U(5) Nambu-Jona-Lasinio model with flavor dependent coupling constants: pseudoscalar and scalar mesons masses

By considering the background field method we calculate one-loop polarization corrections to the coupling constant of the flavor-U(5) Nambu-Jona-Lasinio (NJL) model with degenerate up and down quarks. They break flavor and chiral symmetries and they can be written as $G_{ij}^\Gamma (\bar{\psi} \lambda_i \Gamma \psi) ( \bar{\psi} \lambda_j \Gamma \psi)$, for the scalar and pseudoscalar channels ($\Gamma=I , i \gamma_5$) and $i,j = 0.1,... N_f^2-1$. Their contributions to different observables are computed: quark-antiquark scalar condensates, masses of quark-antiquark meson states (pseudoscalar and scalar) and pseudoscalar meson weak decay constants. The non-covariant three dimensional regularization scheme is employed according to which a three-dimensional momentum cutoff has an unique interpretation for light and heavy quarks. Besides that, flavor dependence of cutoffs is implemented in an unambiguous way.Their values ($\Lambda_f$, f=u,s,c,b) , however, are found to be very close to each other, i.e. the best results are obtained for nearly flavor-independent cutoffs. The NJL-gap equations are found to overestimate the heavy quark condensates at the usual mean field level usually adopted for model. The polarization tensor is calculated entirely in the adjoint representation what may lead to mixing terms. A quite surprisingly good description of all the pseudoscalar -- including the pseudoscalar $\eta$'s -- and most of the scalar meson masses -- is obtained within 5$\%$ to 10$\%$. However, the usual problems to describe the correct mass hierarchy of some light scalar mesons still remains. In spite of the good description of the meson masses, the pseudoscalar meson weak decay constant cannot be described by the NJL model (with relativistic heavy quark propagators) without further interactions or effects.Comment: 20 pages. Abstract above was reduced to cope with filling-form, improved tex

Superstructure based on β-CD self-assembly induced by a small guest molecule

The size, shape and surface chemistry of nanoparticles play an important role in cellular interaction. Thus, the main objective of the present study was the determination of the β-cyclodextrin (β-CD) self-assembly thermodynamic parameters and its structure, aiming to use these assemblies as a possible controlled drug release system. Light scattering measurements led us to obtain the β-CD's critical aggregation concentration (cac) values, and consequently the thermodynamic parameters of the β-CD spontaneous self-assembly in aqueous solution: Δ[subscript agg]G[superscript o] = −16.31 kJ mol[superscript −1], Δ[subscript agg]H[superscript o] = −26.48 kJ mol[superscript −1] and TΔ[subscript agg]S[superscript o] = −10.53 kJ mol[superscript −1] at 298.15 K. Size distribution of the self-assembled nanoparticles below and above cac was 1.5 nm and 60–120 nm, respectively. The number of β-CD molecules per cluster and the second virial coefficient were identified through Debye's plot and molecular dynamic simulations proposed the three-fold assembly for this system below cac. Ampicillin (AMP) was used as a drug model in order to investigate the key role of the guest molecule in the self-assembly process and the β-CD:AMP supramolecular system was studied in solution, aiming to determine the structure of the supramolecular aggregate. Results obtained in solution indicated that the β-CD's cac was not affected by adding AMP. Moreover, different complex stoichiometries were identified by nuclear magnetic resonance and isothermal titration calorimetry experiments.Brazil. National Institute in Science and Technology in Nanobiopharmaceutics (NanoBiofar) (CNPq/MCT/FAPEMIG)Conselho Nacional de Pesquisas (Brazil)National Institutes of Health (U.S.) (Grant 1-R01-DE016516-03)Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior (Process 4597-08-7)Fundacao de Amparo a Pesquisa do Estado de Minas Gerais (CEX APQ-00498/08

Violacein Induces Death of Resistant Leukaemia Cells via Kinome Reprogramming, Endoplasmic Reticulum Stress and Golgi Apparatus Collapse

It is now generally recognised that different modes of programmed cell death (PCD) are intimately linked to the cancerous process. However, the mechanism of PCD involved in cancer chemoprevention is much less clear and may be different between types of chemopreventive agents and tumour cell types involved. Therefore, from a pharmacological view, it is crucial during the earlier steps of drug development to define the cellular specificity of the candidate as well as its capacity to bypass dysfunctional tumoral signalling pathways providing insensitivity to death stimuli. Studying the cytotoxic effects of violacein, an antibiotic dihydro-indolone synthesised by an Amazon river Chromobacterium, we observed that death induced in CD34(+)/c-Kit(+)/P-glycoprotein(+)/MRP1(+) TF1 leukaemia progenitor cells is not mediated by apoptosis and/or autophagy, since biomarkers of both types of cell death were not significantly affected by this compound. To clarify the working mechanism of violacein, we performed kinome profiling using peptide arrays to yield comprehensive descriptions of cellular kinase activities. Pro-death activity of violacein is actually carried out by inhibition of calpain and DAPK1 and activation of PKA, AKT and PDK, followed by structural changes caused by endoplasmic reticulum stress and Golgi apparatus collapse, leading to cellular demise. Our results demonstrate that violacein induces kinome reprogramming, overcoming death signaling dysfunctions of intrinsically resistant human leukaemia cells.TopInstitute pharma (The Netherlands)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Dutch Cancer SocietyErasmus MC Univ Med Ctr, Dept Gastroenterol & Hepatol, Rotterdam, NetherlandsUniv Amsterdam, Acad Med Ctr, Ctr Expt & Mol Med, NL-1105 AZ Amsterdam, NetherlandsUniv Estadual Campinas, Brazil UNICAMP, Dept Biochem, Inst Biol, São Paulo, BrazilFed Univ São Paulo UNIFESP, Dept Biochem, São Paulo, BrazilFed Univ São Paulo UNIFESP, Dept Cell Biol, São Paulo, BrazilUniv Grande Rio UNIGRANRIO, Heath Sci Sch, Multidisciplinary Lab Dent Res, Rio de Janeiro, BrazilNatl Inst Metrol Qual & Technol Inmetro, Biotechnol Lab, Bioengn Sect, Rio de Janeiro, BrazilUniv Campinas UNICAMP, Inst Chem, Biol Chem Lab, Rio de Janeiro, BrazilUniv Groningen, Univ Med Ctr Groningen, Dept Pediat Oncol, Beatrix Childrens Hosp, Groningen, NetherlandsFed Univ São Paulo UNIFESP, Dept Biochem, São Paulo, BrazilFed Univ São Paulo UNIFESP, Dept Cell Biol, São Paulo, BrazilDutch Cancer Society: EMCR 2010-4737Web of Scienc

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Effect of Hydrocortisone on Mortality and Organ Support in Patients With Severe COVID-19: The REMAP-CAP COVID-19 Corticosteroid Domain Randomized Clinical Trial.

Importance: Evidence regarding corticosteroid use for severe coronavirus disease 2019 (COVID-19) is limited. Objective: To determine whether hydrocortisone improves outcome for patients with severe COVID-19. Design, Setting, and Participants: An ongoing adaptive platform trial testing multiple interventions within multiple therapeutic domains, for example, antiviral agents, corticosteroids, or immunoglobulin. Between March 9 and June 17, 2020, 614 adult patients with suspected or confirmed COVID-19 were enrolled and randomized within at least 1 domain following admission to an intensive care unit (ICU) for respiratory or cardiovascular organ support at 121 sites in 8 countries. Of these, 403 were randomized to open-label interventions within the corticosteroid domain. The domain was halted after results from another trial were released. Follow-up ended August 12, 2020. Interventions: The corticosteroid domain randomized participants to a fixed 7-day course of intravenous hydrocortisone (50 mg or 100 mg every 6 hours) (n = 143), a shock-dependent course (50 mg every 6 hours when shock was clinically evident) (n = 152), or no hydrocortisone (n = 108). Main Outcomes and Measures: The primary end point was organ support-free days (days alive and free of ICU-based respiratory or cardiovascular support) within 21 days, where patients who died were assigned -1 day. The primary analysis was a bayesian cumulative logistic model that included all patients enrolled with severe COVID-19, adjusting for age, sex, site, region, time, assignment to interventions within other domains, and domain and intervention eligibility. Superiority was defined as the posterior probability of an odds ratio greater than 1 (threshold for trial conclusion of superiority >99%). Results: After excluding 19 participants who withdrew consent, there were 384 patients (mean age, 60 years; 29% female) randomized to the fixed-dose (n = 137), shock-dependent (n = 146), and no (n = 101) hydrocortisone groups; 379 (99%) completed the study and were included in the analysis. The mean age for the 3 groups ranged between 59.5 and 60.4 years; most patients were male (range, 70.6%-71.5%); mean body mass index ranged between 29.7 and 30.9; and patients receiving mechanical ventilation ranged between 50.0% and 63.5%. For the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively, the median organ support-free days were 0 (IQR, -1 to 15), 0 (IQR, -1 to 13), and 0 (-1 to 11) days (composed of 30%, 26%, and 33% mortality rates and 11.5, 9.5, and 6 median organ support-free days among survivors). The median adjusted odds ratio and bayesian probability of superiority were 1.43 (95% credible interval, 0.91-2.27) and 93% for fixed-dose hydrocortisone, respectively, and were 1.22 (95% credible interval, 0.76-1.94) and 80% for shock-dependent hydrocortisone compared with no hydrocortisone. Serious adverse events were reported in 4 (3%), 5 (3%), and 1 (1%) patients in the fixed-dose, shock-dependent, and no hydrocortisone groups, respectively. Conclusions and Relevance: Among patients with severe COVID-19, treatment with a 7-day fixed-dose course of hydrocortisone or shock-dependent dosing of hydrocortisone, compared with no hydrocortisone, resulted in 93% and 80% probabilities of superiority with regard to the odds of improvement in organ support-free days within 21 days. However, the trial was stopped early and no treatment strategy met prespecified criteria for statistical superiority, precluding definitive conclusions. Trial Registration: ClinicalTrials.gov Identifier: NCT02735707

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost