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Establishment of 3D Cultures of Myometrium, Leiomyoma, and Leiomyosarcoma Cells: Advantages and Disadvantages of Two Different Models
No full textUterine leiomyomas are the most common benign, monoclonal, gynaecological tumors in a woman’s uterus, while leiomyosarcoma is a rare but aggressive condition caused by the malignant transformation of the myometrium. To overcome the common obstacles related to the methods usually used to study these pathologies, we aimed to devise three-dimensional models of myometrium, uterine leiomyoma and leiomyosarcoma cell lines, using two different types of biocompatible scaffolds. Specifically, we exploited the agarose gel matrix in common 6-well plates and the alginate matrix using Bioprinting INKREDIBLE + (CELLINK), a pneumatic extruded base equipped with a system with double printheads, and a UV printer LED curing system. Both methods allowed the development of 3D spheroids of all three cell types, that were also suitable for morphological investigations. We showed that all cell types embedded in both agarose and alginate formed spheroids in their growth medium. The spheroids successfully proliferated and self-organized into complex structures, developing a sustainable system that emulated the condition of the tissues through the accumulation of extracellular matrix. These models could be useful for a better understanding of pathophysiology, etiopathogenesis, and testing new methods or molecules from a preventive and therapeutic point of view
Diagnostic Accuracy of Plasma p-tau217 for Detecting Pathological Cerebrospinal Fluid Changes in Cognitively Unimpaired Subjects Using the Lumipulse Platform
No full textBackground
Plasma biomarkers of Alzheimer’s disease (AD), especially p-tau217, are promising tools to identify subjects with amyloid deposition in the brain, determined either by cerebrospinal fluid (CSF) or positron emission tomography. However, it is essential to measure them in an accurate and fully automated way in order to apply them in clinical practice.
Objectives
To evaluate the diagnostic performance of the fully-automated Lumipulse plasma p-tau217 assay in preclinical AD.
Design
Cross-sectional analyses from a prospective cohort.
Setting
A population-based study.
Participants
Volunteers over 55 years without cognitive impairment or contraindications for complementary tests.
Measurements
Plasma p-tau217 was measured with the fully-automated Lumipulse assay, as well as CSF Aβ40, Aβ42, p-taul81, and t-tau levels. We correlated plasma p-tau217 with CSF Aβ40, Aβ42 and p-tau181, and assessed the differences in plasma p-tau217 according to CSF amyloid status (A−/+), AD status (AD+ being those subjects A+T+ and AD- the rest) and ATN group. We performed ROC curves and measured the areas under the curve (AUC) using CSF amyloid as result, and both p-tau217 and ApoE4 status as predictor.
Results
We screened 209 cognitively unimpaired volunteers with a mean age 64 years (60–69) and 30.2% of ApoE4 carriers. Plasma p-tau217 correlated significantly with CSF Aβ42/Aβ40 (Rho=−0.51; p-value<0.001) and p-tau181 (r=0.59; p-value<0.001). Its levels were significantly higher in A+ subjects (0.26 pg/ml) compared with A- (0.12 pg/ml; p-value<0.001); and along ATN groups. It predicts CSF amyloid pathology with an AUC of 0.85.
Conclusions
Plasma p-tau217 measured using the Lumipulse platform shows promise as an accurate biomarker of preclinical AD pathology
A Plant-Based Food Guide Adapted for Low-Fat Diets: The VegPlate Low-Fat (VP_LF)
No full textStrong evidence supports the paramount importance of the composition of the diet for health. Not only diet should provide nutritional adequacy, but some foods and dietary components can also support the management of common chronic diseases, with mechanisms independent of nutritional adequacy. Among the various intervention diets, low-fat vegan diets have been shown to be effective for cardiometabolic health, mainly influencing insulin resistance, adiposity, and blood lipids. This type of diet relies on reducing or eliminating all added fats and choosing low-fat foods, mainly unprocessed whole-plant foods. We hereby propose a tool for planning low-fat vegan diets, the VegPlate Low-Fat (VP_LF), which has been obtained from a specific adaptation of the VegPlate method, which was already presented in previous publications for adults and some life stages and situations. The reduction in fats in the diet, which ranges between 10% and 15% of total energy, and the varied inclusion of foods from plant groups make it easier to provide adequate amounts of all nutrients with a normal- or lower-calorie intake, in comparison with diets that do not limit fat intakes. We expect that this new proposal will help nutrition professionals embrace low-fat diets as a first-line intervention for individuals affected by different health conditions who can benefit from these diets
La relación entre el equilibrio nutricional y la modulación intestinal con los cambios del neurodesarrollo
No full textLa inflamación sistémica y la neuroinflamación están estrechamente vinculadas, formando un eje crucial en las patologías neuropsiquiátricas y los trastornos del neurodesarrollo, como el Trastorno del Espectro Autista (TEA). Los desequilibrios nutricionales y la inflamación intestinal pueden agravar la neuroinflamación, empeorando las condiciones clínicas. No obstante, la medicina tradicional, enfocada en el manejo sintomático con fármacos, ha relegado el potencial de las intervenciones basadas en nutrición y microbiota intestinal. Este estudio aborda esta brecha evaluando el impacto de una intervención nutricional y probiótica en la salud física y comportamental de niños con trastorno del neurodesarrollo. Objetivo: Analizar los efectos de una alimentación antiinflamatoria, suplementación y probióticos sobre el balance nutricional, manifestaciones gastrointestinales, uso de fármacos y el neurodesarrollo. Metodología: Estudio cuasi-experimental de series temporales realizado durante cuatro meses en dos grupos de niños con TEA: intervención (alimentación antinflamatoria, vitaminas, minerales quelados, omega 3 y probióticos) y control sin intervención. Se evaluaron: i) balance nutricional (perfil bioquímico); ii) síntomas gastrointestinales (selectividad alimentaria, alergias, constipación, dolor intestinal); iii) tratamiento farmacológico (uso de medicamentos y consultas psiquiátricas); iv) comportamiento (agitación psicomotriz, movimientos estereotipados, rendimiento escolar). Resultados: En 23 pacientes (19 masculinos, edad 11±4,3 años), el grupo intervención (n=11) mostró mejoras significativas en niveles de vitamina B12, D y homocisteína; reducción en selectividad alimentaria (67%), alergias (58%) y constipación (50%); disminución en el uso de medicamentos (67%) y consultas psiquiátricas; además de mejoría en agitación psicomotriz (100%) y movimientos estereotipados (55%). Conclusión: La intervención nutricional y la modulación de la microbiota intestinal pueden mejorar el equilibrio nutricional y la salud integral, reduciendo la necesidad de fármacos y promoviendo mejor calidad de vida. Este enfoque integral destaca la relevancia del eje intestino-cerebro en el manejo clínico de trastornos del neurodesarrollo
Transforming Urban Sanitation: Enhancing Sustainability through Machine Learning-Driven Waste Processing
No full textThe enormous increase in the volume of waste caused by the population boom in cities is placing a considerable burden on waste processing in cities. The inefficiency and high costs of conventional approaches exacerbate the risks to the environment and human health. This article proposes a thorough approach that combines deep learning models, IoT technologies, and easily accessible resources to overcome these challenges. Our main goal is to advance a framework for intelligent waste processing that utilizes Internet of Things sensors and deep learning algorithms. The proposed framework is based on Raspberry Pi 4 with a camera module and TensorFlow Lite version 2.13. and enables the classification and categorization of trash in real time. When trash objects are identified, a servo motor mounted on a plastic plate ensures that the trash is sorted into appropriate compartments based on the model’s classification. This strategy aims to reduce overall health risks in urban areas by improving waste sorting techniques, monitoring the condition of garbage cans, and promoting sanitation through efficient waste separation. By streamlining waste handling processes and enabling the creation of recyclable materials, this framework contributes to a more sustainable waste management system
