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1.
Bull Math Biol ; 83(8): 89, 2021 07 03.
Artículo en Inglés | MEDLINE | ID: mdl-34216281

RESUMEN

This work presents a model-agnostic evaluation of four different models that estimate a disease's basic reproduction number. The evaluation presented is twofold: first, the theory behind each of the models is reviewed and compared; then, each model is tested with eight impartial simulations. All scenarios were constructed in an experimental framework that allows each model to fulfill its assumptions and hence, obtain unbiased results for each case. Among these models is the one proposed by Thompson et al. (Epidemics 29:100356, 2019), i.e., a Bayesian estimation method well established in epidemiological practice. The other three models include a novel state-space method and two simulation-based approaches based on a Poisson infection process. The advantages and flaws of each model are discussed from both theoretical and practical standpoints. Finally, we present the evolution of Covid-19 outbreak in Colombia as a case study for computing the basic reproduction number with each one of the reviewed methods.


Asunto(s)
Número Básico de Reproducción/estadística & datos numéricos , COVID-19/epidemiología , COVID-19/transmisión , Pandemias/estadística & datos numéricos , SARS-CoV-2 , Teorema de Bayes , Colombia/epidemiología , Simulación por Computador , Intervalos de Confianza , Epidemias/estadística & datos numéricos , Humanos , Conceptos Matemáticos , Modelos Biológicos , Modelos Estadísticos , Distribución de Poisson
2.
Sensors (Basel) ; 21(12)2021 Jun 16.
Artículo en Inglés | MEDLINE | ID: mdl-34208704

RESUMEN

For robots to execute their navigation tasks both fast and safely in the presence of humans, it is necessary to make predictions about the route those humans intend to follow. Within this work, a model-based method is proposed that relates human motion behavior perceived from RGBD input to the constraints imposed by the environment by considering typical human routing alternatives. Multiple hypotheses about routing options of a human towards local semantic goal locations are created and validated, including explicit collision avoidance routes. It is demonstrated, with real-time, real-life experiments, that a coarse discretization based on the semantics of the environment suffices to make a proper distinction between a person going, for example, to the left or the right on an intersection. As such, a scalable and explainable solution is presented, which is suitable for incorporation within navigation algorithms.


Asunto(s)
Intención , Semántica , Algoritmos , Humanos , Modelos Estadísticos , Movimiento (Física)
3.
Huan Jing Ke Xue ; 42(7): 3118-3126, 2021 Jul 08.
Artículo en Chino | MEDLINE | ID: mdl-34212637

RESUMEN

Ozone pollution has recently become a severe air quality issue in the Beijing-Tianjin-Hebei region. Due to the lack of a precursor emission inventory and complexity of physical and chemical mechanism of ozone generation, numerical modeling still exhibits significant deviations in ozone forecasting. Owing to its simplicity and low calculation costs, the time series analysis model can be effectively applied for ozone pollution forecasting. We conducted a time series analysis of ozone concentration at Shangdianzi, Baoding, and Tianjin sites. Both seasonal and dynamic ARIMA models were established to perform mid- and long-term ozone forecasting. The correlation coefficient R between the predicted and observed value can reach 0.951, and the RMSE is only 10.2 µg·m-3 for the monthly average ozone prediction by the seasonal ARIMA model. The correlation coefficient R between the predicted and observed value increased from 0.296-0.455 to 0.670-0.748, and RMSE was effectively reduced for the 8-hour ozone average predicted by the dynamic ARIMA model.


Asunto(s)
Contaminantes Atmosféricos , Contaminación del Aire , Ozono , Contaminantes Atmosféricos/análisis , Contaminación del Aire/análisis , Beijing , China , Monitoreo del Ambiente , Predicción , Modelos Estadísticos , Ozono/análisis
4.
Front Public Health ; 9: 645405, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34222166

RESUMEN

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.


