Danilo Fiore

2.1k total citations
26 papers, 1.5k citations indexed

About

Danilo Fiore is a scholar working on Molecular Biology, Cancer Research and Oncology. According to data from OpenAlex, Danilo Fiore has authored 26 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 13 papers in Cancer Research and 6 papers in Oncology. Recurrent topics in Danilo Fiore's work include MicroRNA in disease regulation (9 papers), Cancer-related molecular mechanisms research (5 papers) and Acute Kidney Injury Research (5 papers). Danilo Fiore is often cited by papers focused on MicroRNA in disease regulation (9 papers), Cancer-related molecular mechanisms research (5 papers) and Acute Kidney Injury Research (5 papers). Danilo Fiore collaborates with scholars based in Italy, United States and Switzerland. Danilo Fiore's co-authors include Gerolama Condorelli, Cristina Quintavalle, Elvira Donnarumma, Giuseppina Roscigno, Margherita Iaboni, Giorgio Inghirami, Alessandra Affinito, Valentina Russo, Carlo Briguori and Assunta Adamo and has published in prestigious journals such as Nature, Circulation and Nature reviews. Cancer.

In The Last Decade

Danilo Fiore

24 papers receiving 1.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Danilo Fiore Italy 16 898 776 249 225 216 26 1.5k
Weimin Zhang China 22 1.0k 1.1× 562 0.7× 51 0.2× 225 1.0× 263 1.2× 68 1.5k
Chunping Yu China 21 1.1k 1.3× 593 0.8× 120 0.5× 183 0.8× 322 1.5× 50 1.8k
András Szabó Hungary 14 1.1k 1.3× 605 0.8× 103 0.4× 412 1.8× 454 2.1× 39 1.8k
Xiu‐E Xu China 25 1.2k 1.3× 483 0.6× 45 0.2× 297 1.3× 355 1.6× 73 1.7k
Eun-Jin Yeo United States 8 482 0.5× 305 0.4× 124 0.5× 81 0.4× 144 0.7× 9 909
Weiyang He China 18 715 0.8× 343 0.4× 70 0.3× 154 0.7× 89 0.4× 40 1.0k
Fenghua Zeng United States 14 571 0.6× 137 0.2× 107 0.4× 140 0.6× 244 1.1× 24 1.1k
Shujun Lin China 16 822 0.9× 179 0.2× 65 0.3× 206 0.9× 354 1.6× 49 1.4k
Song He China 19 615 0.7× 208 0.3× 75 0.3× 122 0.5× 321 1.5× 49 1.0k

Countries citing papers authored by Danilo Fiore

Since Specialization
Citations

This map shows the geographic impact of Danilo Fiore's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Danilo Fiore with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Danilo Fiore more than expected).

Fields of papers citing papers by Danilo Fiore

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Danilo Fiore. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Danilo Fiore. The network helps show where Danilo Fiore may publish in the future.

Co-authorship network of co-authors of Danilo Fiore

This figure shows the co-authorship network connecting the top 25 collaborators of Danilo Fiore. A scholar is included among the top collaborators of Danilo Fiore based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Danilo Fiore. Danilo Fiore is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Affinito, Alessandra, Danilo Fiore, Cristina Quintavalle, et al.. (2025). Targeting Glioblastoma Stem Cells: A40s Aptamer-NIR-Dye Conjugate for Glioblastoma Visualization and Treatment. Biomolecules. 15(6). 768–768.
2.
Affinito, Alessandra, Cristina Quintavalle, Giuseppina Roscigno, et al.. (2024). MCT4-driven CAF-mediated metabolic reprogramming in breast cancer microenvironment is a vulnerability targetable by miR-425-5p. Cell Death Discovery. 10(1). 140–140. 22 indexed citations
3.
Fiore, Danilo & Salvatore De Rosa. (2023). P443 ACUTE ACALCULOUS CHOLECYSTITIS AND CARDIOVASCULAR DISEASE, WHICH CAME FIRST?. European Heart Journal Supplements. 25(Supplement_D). D218–D218.
4.
Fiore, Danilo, Luca Vincenzo Cappelli, Alessandro Broccoli, et al.. (2020). Peripheral T cell lymphomas: from the bench to the clinic. Nature reviews. Cancer. 20(6). 323–342. 70 indexed citations
5.
Fragliasso, Valentina, Akanksha Verma, Gloria Manzotti, et al.. (2020). The novel lncRNA BlackMamba controls the neoplastic phenotype of ALK− anaplastic large cell lymphoma by regulating the DNA helicase HELLS. Leukemia. 34(11). 2964–2980. 11 indexed citations
6.
García‐Bermúdez, Javier, Lou Baudrier, Erol C. Bayraktar, et al.. (2019). Squalene accumulation in cholesterol auxotrophic lymphomas prevents oxidative cell death. Nature. 567(7746). 118–122. 315 indexed citations
7.
Riccio, Gennaro, Salvatore Antonucci, Carmela Coppola, et al.. (2018). Ranolazine Attenuates Trastuzumab-Induced Heart Dysfunction by Modulating ROS Production. Frontiers in Physiology. 9. 38–38. 41 indexed citations
8.
Russo, Valentina, Alessandra Affinito, Giuseppina Roscigno, et al.. (2018). Aptamer-miR-34c Conjugate Affects Cell Proliferation of Non-Small-Cell Lung Cancer Cells. Molecular Therapy — Nucleic Acids. 13. 334–346. 51 indexed citations
9.
Donnarumma, Elvira, Danilo Fiore, Giuseppina Roscigno, et al.. (2017). Cancer-associated fibroblasts release exosomal microRNAs that dictate an aggressive phenotype in breast cancer. Oncotarget. 8(12). 19592–19608. 268 indexed citations
10.
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12.
Iaboni, Margherita, Valentina Russo, Giuseppina Roscigno, et al.. (2016). Aptamer-miRNA-212 Conjugate Sensitizes NSCLC Cells to TRAIL. Molecular Therapy — Nucleic Acids. 5. e289–e289. 59 indexed citations
13.
Briguori, Carlo, Elvira Donnarumma, Cristina Quintavalle, Danilo Fiore, & Gianluigi Condorelli. (2015). Contrast-induced acute kidney injury. Current Opinion in Nephrology & Hypertension. 24(2). 145–153. 16 indexed citations
14.
Fiore, Danilo, Cristina Quintavalle, Elvira Donnarumma, et al.. (2015). Abstract 3126: Survival in glioblastoma cancer patients is predicted by miR-340, that regulates key cancer hallmarks by inhibiting NRAS. Cancer Research. 75(15_Supplement). 3126–3126. 1 indexed citations
15.
Quintavalle, Cristina, Danilo Fiore, & Francesca De Micco. (2013). Impact of a High Loading Dose of Atorvastatin on Contrast-Induced Acute Kidney Injury. Journal of Vascular Surgery. 57(3). 892–892. 1 indexed citations
16.
Quintavalle, Cristina, Davide Mangani, Giuseppina Roscigno, et al.. (2013). miR-221/222 Target the DNA Methyltransferase MGMT in Glioma Cells. PLoS ONE. 8(9). e74466–e74466. 82 indexed citations
17.
Quintavalle, Cristina, Elvira Donnarumma, Danilo Fiore, Carlo Briguori, & Gerolama Condorelli. (2013). Therapeutic strategies to prevent contrast-induced acute kidney injury. Current Opinion in Cardiology. 28(6). 676–682. 17 indexed citations
18.
Quintavalle, Cristina, Danilo Fiore, Francesca De Micco, et al.. (2012). Impact of a High Loading Dose of Atorvastatin on Contrast-Induced Acute Kidney Injury. Circulation. 126(25). 3008–3016. 148 indexed citations
19.
Quintavalle, Cristina, Elvira Donnarumma, Margherita Iaboni, et al.. (2012). Effect of miR-21 and miR-30b/c on TRAIL-induced apoptosis in glioma cells. Oncogene. 32(34). 4001–4008. 98 indexed citations
20.
Quintavalle, Cristina, Monica Brenca, Francesca De Micco, et al.. (2011). In vivo and in vitro assessment of pathways involved in contrast media-induced renal cells apoptosis. Cell Death and Disease. 2(5). e155–e155. 106 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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