Current and innovative pharmacological options to treat typical and atypical trigeminal neuralgia

Trigeminal neuralgia is a representative neuropathic facial pain condition, characterised by unilateral paroxysmal pain in the distribution territory of one or more divisions of the trigeminal nerve, triggered by innocuous stimuli. A subgroup of patients with trigeminal neuralgia [TN (previously defined as atypical TN)] also suffer from concomitant continuous pain, i.e. a background pain between the paroxysmal attacks. The aim of this review is to provide current, evidence-based, knowledge about the pharmacological treatment of typical and atypical TN, with a specific focus on drugs in development. We searched for relevant papers within PubMed, EMBASE, the Cochrane Database of Systematic Reviews and the Clinical Trials database (ClinicalTrials.gov), taking into account publications up to February 2018. Two authors independently selected studies for inclusions, data extraction, and bias assessment. Carbamazepine and oxcarbazepine are the first-choice drugs for paroxysmal pain. When sodium channel blockers cannot reach full dosage because of side effects, an add-on treatment with lamotrigine or baclofen should be considered. In patients with atypical TN, both gabapentin and antidepressants are expected to be efficacious and should be tried as an add-on to oxcarbazepine or carbamazepine. Although carbamazepine and oxcarbazepine are effective in virtually the totality of patients, they are responsible for side effects causing withdrawal from treatment in an important percentage of cases. A new, better tolerated, Nav1.7 selective state-dependent, sodium channel blocker (vixotrigine) is under development. Future trials testing the effect of combination therapy in patients with TN are needed, especially in patients with concomitant continuous pain and in TN secondary to multiple sclerosis

No money, no honey? Financial versus knowledge and demand constraints on innovation

The paper adds to the literature on the barriers to innovation in two ways. First, we assess comparatively what mostly constrains firms’ ability to translate investment in innovation activity into new products and processes, whether it is mainly finance, as most of the literature would suggest, or whether it is mostly knowledge and market-related aspects. Second, we suggest a method to correct for the sample selection bias that often affects empirical contributions to this scholarship. By filtering out firms that are not interested in innovation from those that struggle to engage in it, we obtain a relevant sample of potential innovators, which allows us to analyse the comparative effect of financial and non-financial barriers on innovation success. We find that demand-side factors, particularly concentrated market structure and lack of demand, are as important as financial constraints in determining firms’ innovation failures. This evidence redirects attention from financial to non-financial barriers by considering traditional demand, market structure and regulation factors involved in reduced firm innovation performance. The empirical analysis is based on an unbalanced panel of firm-level data from four waves of the UK Community Innovation Survey (CIS) between 2002 and 2010 merged with data from the UK Business Structure Database

Query-Answer Causality in Databases: Abductive Diagnosis and View-Updates

Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers). Connections between query causality and consistency-based diagnosis and database repairs (wrt. integrity constrain violations) have been established in the literature. In this work we establish connections between query causality and abductive diagnosis and the view-update problem. The unveiled relationships allow us to obtain new complexity results for query causality -the main focus of our work- and also for the two other areas.Comment: To appear in Proc. UAI Causal Inference Workshop, 2015. One example was fixe

Privacy Preserving Utility Mining: A Survey

In big data era, the collected data usually contains rich information and hidden knowledge. Utility-oriented pattern mining and analytics have shown a powerful ability to explore these ubiquitous data, which may be collected from various fields and applications, such as market basket analysis, retail, click-stream analysis, medical analysis, and bioinformatics. However, analysis of these data with sensitive private information raises privacy concerns. To achieve better trade-off between utility maximizing and privacy preserving, Privacy-Preserving Utility Mining (PPUM) has become a critical issue in recent years. In this paper, we provide a comprehensive overview of PPUM. We first present the background of utility mining, privacy-preserving data mining and PPUM, then introduce the related preliminaries and problem formulation of PPUM, as well as some key evaluation criteria for PPUM. In particular, we present and discuss the current state-of-the-art PPUM algorithms, as well as their advantages and deficiencies in detail. Finally, we highlight and discuss some technical challenges and open directions for future research on PPUM.Comment: 2018 IEEE International Conference on Big Data, 10 page

New probabilistic interest measures for association rules

Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a real-world database from a grocery outlet to explore the behavior of confidence and lift, two popular interest measures used for rule mining. The results show that confidence is systematically influenced by the frequency of the items in the left hand side of rules and that lift performs poorly to filter random noise in transaction data. Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules. The new measures show significantly better performance than lift for applications where spurious rules are problematic

Towards a New Science of a Clinical Data Intelligence

In this paper we define Clinical Data Intelligence as the analysis of data generated in the clinical routine with the goal of improving patient care. We define a science of a Clinical Data Intelligence as a data analysis that permits the derivation of scientific, i.e., generalizable and reliable results. We argue that a science of a Clinical Data Intelligence is sensible in the context of a Big Data analysis, i.e., with data from many patients and with complete patient information. We discuss that Clinical Data Intelligence requires the joint efforts of knowledge engineering, information extraction (from textual and other unstructured data), and statistics and statistical machine learning. We describe some of our main results as conjectures and relate them to a recently funded research project involving two major German university hospitals.Comment: NIPS 2013 Workshop: Machine Learning for Clinical Data Analysis and Healthcare, 201