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1.
Adv Exp Med Biol ; 1269: 137-142, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33966208

RESUMEN

Molecular oxygen (O2) permeability coefficients for lipid bilayers have previously been estimated using both electron paramagnetic resonance (EPR) oximetry and molecular dynamics simulation data. Yet, neither technique captures the fluxes that exist physiologically. Here, the dynamic steady state is modeled using a stochastic approach built on atomic resolution molecular dynamics simulation data. A Monte Carlo Markov chain technique is used to examine membrane-level fluxes of oxygen in lipid-water systems. At steady state, the concentration of oxygen is found to be higher inside the model membranes than in surrounding water, consistent with the known favorable partitioning of O2 toward the lipid phase. Pure phospholipid 1-palmitoyl,2-oleoyl-phosphatidylcholine (POPC) bilayers accrue ~40% more O2 molecules at steady state than POPC/cholesterol bilayers (1:1 molecular ratio) mimicking the red blood cell membrane. Steady-state levels of oxygen were reached inside both bilayer types within the same timeframe, but depletion of oxygen from the bilayer interior occurred 17% faster for POPC than for POPC/cholesterol. Likewise, first-order rate constants estimated for accrual to steady state were the same for POPC and POPC/cholesterol, at 190 µs-1, while first-order rate constants for depletion of the accrued O2 from the bilayers differed, at 95 µs-1 for POPC and 81 µs-1 for POPC/cholesterol (lower by 15%). These results are consistent with prior experiments in red blood cells (RBCs) with varying membrane cholesterol content, in which additional cholesterol slowed oxygen uptake and release. Further work is needed to understand whether differences in RBC membrane cholesterol content would affect the delivery of oxygen to tissues.


Asunto(s)
Oxígeno , Fosfatidilcolinas , Colesterol , Membrana Dobles de Lípidos , Cadenas de Markov
2.
Nat Commun ; 12(1): 2392, 2021 04 22.
Artículo en Inglés | MEDLINE | ID: mdl-33888694

RESUMEN

Cognitive maps are mental representations of spatial and conceptual relationships in an environment, and are critical for flexible behavior. To form these abstract maps, the hippocampus has to learn to separate or merge aliased observations appropriately in different contexts in a manner that enables generalization and efficient planning. Here we propose a specific higher-order graph structure, clone-structured cognitive graph (CSCG), which forms clones of an observation for different contexts as a representation that addresses these problems. CSCGs can be learned efficiently using a probabilistic sequence model that is inherently robust to uncertainty. We show that CSCGs can explain a variety of cognitive map phenomena such as discovering spatial relations from aliased sensations, transitive inference between disjoint episodes, and formation of transferable schemas. Learning different clones for different contexts explains the emergence of splitter cells observed in maze navigation and event-specific responses in lap-running experiments. Moreover, learning and inference dynamics of CSCGs offer a coherent explanation for disparate place cell remapping phenomena. By lifting aliased observations into a hidden space, CSCGs reveal latent modularity useful for hierarchical abstraction and planning. Altogether, CSCG provides a simple unifying framework for understanding hippocampal function, and could be a pathway for forming relational abstractions in artificial intelligence.


Asunto(s)
Cognición/fisiología , Hipocampo/fisiología , Aprendizaje/fisiología , Modelos Neurológicos , Redes Neurales de la Computación , Humanos , Cadenas de Markov
3.
Water Sci Technol ; 83(5): 1085-1102, 2021 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-33724938

RESUMEN

A new parameter optimization and uncertainty assessment procedure using the Bayesian inference with an adaptive Metropolis-Hastings (AM-H) algorithm is presented for extreme rainfall frequency modeling. An efficient Markov chain Monte Carlo sampler is adopted to explore the posterior distribution of parameters and calculate their uncertainty intervals associated with the magnitude of estimated rainfall depth quantiles. Also, the efficiency of AM-H and conventional maximum likelihood estimation (MLE) in parameter estimation and uncertainty quantification are compared. And the procedure was implemented and discussed for the case of Chaohu city, China. Results of our work reveal that: (i) the adaptive Bayesian method, especially for return level associated to large return period, shows better estimated effect when compared with MLE; it should be noted that the implementation of MLE often produces overy optimistic results in the case of Chaohu city; (ii) AM-H algorithm is more reliable than MLE in terms of uncertainty quantification, and yields relatively narrow credible intervals for the quantile estimates to be instrumental in risk assessment of urban storm drainage planning.


