Exploration of moisture activated dry granulation for the development of gastroretentive tablets aided by SeDeM diagram.

Moisture activated dry granulation (MADG) is an attractive granulation process. However, only a few works have explored modified drug release achieved by MADG, and to the best of the authors knowledge, none of them have explored gastroretention. The aim of this study was to explore the applicability of MADG process for developing gastroretentive placebo tablets, aided by SeDeM diagram. Floating and swelling capacities have been identified as critical quality attributes (CQAs). After a formulation screening step, the type and concentration of floating matrix formers and of binders were identified as the most relevant critical material attributes (CMAs) to investigate in ten formulations. A multiple linear regression analysis (MLRA) was applied against the factors that were varied to find the design space. An optimized product based on principal component analysis (PCA) results and MLRA was prepared and characterized. The granulate was also assessed by SeDeM. In conclusion, granulates lead to floating tablets with short floating lag time (<2 min), long floating duration (>4 h), and showing good swelling characteristics. The results obtained so far are promising enough to consider MADG as an advantageous granulation method to obtain gastroretentive tablets or even other controlled delivery systems requiring a relatively high content of absorbent materials in their composition.

Which Variables Are Associated with the Magnitude of the Physical Fitness Response in Older Adults? An Analysis of Their Development and Influence.

Regular physical exercise has proven to be an effective strategy for enhancing the health and well-being of older adults. However, there are still gaps in our understanding of the impacts of exercise on older adults with different health conditions, as well as in the customization of training programs according to individual capabilities. This study aimed to analyze the variables that influence the response of physical capabilities in older adults, considering their development over the aging process, with the goal of assisting professionals in creating personalized training programs. To achieve this, we conducted a cohort study involving 562 previously inactive adults and older adults who underwent anthropometric assessments, blood pressure measurements, and comprehensive physical tests. These assessments were conducted before and after a 14-week training program. Results indicated no significant variations in variables such as waist circumference (p = 0.0455, effect size = 0.10), body mass index (p = 0.0215, effect size = 0.15), systolic (p < 0.0001, effect size = 0.35) and diastolic blood pressure (p < 0.0001, effect size = 0.25) pre- and post-intervention. Strength tests, agility, the 6 min walk test (6MWT), and the back scratch test (BS) showed significant improvements post-intervention, with p-values all below 0.0001 and effect sizes ranging from 0.30 to 0.50. Multiple linear regression analyses revealed that lower initial values in physical capabilities were associated with more significant improvements during training (R2 = 0.73, p < 0.001). These results underscore that individualized guidance in training can lead to clinically meaningful improvements in physical performance and health among older adults, with effect sizes indicating moderate-to-large benefits (effect size range = 0.30 to 0.50). Therefore, personalized training programs are essential to maximize health benefits in this population.

Age-dependent Model for Dynamics of Change in Body Composition Indicators in Professional Firefighters: Serbian Cross-Sectional Study / Modelo Dependiente de la Edad para la Dinámica de Cambio en los Indicadores de Composición Corporal en Bomberos Profesionales: Estudio Transversal Serbio

SUMMARY: The aim of this study was to establish an age-related dynamic of change model for predicting changes in body composition indicators in professional firefighters. The study included a total sample of 145 subjects, comprising professional firefighters from Serbia (Age: 36.6 ± 7.6 yrs., Min - Max: 21.0 - 52.0 yrs.). Four basic variables were analysed: Body Mass - BM, Body Fat Mass - BFM, Skeletal Muscle Mass - SMM, and Visceral Fat Area - VFA, as well as five derived, or index, variables: Body Mass Index - BMI, Percentage of Body Fat - PBF, Percentage of Skeletal Muscle Mass - PSMM, Protein-Fat Index - PFI, and Index of Body - IBC Composition. The results showed a statistically significant dynamic of change as a function of age for eight of the examined variables, while only one (Skeletal Muscle Mass - SMM) was not statistically significant. The highest statistical significance in terms of dynamics of change as a function of age was found for the variable VFA (F = 35.241, p = 000) and the variable PSMM (F = 31.398, p = 0.000). Professional firefighters in Serbia fall into the category of people with normal nutritional indicators. However, due to a dominant increase in visceral fat (VFA) combined with a dominant decrease in the proportion of skeletal muscles in the body (PSMM), it can be concluded that they are exposed to a risk of developing various chronic diseases, while their working conditions, which promote certain negative lifestyle habits, also contribute to the observed increase in body fat components.

