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1.
Mol Syst Biol ; 17(9): e10243, 2021 09.
Artigo em Inglês | MEDLINE | ID: mdl-34487431

RESUMO

Systems serology provides a broad view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. These studies contain structured biophysical profiling across disease-relevant antigen targets, alongside additional measurements made for single antigens or in an antigen-generic manner. Identifying patterns in these measurements helps guide vaccine and therapeutic antibody development, improve our understanding of diseases, and discover conserved regulatory mechanisms. Here, we report that coupled matrix-tensor factorization (CMTF) can reduce these data into consistent patterns by recognizing the intrinsic structure of these data. We use measurements from two previous studies of HIV- and SARS-CoV-2-infected subjects as examples. CMTF outperforms standard methods like principal components analysis in the extent of data reduction while maintaining equivalent prediction of immune functional responses and disease status. Under CMTF, model interpretation improves through effective data reduction, separation of the Fc and antigen-binding effects, and recognition of consistent patterns across individual measurements. Data reduction also helps make prediction models more replicable. Therefore, we propose that CMTF is an effective general strategy for data exploration in systems serology.


Assuntos
Sorodiagnóstico da AIDS , Teste Sorológico para COVID-19 , COVID-19/imunologia , Interpretação Estatística de Dados , Infecções por HIV/imunologia , Sorodiagnóstico da AIDS/métodos , Sorodiagnóstico da AIDS/estatística & dados numéricos , Anticorpos Antivirais/sangue , Anticorpos Antivirais/metabolismo , Teste Sorológico para COVID-19/métodos , Teste Sorológico para COVID-19/estatística & dados numéricos , Humanos , Imunidade Humoral , Células Matadoras Naturais/imunologia , Modelos Logísticos , Receptores Fc/imunologia , Receptores de IgG/imunologia
2.
Sci Rep ; 11(1): 17744, 2021 09 07.
Artigo em Inglês | MEDLINE | ID: mdl-34493760

RESUMO

A simple method is utilised to study and compare COVID-19 infection dynamics between countries based on curve fitting to publicly shared data of confirmed COVID-19 infections. The method was tested using data from 80 countries from 6 continents. We found that Johnson cumulative density functions (CDFs) were extremely well fitted to the data (R2 > 0.99) and that Johnson CDFs were much better fitted to the tails of the data than either the commonly used normal or lognormal CDFs. Fitted Johnson CDFs can be used to obtain basic parameters of the infection wave, such as the percentage of the population infected during an infection wave, the days of the start, peak and end of the infection wave, and the duration of the wave's increase and decrease. These parameters can be easily interpreted biologically and used both for describing infection wave dynamics and in further statistical analysis. The usefulness of the parameters obtained was analysed with respect to the relation between the gross domestic product (GDP) per capita, the population density, the percentage of the population infected during an infection wave, the starting day and the duration of the infection wave in the 80 countries. We found that all the above parameters were significantly associated with GDP per capita, but only the percentage of the population infected was significantly associated with population density. If used with caution, this method has a limited ability to predict the future trajectory and parameters of an ongoing infection wave.


Assuntos
COVID-19/epidemiologia , Previsões/métodos , Modelos Estatísticos , Pandemias/estatística & dados numéricos , Interpretação Estatística de Dados , Estudos de Viabilidade , Carga Global da Doença , Produto Interno Bruto/estatística & dados numéricos , Humanos , Distribuição Normal , Densidade Demográfica
3.
MMWR Morb Mortal Wkly Rep ; 70(37): 1267-1273, 2021 Sep 17.
Artigo em Inglês | MEDLINE | ID: mdl-34529634