Asunto(s)
Algoritmos , Teorema de Bayes , China , Ciudades , Simulación por Computador , Cadenas de Markov , Método de Montecarlo , Incertidumbre
4.
J Med Internet Res ; 23(3): e26516, 2021 03 03.
Artículo en Inglés | MEDLINE | ID: mdl-33656440

RESUMEN

BACKGROUND: The COVID-19 pandemic has caused patients to avoid seeking medical care. Provision of telemonitoring programs in addition to usual care has demonstrated improved effectiveness in managing patients with heart failure (HF). OBJECTIVE: We aimed to examine the potential clinical and health economic outcomes of a telemonitoring program for management of patients with HF during the COVID-19 pandemic from the perspective of health care providers in Hong Kong. METHODS: A Markov model was designed to compare the outcomes of a care under COVID-19 (CUC) group and a telemonitoring plus CUC group (telemonitoring group) in a hypothetical cohort of older patients with HF in Hong Kong. The model outcome measures were direct medical cost, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio. Sensitivity analyses were performed to examine the model assumptions and the robustness of the base-case results. RESULTS: In the base-case analysis, the telemonitoring group showed a higher QALY gain (1.9007) at a higher cost (US $15,888) compared to the CUC group (1.8345 QALYs at US $15,603). Adopting US $48,937/QALY (1 × the gross domestic product per capita of Hong Kong) as the willingness-to-pay threshold, telemonitoring was accepted as a highly cost-effective strategy, with an incremental cost-effective ratio of US $4292/QALY. No threshold value was identified in the deterministic sensitivity analysis. In the probabilistic sensitivity analysis, telemonitoring was accepted as cost-effective in 99.22% of 10,000 Monte Carlo simulations. CONCLUSIONS: Compared to the current outpatient care alone under the COVID-19 pandemic, the addition of telemonitoring-mediated management to the current care for patients with HF appears to be a highly cost-effective strategy from the perspective of health care providers in Hong Kong.


Asunto(s)
Atención Ambulatoria , Análisis de Datos , Insuficiencia Cardíaca/epidemiología , Método de Montecarlo , Telemedicina/economía , Telemedicina/métodos , Atención Ambulatoria/economía , Atención Ambulatoria/métodos , Estudios de Cohortes , Análisis Costo-Beneficio , Hong Kong/epidemiología , Humanos , Cadenas de Markov , Pandemias , Años de Vida Ajustados por Calidad de Vida
5.
Methods Mol Biol ; 2266: 239-259, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33759131

RESUMEN

Molecular dynamics simulations can now routinely access the microsecond timescale, making feasible direct sampling of ligand association events. While Markov State Model (MSM) approaches offer a useful framework for analyzing such trajectory data to gain insight into binding mechanisms, accurate modeling of ligand association pathways and kinetics must be done carefully. We describe methods and good practices for constructing MSMs of ligand binding from unbiased trajectory data and discuss how to use time-lagged independent component analysis (tICA) to build informative models, using as an example recent simulation work to model the binding of phenylalanine to the regulatory ACT domain dimer of phenylalanine hydroxylase. We describe a variety of methods for estimating association rates from MSMs and discuss how to distinguish between conformational selection and induced-fit mechanisms using MSMs. In addition, we review some examples of MSMs constructed to elucidate the mechanisms by which p53 transactivation domain (TAD) and related peptides bind the oncoprotein MDM2.


Asunto(s)
Cadenas de Markov , Simulación de Dinámica Molecular , Fenilalanina Hidroxilasa/química , Fenilalanina/química , Proteínas Proto-Oncogénicas c-mdm2/química , Programas Informáticos , Proteína p53 Supresora de Tumor/química , Cinética , Ligandos , Unión Proteica , Dominios Proteicos , Estructura Terciaria de Proteína
6.
Food Chem ; 354: 129499, 2021 Aug 30.
Artículo en Inglés | MEDLINE | ID: mdl-33752115

RESUMEN

Citrus fruits are the main dietary source of polymethoxylated flavones (PMFs) with significant effects on consumer health. In this study, eleven main PMFs were evaluated in the fruit flavedo or leaves of 116 citrus accessions via UPLC-DAD-ESI-QTOF-MS/MS combined with HPLC-DAD analysis, which revealed significant species-specific and spatiotemporal characteristics. All Citrus reticulata and their natural or artificial hybrids were found to have detectable PMFs, especially in the fruit flavedo of the wild or early-cultivated mandarins at early fruit development stages. However, PMFs were not detected in citrons, pummelos, kumquats, trifoliata oranges, papedas, Chinese box oranges and 'Mangshanyegan'. The results enlightened that PMF accumulation only in mandarins and mandarin hybrids is a phenotype inherited from mandarin ancestors. This study provides a comprehensive PMF profile in various citrus germplasms and will benefit future functional citrus breeding practices aimed at designing plants rich in total or specific PMFs for health benefits.


