IL18R1-Related Molecules as Biomarkers for Asthma Severity and Prognostic Markers for Idiopathic Pulmonary Fibrosis.

To determine the role of inflammation-related proteins in predicting asthma severity and outcome, 92 inflammation-related proteins were measured in the asthmatic serum using Olink analysis. Different bioinformatics algorithms were developed to cross analyze with the single-cell or transcriptome data sets from the Gene Expression Omnibus database to explore the role of IL18R1 and related genes in asthma and idiopathic pulmonary fibrosis (IPF). Olink identified 52 differentially expressed proteins in asthma. They were strongly linked to the cytokine-cytokine receptor interaction, TNF, and NF-κB signaling pathway. Seven proteins were found in both single-cell RNA and Olink analyses. Among them, IL18R1 was predominantly expressed in mast cells, and the results suggested enhanced communication between mast cells and CD 8+ T cells. IL18R1 was upregulated in serum and induced sputum and bronchoalveolar lavage fluid of patients with uncontrolled or severe asthma. IL18R1 was positively correlated with TNFSF1 and OSM and S100A12. The diagnostic efficacy of these serum IL18R1-related molecules for asthma ranged from 0.839 to 0.921. Moreover, high levels of IL18R1, TNFSF1, OSM, and S100A12 were significantly associated with shorter survival times and worse lung function. IL18R1-related molecules may serve as biomarkers for monitoring uncontrolled or severe asthma and as prognostic markers for IPF.

Exploring the role of NAA40 in immune infiltrates and prognostic prediction in hepatocellular carcinoma.

NAA40 belongs to the N-terminal acetyltransferase (NATs) family, responsible for protein N-terminal modification, and it exerts crucial roles across various cancers. However, its impact on patient prognosis and immune infiltration in hepatocellular carcinoma (HCC) remains elusive. To address this, our study delved into the comprehensive analysis of NAA40 in the context of cancer. Our pan-cancer analysis unveiled elevated NAA40 expression in multiple tumor types, including BLCA, BRCA, CHOL, COAD, ESCA, HNSC, LIHC, LUAD, LUSC, STAD, and THCA. Additionally, through a comprehensive examination across various cancer types within TCGA, we discovered that high NAA40 gene expression correlated with poor prognosis in HCC, pointing toward its role in promoting oncogenesis. Further investigation illuminated the association of increased NAA40 expression with T stage, pathologic stage, tumor status, and histologic grade. Interestingly, we noted a significant inverse correlation between NAA40 expression and the infiltration levels of immune cells, such as DC cells, neutrophils, NK cells, and T cells, in liver cancer. This observation underpins the hypothesis that NAA40 influences HCC development by modulating immune cell infiltration. Functional enrichment analysis provided valuable insights into the pathways influenced by NAA40. Enriched pathways encompassed oxidative phosphorylation, xenobiotic metabolism, bile acid metabolism, fatty acid metabolism, G2M checkpoint, and E2F targets. These findings collectively position NAA40 as a potential biomarker for prognostic prediction and monitoring the effects of immunotherapy in HCC.

Immature platelet fraction levels predict the development of prolonged thrombocytopenia after haematopoietic stem cell transplantation.

Prolonged thrombocytopenia (PT) is a serious complication after haematopoietic stem cell transplantation (HSCT). PT has been suggested to be associated with an increased platelet transfusion requirement and poor outcomes after transplantation. Due to the complex mechanism of PT development, it is difficult to diagnose in the early post-transplant period. Our study aimed to identify an early predictive marker for PT after HSCT. Previous studies showed that the clinical utility of immature platelet fraction (IPF) predicts platelet recovery after chemotherapy and successful engraftment. However, the relationship between IPF and PT after HSCT remains unclear. Fifty-two patients with malignant haematological diseases who underwent HSCT were included in the study. We observed the kinetics of recovery of haematological parameters after transplantation and performed receiver operating characteristics (ROC) curve analysis using data from the 52 HSCT patients. The days to rise and peak of IPF, absolute IPF count (A-IPF) and highly fluorescent IPF (H-IPF) were almost synchronised in all patients, at day 10 and day 15, respectively. The begin to rise levels of IPF, H-IPF and A-IPF were all significantly lower in the PT group than in the good engraftment (GE) group (p=0.0016, p=0.0094, p=0.0086, respectively). The peak levels of IPF were significantly lower in the PT group than the GE group (p=0.0036). However, the peaks of H-IPF and A-IPF were not statistically significant between the two groups (p=0.3383, p=0.0887, respectively). The area under the ROC curve (AUC) of IPF rise was 0.739 (95% CI 0.583-0.896; p<0.05) and the cut-off value was 3.5%, while the AUC of IPF peak was 0.800 (95% CI 0.637-0.962; p<0.01) and the cut-off value was 8.0%. In conclusion, early low levels of IPF predict the development of PT after HSCT. These findings may help improve the management and treatment strategies for PT after HSCT.

Unlocking comprehensive molecular design across all scenarios with large language model and unordered chemical language.

Molecular generation stands at the forefront of AI-driven technologies, playing a crucial role in accelerating the development of small molecule drugs. The intricate nature of practical drug discovery necessitates the development of a versatile molecular generation framework that can tackle diverse drug design challenges. However, existing methodologies often struggle to encompass all aspects of small molecule drug design, particularly those rooted in language models, especially in tasks like linker design, due to the autoregressive nature of large language model-based approaches. To empower a language model for a wider range of molecular design tasks, we introduce an unordered simplified molecular-input line-entry system based on fragments (FU-SMILES). Building upon this foundation, we propose FragGPT, a universal fragment-based molecular generation model. Initially pretrained on extensive molecular datasets, FragGPT utilizes FU-SMILES to facilitate efficient generation across various practical applications, such as de novo molecule design, linker design, R-group exploration, scaffold hopping, and side chain optimization. Furthermore, we integrate conditional generation and reinforcement learning (RL) methodologies to ensure that the generated molecules possess multiple desired biological and physicochemical properties. Experimental results across diverse scenarios validate FragGPT's superiority in generating molecules with enhanced properties and novel structures, outperforming existing state-of-the-art models. Moreover, its robust drug design capability is further corroborated through real-world drug design cases.

FFLOM: A Flow-Based Autoregressive Model for Fragment-to-Lead Optimization.

Recently, deep generative models have been regarded as promising tools in fragment-based drug design (FBDD). Despite the growing interest in these models, they still face challenges in generating molecules with desired properties in low data regimes. In this study, we propose a novel flow-based autoregressive model named FFLOM for linker and R-group design. In a large-scale benchmark evaluation on ZINC, CASF, and PDBbind test sets, FFLOM achieves state-of-the-art performance in terms of validity, uniqueness, novelty, and recovery of the generated molecules and can recover over 92% of the original molecules in the PDBbind test set (with at least five atoms). FFLOM also exhibits excellent potential applicability in several practical scenarios encompassing fragment linking, PROTAC design, R-group growing, and R-group optimization. In all four cases, FFLOM can perfectly reconstruct the ground-truth compounds and generate over 74% of molecules with novel fragments, some of which have higher binding affinity than the ground truth.

Comprehensive analysis of immune-related genes for classification and immune microenvironment of asthma.

