Enhancing Prediction for Tumor Pathologic Response to Neoadjuvant Immunochemotherapy in Locally Advanced Esophageal Cancer by Dynamic Parameters from Clinical Assessments.

To develop accurate and accessible prediction methods for assessing pathologic response following NICT prior to surgery, we conducted a retrospective study including 137 patients with esophageal squamous cell carcinoma (ESCC) who underwent surgery after two cycles of NICT between January 2019 and March 2022 at our center. We collected clinical parameters to evaluate the dynamic changes in the primary tumor. Univariate and multivariate analyses were performed to determine the correlations between these parameters and the pathologic response of the primary tumor. Subsequently, we constructed prediction models for pCR and MPR using multivariate logistic regression. The MPR prediction Model 2 was internally validated using bootstrapping and externally validated using an independent cohort from our center. The univariate logistic analysis revealed significant differences in clinical parameters reflecting tumor regression among patients with varying pathologic responses. The clinical models based on these assessments demonstrated excellent predictive performance, with the training cohort achieving a C-index of 0.879 for pCR and 0.912 for MPR, while the testing cohort also achieved a C-index of 0.912 for MPR. Notably, the MPR prediction Model 2, with a threshold cut-off of 0.74, exhibited 92.7% specificity and greater than 70% sensitivity, indicating a low rate of underestimating residual tumors. In conclusion, our study demonstrated the high accuracy of clinical assessment-based models in pathologic response prediction, aiding in decision-making regarding organ preservation and radiotherapy adjustments after induction immunochemotherapy.

Promoting biomethane production from propionate with Fe2O3@carbon nanotubes composites.

Using a batch anaerobic system constructed with 60 mL serum bottles, potential of a composite material with Fe2O3 nanoparticles decorated on carbon nanotubes (CNTs) to enhance biomethane production was investigated. The composites (Fe2O3@CNTs) with well dispersed Fe2O3 nanoparticles (4.5 nm) were fabricated by a facile thermal decomposition method in a muffle furnace under nitrogen atmosphere. Compared with Fe2O3, Fe2O3@CNTs showed a large specific surface area and good electrical conductivity. Supplementation of Fe2O3@CNTs to the propionate-degrading enrichments enhanced the methane production rate, which was 10.4-fold higher than that in the control experiment without material addition. The addition of Fe2O3@CNTs also not only showed a clearly electrochemical response to flavin and cytochrome C, but also reduced the electron transfer resistance when compared to the control. Comparative analysis showed that Fe2O3 in Fe2O3@CNTs played a key role in initiating electrochemical response and triggering rapid methane production, while CNTs functioned as rapid electron conduits to facilitate electron transfer from iron-reducing bacteria (e.g., Acinetobacter, Syntrophomonas, and Geobacter) to methanogens (e.g. Methanosarcina).

Biomarkers for the prediction of esophageal cancer neoadjuvant chemoradiotherapy response: A systemic review.

Neoadjuvant chemoradiotherapy followed by surgery has been established as the standard treatment for locally advanced esophageal cancer. For patients with complete regression after neoadjuvant chemotherapy, active surveillance rather than planned surgery has been proposed as an organ preservation strategy. Reliable biomarkers to predict chemoradiation response is needed. We first summarized the previous reports of biomarkers with the potential to predict the treatment response of esophageal cancer neoadjuvant chemoradiotherapy. These traditional biomarkers are classified into three groups: genetic biomarkers, RNA biomarkers, and protein biomarkers. We then summarized some special types of biomarkers, including metabolites biomarkers, immune and tumor microenvironment biomarkers, and microbiome biomarkers.