0000000000466111

AUTHOR

Stephanie Strobl

showing 5 related works from this author

Enhanced protection of C57 BL/6 vs Balb/c mice to melanoma liver metastasis is mediated by NK cells.

2017

ABSTRACT The B16F10 murine melanoma cell line displays a low expression of MHC class I molecules favoring immune evasion and metastases in immunocompetent C57 BL/6 wild-type mice. Here, we generated metastases to the liver, an organ that is skewed towards immune tolerance, by intrasplenic injection of B16F10 cells in syngeneic C57 BL/6 compared to allogeneic Balb/c mice. Surprisingly, Balb/c mice, which usually display a pronounced M2 macrophage and Th2 T cell polarization, were ∼3 times more susceptible to metastasis than C57 BL/6 mice, despite a much higher M1 and Th1 T cell immune response. The anti-metastatic advantage of C57 BL/6 mice could be attributed to a more potent NK-cell mediat…

0301 basic medicinelcsh:Immunologic diseases. AllergyImmunologyNK cellsMajor histocompatibility complexcancer immunologyliverlcsh:RC254-282BALB/cImmune toleranceMetastasis03 medical and health sciencesImmune systemMHC class ImedicineImmunology and Allergymetastasisinnate immunityOriginal ResearchInnate immune systembiologybiology.organism_classificationmedicine.diseaselcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogensmacrophages030104 developmental biologyOncologyCancer cellCancer researchbiology.proteinlcsh:RC581-607Oncoimmunology
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Multimodal Deep Learning for Prognosis Prediction in Renal Cancer

2021

BackgroundClear-cell renal cell carcinoma (ccRCC) is common and associated with substantial mortality. TNM stage and histopathological grading have been the sole determinants of a patient’s prognosis for decades and there are few prognostic biomarkers used in clinical routine. Management of ccRCC involves multiple disciplines such as urology, radiology, oncology, and pathology and each of these specialties generates highly complex medical data. Here, artificial intelligence (AI) could prove extremely powerful to extract meaningful information to benefit patients.ObjectiveIn the study, we developed and evaluated a multimodal deep learning model (MMDLM) for prognosis prediction in ccRCC.Desig…

OncologyCancer ResearchPrognosis predictionmedicine.medical_specialtyrenal cancerDiseaseRenal cell carcinomaInternal medicinemedicineStage (cooking)Exome sequencingRC254-282Original Researchbusiness.industryDeep learningCancerdeep learningNeoplasms. Tumors. Oncology. Including cancer and carcinogensmedicine.diseaseartificial intelligenceradiologyOncologyCohortpathologyArtificial intelligenceprognosis predictionbusinessFrontiers in Oncology
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P0311 : Balb/c and C57/Bl6 mice exhibit differences in their susceptibility and anti-tumor response to B16F10 melanoma liver metastasis

2015

Antitumor activityC57 bl6 miceHepatologybiologybusiness.industryCancer researchMedicineB16f10 melanomabusinessbiology.organism_classificationmedicine.diseaseBALB/cMetastasisJournal of Hepatology
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P0312 : Preclinical evaluation of dextran-based therapeutic nanoparticles for hepatic drug delivery

2015

chemistry.chemical_compoundDextranHepatologychemistrybusiness.industryDrug deliveryMedicineNanoparticlePharmacologybusinessJournal of Hepatology
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Dextran-based therapeutic nanoparticles for hepatic drug delivery.

2016

Aim: Evaluation of dextran-based nanoparticles (DNP) as a drug delivery system to target myeloid cells of the liver. Materials & methods: DNP were synthesized and optionally PEGylated. Their toxicity and cellular uptake were studied in vitro. Empty and siRNA-carrying DNP were tested in vivo with regard to biodistribution and cellular uptake. Results: In vitro, DNP were taken up by cells of the myeloid lineage without compromising their viability. In vivo, empty and siRNA-carrying DNP distributed to the liver where a single treatment addressed approximately 70% of macrophages and dendritic cells. Serum parameters indicated no in vivo toxicity. Conclusion: DNP are multifunctional liver-s…

0301 basic medicineBiodistributionMaterials scienceCell SurvivalSurface PropertiesBiomedical EngineeringMedicine (miscellaneous)Antigens Differentiation Myelomonocyticchemical and pharmacologic phenomenaBioengineering02 engineering and technologyDevelopmentPharmacologyPolyethylene Glycols03 medical and health scienceschemistry.chemical_compoundMiceIn vivoAntigens CDAnimalsHumansGeneral Materials ScienceTissue DistributionParticle SizeRNA Small InterferingDrug CarriersMice Inbred BALB Corganic chemicalsMacrophageshemic and immune systemsDextransDendritic cell3T3 CellsDendritic Cells021001 nanoscience & nanotechnology030104 developmental biologyDextranRAW 264.7 CellschemistryLiverDrug deliveryToxicityPEGylationNanoparticles0210 nano-technologyDrug carrierNanomedicine (London, England)
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