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Introduction: With limited resources for rehabilitation, burn therapists throughout the world rely on local resources, professional creativity and community relationships to create solutions to help their patients recover. The demands of helping a burn survivor achieve an optimal outcome drive the burn therapist to create tools and strategies that effectively solve common problems after burn injury. Methods: The International Society for Burn Injury (ISBI) invited burn therapists from around the world to describe the innovations that they have created to solve burn rehabilitation problems. The submissions were provided through an online form and inclusion of photographs was encouraged. Each submission was reviewed in a blinded manner by an expert panel of three independent rehabilitation therapists from the ISBI Rehabilitation Committee. The submissions were reviewed for originality, utility, quality and safety, then edited and compiled to be shared and disseminated throughout the world. Results: A total of 77 innovations were submitted for review. 71 were accepted for publication (92%) and given the opportunity to publish in Burns Open. Clinicians from 15 countries submitted innovations. The innovations were categorized as: Orthosis, Scar Management, Techniques, Activities of Daily Living Devices, Educational Tools and Prosthesis. Nineteen submissions were translated from Spanish to English. Discussion: Creative and innovative ideas using local resources are turned into patient solutions by experienced burn therapists around the world. However, because such innovations don’t reach widespread investigation or production, the ideas remain in a silo in the local area when they could be of use to therapists in other cultures. This project resulted in a book of “Innovative Solutions in Burn Rehabilitation from Around the World,” that was printed and is now available as this Special Edition in Burns Open. Future work would include expanding on the book with more innovative solutions and translating it into multiple languages to improve access and usability throughout the world.

BackgroundHepatitis B virus (HBV), the main risk factor for hepatocellular carcinoma (HCC) development, integrates into the host genome, causing genetic instability, which may trigger malignancies to exhibit chronic DNA replication stress, providing exploitable therapeutic vulnerabilities. Therefore, customizing prognostication approach and expanding therapeutic options are of great clinical significance to HBV-associated HCCs.MethodsA robust machine-learning framework was designed to develop a DNA replication stress-related prognostic index (PIRS) based on 606 retrospectively collected HBV-associated HCC cases. Molecular profiles and drug response of HCC cell lines were leveraged to predict therapeutic targets and agents for patients with high mortality risk.ResultsCompared with established population-based predictors, PIRS manifested superior performance for prognostic prediction in HBV-associated HCCs. Lower PIRS tightly associated with higher expression of HBV oncoproteins, activated immune/metabolism pathways and higher likelihood of responding to immunotherapy; while higher PIRS showed co-occurrence manner with elevated Ki-67 progression marker and cancer stemness, and significantly enriched in DNA replication stress, cell cycle pathways, chromatin remodeling regulons, and presented an ‘immune-cold’ phenotype with unfavorable clinical outcome. Through large-scale in silico drug screening, four potential therapeutic targets (TOP2A, PRMT1, CSNK1D, and PPIH) and five agents including three topoisomerase inhibitors (teniposide, doxorubicin, and epirubicin) and two CDK inhibitors (JNJ-7706621 and PHA-793887) were identified for patients with high PIRS.ConclusionsOverall, PIRS holds potential to improve the population-based therapeutic strategies in HCC and sheds new insight to the clinical management for those HBV carriers; current analytic framework provides a roadmap for the rational clinical development of personalized treatment.

Tumors are closely related to the tumor microenvironment (TME). The complex interaction between tumor cells and the TME plays an indisputable role in tumor development. Tumor cells can affect the TME, promote tumor angiogenesis and induce immune tolerance by releasing cell signaling molecules. Immune cell infiltration (ICI) in the TME can affect the prognosis of patients with bladder cancer. However, the pattern of ICI of the TME in bladder cancer has not yet been elucidated. Herein, we identified three distinct ICI subtypes based on the TME immune infiltration pattern of 584 bladder cancer patients using the ESTIMATE and CIBERSORT algorithms. Then, we identified three gene clusters based on the differentially expressed genes (DEGs) between the three ICI subtypes. In addition, the ICI score was determined using single sample gene set enrichment analysis (ssGSEA). The results suggested that patients in the high ICI score subgroup had a favorable prognosis and higher expression of checkpoint-related and immune activity-related genes. The high ICI score subgroup was also linked to increased tumor mutation burden (TMB) and neoantigen burden. A cohort treated with anti-PD-L1 immunotherapy confirmed the therapeutic advantage and clinical benefit of patients with higher ICI scores. In the end, our study also shows that the ICI score represents an effective prognostic predictor for evaluating the response to immunotherapy. In conclusion, our study deepened the understanding of the TME, and it provides new ideas for improving patients’ response to immunotherapy and promoting individualized tumor immunotherapy in the future.

The molecular classification of muscle-invasive bladder cancer (MIBC) based on transcriptomic signatures has been extensively studied. The complementary nature of information provided by different molecular profiles motivated us to refine MIBC classification by aggregating multi-omics data. We generated a consensus ensemble through ten multi-omics integrative clustering approaches on 396 MIBCs from TCGA. A total of 701 MIBCs from different sequencing technologies were used for external validation. Associations between subtypes and prognosis, molecular profiles, the tumour microenvironment, and potential response to frontline therapies were further analyzed. Nearest template prediction and random forest classification were used to develop a predictive signature/classifier for MIBC refinement. We identified four integrative consensus subtypes of MIBC, which were further designated basal-inflamed, basal-noninflamed, luminal-excluded and luminal-desert by immune profiling. Of note, the refinement of basal-like MIBC classification adds to the literature by identifying a basal-noninflamed MIBC subtype presenting with a significantly poor outcome and a global immune-cold phenotype, which might be triggered by Chr4 deletion and high activation of the oncogenic NRF2 pathway. In contrast, basal-inflamed MIBC showed high immunocyte infiltration and high expression of potential targets for immunotherapy. Using an external metastatic MIBC cohort in which patients received anti-PD-L1 treatment, we suggested that basal-inflamed MIBC had a higher likelihood of responding to immunotherapy than other MIBCs. The R package “refineMIBC” was offered as a research tool to refine MIBC from a single-sample perspective (https://github.com/xlucpu/refineMIBC). This consensus ensemble refines the intrinsic MIBC subtypes, which provides a blueprint for the clinical development of rational targeted and immunotherapeutic strategies.

Ovarian epithelial cancer is a gynecological tumor with a high risk of recurrence and death. In the clinical diagnosis of ovarian epithelial cancer, CA125 has become an important indicator of disease burden. To account for patient recurrence and death, a proper method is needed to integrate information from biomarkers and recurrence simultaneously. In the past 10 years, many methods have been proposed for joint modeling of longitudinal biomarkers and survival data, but few of them are applicable to longitudinal data and disease processes, including recurrence and death. In this article, we proposed a new joint frailty model based on functional principal component analysis for dynamic prediction of survival probabilities on the total time scale, which took recurrent history and longitudinal data into account simultaneously. The estimation of the joint frailty model is achieved by maximizing the penalized log-likelihood function. The simulation results demonstrated the advantages of our method in both discrimination and accuracy under different scenarios. To indicate the method's practicality, it is applied to an actual dataset of patients with ovarian epithelial cancer to predict survival dynamically using longitudinal data of biomarker CA125 and recurrent history data.