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1.

001-es BibID:BIBFORM005750
Első szerző:Adarichev, Vyacheslav A.
Cím:Congenic strains displaying similar clinical phenotype of arthritis represent different immunologic models of inflammation / Vyacheslav A. Adarichev, Akos Vegvari, Zoltan Szabo, Katalin Kis-Toth K, Katalin Mikecz, Tibor T. Glant
Dátum:2008
Megjegyzések:Proteoglycan (PG)-induced arthritis (PGIA) is an autoimmune inflammatory disease controlled by multiple genes in the murine genome. BALB/c x DBA/2 congenic strains carrying four major PGIA chromosome loci were immunized, and positions of loci on chromosomes 3, 7, 8 and 19 ( loci Pgia26, Pgia21, Pgia4 and Pgia12, respectively) were confirmed. Each congenic strain exhibited a different pattern of regulation of clinical and immunologic features of PGIA, and these features were significantly influenced by gender. Locus Pgia26 delayed PGIA onset in males and females, and the effect was associated with a lower rate of antigen-induced lymphocyte proliferation and lower production of interferon-gamma (IFN-gamma), tumor necrosis factor-alpha (TNF-alpha) and interleukin-4 (IL-4). Pgia12 similarly delayed onset in males, but the effect was achieved by elevated proliferation of PG-specific lymphocytes and enhanced production of IFN-gamma and IL-4. The effect of the Pgia21 locus was arthritis-suppressive in females but PGIA-permissive in congenic males. These opposite effects are attributed to two-fold higher serum autoantibody and IL-6 levels in males than in females. Our study supports the idea that each congenic strain represents a different immunologic subtype of PGIA, providing an explanation for the complex etiology and various clinical phenotypes of rheumatoid arthritis.
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
proteoglycan-induced arthritis
congenic strains
inflammation
cytokines
autoimmunity
sex effect
Megjelenés:Genes and Immunity. - 9 : 7 (2008), p. 591-601. -
További szerzők:Végvári Ákos Szabó Zoltán (1970-) (belgyógyász, reumatológus) Kis-Tóth Katalin (1975-) (immunológus) Mikecz Katalin Glant Tibor T.
Internet cím:elektronikus változat
DOI
elektronikus változat
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2.

001-es BibID:BIBFORM090154
035-os BibID:(WoS)000585924300006 (Scopus)85095444133
Első szerző:Dániel Bence (molekuláris biológus)
Cím:The transcription factor EGR2 is the molecular linchpin connecting STAT6 activation to the late, stable epigenomic program of alternative macrophage polarization / Daniel Bence, Czimmerer Zsolt, Halasz Laszlo, Boto Pal, Kolostyak Zsuzsanna, Poliska Szilard, Berger Wilhelm K., Tzerpos Petros, Nagy Gergely, Horvath Attila, Hajas György, Cseh Timea, Nagy Aniko, Sauer Sascha, Francois-Deleuze Jean, Szatmari Istvan, Bacsi Attila, Nagy Laszlo
Dátum:2020
ISSN:0890-9369 1549-5477
Tárgyszavak:Orvostudományok Elméleti orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:Genes & Development. - 34 : 21-22 (2020), p. 1474-1492. -
További szerzők:Czimmerer Zsolt (1981-) (molekuláris biológus) Halász László (1989-) (molekuláris biológus) Botó Pál (1986-) (molekuláris biológus) Kolostyák Zsuzsanna Póliska Szilárd (1978-) (biológus) Berger, Wilhelm K. Tzerpos, Petros (1982-) (molekuláris biológus) Nagy Gergely (1986-) (molekuláris biológus) Horváth Attila (orvos) Hajas György (1970-) (biológus) Silye-Cseh Timea Nagy Anikó (orvos) Sauer, Sascha Francois-Deleuze, Jean Szatmári István (1971-) (biológus) Bácsi Attila (1967-) (immunológus) Nagy László (1966-) (molekuláris sejtbiológus, biokémikus)
Pályázati támogatás:ÚNKP-19-4-DE-173
Egyéb
GINOP-2.3 2-15- 2016-0006
GINOP
KKP129909
OTKA
K124298
OTKA
PD124843
OTKA
FK132185
OTKA
K124890
OTKA
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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3.

001-es BibID:BIBFORM102058
Első szerző:Huang, Xufeng (fogorvos)
Cím:Network Pharmacology-Based Approach Combined with Bioinformatic Analytics to Elucidate the Potential of Curcumol against Hepatocellular Carcinoma / Xufeng Huang, Hafiz Muzzammel Rehman, Attila Gábor Szöllősi, Shujing Zhou
Dátum:2022
ISSN:2073-4425
Megjegyzések:Purpose: Modern, open-source databases provide an unprecedented wealth of information to help drug development. By combining data available in these databases with the proper bioinformatical tools, we can elucidate the molecular targets of natural compounds. One such molecule is curcumol, a guaiane-type sesquiterpenoid hemiketal isolated from Rhizoma Curcumae, which is used for a broad range of diseases in traditional Chinese and Indian medicine. It has been reported to exert anti-tumor activity, but the intrinsic molecular mechanism in hepatocellular carcinoma (HCC) is unclear. Therefore, the present study was designed to reveal the predictive targets and biological mechanisms of curcumol against HCC via a network pharmacology-based approach combined with bioinformatic analytics and to provide proof of concept for further similar investigations. Methods: Data available from open-source databases (Traditional Chinese Medicine Systems Pharmacology, Comparative Toxicogenomic Database, The Cancer Genome Atlas, the Human Protein Atlas project) was processed with the help of a variety of open-source tools (SwissADME, SwissTargetPrediction, JVenn, Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, GeneMANIA, Cytoscape). Results: In the present study, the potential of curcumol against HCC was unraveled by network pharmacology-based elucidation. It suggests that curcumol shows exciting druggability with 44 potent homo sapiens biotargets against HCC. The GO terms and KEGG pathways enrichment analyses, curcumol-targets-pathways-HCC network, PPI network, and corresponding in-depth topological analyses, as well as survival analysis, molecular docking simulation indicate that the potential mechanism of curcumol against HCC is complicated, as it may act in various ways, mainly by inducing apoptosis and modulating the inflammatory response, increasing presentation of HCC-specific protein. Conclusion: The present study highlights the potential of curcumol against HCC, giving reference to further experimental study. It also presents a roadmap that can be followed to conduct in silico prescreening of other compounds of interest.
Tárgyszavak:Orvostudományok Klinikai orvostudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
data integration
drug discovery
network modeling
Megjelenés:Genes. - 13 : 4 (2022), p. 1-20. -
További szerzők:Rehman, Hafiz Muzzammel Szöllősi Attila Gábor (1982-) (élettanász) Zhou, Shujing (1997-)
Internet cím:Szerző által megadott URL
DOI
Intézményi repozitóriumban (DEA) tárolt változat
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