Ensino remoto de educação em saúde na pós-graduação em saúde coletiva: possibilidades no contexto da pandemia de covid-19 / Remote teaching of health education in the postgraduate program in collective health: possibilities in the context of the covid-19 pandemic / Enseñanza a distância de educación para la salud en el posgrado en salud colectiva: posibilidades en el contexto de la pandemia del covid-19

O ensino remoto emergencial ocasionou mudanças no processo de ensino-aprendizagem, requisitando criatividade e incorporação de novas estratégias pedagógicas. Aqui, o objetivo é descrever a experiência de ensino-aprendizagem na disciplina educação em saúde, no contexto da pandemia de covid-19. Trata-se de um relato de experiência sobre o ensino remoto de educação em saúde no Programa de Pós-Graduação em Saúde Coletiva da Universidade Estadual do Ceará, no período letivo 2021.1. A disciplina foi ministrada por meio da plataforma Google Meet®, adotando-se estratégias ativas de ensino-aprendizagem. Os conteúdos mostraram--se relevantes. Ademais, a experiência promoveu a articulação teórico-prática, valorizou os saberes prévios dos pós-graduandos e estimulou a interatividade. Buscou-se superar o modelo tradicional de ensino, com vistas a propiciar autonomia e uma aprendizagem significativa. Os desafios encontrados e as possibilidades identificadas permitem a reflexão sobre a práxis docente, no que tange ao estímulo à participação e ao engajamento discente em ambiente virtual, além da incorporação de estratégias ativas de ensino, sobretudo no ensino remoto.

Emergency remote teaching caused changes in the teaching-learning process, requiring creativity and the incorporation of new pedagogical strategies. Here, the objective is to describe the teaching-learning experience in the health education discipline, in the context of the covid-19 pandemic. This is an experience report on remote teaching of health education in the postgraduate program in public health, at the Ceará State University, Brazil, in the 2021.1 academic period. The classes were given using the Google Meet® platform, adopting active teaching-learning strategies. The contents proved to be relevant. Moreover, the experience promoted theoretical-practical articulation, valued the prior knowledge of the postgraduate students and encouraged interactivity. We sought to overcome the traditional teaching model, in order to provide autonomy and a meaningful learning. The challenges experienced and the possibilities identified allow reflection on teaching practice in terms of encouraging student participation and engagement in a virtual environment, in addition to the incorporation of active teaching strategies in especially remote teaching.

La educación remota de emergencia provocó cambios en el proceso de enseñanza-aprendizaje, requiriendo creatividad y la incorporación de nuevas estrategias pedagógicas. El objetivo aquí es describir la experiencia de enseñanza-aprendizaje en la disciplina educación para la salud, en el contexto de la pandemia covid-19. Se trata de un relato de experiencia sobre la enseñanza remota de educación para la salud en el programa de posgrado en Salud Pública, de la Universidad Estadual de Ceará, en el período académico 2021.1. El curso se impartió utilizando la plataforma Google Meet®, adoptando estrategias activas de enseñanza-aprendi-zaje. Los contenidos han demonstrado ser relevantes. Además, la experiencia fomentó la articulación teó-rico-práctica, valoró los conocimientos previos de los estudiantes de posgrado y impulsó la interactividad. Buscamos superar el modelo de enseñanza tradicional, con el propósito de proporcionar autonomía y un aprendizaje significativo. Los desafíos enfrentados y las posibilidades identificadas permiten reflexionar sobre la práctica docente, en relación a incentivar la participación y el compromiso de los estudiantes en un ambiente virtual, además de la incorporación de estrategias activas en la enseñanza remota.

Da superfície para a profundidade: Curva de aprendizado em lifting facial profundo estendido - meus primeiros 100 casos / From Surface to Depth: Learning Curve in Extended Deep Facelift ­ my First 100 Cases

Introdução: O envelhecimento facial é um processo gradual, complexo e multifatorial. É o resultado de mudanças na qualidade, volume e posicionamento dos tecidos. Cirurgiões plásticos têm modificado sua abordagem na cirurgia do rejuvenescimento facial optando pelo plano subaponeurótico (SMAS). O objetivo deste estudo é analisar 100 casos de pacientes operados pela técnica de SMAS profundo, avaliando sua aplicabilidade e eficácia. Método: Foram avaliados 100 pacientes, submetidos a cirurgia plástica facial pela técnica de SMAS profundo - "Deep Smas", e acompanhados por 6 meses. Observou-se a satisfação dos pacientes, número de complicações, número de reoperações, riscos e vantagens da técnica. Resultados: Foram operados 100 pacientes, num período de 3 anos. A idade variou de 41 a 79 anos, sendo 95% sexo feminino. As complicações foram 8 casos (8%) de lesões de ramos do nervo facial, sendo: 4 casos lesão do zigomático, 3 casos de lesão do mandibular e 1 caso de lesão do bucal; houve 1 caso (1%) de queloide retroauricular; 1 caso (1%) de hematoma. Em relação às revisões cirúrgicas, houve 8 casos (8%) de complementação cirúrgica por insatisfação das pacientes. Houve 15% de lesões nervosas entre a 1ª e a 40ª cirurgia, 5% entre a 41ª e a 80ª, e nenhuma lesão entre o 81º e o 100º paciente. Conclusão: O lifting facial profundo ou subSMAS mostrou ser efetivo, proporcionando bons resultados estéticos. Apresenta baixa taxa de recidiva e baixa taxa de morbidade, porém, necessita de uma longa curva de aprendizagem.

