576 research outputs found

    PARP-14 Promotes Survival of Mammalian α but Not β Pancreatic Cells Following Cytokine Treatment

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    PARP-14 (poly-ADP Ribose Polymerase-14), a member of the PARP family, belongs to the group of Bal proteins (B Aggressive Lymphoma). PARP-14 has recently appeared to be involved in the transduction pathway mediated by JNKs (c Jun N terminal Kinases), among which JNK2 promotes cancer cell survival. Several pharmacological PARP inhibitors are currently used as antitumor agents, even though they have also proved to be effective in many inflammatory diseases. Cytokine release from immune system cells characterizes many autoimmune inflammatory disorders, including type I diabetes, in which the inflammatory state causes β cell loss. Nevertheless, growing evidence supports a concomitant implication of glucagon secreting α cells in type I diabetes progression. Here, we provide evidence on the activation of a survival pathway, mediated by PARP-14, in pancreatic α cells, following treatment of αTC1.6 glucagonoma and βTC1 insulinoma cell lines with a cytokine cocktail: interleukin 1 beta (IL-1β), interferon gamma (IFN-γ) and tumor necrosis factor alpha (TNF-α). Through qPCR, western blot and confocal analysis, we demonstrated higher expression levels of PARP-14 in αTC1.6 cells with respect to βTC1 cells under inflammatory stimuli. By cytofluorimetric and caspase-3 assays, we showed the higher resistance of α cells compared to β cells to apoptosis induced by cytokines. Furthermore, the ability of PJ-34 to modulate the expression of the proteins involved in the survival pathway suggests a protective role of PARP-14. These data shed light on a poorly characterized function of PARP-14 in αTC1.6 cells in inflammatory contexts, widening the potential pharmacological applications of PARP inhibitors

    Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

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    Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a ZZ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 <pT<100< p_{\textrm{T}} < 100 GeV and in the pseudorapidity range 2.5<η<42.5 < \eta < 4. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb1^{-1}. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.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-2022-013.html (LHCb public pages

    Study of the BΛc+ΛˉcKB^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} decay

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    The decay BΛc+ΛˉcKB^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} is studied in proton-proton collisions at a center-of-mass energy of s=13\sqrt{s}=13 TeV using data corresponding to an integrated luminosity of 5 fb1\mathrm{fb}^{-1} collected by the LHCb experiment. In the Λc+K\Lambda_{c}^+ K^{-} system, the Ξc(2930)0\Xi_{c}(2930)^{0} state observed at the BaBar and Belle experiments is resolved into two narrower states, Ξc(2923)0\Xi_{c}(2923)^{0} and Ξc(2939)0\Xi_{c}(2939)^{0}, whose masses and widths are measured to be m(Ξc(2923)0)=2924.5±0.4±1.1MeV,m(Ξc(2939)0)=2938.5±0.9±2.3MeV,Γ(Ξc(2923)0)=0004.8±0.9±1.5MeV,Γ(Ξc(2939)0)=0011.0±1.9±7.5MeV, m(\Xi_{c}(2923)^{0}) = 2924.5 \pm 0.4 \pm 1.1 \,\mathrm{MeV}, \\ m(\Xi_{c}(2939)^{0}) = 2938.5 \pm 0.9 \pm 2.3 \,\mathrm{MeV}, \\ \Gamma(\Xi_{c}(2923)^{0}) = \phantom{000}4.8 \pm 0.9 \pm 1.5 \,\mathrm{MeV},\\ \Gamma(\Xi_{c}(2939)^{0}) = \phantom{00}11.0 \pm 1.9 \pm 7.5 \,\mathrm{MeV}, where the first uncertainties are statistical and the second systematic. The results are consistent with a previous LHCb measurement using a prompt Λc+K\Lambda_{c}^{+} K^{-} sample. Evidence of a new Ξc(2880)0\Xi_{c}(2880)^{0} state is found with a local significance of 3.8σ3.8\,\sigma, whose mass and width are measured to be 2881.8±3.1±8.5MeV2881.8 \pm 3.1 \pm 8.5\,\mathrm{MeV} and 12.4±5.3±5.8MeV12.4 \pm 5.3 \pm 5.8 \,\mathrm{MeV}, respectively. In addition, evidence of a new decay mode Ξc(2790)0Λc+K\Xi_{c}(2790)^{0} \to \Lambda_{c}^{+} K^{-} is found with a significance of 3.7σ3.7\,\sigma. The relative branching fraction of BΛc+ΛˉcKB^{-} \to \Lambda_{c}^{+} \bar{\Lambda}_{c}^{-} K^{-} with respect to the BD+DKB^{-} \to D^{+} D^{-} K^{-} decay is measured to be 2.36±0.11±0.22±0.252.36 \pm 0.11 \pm 0.22 \pm 0.25, where the first uncertainty is statistical, the second systematic and the third originates from the branching fractions of charm hadron decays.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-028.html (LHCb public pages

    Measurement of the ratios of branching fractions R(D)\mathcal{R}(D^{*}) and R(D0)\mathcal{R}(D^{0})

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    The ratios of branching fractions R(D)B(BˉDτνˉτ)/B(BˉDμνˉμ)\mathcal{R}(D^{*})\equiv\mathcal{B}(\bar{B}\to D^{*}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(\bar{B}\to D^{*}\mu^{-}\bar{\nu}_{\mu}) and R(D0)B(BD0τνˉτ)/B(BD0μνˉμ)\mathcal{R}(D^{0})\equiv\mathcal{B}(B^{-}\to D^{0}\tau^{-}\bar{\nu}_{\tau})/\mathcal{B}(B^{-}\to D^{0}\mu^{-}\bar{\nu}_{\mu}) are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0 fb1{ }^{-1} of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode τμντνˉμ\tau^{-}\to\mu^{-}\nu_{\tau}\bar{\nu}_{\mu}. The measured values are R(D)=0.281±0.018±0.024\mathcal{R}(D^{*})=0.281\pm0.018\pm0.024 and R(D0)=0.441±0.060±0.066\mathcal{R}(D^{0})=0.441\pm0.060\pm0.066, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is ρ=0.43\rho=-0.43. Results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the Standard Model.Comment: All figures and tables, along with any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-039.html (LHCb public pages

    Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

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    In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

    Dietary Phytoestrogen Intake is Inversely Associated with Hypertension in a Cohort of Adults Living in the Mediterranean Area

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    Background: Dietary polyphenols, including phytoestrogens are abundantly present in a balanced diet. Evidence for their role in preventing non-communicable diseases is emerging. Methods: We examined the association between estimated habitual intakes of dietary phytoestrogens and hypertension in a cohort study. The baseline data included 1936 men and women aged 18 years and older. Intakes of total phytoestrogens, isoflavones, and lignans were calculated from validated food frequency questionnaire. Data on the polyphenols content in foods were retrieved from the Phenol-Explorer database. Results: Individuals in the highest quartile of dietary phytoestrogens intake were less likely to be hypertensive (OR: 0.66, 95% CI: 0.44–0.98); moreover, the association showed a significant decreasing trend. Isoflavones and lignans were not associated with lower odds of hypertension; however, some individual compounds, such as biochanin A and pinoresinol showed an independent inverse association with hypertension. Conclusions: Dietary phytoestrogens are associated with lower likelihood of hypertension in adults living in the Mediterranean area. Future studies are needed to confirm the present findings (i.e., prospective cohort studies) and to better understand the mechanisms underlying such associations

    The Effect of Lutein on Eye and Extra-Eye Health

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    Lutein is a carotenoid with reported anti-inflammatory properties. A large body of evidence shows that lutein has several beneficial effects, especially on eye health. In particular, lutein is known to improve or even prevent age-related macular disease which is the leading cause of blindness and vision impairment. Furthermore, many studies have reported that lutein may also have positive effects in different clinical conditions, thus ameliorating cognitive function, decreasing the risk of cancer, and improving measures of cardiovascular health. At present, the available data have been obtained from both observational studies investigating lutein intake with food, and a few intervention trials assessing the efficacy of lutein supplementation. In general, sustained lutein consumption, either through diet or supplementation, may contribute to reducing the burden of several chronic diseases. However, there are also conflicting data concerning lutein efficacy in inducing favorable effects on human health and there are no univocal data concerning the most appropriate dosage for daily lutein supplementation. Therefore, based on the most recent findings, this review will focus on lutein properties, dietary sources, usual intake, efficacy in human health, and toxicity

    Predictors of ominous outcome in infants who undergo cardiac surgery and cardiopulmonary by-pass: S100B protein

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    S100B protein has been recently proposed as a consolidated marker of brain damage and death in adult, children and newborn patients. The present study evaluates whether the longitudinal measurement of S100B at different perioperative time-points may be a useful tool to identify the occurrence of perioperative early death in congenital heart disease (CHD) newborns. We conducted a case-control study in 88 CHD infants, without pre-existing neurological disorders or other co-morbidities, of whom 22 were complicated by perioperative death in the first week from surgery. Control group was composed by 66 uncomplicated CHD infants matched for age at surgical procedure. Blood samples were drawn at five predetermined timepoints before during and after surgery. In all CHD children, S100B levels showed a pattern characterized by a significant increase in protein’s concentration from hospital admission up to 24-h after procedure reaching their maximum peak (P<0.01) during cardiopulmonary by-pass and at the end of the surgical procedure. Moreover, S100B concentrations in CHD death group were significantly higher (P<0.01) than controls at all monitoring time-points. The ROC curve analysis showed that S100B measured before surgical procedure was the best predictor of perioperative death, among a series of clinical and laboratory parameters, reaching at a cut-off of 0.1 μg/L a sensitivity of 100% and a specificity of 63.7%. The present data suggest that in CHD infants biochemical monitoring in the perioperative period is becoming possible and S100B can be included among a series of parameters for adverse outcome prediction

    Early predictors of perinatal brain damage: the role of neurobiomarkers.

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    The early detection of perinatal brain damage in preterm and term newborns (i.e. intraventricular hemorrhage, periventricular leukomalacia and perinatal asphyxia) still constitute an unsolved issue. To date, despite technological improvement in standard perinatal monitoring procedures, decreasing the incidence of perinatal mortality, the perinatal morbidity pattern has a flat trend. Against this background, the measurement of brain constituents could be particularly useful in the early detection of cases at risk for short-/long-term brain injury. On this scenario, the main European and US international health-care institutions promoted perinatal clinical and experimental neuroprotection research projects aimed at validating and including a panel of biomarkers in the clinical guidelines. Although this is a promising attempt, there are several limitations that do not allow biomarkers to be included in standard monitoring procedures. The main limitations are: (i) the heterogeneity of neurological complications in the perinatal period, (ii) the small cohort sizes, (iii) the lack of multicenter investigations, (iv) the different techniques for neurobiomarkers assessment, (iv) the lack of consensus for the validation of assays in biological fluids such as urine and saliva, and (v), the lack of reference curves according to measurement technique and biological fluid. In the present review we offer an up-to-date overview of the most promising developments in the use of biomarkers in the perinatal period such as calcium binding proteins (S100B protein), vasoactive agents (adrenomedullin), brain biomarkers (activin A, neuron specific enolase, glial fibrillary acidic protein, ubiquitin carboxyl-terminal hydrolase-L1) and oxidative stress markers
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