Prevalencia de la artritis reumatoide en Colombia según información del Sistema Integral de Información de la Protección Social

Objetivo Calcular la prevalencia y describir las principales características demográficas de la artritis reumatoide en adultos en Colombia. Materiales y métodos Estudio descriptivo de corte transversal en el que se tomaron los datos del Sistema Integral de Información de la Protección Social del Ministerio de Salud, durante los años 2012-2016. Resultados Se identificaron 248.995 casos, lo que permite calcular una prevalencia en mayores de 18 años del 0,52%, de los cuales el 80,7% son mujeres, con una relación mujer:hombre de 4,2:1, con mayor prevalencia entre el grupo etario de 70 a 74 años. Los departamentos con mayor número de casos son Bogotá D.C. (64.121), seguido de Antioquia (43.771); sin embargo, los departamentos con mayor prevalencia fueron Cesar (1,13%), Casanare (0,96%) y Risaralda (0,93%). Conclusiones Se presenta información demográfica y epidemiológica de artritis reumatoide en Colombia. Se documentó una prevalencia concordante con datos de la región, similar a Brasil (0,46%), Venezuela (0,4%) y Perú (0,55%), menor que Argentina (0,94%) y México (1,6%).Q4Artículo original83-87Objective To estimate the prevalence and describe the main demographic characteristics of rheumatoid arthritis in adults in Colombia. Material and methods A descriptive cross-sectional study was conducted using the data taken from the Integral Information System of Social Protection of the Ministry of Health during the years 2012–2016. Results A total of 248,995 cases were identified, which gave an estimated prevalence of 0.52% in those older than 18 years of age. The large majority (80.7% were women, with a female:male ratio of 4.2:1, and there was a higher prevalence in the 70–74 years age group. The departments with the highest number of cases were Bogota DC (64,121), followed by Antioquia (43,771), but the departments with the highest prevalence were Cesar (1.13%), Casanare (0.96%), and Risaralda (0.93%). Conclusions Demographic and epidemiological information on rheumatoid arthritis in Colombia is presented. A similar prevalence is reported in other studies from the region, like Brazil (0.46%), Venezuela (0.4%), or Peru (0.55%), but lower than Argentina (0.94%) or Mexico (1.6%)

Electrochemical oxidation of meglumine in a pharmaceutical formulation using a nanocomposite anode

The electrocatalytic oxidation of meglumine and gadoterate meglumine (Gd-DOTA) on a TiO2-Ni(SO4)0.3(OH)1.4 composite anode was investigated in alkaline medium (5 M KOH) using cyclic voltammetry and chronoamperometry. The composite was prepared by hydrothermal method and the morphology and structure of the produced nanoparticles were studied by scanning electron microscopy with energy-dispersive X-ray spectroscopy, X-ray diffraction, atomic force microscopy, BET surface area analysis and Fourier transform infrared spectroscopy. The characterization revealed the formation of Ni(SO4)0.3(OH)1.4 nanobelts dispersed on TiO2 nanoaggregates. The composite was coated onto a porous graphite rod, showing good adherence without requiring any binder (according to their anodic and cathodic charges). The supported composite was electrocatalytic, allowing the oxidation of meglumine, either as pure reagent or contained in gadoterate meglumine solutions. Electrochemical methods allowed determining the kinetic parameters, such as the electron transfer coefficient α, the total number of electrons n and the standard heterogeneous rate constant k0 for the reaction of meglumine. The chronoamperometric tests informed about the good stability of the composite anode upon meglumine oxidation at +0.6 V for 10 h. The electrochemical oxidation of meglumine in a commercial pharmaceutical formulation (Dotarem®) was corroborated via ultra-high performance liquid chromatography coupled to electrospray ionization and quadrupole time-of-flight mass spectrometry

Animal and Cellular Models of Alzheimer’s Disease: Progress, Promise, and Future Approaches

Alzheimer’s disease (AD) is an incurable neurodegenerative disease affecting over 45 million people worldwide. Transgenic mouse models have made remarkable contributions toward clarifying the pathophysiological mechanisms behind the clinical manifestations of AD. However, the limited ability of these in vivo models to accurately replicate the biology of the human disease have precluded the translation of promising preclinical therapies to the clinic. In this review, we highlight several major pathogenic mechanisms of AD that were discovered using transgenic mouse models. Moreover, we discuss the shortcomings of current animal models and the need to develop reliable models for the sporadic form of the disease, which accounts for the majority of AD cases, as well as human cellular models to improve success in translating results into human treatments.Peer reviewe

Co-infections and superinfections complicating COVID-19 in cancer patients: A multicentre, international study

Background: We aimed to describe the epidemiology, risk factors, and clinical outcomes of co-infections and superinfections in onco-hematological patients with COVID-19. Methods: International, multicentre cohort study of cancer patients with COVID-19. All patients were included in the analysis of co-infections at diagnosis, while only patients admitted at least 48 h were included in the analysis of superinfections. Results: 684 patients were included (384 with solid tumors and 300 with hematological malignancies). Co-infections and superinfections were documented in 7.8% (54/684) and 19.1% (113/590) of patients, respectively. Lower respiratory tract infections were the most frequent infectious complications, most often caused by Streptococcus pneumoniae and Pseudomonas aeruginosa. Only seven patients developed opportunistic infections. Compared to patients without infectious complications, those with infections had worse outcomes, with high rates of acute respiratory distress syndrome, intensive care unit (ICU) admission, and case-fatality rates. Neutropenia, ICU admission and high levels of C-reactive protein (CRP) were independent risk factors for infections. Conclusions: Infectious complications in cancer patients with COVID-19 were lower than expected, affecting mainly neutropenic patients with high levels of CRP and/or ICU admission. The rate of opportunistic infections was unexpectedly low. The use of empiric antimicrobials in cancer patients with COVID-19 needs to be optimized

A DNA damage repair gene-associated signature predicts responses of patients with advanced soft-tissue sarcoma to treatment with trabectedin

Predictive biomarkers of trabectedin represent an unmet need in advanced soft-tissue sarcomas (STS). DNA damage repair (DDR) genes, involved in homologous recombination or nucleotide excision repair, had been previously described as biomarkers of trabectedin resistance or sensitivity, respectively. The majority of these studies only focused on specific factors (ERCC1, ERCC5, and BRCA1) and did not evaluate several other DDR-related genes that could have a relevant role for trabectedin efficacy. In this retrospective translational study, 118 genes involved in DDR were evaluated to determine, by transcriptomics, a predictive gene signature of trabectedin efficacy. A six-gene predictive signature of trabectedin efficacy was built in a series of 139 tumor samples from patients with advanced STS. Patients in the high-risk gene signature group showed a significantly worse progression-free survival compared with patients in the low-risk group (2.1 vs 6.0 months, respectively). Differential gene expression analysis defined new potential predictive biomarkers of trabectedin sensitivity (PARP3 and CCNH) or resistance (DNAJB11 and PARP1). Our study identified a new gene signature that significantly predicts patients with higher probability to respond to treatment with trabectedin. Targeting some genes of this signature emerges as a potential strategy to enhance trabectedin efficacy.This study was funded by the Spanish Group for Research on Sarcoma (GEIS) and partially by PharmaMar. The authors would like to thank the GEIS data center for data management. The authors also thank the donors and the Hospital Universitario Virgen del Rocío—Instituto de Biomedicina de Sevilla Biobank (Andalusian Public Health System Biobank and ISCIII-Red de Biobancos PT17/0015/0041) for part of the human specimens used in this study. David S. Moura is recipient of a Sara Borrell postdoctoral fellowship funded by the National Institute of Health Carlos III (ISCIII) (CD20/00155)

Efficacy of β-lactam/β-lactamase inhibitors to treat extended-spectrum beta-lactamase-producing Enterobacterales bacteremia secondary to urinary tract infection in kidney transplant recipients (INCREMENT-SOT Project)

REIPI/INCREMENT-SOT Group.[Background] Whether active therapy with β-lactam/β-lactamase inhibitors (BLBLI) is as affective as carbapenems for extended-spectrum β-lactamase-producing Enterobacterales (ESBL-E) bloodstream infection (BSI) secondary to urinary tract infection (UTI) in kidney transplant recipients (KTRs) remains unclear.[Methods] We retrospectively evaluated 306 KTR admitted to 30 centers from January 2014 to October 2016. Therapeutic failure (lack of cure or clinical improvement and/or death from any cause) at days 7 and 30 from ESBL-E BSI onset was the primary and secondary study outcomes, respectively.[Results] Therapeutic failure at days 7 and 30 occurred in 8.2% (25/306) and 13.4% (41/306) of patients. Hospital-acquired BSI (adjusted OR [aOR]: 4.10; 95% confidence interval [CI]: 1.50-11.20) and Pitt score (aOR: 1.47; 95% CI: 1.21-1.77) were independently associated with therapeutic failure at day 7. Age-adjusted Charlson Index (aOR: 1.25; 95% CI: 1.05-1.48), Pitt score (aOR: 1.72; 95% CI: 1.35-2.17), and lymphocyte count ≤500 cells/μL at presentation (aOR: 3.16; 95% CI: 1.42-7.06) predicted therapeutic failure at day 30. Carbapenem monotherapy (68.6%, primarily meropenem) was the most frequent active therapy, followed by BLBLI monotherapy (10.8%, mostly piperacillin-tazobactam). Propensity score (PS)-adjusted models revealed no significant impact of the choice of active therapy (carbapenem-containing vs any other regimen, BLBLI- vs carbapenem-based monotherapy) within the first 72 hours on any of the study outcomes.[Conclusions] Our data suggest that active therapy based on BLBLI may be as effective as carbapenem-containing regimens for ESBL-E BSI secondary to UTI in the specific population of KTR. Potential residual confounding and unpowered sample size cannot be excluded (ClinicalTrials.gov identifier: NCT02852902).This work was supported by: (1) Plan Nacional de I+D+i 2013-2016 and Instituto de Salud Carlos III (ISCIII), Subdirección General de Redes y Centros de Investigación Cooperativa, Ministerio de Ciencia, Innovación y Universidades, Spanish Network for Research in Infectious Diseases [RD16/0016/0001, RD16/0016/0002, REIPI RD16/0016/0008; RD16/0016/00010], co-financed by European Development Regional Fund “A way to achieve Europe”, Operative Program Intelligent Growth 2014-2020; (2) European Society of Clinical Microbiology and Infectious diseases Study Group for Infections in Compromised Hosts (ESGICH, grant to J.M.A.); (3) Sociedad Andaluza de Trasplante de Órgano Sólido (SATOT, grant to L.M.M.); (4) Research project PI16/01631 integrated into the Plan Estatal de I+D+I 2013-2016 and co-financed by the ISCIII-Subdirección General de Evaluación y Fomento de la Investigación and the Fondo Europeo de Desarrollo Regional (FEDER); (5) M.F.R. holds a research contract “Miguel Servet” (CP 18/00073) from ISCIII, Ministerio de Ciencia, Innovación y Universidades. The work was also supported by the following European Society of Clinical Microbiology and Infectious diseases (ESCMID) study groups: Infections in Compromised Hosts (ESGICH), Bloodstream Infections and Sepsis (ESGBIS) and Antimicrobial Resistance Surveillance (ESGARS).Peer reviewe

