A new urban diagnostics approach for measuring touristification: The case of the Metropolitan Area of Barcelona

Due to the fast growth of the tourism industry in the last decades, many destinations are experiencing touristification. This phenomenon is intended as the socio-economic and spatial transformation process, leading to a tourism monoculture. This study aims to measure the touristification level of the Metropolitan Area of Barcelona (MAB), focusing on social, spatial, and environmental aspects. Its originality lies in the territorial scale of research. The primary outcome of the research is an analytic methodology for supporting informed decision-making for sustainable urban tourism policies at a metropolitan scale. The application of the MAB allows observing a leopard-spotted touristification of the area with two ongoing processes: the increment of tourism supplies in the first crown of MAB municipalities and the distribution of tourism fluxes on the coastal destinations. Besides, four typologies of municipalities have been established according to their touristification growth. Finally, this study proves the importance of discussing it at a metropolitan scale, providing a methodology relevant to other worldwide metropolises

Modulation of systemic cytokine levels by implantation of alginate encapsulated cells

The availability of cell lines that are transfected with IL-4, IL-5 and IFN-γ cytokine genes permits the prolonged in vivo delivery of functional cytokines in relatively large doses for the modulation of specific immune responses. Oft

Amino acids in the first transmembrane domain affecting function of the extracellular domain of truncated α4β2 chimeras of the nicotinic acetylcholine receptor

Important neurological and psychiatric diseases, including Alzheimer disease, nicotine addiction, and schizophrenia, are influenced by nicotinic acetylcholine receptors (nAChRs). Understanding the structure and function of nAChRs will provide a foundation for developing drug therapies for these diseases. nAChRs are members of the Cys-loop receptor superfamily. Each nAChR contains five subunits with each subunit containing four transmembrane domains. Once the ligand binds to the extracellular domain (ECD), the section of the nAChR that is exposed to the external environment, the protein undergoes a conformation change and an ion flux occurs allowing signal transmission. The first transmembrane domain (M1) is specifically critical for the function of the neurotransmitter-binding ECD in nAChRs. The features of M1 that are essential for ECD function, however, are not fully known. M1 sequences from ionotropic serotonin (5HT3A) and from α4, β2, and α7 nAChR subunits are similar but behave differently in ECD α4β2 nAChRs. The sequence differences provide the opportunity to discover amino acids from M1 that promote or inhibit ECD function. Through site-directed mutagenesis and expression of chimeric M1 domains with extracellular α4 and β2 subunits in Xenopus oocytes, we determined amino acids in native M1 controlling expression of ECD α4β2 nAChRs and amino acids from the 5HT3A M1 inhibiting that expression. Generating a feasible ECD will provide insight into the essential properties allowing proper ligand binding, conformational changes, and in result the ion cascade triggering the receptor’s function. The truncated designs of full length receptors will contribute to understanding how nAChR subunits fold and assemble and how their transmembrane domains affect structure and function. The results will aid in identifying the importance of M1 in ECD function and guide the development of ECD Cys-loop receptors as water-soluble structural models of the full length receptors. Water-soluble models are a key step in advancing our understanding of nAChRs due to the difficulty of producing high resolution structural models of full-length nAChR through crystallography. With the advantageous physical properties of water-soluble protein, the shortcomings of crystallography with the full-length nAChRs would be overcome, allowing for further understanding of nAChR assembly, function, and future drug design

The Marian Library Newsletter: Vol. 4, No. 4

https://ecommons.udayton.edu/ml_newsletter/1102/thumbnail.jp

External influences and priority-setting for anti-cancer agents: a case study of media coverage in adjuvant trastuzumab for breast cancer

<p>Abstract</p> <p>Background</p> <p>Setting priorities for the funding of new anti-cancer agents is becoming increasingly complex. The funding of adjuvant trastuzumab for breast cancer has brought this dilemma to the fore. In this paper we review external factors that may influence decision-making bodies and present a case study of media response in Ontario, Canada to adjuvant trastuzumab for breast cancer.</p> <p>Methods</p> <p>A comprehensive search of the databases of Canadian national and local newspapers and television was performed. Articles pertaining to trastuzumab in adjuvant breast cancer as well as 17 other anti-cancer drugs and indications were retrieved. The search period was from the date when individual trial results were announced to the date funding was made available in Ontario.</p> <p>Results</p> <p>During the 2.6 months between the release of the trastuzumab results to funding approval in Ontario, we identified 51 episodes of media coverage. For the 17 other drugs/indications (7 breast and 10 non-breast), the median time to funding approval was 31 months (range 14–46). Other recent major advances in oncology such as adjuvant vinorelbine/cisplatin for resected NSCLC and docetaxel for advanced prostate cancer received considerably less media attention (17 media reports for each) than trastuzumab. The median number of media reports for breast cancer drugs was 4.5 compared to 2.5 for non-breast cancer drugs (p = 0.56).</p> <p>Conclusion</p> <p>Priority-setting for novel anti-cancer agents is a complex process that tries to ensure fair use of constrained resources to fund therapies with the best evidence of clinical benefit. However, this process is subject to external factors including the influence of media, patient advocates, politicians, and industry. The data in this case study serve to illustrate the significant involvement one (or all) of these external factors may play in the debate over priority-setting.</p

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd

Prognostic implications of comorbidity patterns in critically ill COVID-19 patients: A multicenter, observational study

Background: The clinical heterogeneity of COVID-19 suggests the existence of different phenotypes with prognostic implications. We aimed to analyze comorbidity patterns in critically ill COVID-19 patients and assess their impact on in-hospital outcomes, response to treatment and sequelae. Methods: Multicenter prospective/retrospective observational study in intensive care units of 55 Spanish hospitals. 5866 PCR-confirmed COVID-19 patients had comorbidities recorded at hospital admission; clinical and biological parameters, in-hospital procedures and complications throughout the stay; and, clinical complications, persistent symptoms and sequelae at 3 and 6 months. Findings: Latent class analysis identified 3 phenotypes using training and test subcohorts: low-morbidity (n=3385; 58%), younger and with few comorbidities; high-morbidity (n=2074; 35%), with high comorbid burden; and renal-morbidity (n=407; 7%), with chronic kidney disease (CKD), high comorbidity burden and the worst oxygenation profile. Renal-morbidity and high-morbidity had more in-hospital complications and higher mortality risk than low-morbidity (adjusted HR (95% CI): 1.57 (1.34-1.84) and 1.16 (1.05-1.28), respectively). Corticosteroids, but not tocilizumab, were associated with lower mortality risk (HR (95% CI) 0.76 (0.63-0.93)), especially in renal-morbidity and high-morbidity. Renal-morbidity and high-morbidity showed the worst lung function throughout the follow-up, with renal-morbidity having the highest risk of infectious complications (6%), emergency visits (29%) or hospital readmissions (14%) at 6 months (p<0.01). Interpretation: Comorbidity-based phenotypes were identified and associated with different expression of in-hospital complications, mortality, treatment response, and sequelae, with CKD playing a major role. This could help clinicians in day-to-day decision making including the management of post-discharge COVID-19 sequelae.Financial support was provided by Instituto de Salud Carlos III (CIBERESUCICOVID, COV20/00110), co-funded by Fondo Europeo de Desarrollo Regional (FEDER), “Una manera de hacer Europa”, Centro de Investigación Biomédica en Red − Enfermedades Respiratorias (CIBERES) and Donation Program “estar preparados”, UNESPA, Madrid, Spain. JdB acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Miguel Servet 2019: CP19/00108), cofunded by the European Social Fund (ESF), “Investing in your future”. DdGC acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Miguel Servet 2019: CP20/00041), co-funded by the European Social Fund (ESF), “Investing in your future”. AC acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Sara Borrell 2021: CD21/00087).Peer ReviewedArticle signat per 71 autors/es: Iván D. Benítez (a,b,1), Jordi de Batlle (a,b,1), Gerard Torres (a,b), Jessica Gonzáalez (a,b), David de Gonzalo-Calvo (a,b), Adriano D.S. Targa (a,b), Clara Gort-Paniello (a,b), Anna Moncusí-Moix (a,b), Adrián Ceccato (b,c), Laia Fernández-Barat (b,d), Ricard Ferrer (b,e), Dario Garcia-Gasulla (f), Rosario Menéndez (b,g), Anna Motos (b,d), Oscar Peñuelas (b,h), Jordi Riera (b,e), Jesús F. Bermejo-Martin (b,i), Yhivian Peñasco (j), Pilar Ricart (k), María Cruz Martin Delgado(l), Luciano Aguilera(m), Alejandro Rodríguez(n), Maria Victoria Boado Varela (o), Fernando Suarez-Sipmann (p), Juan Carlos Pozo-Laderas (q), Jordi Solé-Violan (r), Maite Nieto (s), Mariana Andrea Novo (t), José Barberán (u), Rosario Amaya Villar (v), José Garnacho-Montero (w), Jose Luis García-Garmendia (x), José M. Gómez (y), José Ángel Lorente (b,h), Aaron Blandino Ortiz (z), Luis Tamayo Lomas (aa), Esther López-Ramos (ab), Alejandro Úbeda (ac), Mercedes Catalán-González (ad), Angel Sánchez-Miralles (ae), Ignacio Martínez Varela (af), Ruth Noemí Jorge García (ag), Nieves