Asunto(s)
COVID-19 , Enfermedades Transmisibles , Enfermedades Transmisibles/epidemiología , Humanos , Modelos Estadísticos , Pandemias , SARS-CoV-2
5.
PLoS One ; 16(7): e0253146, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34197489

RESUMEN

This work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than modeling all cases of the disease. It was tuned using data available for each Brazilian state and updated with daily figures. A figure of merit that assesses the quality of the model fit to the data was defined and used to optimize the free parameters. The parameters of an epidemiological model for the whole of Brazil, comprising a linear combination of the models for each state, were estimated considering the data available for the 26 Brazilian states. The model was validated, and strong adherence was demonstrated in most cases.


Asunto(s)
COVID-19/epidemiología , Brasil/epidemiología , Atención a la Salud , Humanos , Aprendizaje Automático , Modelos Estadísticos , SARS-CoV-2/aislamiento & purificación
6.
Sensors (Basel) ; 21(13)2021 Jun 30.
Artículo en Inglés | MEDLINE | ID: mdl-34209114

RESUMEN

Time-of-Flight (TOF) based Light Detection and Ranging (LiDAR) is a widespread technique for distance measurements in both single-spot depth ranging and 3D mapping. Single Photon Avalanche Diode (SPAD) detectors provide single-photon sensitivity and allow in-pixel integration of a Time-to-Digital Converter (TDC) to measure the TOF of single-photons. From the repetitive acquisition of photons returning from multiple laser shots, it is possible to accumulate a TOF histogram, so as to identify the laser pulse return from unwelcome ambient light and compute the desired distance information. In order to properly predict the TOF histogram distribution and design each component of the LiDAR system, from SPAD to TDC and histogram processing, we present a detailed statistical modelling of the acquisition chain and we show the perfect matching with Monte Carlo simulations in very different operating conditions and very high background levels. We take into consideration SPAD non-idealities such as hold-off time, afterpulsing, and crosstalk, and we show the heavy pile-up distortion in case of high background. Moreover, we also model non-idealities of timing electronics chain, namely, TDC dead-time, limited number of storage cells for TOF data, and TDC sharing. Eventually, we show how the exploit the modelling to reversely extract the original LiDAR return signal from the distorted measured TOF data in different operating conditions.


Asunto(s)
Modelos Estadísticos , Fotones , Electrónica , Luz , Método de Montecarlo
7.
Front Public Health ; 9: 645405, 2021.
Artículo en Inglés | MEDLINE | ID: covidwho-1295716

RESUMEN

In this review, we have discussed the different statistical modeling and prediction techniques for various infectious diseases including the recent pandemic of COVID-19. The distribution fitting, time series modeling along with predictive monitoring approaches, and epidemiological modeling are illustrated. When the epidemiology data is sufficient to fit with the required sample size, the normal distribution in general or other theoretical distributions are fitted and the best-fitted distribution is chosen for the prediction of the spread of the disease. The infectious diseases develop over time and we have data on the single variable that is the number of infections that happened, therefore, time series models are fitted and the prediction is done based on the best-fitted model. Monitoring approaches may also be applied to time series models which could estimate the parameters more precisely. In epidemiological modeling, more biological parameters are incorporated in the models and the forecasting of the disease spread is carried out. We came up with, how to improve the existing modeling methods, the use of fuzzy variables, and detection of fraud in the available data. Ultimately, we have reviewed the results of recent statistical modeling efforts to predict the course of COVID-19 spread.


Asunto(s)
COVID-19 , Enfermedades Transmisibles , Enfermedades Transmisibles/epidemiología , Humanos , Modelos Estadísticos , Pandemias , SARS-CoV-2
8.
Sci Rep ; 11(1): 13822, 2021 07 05.
Artículo en Inglés | MEDLINE | ID: mdl-34226584

RESUMEN

The need for improved models that can accurately predict COVID-19 dynamics is vital to managing the pandemic and its consequences. We use machine learning techniques to design an adaptive learner that, based on epidemiological data available at any given time, produces a model that accurately forecasts the number of reported COVID-19 deaths and cases in the United States, up to 10 weeks into the future with a mean absolute percentage error of 9%. In addition to being the most accurate long-range COVID predictor so far developed, it captures the observed periodicity in daily reported numbers. Its effectiveness is based on three design features: (1) producing different model parameters to predict the number of COVID deaths (and cases) from each time and for a given number of weeks into the future, (2) systematically searching over the available covariates and their historical values to find an effective combination, and (3) training the model using "last-fold partitioning", where each proposed model is validated on only the last instance of the training dataset, rather than being cross-validated. Assessments against many other published COVID predictors show that this predictor is 19-48% more accurate.


Asunto(s)
COVID-19/mortalidad , Enfermedades Transmisibles/mortalidad , Predicción , SARS-CoV-2/patogenicidad , Humanos , Aprendizaje Automático , Modelos Estadísticos , Estados Unidos
9.
Sci Rep ; 11(1): 13860, 2021 07 05.
Artículo en Inglés | MEDLINE | ID: mdl-34226649

RESUMEN

Covid-19 epidemic dramatically relaunched the importance of mathematical modelling in supporting governments decisions to slow down the disease propagation. On the other hand, it remains a challenging task for mathematical modelling. The interplay between different models could be a key element in the modelling strategies. Here we propose a continuous space-time non-linear probabilistic model from which we can derive many of the existing models both deterministic and stochastic as for example SI, SIR, SIR stochastic, continuous-time stochastic models, discrete stochastic models, Fisher-Kolmogorov model. A partial analogy with the statistical interpretation of quantum mechanics provides an interpretation of the model. Epidemic forecasting is one of its possible applications; in principle, the model can be used in order to locate those regions of space where the infection probability is going to increase. The connection between non-linear probabilistic and non-linear deterministic models is analyzed. In particular, it is shown that the Fisher-Kolmogorov equation is connected to linear probabilistic models. On the other hand, a generalized version of the Fisher-Kolmogorov equation is derived from the non-linear probabilistic model and is shown to be characterized by a non-homogeneous time-dependent diffusion coefficient (anomalous diffusion) which encodes information about the non-linearity of the probabilistic model.


Asunto(s)
Algoritmos , COVID-19/epidemiología , Modelos Estadísticos , SARS-CoV-2/patogenicidad , Simulación por Computador , Humanos , Modelos Biológicos , Modelos Teóricos , Procesos Estocásticos
10.
J Environ Public Health ; 2021: 5582589, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34194512

RESUMEN

FluMOMO is a universal formula to forecast mortality in 27 European countries and was developed on EuroMOMO context, http://www.euromomo.eu. The model has a trigonometric baseline and considers any upwards deviation from that to come from flu or extreme temperatures. To measure it, the model considers two variables: influenza activity and extreme temperatures. With the former, the model gives the number of deaths because of flu and with the latter the number of deaths because of extreme temperatures. In this article, we show that FluMOMO lacks important variables to be an accurate measure of all-cause mortality and flu mortality. Indeed, we found, as expected, that population ageing and exposure to the risk of death cannot be excluded from the linear predictor. We model weekly deaths as an autoregressive process (lag of one together with a lead of one week). This step allowed us to avoid FluMOMO trigonometric baseline and have a fit to weekly deaths through demographic variables. Our model uses data from Portugal between 2009 and 2020, on ISO-week basis. We use negative binomial-generalized linear models to estimate the weekly number of deaths as an alternative to traditional overdispersion Poisson. As explanatory variables were found to be statistically significant, we registered the number of deaths from the previous week, the influenza activity index, the population average age, the heat waves, the flu season, the number of deaths with COVID-19, and the population exposed to the risk of dying. Considering as excess mortality the number of deaths above the best estimate of deaths from our model, we conclude that excess mortality in 2020 (net of COVID-19 deaths, heat wave of July, and ageing) is low or inexistent. The model also allows us to have the number of deaths arising from flu and we conclude that FluMOMO is overestimating deaths from flu by 78%. Averages from the probability of dying are obtained as well as the probability of dying from flu. The latter is shown to be decreasing over time, probably due to the increase of flu vaccination. Higher mortality detected with the start of COVID-19, in March-April 2020, was probably due to COVID-19 deaths not recognized as COVID-19 deaths.