RESUMO

Native Hawaiian and Pacific Islander populations have been disproportionately affected by COVID-19 (1-3). Native Hawaiian, Pacific Islander, and Asian populations vary in language; cultural practices; and social, economic, and environmental experiences,† which can affect health outcomes (4).§ However, data from these populations are often aggregated in analyses. Although data aggregation is often used as an approach to increase sample size and statistical power when analyzing data from smaller population groups, it can limit the understanding of disparities among diverse Native Hawaiian, Pacific Islander, and Asian subpopulations¶ (4-7). To assess disparities in COVID-19 outcomes among Native Hawaiian, Pacific Islander, and Asian populations, a disaggregated, descriptive analysis, informed by recommendations from these communities,** was performed using race data from 21,005 COVID-19 cases and 449 COVID-19-associated deaths reported to the Hawaii State Department of Health (HDOH) during March 1, 2020-February 28, 2021.†† In Hawaii, COVID-19 incidence and mortality rates per 100,000 population were 1,477 and 32, respectively during this period. In analyses with race categories that were not mutually exclusive, including persons of one race alone or in combination with one or more races, Pacific Islander persons, who account for 5% of Hawaii's population, represented 22% of COVID-19 cases and deaths (COVID-19 incidence of 7,070 and mortality rate of 150). Native Hawaiian persons experienced an incidence of 1,181 and a mortality rate of 15. Among subcategories of Asian populations, the highest incidences were experienced by Filipino persons (1,247) and Vietnamese persons (1,200). Disaggregating Native Hawaiian, Pacific Islander, and Asian race data can aid in identifying racial disparities among specific subpopulations and highlights the importance of partnering with communities to develop culturally responsive outreach teams§§ and tailored public health interventions and vaccination campaigns to more effectively address health disparities.


Assuntos
COVID-19/etnologia , Grupos de Populações Continentais/estatística & dados numéricos , Disparidades nos Níveis de Saúde , COVID-19/mortalidade , Serviços de Saúde Comunitária/organização & administração , Interpretação Estatística de Dados , Hawaii/epidemiologia , Humanos
4.
Curr Protoc ; 1(8): e174, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34351700

RESUMO

Chromatin Interaction Analysis Using Paired-End Tag Sequencing (ChIA-PET) is an established method to map protein-mediated chromatin interactions. A limitation, however, is that it requires a hundred million cells per experiment, which hampers its broad application in biomedical research, particularly in studies in which it is impractical to obtain a large number of cells from rare samples. To reduce the required input cell number while retaining high data quality, we developed an in situ ChIA-PET protocol, which requires as few as 1 million cells. Here, we describe detailed step-by-step procedures for performing in situ ChIA-PET from cultured cells, including both an experimental protocol for sample preparation and data generation and a computational protocol for data processing and visualization using the ChIA-PIPE pipeline. As the protocol significantly simplifies the experimental procedure, reduces ligation noise, and decreases the required input of cells compared to previous versions of ChIA-PET protocols, it can be applied to generate high-resolution chromatin contact maps mediated by various protein factors for a wide range of human and mouse primary cells. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample preparation and data generation Support Protocol: Bridge linker preparation Basic Protocol 2: Data processing and visualization.


Assuntos
Cromatina , Técnicas Genéticas , Animais , Linhagem Celular , Interpretação Estatística de Dados , Humanos , Camundongos , Análise de Sequência de DNA
6.
Sci Rep ; 11(1): 16312, 2021 08 11.
Artigo em Inglês | MEDLINE | ID: mdl-34381088

RESUMO

Compartmental epidemiological models are, by far, the most popular in the study of dynamics related with infectious diseases. It is, therefore, not surprising that they are frequently used to study the current COVID-19 pandemic. Taking advantage of the real-time availability of COVID-19 related data, we perform a compartmental model fitting analysis of the portuguese case, using an online open-access platform with the integrated capability of solving systems of differential equations. This analysis enabled the data-driven validation of the used model and was the basis for robust projections of different future scenarios, namely, increasing the detected infected population, reopening schools at different moments, allowing Easter celebrations to take place and population vaccination. The method presented in this work can easily be used to perform the non-trivial task of simultaneously fitting differential equation solutions to different epidemiological data sets, regardless of the model or country that might be considered in the analysis.


Assuntos
COVID-19/epidemiologia , Interpretação Estatística de Dados , Métodos Epidemiológicos , Humanos , Modelos Teóricos
7.
Sci Rep ; 11(1): 16331, 2021 08 11.
Artigo em Inglês | MEDLINE | ID: mdl-34381100

RESUMO

COVID-19 is a highly infectious disease that emerged in China at the end of 2019. The COVID-19 pandemic is the first known pandemic caused by a coronavirus, namely, the new and emerging SARS-CoV-2 coronavirus. In the present work, we present simulations of the initial outbreak of this new coronavirus using a modified transmission rate SEIR model that takes into account the impact of government actions and the perception of risk by individuals in reaction to the proportion of fatal cases. The parameters related to these effects were fitted to the number of infected cases in the 33 provinces of China. The data for Hubei Province, the probable site of origin of the current pandemic, were considered as a particular case for the simulation and showed that the theoretical model reproduces the behavior of the data, thus indicating the importance of combining government actions and individual risk perceptions when the proportion of fatal cases is greater than [Formula: see text]. The results show that the adjusted model reproduces the behavior of the data quite well for some provinces, suggesting that the spread of the disease differs when different actions are evaluated. The proposed model could help to predict outbreaks of viruses with a biological and molecular structure similar to that of SARS-CoV-2.