Asunto(s)
Citrus/química , Flavonas/química , Espectrometría de Masas en Tándem/métodos , Cromatografía Líquida de Alta Presión , Citrus/metabolismo , Flavonas/análisis , Frutas/química , Frutas/metabolismo , Hidroxilación , Cadenas de Markov , Metilación , Extractos Vegetales/química , Hojas de la Planta/química , Hojas de la Planta/metabolismo , Análisis de Componente Principal
7.
BMC Bioinformatics ; 22(1): 162, 2021 Mar 26.
Artículo en Inglés | MEDLINE | ID: mdl-33771095

RESUMEN

BACKGROUND: Hidden Markov models (HMM) are a powerful tool for analyzing biological sequences in a wide variety of applications, from profiling functional protein families to identifying functional domains. The standard method used for HMM training is either by maximum likelihood using counting when sequences are labelled or by expectation maximization, such as the Baum-Welch algorithm, when sequences are unlabelled. However, increasingly there are situations where sequences are just partially labelled. In this paper, we designed a new training method based on the Baum-Welch algorithm to train HMMs for situations in which only partial labeling is available for certain biological problems. RESULTS: Compared with a similar method previously reported that is designed for the purpose of active learning in text mining, our method achieves significant improvements in model training, as demonstrated by higher accuracy when the trained models are tested for decoding with both synthetic data and real data. CONCLUSIONS: A novel training method is developed to improve the training of hidden Markov models by utilizing partial labelled data. The method will impact on detecting de novo motifs and signals in biological sequence data. In particular, the method will be deployed in active learning mode to the ongoing research in detecting plasmodesmata targeting signals and assess the performance with validations from wet-lab experiments.


Asunto(s)
Algoritmos , Proteínas , Biología Computacional , Cadenas de Markov , Proteínas/genética
8.
J Transl Med ; 19(1): 109, 2021 03 16.
Artículo en Inglés | MEDLINE | ID: mdl-33726787

RESUMEN

BACKGROUND: No versatile web app exists that allows epidemiologists and managers around the world to comprehensively analyze the impacts of COVID-19 mitigation. The http://covid-webapp.numerusinc.com/ web app presented here fills this gap. METHODS: Our web app uses a model that explicitly identifies susceptible, contact, latent, asymptomatic, symptomatic and recovered classes of individuals, and a parallel set of response classes, subject to lower pathogen-contact rates. The user inputs a CSV file of incidence and, if of interest, mortality rate data. A default set of parameters is available that can be overwritten through input or online entry, and a user-selected subset of these can be fitted to the model using maximum-likelihood estimation (MLE). Model fitting and forecasting intervals are specifiable and changes to parameters allow counterfactual and forecasting scenarios. Confidence or credible intervals can be generated using stochastic simulations, based on MLE values, or on an inputted CSV file containing Markov chain Monte Carlo (MCMC) estimates of one or more parameters. RESULTS: We illustrate the use of our web app in extracting social distancing, social relaxation, surveillance or virulence switching functions (i.e., time varying drivers) from the incidence and mortality rates of COVID-19 epidemics in Israel, South Africa, and England. The Israeli outbreak exhibits four distinct phases: initial outbreak, social distancing, social relaxation, and a second wave mitigation phase. An MCMC projection of this latter phase suggests the Israeli epidemic will continue to produce into late November an average of around 1500 new case per day, unless the population practices social-relaxation measures at least 5-fold below the level in August, which itself is 4-fold below the level at the start of July. Our analysis of the relatively late South African outbreak that became the world's fifth largest COVID-19 epidemic in July revealed that the decline through late July and early August was characterised by a social distancing driver operating at more than twice the per-capita applicable-disease-class (pc-adc) rate of the social relaxation driver. Our analysis of the relatively early English outbreak, identified a more than 2-fold improvement in surveillance over the course of the epidemic. It also identified a pc-adc social distancing rate in early August that, though nearly four times the pc-adc social relaxation rate, appeared to barely contain a second wave that would break out if social distancing was further relaxed. CONCLUSION: Our web app provides policy makers and health officers who have no epidemiological modelling or computer coding expertise with an invaluable tool for assessing the impacts of different outbreak mitigation policies and measures. This includes an ability to generate an epidemic-suppression or curve-flattening index that measures the intensity with which behavioural responses suppress or flatten the epidemic curve in the region under consideration.


Asunto(s)
/epidemiología , Control de Infecciones , Internet , Aplicaciones Móviles , /etiología , Simulación por Computador , Modificador del Efecto Epidemiológico , Inglaterra/epidemiología , Epidemias , Predicción/métodos , Humanos , Control de Infecciones/métodos , Control de Infecciones/organización & administración , Control de Infecciones/normas , Israel/epidemiología , Cadenas de Markov , Vigilancia de la Población/métodos , Factores de Riesgo , Sudáfrica/epidemiología
9.
Math Biosci Eng ; 18(2): 1833-1844, 2021 02 22.
Artículo en Inglés | MEDLINE | ID: mdl-33757213

RESUMEN

In this paper, we present an SEIIaHR epidemic model to study the influence of recessive infection and isolation in the spread of COVID-19. We first prove that the infection-free equilibrium is globally asymptotically stable with condition R0<1 and the positive equilibrium is uniformly persistent when the condition R0>1. By using the COVID-19 data in India, we then give numerical simulations to illustrate our results and carry out some sensitivity analysis. We know that asymptomatic infections will affect the spread of the disease when the quarantine rate is within the range of [0.3519, 0.5411]. Furthermore, isolating people with symptoms is important to control and eliminate the disease.


