The effects of social media usage on vicarious traumatization and the mediation role of recommendation systems usage and peer communication in China after the aircraft flight accident.

Background: Previous research has indicated that continuous exposure to disaster-related information through social media can lead to vicarious trauma. However, scholars have recognized the need for further in-depth research into the underlying mechanisms influencing this relationship.Objective: The purpose of this study is to investigate the impact mechanism of social media usage on vicarious traumatization in users and analyze the roles of recommendation systems and peer communication.Methods: This study was conducted with college students in China, focusing on the context of the MU5735 aircraft flight accident in China in which 123 passengers and 9 crew members died. Data were collected through an online questionnaire. The partial least square structural equation modelling (PLS-SEM) method was used to test the data and model.Results: This study obtained valid responses from 1317 participants. The study findings revealed a significant positive correlation between social media usage(ß = 0.180，P < .001), recommendation systems usage (ß = 0.172, P < .001), peer communication (ß = 0.303, P < .001), and the development of vicarious traumatization. Recommendation systems usage (specific indirect effect = 0.063, P < .001) and peer communication (specific indirect effect = 0.138, P < .001) mediated the relationship between social media use and vicarious trauma. Additionally, the impact of peer communication on vicarious trauma was found to be higher compared to the effects of continuous social media use and recommendation system use.Conclusion: The study found that the use of social media to obtain information about accidents, the frequent pushing of accident information by recommender systems, and the frequent discussion of accidents among peers during unexpected accidents contribute to vicarious traumatization. The study suggests that users' reduced retrieval of accident information via social media, as well as reduced peer-to-peer discussions about accidents, and social media platforms' adjustment of recommender system algorithm rules to reduce accident information pushes, may help reduce the likelihood of users experiencing vicarious traumatization.

Social media usage significantly affected college users to develop vicarious traumatization.Recommendation systems usage and peer communication significantly affected the development of vicarious traumatization.Recommendation systems usage and peer communication mediated the relationship of social media usage and vicarious traumatization.

Modeling the risk of aquatic species invasion spread through boater movements and river connections.

Aquatic invasive species (AIS) are one of the greatest threats to the functioning of aquatic ecosystems worldwide. Once an invasive species has been introduced to a new region, many governments develop management strategies to reduce further spread. Nevertheless, managing AIS in a new region is challenging because of the vast areas that need protection and limited resources. Spatial heterogeneity in invasion risk is driven by environmental suitability and propagule pressure, which can be used to prioritize locations for surveillance and intervention activities. To better understand invasion risk across aquatic landscapes, we developed a simulation model to estimate the likelihood of a waterbody becoming invaded with an AIS. The model included waterbodies connected via a multilayer network that included boater movements and hydrological connections. In a case study of Minnesota, we used zebra mussels (Dreissena polymorpha) and starry stonewort (Nitellopsis obtusa) as model species. We simulated the impacts of management scenarios developed by stakeholders and created a decision-support tool available through an online application provided as part of the AIS Explorer dashboard. Our baseline model revealed that 89% of new zebra mussel invasions and 84% of new starry stonewort invasions occurred through boater movements, establishing it as a primary pathway of spread and offering insights beyond risk estimates generated by traditional environmental suitability models alone. Our results highlight the critical role of interventions applied to boater movements to reduce AIS dispersal.

Modelo del riesgo de la invasión de especies acuáticas dispersadas por movimiento de botes y conexiones entre ríos Resumen Las especies acuáticas invasoras (EAI) son una de las principales amenazas para el funcionamiento de los ecosistemas acuáticos a nivel mundial. Una vez que una especie invasora ha sido introducida a una nueva región, muchos gobiernos desarrollan estrategias de manejo para disminuir la dispersión. Sin embargo, el manejo de las especies acuáticas invasoras en una nueva región se complica debido a las amplias áreas que necesitan protección y los recursos limitados. La heterogeneidad espacial de un riesgo de invasión es causada por la idoneidad ambiental y la presión de propágulo, que puede usarse para priorizar la ubicación de las actividades de vigilancia e intervención. Desarrollamos una simulación para estimar la probabilidad de que un cuerpo de agua sea invadido por EAI para tener un mejor entendimiento del riesgo de invasión en los paisajes acuáticos. El modelo incluyó cuencas conectadas a través de una red multicapa que incluía movimiento de botes y conexiones hidrológicas. Usamos como especies modelo a Dreissena polymorpha y a Nitellopsis obtusa en un estudio de caso en Minnesota. Simulamos el impacto de los escenarios de manejo desarrollado por los actores y creamos una herramienta de decisiones por medio de una aplicación en línea proporcionada como parte del tablero del Explorer de EAI. Nuestro modelo de línea base reveló que el 89% de las invasiones nuevas de D. polymorpha y el 84% de las de N. obtusa ocurrieron debido al movimiento de los botes, lo que lo estableció como una vía primaria de dispersión y nos proporcionó información más allá de las estimaciones de riesgo generadas por los modelos tradicionales de idoneidad ambiental. Nuestros resultados resaltan el papel crítico de las intervenciones aplicadas al movimiento de los botes para reducir la dispersión de especies acuáticas invasoras.

