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Abstract 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. Interestingly, increasing evidence indicates that both PD-L1 on tumor cells and PD-1 on tumor-specific T cells undergo extensive N-linked glycosylation, which is essential for the stability and interaction of these proteins, and this modification promotes tumor evasion. In various preclinical models, targeting the N-linked glycosylation of PD-L1/PD-1 was shown to significantly increase the efficacy of PD-L1/PD-1 blockade therapy. Furthermore, deglycosylation of PD-L1 strengthens the signal intensity in PD-L1 immunohistochemistry (IHC) assays, improving the diagnostic and therapeutic relevance of this protein. In this review, we provide an overview of the regulatory mechanisms underlying the N-linked glycosylation of PD-L1/PD-1 as well as the crucial role of N-linked glycosylation in PD-L1/PD-1-mediated immune evasion. In addition, we highlight the promising implications of targeting the N-linked glycosylation of PD-L1/PD-1 in the clinical diagnosis and treatment of cancer. Our review identifies knowledge gaps and sheds new light on the cancer research field.

Abstract 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. Follow-up examinations were conducted at 1 h, 1 day, 3 days, 1 week, 2 weeks, 1 month, and 3 months postoperatively. A linear mixed model of repeated measures was used to investigate the changes in IOL rotation over time. A 2-week timeframe was utilized to assess differences in IOL rotation between the two groups. Result After PSM, a total of 126 eyes were selected from each group for further analysis. Postoperatively, the time course of IOL rotation change in the two groups remained consistent, with the greatest rotation occurring between 1 h and 1 day postoperatively. At the 2-week postoperative mark, the monofocal toric IOL exhibited a higher degree of rotation compared to the multifocal toric IOL (5.40 ± 7.77° vs. 3.53 ± 3.54°, P = 0.015). In lens thickness(LT) ≥ 4.5 mm and white-to-white distance(WTW) ≥ 11.6 mm subgroups, the monofocal toric IOL rotated greater than the multifocal toric IOL (P = 0.026 and P = 0.011, respectively). Conclusion The diffractive multifocal toric IOL exhibits superior rotational stability compared to the monofocal toric IOL, especially in subgroups LT ≥ 4.5 mm and WTW ≥ 11.6 mm. Moreover, the time course of IOL rotation change is consistent for both, with the maximum rotation occurring between 1 h and 1 day postoperatively.

Abstract 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. Additionally, large-scale pre-training has been shown to be essential in enhancing the generalization capability of complex deep learning models. Based on these considerations, we propose the Reaction Multi-View Pre-training (ReaMVP) framework, which leverages self-supervised learning techniques and a two-stage pre-training strategy to predict chemical reaction yields. By incorporating multi-view learning with 3D geometric information, ReaMVP achieves state-of-the-art performance on two benchmark datasets. Notably, the experimental results indicate that ReaMVP has a significant advantage in predicting out-of-sample data, suggesting an enhanced generalization ability to predict new reactions. Scientific Contribution: This study presents the ReaMVP framework, which improves the generalization capability of machine learning models for predicting chemical reaction yields. By integrating sequential and geometric views and leveraging self-supervised learning techniques with a two-stage pre-training strategy, ReaMVP achieves state-of-the-art performance on benchmark datasets. The framework demonstrates superior predictive ability for out-of-sample data and enhances the prediction of new reactions.

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. Methods: The chatbots' responses to Chinese patients' inquiries about DED were assessed by the evaluation. In the first phase, six senior ophthalmologists subjectively rated the chatbots’ responses using a 5-point Likert scale across five domains: correctness, completeness, readability, helpfulness, and safety. Objective readability analysis was performed using a Chinese readability analysis platform. In the second phase, 46 representative patients with DED asked the two language models (GPT-4 and Baichuan 2) that performed best in the in the first phase questions and then rated the answers for satisfaction and readability. Two senior ophthalmologists then assessed the responses across the five domains. Main outcome measures: Subjective scores for the five domains and objective readability scores in the first phase. The patient satisfaction, readability scores, and subjective scores for the five-domains in the second phase. Results: In the first phase, GPT-4 exhibited superior performance across the five domains (correctness: 4.47; completeness: 4.39; readability: 4.47; helpfulness: 4.49; safety: 4.47, p

BackgroundLiver 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.MethodsThe effect of EGCG on liver fibrosis was examined in a recombinant cccDNA (rcccDNA) chronic HBV mouse model by immunohistochemical staining, Sirius red and Masson’s trichrome staining. The functional relevance between high mobility group box 1 (HMGB1) and inflammasome activation and the role of EGCG in it were analyzed by Western blotting. The effect of EGCG on autophagic flux was determined by Western blotting and flow cytometric analysis.ResultsEGCG treatment efficiently was found to alleviate HBV-induced liver injury and fibrosis in a recombinant cccDNA (rcccDNA) chronic HBV mouse model, a proven suitable research platform for HBV-induced fibrosis. Mechanistically, EGCG was revealed to repress the activation of macrophage NLRP3 inflammasome, a critical trigger of HBV-induced liver fibrosis. Further study revealed that EGCG suppressed macrophage inflammasome through downregulating the level of extracellular HMGB1. Furthermore, our data demonstrated that EGCG treatment downregulated the levels of extracellular HMGB1 through activating autophagic degradation of cytoplasmic HMGB1 in hepatocytes. Accordingly, autophagy blockade was revealed to significantly reverse EGCG-mediated inhibition on extracellular HMGB1-activated macrophage inflammasome and thus suppress the therapeutic effect of EGCG on HBV-induced liver injury and fibrosis.ConclusionEGCG ameliorates HBV-induced liver injury and fibrosis via autophagic degradation of cytoplasmic HMGB1 and the subsequent suppression of macrophage inflammasome activation. These data provided a new pathogenic mechanism for HBV-induced liver fibrosis involving the extracellular HMGB1-mediated macrophage inflammasome activation, and also suggested EGCG administration as a promising therapeutic strategy for this disease.

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. This study proposes a new model called EmbedDTI to enhance the representation of both drugs and target proteins, and improve the performance of DTI prediction. For protein sequences, we leverage language modeling for pretraining the feature embeddings of amino acids and feed them to a convolutional neural network model for further representation learning. For drugs, we build two levels of graphs to represent compound structural information, namely the atom graph and substructure graph, and adopt graph convolutional network with an attention module to learn the embedding vectors for the graphs. We compare EmbedDTI with the existing DTI predictors on two benchmark datasets. The experimental results show that EmbedDTI outperforms the state-of-the-art models, and the attention module can identify the components crucial for DTIs in compounds.