Variation in practice patterns among specialties in the acute management of atrial fibrillation

Abstract Background Atrial fibrillation (AF) is commonly managed by a variety of specialists. Current guidelines differ in their recommendations leading to uncertainty regarding important clinical decisions. We sought to document practice pattern variation among cardiologists, emergency physicians (EP) and hospitalists at a single academic, tertiary-care center. Methods A survey was created containing seven clinical scenarios of patients presenting with AF. We analyzed respondent choices regarding rate vs rhythm control, thromboembolic treatment and hospitalization strategies. Finally, we contrasted our findings with a comparable Australasian survey to provide an international reference. Results There was a 78% response rate (124 of 158), 37% hospitalists, 31.5% cardiologists, and 31.5% EP. Most respondents chose rate over rhythm control (92.2%; 95% CI, 89.1% - 94.5%) and thromboembolic treatment (67.8%; 95% CI, 63.8% - 71.7%). Compared to both hospitalists and EPs, cardiologists were more likely to choose thromboembolic treatment for new and paroxysmal AF (adjusted OR 2.38; 95% CI, 1.05 - 5.41). They were less likely to favor hospital admission across all types of AF (adjusted OR 0.36; 95% CI, 0.17 - 0.79) but thought cardiology consultation was more important (adjusted OR 1.88, 95% CI, 0.97 - 3.64). Australasian physicians were more aggressive with rhythm control for paroxysmal AF with low CHADS2 score compared to US physicians. Conclusions Significant variation exists among specialties in the management of acute AF, likely reflecting a lack of high quality research to direct the provider. Future studies may help to standardize practice leading to decreased rates of hospitalization and overall cost.http://deepblue.lib.umich.edu/bitstream/2027.42/110777/1/12872_2015_Article_9.pd

The Epidemiology of Acute Organ System Dysfunction From Severe Sepsis Outside of the Intensive Care Unit

BACKGROUND: Severe sepsis is a common, costly, and complex problem, the epidemiology of which has only been well studied in the intensive care unit (ICU). However, nearly half of all patients with severe sepsis are cared for outside the ICU. OBJECTIVE: To determine rates of infection and organ sys- tem dysfunction in patients with severe sepsis admitted to non-ICU services. DESIGN: Retrospective cohort study. SETTING: A large, tertiary, academic medical center in the United States. PATIENTS: Adult patients initially admitted to non-ICU medical services from 2009 through 2010. MEASUREMENTS: All International Classification of Dis- eases, 9th Revision, Clinical Modification diagnosis codes were screened for severe sepsis. Three hospitalists reviewed a sample of medical records evaluating the char- acteristics of severe sepsis. The International Consensus Conference (ICC) for sepsis defines severe sepsis as an infection leading to acute organ dysfunction.1,2 Severe sepsis afflicts over 1 million patients each year in Medicare alone, and is substantially more common among older Americans than acute myocardial infarction.3–5 Recently, the Agency for Healthcare Research and Quality identified severe sepsis as the single most expensive cause of hospitalization in the United States.6 The incidence of severe sepsis continues to rise.4,5 Severe sepsis is often mischaracterized as a diagno- sis cared for primarily in the intensive care unit (ICU). Yet, studies indicate that only 32% to 50% of patients with severe sepsis require ICU care, leaving *Address for correspondence and reprint requests: Jeffrey M. Rohde, MD, Department of Internal Medicine, University of Michigan Medical School, 3119 Taubman Center, 1500 E. Medical Center Dr., Ann Arbor, MI 48109-5376; Telephone: 734-647-1599; Fax: 734-233-9343; E-mail: [email protected] Additional Supporting Information may be found in the online version of this article. Received: July 15, 2012; Revised: December 16, 2012; Accepted: December 26, 2012 2013SocietyofHospitalMedicine DOI10.1002/jhm.2012 Published online in Wiley Online Library (Wileyonlinelibrary.com). RESULTS: Of 23,288 hospitalizations, 14% screened posi- tive for severe sepsis. A sample of 111 cases was manually reviewed, identifying 64 cases of severe sepsis. The mean age of patients with severe sepsis was 63 years, and 39% were immunosuppressed prior to presentation. The most common site of infection was the urinary tract (41%). The most common organ system dysfunctions were cardiovas- cular (hypotension) and renal dysfunction occurring in 66% and 64% of patients, respectively. An increase in the num- ber of organ systems affected was associated with an increase in mortality and eventual ICU utilization. Severe sepsis was documented by the treating clinicians in 47% of cases. CONCLUSIONS: Severe sepsis was commonly found and poorly documented on the wards at our medical center. The epidemiology and organ dysfunctions among patients with severe sepsis appear to be different from previously described ICU severe sepsis populations.This work was supported in part by the US National Insti- tutes of Health–K08, HL091249 (TJI) and the University of Michigan Specialist–Hospitalist Allied Research Program (SHARP). This work was also supported in part by VA Ann Arbor Healthcare System, Geriatric Research Education and Clinical Center (GRECC).Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102728/1/Rohde 13 JHM epi acute organ dysfxn sepsis outside ICU.pdf11

Canvass: a crowd-sourced, natural-product screening library for exploring biological space

NCATS thanks Dingyin Tao for assistance with compound characterization. This research was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH). R.B.A. acknowledges support from NSF (CHE-1665145) and NIH (GM126221). M.K.B. acknowledges support from NIH (5R01GM110131). N.Z.B. thanks support from NIGMS, NIH (R01GM114061). J.K.C. acknowledges support from NSF (CHE-1665331). J.C. acknowledges support from the Fogarty International Center, NIH (TW009872). P.A.C. acknowledges support from the National Cancer Institute (NCI), NIH (R01 CA158275), and the NIH/National Institute of Aging (P01 AG012411). N.K.G. acknowledges support from NSF (CHE-1464898). B.C.G. thanks the support of NSF (RUI: 213569), the Camille and Henry Dreyfus Foundation, and the Arnold and Mabel Beckman Foundation. C.C.H. thanks the start-up funds from the Scripps Institution of Oceanography for support. J.N.J. acknowledges support from NIH (GM 063557, GM 084333). A.D.K. thanks the support from NCI, NIH (P01CA125066). D.G.I.K. acknowledges support from the National Center for Complementary and Integrative Health (1 R01 AT008088) and the Fogarty International Center, NIH (U01 TW00313), and gratefully acknowledges courtesies extended by the Government of Madagascar (Ministere des Eaux et Forets). O.K. thanks NIH (R01GM071779) for financial support. T.J.M. acknowledges support from NIH (GM116952). S.M. acknowledges support from NIH (DA045884-01, DA046487-01, AA026949-01), the Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program (W81XWH-17-1-0256), and NCI, NIH, through a Cancer Center Support Grant (P30 CA008748). K.N.M. thanks the California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board for support. B.T.M. thanks Michael Mullowney for his contribution in the isolation, elucidation, and submission of the compounds in this work. P.N. acknowledges support from NIH (R01 GM111476). L.E.O. acknowledges support from NIH (R01-HL25854, R01-GM30859, R0-1-NS-12389). L.E.B., J.K.S., and J.A.P. thank the NIH (R35 GM-118173, R24 GM-111625) for research support. F.R. thanks the American Lebanese Syrian Associated Charities (ALSAC) for financial support. I.S. thanks the University of Oklahoma Startup funds for support. J.T.S. acknowledges support from ACS PRF (53767-ND1) and NSF (CHE-1414298), and thanks Drs. Kellan N. Lamb and Michael J. Di Maso for their synthetic contribution. B.S. acknowledges support from NIH (CA78747, CA106150, GM114353, GM115575). W.S. acknowledges support from NIGMS, NIH (R15GM116032, P30 GM103450), and thanks the University of Arkansas for startup funds and the Arkansas Biosciences Institute (ABI) for seed money. C.R.J.S. acknowledges support from NIH (R01GM121656). D.S.T. thanks the support of NIH (T32 CA062948-Gudas) and PhRMA Foundation to A.L.V., NIH (P41 GM076267) to D.S.T., and CCSG NIH (P30 CA008748) to C.B. Thompson. R.E.T. acknowledges support from NIGMS, NIH (GM129465). R.J.T. thanks the American Cancer Society (RSG-12-253-01-CDD) and NSF (CHE1361173) for support. D.A.V. thanks the Camille and Henry Dreyfus Foundation, the National Science Foundation (CHE-0353662, CHE-1005253, and CHE-1725142), the Beckman Foundation, the Sherman Fairchild Foundation, the John Stauffer Charitable Trust, and the Christian Scholars Foundation for support. J.W. acknowledges support from the American Cancer Society through the Research Scholar Grant (RSG-13-011-01-CDD). W.M.W.acknowledges support from NIGMS, NIH (GM119426), and NSF (CHE1755698). A.Z. acknowledges support from NSF (CHE-1463819). (Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health (NIH); CHE-1665145 - NSF; CHE-1665331 - NSF; CHE-1464898 - NSF; RUI: 213569 - NSF; CHE-1414298 - NSF; CHE1361173 - NSF; CHE1755698 - NSF; CHE-1463819 - NSF; GM126221 - NIH; 5R01GM110131 - NIH; GM 063557 - NIH; GM 084333 - NIH; R01GM071779 - NIH; GM116952 - NIH; DA045884-01 - NIH; DA046487-01 - NIH; AA026949-01 - NIH; R01 GM111476 - NIH; R01-HL25854 - NIH; R01-GM30859 - NIH; R0-1-NS-12389 - NIH; R35 GM-118173 - NIH; R24 GM-111625 - NIH; CA78747 - NIH; CA106150 - NIH; GM114353 - NIH; GM115575 - NIH; R01GM121656 - NIH; T32 CA062948-Gudas - NIH; P41 GM076267 - NIH; R01GM114061 - NIGMS, NIH; R15GM116032 - NIGMS, NIH; P30 GM103450 - NIGMS, NIH; GM129465 - NIGMS, NIH; GM119426 - NIGMS, NIH; TW009872 - Fogarty International Center, NIH; U01 TW00313 - Fogarty International Center, NIH; R01 CA158275 - National Cancer Institute (NCI), NIH; P01 AG012411 - NIH/National Institute of Aging; Camille and Henry Dreyfus Foundation; Arnold and Mabel Beckman Foundation; Scripps Institution of Oceanography; P01CA125066 - NCI, NIH; 1 R01 AT008088 - National Center for Complementary and Integrative Health; W81XWH-17-1-0256 - Office of the Assistant Secretary of Defense for Health Affairs through the Peer Reviewed Medical Research Program; P30 CA008748 - NCI, NIH, through a Cancer Center Support Grant; California Department of Food and Agriculture Pierce's Disease and Glassy Winged Sharpshooter Board; American Lebanese Syrian Associated Charities (ALSAC); University of Oklahoma Startup funds; 53767-ND1 - ACS PRF; PhRMA Foundation; P30 CA008748 - CCSG NIH; RSG-12-253-01-CDD - American Cancer Society; RSG-13-011-01-CDD - American Cancer Society; CHE-0353662 - National Science Foundation; CHE-1005253 - National Science Foundation; CHE-1725142 - National Science Foundation; Beckman Foundation; Sherman Fairchild Foundation; John Stauffer Charitable Trust; Christian Scholars Foundation)Published versionSupporting documentatio