Asunto(s)
Gripe Humana/epidemiología , Mortalidad/tendencias , Población , COVID-19 , Europa (Continente)/epidemiología , Femenino , Humanos , Masculino , Modelos Estadísticos , Portugal , SARS-CoV-2 , Estaciones del Año , Vacunación
11.
Artículo en Inglés | MEDLINE | ID: mdl-34200378

RESUMEN

BACKGROUND: This study intends to identify the best model for predicting the incidence of hand, foot and mouth disease (HFMD) in Ningbo by comparing Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM) models combined and uncombined with exogenous meteorological variables. METHODS: The data of daily HFMD incidence in Ningbo from January 2014 to November 2017 were set as the training set, and the data of December 2017 were set as the test set. ARIMA and LSTM models combined and uncombined with exogenous meteorological variables were adopted to fit the daily incidence of HFMD by using the data of the training set. The forecasting performances of the four fitted models were verified by using the data of the test set. Root mean square error (RMSE) was selected as the main measure to evaluate the performance of the models. RESULTS: The RMSE for multivariate LSTM, univariate LSTM, ARIMA and ARIMAX (Autoregressive Integrated Moving Average Model with Exogenous Input Variables) was 10.78, 11.20, 12.43 and 14.73, respectively. The LSTM model with exogenous meteorological variables has the best performance among the four models and meteorological variables can increase the prediction accuracy of LSTM model. For the ARIMA model, exogenous meteorological variables did not increase the prediction accuracy but became the interference factor of the model. CONCLUSIONS: Multivariate LSTM is the best among the four models to fit the daily incidence of HFMD in Ningbo. It can provide a scientific method to build the HFMD early warning system and the methodology can also be applied to other communicable diseases.


Asunto(s)
Enfermedad de Boca, Mano y Pie , China/epidemiología , Predicción , Enfermedad de Boca, Mano y Pie/epidemiología , Humanos , Incidencia , Conceptos Meteorológicos , Modelos Estadísticos , Redes Neurales de la Computación
12.
Sci Rep ; 11(1): 13939, 2021 07 06.
Artículo en Inglés | MEDLINE | ID: mdl-34230582

RESUMEN

Coronavirus disease 2019 dominated and augmented many aspects of life beginning in early 2020. Related research and data generation developed alongside its spread. We developed a Bayesian spatio-temporal Poisson disease mapping model for estimating real-time characteristics of the coronavirus disease in the United States. We also created several dashboards for visualization of the statistical model for fellow researchers and simpler spatial and temporal representations of the disease for consumption by analysts and data scientists in the policymaking community in our region. Findings suggest that the risk of confirmed cases is higher for health regions under partial stay at home orders and lower in health regions under full stay at home orders, when compared to before stay at home orders were declared. These results confirm the benefit of state-issued stay at home orders as well as suggest compliance to the directives towards the older population for adhering to social distancing guidelines.


Asunto(s)
COVID-19/epidemiología , Modelos Estadísticos , Distanciamiento Físico , SARS-CoV-2/patogenicidad , Factores de Edad , Teorema de Bayes , Humanos , Estados Unidos
13.
Genes (Basel) ; 12(7)2021 06 24.
Artículo en Inglés | MEDLINE | ID: mdl-34202464

RESUMEN

ABO blood system is an inborn trait determined by the ABO gene. The genetic-phenotypic mechanism underneath the four mutually exclusive and collectively exhaustive types of O, A, B and AB could theoretically be elucidated. However, genetic polymorphisms in the human populations render the link elusive, and importantly, past studies using genetically determined rather than biochemically determined ABO types were not and could not be evaluated for the inference errors. Upon both blood-typing and genotyping a cohort of 1008 people of the Han Chinese population, we conducted a genome-wide association study in parallel with both binomial and multinomial log-linear models. Significant genetic variants are all mapped to the ABO gene, and are quantitatively evaluated for binary and multi-class classification performances. Three single nucleotide polymorphisms of rs8176719, rs635634 and rs7030248 would together be sufficient to establish a multinomial predictive model that achieves high accuracy (0.98) and F1 scores (micro 0.99 and macro 0.97). Using the set of identified ABO-associated genetic variants as instrumental variables, we demonstrate the application in causal analysis by Mendelian randomization (MR) studies on blood pressures (one-sample MR) and severe COVID-19 with respiratory failure (two-sample MR).


