University of Southern Denmark

En la actualidad es importante detectar a tiempo la depresión, con el fin de llevar un tratamiento oportuno y mejorar la calidad de vida de las personas, este proceso de evaluación o detección requiere el uso de herramientas o test para llegar a un diagnóstico correcto por parte de profesionales de la salud mental, lo cual puede llegar a ser largo de realizar o en algunos casos ser muy invasivo para las personas. A través de este proyecto se llevó a cabo la investigación de los síntomas que caracterizan el perfil de una persona con depresión y el desarrollo de una aplicación que detecta los posibles síntomas, mediante el uso de webscraping en redes sociales como Instagram, el uso de algoritmos de machine learning, análisis de datos y análisis facial en conjunto para obtener un resultado mas completo del que se puede llegar a tener solo con el texto obtenido en las publicaciones o el análisis aplicado a los rostros. Durante el desarrollo se investigaron los rasgos mas notorios en personas o pacientes con síntomas de depresión, así como los cambios en el lenguaje que puedan generar, con el fin de detectarlo en el texto de las publicaciones, además se investigaron y probaron distintos algoritmos de machine learning con un conjunto de datos para clasificar las publicaciones en suicida o no suicida. Se implementaron módulos de webscraping, clasificación de palabras y una API de análisis facial para descargar y analizar las publicaciones de los perfiles. Durante el desarrollo encontraron varios obstáculos y consideraciones relacionadas a las políticas de uso de Instagram, el manejo de datos personales y los problemas que puede haber al trabajar con este tipo de datos y analizarlos. Este proyecto aporta una base o contexto para crear herramientas de análisis e investigación que sean capaces de detectar síntomas relacionados a la depresión y que trabajen de la mano con otros recursos de diagnóstico y validación clínica.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

Assessment of risk of bias is regarded as an essential component of a systematic review on the effects of an intervention. The most commonly used tool for randomised trials is the Cochrane risk-of-bias tool. We updated the tool to respond to developments in understanding how bias arises in randomised trials, and to address user feedback on and limitations of the original tool.

Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

Chronic obstructive pulmonary disease (COPD) is a global health problem, and since 2001, the Global Initiative for Chronic Obstructive Lung Disease (GOLD) has published its strategy document for the diagnosis and management of COPD. This executive summary presents the main contents of the second 5-year revision of the GOLD document that has implemented some of the vast knowledge about COPD accumulated over the last years. Today, GOLD recommends that spirometry is required for the clinical diagnosis of COPD to avoid misdiagnosis and to ensure proper evaluation of severity of airflow limitation. The document highlights that the assessment of the patient with COPD should always include assessment of (1) symptoms, (2) severity of airflow limitation, (3) history of exacerbations, and (4) comorbidities. The first three points can be used to evaluate level of symptoms and risk of future exacerbations, and this is done in a way that splits patients with COPD into four categories-A, B, C, and D. Nonpharmacologic and pharmacologic management of COPD match this assessment in an evidence-based attempt to relieve symptoms and reduce risk of exacerbations. Identification and treatment of comorbidities must have high priority, and a separate section in the document addresses management of comorbidities as well as COPD in the presence of comorbidities. The revised document also contains a new section on exacerbations of COPD. The GOLD initiative will continue to bring COPD to the attention of all relevant shareholders and will hopefully inspire future national and local guidelines on the management of COPD.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

The PRISMA 2020 statement includes a checklist of 27 items to guide reporting of systematic reviews In this article we explain why reporting of each item is recommended, present bullet points that detail the reporting recommendations, and present examples from published reviews We hope that uptake of the PRISMA 2020 statement will lead to more transparent, complete, and accurate reporting of systematic reviews, thus facilitating evidence based decision making on 1 September

The ESC Guidelines represent the views of the ESC and were produced after careful consideration of the scientific and medical knowledge and the evidence available at the time of their publication.The ESC is not responsible in the event of any contradiction, discrepancy and/or ambiguity between the ESC Guidelines and any other official recommendations or guidelines issued by the relevant public health authorities, in particular in relation to good use of healthcare or therapeutic strategies.Health professionals are encouraged to take the ESC Guidelines fully into account when exercising their clinical judgment, as well as in the determination and the implementation of preventive, diagnostic or therapeutic medical strategies; however, the ESC Guidelines do not override, in any way whatsoever, the individual responsibility of health professionals to make appropriate and accurate decisions in consideration of each patient's health condition and in consultation with that patient and, where appropriate and/or necessary, the patient's caregiver.Nor do the ESC Guidelines exempt health professionals from taking into full and careful consideration the relevant official updated recommendations or guidelines issued by the competent public health authorities, in order to manage each patient's case in light of the scientifically accepted data pursuant to their respective ethical and professional obligations.It is also the health professional's responsibility to verify the applicable rules and regulations relating to drugs and medical devices at the time of prescription.

BACKGROUND: The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. METHODS: We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors-the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). FINDINGS: Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6-58·8) of global deaths and 41·2% (39·8-42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. INTERPRETATION: Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. FUNDING: Bill & Melinda Gates Foundation.

AUTORES: Daniel J Klionsky1745,1749*, Kotb Abdelmohsen840, Akihisa Abe1237, Md Joynal Abedin1762, Hagai Abeliovich425,
\nAbraham Acevedo Arozena789, Hiroaki Adachi1800, Christopher M Adams1669, Peter D Adams57, Khosrow Adeli1981,
\nPeter J Adhihetty1625, Sharon G Adler700, Galila Agam67, Rajesh Agarwal1587, Manish K Aghi1537, Maria Agnello1826,
\nPatrizia Agostinis664, Patricia V Aguilar1960, Julio Aguirre-Ghiso784,786, Edoardo M Airoldi89,422, Slimane Ait-Si-Ali1376,
\nTakahiko Akematsu2010, Emmanuel T Akporiaye1097, Mohamed Al-Rubeai1394, Guillermo M Albaiceta1294,
\nChris Albanese363, Diego Albani561, Matthew L Albert517, Jesus Aldudo128, Hana Alg€ul1164, Mehrdad Alirezaei1198,
\nIraide Alloza642,888, Alexandru Almasan206, Maylin Almonte-Beceril524, Emad S Alnemri1212, Covadonga Alonso544,
\nNihal Altan-Bonnet848, Dario C Altieri1205, Silvia Alvarez1497, Lydia Alvarez-Erviti1395, Sandro Alves107,
\nGiuseppina Amadoro860, Atsuo Amano930, Consuelo Amantini1554, Santiago Ambrosio1458, Ivano Amelio756,
\nAmal O Amer918, Mohamed Amessou2089, Angelika Amon726, Zhenyi An1538, Frank A Anania291, Stig U Andersen6,
\nUsha P Andley2079, Catherine K Andreadi1690, Nathalie Andrieu-Abadie502, Alberto Anel2027, David K Ann58,
\nShailendra Anoopkumar-Dukie388, Manuela Antonioli832,858, Hiroshi Aoki1791, Nadezda Apostolova2007,
\nSaveria Aquila1500, Katia Aquilano1876, Koichi Araki292, Eli Arama2098, Agustin Aranda456, Jun Araya591,
\nAlexandre Arcaro1472, Esperanza Arias26, Hirokazu Arimoto1225, Aileen R Ariosa1749, Jane L Armstrong1930,
\nThierry Arnould1773, Ivica Arsov2120, Katsuhiko Asanuma675, Valerie Askanas1924, Eric Asselin1867, Ryuichiro Atarashi794,
\nSally S Atherton369, Julie D Atkin713, Laura D Attardi1131, Patrick Auberger1787, Georg Auburger379, Laure Aurelian1727,
\nRiccardo Autelli1992, Laura Avagliano1029,1755, Maria Laura Avantaggiati364, Limor Avrahami1166, Suresh Awale1986,
\nNeelam Azad404, Tiziana Bachetti568, Jonathan M Backer28, Dong-Hun Bae1933, Jae-sung Bae677, Ok-Nam Bae409,
\nSoo Han Bae2117, Eric H Baehrecke1729, Seung-Hoon Baek17, Stephen Baghdiguian1368,
\nAgnieszka Bagniewska-Zadworna2, Hua Bai90, Jie Bai667, Xue-Yuan Bai1133, Yannick Bailly884,
\nKithiganahalli Narayanaswamy Balaji473, Walter Balduini2002, Andrea Ballabio316, Rena Balzan1711, Rajkumar Banerjee239,
\nG abor B anhegyi1052, Haijun Bao2109, Benoit Barbeau1363, Maria D Barrachina2007, Esther Barreiro467, Bonnie Bartel997,
\nAlberto Bartolom e222, Diane C Bassham550, Maria Teresa Bassi1046, Robert C Bast Jr1273, Alakananda Basu1798,
\nMaria Teresa Batista1578, Henri Batoko1336, Maurizio Battino970, Kyle Bauckman2085, Bradley L Baumgarner1909,
\nK Ulrich Bayer1594, Rupert Beale1553, Jean-Fran¸cois Beaulieu1360, George R. Beck Jr48,294, Christoph Becker336,
\nJ David Beckham1595, Pierre-Andr e B edard749, Patrick J Bednarski301, Thomas J Begley1135, Christian Behl1419,
\nChristian Behrends757, Georg MN Behrens406, Kevin E Behrns1627, Eloy Bejarano26, Amine Belaid490,
\nFrancesca Belleudi1041, Giovanni B enard497, Guy Berchem706, Daniele Bergamaschi983, Matteo Bergami1401,
\nBen Berkhout1441, Laura Berliocchi714, Am elie Bernard1749, Monique Bernard1354, Francesca Bernassola1880,
\nAnne Bertolotti791, Amanda S Bess272, S ebastien Besteiro1351, Saverio Bettuzzi1828, Savita Bhalla913,
\nShalmoli Bhattacharyya973, Sujit K Bhutia838, Caroline Biagosch1159, Michele Wolfe Bianchi520,1378,1381,
\nMartine Biard-Piechaczyk210, Viktor Billes298, Claudia Bincoletto1314, Baris Bingol350, Sara W Bird1128, Marc Bitoun1112,
\nIvana Bjedov1258, Craig Blackstone843, Lionel Blanc1183, Guillermo A Blanco1496, Heidi Kiil Blomhoff1812,
\nEmilio Boada-Romero1297, Stefan B€ockler1464, Marianne Boes1423, Kathleen Boesze-Battaglia1835, Lawrence H Boise286,287,
\nAlessandra Bolino2063, Andrea Boman693, Paolo Bonaldo1823, Matteo Bordi897, J€urgen Bosch608, Luis M Botana1308,
\nJoelle Botti1375, German Bou1405, Marina Bouch e1038, Marion Bouchecareilh1331, Marie-Jos ee Boucher1901,
\nMichael E Boulton481, Sebastien G Bouret1926, Patricia Boya133, Micha€el Boyer-Guittaut1345, Peter V Bozhkov1141,
\nNathan Brady374, Vania MM Braga469, Claudio Brancolini1997, Gerhard H Braus353, Jos e M Bravo-San Pedro299,393,508,1374,
\nLisa A Brennan322, Emery H Bresnick2022, Patrick Brest490, Dave Bridges1939, Marie-Agn es Bringer124, Marisa Brini1822,
\nGlauber C Brito1311, Bertha Brodin631, Paul S Brookes1872, Eric J Brown352, Karen Brown1690, Hal E Broxmeyer480,
\nAlain Bruhat486,1339, Patricia Chakur Brum1893, John H Brumell446, Nicola Brunetti-Pierri315,1171,
\nRobert J Bryson-Richardson781, Shilpa Buch1777, Alastair M Buchan1819, Hikmet Budak1022, Dmitry V Bulavin118,505,1789,
\nScott J Bultman1792, Geert Bultynck665, Vladimir Bumbasirevic1470, Yan Burelle1356, Robert E Burke216,217,
\nMargit Burmeister1750, Peter B€utikofer1473, Laura Caberlotto1987, Ken Cadwell896, Monika Cahova112, Dongsheng Cai24,
\nJingjing Cai2099, Qian Cai1018, Sara Calatayud2007, Nadine Camougrand1343, Michelangelo Campanella1700,
\nGrant R Campbell1525, Matthew Campbell1249, Silvia Campello556,1876, Robin Candau1769, Isabella Caniggia1983,
\nLavinia Cantoni560, Lizhi Cao116, Allan B Caplan1656, Michele Caraglia1051, Claudio Cardinali1043, Sandra Morais Cardoso1579, Jennifer S Carew208, Laura A Carleton874, Cathleen R Carlin101, Silvia Carloni2002,
\nSven R Carlsson1267, Didac Carmona-Gutierrez1643, Leticia AM Carneiro312, Oliana Carnevali971, Serena Carra1318,
\nAlice Carrier120, Bernadette Carroll900, Caty Casas1324, Josefina Casas1116, Giuliana Cassinelli324, Perrine Castets1462,
\nSusana Castro-Obregon214, Gabriella Cavallini1841, Isabella Ceccherini568, Francesco Cecconi253,555,1884,
\nArthur I Cederbaum459, Valent ın Ce~na199,1281, Simone Cenci1323,2064, Claudia Cerella444, Davide Cervia1996,
\nSilvia Cetrullo1478, Hassan Chaachouay2028, Han-Jung Chae187, Andrei S Chagin634, Chee-Yin Chai626,628,
\nGopal Chakrabarti1502, Georgios Chamilos1601, Edmond YW Chan1142, Matthew TV Chan181, Dhyan Chandra1003,
\nPallavi Chandra548, Chih-Peng Chang818, Raymond Chuen-Chung Chang1653, Ta Yuan Chang345, John C Chatham1434,
\nSaurabh Chatterjee1910, Santosh Chauhan527, Yongsheng Che62, Michael E Cheetham1263, Rajkumar Cheluvappa1783,
\nChun-Jung Chen1153, Gang Chen598,1676, Guang-Chao Chen9, Guoqiang Chen1078, Hongzhuan Chen1077, Jeff W Chen1514,
\nJian-Kang Chen370,371, Min Chen249, Mingzhou Chen2104, Peiwen Chen1823, Qi Chen1674, Quan Chen172,
\nShang-Der Chen138, Si Chen325, Steve S-L Chen10, Wei Chen2125, Wei-Jung Chen829, Wen Qiang Chen979, Wenli Chen1113,
\nXiangmei Chen1133, Yau-Hung Chen1157, Ye-Guang Chen1250, Yin Chen1447, Yingyu Chen953,955, Yongshun Chen2135,
\nYu-Jen Chen712, Yue-Qin Chen1145, Yujie Chen1208, Zhen Chen339, Zhong Chen2123, Alan Cheng1702,
\nChristopher HK Cheng184, Hua Cheng1728, Heesun Cheong814, Sara Cherry1836, Jason Chesney1703,
\nChun Hei Antonio Cheung817, Eric Chevet1359, Hsiang Cheng Chi140, Sung-Gil Chi656, Fulvio Chiacchiera308,
\nHui-Ling Chiang958, Roberto Chiarelli1826, Mario Chiariello235,567,577, Marcello Chieppa835, Lih-Shen Chin290,
\nMario Chiong1285, Gigi NC Chiu878, Dong-Hyung Cho676, Ssang-Goo Cho650, William C Cho982, Yong-Yeon Cho105,
\nYoung-Seok Cho1064, Augustine MK Choi2095, Eui-Ju Choi656, Eun-Kyoung Choi387,400,685, Jayoung Choi1563,
\nMary E Choi2093, Seung-Il Choi2116, Tsui-Fen Chou412, Salem Chouaib395, Divaker Choubey1574, Vinay Choubey1936,
\nKuan-Chih Chow822, Kamal Chowdhury730, Charleen T Chu1856, Tsung-Hsien Chuang827, Taehoon Chun657,
\nHyewon Chung652, Taijoon Chung978, Yuen-Li Chung1194, Yong-Joon Chwae18, Valentina Cianfanelli254,
\nRoberto Ciarcia1775, Iwona A Ciechomska886, Maria Rosa Ciriolo1876, Mara Cirone1042, Sofie Claerhout1694,
\nMichael J Clague1698, Joan Cl aria1457, Peter GH Clarke1687, Robert Clarke361, Emilio Clementi1045,1398, C edric Cleyrat1781,
\nMiriam Cnop1366, Eliana M Coccia574, Tiziana Cocco1459, Patrice Codogno1375, J€orn Coers271, Ezra EW Cohen1533,
\nDavid Colecchia235,567,577, Luisa Coletto25, N uria S Coll123, Emma Colucci-Guyon516, Sergio Comincini1829,
\nMaria Condello578, Katherine L Cook2073, Graham H Coombs1929, Cynthia D Cooper2076, J Mark Cooper1395,
\nIsabelle Coppens601, Maria Tiziana Corasaniti1387, Marco Corazzari485,1884, Ramon Corbalan1566,
\nElisabeth Corcelle-Termeau251, Mario D Cordero1899, Cristina Corral-Ramos1289, Olga Corti507,1109, Andrea Cossarizza1767,
\nPaola Costelli1993, Safia Costes1518, Susan L Cotman721, Ana Coto-Montes946, Sandra Cottet566,1688, Eduardo Couve1301,
\nLori R Covey1015, L Ashley Cowart762, Jeffery S Cox1536, Fraser P Coxon1427, Carolyn B Coyne1846, Mark S Cragg1919,
\nRolf J Craven1679, Tiziana Crepaldi1995, Jose L Crespo1300, Alfredo Criollo1285, Valeria Crippa558, Maria Teresa Cruz1576,
\nAna Maria Cuervo26, Jose M Cuezva1277, Taixing Cui1907, Pedro R Cutillas987, Mark J Czaja27, Maria F Czyzyk-Krzeska1572,
\nRuben K Dagda2068, Uta Dahmen1404, Chunsun Dai800, Wenjie Dai1187, Yun Dai2059, Kevin N Dalby1940,
\nLuisa Dalla Valle1822, Guillaume Dalmasso1340, Marcello D’Amelio557, Markus Damme188, Arlette Darfeuille-Michaud1340,
\nCatherine Dargemont950, Victor M Darley-Usmar1433, Srinivasan Dasarathy205, Biplab Dasgupta202, Srikanta Dash1254,
\nCrispin R Dass242, Hazel Marie Davey8, Lester M Davids1560, David D avila227, Roger J Davis1731, Ted M Dawson604,
\nValina L Dawson606, Paula Daza1898, Jackie de Belleroche470, Paul de Figueiredo1180,1182,
\nRegina Celia Bressan Queiroz de Figueiredo135, Jos e de la Fuente1023, Luisa De Martino1775,
\nAntonella De Matteis1171, Guido RY De Meyer1443, Angelo De Milito631, Mauro De Santi2002,

In the past decade, extracellular vesicles (EVs) have been recognized as potent vehicles of intercellular communication, both in prokaryotes and eukaryotes. This is due to their capacity to transfer proteins, lipids and nucleic acids, thereby influencing various physiological and pathological functions of both recipient and parent cells. While intensive investigation has targeted the role of EVs in different pathological processes, for example, in cancer and autoimmune diseases, the EV‐mediated maintenance of homeostasis and the regulation of physiological functions have remained less explored. Here, we provide a comprehensive overview of the current understanding of the physiological roles of EVs, which has been written by crowd‐sourcing, drawing on the unique EV expertise of academia‐based scientists, clinicians and industry based in 27 European countries, the United States and Australia. This review is intended to be of relevance to both researchers already working on EV biology and to newcomers who will encounter this universal cell biological system. Therefore, here we address the molecular contents and functions of EVs in various tissues and body fluids from cell systems to organs. We also review the physiological mechanisms of EVs in bacteria, lower eukaryotes and plants to highlight the functional uniformity of this emerging communication system.

Quantitative proteomics has traditionally been performed by two-dimensional gel electrophoresis, but recently, mass spectrometric methods based on stable isotope quantitation have shown great promise for the simultaneous and automated identification and quantitation of complex protein mixtures. Here we describe a method, termed SILAC, for stable isotope labeling by amino acids in cell culture, for the in vivo incorporation of specific amino acids into all mammalian proteins. Mammalian cell lines are grown in media lacking a standard essential amino acid but supplemented with a non-radioactive, isotopically labeled form of that amino acid, in this case deuterated leucine (Leu-d3). We find that growth of cells maintained in these media is no different from growth in normal media as evidenced by cell morphology, doubling time, and ability to differentiate. Complete incorporation of Leu-d3 occurred after five doublings in the cell lines and proteins studied. Protein populations from experimental and control samples are mixed directly after harvesting, and mass spectrometric identification is straightforward as every leucine-containing peptide incorporates either all normal leucine or all Leu-d3. We have applied this technique to the relative quantitation of changes in protein expression during the process of muscle cell differentiation. Proteins that were found to be up-regulated during this process include glyceraldehyde-3-phosphate dehydrogenase, fibronectin, and pyruvate kinase M2. SILAC is a simple, inexpensive, and accurate procedure that can be used as a quantitative proteomic approach in any cell culture system. Quantitative proteomics has traditionally been performed by two-dimensional gel electrophoresis, but recently, mass spectrometric methods based on stable isotope quantitation have shown great promise for the simultaneous and automated identification and quantitation of complex protein mixtures. Here we describe a method, termed SILAC, for stable isotope labeling by amino acids in cell culture, for the in vivo incorporation of specific amino acids into all mammalian proteins. Mammalian cell lines are grown in media lacking a standard essential amino acid but supplemented with a non-radioactive, isotopically labeled form of that amino acid, in this case deuterated leucine (Leu-d3). We find that growth of cells maintained in these media is no different from growth in normal media as evidenced by cell morphology, doubling time, and ability to differentiate. Complete incorporation of Leu-d3 occurred after five doublings in the cell lines and proteins studied. Protein populations from experimental and control samples are mixed directly after harvesting, and mass spectrometric identification is straightforward as every leucine-containing peptide incorporates either all normal leucine or all Leu-d3. We have applied this technique to the relative quantitation of changes in protein expression during the process of muscle cell differentiation. Proteins that were found to be up-regulated during this process include glyceraldehyde-3-phosphate dehydrogenase, fibronectin, and pyruvate kinase M2. SILAC is a simple, inexpensive, and accurate procedure that can be used as a quantitative proteomic approach in any cell culture system. Proteomics, the large scale study of the protein complement of a cell or tissue, has its origins in the technology of two-dimensional (2D) 1The abbreviations used are: 2D, two-dimensional; ICAT, isotope-coded affinity tag; MS, mass spectrometry; MALDI-TOF, matrix-assisted laser desorption ionization time-of-flight; MS/MS, tandem MS; 1D, one-dimensional. gel electrophoresis invented more than 25 years ago (1.O’Farrell P.H. High resolution two-dimensional electrophoresis of proteins.J. Biol. Chem. 1975; 250: 4007-4021Google Scholar, 2.Klose J. Kobalz U. Two-dimensional electrophoresis of proteins: an updated protocol and implications for a functional analysis of the genome.Electrophoresis. 1995; 16: 1034-1059Google Scholar). In 2D gel electrophoresis, quantitation is achieved by recording differences in the staining pattern of proteins derived from two states of cell populations or tissues. Therefore, in addition to obtaining increasingly higher resolution, technology improvements in the 2D gel community have been directed toward the image analysis of 2D gels and the relative quantitation of protein spots by their intensity of staining (3.Gorg A. Obermaier C. Boguth G. Harder A. Scheibe B. Wildgruber R. Weiss W. The current state of two-dimensional electrophoresis with immobilized pH gradients.Electrophoresis. 2000; 21: 1037-1053Google Scholar, 4.Herbert B.R. Harry J.L. Packer N.H. Gooley A.A. Pedersen S.K. Williams K.L. What place for polyacrylamide in proteomics?.Trends Biotechnol. 2001; 19: 3-9Abstract Full Text Full Text PDF Google Scholar, 5.Patton W.F. Beechem J.M. Rainbow’s end: the quest for multiplexed fluorescence quantitative analysis in proteomics.Curr. Opin. Chem. Biol. 2002; 6: 63-69Google Scholar, 6.Zhou G. Li H. DeCamp D. Chen S. Shu H. Gong Y. Flaig M. Gillespie J.W. Hu N. Taylor P.R. Emmert-Buck M.R. Liotta L.A. Petricoin III, E.F. Zhao Y. 2D differential in-gel electrophoresis for the identification of esophageal scans cell cancer-specific protein markers.Mol. Cell. Proteomics. 2002; 1: 117-124Google Scholar). Mass spectrometry has long been used in a quantitative manner in the small molecule field (7.Browne T.R. Van Langenhove A. Costello C.E. Biemann K. Greenblatt D.J. Kinetic equivalence of stable-isotope-labeled and unlabeled phenytoin.Clin. Pharmacol. Ther. 1981; 29: 511-515Google Scholar). Pharmacological researchers, for example, use isotopically labeled analogs of the compound of interest and add a known amount to the sample for analysis. This is because mass spectrometry is not quantitative per se because of varying detector response, differential ionization yields for different substances, and other factors. Observed peak ratios for isotopic analogs, however, are highly accurate, because there are no chemical differences between the species, and they are analyzed in the same experiment. One of the first uses of isotopic labels in proteomics was for improved sequence assignment in peptide sequencing by tandem mass spectrometry by incorporating 18O atoms at the C terminus of a peptide (8.Shevchenko A. Chernushevich I. Standing K.G. Thompson B. Wilm M. Mann M. Rapid “de novo” peptide sequencing by a combination of nanoelectrospray, isotopic labeling and a quadrupole/time-of-flight mass spectrometer.Rapid Commun. Mass Spectrom. 1997; 11: 1015-1024Google Scholar, 9.Schnolzer M. Jedrzejewski P. Lehmann W.D. Protease-catalyzed incorporation of 18O into peptide fragments and its application for protein sequencing by electrospray and matrix-assisted laser desorption/ionization mass spectrometry.Electrophoresis. 1996; 17: 945-953Google Scholar, 10.Uttenweiler-Joseph S. Neubauer G. Christoforidis S. Zerial M. Wilm M. Automated de novo sequencing of proteins using the differential scanning technique.Proteomics. 2001; 1: 668-682Google Scholar). The 18O technique had already been used in protein chemistry and was subsequently shown to have interesting uses in quantitation, as well (11.Mirgorodskaya O.A. Kozmin Y.P. Titov M.I. Korner R. Sonksen C.P. Roepstorff P. Quantitation of peptides and proteins by matrix-assisted laser desorption/ionization mass spectrometry using (18)O-labeled internal standards.Rapid Commun. Mass Spectrom. 2000; 14: 1226-1232Google Scholar, 12.Yao X. Freas A. Ramirez J. Demirev P.A. Fenselau C. Proteolytic 18O labeling for comparative proteomics: model studies with two serotypes of adenovirus.Anal. Chem. 2001; 73: 2836-2842Google Scholar, 13.Larsen M.R. Larsen P.M. Fey S.J. Roepstorff P. Characterization of differently processed forms of enolase 2 from Saccharomyces cerevisiae by two-dimensional gel electrophoresis and mass spectrometry.Electrophoresis. 2001; 22: 566-575Google Scholar, 14.Stewart I.I. Thomson T. Figeys D. 18O labeling: a tool for proteomics.Rapid Commun. Mass Spectrom. 2001; 15: 2456-2465Google Scholar). Structural biologists often employ 15N media, in which all 14N atoms are replaced by 15N, to determine phase shifts in NMR studies. Lahm and Langen (15.Lahm H.W. Langen H. Mass spectrometry: a tool for the identification of proteins separated by gels.Electrophoresis. 2000; 21: 2105-2114Google Scholar) and subsequently Chait and co-workers (16.Oda Y. Huang K. Cross F.R. Cowburn D. Chait B.T. Accurate quantitation of protein expression and site-specific phosphorylation.Proc. Natl. Acad. Sci. U. S. A. 1999; 96: 6591-6596Google Scholar) used this 15N-substituted medium for the purpose of quantifying differences between states of microorganisms. The former group used MALDI and 2D gel electrophoresis to quantify the abundance of mixed spots in 2D gels of bacterial proteins, whereas the latter group quantified relative differences in phosphopeptide abundance in yeast. Although clearly showing the power of stable isotope labeling, the particular method employed was limited in its wider applications; 15N-substituted media are difficult and expensive to make for mammalian systems, so the method has generally been limited to microorganisms that can be grown in these media. Additionally, the degree of incorporation is not necessarily 100%, and because there are varying numbers of nitrogen atoms in the different amino acids, automated interpretation of the resulting spectra has proven difficult. Smith and co-workers (17.Veenstra T.D. Martinovic S. Anderson G.A. Pasa-Tolic L. Smith R.D. Proteome analysis using selective incorporation of isotopically labeled amino acids.J. Am. Soc. Mass Spectrom. 2000; 11: 78-82Google Scholar) have used fourier transform-ion cyclotron resonance (FTICR) measurements of intact proteins from microorganisms that were labeled with deuterated leucine-containing media. In this way the number of leucines could be estimated, which helped in the assignment of protein identity to a measured molecular weight (17.Veenstra T.D. Martinovic S. Anderson G.A. Pasa-Tolic L. Smith R.D. Proteome analysis using selective incorporation of isotopically labeled amino acids.J. Am. Soc. Mass Spectrom. 2000; 11: 78-82Google Scholar). In 1999 Aebersold and co-workers (18.Gygi S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.Nat. Biotechnol. 1999; 17: 994-999Google Scholar) introduced the isotope-coded affinity tag (ICAT) method for relative quantitation of protein abundance. In this approach, an isotopically labeled affinity reagent is attached to particular amino acids in all proteins in the population. After digestion of the protein to peptides, as a necessary step in all mainstream proteomic protocols, the labeled peptides are affinity-purified using the newly incorporated affinity tag, thereby achieving a simplification of the peptide mixture at the same time as incorporating the isotopic label. The method has been applied to a range of problems such as the quantification of microsomal proteins in differentiated versus undifferentiated HL-60 cells (19.Han D.K. Eng J. Zhou H. Aebersold R. Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry.Nat. Biotechnol. 2001; 19: 946-951Google Scholar). Limitations of the first iteration of the ICAT principle, which uses biotin as the affinity tag and cysteine as the reactive amino acid, include nonspecific binding to the streptavidin affinity matrix and multiple subsequent reactions at the same site. In recent improvements to the ICAT methodology the cysteines are reacted to solid beads, and a labeled amino acid is attached to the cysteine (20.Zhou H. Ranish J.A. Watts J.D. Aebersold R. Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry.Nat. Biotechnol. 2002; 20: 512-515Google Scholar). This method addresses many of the above limitations and leads to a larger number of identifications of cysteine-containing peptides. However, the method is performed by cross-linking peptides to beads via their cysteine groups and photo-releasing them afterward, which may compromise low analysis. number of isotopic labeling have been that the of chemical of the peptides or proteins M. M. G. P. Quantitation and de novo sequencing of proteins by isotopic labeling of peptides with a Chem. 2000; Scholar, G. A. novo peptide sequencing and quantitative profiling of complex protein mixtures using abundance Biotechnol. 2002; 20: Scholar, A. Watts J.D. R. S. Eng P. Aebersold R. stable isotope labeling of peptides for quantitation and de novo sequence Commun. Mass Spectrom. 2001; 15: Scholar). of these the labeling and peptide step as in the ICAT method, whereas these two or not include the affinity step L. R. L. P. A. C. Thompson proteomics based on stable isotope labeling and affinity Mass Spectrom. 2002; Scholar). quantitation of proteins, labeling and affinity the directly have been Y. T. Chait B.T. analysis of proteins as a tool for the Biotechnol. 2001; 19: Scholar, H. Watts J.D. Aebersold R. approach to the analysis of protein Biotechnol. 2001; 19: Scholar, N.H. T.D. Smith R.D. isotope-coded affinity tag approach for and in Chem. 2001; 73: Scholar). In this we describe a stable isotope labeling that we SILAC isotope labeling by amino acids in cell essential amino acids are to amino acid cell culture media and are incorporated into all proteins as they are into the chemical labeling or affinity are and the method is with all cell culture We that incorporation is and that cells normal in the of labeled media. The method is and and is used in an example, we applied SILAC to the study of cells as they from into This process of muscle necessarily changes in the expression of proteins as the cells from cell to proteins were found to be up-regulated during this of these have not been as up-regulated proteins in this model of muscle differentiation. SILAC cells but may be other quantitative proteomics cell culture is The essential medium and in and was from number The medium was to the the medium was in with and the pH was to The amino acids and were as in and to the media to a of and The medium was a to medium in labeling or were as in and to the media for a of and cells were grown in essential medium supplemented with 2 and in a with in lines were grown for cell in labeling media either normal leucine or Leu-d3 the of differentiation. cells were grown to in normal leucine media. cells that were used for were grown in Leu-d3 media and were the of and the amount of in the media was to medium was replaced with medium every 2 a of were with to proteins and in a pH and The was for two of and to Protein quantitation was performed using the protein and mixtures of were in protein ratios of and the relative quantitation of protein expression during muscle of cell from different and were as After of protein with the mixtures of and samples were in the an undifferentiated at was mixed with an amount of protein from samples at and Protein mixtures were on a gel and to the gel were and to in-gel and digestion as A. Wilm M. Mann M. Mass spectrometric sequencing of proteins polyacrylamide Chem. 1996; Scholar, A. Mann M. of mass spectrometry to study 2000; Scholar). MALDI were with a and a with acid as the M. A. T. S. L. T. Mann M. sequencing of proteins from polyacrylamide gels by mass 1996; were and on a into J. R. Roepstorff P. and technique based on for the analysis of complex peptide mixtures by matrix-assisted laser desorption/ionization mass Mass Spectrom. 1999; Scholar). were with in acid directly into a and the to and analysis on a tandem mass and with a Proteins were by peptide sequence tags M. Wilm identification of peptides in sequence by peptide sequence Chem. derived from spectra of peptides, the protein maintained and updated at the using the quantitative ratios in the and Leu-d3 isotope an isotopic was applied as after peptide the peptide sequence was to the tool which is of the The isotope pattern of the mass in the isotope was from the isotope pattern to the peak of the higher mass Mammalian cells a number of amino acids, these amino acids be in cell culture medium as amino acids for the medium to cell labeled analogs of these amino acids can be and are the labeled of an amino acid is of the abundance amino acid, be incorporated into newly protein After a number of cell of this particular amino acid have been replaced by its isotopically labeled there is no chemical between the labeled amino acid and the amino acid, the cells a control cell grown with the normal amino This is in The experimental cell can be in a specific such as or for Protein populations from samples are and because the is directly into the amino acid sequence of every the can be mixed proteins or peptides the of the labeled to unlabeled as no more is and no can place at the amino acid The proteins and peptides can be analyzed in any of the in which they are analyzed in Quantitation place at the of the peptide mass or peptide mass the same as in any other stable isotope method as is to that the of chemical the same and for SILAC as for SILAC with ICAT labeling, which is the well and method in quantitative proteomics by mass can be from the proteins to be and that can make difficult to the samples in directly states during multiple the chemical and affinity step can be difficult to with small of and peptides are to the in of a large number of affinity to be performed for a experiment. One between SILAC and ICAT methods is that SILAC labels more than of the peptides whereas ICAT labels more than This is based on the 2 and relative abundance of cysteine and and an of amino acids for peptides that can be by mass ICAT in of the peptide mixture whereas SILAC not the peptide resulting from a the in in the case of ICAT is based on the ability to cysteine is to proteins at in ICAT are by the functional group attached to the cysteine whereas in SILAC they are the same as for the unlabeled peptide We use of a labeling medium in amino acids, and for was to the normal amino acids with the of the which be labeled with We leucine in these because is the amino acid, between and and is essential amino acids could have been as mammalian cells media for their amino acids in the can be by the this we used of normal as not of amino the of we cells in media with Leu-d3 but supplemented with normal in place of the 2 clearly that proteins normal leucine can be the incorporation of Leu-d3 in proteins, accurate quantitation of labeled and unlabeled cells not be In the we we have used a essential medium supplemented with the essential amino acids and by SILAC with methods are to the of the amino acid used but are generally not a large of the of the experiment. The SILAC method not in cell culture the of media that we find generally to a of cell lines and that we have in example, we have grown other cell lines a cell cells cells and a cell in culture media not the of this method to any cell system. We performed a time to the time for cells to Leu-d3 in all proteins. The cells were grown in medium for different of shown in incorporation of Leu-d3 was in peptides after of larger incorporation of Leu-d3 was at time with incorporation by This to five doublings for used in this that cell lines can be for use in to protein be that in the time for the cells to five proteins with long incorporation of the as the cells protein to their We were to proteins by peptide mass as well as directed peptide sequencing with In mixtures of and samples were the identification of leucine-containing peptides was by the of peak in the mass the we were to these were and peak by the spectra and peptides to a sample a MS/MS, as in the from and peptides can to the identity of quantitation In peptide mass the of Leu-d3 in peptides peptides peptides a leucine have their peptide by and with more labeled have their mass by the was to and protein based on and peptides in However, the mixtures of proteins in a as well as the from the two cell the process of protein identification by peptide mass we the of mass spectrometric by mass spectra of and peptides were for the mass of fragments the leucine shifts in to the assignment of peptide sequence This is in to (8.Shevchenko A. Chernushevich I. Standing K.G. Thompson B. Wilm M. Mann M. Rapid “de novo” peptide sequencing by a combination of nanoelectrospray, isotopic labeling and a quadrupole/time-of-flight mass spectrometer.Rapid Commun. Mass Spectrom. 1997; 11: 1015-1024Google Scholar, 10.Uttenweiler-Joseph S. Neubauer G. Christoforidis S. Zerial M. Wilm M. Automated de novo sequencing of proteins using the differential scanning technique.Proteomics. 2001; 1: 668-682Google Scholar) incorporation of 18O in peptides to a that in obtaining sequence tag was performed using known of cell from were mixed in ratios of and The ratios of peak of different leucine-containing peptides were found to be in the proteins analyzed and of two peptides mixed in the in ratios are to We performed the not for the isotopic in peptides leucine the ratios were In we in higher differences than We this to be a of the of the peptide mixture in a gel and to be by that not an step of peptide by this this using nanoelectrospray, we to the relative of in the spectra from and Leu-d3 shown in the ratios from the relative from all the well with the of The ratios for a of are shown in I. is to that the in labeled and unlabeled peptides are and no are introduced because of the of a of peptides by their in a we could proteins in based on quantitative changes in their we used cells that have been used as an in model for muscle F. D. J.A. of is to in of protein kinase is for of expression and subsequent Biol. Chem. 1999; Scholar, S.J. A. of mammalian by 2000; Scholar, I. S. K. W. binding and of Biol. Chem. 2002; Scholar). Although the process of is not The of to can be by the cells from the cell expression of and of these cells to of J.A. expression an of and of muscle Biol. Scholar). of the changes that these cells as they in a medium low in changes in protein we the of cells in normal medium and the cells in medium to differentiate. were at different time and analyzed to determine the identity and in abundance of the proteins. the process of is by in cell morphology, and because of changes in expression of matrix proteins, and J.A. expression an of and of muscle Biol. Scholar, F. D.J. matrix is for muscle but not Cell. 1996; Scholar). the SILAC we differential protein expression could be measured in this system. we a combination of gel electrophoresis with mass from different time were separated by gel electrophoresis and or the gel of were in of the mixed which in with samples had shown differential staining between and other time of proteins were quantified in these was to ratios in different leucine-containing peptides for the same The process of quantitation was by the of the because of the large number of protein found in the protein mixture used for analysis. In such we to quantitation on peptide that were well separated and from for isotopic was applied necessary The protein quantitation are by in expression of was up-regulated on 2 and of muscle relative to example, glyceraldehyde-3-phosphate by The of of pyruvate kinase by which with the that the and are more highly in muscle than in other I. K. M. S. cells pyruvate kinase peptide can experimental in 2001; Scholar). Protein such as proteins were found to be up-regulated to in with protein during the of fibronectin, of the of matrix and essential for were found to be Although is known to be an essential in muscle cell F. D.J. matrix is for muscle but not Cell. 1996; Scholar, G. P. A. L. L. G. The is for by growth Biol. had not been shown to be up-regulated during this The relative of other proteins in such as were to the of as an internal have shown that the process of quantitation of protein by SILAC can be performed using standard and in proteomics and can be by groups with cell culture Although we have the of the method with gel electrophoresis and mass the higher in quantitative analysis and protein identification by are to the of this

Rhinosinusitis is a significant and increasing health problem which results in a large financial burden on society. This evidence based position paper describes what is known about rhinosinusitis and nasal polyps, offers evidence based recommendations on diagnosis and treatment, and considers how we can make progress with research in this area. Rhinitis and sinusitis usually coexist and are concurrent in most individuals; thus, the correct terminology is now rhinosinusitis. Rhinosinusitis (including nasal polyps) is defined as inflammation of the nose and the paranasal sinuses characterised by two or more symptoms, one of which should be either nasal blockage/obstruction/congestion or nasal discharge (anterior/posterior nasal drip), +/- facial pain/pressure, +/- reduction or loss of smell; and either endoscopic signs of polyps and/or mucopurulent discharge primarily from middle meatus and/or; oedema/mucosal obstruction primarily in middle meatus, and/or CT changes showing mucosal changes within the ostiomeatal complex and/or sinuses. The paper gives different definitions for epidemiology, first line and second line treatment and for research. Furthermore the paper describes the anatomy and (patho)physiology, epidemiology and predisposing factors, inflammatory mechanisms, evidence based diagnosis, medical and surgical treatment in acute and chronic rhinosinusitis and nasal polyposis in adults and children. Evidence based schemes for diagnosis and treatment are given for the first and second line clinicians. Moreover attention is given to complications and socio-economic cost of chronic rhinosinusitis and nasal polyps. Last but not least the relation to the lower airways is discussed.

Background: The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did and what they found. Over the last decade, there have been many advances in systematic review methodology and terminology, which have necessitated an update to the guideline.Objectives: To develop the PRISMA 2020 statement for reporting systematic reviews.Methods: We reviewed 60 documents with reporting guidance for systematic reviews to generate suggested modifications to the PRISMA 2009 statement. We sought feedback on the suggested modifications through an online survey of 110 systematic review methodologists and journal editors. The results of the review and survey were discussed at a 21-member in-person meeting. Following the meeting, drafts of the PRISMA 2020 checklist, abstract checklist, explanation and elaboration and flow diagram were generated and refined iteratively based on feedback from co-authors and a convenience sample of 15 systematic reviewers.Results: In this statement paper, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews. The checklist includes new reporting guidance that reflects advances in methods to identify, select, appraise and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. The PRISMA 2020 statement replaces the 2009 statement.Conclusions: The PRISMA 2020 statement is intended to facilitate transparent, complete and accurate reporting of systematic reviews. Improved reporting should benefit users of reviews, including guideline developers, policy makers, health care providers, patients and other stakeholders. In order to achieve this, we encourage authors, editors and peer-reviewers to adopt the guideline.

Matthew Page and co-authors describe PRISMA 2020, an updated reporting guideline for systematic reviews and meta-analyses.

BACKGROUND: The contribution of hereditary factors to the causation of sporadic cancer is unclear. Studies of twins make it possible to estimate the overall contribution of inherited genes to the development of malignant diseases. METHODS: We combined data on 44,788 pairs of twins listed in the Swedish, Danish, and Finnish twin registries in order to assess the risks of cancer at 28 anatomical sites for the twins of persons with cancer. Statistical modeling was used to estimate the relative importance of heritable and environmental factors in causing cancer at 11 of those sites. RESULTS: At least one cancer occurred in 10,803 persons among 9512 pairs of twins. An increased risk was found among the twins of affected persons for stomach, colorectal, lung, breast, and prostate cancer. Statistically significant effects of heritable factors were observed for prostate cancer (42 percent; 95 percent confidence interval, 29 to 50 percent), colorectal cancer (35 percent; 95 percent confidence interval, 10 to 48 percent), and breast cancer (27 percent; 95 percent confidence interval, 4 to 41 percent). CONCLUSIONS: Inherited genetic factors make a minor contribution to susceptibility to most types of neoplasms. This finding indicates that the environment has the principal role in causing sporadic cancer. The relatively large effect of heritability in cancer at a few sites suggests major gaps in our knowledge of the genetics of cancer.

The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) statement, published in 2009, was designed to help systematic reviewers transparently report why the review was done, what the authors did, and what they found. Over the past decade, advances in systematic review methodology and terminology have necessitated an update to the guideline. The PRISMA 2020 statement replaces the 2009 statement and includes new reporting guidance that reflects advances in methods to identify, select, appraise, and synthesise studies. The structure and presentation of the items have been modified to facilitate implementation. In this article, we present the PRISMA 2020 27-item checklist, an expanded checklist that details reporting recommendations for each item, the PRISMA 2020 abstract checklist, and the revised flow diagrams for original and updated reviews.

BACKGROUND: Radiotherapy for breast cancer often involves some incidental exposure of the heart to ionizing radiation. The effect of this exposure on the subsequent risk of ischemic heart disease is uncertain. METHODS: We conducted a population-based case-control study of major coronary events (i.e., myocardial infarction, coronary revascularization, or death from ischemic heart disease) in 2168 women who underwent radiotherapy for breast cancer between 1958 and 2001 in Sweden and Denmark; the study included 963 women with major coronary events and 1205 controls. Individual patient information was obtained from hospital records. For each woman, the mean radiation doses to the whole heart and to the left anterior descending coronary artery were estimated from her radiotherapy chart. RESULTS: The overall average of the mean doses to the whole heart was 4.9 Gy (range, 0.03 to 27.72). Rates of major coronary events increased linearly with the mean dose to the heart by 7.4% per gray (95% confidence interval, 2.9 to 14.5; P<0.001), with no apparent threshold. The increase started within the first 5 years after radiotherapy and continued into the third decade after radiotherapy. The proportional increase in the rate of major coronary events per gray was similar in women with and women without cardiac risk factors at the time of radiotherapy. CONCLUSIONS: Exposure of the heart to ionizing radiation during radiotherapy for breast cancer increases the subsequent rate of ischemic heart disease. The increase is proportional to the mean dose to the heart, begins within a few years after exposure, and continues for at least 20 years. Women with preexisting cardiac risk factors have greater absolute increases in risk from radiotherapy than other women. (Funded by Cancer Research UK and others.).

SUMMARY: SOAP2 is a significantly improved version of the short oligonucleotide alignment program that both reduces computer memory usage and increases alignment speed at an unprecedented rate. We used a Burrows Wheeler Transformation (BWT) compression index to substitute the seed strategy for indexing the reference sequence in the main memory. We tested it on the whole human genome and found that this new algorithm reduced memory usage from 14.7 to 5.4 GB and improved alignment speed by 20-30 times. SOAP2 is compatible with both single- and paired-end reads. Additionally, this tool now supports multiple text and compressed file formats. A consensus builder has also been developed for consensus assembly and SNP detection from alignment of short reads on a reference genome. AVAILABILITY: http://soap.genomics.org.cn.