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Resumen Los reportes del exceso de mortalidad durante la pandemia por COVID-19 en Argentina han sido parcia les y fragmentados hasta el momento. Este estudio se propuso cuantificar el exceso de muertes y explorar su distribución demográfica, temporal y geográfica durante el periodo 2020-2022. Utilizando datos de 1 192 963 registros de muertes de estadísticas vitales y proyecciones poblacionales, se estimó la mortalidad esperada mediante modelos de regresión. El exceso de muertes se calculó como la diferencia entre la mortalidad observada y la esperada. Se estimó un exceso de 160 676 muertes (IC 95% 146 861 a 174 491), representando una tasa de 116.9 muer tes (IC 95% 115.5 a 118.3) adicionales por cada 100 000 personas-año. Se verificó una significativa heterogenei dad entre las distintas provincias argentinas. Los resultados indican un impacto desigual de la pandemia, con mayores tasas de exceso de mortalidad en algunas regiones y grupos de edad más vulnerables. Estos patrones sugieren la necesidad de estrategias diferenciadas de respuesta sanitaria y apoyo a las poblaciones más vulnerables en escenarios de nuevas epidemias.

Abstract Reports of excess mortality during the COVID-19 pan demic in Argentina have been partial and fragmented so far. This study aimed to quantify excess deaths and explore their demographic, temporal, and geographic distribution during the period 2020-2022. Using data from 1 192 963 death records from vital statistics and population projections, expected mortality was estimated using regression models. Excess death was calculated as the difference between observed and expected mortality. An excess of 160 676 deaths (95% CI 146 861 to 174 491) was estimated, representing a rate of 116.9 (95% CI 115.5 to 118.3) additional deaths per 100 000 person-years. Significant heterogeneity was found among the different argentine provinces. The results indicate an uneven impact of the pan demic, with higher excess mortality rates in some re gions and more vulnerable age groups. These patterns suggest the need for differentiated strategies of health care response and support to the most vulnerable popu lations in scenarios of new epidemics.

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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.

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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.

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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.

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Introduction: Claw lesions significantly contribute to lameness, greatly affecting sow welfare. This study investigated different factors that would impact the severity of claw lesions in the sows of Brazilian commercial herds. Methods: A total of 129 herds (n = 12,364 sows) were included in the study. Herds were in the Midwest, Southeast, or South regions of Brazil. Inventory sizes were stratified into 250-810 sows, 811-1,300 sows, 1,301-3,000 sows, and 3,001-10,000 sows. Herds belonged to Cooperative (Coop), Integrator, or Independent structures. The herd management was conducted either maintaining breeds from stock on-site (internal), or through purchase of commercially available genetics (external). Herds adopted either individual crates or group housing during gestation. Within each farm, one randomly selected group of sows was scored by the same evaluator (two independent experts evaluated a total of 129 herds) from 0 (none) to 3 (severe) for heel overgrowth and erosion (HOE), heel-sole crack (HSC), separation along the white line (WL), horizontal (CHW) and vertical (CVW) wall cracks, and overgrown toes (T), or dewclaws (DC) in the hind legs after parturition. The study assessed differences and similarities between herds using Principal Component Analysis (PCA) and Hierarchical Agglomerative Clustering (HAC) analysis. The effects of factors (i.e., production structure, management, housing during gestation, and region) were assessed using the partial least squares method (PLS). Results and discussion: Heel overgrowth and erosion had the highest prevalence, followed by WL and CHW, while the lowest scores were observed for T, DC, and CVW. Herds were grouped in three clusters (i.e., C1, C2, and C3). Heel overgrowth and erosion, HSC, WL, CHW, CVW, and T were decreased by 17, 25, 11, 25, 21, and 17%, respectively, in C3 compared to C1 and 2 combined. Independent structure increased the L-Index in all three clusters. Furthermore, individual housing increased the L-Index regardless of the cluster. The results suggest that shifting toward larger, more technologically advanced herds could potentially benefit claw health. Additionally, adopting group gestation housing appears to mitigate the adverse effects on claw health, although further validation is necessary, as Brazil has only recently transitioned from individual housing practices.