OBJECTIVES: To determine the effects of immune-related genes (IRGs) and immune landscape of induced sputum, and develop novel, non-invasive diagnostic molecular therapeutic targets for asthma. METHODS: GSE76262 datasets were used to identify differentially expressed IRGs in asthma. Key IRGs were detected using a protein-protein interaction network. Receiver operating characteristic (ROC) curves were analyzed to investigate the diagnostic value of key IRGs. Gene set enrichment analysis (GSEA) was performed with WebGestalt. Single-sample gene set enrichment analysis and CIBERSORT were used to investigate the immune landscape of induced sputum. RESULTS: A total of 75 potential IRGs were associated with asthma, most of which were involved in the NF-kappa B signaling pathway. ROC analysis showed AUC values for the hub pathway ranging from 0.676-0.767, with moderate diagnostic value for asthma. We also identified IRGs-related cytokines (TNF-α, IL-1ß, IL-8 and IL-6) in 76 asthma and 91 control serum samples to further explore diagnostic efficacy, showing a cumulative AUC of 0.998 for these four related cytokines. Analysis of immune cell infiltration levels showed that follicular helper T cells, activated dendritic cells, activated mast cells and eosinophils were significantly higher and macrophages M0 and macrophages M2 were significantly reduced in sputum from patients with asthma. CONCLUSIONS: IRGs-related cytokines and immune infiltration may contribute to the diagnosis and immune classification of asthma.

The Effect of Elevated Alanine Transaminase on Non-invasive Prenatal Screening Failures.

Objective: To determine the effects of alanine transaminase (ALT) levels on the screening failure rates or "no calls" due to low fetal fraction (FF) to obtain a result in non-invasive prenatal screening (NIPS). Methods: NIPS by sequencing and liver enzyme measurements were performed in 7,910 pregnancies at 12-26 weeks of gestation. Univariate and multivariable regression models were used to evaluate the significant predictors of screening failure rates among maternal characteristics and relevant laboratory parameters. Results: Of the 7,910 pregnancies that met the inclusion criteria, 134 (1.69%) had "no calls." Multiple logistic regression analysis demonstrated that increased body mass index, ALT, prealbumin, albumin levels, and in vitro fertilization (IVF) conception rates were independently associated with screening failures. The test failure rate was higher (4.34 vs. 1.41%; P < 0.001) in IVF pregnancies relative to those with spontaneous conceptions. Meanwhile, the screening failure rates increased with increasing ALT levels from 1.05% at ≤10 U/L to 3.73% at >40 U/L. In particular, IVF pregnancies with an ALT level of >40 U/L had a higher test failure rate (9.52%). Compared with that for an ALT level of ≤10 U/L, the adjusted odds ratio of "no calls" for ALT levels of 10-20, 21-40, and >40 U/L was 1.204 [95% confidence interval (CI), 0.709-2.045], 1.529 (95% CI, 0.865-2.702), and 2.764 (95% CI, 1.500-5.093) (P trend < 0.001), respectively. Conclusions: Increased ALT and IVF conceptions were associated with a higher screening failure rates in NIPS. Therefore, a feasible strategy to adjust these factors to reduce the probability of "no calls" due to low FF would be of great clinical significance.

Lipid Metabolism Affects Fetal Fraction and Screen Failures in Non-invasive Prenatal Testing.

Objective: To assess the association between lipid metabolism and fetal fraction, which is a critical factor in ensuring a highly accurate non-invasive prenatal testing (NIPT), and on the rate of screen failures or "no calls" in NIPT. Methods: A total of 4,514 pregnant women at 12-26 weeks of gestation underwent NIPT sequencing and serum lipid measurements. Univariate analysis and multivariate regression models were used to evaluate the associations of serum lipid concentrations with the fetal fraction and the rate of screen failures. Results: The fetal fraction decreased with increased low-density lipoprotein cholesterol and triglyceride (TG) levels, which were significant factors (standardized coefficient: -0.11). Conversely, high-density lipoprotein cholesterol and the interval between the two tests were positively correlated with the fetal fraction. The median fetal fraction was 10.88% (interquartile range, 8.28-13.89%) and this decreased with TG from 11.56% at ≤1.10 mmol/L to 9.51% at >2.30 mmol/L. Meanwhile, multivariate logistic regression analysis revealed that increased TG levels were independently associated with the risk of screen failures. The rate of screen failures showed an increase with TG levels from 1.20% at ≤1.70 mmol/L to 2.41% at >2.30 mmol/L. Conclusions: The fetal fraction and the rate of screen failures in NIPT are affected by TG levels. Meanwhile, in pregnant women with high TG levels, delaying the time between NIPT blood collections can significantly increase the fetal fraction.