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Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs. We retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs. The WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80). Our study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction. • Whole-slide images integrated with deep learning can predict therapeutic response. • Predictive performance could be improved by combing clinical characteristics. • Deep learning pathological scores could provide more complementary information compare to TILs. [ABSTRACT FROM AUTHOR]

Per- and poly-fluorinated substances (PFASs) are known to have the potential of binding to certain proteins. Protein binding is important to the understanding of cellular toxicities, biotransformation pathways, and the fate of selected PFASs. In this work, we provide a systematic review of major approaches to characterizing PFAS-protein binding, including the techniques based on separation, calorimetry, spectroscopy, mass spectrometry, surface plasmon resonance, molecular docking, and surfactant nature of PFASs. These approaches enable qualitative and/or quantitative characterization of binding parameters, such as the binding affinity constant and binding stoichiometry. For each approach, we review its principles and evaluate inherent strengths and limitations. In addition, the main methodologies for the identification of binding sites and target proteins are reviewed. Through the integrated review and identification of knowledge gaps and challenges, our work will guide the selection of methodologies for better characterizations of the mechanisms, kinetics, and adverse effects of PFAS-protein binding. • A systematic review of major approaches to characterizing PFAS-protein binding. • Approaches for the identification of binding sites and target proteins are reviewed. • The strengths and weaknesses of each method are summarized. • Knowledge gaps and challenges are identified. [ABSTRACT FROM AUTHOR]

Due to the inherent sluggish kinetics of oxygen evolution reaction (OER), designing high-efficient and stable electrocatalysts is essential and urgent for the practical use of energy conversion devices. The transition-metal based layered double hydroxide (LDH) is considered as an alternative candidate with desired OER activity, while its catalytic performance is impeded by the limited active sites, poor conductivity and durability. Herein, the ultrafine and well-dispersed Ru nanoparticles doped NiFe LDH (Ru-NiFe-LDH) is designed as highly efficient electrocatalyst for OER. Benefiting from synergistic electronic modulation, as well as the abundant active interfaces, between Ru nanoparticles and NiFe LDH, Ru-NiFe-LDH shows superior electrocatalytic activity in alkaline electrolyte with a low overpotential of 256 mV at the current density of 50 mA cm−2. Moreover, the Ru-NiFe-LDH exhibits comparable activity toward oxygen reduction (ORR). As a concept verification, a homemade Zn-air battery assembled by Ru-NiFe LDH presents desired performance and stability under practical operating conditions. This work may broaden the family of advanced electrocatalysts for electrochemical energy conversion. The abundant active interfaces were constructed in ultrafine Ru nanoparticles doped NiFe LDH to promote electrocatalytic oxygen evolution in alkaline condition. [Display omitted] [ABSTRACT FROM AUTHOR]