B cell–adaptive immune profile in emphysema-predominant chronic obstructive pulmonary disease

Cigarette smoke, the major risk factor for COPD in developed countries, causes pulmonary inflammation that persists long after smoking cessation, suggesting self-perpetuating adaptive immune responses similar to those that occur in autoimmune diseases. Increases in the number and size of B cell–rich lymphoid follicles (LFs) have been shown in patients in severe stages of COPD (4), and increased B-cell products (autoantibodies) have been observed in the blood and lungs of patients with COPD (5, 6). Oligoclonal rearrangement of the immunoglobulin genes has been observed in B cells isolated from COPD LFs, suggesting that a specific antigenic stimulation drives B-cell proliferation. Consistently, we have shown that in the COPD lung, there is an overexpression of BAFF (B-cell activation factor of the TNF family), which is a key regulator of B-cell homeostasis in several autoimmune diseases (7) and is involved in the growth of LFs in COPD. However, a network analysis of lung transcriptomics showed that a prominent B-cell molecular signature characterized emphysema preferentially but was absent in AD independently of the degree of airflow limitation (8). In the current study, we investigated the correlation between B-cell responses in lung tissue from patients with COPD and healthy smokers, and the extent of emphysema versus airflow limitation

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

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

The Burden of Breast Cancer Predisposition Variants Across The Age Spectrum Among 10 000 Patients.

BACKGROUND/OBJECTIVES: Women diagnosed with breast cancer (BC) at an older age are less likely to undergo genetic cancer risk assessment and genetic testing since the guidelines and referrals are biased toward earlier age at diagnosis. Thus, we determined the prevalence and type of pathogenic cancer predisposition variants among women with a history of BC diagnosed at the age of 65 years or older vs younger than 65 years. DESIGN: Prospective registration cohort. SETTING: The Clinical Cancer Genomics Community Research Network, including 40 community-based clinics in the United States and 5 in Latin America. PARTICIPANTS: Women with BC and genetic testing results. MEASUREMENTS: Sociodemographic characteristics, clinical variables, and genetic profiles were compared between women aged 65 years and older and those younger than 65 years at BC diagnosis. RESULTS: Among 588 women diagnosed with BC and aged 65 years and older and 9412 diagnosed at younger than 65 years, BC-associated pathogenic variants (PVs) were detected in 5.6% of those aged 65 years and older (n = 33) and 14.2% of those younger than 65 years (n = 1340) (P \u3c .01). PVs in high-risk genes (eg, BRCA1 and BRCA2) represented 81.1% of carriers among women aged 65 years and older (n = 27) and 93.1% of those younger than 65 years (n = 1248) (P = .01). BRCA2 PVs represented 42.4% of high-risk gene findings for those aged 65 years and older, whereas BRCA1 PVs were most common among carriers younger than 65 years (49.7%). PVs (n = 7) in moderate-risk genes represented 21.2% for carriers aged 65 years and older and 7.3% of those younger than 65 years (n = 98; P \u3c .01). CHEK2 PVs were the most common moderate-risk gene finding in both groups. CONCLUSION: Clinically actionable BC susceptibility PVs, particularly in BRCA2 and CHEK2, were relatively prevalent among older women undergoing genetic testing. The significant burden of PVs for older women with BC provides a critical reminder to recognize the full spectrum of eligibility and provide genetic testing for older women, rather than exclusion based on chronological age alone. J Am Geriatr Soc 67:884-888, 2019

Alzheimer's disease-related overexpression of the cation-dependent mannose 6-phosphate receptor increases Aβ secretion : role for altered lysosomal hydrolase distribution in β-amyloidogenesis

Prominent endosomal and lysosomal changes are an invariant feature of neurons in sporadic Alzheimer's disease (AD). These changes include increased levels of lysosomal hydrolases in early endosomes and increased expression of the cation-dependent mannose 6-phosphate receptor (CD-MPR), which is partially localized to early endosomes. To determine whether AD-associated redistribution of lysosomal hydrolases resulting from changes in CD-MPR expression affects amyloid precursor protein (APP) processing, we stably transfected APP-overexpressing murine L cells with human CD-MPR. As controls for these cells, we also expressed CD-MPR trafficking mutants that either localize to the plasma membrane (CD-MPRpm) or to early endosomes (CD-MPRendo). Expression of CD-MPR resulted in a partial redistribution of a representative lysosomal hydrolase, cathepsin D, to early endosomal compartments. Turnover of APP and secretion of sAPPalpha and sAPPbeta were not altered by overexpression of any of the CD-MPR constructs. However, secretion of both human Abeta40 and Abeta42 into the growth media nearly tripled in CD-MPR- and CD-MPRendo-expressing cells when compared with parental or CD-MPRpm-expressing cells. Comparable increases were confirmed for endogenous mouse Abeta40 in L cells expressing these CD-MPR constructs but not overexpressing human APP. These data suggest that redistribution of lysosomal hydrolases to early endocytic compartments mediated by increased expression of the CD-MPR may represent a potentially pathogenic mechanism for accelerating Abeta generation in sporadic AD, where the mechanism of amyloidogenesis is unknown

Breast cancer associated pathogenic variants among women 61 years and older with triple negative breast cancer.

Women with triple negative breast cancer (TNBC) have a high prevalence of BRCA1 mutations, and current clinical guidelines recommend genetic testing for patients with TNBC aged ≤60 years. However, studies supporting this recommendation have included few older women with TNBC. METHODS: Genetic testing results from women aged \u3e60 years with TNBC enrolled in the Clinical Cancer Genomics Community Research Network (CCGCRN) registry were included in this analysis. Prevalence of breast cancer-associated pathogenic variants (PVs) was compared across age groups. RESULTS: We identified 151 women with TNBC aged \u3e60 years (median 65 years; SD 5.3). Of these, 130 (86%) underwent genetic testing, and a breast cancer-associated PV was identified in 16 (12.3%; 95% CI 7-19): BRCA1 (n = 6), BRCA2 (n = 5), PALB2 (n = 2), ATM (n = 1) and RAD51C (n = 2). We found no differences in the proportion of patients with close blood relatives with breast (≤50 years) or ovarian cancer (any age) between PV carriers (37.5%) and non-carriers (34.2%) (p = 0.79). Among PV\u27s carriers, the proportion of older women with a BRCA1 PV was lower when compared to younger women (37.5% vs 77.2%; p \u3c 0.01). CONCLUSION: Breast cancer-associated PVs were found in an important proportion of women aged \u3e60 years with TNBC undergoing genetic testing, including greater representation of BRCA2. These results suggest that older women with TNBC should be offered genetic testing, and that their exclusion based on chronologic age alone may not be appropriate

Global dataset of soil organic carbon in tidal marshes.

Funder: The Nature Conservancy through the Bezos Earth Fund and other donor supportFunder: Nelson Mandela UniversityFunder: State Research Agency of Spain (AEI; CGL2007-64915), the Mancomunidad de los Canales del Taibilla (MCT), and the Science and Technology Agency of the Murcia Region (Seneca Foundation; 00593/PI/04 & 08739/PI/08).Funder: Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1)Funder: COOLSTYLE/CARBOSTORE projectFunder: New Zealand Ministry for Business, Innovation and Employment Contract #C01X2109Funder: Portuguese national funds from FCT - Foundation for Science and Technology through projects UIDB/04326/2020, UIDP/04326/2020, LA/P/0101/2020, and 2020.03825.CEECINDFunder: German Research Foundation (DFG project number: GI 171/25-1)Funder: State Research Agency of Spain (AEI; CGL2007-64915), the Mancomunidad de los Canales del Taibilla (MCT), the Science and Technology Agency of the Murcia Region (Seneca Foundation; 00593/PI/04 & 08739/PI/08), and a Ramón y Cajal contract from the Spanish Ministry of Science and Innovation (RYC2020-029322-I)Funder: Velux foundation (#28421, Blå Skove – Havets Skove som kulstofdræn)Funder: LIFE ADAPTA BLUES project Ref. LIFE18 CCA/ES/001160Funder: LIFE ADAPTA BLUES project Ref. LIFE18 CCA/ES/001160, support of national funds through Fundação para a Ciência e Tecnologia, I.P. (FCT), under the projects UIDB/04292/2020, UIDP/04292/2020, granted to MARE, and LA/P/0069/2020, granted to the Associate Laboratory ARNETFunder: Financial support provided by the Welsh Government and Higher Education Funding Council for Wales through the Sêr Cymru National Research Network for Low Carbon, Energy and Environment; as well as the Spanish Ministry of Science and Innovation (project PID2020-113745RB-I00) and FEDERFunder: South African Department of Science and Innovation (DSI)—National Research Foundation (NRF) Research Chair in Shallow Water Ecosystems (UID: 84375), and the Nelson Mandela UniversityFunder: I+D+i projects RYC2019-027073-I and PIE HOLOCENO 20213AT014 funded by MCIN/AEI/10.13039/501100011033 and FEDERFunder: Funding support from the Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1)Funder: Xunta de Galicia (GRC project IN607A 2021-06)Funder: U.S. Army Engineering, Research and Development Center (ACTIONS project, W912HZ2020070)Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2 ± 38.1 Mg SOC ha-1 in the top 30 cm and 231 ± 134 Mg SOC ha-1 in the top 1 m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies

Global dataset of soil organic carbon in tidal marshes

Funding: W.E.N.A. and C.S. would like to acknowledge funding support from the Scottish Government and UK Natural Environment Research Council C-SIDE project (grant NE/R010846/1).Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this data we estimated a median (± median absolute deviation) value of 79.2±38.1 Mg SOC ha−1 in the top 30cm and 231±134 Mg SOC ha−1 in the top 1m of tidal marsh soils globally. This data can serve as a basis for future work, and may contribute to incorporation of tidal marsh ecosystems into climate change mitigation and adaptation strategies and policies.Publisher PDFPeer reviewe

Database: Tidal Marsh Soil Organic Carbon (MarSOC) Dataset

The repository is formatted in the following structure: - README.md: markdown file with repository description - MarSOC-Dataset.Rproj: R project file - useful when using RStudio - Maxwell_MarSOC_dataset.csv: .csv file containing the final dataset. The data structure is described in the metadata file. It contains 17,454 records distributed amongst 29 countries. - Maxwell_MarSOC_dataset_metadata.csv: .csv file containing the main data file metadata (equivalent to Table 1). - data_paper/: folder containing the list of studies included in the dataset, as well as figures for this data paper (generated from the following R script: ‘reports/04_data_process/scripts/04_data-paper_data_clean.R’). - reports/01_litsearchr/: folder containing .bib files with references from the original naive search, a .Rmd document describing the litsearchr analysis using nodes to go from the naive search to the final search string, and the .bib files from this final search, which were then imported into sysrev for abstract screening. - reports/02_sysrev/: folder with .csv files exported from sysrev after abstract screening. These files contain the included studies with their various labels. - reports/03_data_format/: folder containing all original data, associated scripts, and exported data. - reports/04_data_process/: folder containing data processing scripts to bind and clean the exported data, as well as a script testing the different models for predicting soil organic carbon from organic matter and finalising the equation using all available data. A script testing and removing outliers is also included