Investigating potential inhibitory effect of Uncaria tomentosa (Cat's Claw) against the main protease 3CL pro of SARS-CoV-2 by molecular modeling

COVID-19 is a disease caused by severe acute respiratory syndrome coronavirus 2. Presently, there is no effective treatment for COVID-19. As part of the worldwide efforts to find efficient therapies and preventions, it has been reported the crystalline structure of the SARS-CoV-2 main protease M pro (also called 3CL pro) bound to a synthetic inhibitor, which represents a major druggable target. The druggability of M pro could be used for discovering drugs to treat COVID-19. A multilevel computational study was carried out to evaluate the potential antiviral properties of the components of the medicinal herb Uncaria tomentosa (Cat's claw), focusing on the inhibition of M pro. The in silico approach starts with protein-ligand docking of 26 Cat's claw key components, followed by ligand pathway calculations, molecular dynamics simulations, and MM-GBSA calculation of the free energy of binding for the best docked candidates. The structural bioinformatics approaches led to identification of three bioactive compounds of Uncaria tomentosa (speciophylline, cadambine, and proanthocyanidin B2) with potential therapeutic effects by strong interaction with 3CL pro. Additionally, in silico drug-likeness indices for these components were calculated and showed good predicted therapeutic profiles of these phytochemicals. Our findings suggest the potential effectiveness of Cat's claw as complementary and/or alternative medicine for COVID-19 treatment

La vesícula extracelular TGF-β basal es un biomarcador predictivo de la respuesta a los inhibidores del punto de control inmunitario y de la supervivencia en el cáncer de pulmón no microcítico

Antecedentes: Los inhibidores de los puntos de control inmunitarios (ICI) son una estrategia terapéutica eficaz que mejora la supervivencia de los pacientes con cáncer de pulmón en comparación con los tratamientos convencionales. terapéutica eficaz que mejora la supervivencia de los pacientes con cáncer de pulmón en comparación con los tratamientos convencionales. Sin embargo, se necesitan biomarcadores predictivos novedosos para estratificar qué pacientes obtienen un beneficio clínico, ya que el histológico PD-L1, actualmente utilizado y altamente heterogéneo, ha mostrado una baja precisión. La biopsia líquida es el análisis de biomarcadores en fluidos corporales y representa una herramienta mínimamente invasiva que puede utilizarse para monitorizar la evolución del tumor y los efectos del tratamiento, reduciendo potencialmente los sesgos asociados a la heterogeneidad tumoral asociada a las biopsias de tejidos. En este contexto citoquinas, como el factor de crecimiento transformante-β (TGF-β), pueden encontrarse libres en circulación en la sangre y empaquetadas en vesículas extracelulares (VE), que tienen un tropismo de administración específico y pueden afectar a la interacción entre el tumor y el sistema inmunitario. El TGF-β es una citocina inmunosupresora que desempeña un papel crucial en el escape inmunitario de los tumores, la resistencia al tratamiento y la metástasis. Así pues, nuestro objetivo era evaluar el valor predictivo predictivo del TGF-β circulante y EV en pacientes con cáncer de pulmón no microcítico que reciben ICI.Background: Immune‐checkpoint inhibitors (ICIs) are an effective therapeutic strategy, improving the survival of patients with lung cancer compared with conventional treatments. However, novel predictive biomarkers are needed to stratify which patients derive clinical benefit because the currently used and highly heterogenic histological PD‐L1 has shown low accuracy. Liquid biopsy is the analysis of biomarkers in body fluids and represents a minimally invasive tool that can be used to monitor tumor evolution and treatment effects, potentially reducing biases associated with tumor heterogeneity associated with tissue biopsies. In this context, cytokines, such as transforming growth factor‐β (TGF‐β), can be found free in circulation in the blood and packaged into extracellular vesicles (EVs), which have a specific delivery tropism and can affect in tumor/immune system interaction. TGF‐β is an immunosuppressive cytokine that plays a crucial role in tumor immune escape, treatment resistance, and metastasis. Thus, we aimed to evaluate the predictive value of circulating and EV TGF‐β in patients with non–small‐cell lung cancer receiving ICIs

Prioritization strategies in clinical practice guidelines development: a pilot study

Objective: Few methodological studies address the prioritization of clinical topics for the development of Clinical Practice Guidelines (CPGs). The aim of this study was to validate a methodology for Priority Determination of Topics (PDT) of CPGs. Methods and results: Firstly, we developed an instrument for PDT with 41 criteria that were grouped under 10 domains, based on a comprehensive systematic search. Secondly, we performed a survey of stakeholders involved in CPGs development, and end users of guidelines, using the instrument. Thirdly, a pilot testing of the PDT procedure was performed in order to choose 10 guideline topics among 34 proposed projects; using a multicriteria analysis approach, we validated a mechanism that followed five stages: determination of the composition of groups, item/domain scoring, weights determination, quality of the information used to support judgments, and finally, topic selection. Participants first scored the importance of each domain, after which four different weighting procedures were calculated (including the survey results). The process of weighting was determined by correlating the data between them. We also reported the quality of evidence used for PDT. Finally, we provided a qualitative analysis of the process. The main domains used to support judgement, having higher quality scores and weightings, were feasibility, disease burden, implementation and information needs. Other important domains suc

Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation

Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PD

A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES)

In this article, we introduce ARES (Antibiotic Resistance Evolution Simulator) a software device that simulates P-system model scenarios with five types of nested computing membranes oriented to emulate a hierarchy of eco-biological compartments, i.e. a) peripheral ecosystem; b) local environment; c) reservoir of supplies; d) animal host; and e) host's associated bacterial organisms (microbiome). Computational objects emulating molecular entities such as plasmids, antibiotic resistance genes, antimicrobials, and/or other substances can be introduced into this framework and may interact and evolve together with the membranes, according to a set of pre-established rules and specifications. ARES has been implemented as an online server and offers additional tools for storage and model editing and downstream analysisThis work has also been supported by grants BFU2012-39816-C02-01 (co-financed by FEDER funds and the Ministry of Economy and Competitiveness, Spain) to AL and Prometeo/2009/092 (Ministry of Education, Government of Valencia, Spain) and Explora Ciencia y Explora Tecnologia/SAF2013-49788-EXP (Spanish Ministry of Economy and Competitiveness) to AM. IRF is recipient of a "Sara Borrell" postdoctoral fellowship (Ref. CD12/00492) from the Ministry of Economy and Competitiveness (Spain). We are also grateful to the Spanish Network for the Study of Plasmids and Extrachromosomal Elements (REDEEX) for encouraging and funding cooperation among Spanish microbiologists working on the biology of mobile genetic elements (Spanish Ministry of Science and Innovation, reference number BFU2011-14145-E).Campos Frances, M.; Llorens, C.; Sempere Luna, JM.; Futami, R.; Rodríguez, I.; Carrasco, P.; Capilla, R.... (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct. 10(41):1-13. https://doi.org/10.1186/s13062-015-0070-9S1131041Baquero F, Coque TM, Canton R. Counteracting antibiotic resistance: breaking barriers among antibacterial strategies. Expert Opin Ther Targets. 