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This study aimed to determine the feasibility of applying machine-learning methods to assess the progression of chronic kidney disease (CKD) in patients with coronavirus disease (COVID-19) and acute renal injury (AKI). The study was conducted on patients aged 18 years or older who were diagnosed with COVID-19 and AKI between April 2020 and March 2021, and admitted to a second-level hospital in Mérida, Yucatán, México. Of the admitted patients, 47.92% died and 52.06% were discharged. Among the discharged patients, 176 developed AKI during hospitalization, and 131 agreed to participate in the study. The study's results indicated that the area under the receiver operating characteristic curve (AUC-ROC) for the four models was 0.826 for the support vector machine (SVM), 0.828 for the random forest, 0.840 for the logistic regression, and 0.841 for the boosting model. Variable selection methods were utilized to enhance the performance of the classifier, with the SVM model demonstrating the best overall performance, achieving a classification rate of 99.8% ± 0.1 in the training set and 98.43% ± 1.79 in the validation set in AUC-ROC values. These findings have the potential to aid in the early detection and management of CKD, a complication of AKI resulting from COVID-19. Further research is required to confirm these results.

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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.

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BACKGROUND: To develop and validate a serum protein nomogram for colorectal cancer (CRC) screening. METHODS: The serum protein characteristics were extracted from an independent sample containing 30 colorectal cancer and 12 polyp tissues along with their paired samples, and different serum protein expression profiles were validated using RNA microarrays. The prediction model was developed in a training cohort that included 1345 patients clinicopathologically confirmed CRC and 518 normal participants, and data were gathered from November 2011 to January 2017. The lasso logistic regression model was employed for features selection and serum nomogram building. An internal validation cohort containing 576 CRC patients and 222 normal participants was assessed. RESULTS: Serum signatures containing 27 secreted proteins were significantly differentially expressed in polyps and CRC compared to paired normal tissue, and REG family proteins were selected as potential predictors. The C-index of the nomogram1 (based on Lasso logistic regression model) which contains REG1A, REG3A, CEA and age was 0.913 (95% CI, 0.899 to 0.928) and was well calibrated. Addition of CA199 to the nomogram failed to show incremental prognostic value, as shown in nomogram2 (based on logistic regression model). Application of the nomogram1 in the independent validation cohort had similar discrimination (C-index, 0.912 [95% CI, 0.890 to 0.934]) and good calibration. The decision curve (DCA) and clinical impact curve (ICI) analysis demonstrated that nomogram1 was clinically useful. CONCLUSIONS: This study presents a serum nomogram that included REG1A, REG3A, CEA and age, which can be convenient for screening of colorectal cancer.

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All-dielectric magnetophotonic nanostructures are promising for integrated nanophotonic devices with high resolution and sensitivity, but their design requires computationally demanding electromagnetic simulations evaluated through trial and error. In this paper, we propose a machine-learning approach to accelerate the design of these nanostructures. Using a data set of 12 170 samples containing four geometric parameters of the nanostructure and the incidence wavelength, trained neural network and polynomial regression algorithms were capable of predicting the amplitude of the transverse magneto-optical Kerr effect (TMOKE) within a time frame of 10-3 s and mean square error below 4.2%. With this approach, one can readily identify nanostructures suitable for sensing at ultralow analyte concentrations in aqueous solutions. As a proof of principle, we used the machine-learning models to determine the sensitivity (S = |Δθres/Δna|) of a nanophotonic grating, which is competitive with state-of-the-art systems and exhibits a figure of merit of 672 RIU-1. Furthermore, researchers can use the predictions of TMOKE peaks generated by the algorithms to assess the suitability for experimental setups, adding a layer of utility to the machine-learning methodology.

