A 3′ UTR SNP in COL18A1 Is Associated with Susceptibility to HBV Related Hepatocellular Carcinoma in Chinese: Three Independent Case-Control Studies

BACKGROUND: Accumulated evidences indicate that single nucleotide polymorphisms (SNP) in angiogenesis and tumorigenesis related genes are associated with risk of Hepatocellular carcinoma (HCC). COL18A1 encodes the precursor of endostatin, which is a broad-spectrum angiogenesis inhibitor, and we speculate that SNPs in COL18A1 may be associated with susceptibility to HCC. METHODS AND FINDINGS: We carried out a 2-stage association study in 3 independent case-control groups in a total of 1067 chronic hepatitis B (CHB) patients and 808 hepatitis B virus (HBV) related HCC patients in Han Chinese. Four SNPs which can represent all potential functional SNPs with MAF>0.1 recorded in HapMap database were genotyped using TaqMan methods. Levels of total COL18A1 mRNA were also examined using quantitative real-time RT-PCR. We found that rs7499 located in 3'-UTR to be strongly associated with HBV related HCC (P(combined) = 0.0000005, OR = 0.72, 95%CI = 0.63-0.82). COL18A1 mRNA expression was significantly decreased as the disease progressed (P = 0.000026). CONCLUSION: These findings indicate that COL18A1 rs7499 may contribute to the risk of HCC in Han Chinese

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Technical research on engine cycle constructing based on transformation model

This paper presents a technical method to derive engine test cycle by establishing a vehicle-to-engine cycle transform model ; Firstly, input, process and transform vehicle cycle and test vehicle data to get corresponding engine condition; Then, apply model built-in gear use strategy to select gear; Finally, under the selected gear, transform vehicle cycle into engine cycle termed by normalized speed and load. In addition, Comparison between model output cycle and WHTC cycle demonstrates that this transform method features consistency with present emission test standard, adaptability to various engine technologies and representative of real-life test scenarios

Effects of Tactical Periodization on Workload, Physical Fitness, and Well-Being in Professional Rugby Union Players During a Preseason Period

International audienceHu, X, Boisbluche, S, Philippe, K, Maurelli, O, Li, S, Xu, B, and Prioux, J. Effects of tactical periodization on workload, physical fitness, and well-being in professional rugby union players during a preseason period. J Strength Cond Res 38(1): 105–115, 2024—Tactical periodization (TP) emerged approximately 30 years ago and has recently gained considerable attention in rugby union (RU). It aims to develop specific physical fitness components with 3 acquisition days (strength, endurance, and speed). However, no study has investigated the effects of TP on workload, physical fitness, and well-being across an RU preseason. This study aimed to determine how RU players' workload response to TP focusing on positional differences, observe the influence of a TP preseason training program on aerobic fitness and neuromuscular performance between positions, and analyze the variation of well-being reported by forwards and backs from the 3 acquisition days. Thirty-two male players completed a 6-week TP protocol. External and internal workload variables were recorded through global positioning systems and session rating of perceived exertion (s-RPE) separately. Fitness assessments included Bronco and countermovement jump (CMJ) tests. The sum of well-being indices was measured using the Hooper index. Kruskal-Wallis H tests revealed that the highest values of PlayerLoad slow, PlayerLoad slow percentage, and s-RPE were found on endurance day and the lowest on speed day. Mann-Whitney U tests showed that 15 external workload parameters were higher in backs than forwards for each acquisition day. Small improvements were observed on the Bronco test. No differences were observed in CMJ performance during the preseason period and well-being values between acquisition days. This study provides unique insights into external and internal workload variables during each acquisition day. Furthermore, it highlights TP as an efficient theoretical concept to use in an RU context

Effects of preseason training on body composition, running performance, biochemical markers and workload variation in professional rugby union players

International audienceFew studies have examined the impact of a preseason training intervention through systematic measures in Pro D2 rugby union (RU). Therefore, this study aimed to describe the effects of 12 weeks of preseason training (three blocks) on body composition, running performance, biochemical markers, and workload (WL) variation in professional RU players. Physiological (physical and biochemical) responses to preseason WL were analyzed by examining changes in anthropometric characteristics, Yo-Yo intermittent recovery level 1 (Yo-Yo IR1) test, blood samples (BS), Hooper index (1–7), the 10-Hz global positioning system (GPS), and session rating of perceived exertion (s-RPE) in nineteen elite male players. Changes throughout the preseason were analyzed using the one-way and mixed-model analysis of variance. Significant (p < 0.01) improvements occurred in anthropometry and Yo-Yo IR1 running performance in forwards and backs. Total distance (p < 0.01) and impact (p < 0.05) during the second block were meaningfully higher than the other two blocks, with backs showing higher values than forwards. As expected, WL decreased significantly (p < 0.01) during the last training block. The WL variations were correlated with changes in biochemical markers over the preseason period. The collected data can be used for i) profiling French Pro D2 rugby championships players, ii) establishing effective training strategies, and iii) setting preseason WL expectations

Position-specific workload of professional rugby union players during tactical periodization training

