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Objectives: To verify the analytical performance of the HepatoPredict kit, a novel tool developed to stratify Hepatocellular Carcinoma (HCC) patients according to their risk of relapse after a Liver Transplantation (LT). Methods: The HepatoPredict tool combines clinical variables and a gene expression signature in an ensemble of machine-learning algorithms to forecast the benefit of a LT in HCC patients. To ensure the accuracy and reliability of this method, extensive analytical validation was conducted to verify its specificity and robustness. The experiments were designed following the guidelines for multi-target genomic assays such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. The validation process included reproducibility between operators and between RNA extractions and RT-qPCR runs, and interference of input RNA levels or varying reagent levels. A recently retrained version of the HepatoPredict algorithms was also tested. Results: The validation process demonstrated that the HepatoPredict kit met the required standards for robustness (p > 0.05), analytical specificity (inclusivity of 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The operator, equipment, input RNA, and reagents used had no significant effect on the HepatoPredict results. Additionally, the testing of a recently retrained version of the HepatoPredict algorithm, showed that this new version further improved the accuracy of the kit and performed better than existing clinical criteria in accurately identifying HCC patients who are more likely to benefit LT. Conclusions: Even with the introduced variations in molecular and clinical variables, the HepatoPredict kit's prognostic information remains consistent. It can accurately identify HCC patients who are more likely to benefit from a LT. Its robust performance also confirms that it can be easily integrated into standard diagnostic laboratories.
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BACKGROUND: Patch repair of congenital diaphragmatic hernia (CDH) using Gore-Tex® is associated with infection, adhesions, hernia recurrence, long-term musculoskeletal sequels and poor tissue regeneration. To overcome these limitations, the performance of two novel biodegradable membranes was tested to repair CDH in a growing pig model. METHODS: Twelve male pigs were randomly assigned to 3 different groups of 4 animals each, determined by the type of patch used during thoracoscopic diaphragmatic hernia repair (Gore-Tex®, polycaprolactone electrospun membrane-PCLem, and decellularized human chorion membrane-dHCM). After 7 weeks, all animals were euthanized, followed by necropsy for diaphragmatic evaluation and histological analysis. RESULTS: Thoracoscopic defect creation and diaphragmatic repair were performed without any technical difficulty in all groups. However, hernia recurrence rate was 0% in Gore-Tex®, 50% in PCLem and 100% in dHCM groups. At euthanasia, Gore-Tex® patches appeared virtually unchanged and covered with a fibrotic capsule, while PCLem and dHCM patches were replaced by either floppy connective tissue or vascularized and floppy regenerated membranous tissue, respectively. CONCLUSION: Gore-Tex® was associated with a higher survival rate and lower recurrence. Nevertheless, the proposed biodegradable membranes were associated with better tissue integration when compared with Gore-Tex®.