[Bibliometric study and analysis of citation networks of visual screening in primary care]. / Estudio bibliométrico y análisis de redes de citación de los screening visuales en atención primaria.

AIM: Screenings make it possible to detect anomalies that can be treated and identify patients who require referral to a specialist. The objective is to identify the different areas of research and determine the most cited publications on screening in primary care. METHODS: An analysis of publications and visualization of citation networks has been carried out using the Citation Network Explorer software. The bibliographic search was carried out with the Web of Science (WOS) database using the search term: "screening AND (vision OR eye OR ocular OR visual)". RESULTS: We analyzed 16707 publications in all fields, 23919 citation networks have been found. The number of publications has increased, with 2021 being the year with the highest number. The majority are scientific articles and the predominant language is English. The most cited article is a global meta-analysis on the prevalence of glaucoma, showing the importance of screening for its early detection since it is essential to avoid blindness. Using the clustering function we found 8 groups with a significant number of publications where we have bibliography on certain eye diseases: glaucoma, diabetic retinopathy, pediatric amblyopia, keratoconus and dry eye. CONCLUSIONS: The main areas of study in relation to screening are the detection of diseases such as glaucoma, retinopathy of prematurity, keratoconus and dry eye. As well as the detection through visual analysis of childhood amblyopia and vision loss in elderly patients. It also gives importance to performing ocular motility tests in problems of acquired brain damage.

A pilot community science program in Mexico for passive triatomine surveillance and increase Chagas disease awareness / Programa piloto de ciencia ciudadana en México para vigilancia pasiva de triatominos y concientizar sobre la enfermedad de Chagas

Objective. To generate data about Chagas disease vectors through passive surveillance and inform the public using social media and community science. Materials and methods. We used social media to inform, raise awareness and to promote the public to report their triatomine encounters. We received pictures and specimens collected to be tested for Trypanosoma cruzi and to identify recent bloodmeal source through PCR. Results. Community scientists reported 44 triatomines from 15 states in Mexico and one triatomine from Nicaragua, including 9 species with Triatoma dimidiata sensu lato and T. gerstaeckeri being the most common. We received 12 collected specimens and T. cruzi was detected in 8 (67%) of the discrete typing unit TcI. We identified recent bloodmeal source in 6 triatomines including: human (Homo sapiens), dog (Canis lupus familiaris), wood rat (Neotoma sp.), dove (Columbidae) and amphibius (Bufonidae). Conclusion. The use of community science can be a complementary method to generate information about the ecology and epidemiology of Chagas disease vectors.

Objetivo. Generar datos sobre vectores de la enfermedad de Chagas (EC) mediante vigilancia pasiva e informar a la población mediante redes sociales y ciencia ciudadana. Material y métodos. Utilizando redes sociales informamos, concientizamos y alentamos al público a reportarnos sus encuentros con triatominos. Recibimos reportes fotográficos y especímenes colectados a los que analizamos para detectar infección por Trypanosoma cruzi e identificar la fuente reciente de alimentación mediante PCR. Resultados. Nos reportaron 44 triatominos de 15 estados en México y uno de Nicaragua, incluyendo 9 especies siendo Triatoma dimidiata sensu lato y T. gerstaeckeri las más comunes. Recibimos 12 especímenes colectados y encontramos T. cruzi en 8 (67%) de la unidad taxonómica discreta TcI. Identificamos fuente reciente de alimentación en 6 triatominos incluyendo: humano (Homo sapiens), perro (Canis lupus familiaris), rata de campo (Neotoma sp.), paloma (Columbidae) y anfibio (Bufonidae). Conclusión. Ciencia ciudadana puede ser un método complementario para generar información sobre ecología y epidemiología de EC.

Impact of social networks on covid-19 vaccination of the population of Santa Bárbara, Honduras, during the period from march 2021 to march 2022 / Impacto de las redes sociales en la vacunación contra el covid19 Santa Bárbara, Honduras

The present study seeks to analyze the influence of social networks on the anti-vaccination attitude of the population of Santa Bárbara, Honduras. Taking into account the virtuality gap generated during the pandemic and the secondary isolation, as well as the trust placed in speculation and misinformation according to their source of origin.

El presente estudio busca analizar la influencia de las redes sociales en la postura antivacunas de la población de Santa Bárbara, Honduras. Teniendo en cuenta la brecha de la virtualidad generada durante la pandemia y el aislamiento secundario, como también la confianza depositada en las especulaciones y desinformación acorde a su fuente de origen.

Prediction and simulation of PEEP setting effects with machine learning models / Predicción y simulación de los efectos de la configuración de la PEEP con modelos de aprendizaje automático

Objective To establish a new machine learning-based method to adjust positive end-expiratory pressure (PEEP) using only already routinely measured data. Design Retrospective observational study. Setting Intensive care unit (ICU). Patients or participants 51811 mechanically ventilated patients in multiple ICUs in the USA (data from MIMIC-III and eICU databases). Interventions No interventions. Main variables of interest Success parameters of ventilation (arterial partial pressures of oxygen and carbon dioxide and respiratory system compliance). Results The multi-tasking neural network model performed significantly best for all target tasks in the primary test set. The model predicts arterial partial pressures of oxygen and carbon dioxide and respiratory system compliance about 45 min into the future with mean absolute percentage errors of about 21.7%, 10.0% and 15.8%, respectively. The proposed use of the model was demonstrated in case scenarios, where we simulated possible effects of PEEP adjustments for individual cases. Conclusions Our study implies that machine learning approach to PEEP titration is a promising new method which comes with no extra cost once the infrastructure is in place. Availability of databases with most recent ICU patient data is crucial for the refinement of prediction performance. (AU)

Objetivo Establecer un nuevo método basado en el aprendizaje automático para ajustar la presión positiva al final de la espiración (PEEP según sus siglas en inglés) utilizando únicamente datos ya obtenidos de forma rutinaria. Diseño Estudio retrospectivo de observación. Ámbito Unidad de cuidados intesivos (UCI) Pacientes o participantes 51811 pacientes ventilados mecánicamente en múltiples UCIs de EE.UU. (tomados de las bases de datos MIMIC-III y eICU). Intervenciones Sin intervenciones. Variables de interés principales Parametros de éxito de la ventilación (presiones parciales arteriales de oxígeno y dióxido de carbono y distensibilidad del sistema respiratorio). Resultados El modelo de red neuronal multitarea obtuvo los mejores resultados en todos los objetivos del conjunto de pruebas primario. El modelo predice las presiones parciales arteriales de oxígeno y dióxido de carbono así como la distensibilidad del sistema respiratorio con aproximadamente 45 minutos de anticipación, mostrando errores porcentuales absolutos medios de aproximadamente 21.7%, 10.0% y 15.8%, respectivamente. El uso propuesto del modelo se demostró en situaciones hipotéticas en las que se simularon los posibles efectos de los ajustes de PEEP para casos individuales. Conclusiones Nuestro estudio implica que el enfoque de aprendizaje automático para el ajuste de la PEEP es un método nuevo y prometedor que no supone ningún coste adicional una vez que se dispone de la infraestructura necesaria. La disponibilidad de bases de datos con información de pacientes de UCI más recientes es crucial para perfeccionar el rendimiento de la predicción. (AU)

Dynamic symptom associations in posttraumatic stress disorder: a network approach.

Background and objective: The current study aimed to investigate the within-day symptom dynamics in PTSD patients, specifically focusing on symptoms that most predict changes in other symptoms. The study included a baseline diagnostic assessment, followed by an assessment using the experience sampling method (ESM) via a smartphone.Method: Participants answered questions related to their PTSD symptoms four times per day for 15 consecutive days (compliance rate 75%). The clinical sample consisted of 48 treatment-seeking individuals: 44 with PTSD as a primary diagnosis, and four patients with subsyndromal PTSD, all of whom had not yet begun trauma-focused treatment. The ESM assessment included the 20 items from the PTSD Checklist for DSM-5, five items from the International Trauma Questionnaire (ITQ) assessing disturbances in relationships and functional impairment, and two items from the Clinician-Administered PTSD Scale for DSM-5 assessing symptoms of depersonalization and derealization.Results: Temporal networks (prospective associations between symptoms) showed that changes in hypervigilance predicted changes in the greatest number of symptoms at the next time point. Furthermore, hypervigilance showed temporal connections with at least one additional symptom from each of the DSM-5 PTSD symptom clusters.Conclusions: Results show that the contemporaneous network (representing the relationship between given symptoms within the same assessment occasion) and the temporal network (representing prospective associations between symptoms) differ and that it is important to estimate both. Some findings from earlier research are replicated, but heterogeneity across studies remains. Future studies should include potential moderators.

We investigated within-day symptom dynamics in PTSD patients using experience sampling technology.Temporal and contemporaneous symptom networks differed; thus, it is important to estimate both.Changes in hypervigilance were an important predictor of symptoms at the next time point.

Smoking contribution to the global burden of metabolic disorder: A cluster analysis.

INTRODUCTION AND OBJECTIVES: Smoking is associated with various health risks, including cancer, cardiovascular disease, and chronic obstructive pulmonary disease. In this retrospective cohort study, we aimed to determine whether smoking is harmful to the whole metabolic system. METHODS: We collected data from 340 randomly selected participants who were divided into three groups: smokers (n=137), non-smokers (n=134), and ex-smokers (n=69). We obtained information on participants' body mass index, waist circumference, indicators of glucose metabolism, lipid metabolism, bone metabolism, and uric acid from health screen data during the past three years. A cluster analysis was used to synthesize each participant's overall metabolic characteristics. RESULTS: According to the cluster analysis, the 340 participants were divided into three groups: excellent metabolizers (137, 40.3%), adverse metabolizers (32, 9.4%), and intermediate metabolizers (171, 50.3%). The Chi-squared test analysis shows that people with different smoking statuses have different metabolic patterns. Non-smokers had the highest proportion of excellent metabolizers (56%), and current smokers had the highest proportion of adverse metabolizers (15.3%). The proportion of adverse metabolizers (5.8%) in the ex-smoker group was clinically relevantly lower than that of current smokers. CONCLUSION: The statistically significant differences in the distribution of smokers into different metabolic clusters indicate that smoking has adverse effects on the whole metabolic system of the human body, which further increases the existing global burden of metabolic disorders.

