The zebrafish has emerged as a powerful model organism in life science owing to its remarkable biological characteristics and wide-ranging applications. This review provides a comprehensive overview of the recent advancements in research on zebrafish within the field of environmental toxicology, highlighting specific studies where this species was used to investigate various pollutants to elucidate their impacts and underlying mechanisms. The findings of these studies underscore the significant potential of zebrafish as a model to gain crucial insights into the ecological consequences of environmental contamination and toxicity pathways. By incorporating cutting-edge technologies such as artificial intelligence (AI), high-throughput screening, and omics approaches, the use of zebrafish as a model organism is poised to significantly accelerate toxicological investigations, promote environmental conservation efforts, contribute to safeguarding human health, and advance sustainable development objectives.
Animals ; Zebrafish ; Ecotoxicology/methods* ; Artificial Intelligence ; Humans ; Models, Animal ; Environmental Pollutants/toxicity*

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Background: and Purpose Syncope is characterized by the temporary loss of consciousness and is commonly associated with migraine. However, the genetic factors that contribute to this association are not well understood. This study investigated the specific genetic loci that make patients with migraine more susceptible to syncope as well as the genetic factors contributing to syncope and migraine comorbidity in a Han Chinese population in Taiwan. Methods: A genome-wide association study was applied to 1,724 patients with migraine who visited a tertiary hospital in Taiwan. The patients were genotyped using the Affymetrix Axiom Genome-Wide TWB 2.0 array and categorized into the following subgroups based on migraine type: episodic migraine, chronic migraine, migraine with aura, and migraine without aura. Multivariate regression analyses were used to assess the relationships between specific single-nucleotide polymorphisms (SNPs) and the clinical characteristics in patients with syncope and migraine comorbidity. Results: In patients with migraine, SNPs were observed to be associated with syncope. In particular, the rs797384 SNP located in the intron region of LOC102724945 was associated with syncope in all patients with migraine. Additionally, four SNPs associated with syncope susceptibility were detected in the nonmigraine control group, and these SNPs differed from those in the migraine group, suggesting distinct underlying mechanisms. Furthermore, the rs797384 variant in the intron region of LOC102724945 was associated with the score on the Beck Depression Inventory. Conclusions The novel genetic loci identified in this study will improve our understanding of the genetic basis of syncope and migraine comorbidity.

Background: and Purpose Syncope is characterized by the temporary loss of consciousness and is commonly associated with migraine. However, the genetic factors that contribute to this association are not well understood. This study investigated the specific genetic loci that make patients with migraine more susceptible to syncope as well as the genetic factors contributing to syncope and migraine comorbidity in a Han Chinese population in Taiwan. Methods: A genome-wide association study was applied to 1,724 patients with migraine who visited a tertiary hospital in Taiwan. The patients were genotyped using the Affymetrix Axiom Genome-Wide TWB 2.0 array and categorized into the following subgroups based on migraine type: episodic migraine, chronic migraine, migraine with aura, and migraine without aura. Multivariate regression analyses were used to assess the relationships between specific single-nucleotide polymorphisms (SNPs) and the clinical characteristics in patients with syncope and migraine comorbidity. Results: In patients with migraine, SNPs were observed to be associated with syncope. In particular, the rs797384 SNP located in the intron region of LOC102724945 was associated with syncope in all patients with migraine. Additionally, four SNPs associated with syncope susceptibility were detected in the nonmigraine control group, and these SNPs differed from those in the migraine group, suggesting distinct underlying mechanisms. Furthermore, the rs797384 variant in the intron region of LOC102724945 was associated with the score on the Beck Depression Inventory. Conclusions The novel genetic loci identified in this study will improve our understanding of the genetic basis of syncope and migraine comorbidity.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Background: and Purpose Syncope is characterized by the temporary loss of consciousness and is commonly associated with migraine. However, the genetic factors that contribute to this association are not well understood. This study investigated the specific genetic loci that make patients with migraine more susceptible to syncope as well as the genetic factors contributing to syncope and migraine comorbidity in a Han Chinese population in Taiwan. Methods: A genome-wide association study was applied to 1,724 patients with migraine who visited a tertiary hospital in Taiwan. The patients were genotyped using the Affymetrix Axiom Genome-Wide TWB 2.0 array and categorized into the following subgroups based on migraine type: episodic migraine, chronic migraine, migraine with aura, and migraine without aura. Multivariate regression analyses were used to assess the relationships between specific single-nucleotide polymorphisms (SNPs) and the clinical characteristics in patients with syncope and migraine comorbidity. Results: In patients with migraine, SNPs were observed to be associated with syncope. In particular, the rs797384 SNP located in the intron region of LOC102724945 was associated with syncope in all patients with migraine. Additionally, four SNPs associated with syncope susceptibility were detected in the nonmigraine control group, and these SNPs differed from those in the migraine group, suggesting distinct underlying mechanisms. Furthermore, the rs797384 variant in the intron region of LOC102724945 was associated with the score on the Beck Depression Inventory. Conclusions The novel genetic loci identified in this study will improve our understanding of the genetic basis of syncope and migraine comorbidity.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.