Prevalence of vertebral fractures and their prognostic significance in the survival in patients with chronic kidney disease stages 3-5 not on dialysis

The prevalence of vertebral fractures (VF) and their association with clinical risk factors and outcomes are poorly documented in chronic kidney disease (CKD) cohorts. The aim of the study was to evaluate the prevalence of VF in patients with non-dialysis dependent CKD (NDD-CKD), their value in predicting mortality and its correlation with parameters of bone mineral metabolism and vascular calcification. 612 NDD 3-5 stage CKD patients participating in the OSERCE-2 study, a prospective, multicenter, cohort study, were prospectively evaluated and categorized into two groups according to presence or absence of VF at enrollment. VF were assessed with lateral radiographs and Genant semi-quantitative method was applied. Three radiologists specialized in musculoskeletal radiology performed consensual reading of individual images obtained using a Raim DICOM Viewer and a Canon EOS 350 camera to measure with Java Image software in those who had traditional acetate X-ray. Factors related to VF were assessed by logistic regression analysis. Association between VF and death over a 3-year follow-up was assessed by Kaplan-Meier survival curves and Cox-proportional hazard models. VF were detected in 110patients(18%). Serumphosphatelevels(OR0.719,95%CI0.532to0.972,p = 0.032),ankle-brachial index 3 and serum phosphate, the presence of VF (HR 1.983, 95% CI 1.009-3.898, p = 0.047) were an independent predictor of all-cause mortality. In our study 18% of patients with NDD-CKD have VF. Factors associated with VF were age, low serum phosphate levels and peripheral vascular disease. The presence of VF was an independent risk factor for mortality in stages 3-5 NDD-CKD patients. Clinical trials are needed to confirm whether this relationship is causal and reversible with treatment for osteoporosis

Pathogenic mechanisms of SARS-CoV-2 infection and kidney disease: a clinical and molecular perspective

La infección por SARS-CoV-2 se ha convertido en un problema mundial de salud pública. Su presentación clínica es variada, desde benigna hasta un síndrome de distrés respiratorio agudo, afectación sistémica y fallo multiorgánico. La severidad del cuadro clínico depende de factores biológicos del virus y del huésped y de comorbilidades como la enfermedad renal. Además, la interacción entre el virus, la enzima convertidora de angiotensina 2 y la respuesta inmunológica exacerbada podría conducir al desarrollo de lesión renal aguda. Sin embargo, las implicaciones de la infección por SARSCoV-2 sobre las células renales, las repercusiones pronósticas en los pacientes con enfermedad renal crónica y su efecto a largo plazo sobre la función renal no están del todo claras. El objetivo es revisar el papel del SARSCoV-2 en la enfermedad renal aguda y crónica, y sus posibles mecanismos patogénicos en la afectación renal.The SARS-CoV-2 infection has become as a worldwide public health emergency. It exhibits a variety of clinical presentations, ranging from benign to acute respiratory distress syndrome, systemic involvement, and multiorganic failure. The severity of the clinical picture depends on host and virus biological features and the presence of comorbidities such as chronic kidney disease. In addition, the interaction between the virus, angiotensin-converting enzyme 2, and the exacerbated immune response could lead to the development of acute kidney injury. However, the implications of SARSCoV-2 infection on renal cells, the prognosis of patients with chronic kidney disease, and the long-term behavior of renal function are not entirely understood. This review aims to explore the role of SARS-CoV-2 in acute and chronic kidney disease and the possible pathogenic mechanisms of renal involvement

Insights into the high-energy γ-ray emission of Markarian 501 from extensive multifrequency observations in the Fermi era