Asunto(s)
Sistema del Grupo Sanguíneo ABO/sangre , Sistema del Grupo Sanguíneo ABO/genética , COVID-19/genética , Polimorfismo de Nucleótido Simple , Adulto , Grupo de Ascendencia Continental Asiática/genética , Presión Sanguínea/genética , COVID-19/etiología , Estudios de Cohortes , Femenino , Predisposición Genética a la Enfermedad , Estudio de Asociación del Genoma Completo , Humanos , Masculino , Análisis de la Aleatorización Mendeliana , Persona de Mediana Edad , Modelos Estadísticos , Pruebas Serológicas
14.
Int J Mol Sci ; 22(11)2021 May 29.
Artículo en Inglés | MEDLINE | ID: mdl-34072353

RESUMEN

The variability of methicillin-resistant Staphylococcus aureus (MRSA), its rapid adaptive response against environmental changes, and its continued acquisition of antibiotic resistance determinants have made it commonplace in hospitals, where it causes the problem of multidrug resistance. In this study, we used molecular topology to develop several discriminant equations capable of classifying compounds according to their anti-MRSA activity. Topological indices were used as structural descriptors and their relationship with anti-MRSA activity was determined by applying linear discriminant analysis (LDA) on a group of quinolones and quinolone-like compounds. Four extra equations were constructed, named DFMRSA1, DFMRSA2, DFMRSA3 and DFMRSA4 (DFMRSA was built in a previous study), all with good statistical parameters, such as Fisher-Snedecor F (>68 in all cases), Wilk's lambda (<0.13 in all cases), and percentage of correct classification (>94% in all cases), which allows a reliable extrapolation prediction of antibacterial activity in any organic compound. The results obtained clearly reveal the high efficiency of combining molecular topology with LDA for the prediction of anti-MRSA activity.


Asunto(s)
Antiinfecciosos/química , Análisis Discriminante , Descubrimiento de Drogas/métodos , Algoritmos , Antiinfecciosos/farmacología , Staphylococcus aureus Resistente a Meticilina/efectos de los fármacos , Modelos Estadísticos , Relación Estructura-Actividad
15.
Sci Rep ; 11(1): 12213, 2021 06 09.
Artículo en Inglés | MEDLINE | ID: mdl-34108496

RESUMEN

As we enter a chronic phase of the SARS-CoV-2 pandemic, with uncontrolled infection rates in many places, relative regional susceptibilities are a critical unknown for policy planning. Tests for SARS-CoV-2 infection or antibodies are indicative but unreliable measures of exposure. Here instead, for four highly-affected countries, we determine population susceptibilities by directly comparing country-wide observed epidemic dynamics data with that of their main metropolitan regions. We find significant susceptibility reductions in the metropolitan regions as a result of earlier seeding, with a relatively longer phase of exponential growth before the introduction of public health interventions. During the post-growth phase, the lower susceptibility of these regions contributed to the decline in cases, independent of intervention effects. Forward projections indicate that non-metropolitan regions will be more affected during recurrent epidemic waves compared with the initially heavier-hit metropolitan regions. Our findings have consequences for disease forecasts and resource utilisation.


Asunto(s)
COVID-19/epidemiología , Pandemias/estadística & datos numéricos , COVID-19/mortalidad , COVID-19/prevención & control , Ciudades/epidemiología , Susceptibilidad a Enfermedades , Humanos , Modelos Estadísticos , Pandemias/prevención & control
16.
Sci Rep ; 11(1): 12177, 2021 06 09.
Artículo en Inglés | MEDLINE | ID: mdl-34108505

RESUMEN

The stochastic model for epidemic spreading of the novel coronavirus disease based on the data set supply by the public health agencies in countries as Brazil, United States and India is investigated. We perform a numerical analysis using the stochastic differential equation in Itô's calculus for the estimating of novel cases daily, as well as analytical calculations solving the correspondent Fokker-Planck equation for the probability density distribution of novel cases, P(N(t), t). Our results display that the model based in the Itô's diffusion fits well to the results due to uncertainty in the official data and to the number of tests realized in populations of each country.


Asunto(s)
COVID-19/epidemiología , COVID-19/transmisión , Bases de Datos Factuales , Modelos Estadísticos , Humanos , Procesos Estocásticos , Incertidumbre
17.
Sci Prog ; 104(2): 368504211017800, 2021.
Artículo en Inglés | MEDLINE | ID: mdl-34080487

RESUMEN

Accurate modeling of viral outbreaks in living populations and computer networks is a prominent research field. Many researchers are in search for simple and realistic models to manage preventive resources and implement effective measures against hazardous circumstances. The ongoing Covid-19 pandemic has revealed the fact about deficiencies in health resource planning of some countries having relatively high case count and death toll. A unique epidemic model incorporating stochastic processes and queuing theory is presented, which was evaluated by computer simulation using pre-processed data obtained from an urban clinic providing family health services. Covid-19 data from a local corona-center was used as the initial model parameters (e.g. R0, infection rate, local population size, number of contacts with infected individuals, and recovery rate). A long-run trend analysis for 1 year was simulated. The results fit well to the current case data of the sample corona center. Effective preventive and reactive resource planning basically depends on accurately designed models, tools, and techniques needed for the prediction of feature threats, risks, and mitigation costs. In order to sufficiently analyze the transmission and recovery dynamics of epidemics it is important to choose concise mathematical models. Hence, a unique stochastic modeling approach tied to queueing theory and computer simulation has been chosen. The methods used here can also serve as a guidance for accurate modeling and classification of stages (or compartments) of epidemics in general.


Asunto(s)
COVID-19/epidemiología , COVID-19/transmisión , Hospitales Urbanos , Modelos Estadísticos , Pandemias , SARS-CoV-2/patogenicidad , Antivirales/uso terapéutico , COVID-19/tratamiento farmacológico , COVID-19/mortalidad , Simulación por Computador , Trazado de Contacto/estadística & datos numéricos , Medicina Familiar y Comunitaria , Humanos , Incidencia , Modelos Inmunológicos , Densidad de Población , Cuarentena/organización & administración , SARS-CoV-2/inmunología , Procesos Estocásticos , Análisis de Supervivencia , Turquia/epidemiología
18.
Sci Rep ; 11(1): 12051, 2021 06 08.
Artículo en Inglés | MEDLINE | ID: mdl-34103618

RESUMEN

The effect of vaccination coupled with the behavioral response of the population is not well understood. Our model incorporates two important dynamically varying population behaviors: level of caution and sense of safety. Level of caution increases with infectious cases, while an increasing sense of safety with increased vaccination lowers precautions. Our model accurately reproduces the complete time history of COVID-19 infections for various regions of the United States. We propose a parameter [Formula: see text] as a direct measure of a population's caution against an infectious disease that can be obtained from the infectious cases. The model provides quantitative measures of highest disease transmission rate, effective transmission rate, and cautionary behavior. We predict future COVID-19 trends in the United States accounting for vaccine rollout and behavior. Although a high rate of vaccination is critical to quickly ending the pandemic, a return towards pre-pandemic social behavior due to increased sense of safety during vaccine deployment can cause an alarming surge in infections. Our results predict that at the current rate of vaccination, the new infection cases for COVID-19 in the United States will approach zero by August 2021. This model can be used for other regions and for future epidemics and pandemics.


