Objective: Hypoactivity in the reward system among patients with attention deficit hyperactivity disorder (ADHD) is a well-known phenomenon. Whether the activity in the reward pathway is related to harm avoidance, such as in sensitivity to punishment, is unclear. Evidence regarding the potential difference between ADHD patients and controls in terms of this association is scarce. Methods: Event-related functional magnetic resonance imaging was conducted on subjects performing the Iowa gambling test. Fourteen adults with ADHD and 14 controls were enrolled in the study. Results: Harm avoidance was found to be positively correlated with the activities of the bilateral orbitofrontal cortex and right insula in individuals with ADHD. A group difference was also confirmed. Conclusion Understanding the roles of harm avoidance and brain activation during risk tasks is important.

INTRODUCTIONThe exposed section of a traditional nasogastric (NG) tube can interfere with patients' social activities and thereby result in distress. This study was conducted to evaluate the feasibility and safety of a novel two-piece NG tube for patients with dysphagia.

METHODSTen patients with dysphagia were recruited between November 2011 and May 2012. Patients who were unconscious or in critical condition, had a traditional NG tube < 50 cm or > 60 cm in fixed length, or were unable to follow instructions or sign consent forms were excluded. The two-piece NG tube, which was placed in the patients for one week, comprised a removable external tube that can be joined to an internal tube via a T-connector, which was placed close to the naris. Events related to safety (e.g. nasal pressure sores, number of unplanned extubation, displacement and spontaneous migration of the NG tube, other unpredictable injuries) and effectiveness (e.g. liquid food spills, tube obstruction, perfusion rate, other adverse circumstances) were assessed daily.

RESULTSAll patients received feeding without complication using the two-piece NG tube and none experienced premature removal of the tube. No serious NG tube complications or malfunctions were observed.

CONCLUSIONThe results of this study indicate that the two-piece NG feeding tube is a feasible option for patients with dysphagia. Future improvements to the connector may help enhance its performance. A rigorous randomised controlled trial to examine the effects of the two-piece NG tube on patients' quality of life and quality of medical care is being planned.

Aged ; Aged, 80 and over ; Deglutition Disorders ; therapy ; Enteral Nutrition ; instrumentation ; methods ; Equipment Design ; Female ; Humans ; Intubation, Gastrointestinal ; adverse effects ; methods ; Male ; Middle Aged

Objective: Bipolar disorder (BD) is characterized by the poor sleep quality. Whether the striatal dopamine transporter (DAT) availability is related to sleep quality among patients with BD is unclear. Methods: Fifty-three euthymic patients with BD (24 BD-I and 29 BD-II) and sixty-eight healthy controls were enrolled. The Chinese Version of the Pittsburgh Sleep Quality Index (PSQI) was used, and the availability of DAT was assessed by single-photon emission computed tomography (SPECT) using [99mTc] TRODAT-1. Results: The sleep disturbance component of the PSQI was significantly associated with the level of DAT availability among patients with BD. Conclusion The striatal dopaminergic activity that contributes to resilience to adversity was associated with sleep pattern among patients with BD.

Background/Aims: Laryngeal symptoms are largely treated with empiric proton pump inhibitor (PPI) therapy if no apparent pathology shown on ear, nose, and throat evaluation and reflux-related etiologies are suspected. However, treatment response remains unsatisfactory. This study aimed to investigate the clinical and physiological characteristics of patients with PPI-refractory laryngeal symptoms. Methods: Patients with persistent laryngeal symptoms despite PPI treatment for ≥ 8 weeks were recruited. A multidisciplinary evaluationcomprising validated questionnaires for laryngeal symptoms (reflux symptom index [RSI]), gastroesophageal reflux disease symptoms, psychological comorbidity (5-item brief symptom rating scale [BSRS-5]) and sleep disturbance (Pittsburgh sleep quality index [PSQI]), esophagogastroduodenoscopy, ambulatory impedance-pH monitoring, and high-resolution impedance manometry were performed.Healthy asymptomatic individuals were also recruited for comparison of psychological morbidity and sleep disturbances. Results: Ninety-seven adult patients and 48 healthy volunteers were analyzed. The patients had markedly higher prevalence of psychological distress (52.6% vs 2.1%, P < 0.001) and sleep disturbance (82.5% vs 37.5%, P < 0.001) than the healthy volunteers. There were significant correlations between RSI and BSRS-5 scores, and between RSI and PSQI scores (r = 0.26, P = 0.010, and r = 0.29, P = 0.004, respectively). Fifty-eight patients had concurrent gastroesophageal reflux disease symptoms. They had more prominent sleep disturbances (89.7% vs 71.8%, P < 0.001) than those with laryngeal symptoms alone but similar reflux profiles and esophageal motility. Conclusions PPI-refractory laryngeal symptoms are mostly associated with psychological comorbidities and sleep disturbances. Recognition of these psychosocial comorbidities may help optimize management in these 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.

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.

BACKGROUNDThe change of anaerobic exercise abilities during and after a high-altitude expedition or hypoxic exposure is not well studied. To evaluate the effects of an extreme-altitude expedition on anaerobic performance, the 10-second supramaximal test and endocrine hormones were evaluated before and after an expedition to Peak Lenin.

METHODSFour subjects (3 male and 1 female, age (30.5 +/- 16.5) years) were recruited into the study. Three sets of tests were performed, including a basic test at sea level and 20 days before first arrival at the base camp (3600 m), a middle test done at day after returning from the summit to the base camp and the post test at the 10th day after return to the sea level. Both the supramaximal test, performed by a cycle ergometer, and body composition, performed by bioelectrical impedance analysis, were completed before the basic test and post test. The endocrine hormones including cortisol, growth hormone, testosterone, noradrenaline, adrenaline, dopamine, glucagon and beta-endorphin were measured at all tests.

RESULTSComparing the conditions before and after the expedition, the body measurement parameters were decreased after the expedition, i.e., body weight (-4.22%, P < 0.05), fat-free mass (-2.09%, P < 0.01) and body fat (-8.95%, P = 0.172). The peak power relative/body weight ratio (PP/BW) was similar ((9.70 +/- 1.97) vs (9.11 +/- 1.80) W/kg, P = 0.093), while mean power/body weight ratio (MP/BW) was reduced significantly after the expedition ((9.14 +/- 1.77) vs (8.33 +/- 1.74) W/kg, P < 0.05). Peak power/fat-free mass (PP/FFM), mean power/fat-free mass (MP/FFM) and fatigue index (FI) were significantly lower after the expedition (PP/FFM: (11.95 +/- 1.71) vs (10.99 +/- 1.59) W/kg, P < 0.05; MP/FFM: (11.26 +/- 1.50) vs (10.04 +/- 1.55) W/kg, P < 0.005; FI (85.55 +/- 4.17)% vs (77.25 +/- 4.40)%, P < 0.05). Hormone assays showed a significant increase of noradrenaline (basic vs middle, P < 0.05) as well as decrease of adrenaline (P < 0.05). Meanwhile, a trend towards an increase in dopamine (basic vs middle) and a decrease of beta-endorphin (basic vs post) were also noted.

CONCLUSIONSThese results suggested that an expedition to an extreme altitude may have negative effects on anaerobic performance. It showed that a significant increase of noradrenaline (basic vs middle) as well as decrease of adrenaline after the expedition to Peak Lenin had occurred. The real physiological significance needs to be further investigated.

Adaptation, Physiological ; physiology ; Adolescent ; Adult ; Altitude ; Anaerobic Threshold ; physiology ; Dopamine ; blood ; Epinephrine ; blood ; Exercise Test ; Female ; Glucagon ; blood ; Growth Hormone ; blood ; Humans ; Hydrocortisone ; blood ; Male ; Middle Aged ; Norepinephrine ; blood ; Testosterone ; blood ; Young Adult ; beta-Endorphin ; blood