The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Assessment of mycotoxin sequestration efficacy in Saccharomyces cerevisiae by-products cultured in wheat bran and whey protein medium

Abstract Mycotoxins are metabolic products of fungi found in feed for farm animals and pose a major threat to food safety due to their adverse health effects. The development of strategies to reduce their bioavailability is crucial. In this context, the cell wall components of Saccharomyces cerevisiae (YCW), especially β-d-glucans and Mannan-oligosaccharide, have been recognized as potent mycotoxin binders. The objective of this research was to develop a novel culture medium to increase the biomass yield of S. cerevisiae and optimize cell disruption by stepwise physical lysis and hydrolytic preconditioning. This process resulted in a yield of approximately 56% reducing saccharides and 28.54% protein. Subsequently, the β-glucan was extracted after cell wall sequestration. The isolated YCW and extracted β-glucan were characterized both individually and synergistically to evaluate their antibacterial properties and analyze their Fourier transform infrared (FTIR) spectra. In vitro evaluation of antibacterial activity revealed that a concentration greater than 250 μg/mL of YCW-β-glucan blend significantly inhibited the growth of Gram-negative bacteria. In addition, this blend showed good adsorption of various mycotoxins, including Aflatoxin B1, Ochratoxin A, and Zearalenone, the latter of which exhibited a remarkable adsorption rate of 80.85%. This study highlights the promising potential of a combination of YCW and β-glucan as a robust strategy to address the pervasive problem of mycotoxin contamination in feed

Plasma proteomic profiling and pathway analysis of normal and overconditioned dairy cows during the transition from late pregnancy to early lactation

This study applied a quantitative proteomics approach along with bioinformatics analyses to investigate changes in the plasma proteome of normal and overconditioned dairy cows during the transition period. Fifteen weeks before their anticipated calving date, 38 multiparous Holstein cows were selected based on their current and previous body condition scores (BCS) and allocated to either a high or a normal BCS group (19 cows each). They received different diets until dry-off to reach targeted differences in BCS and back fat thickness (BFT) until dry-off. At dry-off, normal BCS cows had a BCS <3.5 (minimum, 2.75) and BFT <1.2 cm (minimum, 0.58), and the high BCS cows had a BCS >3.75 (maximum, 4.50) and BFT >1.4 cm (maximum, 2.90). The proteomics study used a subset of 5 animals from each group. These cows were selected based on their circulating concentrations of fatty acids (FA) on d 14 postpartum and β-hydroxybutyrate (BHB) on d 21 postpartum, representing the greater or the lower extreme values within their BCS group, respectively. The high BCS subset (HE-HBCS) had 4.50 < BCS > 3.75, FA = 1.17 ± 0.46 mmol/L, and BHB = 2.15 ± 0.42 mmol/L (means ± SD), and the low BCS subset (LE-NBCS) had 3.50 < BCS > 2.75, FA = 0.51 ± 0.28 mmol/L, and BHB = 0.84 ± 0.17 mmol/L. Plasma samples from d −49, +7, and +21 relative to parturition were used for proteome profiling by applying the quantitative tandem mass tags (TMT) approach. Nondepleted plasma samples were subjected to reduction and digestion and then labeled with TMT 10plex reagents. High-resolution liquid chromatography-tandem mass spectrometry analysis of TMT-labeled peptides was carried out, and the acquired spectra were analyzed for protein identification and quantification. In total, 254 quantifiable proteins (criteria: 2 unique peptides and 5% false discovery rate) were identified in the plasma samples. From these, 24 differentially abundant proteins (14 more abundant, 10 less abundant) were observed in the LE-NBCS cows compared with the HE-HBCS cows during the transition period. Plasma α-2-macroglobulins were more abundant in HE-HBCS versus LE-NBCS cows at d +7 and +21. Gene Ontology enrichment analyses of differentially abundant proteins revealed that the acute inflammatory response, regulation of complement activation, protein activation cascade, and regulation of humoral immune response were the most enriched terms in the LE-NBCS group compared with the HE-HBCS group. In addition, we identified 24 differentially abundant proteins (16 in the LE-NBCS group, and 8 in the HE-HBCS group) during the transition period. The complement components C1q and C5 were less abundant, while C3 and C3d were more abundant in LE-NBCS compared with HE-HBCS cows. Overall, overconditioning around calving was associated with alterations in protein pathways related to acute inflammatory response and regulation of complement and coagulation cascades in transition cows

Fuzzy modeling of a piezoelectric actuator

In this research, a piezoelectric actuator was modeled using fuzzy subtractive clustering and neuro-fuzzy networks. In the literature, the use of various modeling techniques (excluding techniques used in this article) and different arrangements of inputs in black box modeling of piezoelectric actuators for the purpose of displacement prediction has been reported. Nowadays, universal approximators are available with proven ability in system modeling; hence, the modeling technique is no longer such a critical issue. Appropriate selection of the inputs to the model is, however, still an unsolved problem, with an absence of comparative studies. While the extremum values of input voltage and/or displacement in each cycle of operation have been used in black box modeling inspired by classical phenomenological methods, some researchers have ignored them. This article focuses on addressing this matter. Despite the fact that classical artificial neural networks, the most popular black box modeling tools, provide no visibility of the internal operation, neuro-fuzzy networks can be converted to fuzzy models. Fuzzy models comprise of fuzzy rules which are formed by a number of fuzzy or linguistic values, and this lets the researcher understand the role of each input in the model in comparison with other inputs, particularly, if fuzzy values (sets) have been selected through subtractive clustering. This unique advantage was employed in this research together with consideration of a few critical but subtle points in model verification which are usually overlooked in black box modeling of piezoelectric actuators.Morteza Mohammadzaheri, Steven Grainger and Mohsen Bazghale