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%.     

Insulin-like growth factor-1 as predictive factor of difficult laryngoscopy in patients with GH-producing pituitary adenoma: A pilot study

BackgroundGrowth hormone (GH)-producing pituitary tumors account for 10 to 15% of pituitary tumors. The hypersecretion of GH may induce changes in the airway anatomy through the activation of Insulin-like Growth factor 1(IGF-1) pathway. We sought investigate the role IGF-1 as a potential predictive factor of difficult laryngoscopy in patients with GH-producing pituitary adenoma.MethodsThis study was a single center retrospective study. We included 33 patients undergoing transsphenoidal resection of GH-producing pituitary. We recorded demographic data, el-Ganzouri risk index (EGRI) and modified Look-Evaluate-Mallampati-Obstruction-Neck mobility (mLEMON) score, and pituitary hormone plasma levels. We performed ordinal logistic regression to analyze the relationship between IGF-1 and EGRI, mLEMON, and Cormack-Lehane Grade score and a multiple logistic regression to test the capability of EGRI, mLEMON and IGF-1 levels to predict Cormack-Lehane score. Receiver operating curve (ROC), area under the curve (AUC), and cut-off value of IGF-1 were calculated.ResultsOnly 14 (42.8%) and 12 (36.36%) patients showed predictive factors of difficult intubation according to EGRI and mLEMON score, respectively. IGF-1 significantly correlated with Cormack-Lehane (p = 0.005879) but not with mLEMON and EGRI (p = 0.3080 and 0.4146, respectively). In multiple regression model IGF-1 correlated only with Cormack-Lehane grade (p = 0.0089). Area under ROC was 0.8571 and cut-off value of IGF-1 was 186.15 ng/ml.ConclusionHigher IGF-1 levels correlate with the probability of having a higher Cormack-Lehane score; classical bedside scores, such as mLEMON and EGRI, were not able to predict difficult laryngoscopy in our population.     

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。     

Proteases and their inhibitors as prognostic factors for high-grade serous ovarian cancer

Ovarian carcinoma is one of the most lethal malignancies, but only very few prognostic biomarkers are known. The degradome, comprising proteases, protease non-proteolytic homologues and inhibitors, have been involved in the prognosis of many cancer types, including ovarian carcinoma. The prognostic significance of the whole degradome family has not been specifically studied in high-grade serous ovarian cancer. A targeted DNA microarray known as the CLIP-CHIP microarray was used to identify potential prognostic factors in ten high-grade serous ovarian cancer women who had early recurrence (<1.6 years) or late/no recurrence after first line surgery and chemotherapy. In women with early recurrence, we identified seven upregulated genes (TMPRSS4, MASP1/3, SPC18, PSMB1, IGFBP2, CFI – encoding Complement Factor I – and MMP9) and one down-regulated gene (ADAM-10). Using immunohistochemistry, we evaluated the prognostic effect of these 8 candidate genes in an independent cohort of 112 high-grade serous ovarian cancer women. Outcomes were progression, defined according to CA-125 criteria, and death. Multivariate Cox proportional hazard regression models were done to estimate the associations between each protein and each outcome. High ADAM-10 expression (intensity of 2–3) was associated with a lower risk of progression (adjusted hazard ratio (HR): 0.51; 95% confidence interval (CI): 0.29-0.87). High complement factor I expression (intensity 2–3) was associated with a higher risk of progression (adjusted HR: 2.30, 95% CI: 1.17–4.53) and death (adjusted HR: 3.42; 95% CI: 1.72–6.79). Overall, we identified the prognostic value of two proteases, ADAM-10 and complement factor I, for high-grade serous ovarian cancer which could have clinical significance.