A protocol for harvesting biodiversity data from Facebook.

The expanding use of community science platforms has led to an exponential increase in biodiversity data in global repositories. Yet, understanding of species distributions remains patchy. Biodiversity data from social media can potentially reduce the global biodiversity knowledge gap. However, practical guidelines and standardized methods for harvesting such data are nonexistent. Following data privacy and protection safeguards, we devised a standardized method for extracting species distribution records from Facebook groups that allow access to their data. It involves 3 steps: group selection, data extraction, and georeferencing the record location. We present how to structure keywords, search for species photographs, and georeference localities for such records. We further highlight some challenges users might face when extracting species distribution data from Facebook and suggest solutions. Following our proposed framework, we present a case study on Bangladesh's biodiversity-a tropical megadiverse South Asian country. We scraped nearly 45,000 unique georeferenced records across 967 species and found a median of 27 records per species. About 12% of the distribution data were for threatened species, representing 27% of all species. We also obtained data for 56 DataDeficient species for Bangladesh. If carefully harvested, social media data can significantly reduce global biodiversity knowledge gaps. Consequently, developing an automated tool to extract and interpret social media biodiversity data is a research priority.

Un protocolo para recolectar datos sobre biodiversidad en Facebook Resumen El uso creciente de plataformas de ciencia comunitaria ha causado un incremento exponencial de los datos sobre biodiversidad en los repositorios mundiales. Sin embargo, el conocimiento sobre la distribución de las especies todavía está incompleto. Los datos sobre biodiversidad obtenidos de las redes sociales tienen el potencial para disminuir el vacío de conocimiento sobre la biodiversidad mundial. No obstante, no existe una guía práctica o un método estandarizado para recolectar dichos datos. Seguimos los protocolos de privacidad y protección de datos para diseñar un método estandarizado para extraer registros de la distribución de especies de grupos en Facebook que permiten el acceso a sus datos. El método consta de tres pasos: selección del grupo, extracción de datos y georreferenciación de la localidad registrada. También planteamos cómo estructurar las palabras clave, buscar fotografías de especies y georreferenciar las localidades de dichos registros. Además, resaltamos algunos retos que los usuarios pueden enfrentar al extraer los datos de distribución de Facebook y sugerimos algunas soluciones. Aplicamos nuestro marco de trabajo propuesto a un estudio de caso de la biodiversidad en Bangladesh, un país tropical megadiverso en el sureste de Asia. Reunimos casi 45,000 registros georreferenciados únicos para 967 especies y encontramos una media de 27 registros por especie. Casi el 12% de los datos de distribución correspondió a especies amenazadas, que representaban el 27% de todas las especies. También obtuvimos datos para 56 especies deficientes de datos en Bangladesh. Si los datos de las redes sociales se recolectan con cuidado, éstos pueden reducir de forma significativa el vacío de conocimiento para la biodiversidad mundial. Como consecuencia, es una prioridad para la investigación el desarrollo de una herramienta automatizada para extraer e interpretar los datos sobre biodiversidad de las redes sociales.

Exploring the potential for online data sources to enhance species threat mapping through the case study of global bat exploitation.

Expanding digital data sources, including social media and online news, provide a low-cost way to examine human-nature interactions, such as wildlife exploitation. However, the extent to which using such data sources can expand or bias understanding of the distribution and intensity of threats has not been comprehensively assessed. To address this gap, we quantified the geographical and temporal distribution of online sources documenting the hunting and trapping, consumption, or trade of bats (Chiroptera) and compared these with the distribution of studies obtained from a systematic literature search and species listed as threatened by exploitation on the International Union for Conservation of Nature Red List. Online records were collected using automated searches of Facebook, Twitter, Google, and Bing and were filtered using machine classification. This yielded 953 relevant social media posts and web pages, encompassing 1099 unique records of bat exploitation from 84 countries. Although the number of records per country was significantly predicted by the number of academic studies per country, online records provided additional locations and more recent records of bat exploitation, including 22 countries not present in academic literature. This demonstrates the value of online resources in providing more complete geographical representation. However, confounding variables can bias the analysis of spatiotemporal trends. Online bat exploitation records showed peaks in 2020 and 2014, after accounting for increases in internet users through time. The second of these peaks could be attributed to the COVID-19 outbreak, and speculation about the role of bats in its epidemiology, rather than to true changes in exploitation. Overall, our results showed that data from online sources provide additional knowledge on the global extent of wildlife exploitation, which could be used to identify early warnings of emerging threats and pinpoint locations for further research.