El objetivo de este estudio fue establecer un modelo de dinámica de cambio relacionada con la edad para predecir cambios en los indicadores de composición corporal en bomberos profesionales. El estudio incluyó una muestra total de 145 sujetos, incluidos bomberos profesionales de Serbia (Edad: 36,6 ± 7,6 años, mín. - máx.: 21,0 - 52,0 años). Se analizaron cuatro variables básicas: Masa Corporal - MC, Masa Grasa Corporal - MGC, - Masa Muscular Esquelética - MME y Área Grasa Visceral - AGV, así como cinco variables derivadas o indexadas: Índice de Masa Corporal - IMC, Porcentaje de grasa corporal - PGC, porcentaje de masa muscular esquelética - PMME, índice proteína-grasa - IPG e índice de composición corporal - ICC. Los resultados mostraron una dinámica de cambio estadísticamente significativa en función de la edad para ocho de las variables examinadas, mientras que sólo una, MME no fue estadísticamente significativa. La mayor significancia estadística en términos de dinámica de cambio en función de la edad se encontró para la variable AGV (F = 35,241, p = 000) y la variable PMME (F = 31,398, p = 0,000). Los bomberos profesionales de Serbia pertenecen a la categoría de personas con indicadores nutricionales normales. Sin embargo, debido a un aumento dominante de la grasa visceral combinado con una disminución dominante de la PMME, se puede concluir que están expuestos a un riesgo de desarrollar diversas enfermedades crónicas, mientras que las condiciones de trabajo, que promueven ciertos hábitos de vida negativos, también contribuyen al aumento observado de los componentes de la grasa corporal.

Age Prediction Using Pixel Value Sum from Radiographic Proximal Femur in a Thai Population / Predicción de la Edad Utilizando la Suma de Valores de Pixeles del Fémur Proximal Radiográfico en una Población Tailandesa

SUMMARY: The present study aimed to investigate the utility of the proximal femur in the forensic age estimation by assessing changes in bone densities through radiographs. Using Otsu's threshold, bone density was quantified by counting all white pixel values within selected regions of interest, which include femoral head (FH), femoral neck (FN), Ward's triangle (WT), and greater trochanter (GT) from 354 left femora of Northern Thai descent. The pixel width of medullary cavity (MC) was also estimated. Furthermore, the study evaluated the performance of linear regression (LR) models for age estimation from radiographic images of proximal femora. Negative correlations were observed between FH, FN, WT, and GT pixel intensity with the age-at-death of the samples, with females exhibiting stronger correlations than males. Moreover, a positive correlation was found between age and MC width in female samples, while male MC widths did not show any relationship with increasing age. The results showed a slight difference between the LR model applied to both sexes, which integrated all variables, and the alternative configuration that only utilized relevant attributes. Both models exhibited similar performance, with a narrow range of root mean square error (RMSE) values, ranging from 12.67 to 12.71 years, and a correlation coefficient range of 0.51 to 0.52. For females, the LR model with FN and WT as selected attributes (RMSE = 11.85 years, correlation coefficient = 0.65) performed decently, while for males, the LR model with all variables showed RMSE of 12.52 years and correlation coefficient of 0.46. This study showcased the potential application of pixel intensity in predicting age.

El presente estudio tuvo como objetivo investigar la utilidad del fémur proximal en la estimación forense de la edad mediante la evaluación de cambios en las densidades óseas a través de radiografías. Utilizando el umbral de Otsu, la densidad ósea se cuantificó contando todos los valores de pixeles blancos dentro de regiones de interés seleccionadas, que incluyen la cabeza femoral (CF), el cuello femoral (CF), el triángulo de Ward (WT) y el trocánter mayor (TM) de 354 fémures izquierdos de ascendencia del norte de Tailandia. También se estimó el ancho de pixeles de la cavidad medular (CM). Además, el estudio evaluó el rendimiento de modelos de regresión lineal (RL) para la estimación de la edad a partir de imágenes radiográficas de fémur proximal. Se observaron correlaciones negativas entre la intensidad de los pixeles CF, CF, WT y TM con la edad de muerte, y las mujeres exhibieron correlaciones más fuertes que los hombres. Además, se encontró una correlación positiva entre la edad y el ancho del CM en muestras de mujeres, mientras que el ancho del CM del hombre no mostró ninguna relación con el aumento de la edad. Los resultados mostraron una ligera diferencia entre el modelo RL aplicado a ambos sexos, que integraba todas las variables, y la configuración alternativa que sólo utilizaba atributos relevantes. Ambos modelos mostraron un rendimiento similar, con un rango estrecho de valores del error cuadrático medio (RMSE), que oscilaba entre 12,67 y 12,71 años, y un rango de coeficiente de correlación de 0,51 a 0,52. Para las mujeres, el modelo RL con CF y WT como atributos seleccionados (RMSE = 11,85 años, coeficiente de correlación = 0,65) tuvo un desempeño satisfactorio, mientras que para los hombres, el modelo RL con todas las variables mostró un RMSE de 12,52 años y un coeficiente de correlación de 0,46. Este estudio mostró la posible aplicación de la intensidad de los pixeles en la predicción de la edad.

Prediction of Bioactive Compounds and Antioxidant Activity in Bananas during Ripening Using Non-Destructive Parameters as Input Data.

Vegetable quality parameters are established according to standards primarily based on visual characteristics. Although knowledge of biochemical changes in the secondary metabolism of plants throughout development is essential to guide decision-making about consumption, harvesting and processing, these determinations involve the use of reagents, specific equipment and sophisticated techniques, making them slow and costly. However, when non-destructive methods are employed to predict such determinations, a greater number of samples can be tested with adequate precision. Therefore, the aim of this work was to establish an association capable of modeling between non-destructive-physical and colorimetric aspects (predictive variables)-and destructive determinations-bioactive compounds and antioxidant activity (variables to be predicted), quantified spectrophotometrically and by HPLC in 'Nanicão' bananas during ripening. It was verified that to predict some parameters such as flavonoids, a regression equation using predictive parameters indicated the importance of R2, which varied from 83.43 to 98.25%, showing that some non-destructive parameters can be highly efficient as predictors.

Overweight, Obesity, and Age Are the Main Determinants of Cardiovascular Risk Aggregation in the Current Mexican Population: The FRIMEX III Study.

Background: The Mexican population exhibits several cardiovascular risk factors (CVRF) including high blood pressure (HBP), dysglycemia, dyslipidemia, overweight, and obesity. This study is an extensive observation of the most important CVFRs in six of the most populated cities in Mexico. Methods: In a cohort of 297,370 participants (54% female, mean age 43 ± 12.6 years), anthropometric (body mass index (BMI)), metabolic (glycemia and total cholesterol (TC)), and blood pressure (BP) data were obtained. Results: From age 40, 40% and 30% of the cohort's participants were overweight or obese, respectively. HBP was found in 27% of participants. However, only 8% of all hypertensive patients were controlled. Fifty percent of the subjects 50 years and older were hypercholesterolemic. Glycemia had a constant linear relation with age. BMI had a linear correlation with SBP, glycemia, and TC, with elevated coefficients in all cases and genders. The ß1 coefficient for BMI was more significant in all equations than the other ß, indicating that it greatly influences the other CVRFs. Conclusions: TC, glycemia, and SBP, the most critical atherogenic factors, are directly related to BMI.

Spatio-temporal evaluation of air pollution using ground-based and satellite data during COVID-19 in Ecuador.

The concentration of gases in the atmosphere is a topic of growing concern due to its effects on health, ecosystems etc. Its monitoring is commonly carried out through ground stations which offer high precision and temporal resolution. However, in countries with few stations, such as Ecuador, these data fail to adequately describe the spatial variability of pollutant concentrations. Remote sensing data have great potential to solve this complication. This study evaluates the spatiotemporal distribution of nitrogen dioxide (NO2) and ozone (O3) concentrations in Quito and Cuenca, using data obtained from ground-based and Sentinel-5 Precursor mission sources during the years 2019 and 2020. Moreover, a Linear Regression Model (LRM) was employed to analyze the correlation between ground-based and satellite datasets, revealing positive associations for O3 (R2 = 0.83, RMSE = 0.18) and NO2 (R2 = 0.83, RMSE = 0.25) in Quito; and O3 (R2 = 0.74, RMSE = 0.23) and NO2, (R2 = 0.73, RMSE = 0.23) for Cuenca. The agreement between ground-based and satellite datasets was analyzed by employing the intra-class correlation coefficient (ICC), reflecting good agreement between them (ICC ≥0.57); and using Bland and Altman coefficients, which showed low bias and that more than 95% of the differences are within the limits of agreement. Furthermore, the study investigated the impact of COVID-19 pandemic-related restrictions, such as social distancing and isolation, on atmospheric conditions. This was categorized into three periods for 2019 and 2020: before (from January 1st to March 15th), during (from March 16th to May 17th), and after (from March 18th to December 31st). A 51% decrease in NO2 concentrations was recorded for Cuenca, while Quito experienced a 14.7% decrease. The tropospheric column decreased by 27.3% in Cuenca and 15.1% in Quito. O3 showed an increasing trend, with tropospheric concentrations rising by 0.42% and 0.11% for Cuenca and Quito respectively, while the concentration in Cuenca decreased by 14.4%. Quito experienced an increase of 10.5%. Finally, the reduction of chemical species in the atmosphere as a consequence of mobility restrictions is highlighted. This study compared satellite and ground station data for NO2 and O3 concentrations. Despite differing units preventing data validation, it verified the Sentinel-5P satellite's effectiveness in anomaly detection. Our research's value lies in its applicability to developing countries, which may lack extensive monitoring networks, demonstrating the potential use of satellite technology in urban planning.

Machine learning regression algorithms to predict emissions from steam boilers.

Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature.

A Low-Cost Wearable Device to Estimate Body Temperature Based on Wrist Temperature.

The remote monitoring of vital signs and healthcare provision has become an urgent necessity due to the impact of the COVID-19 pandemic on the world. Blood oxygen level, heart rate, and body temperature data are crucial for managing the disease and ensuring timely medical care. This study proposes a low-cost wearable device employing non-contact sensors to monitor, process, and visualize critical variables, focusing on body temperature measurement as a key health indicator. The wearable device developed offers a non-invasive and continuous method to gather wrist and forehead temperature data. However, since there is a discrepancy between wrist and actual forehead temperature, this study incorporates statistical methods and machine learning to estimate the core forehead temperature from the wrist. This research collects 2130 samples from 30 volunteers, and both the statistical least squares method and machine learning via linear regression are applied to analyze these data. It is observed that all models achieve a significant fit, but the third-degree polynomial model stands out in both approaches. It achieves an R2 value of 0.9769 in the statistical analysis and 0.9791 in machine learning.

Biopsychosocial factors associated with the state of disability after hemiparesis in the chronic phase of stroke: exploratory analysis based on the International Classification of Functioning, Disability and Health.

OBJECTIVE: To identify the main biopsychosocial factors associated with disability level after stroke using the International Classification of Functioning, Disability and Health (ICF) model. METHODS: A cross-sectional study was conducted with chronic stroke survivors. Disability was assessed using the World Health Disability Assessment Schedule 2.0. The independent variables were: Body functions: emotional functioning and whether the dominant upper limb was affected. For the Activities & Participation component, satisfaction regarding the execution of activities and participation were assessed using the SATIS-Stroke, as well as the locomotion ability for adults (ABILOCO), manual ability (ABILHAND) and the return to work. For environmental factors, income and facilitators and obstacles were assessed using the Measure of the Quality of the Environment (MQE). Personal factors: age and sex. Multiple Linear Regression was employed. RESULTS: Limited locomotor ability (ß = -0.281; t = -3.231 p = 0.002), dissatisfaction regarding activities and participation (ß = -0.273; t = -3.070 p = 0.003), and the non-return to work (ß = 0.162; t = 2.085 p = 0.04) were associated with disability. CONCLUSION: The reduction in locomotor ability, dissatisfaction regarding activities and participation and the non-return to work were associated with disability in the chronic phase following a stroke.

The reduction in locomotion ability, dissatisfaction regarding activities and participation, and the non-return to work were associated with disability in the chronic phase following a stroke.Clinicians will be able to develop rehabilitation strategies focused on diminishing locomotor limitations, increasing satisfaction with activities and participation, and improving vocational planning for the return to work after a strokeThese findings underscore the importance of assessments and intervention strategies based on the individual rather than the disease as well as focusing on social and personal issues to guide clinical decision making.

Study of some morphometric and meristic characteristics of Alepes vari (Cuvier, 1833) collected from the Arabian coast

Abstract The present study examines the correlations between fifteen morphometric and ten meristic characters and total length (TL) of males, females, and combined sexes of Alepes vari (Cuvier, 1833) collected from Karachi fish harbor, West Wharf of Karachi Coast. Statistical analyses of linear regression relationships show mostly strong correlations (r0.70; p 0.05) between total length (TL) and most morphometric characters in males, females, and combined sexes, except the height of pectoral-fin (PFH), and pelvic-fin base length (PelFL); whereas, meristic characters were found to be constant and indicate weak or negative type correlations (r0.50; p>0.05) with total length (TL). Hence, according to our present results, there is a direct relationship between the total length of fish and all morphometric characters, which were found to be the best indicators of positive allometric pattern growth in fish. Moreover, analysis of the 2-sample t-test revealed (t-test; p>0.05) that no sexual dimorphism was reported in Alepes vari. Thus, our present study could be valuable in systematic classification, sexual dimorphism, and management of this species on the Karachi coast.

Resumo O presente estudo examina as correlações entre 15 caracteres morfométricos e 10 caracteres merísticos e comprimento total (CT) de machos, fêmeas e sexos combinados de Alepes vari (Cuvier, 1833), coletados do porto de Karachi, West Wharf, na costa de Karachi. As análises estatísticas das relações de regressão linear mostraram, principalmente, correlações fortes (r 0,70; p 0,05) entre o CT e a maioria dos caracteres morfométricos em machos, fêmeas e sexos combinados, exceto a altura da nadadeira peitoral e o comprimento da base da nadadeira pélvica, enquanto os caracteres merísticos foram constantes, indicando correlações fracas ou negativas (r 0,50; p > 0,05) com o CT. Portanto, de acordo com nossos resultados, existe uma relação direta entre o CT dos peixes e todos os caracteres morfométricos, que foram considerados os melhores indicadores de crescimento do padrão alométrico positivo em peixes. Além disso, a análise do teste t de duas amostras revelou (teste t; p > 0,05) que nenhum dimorfismo sexual foi relatado em A. vari. Assim, o presente estudo pode ser valioso na classificação sistemática, dimorfismo sexual e manejo dessa espécie na costa de Karachi.

Study of some morphometric and meristic characteristics of Alepes vari (Cuvier, 1833) collected from the Arabian coast / Estudo de características morfométricas e merísticas de Alepes vari (Cuvier, 1833) coletados na costa da Arábia

The present study examines the correlations between fifteen morphometric and ten meristic characters and total length (TL) of males, females, and combined sexes of Alepes vari (Cuvier, 1833) collected from Karachi fish harbor, West Wharf of Karachi Coast. Statistical analyses of linear regression relationships show mostly strong correlations (r≥0.70; p<0.05) between total length (TL) and most morphometric characters in males, females, and combined sexes, except the height of pectoral-fin (PFH), and pelvic-fin base length (PelFL); whereas, meristic characters were found to be constant and indicate weak or negative type correlations (r≤0.50; p>0.05) with total length (TL). Hence, according to our present results, there is a direct relationship between the total length of fish and all morphometric characters, which were found to be the best indicators of positive allometric pattern growth in fish. Moreover, analysis of the 2-sample t-test revealed (t-test; p>0.05) that no sexual dimorphism was reported in Alepes vari. Thus, our present study could be valuable in systematic classification, sexual dimorphism, and management of this species on the Karachi coast.

O presente estudo examina as correlações entre 15 caracteres morfométricos e 10 caracteres merísticos e comprimento total (CT) de machos, fêmeas e sexos combinados de Alepes vari (Cuvier, 1833), coletados do porto de Karachi, West Wharf, na costa de Karachi. As análises estatísticas das relações de regressão linear mostraram, principalmente, correlações fortes (r ≥ 0,70; p < 0,05) entre o CT e a maioria dos caracteres morfométricos em machos, fêmeas e sexos combinados, exceto a altura da nadadeira peitoral e o comprimento da base da nadadeira pélvica, enquanto os caracteres merísticos foram constantes, indicando correlações fracas ou negativas (r ≤ 0,50; p > 0,05) com o CT. Portanto, de acordo com nossos resultados, existe uma relação direta entre o CT dos peixes e todos os caracteres morfométricos, que foram considerados os melhores indicadores de crescimento do padrão alométrico positivo em peixes. Além disso, a análise do teste t de duas amostras revelou (teste t; p > 0,05) que nenhum dimorfismo sexual foi relatado em A. vari.

