Strength training in elderly: An useful tool against sarcopenia

The loss of muscle mass and strength in elderly population (especially after the age of 65-70) represents a public health problem. Due to the high prevalence of frailty in older adults, cardiovascular or low-intensity exercise is implemented as first choice option. Although beneficial these training schemes are not as effective as strength-based resistance training for increasing muscle strength and hypertrophy. In fact, when performed progressively and under professional supervision, strength-based training has been proposed as an important and valid methodology to reduce sarcopenia-related problems. In this mini-review, we not only summarize the benefits of weight resistance training but also highlight practical recommendations and other non-conventional methods (e.g., suspension training) as part of an integral anti-sarcopenia strategy. Future directions including cluster set configurations and high-speed resistance training are also outlined

Snazer: the simulations and networks analyzer

<p>Abstract</p> <p>Background</p> <p>Networks are widely recognized as key determinants of structure and function in systems that span the biological, physical, and social sciences. They are static pictures of the interactions among the components of complex systems. Often, much effort is required to identify networks as part of particular patterns as well as to visualize and interpret them.</p> <p>From a pure dynamical perspective, simulation represents a relevant <it>way</it>-<it>out</it>. Many simulator tools capitalized on the "noisy" behavior of some systems and used formal models to represent cellular activities as temporal trajectories. Statistical methods have been applied to a fairly large number of replicated trajectories in order to infer knowledge.</p> <p>A tool which both graphically manipulates reactive models and deals with sets of simulation time-course data by aggregation, interpretation and statistical analysis is missing and could add value to simulators.</p> <p>Results</p> <p>We designed and implemented <it>Snazer</it>, the simulations and networks analyzer. Its goal is to aid the processes of visualizing and manipulating reactive models, as well as to share and interpret time-course data produced by stochastic simulators or by any other means.</p> <p>Conclusions</p> <p><it>Snazer </it>is a solid prototype that integrates biological network and simulation time-course data analysis techniques.</p

Effect of Soy Protein Supplementation on Muscle Adaptations, Metabolic and Antioxidant Status, Hormonal Response, and Exercise Performance of Active Individuals and Athletes: A Systematic Review of Randomised Controlled Trials

This is the final version. Available on open access from Springer via the DOI in this recordAvailability of data and material: Not applicable.Background Protein supplements are important to maintain optimum health and physical performance, particularly in athletes and active individuals to repair and rebuild their skeletal muscles and connective tissues. Soy protein (SP) has gained popularity in recent years as an alternative to animal proteins. Objectives This systematic review evaluates the evidence from randomised controlled clinical trials of the effects of SP supplementation in active individuals and athletes in terms of muscle adaptations, metabolic and antioxidant status, hormonal response and exercise performance. It also explores the differences in SP supplementation effects in comparison to whey protein. Methods A systematic search was conducted in PubMed, Embase and Web of Science, as well as a manual search in Google Scholar and EBSCO, on 27 June 2023. Randomised controlled trials that evaluated the applications of SPs supplementation on sports and athletic-related outcomes that are linked with exercise performance, adaptations and biomarkers in athletes and physically active adolescents and young adults (14 to 39 years old) were included, otherwise, studies were excluded. The risk of bias was assessed according to Cochrane’s revised risk of bias tool. Results A total of 19 eligible original research articles were included that investigated the effect of SP supplementation on muscle adaptations (n = 9), metabolic and antioxidant status (n = 6), hormonal response (n = 6) and exercise performance (n = 6). Some studies investigated more than one effect. SP was found to provide identical increases in lean mass compared to whey in some studies. SP consumption promoted the reduction of exercise-induced metabolic/blood circulating biomarkers such as triglycerides, uric acid and lactate. Better antioxidant capacity against oxidative stress has been seen with respect to whey protein in long-term studies. Some studies reported testosterone and cortisol fluctuations related to SP; however, more research is required. All studies on SP and endurance performance suggested the potential beneficial effects of SP supplementation (10–53.3 g) on exercise performance by improving high-intensity and high-speed running performance, enhancing maximal cardiac output, delaying fatigue and improving isometric muscle strength, improving endurance in recreational cyclists, increasing running velocity and decreasing accumulated lactate levels; however, studies determining the efficacy of soy protein on VO2max provided conflicted results. Conclusion It is possible to recommend SP to athletes and active individuals in place of conventional protein supplements by assessing their dosage and effectiveness in relation to different types of training. SP may enhance lean mass compared with other protein sources, enhance the antioxidant status, and reduce oxidative stress. SP supplementation had an inconsistent effect on testosterone and cortisol levels. SP supplementation may be beneficial, especially after muscle damage, high-intensity/high-speed or repeated bouts of strenuous exercise