Sondeo del potencial de las fuentes virtuales de datos para mejorar el mapeo de amenazas para las especies por medio del estudio de caso de la explotación mundial de murciélagos Resumen La expansión de las fuentes virtuales, incluidas las redes sociales y las noticias en línea, proporciona una forma asequible de analizar las interacciones entre el humano y la naturaleza, como la explotación de fauna. Sin embargo, no se ha analizado por completo el rango al que dichas fuentes pueden expandir o sesgar el conocimiento de la distribución e intensidad de las amenazas. Para abordar este vacío cuantificamos la distribución geográfica y temporal de las fuentes virtuales que documentan la caza, captura, consumo o mercado de murciélagos (Chiroptera) y las comparamos con la distribución de los estudios obtenidos de una búsqueda sistemática en la literatura y con las especies catalogadas como amenazadas por la explotación según la Lista Roja de la Unión Internacional para la Conservación de la Naturaleza. Recolectamos los registros virtuales por medio de búsquedas automatizadas en Facebook, Twitter, Google y Bing y después las filtramos con clasificaciones automatizadas. Esto arrojó 953 publicaciones relevantes en redes sociales y sitios web que englobaban 1099 registros únicos de la explotación de murciélagos en 84 países. Aunque pronosticamos de forma significativa el número de registros por país con el número de estudios académicos por país, los registros virtuales proporcionaron localidades adicionales y registros más recientes de la explotación de murciélagos, incluyendo a 22 países que no se encuentran en la literatura académica. Lo anterior demuestra el valor que tienen los recursos en línea para proporcionar una representación geográfica más completa. Sin embargo, las variables confusas pueden sesgar el análisis de las tendencias espaciotemporales. Los registros virtuales de la explotación de murciélagos mostraron picos en 2020 y en 2014, esto después de considerar el incremento de usuarios de internet con el tiempo. El segundo pico podría atribuirse al brote de COVID-19 y la especulación en torno al papel que tenían los murciélagos en su epidemiología y no tanto a un verdadero cambio en la explotación. En general, nuestros resultados mostraron que los datos de las fuentes virtuales proporcionan conocimiento adicional sobre el alcance mundial de la explotación de fauna, el cual podría usarse para identificar señales tempranas de amenazas emergentes y ubicar localidades para su mayor investigación.

Salud, género y reciclaje: un encuentro impostergable / Health, gender and recycling: a meeting that cannot be put off

Introducción. En Asunción existe una zona geográfica llamada Bañados, en esta zona se conforman los llamados «cinturones de pobreza», donde el trabajo informal se impone como principal medio de sustento. El oficio del reciclaje corresponde a uno de los trabajos informales más practicados. Objetivos. Describir aspectos de la zona donde desarrollan su vida trabajadora de la recolección. Incorporar elementos de resignificación positiva acerca del trabajo de reciclaje a mujeres recicladoras organizadas o no del Bañado Sur de la ciudad de Asunción, Paraguay. Material y métodos. Se realizaron 28 encuentros con 153 mujeres agrupadas en 7 grupos, durante el período del 2019 al 2022. La muestra fue seleccionada fue por conveniencia. Se eligió la modalidad de «taller» debido a las prácticas ya conocidas y aceptadas por la comunidad. Resultados. Se realizó conjuntamente la clasificación de la basura o residuos urbanos en sus categorías orgánica/inorgánica/tóxica, pero también en una clasificación más cercana a su realidad concreta. Se identificó cómo son las relaciones familiares y comunitarias, las preocupaciones por los hijos e hijas, el cambio climático, las inundaciones, las viviendas precarias, el acceso al agua, la inseguridad en el barrio y la problemática de drogas en la comunidad. Conclusión. Las jornadas se desarrollaron con mujeres recicladoras organizadas, miembros de una organización civil, que residen en el Bañado Sur – Tacumbú, Asunción (Paraguay). Durante el proceso se logró acercar a los grupos de mujeres que han podido participar de la experiencia, una resignificación positiva del trabajo y su rol en la sociedad, mediante el diálogo. (AU)

Introduction. In Asunción there is a geographic area called Bañados. They make up the so-called “poverty belts”, where informal work is laid down as the main means of support. The job of recycling is one of the most performed informal jobs. Aims. To report aspects of the area where collection workers go about their lives. Incorporate elements of positive new meaning about the recycling work to organized or disorganized women recyclers from Bañado Sur in the city of Asunción, Paraguay. Methods. In total 28 meetings were held with 153 women grouped into seven groups, during the period from 2019 to 2022. The sample was selected by convenience. The “workshop” modality was chosen due to the practices already known and accepted by the community. Results. Garbage or urban waste could be classified together in its organic/inorganic/toxic categories but also in a classification more akin to its specific reality. Family and community relationships, concerns for children, climate change, floods, precarious housing, access to water, insecurity in the neighbourhood and drug problems in the community were all identified. Conclusion. The sessions were held with organized women recyclers, members of a civil organization residing in Bañado Sur – Tacumbú, Asunción, Paraguay. During the process, it was possible to bring together the groups of women who were able to take part in the experience, a positive new meaning for work and their role in society, by means of dialogue. (AU)

Guía en redes sociales de radiología. ¿Hacia un nuevo paradigma en educación médica? / Social networks in radiology: Toward a new paradigm in medical education?

