Research Progress on the Cardiotoxicity of EGFR-TKIs in Non-Small Cell Lung Cancer.

OPINION STATEMENT: With the development of molecular biology and histology techniques, targeted therapy for non-small cell lung cancer (NSCLC) has emerged, which is highly effective and has marginal side effects. Epidermal growth factor receptor (EGFR) was the first driver gene discovered, whose three generations of therapeutic use have its characteristics and benefits in clinical practice. However, cardiovascular complications by EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in preclinical studies have been increasingly reported, including heart failure, cardiomyopathy, and QT prolongation, among others. Cardiotoxicity of targeted drugs significantly affects the therapeutic effect of NSCLC and has become the second leading cause of death in NSCLC. The aim of the present review was to recognize the potential cardiotoxicity of third-generation targeted drugs in the treatment of NSCLC and their associated mechanisms to help clinicians identify and prevent it early in the treatment, minimize the cardiotoxicity of targeted drugs, and improve the therapeutic effect of patients.

Sentiment and semantic analysis: Urban quality inference using machine learning algorithms.

Sustainable urban transformation requires comprehensive knowledge about the built environment, including people's perceptions, use of sites, and wishes. Qualitative interviews are conducted to understand better people's opinions about a specific topic or location. This study explores the automatization of the interview coding process by investigating how state-of-the-art natural language processing techniques classify sentiment and semantic orientation from interviews transcribed in Swedish. For the sentiment analysis, the Swedish bidirectional encoder representations from transformers (BERT) model KB-BERT was used to perform a multi-class classification task on a text sentence level into three different classes: positive, negative, and neutral. Named entity recognition (NER) and string search were used for the semantic analysis to perform multi-label classification to match domain-related topics to the sentence. The models were trained and evaluated on partially annotated datasets. The results demonstrate that the implemented deep learning techniques are a possible and promising solution to achieve the stated goal.