International audienceThe positional workload characteristics in rugby union on three acquisition days (i.e. strength, endurance, and speed days) of tactical periodization are still relatively unknown. Therefore, the primary aim of this study was to shed light on the positional external workload variables (10 Hz Global Positioning System and accelerometer microtechnology) and internal workload indicators (the session rating of perceived exertion) of players in a professional rugby union team by utilizing and comparing two tactical periodization models. Twenty-six male players (15 forwards and 11 backs) were recruited from a French second-division rugby club. Data were obtained over 10 weeks of in-season home games: a total of 780 observations were analyzed. Student’s t-test observed different external workload profiles between positions among acquisition days. Mean external workload values, except PlayerLoadslow, were significantly higher (p≤0.01; effect size: 0.41–1.93) for backs than forwards for all acquisition days. Moreover, forwards perceived a higher internal workload than backs on the strength day of both models. The findings demonstrate that applying these two tactical periodization models could result in effective rugby union training. Validating external and internal workload characteristics on tactical periodization acquisition days enables extensive analysis of training load monitoring data; these data can be utilized to discover the unique characteristics of each position and design position-specific acquisition days to improve performance

Research on the influence of soak in the cold environment on the energy consumption of electric vehicles

The discharge mode has a great influence on the energy consumption of electric vehicles, especially at cold environment, soak in cold environment will lead to the decrease of the temperature of the vehicle/battery, resulting in the increase of energy consumption. In this paper, a segmented driving energy consumption test method was designed according to the real road discharge low of private vehicle, and the continuous and segmented energy consumption tests were carried out on the laboratory drum under −10 °C. From the perspective of energy flow, the differences of driving energy consumption, air conditioner energy consumption and brake energy recovery under the two test methods are analyzed. Results shown that soak in cold environment will cause the energy consumption of driving and air conditioner to increase by 5.5% and 43.8%, respectively, and also lead to a 17.1% decrease in brake recovery energy

Relationship between Gingival Crevicular Fluid Microbiota and Cytokine Profile in Periodontal Host Homeostasis

As potential biomarkers in periodontitis, microbiome, and cytokines have recently been extensively investigated, but combined analyses of the variations between the microbial structure and cytokine composition are rare. The present study aimed to investigate whether there are differences in the combined profile of microbiome and cytokines in individuals with or without periodontitis. The microbiome and cytokine composition in gingival crevicular fluid (GCF) from 16 patients and 15 controls from Jishi Shan (Gansu, China) were analyzed using 454 pyrosequencing and RayBio Quantibody Arrays. The results showed that a higher co-occurrence of genera in periodontitis group compared with the healthy group, as evaluated by Schoener's abundance-based co-occurrence index. C-reactive protein (CRP) was significantly (P &lt; 0.05) higher in the GCF of the periodontitis group while interleukin (IL)-8 was significantly (P &lt; 0.01) higher in the GCF of the healthy group. The Mantel test revealed a significant concordance between cytokines and microbiota, in the healthy group (Mantel statistic r = 0.36, P ≤ 0.05) but not in the periodontitis group (Mantel statistic r = 0.013, P = 0.434). The results were further confirmed by the Procrustes test. Matrix metalloproteinase (MMP)-9, osteoactivin, IL-8, and macrophage inflammatory protein (MIP)-1a were significantly associated with bacterial composition at the phylum, class, order, family, and genus levels. CRP was also associated with bacterial composition at the species level. In conclusion, alterations in the polymicrobial community structure leads to disruption in the healthy correlation between cytokines and microbiomes. This dysbiosis between the microbiota and the immune response could be one of the major etiological mechanisms underlying periodontitis

Exploration of Human Salivary Microbiomes—Insights into the Novel Characteristics of Microbial Community Structure in Caries and Caries-Free Subjects

<div><p>Recently, high-throughput sequencing has improved the understanding of the microbiological etiology of caries, but the characteristics of the microbial community structure in the human oral cavity with and without caries are not completely clear. To better understand these characteristics, Illumina MiSeq high-throughput sequencing was utilized to analyze 20 salivary samples (10 caries-free and 10 caries) from subjects from the same town in Dongxiang, Gansu, China. A total of 5,113 OTUs (Operational Taxonomic Units, 97% cutoff) were characterized in all of the salivary samples obtained from the 20 subjects. A comparison of the two groups revealed that (i) the predominant phyla were constant between the two groups; (ii) the relative abundance of the genera <i>Veillonella</i>, <i>Bifidobacterium</i>, <i>Selenomonas</i>, <i>Olsenella</i>, <i>Parascardovia</i>, <i>Scardovia</i>, <i>Chryseobacterium</i>, <i>Terrimonas</i>, <i>Burkholderia</i> and <i>Sporobacter</i> was significantly higher in the group with caries (P < 0.05); and (iii) four genera with low relative abundance (< 0.01% on average), including two characteristic genera in caries (<i>Chryseobacterium</i> and <i>Scardovia</i>), significantly influenced the microbial community structure at the genus and OTU levels. Moreover, via co-occurrence and principal component analyses, the co-prevalence of the pathogenic genera was detected in the caries samples, but in the caries-free samples, the function of clustered genera was more random. This result suggests that a synergistic effect may be influencing the assembly of the caries microbial community, whereas competition may play a more dominant role in governing the microbial community in the caries-free group. Our findings regarding the characteristics of the microbial communities of the groups with and without caries might improve the understanding of the microbiological etiology of caries and might improve the prevention and cure of caries in the future.</p></div