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Podocytes are terminally differentiated epithelial cells that reside along the glomerular filtration barrier. Evidence suggests that after podocyte injury, endoplasmic reticulum stress response is activated, but the molecular mechanisms involved are incompletely defined. In a mouse model, we confirmed that podocyte injury induces endoplasmic reticulum stress response and upregulated unfolded protein response pathways, which have been shown to mitigate damage by preventing the accumulation of misfolded proteins in the endoplasmic reticulum. Furthermore, simultaneous podocyte-specific genetic inactivation of X-box binding protein-1 (Xbp1), a transcription factor activated during endoplasmic reticulum stress and critically involved in the untranslated protein response, and Sec63, a heat shock protein-40 chaperone required for protein folding in the endoplasmic reticulum, resulted in progressive albuminuria, foot process effacement, and histology consistent with ESRD. Finally, loss of both Sec63 and Xbp1 induced apoptosis in podocytes, which associated with activation of the JNK pathway. Collectively, our results indicate that an intact Xbp1 pathway operating to mitigate stress in the endoplasmic reticulum is essential for the maintenance of a normal glomerular filtration barrier.

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Intermediate-stage Hepatocellular Carcinoma (HCC) represents a wide range of disease burden. Patients with different levels of liver function, tumor size, and number of lesions may all have intermediate-stage disease according to the Barcelona Clinic Liver Cancer (BCLC) staging system. Several minimally invasive image-guided locoregional therapies are available for the treatment of intermediate-stage HCC, including conventional transarterial chemoembolization (cTACE), drug-eluting bead TACE (DEB-TACE), yttrium-90 radioembolization (Y-90 RE), thermal ablation, bland embolization, and combination therapy. Available clinical evidence points to cTACE as the current gold standard for the locoregional treatment of intermediate-stage HCC. DEB-TACE is at best non-inferior to cTACE in terms of survival benefit. Y-90 RE is a maturing therapy, and some institutions have adopted it as first-line therapy for intermediate-stage HCC. Thermal ablation combined with TACE may be used in select patients, while bland embolization has only limited evidence for its use. The combination of locoregional therapy with VEGF inhibitors or immune checkpoint inhibitors has also been explored. This article will examine in detail the clinical evidence supporting available locoregional treatment options for intermediate-stage HCC.

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Tumor recurrence affects up to 70% of early-stage hepatocellular carcinoma (HCC) patients, depending on treatment option. Deep learning algorithms allow in-depth exploration of imaging data to discover imaging features that may be predictive of recurrence. This study explored the use of convolutional neural networks (CNN) to predict HCC recurrence in patients with early-stage HCC from pre-treatment magnetic resonance (MR) images. This retrospective study included 120 patients with early-stage HCC. Pre-treatment MR images were fed into a machine learning pipeline (VGG16 and XGBoost) to predict recurrence within six different time frames (range 1–6 years). Model performance was evaluated with the area under the receiver operating characteristic curves (AUC–ROC). After prediction, the model’s clinical relevance was evaluated using Kaplan–Meier analysis with recurrence-free survival (RFS) as the endpoint. Of 120 patients, 44 had disease recurrence after therapy. Six different models performed with AUC values between 0.71 to 0.85. In Kaplan–Meier analysis, five of six models obtained statistical significance when predicting RFS (log-rank p < 0.05). Our proof-of-concept study indicates that deep learning algorithms can be utilized to predict early-stage HCC recurrence. Successful identification of high-risk recurrence candidates may help optimize follow-up imaging and improve long-term outcomes post-treatment.