Assuntos
COVID-19 , Infecções por Coronavirus/epidemiologia , Surtos de Doenças , Modelos Teóricos , COVID-19/epidemiologia , COVID-19/fisiopatologia , China/epidemiologia , Biologia Computacional , Simulação por Computador , Interpretação Estatística de Dados , Programas Governamentais , Política de Saúde , Humanos , Medição de Risco
8.
Artigo em Inglês | MEDLINE | ID: mdl-34206234

RESUMO

(1) Background: Propensity score methods gained popularity in non-interventional clinical studies. As it may often occur in observational datasets, some values in baseline covariates are missing for some patients. The present study aims to compare the performances of popular statistical methods to deal with missing data in propensity score analysis. (2) Methods: Methods that account for missing data during the estimation process and methods based on the imputation of missing values, such as multiple imputations, were considered. The methods were applied on the dataset of an ongoing prospective registry for the treatment of unprotected left main coronary artery disease. The performances were assessed in terms of the overall balance of baseline covariates. (3) Results: Methods that explicitly deal with missing data were superior to classical complete case analysis. The best balance was observed when propensity scores were estimated with a method that accounts for missing data using a stochastic approximation of the expectation-maximization algorithm. (4) Conclusions: If missing at random mechanism is plausible, methods that use missing data to estimate propensity score or impute them should be preferred. Sensitivity analyses are encouraged to evaluate the implications methods used to handle missing data and estimate propensity score.


Assuntos
Modelos Estatísticos , Projetos de Pesquisa , Interpretação Estatística de Dados , Humanos , Estudos Longitudinais , Pontuação de Propensão
9.
Nucleic Acids Res ; 49(15): 8471-8487, 2021 09 07.
Artigo em Inglês | MEDLINE | ID: mdl-34313777

RESUMO

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called 'VarSAn' that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.


Assuntos
Genômica/métodos , Polimorfismo de Nucleotídeo Único , Software , Algoritmos , Neoplasias da Mama/genética , Neoplasias da Mama/metabolismo , Interpretação Estatística de Dados , Feminino , Genes , Humanos , Síndrome do Coração Esquerdo Hipoplásico/genética , Síndrome do Coração Esquerdo Hipoplásico/metabolismo , Masculino , Neoplasias da Próstata/genética , Neoplasias da Próstata/metabolismo , Transdução de Sinais/genética
10.
Br J Anaesth ; 127(3): 487-494, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34275603

RESUMO

BACKGROUND: Multicentre RCTs are widely used by critical care researchers to answer important clinical questions. However, few trials evaluating mortality outcomes report statistically significant results. We hypothesised that the low proportion of trials reporting statistically significant differences for mortality outcomes is plausibly explained by lower-than-expected effect sizes combined with a low proportion of participants who could realistically benefit from studied interventions. METHODS: We reviewed multicentre trials in critical care published over a 10-yr period in the New England Journal of Medicine, the Journal of the American Medical Association, and the Lancet. To test our hypothesis, we analysed the results using a Bayesian model to investigate the relationship between the proportion of effective interventions and the proportion of statistically significant results for prior distributions of effect size and trial participant susceptibility. RESULTS: Five of 54 trials (9.3%) reported a significant difference in mortality between the control and the intervention groups. The median expected and observed differences in absolute mortality were 8.0% and 2.0%, respectively. Our modelling shows that, across trials, a lower-than-expected effect size combined with a low proportion of potentially susceptible participants is consistent with the observed proportion of trials reporting significant differences even when most interventions are effective. CONCLUSIONS: When designing clinical trials, researchers most likely overestimate true population effect sizes for critical care interventions. Bayesian modelling demonstrates that that it is not necessarily the case that most studied interventions lack efficacy. In fact, it is plausible that many studied interventions have clinically important effects that are missed.