Introduction: Facial aging is a gradual, complex, and multifactorial process. It is the result of changes in the quality, volume, and positioning of tissues. Plastic surgeons have modified their approach to facial rejuvenation surgery, opting for the subaponeurotic plane (SMAS). The objective of this study is to analyze 100 cases of patients operated on using the deep SMAS technique, evaluating its applicability and effectiveness. Method: 100 patients were evaluated, undergoing facial plastic surgery using the deep SMAS technique - "Deep Smas", and followed up for 6 months. Patient satisfaction, number of complications, number of reoperations, risks, and advantages of the technique were observed. Results: 100 patients were operated on over 3 years. Age ranged from 41 to 79 years, with 95% being female. The complications were 8 cases (8%) of injuries to branches of the facial nerve, of which 4 cases of zygomatic injury, 3 cases of mandibular injury, and 1 case of buccal injury; there was 1 case (1%) of post-auricular keloid; 1 case (1%) of hematoma. Regarding surgical revisions, there were 8 cases (8%) of surgical completion due to patient dissatisfaction. There were 15% of nerve injuries between the 1st and 40th surgery, 5% between the 41st and 80th, and no injuries between the 81st and 100th patient. Conclusion: Deep facial lifting or subSMAS has proven to be effective, providing good aesthetic results. It has a low recurrence rate and low morbidity rate; however, it requires a long learning curve.

ChatGPT's learning and reasoning capacity in anesthesiology

Introduction: Over the past few months, ChatGPT has raised a lot of interest given its ability to perform complex tasks through natural language and conversation. However, its use in clinical decision-making is limited and its application in the field of anesthesiology is unknown. Objective: To assess ChatGPT's basic and clinical reasoning and its learning ability in a performance test on general and specific anesthesia topics. Methods: A three-phase assessment was conducted. Basic knowledge of anesthesia was assessed in the first phase, followed by a review of difficult airway management and, finally, measurement of decision-making ability in ten clinical cases. The second and the third phases were conducted before and after feeding ChatGPT with the 2022 guidelines of the American Society of Anesthesiologists on difficult airway management. Results: On average, ChatGPT succeded 65% of the time in the first phase and 48% of the time in the second phase. Agreement in clinical cases was 20%, with 90% relevance and 10% error rate. After learning, ChatGPT improved in the second phase, and was correct 59% of the time, with agreement in clinical cases also increasing to 40%. Conclusions: ChatGPT showed acceptable accuracy in the basic knowledge test, high relevance in the management of specific difficult airway clinical cases, and the ability to improve after learning.

Introducción: En los últimos meses, ChatGPT ha suscitado un gran interés debido a su capacidad para realizar tareas complejas a través del lenguaje natural y la conversación. Sin embargo, su uso en la toma de decisiones clínicas es limitado y su aplicación en el campo de anestesiología es desconocido. Objetivo: Evaluar el razonamiento básico, clínico y la capacidad de aprendizaje de ChatGPT en una prueba de rendimiento sobre temas generales y específicos de anestesiología. Métodos: Se llevó a cabo una evaluación dividida en tres fases. Se valoraron conocimientos básicos de anestesiología en la primera fase, seguida de una revisión del manejo de vía aérea difícil y, finalmente, se midió la toma de decisiones en diez casos clínicos. La segunda y tercera fases se realizaron antes y después de alimentar a ChatGPT con las guías de la Sociedad Americana de Anestesiólogos del manejo de la vía aérea difícil del 2022. Resultados: ChatGPT obtuvo una tasa de acierto promedio del 65 % en la primera fase y del 48 % en la segunda fase. En los casos clínicos, obtuvo una concordancia del 20 %, una relevancia del 90 % y una tasa de error del 10 %. Posterior al aprendizaje, ChatGPT mejoró su tasa de acierto al 59 % en la segunda fase y aumentó la concordancia al 40 % en los casos clínicos. Conclusiones: ChatGPT demostró una precisión aceptable en la prueba de conocimientos básicos, una alta relevancia en el manejo de los casos clínicos específicos de vía aérea difícil y la capacidad de mejoría secundaria a un aprendizaje.

Artificial intelligence, applications and challenges in simulation-based education

The rapid advancement of Artificial Intelligence (AI) has taken the world by "surprise" due to the lack of regulation over this technological innovation which, while promising application opportunities in different fields of knowledge, including education, simultaneously generates concern, rejection and even fear. In the field of Health Sciences Education, clinical simulation has transformed educational practice; however, its formal insertion is still heterogeneous, and we are now facing a new technological revolution where AI has the potential to transform the way we conceive its application.

El rápido avance de la inteligencia artificial (IA) ha tomado al mundo por "sorpresa" debido a la falta de regulación sobre esta innovación tecnológica, que si bien promete oportunidades de aplicación en diferentes campos del conocimiento, incluido el educativo, también genera preocupación e incluso miedo y rechazo. En el campo de la Educación en Ciencias de la Salud la Simulación Clínica ha transformado la práctica educativa; sin embargo, aún es heterogénea su inserción formal, y ahora nos enfrentamos a una nueva revolución tecnológica, en la que las IA tienen el potencial de transformar la manera en que concebimos su aplicación.

Una revisión sistemática del uso de la inteligencia artificial en la educación / A systematic review of the use of artificial intelligence in education

Introducción. El uso de la inteligencia artificial (IA) en la educación ha sido objeto de una creciente atención en los últimos años. La IA se ha utilizado para mejorar la personalización del aprendizaje, la retroalimentación y la evaluación de los estudiantes. Sin embargo, también hay desafíos y limitaciones asociados. El objetivo de este trabajo fue identificar las principales tendencias y áreas de aplicación de la inteligencia artificial en la educación, así como analizar los beneficios y limitaciones de su uso en este ámbito. Métodos. Se llevó a cabo una revisión sistemática que exploró el empleo de la inteligencia artificial en el ámbito educativo. Esta revisión siguió una metodología de investigación basada en la búsqueda de literatura, compuesta por cinco etapas. La investigación se realizó utilizando Scopus como fuente de consulta primaria y se empleó la herramienta VOSviewer para analizar los resultados obtenidos. Resultados. Se encontraron numerosos estudios que investigan el uso de la IA en la educación. Los resultados sugieren que la IA puede mejorar significativamente la personalización del aprendizaje, proporcionando recomendaciones de actividades y retroalimentación adaptadas a las necesidades individuales de cada estudiante. Conclusiones. A pesar de las ventajas del uso de la IA en la educación, también hay desafíos y limitaciones que deben abordarse, como la calidad de los datos utilizados por la IA, la necesidad de capacitación para educadores y estudiantes, y las preocupaciones sobre la privacidad y la seguridad de los datos de los estudiantes. Es importante seguir evaluando los efectos del uso de la IA en la educación para garantizar su uso efectivo y responsable.

Introduction. The use of artificial intelligence (AI) in education has been the subject of increasing attention in recent years. AI has been used to improve personalized learning, feedback, and student assessment. However, there are also challenges and limitations. The aim of this study was to identify the main trends and areas of application of artificial intelligence in education, as well as to analyze the benefits and limitations of its use in this field. Methods. A systematic review was carried out on the use of artificial intelligence in education, using a literature search research methodology with five stages, based on the Scopus query and the tool for analyzing results with VOSviewer. Results. Numerous studies investigating the use of AI in education were found. The results suggest that AI can significantly improve personalized learning by providing activity recommendations and feedback tailored to the individual needs of each student. Conclusions. Despite the advantages of using AI in education, there are also challenges and limitations that need to be addressed, such as the quality of data used by AI, the need for training for educators and students, and concerns about the privacy and security of student data. It is important to continue evaluating the effects of AI use in education to ensure its effective and responsible use.

Diagnóstico da retinopatia diabética por inteligência artificial por meio de smartphone / Diagnosis of diabetic retinopathy by artificial intelligence using smartphone

RESUMO Objetivo: Obter imagens de fundoscopia por meio de equipamento portátil e de baixo custo e, usando inteligência artificial, avaliar a presença de retinopatia diabética. Métodos: Por meio de um smartphone acoplado a um dispositivo com lente de 20D, foram obtidas imagens de fundo de olhos de pacientes diabéticos; usando a inteligência artificial, a presença de retinopatia diabética foi classificada por algoritmo binário. Resultados: Foram avaliadas 97 imagens da fundoscopia ocular (45 normais e 52 com retinopatia diabética). Com auxílio da inteligência artificial, houve acurácia diagnóstica em torno de 70 a 100% na classificação da presença de retinopatia diabética. Conclusão: A abordagem usando dispositivo portátil de baixo custo apresentou eficácia satisfatória na triagem de pacientes diabéticos com ou sem retinopatia diabética, sendo útil para locais sem condições de infraestrutura.

ABSTRACT Introduction: To obtain fundoscopy images through portable and low-cost equipment using artificial intelligence to assess the presence of DR. Methods: Fundus images of diabetic patients' eyes were obtained by using a smartphone coupled to a device with a 20D lens. By using artificial intelligence (AI), the presence of DR was classified by a binary algorithm. Results: 97 ocular fundoscopy images were evaluated (45 normal and 52 with DR). Through AI diagnostic accuracy around was 70% to 100% in the classification of the presence of DR. Conclusion: The approach using a low-cost portable device showed satisfactory efficacy in the screening of diabetic patients with or without diabetic retinopathy, being useful for places without infrastructure conditions.

Performance analysis of pretrained convolutional neural network models for ophthalmological disease classification / Análise de desempenho de modelos de rede neural convolucional pré-treinados para classificação de doenças oftalmológicas

ABSTRACT Purpose: This study aimed to evaluate the classification performance of pretrained convolutional neural network models or architectures using fundus image dataset containing eight disease labels. Methods: A publicly available ocular disease intelligent recognition database has been used for the diagnosis of eight diseases. This ocular disease intelligent recognition database has a total of 10,000 fundus images from both eyes of 5,000 patients for the following eight diseases: healthy, diabetic retinopathy, glaucoma, cataract, age-related macular degeneration, hypertension, myopia, and others. Ocular disease classification performances were investigated by constructing three pretrained convolutional neural network architectures including VGG16, Inceptionv3, and ResNet50 models with adaptive moment optimizer. These models were implemented in Google Colab, which made the task straight-forward without spending hours installing the environment and supporting libraries. To evaluate the effectiveness of the models, the dataset was divided into 70%, 10%, and 20% for training, validation, and testing, respectively. For each classification, the training images were augmented to 10,000 fundus images. Results: ResNet50 achieved an accuracy of 97.1%; sensitivity, 78.5%; specificity, 98.5%; and precision, 79.7%, and had the best area under the curve and final score to classify cataract (area under the curve = 0.964, final score = 0.903). By contrast, VGG16 achieved an accuracy of 96.2%; sensitivity, 56.9%; specificity, 99.2%; precision, 84.1%; area under the curve, 0.949; and final score, 0.857. Conclusions: These results demonstrate the ability of the pretrained convolutional neural network architectures to identify ophthalmological diseases from fundus images. ResNet50 can be a good architecture to solve problems in disease detection and classification of glaucoma, cataract, hypertension, and myopia; Inceptionv3 for age-related macular degeneration, and other disease; and VGG16 for normal and diabetic retinopathy.

RESUMO Objetivo: Avaliar o desempenho de classificação de modelos ou arquiteturas de rede neural convolucional pré--treinadas usando um conjunto de dados de imagem de fundo de olho contendo oito rótulos de doenças diferentes. Métodos: Neste artigo, o conjunto de dados de reconhecimento inteligente de doenças oculares publicamente disponível foi usado para o diagnóstico de oito rótulos de doenças diferentes. O banco de dados de reconhecimento inteligente de doenças oculares tem um total de 10.000 imagens de fundo de olho de ambos os olhos de 5.000 pacientes para oito categorias que contêm rótulos saudáveis, retinopatia diabética, glaucoma, catarata, degeneração macular relacionada à idade, hipertensão, miopia, outros. Investigamos o desempenho da classificação de doenças oculares construindo três arquiteturas de rede neural convolucional pré-treinadas diferentes, incluindo os modelos VGG16, Inceptionv3 e ResNet50 com otimizador de Momento Adaptativo. Esses modelos foram implementados no Google Colab o que facilitou a tarefa sem gastar horas instalando o ambiente e suportando bibliotecas. Para avaliar a eficácia dos modelos, o conjunto de dados é dividido em 70% para treinamento, 10% para validação e os 20% restantes utilizados para teste. As imagens de treinamento foram expandidas para 10.000 imagens de fundo de olho para cada tal. Resultados: Observou-se que o modelo ResNet50 alcançou acurácia de 97,1%, sensibilidade de 78,5%, especificidade de 98,5% e precisão de 79,7% e teve a melhor área sob a curva e pontuação final para classificar a categoria da catarata (área sob a curva=0,964, final=0,903). Em contraste, o modelo VGG16 alcançou uma precisão de 96,2%, sensibilidade de 56,9%, especificidade de 99,2% e precisão de 84,1%, área sob a curva 0,949 e pontuação final de 0,857. Conclusão: Esses resultados demonstram a capacidade das arquiteturas de rede neural convolucional pré-treinadas em identificar doenças oftalmológicas a partir de imagens de fundo de olho. ResNet50 pode ser uma boa solução para resolver problemas na detecção e classificação de doenças como glaucoma, catarata, hipertensão e miopia; Inceptionv3 para degeneração macular relacionada à idade e outras doenças; e VGG16 para retinopatia normal e diabética.

Assessing the reaction to and efficacy of the Screener drug discovery and development board game as a pedagogical tool in postgraduate courses

Screener, a board game supplemented with online resources, was introduced and distributed by the Brazilian Society of Pharmacology and Experimental Therapeutics to postgraduate programs as an instructional tool for the process of drug discovery and development (DDD). In this study, we provided a comprehensive analysis of five critical aspects for evaluating the quality of educational games, namely: 1) description of the intervention; 2) underlying pedagogical theory; 3) identification of local educational gaps; 4) impact on diverse stakeholders; and 5) elucidation of iterative quality enhancement processes. We also present qualitative and quantitative assessments of the effectiveness of this game in 11 postgraduate courses. We employed the MEEGA+ online survey, comprising thirty-three close-ended unipolar items with 5-point Likert-type response scales, to assess student perceptions of the quality and utility of Screener. Based on 115 responses, the results indicated a highly positive outlook among students. In addition, we performed a preliminary evaluation of learning outcomes in two courses involving 28 students. Pre- and post-quizzes were applied, each consisting of 20 True/False questions directly aligned with the game's content. The analysis revealed significant improvement in students' performance following engagement with the game, with scores rising from 8.4 to 13.3 (P<0.0001, paired t-test) and 9.7 to 12.7 (P<0.0001, paired t-test). These findings underscore the utility of Screener as an enjoyable and effective tool for facilitating a positive learning experience in the DDD process. Notably, the game can also reduce the educational disparities across different regions of our continental country.

