Paget-Schroetter syndrome: the importance of early detection and effective surgical intervention.

Paget-Schroetter syndrome (PSS) is relatively rare condition of thoracic outlet syndrome characterized by thrombosis or blood clot formation in the subclavian vein. Due to the non-specific symptoms and low incidence rate, PSS is frequently missed by medical professionals, and as such it often leads to wrong diagnosis and untreated patients. We present the case of a 30-year-old CrossFit trainer who developed a thrombosis of the subclavian vein. Initially, the patient consulted an internist after experiencing swelling in the right shoulder region and discoloration of the right upper extremity. Angiography revealed occlusion of the subclavian vein and anticoagulant therapy was prescribed. For more than a year, the patient's symptoms remained unchanged, and the subclavian vein occlusion persisted. Venography suspected effort thrombosis of the subclavian vein. The patient underwent surgery for decompression of the subclavian vein. After six months, results from post-operative computed tomography angiography showed that venous flow was fully restored and no pathology of the venous vessel wall could be demonstrated. This report aims to increase awareness of PSS among medical professionals, leading to earlier diagnosis and adequate clinical-surgical management.

Disease progression in Parkinson subtypes: the PPMI dataset.

INTRODUCTION: Discrete patterns of progression have been suggested for patients with Parkinson disease and presenting tremor dominant (TD) or postural instability gait disorders (PIGD). However, longitudinal prospective assessments need to take into consideration the variability in clinical manifestations and the evidence that only 40% of initially classified PIGD remain in this subtype at subsequent visits. METHODS: We analyzed clinical progression of PIGD compared to TD using longitudinal clinical data from the PPMI. Given the reported instability of such clinical classification, we only included patients who were reported as PIGD/TD at each visit during the 4-year observation. We used linear mixed-effects models to test differences in progression in these subgroups in 51 dependent variables. RESULTS: There were 254 patients with yearly assessment. The number of PIGD was 36/254 vs 144/254 TD. PIGD had more severe motor disease at baseline but progressed faster than TD only in three non-motor items of the MDS-UPDRS: cognitive impairment, hallucinations, and psychosis plus features of DDS. Our analysis also showed in PIGD faster increase in the average time with dyskinesia. CONCLUSIONS: PIGD are characterized by more severe disease manifestations at diagnosis and greater cognitive progression, more frequent hallucinations, psychosis as well as features of DDS than TD patients. We interpret these findings as expression of greater cortical and subcortical involvement in PIGD already at onset. Since PIGD/TD classification is very unstable at onset, our analysis based on stricter definition criteria provides important insight for clinical trial stratification and definition of related outcome measures.

Twitter sentiment around the Earnings Announcement events.

We investigate the relationship between social media, Twitter in particular, and stock market. We provide an in-depth analysis of the Twitter volume and sentiment about the 30 companies in the Dow Jones Industrial Average index, over a period of three years. We focus on Earnings Announcements and show that there is a considerable difference with respect to when the announcements are made: before the market opens or after the market closes. The two different timings of the Earnings Announcements were already investigated in the financial literature, but not yet in the social media. We analyze the differences in terms of the Twitter volumes, cumulative abnormal returns, trade returns, and earnings surprises. We report mixed results. On the one hand, we show that the Twitter sentiment (the collective opinion of the users) on the day of the announcement very well reflects the stock moves on the same day. We demonstrate this by applying the event study methodology, where the polarity of the Earnings Announcements is computed from the Twitter sentiment. Cumulative abnormal returns are high (2-4%) and statistically significant. On the other hand, we find only weak predictive power of the Twitter sentiment one day in advance. It turns out that it is important how to account for the announcements made after the market closes. These after-hours announcements draw high Twitter activity immediately, but volume and price changes in trading are observed only on the next day. On the day before the announcements, the Twitter volume is low, and the sentiment has very weak predictive power. A useful lesson learned is the importance of the proper alignment between the announcements, trading and Twitter data.

The Effects of Twitter Sentiment on Stock Price Returns.

Social media are increasingly reflecting and influencing behavior of other complex systems. In this paper we investigate the relations between a well-known micro-blogging platform Twitter and financial markets. In particular, we consider, in a period of 15 months, the Twitter volume and sentiment about the 30 stock companies that form the Dow Jones Industrial Average (DJIA) index. We find a relatively low Pearson correlation and Granger causality between the corresponding time series over the entire time period. However, we find a significant dependence between the Twitter sentiment and abnormal returns during the peaks of Twitter volume. This is valid not only for the expected Twitter volume peaks (e.g., quarterly announcements), but also for peaks corresponding to less obvious events. We formalize the procedure by adapting the well-known "event study" from economics and finance to the analysis of Twitter data. The procedure allows to automatically identify events as Twitter volume peaks, to compute the prevailing sentiment (positive or negative) expressed in tweets at these peaks, and finally to apply the "event study" methodology to relate them to stock returns. We show that sentiment polarity of Twitter peaks implies the direction of cumulative abnormal returns. The amount of cumulative abnormal returns is relatively low (about 1-2%), but the dependence is statistically significant for several days after the events.