Models and Biomarkers for Local Response Prediction in Early-Stage and Oligometastatic Non-small Cell Lung Cancer Patients Treated With Stereotactic Body Radiation Therapy Using Machine Learning.

Background A minority of patients receiving stereotactic body radiation therapy (SBRT) for non-small cell lung cancer (NSCLC) are not good responders. Radiomic features can be used to generate predictive algorithms and biomarkers that can determine treatment outcomes and stratify patients to their therapeutic options. This study investigated and attempted to validate the radiomic and clinical features obtained from early-stage and oligometastatic NSCLC patients who underwent SBRT, to predict local response.

miR-92a-3p regulates egg fertilization through ribogenesis in the invasive fall armyworm Spodoptera frugiperda.

Understanding the epigenetic molecular mechanisms (EMMs) of reproduction is crucial for developing advanced and targeted control strategies for Spodoptera frugiperda. Differential expression analysis revealed 11 known miRNAs with varying expression levels, including nine upregulated and two downregulated miRNAs, in virgin females compared with males. The predictive analysis identified 426 target genes for these miRNAs, with ribogenesis highlighted as a key process in oogenesis and egg fertilization.

Intrusion detection system for V2X communication in VANET networks using machine learning-based cryptographic protocols.

Vehicle-to-everything (V2X) communication has many benefits. It improves fuel efficiency, road safety, and traffic management. But it raises privacy and security concerns.

Radiomics As Biomarkers for the Treatment of Non-small Cell Lung Cancer With Stereotactic Body Radiation Therapy: A Review of Concepts.

Stereotactic body radiation therapy (SBRT) is currently the alternative for inoperable early-stage and oligometastatic non-small cell lung cancer (NSCLC) patients. While most patients are good responders among this specific group, some patients do not experience the benefits of this treatment. Even though physicians use clinical variables and semantic radiological features to make treatment decisions, medical images contain a wealth of personalized pathophysiological information that can be extracted and used for clinical decision support systems.

Suitability of large language models for extraction of high-quality chemical reaction dataset from patent literature.

With the advent of artificial intelligence (AI), it is now possible to design diverse and novel molecules from previously unexplored chemical space. However, a challenge for chemists is the synthesis of such molecules. Recently, there have been attempts to develop AI models for retrosynthesis prediction, which rely on the availability of a high-quality training dataset.