RESUMEN
BACKGROUND: The preoperative prediction of peritoneal metastasis (PM) in gastric cancer would prevent unnecessary surgery and promptly indicate an appropriate treatment plan. AIM: To explore the predictive value of visceral fat (VF) parameters obtained from preoperative computed tomography (CT) images for occult PM and to develop an individualized model for predicting occult PM in patients with gastric carcinoma (GC). METHODS: A total of 128 confirmed GC cases (84 male and 44 female patients) that underwent CT scans were analyzed and categorized into PM-positive (n = 43) and PM-negative (n = 85) groups. The clinical characteristics and VF parameters of two regions of interest (ROIs) were collected. Univariate and stratified analyses based on VF volume were performed to screen for predictive characteristics for occult PM. Prediction models with and without VF parameters were established by multivariable logistic regression analysis. RESULTS: The mean attenuations of VFROI 1 and VFROI 2 varied significantly between the PM-positive and PM-negative groups (P = 0.044 and 0.001, respectively). The areas under the receiver operating characteristic curves (AUCs) of VFROI 1 and VFROI 2 were 0.599 and 0.657, respectively. The mean attenuation of VFROI 2 was included in the final prediction combined model, but not an independent risk factor of PM (P = 0.068). No significant difference was observed between the models with and without mean attenuation of VF (AUC: 0.749 vs 0.730, P = 0.339). CONCLUSION: The mean attenuation of VF is a potential auxiliary parameter for predicting occult PM in patients with GC.
Asunto(s)
Neoplasias Peritoneales , Neoplasias Gástricas , Humanos , Masculino , Femenino , Neoplasias Gástricas/patología , Neoplasias Peritoneales/diagnóstico por imagen , Neoplasias Peritoneales/secundario , Grasa Intraabdominal/diagnóstico por imagen , Grasa Intraabdominal/patología , Estudios Retrospectivos , Tomografía Computarizada por Rayos X/métodosRESUMEN
With the development of electrical stimulation technology, especially the emergence of temporally interfering (TI) stimulation, it is necessary to discuss the influence of current frequency on stimulation intensity. Accurate skull modeling is important for transcranial current stimulation (tCS) simulation prediction because of its large role in dispersing current. In this study, we simulated different frequencies of transcranial alternating current stimulation (tACS) and TI stimulation in single-layer and layered skull model, compared the electric field via error parameters such as the relative difference measure and relative magnification factor. Pearson correlation analysis and t-test were used to measure the differences in envelope amplitude. The results showed that the intensity of electric field in the brain generated by per unit of stimulation current will increase with current frequency, and the layered skull model had a better response to frequency. An obvious pattern difference was found between the electric fields of the layered and single-layer skull individualized models. For TI stimulation, the Pearson correlation coefficient between the envelope distribution of the layered skull model and the single-layer skull was only 0.746 in the individualized model, which is clearly lower than the correlation coefficient of 0.999 determined from the spherical model. Higher carrier frequencies seemed to be easier to generate a large enough brain electric field envelope in TI stimulation. In conclusion, we recommend using layered skull models instead of single-layer skull models in tCS (particularly TI stimulation) simulation studies in order to improve the accuracy of the prediction of stimulus intensity and stimulus target.
Asunto(s)
Modelos Teóricos , Cráneo/anatomía & histología , Estimulación Transcraneal de Corriente Directa/métodos , Estimulación Transcraneal de Corriente Directa/normas , HumanosRESUMEN
BACKGROUND: Exposure to air pollution aggravates symptoms of atopic dermatitis (AD) in children in the population studies. Variability in individual patient's response from individual susceptibility is needed to be explored. OBJECTIVE: This study aimed to investigate spectrum of individual variability in the associations between AD symptoms and air quality. METHODS: We enrolled 89 children aged 0-6 years with AD (22 890 person-days). Daily manifestation of symptoms was recorded for an average of 257 days (range 100-499). Both an individual analysis using logistic regression models and an overall analysis using a generalized estimating equation were performed. RESULTS: The odds ratios of an individual ranged 0.24-8.11 for particulate matter <10 µm in diameter (PM10 ), 0.09-101.92 for nitrogen oxide (NO2 ), 0.03-44.00 for ozone (O3 ), 0.11-58.30 for sulfur dioxide (SO2 ), 0.00-15.83 for carbon monoxide (CO), 0.00-39 446.94 for temperature, and 0.03-5.18 for relative humidity, demonstrating a wide individual variability. In the overall analysis, PM10 , NO2 , SO2 , and CO had a significantly positive association, whereas temperature and relative humidity were negatively associated with AD symptoms. Air pollution was responsible for aggravation of symptoms from 24.7% (O3 ) to 39.3% (SO2 ) of AD children. Overall, 71.9% of the AD children responded to at least one or more air pollution and weather variable. CONCLUSION: Responses of AD children to air pollution and weather variable were considerably variable among individuals. An individualized model would be useful to forecast and manage AD symptoms in patients.
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Contaminantes Atmosféricos/inmunología , Contaminación del Aire/efectos adversos , Variación Biológica Poblacional/inmunología , Dermatitis Atópica/etiología , Exposición a Riesgos Ambientales/efectos adversos , Contaminantes Atmosféricos/análisis , Niño , Preescolar , Femenino , Humanos , Lactante , Recién Nacido , Masculino , República de Corea , Tiempo (Meteorología)RESUMEN
Caffeine is the most widely consumed stimulant to counter sleep-loss effects. While the pharmacokinetics of caffeine in the body is well-understood, its alertness-restoring effects are still not well characterized. In fact, mathematical models capable of predicting the effects of varying doses of caffeine on objective measures of vigilance are not available. In this paper, we describe a phenomenological model of the dose-dependent effects of caffeine on psychomotor vigilance task (PVT) performance of sleep-deprived subjects. We used the two-process model of sleep regulation to quantify performance during sleep loss in the absence of caffeine and a dose-dependent multiplier factor derived from the Hill equation to model the effects of single and repeated caffeine doses. We developed and validated the model fits and predictions on PVT lapse (number of reaction times exceeding 500 ms) data from two separate laboratory studies. At the population-average level, the model captured the effects of a range of caffeine doses (50-300 mg), yielding up to a 90% improvement over the two-process model. Individual-specific caffeine models, on average, predicted the effects up to 23% better than population-average caffeine models. The proposed model serves as a useful tool for predicting the dose-dependent effects of caffeine on the PVT performance of sleep-deprived subjects and, therefore, can be used for determining caffeine doses that optimize the timing and duration of peak performance.
Asunto(s)
Atención/efectos de los fármacos , Cafeína/administración & dosificación , Privación de Sueño/fisiopatología , Cafeína/farmacología , Relación Dosis-Respuesta a Droga , HumanosRESUMEN
The incretin effect, which is a unique stimulus of insulin secretion in response to oral ingestion of nutrients, is calculated by the difference in insulin secretory responses from an oral glucose tolerance test (OGTT) and a corresponding isoglycemic intravenous glucose infusion (IIGI) study. The OGTT model of this study, which is individualized by fitting the glucose profiles during an OGTT, was developed to predict the glucose profile during an IIGI study in the same subject. Also, the model predicts the insulin and incretin profiles during both studies. The incretin effect, estimated by simulation, was compared with that measured by physiologic studies from eight human subjects with normal glucose tolerance, and the result exhibited a good correlation (r > 0.8); the incretin effect from the simulation was 56.5% ± 10.6% while the one from the measured data was 52.5% ± 19.6%. In conclusion, the parameters of the OGTT model have been successfully estimated to predict the profiles of both OGTTs and IIGI studies. Therefore, with glucose data from the OGTT alone, this model could control and predict the physiologic responses, including insulin secretion during OGTTs and IIGI studies, which could eventually eliminate the need for complex and cumbersome IIGI studies in incretin research.
Asunto(s)
Simulación por Computador , Incretinas/sangre , Modelos Teóricos , Administración Oral , Adulto , Área Bajo la Curva , Glucemia/análisis , Femenino , Glucosa/metabolismo , Glucosa/farmacología , Prueba de Tolerancia a la Glucosa , Humanos , Insulina/sangre , Hígado/efectos de los fármacos , Persona de Mediana Edad , Curva ROCRESUMEN
The incretin effect, which is a unique stimulus of insulin secretion in response to oral ingestion of nutrients, is calculated by the difference in insulin secretory responses from an oral glucose tolerance test (OGTT) and a corresponding isoglycemic intravenous glucose infusion (IIGI) study. The OGTT model of this study, which is individualized by fitting the glucose profiles during an OGTT, was developed to predict the glucose profile during an IIGI study in the same subject. Also, the model predicts the insulin and incretin profiles during both studies. The incretin effect, estimated by simulation, was compared with that measured by physiologic studies from eight human subjects with normal glucose tolerance, and the result exhibited a good correlation (r > 0.8); the incretin effect from the simulation was 56.5% +/- 10.6% while the one from the measured data was 52.5% +/- 19.6%. In conclusion, the parameters of the OGTT model have been successfully estimated to predict the profiles of both OGTTs and IIGI studies. Therefore, with glucose data from the OGTT alone, this model could control and predict the physiologic responses, including insulin secretion during OGTTs and IIGI studies, which could eventually eliminate the need for complex and cumbersome IIGI studies in incretin research.