Amber M. Smith

13.7k total citations · 8 hit papers
80 papers, 10.2k citations indexed

About

Amber M. Smith is a scholar working on Immunology, Epidemiology and Infectious Diseases. According to data from OpenAlex, Amber M. Smith has authored 80 papers receiving a total of 10.2k indexed citations (citations by other indexed papers that have themselves been cited), including 43 papers in Immunology, 34 papers in Epidemiology and 16 papers in Infectious Diseases. Recurrent topics in Amber M. Smith's work include Influenza Virus Research Studies (23 papers), Immune Response and Inflammation (19 papers) and Respiratory viral infections research (15 papers). Amber M. Smith is often cited by papers focused on Influenza Virus Research Studies (23 papers), Immune Response and Inflammation (19 papers) and Respiratory viral infections research (15 papers). Amber M. Smith collaborates with scholars based in United States, Germany and Canada. Amber M. Smith's co-authors include Edward J. Pearce, Erika L. Pearce, Stanley Ching‐Cheng Huang, Bart Everts, Peter J. Murray, Jonathan J. Miner, Michael Diamond, Wing Y. Lam, Jennifer Govero and Jonathan A. McCullers and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Journal of Biological Chemistry.

In The Last Decade

Amber M. Smith

76 papers receiving 10.1k citations

Hit Papers

Cell-intrinsic lysosomal lipolysis is essential for alter... 2009 2026 2014 2020 2014 2014 2016 2009 2016 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Cell-intrinsic lysosomal lipolysis is essential for alternative activation of macrophages
    2014 897 citations Stanley Ching‐Cheng Huang, Bart Everts et al. profile →
  2. TLR-driven early glycolytic reprogramming via the kinases TBK1-IKKɛ supports the anabolic demands of dendritic cell activation
    2014 841 citations Bart Everts, Stanley Ching‐Cheng Huang et al. profile →
  3. A Mouse Model of Zika Virus Pathogenesis
    2016 698 citations Amber M. Smith, Jonathan J. Miner et al. profile →
  4. Arginase-1–Expressing Macrophages Suppress Th2 Cytokine–Driven Inflammation and Fibrosis
    2009 643 citations Karim C. El Kasmi, Amber M. Smith et al. PLoS Pathogens profile →
  5. Zika Virus Infection during Pregnancy in Mice Causes Placental Damage and Fetal Demise
    2016 621 citations Jonathan J. Miner, Amber M. Smith et al. profile →
  6. Memory CD8+ T Cells Use Cell-Intrinsic Lipolysis to Support the Metabolic Programming Necessary for Development
    2014 611 citations David O’Sullivan, Gerritje J. W. van der Windt et al. Immunity profile →
  7. Metabolic Reprogramming Mediated by the mTORC2-IRF4 Signaling Axis Is Essential for Macrophage Alternative Activation
    2016 495 citations Stanley Ching‐Cheng Huang, Amber M. Smith et al. Immunity profile →
  8. TBK1 and IKKε Act Redundantly to Mediate STING-Induced NF-κB Responses in Myeloid Cells
    2020 325 citations Katherine R. Balka, Cynthia Louis et al. Cell Reports profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Amber M. Smith United States 43 5.5k 2.6k 2.5k 2.3k 1.9k 80 10.2k
Michael Poidinger Singapore 43 3.4k 0.6× 1.2k 0.5× 2.8k 1.1× 1.8k 0.8× 1.3k 0.7× 106 8.2k
Ulrich Kalinke Germany 58 8.2k 1.5× 2.3k 0.9× 3.8k 1.5× 2.1k 0.9× 883 0.5× 241 14.1k
Daniel F. Hoft United States 50 4.1k 0.8× 2.7k 1.0× 1.4k 0.6× 1.9k 0.8× 889 0.5× 159 7.2k
Søren R. Paludan Denmark 64 9.1k 1.7× 3.9k 1.5× 5.0k 2.0× 2.7k 1.2× 822 0.4× 169 14.2k
Mark R. Alderson United States 56 7.4k 1.4× 3.3k 1.3× 2.9k 1.2× 2.3k 1.0× 559 0.3× 115 12.0k
Matthew L. Albert France 60 9.1k 1.7× 2.4k 1.0× 4.7k 1.9× 2.5k 1.1× 2.3k 1.2× 151 16.5k
Himanshu Kumar Japan 34 6.9k 1.3× 2.0k 0.8× 3.2k 1.3× 1.6k 0.7× 442 0.2× 63 9.6k
David J. Kelvin Canada 56 4.3k 0.8× 1.9k 0.7× 3.0k 1.2× 2.0k 0.9× 591 0.3× 180 10.5k
Olaf Groß Germany 34 5.0k 0.9× 1.9k 0.7× 4.2k 1.7× 1.8k 0.8× 438 0.2× 71 9.1k
Heung Kyu Lee South Korea 39 5.1k 0.9× 2.5k 1.0× 2.7k 1.1× 1000 0.4× 410 0.2× 115 9.5k

Countries citing papers authored by Amber M. Smith

Since Specialization
Citations

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

Fields of papers citing papers by Amber M. Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Amber M. Smith. 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 Amber M. Smith. The network helps show where Amber M. Smith may publish in the future.

Co-authorship network of co-authors of Amber M. Smith

This figure shows the co-authorship network connecting the top 25 collaborators of Amber M. Smith. A scholar is included among the top collaborators of Amber M. Smith 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 Amber M. Smith. Amber M. Smith 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.
2.
Laubenbacher, Reinhard, Gary An, Filippo Castiglione, et al.. (2024). Forum on immune digital twins: a meeting report. npj Systems Biology and Applications. 10(1). 19–19. 6 indexed citations
3.
Schughart, Klaus, Amber M. Smith, Ephraim L. Tsalik, et al.. (2024). Host response to influenza infections in human blood: association of influenza severity with host genetics and transcriptomic response. Frontiers in Immunology. 15. 1385362–1385362. 3 indexed citations
4.
Williams, Evan P., Piroon Jenjaroenpun, Thidathip Wongsurawat, et al.. (2023). Dissecting Phenotype from Genotype with Clinical Isolates of SARS-CoV-2 First Wave Variants. Viruses. 15(3). 611–611.
5.
6.
Smith, Amanda, Evan P. Williams, Muneeswaran Selvaraj, et al.. (2022). Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infections During SARS-CoV-2. Frontiers in Immunology. 13. 894534–894534. 12 indexed citations
7.
Smith, Amanda, Tim van Opijnen, Rob Carter, et al.. (2021). Dynamic Pneumococcal Genetic Adaptations Support Bacterial Growth and Inflammation during Coinfection with Influenza. Infection and Immunity. 89(7). e0002321–e0002321. 5 indexed citations
8.
Warren, Donald C., et al.. (2021). Time to revisit the endpoint dilution assay and to replace the TCID50 as a measure of a virus sample’s infection concentration. PLoS Computational Biology. 17(10). e1009480–e1009480. 26 indexed citations
9.
Jenner, Adrianne L., Rosemary A. Aogo, Xiaoyan Deng, et al.. (2021). COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes. PLoS Pathogens. 17(7). e1009753–e1009753. 50 indexed citations
10.
Balka, Katherine R., Cynthia Louis, Tahnee L. Saunders, et al.. (2020). TBK1 and IKK epsilon Act Redundantly to Mediate STING-Induced NF-kappa B Responses in Myeloid Cells. Cell Reports. 31(1). 10 indexed citations
11.
McCullers, Jonathan A., et al.. (2019). Enhanced IL-1β production is mediated by a TLR2-MYD88-NLRP3 signaling axis during coinfection with influenza A virus and Streptococcus pneumoniae. PLoS ONE. 14(2). e0212236–e0212236. 29 indexed citations
12.
Wu, Duojiao, David E. Sanin, Bart Everts, et al.. (2016). Type 1 Interferons Induce Changes in Core Metabolism that Are Critical for Immune Function. Immunity. 44(6). 1325–1336. 254 indexed citations
13.
O’Sullivan, David, Gerritje J. W. van der Windt, Stanley Ching‐Cheng Huang, et al.. (2014). Memory CD8+ T Cells Use Cell-Intrinsic Lipolysis to Support the Metabolic Programming Necessary for Development. Immunity. 41(1). 75–88. 611 indexed citations breakdown →
14.
Smith, Amber M. & Jonathan A. McCullers. (2014). Secondary Bacterial Infections in Influenza Virus Infection Pathogenesis. Current topics in microbiology and immunology. 385. 327–356. 105 indexed citations
15.
Conley, Mary Ellen, Kerry Dobbs, Anita M. Quintana, et al.. (2012). Agammaglobulinemia and absent B lineage cells in a patient lacking the p85α subunit of PI3K. The Journal of Experimental Medicine. 209(3). 463–470. 137 indexed citations
16.
Qualls, Joseph E., Geoffrey Neale, Jessica M. Haverkamp, et al.. (2012). MyD88 and Stat3 signaling are fundamental to tumor associated macrophage function (162.34). The Journal of Immunology. 188(1_Supplement). 162.34–162.34. 1 indexed citations
17.
Qualls, Joseph E., et al.. (2009). Direct and indirect type-1 arginase (Arg1) induction following Mycobacterium bovis (BCG) infection (43.1). The Journal of Immunology. 182(Supplement_1). 43.1–43.1. 2 indexed citations
18.
Moreira, Lilian O., et al.. (2008). Modulation of adaptive immunity by different adjuvant–antigen combinations in mice lacking Nod2. Vaccine. 26(46). 5808–5813. 32 indexed citations
19.
Kasmi, Karim C. El, Amber M. Smith, Lynn Williams, et al.. (2007). Cutting Edge: A Transcriptional Repressor and Corepressor Induced by the STAT3-Regulated Anti-Inflammatory Signaling Pathway. The Journal of Immunology. 179(11). 7215–7219. 132 indexed citations
20.
Kasmi, Karim C. El, Jeff Holst, Maryaline Coffre, et al.. (2006). General Nature of the STAT3-Activated Anti-Inflammatory Response. The Journal of Immunology. 177(11). 7880–7888. 186 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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