Machine learning in predicting severe acute respiratory infection outbreaks / Aprendizado de máquina na previsão de surtos de síndrome respiratória aguda grave / Aprendizaje automático en la predicción de brotes de síndrome respiratorio agudo grave

Abstract: Severe acute respiratory infection (SARI) outbreaks occur annually, with seasonal peaks varying among geographic regions. Case notification is important to prepare healthcare networks for patient attendance and hospitalization. Thus, health managers need adequate resource planning tools for SARI seasons. This study aims to predict SARI outbreaks based on models generated with machine learning using SARI hospitalization notification data. In this study, data from the reporting of SARI hospitalization cases in Brazil from 2013 to 2020 were used, excluding SARI cases caused by COVID-19. These data were prepared to feed a neural network configured to generate predictive models for time series. The neural network was implemented with a pipeline tool. Models were generated for the five Brazilian regions and validated for different years of SARI outbreaks. By using neural networks, it was possible to generate predictive models for SARI peaks, volume of cases per season, and for the beginning of the pre-epidemic period, with good weekly incidence correlation (R2 = 0.97; 95%CI: 0.95-0.98, for the 2019 season in the Southeastern Brazil). The predictive models achieved a good prediction of the volume of reported cases of SARI; accordingly, 9,936 cases were observed in 2019 in Southern Brazil, and the prediction made by the models showed a median of 9,405 (95%CI: 9,105-9,738). The identification of the period of occurrence of a SARI outbreak is possible using predictive models generated with neural networks and algorithms that employ time series.

Resumo: Surtos de síndrome respiratória aguda grave (SRAG) ocorrem anualmente, com picos sazonais variando entre regiões geográficas. A notificação dos casos é importante para preparar as redes de atenção à saúde para o atendimento e internação dos pacientes. Portanto, os gestores de saúde precisam ter ferramentas adequadas de planejamento de recursos para as temporadas de SRAG. Este estudo tem como objetivo prever surtos de SRAG com base em modelos gerados com aprendizado de máquina usando dados de internação por SRAG. Foram incluídos dados sobre casos de hospitalização por SRAG no Brasil de 2013 a 2020, excluindo os casos causados pela COVID-19. Estes dados foram preparados para alimentar uma rede neural configurada para gerar modelos preditivos para séries temporais. A rede neural foi implementada com uma ferramenta de pipeline. Os modelos foram gerados para as cinco regiões brasileiras e validados para diferentes anos de surtos de SRAG. Com o uso de redes neurais, foi possível gerar modelos preditivos para picos de SRAG, volume de casos por temporada e para o início do período pré-epidêmico, com boa correlação de incidência semanal (R2 = 0,97; IC95%: 0,95-0,98, para a temporada de 2019 na Região Sudeste). Os modelos preditivos obtiveram uma boa previsão do volume de casos notificados de SRAG; dessa forma, foram observados 9.936 casos em 2019 na Região Sul, e a previsão feita pelos modelos mostrou uma mediana de 9.405 (IC95%: 9.105-9.738). A identificação do período de ocorrência de um surto de SRAG é possível por meio de modelos preditivos gerados com o uso de redes neurais e algoritmos que aplicam séries temporais.

Resumen: Brotes de síndrome respiratorio agudo grave (SRAG) ocurren todos los años, con picos estacionales que varían entre regiones geográficas. La notificación de los casos es importante para preparar las redes de atención a la salud para el cuidado y hospitalización de los pacientes. Por lo tanto, los gestores de salud deben tener herramientas adecuadas de planificación de recursos para las temporadas de SRAG. Este estudio tiene el objetivo de predecir brotes de SRAG con base en modelos generados con aprendizaje automático utilizando datos de hospitalización por SRAG. Se incluyeron datos sobre casos de hospitalización por SRAG en Brasil desde 2013 hasta 2020, salvo los casos causados por la COVID-19. Se prepararon estos datos para alimentar una red neural configurada para generar modelos predictivos para series temporales. Se implementó la red neural con una herramienta de canalización. Se generaron los modelos para las cinco regiones brasileñas y se validaron para diferentes años de brotes de SRAG. Con el uso de redes neurales, se pudo generar modelos predictivos para los picos de SRAG, el volumen de casos por temporada y para el inicio del periodo pre-epidémico, con una buena correlación de incidencia semanal (R2 = 0,97; IC95%: 0,95-0,98, para la temporada de 2019 en la Región Sudeste). Los modelos predictivos tuvieron una buena predicción del volumen de casos notificados de SRAG; así, se observaron 9.936 casos en 2019 en la Región Sur, y la predicción de los modelos mostró una mediana de 9.405 (IC95%: 9.105-9.738). La identificación del periodo de ocurrencia de un brote de SRAG es posible a través de modelos predictivos generados con el uso de redes neurales y algoritmos que aplican series temporales.

Assessment of school readiness and the importance of executive functions for learning / Avaliação da prontidão escolar e a importância das funções executivas para o aprendizado

ABSTRACT Objective: Considering the importance of the beginning of the academic trajectory for children to reach their full development, this work aims to evaluate the school readiness of preschool-age children and identify which factors influence these results, in order to contribute to the proposition of strategies that allow improving the teaching-learning process and child development. Methods: This is a cross-sectional, descriptive and analytical study with 443 preschool children belonging to the West Region Cohort (ROC Cohort), from the public school system of the city of São Paulo. School readiness was assessed by the International Development and Early Learning Assessment (IDELA) tool. Non-parametric techniques were used for the correlation analysis between IDELA scores and sociodemographic and socioeconomic conditions: Spearman's parametric correlation, Mann-Whitney and Kruskal-Wallis tests. Results: The children's mean age was 69 months (standard deviation — SD=2.8; ranging from 55 to 72 months) and most of them came from families with low socioeconomic level. Most children showed adequate readiness in the overall score (65%) and in most domains, except for emergent literacy, in which most (56.9%) were classified as "emergent". The highest percentage of insufficiency was identified in executive functions (4.1%), which showed a correlation only with the caregiver's education. Conclusions: Children had adequate school readiness scores, except for emergent literacy, but the insufficiency in executive functions may compromise the future schooling of these children. Thus, pedagogical proposals should consider these aspects for learning and pediatricians need to reinforce the habit of reading and playing games to stimulate child development.

RESUMO Objetivo: Considerando-se a importância do início da trajetória acadêmica para as crianças alcançarem o seu pleno potencial de desenvolvimento, este trabalho tem como objetivo avaliar a prontidão escolar de crianças em idade pré-escolar e identificar que fatores influenciam esses resultados, com a finalidade de propor estratégias que possam melhorar o processo de ensino-aprendizagem e o desenvolvimento da criança. Métodos: Trata-se de um estudo transversal, descritivo e analítico, com 443 pré-escolares pertencentes à Coorte da Região Oeste (Coorte ROC) da rede pública de ensino da cidade de São Paulo. A prontidão escolar foi avaliada pela ferramenta International Development and Early Learning Assessment (IDELA). Técnicas não paramétricas foram utilizadas para a análise de correlação entre escores de IDELA e as condições sociodemográficas e socioeconômicas: correlação paramétrica de Spearman, testes de Mann-Whitney e Kruskal-Wallis. Resultados: A média de idade das crianças foi de 69 meses (desvio padrão — DP=2,8; variando de 55 a 72 meses) e maioria era proveniente de famílias com baixo nível socioeconômico. A maioria das crianças apresentou prontidão adequada na pontuação geral (65%) e na maior parte dos domínios, com exceção da pré-escrita, na qual as crianças foram predominantemente (56,9%) classificadas como "emergentes". O maior percentual de insuficiência foi identificado nas funções executivas (4,1%), apresentando correlação apenas com a formação do cuidador. Conclusões: As crianças apresentaram escores adequados de prontidão escolar, exceto para a pré-escrita, mas a insuficiência nas funções executivas pode comprometer a escolaridade futura dessas crianças. Assim, as propostas pedagógicas devem considerar esses aspectos para a aprendizagem, e os pediatras precisam reforçar o hábito de ler e dos jogos e brincadeiras para estimular o desenvolvimento infantil.

A retrospective study of the online self-regulated learning skills of occupational therapy students enrolled in a full online curriculum at the University of Santo Tomas amidst the COVID-19 Pandemic

Background@#The pandemic brought permanent changes in education in terms of set-up and delivery. In the Philippines, most universities switched to online learning to provide educational continuity to their students. Without direct supervision from instructors, higher educational level learners bear greater responsibility for their learning behaviors, emphasizing the need to employ online self-regulated learning (OSRL) skills, which are goal setting, environment structuring, time management, help-seeking, self-evaluation, and metacognition.@*Objectives@#This study examined the OSRL skills of occupational therapy (OT) students enrolled in a full online curriculum at the University of Santo Tomas (UST) during the academic year (A.Y.) 2020-2021. It also describes the differences between students' OSRL skills and their demographic characteristics—sex, age, year level, and student status.@*Methods@#The study employed a cross-sectional records review of the 2021 Student Life Survey, which was deployed through Google Forms to a total of 205 respondents. Responses from the Online Self-Regulated Questionnaire were extracted and analyzed through descriptive and inferential statistics in SPSS version 27, using the Mann-Whitney U Test and Kruskal-Wallis Test with a significance level set at 0.050.@*Results@#Data analysis showed that UST OT students reported average to high levels of online self-regulated learning, with the highest SRL mean score in environmental structuring and goal setting. The students’ online self-regulated learning in goal setting is statistically significant to sex (p= 0.021) and age (p= 0.036). Additionally, year levels have a significant difference in task strategies (p= 0.042) and time management (p= 0.006).@*Conclusion@#OSRL skills vary depending on the students’ contexts and learning environment. They are independent of the students’ demographic characteristics. These findings could inform stakeholders and researchers about students’ OSRL levels, which can help in providing pedagogical strategies that will enhance students' self-regulated learning in online education.

Research progress of artificial intelligence in pathological subtypes classification and gene expression analysis of lung adenocarcinoma / 中国胸心血管外科临床杂志

@#Lung adenocarcinoma is a prevalent histological subtype of non-small cell lung cancer with different morphologic and molecular features that are critical for prognosis and treatment planning. In recent years, with the development of artificial intelligence technology, its application in the study of pathological subtypes and gene expression of lung adenocarcinoma has gained widespread attention. This paper reviews the research progress of machine learning and deep learning in pathological subtypes classification and gene expression analysis of lung adenocarcinoma, and some problems and challenges at the present stage are summarized and the future directions of artificial intelligence in lung adenocarcinoma research are foreseen.

Construction of a machine learning model for identifying clinical high-risk carotid plaques based on radiomics / 中国胸心血管外科临床杂志

@#Objective     To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods     A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results    Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion     Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.

Development and prospects of predicting drug polymorphs technology / 药学学报

Most chemical medicines have polymorphs. The difference of medicine polymorphs in physicochemical properties directly affects the stability, efficacy, and safety of solid medicine products. Polymorphs is incomparably important to pharmaceutical chemistry, manufacturing, and control. Meantime polymorphs is a key factor for the quality of high-end drug and formulations. Polymorph prediction technology can effectively guide screening of trial experiments, and reduce the risk of missing stable crystal form in the traditional experiment. Polymorph prediction technology was firstly based on theoretical calculations such as quantum mechanics and computational chemistry, and then was developed by the key technology of machine learning using the artificial intelligence. Nowadays, the popular trend is to combine the advantages of theoretical calculation and machine learning to jointly predict crystal structure. Recently, predicting medicine polymorphs has still been a challenging problem. It is expected to learn from and integrate existing technologies to predict medicine polymorphs more accurately and efficiently.

Inheritance and Innovation of Basic Theories of Traditional Chinese Medicine in the Context of Mutual Understanding of Civilisations / 中医杂志

In the context of the current era when different civilizations are learning from each other， traditional Chinese medicine （TCM） needs to continuously draw the essence from modern scientific and technological civilization for creative inheritance and innovative development. It is essential to master the classic texts of TCM to solidify its theoretical foundation， while also read extensively to broaden the academic horizons. TCM's inheritance and innovation should be integrated with the current era's context and social reality， and delve into the essence of TCM's academic experience and clinical practice. Emphasis should be placed on the inheritance of original TCM thinking， and return to the original image creation and transformation. Using image thinking as the main approach， and combined it with modern complex systems science and front-edge methodologies， we could elaborate the TCM theories such as correspondence between nature and human， five circuits and six qi， constitution and endowment， and harmony of body and spirit. The training of academic talents must be at the forefront， closely follow the new trends of the development of contemporary scientific and technological civilization， identify the “pain points” of discipline development， focus on the key areas for improvement， and seek the highlights of academic research. There should be a courage to question and propose new insights， construct new concepts， establish new theories， transform the weakness in original theoretical innovation， and enrich the evolving connotation of TCM.

MobileNetV3 network-based diagnosis of caries and periapical periodontitis from periapical films / 口腔疾病防治

Objective@#To research the effectiveness of deep learning techniques in intelligently diagnosing dental caries and periapical periodontitis and to explore the preliminary application value of deep learning in the diagnosis of oral diseases@*Methods@#A dataset containing 2 298 periapical films, including healthy teeth, dental caries, and periapical periodontitis, was used for the study. The dataset was randomly divided into 1 573 training images, 233 validation images, and 492 test images. By comparing various neural network models, the MobileNetV3 network model with better performance was selected for dental disease diagnosis, and the model was optimized by tuning the network hyperparameters. The accuracy, precision, recall, and F1 score were used to evaluate the model's ability to recognize dental caries and periapical periodontitis. Class activation map was used to visualization analyze the performance of the network model@*Results@#The algorithm achieved a relatively ideal intelligent diagnostic effect with precision, recall, and accuracy of 99.42%, 99.73%, and 99.60%, respectively, and the F1 score was 99.57% for classifying healthy teeth, dental caries, and periapical periodontitis. The visualization of the class activation maps also showed that the network model can accurately extract features of dental diseases.@*Conclusion@#The tooth lesion detection algorithm based on the MobileNetV3 network model can eliminate interference from image quality and human factors and has high diagnostic accuracy, which can meet the needs of dental medicine teaching and clinical applications.

Effects of Jiaohong Pills and Its Prescription on Scopolamine-induced Alzheimer's Disease Mice / 中国实验方剂学杂志

