A. de la Fuente

754 total citations
27 papers, 602 citations indexed

About

A. de la Fuente is a scholar working on Molecular Biology, Oncology and Cellular and Molecular Neuroscience. According to data from OpenAlex, A. de la Fuente has authored 27 papers receiving a total of 602 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Molecular Biology, 7 papers in Oncology and 6 papers in Cellular and Molecular Neuroscience. Recurrent topics in A. de la Fuente's work include Drug Transport and Resistance Mechanisms (7 papers), Neuropeptides and Animal Physiology (6 papers) and Antibiotic Resistance in Bacteria (4 papers). A. de la Fuente is often cited by papers focused on Drug Transport and Resistance Mechanisms (7 papers), Neuropeptides and Animal Physiology (6 papers) and Antibiotic Resistance in Bacteria (4 papers). A. de la Fuente collaborates with scholars based in Spain, Germany and Chile. A. de la Fuente's co-authors include E. F. Pfeiffer, V. Schusdziarra, V. Brantl, Paloma Liras, Carlos Polanco, Gabriel A. Dover, Ana Isabel González, R. Schick, Juan F. Martı́n and Ana I. Álvarez and has published in prestigious journals such as Genetics, Food Chemistry and Journal of Bacteriology.

In The Last Decade

A. de la Fuente

26 papers receiving 577 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
A. de la Fuente Spain 16 281 100 93 75 60 27 602
Wenwen Huo China 17 381 1.4× 52 0.5× 32 0.3× 30 0.4× 80 1.3× 39 819
Sarika Chaudhary India 13 296 1.1× 15 0.1× 21 0.2× 25 0.3× 65 1.1× 43 609
Julie A. Nicholson United Kingdom 14 159 0.6× 9 0.1× 67 0.7× 115 1.5× 83 1.4× 21 668
Jessica Kim United States 11 536 1.9× 9 0.1× 41 0.4× 13 0.2× 56 0.9× 19 761
Hanseul Oh South Korea 15 169 0.6× 20 0.2× 32 0.3× 11 0.1× 21 0.3× 50 618
Ransome van der Hoeven United States 14 300 1.1× 19 0.2× 5 0.1× 14 0.2× 35 0.6× 30 679
Simos Simeonidis United States 11 326 1.2× 38 0.4× 140 1.5× 93 1.2× 86 1.4× 11 820
Ángeles Sánchez-Pérez Australia 13 400 1.4× 25 0.3× 27 0.3× 29 0.4× 39 0.7× 23 921
Roland Pálffy Slovakia 15 327 1.2× 16 0.2× 45 0.5× 28 0.4× 185 3.1× 22 760
Jinshan Cao China 16 179 0.6× 114 1.1× 51 0.5× 22 0.3× 108 1.8× 57 718

Countries citing papers authored by A. de la Fuente

Since Specialization
Citations

This map shows the geographic impact of A. de la Fuente'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 A. de la Fuente with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. de la Fuente more than expected).

Fields of papers citing papers by A. de la Fuente

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. de la Fuente. 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 A. de la Fuente. The network helps show where A. de la Fuente may publish in the future.

Co-authorship network of co-authors of A. de la Fuente

This figure shows the co-authorship network connecting the top 25 collaborators of A. de la Fuente. A scholar is included among the top collaborators of A. de la Fuente 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 A. de la Fuente. A. de la Fuente 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.
Fuente, A. de la, et al.. (2025). Quercetin and Silybin Decrease Intracellular Replication of Piscirickettsia salmonis in SHK-1 Cell. International Journal of Molecular Sciences. 26(3). 1184–1184. 2 indexed citations
2.
Gomez‐Gómez, Àlex, et al.. (2020). Analysis of the interaction between tryptophan-related compounds and ATP-binding cassette transporter G2 (ABCG2) using targeted metabolomics. Food Chemistry. 344. 128665–128665. 6 indexed citations
3.
Fuente, A. de la, et al.. (2019). Role of ABCG2 in Secretion into Milk of the Anti-Inflammatory Flunixin and Its Main Metabolite: In Vitro-In Vivo Correlation in Mice and Cows. Drug Metabolism and Disposition. 47(5). 516–524. 13 indexed citations
4.
Otero, Jon A., et al.. (2016). Effect of bovine ABCG2 Y581S polymorphism on concentrations in milk of enrofloxacin and its active metabolite ciprofloxacin. Journal of Dairy Science. 99(7). 5731–5738. 15 indexed citations
5.
Otero, Jon A., A. de la Fuente, Julio G. Prieto, et al.. (2014). Short communication: The gain-of-function Y581S polymorphism of the ABCG2 transporter increases secretion into milk of danofloxacin at the therapeutic dose for mastitis treatment. Journal of Dairy Science. 98(1). 312–317. 20 indexed citations
6.
Real, Rebeca, A. de la Fuente, Julio G. Prieto, et al.. (2014). Novelin vitrosystems for prediction of veterinary drug residues in ovine milk and dairy products. Food Additives & Contaminants Part A. 31(6). 1026–1037. 20 indexed citations
7.
Kau, Chung How, et al.. (2013). Photobiomodulation accelerates orthodontic alignment in the early phase of treatment. Progress in Orthodontics. 14(1). 30–30. 87 indexed citations
8.
Otero, Jon A., Rebeca Real, A. de la Fuente, et al.. (2012). The Bovine ATP-Binding Cassette Transporter ABCG2 Tyr581Ser Single-Nucleotide Polymorphism Increases Milk Secretion of the Fluoroquinolone Danofloxacin. Drug Metabolism and Disposition. 41(3). 546–549. 17 indexed citations
9.
10.
Rodríguez‐García, Antonio, et al.. (2000). Characterization and expression of the arginine biosynthesis gene cluster of Streptomyces clavuligerus.. PubMed. 2(4). 543–50. 26 indexed citations
11.
Polanco, Carlos, Ana Isabel González, A. de la Fuente, & Gabriel A. Dover. (1998). Multigene Family of Ribosomal DNA in Drosophila melanogaster Reveals Contrasting Patterns of Homogenization for IGS and ITS Spacer Regions: A Possible Mechanism to Resolve This Paradox. Genetics. 149(1). 243–256. 81 indexed citations
12.
López, José Manuel García, et al.. (1987). Beta Cell Response to the Hyperglycaemic Clamp in Three Patients with Insulinoma: A Study Using a Hyperglycaemic Glucose Clamp. Hormone and Metabolic Research. 19(4). 160–163. 6 indexed citations
13.
Olivera, Javier, et al.. (1987). [Ingestion of foreign bodies in a prison population; surgical treatment].. PubMed. 72(5 Pt 2). 617–21. 4 indexed citations
14.
Fuente, A. de la, et al.. (1986). Acromegaly and insulin resistance: a case study. European Journal of Endocrinology. 111(4). 445–451. 2 indexed citations
15.
Glock, Y, et al.. (1985). [Superior vena cava compression syndrome. Our experience apropos of 26 cases].. PubMed. 39(7). 495–500. 1 indexed citations
16.
Schusdziarra, V., et al.. (1983). Milk-derived opiates stimulate insulin release in dogs. Regulatory Peptides. 5. 42–43. 1 indexed citations
17.
Schusdziarra, V., et al.. (1983). Effect of Morphine, Leu-Enkephalin and β-Casomorphins on Basal Somatostatin Release in Dogs. Hormone and Metabolic Research. 15(8). 407–408. 16 indexed citations
18.
Schusdziarra, V., A. de la Fuente, Jennifer M. Specht, et al.. (1983). Effect of β-Casomorphins and Analogs on Insulin Release in Dogs*. Endocrinology. 112(3). 885–889. 42 indexed citations
19.
Schusdziarra, V., R. Schick, A. de la Fuente, et al.. (1983). Effect of opiate-active substances on pancreatic polypeptide levels in dogs. Peptides. 4(2). 205–210. 26 indexed citations
20.
Fuente, A. de la, et al.. (1976). Echocardiogram in Right Atrial Myxoma. CHEST Journal. 69(1). 94–96. 8 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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