Orientation and mobility outcome measures

Despite orientation and mobility (O&M) being a significant factor determining quality of life of people with low vision or blindness, there are no gold standard measures or agreement on how to measure O&M performance. In the first part of this systematic review, an inventory of O&M outcome measures used by recent studies to assess the performance of orientation and/or mobility of adults with vision impairment (low vision and blindness) is presented. A wide variety of O&M outcome measures have been implemented in different fields of study, such as epidemiologic research and interventional studies evaluating training, assistive technology, vision rehabilitation and vision restoration. The most frequent aspect of outcome measures is efficiency such as time, distance, speed and percentage of preferred walking speed, followed by obstacle contacts and avoidance, and dis/orientation and veering. Other less commonly used aspects are target identification, safety and social interaction and self-reported outcome measures. Some studies employ sophisticated equipment to capture and analyse O&M performance in a laboratory setting, while others carry out their assessment in real-world indoor or outdoor environments. In the second part of this review, the appropriateness of implementing the identified outcome measures to assess O&M performance in clinical and functional O&M practice is evaluated. Nearly a half of these outcome measures meet all four criteria of face validity (either clinical or functional), responsiveness, reliability and feasibility and have the potential to be implemented in clinical or functional O&M practice. The findings of this review confirm the complicated and dynamic nature of O&M. Multiple measures are required in any evaluation of O&M performance to facilitate holistic assessment of O&M abilities and limitations of each individual.     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.     

低密度脂蛋白在2型糖尿病骨质疏松中的作用研究进展

2型糖尿病及骨质疏松已成为我国最主要的慢性代谢性疾病。2型糖尿病常伴有血脂紊乱，常表现为低密度脂蛋白升高，而越来越多的研究表明血脂通过不同的方式影响骨代谢，其机制可能是通过抑制骨髓间充质干细胞成骨分化，通过RANK/RNAKL/OPG信号通路及炎症反应调节破骨细胞等方式调节骨代谢。     

Tedium vitae in stroke survivors: a comparative cross-sectional study

Objectives: Suicide is best studied by deconstructing the psychological experiences preceding suicidal death. We assessed the characteristics of tedium vitae (feeling tired of life) after first ever stroke in Nigerian survivors.

Methods: Using the Schedule for Clinical Assessment in Neuropsychiatry, tedium vitae was assessed in 130 stroke survivors attending rehabilitation in a large Nigerian university hospital. Global cognitive and executive dysfunctions were evaluated, respectively, using the Mini Mental State Examination and the modi?ed Indiana University Token test. All participants had their index stroke 3 to 24 months before recruitment into the study. We also examined a comparative group of 130 age, gender, and education matched apparently normal persons who were unrelated to the stroke survivors. Associations were explored using univariate and multivariate logistic regression analyses.

Results: Tedium vitae was experienced by 16 (12.3%) stroke survivors compared with 5 (3.9%) in the comparative group (O. R = 3.5, 95% C. I = 1.3–9.9, p = 0.018). Among stroke survivors, those who were retired were more likely to experience tedium vitae (56.2%, p = 0.045). In analyses adjusting for the effect of systemic hypertension, cognitive dysfunction, retirement and marital separation, there was a 3.5-fold increase in the odds of experiencing tedium vitae after surviving a stroke (O. R = 3.5, 95% C. I = 1.1–11.6, p = 0.042).

Conclusions: Tedium vitae is a common suicidal experience after stroke and may be among the earliest perceptible pointer to impending poststroke suicide. It is easy to assess and may be less costly to obtain an adequate sample size in studies aiming to understand the phenomenon of suicide in the stroke population.