Asunto(s)
/epidemiología , Epidemias , Modelos Biológicos , Infecciones Asintomáticas/epidemiología , Número Básico de Reproducción/estadística & datos numéricos , /transmisión , Simulación por Computador , Epidemias/prevención & control , Epidemias/estadística & datos numéricos , Humanos , India/epidemiología , Cadenas de Markov , Conceptos Matemáticos , Método de Montecarlo , Pandemias/prevención & control , Pandemias/estadística & datos numéricos , Cuarentena/estadística & datos numéricos
10.
J Altern Complement Med ; 27(4): 331-341, 2021 Apr.
Artículo en Inglés | MEDLINE | ID: mdl-33571026

RESUMEN

Objective: To evaluate the long-term cost-effectiveness of ginkgolide plus aspirin compared with placebo plus aspirin treatment of ischemic stroke. Background: Stroke is the leading cause of death and long-term disability in China, with high incidence, high mortality, and heavy disease burden. In addition to Western medicines, Chinese clinical guidelines for diagnosis and treatment of acute ischemic stroke recommend application of Chinese patent medicines. Ginkgolide injection is commonly used in the clinical treatment of stroke in China to promote blood circulation and remove blood stasis. The economy of ginkgolide injection needs to be evaluated. Methods: A Markov model was constructed consisting of four disease states: no significant disability, disability, stroke recurrence, and death. Therapeutic data were taken from the Ginkgolide in Ischemic Stroke Patients with Large Artery Atherosclerosis (GISAA) study. Utilities and transition probabilities were extracted from the literature. Cost data were obtained from the China Health Statistics Yearbook and hospital record survey. Expected costs and quality-adjusted life-years (QALYs) of 13 years of cycles (calculated by average age of subjects and Chinese life expectancy) were calculated through TreeAge Pro11 software. The willingness-to-pay (WTP) threshold was set as the Chinese per capita Gross Domestic Product (GDP) in 2019, CN¥70,892/QALY. The results were analyzed by single factor and probability sensitivity analyses. Results: Ginkgolide plus aspirin had a higher expected per-patient cost than placebo plus aspirin but a higher QALYs. Compared with placebo plus aspirin, ginkgolide plus aspirin produced an incremental cost-effectiveness ratio of CN¥14,866.06/QALY, which is below the WTP threshold. Probabilistic sensitivity analysis suggested the acceptability of ginkgolide plus aspirin was higher than that of placebo plus aspirin. Conclusions: The present cost-effectiveness analysis showed that addition of ginkgolides to conventional treatment is cost-effective at a threshold the Chinese per capita GDP.


Asunto(s)
Análisis Costo-Beneficio , Ginkgólidos , Aspirina/economía , Aspirina/uso terapéutico , Ginkgólidos/economía , Ginkgólidos/uso terapéutico , Humanos , /economía , Cadenas de Markov , Estudios Prospectivos , Años de Vida Ajustados por Calidad de Vida , Ensayos Clínicos Controlados Aleatorios como Asunto
11.
Nature ; 591(7849): 265-269, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33597750

RESUMEN

Temporal genomic data hold great potential for studying evolutionary processes such as speciation. However, sampling across speciation events would, in many cases, require genomic time series that stretch well back into the Early Pleistocene subepoch. Although theoretical models suggest that DNA should survive on this timescale1, the oldest genomic data recovered so far are from a horse specimen dated to 780-560 thousand years ago2. Here we report the recovery of genome-wide data from three mammoth specimens dating to the Early and Middle Pleistocene subepochs, two of which are more than one million years old. We find that two distinct mammoth lineages were present in eastern Siberia during the Early Pleistocene. One of these lineages gave rise to the woolly mammoth and the other represents a previously unrecognized lineage that was ancestral to the first mammoths to colonize North America. Our analyses reveal that the Columbian mammoth of North America traces its ancestry to a Middle Pleistocene hybridization between these two lineages, with roughly equal admixture proportions. Finally, we show that the majority of protein-coding changes associated with cold adaptation in woolly mammoths were already present one million years ago. These findings highlight the potential of deep-time palaeogenomics to expand our understanding of speciation and long-term adaptive evolution.


