Association between dental factors and mortality

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Trees as carbon sinks and sources in the European Union

http://www.elsevier.com/locate/issn/14629011The carbon (C) sinks and sources of trees that may be accounted for under Article 3.3 of the Kyoto Protocol during the first commitment period from 2008 to 2012 were estimated for the countries of the European Union (EU) based on existing forest inventory data. Two sets of definitions for the accounted activities, afforestation, reforestation and deforestation, were applied. Applying the definitions by the Food and Agricultural Organization of the United Nations (FAO), the trees were estimated to be a C source in eight and a C sink in seven countries, and in the whole EU a C source of 5.4 Tg year-1. Applying the definitions by the Intergovernmental Panel of Climate Change (IPCC), the trees were estimated to be a C source in three and a C sink in 12 countries, and in the whole EU a C sink of 0.1 Tg year-1. These estimates are small compared with the C sink of trees in all EU forests, 63 Tg year-1, the anthropogenic CO2 emissions of the EU, 880 Tg C year-1, and the reduction target of the CO2 emissions, 8%. In individual countries, the estimated C sink of the trees accounted for under Article 3.3 was at largest 8% and the C source 12% compared with the CO2 emissions. 7 2000 Elsevier Science Ltd. All rights reserved

Salivary IgA to MAA-LDL and Oral Pathogens Are Linked to Coronary Disease

A large body of literature has established the link between periodontal disease and cardiovascular disease. Oxidized low-density lipoproteins (OxLDLs) have a crucial role in atherosclerosis progression through initiation of immunological response. Monoclonal IgM antibodies to malondialdehyde-modified low-density lipoprotein (MDA-LDL) and to malondialdehyde acetaldehyde-modified low-density lipoprotein (MAA-LDL) have been shown to cross-react with the key virulence factors of periodontal pathogens Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans. We have previously shown that salivary IgA antibodies to MAA-LDL cross-react with P. gingivalis in healthy humans. In this study, we aim to assess whether oral mucosal immune response represented by salivary IgA to MAA-LDL and oral pathogens is associated with coronary artery disease (CAD). Also, the molecular mimicry through antibody cross-reaction between salivary IgA to MAA-LDL and oral pathogens was evaluated. The study subjects consisted of 451 patients who underwent a coronary angiography with no CAD (n = 133), stable CAD (n = 169), and acute coronary syndrome (ACS, n = 149). Elevated salivary IgA antibody levels to MAA-LDL, Rgp44 (gingipain A hemagglutinin domain of P. gingivalis), and Aa-HSP60 (heat shock protein 60 of A. actinomycetemcomitans) were discovered in stable-CAD and ACS patients when compared to no-CAD patients. In a multinomial regression model adjusted for known cardiovascular risk factors, stable CAD and ACS were associated with IgA to MAA-LDL (P = 0.016, P = 0.043), Rgp44 (P = 0.012, P = 0.004), Aa-HSP60 (P = 0.032, P = 0.030), Tannerella forsythia (P = 0.002, P = 0.004), Porphyromonas endodontalis (P = 0.016, P = 0.020), Prevotella intermedia (P = 0.038, P = 0.005), and with total IgA antibody concentration (P = 0.002, P = 0.016). Salivary IgA to MAA-LDL showed cross-reactivity with the oral pathogens tested in the study patients. The study highlights an association between salivary IgA to MAA-LDL and atherosclerosis. However, whether salivary IgA to MAA-LDL and the related oral humoral responses play a causal role in the development in the CAD should be elucidated in the future.Peer reviewe

