Pancreatic ductal adenocarcinoma (PDAC) is an extremely lethal malignancy, with an average 5-year survival rate of 9% (Siegel RL, Miller KD, Jemal A. Ca Cancer J Clin. 2019;69(1):7-34). The steady increase in mortality rate indicates limited efficacy of the conventional regimen. The heterogeneity of PDAC calls for personalized treatment in clinical practice, which requires the construction of a preclinical system for generating patient-derived models. Currently, the lack of high-quality preclinical models results in ineffective translation of novel targeted therapeutics. This review summarizes applications of commonly used models, discusses major difficulties in PDAC model construction and provides recommendations for integrating workflows for precision medicine.

Postoperative wound healing is permanently of great concern to both surgeons and patients because pathological scars, including hypertrophic scars and keloids, profoundly affect an individual's physical and psychological well-being by causing pruritus, ulceration, pain, and contractures. Normal scar formation is an overlapping and orderly process involving hemostasis, inflammation, proliferation, and remodeling phases. 1 Although the pathogenesis of abnormal scar formation remains obscure, it is believed to be attributed to an

BackgroundThe indications for sentinel lymph node biopsy (SLNB) for thin melanoma are still unclear. This meta-analysis aims to determine the positive rate of SLNB in thin melanoma and to summarize the predictive value of different high-risk features for positive results of SLNB.MethodsFour databases were searched for literature on SLNB performed in patients with thin melanoma published between January 2000 and December 2020. The overall positive rate and positive rate of each high-risk feature were calculated and obtained with 95% confidence intervals (CIs). Both unadjusted odds ratios (ORs) and adjusted ORs (AORs) of high-risk features were analyzed. Pooled effects were estimated using random-effects model meta-analyses.ResultsSixty-six studies reporting 38,844 patients with thin melanoma who underwent SLNB met the inclusion criteria. The pooled positive rate of SLNB was 5.1% [95% confidence interval (CI) 4.9%-5.3%]. Features significantly predicted a positive result of SLNB were thickness≥0.8 mm [AOR 1.94 (95%CI 1.28-2.95); positive rate 7.0% (95%CI 6.0-8.0%)]; ulceration [AOR 3.09 (95%CI 1.75-5.44); positive rate 4.2% (95%CI 1.8-7.2%)]; mitosis rate >0/mm2 [AOR 1.63 (95%CI 1.13-2.36); positive rate 7.7% (95%CI 6.3-9.1%)]; microsatellites [OR 3.8 (95%CI 1.38-10.47); positive rate 16.6% (95%CI 2.4-36.6%)]; and vertical growth phase [OR 2.76 (95%CI 1.72-4.43); positive rate 8.1% (95%CI 6.3-10.1%)].ConclusionsThe overall positive rate of SLNB in thin melanoma was 5.1%. The strongest predictor for SLN positivity identified was microsatellites on unadjusted analysis and ulceration on adjusted analysis. Breslow thickness ≥0.8 mm and mitosis rate >0/mm2 both predict SLN positivity in adjusted analysis and increase the positive rate to 7.0% and 7.7%. We suggest patients with thin melanoma with the above high-risk features should be considered for giving an SLNB.