Asunto(s)
ADN Antiguo/análisis , Evolución Molecular , Genoma Mitocondrial/genética , Genómica , Mamuts/genética , Filogenia , Aclimatación/genética , Alelos , Animales , Teorema de Bayes , ADN Antiguo/aislamiento & purificación , Elefantes/genética , Europa (Continente) , Femenino , Fósiles , Variación Genética/genética , Cadenas de Markov , Diente Molar , América del Norte , Datación Radiométrica , Siberia , Factores de Tiempo
12.
J Trauma Acute Care Surg ; 90(3): 451-458, 2021 03 01.
Artículo en Inglés | MEDLINE | ID: mdl-33559982

RESUMEN

BACKGROUND: Surgical stabilization of rib fracture (SSRF) is increasingly used to manage patients with rib fractures. Benefits of performing SSRF appear variable, and the procedure is costly, necessitating cost-effectiveness analysis for distinct subgroups. We aimed to assess the cost-effectiveness of SSRF versus nonoperative management among patients with rib fractures younger than 65 years versus 65 years or older, with versus without flail chest. We hypothesized that, compared with nonoperative management, SSRF is cost-effective only for patients with flail chest. METHODS: This economic evaluation used a decision-analytic Markov model with a lifetime time horizon incorporating US population-representative inputs to simulate benefits and risks of SSRF compared with nonoperative management. We report quality-adjusted life years (QALYs), costs, and incremental cost-effectiveness ratios. Deterministic and probabilistic sensitivity analyses accounted for most plausible clinical scenarios. RESULTS: Compared with nonoperative management, SSRF was cost-effective for patients with flail chest at willingness-to-pay threshold of US $150,000/QALY gained. Surgical stabilization of rib fracture costs US $25,338 and US $123,377/QALY gained for those with flail chest younger than 65 years and 65 years or older, respectively. Surgical stabilization of rib fracture was not cost-effective for patients without flail chest, costing US $172,704 and US $243,758/QALY gained for those younger than 65 years and 65 years or older, respectively. One-way sensitivity analyses showed that, under most plausible scenarios, SSRF remained cost-effective for subgroups with flail chest, and nonoperative management remained cost-effective for patients older than 65 years without flail chest. Probability that SSRF is cost-effective ranged from 98% among patients younger than 65 years with flail chest to 35% among patients 65 years or older without flail chest. CONCLUSIONS: Surgical stabilization of rib fracture is cost-effective for patients with flail chest. Surgical stabilization of rib fracture may be cost-effective in some patients without flail chest, but delineating these patients requires further study. LEVEL OF EVIDENCE: Economic/decision, level II.


Asunto(s)
Tórax Paradójico/complicaciones , Tórax Paradójico/cirugía , Fijación de Fractura/economía , Fracturas de las Costillas/complicaciones , Fracturas de las Costillas/cirugía , Factores de Edad , Anciano , Análisis Costo-Beneficio , Femenino , Tórax Paradójico/economía , Humanos , Tiempo de Internación , Masculino , Cadenas de Markov , Persona de Mediana Edad , Años de Vida Ajustados por Calidad de Vida , Estudios Retrospectivos , Fracturas de las Costillas/economía , Sensibilidad y Especificidad , Resultado del Tratamiento
13.
Epidemics ; 34: 100439, 2021 03.
Artículo en Inglés | MEDLINE | ID: mdl-33556763

RESUMEN

Epidemiological models are widely used to analyze the spread of diseases such as the global COVID-19 pandemic caused by SARS-CoV-2. However, all models are based on simplifying assumptions and often on sparse data. This limits the reliability of parameter estimates and predictions. In this manuscript, we demonstrate the relevance of these limitations and the pitfalls associated with the use of overly simplistic models. We considered the data for the early phase of the COVID-19 outbreak in Wuhan, China, as an example, and perform parameter estimation, uncertainty analysis and model selection for a range of established epidemiological models. Amongst others, we employ Markov chain Monte Carlo sampling, parameter and prediction profile calculation algorithms. Our results show that parameter estimates and predictions obtained for several established models on the basis of reported case numbers can be subject to substantial uncertainty. More importantly, estimates were often unrealistic and the confidence/credibility intervals did not cover plausible values of critical parameters obtained using different approaches. These findings suggest, amongst others, that standard compartmental models can be overly simplistic and that the reported case numbers provide often insufficient information for obtaining reliable and realistic parameter values, and for forecasting the evolution of epidemics.


