Neurological disease in adults with Zika and chikungunya virus infection in Northeast Brazil: a prospective observational study

Background: Since 2015, the arthropod-borne viruses (arboviruses) Zika and chikungunya have spread across the Americas causing outbreaks, accompanied by increases in immune-mediated and infectious neurological disease. The spectrum of neurological manifestations linked to these viruses, and the importance of dual infection, are not known fully. We aimed to investigate whether neurological presentations differed according to the infecting arbovirus, and whether patients with dual infection had a different disease spectrum or severity. Methods: We report a prospective observational study done during epidemics of Zika and chikungunya viruses in Recife, Pernambuco, a dengue-endemic area of Brazil. We recruited adults aged 18 years or older referred to Hospital da Restauração, a secondary-level and tertiary-level hospital, with suspected acute neurological disease and a history of suspected arboviral infection. We looked for evidence of Zika, chikungunya, or dengue infection by viral RNA or specific IgM antibodies in serum or CSF. We grouped patients according to their arbovirus laboratory diagnosis and then compared demographic and clinical characteristics. Findings: Between Dec 4, 2014, and Dec 4, 2016, 1410 patients were admitted to the hospital neurology service; 201 (14%) had symptoms consistent with arbovirus infection and sufficient samples for diagnostic testing and were included in the study. The median age was 48 years (IQR 34–60), and 106 (53%) were women. 148 (74%) of 201 patients had laboratory evidence of arboviral infection. 98 (49%) of them had a single viral infection (41 [20%] had Zika, 55 [27%] had chikungunya, and two [1%] had dengue infection), whereas 50 (25%) had evidence of dual infection, mostly with Zika and chikungunya viruses (46 [23%] patients). Patients positive for arbovirus infection presented with a broad range of CNS and peripheral nervous system (PNS) disease. Chikungunya infection was more often associated with CNS disease (26 [47%] of 55 patients with chikungunya infection vs six [15%] of 41 with Zika infection; p=0·0008), especially myelitis (12 [22%] patients). Zika infection was more often associated with PNS disease (26 [63%] of 41 patients with Zika infection vs nine [16%] of 55 with chikungunya infection; p≤0·0001), particularly Guillain-Barré syndrome (25 [61%] patients). Patients with Guillain-Barré syndrome who had Zika and chikungunya dual infection had more aggressive disease, requiring intensive care support and longer hospital stays, than those with mono-infection (median 24 days [IQR 20–30] vs 17 days [10–20]; p=0·0028). Eight (17%) of 46 patients with Zika and chikungunya dual infection had a stroke or transient ischaemic attack, compared with five (6%) of 96 patients with Zika or chikungunya mono-infection (p=0·047). Interpretation: There is a wide and overlapping spectrum of neurological manifestations caused by Zika or chikungunya mono-infection and by dual infections. The possible increased risk of acute cerebrovascular disease in patients with dual infection merits further investigation. Funding: Fundação do Amparo a Ciência e Tecnologia de Pernambuco (FACEPE), EU's Horizon 2020 research and innovation programme, National Institute for Health Research. Translations: For the Portuguese and Spanish translations of the abstract see Supplementary Materials section

Guillain-Barré syndrome during the Zika virus outbreak in Northeast Brazil: An observational cohort study

Objective: To determine the clinical phenotype of Guillain-Barré syndrome (GBS) after Zika virus (ZIKV) infection, the anti-glycolipid antibody signature, and the role of other circulating arthropod-borne viruses, we describe a cohort of GBS patients identified during ZIKV and chikungunya virus (CHIKV) outbreaks in Northeast Brazil. Methods: We prospectively recruited GBS patients from a regional neurology center in Northeast Brazil between December 2014 and February 2017. Serum and CSF were tested for ZIKV, CHIKV, and dengue virus (DENV), by RT-PCR and antibodies, and serum was tested for GBS-associated antibodies to glycolipids. Results: Seventy-one patients were identified. Forty-eight (68%) had laboratory evidence of a recent arbovirus infection; 25 (52%) ZIKV, 8 (17%) CHIKV, 1 (2%) DENV, and 14 (29%) ZIKV and CHIKV. Most patients with a recent arbovirus infection had motor and sensory symptoms (72%), a demyelinating electrophysiological subtype (67%) and a facial palsy (58%). Patients with a recent infection with ZIKV and CHIKV had a longer hospital admission and more frequent mechanical ventilation compared to the other patients. No specific anti-glycolipid antibody signature was identified in association with arbovirus infection, although significant antibody titres to GM1, GalC, LM1, and GalNAc-GD1a were found infrequently. Conclusion: A large proportion of cases had laboratory evidence of a recent infection with ZIKV or CHIKV, and recent infection with both viruses was found in almost one third of patients. Most patients with a recent arbovirus infection had a sensorimotor, demyelinating GBS. We did not find a specific anti-glycolipid antibody signature in association with arbovirus-related GBS

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost