A personalized stepwise dynamic predictive algorithm of the time to first treatment in chronic lymphocytic leukemia.

Personalized prediction is ideal in chronic lymphocytic leukemia (CLL). Although refined models have been developed, stratifying patients in risk groups, it is required to accommodate time-dependent information of patients, to address the clinical heterogeneity observed within these groups. In this direction, this study proposes a personalized stepwise dynamic predictive algorithm (PSDPA) for the time-to-first-treatment of the individual patient. The PSDPA introduces a personalized Score, reflecting the evolution in the patient's follow-up, employed to develop a reference pool of patients. Score evolution's similarity is used to predict, at a selected time point, the time-to-first-treatment for a new patient. Additional patient's biological information may be utilized. The algorithm was applied to 20 CLL patients, indicating that stricter assessment criteria for the Score evolution's similarity, and biological similarity exploitation, may improve prediction. The PSDPA capitalizes on both the follow-up and the biological background of the individual patient, dynamically promoting personalized prediction in CLL.

Mobile App Interventions for Parkinson's Disease, Multiple Sclerosis and Stroke: A Systematic Literature Review.

Central nervous system diseases (CNSDs) lead to significant disability worldwide. Mobile app interventions have recently shown the potential to facilitate monitoring and medical management of patients with CNSDs. In this direction, the characteristics of the mobile apps used in research studies and their level of clinical effectiveness need to be explored in order to advance the multidisciplinary research required in the field of mobile app interventions for CNSDs. A systematic review of mobile app interventions for three major CNSDs, i.e., Parkinson's disease (PD), multiple sclerosis (MS), and stroke, which impose significant burden on people and health care systems around the globe, is presented. A literature search in the bibliographic databases of PubMed and Scopus was performed. Identified studies were assessed in terms of quality, and synthesized according to target disease, mobile app characteristics, study design and outcomes. Overall, 21 studies were included in the review. A total of 3 studies targeted PD (14%), 4 studies targeted MS (19%), and 14 studies targeted stroke (67%). Most studies presented a weak-to-moderate methodological quality. Study samples were small, with 15 studies (71%) including less than 50 participants, and only 4 studies (19%) reporting a study duration of 6 months or more. The majority of the mobile apps focused on exercise and physical rehabilitation. In total, 16 studies (76%) reported positive outcomes related to physical activity and motor function, cognition, quality of life, and education, whereas 5 studies (24%) clearly reported no difference compared to usual care. Mobile app interventions are promising to improve outcomes concerning patient's physical activity, motor ability, cognition, quality of life and education for patients with PD, MS, and Stroke. However, rigorous studies are required to demonstrate robust evidence of their clinical effectiveness.

Enhancing user awareness on inferences obtained from fitness trackers data.

In the IoT era, sensitive and non-sensitive data are recorded and transmitted to multiple service providers and IoT platforms, aiming to improve the quality of our lives through the provision of high-quality services. However, in some cases these data may become available to interested third parties, who can analyse them with the intention to derive further knowledge and generate new insights about the users, that they can ultimately use for their own benefit. This predicament raises a crucial issue regarding the privacy of the users and their awareness on how their personal data are shared and potentially used. The immense increase in fitness trackers use has further increased the amount of user data generated, processed and possibly shared or sold to third parties, enabling the extraction of further insights about the users. In this work, we investigate if the analysis and exploitation of the data collected by fitness trackers can lead to the extraction of inferences about the owners routines, health status or other sensitive information. Based on the results, we utilise the PrivacyEnhAction privacy tool, a web application we implemented in a previous work through which the users can analyse data collected from their IoT devices, to educate the users about the possible risks and to enable them to set their user privacy preferences on their fitness trackers accordingly, contributing to the personalisation of the provided services, in respect of their personal data.

Electrochemical primer extension based on polyoxometalate electroactive labels for multiplexed detection of single nucleotide polymorphisms.

Polyoxymetalates (POMs) ([SiW11O39{Sn(CH2)2CO)}]4- and [P2W17O61{Sn(CH2)2CO)}]6-) were used to modify dideoxynucleotides (ddNTPs) through amide bond formation, and applied to the multiplexed detection of single nucleotide polymorphisms (SNPs) in an electrochemical primer extension reaction. Each gold electrode of an array was functionalised with a short single stranded thiolated DNA probe, specifically designed to extend with the POM-ddNTP at the SNP site to be interrogated. The system was applied to the simultaneous detection of 4 SNPs within a single stranded 103-mer model target generated using asymmetric PCR, highlighting the potential of POM-ddNTPs for targeted, multiplexed SNP detection. The four DNA bases were successfully labelled with both ([SiW11O39{Sn(CH2)2CO)}]4- and [P2W17O61{Sn(CH2)2CO)}]6-), and [SiW11O39{Sn(CH2)2CO)}]4- demonstrated to be the more suitable due to its single oxidation peak, which provides an unequivocal signal. The POM-ddNTP enzymatically incorporated to the DNA anchored to the surface was visualised by AFM using gold coated mica. The developed assay has been demonstrated to be highly reproducible, simple to carry out and with very low non-specific background signals. Future work will focus on applying the developed platform to the detection of SNPs associated with rifampicin resistance in real samples from patients suffering from tuberculosis.

Social voting advice applications-definitions, challenges, datasets and evaluation.

Voting advice applications (VAAs) are online tools that have become increasingly popular and purportedly aid users in deciding which party/candidate to vote for during an election. In this paper we present an innovation to current VAA design which is based on the introduction of a social network element. We refer to this new type of online tool as a social voting advice application (SVAA). SVAAs extend VAAs by providing (a) community-based recommendations, (b) comparison of users' political opinions, and (c) a channel of user communication. In addition, SVAAs enriched with data mining modules, can operate as citizen sensors recording the sentiment of the electorate on issues and candidates. Drawing on VAA datasets generated by the Preference Matcher research consortium, we evaluate the results of the first VAA-Choose4Greece-which incorporated social voting features and was launched during the landmark Greek national elections of 2012. We demonstrate how an SVAA can provide community based features and, at the same time, serve as a citizen sensor. Evaluation of the proposed techniques is realized on a series of datasets collected from various VAAs, including Choose4Greece. The collection is made available online in order to promote research in the field.

Label free optical sensor for Avidin based on single gold nanoparticles functionalized with aptamers.

Optical spectroscopy of a single gold nanoparticle, functionalized with an aptamer, is used to sense the specific binding of avidin. Herewith, the field of single noble metal nanoparticle biosensors is extended to the important field of aptamer based assays. The sensitivity of this initial, but not yet optimized apta-nano-sensor is in the range of 20 nM. Due to its nanoscopic size, this single nanoparticle based apta-sensor may be used in nanoscopic volumes such as in array type assays or even inside cells.