Type I Interferon Regulates the Expression of Long Non-Coding RNAs

Interferons (IFNs) are key players in the antiviral response. IFN sensing by the cell activates transcription of IFN-stimulated genes (ISGs) able to induce an antiviral state by affecting viral replication and release. IFN also induces the expression of ISGs that function as negative regulators to limit the strength and duration of IFN response. The ISGs identified so far belong to coding genes. However, only a small proportion of the transcriptome corresponds to coding transcripts and it has been estimated that there could be as many coding as long noncoding RNAs (lncRNAs). To address whether IFN can also regulate the expression of lncRNAs, we analyzed the transcriptome of HuH7 cells treated or not with IFNα2 by expression arrays. Analysis of the arrays showed increased levels of several well-characterized coding genes that respond to IFN both at early or late times. Furthermore, we identified several IFN-stimulated or -downregulated lncRNAs (ISRs and IDRs). Further validation showed that ISR2, 8 and 12 expression mimics that of their neighboring genes GBP1, IRF1 and IL6, respectively, all related to the IFN response. These genes are induced in response to different doses of IFNα2 in different cell lines at early (ISR2 or 8) or later (ISR12) time points. IFNβ also induced the expression of these lncRNAs. ISR2 and 8 were also induced by an influenza virus unable to block the IFN response but not by other wild-type lytic viruses tested. Surprisingly, both ISR2 and 8 were significantly upregulated in cultured cells and livers from patients infected with HCV. Increased levels of ISR2 were also detected in patients chronically infected with HIV. This is relevant as genome-wide guilt-by-association studies predict that ISR2, 8 and 12 may function in viral processes, in the IFN pathway and the antiviral response. Therefore, we propose that these lncRNAs could be induced by IFN to function as positive or negative regulators of the antiviral response

Identification of tissue microRNAs predictive of sunitinib activity in patients with metastatic renal cell carcinoma

PURPOSE: To identify tissue microRNAs predictive of sunitinib activity in patients with metastatic renal-cell-carcinoma (MRCC) and to evaluate in vitro their mechanism of action in sunitinib resistance. METHODS: We screened 673 microRNAs using TaqMan Low-density-Arrays (TLDAs) in tumors from MRCC patients with extreme phenotypes of marked efficacy and resistance to sunitinib, selected from an identification cohort (n = 41). The most relevant differentially expressed microRNAs were selected using bioinformatics-based target prediction analysis and quantified by qRT-PCR in tumors from patients presenting similar phenotypes selected from an independent cohort (n = 101). In vitro experiments were conducted to study the role of miR-942 in sunitinib resistance. RESULTS: TLDAs identified 64 microRNAs differentially expressed in the identification cohort. Seven candidates were quantified by qRT-PCR in the independent series. MiR-942 was the most accurate predictor of sunitinib efficacy (p = 0.0074). High expression of miR-942, miR-628-5p, miR-133a, and miR-484 was significantly associated with decreased time to progression and overall survival. These microRNAs were also overexpressed in the sunitinib resistant cell line Caki-2 in comparison with the sensitive cell line. MiR-942 overexpression in Caki-2 up-regulates MMP-9 and VEGF secretion which, in turn, promote HBMEC endothelial migration and sunitinib resistance. CONCLUSIONS: We identified differentially expressed microRNAs in MRCC patients presenting marked sensitivity or resistance to sunitinib. MiR-942 was the best predictor of efficacy. We describe a novel paracrine mechanism through which high miR-942 levels in MRCC cells up-regulates MMP-9 and VEGF secretion to enhance endothelial migration and sunitinib resistance. Our results support further validation of these miRNA in clinical confirmatory studies

Future-ai:International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI

Jardins per a la salut

Facultat de Farmàcia, Universitat de Barcelona. Ensenyament: Grau de Farmàcia. Assignatura: Botànica farmacèutica. Curs: 2014-2015. Coordinadors: Joan Simon, Cèsar Blanché i Maria Bosch.Els materials que aquí es presenten són el recull de les fitxes botàniques de 128 espècies presents en el Jardí Ferran Soldevila de l’Edifici Històric de la UB. Els treballs han estat realitzats manera individual per part dels estudiants dels grups M-3 i T-1 de l’assignatura Botànica Farmacèutica durant els mesos de febrer a maig del curs 2014-15 com a resultat final del Projecte d’Innovació Docent «Jardins per a la salut: aprenentatge servei a Botànica farmacèutica» (codi 2014PID-UB/054). Tots els treballs s’han dut a terme a través de la plataforma de GoogleDocs i han estat tutoritzats pels professors de l’assignatura. L’objectiu principal de l’activitat ha estat fomentar l’aprenentatge autònom i col·laboratiu en Botànica farmacèutica. També s’ha pretès motivar els estudiants a través del retorn de part del seu esforç a la societat a través d’una experiència d’Aprenentatge-Servei, deixant disponible finalment el treball dels estudiants per a poder ser consultable a través d’una Web pública amb la possibilitat de poder-ho fer in-situ en el propi jardí mitjançant codis QR amb un smartphone

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Synthesis and Pharmacological Screening of Several Aroyl and Heteroaroyl Selenylacetic Acid Derivatives as Cytotoxic and Antiproliferative Agents

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Phenotypic and Genetic Characterization of Circulating Tumor Cells by Combining Immunomagnetic Selection and FICTION Techniques

The presence of circulating tumor cells (CTCs) in breast cancer patients has been proven to have clinical relevance. Cytogenetic characterization of these cells could have crucial relevance for targeted cancer therapies. We developed a method that combines an immunomagnetic selection of CTCs from peripheral blood with the fluorescence immunophenotyping and interphase cytogenetics as a tool for investigation of neoplasm (FICTION) technique. Briefly, peripheral blood (10 ml) from healthy donors was spiked with a predetermined number of human breast cancer cells. Nucleated cells were separated by double density gradient centrifugation of blood samples. Tumor cells (TCs) were immunomagnetically isolated with an anti-cytokeratin antibody and placed onto slides for FICTION analysis. For immunophenotyping and genetic characterization of TCs, a mixture of primary monoclonal anti-pancytokeratin antibodies was used, followed by fluorescent secondary antibodies, and finally hybridized with a TOP2A/HER-2/CEP17 multicolor probe. Our results show that TCs can be efficiently isolated from peripheral blood and characterized by FICTION. Because genetic amplification of TOP2A and ErbB2 (HER-2) in breast cancer correlates with response to anthracyclines and herceptin therapies, respectively, this novel methodology could be useful for a better classification of patients according to the genetic alterations of CTCs and for the application of targeted therapies. (J Histochem Cytochem 56:667–675, 2008