Analysis of confounding caused by creatinine and age in the correlation between soluble tumor necrosis factor a receptor 1 (sTNFR1) levels and estimated glomerular filtration rate (eGFR) in Colombian patients with type 2 diabetes / Análisis de la confusión producida por la creatinina y la edad en la correlación entre los niveles del receptor soluble 1 del factor de necrosis tumoral a (sTNFR1) y la tasa de filtración glomerular estimada (TFGe) en pacientes colombianos con diabetes mellitus tipo 2

Abstract Introduction. Tumor necrosis factor a (TNF-α) is a cytokine involved in inflammatory processes associated with type 2 diabetes mellitus (DM2). Although the correlation between soluble TNF-a receptor 1 (sTNFRI) levels and estimated glomerular filtration rate (eGFR) has been already described in Colombian population with DM2, the influence of sTNFR1 on eGFR in a model adjusted for age and creatinine level has not yet been evaluated. Objectives. To identify and evaluate the linear correlations between sTNFR1 levels, routine clinical variables, and eGFR in Colombian patients with DM2. Materials and methods. Cross-sectional study conducted in March 2020 in 69 patients with DM2 who were enrolled in the Program for the Prevention of Diabetes Complications and Dyslipidemias of the Faculty of Medicine of the Universidad Nacional de Colombia. Medical records were reviewed in order to obtain sociodemographic, anthropometric and clinical data. Serum sTNFR1 levels were determined by means of an ELISA test. A multiple linear regression model (stepwise regression) was performed to evaluate correlations between sTNFR1, clinical variables, and eGFR. Results. The final multiple linear regression model, which includes creatinine levels, sTNFR1 levels, and age, explained 72% of the variance of eGFR (p=0.023). Furthermore, sTNFR1 levels explained 20% of the variance of eGFR independently (standardized ß coefficient: -0.2; 95%CI: [-0.008]-[-0.001]; p=0.02). Conclusion. In the final multiple linear regression model, an inversely proportional and statistically significant linear correlation was found between sTNFR1 levels and eGFR, independent of serum creatinine levels and age. Compared with age, sTNFR1 levels have a superior effect in terms of changes in eGFR.

Resumen Introducción. El factor de necrosis tumoral a (TNF-α) es una citoquina involucrada en los procesos inflamatorios de la diabetes mellitus tipo 2 (DM2). Aunque la correlación entre los niveles del receptor soluble 1 del TNF-a (sTNFR1) y la tasa de filtración glomerular estimada (TFGe) ya ha sido descrita previamente en población colombiana con DM2, la influencia del sTNFR1 en la TFGe en un modelo ajustado a edad y creatinina no ha sido evaluada. Objetivos. Identificar y evaluar las correlaciones lineales entre los niveles del sTNFR1, las variables clínicas de uso rutinario y la TFGe en pacientes colombianos con DM2. Materiales y métodos. Estudio transversal realizado en marzo de 2020 en 69 pacientes con DM2 que estaban inscritos en el Programa para la prevención de las complicaciones de la diabetes y las dislipidemias de la Facultad de Medicina de la Universidad Nacional de Colombia. Los datos sociodemográficos, antropométricos y clínicos se recolectaron a partir de la revisión de las historias clínicas. Los niveles séricos del sTNFR1 se determinaron mediante prueba de ELISA. Se realizó un modelo de regresión lineal múltiple (regresión paso a paso) para evaluar las correlaciones entre el sTNFR1, las variables clínicas y la TFGe. Resultados. El modelo final de regresión lineal múltiple, que incluye niveles de creatinina, niveles del sTNFR1 y edad, explica el 72% de la varianza de la TFGe (p=0.023); además, los niveles del sTNFR1 explican el 20% de la varianza de la TFGe de forma independiente (coeficiente ß estandarizado: -0.2; IC95%: [-0.008]-[-0.001]; p=0.02). Conclusión. En el modelo final de regresión lineal múltiple se encontró una correlación lineal inversamente proporcional y estadísticamente significativa entre los niveles del sTNFR1 y la TFGe, independientemente de los niveles séricos de creatinina y la edad. Comparado con la edad, los niveles del sTNFR1 tienen un efecto superior en términos de cambios en la TFGe.

Quantification of pharmaceuticals in hospital effluent: Weighted ranking of environmental risk using a fuzzy hybrid multicriteria method.

An analytical method for quantification of seventeen pharmaceuticals and one metabolite was validated and applied in the analysis of hospital effluent samples. Two different sampling strategies were used: seasonal sampling, with 7 samples collected bimonthly; and hourly sampling, with 12 samples collected during 12 h. Thus, the variability was both seasonal and within the same day. High variability was observed in the measured concentrations of the pharmaceuticals and the metabolite. The quantification method, performed using weighted linear regression model, demonstrated results of average concentrations in seasonal samples ranged between 0.19 µgL-1 (carbamazepine) and higher than 61.56 µgL-1 (acetaminophen), while the hourly samples showed average concentrations between 0.07 µgL-1 (diazepam) and higher than 54.91 µgL-1 (acetaminophen). It is described as higher because the maximum concentration of the calibration curve took into account the dilution factor provided by DLLME. The diurnal results showed a trend towards higher concentrations in the first and last hours of sampling. The risk quotient (RQ) was calculated using organisms from three different trophic levels, for all the analytes quantified in the samples. Additionally, in order to understand the level of importance of each RQ, an expert panel was established, with contributions from 23 specialists in the area. The results were analyzed using a hybrid decision-making approach based on a Fuzzy Analytic Hierarchy Process (FAHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, in order to rank the compounds by environmental risk priority. The compounds of greatest concern were losartan, acetaminophen, 4-aminoantipyrine, sulfamethoxazole, and metoclopramide. Comparison of the environmental risk priority ranking with the potential human health risk was performed by applying the same multicriteria approach, with the prediction of endpoints using in silico (Q)SAR models. The results obtained suggested that sulfamethoxazole and acetaminophen were the most important analytes to be considered for monitoring.

Artificial neural network approach for predicting the sesame (Sesamum indicum L.) leaf area: A non-destructive and accurate method.

The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R2). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R2: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R2: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

Morphological Description and Live Weight Prediction from Body Measurements of Socorro Island Merino Lambs.

The aim of this study was to describe the morphology and estimate live weight from body measurements of Socorro Island Merino lambs. A group of Socorro Island Merino lambs was recorded from birth to year for live weight, rump width, rump length, withers height, body length, cannon bone perimeter, and chest girth, width, and depth. The effect of the lamb type on body measurements and live weight was analyzed using ANOVA, Pearson's correlation analysis was performed to estimate the relationship between body measurements and live weight, multiple linear regressions were fitted to obtain prediction equations of live weight from the body measurements and finally, chest girth was used to generate prediction equations using linear and exponential models. At birth and at year, differences were observed in body measurements, especially those related to the thoracic region, with crossbred males showing the highest values. Live weight was correlated with almost all the body measurements, with the highest coefficients observed with chest girth, chest width, and chest depth. Live weight can be accurately predicted from multiple regression equations using several body measurements, but using only chest girth (CG) as a predictor, the exponential equation W0-365 = 0.9142 exp(0.0462 CG) showed the best accuracy.

NASA POWER satellite meteorological system is a good tool for obtaining estimates of the temperature-humidity index under Brazilian conditions compared to INMET weather stations data.

Heat stress negatively affects livestock, with undesirable effects on animals' production and reproduction. Temperature and humidity index (THI) is a climatic variable used worldwide to study the effect of heat stress on farm animals. Temperature and humidity data can be obtained in Brazil through the National Institute of Meteorology (INMET), but complete data may not be available due to temporary failures on weather stations. An alternative to obtaining meteorological data is the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) satellite-based weather system. We aimed to compare THI estimates obtained from INMET weather stations and NASA POWER meteorological information sources using Pearson correlation and linear regression. After quality check, data from 489 INMET weather stations were used. The hourly, average daily and maximum daily THI were evaluated. We found greater correlations and better regression evaluation metrics when average daily THI values were considered, followed by maximum daily THI, and hourly THI. NASA POWER satellite-based weather system is a suitable tool for obtaining the average and maximum THI values using information collected from Brazil, showing high correlations with THI estimates from INMET and good regression evaluation metrics, and can assist studies that aim to analyze the impact of heat stress on livestock production in Brazil, providing additional data to complement the existing information available in the INMET database.

State of Charge Estimation Model Based on Genetic Algorithms and Multivariate Linear Regression with Applications in Electric Vehicles.