Salivary IgA to MAA-LDL and Oral Pathogens Are Linked to Coronary Disease

A large body of literature has established the link between periodontal disease and cardiovascular disease. Oxidized low-density lipoproteins (OxLDLs) have a crucial role in atherosclerosis progression through initiation of immunological response. Monoclonal IgM antibodies to malondialdehyde-modified low-density lipoprotein (MDA-LDL) and to malondialdehyde acetaldehyde-modified low-density lipoprotein (MAA-LDL) have been shown to cross-react with the key virulence factors of periodontal pathogens Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans. We have previously shown that salivary IgA antibodies to MAA-LDL cross-react with P. gingivalis in healthy humans. In this study, we aim to assess whether oral mucosal immune response represented by salivary IgA to MAA-LDL and oral pathogens is associated with coronary artery disease (CAD). Also, the molecular mimicry through antibody cross-reaction between salivary IgA to MAA-LDL and oral pathogens was evaluated. The study subjects consisted of 451 patients who underwent a coronary angiography with no CAD (n = 133), stable CAD (n = 169), and acute coronary syndrome (ACS, n = 149). Elevated salivary IgA antibody levels to MAA-LDL, Rgp44 (gingipain A hemagglutinin domain of P. gingivalis), and Aa-HSP60 (heat shock protein 60 of A. actinomycetemcomitans) were discovered in stable-CAD and ACS patients when compared to no-CAD patients. In a multinomial regression model adjusted for known cardiovascular risk factors, stable CAD and ACS were associated with IgA to MAA-LDL (P = 0.016, P = 0.043), Rgp44 (P = 0.012, P = 0.004), Aa-HSP60 (P = 0.032, P = 0.030), Tannerella forsythia (P = 0.002, P = 0.004), Porphyromonas endodontalis (P = 0.016, P = 0.020), Prevotella intermedia (P = 0.038, P = 0.005), and with total IgA antibody concentration (P = 0.002, P = 0.016). Salivary IgA to MAA-LDL showed cross-reactivity with the oral pathogens tested in the study patients. The study highlights an association between salivary IgA to MAA-LDL and atherosclerosis. However, whether salivary IgA to MAA-LDL and the related oral humoral responses play a causal role in the development in the CAD should be elucidated in the future.Peer reviewe

CO2FIX V2.0 : manual of a modeling framework for quantifying carbon sequestration in forest ecosystems and wood products

This reports presents a manual of the CO2FIX V 2.0 model. CO2FIX V 2.0 is a simple bookkeeping model that converts volumetric net annual increment data (and additional parameters) to annual carbon stocks and fluxes of the forest ecosystem-soil-wood products chain. It calculates on the hectare scale with time steps of one year. This Version 2.0 is a hectare scale model which was improved on the ability to simulate multi-species and uneven aged stands in multiple cohorts (e.g. selective tropical selective logging systems, and agroforestry systems); the ability to parametrize the growth also by stand density; the ability to deal with inter cohort competition; harvesting, allocation, processing lines, and end-of-life disposal of harvested wood; soil dynamics; the ability to deal with a wider variety of forest types including agro-forestry systems, selective logging systems, and post harvesting mortality; output viewing charts

A machine learning approach to predict resilience and sickness absence in the healthcare workforce during the COVID-19 pandemic

During the COVID-19 pandemic, healthcare workers (HCWs) have faced unprecedented workloads and personal health risks leading to mental disorders and surges in sickness absence. Previous work has shown that interindividual differences in psychological resilience might explain why only some individuals are vulnerable to these consequences. However, no prognostic tools to predict individual HCW resilience during the pandemic have been developed. We deployed machine learning (ML) to predict psychological resilience during the pandemic. The models were trained in HCWs of the largest Finnish hospital, Helsinki University Hospital (HUS, N = 487), with a six-month follow-up, and prognostic generalizability was evaluated in two independent HCW validation samples (Social and Health Services in Kymenlaakso: Kymsote, N = 77 and the City of Helsinki, N = 322) with similar follow-ups never used for training the models. Using the most predictive items to predict future psychological resilience resulted in a balanced accuracy (BAC) of 72.7-74.3% in the HUS sample. Similar performances (BAC = 67-77%) were observed in the two independent validation samples. The models' predictions translated to a high probability of sickness absence during the pandemic. Our results provide the first evidence that ML techniques could be harnessed for the early detection of COVID-19-related distress among HCWs, thereby providing an avenue for potential targeted interventions.Peer reviewe

Missing Teeth Predict Incident Cardiovascular Events, Diabetes, and Death

Periodontitis, the main cause of tooth loss in the middle-aged and elderly, associates with the risk of atherosclerotic vascular disease. The objective was to study the capability of the number of missing teeth in predicting incident cardiovascular diseases (CVDs), diabetes, and all-cause death. The National FINRISK 1997 Study is a Finnish population-based survey of 8,446 subjects with 13 y of follow-up. Dental status was recorded at baseline in a clinical examination by a trained nurse, and information on incident CVD events, diabetes, and death was obtained via national registers. The registered CVD events included coronary heart disease events, acute myocardial infarction, and stroke. In Cox regression analyses, having >= 5 teeth missing was associated with 60% to 140% increased hazard for incident coronary heart disease events (P = 9 missing teeth. No association with stroke was observed. Adding information on missing teeth to established risk factors improved risk discrimination of death (P = 0.0128) and provided a statistically significant net reclassification improvement for all studied end points. Even a few missing teeth may indicate an increased risk of CVD, diabetes, or all-cause mortality. When individual risk factors for chronic diseases are assessed, the number of missing teeth could be a useful additional indicator for general medical practitioners.Peer reviewe

Quantitative PCR analysis of salivary pathogen burden in periodontitis

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