Franco (ah), Víctor D. Gumucio-Sanguino (ai), Arturo Huerta Garcia (aj), Elena Bustamante-Munguira (ak), Luis Jorge Valdivia (al), Jesús Caballero (am), Elena Gallego (an), Amalia Martínez de la Gándara (ao), Álvaro Castellanos-Ortega (ap), Josep Trenado (aq), Judith Marin-Corral (ar), Guillermo M Albaiceta (b,as), Maria del Carmen de la Torre (at), Ana Loza-Vázquez (au), Pablo Vidal (av), Juan Lopez Messa (aw), Jose M. Añon (b,ax), Cristina Carbajales Pérez (ay), Victor Sagredo (az), Neus Bofill (ba), Nieves Carbonell (bb), Lorenzo Socias(bc), Carme Barberá (bd), Angel Estella (be), Manuel Valledor Mendez (bf), Emili Diaz (bg), Ana López Lago (bh), Antoni Torres (b,d) and Ferran Barbé (a,b*), on behalf of the CIBERESUCICOVID Project (COV20/00110, ISCIII)2 // (a) Translational Research in Respiratory Medicine, University Hospital Arnau de Vilanova and Santa Maria, IRBLleida, Lleida, Spain; (b) CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, Madrid, Spain; (c) Critical Care Center, ParcTaulí Hospital Universitari, Institut d'Investigació i Innovació Parc Taulí I3PT, Sabadell, Spain; (d) Department of Pneumology, Hospital Clinic of Barcelona; August Pi i Sunyer Biomedical Research Institute−IDIBAPS, University of Barcelona, Barcelona, Spain; (e) Intensive Care Department, Vall d’Hebron Hospital Universitari. SODIR Research Group, Vall d’Hebron Institut de Recerca (VHIR), Barcelona, Spain; (f) Barcelona Supercomputing Center (BSC), Barcelona, Spain; (g) Pulmonology Service, University and Polytechnic Hospital La Fe, Valencia, Spain; (h) Hospital Universitario de Getafe, Madrid, Spain; Universidad Europea, Madrid, Spain; (i) Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain; Group for Biomedical Research in Sepsis (BioSepsis), Instituto de Investigación Biomédica de Salamanca (IBSAL), Salamanca, Spain; (j) Servicio de Medicina Intensiva, Hospital Universitario Marqués de Valdecilla, Santander, Spain; (k) Servei de Medicina Intensiva, Hospital Universitari Germans Trias, Badalona, Spain; (l) Hospital Universitario Torrejón-Universidad Francisco de Vitoria, Madrid, Spain; (m) Servicio de Anestesiología y Reanimación, Hospital Universitario Basurto, Bilbao, Spain; (n) Critical Care Department, Hospital Joan XXIII, Tarragona, Spain; (o) Servicio de Medicina Intensiva, Hospital de Cruces, Baracaldo, Vizcaya, Spain; (p) Intensive Care Unit, Hospital Universitario La Princesa, Madrid, Spain; (q) UGC-Medicina Intensiva, Hospital Universitario Reina Sofia, Instituto Maimonides IMIBIC, Córdoba, Spain; (r) Critical Care Department, Hospital Dr. Negrín Gran Canaria, Las Palmas, Gran Canaria, Spain. Universidad Fernando Pessoa, Canarias, Spain; (s) Hospital Universitario de Segovia, Segovia, Spain; (t) Servei de Medicina Intensiva, Hospital Universitari Son Espases, Palma de Mallorca, Illes Balears, Spain; (u) Hospital Universitario HM Montepríncipe, Universidad San Pablo-CEU, Madrid, Spain; vIntensive Care Clinical Unit, Hospital Universitario Virgen de Rocío, Sevilla, Spain; (w) Intensive Care Clinical Unit, Hospital Universitario Virgen Macarena, Seville, Spain; (x) Intensive Care Unit, Hospital San Juan de Dios del Aljarafe, Bormujos, Sevilla, Spain; (y) Hospital General Universitario Gregorio Marañon, Madrid, Spain; (z) Servicio de Medicina Intensiva, Hospital Universitario Ramón y Cajal, Madrid, Spain; (aa) Critical Care Department, Hospital Universitario Río Hortega de Valladolid, Valladolid, Spain; (ab) Servicio de Medicina Intensiva, Hospital Universitario Príncipe de Asturias, Madrid, Spain; (ac) Servicio de Medicina Intensiva, Hospital Punta de Europa, Algeciras, Spain; (ad) Department of Intensive Care Medicine, Hospital Universitario 12 de Octubre, Madrid, Spain; (ae) Hospital de Sant Joan d’Alacant, Alacant, Spain; (af) Critical Care Department, Hospital Universitario Lucus Augusti, Lugo, Spain; (ag) Intensive Care Department, Hospital Nuestra Señora de Gracia, Zaragoza, Spain; (ah) Hospital Universitario de Móstoles, Madrid, Spain; (ai) Department of Intensive Care. Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, Barcelona, Spain. Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona, Spain; (aj) Pulmonary and Critical Care Division; Emergency Department, Clínica Sagrada Família, Barcelona, Spain; (ak) Department of Intensive Care Medicine, Hospital Clínico Universitario Valladolid, Valladolid, Spain; (al) Hospital Universitario de León, León, Spain; (am) Critical Care Department, Hospital Universitari Arnau de Vilanova; IRBLleida, Lleida, Spain; (an) Unidad de Cuidados Intensivos, Hospital Universitario San Pedro de Alcántara, Cáceres, Spain; (ao) Department of Intensive Medicine, Hospital Universitario Infanta Leonor, Madrid, Spain; (ap) Servicio de medicina intensiva. Hospital Universitario y Politécnico La Fe, Valencia, Spain; (aq) Servicio de Medicina Intensiva, Hospital Universitario Mútua de Terrassa, Terrassa, Barcelona, Spain; (ar) Critical Care Department, Hospital del Mar-IMIM, Barcelona, Spain; (as) Departamento de Biología Funcional. Instituto Universitario de Oncología del Principado de Asturias, Universidad de Oviedo; Instituto de Investigación Sanitaria del Principado de Asturias, Hospital Central de Asturias, Oviedo, Spain; (at) Hospital de Mataró de Barcelona, Spain; (au) Unidad de Medicina Intensiva, Hospital Universitario Virgen de Valme, Sevilla, Spain; (av) Complexo Hospitalario Universitario de Ourense, Ourense, Spain; (aw) Complejo Asistencial Universitario de Palencia, Palencia, Spain; (ax) Servicio de Medicina Intensiva. Hospital Universitario La Paz, IdiPAZ, Madrid, Spain; (ay) Intensive Care Unit, Hospital Álvaro Cunqueiro, Vigo, Spain; (az) Hospital Universitario de Salamanca, Salamanca, Spain; (ba) Department of Physical Medicine and Rehabilitation, Hospital Verge de la Cinta, Tortosa, Tarragona, Spain; (bb) Intensive Care Unit, Hospital Clínico y Universitario de Valencia, Valencia, Spain; (bc) Intensive Care Unit, Hospital Son Llàtzer, Palma de Mallorca, Illes Balears, Spain; (bd) Hospital Santa Maria; IRBLleida, Lleida, Spain; (be) Intensive Care Unit, University Hospital of Jerez. Medicine Department University of Cadiz. INiBICA, Spain; (bf) Hospital Universitario San Agustín, Asturias, Spain; (bg) Department of Medicine, Universitat Autónoma de Barcelona (UAB); Critical Care Department, Corporació Sanitària Parc Taulí, Sabadell, Barcelona, Spain; (bh) Department of Intensive care Medicine, Complejo Hospitalario Universitario de Santiago de Compostela, Santiago de Compostela, SpainPostprint (published version

Characterization and cartography of some Mediterranean soft-bottom benthic communities (Ligurian Sea, Italy)

Soft-bottom benthic communities were studied along the Western coast of the Ligurian Sea with a new approach using both videocamera surveys and collected samples. The preliminary distribution of soft-bottoms and the definition of the limits and status of seagrass beds were carried out in September 1991, using an underwater vehicle provided with a videocamera and towed by a boat. Moreover, 90 benthic samples were collected at 5-40 m depth in order to characterize the macrobenthic soft-bottom communities. Six soft-bottom benthic assemblages and two sea grass biotopes (Cymodocea nodosa and Posidonia oceanica) were revealed by means of underwater images and multivariate analysis (TWINSPAN) on samples collected. The communities inhabiting the infralittoral sandy and coarse sediments corresponded to those previously described in the Mediterranean Sea, whereas a large complex transition between sandy and muddy communities was recognized on circalittoral soft-bottoms. Information obtained with this approach was used to draw a map of the investigated areas at 1:10,000 scale. The employment of the two techniques was cost effective for both time and research effort

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