Nowadays, the use of renewable, green/eco-friendly technologies is attracting the attention of researchers, with a view to overcoming recent challenges that must be faced to guarantee the availability of Electric Vehicles (EVs). Therefore, this work proposes a methodology based on Genetic Algorithms (GA) and multivariate regression for estimating and modeling the State of Charge (SOC) in Electric Vehicles. Indeed, the proposal considers the continuous monitoring of six load-related variables that have an influence on the SOC (State of Charge), specifically, the vehicle acceleration, vehicle speed, battery bank temperature, motor RPM, motor current, and motor temperature. Thus, these measurements are evaluated in a structure comprised of a Genetic Algorithm and a multivariate regression model in order to find those relevant signals that better model the State of Charge, as well as the Root Mean Square Error (RMSE). The proposed approach is validated under a real set of data acquired from a self-assembly Electric Vehicle, and the obtained results show a maximum accuracy of approximately 95.5%; thus, this proposed method can be applied as a reliable diagnostic tool in the automotive industry.

Estimation of cropland prices in Rio Grande do Sul by multiple linear regression and principal component analysis / Estimação do preço das lavouras no Rio Grande do Sul por meio de regressão linear múltipla e análise de componentes principais

This study aimed to price croplands in Rio Grande do Sul State (southern Brazil) and point which variables had the most significant impact on prices. The main purpose was achieved using multiple linear regression and principal component analysis. The variables used in this study were planted area, production, price, and yield of the commodities soybean, wheat, and corn. The period under analysis was from January 1994 to December 2017 (biannual observations). Multiple linear regression showed that five variables contributed to land pricing, being three related to soybean and two to wheat. Multivariate analysis grouped the investigated variables into clusters and indicated their influence, in addition to providing information on land prices and reducing variable dimensionality from fourteen original variables to three principal components to be analyzed. The two analyses complemented each other so that the croplands' price was explained by three variables, in which two corroborated in constructing the pricing model for croplands.

Este estudo teve como objetivo a precificação de terra para lavouras no Rio Grande do Sul e apresentar quais variáveis possuem maior impacto no preço. O objetivo foi alcançado por meio da aplicação da análise de regressão linear múltipla e de componentes principais. Variáveis relacionadas às commodities soja, trigo e milho, como a área plantada, produção, cotação e rendimento, formaram o banco amostral para as duas metodologias, compreendendo o período de janeiro de 1994 a dezembro de 2017, em observações bianuais. A regressão linear múltipla mostrou que três variáveis relacionadas à soja e duas ao trigo contribuem na precificação das terras. A análise multivariada agrupou as variáveis investigadas, indicando a influência entre as mesmas, fornecendo informações sobre o preço de terras e diminuindo a dimensionalidade do problema de 14 variáveis originais para três componentes a serem analisados. As duas análises se complementaram de forma que o preço de terras foi explicado por três variáveis e duas corroboraram na construção do modelo de precificação das lavouras.

Estimation of cropland prices in Rio Grande do Sul by multiple linear regression and principal component analysis / Estimação do preço das lavouras no Rio Grande do Sul por meio de regressão linear múltipla e análise de componentes principais

ABSTRACT: This study aimed to price croplands in Rio Grande do Sul State (southern Brazil) and point which variables had the most significant impact on prices. The main purpose was achieved using multiple linear regression and principal component analysis. The variables used in this study were planted area, production, price, and yield of the commodities soybean, wheat, and corn. The period under analysis was from January 1994 to December 2017 (biannual observations). Multiple linear regression showed that five variables contributed to land pricing, being three related to soybean and two to wheat. Multivariate analysis grouped the investigated variables into clusters and indicated their influence, in addition to providing information on land prices and reducing variable dimensionality from fourteen original variables to three principal components to be analyzed. The two analyses complemented each other so that the croplands' price was explained by three variables, in which two corroborated in constructing the pricing model for croplands.

RESUMO: Este estudo teve como objetivo a precificação de terra para lavouras no Rio Grande do Sul e apresentar quais variáveis possuem maior impacto no preço. O objetivo foi alcançado por meio da aplicação da análise de regressão linear múltipla e de componentes principais. Variáveis relacionadas às commodities soja, trigo e milho, como a área plantada, produção, cotação e rendimento, formaram o banco amostral para as duas metodologias, compreendendo o período de janeiro de 1994 a dezembro de 2017, em observações bianuais. A regressão linear múltipla mostrou que três variáveis relacionadas à soja e duas ao trigo contribuem na precificação das terras. A análise multivariada agrupou as variáveis investigadas, indicando a influência entre as mesmas, fornecendo informações sobre o preço de terras e diminuindo a dimensionalidade do problema de 14 variáveis originais para três componentes a serem analisados. As duas análises se complementaram de forma que o preço de terras foi explicado por três variáveis e duas corroboraram na construção do modelo de precificação das lavouras.