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We read with interest the study entitled "Spatial proximity of tumor-immune interactions predicts patient outcome in hepatocellular "in Hepatology (Maestri E, Kedei N, Khatib S, et al. in Hepatology 79(4):768-79, 2024). This study utilized highly multiplexed CODEX imaging to comprehensively analyze the spatial interactions between tumor and immune cells in hepatocellular carcinoma (HCC), yielding several meaningful findings and innovatively proposing the concept of the spatial neighborhood of immune cell infiltration and confirming its association with patient prognosis.

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Pesticide residues in three fishing sites in Burkina Faso.

Environ Monit Assess

December 2024

Center Region, National Public Health Laboratory, Ouagadougou, Burkina Faso.

Pesticides control pests and diseases in crops in order to enhance agricultural productivity; however, they are responsible for damaging aquatic resources. This study aimed to assess pesticide residues in aquatic ecosystems in Burkina Faso. Data were collected in two sampling sites: Bama and Banzon.

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Environmental pollution is a problem that affects food safety throughout the world. In this study, a health risk assessment of possible toxic metal and aflatoxin contamination in homemade and industrial noodles produced in Türkiye was carried out. Samples were taken from 67 kinds of homemade and five kinds of industrial noodles from different cities.

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Article Synopsis
  • Pesticides present health risks to consumers and ecosystems globally, prompting researchers to recommend ongoing assessments of pesticide residues in food and environments.
  • This study focused on quantifying pesticide residues in cabbage and fish from Ethiopia, using advanced techniques to analyze samples collected in 2023, confirming contamination and significant health risks.
  • The results indicated high levels of organochlorine and organophosphate pesticides in both cabbage (over 92% above limits) and fish, raising concerns for agricultural practices in the Fogera District and highlighting the need for improved pesticide monitoring and management.
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Background: This study aims to validate a machine learning algorithm previously developed in a training population on a different randomly chosen population (i.e., test set).

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