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Through enviromics, precision breeding leverages innovative geotechnologies to customize crop varieties to specific environments, potentially improving both crop yield and genetic selection gains. In Brazil's four southernmost states, data from 183 distinct geographic field trials (also accounting for 2017-2021) covered information on 164 genotypes: 79 phenotyped maize hybrid genotypes for grain yield and their 85 nonphenotyped parents. Additionally, 1342 envirotypic covariates from weather, soil, sensor-based, and satellite sources were collected to engineer 10 K synthetic enviromic markers via machine learning. Soil, radiation light, and surface temperature variations remarkably affect differential genotype yield, hinting at ecophysiological adjustments including evapotranspiration and photosynthesis. The enviromic ensemble-based random regression model showcases superior predictive performance and efficiency compared to the baseline and kernel models, matching the best genotypes to specific geographic coordinates. Clustering analysis has identified regions that minimize genotype-environment (G × E) interactions. These findings underscore the potential of enviromics in crafting specific parental combinations to breed new, higher-yielding hybrid crops. The adequate use of envirotypic information can enhance the precision and efficiency of maize breeding by providing important inputs about the environmental factors that affect the average crop performance. Generating enviromic markers associated with grain yield can enable a better selection of hybrids for specific environments.

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The article proposes a new regression based on the generalized odd log-logistic family for interval-censored data. The survival times are not observed for this type of data, and the event of interest occurs at some random interval. This family can be used in interval modeling since it generalizes some popular lifetime distributions in addition to its ability to present various forms of the risk function. The estimation of the parameters is addressed by the classical and Bayesian methods. We examine the behavior of the estimates for some sample sizes and censorship percentages. Selection criteria, likelihood ratio tests, residual analysis, and graphical techniques assess the goodness of fit of the fitted models. The usefulness of the proposed models is red shown by means of two real data sets.

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This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line.

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The success of obtaining solid dispersions for solubility improvement invariably depends on the miscibility of the drug and polymeric carriers. This study aimed to categorize and select polymeric carriers via the classical group contribution method using the multivariate analysis of the calculated solubility parameter of RX-HCl. The total, partial, and derivate parameters for RX-HCl were calculated. The data were compared with the results of excipients (N = 36), and a hierarchical clustering analysis was further performed. Solid dispersions of selected polymers in different drug loads were produced using solvent casting and characterized via X-ray diffraction, infrared spectroscopy and scanning electron microscopy. RX-HCl presented a Hansen solubility parameter (HSP) of 23.52 MPa1/2. The exploratory analysis of HSP and relative energy difference (RED) elicited a classification for miscible (n = 11), partially miscible (n = 15), and immiscible (n = 10) combinations. The experimental validation followed by a principal component regression exhibited a significant correlation between the crystallinity reduction and calculated parameters, whereas the spectroscopic evaluation highlighted the hydrogen-bonding contribution towards amorphization. The systematic approach presented a high discrimination ability, contributing to optimal excipient selection for the obtention of solid solutions of RX-HCl.

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RESUMEN La fotocatálisis homogénea foto-Fenton es uno de los procesos de oxidación avanzada más utilizados en el tratamiento de aguas residuales con contenido de pesticidas, donde la optimización de la dosis de catalizador y el agente oxidante, teniendo como respuesta la mineralización en términos de carbón orgánico total (COT) o la eliminación del contaminante específico, son el objetivo de cualquier diseño experimental. El desarrollo experimental en los procesos de tratamiento requiere la ejecución de una cantidad significativa de condiciones experimentales que necesitan el uso de reactivos, energía y tiempo de ejecución, por lo tanto, el modelamiento de este tipo de fenómenos surge como una alternativa a esta limitante en los tratamientos de aguas residuales. En esta investigación, se evaluó la influencia de los factores FeSO4 y H2O2, cada uno en tres niveles, en la mineralización de una solución sintética del insecticida Carbendazim (50 mg.L-1), en términos de COT, mediante un modelo de regresión lineal múltiple y optimizado por una superficie de respuesta. Los principales resultados establecieron que el mejor ajuste del modelo se da teniendo en cuenta la interacción entre el FeSO4 y el H2O2 (X1*X2) y los términos cuadráticos de cada una de ellas con p-values <0,05 y que la validación del modelo, mediante la técnica Leave-One-Out Cross Validation (LOOCV), así como la exactitud y la precisión, mediante el análisis de residuos y el supuesto de mínimos cuadrados ordinarios, establecen que las conclusiones que se deriven de él son válidas.

ABSTRACT Homogeneous photo-Fenton photocatalysis is one of the most widely used advanced oxidation processes in treating wastewater containing pesticides, where optimizing the catalyst dosage and oxidizing agent, with the response being mineralization in terms of total organic carbon (TOC) or removal of the specific contaminant, is the goal of any experimental design. Experimental development in treatment processes necessitates executing a significant number of experimental conditions that require the use of reagents, energy, and execution time. Therefore, modeling such phenomena emerges as an alternative to these limitations in wastewater treatment. In this research, the influence of factors FeSO4 and H2O2, each at three levels, on the mineralization of a synthetic solution of the insecticide Carbendazim (50 mg.L-1) in terms of TOC was evaluated using a multiple linear regression model optimized by response surface methodology. The main results established that the best model fit considers the interaction between FeSO4 and H2O2 (X 1 *X 2 ) and the quadratic terms of each with p-values<0.05. The validation of the model using the Leave-One-Out Cross Validation (LOOCV) technique, as well as accuracy and precision through residual analysis and ordinary least squares assumptions, confirms the validity of the conclusions derived from it.

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BACKGROUND: Breast cancer is a leading cause of cancer-related deaths in females, and the hormone receptor-positive subtype is the most frequent. Breast cancer is a common source of brain metastases; therefore, we aimed to generate a brain metastases prediction model in females with hormone receptor-positive breast cancer. METHODS: The primary cohort included 3,682 females with hormone receptor-positive breast cancer treated at a single center from May 2009 to May 2020. Patients were randomly divided into a training dataset (n = 2,455) and a validation dataset (n = 1,227). In the training dataset, simple logistic regression analyses were used to measure associations between variables and the diagnosis of brain metastases and to build multivariable models. The model with better calibration and discrimination capacity was tested in the validation dataset to measure its predictive performance. RESULTS: The variables incorporated in the model included age, tumor size, axillary lymph node status, clinical stage at diagnosis, HER2 expression, Ki-67 proliferation index, and the modified Scarff-Bloom-Richardson grade. The area under the curve was 0.81 (95 % CI 0.75-0.86), p < 0.001 in the validation dataset. The study presents a guide for the clinical use of the model. CONCLUSION: A brain metastases prediction model in females with hormone receptor-positive breast cancer helps assess the individual risk of brain metastases.

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OBJECTIVES: To analyse the knowledge of dental undergraduates and dentists on the prevention, diagnosis and management of dentin hypersensitivity (DH); to compare their knowledge scores; and to understand the related variables using a regression model. METHODS: An original online questionnaire investigated the attitudes, self-reported knowledge ('how much they thought they knew') and real knowledge ('how much they really knew') of 132 students and 338 dentists. Data were analysed descriptively, both knowledge scores were compared using Mann-Whitney and Wilcoxon signed-rank tests and data were subjected to two multiple linear regression analyses considering real knowledge scores as the dependent variable (α < 0.05). RESULTS: The self-reported knowledge on DH was higher than the real knowledge for both students and dentists, but dentists presented the highest scores. Gingival recession and acidic diet were reported as the main predisposing factors for DH by undergraduates and dentists. Students normally managed DH with dietary and hygiene instructions followed by a desensitizing agent application, while dentists managed with occlusal adjustments. The mechanism of glutaraldehyde/HEMA and bioactive fillers on DH is widely unknown by students and dentists. The majority of the questioned individuals cannot differentiate DH from sensitivity of caries or molar-incisor hypomineralization. CONCLUSION: Both students and dentists overestimate their knowledge of DH, revealing deficiencies in prevention, diagnosis and management. Students' knowledge improves towards the end of the Dentistry course, while younger dentists and PhD holders are more knowledgeable. Institutions should implement ongoing DH education for undergraduates and conduct interventions for experienced professionals, especially older ones.

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Monitoring weight development is essential for decision-making and assessing the effectiveness of management strategies. However, this practice is often hindered by the lack of scales on farms. This study aimed to characterize the weight development and growth curves of male and female Santa Inês lambs from birth to weaning, managed on pasture with creep-fed concentrate supplementation. Data from 212 lambs during the pre-weaning phase were analyzed. The animals were weighed every seven days to evaluate total weight gain and average daily gain. Biometric measurements were taken every 28 days. Mixed models were used to assess the effects of sex and birth type on birth and weaning weights. Simple and multiple linear regression models were employed to estimate live weight using biometric measurements. The non-linear Gompertz model was utilized to describe weight development and formulate growth curves. Results were considered significant at p < 0.05. An interaction effect between birth type and sex (p < 0.05) was noted for birth weight, with the lowest weight observed in twin-birth females (2.96 kg) and the highest in single-birth males (3.73 kg) and females (3.65 kg) (p > 0.05). Birth type significantly influenced average daily gain, total weight gain, and weaning weight (p < 0.05). The Gompertz model accurately depicted the growth curves, effectively describing the weight development. Pearson's correlation coefficients between biometric measurements and weight were positive and significant (p < 0.05), ranging from 0.599 for hip height to 0.847 for heart girth. Consequently, the simple and multiple regression equations demonstrated high precision in predicting weaning weight. In conclusion, twin-birth lambs receiving concentrate supplementation via creep-feeding and managed on pasture showed different developmental patterns compared to single-birth lambs under the same conditions. The Gompertz model proved effective for monitoring development during the pre-weaning phase. All simple and multiple linear regression models were effective in predicting weaning weight through biometric measurements. However, for practical application, the model incorporating two measurements-body length and abdominal circumference-is recommended.

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Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.

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Ensuring that the proposed probabilistic model accurately represents the problem is a critical step in statistical modeling, as choosing a poorly fitting model can have significant repercussions on the decision-making process. The primary objective of statistical modeling often revolves around predicting new observations, highlighting the importance of assessing the model's accuracy. However, current methods for evaluating predictive ability typically involve model comparison, which may not guarantee a good model selection. This work presents an accuracy measure designed for evaluating a model's predictive capability. This measure, which is straightforward and easy to understand, includes a decision criterion for model rejection. The development of this proposal adopts a Bayesian perspective of inference, elucidating the underlying concepts and outlining the necessary procedures for application. To illustrate its utility, the proposed methodology was applied to real-world data, facilitating an assessment of its practicality in real-world scenarios.

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BACKGROUND: Metal(oid)s have been cross-sectionally associated with lung function outcomes in childhood but there is limited data on their combined effects starting in utero. Child sex may further modify these effects. OBJECTIVE: Examine associations between in utero and early life exposure to metals assessed via novel dentine biomarkers and childhood lung function and explore effect modification by child sex. METHODS: Analyses included 291 children enrolled in the Programming Research in Obesity, Growth, Environment and Social Stressors (PROGRESS) study, a longitudinal birth cohort study in Mexico City. Weekly dentine levels of arsenic (As), cadmium (Cd), cobalt (Co), copper (Cu), manganese (Mn), nickel (Ni), and lead (Pb) were measured from 15 weeks pre-birth to 15 weeks post birth in deciduous children's teeth. Lung function was tested at ages 8-14 years and then modeled as age, height and sex adjusted z-scores. Associations were modeled using lagged weighted quantile sum (LWQS) regression to evaluate the potential for a time-varying mixture effect adjusting for maternal age and education at enrollment and exposure to environmental tobacco smoke in pregnancy. Models were also stratified by sex. RESULTS: We identified a window of susceptibility at 12-15 weeks pre-birth in which the metal mixture was associated with lower FVC z-scores in children aged 8-14 years. Cd and Mn were the largest contributors to the mixture effect (70 %). There was also some evidence of effect modification by sex, in which the mean weights and weighted correlations over the identified window was more evident in males when compared to females. In the male stratum, Cd, Mn and additionally Pb also dominated the mixture association. CONCLUSIONS: Prenatal metal(oid) exposure was associated with lower lung function in childhood. These findings underscore the need to consider both mixtures and windows of susceptibility to fully elucidate effects of prenatal metal(oid) exposure on childhood lung function.

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