Objective: To develop an imaging-derived biomarker for prediction of overall survival (OS) of pancreatic cancer by analyzing preoperative multiphase contrast-enhanced computed topography (CECT) using deep learning. Background: Exploiting prognostic biomarkers for guiding neoadjuvant and adjuvant treatment decisions may potentially improve outcomes in patients with resectable pancreatic cancer. Methods: This multicenter, retrospective study included 1516 patients with resected pancreatic ductal adenocarcinoma (PDAC) from 5 centers located in China. The discovery cohort (n=763), which included preoperative multiphase CECT scans and OS data from 2 centers, was used to construct a fully automated imaging-derived prognostic biomarker—DeepCT-PDAC—by training scalable deep segmentation and prognostic models (via self-learning) to comprehensively model the tumor-anatomy spatial relations and their appearance dynamics in multiphase CECT for OS prediction. The marker was independently tested using internal (n=574) and external validation cohorts (n=179, 3 centers) to evaluate its performance, robustness, and clinical usefulness. Results: Preoperatively, DeepCT-PDAC was the strongest predictor of OS in both internal and external validation cohorts [hazard ratio (HR) for high versus low risk 2.03, 95% confidence interval (CI): 1.50–2.75; HR: 2.47, CI: 1.35–4.53] in a multivariable analysis. Postoperatively, DeepCT-PDAC remained significant in both cohorts (HR: 2.49, CI: 1.89–3.28; HR: 2.15, CI: 1.14–4.05) after adjustment for potential confounders. For margin-negative patients, adjuvant chemoradiotherapy was associated with improved OS in the subgroup with DeepCT-PDAC low risk (HR: 0.35, CI: 0.19–0.64), but did not affect OS in the subgroup with high risk. Conclusions: Deep learning-based CT imaging-derived biomarker enabled the objective and unbiased OS prediction for patients with resectable PDAC. This marker is applicable across hospitals, imaging protocols, and treatments, and has the potential to tailor neoadjuvant and adjuvant treatments at the individual level.

With the continuous increase in the use of haploidentical donors for transplantation, the selection of donors becomes increasingly important. Haploidentical donors have been selected primarily based on clinical characteristics, while the effects of killer cell immunoglobulin-like receptors (KIRs) on outcomes of haploidentical-hematopoietic stem cell transplantation (haplo-HSCT) with post-transplant cyclophosphamide (PTCy) remain inconclusive. The present study aimed to thoroughly evaluate the effect of KIRs and binding ligands assessed by various models, in addition to other patient/donor variables, on clinical outcomes in haplo-HSCT. In a cohort of 354 patients undergoing their first haplo-HSCT, we found that a higher Count Functional inhibitory KIR score (CF-iKIR) was associated with improved progression-free survival (adjusted hazard ratio [HR], 0.71; P = .029) and overall survival (OS) (HR, 0.66; P = .016), while none of the other models predicted for survival in these patients. Moreover, using exploratory classification and regression tree analysis, we found that donor age 58 years was associated with the worst OS. In the rest of our cohort (80%), cytomegalovirus-reactive recipients with a donor <58 years old, a higher CF-iKIR was associated with superior OS. The 3-year OS rates were 73.9%, 54.1% (HR, 1.84; P = .044), 44.5% (HR, 2.01; P = .003), and 18.5% (HR, 5.44; P <.001) in the best, better, poor, and worse donor groups, respectively. Our results suggest that KIR alloreactivity assessed by CF-iKIR score can help optimize donor selection in haplo-HSCT.

Immune-mediated skin diseases have a high prevalence and seriously affect patients’ quality of life. Gold compounds have been considered promising therapeutic agents in dermatology, but the high incidence of adverse reactions have limited their clinical application. There is a great need to develop more effective and less toxic gold-based drugs. Gold nanoclusters fabricated by using peptides (pep-AuNCs) have appeared as potential biomedical nanomaterials because of their excellent biocompatibility, ease of fabrication and unique physicochemical properties. Glutathione (GSH) is an endogenous tripeptide and has been used for lightening the skin color. Therefore, we fabricated a well-defined gold nanocluster with GSH as an example to explore the immunomodulatory effect of AuNCs on a TNF-α-treated human keratinocyte cell line (HaCaT) in vitro, the 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced irritant contact dermatitis (ICD) model and the oxazolone (OXA)-induced psoriatic model in vivo. The results indicated that topically applied AuNCs successfully attenuated the severity of ICD and psoriasis-like lesions. In vitro and in vivo, AuNCs effectively inhibited the abnormal activation of the NF-κB pathway and the consequent overexpression of proinflammatory cytokines in keratinocytes. In particular, the transactivation of IL-17A, the most important cytokine in psoriasis pathology, was effectively inhibited by AuNCs treatment. In addition, AuNCs did not show any obvious cytotoxicity in HaCaT cells at doses even up to 100 µM and did not induce any irritation in the healthy skin and major organs, which indicated their favorable biosafety. These results indicate that biocompatible pep-AuNCs might be a promising gold-based nanomedicine for the treatment of inflammatory skin diseases.