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Experiments show that the absorption spectrum of the hydrated electron (ehyd-) blue-shifts in electrolyte solutions compared with what is seen in pure water. This shift has been assigned to the ehyd-s competitive ion-pairing interactions with the salt cation relative to the salt anion based on the ions positions on the Hofmeister series. Remarkably, little work has been done investigating the ehyd-s behavior when the salts have chaotropic cations, which should greatly change the ion-pairing interactions given that the ehyd- is a champion chaotrope. In this work, we remedy this by using mixed quantum/classical simulations to analyze the behavior of two different models of the ehyd- in aqueous RbF and RbI electrolyte solutions as a function of salt concentration. We find that the magnitude of the salt-induced spectral blue-shift is determined by a combination of the number of chaotropic Rb+ cations near the ehyd- and the number of salt anions near those cations so that the spectrum of the ehyd- directly reflects its local environment. We also find that the use of a soft-cavity ehyd- model predicts stronger competitive interactions with Rb+ relative to I- than a more traditional hard cavity model, leading to different predicted spectral shifts that should provide a way to distinguish between the two models experimentally. Our simulations predict that at the same concentration, salts with chaotropic cations should produce larger spectral blue-shifts than salts with kosmotropic cations. We also found that at high salt concentrations with chaotropic cations, the predicted blue-shift is greater when the salt anion is kosmotropic instead of chaotropic. Our goal is for this work to inspire experimentalists to make such measurements, which will help provide a spectroscopic means to distinguish between simulations models that predict different hydration structures for the ehyd-.

Radiomics has been applied to predict recurrence in several disease sites, but current approaches are typically restricted to analyzing tumor features, neglecting nontumor information in the rest of the body. The purpose of this work was to develop and validate a model incorporating nontumor radiomics, including whole-body features, to predict treatment outcomes in patients with previously untreated locoregionally advanced cervical cancer. Methods: We analyzed 127 cervical cancer patients treated definitively with chemoradiotherapy and intracavitary brachytherapy. All patients underwent pretreatment whole-body 18F-FDG PET/CT. To quantify effects due to the tumor itself, the gross tumor volume (GTV) was directly contoured on the PET/CT image. Meanwhile, to quantify effects arising from the rest of the body, the planning target volume (PTV) was deformably registered from each planning CT to the PET/CT scan, and a semiautomated approach combining seed-growing and manual contour review generated whole-body muscle, bone, and fat segmentations on each PET/CT image. A total of 965 radiomic features were extracted for GTV, PTV, muscle, bone, and fat. Ninety-five patients were used to train a Cox model of disease recurrence including both radiomic and clinical features (age, stage, tumor grade, histology, and baseline complete blood cell counts), using bagging and split-sample-validation for feature reduction and model selection. To further avoid overfitting, the resulting models were tested for generalization on the remaining 32 patients, by calculating a risk score based on Cox regression and evaluating the c-index (c-index > 0.5 indicates predictive power). Results: Optimal performance was seen in a Cox model including 1 clinical biomarker (whether or not a tumor was stage III-IVA), 2 GTV radiomic biomarkers (PET gray-level size-zone matrix small area low gray level emphasis and zone entropy), 1 PTV radiomic biomarker (major axis length), and 1 whole-body radiomic biomarker (CT bone root mean square). In particular, stratification into high- and low-risk groups, based on the linear risk score from this Cox model, resulted in a hazard ratio of 0.019 (95% CI, 0.004, 0.082), an improvement over stratification based on clinical stage alone, which had a hazard ratio of 0.36 (95% CI, 0.16, 0.83). Conclusion: Incorporating nontumor radiomic biomarkers can improve the performance of prognostic models compared with using only clinical and tumor radiomic biomarkers. Future work should look to further test these models in larger, multiinstitutional cohorts.