Portfólio: percepção de estudantes de graduação da área da saúde / Portafolio: percepción de los estudiantes de pregrado en eláreade la salud / Portfolio: perception of undergraduate students in the healthcare field

Introdução:A formação em saúde norteia a prática profissional, incidindo diretamente na atenção e assistência à saúde ofertada à população. Nesse sentido, o uso de métodos ativos de aprendizagem e avaliação, como por exemplo, o portfólio, podem contribuir para a construção de conhecimentos crítico-reflexivos. Objetivo:Evidenciara percepção de estudantes dos cursos da área da saúde, que cursam a disciplina de Saúde e Cidadania na Universidade Federal do Rio Grande do Norte sobre o uso do portfólio enquanto instrumento de avaliação do ensino aprendizagem. Metodologia:Os dados foram obtidos por meio da formação de grupo focaleanalisados pela análise do conteúdo. Definiram-se, então, as categorias temáticas:percepção sobre o portfólio; a elaboração do portfólio e a sua contribuição para a formação; dificuldades para formulação doportfólio;o portfólio como instrumento de avaliação. Resultados:Os estudantes compreendem o portfólio como instrumento de diálogo entre docentes e discentes, através dos relatos das vivências em grupo nos equipamentos sociais e reflexões individuais na construção de conceitos e aprofundamento teórico. Ainda referem inseguranças e dúvidas acerca da estruturação e confecção do instrumento, no entanto, percebem o portfólio como potente e inovador no auxílio aconstrução do conhecimento uma vez que permite oacompanhamento do processo de ensino-aprendizagem, possibilitando maior interação entre educador-educando, com produção de uma aprendizagem significativa.Conclusões:o portfólio estimula a reflexão e a crítica acerca das vivências nos cenários de práticas onde se desenvolve o componente curricular Saúde e Cidadaniacorroborando, sobremaneira, para a construção do conhecimento dos estudantes (AU).

Introduction:A degreein healthcare guides the professional practice, directly affecting the healthcare attention and assistance offered to the population. In this sense, the use of active learning and assessment methods, such as portfolios, can contribute to the construction of critical-reflective knowledge. Objective:To highlight the perception of students from health courses, who study the Health and Citizenship discipline at the Federal University of Rio Grande do Norte, regarding the use of the portfolio as an instrument for evaluating teaching and learning.Methodology:Data were obtained through the formation of a focus group and analyzed using content analysis. Thematic categories were then defined: perception of the portfolio; the preparation of the portfolio and its contribution to training; difficulties in formulating the portfolio; the portfolio as an assessment tool. Results:Students understand the portfolio as an instrument of dialogue between teachers and students, through reports of group experiences in social facilities and individual reflections in the construction of concepts and theoretical deepening. They still report insecurities and doubts about the structuring and creation of the instrument, however, they perceive the portfolio as powerful and innovativein helping to build knowledge as it allows the monitoring of the teaching-learning process, enabling greater interaction between educator and student, with the production of significant learning. Conclusions:The portfolio encourages reflection and criticism about the experiences in the practical scenarios where the curricular component -SACI is developed, greatly supporting the construction of students' knowledge (AU).

Introducción:La formación en salud orienta la práctica profesional, incidiendo directamente en la atención y asistencia sanitaria que se ofrece a la población. En este sentido, el uso de métodos activos de aprendizaje y evaluación, como los portafolios, puedecontribuir a la construcción de conocimiento crítico-reflexivo. Objetivo:Resaltar la percepción de estudiantes de carreras de salud, que cursan la disciplina Salud y Ciudadanía de la Universidad Federal de Rio Grande do Norte, sobre el uso del portafolios como instrumento de evaluación de la enseñanza y del aprendizaje. Metodología:Los datos se obtuvieron mediante la formación de un grupo focal y se analizaron mediante análisis de contenido. Luego se definieron categorías temáticas: percepción del portafolio; la elaboración del portafolio y su contribución a la formación; dificultades para formular el portafolio; el portafolio como herramienta de evaluación.Resultados:Los estudiantes entienden el portafolio como un instrumento de diálogo entre docentes y estudiantes, a través de relatos de experiencias grupales en establecimientos sociales y reflexiones individuales en la construcción de conceptos y profundización teórica. Aún reportan inseguridades y dudas sobre la estructuración y creación del instrumento, sin embargo, perciben el portafolio como poderoso e innovador para ayudar a la construcción de conocimiento ya que permite el seguimiento del proceso de enseñanza-aprendizaje, posibilitando una mayor interacción entre educador y estudiante, con la producción de aprendizajes significativos.Conclusiones: El portafolio incentiva la reflexión y crítica sobre las experiencias en los escenarios prácticos donde se desarrolla el componente curricular -SACI, apoyando en gran medida la construcción del conocimiento de los estudiantes (AU).

Cooperación entre médicos del departamento de emergencias y dermatólogos por telemedicina / Cooperation between emergency department physicians and dermatologists via telemedicine

Las intercurrencias dermatológicas agudas son un motivo de consulta frecuente a las centrales de emergencias, y generalmente los médicos de atención primaria se ocupan del primer nivel de atención. Puede ser necesaria una interconsulta con expertos, aunque no siempre estén disponibles. Ante la necesidad de facilitar dicha interacción a distancia, en Julio 2022 se implementó una herramienta de teledermatología en un hospital de alta complejidad en Buenos Aires, Argentina. Este servicio se limitó a días hábiles con horario restringido, permitiendo la comunicación entre médicos del departamento de emergencias y dermatólogos, a través de WhatsApp institucional. El dermatólogo podía verificar datos de salud relacionados al paciente (ej: comorbilidades y medicación crónica) mediante revisión de la historia clínica electrónica, para decidir sobre un plan de acción. Se evaluó la perspectiva de los usuarios a través de un formulario electrónico tras 3 meses de implementación. Los resultados evidenciaron que la mayoría (85%) de los profesionales conocía la herramienta, y el 57% la había usado al menos una vez. Se obtuvo una mediana de 9 puntos (de una escala de Likert del 1 al 10) sobre la recomendación hacia otro profesional. El teletriage dermatológico resultó beneficioso y fue aceptado, tanto por médicos de guardia como por especialistas. Ante las demoras en la atención ambulatoria, ha resultado una alternativa útil para evitar derivaciones innecesarias y/o acelerar aquellas que verdaderamente lo ameritan. Sin embargo, representa una forma de comunicación informal desde el punto de vista de almacenamiento de datos. Será necesario reflexionar sobre estos tópicos pendientes de esta experiencia asistencial como legalidad, seguridad y confidencialidad (AU)

Acute skin conditions are a frequent reason for consultation in emergency departments, and primary care physicians generally handle them. They might require referrals to experts, who are not always readily available. Recognizing the need to facilitate such interactions remotely, a teledermatology triage tool was implemented in July 2022 at a high-complexity hospital in Buenos Aires, Argentina. The service was limited to business days with restricted hours, enabling communication between emergency department physicians and dermatologists through institutional WhatsApp. Dermatologists could access patient-related health data (e.g., comorbidities and chronic medication) through the electronic medical record to determine an appropriate course of action. The perspective of users was evaluated through an electronic questionnaire after three months of application. Results showed that most professionals were aware of the tool (85%), and 57% used it at least once. The median rating for recommending the tool to other professionals was 9 points (on a Likert scale from 1 to 10). Dermatological teletriage proved beneficial and was well-received by emergency physicians and specialists. In the face of delays in outpatient care, it has been a useful alternative to avoid unnecessary referrals and expedite those that are warranted. However, it represents an informal method of communication with regard to data storage. It will be necessary to rethink on improvements in pending topics such as legal limitations, security, and confidentiality of this healthcare experience (AU)

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabes (LM), Willmotts index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabes (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.

Prediction of visceral leishmaniasis incidence using the Seasonal Autoregressive Integrated Moving Average model (SARIMA) in the state of Maranhão, Brazil

Abstract Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.

Resumo A leishmaniose visceral (LV) é uma doença de natureza infecciosa, predominante em países de zonas tropicais. A predição de ocorrência de doenças infecciosas através da modelagem epidemiológica tem se revelado uma importante ferramenta no entendimento de sua dinâmica de ocorrência. O objetivo deste estudo foi desenvolver um modelo de previsão da incidência da LV no Maranhão usando o modelo de Média Móvel Integrada Autocorrelacionada Sazonal (SARIMA). Foram coletados os dados mensais de casos de LV através do Sistema de Informação de Agravos de Notificação (SINAN) correspondentes ao período de 2001 a 2018. O método de Box-Jenkins foi aplicado para ajustar um modelo de predição SARIMA para incidência geral e por sexo (masculino e feminino) de LV para o período de janeiro de 2019 a dezembro de 2023. Durante o período de 216 meses dessa série temporal, foram registrados 10.431 casos de LV no Maranhão, com uma média de 579 casos por ano. Em relação à faixa etária, houve maior registro no público pediátrico (0 a 14 anos). Houve predominância do sexo masculino, com 6437 casos (61,71%). Os valores do teste de Box-Pierce para incidência geral, sexo masculino e feminino reforçados pelos resultados do teste Ljung-Box sugerem que as autocorrelações de resíduos apresentam um comportamento de ruído branco. Para incidência mensal geral e por sexo masculino e feminino, os modelos SARIMA (2,0,0) (2,0,0), (0,1,1) (0,1,1) e (0,1,1) (2, 0, 0) foram os que mais se ajustaram aos dados, respectivamente. O modelo SARIMA se mostrou uma ferramenta adequada de previsão e análise da tendência de incidência da LV no Maranhão. A determinação da variação temporal e sua predição são determinantes no norteamento de medidas de intervenção em saúde.

Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan / Previsão de produção florestal em diferentes cenários de mudanças climáticas usando modelos inteligentes de dados no Paquistão

Abstract This study aimed to develop and evaluate data driven models for prediction of forest yield under different climate change scenarios in the Gallies forest division of district Abbottabad, Pakistan. The Random Forest (RF) and Kernel Ridge Regression (KRR) models were developed and evaluated using yield data of two species (Blue pine and Silver fir) as an objective variable and climate data (temperature, humidity, rainfall and wind speed) as predictive variables. Prediction accuracy of both the models were assessed by means of root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (r), relative root mean squared error (RRMSE), Legates-McCabe's (LM), Willmott's index (WI) and Nash-Sutcliffe (NSE) metrics. Overall, the RF model outperformed the KRR model due to its higher accuracy in forecasting of forest yield. The study strongly recommends that RF model should be applied in other regions of the country for prediction of forest growth and yield, which may help in the management and future planning of forest productivity in Pakistan.

Resumo Este estudo teve como objetivo desenvolver e avaliar modelos baseados em dados para previsão da produção florestal em diferentes cenários de mudanças climáticas na divisão florestal Gallies do distrito de Abbottabad, Paquistão. Os modelos Random Forest (RF) e Kernel Ridge Regression (KRR) foram desenvolvidos e avaliados usando dados de produção de duas espécies (pinheiro-azul e abeto-prateado) como uma variável objetiva e dados climáticos (temperatura, umidade, precipitação e velocidade do vento) como preditivos variáveis. A precisão da previsão de ambos os modelos foi avaliada por meio de erro quadrático médio (RMSE), erro absoluto médio (MAE), coeficiente de correlação (r), erro quadrático médio relativo (RRMSE), Legates-McCabe's (LM), índice de Willmott (WI) e métricas Nash-Sutcliffe (NSE). No geral, o modelo RF superou o modelo KRR devido à sua maior precisão na previsão do rendimento florestal. O estudo recomenda fortemente que o modelo RF seja aplicado em outras regiões do país para previsão do crescimento e produtividade florestal, o que pode ajudar no manejo e planejamento futuro da produtividade florestal no Paquistão.

Prediction of visceral leishmaniasis incidence using the Seasonal Autoregressive Integrated Moving Average model (SARIMA) in the state of Maranhão, Brazil / Previsão da incidência da leishmaniose visceral usando o modelo de média móvel integrado autorregressivo sazonal (SARIMA) no Maranhão, Brasil

Abstract Visceral leishmaniasis (VL) is an infectious disease predominant in countries located in the tropics. The prediction of occurrence of infectious diseases through epidemiologic modeling has revealed to be an important tool in the understanding of its occurrence dynamic. The objective of this study was to develop a forecasting model for the incidence of VL in Maranhão using the Seasonal Autoregressive Integrated Moving Average model (SARIMA). We collected monthly data regarding VL cases from the National Disease Notification System (SINAN) corresponding to the period between 2001 and 2018. The Box-Jenkins method was applied in order to adjust a SARIMA prediction model for VL general incidence and by sex (male or female) for the period between January 2019 and December 2013. For 216 months of this time series, 10,431 cases of VL were notified in Maranhão, with an average of 579 cases per year. With regard to age range, there was a higher incidence among the pediatric public (0 to 14 years of age). There was a predominance in male cases, 6437 (61.71%). The Box-Pierce test figures for overall, male and female genders supported by the results of the Ljung-Box test suggest that the autocorrelations of residual values act as white noise. Regarding monthly occurrences in general and by gender, the SARIMA models (2,0,0) (2,0,0), (0,1,1) (0,1,1) and (0,1,1) (2, 0, 0) were the ones that mostly adjusted to the data respectively. The model SARIMA has proven to be an adequate tool for predicting and analyzing the trends in VL incidence in Maranhão. The time variation determination and its prediction are decisive in providing guidance in health measure intervention.

