Use of tissue chromogranin A as chronic and acute stress marker in fish

Herrera, Marcelino; Salamanca, N.; Ferrer, J.F.; Rosa, Ignacio de la

doi:10.1016/j.aquaculture.2022.739120

Cited by 3 publications

(1 citation statement)

References 77 publications

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“…Its value is normalized to a range of 0 to 1 by summing each weighted and normalized stress value. For a certain fish size, the total fish stress value S t total is calculated as shown in Equation (10).…”

Section: Health Level Assessment Modeling 231 Health Level Calculationsmentioning

confidence: 99%

Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions

Zhang,

Chen,

Feng

et al. 2023

Sensors

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(1) Background: At present, physiological stress detection technology is a critical means for precisely evaluating the comprehensive health status of live fish. However, the commonly used biochemical tests are invasive and time-consuming and cannot simultaneously monitor and dynamically evaluate multiple stress levels in fish and accurately classify their health levels. The purpose of this study is to deploy wearable bioelectrical impedance analysis (WBIA) sensors on fish skin to construct a deep learning-based stress dynamic evaluation model for precisely estimating their accurate health status. (2) Methods: The correlation of fish (turbot) muscle nutrients and their stress indicators are calculated using grey relation analysis (GRA) for allocating the weight of the stress factors. Next, WBIA features are sieved using the maximum information coefficient (MIC) in stress trend evaluation modeling, which is closely related to the key stress factors. Afterward, a convolutional neural network (CNN) is utilized to obtain the features of the WBIA signals. Then, the long short-term memory (LSTM) method learns the stress trends with residual rectification using bidirectional gated recurrent units (BiGRUs). Furthermore, the Z-shaped fuzzy function can accurately classify the fish health status by the total evaluated stress values. (3) Results: The proposed CNN-LSTM-BiGRU-based stress evaluation model shows superior accuracy compared to the other machine learning models (CNN-LSTM, CNN-GRU, LSTM, GRU, SVR, and BP) based on the MAPE, MAE, and RMSE. Moreover, the fish health classification under waterless and low-temperature conditions is thoroughly verified. High accuracy is proven by the classification validation criterion (accuracy, F1 score, precision, and recall). (4) Conclusions: the proposed health evaluation technology can precisely monitor and track the health status of live fish and provides an effective technical reference for the field of live fish vital sign detection.

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Section: Health Level Assessment Modeling 231 Health Level Calculationsmentioning

confidence: 99%

Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions

Zhang,

Chen,

Feng

et al. 2023

Sensors

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Pikeperch (Sander lucioperca) adaptive or maladaptive reactions to environmental stressors؟ a review

Pourhosein-Sarameh,

Falahatkar

2024

Aquacult Int

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Can portable glucose and lactate meters be a useful tool in quantifying stress of juvenile Chinook salmon?

Vaage

Liss

Fischer

et al. 2023

Conservation Physiology

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Blood plasma analyses can provide researchers, aquaculture facilities and fisheries managers with valuable insights into the physiological state and welfare of fish. For example, glucose and lactate are part of the secondary stress response system, and elevated concentrations are indicators of stress. However, analysing blood plasma in the field can be logistically difficult and typically involves sample storage and transport to quantify concentrations in a laboratory setting. Portable glucose and lactate meters offer an alternative to laboratory assays and have shown to be relatively accurate in fish, but these tools have only been validated for a few fish species. The objective of this study was to investigate if portable meters could be reliably used in Chinook salmon (Oncorhynchus tshawytscha). As part of a larger stress response study, juvenile Chinook salmon (157 ± 17 mm fork length [mean ± standard deviation; SD]) were exposed to stress-inducing treatments and sampled for blood. Laboratory reference glucose concentrations (milligrams per deciliter; mg/dl; n = 70) were positively correlated with the Accu-Check Aviva meter (Roche Diagnostics, Indianapolis, IN) measurements (R2 = 0.79), although glucose values were 1.21 ± 0.21 (mean ± SD) times higher in the laboratory than with the portable meter. Lactate concentrations (milliMolar; mM; n = 52) of the laboratory reference were also positively correlated (R2 = 0.76) with the Lactate Plus meter (Nova Biomedical, Waltham, MA) and were 2.55 ± 0.50 times higher than portable meter. Our results indicate both meters could be used to measure relative glucose and lactate concentrations in Chinook salmon and provide fisheries professionals with a valuable tool, particularly in remote field settings.

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Use of tissue chromogranin A as chronic and acute stress marker in fish

Cited by 3 publications

References 77 publications

Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions

Wearable Bioimpedance-Based Deep Learning Techniques for Live Fish Health Assessment under Waterless and Low-Temperature Conditions

Pikeperch (Sander lucioperca) adaptive or maladaptive reactions to environmental stressors؟ a review

Can portable glucose and lactate meters be a useful tool in quantifying stress of juvenile Chinook salmon?

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