The HSP70 modulator MAL3-101 inhibits Merkel cell carcinoma

Merkel Cell Carcinoma (MCC) is a rare and highly aggressive neuroendocrine skin cancer for which no effective treatment is available. MCC represents a human cancer with the best experimental evidence for a causal role of a polyoma virus. Large T antigens (LTA) encoded by polyoma viruses are oncoproteins, which are thought to require support of cellular heat shock protein 70 (HSP70) to exert their transforming activity. Here we evaluated the capability of MAL3-101, a synthetic HSP70 inhibitor, to limit proliferation and survival of various MCC cell lines. Remarkably, MAL3-101 treatment resulted in considerable apoptosis in 5 out of 7 MCC cell lines. While this effect was not associated with the viral status of the MCC cells, quantitative mRNA expression analysis of the known HSP70 isoforms revealed a significant correlation between MAL3-101 sensitivity and HSC70 expression, the most prominent isoform in all cell lines. Moreover, MAL3-101 also exhibited in vivo antitumor activity in an MCC xenograft model suggesting that this substance or related compounds are potential therapeutics for the treatment of MCC in the future. © 2014 Adam et al

Concepts in Animal Parasitology, Part 3: Endoparasitic Platyhelminths

Part III: Endoparasitic Platyhelminths, chapters 15-47, pages 231-532, in Concepts in Animal Parasitology. 2024. Scott L. Gardner and Sue Ann Gardner, editors. Zea Books, Lincoln, Nebraska, United States; part III doi: 10.32873/unl.dc.ciap073 Platyhelminthes Chapter 15: Introduction to Endoparasitic Platyhelminths (Phylum Platyhelminthes) by Larry S. Roberts, John J. Janovy, Jr., Steve Nadler, and Scott L. Gardner, pages 231-240 Cestoda Chapter 16: Introduction to Cestodes (Class Cestoda) by Scott L. Gardner, pages 241-246 Eucestoda Chapter 17: Introduction to Cyclophyllidea Beneden in Braun, 1900 (Order) by Scott L. Gardner, pages 247-250 Chapter 18: Taenia (Genus) by Sumiya Ganzorig and Scott. L. Gardner, pages 251-261 Chapter 19: Echinococcus (Genus) by Akira Ito and Scott. L. Gardner, pages 262-275 Chapter 20: Proteocephalidae La Rue, 1911 (Family) by Tomáš Scholz and Roman Kuchta, pages 276-282 Chapter 21: Bothriocephalidea Kuchta et al., 2008 (Order) by Jorge Falcón-Ordaz and Luis García-Prieto, pages 283-288 Chapter 22: Diphyllobothriidea Kuchta et al., 2008 (Order): The Broad Tapeworms by Tomáš Scholz and Roman Kuchta, pages 289-296 Chapter 23: Trypanorhyncha Diesing, 1863 (Order) by Francisco Zaragoza-Tapia and Scott Monks, pages 297-305 Chapter 24: Cathetocephalidea Schmidt and Beveridge, 1990 (Order) by Luis García-Prieto, Omar Lagunas-Calvo, Brenda Atziri García-García, and Berenice Adán-Torres, pages 306-309 Chapter 25: Diphyllidea van Beneden in Carus, 1863 (Order) by Luis García-Prieto, Brenda Atziri García-García, Omar Lagunas-Calvo, and Berenice Adán-Torres, pages 310-315 Chapter 26: Lecanicephalidea Hyman, 1951 (Order) by Luis García-Prieto, Berenice Adán-Torres, Omar Lagunas-Calvo, and Brenda Atziri García- García, pages 316-320 Chapter 27: Litobothriidea Dailey, 1969 (Order) by Luis García-Prieto, Berenice Adán-Torres, Brenda Atziri García-García, and Omar Lagunas-Calvo, pages 321-325 Chapter 28: Phyllobothriidea Caira et al., 2014 (Order) by Brenda Atziri García-García, Omar Lagunas-Calvo, Berenice Adán-Torres, and Luis García-Prieto, pages 326-331 Chapter 29: Rhinebothriidea Healy et al., 2009 (Order) by Omar Lagunas-Calvo, Brenda Atziri García-García, Berenice Adán-Torres, and Luis García-Prieto, pages 332-339 Chapter 30: Relics of “Tetraphyllidea” van Beneden, 1850 (Order) by Berenice Adán-Torres, Omar Lagunas-Calvo, Brenda Atziri García-García, and Luis García-Prieto, pages 340-346 Amphilinidea Chapter 31: Amphilinidea Poche 1922 (Order) by Klaus Rohde, pages 347-353 Gyrocotylidea Chapter 32: Gyrocotylidea (Order): The Most Primitive Group of Tapeworms by Willi E. R. Xylander and Klaus Rohde, pages 354-360 Trematoda Aspidogastrea Chapter 33: Aspidogastrea (Subclass) by Klaus Rohde, pages 361-377 Digenea: Diplostomida Chapter 34: Introduction to Diplostomida Olson et al., 2003 (Order) by Lucrecia Acosta Soto, Bernard Fried, and Rafael Toledo, pages 378-393 Chapter 35: Aporocotylidae (Family): Fish Blood Flukes by Russell Q.-Y. Yong, pages 394-401 Digenea: Plagiorchiida Chapter 36: Introduction to Plagiorchiida La Rue, 1957 (Order) by Rafael Toledo, Bernard Fried, and Lucrecia Acosta Soto, pages 402-404 Chapter 37: Bivesiculata Olson et al., 2003 (Suborder): Small, Rare, but Important by Thomas H. Cribb and Scott C. Cutmore, pages 405-408 Chapter 38: Echinostomata La Rue, 1926 (Suborder) by Rafael Toledo, Bernard Fried, and Lucrecia Acosta Soto, pages 409-422 Chapter 39: Haplosplanchnata Olson et al., 2003 (Suborder): Two Hosts with Half the Guts by Daniel C. Huston, pages 423-427 Chapter 40: Hemiurata Skrjabin & Guschanskaja, 1954 (Suborder) by Lucrecia Acosta Soto, Bernard Fried, and Rafael Toledo, pages 428-435 Chapter 41: Monorchiata Olson et al., 2003 (Suborder): Two Families Separated by Salinity by Nicholas Q.-X. Wee, pages 436-442 Chapter 42: Opisthorchis (Genus) compiled from material from the United States Centers for Disease Control and Prevention, Division of Parasitic Diseases and Malaria by Sue Ann Gardner, pages 443-445 Xiphidiata Chapter 43: Allocreadiidae Looss, 1902 (Family) by Gerardo Pérez-Ponce de León, David Iván Hernández-Mena, and Brenda Solórzano-García, pages 446-459 Chapter 44: Haematoloechidae Odening, 1964 (Family) by Virginia León-Règagnon, pages 460-469 Chapter 45: Lecithodendriidae Lühe, 1901 (Family) by Jeffrey M. Lotz, pages 470-479 Chapter 46: Opecoelidae Ozaki, 1925 (Family): The Richest Trematode Family by Storm B. Martin, pages 480-489 Digenea Summary Chapter 47: Summary of the Digenea (Subclass): Insights and Lessons from a Prominent Parasitologist by Robin M. Overstreet, pages 490-53

A structural comparison of human serum transferrin and human lactoferrin

The transferrins are a family of proteins that bind free iron in the blood and bodily fluids. Serum transferrins function to deliver iron to cells via a receptor-mediated endocytotic process as well as to remove toxic free iron from the blood and to provide an anti-bacterial, low-iron environment. Lactoferrins (found in bodily secretions such as milk) are only known to have an anti-bacterial function, via their ability to tightly bind free iron even at low pH, and have no known transport function. Though these proteins keep the level of free iron low, pathogenic bacteria are able to thrive by obtaining iron from their host via expression of outer membrane proteins that can bind to and remove iron from host proteins, including both serum transferrin and lactoferrin. Furthermore, even though human serum transferrin and lactoferrin are quite similar in sequence and structure, and coordinate iron in the same manner, they differ in their affinities for iron as well as their receptor binding properties: the human transferrin receptor only binds serum transferrin, and two distinct bacterial transport systems are used to capture iron from serum transferrin and lactoferrin. Comparison of the recently solved crystal structure of iron-free human serum transferrin to that of human lactoferrin provides insight into these differences

Mycobacterium tuberculosis releases an antacid that remodels phagosomes

International audienc

The World Federation of ADHD International Consensus Statement:208 Evidence-based conclusions about the disorder

Background: Misconceptions about ADHD stigmatize affected people, reduce credibility of providers, and prevent/delay treatment. To challenge misconceptions, we curated findings with strong evidence base. Methods: We reviewed studies with more than 2000 participants or meta-analyses from five or more studies or 2000 or more participants. We excluded meta-analyses that did not assess publication bias, except for meta-analyses of prevalence. For network meta-analyses we required comparison adjusted funnel plots. We excluded treatment studies with waiting-list or treatment as usual controls. From this literature, we extracted evidence-based assertions about the disorder. Results: We generated 208 empirically supported statements about ADHD. The status of the included statements as empirically supported is approved by 80 authors from 27 countries and 6 continents. The contents of the manuscript are endorsed by 366 people who have read this document and agree with its contents. Conclusions: Many findings in ADHD are supported by meta-analysis. These allow for firm statements about the nature, course, outcome causes, and treatments for disorders that are useful for reducing misconceptions and stigma.</p

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery