Microplastics in the Olfactory Bulb of the Human Brain

Importance Microplastic (MP) pollution is an emerging environmental and health concern. While MPs have been detected in various human tissues, their presence in the human brain has not been documented, raising important questions about potential neurotoxic effects and the mechanisms by which MPs might reach brain tissues. Objective To determine the presence of MPs in the human olfactory bulb and to analyze their characteristics such as size, morphology, color, and polymeric composition. Design, Setting, and Participants This case series study used a cross-sectional design involving the analysis of olfactory bulb tissues obtained from deceased individuals during routine coroner autopsies. The sampling procedures were conducted at São Paulo City Death Verification Service, with laboratory analysis carried out at the Brazilian Synchrotron Light Laboratory (LNLS). Participants included 15 adult individuals who had been residents of São Paulo for more than 5 years and underwent coroner autopsies. Exclusion criteria included previous neurosurgical interventions. Data analysis was performed in April 2024. Exposure The primary exposure assessed was the presence of MPs in the olfactory bulb, analyzed through direct tissue examination and digested tissue filtration followed by micro-Fourier transform infrared spectroscopy. Main Outcomes and Measures The main outcomes were the identification and characterization of MPs within the olfactory bulb, including their size, morphology, color, and polymeric composition. Results The median age of the 15 deceased individuals was 69.5 years, ranging from 33 to 100 years, with 12 males and 3 females. MPs were detected in the olfactory bulbs of 8 out of 15 individuals. A total of 16 synthetic polymer particles and fibers were identified, with 75% being particles and 25% being fibers. The most common polymer detected was polypropylene (43.8%). Sizes of MPs ranged from 5.5 μm to 26.4 μm for particles, and the mean fiber length was 21.4 μm. Polymeric materials were absent in procedural blank and negative control filters, indicating minimal contamination risk. Conclusions and Relevance This case series provides evidence of MPs found in the human olfactory bulb, suggesting a potential pathway for the translocation of MPs to the brain. The findings underscore the need for further research on the health implications of MP exposure, particularly concerning neurotoxicity and the potential for MPs to bypass the blood-brain barrier

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

In vitro additive interaction between ketoconazole and antimony against intramacrophage Leishmania (Leishmania) amazonensis amastigotes.

Leishmaniasis is a group of diseases caused by protozoa of Leishmania genus. The currently available treatments for this disease are expensive, present high toxicity and are associated to difficulties of healing and parasite resistance. Therefore, the development of strategies for leishmaniasis treatment is indispensable and includes reposition of existing drugs, as well as drug combination therapy. The aim of this study was to assess the nature of ketoconazole and antimony association on the cytotoxic effect against Leishmania (Leishmania) amazonensis amastigotes. The calculated mean sum of fractional 50% inhibitory concentration ([Formula: see text]ΣFIC50) was 2.54 and 1.43 for free and intracellular amastigotes, respectively, values that suggest an additive interaction between ketoconazole and antimony concerning to Leishmania toxicity only in the intramacrophage parasite form. Despite the clinical efficacy of ketoconazole-antimony combination has been shown in the literature, our study is the first to describe the nature of ketoconazole-antimony interaction against L. (L.) amazonensis amastigotes. Moreover, our results point out the need for future in vivo studies to confirm the nature of ketoconazole-antimony interaction and also to determine possible effective dosage regimens related to ketoconazole administration in association with the optimal lower dose of antimony

IC₅₀, FIC₅₀ and ΣFIC₅₀ of antimony-ketoconazole combination against <i>L</i>. <i>(L</i>.<i>) amazonensis</i> free and intracellular amastigotes.

<p>IC₅₀, FIC₅₀ and ΣFIC₅₀ of antimony-ketoconazole combination against <i>L</i>. <i>(L</i>.<i>) amazonensis</i> free and intracellular amastigotes.</p

Cytotoxic effects of isolated drugs (ketoconazole and antimony) on free amastigotes, murine macrophages cell line RAW264.7 and intracellular amastigotes.

<p>Concentration-effect curves to ketoconazole (white points) and antimony (black points) against <i>L</i>. <i>(L</i>.<i>) amazonensis</i> free amastigotes <b>(A),</b> murine macrophages <b>(B)</b> or intracellular amastigotes <b>(C)</b>. Data represent mean ± SEM of two to three independent experiments performed in triplicate.</p