Vassilis Aidinis

Dr. Vassilis Aidinis

Biomedical Sciences Research Center - Alexander Flemingdisabled, Vari, Greece
orcid

Research Focus & Metrics

Academic Career Length

34Years (1993-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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2 September 2005 in Science
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Journal Article
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Piero CarninciPiero Carninci
Piero Carninci
TKT. Kasukawa
T. Kasukawa
SKS. Katayama
S. Katayama
JGJ. Gough
J. Gough
MFM. C. Frith
M. C. Frith
YHY. Hayashizaki
Y. Hayashizaki
This study describes comprehensive polling of transcription start and termination sites and analysis of previously unidentified full-length complementary DNAs derived from the mouse genome. We identify the 5′ and 3′ boundaries of 181,047 transcripts with extensive variation in transcripts arising from alternative promoter usage, splicing, and polyadenylation. There are 16,247 new mouse protein-coding transcripts, including 5154 encoding previously unidentified proteins. Genomic mapping of the transcriptome reveals transcriptional forests, with overlapping transcription on both strands, separated by deserts in which few transcripts are observed. The data provide a comprehensive platform for the comparative analysis of mammalian transcriptional regulation in differentiation and development.

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VMVarnavas Mouchlis
Varnavas Mouchlis
Antreas AfantitisAntreas Afantitis
Antreas Afantitis
ASAngela Serra
Angela Serra
MFMichele Fratello
Michele Fratello
APAnastasios Papadiamantis
Anastasios Papadiamantis
GMGeorgia Melagraki
Georgia Melagraki
De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.

28 July 2017 in Frontiers in Medicine
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JTJun Tashiro
Jun Tashiro
GRGustavo A. Rubio
Gustavo A. Rubio
ALAndrew H. Limper
Andrew H. Limper
KWKurt Williams
Kurt Williams
SESharon J. Elliot
Sharon J. Elliot
MGMarilyn K. Glassberg
Marilyn K. Glassberg
Large multicenter clinical trials have led to two recently approved drugs for patients with idiopathic pulmonary fibrosis (IPF); yet, both of these therapies only slow disease progression and do not provide a definitive cure. Traditionally, preclinical trials have utilized mouse models of bleomycin-induced pulmonary fibrosis – though several limitations prevent direct translation to human IPF. Spontaneous pulmonary fibrosis occurs in other animal species, including dogs, horses, donkeys, and cats. While the fibrotic lungs of these animals share many characteristics with lungs of patients with IPF, current veterinary classifications of fibrotic lung disease are not entirely equivalent. Additional studies that profile these examples of spontaneous fibroses in animals for similarities to human IPF should prove useful for both human and animal investigators. In the meantime, studies of bleomycin-induced fibrosis in aged male mice remain the most clinically relevant model for preclinical study for human IPF. Addressing issues such as time course of treatment, animal size and characteristics, clinically irrelevant treatment endpoints and reproducibility of therapeutic outcomes will improve the current status of preclinical studies. Elucidating the mechanisms responsible for the development of fibrosis and disrepair associated with aging through a collaborative approach between researchers will promote the development of models that more accurately represent the realm of interstitial lung diseases in humans.

1 April 2020 in Nature
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MRManolis Roulis
Manolis Roulis
AKAimilios Kaklamanos
Aimilios Kaklamanos
MSMarina Schernthanner
Marina Schernthanner
PBPiotr Bielecki
Piotr Bielecki
JZJun Zhao
Jun Zhao
RFRichard A. Flavell
Richard A. Flavell

15 June 2009 in American Journal of Respiratory and Critical Care Medicine
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Journal Article
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IKIOANNIS KOTSIANIDIS
IOANNIS KOTSIANIDIS
ENEvangelia Nakou
Evangelia Nakou
IBIrene Bouchliou
Irene Bouchliou
ATArgyrios Tzouvelekis
Argyrios Tzouvelekis
ESEmmanouil Spanoudakis
Emmanouil Spanoudakis
DBDEMOSTHENES BOUROS BOUROS
DEMOSTHENES BOUROS BOUROS
Rationale: The implication of T cells in the pathogenesis of idiopathic pulmonary fibrosis (IPF) is controversial. CD4+CD25+FOXP3+ regulatory T cells (Tregs) are pivotal in maintaining immune homeostasis, but their role in IPF pathophysiology has not yet been studied. Objectives: To explore Treg dynamics and function in IPF. Methods: Treg levels and dynamics were analyzed by flow cytometry in the peripheral blood (PB) and bronchoalveolar lavage (BAL) of 21 patients with IPF, 35 patients with lung diseases other than IPF (patients without IPF), 20 patients with collagen vascular diseases with pulmonary parenchymal involvement (CVD-IP), and 28 healthy volunteers. The suppression of autologous CD4+CD25− cell-proliferative responses and cytokine release by magnetic bead–isolated Tregs was evaluated by proliferation assays and cytometric bead array. Correlations of Treg function and levels with lung function parameters were also performed. Measurements and Main Results: In patients with IPF, both BAL and PB Tregs were reduced compared with those of healthy volunteers and patients without IPF, although not always significantly. Treg levels were not affected by the administration of low-dose prednisone in four nonresponding patients. The suppressor potential of BAL and PB Tregs was compromised in patients with IPF and patients with CVD-IP, compared with healthy volunteers and patients without IPF. Similarly, the Treg-induced suppression of helper T-cell type 1 and 2 cytokine secretion was impaired in the BAL of patients with IPF and patients with CVD-IP. Moreover, the defective function of BAL Tregs correlated highly with parameters of disease severity. Conclusions: This study provides the first evidence of global Treg impairment in IPF that strongly correlates with disease severity, suggesting a role for Tregs in the fibrotic process.

1 November 2012 in American Journal of Respiratory Cell and Molecular Biology
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Journal Article
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NONikos Oikonomou
Nikos Oikonomou
MMMarios-Angelos Mouratis
Marios-Angelos Mouratis
ATArgyris Tzouvelekis
Argyris Tzouvelekis
EKEleanna Kaffe
Eleanna Kaffe
CVChristos Valavanis
Christos Valavanis
Vassilis AidinisVassilis Aidinis
Vassilis Aidinis
Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive, fibrotic form of diffuse lung disease occurring mainly in older adults. Increased lysophosphatidic acid (LPA) concentrations have been reported in the alveolar space of both idiopathic pulmonary fibrosis patients and a corresponding animal model, whereas the genetic deletion or pharmacological inhibition of LPA receptor 1 attenuated the development of the modeled disease, suggesting a direct involvement of LPA in disease pathogenesis. In this report, increased concentrations of autotaxin (ATX; ENPP2), the enzyme largely responsible for extracellular LPA production, were detected in both murine and human fibrotic lungs. The genetic deletion of ATX from bronchial epithelial cells or macrophages attenuated disease severity, establishing ATX as a novel player in IPF pathogenesis. Furthermore, the pharmacological inhibition of ATX attenuated the development of the modeled disease, suggesting that ATX is a possible therapeutic target in IPF.