El sistema universalmente aceptado para la transmisión del conocimiento científico en medicina se basa desde hace mucho en las publicaciones científicas. Las redes sociales (RRSS) son una alternativa o complemento que puede ser útil. Las RRSS (Twitter, Instagram, Facebook, LinkedIn, YouTube, TikTok) tienen generadores de contenidos educativos que pueden proporcionar formación de calidad, a pesar de su informalidad. Cada una tiene sus puntos fuertes y sus debilidades, que conviene conocer. Son gratuitas y permiten discutir en vivo, incorporar contenidos ágilmente y contactar directamente con expertos o fuentes de conocimiento. Las editoriales son conscientes de su influencia y han incorporado métricas que miden el impacto en ellas de los artículos (Altmetrics). La estrategia formativa de cualquier servicio debe incorporarlas ya. Sin embargo, navegar en ellas es complejo y el sistema de búsqueda, basado en hashtags, es ineficiente, por lo que su uso en educación sigue siendo cosa de excéntricos. El conocimiento generado en las RRSS, a pesar de su informalidad, es una fuente cada vez más importante de conocimiento. Los servicios de radiología deben definir una estrategia de RRSS, no con fines de propaganda, sino educativos, creando grupos focales bien formados que busquen contenidos mediante revisión sistemática y filtros, repositorios digitales y sesiones de revisión y los compartan dentro y fuera del servicio. Igualmente, debe ser implementada una estrategia de comunicación a través de redes.(AU)

The universally accepted system for the transmission of scientific knowledge in the field of medicine has long been grounded in scientific publications. Social networks can be a useful alternative or complementary method of transmitting this knowledge. Social networks (e.g., Twitter, Instagram, Facebook, LinkedIn, YouTube, and TikTok) generate educational contents that enable quality training, despite their informality. Each of these networks has strengths and weaknesses that users should know about. These platforms are free and allow for real-time discussion. They make it easy to incorporate content and to contact experts or access sources of knowledge directly. Aware of their influence, publishers have incorporated metrics to measure the impact of their articles in social networks (Altmetrics). These networks should be incorporated into departmental training programs immediately. Nevertheless, navigating through social networks is complex, and the hashtag-based system of searching is inefficient, limiting their use in education. Despite the informality of the knowledge generated on social networks, the importance of these networks as a source of knowledge is growing. Radiology departments must design a strategy for using social networks for education rather than for propaganda, creating well-organized focal groups that search for contents through systematic, filtered review of information, digital repositories, and review sessions and for sharing this knowledge both inside and outside the department. Departments must also implement a strategy for communicating through these networks.(AU)

Social networks in radiology: Toward a new paradigm in medical education?

The universally accepted system for the transmission of scientific knowledge in the field of medicine has long been grounded in scientific publications. Social networks can be a useful alternative or complementary method of transmitting this knowledge. Social networks (e.g., Twitter, Instagram, Facebook, LinkedIn, YouTube, and TikTok) generate educational contents that enable quality training, despite their informality. Each of these networks has strengths and weaknesses that users should know about. These platforms are free and allow for real-time discussion. They make it easy to incorporate content and to contact experts or access sources of knowledge directly. Aware of their influence, publishers have incorporated metrics to measure the impact of their articles in social networks (Altmetrics). These networks should be incorporated into departmental training programs immediately. Nevertheless, navigating through social networks is complex, and the hashtag-based system of searching is inefficient, limiting their use in education. Despite the informality of the knowledge generated on social networks, the importance of these networks as a source of knowledge is growing. Radiology departments must design a strategy for using social networks for education rather than for propaganda, creating well-organized focal groups that search for contents through systematic, filtered review of information, digital repositories, and review sessions and for sharing this knowledge both inside and outside the department. Departments must also implement a strategy for communicating through these networks.

Using social media records to inform conservation planning.

Citizen science plays a crucial role in helping monitor biodiversity and inform conservation. With the widespread use of smartphones, many people share biodiversity information on social media, but this information is still not widely used in conservation. Focusing on Bangladesh, a tropical megadiverse and mega-populated country, we examined the importance of social media records in conservation decision-making. We collated species distribution records for birds and butterflies from Facebook and Global Biodiversity Information Facility (GBIF), grouped them into GBIF-only and combined GBIF and Facebook data, and investigated the differences in identifying critical conservation areas. Adding Facebook data to GBIF data improved the accuracy of systematic conservation planning assessments by identifying additional important conservation areas in the northwest, southeast, and central parts of Bangladesh, extending priority conservation areas by 4,000-10,000 km2 . Community efforts are needed to drive the implementation of the ambitious Kunming-Montreal Global Biodiversity Framework targets, especially in megadiverse tropical countries with a lack of reliable and up-to-date species distribution data. We highlight that conservation planning can be enhanced by including available data gathered from social media platforms.

