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Author snapshot — Lívia C R Teixeira

Active mid-career researcher focused on inflammation, macrophage biology and neurodegeneration with multiple open-access reviews and experimentally-focused papers; visible impact (multiple papers with 20–70 citations each).

Representative high-impact experimental and review contributions include a 2020 Cells paper (cited 69×), a 2021 JCI Insight article (cited 50×), and JCI Insight 2023 (cited 28×) — these support moderate field influence and cross-disciplinary reach.
Click to inspect key papers below for source detail.

Long Explanation

Author Review: Lívia C R Teixeira

Visual summary first — citation footprint across selected top works

What the numbers show (evidence + inline sources)

Multiple experimentally-focused papers led or co-authored by Teixeira have moderate citation uptake (example: "Cyclic AMP..." — 69 citations), consistent with solid engagement in inflammation/macrophage research and reproducible experimental approaches.
High-quality venue JCI Insight publications (2021, 2023) address macrophage migration and systemic inflammation in sepsis; both are mechanistic, preclinical, and show community interest (50 and 28 citations respectively), strengthening the author's experimental credibility.

Qualitative strengths — evidence-based

Experimental breadth: several papers combine in vitro assays and in vivo models (pleural injections, sepsis models), showing practical laboratory skills and mechanistic focus ().
Translational orientation: author co-authors reviews on neurodegeneration/GLP‑1RAs and miRNA analyses linking molecular pathways to disease contexts, indicating movement between basic immunology and clinical-relevant topics ().

Limitations, blind spots, and potential biases

Predominantly preclinical: major experimental papers use animal and in vitro models — translational gaps to human pathology remain; clinical evidence is largely from reviews summarizing others' trials rather than novel clinical trials ().
Authorship position: many papers list the author as a middle author (contributor) rather than first/last — this indicates strong collaborative work but complicates attribution of leadership/independent intellectual contribution (OpenAlex metadata shows mixed positions across works).
Metric inconsistency between data sources: local summary listed h-index=6 & 187 citations (paper-count 9) whereas OpenAlex lists works_count=17, cited_by_count=276, h_index=8 — this discrepancy suggests different time snapshots, coverage differences (some databases index more items), or author name disambiguation issues; interpret metrics cautiously (see sources below).

Concrete suggestions to raise scientific impact

Lead more first/last-author mechanistic papers with multi-site validation or larger animal cohorts to strengthen causal claims and translational readiness.
Publish more open, reproducible datasets and methods (raw data deposits) to increase reuse and citation potential; several current papers are open access which is positive ().
Where possible, include orthogonal human data (biomarker measurements, patient samples) or collaborate with clinical groups to add translational weight to mechanistic findings (current reviews show interest in clinical links e.g., GLP‑1RA reviews).

Key-data citations used to form this review

Conclusion — evidence-weighted

The author demonstrates solid experimental competence in immunology/inflammation with moderate citation impact and topical breadth (macrophage biology, resolution of inflammation, sepsis, and neurodegeneration reviews). Evidence quality is predominantly moderate: well-designed preclinical work with reproducible methods but with limited direct human interventional data; collaborative co-authorship is a strength but makes individual leadership attribution mixed. Further leadership in first/last author mechanistic studies and addition of human translational data would raise the author's scientific standing and translational influence.

Use the AI Scientist to do deeper bibliometrics, co-authorship networks, or raw-data extraction from chosen full texts.

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Updated: March 17, 2026

BGPT Author Review

Scientific Quality

70%

Produces reproducible, mechanistic preclinical work with moderate community uptake; demonstrates experimental breadth (macrophage biology, resolution of inflammation, sepsis) and topical translational interest via reviews; limitations: mostly preclinical data, mixed authorship positions, and moderate total citations — overall competent mid-career scientist with potential to increase impact via lead-authored translational studies.

Communication Quality

70%

Writes clearly for specialists (open-access reviews and methods-rich experimental papers aid reproducibility). Some review articles increase visibility; could improve lay summaries and data-sharing for broader accessibility.

Author Novelty

60%

Work addresses important questions (e.g., efferocytosis, NETs, macrophage regulatory migration) with mechanistic insights but largely within established fields; novelty arises from specific mechanistic links (Ang-(1-7), plasmin roles) rather than paradigm-shifting discoveries.

Scientific Rigor

70%

Experimental papers show multiple orthogonal assays, in vivo models and appropriate endpoints — methods appear rigorous, but independent replication and human validation are limited, reducing translational rigor.

Key Insight

Mechanistic preclinical strengths in macrophage efferocytosis and inflammation-resolution position the author to translate findings into therapeutic biomarkers or adjunctive treatments for sepsis and neuroinflammation if human-sample work is added.

Keep Exploring

Which specific Teixeira-led papers would benefit most from independent replication and which assays are highest priority for translation?

Can we map co-author network centrality to identify potential clinical collaborators for human studies?

Analysis Wizard

Generating a reproducible bibliometric table and co-author network from OpenAlex/DOI lists to quantify citations, h-index differences, and collaboration centrality for Teixeira's works.

Hypothesis Graveyard

Hypothesis: Current preclinical macrophage findings already prove clinical efficacy — rejected because preclinical-to-human translation is unproven and human pathophysiology is more complex.

Hypothesis: Citation counts alone show leadership — rejected because co-authorship positions and cross-database metric discrepancies require nuanced interpretation.

Potential Experiments

Run a multi-cohort rodent sepsis trial (n≥12 per group) with plasminogen treatment vs control, measure NETs (MPO-DNA ELISA), coagulation markers (D-dimer), organ injury scores and survival — this would directly test translational potential.

Collect peripheral blood monocytes from sepsis patients, expose ex vivo to Ang-(1-7) and measure efferocytosis and gene-expression shifts vs controls to bridge preclinical mechanism to human cells.

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Discussion

BGPT Bias

I prioritize reproducible experimental evidence and bibliometrics; training-data biases may underweight regional journals and over-represent English OA sources.

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