Exercise Intervention on Insomnia in Patients with a Cancer: A Systematic Review of the Literature.

Cancer is associated with increased muscle weakness, reduced physical functioning, increased fatigue, but also sleep disturbances, including insomnia, that affect quality of life (QoL). Physical activity demonstrated benefits on functional capacity, resilience and cancer-related fatigue, but there is a paucity of available data regarding its effects on insomnia in patients with cancer. This systematic review aims to examine the efficacy of exercise levels with insomnia in cancer patients.

Interpreting Neural Network Models for Toxicity Prediction by Extracting Learned Chemical Features.

Neural network models have become a popular machine-learning technique for the toxicity prediction of chemicals. However, due to their complex structure, it is difficult to understand predictions made by these models which limits confidence. Current techniques to tackle this problem such as SHAP or integrated gradients provide insights by attributing importance to the input features of individual compounds.

Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors.

De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge.

A randomized trial to evaluate the effects of a supervised exercise program on insomnia in patients with non-metastatic breast cancer undergoing chemotherapy: design of the FATSOMCAN study.

Background: Up to 70% of breast cancer patients report symptoms of insomnia during and after treatment. Despite the ubiquity of insomnia symptoms, they are under-screened, under-diagnosed and poorly managed in breast cancer patients. Sleep medications treat symptoms but are ineffective to cure insomnia.

Gradual Advance of Sleep-Wake Schedules Before an Eastward Flight and Phase Adjustment After Flight in Elite Cross-Country Mountain Bikers: Effects on Sleep and Performance.

Garbellotto, L, Petit, E, Brunet, E, Guirronnet, S, Clolus, Y, Gillet, V, Bourdin, H, and Mougin, F. Gradual advance of sleep-wake schedules before an eastward flight and phase adjustment after flight in elite cross-country mountain bikers: effects on sleep and performance. J Strength Cond Res 37(4): 872-880, 2023-Strategies, for alleviating jet lag, specifically targeted to competitive athletes have never been studied, in ecological conditions.

Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction.

Recently, imputation techniques have been adapted to predict activity values among sparse bioactivity matrices, showing improvements in predictive performance over traditional QSAR models. These models are able to use experimental activity values for auxiliary assays when predicting the activity of a test compound on a specific assay. In this study, we tested three different multi-task imputation techniques on three classification-based toxicity datasets: two of small scale (12 assays each) and one large scale with 417 assays.

RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de novo Design.

Reaction-based de novo design refers to the generation of synthetically accessible molecules using transformation rules extracted from known reactions in the literature. In this context, we have previously described the extraction of reaction vectors from a reactions database and their coupling with a structure generation algorithm for the generation of novel molecules from a starting material. An issue when designing molecules from a starting material is the combinatorial explosion of possible product molecules that can be generated, especially for multistep syntheses.

Amyloid binding and beyond: a new approach for Alzheimer's disease drug discovery targeting Aβo-PrP binding and downstream pathways.

Amyloid β oligomers (Aβo) are the main toxic species in Alzheimer's disease, which have been targeted for single drug treatment with very little success. In this work we report a new approach for identifying functional Aβo binding compounds. A tailored library of 971 fluorine containing compounds was selected by a computational method, developed to generate molecular diversity.

Complete sleep evaluation of top professional cross-country mountain bikers' athletes.

Background: Despite growing interest in athletes' sleep, few studies have focused on professional athletes, especially in individual sports. Moreover, limited investigations included female athletes. This study aimed to evaluate sleep chronotype, as well as objective and subjective sleep characteristics in male and female professional cross-country mountain bikers.

Enhancing reaction-based de novo design using a multi-label reaction class recommender.

Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases.

Sleep-disordered breathing in adolescents with obesity: When does it start to affect cardiometabolic health?

Background And Aims: Pediatric obesity and sleep-disordered breathing (SDB) are associated with cardiometabolic risk (CMR), but the degree of severity at which SDB affects cardiometabolic health is unknown. We assessed the relationship between the CMR and the apnea-hypopnea index (AHI), to identify a threshold of AHI from which an increase in the CMR is observed, in adolescents with obesity. We also compared the clinical, cardiometabolic and sleep characteristics between adolescents presenting a high (CMR+) and low CMR (CMR-), according to the threshold of AHI.

Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature.

Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables data-driven approaches to be developed. We present the development and validation of a 336-class machine learning-based classification model integrated within a Conformal Prediction (CP) framework to associate reaction class predictions with confidence estimations.

Identification of compounds that rescue otic and myelination defects in the zebrafish () mutant.

Adgrg6 (Gpr126) is an adhesion class G protein-coupled receptor with a conserved role in myelination of the peripheral nervous system. In the zebrafish, mutation of also results in defects in the inner ear: otic tissue fails to down-regulate gene expression and morphogenesis is disrupted. We have designed a whole-animal screen that tests for rescue of both up- and down-regulated gene expression in mutant embryos, together with analysis of weak and strong alleles.

Beneficial effects of a lifestyle intervention program on C-reactive protein: impact of cardiorespiratory fitness in obese adolescents with sleep disturbances.

The objectives of this study were to assess the relationship between inflammation and obstructive sleep apnea (OSA) and determine whether the lifestyle program's effects on inflammatory markers are associated with changes in anthropometric parameters, cardiorespiratory fitness, sleep duration, and OSA severity in severely obese adolescents. Participants were aged 14.6 (SD 1.

Alignment-Free Molecular Shape Comparison Using Spectral Geometry: The Framework.

A framework is presented for the calculation of novel alignment-free descriptors of molecular shape. The methods are based on the technique of spectral geometry which has been developed in the field of computer vision where it has shown impressive performance for the comparison of deformable objects such as people and animals. Spectral geometry techniques encode shape by capturing the curvature of the surface of an object into a compact, information-rich representation that is alignment-free while also being invariant to isometric deformations, that is, changes that do not distort distances over the surface.

Obstructive Sleep Apnea and Sleep Architecture in Adolescents With Severe Obesity: Effects of a 9-Month Lifestyle Modification Program Based on Regular Exercise and a Balanced Diet.

Study Objectives: Physical exercise and lifestyle modification are recognized as adjunct therapy for obstructive sleep apnea (OSA) in overweight adults. The objectives of this study were to investigate the effects of long-term physical exercise combined with a balanced diet on sleep architecture, sleep duration, and OSA in adolescents with severe obesity.

Methods: This interventional study was conducted in a nursing institution.

Effect of missing data on multitask prediction methods.

There has been a growing interest in multitask prediction in chemoinformatics, helped by the increasing use of deep neural networks in this field. This technique is applied to multitarget data sets, where compounds have been tested against different targets, with the aim of developing models to predict a profile of biological activities for a given compound. However, multitarget data sets tend to be sparse; i.

Bioisosteric Replacements Extracted from High-Quality Structures in the Protein Databank.

Bioisosterism is an important concept in the lead optimisation phase of drug discovery where the aim is to make modifications to parts of a molecule in order to improve some properties while maintaining others. We present an analysis of bioisosteric fragments extracted from the ligands in an established data set consisting of 121 protein targets. A pairwise analysis is carried out of all ligands for a given target.

Cardiometabolic risk is associated with the severity of sleep-disordered breathing in children with obesity.

Background: The alarming progression of pediatric obesity is associated with the development of sleep-disordered breathing (SDB), and both exhibit similar adverse cardiometabolic health outcomes. Physical activity level (PAL) may counteract sleep and metabolic disturbances. The present study investigates i) the association between the metabolic syndrome in childhood obesity and SDB, ii) the impact of SDB severity on cardiometabolic risk scores and PAL in children with obesity.

Chemoinformatics at the University of Sheffield 2002-2014.

This paper summarises work in chemoinformatics carried out in the Information School of the University of Sheffield during the period 2002-2014. Research studies are described on fingerprint-based similarity searching, data fusion, applications of reduced graphs and pharmacophore mapping, and on the School's teaching in chemoinformatics.

Perspectives on Knowledge Discovery Algorithms Recently Introduced in Chemoinformatics: Rough Set Theory, Association Rule Mining, Emerging Patterns, and Formal Concept Analysis.

Knowledge Discovery in Databases (KDD) refers to the use of methodologies from machine learning, pattern recognition, statistics, and other fields to extract knowledge from large collections of data, where the knowledge is not explicitly available as part of the database structure. In this paper, we describe four modern data mining techniques, Rough Set Theory (RST), Association Rule Mining (ARM), Emerging Pattern Mining (EP), and Formal Concept Analysis (FCA), and we have attempted to give an exhaustive list of their chemoinformatics applications. One of the main strengths of these methods is their descriptive ability.

Investigation of the use of spectral clustering for the analysis of molecular data.

Spectral clustering involves placing objects into clusters based on the eigenvectors and eigenvalues of an associated matrix. The technique was first applied to molecular data by Brewer [J. Chem.

Emerging pattern mining to aid toxicological knowledge discovery.

Knowledge-based systems for toxicity prediction are typically based on rules, known as structural alerts, that describe relationships between structural features and different toxic effects. The identification of structural features associated with toxicological activity can be a time-consuming process and often requires significant input from domain experts. Here, we describe an emerging pattern mining method for the automated identification of activating structural features in toxicity data sets that is designed to help expedite the process of alert development.

Automating knowledge discovery for toxicity prediction using jumping emerging pattern mining.

The design of new alerts, that is, collections of structural features observed to result in toxicological activity, can be a slow process and may require significant input from toxicology and chemistry experts. A method has therefore been developed to help automate alert identification by mining descriptions of activating structural features directly from toxicity data sets. The method is based on jumping emerging pattern mining which is applied to a set of toxic and nontoxic compounds that are represented using atom pair descriptors.