1.An animal model of severe acute respiratory distress syndrome for translational research
Kuo‑An CHU ; Chia‑Yu LAI ; Yu‑Hui CHEN ; Fu‑Hsien KUO ; I.‑Yuan CHEN ; You‑Cheng JIANG ; Ya‑Ling LIU ; Tsui‑Ling KO ; Yu‑Show FU
Laboratory Animal Research 2025;41(1):81-92
Background:
Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency.
Results:
In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation (SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen (PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide (PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7.
Conclusions
This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.
2.An animal model of severe acute respiratory distress syndrome for translational research
Kuo‑An CHU ; Chia‑Yu LAI ; Yu‑Hui CHEN ; Fu‑Hsien KUO ; I.‑Yuan CHEN ; You‑Cheng JIANG ; Ya‑Ling LIU ; Tsui‑Ling KO ; Yu‑Show FU
Laboratory Animal Research 2025;41(1):81-92
Background:
Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency.
Results:
In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation (SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen (PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide (PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7.
Conclusions
This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.
3.An animal model of severe acute respiratory distress syndrome for translational research
Kuo‑An CHU ; Chia‑Yu LAI ; Yu‑Hui CHEN ; Fu‑Hsien KUO ; I.‑Yuan CHEN ; You‑Cheng JIANG ; Ya‑Ling LIU ; Tsui‑Ling KO ; Yu‑Show FU
Laboratory Animal Research 2025;41(1):81-92
Background:
Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency.
Results:
In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation (SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen (PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide (PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7.
Conclusions
This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.
4.An animal model of severe acute respiratory distress syndrome for translational research
Kuo‑An CHU ; Chia‑Yu LAI ; Yu‑Hui CHEN ; Fu‑Hsien KUO ; I.‑Yuan CHEN ; You‑Cheng JIANG ; Ya‑Ling LIU ; Tsui‑Ling KO ; Yu‑Show FU
Laboratory Animal Research 2025;41(1):81-92
Background:
Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency.
Results:
In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation (SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen (PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide (PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7.
Conclusions
This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.
5.An animal model of severe acute respiratory distress syndrome for translational research
Kuo‑An CHU ; Chia‑Yu LAI ; Yu‑Hui CHEN ; Fu‑Hsien KUO ; I.‑Yuan CHEN ; You‑Cheng JIANG ; Ya‑Ling LIU ; Tsui‑Ling KO ; Yu‑Show FU
Laboratory Animal Research 2025;41(1):81-92
Background:
Despite the fact that an increasing number of studies have focused on developing therapies for acute lung injury, managing acute respiratory distress syndrome (ARDS) remains a challenge in intensive care medicine.Whether the pathology of animal models with acute lung injury in prior studies differed from clinical symptoms of ARDS, resulting in questionable management for human ARDS. To evaluate precisely the therapeutic effect of trans‑ planted stem cells or medications on acute lung injury, we developed an animal model of severe ARDS with lower lung function, capable of keeping the experimental animals survive with consistent reproducibility. Establishing this animal model could help develop the treatment of ARDS with higher efficiency.
Results:
In this approach, we intratracheally delivered bleomycin (BLM, 5 mg/rat) into rats’ left trachea via a needle connected with polyethylene tube, and simultaneously rotated the rats to the left side by 60 degrees. Within sevendays after the injury, we found that arterial blood oxygen saturation (SpO2 ) significantly decreased to 83.7%, partial pressure of arterial oxygen (PaO2 ) markedly reduced to 65.3 mmHg, partial pressure of arterial carbon dioxide (PaCO2 )amplified to 49.2 mmHg, and the respiratory rate increased over time. Morphologically, the surface of the left lung appeared uneven on Day 1, the alveoli of the left lung disappeared on Day 2, and the left lung shrank on Day 7. A his‑ tological examination revealed that considerable cell infiltration began on Day 1 and lasted until Day 7, with a larger area of cell infiltration. Serum levels of IL-5, IL-6, IFN-γ, MCP-1, MIP-2, G-CSF, and TNF-α substantially rose on Day 7.
Conclusions
This modified approach for BLM-induced lung injury provided a severe, stable, and one-sided (left-lobe) ARDS animal model with consistent reproducibility. The physiological symptoms observed in this severe ARDS animal model are entirely consistent with the characteristics of clinical ARDS. The establishment of this ARDS animal model could help develop treatment for ARDS.
6.Atrial fibrillation detection using millimeter-wave radar
Hengji ZHOU ; Yihan YANG ; Yuanhui HU ; Yuguang CHU ; Xintian SHOU ; Yaping YOU ; Wenjing XUE ; Shaowei FAN ; Yong WANG ; Huiliang SHEN
Chinese Journal of Medical Physics 2024;41(1):81-87
A novel technology is proposed for non-contact and real-time detection of atrial fibrillation using millimeter-wave radar.A 60 GHz PCR millimeter wave radar is used to continuously detect the chest echo signal of the subject.After signal acquisition,I-Q signal is generated through I-Q demodulation,and the signal phase information is extracted using effective points phase trend evaluation for obtaining the signals from oscillations in the chest wall,from which the respiratory signals and cardiac signals are extracted through digital filtering for the analysis of cardiac movement.Whether the atrial fibrillation occurs or not is determined by the characteristics of atrial fibrillation wave in the time domain.The effective points phase trend evaluation for extracting more accurate signal phase information and the time-domain method for real-time atrial fibrillation detection are the innovations of the study.The experimental results show that the proposed method achieves a detection accuracy of 99.2%in clinic.
7.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.
8.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.
9.Dawn of CAR-T cell therapy in autoimmune diseases
Yuxin LIU ; Minghao DONG ; Yunhui CHU ; Luoqi ZHOU ; Yunfan YOU ; Xiaowei PANG ; Sheng YANG ; Luyang ZHANG ; Lian CHEN ; Lifang ZHU ; Jun XIAO ; Wei WANG ; Chuan QIN ; Daishi TIAN
Chinese Medical Journal 2024;137(10):1140-1150
Chimeric antigen receptor (CAR)-T cell therapy has achieved remarkable success in the treatment of hematological malignancies. Based on the immunomodulatory capability of CAR-T cells, efforts have turned toward exploring their potential in treating autoimmune diseases. Bibliometric analysis of 210 records from 128 academic journals published by 372 institutions in 40 countries/regions indicates a growing number of publications on CAR-T therapy for autoimmune diseases, covering a range of subtypes such as systemic lupus erythematosus, multiple sclerosis, among others. CAR-T therapy holds promise in mitigating several shortcomings, including the indiscriminate suppression of the immune system by traditional immunosuppressants, and non-sustaining therapeutic levels of monoclonal antibodies due to inherent pharmacokinetic constraints. By persisting and proliferating in vivo, CAR-T cells can offer a tailored and precise therapeutics. This paper reviewed preclinical experiments and clinical trials involving CAR-T and CAR-related therapies in various autoimmune diseases, incorporating innovations well-studied in the field of hematological tumors, aiming to explore a safe and effective therapeutic option for relapsed/refractory autoimmune diseases.
10.The Quantitative Evaluation of Automatic Segmentation in Lumbar Magnetic Resonance Images
Yao-Wen LIANG ; Yu-Ting FANG ; Ting-Chun LIN ; Cheng-Ru YANG ; Chih-Chang CHANG ; Hsuan-Kan CHANG ; Chin-Chu KO ; Tsung-Hsi TU ; Li-Yu FAY ; Jau-Ching WU ; Wen-Cheng HUANG ; Hsiang-Wei HU ; You-Yin CHEN ; Chao-Hung KUO
Neurospine 2024;21(2):665-675
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.

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