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.

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges.

Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.

Deep learning derived automated ASPECTS on non-contrast CT scans of acute ischemic stroke patients.

Ischemic stroke is the most common type of stroke, ranked as the second leading cause of death worldwide. The Alberta Stroke Program Early CT Score (ASPECTS) is considered as a systematic method of assessing ischemic change on non-contrast CT scans (NCCT) of acute ischemic stroke (AIS) patients, while still suffering from the requirement of experts' experience and also the inconsistent results between readers. In this study, we proposed an automated ASPECTS method to utilize the powerful learning ability of neural networks for objectively scoring CT scans of AIS patients. First, we proposed to use the CT perfusion (CTP) from one-stop stroke imaging to provide the golden standard of ischemic regions for ASPECTS scoring. Second, we designed an asymmetry network to capture features when comparing the left and right sides for each ASPECTS region to estimate its ischemic status. Third, we performed experiments in a large main dataset of 870 patients, as well as an independent testing dataset consisting of 207 patients with radiologists' scorings. Experimental results show that our network achieved remarkable performance, as sensitivity and accuracy of 93.7 and 92.4% in the main dataset, and 95.5 and 91.3% in the independent testing dataset, respectively. In the latter dataset, our analysis revealed a high positive correlation between the ASPECTS score and the prognosis of patients in 90DmRs. Also, we found ASPECTS score is a good indicator of the size of CTP core volume of an infraction. The proposed method shows its potential for automated ASPECTS scoring on NCCT images.

The aptamer DNA-templated fluorescence silver nanoclusters: ATP detection and preliminary mechanism investigation.

Two general and reliable fluorescence sensors were proposed in this work utilizing aptamer DNA-templated silver nanoclusters (Ag NCs). Both DNA-AgNCs could be used for label-free detecting of ATP with the limits of detection of 0.44 and 0.65mM. One of them was further applied to monitor the activity of adenosine deaminase (ADA). In our effort to elucidate the light-up mechanism, we studied a total of six Ag NCs prepared by different DNA sequences, and found that they showed different sensitivity to ATP. Both BT3T3- and BT3T3(R)-templated Ag NCs were chose to make particular studies by UV-vis, TEM, fluorescence, and TCSPC methods. The results showed that when DNA-Ag NCs was kept for 1.5h and presented a strong fluorescence, the addition of ATP failed to cause a large change of fluorescence intensity; on the contrary, after Ag NCs was kept for 24h and emitted a weak fluorescence, adding ATP was able to result in the large fluorescence enhanced of 43 and 33 times for BT3T3- and BT3T3(R)-templated Ag NCs, respectively. The possible mechanism was also suggested that ATP binding to aptamer segment of template induced the change of the DNA secondary structure, which made the aggregated Ag nanoparticles disperse into Ag NCs with an average diameter of about 2nm that were responsible for the large fluorescence increase. Moreover, ATP could protect the fluorescence intensity of BT3T3(R)-templated Ag NCs from quenching for at least 9h.