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.

Claw lesion status in Brazilian commercial sow herds from 2013 to 2023.

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.

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.

Machine Learning for Predicting Chronic Renal Disease Progression in COVID-19 Patients with Acute Renal Injury: A Feasibility Study.

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.

Toward Machine-Learning-Accelerated Design of All-Dielectric Magnetophotonic Nanostructures.

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.

Serum biomarkers REG1A and REG3A combined with the traditional CEA represent a novel nomogram for the screening and risk stratification of colorectal cancer.

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.

GIS-based G × E modeling of maize hybrids through enviromic markers engineering.

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.

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.

Brain metastasis risk prediction model in females with hormone receptor-positive breast cancer.

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.

Multivariate Analysis of Solubility Parameters for Drug-Polymer Miscibility Assessment in Preparing Raloxifene Hydrochloride Amorphous Solid Dispersions.

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.

Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia.

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.

A Bayesian Measure of Model Accuracy.

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.

Weight Development and Growth Curves of Grazing Santa Inês Sheep Supplemented with Concentrate in the Pre-Weaning Phase.

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.

The generalized odd log-logistic-G regression with interval-censored survival data.

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.

A new family of quantile regression models applied to nutritional data.

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.

Brazilian undergraduates' and dentists' knowledge on preventing, diagnosing and managing dentin hypersensitivity: A cross-sectional questionnaire study.

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.

Associations between prenatal metal and metalloid mixtures in teeth and reductions in childhood lung function.

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.

Unveiling ecological/evolutionary insights in HIV viral load dynamics: Allowing random slopes to observe correlational changes to CpG-contents and other molecular and clinical predictors.

In the context of infectious diseases, the dynamic interplay between ever-changing host populations and viral biology demands a more flexible modeling approach than common fixed correlations. Embracing random-effects regression models allows for a nuanced understanding of the intricate ecological and evolutionary dynamics underlying complex phenomena, offering valuable insights into disease progression and transmission patterns. In this article, we employed a random-effects regression to model an observed decreasing median plasma viral load (pVL) among individuals with HIV in Mexico City during 2019-2021. We identified how these functional slope changes (i.e. random slopes by year) improved predictions of the observed pVL median changes between 2019 and 2021, leading us to hypothesize underlying ecological and evolutionary factors. Our analysis involved a dataset of pVL values from 7325 ART-naïve individuals living with HIV, accompanied by their associated clinical and viral molecular predictors. A conventional fixed-effects linear model revealed significant correlations between pVL and predictors that evolved over time. However, this fixed-effects model could not fully explain the reduction in median pVL; thus, prompting us to adopt random-effects models. After applying a random effects regression model-with random slopes and intercepts by year-, we observed potential "functional changes" within the local HIV viral population, highlighting the importance of ecological and evolutionary considerations in HIV dynamics: A notably stronger negative correlation emerged between HIV pVL and the CpG content in the pol gene, suggesting a changing immune landscape influenced by CpG-induced innate immune responses that could impact viral load dynamics. Our study underscores the significance of random effects models in capturing dynamic correlations and the crucial role of molecular characteristics like CpG content. By enriching our understanding of changing host-virus interactions and HIV progression, our findings contribute to the broader relevance of such models in infectious disease research. They shed light on the changing interplay between host and pathogen, driving us closer to more effective strategies for managing infectious diseases. SIGNIFICANCE OF THE STUDY: This study highlights a decreasing trend in median plasma viral loads among ART-naïve individuals living with HIV in Mexico City between 2019 and 2021. It uncovers various predictors significantly correlated with pVL, shedding light on the complex interplay between host-virus interactions and disease progression. By employing a random-slopes model, the researchers move beyond traditional fixed-effects models to better capture dynamic correlations and evolutionary changes in HIV dynamics. The discovery of a stronger negative correlation between pVL and CpG content in HIV-pol sequences suggests potential changes in the immune landscape and innate immune responses, opening avenues for further research into adaptive changes and responses to environmental shifts in the context of HIV infection. The study's emphasis on molecular characteristics as predictors of pVL adds valuable insights to epidemiological and evolutionary studies of viruses, providing new avenues for understanding and managing HIV infection at the population level.

Spatiotemporal hierarchical Bayesian analysis to identify factors associated with COVID-19 in suburban areas in Colombia.

Introduction: The pandemic had a profound impact on the provision of health services in Cúcuta, Colombia where the neighbourhood-level risk of Covid-19 has not been investigated. Identifying the sociodemographic and environmental risk factors of Covid-19 in large cities is key to better estimate its morbidity risk and support health strategies targeting specific suburban areas. This study aims to identify the risk factors associated with the risk of Covid-19 in Cúcuta considering inter -spatial and temporal variations of the disease in the city's neighbourhoods between 2020 and 2022. Methods: Age-adjusted rate of Covid-19 were calculated in each Cúcuta neighbourhood and each quarter between 2020 and 2022. A hierarchical spatial Bayesian model was used to estimate the risk of Covid-19 adjusting for socioenvironmental factors per neighbourhood across the study period. Two spatiotemporal specifications were compared (a nonparametric temporal trend; with and without space-time interaction). The posterior mean of the spatial and spatiotemporal effects was used to map the Covid-19 risk. Results: There were 65,949 Covid-19 cases in the study period with a varying standardized Covid-19 rate that peaked in October-December 2020 and April-June 2021. Both models identified an association of the poverty and stringency indexes, education level and PM10 with Covid-19 although the best fit model with a space-time interaction estimated a strong association with the number of high-traffic roads only. The highest risk of Covid-19 was found in neighbourhoods in west, central, and east Cúcuta. Conclusions: The number of high-traffic roads is the most important risk factor of Covid-19 infection in Cucuta. This indicator of mobility and connectivity overrules other socioenvironmental factors when Bayesian models include a space-time interaction. Bayesian spatial models are important tools to identify significant determinants of Covid-19 and identifying at-risk neighbourhoods in large cities. Further research is needed to establish causal links between these factors and Covid-19.