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      Association between urinary glyphosate levels and serum neurofilament light chain in a representative sample of US adults: NHANES 2013–2014

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          Neurofilament light chain as a biomarker in neurological disorders

          In the management of neurological diseases, the identification and quantification of axonal damage could allow for the improvement of diagnostic accuracy and prognostic assessment. Neurofilament light chain (NfL) is a neuronal cytoplasmic protein highly expressed in large calibre myelinated axons. Its levels increase in cerebrospinal fluid (CSF) and blood proportionally to the degree of axonal damage in a variety of neurological disorders, including inflammatory, neurodegenerative, traumatic and cerebrovascular diseases. New immunoassays able to detect biomarkers at ultralow levels have allowed for the measurement of NfL in blood, thus making it possible to easily and repeatedly measure NfL for monitoring diseases’ courses. Evidence that both CSF and blood NfL may serve as diagnostic, prognostic and monitoring biomarkers in neurological diseases is progressively increasing, and NfL is one of the most promising biomarkers to be used in clinical and research setting in the next future. Here we review the most important results on CSF and blood NfL and we discuss its potential applications and future directions.
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            Serum Neurofilament light: A biomarker of neuronal damage in multiple sclerosis

            Objective Neurofilament light chains (NfL) are unique to neuronal cells, are shed to the cerebrospinal fluid (CSF), and are detectable at low concentrations in peripheral blood. Various diseases causing neuronal damage have resulted in elevated CSF concentrations. We explored the value of an ultrasensitive single‐molecule array (Simoa) serum NfL (sNfL) assay in multiple sclerosis (MS). Methods sNfL levels were measured in healthy controls (HC, n = 254) and two independent MS cohorts: (1) cross‐sectional with paired serum and CSF samples (n = 142), and (2) longitudinal with repeated serum sampling (n = 246, median follow‐up = 3.1 years, interquartile range [IQR] = 2.0–4.0). We assessed their relation to concurrent clinical, imaging, and treatment parameters and to future clinical outcomes. Results sNfL levels were higher in both MS cohorts than in HC (p < 0.001). We found a strong association between CSF NfL and sNfL (β = 0.589, p < 0.001). Patients with either brain or spinal (43.4pg/ml, IQR = 25.2–65.3) or both brain and spinal gadolinium‐enhancing lesions (62.5pg/ml, IQR = 42.7–71.4) had higher sNfL than those without (29.6pg/ml, IQR = 20.9–41.8; β = 1.461, p = 0.005 and β = 1.902, p = 0.002, respectively). sNfL was independently associated with Expanded Disability Status Scale (EDSS) assessments (β = 1.105, p < 0.001) and presence of relapses (β = 1.430, p < 0.001). sNfL levels were lower under disease‐modifying treatment (β = 0.818, p = 0.003). Patients with sNfL levels above the 80th, 90th, 95th, 97.5th, and 99th HC‐based percentiles had higher risk of relapses (97.5th percentile: incidence rate ratio = 1.94, 95% confidence interval [CI] = 1.21–3.10, p = 0.006) and EDSS worsening (97.5th percentile: OR = 2.41, 95% CI = 1.07–5.42, p = 0.034). Interpretation These results support the value of sNfL as a sensitive and clinically meaningful blood biomarker to monitor tissue damage and the effects of therapies in MS. Ann Neurol 2017;81:857–870
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              Trends in glyphosate herbicide use in the United States and globally

              Background Accurate pesticide use data are essential when studying the environmental and public health impacts of pesticide use. Since the mid-1990s, significant changes have occurred in when and how glyphosate herbicides are applied, and there has been a dramatic increase in the total volume applied. Methods Data on glyphosate applications were collected from multiple sources and integrated into a dataset spanning agricultural, non-agricultural, and total glyphosate use from 1974–2014 in the United States, and from 1994–2014 globally. Results Since 1974 in the U.S., over 1.6 billion kilograms of glyphosate active ingredient have been applied, or 19 % of estimated global use of glyphosate (8.6 billion kilograms). Globally, glyphosate use has risen almost 15-fold since so-called “Roundup Ready,” genetically engineered glyphosate-tolerant crops were introduced in 1996. Two-thirds of the total volume of glyphosate applied in the U.S. from 1974 to 2014 has been sprayed in just the last 10 years. The corresponding share globally is 72 %. In 2014, farmers sprayed enough glyphosate to apply ~1.0 kg/ha (0.8 pound/acre) on every hectare of U.S.-cultivated cropland and nearly 0.53 kg/ha (0.47 pounds/acre) on all cropland worldwide. Conclusions Genetically engineered herbicide-tolerant crops now account for about 56 % of global glyphosate use. In the U.S., no pesticide has come remotely close to such intensive and widespread use. This is likely the case globally, but published global pesticide use data are sparse. Glyphosate will likely remain the most widely applied pesticide worldwide for years to come, and interest will grow in quantifying ecological and human health impacts. Accurate, accessible time-series data on glyphosate use will accelerate research progress. Electronic supplementary material The online version of this article (doi:10.1186/s12302-016-0070-0) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                Journal of Exposure Science & Environmental Epidemiology
                J Expo Sci Environ Epidemiol
                Springer Science and Business Media LLC
                1559-0631
                1559-064X
                September 06 2023
                Article
                10.1038/s41370-023-00594-2
                fc39af78-bda8-408b-8854-08b36bd546cc
                © 2023

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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