Tokyo Women's Medical 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,

Abstract Somatic mutations in cancer genomes are caused by multiple mutational processes, each of which generates a characteristic mutational signature 1 . Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium 2 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), we characterized mutational signatures using 84,729,690 somatic mutations from 4,645 whole-genome and 19,184 exome sequences that encompass most types of cancer. We identified 49 single-base-substitution, 11 doublet-base-substitution, 4 clustered-base-substitution and 17 small insertion-and-deletion signatures. The substantial size of our dataset, compared with previous analyses 3–15 , enabled the discovery of new signatures, the separation of overlapping signatures and the decomposition of signatures into components that may represent associated—but distinct—DNA damage, repair and/or replication mechanisms. By estimating the contribution of each signature to the mutational catalogues of individual cancer genomes, we revealed associations of signatures to exogenous or endogenous exposures, as well as to defective DNA-maintenance processes. However, many signatures are of unknown cause. This analysis provides a systematic perspective on the repertoire of mutational processes that contribute to the development of human cancer.

Abstract Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale 1–3 . Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4–5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter 4 ; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation 5,6 ; analyses timings and patterns of tumour evolution 7 ; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity 8,9 ; and evaluates a range of more-specialized features of cancer genomes 8,10–18 .

Japanese mortality due to colorectal cancer is on the rise, surpassing 49,000 in 2015. Many new treatment methods have been developed during recent decades. The Japanese Society for Cancer of the Colon and Rectum Guidelines 2016 for the treatment of colorectal cancer (JSCCR Guidelines 2016) were prepared to show standard treatment strategies for colorectal cancer, to eliminate disparities among institutions in terms of treatment, to eliminate unnecessary treatment and insufficient treatment, and to deepen mutual understanding between health-care professionals and patients by making these Guidelines available to the general public. These Guidelines were prepared by consensus reached by the JSCCR Guideline Committee, based on a careful review of the evidence retrieved by literature searches, and in view of the medical health insurance system and actual clinical practice settings in Japan. Therefore, these Guidelines can be used as a tool for treating colorectal cancer in actual clinical practice settings. More specifically, they can be used as a guide to obtaining informed consent from patients and choosing the method of treatment for each patient. As a result of the discussions held by the Guideline Committee, controversial issues were selected as Clinical Questions, and recommendations were made. Each recommendation is accompanied by a classification of the evidence and a classification of recommendation categories based on the consensus reached by the Guideline Committee members. Here we present the English version of the JSCCR Guidelines 2016.

BACKGROUND: Spinal muscular atrophy is an autosomal recessive neuromuscular disorder that is caused by an insufficient level of survival motor neuron (SMN) protein. Nusinersen is an antisense oligonucleotide drug that modifies pre-messenger RNA splicing of the SMN2 gene and thus promotes increased production of full-length SMN protein. METHODS: We conducted a randomized, double-blind, sham-controlled, phase 3 efficacy and safety trial of nusinersen in infants with spinal muscular atrophy. The primary end points were a motor-milestone response (defined according to results on the Hammersmith Infant Neurological Examination) and event-free survival (time to death or the use of permanent assisted ventilation). Secondary end points included overall survival and subgroup analyses of event-free survival according to disease duration at screening. Only the first primary end point was tested in a prespecified interim analysis. To control the overall type I error rate at 0.05, a hierarchical testing strategy was used for the second primary end point and the secondary end points in the final analysis. RESULTS: In the interim analysis, a significantly higher percentage of infants in the nusinersen group than in the control group had a motor-milestone response (21 of 51 infants [41%] vs. 0 of 27 [0%], P<0.001), and this result prompted early termination of the trial. In the final analysis, a significantly higher percentage of infants in the nusinersen group than in the control group had a motor-milestone response (37 of 73 infants [51%] vs. 0 of 37 [0%]), and the likelihood of event-free survival was higher in the nusinersen group than in the control group (hazard ratio for death or the use of permanent assisted ventilation, 0.53; P=0.005). The likelihood of overall survival was higher in the nusinersen group than in the control group (hazard ratio for death, 0.37; P=0.004), and infants with a shorter disease duration at screening were more likely than those with a longer disease duration to benefit from nusinersen. The incidence and severity of adverse events were similar in the two groups. CONCLUSIONS: Among infants with spinal muscular atrophy, those who received nusinersen were more likely to be alive and have improvements in motor function than those in the control group. Early treatment may be necessary to maximize the benefit of the drug. (Funded by Biogen and Ionis Pharmaceuticals; ENDEAR ClinicalTrials.gov number, NCT02193074 .).

Abstract The number of deaths from colorectal cancer in Japan continues to increase. Colorectal cancer deaths exceeded 50,000 in 2016. In the 2019 edition, revision of all aspects of treatments was performed, with corrections and additions made based on knowledge acquired since the 2016 version (drug therapy) and the 2014 version (other treatments). The Japanese Society for Cancer of the Colon and Rectum guidelines 2019 for the treatment of colorectal cancer (JSCCR guidelines 2019) have been prepared to show standard treatment strategies for colorectal cancer, to eliminate disparities among institutions in terms of treatment, to eliminate unnecessary treatment and insufficient treatment and to deepen mutual understanding between healthcare professionals and patients by making these guidelines available to the general public. These guidelines have been prepared by consensuses reached by the JSCCR Guideline Committee, based on a careful review of the evidence retrieved by literature searches and in view of the medical health insurance system and actual clinical practice settings in Japan. Therefore, these guidelines can be used as a tool for treating colorectal cancer in actual clinical practice settings. More specifically, they can be used as a guide to obtaining informed consent from patients and choosing the method of treatment for each patient. Controversial issues were selected as clinical questions, and recommendations were made. Each recommendation is accompanied by a classification of the evidence and a classification of recommendation categories based on the consensus reached by the Guideline Committee members. Here, we present the English version of the JSCCR guidelines 2019.

A total of 850 patients with hepatocellular carcinoma seen during the last 8 years were analyzed retrospectively for survival in relation to treatment and disease stage. A new staging scheme based on tumor size, ascites, jaundice and serum albumin was used. Clearly, the prognosis depended on disease stage. The median survival of 229 patients who received no specific treatment was 1.6 months, 0.7 month for Stage III patients, 2.0 months for Stage II, and 8.3 months for Stage I. The median survival of Stage I patients who had hepatic resection (n = 115) was 25.6 months and Stage II patients with resection (n = 42) was 12.2 months. In patients who had a small cancer (less than or equal to 25% of liver area in size) the median survival was 29.0 months. Survival of the surgically treated patients, which represented a highly selected group, was better than that of medically treated patients of a comparable stage. Median survival of Stage I medically treated patients (n = 124) was 9.4 months, for Stage II (n = 290) 3.5 months, and for Stage III (n = 50) 1.6 months. Medical treatment prolonged survival in Stage II and III patients, but not in Stage I. Transcatheter arterial embolization gave a better survival compared with chemotherapy, whether intra-arterial bolus administration of mitomycin C, systemic mitomycin C, or oral/rectal tegafur, in Stage II. Among various chemotherapeutic modalities, intra-arterial bolus injection was superior to systemic chemotherapy in survival in Stage II. In Stage III, chemotherapy improved survival as compared with no specific treatment. The major causes of death were hepatic failure and gastrointestinal bleeding, probably due to the coexistent advanced cirrhosis. These results in survival are much improved over the past reports, and the differences are probably a result of earlier diagnosis and frequent hepatic resections.

Concept of Diabetes Mellitus: Diabetes mellitus is a group of diseases associated with various metabolic disorders, the main feature of which is chronic hyperglycemia due to insufficient insulin action. Its pathogenesis involves both genetic and environmental factors. The long-term persistence of metabolic disorders can cause susceptibility to specific complications and also foster arteriosclerosis. Diabetes mellitus is associated with a broad range of clinical presentations, from being asymptomatic to ketoacidosis or coma, depending on the degree of metabolic disorder. Classification (Tables 1 and 2, and Figure 1): Table 1. Etiological classification of diabetes mellitus and glucose metabolism disorders I. Type 1 (destruction of pancreatic β-cells, usually leading to absolute insulin deficiency) A. Autoimmune B. Idiopathic II. Type 2 (ranging from predominantly insulin secretory defect, to predominantly insulin resistance with varying degrees of insulin secretory defect) III. Due to other specific mechanisms or diseases (see Table 2 for details) A. Those in which specific mutations have been identified as a cause of genetic susceptibility (1) Genetic abnormalities of pancreatic β-cell function (2) Genetic abnormalities of insulin action B. Those associated with other diseases or conditions (1) Diseases of exocrine pancreas (2) Endocrine diseases (3) Liver disease (4) Drug- or chemical-induced (5) Infections (6) Rare forms of immune-mediated diabetes (7) Various genetic syndromes often associated with diabetes IV. Gestational diabetes mellitus Note: Those that cannot at present be classified as any of the above are called unclassifiable. The occurrence of diabetes-specific complications has not been confirmed in some of these conditions. Table 2. Diabetes mellitus and glucose metabolism disorders due to other specific mechanisms and diseases A. Those in which specific mutations have been identified as a cause of genetic susceptibility B. Those associated with other diseases or conditions (1) Genetic abnormalities of pancreatic β-cell functionInsulin gene (abnormal insulinemia, abnormal proinsulinemia, neonatal diabetes mellitus) HNF 4α gene (MODY1) Glucokinase gene (MODY2) HNF 1α gene (MODY3) IPF-1 gene (MODY4) HNF 1β gene (MODY5) Mitochondria DNA (MIDD) NeuroD1 gene (MODY6) Kir6.2 gene (neonatal diabetes mellitus) SUR1 gene (neonatal diabetes mellitus) AmylinOthers(2) Genetic abnormalities of insulin actionInsulin receptor gene (type A insulin resistance, leprechaunism, Rabson–Mendenhall syndrome etc.) Others (1) Diseases of exocrine pancreasPancreatitisTrauma/pancreatectomyNeoplasmHemochromatosisOthers(2) Endocrine diseasesCushing’s syndromeAcromegalyPheochromocytomaGlucagonomaAldosteronismHyperthyroidismSomatostatinomaOthers(3) Liver diseaseChronic hepatitisLiver cirrhosis Others(4) Drug- or chemical-inducedGlucocorticoidsInterferonOthers(5) InfectionsCongenital rubellaCytomegalovirusOthers(6) Rare forms of immune-mediated diabetesAnti-insulin receptor antibodiesStiffman syndromeInsulin autoimmune syndromeOthers(7) Various genetic syndromes often associated with diabetesDown syndromePrader-Willi syndromeTurner syndromeKlinefelter syndromeWerner syndromeWolfram syndromeCeruloplasmin deficiencyLipoatrophic diabetes mellitusMyotonic dystrophyFriedreich ataxiaLaurence-Moon-Biedl syndromeOthers The occurrence of diabetes-specific complications has not been confirmed in some of these conditions. Figure 1Open in figure viewerPowerPoint A scheme of the relationship between etiology (mechanism) and patho-physiological stages (states) of diabetes mellitus. Arrows pointing right represent worsening of glucose metabolism disorders (including onset of diabetes mellitus). Among the arrow lines, indicates the condition classified as ‘diabetes mellitus’. Arrows pointing left represent improvement in the glucose metabolism disorder. The broken lines indicate events of low frequency. For example, in type 2 diabetes mellitus, infection can lead to ketoacidosis and require temporary insulin treatment for survival. Also, once diabetes mellitus has developed, it is treated as diabetes mellitus regardless of improvement in glucose metabolism, therefore, the arrow lines pointing left are filled in black. In such cases, a broken line is used, because complete normalization of glucose metabolism is rare. The classification of glucose metabolism disorders is principally derived from etiology, and includes staging of pathophysiology based on the degree of deficiency of insulin action. These disorders are classified into four groups: (i) type 1 diabetes mellitus; (ii) type 2 diabetes mellitus; (iii) diabetes mellitus due to other specific mechanisms or diseases; and (iv) gestational diabetes mellitus. Type 1 diabetes is characterized by destruction of pancreatic β-cells. Type 2 diabetes is characterized by combinations of decreased insulin secretion and decreased insulin sensitivity (insulin resistance). Glucose metabolism disorders in category (iii) are divided into two subgroups; subgroup A is diabetes in which a genetic abnormality has been identified, and subgroup B is diabetes associated with other pathologic disorders or clinical conditions. The staging of glucose metabolism includes normal, borderline and diabetic stages depending on the degree of hyperglycemia occurring as a result of the lack of insulin action or clinical condition. The diabetic stage is then subdivided into three substages: non-insulin- requiring, insulin-requiring for glycemic control, and insulin-dependent for survival. The two former conditions are called non-insulin-dependent diabetes and the latter is known as insulin-dependent diabetes. In each individual, these stages may vary according to the deterioration or the improvement of the metabolic state, either spontaneously or by treatment. Diagnosis (Tables 3–7 and Figure 2): Table 3. Criteria of fasting plasma glucose levels and 75 g oral glucose tolerance test 2-h value Normal range Diabetic range Fasting value <110 mg/dL (6.1 mmol/L) ≥126 mg/dL (7.0 mmol/L) 75 g OGTT 2-h value <140 mg/dL (7.8 mmol/L) ≥200 mg/dL (11.1 mmol/L) Evaluation of OGTT Normal type: If both values belong to normal range *Diabetic type: If any of the two values falls into diabetic range Borderline typeNeither normal nor diabetic types *Casual plasma glucose ≥200 mg/dL (≥11.1 mmol/L) and HbA1c≥6.5% are also regarded as to indicate diabetic type. Even for normal type, if 1-h value is 180 mg/dL (10.0 mmol/L), the risk of progression to diabetes mellitus is greater than for <180 mg/dL (10.0 mmol/L) and should be treated as with borderline type (follow-up observation, etc.). Fasting plasma glucose level of 100–109 mg/dL (5.5–6.0 mmol/L) is called ‘high-normal’: within the range of normal fasting plasma glucose. Plasma glucose level after glucose load in oral glucose tolerance test (OGTT) is not included in casual plasma glucose levels. The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). Table 4. Procedures for diagnosing diabetes mellitus Clinical diagnosis (1) At initial examination, a ‘diabetic type’ is diagnosed if any of the following criteria are met: (i) fasting plasma glucose level ≥126 mg/dL (7.0 mmol/L), (ii) 75 g OGTT 2-h value ≥200 mg/dL (11.1 mmol/L), (iii) casual plasma glucose level ≥200 mg/dL (11.1 mmol/L) or (iv) *HbA1c≥6.5%. Re-examination is carried out at another date and diabetes mellitus is diagnosed if ‘diabetic type’ is confirmed again**. However, diagnosis cannot be made on the basis of a repeated HbA1c test alone. If the same blood sample is confirmed to be diabetic type by both plasma glucose and HbA1c levels (any of [i] to [iii] plus [iv]), then diabetes mellitus can be diagnosed from the initial test (2) If plasma glucose level shows diabetic type (any of [i] to [iii]) and either of the following conditions exists, diabetes mellitus can be diagnosed immediately at the initial examination• The presence of typical symptoms of diabetes mellitus (thirst, polydipsia, polyuria, weight loss)• The presence of definite diabetic retinopathy (3) If it can be confirmed that either of the above conditions 1 or 2 existed in the past, diabetes mellitus must be diagnosed or suspected even if present test values do not meet the above conditions (4) If diabetes mellitus is suspected but the diagnosis cannot be made by the above (1) to (3), the patient should be followed-up (5) The following points should be kept in mind when selecting the method of determination in initial examination and re-examination• If HbA1c is used at initial examination, another method of determination is required for diagnosis at re-examination. As a rule, both plasma glucose level and HbA1c should be measured• If casual plasma glucose level is ≥200 mg/dL (11.1 mmol/L) at the initial test, a different test method is desirable for re-examination• In the case of disorders and conditions in which HbA1c may be inappropriately low, plasma glucose level should be used for diagnosis (Table 5) Epidemiological study For the purpose of estimating the frequency of diabetes mellitus, determination of ‘diabetic type’ from a single test can be considered to represent ‘diabetes mellitus’. Whenever possible, the criteria to be used are HbA1c≥6.5% or OGTT 2-h value ≥200 mg/dL (11.1 mmol/L) Health screening It is important to detect diabetes mellitus and identify high risk groups without overlooking anyone. Therefore, besides measuring plasma glucose and HbA1c, clinical information such as family history and obesity should be referred *The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). **Hyperglycemia must be confirmed in a non-stressful condition. OGTT, oral glucose tolerance test. Table 5. Disorders and conditions associated with low HbA1c values Anemia Liver disease Dialysis Major hemorrhage Blood transfusion Chronic malaria Hemoglobinopathy Others Table 6. Situations where a 75-g oral glucose tolerance test is recommended Strongly recommended (suspicion of present diabetes mellitus cannot be ruled out) Fasting plasma glucose level is 110–125 mg/dL (6.1–6.9 mmol/L) Casual plasma glucose level is 140–199 mg/dL (7.8–11.0 mmol/L) *HbA1c is 6.0–6.4% (excluding those having overt symptoms of diabetes mellitus) Testing is desirable (high risk of developing diabetes mellitus in the future;Testing is especially advisable for patients with risk factors for arteriosclerosis such as hypertension, dyslipidemia and obesity.) Fasting plasma glucose level is 100–109 mg/dL (5.5–6.0 mmol/L) *HbA1c is 5.6–5.9% Strong family history of diabetes mellitus or present obesity regardless of above criteria *The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). Table 7. Definition and diagnostic criteria of gestational diabetes mellitus Definition of gestational diabetes mellitus Glucose metabolism disorder with first recognition or onset during pregnancy, but that has not developed into diabetes mellitus Diagnostic criteria of gestational diabetes mellitus Diagnosed if one or more of the following criteria is met in a 75 g OGTT Fasting plasma glucose ≥92 mg/dL (5.1 mmol/L) 1-h value ≥180 mg/dL (10.0 mmol/L) 2-h value ≥153 mg/dL (8.5 mmol/L) However, diabetes mellitus that is diagnosed according to ‘Clinical diagnosis’ outlined in Table 4 is excluded from gestational diabetes mellitus (IADPSG Consensus Panel, Reference 42, partly modified with permission of Diabetes Care). Figure 2Open in figure viewerPowerPoint Flow chart outlining steps in the clinical diagnosis of diabetes mellitus. *The value for HbA1c (%) is indicated with 0.4% added to HbA1c (JDS) (%). Categories of the State of Glycemia: Confirmation of chronic hyperglycemia is essential for the diagnosis of diabetes mellitus. When plasma glucose levels are used to determine the categories of glycemia, patients are classified as having a diabetic type if they meet one of the following criteria: (i) fasting plasma glucose level of ≥126 mg/dL (≥7.0 mmol/L); (ii) 2-h value of ≥200 mg/dL (≥11.1 mmol/L) in 75 g oral glucose tolerance test (OGTT); or (iii) casual plasma glucose level of ≥200 mg/dL (≥11.1 mmol/L). Normal type is defined as fasting plasma glucose level of <110 mg/dL (<6.1 mmol/L) and 2-h value of <140 mg/dL (<7.8 mmol/L) in OGTT. Borderline type (neither diabetic nor normal type) is defined as falling between the diabetic and normal values. According to the current revision, in addition to the earlier listed plasma glucose values, hemoglobin A1c (HbA1c) has been given a more prominent position as one of the diagnostic criteria. That is, (iv) HbA1c≥6.5% is now also considered to indicate diabetic type. The value of HbA1c, which is equivalent to the internationally used HbA1c (%) (HbA1c [NGSP]) defined by the NGSP (National Glycohemoglobin Standardization Program), is expressed by adding 0.4% to the HbA1c (JDS) (%) defined by the Japan Diabetes Society (JDS). Subjects with borderline type have a high rate of developing diabetes mellitus, and correspond to the combination of impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) noted by the American Diabetes Association (ADA) and WHO. Although borderline cases show few of the specific complications of diabetes mellitus, the risk of arteriosclerosis is higher than those of normal type. When HbA1c is 6.0–6.4%, suspected diabetes mellitus cannot be excluded, and when HbA1c of 5.6–5.9% is included, it forms a group with a high risk for developing diabetes mellitus in the future, even if they do not have it currently. Clinical Diagnosis: 1 If any of the criteria for diabetic type (i) through to (iv) is observed at the initial examination, the patient is judged to be ‘diabetic type’. Re-examination is conducted on another day, and if ‘diabetic type’ is reconfirmed, diabetes mellitus is diagnosed. However, a diagnosis cannot be made only by the re-examination of HbA1c alone. Moreover, if the plasma glucose values (any of criteria [i], [ii], or [iii]) and the HbA1c (criterion [iv]) in the same blood sample both indicate diabetic type, diabetes mellitus is diagnosed based on the initial examination alone. If HbA1c is used, it is essential that the plasma glucose level (criteria [i], [ii] or [iii]) also indicates diabetic type for a diagnosis of diabetes mellitus. When diabetes mellitus is suspected, HbA1c should be measured at the same time as examination for plasma glucose. 2 If the plasma glucose level indicates diabetic type (any of [i], [ii], or [iii]) and either of the following conditions exists, diabetes mellitus can be diagnosed immediately at the initial examination. • The presence of typical symptoms of diabetes mellitus (thirst, polydipsia, polyuria, weight loss) • The presence of definite diabetic retinopathy 3 If it can be confirmed that the above conditions 1 or 2 existed in the past, diabetes mellitus can be diagnosed or suspected regardless of the current test results. 4 If the diagnosis of diabetes cannot be established by these procedures, the patient is followed up and re-examined after an appropriate interval. 5 The physician should assess not only the presence or absence of diabetes, but also its etiology and glycemic stage, and the presence and absence of diabetic complications or associated conditions. Epidemiological Study: For the purpose of estimating the frequency of diabetes mellitus, ‘diabetes mellitus’ can be substituted for the determination of ‘diabetic type’ from a single examination. In this case, HbA1c≥6.5% alone can be defined as ‘diabetes mellitus’. Health Screening: It is important not to misdiagnose diabetes mellitus, and thus clinical information such as family history and obesity should be referred to at the time of screening in addition to an index for plasma glucose level. Gestational Diabetes Mellitus: There are two hyperglycemic disorders in pregnancy: (i) gestational diabetes mellitus (GDM); and (ii) diabetes mellitus. GDM is diagnosed if one or more of the following criteria is met in a 75 g OGTT during pregnancy: 1 Fasting plasma glucose level of ≥92 mg/dL (5.1 mmol/L) 2 1-h value of ≥180 mg/dL (10.0 mmol/L) 3 2-h value of ≥153 mg/dL (8.5 mmol/L) However, diabetes mellitus that is diagnosed by the clinical diagnosis of diabetes mellitus defined earlier is excluded from GDM. (J Diabetes Invest, doi: 10.1111/j.2040-1124.2010.00074.x, 2010)

BACKGROUND: Lenvatinib in combination with pembrolizumab or everolimus has activity against advanced renal cell carcinoma. The efficacy of these regimens as compared with that of sunitinib is unclear. METHODS: In this phase 3 trial, we randomly assigned (in a 1:1:1 ratio) patients with advanced renal cell carcinoma and no previous systemic therapy to receive lenvatinib (20 mg orally once daily) plus pembrolizumab (200 mg intravenously once every 3 weeks), lenvatinib (18 mg orally once daily) plus everolimus (5 mg orally once daily), or sunitinib (50 mg orally once daily, alternating 4 weeks receiving treatment and 2 weeks without treatment). The primary end point was progression-free survival, as assessed by an independent review committee in accordance with Response Evaluation Criteria in Solid Tumors, version 1.1. Overall survival and safety were also evaluated. RESULTS: A total of 1069 patients were randomly assigned to receive lenvatinib plus pembrolizumab (355 patients), lenvatinib plus everolimus (357), or sunitinib (357). Progression-free survival was longer with lenvatinib plus pembrolizumab than with sunitinib (median, 23.9 vs. 9.2 months; hazard ratio for disease progression or death, 0.39; 95% confidence interval [CI], 0.32 to 0.49; P<0.001) and was longer with lenvatinib plus everolimus than with sunitinib (median, 14.7 vs. 9.2 months; hazard ratio, 0.65; 95% CI, 0.53 to 0.80; P<0.001). Overall survival was longer with lenvatinib plus pembrolizumab than with sunitinib (hazard ratio for death, 0.66; 95% CI, 0.49 to 0.88; P = 0.005) but was not longer with lenvatinib plus everolimus than with sunitinib (hazard ratio, 1.15; 95% CI, 0.88 to 1.50; P = 0.30). Grade 3 or higher adverse events emerged or worsened during treatment in 82.4% of the patients who received lenvatinib plus pembrolizumab, 83.1% of those who received lenvatinib plus everolimus, and 71.8% of those who received sunitinib. Grade 3 or higher adverse events occurring in at least 10% of the patients in any group included hypertension, diarrhea, and elevated lipase levels. CONCLUSIONS: Lenvatinib plus pembrolizumab was associated with significantly longer progression-free survival and overall survival than sunitinib. (Funded by Eisai and Merck Sharp and Dohme; CLEAR ClinicalTrials.gov number, NCT02811861.).

IL-17 is a newly discovered T cell-derived cytokine whose role in osteoclast development has not been fully elucidated. Treatment of cocultures of mouse hemopoietic cells and primary osteoblasts with recombinant human IL-17 induced the formation of multinucleated cells, which satisfied major criteria of osteoclasts, including tartrate-resistant acid phosphatase activity, calcitonin receptors, and pit formation on dentine slices. Direct interaction between osteoclast progenitors and osteoblasts was required for IL-17-induced osteoclastogenesis, which was completely inhibited by adding indomethacin or NS398, a selective inhibitor of cyclooxgenase-2 (COX-2). Adding IL-17 increased prostaglandin E2 (PGE2) synthesis in cocultures of bone marrow cells and osteoblasts and in single cultures of osteoblasts, but not in single cultures of bone marrow cells. In addition, IL-17 dose-dependently induced expression of osteoclast differentiation factor (ODF) mRNA in osteoblasts. ODF is a membrane-associated protein that transduces an essential signal(s) to osteoclast progenitors for differentiation into osteoclasts. Osteoclastogenesis inhibitory factor (OCIF), a decoy receptor of ODF, completely inhibited IL-17-induced osteoclast differentiation in the cocultures. Levels of IL-17 in synovial fluids were significantly higher in rheumatoid arthritis (RA) patients than osteoarthritis (OA) patients. Anti-IL-17 antibody significantly inhibited osteoclast formation induced by culture media of RA synovial tissues. These findings suggest that IL-17 first acts on osteoblasts, which stimulates both COX-2-dependent PGE2 synthesis and ODF gene expression, which in turn induce differentiation of osteoclast progenitors into mature osteoclasts, and that IL-17 is a crucial cytokine for osteoclastic bone resorption in RA patients.

BACKGROUND: Ocular trauma or disease may lead to severe corneal opacification and, consequently, severe loss of vision as a result of complete loss of corneal epithelial stem cells. Transplantation of autologous corneal stem-cell sources is an alternative to allograft transplantation and does not require immunosuppression, but it is not possible in many cases in which bilateral disease produces total corneal stem-cell deficiency in both eyes. We studied the use of autologous oral mucosal epithelial cells as a source of cells for the reconstruction of the corneal surface. METHODS: We harvested 3-by-3-mm specimens of oral mucosal tissue from four patients with bilateral total corneal stem-cell deficiencies. Tissue-engineered epithelial-cell sheets were fabricated ex vivo by culturing harvested cells for two weeks on temperature-responsive cell-culture surfaces with 3T3 feeder cells that had been treated with mitomycin C. After conjunctival fibrovascular tissue had been surgically removed from the ocular surface, sheets of cultured autologous cells that had been harvested with a simple reduced-temperature treatment were transplanted directly to the denuded corneal surfaces (one eye of each patient) without sutures. RESULTS: Complete reepithelialization of the corneal surfaces occurred within one week in all four treated eyes. Corneal transparency was restored and postoperative visual acuity improved remarkably in all four eyes. During a mean follow-up period of 14 months, all corneal surfaces remained transparent. There were no complications. CONCLUSIONS: Sutureless transplantation of carrier-free cell sheets composed of autologous oral mucosal epithelial cells may be used to reconstruct corneal surfaces and can restore vision in patients with bilateral severe disorders of the ocular surface.

Abstract The Tokyo Guidelines 2013 (TG13) for acute cholangitis and cholecystitis were globally disseminated and various clinical studies about the management of acute cholecystitis were reported by many researchers and clinicians from all over the world. The 1 st edition of the Tokyo Guidelines 2007 ( TG 07) was revised in 2013. According to that revision, the TG 13 diagnostic criteria of acute cholecystitis provided better specificity and higher diagnostic accuracy. Thorough our literature search about diagnostic criteria for acute cholecystitis, new and strong evidence that had been released from 2013 to 2017 was not found with serious and important issues about using TG 13 diagnostic criteria of acute cholecystitis. On the other hand, the TG 13 severity grading for acute cholecystitis has been validated in numerous studies. As a result of these reviews, the TG 13 severity grading for acute cholecystitis was significantly associated with parameters including 30‐day overall mortality, length of hospital stay, conversion rates to open surgery, and medical costs. In terms of severity assessment, breakthrough and intensive literature for revising severity grading was not reported. Consequently, TG 13 diagnostic criteria and severity grading were judged from numerous validation studies as useful indicators in clinical practice and adopted as TG 18/ TG 13 diagnostic criteria and severity grading of acute cholecystitis without any modification. Free full articles and mobile app of TG18 are available at: http://www.jshbps.jp/modules/en/index.php?content_id=47 . Related clinical questions and references are also included.

BACKGROUND: Nusinersen is an antisense oligonucleotide drug that modulates pre-messenger RNA splicing of the survival motor neuron 2 ( SMN2) gene. It has been developed for the treatment of spinal muscular atrophy (SMA). METHODS: We conducted a multicenter, double-blind, sham-controlled, phase 3 trial of nusinersen in 126 children with SMA who had symptom onset after 6 months of age. The children were randomly assigned, in a 2:1 ratio, to undergo intrathecal administration of nusinersen at a dose of 12 mg (nusinersen group) or a sham procedure (control group) on days 1, 29, 85, and 274. The primary end point was the least-squares mean change from baseline in the Hammersmith Functional Motor Scale-Expanded (HFMSE) score at 15 months of treatment; HFMSE scores range from 0 to 66, with higher scores indicating better motor function. Secondary end points included the percentage of children with a clinically meaningful increase from baseline in the HFMSE score (≥3 points), an outcome that indicates improvement in at least two motor skills. RESULTS: In the prespecified interim analysis, there was a least-squares mean increase from baseline to month 15 in the HFMSE score in the nusinersen group (by 4.0 points) and a least-squares mean decrease in the control group (by -1.9 points), with a significant between-group difference favoring nusinersen (least-squares mean difference in change, 5.9 points; 95% confidence interval, 3.7 to 8.1; P<0.001). This result prompted early termination of the trial. Results of the final analysis were consistent with results of the interim analysis. In the final analysis, 57% of the children in the nusinersen group as compared with 26% in the control group had an increase from baseline to month 15 in the HFMSE score of at least 3 points (P<0.001), and the overall incidence of adverse events was similar in the nusinersen group and the control group (93% and 100%, respectively). CONCLUSIONS: Among children with later-onset SMA, those who received nusinersen had significant and clinically meaningful improvement in motor function as compared with those in the control group. (Funded by Biogen and Ionis Pharmaceuticals; CHERISH ClinicalTrials.gov number, NCT02292537 .).

BACKGROUND: The implantable cardioverter-defibrillator (ICD) is highly effective in reducing mortality among patients at risk for fatal arrhythmias, but inappropriate ICD activations are frequent, with potential adverse effects. METHODS: We randomly assigned 1500 patients with a primary-prevention indication to receive an ICD with one of three programming configurations. The primary objective was to determine whether programmed high-rate therapy (with a 2.5-second delay before the initiation of therapy at a heart rate of ≥200 beats per minute) or delayed therapy (with a 60-second delay at 170 to 199 beats per minute, a 12-second delay at 200 to 249 beats per minute, and a 2.5-second delay at ≥250 beats per minute) was associated with a decrease in the number of patients with a first occurrence of inappropriate antitachycardia pacing or shocks, as compared with conventional programming (with a 2.5-second delay at 170 to 199 beats per minute and a 1.0-second delay at ≥200 beats per minute). RESULTS: During an average follow-up of 1.4 years, high-rate therapy and delayed ICD therapy, as compared with conventional device programming, were associated with reductions in a first occurrence of inappropriate therapy (hazard ratio with high-rate therapy vs. conventional therapy, 0.21; 95% confidence interval [CI], 0.13 to 0.34; P<0.001; hazard ratio with delayed therapy vs. conventional therapy, 0.24; 95% CI, 0.15 to 0.40; P<0.001) and reductions in all-cause mortality (hazard ratio with high-rate therapy vs. conventional therapy, 0.45; 95% CI, 0.24 to 0.85; P=0.01; hazard ratio with delayed therapy vs. conventional therapy, 0.56; 95% CI, 0.30 to 1.02; P=0.06). There were no significant differences in procedure-related adverse events among the three treatment groups. CONCLUSIONS: Programming of ICD therapies for tachyarrhythmias of 200 beats per minute or higher or with a prolonged delay in therapy at 170 beats per minute or higher, as compared with conventional programming, was associated with reductions in inappropriate therapy and all-cause mortality during long-term follow-up. (Funded by Boston Scientific; MADIT-RIT ClinicalTrials.gov number, NCT00947310.).

The design and use of materials in the nanoscale size range for addressing medical and health-related issues continues to receive increasing interest. Research in nanomedicine spans a multitude of areas, including drug delivery, vaccine development, antibacterial, diagnosis and imaging tools, wearable devices, implants, high-throughput screening platforms, etc. using biological, nonbiological, biomimetic, or hybrid materials. Many of these developments are starting to be translated into viable clinical products. Here, we provide an overview of recent developments in nanomedicine and highlight the current challenges and upcoming opportunities for the field and translation to the clinic.

The use of laparoscopy for liver surgery is increasing rapidly. The Second International Consensus Conference on Laparoscopic Liver Resections (LLR) was held in Morioka, Japan, from October 4 to 6, 2014 to evaluate the current status of laparoscopic liver surgery and to provide recommendations to aid its future development. Seventeen questions were addressed. The first 7 questions focused on outcomes that reflect the benefits and risks of LLR. These questions were addressed using the Zurich-Danish consensus conference model in which the literature and expert opinion were weighed by a 9-member jury, who evaluated LLR outcomes using GRADE and a list of comparators. The jury also graded LLRs by the Balliol Classification of IDEAL. The jury concluded that MINOR LLRs had become standard practice (IDEAL 3) and that MAJOR liver resections were still innovative procedures in the exploration phase (IDEAL 2b). Continued cautious introduction of MAJOR LLRs was recommended. All of the evidence available for scrutiny was of LOW quality by GRADE, which prompted the recommendation for higher quality evaluative studies. The last 10 questions focused on technical questions and the recommendations were based on literature review and expert panel opinion. Recommendations were made regarding preoperative evaluation, bleeding controls, transection methods, anatomic approaches, and equipment. Both experts and jury recognized the need for a formal structure of education for those interested in performing major laparoscopic LLR because of the steep learning curve.

Osteoclast differentiation factor (ODF, also called RANKL/TRANCE/OPGL) stimulates the differentiation of osteoclast progenitors of the monocyte/macrophage lineage into osteoclasts in the presence of macrophage colony-stimulating factor (M-CSF, also called CSF-1). When mouse bone marrow cells were cultured with M-CSF, M-CSF-dependent bone marrow macrophages (M-BMM phi) appeared within 3 d. Tartrate-resistant acid phosphatase-positive osteoclasts were also formed when M-BMM phi were further cultured for 3 d with mouse tumor necrosis factor alpha (TNF-alpha) in the presence of M-CSF. Osteoclast formation induced by TNF-alpha was inhibited by the addition of respective antibodies against TNF receptor 1 (TNFR1) or TNFR2, but not by osteoclastogenesis inhibitory factor (OCIF, also called OPG, a decoy receptor of ODF/RANKL), nor the Fab fragment of anti-RANK (ODF/RANKL receptor) antibody. Experiments using M-BMM phi prepared from TNFR1- or TNFR2-deficient mice showed that both TNFR1- and TNFR2-induced signals were important for osteoclast formation induced by TNF-alpha. Osteoclasts induced by TNF-alpha formed resorption pits on dentine slices only in the presence of IL-1alpha. These results demonstrate that TNF-alpha stimulates osteoclast differentiation in the presence of M-CSF through a mechanism independent of the ODF/RANKL-RANK system. TNF-alpha together with IL-1alpha may play an important role in bone resorption of inflammatory bone diseases.

BACKGROUND: Uncontrolled pilot studies have suggested the efficacy of focused ultrasound thalamotomy with magnetic resonance imaging (MRI) guidance for the treatment of essential tremor. METHODS: We enrolled patients with moderate-to-severe essential tremor that had not responded to at least two trials of medical therapy and randomly assigned them in a 3:1 ratio to undergo unilateral focused ultrasound thalamotomy or a sham procedure. The Clinical Rating Scale for Tremor and the Quality of Life in Essential Tremor Questionnaire were administered at baseline and at 1, 3, 6, and 12 months. Tremor assessments were videotaped and rated by an independent group of neurologists who were unaware of the treatment assignments. The primary outcome was the between-group difference in the change from baseline to 3 months in hand tremor, rated on a 32-point scale (with higher scores indicating more severe tremor). After 3 months, patients in the sham-procedure group could cross over to active treatment (the open-label extension cohort). RESULTS: Seventy-six patients were included in the analysis. Hand-tremor scores improved more after focused ultrasound thalamotomy (from 18.1 points at baseline to 9.6 at 3 months) than after the sham procedure (from 16.0 to 15.8 points); the between-group difference in the mean change was 8.3 points (95% confidence interval [CI], 5.9 to 10.7; P<0.001). The improvement in the thalamotomy group was maintained at 12 months (change from baseline, 7.2 points; 95% CI, 6.1 to 8.3). Secondary outcome measures assessing disability and quality of life also improved with active treatment (the blinded thalamotomy cohort)as compared with the sham procedure (P<0.001 for both comparisons). Adverse events in the thalamotomy group included gait disturbance in 36% of patients and paresthesias or numbness in 38%; these adverse events persisted at 12 months in 9% and 14% of patients, respectively. CONCLUSIONS: MRI-guided focused ultrasound thalamotomy reduced hand tremor in patients with essential tremor. Side effects included sensory and gait disturbances. (Funded by InSightec and others; ClinicalTrials.gov number, NCT01827904.).

Abstract Cancer develops through a process of somatic evolution 1,2 . Sequencing data from a single biopsy represent a snapshot of this process that can reveal the timing of specific genomic aberrations and the changing influence of mutational processes 3 . Here, by whole-genome sequencing analysis of 2,658 cancers as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) 4 , we reconstruct the life history and evolution of mutational processes and driver mutation sequences of 38 types of cancer. Early oncogenesis is characterized by mutations in a constrained set of driver genes, and specific copy number gains, such as trisomy 7 in glioblastoma and isochromosome 17q in medulloblastoma. The mutational spectrum changes significantly throughout tumour evolution in 40% of samples. A nearly fourfold diversification of driver genes and increased genomic instability are features of later stages. Copy number alterations often occur in mitotic crises, and lead to simultaneous gains of chromosomal segments. Timing analyses suggest that driver mutations often precede diagnosis by many years, if not decades. Together, these results determine the evolutionary trajectories of cancer, and highlight opportunities for early cancer detection.

Abstract The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.