Asunto(s)
COVID-19/epidemiología , COVID-19/prevención & control , Conductas Relacionadas con la Salud , Modelos Estadísticos , Vacunación/estadística & datos numéricos , COVID-19/transmisión , Humanos
19.
Medicine (Baltimore) ; 100(25): e26462, 2021 Jun 25.
Artículo en Inglés | MEDLINE | ID: mdl-34160448

RESUMEN

ABSTRACT: To develop a noninvasive model to predict significant fibrosis in children with chronic hepatitis B (CHB).A total of 116 CHB pediatric patients who underwent liver biopsy were included in the study. Liver histology, which is the gold standard for assessing fibrosis, was performed. Blood routine examination, coagulation function, liver biochemistry, viral serology, and viral load were analyzed. Receiver operating characteristic curve analysis was used to analyze the sensitivity and specificity of all possible cut-off values.Based on the correlation and difference analyses, 7 available clinical parameters (total bile acid, gamma-glutamyl transpeptidase [GGT], aspartate transaminase, direct bilirubin to total bilirubin ratio, alanine aminotransferase, prealbumin [PA], and cholinesterase) were included in the modeling analysis. A model to predict significant liver fibrosis was derived using the 2 best parameters (PA and GGT). The original model was . After the mathematical calculation, the G index=600 × GGT/PA2 predicted significant fibrosis, with an area under the receiving operating characteristics (AUROC) curve of 0.733, 95% confidence interval (0.643-0.811). The AUROC of the G index (0.733) was higher than that of aminotransferase to platelet ratio index (APRI) (0.680) and Fibrosis index based on 4 factors (FIB-4) (0.601) in predicting significant fibrosis in children with CHB. If the values of the G index were outside the range of 0.28 to 1.16, 52% of children with CHB could avoid liver biopsy, with an overall accuracy of 75%.The G index can predict and exclude significant fibrosis in children with CHB, and it may reduce the need for liver biopsy in children with CHB.


Asunto(s)
Hepatitis B Crónica/sangre , Cirrosis Hepática/diagnóstico , Hígado/patología , Modelos Estadísticos , Índice de Severidad de la Enfermedad , Biopsia , Niño , Preescolar , Progresión de la Enfermedad , Estudios de Factibilidad , Femenino , Hepatitis B Crónica/patología , Hepatitis B Crónica/virología , Humanos , Cirrosis Hepática/sangre , Cirrosis Hepática/patología , Cirrosis Hepática/virología , Pruebas de Función Hepática/métodos , Masculino , Recuento de Plaquetas , Valor Predictivo de las Pruebas , Pronóstico , Curva ROC , Estudios Retrospectivos
20.
Nat Commun ; 12(1): 3718, 2021 06 17.
Artículo en Inglés | MEDLINE | ID: mdl-34140479

RESUMEN

Identification of small molecules is a critical task in various areas of life science. Recent advances in mass spectrometry have enabled the collection of tandem mass spectra of small molecules from hundreds of thousands of environments. To identify which molecules are present in a sample, one can search mass spectra collected from the sample against millions of molecular structures in small molecule databases. The existing approaches are based on chemistry domain knowledge, and they fail to explain many of the peaks in mass spectra of small molecules. Here, we present molDiscovery, a mass spectral database search method that improves both efficiency and accuracy of small molecule identification by learning a probabilistic model to match small molecules with their mass spectra. A search of over 8 million spectra from the Global Natural Product Social molecular networking infrastructure shows that molDiscovery correctly identify six times more unique small molecules than previous methods.


Asunto(s)
Ensayos Analíticos de Alto Rendimiento/métodos , Metabolómica/métodos , Bibliotecas de Moléculas Pequeñas/análisis , Espectrometría de Masas en Tándem/métodos , Algoritmos , Bacterias/aislamiento & purificación , Bacterias/metabolismo , Benchmarking , Simulación por Computador , Bases de Datos de Compuestos Químicos , Humanos , Lípidos/aislamiento & purificación , Modelos Estadísticos , Plantas/metabolismo , Metabolismo Secundario , Programas Informáticos
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