A Case-Control Study of the Association Between<i>Karenia Brevis</i>(Red Tide) and Biliary Atresia

Wyrebek, Rita; Fierstein, Jamie; Wells, Rebecca G.; Machry, Joana; Karjoo, Sara

doi:10.1101/2022.10.24.22279447

Cited by 2 publications

(1 citation statement)

References 42 publications

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“…On the one hand, the excessive growth in HABs not only leads to the discoloration of water bodies and reduced oxygen levels in aquatic habitats but also causes mass fish mortality [7]. On the other hand, the "red tide toxins" released by certain algae can cause respiratory irritation, coughing, and asthma-like symptoms when inhaled by Sustainability 2023, 15, 15386 2 of 26 humans [8][9][10][11]. In order to promptly detect the occurrence of HABs, scholars from various countries have conducted extensive research on harmful algal blooms.…”

Section: Introductionmentioning

confidence: 99%

A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data

Bu,

Liu,

Liu

et al. 2023

Sustainability

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Over the past few decades, harmful algal blooms (HABs) have occurred frequently worldwide. The application of harmful algal bloom detection when based solely on water quality measurements proves challenging in achieving broad generalization across various regions. Satellite remote sensing, due to its low risk, cost effectiveness, and wide ground-coverage capabilities, has been extensively employed in HAB detection tasks. However, relying solely on remote sensing data poses issues of false positives, false negatives, and the incomplete consideration of contributing factors in HAB detection. This study proposes a model for harmful algal bloom detection by integrating MODIS multifactor data with heterogeneous meteorological data. Initially, a dataset named MODIS_MI_HABs is constructed by gathering information from 192 instances of harmful algal bloom events worldwide. Subsequently, remote sensing data corresponding to specific regions are collected; all were obtained from a moderate resolution imaging spectroradiometer (MODIS) aboard an ocean-color-detecting satellite. This dataset encompasses variables such as chlorophyll-a concentration, the sea surface temperature, photosynthetically active radiation, the relative radiation stability differences, the six seawater-absorption coefficients, and three scattering coefficients. By fusing six meteorological factors, latitude and longitude information, and remote sensing data, a regression dataset for harmful algal bloom detection is established. Finally, employing harmful algal bloom cell concentration as the data label, seven machine learning models are employed to establish correlations between the remote sensing data, heterogeneous meteorological data, and harmful algal bloom cell concentrations. The root mean square error (RMSE), mean absolute error (MAE), explained variance (EV), and coefficient of determination (R2) parameters are used to evaluate the regression performance. The results indicate that the extreme gradient boosting (XGR) model demonstrates the best predictive capability for harmful algal blooms (leave-one-out: RMSE/MAE = 0.0714). The XGR model, trained with the entire dataset, yields the optimal predictive performance (RMSE = 0.0236, MAE = 0.0151, EV = 0.9593, R2 = 0.9493). When compared to the predictions based on the fixed-area water quality analysis and single-source remote sensing data usage, the proposed approach in this paper displays wide applicability, offering valuable support for the sustainable development of marine ecology.

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Section: Introductionmentioning

confidence: 99%

A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data

Bu,

Liu,

Liu

et al. 2023

Sustainability

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Microcystin-RR is a biliary toxin selective for neonatal cholangiocytes

Gupta,

Chen,

Wells

2023

Preprint

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BACKGROUND AND AIMS: Biliary atresia is a fibrosing cholangiopathy affecting neonates that is thought to be caused by a prenatal environmental insult to the bile duct. Biliatresone, a plant toxin with an α-methylene ketone group, was previously implicated in toxin-induced biliary atresia in Australian livestock, but is found in a limited location and is highly unlikely to be a significant human toxin. We hypothesized that other molecules with α-methylene ketone groups, some with the potential for significant human exposure, might also be biliary toxins. APPROACH AND RESULTS: We focused on the family of microcystins, cyclic peptide toxins from blue-green algae that have an α-methylene ketone group and are found worldwide, particularly during harmful algal blooms. We found that microcystin-RR, but not 6 other microcystins, caused damage to cell spheroids made using cholangiocytes isolated from 2-3-day-old mice, but not from adult mice. We also found that microcystin-RR caused occlusion of extrahepatic bile duct explants from 2-day-old mice, but not 18-day-old mice. Microcystin-RR caused elevated reactive oxygen species in neonatal cholangiocytes, and treatment with N-acetyl cysteine partially prevented microcystin-RR-induced lumen closure, suggesting a role for redox homeostasis in its mechanism of action. CONCLUSIONS: This study highlights the potential for environmental toxins to cause neonatal biliary disease and identifies microcystin-RR acting via increased redox stress as a possible neonatal bile duct toxin.

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A Case-Control Study of the Association BetweenKarenia Brevis(Red Tide) and Biliary Atresia

Cited by 2 publications

References 42 publications

A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data

A Harmful Algal Bloom Detection Model Combining Moderate Resolution Imaging Spectroradiometer Multi-Factor and Meteorological Heterogeneous Data

Microcystin-RR is a biliary toxin selective for neonatal cholangiocytes

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