Combustion Melting Characterisation of Solid Fuel Obtained from Sewage Sludge

Solid fuelization technology can increase the heating value of sewage sludge such that it can be utilised as a fossil fuel substitutes. Reducing landfilling of bottom and fly ash resulting from heavy metals contained in sewage sludge is challenging. Hence, combustion melting technology (CMT), which can discharge bottom ash in the form of slag, has been proposed herein as an alternative to the conventional incineration technology. However, further research is required to improve the flowability of slag. Applicability of CMT for the stable treatment of heavy metals in the ash generated during the energisation of sewage sludge solid fuel has been reviewed. The change in the degree of fluidity was identified via a laboratory-scale fluidity measurement experiment following changes in melting temperature, mixing ratio of sewage sludge and sawdust, and basicity. The pouring index (PI) of sewage sludge solid fuel (pellet) was maintained at a level of about 60% at a basicity index of 0.8. Based on the results, the slagging rates and volume reduction rates, exhaust gas analysis, and heavy metal elution characteristics under oxygen enrichment were derived from a 2 ton/day combustion melting pilot plant experiment; thereafter, the feasibility of combustion melting of sewage sludge solid fuel was determined

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

Abstract 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