Genome-wide analysis reveals the extent of EAV-HP integration in domestic chicken

Background: EAV-HP is an ancient retrovirus pre-dating Gallus speciation, which continues to circulate in modern chicken populations, and led to the emergence of avian leukosis virus subgroup J causing significant economic losses to the poultry industry. We mapped EAV-HP integration sites in Ethiopian village chickens, a Silkie, Taiwan Country chicken, red junglefowl Gallusgallus and several inbred experimental lines using whole-genome sequence data. Results: An average of 75.22 ± 9.52 integration sites per bird were identified, which collectively group into 279 intervals of which 5% are common to 90% of the genomes analysed and are suggestive of pre-domestication integration events. More than a third of intervals are specific to individual genomes, supporting active circulation of EAV-HP in modern chickens. Interval density is correlated with chromosome length (P<2.31−6), and 27 % of intervals are located within 5 kb of a transcript. Functional annotation clustering of genes reveals enrichment for immune-related functions (P<0.05). Conclusions: Our results illustrate a non-random distribution of EAV-HP in the genome, emphasising the importance it may have played in the adaptation of the species, and provide a platform from which to extend investigations on the co-evolutionary significance of endogenous retroviral genera with their hosts

Functional interaction between mouse erbB3 and wild-type rat c-neu in transgenic mouse mammary tumor cells

INTRODUCTION: Co-expression of several receptor tyrosine kinases (RTKs), including erbB2 and erbB3, is frequently identified in breast cancers. A member of the RTK family, the kinase-deficient erbB3 can activate downstream signaling via heterodimer formation with erbB2. We studied the expression of RTK receptors in mammary tumors from the wild-type (wt) rat c-neu transgenic model. We hypothesized that physical and functional interactions between the wt rat neu/ErbB2 transgene and mouse ErbB3-encoded proteins could occur, activating downstream signaling and promoting mammary oncogenesis. METHODS: Immunohistochemical and Western blot analyses were performed to study the expression of rat c-neu/ErbB2 and mouse erbB3 in mammary tumors and tumor-derived cell lines from the wt rat c-neu transgenic mice. Co-immunoprecipitation methods were employed to quantitate heterodimerization between the transgene-encoded protein erbB2 and the endogenous mouse erbB3. Tumor cell growth in response to growth factors, such as Heregulin (HRG), epidermal growth factor (EGF), or insulin-like growth factor-1 (IGF-1), was also studied. Post-HRG stimulation, activation of the RTK downstream signaling was determined by Western blot analyses using antibodies against phosphorylated Akt and mitogen-activated protein kinase (MAPK), respectively. Specific inhibitors were then used with cell proliferation assays to study the phosphoinositide-3 kinase (PI-3K)/Akt and MAPK kinase (MEK)/MAPK pathways as possible mechanisms of HRG-induced tumor cell proliferation. RESULTS: Mammary tumors and tumor-derived cell lines frequently exhibited elevated co-expression of erbB2 and erbB3. The transgene-encoded protein erbB2 formed a stable heterodimer complex with endogenous mouse erbB3. HRG stimulation promoted physical and functional erbB2/erbB3 interactions and tumor cell growth, whereas no response to EGF or IGF-1 was observed. HRG treatment activated both the Akt and MAPK pathways in a dose- and time-dependent manner. Both the PI-3K inhibitor LY 294002 and MEK inhibitor PD 98059 significantly decreased the stimulatory effect of HRG on tumor cell proliferation. CONCLUSION: The co-expression of wt rat neu/ErbB2 transgene and mouse ErbB3, with physical and functional interactions between these two species of RTK receptors, was demonstrated. These data strongly suggest a role for erbB3 in c-neu (ErbB2)-associated mammary tumorigenesis, as has been reported in human breast cancers

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p&lt;0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p&lt;0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

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

Educational Attainment Moderates the Effect of a Brief Diabetes Intervention

Aims: Those with less education are at increased risk for developing diabetes and have a poorer prognosis. Intensive diabetes self-care interventions have been more effective at improving glycemic control in those with lower educational attainment. Due to limited resources, the focus has shifted to brief, cost-effective health interventions. This study examined whether educational attainment moderates the effect of a brief, telephone delivered self-care intervention on glycemic control in people with type 2 diabetes. Methods Randomized clinical trial. Participants (N = 62) were assigned to receive treatment as usual or treatment as usual plus a brief telephone intervention. The primary outcome measure was hemoglobin A1c. Results: A significant education by intervention group interaction effect indicated that participants with higher educational attainment had greater improvement in glycemic control (A1c) than those with less educational attainment; whereas, educational attainment was unrelated to change in glycemic control (A1c) within the control group. Conclusions: People with higher educational attainment may benefit to a greater extent from brief self-care interventions for diabetes, while those with lower educational attainment may require more intensive treatment