Targeting Synthesis of Diatomic Catalysts by Selective Etching and Sequential Adsorption of Metal Atom.

Diatomic catalysts featuring a tunable structure and synergetic effects hold great promise for various reactions. However, their precise construction with specific configurations and diverse metal combinations is still challenging. Here, a selective etching and metal ion adsorption strategy is proposed to accurately assign a second metal atom (M) geminal to the single atom site (M-N) for constructing diatomic sites (e.

Identification of 3-methoxytyramine as a specific biomarker for beet-sugar-fed honey: A two year surveillance study in South Korea.

Honey is highly vulnerable to food fraud, and there are growing concerns about product authenticity. The commonly used stable carbon isotope ratios in the Calvin (C3) and Hatch-Slack (C4) photosynthesis cycles in plant feed cannot distinguish between beet-sugar-fed honey and natural honey. However, 3-methoxytyramine (3-MT) can be used as specific biomarker for identifying adulteration of beet-sugar-fed honey.

Comparing Efficacy of Steroid Irrigation + Steroid-eluting Sinus Stent Versus Steroid Irrigation Alone for Maintaining Frontal Sinus Patency After Sinus Surgery: A Randomized Controlled Trial.

Background: Steroid rinses and steroid-eluting stents are both options for preventing postoperative stenosis after frontal sinus surgery. This study aimed to assess whether steroid-eluting stents offer added benefit over steroid rinses alone in postoperative healing and long-term frontal sinus patency.

Methods: A randomized controlled trial enrolled patients with CRS with nasal polyps (CRSwNP) who underwent surgery for bilateral and equal frontal sinusitis after failing prior medical therapy.

Utilization of ChatGPT for Rhinology Patient Education: Limitations in a Surgical Sub-Specialty.

Objective: To analyze the accuracy of ChatGPT-generated responses to common rhinologic patient questions.

Methods: Ten common questions from rhinology patients were compiled by a panel of 4 rhinology fellowship-trained surgeons based on clinical patient experience. This panel (Panel 1) developed consensus "expert" responses to each question.

Machine learning of endoscopy images to identify, classify, and segment sinonasal masses.

Background: We developed and assessed the performance of a machine learning model (MLM) to identify, classify, and segment sinonasal masses based on endoscopic appearance.

Methods: A convolutional neural network-based model was constructed from nasal endoscopy images from patients evaluated at an otolaryngology center between 2013 and 2024. Images were classified into four groups: normal endoscopy, nasal polyps, benign, and malignant tumors.