We report on the γ-ray activity of the blazar Mrk 501 during the first 480 days of Fermi operation. We find that the average Large Area Telescope (LAT) γ-ray spectrum of Mrk 501 can be well described by a single power-law function with a photon index of 1.78 ± 0.03. While we observe relatively mild flux variations with the Fermi-LAT (within less than a factor of two), we detect remarkable spectral variability where the hardest observed spectral index within the LAT energy range is 1.52 ± 0.14, and the softest one is 2.51 ± 0.20. These unexpected spectral changes do not correlate with the measured flux variations above 0.3 GeV. In this paper, we also present the first results from the 4.5 month long multifrequency campaign (2009 March 15-August 1) on Mrk 501, which included the Very Long Baseline Array (VLBA), Swift, RXTE, MAGIC, and VERITAS, the F-GAMMA, GASP-WEBT, and other collaborations and instruments which provided excellent temporal and energy coverage of the source throughout the entire campaign. The extensive radio to TeV data set from this campaign provides us with the most detailed spectral energy distribution yet collected for this source during its relatively low activity. The average spectral energy distribution of Mrk 501 is well described by the standard one-zone synchrotron self-Compton (SSC) model. In the framework of this model, we find that the dominant emission region is characterized by a size ≲0.1 pc (comparable within a factor of few to the size of the partially resolved VLBA core at 15-43 GHz), and that the total jet power (≃1044 erg s-1) constitutes only a small fraction (∼10-3) of the Eddington luminosity. The energy distribution of the freshly accelerated radiating electrons required to fit the time-averaged data has a broken power-law form in the energy range 0.3 GeV-10 TeV, with spectral indices 2.2 and 2.7 below and above the break energy of 20 GeV. We argue that such a form is consistent with a scenario in which the bulk of the energy dissipation within the dominant emission zone of Mrk 501 is due to relativistic, proton-mediated shocks. We find that the ultrarelativistic electrons and mildly relativistic protons within the blazar zone, if comparable in number, are in approximate energy equipartition, with their energy dominating the jet magnetic field energy by about two orders of magnitude. © 2011. The American Astronomical Society

Multi-omic approaches to breast cancer metabolic phenotyping: applications in diagnosis, prognosis, and the development of novel treatments

Simple Summary Breast cancer (BC) is a heterogeneous tumor type and has become the leading cause of cancer worldwide, with 685,000 deaths forecast in 2020. The clinical management of BC patients remains challenging, and there exists an urgent need for improved diagnostic, prognostic, and therapeutic strategies. Multi-omics platforms represent a promising tool for discovering novel biomarkers and identifying new therapeutic targets. In addition, the ongoing development of multi-omics approaches may foster the identification of more robust and accurate algorithms for data analysis. This review aims to summarize the results of recent multi-omics-based studies focused on the characterization of the metabolic phenotype of BC. Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment

Multi-omic approaches to breast cancer metabolic phenotyping: applications in diagnosis, prognosis, and the development of novel treatments

Simple Summary Breast cancer (BC) is a heterogeneous tumor type and has become the leading cause of cancer worldwide, with 685,000 deaths forecast in 2020. The clinical management of BC patients remains challenging, and there exists an urgent need for improved diagnostic, prognostic, and therapeutic strategies. Multi-omics platforms represent a promising tool for discovering novel biomarkers and identifying new therapeutic targets. In addition, the ongoing development of multi-omics approaches may foster the identification of more robust and accurate algorithms for data analysis. This review aims to summarize the results of recent multi-omics-based studies focused on the characterization of the metabolic phenotype of BC. Breast cancer (BC) is characterized by high disease heterogeneity and represents the most frequently diagnosed cancer among women worldwide. Complex and subtype-specific gene expression alterations participate in disease development and progression, with BC cells known to rewire their cellular metabolism to survive, proliferate, and invade. Hence, as an emerging cancer hallmark, metabolic reprogramming holds great promise for cancer diagnosis, prognosis, and treatment. Multi-omics approaches (the combined analysis of various types of omics data) offer opportunities to advance our understanding of the molecular changes underlying metabolic rewiring in complex diseases such as BC. Recent studies focusing on the combined analysis of genomics, epigenomics, transcriptomics, proteomics, and/or metabolomics in different BC subtypes have provided novel insights into the specificities of metabolic rewiring and the vulnerabilities that may guide therapeutic development and improve patient outcomes. This review summarizes the findings of multi-omics studies focused on the characterization of the specific metabolic phenotypes of BC and discusses how they may improve clinical BC diagnosis, subtyping, and treatment

Cost-effective SU-8 micro-structures by DUV excimer laser lithography for label-free biosensing

Cost-effective SU-8 micro-structures on a silicon substrate were developed using 248 nm excimer laser KrF projection, studying the influence of the different variables on the final pattern geometry, finding out that the most critical are exposure dose and post-bake condition. Also a novel and cost effective type of photomask based on commercial polyimide Kapton produced by 355 nm DPSS laser microprocessing was developed, studying the influence of the cutting conditions on the photomask. Finally, as a likely application the biosensing capability with a standard BSA/antiBSA immunoassay over a 10 × 10 micro-plates square lattice of around 10 ¿m in diameter, 15 ¿m of spacing and 400 nm in height was demonstrated, finding a limit of detection (LOD) of 33.4 ng/ml which is in the order of magnitude of bioapplications such as detection of cortisol hormone or insulin-like growth factor. Low cost fabrication and vertical interrogation characterization techniques lead to a promising future in the biosensing technology field. © 2010 Elsevier B.V. All rights reserved.Funding for the study was provided by the Spanish Ministry of Science and Innovation under BIOPSIA project no. TEC2008-06574-C03.Sanza, FJ.; Laguna, MF.; Casquel Del Campo, R.; Holgado, M.; Angulo Barrios, C.; Ortega Higueruelo, FJ.; López-Romero, D.... (2011). Cost-effective SU-8 micro-structures by DUV excimer laser lithography for label-free biosensing. Applied Surface Science. 257(12):5403-5407. https://doi.org/10.1016/j.apsusc.2010.10.010S540354072571

Bio-Photonic Sensing Cells over transparent substrates for anti-gestrinone antibodies biosensing

[EN] In a previous work we introduced the term Bio-Photonic Sensing Cells (BICELLs), referred to periodic networks of nano-pillar suitable for biosensing when are vertically interrogated. In this article, we demonstrate the biosensing capabilities of a type of micrometric size BICELLs made of SU-8 nano-pillars fabricated over transparent substrates. We verify the biochips functionality comparing the theoretical simulations with the experimental results when are optically interrogated in transmission. We also demonstrate a sensitivity enhancement by reducing the pitch among nano-pillars from 800 to 700. nm. Thus, the Limit of Detection achievable in these types of BICELLs is in the order of 64. pg/mL for 700. nm in pitch among nano-pillars in comparison with 292. pg/mL for 800. nm in pitch when are interrogated by Fourier Transform Visible and Infrared Spectrometry. The experiments exhibited a good reproducibility with a relative standard deviation of 0.29% measured within 8 days for a specific concentration. Finally, BICELLs functionality was tested in real conditions with unpurified rabbit serum for detecting anti-gestrinone antibodies, demonstrating the high performance of this type of BICELLs to detect specific antibodies having immobilized the suitable bioreceptors onto the sensing surface. © 2011 Elsevier B.V.This work is done within the support of the Spanish Ministry of Science and Innovation under project BIOPSIA (REF: TEC2008-06574). The authors thanks the Comunidad de Madrid and Universidad Politecncia de Madrid (Project BIO-VERSATIL, Ref CCG10-UPM/SEM-5096), Generalitat Valenciana (project ACOMP/2010/009 and PROMETEO 2010/008) and Dr Eva Brun for providing the rabbit serum containing polyclonal antibodies for gestrinone studies. F.J.O. is grateful to the Generalitat Valenciana for the postdoctoral grant included in the VALi + d 2010 Programme for Postdoctoral Researchers.Sanza, F.; Holgado, M.; Ortega Higueruelo, FJ.; Casquel Del Campo, R.; López-Romero, D.; Bañuls Polo, MJ.; Laguna, MF.... (2011). Bio-Photonic Sensing Cells over transparent substrates for anti-gestrinone antibodies biosensing. Biosensors and Bioelectronics. 26:4842-4847. https://doi.org/10.1016/j.bios.2011.06.010S484248472

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac

Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach

Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused by benign conditions, and the identification of their etiology still remains a clinical challenge. We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36) and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was performed in five patients per group. We implemented artificial intelligence tools for the selection of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included the generation of synthetic data with properties of real data, the selection of potential biomarkers (metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when analyzed with NN algorithms discriminated between patients with and without cancer with an unprecedented accurac