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Purpose: Electronic health records (EHRs) are valuable information repositories that offer insights for enhancing clinical research on breast cancer (BC) using real-world data. The objective of this study was to develop a natural language processing (NLP) model specifically designed to extract structured data from BC pathology reports written in natural language., Methods: During the initial phase, the algorithm's development cohort comprised 193 pathology reports from 116 patients with BC from 2012 to 2016. A rule-based NLP algorithm was applied to extract 26 variables for analysis and was compared with the manual extraction of data performed by both a data entry specialist and an oncologist. Following the first approach, the data set was expanded to include 513 reports, and a Named Entity Recognition (NER)-NLP model was trained and evaluated using K-fold cross-validation., Results: The first approach led to a concordance analysis, which revealed an 82.9% agreement between the algorithm and the oncologist, whereas the concordance between the data entry specialist and the oncologist was 90.8%. The second training approach introduced the definition of an NER-NLP model, in which the accuracy showed remarkable potential (97.8%). Notably, the model demonstrated remarkable performance, especially for parameters such as estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2, and Ki-67 (F1-score 1.0)., Conclusion: The present study aligns with the rapidly evolving field of artificial intelligence (AI) applications in oncology, seeking to expedite the development of complex cancer databases and registries. The results of the model are currently undergoing postprocessing procedures to organize the data into tabular structures, facilitating their utilization in real-world clinical and research endeavors.

Hemp ( Cannabis sativa L.) is known to tolerate high concentrations of soil contaminants which however can limit its biomass yield. On the other hand, organic-based amendments such as biochar can immobilize soil contaminants and assist hemp growth in soils contaminated by potentially toxic elements (PTEs), allowing for environmental recovery and income generation, e.g. due to green energy production from plant biomass. The aim of this study was therefore to evaluate the suitability of a softwood-derived biochar to enhance hemp growth and promote the assisted phytoremediation of a PTE-contaminated soil (i.e., Sb 2175 mg kg -1 ; Zn 3149 mg kg -1 ; Pb 403 mg kg -1 ; and Cd 12 mg kg -1 ). Adding 3% (w/w) biochar to soil favoured the reduction of soluble and exchangeable PTEs, decreased soil dehydrogenase activity (by ∼2.08-fold), and increased alkaline phosphomonoesterase and urease activities, basal respiration and soil microbial carbon (by ∼1.18-, 1.22-, 1.22-, and 1.66-fold, respectively). Biochar increased the abundance of selected soil culturable microorganisms, while amplicon sequencing analysis showed a positive biochar impact on α-diversity and the induction of structural changes on soil bacterial community structure. Biochar did not affect root growth of hemp but significantly increased its aboveground biomass by ∼1.67-fold for shoots, and by ∼2-fold for both seed number and weight. Biochar increased the PTEs phytostabilisation potential of hemp with respect to Cd, Pb and Zn, and also stimulated hemp phytoextracting capacity with respect to Sb. Overall, the results showed that biochar can boost hemp yield and its phytoremediation effectiveness in soils contaminated by PTEs providing valuable biomass that can generate profit in economic, environmental and sustainability terms., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)