Tag: nevin manimala

PLoS One. 2021 Apr 19;16(4):e0249853. doi: 10.1371/journal.pone.0249853. eCollection 2021.

ABSTRACT

BACKGROUND: Knowledge, attitudes, perception, and preventative practices regarding coronavirus- 2019 (COVID-19) are crucial in its prevention and control. Several studies have noted that the majority of people in sub-Saharan African are noncompliant with proposed health and safety measures recommended by the World Health Organization (WHO) and respective country health departments. In most sub-Saharan African countries, noncompliance is attributable to ignorance and misinformation, thereby raising questions about people’s knowledge, attitudes, perception, and practices towards COVID-19 in these settings. This situation is particularly of concern for governments and public health experts. Thus, this scoping review is aimed at mapping evidence on the knowledge, attitudes, perceptions, and preventive practices (KAP) towards COVID-19 in sub-Saharan Africa (SSA).

METHODS: Systematic searches of relevant articles were performed using databases such as the EBSCOhost, PubMed, Science Direct, Google Scholar, the WHO library and grey literature. Arksey and O’Malley’s framework guided the study. The risk of bias for included primary studies was assessed using the Mixed Method Appraisal Tool (MMAT). NVIVO version 10 was used to analyse the data and a thematic content analysis was used to present the review’s narrative account.

RESULTS: A total of 3037 eligible studies were identified after the database search. Only 28 studies met the inclusion criteria after full article screening and were included for data extraction. Studies included populations from the following SSA countries: Ethiopia, Nigeria, Cameroon, Uganda, Rwanda, Ghana, Democratic Republic of Congo, Sudan, and Sierra Leone. All the included studies showed evidence of knowledge related to COVID-19. Eleven studies showed that participants had a positive attitude towards COVID-19, and fifteen studies showed that participants had good practices towards COVID-19.

CONCLUSIONS: Most of the participants had adequate knowledge related to COVID-19. Despite adequate knowledge, the attitude was not always positive, thereby necessitating further education to convey the importance of forming a positive attitude and continuous preventive practice towards reducing contraction and transmission of COVID-19.

PMID:33872330 | DOI:10.1371/journal.pone.0249853

PLoS Pathog. 2021 Apr 19;17(4):e1009522. doi: 10.1371/journal.ppat.1009522. Online ahead of print.

ABSTRACT

Although HIV infection inhibits interferon responses in its target cells in vitro, interferon signatures can be detected in vivo soon after sexual transmission, mainly attributed to plasmacytoid dendritic cells (pDCs). In this study, we examined the physiological contributions of pDCs to early HIV acquisition using coculture models of pDCs with myeloid DCs, macrophages and the resting central, transitional and effector memory CD4 T cell subsets. pDCs impacted infection in a cell-specific manner. In myeloid cells, HIV infection was decreased via antiviral effects, cell maturation and downregulation of CCR5 expression. In contrast, in resting memory CD4 T cells, pDCs induced a subset-specific increase in intracellular HIV p24 protein expression without any activation or increase in CCR5 expression, as measured by flow cytometry. This increase was due to reactivation rather than enhanced viral spread, as blocking HIV entry via CCR5 did not alter the increased intracellular p24 expression. Furthermore, the load and proportion of cells expressing HIV DNA were restricted in the presence of pDCs while reverse transcriptase and p24 ELISA assays showed no increase in particle associated reverse transcriptase or extracellular p24 production. In addition, PDCs also markedly induced the expression of CD69 on infected CD4 T cells and other markers of CD4 T cell tissue retention. These phenotypic changes showed marked parallels with resident memory CD4 T cells isolated from anogenital tissue using enzymatic digestion. Production of IFNα by pDCs was the main driving factor for all these results. Thus, pDCs may reduce HIV spread during initial mucosal acquisition by inhibiting replication in myeloid cells while reactivating latent virus in resting memory CD4 T cells and retaining them for immune clearance.

PMID:33872331 | DOI:10.1371/journal.ppat.1009522

PLoS One. 2021 Apr 19;16(4):e0248791. doi: 10.1371/journal.pone.0248791. eCollection 2021.

ABSTRACT

BACKGROUND AND PURPOSE: The role of copy number variation (CNV) variation in stroke susceptibility and outcome has yet to be explored. The Copy Number Variation and Stroke (CaNVAS) Risk and Outcome study addresses this knowledge gap.

METHODS: Over 24,500 well-phenotyped IS cases, including IS subtypes, and over 43,500 controls have been identified, all with readily available genotyping on GWAS and exome arrays, with case measures of stroke outcome. To evaluate CNV-associated stroke risk and stroke outcome it is planned to: 1) perform Risk Discovery using several analytic approaches to identify CNVs that are associated with the risk of IS and its subtypes, across the age-, sex- and ethnicity-spectrums; 2) perform Risk Replication and Extension to determine whether the identified stroke-associated CNVs replicate in other ethnically diverse datasets and use biomarker data (e.g. methylation, proteomic, RNA, miRNA, etc.) to evaluate how the identified CNVs exert their effects on stroke risk, and lastly; 3) perform outcome-based Replication and Extension analyses of recent findings demonstrating an inverse relationship between CNV burden and stroke outcome at 3 months (mRS), and then determine the key CNV drivers responsible for these associations using existing biomarker data.

RESULTS: The results of an initial CNV evaluation of 50 samples from each participating dataset are presented demonstrating that the existing GWAS and exome chip data are excellent for the planned CNV analyses. Further, some samples will require additional considerations for analysis, however such samples can readily be identified, as demonstrated by a sample demonstrating clonal mosaicism.

CONCLUSION: The CaNVAS study will cost-effectively leverage the numerous advantages of using existing case-control data sets, exploring the relationships between CNV and IS and its subtypes, and outcome at 3 months, in both men and women, in those of African and European-Caucasian descent, this, across the entire adult-age spectrum.

PMID:33872305 | DOI:10.1371/journal.pone.0248791

PLoS Genet. 2021 Apr 19;17(4):e1009455. doi: 10.1371/journal.pgen.1009455. Online ahead of print.

ABSTRACT

Expression quantitative trait loci (eQTL) studies are used to understand the regulatory function of non-coding genome-wide association study (GWAS) risk loci, but colocalization alone does not demonstrate a causal relationship of gene expression affecting a trait. Evidence for mediation, that perturbation of gene expression in a given tissue or developmental context will induce a change in the downstream GWAS trait, can be provided by two-sample Mendelian Randomization (MR). Here, we introduce a new statistical method, MRLocus, for Bayesian estimation of the gene-to-trait effect from eQTL and GWAS summary data for loci with evidence of allelic heterogeneity, that is, containing multiple causal variants. MRLocus makes use of a colocalization step applied to each nearly-LD-independent eQTL, followed by an MR analysis step across eQTLs. Additionally, our method involves estimation of the extent of allelic heterogeneity through a dispersion parameter, indicating variable mediation effects from each individual eQTL on the downstream trait. Our method is evaluated against other state-of-the-art methods for estimation of the gene-to-trait mediation effect, using an existing simulation framework. In simulation, MRLocus often has the highest accuracy among competing methods, and in each case provides more accurate estimation of uncertainty as assessed through interval coverage. MRLocus is then applied to five candidate causal genes for mediation of particular GWAS traits, where gene-to-trait effects are concordant with those previously reported. We find that MRLocus’s estimation of the causal effect across eQTLs within a locus provides useful information for determining how perturbation of gene expression or individual regulatory elements will affect downstream traits. The MRLocus method is implemented as an R package available at https://mikelove.github.io/mrlocus.

PMID:33872308 | DOI:10.1371/journal.pgen.1009455

PLoS Comput Biol. 2021 Apr 19;17(4):e1008856. doi: 10.1371/journal.pcbi.1008856. Online ahead of print.

ABSTRACT

Convolutional Neural Networks (CNNs) are statistical models suited for learning complex visual patterns. In the context of Species Distribution Models (SDM) and in line with predictions of landscape ecology and island biogeography, CNN could grasp how local landscape structure affects prediction of species occurrence in SDMs. The prediction can thus reflect the signatures of entangled ecological processes. Although previous machine-learning based SDMs can learn complex influences of environmental predictors, they cannot acknowledge the influence of environmental structure in local landscapes (hence denoted “punctual models”). In this study, we applied CNNs to a large dataset of plant occurrences in France (GBIF), on a large taxonomical scale, to predict ranked relative probability of species (by joint learning) to any geographical position. We examined the way local environmental landscapes improve prediction by performing alternative CNN models deprived of information on landscape heterogeneity and structure (“ablation experiments”). We found that the landscape structure around location crucially contributed to improve predictive performance of CNN-SDMs. CNN models can classify the predicted distributions of many species, as other joint modelling approaches, but they further prove efficient in identifying the influence of local environmental landscapes. CNN can then represent signatures of spatially structured environmental drivers. The prediction gain is noticeable for rare species, which open promising perspectives for biodiversity monitoring and conservation strategies. Therefore, the approach is of both theoretical and practical interest. We discuss the way to test hypotheses on the patterns learnt by CNN, which should be essential for further interpretation of the ecological processes at play.

PMID:33872302 | DOI:10.1371/journal.pcbi.1008856

Clin Spine Surg. 2021 Apr 19. doi: 10.1097/BSD.0000000000001184. Online ahead of print.

ABSTRACT

STUDY DESIGN: Retrospective review of spine surgery malpractice cases.

OBJECTIVES: The aim was to compare medical malpractice outcomes among different types of spine surgery and identify predictors of litigation outcomes.

SUMMARY OF BACKGROUND DATA: Spine surgery is highly litigious in the United States with data suggesting favorable outcomes for defendant surgeons. However, factor specific data and explanations for plaintiff verdicts are lacking.

METHODS: Westlaw legal database was queried for spine surgery malpractice outcomes from 2010 to 2019. Clinical data, reasons for litigation, and legal outcomes were tabulated. Statistical analysis was performed to identify factors associated with litigation outcomes.

RESULTS: A total of 257 cases were identified for inclusion. There were 98 noninstrumented and 148 instrumented cases; 110 single-level and 99 multilevel; 83 decompressions, 95 decompression and fusions, and 47 fusion only. In all, 182 (71%) resulted in a defendant verdict, 44 (17%) plaintiff verdict, and 31 (12%) settlement. Plaintiff verdicts resulted in payouts of $2.03 million, while settlements resulted in $1.11 million (P=0.34). Common reasons for litigation were intraoperative error, hardware complication, and improper postoperative management. Cases were more likely to result for the plaintiff if postoperative cauda equina syndrome (55% vs. 26%, P<0.01), a surgical site infection (46% vs. 27%, P=0.03), or other catastrophic injury (40% vs. 26%, P=0.03) occurred. Higher monetary awards were associated with multi versus single-level (median: $2.61 vs. $0.92 million, P=0.01), improper postoperative management cited (median: $2.29 vs. $1.12 million, P=0.04), and permanent neurological deficits ($2.29 vs. $0.78 million, P<0.01). Plaintiff payouts were more likely if defendant specialty was neurosurgery versus orthopedic surgery (33% vs. 18%, P=0.01).

CONCLUSIONS: Spine surgery is a litigious field with multiple factors associated with outcomes. Efforts to reduce intraoperative errors and complications may improve patient care and decrease the risk of litigation.

PMID:33872221 | DOI:10.1097/BSD.0000000000001184

PLoS Comput Biol. 2021 Apr 19;17(4):e1008054. doi: 10.1371/journal.pcbi.1008054. Online ahead of print.

ABSTRACT

Transfer entropy (TE) is a widely used measure of directed information flows in a number of domains including neuroscience. Many real-world time series for which we are interested in information flows come in the form of (near) instantaneous events occurring over time. Examples include the spiking of biological neurons, trades on stock markets and posts to social media, amongst myriad other systems involving events in continuous time throughout the natural and social sciences. However, there exist severe limitations to the current approach to TE estimation on such event-based data via discretising the time series into time bins: it is not consistent, has high bias, converges slowly and cannot simultaneously capture relationships that occur with very fine time precision as well as those that occur over long time intervals. Building on recent work which derived a theoretical framework for TE in continuous time, we present an estimation framework for TE on event-based data and develop a k-nearest-neighbours estimator within this framework. This estimator is provably consistent, has favourable bias properties and converges orders of magnitude more quickly than the current state-of-the-art in discrete-time estimation on synthetic examples. We demonstrate failures of the traditionally-used source-time-shift method for null surrogate generation. In order to overcome these failures, we develop a local permutation scheme for generating surrogate time series conforming to the appropriate null hypothesis in order to test for the statistical significance of the TE and, as such, test for the conditional independence between the history of one point process and the updates of another. Our approach is shown to be capable of correctly rejecting or accepting the null hypothesis of conditional independence even in the presence of strong pairwise time-directed correlations. This capacity to accurately test for conditional independence is further demonstrated on models of a spiking neural circuit inspired by the pyloric circuit of the crustacean stomatogastric ganglion, succeeding where previous related estimators have failed.

PMID:33872296 | DOI:10.1371/journal.pcbi.1008054

Int J Radiat Biol. 2021 Apr 19:1-5. doi: 10.1080/09553002.2021.1918972. Online ahead of print.

NO ABSTRACT

PMID:33872125 | DOI:10.1080/09553002.2021.1918972

IEEE Trans Biomed Eng. 2021 Apr 19;PP. doi: 10.1109/TBME.2021.3073833. Online ahead of print.

ABSTRACT

OBJECTIVE: While understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience, existing methods either do not consider the inherent point-process nature of spike trains or are based on parametric assumptions. This work presents an information-theoretic framework for the model-free, continuous-time estimation of both undirected (symmetric) and directed (Granger-causal) interactions between spike trains.

METHODS: The framework computes the mutual information rate (MIR) and the transfer entropy rate (TER) for two point processes X and Y, showing that the MIR between X and Y can be decomposed as the sum of the TER along the directions X Y and Y X. We present theoretical expressions and introduce strategies to estimate efficiently the two measures through nearest neighbor statistics.

RESULTS: Using simulations of independent and coupled spike train processes, we show the accuracy of MIR and TER to assess interactions even for weakly coupled and short realizations, and prove the superiority of continuous-time estimation over the standard discrete-time approach. In a real data scenario of recordings from in-vitro preparations of spontaneously-growing cultures of cortical neurons, we show the ability of MIR and TER to describe how the functional organization of the networks of spike train interactions emerges through maturation of the neuronal cultures.

CONCLUSION AND SIGNIFICANCE: the proposed framework provides principled measures to assess undirected and directed spike train interactions with more efficiency and flexibility than previous discrete-time or parametric approaches, opening new perspectives for the analysis of point-process data in neuroscience and many other fields.

PMID:33872139 | DOI:10.1109/TBME.2021.3073833

J Med Internet Res. 2021 Apr 17. doi: 10.2196/28973. Online ahead of print.

ABSTRACT

BACKGROUND: On the 30th of January 2020, the WHO’s Emergency Committee declared the rapid worldwide spread of COVID-19, a global health emergency. Since then tireless efforts have been made to mitigate the spread of the disease and its impact, mostly relying on non-pharmaceutical interventions. By December 2020, the safety and efficacy of the first COVID-19 vaccines have been demonstrated. Lately, the large social media platform Twitter has been utilized by medical research for the analysis of important public health topics, such as the publics´ perception on antibiotic use and misuse and human papillomavirus vaccination. Analysis of Twitter-generated data can be further facilitated by utilizing the inbuilt, anonymous, polling tool, in order to gain insight into public health issues with rapid feedback on an international scale. During the fast-paced course of the COVID-19 pandemic the Twitter polling system offers a viable method to gain rapid large-scale international public health insights on highly relevant and timely SARS-CoV-2 related topics.

OBJECTIVE: The purpose of this study was to understand the public’s perception on the safety and acceptance of the COVID-19 vaccines in real-time through Twitter polls.

METHODS: Two Twitter polls were developed to explore the public’s views on the currently available COVID-19 vaccines. The surveys were pinned to the Digital Health and Patient Safety Platform Twitter timeline for one week in mid-February 2021 and Twitter users and influencers were asked to participate and re-tweet the polls to reach the largest possible audience.

RESULTS: Adequacy of COVID-19 vaccine safety (of currently available vaccines; Poll 1) was agreed upon by 1,579 out of 3,439 (45.9%) Twitter users, in contrast to almost as many Twitter users (n=1,434/3,439; 41.7%) being unsure about their safety. Only 5.2% (179/3,439) rated the currently available COVID-19 vaccines as generally unsafe. Poll 2, addressing the question whether users would get vaccinated, was answered affirmatively by 82.8% (2,862/3,457) and only 8% (277/3,457) categorically rejected vaccination at the time of polling.

CONCLUSIONS: In contrast to the perceived high level of uncertainty about the safety of currently available COVID-19 vaccines, there is an elevated willingness to get vaccinated among this study sample. Since people’s perceptions and views are strongly influenced by the (social) media, snapshots provided from these media represent a static image of a moving target. Thus, the results of this work need to be followed by long-term surveys in an effort to keep validity. This is especially relevant under the circumstances of a fast-paced pandemic course, in order not to miss sudden rises of hesitancy, which may have detrimental effects on the pandemics course.

PMID:33872185 | DOI:10.2196/28973