Registros de las redes sociales para guiar la planeación de la conservación Resumen La ciencia ciudadana es importante para monitorear la biodiversidad e informar la conservación. Con el creciente uso de los teléfonos inteligentes, muchas personas comparten información de la biodiversidad en redes sociales, pero todavía no se usa ampliamente en la conservación. Analizamos la importancia de los registros de las redes sociales para las decisiones de conservación enfocados en Bangladesh, un país tropical megadiverso y mega poblado. Cotejamos los registros de distribución de especies de aves y mariposas en Facebook y Global Biodiversity Information Facility (GBIF), las agrupamos en datos sólo de GBIF o datos combinados de Facebook y GBIF e investigamos las diferencias en la identificación de las áreas de conservación críticas. La combinación de los datos de Facebook con los de GBIF mejoró la precisión de las evaluaciones de la planeación de la conservación sistemática al identificar otras áreas importantes de conservación en el noroeste, sureste y centro de Bangladesh, extendiendo así las áreas prioritarias de conservación en unos 4,000-10,000 km2 . Se requieren esfuerzos comunitarios para impulsar la implementación de los objetivos ambiciosos del Marco Global de Biodiversidad Kunming-Montreal, especialmente en países tropicales que carecen de datos confiables y actuales sobre la distribución de las especies. Destacamos que la planeación de la conservación puede mejorarse si se incluye información tomada de las redes sociales.

Prediction and simulation of PEEP setting effects with machine learning models.

OBJECTIVE: To establish a new machine learning-based method to adjust positive end-expiratory pressure (PEEP) using only already routinely measured data. DESIGN: Retrospective observational study. SETTING: Intensive care unit (ICU). PATIENTS OR PARTICIPANTS: 51811 mechanically ventilated patients in multiple ICUs in the USA (data from MIMIC-III and eICU databases). INTERVENTIONS: No interventions. MAIN VARIABLES OF INTEREST: Success parameters of ventilation (arterial partial pressures of oxygen and carbon dioxide and respiratory system compliance) RESULTS: The multi-tasking neural network model performed significantly best for all target tasks in the primary test set. The model predicts arterial partial pressures of oxygen and carbon dioxide and respiratory system compliance about 45 min into the future with mean absolute percentage errors of about 21.7%, 10.0% and 15.8%, respectively. The proposed use of the model was demonstrated in case scenarios, where we simulated possible effects of PEEP adjustments for individual cases. CONCLUSIONS: Our study implies that machine learning approach to PEEP titration is a promising new method which comes with no extra cost once the infrastructure is in place. Availability of databases with most recent ICU patient data is crucial for the refinement of prediction performance.

Artificial intelligence to predict bed bath time in Intensive Care Units / Inteligencia artificial para predecir la hora del baño en cama en Unidades de Cuidados Intensivos / Inteligência artificial para predição do tempo de banho no leito em Unidades de Terapia Intensiva

ABSTRACT Objectives: to assess the predictive performance of different artificial intelligence algorithms to estimate bed bath execution time in critically ill patients. Methods: a methodological study, which used artificial intelligence algorithms to predict bed bath time in critically ill patients. The results of multiple regression models, multilayer perceptron neural networks and radial basis function, decision tree and random forest were analyzed. Results: among the models assessed, the neural network model with a radial basis function, containing 13 neurons in the hidden layer, presented the best predictive performance to estimate the bed bath execution time. In data validation, the squared correlation between the predicted values and the original values was 62.3%. Conclusions: the neural network model with radial basis function showed better predictive performance to estimate bed bath execution time in critically ill patients.

RESUMEN Objetivos: evaluar el rendimiento predictivo de diferentes algoritmos de inteligencia artificial para estimar el tiempo de ejecución del baño en cama en pacientes críticos. Métodos: estudio metodológico, que utilizó algoritmos de inteligencia artificial para predecir el tiempo de baño en cama en pacientes críticos. Se analizaron los resultados de modelos de regresión múltiple, redes neuronales perceptrón multicapa y función de base radial, árbol de decisión y random forest. Resultados: entre los modelos evaluados, el modelo de red neuronal con función de base radial, que contiene 13 neuronas en la capa oculta, presentó el mejor desempeño predictivo para estimar el tiempo de ejecución del baño en cama. En la validación de datos, la correlación al cuadrado entre los valores predichos y los valores originales fue del 62,3%. Conclusiones: el modelo de red neuronal con función de base radial mostró mejor rendimiento predictivo para estimar el tiempo de ejecución del baño en cama en pacientes críticos.

RESUMO Objetivos: avaliar a performance preditiva de diferentes algoritmos de inteligência artificial para estimar o tempo de execução do banho no leito em pacientes críticos. Métodos: estudo metodológico, que utilizou algoritmos de inteligência artificial para predizer o tempo de banho no leito em pacientes críticos. Foram analisados os resultados dos modelos de regressão múltipla, redes neurais perceptron multicamadas e função de base radial, árvore de decisão e random forest. Resultados: entre os modelos avaliados, o modelo de rede neural com função de base radial, contendo 13 neurônios na camada oculta, apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito. Na validação dos dados, o quadrado da correlação entre os valores preditos e os valores originais foi de 62,3%. Conclusões: o modelo de rede neural com função de base radial apresentou melhor performance preditiva para estimar o tempo de execução do banho no leito em pacientes críticos.

As redes vivas na produção do cuidado com o usuário na centralidade do tratamento para tuberculose multidroga resistente / Las redes vivas en la producción del cuidado con el usuario en la centralidad del tratamiento para la tuberculosis multidrogo resistente / Live networks in care production with the user at the center of the treatment for multidrugresistant tuberculosis

Vivenciamos a trajetória de uma usuária-guia no tratamento para tuberculose multidroga resistente (TB-MDR). As narrativas das redes vivas na produção de cuidado apontam para os seguintes itens: 1) cuidar no ato de viver: suplantar os estigmas e cultivar vínculos que ajudem a superar os discursos fomentados pelo medo, preconceitos, exclusão e invisibilidade dos sujeitos; 2) redes vivas de cuidado: os entremeios da norma; e 3) as interfaces de atenção usuário-trabalhador da saúde: como desmistificar o julgamento dos trabalhadores da saúde, que, subordinados a protocolos limitantes, muitas vezes estigmatizam o usuário como "abandonador de tratamento"?. A usuária-guia vislumbrou que cuidar é se desterritorializar, é colocar os desejos como potência para transformação, saindo do modus operandi rumo à criatividade, tendo o usuário no centro do processo. (AU)

Presenciamos la trayectoria de una usuaria-guía en el tratamiento para tuberculosis multidrogo resistente (TB-MDR). Las narrativas de las Redes Vivas en la producción de cuidado señalan: 1) cuidar en el acto de vivir: suplantar los estigmas y cultivar vínculos que ayuden a superar los discursos fomentados por el miedo, prejuicios, exclusión e invisibilidad de los sujetos. 2) Redes Vivas de cuidado: los entresijos de la norma y 3) las interfaces de atención usuario-trabajador de la salud: ¿cómo desmistificar el juicio de los trabajadores de la salud quienes, subordinados a protocolos limitantes, muchas veces estigmatizan al usuario como "abandonador de tratamiento"? La usuaria-guía vislumbró que cuidar es desterritorializarse, es colocar los deseos como potencia para trasformación, saliendo del modus operandi rumbo a la creatividad, colocando al usuario en el centro del proceso. (AU)

We followed the trajectory of a guiding user undergoing treatment for multidrug-resistant tuberculosis (MDR-TB). The narratives of Live Networks in care production showed: 1) Caring in the act of living: Overcoming stigmas and cultivating bonds that help overcome discourses fostered by fear, prejudice, exclusion and invisibility of subjects; 2) Live Networks of care: The in-betweens of the norm; and 3) Interfaces of user-health worker care: How can we demystify the judgment of health workers who, subordinated to limiting protocols, often stigmatize the user as someone who "abandons the treatment"? The guiding user perceived that caring means deterritorializing oneself, expressing one's desires as power for transformation, and leaving the modus operandi towards creativity, with the user at the center of the process. (AU)

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.

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.

Sobre ativismos e conhecimentos: a experiência de associações canábicas no Brasil / On activism and knowledge: the experience of cannabis associations in Brazil

Resumo O surgimento de associações civis em prol da cannabis se iniciou na década de 2010. Diante da inércia do Estado, essas organizações têm atuado no acolhimento, apoio, informação, capacitação e facilitação do acesso de pacientes e familiares a medicamento produzido à base de maconha, substância proibida no Brasil. Este estudo visa analisar como o ativismo canábico promovido pelas associações brasileiras se fundamenta em conhecimentos científicos ou adquiridos pela vivência dos associados. A metodologia englobou entrevistas com participantes das associações ACuCa, Ama+me e Apepi e análise de conteúdo dos perfis dessas instituições no Instagram. Verificou-se que o ativismo canábico no Instagram apresenta semelhanças com aquele praticado presencialmente, no entanto, o ativismo nas mídias sociais prioriza a divulgação do conhecimento pela informação e capacitação de seus seguidores, tendo o cuidado de tratar o conteúdo para se adequar às diretrizes da plataforma. Além disso, as principais linhas de atuação do associativismo canábico (acolhimento e distribuição de óleos medicinais) aparecem de forma velada nas publicações, sendo que em sua maioria ocorrem em conversas privadas nos meios de comunicação com as associações.

Abstract The emergence of civil associations in favor of cannabis began in the 2010s. Faced with the inertia of the State, these organizations have acted in the reception, support, information, training, and facilitation of access for patients and their families to the medicine produced from marijuana, a prohibited substance in Brazil. This study aims to analyze how cannabis activism promoted by Brazilian associations is based on scientific knowledge or knowledge acquired through the experience of members. The methodology included interviews with participants from the ACuCa, Ama+me, and Apepi associations, as well as the Content Analysis of the profiles of these institutions on Instagram. It was found that cannabis activism on Instagram is similar to that practiced in person; however, activism on social media prioritizes the dissemination of knowledge through information and training of its followers, being careful to treat the content in order to suit the guidelines of the platform. In addition, the main lines of action of cannabis associations (reception and distribution of medicinal oils) appear in a veiled way in the publications, most of which occur through private conversations in the media with the associations.