Asunto(s)
/epidemiología , Modelos Estadísticos , Pandemias , Algoritmos , China/epidemiología , Predicción , Humanos , Cadenas de Markov , Método de Montecarlo , Reproducibilidad de los Resultados , Incertidumbre
14.
J Med Econ ; 24(1): 339-344, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-33571036

RESUMEN

OBJECTIVES: The aim of this study is to assess the cost-effectiveness of fruquintinib compared to regorafenib as third-line treatment for patients with metastatic colorectal cancer (mCRC) in China. METHODS: A three-state Markov model with monthly cycle was constructed to estimate lifetime incremental cost-effectiveness ratio (ICER) of fruquintinib versus regorafenib as third-line treatment for patients with mCRC from Chinese health care perspective. Survival analysis was applied to calculate transition probabilities using the data from the clinical trials FRESCO and CONCUR, which were also the data sources accessing probabilities of adverse events. Background mortality rate and drug costs were derived from government published data. Costs for medical services were obtained from real-world data and published literatures. Utilities applied to calculate the quality-adjusted life years (QALYs) were obtained from literature review. One-way sensitivity analysis and probabilistic sensitivity analysis were adopted to verify the robustness of the results. RESULTS: Fruquintinib provided 0.74 QALYs at a cost of CNY 151,058 (USD 22,888), whereas regorafenib provided 0.79 QALYs at a cost of CNY 226,657 (USD 32,224). Compared to fruquintinib, the ICER of regorafenib was CNY 1,529,197/QALY (USD 231,697/QALY) from Chinese health care perspective, which was above the triple GDP per capita of China in 2019 (CNY 212,676) (USD 32,224) as the threshold to define the cost-effectiveness. One-way sensitivity analysis showed the results were generally robust. Cost-effectiveness acceptability curves derived from probabilistic sensitivity analysis demonstrated the probability that fruquintinib was more cost-effective was 100% when the threshold was the triple GDP per capita of China. CONCLUSIONS: Compared to regorafenib, fruquintinib, which leads to forego about 0.05 QALYs and save about CNY 75,599 (USD 11,454), is a cost-effective choice as the third-line treatment for patients with mCRC in China.


Asunto(s)
Antineoplásicos/economía , Benzofuranos/economía , Benzofuranos/uso terapéutico , Neoplasias Colorrectales/tratamiento farmacológico , Compuestos de Fenilurea/economía , Compuestos de Fenilurea/uso terapéutico , Piridinas/economía , Piridinas/uso terapéutico , Quinazolinas/economía , Quinazolinas/uso terapéutico , Antineoplásicos/efectos adversos , Antineoplásicos/uso terapéutico , Benzofuranos/efectos adversos , China , Neoplasias Colorrectales/patología , Análisis Costo-Beneficio , Gastos en Salud/estadística & datos numéricos , Recursos en Salud/economía , Recursos en Salud/estadística & datos numéricos , Humanos , Cadenas de Markov , Modelos Económicos , Metástasis de la Neoplasia , Compuestos de Fenilurea/efectos adversos , Piridinas/efectos adversos , Años de Vida Ajustados por Calidad de Vida , Quinazolinas/efectos adversos
15.
Sci Rep ; 11(1): 3354, 2021 02 08.
Artículo en Inglés | MEDLINE | ID: mdl-33558571

RESUMEN

The application, timing, and duration of lockdown strategies during a pandemic remain poorly quantified with regards to expected public health outcomes. Previous projection models have reached conflicting conclusions about the effect of complete lockdowns on COVID-19 outcomes. We developed a stochastic continuous-time Markov chain (CTMC) model with eight states including the environment (SEAMHQRD-V), and derived a formula for the basic reproduction number, R0, for that model. Applying the [Formula: see text] formula as a function in previously-published social contact matrices from 152 countries, we produced the distribution and four categories of possible [Formula: see text] for the 152 countries and chose one country from each quarter as a representative for four social contact categories (Canada, China, Mexico, and Niger). The model was then used to predict the effects of lockdown timing in those four categories through the representative countries. The analysis for the effect of a lockdown was performed without the influence of the other control measures, like social distancing and mask wearing, to quantify its absolute effect. Hypothetical lockdown timing was shown to be the critical parameter in ameliorating pandemic peak incidence. More importantly, we found that well-timed lockdowns can split the peak of hospitalizations into two smaller distant peaks while extending the overall pandemic duration. The timing of lockdowns reveals that a "tunneling" effect on incidence can be achieved to bypass the peak and prevent pandemic caseloads from exceeding hospital capacity.


Asunto(s)
/epidemiología , Modelos Estadísticos , Pandemias , Cuarentena/métodos , Adolescente , Adulto , Anciano , Número Básico de Reproducción , /virología , Canadá/epidemiología , Niño , Preescolar , China/epidemiología , Hospitalización , Humanos , Incidencia , Lactante , Recién Nacido , Cadenas de Markov , México/epidemiología , Persona de Mediana Edad , Niger/epidemiología , Salud Pública , Factores de Tiempo , Adulto Joven
16.
Math Biosci Eng ; 18(1): 950-967, 2021 01 04.
Artículo en Inglés | MEDLINE | ID: mdl-33525127

RESUMEN

In this paper, deterministic and stochastic models are proposed to study the transmission dynamics of the Coronavirus Disease 2019 (COVID-19) in Wuhan, China. The deterministic model is formulated by a system of ordinary differential equations (ODEs) that is built upon the classical SEIR framework. The stochastic model is formulated by a continuous-time Markov chain (CTMC) that is derived based on the ODE model with constant parameters. The nonlinear CTMC model is approximated by a multitype branching process to obtain an analytical estimate for the probability of a disease outbreak. The local and global dynamics of the disease are analyzed by using the deterministic model with constant parameters, and the result indicates that the basic reproduction number $ \mathcal{R}_0 $ serves as a sharp disease threshold: the disease dies out if $ \mathcal{R}_0\le 1 $ and persists if $ \mathcal{R}_0 > 1 $. In contrast to the deterministic dynamics, the stochastic dynamics indicate that the disease may not persist when $ \mathcal{R}_0 > 1 $. Parameter estimation and validation are performed to fit our ODE model to the public reported data. Our result indicates that both the exposed and infected classes play an important role in shaping the epidemic dynamics of COVID-19 in Wuhan, China. In addition, numerical simulations indicate that a second wave of the ongoing pandemic is likely to occur if the prevention and control strategies are not implemented properly.


Asunto(s)
/epidemiología , Epidemias , Número Básico de Reproducción , China/epidemiología , Humanos , Cadenas de Markov , Modelos Teóricos , Pandemias , Probabilidad , Procesos Estocásticos , Factores de Tiempo
17.
Value Health ; 24(2): 174-181, 2021 02.
Artículo en Inglés | MEDLINE | ID: mdl-33518023

RESUMEN

OBJECTIVES: To assess the cost-effectiveness of an opioid abuse-prevention program embedded in the Narcotics Information Management System ("the Network System to Prevent Doctor-Shopping for Narcotics") in South Korea. METHODS: Using a Markov model with a 1-year cycle length and 30-year time horizon, we estimated the incremental cost-utility ratio (ICUR) of implementing an opioid abuse-prevention program in patients prescribed outpatient opioids from a Korean healthcare payer's perspective. The model has 6 health states: no opioid use, therapeutic opioid use, opioid abuse, overdose, overdose death, and all-cause death. Patient characteristics, healthcare costs, and transition probabilities were estimated from national population-based data and published literature. Age- and sex-specific utilities of the general Korean population were used for the no-use state, whereas the other health-state utilities were obtained from published studies. Costs (in 2019 US dollars) included the expenses of the program, opioids, and overdoses. An annual 5% discount rate was applied to the costs and quality-adjusted life-years (QALYs). Parameter uncertainties were explored via deterministic and probabilistic sensitivity analyses. RESULTS: The program was associated with 2.27 fewer overdoses per 100 000 person-years, with an ICUR of $227/QALY. The ICURs were generally robust to parameter changes, although the program's effect on abuse reduction was the most influential parameter. Probabilistic sensitivity analysis showed that the program reached a 100% probability of cost-effectiveness at a willingness-to-pay threshold of $900/QALY. CONCLUSIONS: The opioid abuse-prevention program appears to be cost-effective in South Korea. Mandatory use of the program should be considered to maximize clinical and economic benefits of the program.


Asunto(s)
Sistemas de Información en Salud/organización & administración , Promoción de la Salud/organización & administración , Trastornos Relacionados con Opioides/prevención & control , Factores de Edad , Análisis Costo-Beneficio , Gastos en Salud , Sistemas de Información en Salud/economía , Promoción de la Salud/economía , Humanos , Cadenas de Markov , Modelos Económicos , Trastornos Relacionados con Opioides/economía , Años de Vida Ajustados por Calidad de Vida , República de Corea , Factores Sexuales , Factores Socioeconómicos
18.
BMC Bioinformatics ; 22(1): 33, 2021 Jan 28.
Artículo en Inglés | MEDLINE | ID: mdl-33509079

