Abstract: Syndromic surveillance using primary health care (PHC) data is a valuable tool for early outbreak detection, as demonstrated by the potential to identify COVID-19 outbreaks. However, the potential of such an early warning system in the post-COVID-19 era remains largely unexplored. We analyzed PHC encounter counter of respiratory complaints registered in the database of the Brazilian Unified National Health System from October 2022 to July 2023. We applied EARS (variations C1/C2/C3) and EVI to estimate the weekly thresholds. An alarm was determined when the number of encounters exceeded the week-specific threshold. We used data on hospitalization due to respiratory disease to classify as anomalies the weeks in which the number of cases surpassed predetermined thresholds. We compared EARS and EVI efficacy in anticipating anomalies. A total of 119 anomalies were identified across 116 immediate regions during the study period. The EARS-C2 presented the highest early alarm rate, with 81/119 (68%) early alarms, and C1 the lowest, with 71 (60%) early alarms. The lowest true positivity was the EARS-C1 118/1,354 (8.7%) and the highest was EARS-C3 99/856 (11.6%). Routinely collected PHC data can be successfully used to detect respiratory disease outbreaks in Brazil. Syndromic surveillance enhances timeliness in surveillance strategies, albeit with lower specificity. A combined approach with other strategies is essential to strengthen accuracy, offering a proactive and effective public health response against future outbreaks.

Resumo: A vigilância sindrômica utilizando dados de atenção primária à saúde (APS) é uma ferramenta valiosa para a detecção precoce de surtos, conforme demonstrado no potencial de identificar surtos de COVID-19. No entanto, o potencial desse sistema de alerta antecipado na era pós-COVID-19 continua amplamente inexplorado. Foram analisadas as contagens de atendimentos na APS por queixas respiratórias registradas na base de dados do Sistema Único de Saúde de outubro de 2022 a julho de 2023. O EARS (variações C1/C2/C3) e o EVI foram aplicados para estimar os limiares semanais. Um alarme foi criado para quando o número de atendimentos excedesse o limite específico da semana. Dados de hospitalização por doença respiratória foram utilizados para classificar semanas em que o número de casos ultrapassou os limites predeterminados como anomalias. Comparamos a eficácia do EARS e do EVI na antecipação de anomalias. Um total de 119 anomalias foram identificadas em 116 regiões imediatas durante o período do estudo. O EARS-C2 apresentou a maior taxa de alarmes precoces, com 81 de 119 (68%) alarmes precoces, enquanto o C1 apresentou a menor, com 71 (60%) alarmes precoces. A menor taxa de verdadeiros positivos foi a EARS-C1 118/1.354 (8,7%) e a maior EARS-C3 99/856 (11,6%). Os dados de APS coletados rotineiramente podem ser usados com sucesso para detectar surtos de doenças respiratórias no Brasil. A vigilância sindrômica melhora a prontidão das estratégias de vigilância, embora com menor especificidade. Uma abordagem combinada com outras estratégias é essencial para fortalecer a precisão, oferecendo uma resposta proativa e eficaz de saúde pública contra futuros surtos.

Resumen: La vigilancia sindrómica, que utiliza datos de la atención primaria de salud (APS), es una herramienta valiosa para la detección temprana de brotes, como lo demuestra el capacidad para identificar brotes de COVID-19. Sin embargo, el uso de este sistema de alerta temprana en la era posterior a la COVID-19 sigue en gran medida inexplorado. Se analizaron los conteos de atenciones en la APS por afecciones respiratorias registradas en la base de datos del Sistema Único de Salud desde octubre del 2022 hasta julio del 2023. Se aplicaron el EARS (rangos C1/C2/C3) y el EVI para estimar los umbrales semanales. Se emitió una alarma en caso de que el número de atenciones excediera el límite específico para la semana. Los datos de hospitalización por enfermedad respiratoria se utilizaron para clasificar como anómalas las semanas en las que el número de casos superó los umbrales predeterminados. Comparamos la eficacia del EARS y del EVI para anticipar anomalías. Durante el período del estudio se identificó un total de 119 anomalías en 116 regiones. El EARS-C2 presentó la tasa más alta de alarmas tempranas, con 81 de 119 (68%) alarmas tempranas, mientras que el C1 tuvo la más baja, con 71 (60%) alarmas tempranas. La tasa de positividad más baja fue la EARS-C1 118/1.354 (8,7%) y la más alta EARS-C3 99/856 (11,6%). Los datos de APS recopilados de forma rutinaria pueden utilizarse con éxito para detectar brotes de enfermedades respiratorias en Brasil. La vigilancia sindrómica aumenta la prontitud de las estrategias de vigilancia, aunque con menor especificidad. Un enfoque combinado con otras estrategias es esencial para fortalecer la precisión y proporcionar una respuesta de salud pública proactiva y eficaz contra futuros brotes.

The mosquito Aedes aegypti is the main vector of several arthropod-borne diseases that have global impacts. In a previous meta-analysis, our group identified a vector gene set containing 110 genes strongly associated with infections of dengue, West Nile and yellow fever viruses. Of these 110 genes, four genes allowed a highly accurate classification of infected status. More recently, a new study of Ae. aegypti infected with Zika virus (ZIKV) was published, providing new data to investigate whether this “infection” gene set is also altered during a ZIKV infection. Our hypothesis is that the infection-associated signature may also serve as a proxy to classify the ZIKV infection in the vector. Raw data associated with the NCBI/BioProject were downloaded and re-analysed. A total of 18 paired-end replicates corresponding to three ZIKV-infected samples and three controls were included in this study. The nMDS technique with a logistic regression was used to obtain the probabilities of belonging to a given class. Thus, to compare both gene sets, we used the area under the curve and performed a comparison using the bootstrap method. Our meta-signature was able to separate the infected mosquitoes from the controls with good predictive power to classify the Zika-infected mosquitoes.

The phytopathogenic fungus Moniliophthora perniciosa (Stahel) Aime & Philips-Mora, causal agent of witches' broom disease of cocoa, causes countless damage to cocoa production in Brazil. Molecular studies have attempted to identify genes that play important roles in fungal survival and virulence. In this study, sequences deposited in the M. perniciosa Genome Sequencing Project database were analyzed to identify potential biological targets. For the first time, the ergosterol biosynthetic pathway in M. perniciosa was studied and the lanosterol 14α-demethylase gene (ERG11) that encodes the main enzyme of this pathway and is a target for fungicides was cloned, characterized molecularly and its phylogeny analyzed. ERG11 genomic DNA and cDNA were characterized and sequence analysis of the ERG11 protein identified highly conserved domains typical of this enzyme, such as SRS1, SRS4, EXXR and the heme-binding region (HBR). Comparison of the protein sequences and phylogenetic analysis revealed that the M. perniciosa enzyme was most closely related to that of Coprinopsis cinerea.