Alzheimer's disease image classification based on enhanced residual attention network.

With the increasing number of patients with Alzheimer's Disease (AD), the demand for early diagnosis and intervention is becoming increasingly urgent. The traditional detection methods for Alzheimer's disease mainly rely on clinical symptoms, biomarkers, and imaging examinations. However, these methods have limitations in the early detection of Alzheimer's disease, such as strong subjectivity in diagnostic criteria, high detection costs, and high misdiagnosis rates.

Research of Orthodontic Soft Tissue Profile Prediction Based on Conditional Generative Adversarial Networks.

Objective: This study constructed a new conditional generative adversarial network (CGAN) model to predict changes in lateral appearance following orthodontic treatment.

Methods: Lateral cephalometric radiographs of adult patients were obtained before (T1) and after (T2) orthodontic treatment. The expanded dataset was divided into training, validation, and test sets by random sampling in a ratio of 8:1:1.

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Glucagon-like peptide 2 (GLP-2) is a proglucagon-derived peptide released by intestinal endocrine cells. However, its therapeutic potential is limited by rapid inactivation via dipeptidyl peptidase-IV. The elucidation of three-dimensional structures of G-protein-coupled receptors, including GLP-2 receptor, has facilitated the rational design of novel peptide therapeutics.

The GlnE protein of Azorhizobium caulinodans ORS571 plays a crucial role in the nodulation process of the legume host Sesbania rostrata.

The GlnE enzyme, functioning as an adenylyltransferase/adenylyl-removing enzyme, plays a crucial role in reversible adenylylation of glutamine synthetase (GS), which in turn regulates bacterial nitrogen assimilation. Genomic analysis of Azorhizobium caulinodans ORS571 revealed an open reading frame encoding a GlnE protein, whose function in the free-living and symbiotic states remains to be elucidated. A glnE deletion mutant retained high GS activity even under nitrogen-rich conditions.

Switchable Deep Eutectic Solvents for the Green Extraction of Tea Saponins From Camellia Oleifera Seed Meal.

Tea saponin has garnered tremendous interest for its potential use in surfactant and drug synthesis. This research was designed to develop a technique based on pH-responsive switchable deep eutectic solvents (SDESs) for extracting tea saponins from Camellia oleifera seed meal. SDES synthesized from hexanoic acid and triethanolamine (1:1 molar ratio) offered the optimum extractive performance and the optimal conditions were obtained through single-factor experiments: 30 wt% water content in SDES, solid-liquid ratio of 1:30 g/mL, 60°C extraction temperature, 30 min extraction time, and acid volume of 1500 µL.