ObjectiveTo investigate the effects of Jiaohong pills （JHP） and its prescription， Pericarpium Zanthoxyli （PZ） and Rehmanniae Radix （RR） cognitive dysfunction in scopolamine-induced Alzheimer's disease （AD） mice and its mechanism through pharmacodynamic and metabolomics study. MethodThe animal model of AD induced by scopolamine was established and treated with PZ， RG and JHP， respectively. The effects of JHP and its formulations were investigated by open field test， water maze test， object recognition test， avoidance test， cholinergic system and oxidative stress related biochemical test. Untargeted metabolomics analysis of cerebral cortex was performed by ultra-performance liquid chromatography-Quadrupole/Orbitrap high resolution mass spectrometry （UPLC Q-Exactive Orbitrap MS）. ResultThe behavioral data showed that， compared with the model group， the discrimination indexes of the high dose of JHP， PZ and RR groups was significantly increased （P<0.05）. The staging rate of Morris water maze test in the PZ， RR， high and low dose groups of JHP was significantly increased （P<0.05， P<0.01）， the crossing numbers in the PZ， JHP high and low dose groups were significantly increased （P<0.05， P<0.01）； the number of errors in the avoidance test were significantly reduced in the PZ and high-dose JHP groups （P<0.01）， and the error latencies were significantly increased in the JHP and its prescription drug groups （P<0.01）. Compared with the model group， the activities of acetylcholinesterase in the cerebral cortex of the two doses of JHP group and the PZ group were significantly increased （P<0.05， P<0.01）， and the activity of acetylcholinesterase in the high-dose JHP group was significantly decreased （P<0.05）， and the level of acetylcholine was significantly increased （P<0.01）. At the same time， the contents of malondialdehyde in the serum of the two dose groups of JHP decreased significantly （P<0.05， P<0.01）. The results of metabolomics study of cerebral cortex showed that 149 differential metabolites were identified between the JHP group and the model group， which were involved in neurotransmitter metabolism， energy metabolism， oxidative stress and amino acid metabolism. ConclusionJHP and its prescription can antagonize scopolamine-induced cognitive dysfunction， regulate cholinergic system， and reduce oxidative stress damage. The mechanism of its therapeutic effect on AD is related to the regulation of neurotransmitter， energy， amino acid metabolism， and improvement of oxidative stress.

Application status of artificial intelligence techniques in retinoblastoma / 国际眼科杂志(Guoji Yanke Zazhi)

Retinoblastoma is a kind of malignant eye tumor commonly seen in children, which is one of the main causes threatening children's vision and life. The diagnosis and evaluation of retinoblastoma has always been a hot topic in clinic. In the past few years, the application of artificial intelligence(AI)technology has made significant progress in the medical field, providing new opportunities and challenges for the diagnosis and treatment of retinoblastoma, for example, the use of AI algorithms to analyze massive clinical data, which can help doctors diagnose the disease more accurately and provide personalized treatment plans. In addition, AI technology also plays an important role in medical image analysis, genomics research and other aspects, which can help the development of new drugs and improve patient prognosis. This article reviews the application progress of AI in retinoblastoma.

Learning Curve for Using Endoscopic Saphenous Vein Harvesting in Coronary Artery Bypass Grafting / 中山大学学报(医学科学版)

ObjectiveTo investigate the application of endoscopy in obtaining the great saphenous vein （GSV） during coronary artery bypass grafting （CABG） and explore the learning curve， with a particular focus on common challenges encountered during the learning process and their impact on early clinical outcomes. MethodsA retrospective analysis was conducted on clinical data from 83 patients who underwent off-pump CABG with endoscopic GSV harvesting at the First Affiliated Hospital of Zhengzhou University from July 2013 to April 2014. Patients were categorized into four groups based on the chronological order of their hospitalization： Group A （novice group， n=20）， Group B （proficient group， n=20）， Group C （progressive group， n=20）， and Group D （mature group， n=23）. Differences in perioperative and midterm follow-up outcomes among the groups were analyzed to determine the learning curve period. ResultsThe study population had a mean age of （60.22±8.06） years and a mean body weight of （69.77±11.66） kg. Comorbidities included hypertension （24 cases）， diabetes （26 cases）， and subacute cerebral infarction （14 cases）. The novice group exhibited significantly shorter GSV length-to-harvest time ratio relative to the other three groups （P<0.001） and a significantly higher incidence of main vein damage （P=0.006）. However， there was no statistically significant difference in graft patency at the 1-year follow-up. ConclusionThorough and reliable technical training in endoscopic GSV harvesting is essential to minimize vascular injury caused by novice operators. Approximately 20 cases of hands-on experience and a careful self-analysis of procedural challenges are likely required to achieve proficiency in GSV harvesting.

Effect of safflower yellow on learning and memory ability of APP/PS1 at different months based on TLR4/NF-KB signaling pathway / 中国药理学通报

Aim To investigate the effect of safflower yellow (SY) on learning and memory ability of APP/ PS1 mice at different disease stages, and to explore the mechanism of SY anti- Alzheimer's disease by using 3-,6- and 9-month-old APP/PS 1 transgenic mice as experimental animal models. Methods Behavioral experiments were conducted to observe the effects of SY on learning and memory of APP/PS1 mice of different months. ELISA was used to detect the effect of SY on the expression of inflammatory factors in cortex of mice of different months. Western blot was used to detect the microglia activation marker protein, and its mechanism of action was further analyzed. Results SY could enhance the learning and memory ability of mice aged 3, 6 and 9 months, reduce the content of IL-6 and increase the content of TGF-β1 in brain tissue, up-regulate the expression levels of arginase-1 (arg-1) and triggering receptor expressed on myeloid cells 2 (tREM2) in brain tissue of mice of different months, and down-regulate the expression levels of inducible nitric oxide synthase (iNOS), Toll-like receptors 4 (tlr4) and nuclear factor-kappa B (nf-KB). Conclusions Compared with 3- and 9-month-old mice, SY is the most effective in improving learning memory in 6-month-old APP/PS1 mice. SY inhibits TLR4/NF-KB pathway activation by inducing TREM2 expression in brain tissue of APP/PS 1 transgenic mice, promotes microglia phenotype shift to anti-inflammatory phenotype, reduces chronic neuroinflammatory response, and improves learning memory in APP/PS1 mice at all months of age.