Insight of neonicotinoid insecticides: Exploring exposure, mechanisms in non-target organisms, and removal technologies.

Neonicotinoid insecticides (NEOs) have garnered global attention due to their selective toxicity to insects and minimal impact on mammals. However, growing concerns about their extensive use and potential adverse effects on the ecological environment and non-target organisms necessitate further investigation. This study utilized bibliometric tools to analyze Web of Science data from 2003 to 2024, elucidating the current research landscape, identifying key research areas, and forecasting future trends related to NEOs.

Establishment of a rapid and sensitive ic-ELISA for the detection of thiacloprid residues in honey and medicinal herbs using a novel highly specific monoclonal antibody.

Thiacloprid is one of the first generation of neonicotinoid insecticide with a chloropyridine structure like imidacloprid and acetamiprid. Recent studies have revealed its environmental and non-target organism toxicity, leading to restrictions on its use in many countries and regions. Despite limitations, thiacloprid has been detected in various environmental samples, food sources, and biological specimens, posing a significant threat to human health, necessitating advanced detection methods for monitoring.

Training a Deep Contextualized Language Model for International Classification of Diseases, 10th Revision Classification via Federated Learning: Model Development and Validation Study.

Background: The automatic coding of clinical text documents by using the International Classification of Diseases, 10th Revision (ICD-10) can be performed for statistical analyses and reimbursements. With the development of natural language processing models, new transformer architectures with attention mechanisms have outperformed previous models. Although multicenter training may increase a model's performance and external validity, the privacy of clinical documents should be protected.

Successful real-world application of an osteoarthritis classification deep-learning model using 9210 knees-An orthopedic surgeon's view.

This study aimed to evaluate the performance of a deep-learning model to evaluate knee osteoarthritis using Kellgren-Lawrence grading in real-life knee radiographs. A deep convolutional neural network model was trained using 8964 knee radiographs from the osteoarthritis initiative (OAI), including 962 testing set images. Another 246 knee radiographs from the Far Eastern Memorial Hospital were used for external validation.