Júlia Rodríguez‐Comas

465 total citations
22 papers, 306 citations indexed

About

Júlia Rodríguez‐Comas is a scholar working on Surgery, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Júlia Rodríguez‐Comas has authored 22 papers receiving a total of 306 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Surgery, 6 papers in Radiology, Nuclear Medicine and Imaging and 5 papers in Oncology. Recurrent topics in Júlia Rodríguez‐Comas's work include Pancreatic function and diabetes (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Endoplasmic Reticulum Stress and Disease (5 papers). Júlia Rodríguez‐Comas is often cited by papers focused on Pancreatic function and diabetes (9 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Endoplasmic Reticulum Stress and Disease (5 papers). Júlia Rodríguez‐Comas collaborates with scholars based in Spain, China and Chile. Júlia Rodríguez‐Comas's co-authors include Javier Ramón‐Azcón, Anna Novials, Joan‐Marc Servitja, Antoni Riéra, Carlos Castaño, Ana Vázquez‐Romero, Agustí Lledó, Xavier Verdaguer, Marta Bou and Isabel Navarro and has published in prestigious journals such as SHILAP Revista de lepidopterología, Diabetes and Scientific Reports.

In The Last Decade

Júlia Rodríguez‐Comas

22 papers receiving 303 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Júlia Rodríguez‐Comas Spain 11 83 81 75 41 40 22 306
Maria A Missinato United States 10 95 1.1× 122 1.5× 304 4.1× 49 1.2× 25 0.6× 12 490
Mayuri Prasad United States 10 41 0.5× 42 0.5× 236 3.1× 17 0.4× 33 0.8× 15 351
Tzuen Yih Saw Singapore 10 35 0.4× 44 0.5× 250 3.3× 24 0.6× 34 0.8× 13 461
Neha R. Dhoke India 12 44 0.5× 68 0.8× 227 3.0× 27 0.7× 36 0.9× 16 412
Daiyoon Lee Canada 12 93 1.1× 58 0.7× 199 2.7× 57 1.4× 7 0.2× 17 441
Siyuan Hou China 11 96 1.2× 38 0.5× 159 2.1× 13 0.3× 23 0.6× 17 430
Emanuela Pessolano Italy 13 44 0.5× 33 0.4× 243 3.2× 33 0.8× 11 0.3× 21 450
XiangDi Wang United States 10 21 0.3× 125 1.5× 139 1.9× 9 0.2× 51 1.3× 24 369
Elizabeth M. Perruccio United States 4 52 0.6× 34 0.4× 139 1.9× 42 1.0× 6 0.1× 4 332

Countries citing papers authored by Júlia Rodríguez‐Comas

Since Specialization
Citations

This map shows the geographic impact of Júlia Rodríguez‐Comas'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 Júlia Rodríguez‐Comas with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Júlia Rodríguez‐Comas more than expected).

Fields of papers citing papers by Júlia Rodríguez‐Comas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Júlia Rodríguez‐Comas. 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 Júlia Rodríguez‐Comas. The network helps show where Júlia Rodríguez‐Comas may publish in the future.

Co-authorship network of co-authors of Júlia Rodríguez‐Comas

This figure shows the co-authorship network connecting the top 25 collaborators of Júlia Rodríguez‐Comas. A scholar is included among the top collaborators of Júlia Rodríguez‐Comas 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 Júlia Rodríguez‐Comas. Júlia Rodríguez‐Comas 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.
Serra‐Prat, Mateu, et al.. (2025). Machine Learning-Driven Radiomics Analysis for Distinguishing Mucinous and Non-Mucinous Pancreatic Cystic Lesions: A Multicentric Study. Journal of Imaging. 11(3). 68–68. 1 indexed citations
2.
Galdrán, Adrián, et al.. (2025). Uncertainty-aware segmentation quality prediction via deep learning Bayesian Modeling: Comprehensive evaluation and interpretation on skin cancer and liver segmentation. Computerized Medical Imaging and Graphics. 123. 102547–102547. 1 indexed citations
3.
Galdrán, Adrián, et al.. (2024). Uncertainty Aware Segmentation Quality Assessment in Medical Images. TECNALIA Publications (Fundación TECNALIA Research & Innovation). 1–5. 2 indexed citations
4.
Munuera, Josep, et al.. (2024). Artificial intelligence for detection and characterization of focal hepatic lesions: a review. Abdominal Radiology. 50(4). 1564–1583. 1 indexed citations
5.
Rodríguez‐Comas, Júlia, Carlos Castaño, María Alejandra Ortega, et al.. (2023). Immunoaffinity‐Based Microfluidic Platform for Exosomal MicroRNA Isolation from Obese and Lean Mouse Plasma. Advanced Materials Technologies. 8(15). 3 indexed citations
6.
Berbís, M. Álvaro, et al.. (2023). Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdominal Radiology. 49(1). 322–340. 14 indexed citations
7.
Rodríguez‐Comas, Júlia, et al.. (2023). Deep Learning to Detect Pancreatic Cystic Lesions on Abdominal Computed Tomography Scans: Development and Validation Study. SHILAP Revista de lepidopterología. 2. e40702–e40702. 6 indexed citations
8.
Fernández‐Costa, Juan M., et al.. (2022). Training‐on‐a‐Chip: A Multi‐Organ Device to Study the Effect of Muscle Exercise on Insulin Secretion in Vitro. Advanced Materials Technologies. 8(7). 19 indexed citations
9.
García, Javier, et al.. (2022). Artificial intelligence for the detection of pancreatic lesions. International Journal of Computer Assisted Radiology and Surgery. 17(10). 1855–1865. 10 indexed citations
10.
Chiara, Francesco De, Júlia Rodríguez‐Comas, Jordi Comelles, et al.. (2022). Collagen‐Tannic Acid Spheroids for β‐Cell Encapsulation Fabricated Using a 3D Bioprinter. Advanced Materials Technologies. 7(7). 2101696–2101696. 15 indexed citations
11.
12.
Rodríguez‐Comas, Júlia, et al.. (2021). Cellulose-based scaffolds enhance pseudoislets formation and functionality. Biofabrication. 13(3). 35044–35044. 18 indexed citations
13.
Rodríguez‐Comas, Júlia, Gema Alcarraz‐Vizán, Carlos Castaño, et al.. (2021). 4-Phenylbutyrate (PBA) treatment reduces hyperglycemia and islet amyloid in a mouse model of type 2 diabetes and obesity. Scientific Reports. 11(1). 11878–11878. 5 indexed citations
14.
Rodríguez‐Comas, Júlia & Javier Ramón‐Azcón. (2021). Islet-on-a-chip for the study of pancreatic β-cell function. PubMed. 1(1). 41–57. 9 indexed citations
15.
Alcarraz‐Vizán, Gema, Carlos Castaño, Júlia Rodríguez‐Comas, et al.. (2021). BACE2 suppression in mice aggravates the adverse metabolic consequences of an obesogenic diet. Molecular Metabolism. 53. 101251–101251. 5 indexed citations
16.
Ortega, María Alejandra, Júlia Rodríguez‐Comas, Ozlem Yavas, et al.. (2021). In Situ LSPR Sensing of Secreted Insulin in Organ-on-Chip. Biosensors. 11(5). 138–138. 33 indexed citations
17.
Rodríguez‐Comas, Júlia, Mercè Obach, Carlos Castaño, et al.. (2020). Alpha1-antitrypsin ameliorates islet amyloid-induced glucose intolerance and β-cell dysfunction. Molecular Metabolism. 37. 100984–100984. 15 indexed citations
18.
Rodríguez‐Comas, Júlia, Alba Moreno‐Asso, Mercè Martı́n, et al.. (2017). Stress-Induced MicroRNA-708 Impairs β-Cell Function and Growth. Diabetes. 66(12). 3029–3040. 37 indexed citations
19.
Montané, Joel, Mercè Obach, Carlos Castaño, et al.. (2015). Protein disulfide isomerase ameliorates β-cell dysfunction in pancreatic islets overexpressing human islet amyloid polypeptide. Molecular and Cellular Endocrinology. 420. 57–65. 13 indexed citations
20.
Bou, Marta, Marijana Todorčević, Júlia Rodríguez‐Comas, et al.. (2014). Interplay of adiponectin, TNFα and insulin on gene expression, glucose uptake and PPARγ, AKT and TOR pathways in rainbow trout cultured adipocytes. General and Comparative Endocrinology. 205. 218–225. 33 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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