Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment for patients hinges on their responsiveness to it. Currently, no available biomarkers can promptly predict responses to platinum-based treatment. Therefore, we developed the Pathologic Risk Classifier for HGSOC (PathoRiCH), a histopathologic image-based classifier. PathoRiCH was trained on an in-house cohort (n = 394) and validated on two independent external cohorts (n = 284 and n = 136). The PathoRiCH-predicted favorable and poor response groups show significantly different platinum-free intervals in all three cohorts. Combining PathoRiCH with molecular biomarkers provides an even more powerful tool for the risk stratification of patients. The decisions of PathoRiCH are explained through visualization and a transcriptomic analysis, which bolster the reliability of our model's decisions. PathoRiCH exhibits better predictive performance than current molecular biomarkers. PathoRiCH will provide a solid foundation for developing an innovative tool to transform the current diagnostic pipeline for HGSOC.

Thermochemical conversion of silkworm by-product into syngas.

This study explored the valorisation of silkworm by-product, a major by-product of the silk industry (sericulture), which amounts to 16 million tonnes annually. The focus was on transforming waste into energy resources through pyrolysis under CO2 conditions. In one-stage pyrolysis, the evolution of syngas under N2 was found to be comparable to that under CO2. A notable allocation of carbon to biocrude rather than syngas was observed. The two-stage pyrolysis resulted in increased syngas production. However, achieving a homogeneous reaction between CO2 and the volatiles liberated from silkworm byproduct proved challenging. Indeed, the reaction kinetics governing CO2 reactivity was not fast although the temperature windows of the reaction were aligned in the two-stage pyrolysis. To address this issue, pyrolysis was performed using a Ni-based catalyst to expedite the reaction kinetics. Consequently, syngas formation, particularly CO formation, was significantly enhanced under CO2 conditions compared to that under N2 conditions. The syngas yield under CO2 was 36.42 wt% which was 2-fold higher than that of N2. This suggested the potential of CO2 altering the carbon distribution from biocrude to syngas. This strategy would contribute to the establishment of sustainable production of silk by converting sericulture by-product into energy/chemical resources.