Beachseerup1649

Preliminary research has shown the effectiveness of supervised exercise-based interventions in alleviating sequela resulting from metastatic prostate cancer. Despite this, many individuals do not engage in sufficient exercise to gain the benefits. There are many barriers, which limit the uptake of face-to-face exercise in this population including lack of suitable facilities, remoteness, and access to experts, significant fatigue, urinary incontinence and motivation. Technology-enabled interventions offer a distance-based alternative. This protocol describes a pilot two-armed randomised controlled study that will investigate the feasibility and preliminary efficacy of an online exercise and behavioural change tool (ExerciseGuide) amongst individuals with metastatic prostate cancer.

Sixty-six participants with histologically diagnosed metastatic prostate cancer will be randomised into either the 8-week intervention or a wait-list control. The intervention arm will have access to a tailored website, remote 8001979246.aspx.

ANZCTR, ACTRN12614001268639 . Registered 10 December 2018, https//anzctr.org.au/ACTRN12618001979246.aspx.

The identification of central sensitization (CS) is an important aspect in the management of patients with chronic musculoskeletal pain. Several methods have been developed, including clinical indicators and psychophysical measures. However, whether clinical indicators coincide with the psychophysical test of CS-related sign and symptoms is still unknown. Therefore, the present study aimed to analyze the diagnostic accuracy of the clinical indicators in identifying CS-related sign and symptoms in patients with musculoskeletal pain.

One-hundred consecutive patients with musculoskeletal pain were included. Clinical indicators (index method) based on a combination of patient self-report pain characteristics and physical examination were used to identify the phenotype of patients with musculoskeletal pain and the predominance of the CS-related sign and symptoms. Conditioned pain modulation (CPM) was assessed by the Cold Pressor Test (reference standard), which is a psychophysical test used to detect impairmense the clinical indicators in the management of patients with musculoskeletal pain.An affinity fingerprint is the vector consisting of compound's affinity or potency against the reference panel of protein targets. Here, we present the QAFFP fingerprint, 440 elements long in silico QSAR-based affinity fingerprint, components of which are predicted by Random Forest regression models trained on bioactivity data from the ChEMBL database. Both real-valued (rv-QAFFP) and binary (b-QAFFP) versions of the QAFFP fingerprint were implemented and their performance in similarity searching, biological activity classification and scaffold hopping was assessed and compared to that of the 1024 bits long Morgan2 fingerprint (the RDKit implementation of the ECFP4 fingerprint). In both similarity searching and biological activity classification, the QAFFP fingerprint yields retrieval rates, measured by AUC (~ 0.65 and ~ 0.70 for similarity searching depending on data sets, and ~ 0.85 for classification) and EF5 (~ 4.67 and ~ 5.82 for similarity searching depending on data sets, and ~ 2.10 for classification), comparable to that of the Morgan2 fingerprint (similarity searching AUC of ~ 0.57 and ~ 0.66, and EF5 of ~ 4.09 and ~ 6.41, depending on data sets, classification AUC of ~ 0.87, and EF5 of ~ 2.16). However, the QAFFP fingerprint outperforms the Morgan2 fingerprint in scaffold hopping as it is able to retrieve 1146 out of existing 1749 scaffolds, while the Morgan2 fingerprint reveals only 864 scaffolds.We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.In silico prediction of drug-target interactions is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. However, developing such computational methods is not an easy task, but is much needed, as current methods that predict potential drug-target interactions suffer from high false-positive rates. Here we introduce DTiGEMS+, a computational method that predicts Drug-Target interactions using Graph Embedding, graph Mining, and Similarity-based techniques. DTiGEMS+ combines similarity-based as well as feature-based approaches, and models the identification of novel drug-target interactions as a link prediction problem in a heterogeneous network. DTiGEMS+ constructs the heterogeneous network by augmenting the known drug-target interactions graph with two other complementary graphs namely drug-drug similarity, target-target similarity. DTiGEMS+ combines different computational techniques to provide the final drug target prediction, these techniques include graph embeddings, graph mining, and machine learning. https://www.selleckchem.com/products/mrtx1257.html DTiGEMS+ integrates multiple drug-drug similarities and target-target similarities into the final heterogeneous graph construction after applying a similarity selection procedure as well as a similarity fusion algorithm. Using four benchmark datasets, we show DTiGEMS+ substantially improves prediction performance compared to other state-of-the-art in silico methods developed to predict of drug-target interactions by achieving the highest average AUPR across all datasets (0.92), which reduces the error rate by 33.3% relative to the second-best performing model in the state-of-the-art methods comparison.