- 作者列表："Yi KH","Choi YJ","Cong L","Lee KL","Hu KS","Kim HJ
:The aim of this study was to elucidate the distribution of the accessory nerve within the sternocleidomastoid muscle (SCM) to aid identifying the optimum sites for botulinum neurotoxin (BoNT) injections and applying chemical neurolysis. Thirty SCM specimens from 15 Korean cadavers were used in this study. Sihler's staining was applied to 10 of the SCM specimens. Transverse lines were drawn in 20 sections to divide the SCM into 10 divisions vertically, and a vertical line was drawn into the medial and lateral halves from the mastoid process to the sternoclavicular joint. The most densely innervated areas were 5/10-6/10 and 6/10-7/10 along the lateral and medial parts of the muscle, respectively. We suggest injecting BoNT in the medial region 6/10-7/10 along the SCM prior to injecting in the lateral region 5/10-6/10 along the muscle to ensure safe and effective treatment. Clin. Anat. 33:192-198, 2020. © 2019 Wiley Periodicals, Inc.
: 本研究的目的是阐明胸锁乳突肌 (SCM) 内副神经的分布，以帮助确定肉毒杆菌神经毒素 (BoNT) 注射和应用化学神经松解的最佳部位。本研究使用了来自15具韩国尸体的30个SCM标本。对10个SCM标本进行Sihler染色。在20个切片中绘制横向线，将SCM垂直分成10个分区，并从乳突到胸锁关节的内侧和外侧一半绘制垂直线。最密集的神经支配区域沿肌肉的外侧和内侧分别为5/10-6/10和6/10-7/10。我们建议在沿肌肉向外侧区6/10-7/10注射前沿SCM向内侧区5/10-6/10注射BoNT，以确保安全有效的治疗。克林。Anat.33:192-198、2020。©2019威利期刊公司
METHODS:BACKGROUND:Whether vitamin D receptor (VDR) genetic variants influence individual susceptibility to neurodegenerative disorders remains controversial. AIMS:This meta-analysis was conducted to analyze correlations of VDR genetic variants with two types of most common neurodegenerative disorders, Parkinson's disease (PD) and Alzheimer's disease (AD). METHODS:Systematic literature research of PubMed and Embase was performed to identify eligible articles. Q test and I2 statistic were employed to decide whether pooled analyses would be performed with random-effect models (REMs) or fixed-effect models (FEMs). All statistical analyses were conducted with Review Manager. RESULTS:Totally sixteen studies were enrolled for analyses. Among these eligible studies, ten studies were about PD (2356 cases and 2815 controls) and six studies were about AD (1256 cases and 1205 controls). Pooled overall analyses suggested that VDR rs7975232 (additive model: p = 0.03, OR = 1.19, 95% CI 1.01-1.39) and rs2228570 (recessive model: p < 0.008, OR = 1.26, 95% CI 1.06-1.50; allele model: p < 0.001, OR = 0.80, 95% CI 0.71-0.91) variants were significantly correlated with PD, and VDR rs731236 (dominant model: p = 0.003, OR = 0.70, 95% CI 0.56-0.89; additive model: p = 0.02, OR = 1.32, 95% CI 1.06-1.66; allele model: p = 0.02, OR = 0.82, 95% CI 0.69-0.96) variant was significantly correlated with AD. Further subgroup analyses by ethnicity revealed that the positive results were mainly driven by the Asians, whereas no significant associations were observed in Caucasians. CONCLUSION:Our meta-analysis suggested that VDR rs7975232 and rs2228570 variants might serve as genetic biomarkers of PD, whereas VDR rs731236 variant might serve as a genetic biomarker of AD.
METHODS::Elderly people can be provided with safer and more independent living by the early detection of abnormalities in their performing actions and the frequent assessment of the quality of their motion. Low-cost depth sensing is one of the emerging technologies that can be used for unobtrusive and inexpensive motion abnormality detection and quality assessment. In this study, we develop and evaluate vision-based methods to detect and assess neuromusculoskeletal disorders manifested in common daily activities using three-dimensional skeletal data provided by the SDK of a depth camera (e.g., MS Kinect and Asus Xtion PRO). The proposed methods are based on extracting medically -justified features to compose a simple descriptor. Thereafter, a probabilistic normalcy model is trained on normal motion patterns. For abnormality detection, a test sequence is classified as either normal or abnormal based on its likelihood, which is calculated from the trained normalcy model. For motion quality assessment, a linear regression model is built using the proposed descriptor in order to quantitatively assess the motion quality. The proposed methods were evaluated on four common daily actions-sit to stand, stand to sit, flat walk, and gait on stairs-from two datasets, a publicly released dataset and our dataset that was collected in a clinic from 32 patients suffering from different neuromusculoskeletal disorders and 11 healthy individuals. Experimental results demonstrate promising results, which is a step toward having convenient in-home automatic health care services.
METHODS:BACKGROUND:Parkinson's disease (PD) is responsible for significant changes in body composition. AIMS:We aimed to test the association between PD severity and fat distribution patterns, and to investigate the potential modifier effect of nutritional status in this association. METHODS:We enrolled 195 PD subjects consecutively admitted to a university geriatric day hospital. All participants underwent comprehensive clinical evaluation, including assessment of total and regional body composition (dual-energy X-ray absorptiometry, DXA), body mass index, nutritional status (Mini-Nutritional Assessment, MNA), motor disease severity (UPDRS III), comorbidities, and pharmacotherapy. RESULTS:The fully adjusted linear regression model showed a negative association between UPDRS III and total body fat in kg and percentage (respectively, B - 0.79; 95% CI - 1.54 to - 0.05 and B - 0.55; 95% CI - 1.04 to - 0.05), percentage android fat (B - 1.07; 95% CI - 1.75 to - 0.39), trunk-leg fat ratio (B - 0.02; 95% CI - 0.04 to - 0.01), trunk-limb fat ratio (B - 0.01; 95% CI - 0.06 to - 0.01) and android-gynoid fat ratio (B - 0.01; 95% CI - 0.03 to - 0.01). After stratification by MNA score, all the parameters of android-like fat distribution resulted negatively associated (p < 0.001 for all) with UPDRS III, but only among subjects with a MNA < 23.5 (risk of malnutrition or malnutrition). CONCLUSION:We found a negative association between severity of motor impairment and total fat mass in PD, more specific with respect to an android pattern of fat distribution. This association seems to be driven by nutritional status, and is significant only among patients at risk of malnutrition or with overt malnutrition.