Evaluating the effectiveness of large language models in patient education for conjunctivitis.

Aims: To evaluate the quality of responses from large language models (LLMs) to patient-generated conjunctivitis questions.

Methods: A two-phase, cross-sectional study was conducted at the Eye and ENT Hospital of Fudan University. In phase 1, four LLMs (GPT-4, Qwen, Baichuan 2 and PaLM 2) responded to 22 frequently asked conjunctivitis questions.

Benchmarking four large language models' performance of addressing Chinese patients' inquiries about dry eye disease: A two-phase study.

Purpose: To evaluate the performance of four large language models (LLMs)-GPT-4, PaLM 2, Qwen, and Baichuan 2-in generating responses to inquiries from Chinese patients about dry eye disease (DED).

Design: Two-phase study, including a cross-sectional test in the first phase and a real-world clinical assessment in the second phase.

Subjects: Eight board-certified ophthalmologists and 46 patients with DED.

N-linked glycosylation of PD-L1/PD-1: an emerging target for cancer diagnosis and treatment.

During tumorigenesis and progression, the immune checkpoint programmed death-1 (PD-1) and its ligand programmed death ligand-1 (PD-L1) play critical roles in suppressing T cell-mediated anticancer immune responses, leading to T-cell exhaustion and subsequent tumor evasion. Therefore, anti-PD-L1/PD-1 therapy has been an attractive strategy for treating cancer over the past decade. However, the overall efficacy of this approach remains suboptimal, revealing an urgent need for novel insights.

Prediction of chemical reaction yields with large-scale multi-view pre-training.

Developing machine learning models with high generalization capability for predicting chemical reaction yields is of significant interest and importance. The efficacy of such models depends heavily on the representation of chemical reactions, which has commonly been learned from SMILES or graphs of molecules using deep neural networks. However, the progression of chemical reactions is inherently determined by the molecular 3D geometric properties, which have been recently highlighted as crucial features in accurately predicting molecular properties and chemical reactions.

Rotational stability of monofocal and diffractive multifocal toric intraocular lens with identical design and material: a propensity score based prospective comparative study.

Purpose: To compare the rotational stability of a monofocal and a diffractive multifocal toric intraocular lens(IOLs) with identical design and material.

Methods: This prospective study enrolled patients who underwent plate-haptic toric IOL (AT TORBI 709 M and AT LISA 909 M) implantation. Propensity score matching (PSM) was performed to balance baseline factors.

miR-1273h-5p protects the human corneal epithelium against UVR-induced oxidative stress and apoptosis: Role of miR-1273h-5p in climatic droplet keratopathy.

Climatic droplet keratopathy (CDK) is characterized by an increased number of oil-like deposits on the most anterior corneal layers, which affect vision and can cause blindness. Environmental ultraviolet radiation (UVR) exposure is a major risk factor, but the underlying mechanism of CDK pathogenesis is unclear. Increasing evidence has demonstrated that miRNAs participate in the cross-talk with oxidative stress.

Inhibition of macrophages inflammasome activation autophagic degradation of HMGB1 by EGCG ameliorates HBV-induced liver injury and fibrosis.

Background: Liver fibrosis is a reversible wound-healing response that can lead to end-stage liver diseases without effective treatment, in which HBV infection is a major cause. However, the underlying mechanisms for the development of HBV-induced fibrosis remains elusive, and efficacious therapies for this disease are still lacking. In present investigation, we investigated the effect and mechanism of green tea polyphenol epigallocatechin-3-gallate (EGCG) on HBV-induced liver injury and fibrosis.

ReLMole: Molecular Representation Learning Based on Two-Level Graph Similarities.

Molecular representation is a critical part of various prediction tasks for physicochemical properties of molecules and drug design. As graph notations are common in expressing the structural information of chemical compounds, graph neural networks (GNNs) have become the mainstream backbone model for learning molecular representation. However, the scarcity of task-specific labels in the biomedical domain limits the power of GNNs.

EmbedDTI: Enhancing the Molecular Representations via Sequence Embedding and Graph Convolutional Network for the Prediction of Drug-Target Interaction.

The identification of drug-target interaction (DTI) plays a key role in drug discovery and development. Benefitting from large-scale drug databases and verified DTI relationships, a lot of machine-learning methods have been developed to predict DTIs. However, due to the difficulty in extracting useful information from molecules, the performance of these methods is limited by the representation of drugs and target proteins.

Evidence for the Involvement of Auxin, Ethylene and ROS Signaling During Primary Root Inhibition of Arabidopsis by the Allelochemical Benzoic Acid.

Allelopathy is mediated by plant-derived secondary metabolites (allelochemicals) which are released by donor plants and affect the growth and development of receptor plants. The plant root is the first organ which senses soil allelochemicals this results in the production of a shorter primary root. However, the mechanisms underlying this process remain elusive.

Auxin signalling of Arachis hypogaea activated by colonization of mutualistic fungus Phomopsis liquidambari enhances nodulation and N -fixation.

Beneficial fungal and rhizobial symbioses share commonalities in phytohormones responses, especially in auxin signalling. Mutualistic fungus Phomopsis liquidambari effectively increases symbiotic efficiency of legume peanut (Arachis hypogaea L.) with another microsymbiont, bradyrhizobium, but the underlying mechanisms are not well understood.