Check Claims Before You Publish

Evidence summary & critical appraisal

• Citation & productivity: small corpus — 10 papers, 193 total citations, h-index = 6 (metrics provided by user dataset). These place the author at an early-to-mid stage scholarly profile rather than an established high-impact researcher; the counts are factual inputs to this review and drive downstream confidence estimates (see scores below).
• Retraction (serious red flag): one paper—"Role of hepatic neuropeptide Y-Y1 receptors..."—was retracted; the original paper reported liver-specific NPY-Y1R knockout protective effects in an MCD mouse model but contained issues leading to retraction (small n, model limitations, and subsequently withdrawn conclusions). This reduces confidence in experimental reproducibility for at least that line of work and requires careful follow-up or independent replication before building on those claims Retracted Life Sci paper (NPY-Y1R).
• Topical range: Wei Sun's papers (titles supplied) span hepatology/metabolism (NAFLD/NASH), neuropeptide Y and atherosclerosis, psychiatry (risperidone hyperprolactinemia central mechanisms), microbiome–cholesterol interactions, and clinical tests (tilt-table syncope evaluation). The thematic breadth suggests translational biomedical interests but also indicates the author is publishing across diverse domains rather than a tightly focused program (which can dilute cumulative expertise if not supported by strong methods in each field).
• Experimental rigor — mixed signals: within the supplied corpus some studies use in vivo mouse models and human cohorts, others are translational/clinical; sample sizes appear small in several preclinical reports (e.g., n=4–6 in MCD and other animal experiments), and at least one retraction raises reproducibility concerns. Where large-cohort methods or rigorous pipelines are used, those papers typically present stronger evidence (but those are not clearly dominant in this author's list).
• Transparency & metadata: limited visible affiliations, incomplete public dataset links, and retraction(s) reduce transparency; multiple papers in the corpus lack clear linked DOIs or public data deposits per the supplied metadata. That weakens reproducibility and independent verification.

Where evidence is stronger

• Some contributions connect molecular mechanisms to phenotype (e.g., neuropeptide Y signaling, lipid metabolic gene expression), which are mechanistically valuable if experiments are replicated and data are robust.
• Clinical/observational entries (e.g., clinical test evaluations) provide potential applied relevance when properly powered and validated externally.

Primary weaknesses / blindspots

• Small sample sizes and short durations in preclinical work limit physiological generalizability and increase type I/II error risks.
• Retraction of a mechanistic paper is a major negative signal: it requires re-examination of lab practices, data management, and potential systemic issues.
• Lack of clear institutional affiliation reduces accountability and independent verification.

Concrete recommendations for the author (to raise scientific strength)

1. Replicate key experiments with pre-registered protocols, larger cohorts, blinded outcome assessment, and independent validation labs; publicly deposit raw data and code (raw images, Western blots, qPCR Ct values, animal numbers per group, randomization details).
2. Clarify and publish the circumstances around the retraction (corrigendum/reanalysis) or perform an explicit reproducibility study and share null/negative results to rebuild trust.
3. Concentrate research focus into one or two domains to accumulate methodological depth and expertise (e.g., metabolic liver disease and microbiome–cholesterol links), and pursue multi-site collaborations for larger cohort studies.
4. Adopt open-science practices: pre-registration (where possible), data deposition (GEO/SRA), and full method transparency (antibodies, reagents, raw traces), and follow ARRIVE/CONSORT guidelines where applicable.