2014;18:851–61.Baquero F, Lanza VF, Canton R, Coque TM. Public health evolutionary biology of antimicrobial resistance: priorities for intervention. Evol Appl. 2014;8:223–39.Baquero F, Coque TM, de la Cruz F. Ecology and evolution as targets: the need for novel eco-evo drugs and strategies to fight antibiotic resistance. Antimicrob Agents Chemother. 2011;55:3649–60.Carlet J, Jarlier V, Harbarth S, Voss A, Goossens H, Pittet D, et al. Ready for a world without antibiotics? The pensieres antibiotic resistance call to action. Antimicrob Resist Infect Control. 2012;1:11.Laxminarayan R, Duse A, Wattal C, Zaidi AK, Wertheim HF, Sumpradit N, et al. Antibiotic resistance-the need for global solutions. Lancet Infect Dis. 2013;13:1057–98.G8-Science-Ministers-Statement. 2013. https://www.gov.uk/government/news/g8-science-ministers-statement .Levy SB, Marshall B. Antibacterial resistance worldwide: causes, challenges and responses. Nat Med. 2004;10:S122–9.Wellington EM, Boxall AB, Cross P, Feil EJ, Gaze WH, Hawkey PM, et al. The role of the natural environment in the emergence of antibiotic resistance in gram-negative bacteria. Lancet Infect Dis. 2013;13:155–65.Marshall BM, Levy SB. Food animals and antimicrobials: impacts on human health. Clin Microbiol Rev. 2011;24:718–33.Marshall BM, Ochieng DJ, Levy SB. Commensals: underappreciated reservoir of antibiotic resistance. Microbe. 2009;4:231–8.Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MO, Dantas G. The shared antibiotic resistome of soil bacteria and human pathogens. Science. 2012;337:1107–11.Heuer H, Schmitt H, Smalla K. Antibiotic resistance gene spread due to manure application on agricultural fields. Curr Opin Microbiol. 2011;14:236–43.Teillant A, Laxminarayan R. Economics of Antibiotic Use in U.S. Swine and Poultry Production. Choices. 2015;30:1. 1st Quarter 2015.ANTIBIOTIC RESISTANCE THREATS in the United States. http://www.cdc.gov/drugresistance/threat-report-2013/pdf/ar-threats-2013-508.pdf .Gillings MR. Evolutionary consequences of antibiotic use for the resistome, mobilome and microbial pangenome. Front Microbiol. 2013;4:4.Davies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev. 2010;74:417–33.Palmer AC, Kishony R. Understanding, predicting and manipulating the genotypic evolution of antibiotic resistance. Nat Rev Genet. 2013;14:243–8.Baquero F, Tedim AP, Coque TM. Antibiotic resistance shaping multi-level population biology of bacteria. Front Microbiol. 2013;4:15.Partridge SR. Analysis of antibiotic resistance regions in Gram-negative bacteria. FEMS Microbiol Rev. 2011;35:820–55.Baquero F, Coque TM. Multilevel population genetics in antibiotic resistance. FEMS Microbiol Rev. 2011;35:705–6.Martinez JL, Baquero F, Andersson DI. Predicting antibiotic resistance. Nat Rev Microbiol. 2007;5:958–65.Martinez JL, Baquero F. Emergence and spread of antibiotic resistance: setting a parameter space. Upsala Journal of Medical Sciences. Upsala J Med Sci. 2014, Early Online: 1–10, doi: 10.3109/03009734.2014.901444 ).Baquero F, Nombela C. The microbiome as a human organ. Clin Microbiol Infect. 2012;18 Suppl 4:2–4.Kumsa B, Socolovschi C, Parola P, Rolain JM, Raoult D. Molecular detection of Acinetobacter species in lice and keds of domestic animals in Oromia Regional State. Ethiopia PLoS One. 2012;7:e52377.Ahmad A, Ghosh A, Schal C, Zurek L. Insects in confined swine operations carry a large antibiotic resistant and potentially virulent enterococcal community. BMC Microbiol. 2011;11:23.Graczyk TK, Knight R, Gilman RH, Cranfield MR. The role of non-biting flies in the epidemiology of human infectious diseases. Microbes Infect. 2001;3:231–5.Limoee M, Enayati AA, Khassi K, Salimi M, Ladonni H. Insecticide resistance and synergism of three field-collected strains of the German cockroach Blattella germanica (L.) (Dictyoptera: Blattellidae) from hospitals in Kermanshah, Iran. Trop Biomed. 2011;28:111–8.Salehzadeha A, Tavacolb P, Mahjubc H. Bacterial, fungal and parasitic contamination of cockroaches in public hospitals of Hamadan, Iran. J Vect Borne Dis. 2007;44:105–10.Akinjogunla OJ, Odeyemi AT, Udoinyang EP. Cockroaches (periplaneta americana and blattella germanica): reservoirs of multi drug resistant (MDR) bacteria in Uyo, Akwa Ibom State. Scientific J Biol Sci. 2012;1:19–30.Mideo N, Alizon S, Day T. Linking within- and between-host dynamics in the evolutionary epidemiology of infectious diseases. Trends Ecol Evol. 2008;23:511–7.Gillings MR, Stokes HW. Are humans increasing bacterial evolvability? Trends EcolEvol. 2012;27:346–52.Baquero F. Environmental stress and evolvability in microbial systems. Clin Microbiol Infect. 2009;15 Suppl 1:5–10.Paun G, Rozemberg G, Salomaa A. The Oxford Handbook of Membrane Computing. Oxford, London. Oxford University Press. 2010.Paun G. Membrane Computing. An Introduction. Berlin, Heidelberg. Springer-Verlag GmbH. 2002.Paun G. Computing with membranes. J Comput Syst Sci. 2000;61:108–43.Fontana F, Biancom L, Manca V. P systems and the modeling of biochemical oscillations. Lect Notes Comput Sci. 2006;3850:199–208.Cheruku S, Paun A, Romero-Campero FJ, Perez-Jimenez MJ, Ibarra OH. Simulating FAS-induced apoptosis by using P systems. Prog Nat Sci. 2007;4:424–31.Perez-Jimenez MJ, Romero-Campero FJ. P systems, a new computational modelling tool for systems biology. Transactions on computational systems. Lect N Bioinformat. 2006;Biology VI:176–97.Romero-Campero FJ, Perez-Jimenez MJ. Modelling gene expression control using P systems: The Lac Operon, a case study. Biosystems. 2008;91:438–57.Romero-Campero FJ, Perez-Jimenez MJ. A model of the quorum sensing system in Vibrio fischeri using P systems. Artif Life. 2008;14:95–109.Besozzi D, Cazzaniga P, Pescini D, Mauri G. Modelling metapopulations with stochastic membrane systems. Biosystems. 2008;91:499–514.Cardona M, Colomer MA, Perez-Jimenez MJ, Sanuy D, Margalida A. Modelling ecosystems using P Systems: The Bearded Vulture, a case of study. Lect Notes Comput Sci. 2009;5391:137–56.Cardona M, Colomer MA, Margalida A, Perez-Hurtado I, Perez-Jimenez MJ, Sanuy D. A P system based model of an ecosystem of some scavenger birds. Lect Notes Comput Sci. 2010;5957:182–95.Frisco P, Gheorghe M, Perez-Jimenez M. Applications of Membrane Computing in Systems and Synthetic biology. Cham. Springer International Publishing. 2014.Membrane Computing Community. http://ppage.psystems.eu .P-Lingua. http://www.p-lingua.org/wiki/index.php/Main_Page .Llorens C, Futami R, Covelli L, Dominguez-Escriba L, Viu JM, Tamarit D, et al. The Gypsy Database (GyDB) of mobile genetic elements: release 2.0. Nucleic Acids Res. 2011;39:D70–4.Baquero F. From pieces to patterns: evolutionary engineering in bacterial pathogens. Nat Rev Microbiol. 2004;2:510–8.Java. http://www.java.com .Garcia-Quismondo M, Gutierrez-Escudero R, Martinez-del-Amor MA, Orejuela-Pinedo E, Pérez-Hurtado I. P-Lingua 2.0: a software framework for cell-like P systems. Int J Comput Commun. 2009;IV:234.R programming language. http://www.r-project.org .Maciel A, Sankaranarayanan G, Halic T, Arikatla VS, Lu Z, De S. Surgical model-view-controller simulation software framework for local and collaborative applications. Int J Comput Assist Radiol Surg. 2011;6:457–71.Dethlefsen L, McFall-Ngai M, Relman DA. An ecological and evolutionary perspective on human-microbe mutualism and disease. Nature. 2007;449:811–8.Ley RE, Lozupone CA, Hamady M, Knight R, Gordon JI. Worlds within worlds: evolution of the vertebrate gut microbiota. Nat Rev Microbiol. 2008;6:776–88.Pallen MJ, Wren BW. Bacterial pathogenomics. Nature. 2007;449:835–42.Carrasco P, Perez-Cobas AE, Van de Pol C, Baixeras J, Moya A, Latorre A. Succession of the gut microbiota in the cockroach Blattella germanica. Int Microbiol. 2014;17:99–109

Genome-wide association study of Tourette Syndrome

Tourette Syndrome (TS) is a developmental disorder that has one of the highest familial recurrence rates among neuropsychiatric diseases with complex inheritance. However, the identification of definitive TS susceptibility genes remains elusive. Here, we report the first genome-wide association study (GWAS) of TS in 1285 cases and 4964 ancestry-matched controls of European ancestry, including two European-derived population isolates, Ashkenazi Jews from North America and Israel, and French Canadians from Quebec, Canada. In a primary meta-analysis of GWAS data from these European ancestry samples, no markers achieved a genome-wide threshold of significance (p<5 × 10−8); the top signal was found in rs7868992 on chromosome 9q32 within COL27A1 (p=1.85 × 10−6). A secondary analysis including an additional 211 cases and 285 controls from two closely-related Latin-American population isolates from the Central Valley of Costa Rica and Antioquia, Colombia also identified rs7868992 as the top signal (p=3.6 × 10−7 for the combined sample of 1496 cases and 5249 controls following imputation with 1000 Genomes data). This study lays the groundwork for the eventual identification of common TS susceptibility variants in larger cohorts and helps to provide a more complete understanding of the full genetic architecture of this disorder

Studies of η\eta and η′\eta' production in pppp and ppPb collisions

The production of $\eta$ and $\eta'$ mesons is studied in proton-proton and proton-lead collisions collected with the LHCb detector. Proton-proton collisions are studied at center-of-mass energies of $5.02$ and $13~{\rm TeV}$, and proton-lead collisions are studied at a center-of-mass energy per nucleon of $8.16~{\rm TeV}$. The studies are performed in center-of-mass rapidity regions $2.5<y_{\rm c.m.}<3.5$ (forward rapidity) and $-4.0<y_{\rm c.m.}<-3.0$ (backward rapidity) defined relative to the proton beam direction. The $\eta$ and $\eta'$ production cross sections are measured differentially as a function of transverse momentum for $1.5<p_{\rm T}<10~{\rm GeV}$ and $3<p_{\rm T}<10~{\rm GeV}$, respectively. The differential cross sections are used to calculate nuclear modification factors. The nuclear modification factors for $\eta$ and $\eta'$ mesons agree at both forward and backward rapidity, showing no significant evidence of mass dependence. The differential cross sections of $\eta$ mesons are also used to calculate $\eta/\pi^0$ cross section ratios, which show evidence of a deviation from the world average. These studies offer new constraints on mass-dependent nuclear effects in heavy-ion collisions, as well as $\eta$ and $\eta'$ meson fragmentation.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://lhcbproject.web.cern.ch/Publications/p/LHCb-PAPER-2023-030.html (LHCb public pages

Fraction of χc\chi_c decays in prompt J/ψJ/\psi production measured in pPb collisions at sNN=8.16\sqrt{s_{NN}}=8.16 TeV

The fraction of $\chi_{c1}$ and $\chi_{c2}$ decays in the prompt $J/\psi$ yield, $F_{\chi c}=\sigma_{\chi_c \to J/\psi}/\sigma_{J/\psi}$, is measured by the LHCb detector in pPb collisions at $\sqrt{s_{NN}}=8.16$ TeV. The study covers the forward ($1.5<y^*<4.0$) and backward ($-5.0<y^*<-2.5$) rapidity regions, where $y^*$ is the $J/\psi$ rapidity in the nucleon-nucleon center-of-mass system. Forward and backward rapidity samples correspond to integrated luminosities of 13.6 $\pm$ 0.3 nb$^{-1}$ and 20.8 $\pm$ 0.5 nb$^{-1}$, respectively. The result is presented as a function of the $J/\psi$ transverse momentum $p_{T,J/\psi}$ in the range 1$<p_{T, J/\psi}<20$ GeV/$c$. The $F_{\chi c}$ fraction at forward rapidity is compatible with the LHCb measurement performed in $pp$ collisions at $\sqrt{s}=7$ TeV, whereas the result at backward rapidity is 2.4 $\sigma$ larger than in the forward region for $1<p_{T, J/\psi}<3$ GeV/$c$. The increase of $F_{\chi c}$ at low $p_{T, J/\psi}$ at backward rapidity is compatible with the suppression of the $\psi$(2S) contribution to the prompt $J/\psi$ yield. The lack of in-medium dissociation of $\chi_c$ states observed in this study sets an upper limit of 180 MeV on the free energy available in these pPb collisions to dissociate or inhibit charmonium state formation.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-028.html (LHCb public pages

Observation of strangeness enhancement with charmed mesons in high-multiplicity pPbp\mathrm{Pb} collisions at sNN=8.16 \sqrt {s_{\mathrm{NN}}}=8.16\,TeV

The production of prompt $D^+_{s}$ and $D^+$ mesons is measured by the LHCb experiment in proton-lead ($p\mathrm{Pb}$) collisions in both the forward ($1.5<y^*<4.0$) and backward ($-5.0<y^*<-2.5$) rapidity regions at a nucleon-nucleon center-of-mass energy of $\sqrt {s_{\mathrm{NN}}}=8.16\,$TeV. The nuclear modification factors of both $D^+_{s}$ and $D^+$ mesons are determined as a function of transverse momentum, $p_{\mathrm{T}}$, and rapidity. In addition, the $D^+_{s}$ to $D^+$ cross-section ratio is measured as a function of the charged particle multiplicity in the event. An enhanced $D^+_{s}$ to $D^+$ production in high-multiplicity events is observed for the whole measured $p_{\mathrm{T}}$ range, in particular at low $p_{\mathrm{T}}$ and backward rapidity, where the significance exceeds six standard deviations. This constitutes the first observation of strangeness enhancement in charm quark hadronization in high-multiplicity $p\mathrm{Pb}$ collisions. The results are also qualitatively consistent with the presence of quark coalescence as an additional charm quark hadronization mechanism in high-multiplicity proton-lead collisions.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2023-021.html (LHCb public pages