RESUMEN

BACKGROUND: Predicting the response of cancer cell lines to specific drugs is an essential problem in personalized medicine. Since drug response is closely associated with genomic information in cancer cells, some large panels of several hundred human cancer cell lines are organized with genomic and pharmacogenomic data. Although several methods have been developed to predict the drug response, there are many challenges in achieving accurate predictions. This study proposes a novel feature selection-based method, named Auto-HMM-LMF, to predict cell line-drug associations accurately. Because of the vast dimensions of the feature space for predicting the drug response, Auto-HMM-LMF focuses on the feature selection issue for exploiting a subset of inputs with a significant contribution. RESULTS: This research introduces a novel method for feature selection of mutation data based on signature assignments and hidden Markov models. Also, we use the autoencoder models for feature selection of gene expression and copy number variation data. After selecting features, the logistic matrix factorization model is applied to predict drug response values. Besides, by comparing to one of the most powerful feature selection methods, the ensemble feature selection method (EFS), we showed that the performance of the predictive model based on selected features introduced in this paper is much better for drug response prediction. Two datasets, the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) are used to indicate the efficiency of the proposed method across unseen patient cell-line. Evaluation of the proposed model showed that Auto-HMM-LMF could improve the accuracy of the results of the state-of-the-art algorithms, and it can find useful features for the logistic matrix factorization method. CONCLUSIONS: We depicted an application of Auto-HMM-LMF in exploring the new candidate drugs for head and neck cancer that showed the proposed method is useful in drug repositioning and personalized medicine. The source code of Auto-HMM-LMF method is available in https://github.com/emdadi/Auto-HMM-LMF .


Asunto(s)
Variaciones en el Número de Copia de ADN , Preparaciones Farmacéuticas , Farmacogenética , Algoritmos , Predicción , Humanos , Cadenas de Markov , Programas Informáticos
19.
Ecotoxicol Environ Saf ; 210: 111867, 2021 Mar 01.
Artículo en Inglés | MEDLINE | ID: mdl-33387907

RESUMEN

The antimicrobial residues of aquacultural production is a growing public concern, leading to reexamine the method for establishing robust withdrawal time and ensuring food safety. Our study aims to develop the optimizing population physiologically-based pharmacokinetic (PBPK) model for assessing florfenicol residues in the tilapia tissues, and for evaluating the robustness of the withdrawal time (WT). Fitting with published pharmacokinetic profiles that experimented under temperatures of 22 and 28 °C, a PBPK model was constructed by applying with the Bayesian Markov chain Monte Carol (MCMC) algorithm to estimate WTs under different physiological, environmental and dosing scenarios. Results show that the MCMC algorithm improves the estimates of uncertainty and variability of PBPK-related parameters, and optimizes the simulation of the PBPK model. It is noteworthy that posterior sets generated from temperature-associated datasets to be respectively used for simulating residues under corresponding temperature conditions. Simulating the residues under regulated regimen and overdosing scenarios for Taiwan, the estimated WTs were 12-16 days at 22 °C and 9-12 days at 28 °C, while for the USA, the estimated WTs were 14-18 and 11-14 days, respectively. Comparison with the regulated WT of 15 days, results indicate that the current WT has well robustness and resilience in the environment of higher temperatures. The optimal Bayesian population PBPK model provides effective analysis for determining WTs under scenario-specific conditions. It is a new insight into the increasing body of literature on developing the Bayesian-PBPK model and has practical implications for improving the regulation of food safety.


Asunto(s)
Antibacterianos/farmacocinética , Modelos Biológicos , Tianfenicol/análogos & derivados , Tilapia/metabolismo , Animales , Acuicultura , Teorema de Bayes , Cadenas de Markov , Método de Montecarlo , Taiwán , Tianfenicol/farmacocinética
20.
Artículo en Inglés | MEDLINE | ID: mdl-33418973

RESUMEN

Geosynthetics are extensively utilized to improve the stability of geotechnical structures and slopes in urban areas. Among all existing geosynthetics, geotextiles are widely used to reinforce unstable slopes due to their capabilities in facilitating reinforcement and drainage. To reduce settlement and increase the bearing capacity and slope stability, the classical use of geotextiles in embankments has been suggested. However, several catastrophic events have been reported, including failures in slopes in the absence of geotextiles. Many researchers have studied the stability of geotextile-reinforced slopes (GRSs) by employing different methods (analytical models, numerical simulation, etc.). The presence of source-to-source uncertainty in the gathered data increases the complexity of evaluating the failure risk in GRSs since the uncertainty varies among them. Consequently, developing a sound methodology is necessary to alleviate the risk complexity. Our study sought to develop an advanced risk-based maintenance (RBM) methodology for prioritizing maintenance operations by addressing fluctuations that accompany event data. For this purpose, a hierarchical Bayesian approach (HBA) was applied to estimate the failure probabilities of GRSs. Using Markov chain Monte Carlo simulations of likelihood function and prior distribution, the HBA can incorporate the aforementioned uncertainties. The proposed method can be exploited by urban designers, asset managers, and policymakers to predict the mean time to failures, thus directly avoiding unnecessary maintenance and safety consequences. To demonstrate the application of the proposed methodology, the performance of nine reinforced slopes was considered. The results indicate that the average failure probability of the system in an hour is 2.8×10-5 during its lifespan, which shows that the proposed evaluation method is more realistic than the traditional methods.


Asunto(s)
Teorema de Bayes , Cadenas de Markov , Método de Montecarlo , Reproducibilidad de los Resultados , Incertidumbre
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