Co-limitation towards lower latitudes shapes global forest diversity gradients

Funding Information: The team collaboration and manuscript development are supported by the web-based team science platform: science-i.org, with the project number 202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the Global Forest Biodiversity Initiative (GFBI) for establishing the data standards and collaborative framework; United States Department of Agriculture, Forest Service, Forest Inventory and Analysis (FIA) Program; University of Alaska Fairbanks; the SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast); the Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments and staff for data collection for over seven decades; and the National Forestry Commission of Mexico (CONAFOR). We thank M. Baker (Carbon Tanzania), together with a team of field assistants (Valentine and Lawrence); all persons who made the Third Spanish Forest Inventory possible, especially the main coordinator, J. A. Villanueva (IFN3); the French National Forest Inventory (NFI campaigns (raw data 2005 and following annual surveys, were downloaded by GFBI at https://inventaire-forestier.ign.fr/spip.php?rubrique159 ; site accessed on 1 January 2015)); the Italian Forest Inventory (NFI campaigns raw data 2005 and following surveys were downloaded by GFBI at https://inventarioforestale.org/ ; site accessed on 27 April 2019); Swiss National Forest Inventory, Swiss Federal Institute for Forest, Snow and Landscape Research WSL and Federal Office for the Environment FOEN, Switzerland; the Swedish NFI, Department of Forest Resource Management, Swedish University of Agricultural Sciences SLU; the National Research Foundation (NRF) of South Africa (89967 and 109244) and the South African Research Chair Initiative; the Danish National Forestry, Department of Geosciences and Natural Resource Management, UCPH; Coordination for the Improvement of Higher Education Personnel of Brazil (CAPES, grant number 88881.064976/2014-01); R. Ávila and S. van Tuylen, Instituto Nacional de Bosques (INAB), Guatemala, for facilitating Guatemalan data; the National Focal Center for Forest condition monitoring of Serbia (NFC), Institute of Forestry, Belgrade, Serbia; the Thünen Institute of Forest Ecosystems (Germany) for providing National Forest Inventory data; the FAO and the United Nations High Commissioner for Refugees (UNHCR) for undertaking the SAFE (Safe Access to Fuel and Energy) and CBIT-Forest projects; and the Amazon Forest Inventory Network (RAINFOR), the African Tropical Rainforest Observation Network (AfriTRON) and the ForestPlots.net initiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the LUCAS programme for the New Zealand Ministry for the Environment are provided by the New Zealand National Vegetation Survey Databank https://nvs.landcareresearch.co.nz/. We thank the International Boreal Forest Research Association (IBFRA); the Forestry Corporation of New South Wales, Australia; the National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS) for the plot data of the Second National Forest Inventory (INBN2); the National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS) dataset; and the Sabah Biodiversity Council and the staff from Sabah Forest Research Centre. All TEAM data are provided by the Tropical Ecology Assessment and Monitoring (TEAM) Network, a collaboration between Conservation International, the Missouri Botanical Garden, the Smithsonian Institution and the Wildlife Conservation Society, and partially funded by these institutions, the Gordon and Betty Moore Foundation and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data. We thank the people of the Redidoti, Pierrekondre and Cassipora village who were instrumental in assisting with the collection of data and sharing local knowledge of their forest and the dedicated members of the field crew of Kabo 2012 census. We are also thankful to FAPESC, SFB, FAO and IMA/SC for supporting the IFFSC. This research was supported in part through computational resources provided by Information Technology at Purdue, West Lafayette, Indiana.This work is supported in part by the NASA grant number 12000401 ‘Multi-sensor biodiversity framework developed from bioacoustic and space based sensor platforms’ (J. Liang, B.P.); the USDA National Institute of Food and Agriculture McIntire Stennis projects 1017711 (J. Liang) and 1016676 (M.Z.); the US National Science Foundation Biological Integration Institutes grant NSF‐DBI‐2021898 (P.B.R.); the funding by H2020 VERIFY (contract 776810) and H2020 Resonate (contract 101000574) (G.-J.N.); the TEAM project in Uganda supported by the Moore foundation and Buffett Foundation through Conservation International (CI) and Wildlife Conservation Society (WCS); the Danish Council for Independent Research | Natural Sciences (TREECHANGE, grant 6108-00078B) and VILLUM FONDEN grant number 16549 (J.-C.S.); the Natural Environment Research Council of the UK (NERC) project NE/T011084/1 awarded to J.A.-G. and NE/ S011811/1; ERC Advanced Grant 291585 (‘T-FORCES’) and a Royal Society-Wolfson Research Merit Award (O.L.P.); RAINFOR plots supported by the Gordon and Betty Moore Foundation and the UK Natural Environment Research Council, notably NERC Consortium Grants ‘AMAZONICA’ (NE/F005806/1), ‘TROBIT’ (NE/D005590/1) and ‘BIO-RED’ (NE/N012542/1); CIFOR’s Global Comparative Study on REDD+ funded by the Norwegian Agency for Development Cooperation, the Australian Department of Foreign Affairs and Trade, the European Union, the International Climate Initiative (IKI) of the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety and the CGIAR Research Program on Forests, Trees and Agroforestry (CRP-FTA) and donors to the CGIAR Fund; AfriTRON network plots funded by the local communities and NERC, ERC, European Union, Royal Society and Leverhume Trust; a grant from the Royal Society and the Natural Environment Research Council, UK (S.L.L.); National Science Foundation CIF21 DIBBs: EI: number 1724728 (A.C.C.); National Natural Science Foundation of China (31800374) and Shandong Provincial Natural Science Foundation (ZR2019BC083) (H.L.). UK NERC Independent Research Fellowship (grant code: NE/S01537X/1) (T.J.); a Serra-Húnter Fellowship provided by the Government of Catalonia (Spain) (S.d.-M.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.A.S.); a grant from the Franklinia Foundation (D.A.C.); Russian Science Foundation project number 19-77-300-12 (R.V.); the Takenaka Scholarship Foundation (A.O.A.); the German Research Foundation (DFG), grant number Am 149/16-4 (C.A.); the Romania National Council for Higher Education Funding, CNFIS, project number CNFIS-FDI-2022-0259 (O.B.); Natural Sciences and Engineering Research Council of Canada (RGPIN-2019-05109 and STPGP506284) and the Canadian Foundation for Innovation (36014) (H.Y.H.C.); the project SustES—Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797) (E.C.); Consejo de Ciencia y Tecnología del estado de Durango (2019-01-155) (J.J.C.-R.); Science and Engineering Research Board (SERB), New Delhi, Government of India (file number PDF/2015/000447)—‘Assessing the carbon sequestration potential of different forest types in Central India in response to climate change ’ (J.A.D.); Investissement d’avenir grant of the ANR (CEBA: ANR-10-LABEX-0025) (G.D.); National Foundation for Science & Technology Development of Vietnam, 106-NN.06-2013.01 (T.V.D.); Queensland government, Department of Environment and Science (T.J.E.); a Czech Science Foundation Standard grant (19-14620S) (T.M.F.); European Union Seventh Framework Program (FP7/2007–2013) under grant agreement number 265171 (L. Finer, M. Pollastrini, F. Selvi); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (J.F.); CNPq productivity grant number 311303/2020-0 (A.L.d.G.); DFG grant HE 2719/11-1,2,3; HE 2719/14-1 (A. Hemp); European Union’s Horizon Europe research project OpenEarthMonitor grant number 101059548, CGIAR Fund INIT-32-MItigation and Transformation Initiative for GHG reductions of Agrifood systems RelaTed Emissions (MITIGATE+) (M.H.); General Directorate of the State Forests, Poland (1/07; OR-2717/3/11; OR.271.3.3.2017) and the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (A.M.J.); Czech Science Foundation 18-10781 S (S.J.); Danish of Ministry of Environment, the Danish Environmental Protection Agency, Integrated Forest Monitoring Program—NFI (V.K.J.); State of São Paulo Research Foundation/FAPESP as part of the BIOTA/FAPESP Program Project Functional Gradient-PELD/BIOTA-ECOFOR 2003/12595-7 & 2012/51872-5 (C.A.J.); Danish Council for Independent Research—social sciences—grant DFF 6109–00296 (G.A.K.); Russian Science Foundation project 21-46-07002 for the plot data collected in the Krasnoyarsk region (V.K.); BOLFOR (D.K.K.); Department of Biotechnology, New Delhi, Government of India (grant number BT/PR7928/NDB/52/9/2006, dated 29 September 2006) (M.L.K.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (J.N.K.); Korea Forest Service (2018113A00-1820-BB01, 2013069A00-1819-AA03, and 2020185D10-2022-AA02) and Seoul National University Big Data Institute through the Data Science Research Project 2016 (H.S.K.); the Brazilian National Council for Scientific and Technological Development (CNPq, grant 442640/2018-8, CNPq/Prevfogo-Ibama number 33/2018) (C.K.); CSIR, New Delhi, government of India (grant number 38(1318)12/EMR-II, dated: 3 April 2012) (S.K.); Department of Biotechnology, New Delhi, government of India (grant number BT/ PR12899/ NDB/39/506/2015 dated 20 June 2017) (A.K.); Coordination for the Improvement of Higher Education Personnel (CAPES) #88887.463733/2019-00 (R.V.L.); National Natural Science Foundation of China (31800374) (H.L.); project of CEPF RAS ‘Methodological approaches to assessing the structural organization and functioning of forest ecosystems’ (AAAA-A18-118052590019-7) funded by the Ministry of Science and Higher Education of Russia (N.V.L.); Leverhulme Trust grant to Andrew Balmford, Simon Lewis and Jon Lovett (A.R.M.); Russian Science Foundation, project 19-77-30015 for European Russia data processing (O.M.); grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (M.T.E.M.); the National Centre for Research and Development, Poland (BIOSTRATEG1/267755/4/NCBR/2015) (S.M.); the Secretariat for Universities and of the Ministry of Business and Knowledge of the Government of Catalonia and the European Social Fund (A. Morera); Queensland government, Department of Environment and Science (V.J.N.); Pinnacle Group Cameroon PLC (L.N.N.); Queensland government, Department of Environment and Science (M.R.N.); the Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-05201) (A.P.); the Russian Foundation for Basic Research, project number 20-05-00540 (E.I.P.); European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement number 778322 (H.P.); Science and Engineering Research Board, New Delhi, government of India (grant number YSS/2015/000479, dated 12 January 2016) (P.S.); the Chilean Government research grants Fondecyt number 1191816 and FONDEF number ID19 10421 (C.S.-E.); the Deutsche Forschungsgemeinschaft (DFG) Priority Program 1374 Biodiversity Exploratories (P.S.); European Space Agency projects IFBN (4000114425/15/NL/FF/gp) and CCI Biomass (4000123662/18/I-NB) (D. Schepaschenko); FunDivEUROPE, European Union Seventh Framework Programme (FP7/2007–2013) under grant agreement number 265171 (M.S.-L.); APVV 20-0168 from the Slovak Research and Development Agency (V.S.); Manchester Metropolitan University’s Environmental Science Research Centre (G.S.); the project ‘LIFE+ ForBioSensing PL Comprehensive monitoring of stand dynamics in Białowieża Forest supported with remote sensing techniques’ which is co-funded by the EU Life Plus programme (contract number LIFE13 ENV/PL/000048) and the National Fund for Environmental Protection and Water Management in Poland (contract number 485/2014/WN10/OP-NM-LF/D) (K.J.S.); Global Challenges Research Fund (QR allocation, MMU) (M.J.P.S.); Czech Science Foundation project 21-27454S (M.S.); the Russian Foundation for Basic Research, project number 20-05-00540 (N. Tchebakova); Botanical Research Fund, Coalbourn Trust, Bentham Moxon Trust, Emily Holmes scholarship (L.A.T.); the programmes of the current scientific research of the Botanical Garden of the Ural Branch of Russian Academy of Sciences (V.A.U.); FCT—Portuguese Foundation for Science and Technology—Project UIDB/04033/2020. Inventário Florestal Nacional—ICNF (H. Viana); Grant from Kenya Coastal Development Project (KCDP), which was funded by World Bank (C.W.); grants from the Swedish National Forest Inventory, Swedish University of Agricultural Sciences (B.W.); ATTO project (grant number MCTI-FINEP 1759/10 and BMBF 01LB1001A, 01LK1602F) (F.W.); ReVaTene/PReSeD-CI 2 is funded by the Education and Research Ministry of Côte d’Ivoire, as part of the Debt Reduction-Development Contracts (C2Ds) managed by IRD (I.C.Z.-B.); the National Research Foundation of South Africa (NRF, grant 89967) (C.H.). The Tropical Plant Exploration Group 70 1 ha plots in Continental Cameroon Mountains are supported by Rufford Small Grant Foundation, UK and 4 ha in Sierra Leone are supported by the Global Challenge Research Fund through Manchester Metropolitan University, UK; the National Geographic Explorer Grant, NGS-53344R-18 (A.C.-S.); University of KwaZulu-Natal Research Office grant (M.J.L.); Universidad Nacional Autónoma de México, Dirección General de Asuntos de Personal Académico, Grant PAPIIT IN-217620 (J.A.M.). Czech Science Foundation project 21-24186M (R.T., S. Delabye). Czech Science Foundation project 20-05840Y, the Czech Ministry of Education, Youth and Sports (LTAUSA19137) and the long-term research development project of the Czech Academy of Sciences no. RVO 67985939 (J.A.). The American Society of Primatologists, the Duke University Graduate School, the L.S.B. Leakey Foundation, the National Science Foundation (grant number 0452995) and the Wenner-Gren Foundation for Anthropological Research (grant number 7330) (M.B.). Research grants from Conselho Nacional de Desenvolvimento Científico e Tecnologico (CNPq, Brazil) (309764/2019; 311303/2020) (A.C.V., A.L.G.). The Project of Sanya Yazhou Bay Science and Technology City (grant number CKJ-JYRC-2022-83) (H.-F.W.). The Ugandan NBS was supported with funds from the Forest Carbon Partnership Facility (FCPF), the Austrian Development Agency (ADC) and FAO. FAO’s UN-REDD Program, together with the project on ‘Native Forests and Community’ Loan BIRF number 8493-AR UNDP ARG/15/004 and the National Program for the Protection of Native Forests under UNDP funded Argentina’s INBN2. Publisher Copyright: © 2022, The Author(s), under exclusive licence to Springer Nature Limited.Peer reviewedPostprin

Role of age and comorbidities in mortality of patients with infective endocarditis

[Purpose]: The aim of this study was to analyse the characteristics of patients with IE in three groups of age and to assess the ability of age and the Charlson Comorbidity Index (CCI) to predict mortality. [Methods]: Prospective cohort study of all patients with IE included in the GAMES Spanish database between 2008 and 2015.Patients were stratified into three age groups:<65 years,65 to 80 years,and ≥ 80 years.The area under the receiver-operating characteristic (AUROC) curve was calculated to quantify the diagnostic accuracy of the CCI to predict mortality risk. [Results]: A total of 3120 patients with IE (1327 < 65 years;1291 65-80 years;502 ≥ 80 years) were enrolled.Fever and heart failure were the most common presentations of IE, with no differences among age groups.Patients ≥80 years who underwent surgery were significantly lower compared with other age groups (14.3%,65 years; 20.5%,65-79 years; 31.3%,≥80 years). In-hospital mortality was lower in the <65-year group (20.3%,<65 years;30.1%,65-79 years;34.7%,≥80 years;p < 0.001) as well as 1-year mortality (3.2%, <65 years; 5.5%, 65-80 years;7.6%,≥80 years; p = 0.003).Independent predictors of mortality were age ≥ 80 years (hazard ratio [HR]:2.78;95% confidence interval [CI]:2.32–3.34), CCI ≥ 3 (HR:1.62; 95% CI:1.39–1.88),and non-performed surgery (HR:1.64;95% CI:11.16–1.58).When the three age groups were compared,the AUROC curve for CCI was significantly larger for patients aged <65 years(p < 0.001) for both in-hospital and 1-year mortality. [Conclusion]: There were no differences in the clinical presentation of IE between the groups. Age ≥ 80 years, high comorbidity (measured by CCI),and non-performance of surgery were independent predictors of mortality in patients with IE.CCI could help to identify those patients with IE and surgical indication who present a lower risk of in-hospital and 1-year mortality after surgery, especially in the <65-year group

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

Management of multidrug resistant Gram-negative bacilli infections in solid organ transplant recipients: SET/GESITRA-SEIMC/REIPI recommendations

Solid organ transplant (SOT) recipients are especially at risk of developing infections by multidrug resistant (MDR) Gram-negative bacilli (GNB), as they are frequently exposed to antibiotics and the healthcare setting, and are regulary subject to invasive procedures. Nevertheless, no recommendations concerning prevention and treatment are available. A panel of experts revised the available evidence; this document summarizes their recommendations: (1) it is important to characterize the isolate´s phenotypic and genotypic resistance profile; (2) overall, donor colonization should not constitute a contraindication to transplantation, although active infected kidney and lung grafts should be avoided; (3) recipient colonization is associated with an increased risk of infection, but is not a contraindication to transplantation; (4) different surgical prophylaxis regimens are not recommended for patients colonized with carbapenem-resistant GNB; (5) timely detection of carriers, contact isolation precautions, hand hygiene compliance and antibiotic control policies are important preventive measures; (6) there is not sufficient data to recommend intestinal decolonization; (7) colonized lung transplant recipients could benefit from prophylactic inhaled antibiotics, specially for Pseudomonas aeruginosa; (8) colonized SOT recipients should receive an empirical treatment which includes active antibiotics, and directed therapy should be adjusted according to susceptibility study results and the severity of the infection.J.T.S. holds a research contract from the Fundación para la Formación e Investigación de los Profesionales de la Salud de Extremadura (FundeSalud), Instituto de Salud Carlos III. M.F.R. holds a clinical research contract “Juan Rodés” (JR14/00036) from the Spanish Ministry of Economy and Competitiveness, Instituto de Salud Carlos III