Self-reported Physical Activity and Objective Aerobic Fitness: Differential Associations with Gray Matter Density in Healthy Aging

Aerobic fitness (AF) and self-reported physical activity (srPA) do not represent the same construct. However, many exercise and brain aging studies interchangeably use AF and srPA measures, which may be problematic with regards to how these metrics are associated with brain outcomes, such as morphology. If AF and PA measures captured the same phenomena, regional brain volumes associated with these measures should directly overlap. This study employed the general linear model to examine the differential association between objectively-measured AF (treadmill assessment) and srPA (questionnaire) with gray matter density (GMd) in 29 cognitively unimpaired community- dwelling older adults using voxel based morphometry. The results show significant regional variance in terms of GMd when comparing AF and srPA as predictors. Higher AF was associated with greater GMd in the cerebellum only, while srPA displayed positive associations with GMd in occipito-temporal, left perisylvian, and frontal regions after correcting for age. Importantly, only AF level, and not srPA, modified the relationship between age and GMd, such that higher levels of AF were associated with increased GMd in older age, while decreased GMd was seen in those with lower AF as a function of age. These results support existing literature suggesting that both AF and PA exert beneficial effects on GMd, but only AF served as a buffer against age-related GMd loss. Furthermore, these results highlight the need for use of objective PA measurement and comparability of tools across studies, since results vary dependent upon the measures used and whether these are objective or subjective in nature

Aging, Aerobic Activity and Interhemispheric Communication

Recent studies have shown that during unimanual motor tasks, aging adults show bilateral recruitment of primary motor cortex (M1), while younger adults show a suppression of the ipsilateral motor cortex. Additional work has indicated that increased bilateral M1 recruitment in older adults may be deleterious when performing some motor tasks. However, higher levels of physical fitness are associated with improved dexterity and fitness may mitigate the loss of both inhibitory and excitatory communication in aging adults. The goal of this study was to assess dexterity and interhemispheric motor communication in physically fit and sedentary middle-age (40–60 years) right handed participants using tests of hand deftness and transcranial magnetic stimulation (TMS). To behaviorally assess the influence of interhemispheric communication on motor performance, participants also perform the coin rotation deftness task while maintaining pinch force with the opposite hand (bimanual condition). We correlated these behavioral measures with the ipsilateral silent period using TMS to assess interhemispheric inhibition. Our results show that the middle-aged adults who were physically fit had better dexterity of their right hand (finger tapping and peg-board). When performing the coin rotation task the fit group had no between hand differences, but the sedentary group’s left hand performance was inferior to the their right hand. We found that better dexterity correlated with ipsilateral silent period duration (greater inhibition) thereby supporting the postulate that fitness improves interhemispheric motor communication

Effects of a 12-Week Aerobic Spin Intervention on Resting State Networks in Previously Sedentary Older Adults

Objective: We have previously demonstrated that aerobic exercise improves upper extremity motor function concurrent with changes in motor cortical activity using task-based functional magnetic resonance imaging (fMRI). However, it is currently unknown how a 12-week aerobic exercise intervention affects resting-state functional connectivity (rsFC) in motor networks. Previous work has shown that over a 6-month or 1-year exercise intervention, older individuals show increased resting state connectivity of the default mode network and the sensorimotor network (Voss et al., 2010b; Flodin et al., 2017). However, the effects of shorter-term 12-week exercise interventions on functional connectivity have received less attention.Method: Thirty-seven sedentary right-handed older adults were randomized to either a 12-week aerobic, spin cycling exercise group or a 12-week balance-toning exercise group. Resting state functional magnetic resonance images were acquired in sessions PRE/POST interventions. We applied seed-based correlation analysis to left and right primary motor cortices (L-M1 and R-M1) and anterior default mode network (aDMN) to test changes in rsFC between groups after the intervention. In addition, we performed a regression analysis predicting connectivity changes PRE/POST intervention across all participants as a function of time spent in aerobic training zone regardless of group assignment.Results: Seeding from L-M1, we found that participants in the cycling group had a greater PRE/POST change in rsFC in aDMN as compared to the balance group. When accounting for time in aerobic HR zone, we found increased heart rate workload was positively associated with increased change of rsFC between motor networks and aDMN. Interestingly, L-M1 to aDMN connectivity changes were also related to motor behavior changes in both groups. Respective of M1 laterality, comparisons of all participants from PRE to POST showed a reduction in the extent of bilateral M1 connectivity after the interventions with increased connectivity in dominant M1.Conclusion: A 12-week physical activity intervention can change rsFC between primary motor regions and default mode network areas, which may be associated with improved motor performance. The decrease in connectivity between L-M1 and R-M1 post-intervention may represent a functional consolidation to the dominant M1.Topic Areas: Neuroimaging, Aging

