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Cancer cachexia is a complex multifactorial syndrome marked by a continuous depletion of skeletal muscle mass associated, in some cases, with a reduction in fat mass. It is irreversible by nutritional support alone and affects up to 74% of patients with cancer—dependent on the underlying type of cancer—and is associated with physical function impairment, reduced response to cancer-related therapy, and higher mortality. Organs, like muscle, adipose tissue, and liver, play an important role in the progression of cancer cachexia by exacerbating the pro- and anti-inflammatory response initially activated by the tumor and the immune system of the host. Moreover, this metabolic dysfunction is produced by alterations in glucose, lipids, and protein metabolism that, when maintained chronically, may lead to the loss of skeletal muscle and adipose tissue. Although a couple of drugs have yielded positive results in increasing lean body mass with limited impact on physical function, a single therapy has not lead to effective treatment of this condition. Therefore, a multimodal intervention, including pharmacological agents, nutritional support, and physical exercise, may be a reasonable approach for future studies to better understand and prevent the wasting of body compartments in patients with cancer cachexia.

Synthetic biomaterials have played an increasingly prominent role in the substitution of naturally derived biomaterials in current surgery practice. In vitro and in vivo characterization studies of new synthetic biomaterials are essential to analyze their physicochemical properties and the underlying mechanisms associated with the modulation of the inflammatory process and bone healing. This study compares the in vivo tissue behavior of a synthetic biomaterial nano-hydroxyapatite/beta-tricalcium phosphate (nano-HA/ß-TCP mixture) and deproteinized bovine bone mineral (DBBM) in a rat calvarial defect model. The innovation of this work is in the comparative analysis of the effect of new synthetic and commercially xenogenic biomaterials on the inflammatory response, bone matrix gain, and stimulation of osteoclastogenesis and osteoblastogenesis. Both biomaterials were inserted in rat defects. The animals were divided into three groups, in which calvarial defects were filled with xenogenic biomaterials (group 1) and synthetic biomaterials (group 2), or left unfilled (group 3, controls). Sixty days after calvarial bone defects filled with biomaterials, periodic acid Schiff (PAS) and Masson's trichrome staining, immunohistochemistry tumor necrosis factor-alpha (TNF-α), matrix metalloproteinase-9 (MMP-9), and electron microscopy analyses were conducted. Histomorphometric analysis revealed powerful effects such as a higher amount of proteinaceous matrix and higher levels of TNF-α and MMP-9 in bone defects treated with alloplastic nano-HA/ß-TCP mixture than xenogenicxenogic biomaterial, as well as collagen-proteinaceous material in association with hydroxyapatite crystalloids. These data indicate that the synthetic nano-HA/ß-TCP mixture enhanced bone formation/remodeling in rat calvarial bone defects. The nano-HA/ß-TCP did not present risks of cross-infection/disease transmission. The synthetic nano-hydroxyapatite/beta-tricalcium phosphate mixture presented adequate properties for guided bone regeneration and guided tissue regeneration for dental surgical procedures.

A plateau in oxygen uptake ([Formula: see text]) during an incremental cardiopulmonary exercise test (CPET) to volitional exhaustion appears less likely to occur in special and clinical populations. Secondary maximal oxygen uptake ([Formula: see text]) criteria have been shown to commonly underestimate the actual [Formula: see text]. The verification phase protocol might determine the occurrence of 'true' [Formula: see text] in these populations. The primary aim of the current study was to systematically review and provide a meta-analysis on the suitability of the verification phase for confirming 'true' [Formula: see text] in special and clinical groups. Secondary aims were to explore the applicability of the verification phase according to specific participant characteristics and investigate which test protocols and procedures minimise the differences between the highest [Formula: see text] values attained in the CPET and verification phase. Electronic databases (PubMed, Web of Science, SPORTDiscus, Scopus, and EMBASE) were searched using specific search strategies and relevant data were extracted from primary studies. Studies meeting inclusion criteria were systematically reviewed. Meta-analysis techniques were applied to quantify weighted mean differences (standard deviations) in peak [Formula: see text] from a CPET and a verification phase within study groups using random-effects models. Subgroup analyses investigated the differences in [Formula: see text] according to individual characteristics and test protocols. The methodological quality of the included primary studies was assessed using a modified Downs and Black checklist to obtain a level of evidence. Participant-level [Formula: see text] data were analysed according to the threshold criteria reported by the studies or the inherent measurement error of the metabolic analysers and displayed as Bland-Altman plots. Forty-three studies were included in the systematic review, whilst 30 presented quantitative information for meta-analysis. Within the 30 studies, the highest mean [Formula: see text] values attained in the CPET and verification phase protocols were similar (mean difference = -0.00 [95% confidence intervals, CI = -0.03 to 0.03] L·min-1, p = 0.87; level of evidence, LoE: strong). The specific clinical groups with sufficient primary studies to be meta-analysed showed a similar [Formula: see text] between the CPET and verification phase (p > 0.05, LoE: limited to strong). Across all 30 studies, [Formula: see text] was not affected by differences in test protocols (p > 0.05; LoE: moderate to strong). Only 23 (53.5%) of the 43 reviewed studies reported how many participants achieved a lower, equal, or higher [Formula: see text] value in the verification phase versus the CPET or reported or supplied participant-level [Formula: see text] data for this information to be obtained. The percentage of participants that achieved a lower, equal, or higher [Formula: see text] value in the verification phase was highly variable across studies (e.g. the percentage that achieved a higher [Formula: see text] in the verification phase ranged from 0% to 88.9%). Group-level verification phase data appear useful for confirming a specific CPET protocol likely elicited [Formula: see text], or a reproducible [Formula: see text], for a given special or clinical group. Participant-level data might be useful for confirming whether specific participants have likely elicited [Formula: see text], or a reproducible [Formula: see text], however, more research reporting participant-level data is required before evidence-based guidelines can be given. PROSPERO (CRD42021247658) https://www.crd.york.ac.uk/prospero.

A set S ⊂ Z 2 of integer points is digital convex if conv ( S ) ∩ Z 2 = S , where conv ( S ) denotes the convex hull of S . In this paper, we consider the following two problems. The first one is to test whether a given set S of n lattice points is digital convex. If the answer to the first problem is positive, then the second problem is to find a polygon P ⊂ Z 2 with minimum number of edges and whose intersection with the lattice P ∩ Z 2 is exactly S . We provide linear-time algorithms for these two problems. The algorithm is based on the well-known quickhull algorithm. The time to solve both problems is O ( n + h log r ) = O ( n + n 1 / 3 log r ) , where h = min ( | conv ( S ) | , n 1 / 3 ) and r is the diameter of S .

Purpose of Review Sarcopenia and frailty are common in patients with heart failure (HF) and are strongly associated with prognosis. This review aims to examine promising biomarkers that can guide physicians in identifying sarcopenia and frailty in HF. Recent Findings Traditional biomarkers including C-reactive protein, aminotransaminase, myostatin, and urinary creatinine as well as novel biomarkers including microRNAs, suppression of tumorigenicity 2 (ST2), galectin-3, and procollagen type III N-terminal peptide may help in predicting the development of sarcopenia and frailty in HF patients. Among those biomarkers, aminotransferase, urinary creatinine, and ST2 predicted the prognosis in HF patients with sarcopenia and frailty. Summary This review outlines the current knowledge of biomarkers that are considered promising for diagnosing sarcopenia and frailty in HF. The listed biomarkers might support the diagnosis, prognosis, and therapeutic decisions for sarcopenia and frailty in HF patients.

Approximating convex bodies succinctly by convex polytopes is a fundamental problem in discrete geometry. A convex body K of diameter diam ( K ) is given in Euclidean d -dimensional space, where d is a constant. Given an error parameter ε > 0 , the objective is to determine a polytope of minimum combinatorial complexity whose Hausdorff distance from K is at most ε · diam ( K ) . By combinatorial complexity we mean the total number of faces of all dimensions of the polytope. A well-known result by Dudley implies that O ( 1 / ε ( d - 1 ) / 2 ) facets suffice, and a dual result by Bronshteyn and Ivanov similarly bounds the number of vertices, but neither result bounds the total combinatorial complexity. We show that there exists an approximating polytope whose total combinatorial complexity is O ~ ( 1 / ε ( d - 1 ) / 2 ) , where O ~ conceals a polylogarithmic factor in 1 / ε . This is a significant improvement upon the best known bound, which is roughly O ( 1 / ε d - 2 ) . Our result is based on a novel combination of both old and new ideas. First, we employ Macbeath regions, a classical structure from the theory of convexity. The construction of our approximating polytope employs a new stratified placement of these regions. Second, in order to analyze the combinatorial complexity of the approximating polytope, we present a tight analysis of a width-based variant of Bárány and Larman’s economical cap covering . Finally, we use a deterministic adaptation of the witness-collector technique (developed recently by Devillers et al.) in the context of our stratified construction.

Sacubitril/valsartan reduces mortality in patients with heart failure with reduced ejection fraction (HFrEF) when compared with enalapril. However, it is unknown the effect of both treatments on exercise capacity. We compared sacubitril/valsartan versus enalapril in patients with HFrEF based on peak oxygen consumption (VO2) and 6-minute walk test (6-MWT). We included 52 participants with HFrEF with a left ventricular ejection fraction <40% to receive either sacubitril/valsartan (target dose of 400 mg daily) or enalapril (target dose of 40 mg daily). Peak VO2 was measured by using cardiopulmonary exercise testing. Six-minute walk test was also performed. At 12 weeks, the sacubitril/valsartan (mean dose 382.6 ± 57.6 mg daily) group had increased peak VO2 of 13.1% (19.35 ± 0.99 to 21.89 ± 1.04 mL/kg/min) and enalapril (mean dose 34.4 ± 9.2 mg daily) 5.6% (18.58 ± 1.19 to 19.62 ± 1.25 mL/kg/min). However, no difference was found between groups (P = .332 interaction). At 24 weeks, peak VO2 increased 13.5% (19.35 ± 0.99 to 21.96 ± 0.98 mL/kg/min) and 12.0% (18.58 ± 1.19 to 20.82 ± 1.18 mL/kg/min) in sacubitril/valsartan (mean dose 400 ± 0 mg daily) and enalapril (mean dose 32.7 ± 11.0 mg daily), respectively. However, no differences were found between groups (P= .332 interaction). At 12 weeks, 6-MWT increased in both groups (sacubitril/valsartan: 459 ± 18 to 488 ± 17 meters [6.3%] and enalapril: 443 ± 22 to 477 ± 21 meters [7.7%]). At 24 weeks, sacubitril/valsartan increased 18.3% from baseline (543 ± 26 meters) and enalapril decreased slightly to 6.8% (473 ± 31 meters), but no differences existed between groups (P= .257 interaction). Compared to enalapril, sacubitril/valsartan did not substantially improve peak VO2 or 6-MWT after 12 or 24 weeks in participants with HFrEF. (NEPRIExTol-HF Trial, ClinicalTrials.gov number, NCT03190304)

Blue whiting (BW) represents an underutilised fish species containing a high-quality protein and amino acid (AA) profile with numerous potentially bioactive peptide sequences, making BW an economic and sustainable alternative source of protein. This study investigated the impact of three different BW protein hydrolysates (BWPH-X, Y and Z) on growth, proliferation and muscle protein synthesis (MPS) in skeletal muscle (C2C12) myotubes. BWPHs were hydrolysed using different enzymatic and heat exposures and underwent simulated gastrointestinal digestion (SGID), each resulting in a high degree of hydrolysis (33.41–37.29%) and high quantities of low molecular mass peptides (86.17–97.12% <1 kDa). C2C12 myotubes were treated with 1 mg protein equivalent/mL of SGID-BWPHs for 4 h. Muscle growth and myotube thickness were analysed using an xCelligence™ platform. Anabolic signalling (phosphorylation of mTOR, rpS6 and 4E-BP1) and MPS measured by puromycin incorporation were assessed using immunoblotting. BWPH-X significantly increased muscle growth (p < 0.01) and myotube thickness (p < 0.0001) compared to the negative control (amino acid and serum free media). Muscle protein synthesis (MPS), as measured by puromycin incorporation, was significantly higher after incubation with BWPH-X compared with the negative control, but did not significantly change in response to BWPH-Y and Z treatments. Taken together, these preliminary findings demonstrate the anabolic potential of some but not all BWPHs on muscle enhancement, thus providing justification for human dietary intervention studies to confirm and translate the results of such investigations to dietary recommendations and practices.

Composite materials, valued for their adaptability, face challenges associated with degradation over time. Characterising their durability through traditional experimental methods has shown limitations, highlighting the need for accelerated testing and computational modelling to reduce time and costs. This study presents an overview of the current landscape and future prospects of multi-scale modelling for predicting the long-term durability of composite materials under different environmental conditions. These models offer detailed insights into complex degradation phenomena, including hydrolytic, thermo-oxidative, and mechano-chemical processes. Recent research trends indicate a focus on hygromechanical models across various materials, with future directions aiming to explore less-studied environmental factors, integrate multiple stressors, investigate emerging materials, and advance computational techniques for improved predictive capabilities. The importance of the synergistic relationship between experimental testing and modelling is emphasised as essential for a comprehensive understanding of composite material behaviour in diverse environments. Ultimately, multi-scale modelling is seen as a vital contributor to accurate predictions of environmental effects on composite materials, offering valuable insights for sustainable development across industries.

MicroRNAs are key regulators of eukaryotic gene expression whose fundamental role has already been identified in many cell pathways. The correct identification of miRNAs targets is still a major challenge in bioinformatics and has motivated the development of several computational methods to overcome inherent limitations of experimental analysis. Indeed, the best results reported so far in terms of specificity and sensitivity are associated to machine learning-based methods for microRNA-target prediction. Following this trend, in the current paper we discuss and explore a microRNA-target prediction method based on a random forest classifier, namely RFMirTarget. Despite its well-known robustness regarding general classifying tasks, to the best of our knowledge, random forest have not been deeply explored for the specific context of predicting microRNAs targets. Our framework first analyzes alignments between candidate microRNA-target pairs and extracts a set of structural, thermodynamics, alignment, seed and position-based features, upon which classification is performed. Experiments have shown that RFMirTarget outperforms several well-known classifiers with statistical significance, and that its performance is not impaired by the class imbalance problem or features correlation. Moreover, comparing it against other algorithms for microRNA target prediction using independent test data sets from TarBase and starBase, we observe a very promising performance, with higher sensitivity in relation to other methods. Finally, tests performed with RFMirTarget show the benefits of feature selection even for a classifier with embedded feature importance analysis, and the consistency between relevant features identified and important biological properties for effective microRNA-target gene alignment.