Roman urdu hate speech detection using hybrid machine learning models and hyperparameter optimization
No full textWith the rapid increase of users over social media, cyberbullying, and hate speech problems have arisen over the past years. Automatic hate speech detection (HSD) from text is an emerging research problem in natural language processing (NLP). Researchers developed various approaches to solve the automatic hate speech detection problem using different corpora in various languages, however, research on the Urdu language is rather scarce. This study aims to address the HSD task on Twitter using Roman Urdu text. The contribution of this research is the development of a hybrid model for Roman Urdu HSD, which has not been previously explored. The novel hybrid model integrates deep learning (DL) and transformer models for automatic feature extraction, combined with machine learning algorithms (MLAs) for classification. To further enhance model performance, we employ several hyperparameter optimization (HPO) techniques, including Grid Search (GS), Randomized Search (RS), and Bayesian Optimization with Gaussian Processes (BOGP). Evaluation is carried out on two publicly available benchmarks Roman Urdu corpora comprising HS-RU-20 corpus and RUHSOLD hate speech corpus. Results demonstrate that the Multilingual BERT (MBERT) feature learner, paired with a Support Vector Machine (SVM) classifier and optimized using RS, achieves state-of-the-art performance. On the HS-RU-20 corpus, this model attained an accuracy of 0.93 and an F1 score of 0.95 for the Neutral-Hostile classification task, and an accuracy of 0.89 with an F1 score of 0.88 for the Hate Speech-Offensive task. On the RUHSOLD corpus, the same model achieved an accuracy of 0.95 and an F1 score of 0.94 for the Coarse-grained task, alongside an accuracy of 0.87 and an F1 score of 0.84 for the Fine-grained task. These results demonstrate the effectiveness of our hybrid approach for Roman Urdu hate speech detection
Integrating artificial intelligence to assess emotions in learning environments: a systematic literature review
No full textIntroduction: Artificial Intelligence (AI) is transforming multiple sectors within our society, including education. In this context, emotions play a fundamental role in the teaching-learning process given that they influence academic performance, motivation, information retention, and student well-being. Thus, the integration of AI in emotional assessment within educational environments offers several advantages that can transform how we understand and address the socio-emotional development of students. However, there remains a lack of comprehensive approach that systematizes advancements, challenges, and opportunities in this field.
Aim: This systematic literature review aims to explore how artificial intelligence (AI) is used to evaluate emotions within educational settings. We provide a comprehensive overview of the current state of research, focusing on advancements, challenges, and opportunities in the domain of AI-driven emotional assessment within educational settings.
Method: The review involved a search across the following academic databases: Pubmed, Web of Science, PsycINFO and Scopus. Forty-one articles were selected that meet the established inclusion criteria. These articles were analyzed to extract key insights related to the integration of AI and emotional assessment within educational environments.
Results: The findings reveal a variety of AI-driven approaches that were developed to capture and analyze students’ emotional states during learning activities. The findings are summarized in four fundamental topics: (1) emotion recognition in education, (2) technology integration and learning outcomes, (3) special education and assistive technology, (4) affective computing. Among the key AI techniques employed are machine learning and facial recognition, which are used to assess emotions. These approaches demonstrate promising potential in enhancing pedagogical strategies and creating adaptive learning environments that cater to individual emotional needs. The review identified emerging factors that, while important, require further investigation to understand their relationships and implications fully. These elements could significantly enhance the use of AI in assessing emotions within educational settings. Specifically, we are referring to: (1) federated learning, (2) convolutional neural network (CNN), (3) recurrent neural network (RNN), (4) facial expression databases, and (5) ethics in the development of intelligent systems.
Conclusion: This systematic literature review showcases the significance of AI in revolutionizing educational practices through emotion assessment. While advancements are evident, challenges related to accuracy, privacy, and cross-cultural validity were also identified. The synthesis of existing research highlights the need for further research into refining AI models for emotion recognition and emphasizes the importance of ethical considerations in implementing AI technologies within educational contexts
Premorbid adjustment as predictor of long-term functionality: Findings from a 10-year follow-up study in the PAFIP-cohort
No full textThe literature indicates that patients with schizophrenia spectrum disorders often show deficits in premorbid adjustment. Additionally, these impairments have been correlated with critical disease parameters, evident in both early and advanced stages. The principal objective of this study was to investigate the association between premorbid adjustment and functional outcomes a decade following the initial episode of psychosis. A cluster analysis was performed to group patients according to their premorbid adjustment scores as assessed with the Premorbid Adjustment Scale (PAS). The measurements of The Disability Assessment Scale (DAS), The Global Assessment of Function (GAF) scale, and The Quality of Life Scale (QLS) were used to compare the functionality of the groups at a 10-year follow-up. A total of 231 patients were classified into three groups based on their premorbid adjustment: “good PAS”, “deteriorating PAS”, and “chronically poor PAS”. The three groups differed significantly in their sociodemographic and cognitive baseline characteristics. At the 10-year follow-up, “good PAS” group had better scores than the other groups in the variables of functionality and quality of life. The relationship found between premorbid adjustment and long-term functional results in patients with psychosis can help us predict the evolution of patients and act accordingly
Efectos del ayuno intermitente en comparación a la restricción calórica en pacientes con síndrome metabólico entre las edades de 20-70 años atendidos en consulta externa del hospital escuela Oscar Danilo Rosales Arguello-HEODRA, León-Nicaragua en el 2023
No full textSe realizó este estudio con el propósito de comparar el efecto del ayuno intermitente vs. la restricción calórica en pacientes con síndrome metabólico entre las edades 20-70 años en el hospital Escuela Oscar Danilo Rosales Arguello – HEODRA, león- Nicaragua en el año 2023. El tipo de estudio fue experimental, ensayo clínico con 2 grupos comparativos (30 pacientes cada uno), fuente de recolección primaria. Se realizó un análisis univariado de frecuencias y porcentajes. Se estimaron las medidas de tendencia central y de dispersión para variables cuantitativas. Se estimaron pruebas estadísticas paramétricas como la t de Student, y pruebas no paramétricas como la U Mann de Whitney. El chi cuadrado se estimó en variables cualitativas, siendo significativo si es menor de 0.05. Los resultados muestran entre las principales características sociodemográficas que predominaron fueron el grupo etario de 30 a 45 años (43.3%), el sexo femenino (68.3%), la procedencia del municipio de León (95%), la escolaridad superior (51.7%), la ocupación profesional (63.6%) y un estado civil de unión libre (41.7%). La intervención basada en el ayuno intermitente demostró una perdida significativamente estadística de peso en pacientes con síndrome metabólico(p:0.03). Se observó una tendencia mayor de cambios favorables en los parámetros corporales con el ayuno intermitente que con la restricción calórica, sin haber obtenido diferencias significativas estadísticas. La calidad de vida de los pacientes en estudio fue determinada como “Buena” (65%). Dicho instrumento tomo en cuenta dimensiones laborales, familiares, sociales y personales, no una diferencia significativa entre los grupos intervenidos con estrategias diferentes. La adherencia en el tiempo de estudio del grupo con ayuno intermitente fue similar a la del grupo con restricción calórica. Los pacientes con intervención de ayuno intermitente y con restricción calórica no presentaron efectos adversos o no deseados relevantes. Se concluyó que los resultados encontrados son similares a estudios observados en el contexto internacional siendo el ayuno intermitente un método con buenos resultados para el abordaje de los pacientes con síndrome metabólico, y es necesario continuar con estos estudios utilizando más pacientes por grupo, y con mayor tiempo de seguimiento. Se recomienda tomar en cuenta otros aspectos relacionados al tema como tipo de alimentos, factores de riesgo, prevalencia, conocimientos, entre otros
An empirical analysis of factors determining changes in physical exercise during the COVID-19 pandemic
No full textAim
The main objective of the study was to report the changes that have taken place in the practice of physical exercise during confinement and to examine the factors that favor or detract from it.
Material and methods
To determine the objective, a survey was carried out in the United States during the pandemic and a sample of 511 participants was obtained. A binary logit model was used to process the data, as well as several independence tests.
Results
The main result of this study is the increase in the practice of physical activity of the individuals surveyed during the pandemic. Some of the elements that most influenced this increase were annual family income, education level, and eating habits, but these results are subject to change depending on the respondent’s body mass index. On the other hand, the results also show changes in physical exercise habits during the pandemic, especially in the time of the week when it is performed, and these changes are highly correlated with the use of electronic devices, hours of sleep, and physical condition of the respondents before the pandemic.
Conclusion
Determining the different factors that affect the practice of physical exercise during pandemic periods seems to be important to determine in which populations it is more important to act or what resources are necessary when implementing physical exercise programs in specific situations such as pandemics