Resumo A leishmaniose visceral (LV) é uma doença de natureza infecciosa, predominante em países de zonas tropicais. A predição de ocorrência de doenças infecciosas através da modelagem epidemiológica tem se revelado uma importante ferramenta no entendimento de sua dinâmica de ocorrência. O objetivo deste estudo foi desenvolver um modelo de previsão da incidência da LV no Maranhão usando o modelo de Média Móvel Integrada Autocorrelacionada Sazonal (SARIMA). Foram coletados os dados mensais de casos de LV através do Sistema de Informação de Agravos de Notificação (SINAN) correspondentes ao período de 2001 a 2018. O método de Box-Jenkins foi aplicado para ajustar um modelo de predição SARIMA para incidência geral e por sexo (masculino e feminino) de LV para o período de janeiro de 2019 a dezembro de 2023. Durante o período de 216 meses dessa série temporal, foram registrados 10.431 casos de LV no Maranhão, com uma média de 579 casos por ano. Em relação à faixa etária, houve maior registro no público pediátrico (0 a 14 anos). Houve predominância do sexo masculino, com 6437 casos (61,71%). Os valores do teste de Box-Pierce para incidência geral, sexo masculino e feminino reforçados pelos resultados do teste Ljung-Box sugerem que as autocorrelações de resíduos apresentam um comportamento de ruído branco. Para incidência mensal geral e por sexo masculino e feminino, os modelos SARIMA (2,0,0) (2,0,0), (0,1,1) (0,1,1) e (0,1,1) (2, 0, 0) foram os que mais se ajustaram aos dados, respectivamente. O modelo SARIMA se mostrou uma ferramenta adequada de previsão e análise da tendência de incidência da LV no Maranhão. A determinação da variação temporal e sua predição são determinantes no norteamento de medidas de intervenção em saúde.

Research progress on bone metabolism and molecular mechanisms in accelerated aging SAMP6 mice / 中国药理学通报

Senile osteoporosis (SOP) is a systemic bone disease characterized by increased susceptibility to fractures. The pathogenesis of SOP is complex and not well understood. Currently, the rapid aging model mouse, senescence accelerated mouse prone 6 (SAMP6), is an ideal model for studying the mechanisms of SOP development and exploring its prevention and treatment. This model exhibits characteristics including increased bone fragility, degradation of bone microstructure, loss of bone matrix, and abnormal metabolism and dysfunction of bone cells, faithfully replicating the process of SOP occurrence and progression at both macroscopic and microscopic levels.

Application of a polygenic risk score in the early prediction of myopia in children and adolescents / 中国学校卫生

Abstract@#Myopia has become a major public health issue of global concern. Scientific and effective myopia prediction models can help identify high risk groups for myopia, thereby achieving precise prevention. With the rapid development of genome wide association studies and the establishment of large scale prospective population cohorts, the polygenic risk score (PRS) model has been used to predict myopia phenotypes, advancing the myopia prediction window and thus predicting high myopia risk for early screening and intervention for at risk groups. The review aims to systematically elaborate the identification and verification of myopia genes in recent years, briefly describe the practice and effectiveness evaluation of the PRS model in myopia prevention research at home and abroad, reveal the application value in myopia prediction research, and emphasize the relationship between the PRS prediction model and outdoor activities. Close eye use and other preventive measures are of great significance to promote the precise prevention of myopia in children and adolescents.

Comparative study of different large language models and medical professionals of different levels responding to ophthalmology questions / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To evaluate the performance of three distinct large language models(LLM), including GPT-3.5, GPT-4, and PaLM2, in responding to queries within the field of ophthalmology, and to compare their performance with three different levels of medical professionals: medical undergraduates, master of medicine, and attending physicians.METHODS: A total of 100 ophthalmic multiple-choice tests, which covered ophthalmic basic knowledge, clinical knowledge, ophthalmic examination and diagnostic methods, and treatment for ocular disease, were conducted on three different kinds of LLM and three different levels of medical professionals(9 undergraduates, 6 postgraduates and 3 attending physicians), respectively. The performance of LLM was comprehensively evaluated from the aspects of mean scores, consistency and confidence of response, and it was compared with human.RESULTS: Notably, each LLM surpassed the average performance of undergraduate medical students(GPT-4:56, GPT-3.5:42, PaLM2:47, undergraduate students:40). Specifically, performance of GPT-3.5 and PaLM2 was slightly lower than those of master's students(51), while GPT-4 exhibited a performance comparable to attending physicians(62). Furthermore, GPT-4 showed significantly higher response consistency and self-confidence compared with GPT-3.5 and PaLM2.CONCLUSION: LLM represented by GPT-4 performs well in the field of ophthalmology, and the LLM model can provide clinical decision-making and teaching aids for clinicians and medical education.