Biosimilars: a position paper of the European Society for Medical Oncology, with particular reference to oncology prescribers.

Biosimilars present a necessary and timely opportunity for physicians, patients and healthcare systems. If suitably developed clinically, manufactured to the correct standards and used appropriately, they can positively impact on the financial sustainability of healthcare systems. A critical consideration regarding the introduction of biosimilars into the clinic centres on the required information concerning all the respective procedures. This position paper aims to describe the issues revolving around biosimilars that are relevant to the field of oncology, especially the prescribers. More specifically, we discuss aspects related to definition, forms of biosimilars, labelling, extrapolation, interchangeability, switching, automatic substitution, clinical standards on safety and efficacy, responsibilities among prescribers and pharmacists, potential impact on financial burden in healthcare and the current scenario and future prospects of biosimilars in Europe and the rest of the world

Discovering patterns in drug-protein interactions based on their fingerprints

<p>Abstract</p> <p>Background</p> <p>The discovering of interesting patterns in drug-protein interaction data at molecular level can reveal hidden relationship among drugs and proteins and can therefore be of paramount importance for such application as drug design. To discover such patterns, we propose here a computational approach to analyze the molecular data of drugs and proteins that are known to have interactions with each other. Specifically, we propose to use a data mining technique called <it>Drug-Protein Interaction Analysis </it>(<it>D-PIA</it>) to determine if there are any commonalities in the fingerprints of the substructures of interacting drug and protein molecules and if so, whether or not any patterns can be generalized from them.</p> <p>Method</p> <p>Given a database of drug-protein interactions, <it>D-PIA </it>performs its tasks in several steps. First, for each drug in the database, the fingerprints of its molecular substructures are first obtained. Second, for each protein in the database, the fingerprints of its protein domains are obtained. Third, based on known interactions between drugs and proteins, an interdependency measure between the fingerprint of each drug substructure and protein domain is then computed. Fourth, based on the interdependency measure, drug substructures and protein domains that are significantly interdependent are identified. Fifth, the existence of interaction relationship between a previously unknown drug-protein pairs is then predicted based on their constituent substructures that are significantly interdependent.</p> <p>Results</p> <p>To evaluate the effectiveness of <it>D-PIA</it>, we have tested it with real drug-protein interaction data. <it>D-PIA </it>has been tested with real drug-protein interaction data including enzymes, ion channels, and protein-coupled receptors. Experimental results show that there are indeed patterns that one can discover in the interdependency relationship between drug substructures and protein domains of interacting drugs and proteins. Based on these relationships, a testing set of drug-protein data are used to see if <it>D-PIA </it>can correctly predict the existence of interaction between drug-protein pairs. The results show that the prediction accuracy can be very high. An AUC score of a ROC plot could reach as high as 75% which shows the effectiveness of this classifier.</p> <p>Conclusions</p> <p><it>D-PIA </it>has the advantage that it is able to perform its tasks effectively based on the fingerprints of drug and protein molecules without requiring any 3D information about their structures and <it>D-PIA </it>is therefore very fast to compute. <it>D-PIA </it>has been tested with real drug-protein interaction data and experimental results show that it can be very useful for predicting previously unknown drug-protein as well as protein-ligand interactions. It can also be used to tackle problems such as ligand specificity which is related directly and indirectly to drug design and discovery.</p

The Health Informatics Trial Enhancement Project (HITE): Using routinely collected primary care data to identify potential participants for a depression trial

<p>Abstract</p> <p>Background</p> <p>Recruitment to clinical trials can be challenging. We identified anonymous potential participants to an existing pragmatic randomised controlled depression trial to assess the feasibility of using routinely collected data to identify potential trial participants. We discuss the strengths and limitations of this approach, assess its potential value, report challenges and ethical issues encountered.</p> <p>Methods</p> <p>Swansea University's Health Information Research Unit's Secure Anonymised Information Linkage (SAIL) database of routinely collected health records was interrogated, using Structured Query Language (SQL). Read codes were used to create an algorithm of inclusion/exclusion criteria with which to identify suitable anonymous participants. Two independent clinicians rated the eligibility of the potential participants' identified. Inter-rater reliability was assessed using the kappa statistic and inter-class correlation.</p> <p>Results</p> <p>The study population (N = 37263) comprised all adults registered at five general practices in Swansea UK. Using the algorithm 867 anonymous potential participants were identified. The sensitivity and specificity results > 0.9 suggested a high degree of accuracy from the algorithm. The inter-rater reliability results indicated strong agreement between the confirming raters. The Intra Class Correlation Coefficient (Cronbach's Alpha) > 0.9, suggested excellent agreement and Kappa coefficient > 0.8; almost perfect agreement.</p> <p>Conclusions</p> <p>This proof of concept study showed that routinely collected primary care data can be used to identify potential participants for a pragmatic randomised controlled trial of folate augmentation of antidepressant therapy for the treatment of depression. Further work will be needed to assess generalisability to other conditions and settings and the inclusion of this approach to support Electronic Enhanced Recruitment (EER).</p

Analysis of Tp53 Codon 72 Polymorphisms, Tp53 Mutations, and HPV Infection in Cutaneous Squamous Cell Carcinomas

Non-melanoma skin cancers are one of the most common human malignancies accounting for 2-3% of tumors in the US and represent a significant health burden. Epidemiology studies have implicated Tp53 mutations triggered by UV exposure, and human papilloma virus (HPV) infection to be significant causes of non-melanoma skin cancer. However, the relationship between Tp53 and cutaneous HPV infection is not well understood in skin cancers. In this study we assessed the association of HPV infection and Tp53 polymorphisms and mutations in lesional specimens with squamous cell carcinomas.We studied 55 cases of histologically confirmed cutaneous squamous cell carcinoma and 41 controls for the presence of HPV infection and Tp53 genotype (mutations and polymorphism).We found an increased number of Tp53 mutations in the squamous cell carcinoma samples compared with perilesional or control samples. There was increased frequency of homozygous Tp53-72R polymorphism in cases with squamous cell carcinomas, while the Tp53-72P allele (Tp53-72R/P and Tp53-72P/P) was more frequent in normal control samples. Carcinoma samples positive for HPV showed a decreased frequency of Tp53 mutations compared to those without HPV infection. In addition, carcinoma samples with a Tp53-72P allele showed an increased incidence of Tp53 mutations in comparison carcinomas samples homozygous for Tp53-72R.These studies suggest there are two separate pathways (HPV infection and Tp53 mutation) leading to cutaneous squamous cell carcinomas stratified by the Tp53 codon-72 polymorphism. The presence of a Tp53-72P allele is protective against cutaneous squamous cell carcinoma, and carcinoma specimens with Tp53-72P are more likely to have Tp53 mutations. In contrast Tp53-72R is a significant risk factor for cutaneous squamous cell carcinoma and is frequently associated with HPV infection instead of Tp53 mutations. Heterozygosity for Tp53-72R/P is protective against squamous cell carcinomas, possibly reflecting a requirement for both HPV infection and Tp53 mutations