PURPOSE: The purpose of this project was to explore the parental experience of making a "do not resuscitate" (DNR) decision for their child who is or was cared for in a pediatric intensive care unit in Taiwan. METHODS: A descriptive qualitative study was conducted following parental signing of a standard hospital DNR form on behalf of their critically ill child. Sixteen Taiwanese parents of 11 children aged 1 month to 18 years were interviewed. Interviews were recorded, transcribed, analyzed and sorted into themes by the sole interviewer plus other researchers. RESULTS: Three major themes were identified: (a) "convincing points to sign", (b) "feelings immediately after signing", and (c) "postsigning relief or regret". Feelings following signing the DNR form were mixed and included "frustration", "guilt", and "conflicting hope". Parents adjusted their attitudes to thoughts such as "I have done my best," and "the child's life is beyond my control." Some parents whose child had died before the time of the interview expressed among other things "regret not having enough time to be with and talk to my child". CONCLUSION: Open family visiting hours plus staff sensitivity and communication skills training are needed. To help parents with this difficult signing process, nurses and other professionals in the pediatric intensive care unit need education on initiating the conversation, guiding the parents in expressing their fears, and providing continuing support to parents and children throughout the child's end of life process.
Adolescent ; Adult ; Child ; Child, Preschool ; *Decision Making ; Female ; Humans ; Infant ; Intensive Care Units, Pediatric ; Male ; Middle Aged ; Palliative Care/*psychology ; Parents/*psychology ; *Professional-Family Relations ; Qualitative Research ; Resuscitation Orders/*psychology ; Taiwan ; Young Adult

Numerous studies have indicated that organic fertilizers (OFer) might contain heavy metals (HMs) that present health risks to organic farmers (OFar). This study compared the concentrations of six HMs (Zn, Ni, Cd, Cu, Pb, Cr) in the blood of two distinct groups of farmers: 30 OFar from a designated organic area in eastern Taiwan, and 74 conventional farmers (CFar) from neighboring non-organic designated regions. The findings revealed that the OFar exhibited higher levels of Zn (1202.70 ± 188.74 μg/L), Cr (0.20 ± 0.09 μg/L), and Ni (2.14 ± 1.48 μg/L) in their blood compared to the CFar (988.40 ± 163.16 μg/L, 0.18 ± 0.15 μg/L, and 0.77 ± 1.23 μg/L), respectively. The disparities in Zn, Cr, and Ni levels were measured at 214.3 μg/L, 0.02 μg/L, and 1.37 μg/L, respectively. Furthermore, among the OFar, those who utilized green manures (GM) displayed significantly elevated blood levels of Zn (1279.93 ± 156.30 μg/L), Cr (0.24 ± 0.11 μg/L), and Ni (1.94 ± 1.38 μg/L) compared to individuals who exclusively employed chemical fertilizers (CFer) (975.42 ± 165.35 μg/L, 0.19 ± 0.16 μg/L, and 0.74 ± 1.20 μg/L), respectively. The differences in Zn, Cr, and Ni levels were measured at 304.51 μg/L, 0.05 μg/L, and 1.20 μg/L, respectively. As a result, OFar should be careful in choosing OFer and avoid those that may have heavy metal contamination.

Objective: aaWilson’s disease (WD) is a rare genetic disorder of copper metabolism, and longitudinal follow-up studies are limited. We performed a retrospective analysis to determine the clinical characteristics and long-term outcomes in a large WD cohort. Methods: aaMedical records of WD patients diagnosed from 2006–2021 at National Taiwan University Hospital were retrospectively evaluated for clinical presentations, neuroimages, genetic information, and follow-up outcomes. Results: aaThe present study enrolled 123 WD patients (mean follow-up: 11.12 ± 7.41 years), including 74 patients (60.2%) with hepatic features and 49 patients (39.8%) with predominantly neuropsychiatric symptoms. Compared to the hepatic group, the neuropsychiatric group exhibited more Kayser-Fleischer rings (77.6% vs. 41.9%, p < 0.01), lower serum ceruloplasmin levels (4.9 ± 3.9 vs. 6.3 ± 3.9 mg/dL, p < 0.01), smaller total brain and subcortical gray matter volumes (p < 0.0001), and worse functional outcomes during follow-up (p = 0.0003). Among patients with available DNA samples (n = 59), the most common mutations were p.R778L (allelic frequency of 22.03%) followed by p.P992L (11.86%) and p.T935M (9.32%). Patients with at least one allele of p.R778L had a younger onset age (p = 0.04), lower ceruloplasmin levels (p < 0.01), lower serum copper levels (p = 0.03), higher percentage of the hepatic form (p = 0.03), and a better functional outcome during follow-up (p = 0.0012) compared to patients with other genetic variations. Conclusion aaThe distinct clinical characteristics and long-term outcomes of patients in our cohort support the ethnic differences regarding the mutational spectrum and clinical presentations in WD.

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.

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.