Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease.

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.

Potential significance of podoplanin immunohistochemical expression in papillary thyroid carcinoma.

Podoplanin (PDPN) is a lymphatic endothelial marker expressed by a range of human malignancies in which it has been shown to contribute to tumor progression and metastasis. However, there is a lack of the studies, examining the function of PDPN in thyroid cancer. The current study was performed to explore the possible diagnostic value of PDPN expression in papillary thyroid cancer (PTC) and to evaluate the marker's potential for prediction of regional lymph node metastasis. Lymphatic vascular density (LVD) and the stromal/cancer-associated fibroblasts (CAFs), labeled by PDPN, were examined in PTC compared to the other thyroid lesions. The current study included 50 cases of PTC and 50 cases of non-PTC thyroid lesions. Immunohistochemical staining was performed using monoclonal PDPN antibodies. Podoplanin expression was scored as positive and negative. Podoplanin expression was found in 36% of PTC cases, but it was not found in benign, low risk (borderline), or malignant lesions other than PTC. Furthermore, lymph node metastasis was significantly correlated with PDPN expression, LVD and CAFs (p-values < 0.00001, < 0.001 and 0.0002 respectively). These findings support the diagnostic utility of PDPN expression in PTC and its predictive value for LN metastasis.