significant, with the pathological results indicating a significant reduction in the degree of hepatic fibrosis (p < 0.01). The therapeutic effectiveness of group D was less than that of group C (p < 0.05). Significant differences existed between the portal venous pressure of the experimental groups and of the control group (p < 0.01). For the abdominal aortic elasticity parameter measured by radio frequency ultrasound imaging, differences existed between the values obtained from the experimental groups and from that of the control group (p < 0.05), while statistically significant differences were not found among the various experimental groups. 3. Continuous ultrasound examination results indicated that the elasticity value of group A was significantly different from those of the other groups after 2 weeks of model establishment (p < 0.01); after 6 weeks, the elasticity values of groups C and E were significantly different compared with those of groups B and D (p < 0.01). For the abdominal aortic elasticity parameter and pulse wave velocity (PWV), there were no significant differences among the various groups (p > 0.05).

CONCLUSION: CCl4-induced hepatic fibrosis can be treated through shRNA silencing of TβR1. Ultrasound ARFI elastography is superior to external force-assisted elastography as it can reflect the degree of fibrosis in moderate to severe hepatic fibrosis and the variations in the degree of fibrosis after treatment. Portal venous pressure was positively correlated with the degree of fibrosis; with early combination therapy, both the degree of fibrosis and portal venous pressure could be effectively reduced.

PMID:34181670 | DOI:10.1371/journal.pone.0253150

PLoS One. 2021 Jun 28;16(6):e0253349. doi: 10.1371/journal.pone.0253349. eCollection 2021.

ABSTRACT

Beta regressions are commonly used with responses that assume values in the standard unit interval, such as rates, proportions and concentration indices. Hypothesis testing inferences on the model parameters are typically performed using the likelihood ratio test. It delivers accurate inferences when the sample size is large, but can otherwise lead to unreliable conclusions. It is thus important to develop alternative tests with superior finite sample behavior. We derive the Bartlett correction to the likelihood ratio test under the more general formulation of the beta regression model, i.e. under varying precision. The model contains two submodels, one for the mean response and a separate one for the precision parameter. Our interest lies in performing testing inferences on the parameters that index both submodels. We use three Bartlett-corrected likelihood ratio test statistics that are expected to yield superior performance when the sample size is small. We present Monte Carlo simulation evidence on the finite sample behavior of the Bartlett-corrected tests relative to the standard likelihood ratio test and to two improved tests that are based on an alternative approach. The numerical evidence shows that one of the Bartlett-corrected typically delivers accurate inferences even when the sample is quite small. An empirical application related to behavioral biometrics is presented and discussed.

PMID:34181678 | DOI:10.1371/journal.pone.0253349

PLoS Negl Trop Dis. 2021 Jun 28;15(6):e0009457. doi: 10.1371/journal.pntd.0009457. Online ahead of print.

ABSTRACT

BACKGROUND: Serological surveys with multiplex bead assays can be used to assess seroprevalence to multiple pathogens simultaneously. However, multiple methods have been used to generate cut-off values for seropositivity and these may lead to inconsistent interpretation of results. A literature review was conducted to describe the methods used to determine cut-off values for data generated by multiplex bead assays.

METHODOLOGY/PRINCIPAL FINDINGS: A search was conducted in PubMed that that included articles published from January 2010 to January 2020, and 291 relevant articles were identified that included the terms “serology”, “cut-offs”, and “multiplex bead assays”. After application of exclusion of articles not relevant to neglected tropical diseases (NTD), vaccine preventable diseases (VPD), or malaria, 55 articles were examined based on their relevance to NTD or VPD. The most frequently applied approaches to determine seropositivity included the use of presumed unexposed populations, mixture models, receiver operating curves (ROC), and international standards. Other methods included the use of quantiles, pre-exposed endemic cohorts, and visual inflection points.

CONCLUSIONS/SIGNIFICANCE: For disease control programmes, seropositivity is a practical and easily interpretable health metric but determining appropriate cut-offs for positivity can be challenging. Considerations for optimal cut-off approaches should include factors such as methods recommended by previous research, transmission dynamics, and the immunological backgrounds of the population. In the absence of international standards for estimating seropositivity in a population, the use of consistent methods that align with individual disease epidemiological data will improve comparability between settings and enable the assessment of changes over time.

PMID:34181665 | DOI:10.1371/journal.pntd.0009457

PLoS Comput Biol. 2021 Jun 28;17(6):e1009119. doi: 10.1371/journal.pcbi.1009119. Online ahead of print.

ABSTRACT

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or “mutational signatures”. Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features.

PMID:34181655 | DOI:10.1371/journal.pcbi.1009119

COPD. 2021 Jun 28:1-6. doi: 10.1080/15412555.2021.1941834. Online ahead of print.

ABSTRACT

Since the outbreak of the SARS-CoV-2 pandemic in 2020, many governments have been imposing confinement and physical distancing measures. No data exist on the effects of lockdowns on the health status of patients affected by chronic pathologies, specifically those with Chronic Obstructive Pulmonary Disease (COPD). Our study aims to establish variations across the psychological and cognitive profile of patients during the isolation period in Italy, in a cohort of patients affected by COPD, between February and May 2020. Forty patients with established COPD were comprehensively evaluated by geriatric multidimensional assessment before the spread of the epidemic in Italy, and submitted to a second evaluation during the subsequent lockdown. We assessed functional ability, basic and instrumental Activities of Daily Living (ADL and IADL), cognition and mood status. We compared the scores obtained at baseline against those obtained during the pandemic, and used mean differences for correlation with major clinical and functional indexes. The score differences from MMSE, ADL and IADL were statistically significant. Such differences were correlated to the presence of a caregiver and to the total number of family members living together. Remarkably, the loneliness dimension, more than the restrictions themselves, seemed to represent the major determinant of altered health status and depressed psycho-cognitive profile in our population. Also remarkably, we detected no correlation between the score variation and the respiratory function indexes of disease severity. The isolation measures adopted during the SARS-CoV-2 pandemic have triggered the classic clinical string associated to geriatric isolation, which leads to a deterioration of cognitive functions, independence and frailty levels in a population affected by a chronic degenerative disease, such as COPD. If considered from a multidimensional geriatric point of view, the individual benefit of isolation measures could be small or non-existent.

PMID:34180766 | DOI:10.1080/15412555.2021.1941834

J Abnorm Psychol. 2021 May;130(4):377-387. doi: 10.1037/abn0000666.

ABSTRACT

The objective of this study was to characterize the temporal sequencing of symptom emergence for anorexia nervosa (AN), bulimia nervosa (BN), binge eating disorder (BED), and purging disorder (PD), as well as to test whether prodromal symptoms increase risk for future onset of each type of eating disorder and compare the predictive effects to those of established risk factors. Data from four prevention trials that targeted high-risk young women with body image concerns (N = 1,952; Mage = 19.7, SD = 5.7) and collected annual diagnostic interview data over 3-year follow-up were combined to address these aims. Regarding behavioral symptoms, compensatory weight control behaviors typically emerged first for AN, BN, and PD, whereas binge eating typically emerged first for BED. Regarding cognitive symptoms, for AN, weight/shape overvaluation typically emerged first, whereas for BN, BED, and PD, overvaluation typically emerged simultaneously with feeling fat and fear of weight gain. Binge eating, compensatory behaviors, weight/shape overvaluation, fear of weight gain, and feeling fat predicted BN, BED, and PD onset, whereas weight/shape overvaluation, fear of weight gain, and lower than expected body mass index predicted AN onset. Predictive effects of prodromal symptoms were similar in magnitude to those of established risk factors: Collectively, prodromal symptoms and risk factors predicted onset of specific eating disorders with 67-83% accuracy. Results suggest that compensatory weight control behaviors and cognitive symptoms are likely to emerge before binge eating in the various eating disorders and that offering indicated prevention programs to youth with prodromal symptoms may be an effective way to prevent eating disorders. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

PMID:34180702 | DOI:10.1037/abn0000666