Distributed generative data mining

A process of Knowledge Discovery in Databases (KDD) involving large amounts of data requires a considerable amount of computational power. The process may be done on a dedicated and expensive machinery or, for some tasks, one can use distributed computing techniques on a network of affordable machines. In either approach it is usual the user to specify the workflow of the sub-tasks composing the whole KDD process before execution starts.In this paper we propose a technique that we call Distributed Generative Data Mining. The generative feature of the technique is due to its capability of generating new sub-tasks of the Data Mining analysis process at execution time. The workflow of sub-tasks of the DM is, therefore, dynamic.To deploy the proposed technique we extended the Distributed Data Mining system HARVARD and adapted an Inductive Logic Programming system (IndLog) used in a Relational Data Ming task.As a proof-of-concept, the extended system was used to analyse an artificialdataset of a credit scoring problem with eighty million records

AlignNemo: A Local Network Alignment Method to Integrate Homology and Topology

Local network alignment is an important component of the analysis of protein-protein interaction networks that may lead to the identification of evolutionary related complexes. We present AlignNemo, a new algorithm that, given the networks of two organisms, uncovers subnetworks of proteins that relate in biological function and topology of interactions. The discovered conserved subnetworks have a general topology and need not to correspond to specific interaction patterns, so that they more closely fit the models of functional complexes proposed in the literature. The algorithm is able to handle sparse interaction data with an expansion process that at each step explores the local topology of the networks beyond the proteins directly interacting with the current solution. To assess the performance of AlignNemo, we ran a series of benchmarks using statistical measures as well as biological knowledge. Based on reference datasets of protein complexes, AlignNemo shows better performance than other methods in terms of both precision and recall. We show our solutions to be biologically sound using the concept of semantic similarity applied to Gene Ontology vocabularies. The binaries of AlignNemo and supplementary details about the algorithms and the experiments are available at: sourceforge.net/p/alignnemo

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

<p>Abstract</p> <p>Introduction</p> <p>Raw spectral data from matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) with MS profiling techniques usually contains complex information not readily providing biological insight into disease. The association of identified features within raw data to a known peptide is extremely difficult. Data preprocessing to remove uncertainty characteristics in the data is normally required before performing any further analysis. This study proposes an alternative yet simple solution to preprocess raw MALDI-TOF-MS data for identification of candidate marker ions. Two in-house MALDI-TOF-MS data sets from two different sample sources (melanoma serum and cord blood plasma) are used in our study.</p> <p>Method</p> <p>Raw MS spectral profiles were preprocessed using the proposed approach to identify peak regions in the spectra. The preprocessed data was then analysed using bespoke machine learning algorithms for data reduction and ion selection. Using the selected ions, an ANN-based predictive model was constructed to examine the predictive power of these ions for classification.</p> <p>Results</p> <p>Our model identified 10 candidate marker ions for both data sets. These ion panels achieved over 90% classification accuracy on blind validation data. Receiver operating characteristics analysis was performed and the area under the curve for melanoma and cord blood classifiers was 0.991 and 0.986, respectively.</p> <p>Conclusion</p> <p>The results suggest that our data preprocessing technique removes unwanted characteristics of the raw data, while preserving the predictive components of the data. Ion identification analysis can be carried out using MALDI-TOF-MS data with the proposed data preprocessing technique coupled with bespoke algorithms for data reduction and ion selection.</p

What Every Reader Should Know About Studies Using Electronic Health Record Data but May Be Afraid to Ask

Coincident with the tsunami of COVID-19-related publications, there has been a surge of studies using real-world data, including those obtained from the electronic health record (EHR). Unfortunately, several of these high-profile publications were retracted because of concerns regarding the soundness and quality of the studies and the EHR data they purported to analyze. These retractions highlight that although a small community of EHR informatics experts can readily identify strengths and flaws in EHR-derived studies, many medical editorial teams and otherwise sophisticated medical readers lack the framework to fully critically appraise these studies. In addition, conventional statistical analyses cannot overcome the need for an understanding of the opportunities and limitations of EHR-derived studies. We distill here from the broader informatics literature six key considerations that are crucial for appraising studies utilizing EHR data: data completeness, data collection and handling (eg, transformation), data type (ie, codified, textual), robustness of methods against EHR variability (within and across institutions, countries, and time), transparency of data and analytic code, and the multidisciplinary approach. These considerations will inform researchers, clinicians, and other stakeholders as to the recommended best practices in reviewing manuscripts, grants, and other outputs from EHR-data derived studies, and thereby promote and foster rigor, quality, and reliability of this rapidly growing field

International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality

Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach