Magna Graecia University

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,

OBJECTIVE: To provide an update to "Surviving Sepsis Campaign Guidelines for Management of Sepsis and Septic Shock: 2012." DESIGN: A consensus committee of 55 international experts representing 25 international organizations was convened. Nominal groups were assembled at key international meetings (for those committee members attending the conference). A formal conflict-of-interest (COI) policy was developed at the onset of the process and enforced throughout. A stand-alone meeting was held for all panel members in December 2015. Teleconferences and electronic-based discussion among subgroups and among the entire committee served as an integral part of the development. METHODS: The panel consisted of five sections: hemodynamics, infection, adjunctive therapies, metabolic, and ventilation. Population, intervention, comparison, and outcomes (PICO) questions were reviewed and updated as needed, and evidence profiles were generated. Each subgroup generated a list of questions, searched for best available evidence, and then followed the principles of the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system to assess the quality of evidence from high to very low, and to formulate recommendations as strong or weak, or best practice statement when applicable. RESULTS: The Surviving Sepsis Guideline panel provided 93 statements on early management and resuscitation of patients with sepsis or septic shock. Overall, 32 were strong recommendations, 39 were weak recommendations, and 18 were best-practice statements. No recommendation was provided for four questions. CONCLUSIONS: Substantial agreement exists among a large cohort of international experts regarding many strong recommendations for the best care of patients with sepsis. Although a significant number of aspects of care have relatively weak support, evidence-based recommendations regarding the acute management of sepsis and septic shock are the foundation of improved outcomes for these critically ill patients with high mortality.

autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.

Noninvasive mechanical ventilation (NIV) is widely used in the acute care setting for acute respiratory failure (ARF) across a variety of aetiologies. This document provides European Respiratory Society/American Thoracic Society recommendations for the clinical application of NIV based on the most current literature. The guideline committee was composed of clinicians, methodologists and experts in the field of NIV. The committee developed recommendations based on the GRADE (Grading, Recommendation, Assessment, Development and Evaluation) methodology for each actionable question. The GRADE Evidence to Decision framework in the guideline development tool was used to generate recommendations. A number of topics were addressed using technical summaries without recommendations and these are discussed in the supplementary material. This guideline committee developed recommendations for 11 actionable questions in a PICO (population–intervention–comparison–outcome) format, all addressing the use of NIV for various aetiologies of ARF. The specific conditions where recommendations were made include exacerbation of chronic obstructive pulmonary disease, cardiogenic pulmonary oedema, de novo hypoxaemic respiratory failure, immunocompromised patients, chest trauma, palliation, post-operative care, weaning and post-extubation. This document summarises the current state of knowledge regarding the role of NIV in ARF. Evidence-based recommendations provide guidance to relevant stakeholders.

Tractography based on non-invasive diffusion imaging is central to the study of human brain connectivity. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain data set with ground truth tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. Here, we report the encouraging finding that most state-of-the-art algorithms produce tractograms containing 90% of the ground truth bundles (to at least some extent). However, the same tractograms contain many more invalid than valid bundles, and half of these invalid bundles occur systematically across research groups. Taken together, our results demonstrate and confirm fundamental ambiguities inherent in tract reconstruction based on orientation information alone, which need to be considered when interpreting tractography and connectivity results. Our approach provides a novel framework for estimating reliability of tractography and encourages innovation to address its current limitations.

Purines perform many important functions in the cell, being the formation of the monomeric precursors of nucleic acids DNA and RNA the most relevant one. Purines which also contribute to modulate energy metabolism and signal transduction, are structural components of some coenzymes and have been shown to play important roles in the physiology of platelets, muscles and neurotransmission. All cells require a balanced quantity of purines for growth, proliferation and survival. Under physiological conditions the enzymes involved in the purine metabolism maintain in the cell a balanced ratio between their synthesis and degradation. In humans the final compound of purines catabolism is uric acid. All other mammals possess the enzyme uricase that converts uric acid to allantoin that is easily eliminated through urine. Overproduction of uric acid, generated from the metabolism of purines, has been proven to play emerging roles in human disease. In fact the increase of serum uric acid is inversely associated with disease severity and especially with cardiovascular disease states. This review describes the enzymatic pathways involved in the degradation of purines, getting into their structure and biochemistry until the uric acid formation.

Next-Generation Gene Therapy Few disciplines in contemporary clinical research have experienced the high expectations directed at the gene therapy field. However, gene therapy has been challenging to translate to the clinic, often because the therapeutic gene is expressed at insufficient levels in the patient or because the gene delivery vector integrates near protooncogenes, which can cause leukemia (see the Perspective by Verma ). Biffi et al. ( 1233158 , published online 11 July) and Aiuti et al. ( 1233151 ; published online 11 July) report progress on both fronts in gene therapy trials of three patients with metachromatic leukodystrophy (MLD), a neurodegenerative disorder, and three patients with Wiskott-Aldrich syndrome (WAS), an immunodeficiency disorder. Optimized lentiviral vectors were used to introduce functional MLD or WAS genes into the patients' hematopoietic stem cells (HSCs) ex vivo, and the transduced cells were then infused back into the patients, who were then monitored for up to 2 years. In both trials, the patients showed stable engraftment of the transduced HSC and high expression levels of functional MLD or WAS genes. Encouragingly, there was no evidence of lentiviral vector integration near proto-oncogenes, and the gene therapy treatment halted disease progression in most patients. A longer follow-up period will be needed to further validate efficacy and safety.

In mammals, the insulin receptor (IR) gene has acquired an additional exon, exon 11. This exon may be skipped in a developmental and tissue-specific manner. The IR, therefore, occurs in two isoforms (exon 11 minus IR-A and exon 11 plus IR-B). The most relevant functional difference between these two isoforms is the high affinity of IR-A for IGF-II. IR-A is predominantly expressed during prenatal life. It enhances the effects of IGF-II during embryogenesis and fetal development. It is also significantly expressed in adult tissues, especially in the brain. Conversely, IR-B is predominantly expressed in adult, well-differentiated tissues, including the liver, where it enhances the metabolic effects of insulin. Dysregulation of IR splicing in insulin target tissues may occur in patients with insulin resistance; however, its role in type 2 diabetes is unclear. IR-A is often aberrantly expressed in cancer cells, thus increasing their responsiveness to IGF-II and to insulin and explaining the cancer-promoting effect of hyperinsulinemia observed in obese and type 2 diabetic patients. Aberrant IR-A expression may favor cancer resistance to both conventional and targeted therapies by a variety of mechanisms. Finally, IR isoforms form heterodimers, IR-A/IR-B, and hybrid IR/IGF-IR receptors (HR-A and HR-B). The functional characteristics of such hybrid receptors and their role in physiology, in diabetes, and in malignant cells are not yet fully understood. These receptors seem to enhance cell responsiveness to IGFs.

Much evidence shows that physical exercise (PE) is a strong gene modulator that induces structural and functional changes in the brain, determining enormous benefit on both cognitive functioning and wellbeing. PE is also a protective factor for neurodegeneration. However, it is unclear if such protection is granted through modifications to the biological mechanisms underlying neurodegeneration or through better compensation against attacks. This concise review addresses the biological and psychological positive effects of PE describing the results obtained on brain plasticity and epigenetic mechanisms in animal and human studies, in order to clarify how to maximize the positive effects of PE while avoiding negative consequences, as in the case of exercise addiction.

BACKGROUND: Coronary revascularization guided by fractional flow reserve (FFR) is associated with better patient outcomes after the procedure than revascularization guided by angiography alone. It is unknown whether the instantaneous wave-free ratio (iFR), an alternative measure that does not require the administration of adenosine, will offer benefits similar to those of FFR. METHODS: We randomly assigned 2492 patients with coronary artery disease, in a 1:1 ratio, to undergo either iFR-guided or FFR-guided coronary revascularization. The primary end point was the 1-year risk of major adverse cardiac events, which were a composite of death from any cause, nonfatal myocardial infarction, or unplanned revascularization. The trial was designed to show the noninferiority of iFR to FFR, with a margin of 3.4 percentage points for the difference in risk. RESULTS: At 1 year, the primary end point had occurred in 78 of 1148 patients (6.8%) in the iFR group and in 83 of 1182 patients (7.0%) in the FFR group (difference in risk, -0.2 percentage points; 95% confidence interval [CI], -2.3 to 1.8; P<0.001 for noninferiority; hazard ratio, 0.95; 95% CI, 0.68 to 1.33; P=0.78). The risk of each component of the primary end point and of death from cardiovascular or noncardiovascular causes did not differ significantly between the groups. The number of patients who had adverse procedural symptoms and clinical signs was significantly lower in the iFR group than in the FFR group (39 patients [3.1%] vs. 385 patients [30.8%], P<0.001), and the median procedural time was significantly shorter (40.5 minutes vs. 45.0 minutes, P=0.001). CONCLUSIONS: Coronary revascularization guided by iFR was noninferior to revascularization guided by FFR with respect to the risk of major adverse cardiac events at 1 year. The rate of adverse procedural signs and symptoms was lower and the procedural time was shorter with iFR than with FFR. (Funded by Philips Volcano; DEFINE-FLAIR ClinicalTrials.gov number, NCT02053038 .).

AIMS: To evaluate the impact of the COVID-19 pandemic on patient admissions to Italian cardiac care units (CCUs). METHODS AND RESULTS: We conducted a multicentre, observational, nationwide survey to collect data on admissions for acute myocardial infarction (AMI) at Italian CCUs throughout a 1 week period during the COVID-19 outbreak, compared with the equivalent week in 2019. We observed a 48.4% reduction in admissions for AMI compared with the equivalent week in 2019 (P < 0.001). The reduction was significant for both ST-segment elevation myocardial infarction [STEMI; 26.5%, 95% confidence interval (CI) 21.7-32.3; P = 0.009] and non-STEMI (NSTEMI; 65.1%, 95% CI 60.3-70.3; P < 0.001). Among STEMIs, the reduction was higher for women (41.2%; P = 0.011) than men (17.8%; P = 0.191). A similar reduction in AMI admissions was registered in North Italy (52.1%), Central Italy (59.3%), and South Italy (52.1%). The STEMI case fatality rate during the pandemic was substantially increased compared with 2019 [risk ratio (RR) = 3.3, 95% CI 1.7-6.6; P < 0.001]. A parallel increase in complications was also registered (RR = 1.8, 95% CI 1.1-2.8; P = 0.009). CONCLUSION: Admissions for AMI were significantly reduced during the COVID-19 pandemic across Italy, with a parallel increase in fatality and complication rates. This constitutes a serious social issue, demanding attention by the scientific and healthcare communities and public regulatory agencies.

AIMS: To provide contemporary data on the implementation of European guideline recommendations for lipid-lowering therapies (LLTs) across different settings and populations and how this impacts low-density lipoprotein cholesterol (LDL-C) goal achievement. METHODS AND RESULTS: An 18 country, cross-sectional, observational study of patients prescribed LLT for primary or secondary prevention in primary or secondary care across Europe. Between June 2017 and November 2018, data were collected at a single visit, including LLT in the preceding 12 months and most recent LDL-C. Primary outcome was the achievement of risk-based 2016 European Society of Cardiology (ESC)/European Atherosclerosis Society (EAS) LDL-C goal while receiving stabilized LLT; 2019 goal achievement was also assessed. Overall, 5888 patients (3000 primary and 2888 secondary prevention patients) were enrolled; 54% [95% confidence interval (CI) 52-56] achieved their risk-based 2016 goal and 33% (95% CI 32-35) achieved their risk-based 2019 goal. High-intensity statin monotherapy was used in 20% and 38% of very high-risk primary and secondary prevention patients, respectively. Corresponding 2016 goal attainment was 22% and 45% (17% and 22% for 2019 goals) for very high-risk primary and secondary prevention patients, respectively. Use of moderate-high-intensity statins in combination with ezetimibe (9%), or any LLT with PCSK9 inhibitors (1%), was low; corresponding 2016 and 2019 goal attainment was 53% and 20% (ezetimibe combination), and 67% and 58% (PCSK9i combination). CONCLUSION: Gaps between clinical guidelines and clinical practice for lipid management across Europe persist, which will be exacerbated by the 2019 guidelines. Even with optimized statins, greater utilization of non-statin LLT is likely needed to reduce these gaps for patients at highest risk.

Although the discovery of the critical role of α-synuclein (α-syn) in the pathogenesis of Parkinson's disease (PD) is now twenty-five years old, it still represents a milestone in PD research. Abnormal forms of α-syn trigger selective and progressive neuronal death through mitochondrial impairment, lysosomal dysfunction, and alteration of calcium homeostasis not only in PD but also in other α-syn-related neurodegenerative disorders such as dementia with Lewy bodies, multiple system atrophy, pure autonomic failure, and REM sleep behavior disorder. Furthermore, α-syn-dependent early synaptic and plastic alterations and the underlying mechanisms preceding overt neurodegeneration have attracted great interest. In particular, the presence of early inflammation in experimental models and PD patients, occurring before deposition and spreading of α-syn, suggests a mechanistic link between inflammation and synaptic dysfunction. The knowledge of these early mechanisms is of seminal importance to support the research on reliable biomarkers to precociously identify the disease and possible disease-modifying therapies targeting α-syn. In this review, we will discuss these critical issues, providing a state of the art of the role of this protein in early PD and other synucleinopathies.

Self-propelling bacteria are a nanotechnology dream. These unicellular organisms are not just capable of living and reproducing, but they can swim very efficiently, sense the environment, and look for food, all packaged in a body measuring a few microns. Before such perfect machines can be artificially assembled, researchers are beginning to explore new ways to harness bacteria as propelling units for microdevices. Proposed strategies require the careful task of aligning and binding bacterial cells on synthetic surfaces in order to have them work cooperatively. Here we show that asymmetric environments can produce a spontaneous and unidirectional rotation of nanofabricated objects immersed in an active bacterial bath. The propulsion mechanism is provided by the self-assembly of motile Escherichia coli cells along the rotor boundaries. Our results highlight the technological implications of active matter's ability to overcome the restrictions imposed by the second law of thermodynamics on equilibrium passive fluids.

Objective: Machine learning classification has been the most important computational development in the last years to satisfy the primary need of clinicians for automatic early diagnosis and prognosis. Nowadays, Random Forest (RF) algorithm has been successfully applied for reducing high dimensional and multi-source data in many scientific realms. Our aim was to explore the state of the art of the application of RF on single and multi-modal neuroimaging data for the prediction of Alzheimer’s disease. Methods: A systematic review following PRISMA guidelines was conducted on this field of study. In particular, we constructed an advanced query using boolean operators as follows: ("random forest" OR "random forests") AND neuroimaging AND ("alzheimer's disease" OR alzheimer's OR alzheimer) AND (prediction OR classification). The query was then searched in four well-known scientific databases: Pubmed, Scopus, Google Scholar and Web of Science. Results: Twelve articles – published between the 2007 and 2017 - have been included in this systematic review after a quantitative and qualitative selection. The lesson learnt from these works suggest that when RF was applied on multi-modal data for prediction of Alzheimer’s disease (AD) conversion from the Mild Cognitive Impairment (MCI), it produces one of the best accuracies to date. Moreover, the RF has important advantages in terms of robustness to overfitting, ability to handle highly non-linear data, stability in the presence of outliers and opportunity for efficient parallel processing mainly when applied on multi-modality neuroimaging data, such as MRI morphometric, diffusion tensor imaging and PET images. Conclusions: We discussed the strengths of RF, considering also possible limitations and by encouraging further studies on the comparisons of this algorithm with other commonly used classification approaches, particularly in the early prediction of the progression from MCI to AD.

Chronic leg ulcers affect 1-2% of the general population and are related to increased morbidity and health costs. Staphylococcus aureus and Pseudomonas aeruginosa are the most common bacteria isolated from chronic wounds. They can express virulence factors and surface proteins affecting wound healing. The co-infection of S. aureus and P. aeruginosa is more virulent than single infection. In particular, S. aureus and P. aeruginosa have both intrinsic and acquired antibiotic resistance, making clinical management of infection a real challenge, particularly in patients with comorbidity. Therefore, a correct and prompt diagnosis of chronic wound infection requires a detailed knowledge of skin bacterial flora. This is a necessary prerequisite for tailored pharmacological treatment, improving symptoms, and reducing side effects and antibiotic resistance.

Studying proteins and their structures has an important role for understanding protein functionalities. Recently, due to important results obtained with proteomics, a great interest has been given to interactomics , that is, the study of protein-to-protein interactions, called PPI, or more generally, interactions among macromolecules, particularly within cells. Interactomics means studying, modeling, storing, and retrieving protein-to-protein interactions as well as algorithms for manipulating, simulating, and predicting interactions. PPI data can be obtained from biological experiments studying interactions. Modeling and storing PPIs can be realized by using graph theory and graph data management, thus graph databases can be queried for further experiments. PPI graphs can be used as input for data-mining algorithms, where raw data are binary interactions forming interaction graphs, and analysis algorithms retrieve biological interactions among proteins (i.e., PPI biological meanings). For instance, predicting the interactions between two or more proteins can be obtained by mining interaction networks stored in databases. In this article we survey modeling, storing, analyzing, and manipulating PPI data. After describing the main PPI models, mostly based on graphs, the article reviews PPI data representation and storage, as well as PPI databases. Algorithms and software tools for analyzing and managing PPI networks are discussed in depth. The article concludes by discussing the main challenges and research directions in PPI networks.

Insulin resistance is common in individuals with obesity or type 2 diabetes (T2D), in which circulating insulin levels are frequently increased. Recent epidemiological and clinical evidence points to a link between insulin resistance and cancer. The mechanisms for this association are unknown, but hyperinsulinaemia (a hallmark of insulin resistance) and the increase in bioavailable insulin-like growth factor I (IGF-I) appear to have a role in tumor initiation and progression in insulin-resistant patients. Insulin and IGF-I inhibit the hepatic synthesis of sex-hormone binding globulin (SHBG), whereas both hormones stimulate the ovarian synthesis of sex steroids, whose effects, in breast epithelium and endometrium, can promote cellular proliferation and inhibit apoptosis. Furthermore, an increased risk of cancer among insulin-resistant patients can be due to overproduction of reactive oxygen species (ROS) that can damage DNA contributing to mutagenesis and carcinogenesis. On the other hand, it is possible that the abundance of inflammatory cells in adipose tissue of obese and diabetic patients may promote systemic inflammation which can result in a protumorigenic environment. Here, we summarize recent progress on insulin resistance and cancer, focusing on various implicated mechanisms that have been described recently, and discuss how these mechanisms may contribute to cancer initiation and progression.

In cancer therapy and imaging, the systemic passive delivery of particulate systems has relied on the enhanced permeability and retention (EPR) effect: sufficiently small particles can cross the endothelial fenestrations and accumulate in the tumor parenchyma. The vast majority of man-made particulates exhibit a spherical shape as a result of surface energy minimization during their synthesis. The advent of phage display libraries, which are revealing the extraordinary molecular diversity of endothelial cells, and the development of processes for fabricating particles with shapes other than spherical are opening the path to new design solutions for systemically administered targeted particulates. In this paper, the role of particle geometry (i.e., size and shape) is discussed at the tissue and cellular scales. Emphasis is placed on how the synergistic effect of particle geometry and molecular targeting can enhance the specificity of delivery. The intravascular delivery process has been broken into three events: margination, firm adhesion and control of internalization. Predictions from mathematical models and observations from in-vitro experiments were used to show the relevance of particle geometry in systemic delivery. Rational design of particulate systems should consider, beside the physico-chemical properties of the surface coatings, geometrical features as size and shape. The integration of mathematical modeling with in-vitro and in-vivo testing provides the tools for establishing a rational design of nanoparticles.

Nowadays, obesity is one of the most prevalent human health problems. Research from the last 30 years has clarified the role of the imbalance between energy intake and expenditure, unhealthy lifestyle, and genetic variability in the development of obesity. More recently, the composition and metabolic functions of gut microbiota have been proposed as being able to affect obesity development. Here, we will report the current knowledge on the definition, composition, and functions of intestinal microbiota. We have performed an extensive review of the literature, searching for the following keywords: metabolism, gut microbiota, dysbiosis, obesity. There is evidence for the association between gut bacteria and obesity both in infancy and in adults. There are several genetic, metabolic, and inflammatory pathophysiological mechanisms involved in the interplay between gut microbes and obesity. Microbial changes in the human gut can be considered a factor involved in obesity development in humans. The modulation of the bacterial strains in the digestive tract can help to reshape the metabolic profile in the human obese host as suggested by several data from animal and human studies. Thus, a deep revision of the evidence pertaining to the use probiotics, prebiotics, and antibiotics in obese patients is conceivable.