Simultaneous Therapy with Intravitreal Dexamethasone Implant and Bevacizumab for the Treatment of Macular Edema

To investigate the safety profile and benefits of a short-term simultaneous treatment regimen combining two drugs—an intravitreal implant of dexamethasone with an intravitreal injection of bevacizumab—in patients with macular edema. This was a retrospective, non-randomized, open-label case series study.  Patients were treated between April 2014 and July 2015 and were diagnosed with recurrent macular edema secondary to diabetic retinopathy and retinal vein occlusion. They underwent simultaneous treatment with an intravitreal injection of bevacizumab (1.25 mg) and an intravitreal implant of dexamethasone (0.7 mg). Patients were evaluated at baseline and at each subsequent visit with a complete ophthalmological examination and spectral-domain optical coherence tomography (OCT) scans. They were examined 24 hours after the treatment, and then followed up after 30 days and 60 days. Twenty patients (representing 20 eyes) were included in the study. At the time of injection (i.e., baseline), the best-corrected visual acuity (BCVA) was 0.758 ± 0.42 logarithm of the minimum angle of resolution (logMAR). It improved significantly to 0.51 ± 0.33 logMAR at 1 month and to 0.5 ± 0.34 logMAR at 2 months (P ≤ 0.03). The median baseline central macular thickness (CMT) was 542 µm (interquartile range, 466 – 751 µm). The median CMT decreased significantly to 321 µm (interquartile range, 288–381 µm) at 1 month and 310 µm (interquartile range, 286 – 354 µm) at 2 months (P ≤ 0.0002). The mean intraocular pressure (IOP) increased from 14.9 ± 2.29 mmHg (at baseline) to 16.5 ± 2.99 mmHg (P = 0.04) after 2 months. Two (10%) eyes showed cataract progression. There were no other ocular or systemic complications for the duration of this study. Simultaneous therapy combining a dexamethasone implant plus bevacizumab for macular edema may be an attractive treatment regimen with an acceptable safety profile

An Intelligent Multicriteria Model for Diagnosing Dementia in People Infected with Human Immunodeficiency Virus

Hybrid models to detect dementia based on Machine Learning can provide accurate diagnoses in individuals with neurological disorders and cognitive complications caused by Human Immunodeficiency Virus (HIV) infection. This study proposes a hybrid approach, using Machine Learning algorithms associated with the multicriteria method of Verbal Decision Analysis (VDA). Dementia, which affects many HIV-infected individuals, refers to neurodevelopmental and mental disorders. Some manuals standardize the information used in the correct detection of neurological disorders with cognitive complications. Among the most common manuals used are the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th edition) of the American Psychiatric Association and the International Classification of Diseases, 10th edition (ICD-10)—both published byWorld Health Organization (WHO). The model is designed to explore the predictive of specific data. Furthermore, a well-defined database data set improves and optimizes the diagnostic models sought in the research.info:eu-repo/semantics/publishedVersio

Enhancing spatial inference of air pollution using machine learning techniques with low-cost monitors in data-limited scenarios

Ensuring environmental justice necessitates equitable access to air quality data, particularly for vulnerable communities. However, traditional air quality data from reference monitors can be costly and challenging to interpret without in-depth knowledge of local meteorology. Low-cost monitors present an opportunity to enhance data availability in developing countries and enable the establishment of local monitoring networks. While machine learning models have shown promise in atmospheric dispersion modelling, many existing approaches rely on complementary data sources that are inaccessible in low-income areas, such as smartphone tracking and real-time traffic monitoring. This study addresses these limitations by introducing deep learning-based models for particulate matter dispersion at the neighbourhood scale. The models utilize data from low-cost monitors and widely available free datasets, delivering root mean square errors (RMSE) below 2.9 μg m−3 for PM1, PM2.5, and PM10. The sensitivity analysis shows that the most important inputs to the models were the nearby monitors' PM concentrations, boundary layer dissipation and height, and precipitation variables. The models presented different sensitivities to each road type, and an RMSE below the regional differences, evidencing the learning of the spatial dependencies. This breakthrough paves the way for applications in various vulnerable localities, significantly improving air pollution data accessibility and contributing to environmental justice. Moreover, this work sets the stage for future research endeavours in refining the models and expanding data accessibility using alternative sources

POTENCIAL AGRONÔMICO DE Eugenia candolleana D.C. EM ÁREA DE RESTINGA LITORÂNEA EM ARAQUARI-SC, BRASIL: RESULTADOS PARCIAIS

Eugenia candolleana D.C. (Myrtaceae) é uma espécie florestal frutífera endêmica da Mata Atlântica. Apesar da planta possuir propriedades medicinais e de seus frutos serem apreciados para o consumo in natura e na culinária, carece de informações técnicas no âmbito agronômico, principalmente para as condições do litoral nordeste de Santa Catarina. Este trabalho teve por objetivos verificar aspectos relativos ao potencial agronômico de E. candolleana D.C. em área de restinga litorânea. em Araquari-SC, Brasil. O estudo foi realizado em uma população plantas existentes na área do IFC-Campus Araquari (26°22'12'' S e 48°43' 19'' O), durante o período 01/08/2019 a 31/07/2020. Foram analisados: o período de colheita; a estimativa de produção de frutos, determinada pelo acumulado de colheita de todos os frutos maduros por planta; o comportamento pós-colheita, por meio da determinação do “tempo de vida de prateleira” de frutos frescos maduros e recém colhidos, mantidos a 10°C e 25°C, sendo determinadas a matéria fresca (g) e o teor total de sólidos solúveis (°Brix) aos 0, 2, 4, 6 e 8 dias após a colheita, bem como verificadas a ocorrência de alterações visuais nos frutos. Também foi observada a ocorrência de danos causados por pássaros, insetos ou doenças, obtida pela análise visual dos frutos durante a colheita e respectivo cálculo do percentual de frutos danificados. Os frutos foram acondicionados em bandejas plásticas. Da população de plantas observadas, foi possível realizar a colheita em apenas uma, possivelmente devido à juvenilidade das demais. O período de colheita de frutos foi de 29 dias, de 28/02/2020 à 28/03/2020, sendo realizadas ao todo 11 colheitas. Foi possível verificar duas etapas de produção, a primeira de 11 dias, referente à primeira florada e a outra de 14 dias de maior intensidade e produção. A produção total estimada foi 22,17 kg.planta-1 e o pesomédio dos frutos foi de 5,09g. Houveram danos decorrentes de pássaros e/ou queda natural em 21% dos frutos. O tempo de prateleira foi maior para frutos mantidos refrigerados, com redução de 12,06 para 11,24 °Brix e de 3% para matéria fresca aos 8 dias após a colheita. Frutos mantidos à 25°C reduziram de 12,5 para 11,42, 7,42 e 7,24 °Brix aos 4, 6 e 8 dias após a colheita, respectivamente, com cerca de 14% de redução de matéria fresca no final do período. Aos quatro dias após a colheita à 25°C, os frutos apresentaram odor desagradável, possivelmente devido a presença de larvas de insetos (mosca das frutas), sendo esta a principal causa de danos observada em frutos no final do tempo de prateleira. De acordo com as condições em que o trabalho foi desenvolvido pode-se concluir que: E. candolleana apresenta potencial agronômico quanto à produtividade de frutos; o reduzido tempo de prateleira demanda cuidados na comercialização de frutos frescos in natura e a refrigeração é uma alternativa na conservação da qualidade dos frutos em pós-colheita; Mosca das frutas é a principal praga encontrada, com potencial de dano econômico principalmente em pós-colheita

Hybrid model for early identification post-Covid-19 sequelae

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.info:eu-repo/semantics/publishedVersio

Vascular retinal findings after COVID-19 vaccination in 11 cases: a coincidence or consequence?

Purpose: The primary purpose of this study was to assess vascular retinal findings temporally related to COVID-19 vaccination. With greater information regarding all possible future adverse events, we hope to understand the real dimension and relevance of what was presented. Methods: Eleven patients with visual complaints after COVID-19 vaccination were enrolled. Data on the following were included: age, sex, vaccine, time of symptom onset, systemic findings, medical history, best-corrected visual acuity, and ocular findings by slit-lamp biomicroscopy as well as multimodal retinal imaging (color fundus, red-free photography, spectral-domain optical coherence tomography, optical coherence tomography angiography, and fluorescein-angiography). Inclusion criteria were the presence of ophthalmologic signs within 30 days after the first or second dose of any COVID-19 vaccine. Results: Of 11 patients, five had arterial occlusion (45.4%), four had venous occlusion (36.4%), and two (18.2%) had nonspecific vascular alterations suggestive of retinal ischemia such as cotton-wool spots. The mean age was 57 (SD = 16; range: 27-84) years. The mean time of symptoms onset was 10 (SD = 5.4; range: 3-16) days. Nine patients were female (81.8%). Systemic risk factors were observed in 36.4% of patients. Two patients had both neurological and visual symptoms, with arterial occlusion. Overall, 36.4% patients had COVID-19 in the previous year. Seven patients (63.6%) received ChAdOx1 nCoV- 19 (AZD1222) vaccine. Conclusions: Our data suggest that retinal events temporally related to COVID-19 vaccination are possible but are very rare. The relationship of these events with post-COVID-19 vaccination warrants further attention to derive a meaningful conclusion.RESUMO Objetivos: o principal objetivo deste estudo foi descrever pacientes com achados vasculares retinianos temporalmente relacionados à vacinação contra COVID-19. Com maior notificação de possíveis eventos adversos similares, esperamos compreender a real dimensão e relevância do que foi apresentado. Métodos: Onze pacientes com queixas visuais após vacinação contra COVID-19 foram estudados. Os dados analisados foram: idade, gênero, tipo de vacinação, tempo de aparecimento de sintomas, achados sistêmicos, antecedentes pessoais, acuidade visual com melhor correção, biomicroscopia e imagem retiniana multimodal (retinografia colorida, red-free, SD-OCT, OCTA e angiofluoresceinografia). Os critérios de inclusão foram a presença de alterações oftalmológicas ocorridas dentro de 30 dias após a primeira ou segunda dose de qualquer vacina contra COVID-19. Resultados: Onze pacientes foram incluídos: 5 com oclusão arterial (45,4%), 4 com oclusão venosa (36,4%) e 2 (18,2%) com alterações não específicas vasculares sugestivas de isquemia retiniana como exsudatos algodonosos. A idade média dos pacientes foi de 57 anos (DP=16; com intervalo de 27 a 84 anos). A média de tempo de aparecimento de sintomas após a vacinação foi de 10 dias (DP=5,4; com intervalo de 3 a 16 dias). Nove dos onze pacientes eram do sexo feminino (81,8%). Fatores de risco sistêmicos foram observados em 36,4% dos pacientes. Dois pacientes tiveram sintomas neurológicos e visuais, com oclusão arterial. 36,4% dos pacientes tiveram infecção prévia por COVID-19 no último ano. Sete pacientes (63,6%) receberam a vacina ChAdOx1 nCoV-19 (AZD1222). Conclusões: nossos dados sugerem que eventos retinianos temporalmente relacionados à vacinação contra COVID-19 são possíveis, porém raros. A relação entre estes eventos pós-vacinais exigem futura atenção antes de maiores conclusões

MobiSaúde: Sistema de Informação para Visitas Domiciliares na Área de Saúde / MobiSaúde: Information System for Home Visits in the Health Area

Com a popularização de dispositivos móveis e o aumento das capacidades dos mesmos, houve a integração entre sistemas de informação da área de saúde e estes dispositivos, chamada de mHealth. Neste trabalho discutimos um SIS voltado para visitas domiciliares medicas, com foco na coleta de dados e nas dificuldades envolvendo a locomoção do agente realizador

Rarity of monodominance in hyperdiverse Amazonian forests.

Tropical forests are known for their high diversity. Yet, forest patches do occur in the tropics where a single tree species is dominant. Such "monodominant" forests are known from all of the main tropical regions. For Amazonia, we sampled the occurrence of monodominance in a massive, basin-wide database of forest-inventory plots from the Amazon Tree Diversity Network (ATDN). Utilizing a simple defining metric of at least half of the trees ≥ 10 cm diameter belonging to one species, we found only a few occurrences of monodominance in Amazonia, and the phenomenon was not significantly linked to previously hypothesized life history traits such wood density, seed mass, ectomycorrhizal associations, or Rhizobium nodulation. In our analysis, coppicing (the formation of sprouts at the base of the tree or on roots) was the only trait significantly linked to monodominance. While at specific locales coppicing or ectomycorrhizal associations may confer a considerable advantage to a tree species and lead to its monodominance, very few species have these traits. Mining of the ATDN dataset suggests that monodominance is quite rare in Amazonia, and may be linked primarily to edaphic factors