Association of 24hour movement behaviors with emotional and behavioral problems among left-behind children / 中国学校卫生

Objective@#To examine the association of 24 hour movement behaviors with emotional and behavioral problems among left behind children, so as to provide a theoretical reference for the practice of 24 hour activity interventions to promote emotional and behavioral problems in this population.@*Methods@#From February to May 2023, 1 117 left behind children in grades 4-6 from 10 primary schools in five cities in Zhejiang Province were selected using a convenient cluster sampling method to conduct a questionnaire survey examining 24 hour movement behaviors, as well as emotional and behavioral problems. The general linear model was adopted to analyze the association between satisfying the 24 hour movement behavior guidelines, and emotional and behavioral problems among left behind children.@*Results@#The sleep duration compliance rate was the highest (52.19%), while the moderate-to-vigorous PA (MVPA) compliance rate was the lowest (17.73%). The compliance rate of the three activities accounted for 7.43 %. There was a dose response between the number of guidelines satisfied, and the emotional and behavior of left behind children; that was, satisfaction of a higher number of guidelines was associated with a lower risk of emotional and behavioral problems among left behind children (difficulty factor: β=-0.56, 95%CI =-1.23--0.19; strength factor: β=0.50, 95%CI =-0.48-1.22, P < 0.01). Compared to satisfying none of the guidelines, satisfying the guidelines for screen time ( β=-0.23, 95%CI =-2.18- -0.14 ) and sleep duration ( β=-0.13, 95%CI =-1.66--0.11) was negatively correlated with the difficulty factor, while satisfying the guideline for MVPA ( β=0.13, 95%CI =0.09-1.08) and sleep duration ( β=0.18, 95%CI =0.09-1.40) was positively associated with the strength factor. In addition, satisfying two or all three of the guidelines was more strongly associated with these outcomes than satisfying one of the recommendations ( P <0.01).@*Conclusions@#Meeting the 24 hour movement behavior guidelines can improve emotional and behavioral problems among left behind children. It is necessary to raise their awareness of the effect of satisfying the 24 hour movement behavior guidelines and formulate comprehensive intervention measures.

Application status of risk assessment models for periodontal disease / 口腔疾病防治

@#Risk assessment models for periodontal disease provide dentists with a precise and consolidated evaluation of the prognosis of periodontitis, enabling the formulation of personalized treatment plans. Periodontal risk assessment systems have been widely applied in clinical practice and research. The application fields of periodontal risk assessment systems vary based on the distinctions between clinical periodontal parameters and risk factors. The assessment models listed below are commonly used in clinical practice, including the periodontal risk calculator (PRC), which is an individual-based periodontal risk assessment tool that collects both periodontal and systemic information for prediction; the periodontal assessment tool (PAT), which allows for quantitative differentiation of stages of periodontal disease; the periodontal risk assessment (PRA) and modified periodontal risk assessment (mPRA), which are easy to use; and the classification and regression trees (CART), which assess the periodontal prognosis based on a single affected tooth. Additionally, there are orthodontic-periodontal combined risk assessment systems and implant periapical risk assessment systems tailored for patients needing multidisciplinary treatment. This review focuses on the current application status of periodontal risk assessment systems.

Current research status of animal models for acute-on-chronic liver failure / 临床肝胆病杂志

Acute-on-chronic liver failure has complex conditions， rapid progression， and a high mortality rate， and further studies are still needed to clarify its pathogenesis and etiology. The establishment of animal models for acute-on-chronic liver failure can not only provide a good basis for exploring the pathogenesis of acute-on-chronic liver failure， but also provide an experimental basis for clinical treatment. Through a literature review， this article summarizes the methods commonly used to establish the animal models of acute-on-chronic liver failure， including carbon tetrachloride combined with LPS/GaIN， thioacetamide combined with LPS， serum albumin， and bile duct ligation. This article analyzes the characteristics of various animal models， so as to provide documentary and experimental bases for further exploration of more ideal animal models.

Establishment and application of animal models for portal vein thrombosis / 临床肝胆病杂志

Portal vein thrombosis （PVT） refers to thromboembolism that occurs in the extrahepatic main portal vein and/or intrahepatic portal vein branches. PVT is the result of the combined effect of multiple factors， but its pathogenesis remains unclear. Animal models are an important method for exploring the pathophysiological mechanism of PVT. Based on the different species of animals， this article reviews the existing animal models of PVT in terms of modeling methods， principles， advantages and disadvantages， and application.

Artificial intelligence ChatGPT in ophthalmology and scientific writing / 国际眼科杂志(Guoji Yanke Zazhi)

ChatGPT is a large language models(LLMs)that uses deep learning techniques to produce human-like responses to natural language inputs. It belongs to the family of generative pre-training transformer(GPT)models currently publicly available developed by OpenAI in November 2022. ChatGPT is capable of capturing the nuances and intricacies of human language, generating appropriate and contextually relevant responses. It can assist medical professionals in various tasks, such as research, diagnosis, patient monitoring, and medical education, from identifying research programs to assisting in clinical and laboratory diagnosis, to know new developments in their fields and scientific writing. ChatGPT has also attracted increasing attention and widely used in ophthalmology. However, the use of ChatGPT and other artificial intelligence tools in such tasks comes now with several limitations, ethical and legal concerns, such as credibility, plagiarism, copyright infringement, and biases. Future research can focus on developing new methods to mitigate these limitations while harnessing the benefits of ChatGPT in medicine and related aspects.

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.

An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience / Predição do influxo de atendimentos e casos de trauma em um serviço de urgência oftalmológico brasileiro realizado por um oftalmologista sem experiência em programação

ABSTRACT Purpose: The emergency medical service is a fundamental part of healthcare, albeit crowded emergency rooms lead to delayed and low-quality assistance in actual urgent cases. Machine-learning algorithms can provide a smart and effective estimation of emergency patients' volume, which was previously restricted to artificial intelligence (AI) experts in coding and computer science but is now feasible by anyone without any coding experience through auto machine learning. This study aimed to create a machine-learning model designed by an ophthalmologist without any coding experience using AutoML to predict the influx in the emergency department and trauma cases. Methods: A dataset of 356,611 visits at Hospital da Universidade Federal de São Paulo from January 01, 2014 to December 31, 2019 was included in the model training, which included visits/day and the international classification disease code. The training and prediction were made with the Amazon Forecast by 2 ophthalmologists with no prior coding experience. Results: The forecast period predicted a mean emergency patient volume of 216.27/day in p90, 180.75/day in p50, and 140.35/day in p10, and a mean of 7.42 trauma cases/ day in p90, 3.99/day in p50, and 0.56/day in p10. In January of 2020, there were a total of 6,604 patient visits and a mean of 206.37 patients/day, which is 13.5% less than the p50 prediction. This period involved a total of 199 trauma cases and a mean of 6.21 cases/day, which is 55.77% more traumas than that by the p50 prediction. Conclusions: The development of models was previously restricted to data scientists' experts in coding and computer science, but transfer learning autoML has enabled AI development by any person with no code experience mandatory. This study model showed a close value to the actual 2020 January visits, and the only factors that may have influenced the results between the two approaches are holidays and dataset size. This is the first study to apply AutoML in hospital visits forecast, showing a close prediction of the actual hospital influx.

RESUMO Objetivo: Esse estudo tem como objetivo criar um modelo de Machine Learning por um oftalmologista sem experiência em programação utilizando auto Machine Learning predizendo influxo de pacientes em serviço de emergência e casos de trauma. Métodos: Um dataset de 366,610 visitas em Hospital Universitário da Universidade Federal de São Paulo de 01 de janeiro de 2014 até 31 de dezembro de 2019 foi incluído no treinamento do modelo, incluindo visitas/dia e código internacional de doenças. O treinamento e predição foram realizados com o Amazon Forecast por dois oftalmologistas sem experiência com programação. Resultados: O período de previsão estimou um volume de 206,37 pacientes/dia em p90, 180,75 em p50, 140,35 em p10 e média de 7,42 casos de trauma/dia em p90, 3,99 em p50 e 0,56 em p10. Janeiro de 2020 teve um total de 6.604 pacientes e média de 206,37 pacientes/dia, 13,5% menos do que a predição em p50. O período teve um total de 199 casos de trauma e média de 6,21 casos/dia, 55,77% mais casos do que a predição em p50. Conclusão: O desenvolvimento de modelos era restrito a cientistas de dados com experiencia em programação, porém a transferência de ensino com a tecnologia de auto Machine Learning permite o desenvolvimento de algoritmos por qualquer pessoa sem experiencia em programação. Esse estudo mostra um modelo com valores preditos próximos ao que ocorreram em janeiro de 2020. Fatores que podem ter influenciados no resultado foram feriados e tamanho do banco de dados. Esse é o primeiro estudo que aplicada auto Machine Learning em predição de visitas hospitalares com resultados próximos aos que ocorreram.

Fully independent validation of eleven prognostic scores predicting progression to critically ill condition in hospitalized patients with COVID-19

Abstract Introduction COVID-19 remains an important threat to global health and maintains the challenge of COVID-19 hospital care. To assist decision making regarding COVID-19 hospital care many instruments to predict COVID-19 progression to critical condition were developed and validated. Objective To validate eleven COVID-19 progression prediction scores for critically ill hospitalized patients in a Brazilian population. Methodology Observational study with retrospective follow-up, including 301 adults confirmed for COVID-19 sequentially. Participants were admitted to non-critical units for treatment of the disease, between January and April 2021 and between September 2021 and February 2022. Eleven prognostic scores were applied using demographic, clinical, laboratory and imaging data collected in the first 48 of the hospital admission. The outcomes of greatest interest were as originally defined for each score. The analysis plan was to apply the instruments, estimate the outcome probability reproducing the original development/validation of each score, then to estimate performance measures (discrimination and calibration) and decision thresholds for risk classification. Results The overall outcome prevalence was 41.8 % on 301 participants. There was a greater risk of the occurrence of the outcomes in older and male patients, and a linear trend with increasing comorbidities. Most of the patients studied were not immunized against COVID-19. Presence of concomitant bacterial infection and consolidation on imaging increased the risk of outcomes. College of London COVID-19 severity score and the 4C Mortality Score were the only with reasonable discrimination (ROC AUC 0.647 and 0.798 respectively) and calibration. The risk groups (low, intermediate and high) for 4C score were updated with the following thresholds: 0.239 and 0.318 (https://pedrobrasil.shinyapps.io/INDWELL/). Conclusion The 4C score showed the best discrimination and calibration performance among the tested instruments. We suggest different limits for risk groups. 4C score use could improve decision making and early therapeutic management at hospital care.

Exploring the effect of the structural model of active aging on the self-assessment of quality of life among older people: A cross-sectional and analytical study

ABSTRACT BACKGROUND: Although studies have examined the relationship between variables associated with active aging and quality of life (QoL), no studies have been identified to have investigated the effect of a structural model of active aging on QoL in a representative sample of older people in the community. OBJECTIVE: To measure the domains and facets of QoL in older people and identify the effect of the structural model of active aging on the self-assessment of QoL. DESIGN AND SETTING: This cross-sectional analytical study included 957 older people living in urban areas. Data were collected from households using validated instruments between March and June 2018. Descriptive, confirmatory factor, and structural equation modeling analyses were performed. RESULTS: Most older people self-rated their QoL as good (58.7%), and the highest mean scores were for the social relationships domain (70.12 ± 15.4) and the death and dying facet (75.43 ± 26.7). In contrast, the lowest mean scores were for the physical domains (64.41 ± 17.1) and social participation (67.20 ± 16.2) facets. It was found that active aging explained 50% of the variation in self-assessed QoL and directly and positively affected this outcome (λ = 0.70; P < 0.001). CONCLUSION: Active aging had a direct and positive effect on the self-assessment of QoL, indicating that the more individuals actively aged, the better the self-assessment of QoL.

Isotemporal substitution analysis of time between sleep, sedentary behavior, and physical activity on depressive symptoms in older adults: a cross-sectional study

ABSTRACT BACKGROUND: Compared to young individuals, older adults participate more in sedentary behavior (SB) and less in physical activity (PA). These behaviors are associated with numerous adverse health factors. OBJECTIVE: The purpose of the study was to examine the hypothetical effects of substituting time spent sleeping, performing SB, and performing moderate-to-vigorous physical activity (MVPA) on depressive symptomatology in older adults. DESIGN AND SETTING: An analytical cross-sectional study employing exploratory survey methods was conducted in the city of Alcobaça in the state of Bahia, Brazil METHODS: The study included 473 older adults who answered a structured questionnaire during an interview. Exposure time to SB and PA level were assessed using the International Physical Activity Questionnaire, and depressive symptoms were analyzed using the short version of the Geriatric Depression Scale. An isotemporal replacement model was used to evaluate the effects of different SB sessions on depressive symptomatology. RESULTS: An increase in the risk of depressive symptoms was observed when MVPA and sleep time were substituted for the same SB time at all times tested, with maximum values of 40% and 20%, respectively. Opposite substitution of MVPA and sleep time increments reduced the risk of depressive symptomatology by 28% and 17%, respectively. CONCLUSIONS: The results of the present study indicate that replacing SB with the same amount of sleep or MVPA may reduce depressive symptoms. The longer the reallocation time, the greater are the benefits.

Modelos de agendamento e o acesso a imunização na atenção primária a saúde / Appointment models and access to immunization in primary health care / Modelos de cita y acceso a inmunización en atención primaria de salud

A Atenção Primária à Saúde é caracterizada como a porta de entrada preferencial do sistema de saúde com território adscrito. Desta forma, deve prover ações de promoção, prevenção e reabilitação da saúde. Sendo assim, a escassez de investigações sobre o acesso à saúde e a imunização levanta questões sobre como os modelos de agendamento impactam a disponibilidade e a acessibilidade das vacinas. Trata-se de um estudo reflexivo conduzido no período de junho a agosto de 2023. O estudo tem como base a análise discursiva das orientações do Ministério da Saúde, assim como a aceitabilidade, satisfação e, consequentemente, adesão à vacinação por parte dos usuários. A pesquisa originou-se a partir das provocações e debates realizados pelo Grupo de Pesquisa "Abordagens Tecnológicas no Cuidado à Saúde e Promoção da Saúde" da Escola de Enfermagem da Universidade de São Paulo (EEUSP). Está dividido em três seções que abordam os principais pontos de reflexão. O estudo considerou que é notável que a maneira como o usuário é recebido e tratado pelo sistema influencia diretamente a aceitabilidade, satisfação e, consequentemente, adesão à vacinação. Portanto, ao escolher e planejar o método de agendamento do serviço de saúde, as particularidades da área de vacinação devem ser consideradas, contemplando a demanda espontânea e a abordagem e controle do usuário.

Primary Health Care is characterized as the preferred gateway to the health system with an assigned territory. In this way, it must provide health promotion, prevention and rehabilitation actions. Therefore, the scarcity of research on access to healthcare and immunization raises questions about how scheduling models impact the availability and accessibility of vaccines. This is a reflective study conducted from June to August 2023. The study is based on the discursive analysis of the Ministry of Health's guidelines, as well as the acceptability, satisfaction and, consequently, adherence to vaccination by users. The research originated from the provocations and debates carried out by the Research Group "Technological Approaches in Health Care and Health Promotion" at the School of Nursing of the University of São Paulo (EEUSP). It is divided into three sections that address the main points of reflection. The study considered that it is notable that the way the user is received and treated by the system directly influences acceptability, satisfaction and, consequently, adherence to vaccination. Therefore, when choosing and planning the health service scheduling method, the particularities of the vaccination area must be considered, considering spontaneous demand and user approach and control.

La Atención Primaria de Salud se caracteriza por ser la puerta de entrada preferente al sistema de salud con un territorio asignado. De esta manera, debe brindar acciones de promoción, prevención y rehabilitación de la salud. Por lo tanto, la escasez de investigaciones sobre el acceso a la atención sanitaria y la inmunización plantea interrogantes sobre cómo los modelos de programación afectan la disponibilidad y accesibilidad de las vacunas. Se trata de un estudio reflexivo realizado de junio a agosto de 2023. El estudio se basa en el análisis discursivo de las directrices del Ministerio de Salud, así como de la aceptabilidad, satisfacción y, en consecuencia, adherencia a la vacunación por parte de los usuarios. La investigación surgió de las provocaciones y debates realizados por el Grupo de Investigación "Enfoques Tecnológicos en Atención y Promoción de la Salud" de la Escuela de Enfermería de la Universidad de São Paulo (EEUSP). Se divide en tres apartados que abordan los principales puntos de reflexión. El estudio consideró que se destaca que la forma en que el usuario es recibido y tratado por el sistema influye directamente en la aceptabilidad, satisfacción y, en consecuencia, en la adherencia a la vacunación. Por lo tanto, en la elección y planificación del método de programación de los servicios de salud se deben considerar las particularidades del área de vacunación, considerando la demanda espontánea y el abordaje y control de los usuarios.