Assuntos
Cuidados Críticos/estatística & dados numéricos , Determinação de Ponto Final/estatística & dados numéricos , Mortalidade , Estudos Multicêntricos como Assunto/estatística & dados numéricos , Ensaios Clínicos Controlados Aleatórios como Assunto/estatística & dados numéricos , Projetos de Pesquisa/estatística & dados numéricos , Teorema de Bayes , Interpretação Estatística de Dados , Humanos , Modelos Estatísticos , Tamanho da Amostra , Resultado do Tratamento
14.
Br J Anaesth ; 127(1): 110-132, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34147158

RESUMO

BACKGROUND: For most procedures, there is insufficient evidence to guide clinicians in the optimal timing of advanced analgesic methods, which should be based on the expected time course of acute postoperative pain severity and aimed at time points where basic analgesia has proven insufficient. METHODS: We conducted a systematic search of the literature of analgesic trials for total hip arthroplasty (THA), extracting and pooling pain scores across studies, weighted for study size. Patients were grouped according to basic anaesthetic method used (general, spinal), and adjuvant analgesic interventions such as nerve blocks, local infiltration analgesia, and multimodal analgesia. Special consideration was given to high-risk populations such as chronic pain or opioid-dependent patients. RESULTS: We identified and analysed 71 trials with 5973 patients and constructed pain trajectories from the available pain scores. In most patients undergoing THA under general anaesthesia on a basic analgesic regimen, postoperative acute pain recedes to a mild level (<4/10) by 4 h after surgery. We note substantial variability in pain intensity even in patients subjected to similar analgesic regimens. Chronic pain or opioid-dependent patients were most often actively excluded from studies, and never analysed separately. CONCLUSIONS: We have demonstrated that it is feasible to construct procedure-specific pain curves to guide clinicians on the timing of advanced analgesic measures. Acute intense postoperative pain after THA should have resolved by 4-6 h after surgery in most patients. However, there is a substantial gap in knowledge on the management of patients with chronic pain and opioid-dependent patients.


Assuntos
Artroplastia de Quadril/tendências , Interpretação Estatística de Dados , Procedimentos Cirúrgicos Eletivos/tendências , Medição da Dor/tendências , Dor Pós-Operatória/diagnóstico , Dor Pós-Operatória/etiologia , Artroplastia de Quadril/efeitos adversos , Ensaios Clínicos como Assunto/métodos , Procedimentos Cirúrgicos Eletivos/efeitos adversos , Humanos , Manejo da Dor/métodos , Manejo da Dor/tendências , Medição da Dor/métodos
17.
Ann Intern Med ; 174(8): 1151-1158, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34125574

RESUMO

The development of the National Institutes of Health (NIH) COVID-19 Treatment Guidelines began in March 2020 in response to a request from the White House Coronavirus Task Force. Within 4 days of the request, the NIH COVID-19 Treatment Guidelines Panel was established and the first meeting took place (virtually-as did subsequent meetings). The Panel comprises 57 individuals representing 6 governmental agencies, 11 professional societies, and 33 medical centers, plus 2 community members, who have worked together to create and frequently update the guidelines on the basis of evidence from the most recent clinical studies available. The initial version of the guidelines was completed within 2 weeks and posted online on 21 April 2020. Initially, sparse evidence was available to guide COVID-19 treatment recommendations. However, treatment data rapidly accrued based on results from clinical studies that used various study designs and evaluated different therapeutic agents and approaches. Data have continued to evolve at a rapid pace, leading to 24 revisions and updates of the guidelines in the first year. This process has provided important lessons for responding to an unprecedented public health emergency: Providers and stakeholders are eager to access credible, current treatment guidelines; governmental agencies, professional societies, and health care leaders can work together effectively and expeditiously; panelists from various disciplines, including biostatistics, are important for quickly developing well-informed recommendations; well-powered randomized clinical trials continue to provide the most compelling evidence to guide treatment recommendations; treatment recommendations need to be developed in a confidential setting free from external pressures; development of a user-friendly, web-based format for communicating with health care providers requires substantial administrative support; and frequent updates are necessary as clinical evidence rapidly emerges.


