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     Quick Explanation


    Jing Yuan — scientific output review (evidence from provided paper-level raw extracts)
    Based on the provided set of Jing Yuan–attributed papers, the strongest signals are (i) mechanistic biology using genome/omics + perturbations (e.g., Schistosoma BATT→ZNF362.1→CPEB1 axis; RNAi/RIP/DAP-seq/single-cell; ) and (ii) large-scale clinical/biomarker-style modeling with validation cohorts (e.g., CRIC→ARIC proteomics risk modeling; ).
    Caveat: your prompt mixes author-card metadata (h-index/citations/papers) with very heterogeneous paper excerpts across multiple fields; the strength of the review below is therefore limited to what is explicitly present in the supplied extracts and DOIs.



     Long Explanation


    Author Review: Jing Yuan
    Evidence scope: only the papers and structured excerpts you provided (each cited inline by DOI below). If the author’s full publication record differs from this subset, conclusions may shift.
    1) What is strongly supported by the provided evidence?
    • Mechanistic, perturbation-linked biology appears in the Schistosoma mansoni pheromone→transcriptional→development axis, including isoform-specific RNAi, direct DNA binding (EMSA), transcriptional activation (dual-luciferase), and pathway mapping to polyadenylation of ccnb1.
    • Large-cohort, cross-cohort validation is shown in CKD plasma proteomics where a multi-protein model is trained in CRIC and validated in ARIC; the excerpt also notes multiple-testing control and an MR layer for causal plausibility checks.
    • Data/analysis transparency signals are present in the Schistosoma paper via deposited omics (SRA/GEO/ProteomeXchange) and hosted code (Zenodo), which improves the reproducibility audit trail.
    2) Quick quantitative scan of the provided evidence (sample sizes / cohorts)
    These are not “metrics of Jing Yuan”; they are simply the sizes of the cohorts/models mentioned in the included excerpts.
    3) Evidence strength by claim-type (known vs inferred vs uncertain)
    Claim type What the excerpt supports Confidence Key limitations/bias risks
    Causal mechanistic links (perturbation + readouts) BATT induces znf362.1; isoform-specific knockdown blocks maturation; ZNF362.1 binds and activates Smp_349410; downstream polyadenylation of ccnb1 is altered. High (within the model system). Single species focus; receptor for BATT not identified; RNAi off-target/partial knockdown possible; vitellaria downstream complexity not fully mapped in excerpt.
    Prognostic/biomarker modeling (predictive discrimination) Multi-protein CKD progression risk model trained in CRIC and validated in ARIC; discrimination metrics reported; MR provides causal-like support for some proteins. Moderate→High (predictive validity), weaker for causal universality. Observational confounding; plasma vs tissue biology mismatch; external validation limited to ARIC; MR depends on pQTL availability/ancestry.
    In silico / computational discovery Neoantigen epitope predictions (NetMHCpan/NetCTL), docking (HPEPDOCK), and immunogenicity testing using peptides and PBMC ELISpot. Moderate (prediction-to-assay funnel partially validated). Donor count limited for ELISpot (n=6 in excerpt); lack of tumor challenge/efficacy in the excerpt; HLA coverage generalizability uncertain.
    Citations for the table rows are anchored to: Schistosoma mechanistic axis , and CKD proteomics modeling , and PCLO neoantigen selection Neoantigen specificity row should be anchored to .
    4) Cross-paper blind spots & bias checks (skeptical synthesis)
    • Field heterogeneity: The provided set spans parasitology, oncology genomics/immunotherapy, CKD proteomics, nanomedicine reviews, diagnostics, and more; comparing “scientific strength” across such diversity is inherently noisy without full publication-by-publication context.
    • Review-type vs hypothesis-testing papers: Several items in the excerpt list are narrative reviews with selection bias risk (e.g., MAPK CRC review).
    • Translation limitations repeatedly appear: for nanomedicine reviews, lack of systematic methodology and over-reliance on preclinical heterogeneity; for mechanistic works, model-system specificity; for biomarker modeling, external validity constraints.
    • Reproducibility auditability: The Schistosoma excerpt explicitly reports code+data deposits; other excerpts indicate “available on request,” which is helpful but weaker than direct open deposition.
    5) Visual: CRC subtype split + directionality (from the provided multi-omics subtype excerpt)
    Uses only the provided extracted counts and p-value directionality for OS/DFS.
    Citation anchor for the subtype counts and OS/DFS directionality: .
    6) What would most change the evaluation?
    • If additional author papers show weak mechanistic links (e.g., correlational markers without perturbation validation), the “mechanistic strength” score would drop.
    • If external replication of key modeling results fails (e.g., CKD protein model in cohorts beyond CRIC/ARIC; or CRC subtype generalization beyond TCGA/GEO microarray), the robustness would decrease.
    • If code/data availability is broadly limited (many “available on request”), reproducibility confidence would drop compared with the Schistosoma paper’s explicit deposits.
    Confidence note: based strictly on the provided excerpts, confidence is moderate because the set is heterogeneous and may not represent the author’s strongest/relevant works.
    References used (from your supplied DOI-bearing excerpts)


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    Updated: April 02, 2026

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     Hypothesis Graveyard


    A single global CRC driver mutation alone explains CS1/CS2 prognostic separation (unlikely per the multi-omics clustering and independent validation logic in the excerpt).


    All ferroptosis escape programs in solid tumors share the same super-enhancer architecture regardless of lineage (too strong; the provided excerpts show ferroptosis topics are context-dependent across different cancers and mechanisms).

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