Assuntos
COVID-19/terapia , Pandemias , Guias de Prática Clínica como Assunto , Comitês Consultivos , COVID-19/tratamento farmacológico , COVID-19/epidemiologia , Criança , Interpretação Estatística de Dados , Aprovação de Drogas , Medicina Baseada em Evidências , Feminino , Humanos , Relações Interprofissionais , National Institutes of Health (U.S.) , Gravidez , SARS-CoV-2 , Participação dos Interessados , Estados Unidos
18.
Am J Hum Genet ; 108(7): 1270-1282, 2021 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-34157305

RESUMO

Publicly available genetic summary data have high utility in research and the clinic, including prioritizing putative causal variants, polygenic scoring, and leveraging common controls. However, summarizing individual-level data can mask population structure, resulting in confounding, reduced power, and incorrect prioritization of putative causal variants. This limits the utility of publicly available data, especially for understudied or admixed populations where additional research and resources are most needed. Although several methods exist to estimate ancestry in individual-level data, methods to estimate ancestry proportions in summary data are lacking. Here, we present Summix, a method to efficiently deconvolute ancestry and provide ancestry-adjusted allele frequencies (AFs) from summary data. Using continental reference ancestry, African (AFR), non-Finnish European (EUR), East Asian (EAS), Indigenous American (IAM), South Asian (SAS), we obtain accurate and precise estimates (within 0.1%) for all simulation scenarios. We apply Summix to gnomAD v.2.1 exome and genome groups and subgroups, finding heterogeneous continental ancestry for several groups, including African/African American (∼84% AFR, ∼14% EUR) and American/Latinx (∼4% AFR, ∼5% EAS, ∼43% EUR, ∼46% IAM). Compared to the unadjusted gnomAD AFs, Summix's ancestry-adjusted AFs more closely match respective African and Latinx reference samples. Even on modern, dense panels of summary statistics, Summix yields results in seconds, allowing for estimation of confidence intervals via block bootstrap. Given an accompanying R package, Summix increases the utility and equity of public genetic resources, empowering novel research opportunities.


Assuntos
Grupos de Populações Continentais/genética , Interpretação Estatística de Dados , Metagenômica/métodos , Linhagem , Alelos , Simulação por Computador , Frequência do Gene , Humanos , Padrões de Herança , Software
19.
Asian Nurs Res (Korean Soc Nurs Sci) ; 15(3): 157-162, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34144201

RESUMO

PURPOSE: This study develops a checklist with guidelines for the methods and important factors to consider in research using structural equation modeling (SEM). METHOD: The paper discusses the factors to consider in the process across the three stages of 1) model setting, 2) model evaluation and modification, and 3) interpretation and reporting of SEM-based studies. RESULTS: The authors present a checklist for researchers during the stages of model setting, model evaluation and modification, result analysis, and reporting, along with examples of figures and tables with explanations. CONCLUSION: A checklist will help to improve the reporting quality of SEM-based studies.


Assuntos
Lista de Checagem , Análise de Classes Latentes , Modelos Estatísticos , Pesquisa em Enfermagem , Interpretação Estatística de Dados , Humanos , Pesquisa em Enfermagem/métodos , Pesquisa em Enfermagem/normas
20.
Eur J Epidemiol ; 36(7): 659-667, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34114186

RESUMO

Causal graphs provide a key tool for optimizing the validity of causal effect estimates. Although a large literature exists on the mathematical theory underlying the use of causal graphs, less literature exists to aid applied researchers in understanding how best to develop and use causal graphs in their research projects. We sought to understand why researchers do or do not regularly use DAGs by surveying practicing epidemiologists and medical researchers on their knowledge, level of interest, attitudes, and practices towards the use of causal graphs in applied epidemiology and health research. We used Twitter and the Society for Epidemiologic Research to disseminate the survey. Overall, a majority of participants reported being comfortable with using causal graphs and reported using them 'sometimes', 'often', or 'always' in their research. Having received training appeared to improve comprehension of the assumptions displayed in causal graphs. Many of the respondents who did not use causal graphs reported lack of knowledge as a barrier to using DAGs in their research. Causal graphs are of interest to epidemiologists and medical researchers, but there are several barriers to their uptake. Additional training and clearer guidance are needed. In addition, methodological developments regarding visualization of effect measure modification and interaction on causal graphs is needed.


Assuntos
Atitude do Pessoal de Saúde , Causalidade , Gráficos por Computador , Interpretação Estatística de Dados , Projetos de Pesquisa Epidemiológica , Epidemiologistas , Feminino , Humanos , Masculino , Pesquisa Qualitativa , Pesquisadores , Inquéritos e Questionários
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