Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors.

Supramolecular hydrogels derived from nucleosides have been gaining significant attention in the biomedical field due to their unique properties and excellent biocompatibility. However, a major challenge in this field is that there is no model for predicting whether nucleoside derivative will form a hydrogel. Here, we successfully develop a machine learning model to predict the hydrogel-forming ability of nucleoside derivatives. The optimal model with a 71% (95% Confidence Interval, 0.69-0.73) accuracy is established based on a dataset of 71 reported nucleoside derivatives. 24 molecules are selected via the optimal model external application and the hydrogel-forming ability is experimentally verified. Among these, two rarely reported cation-independent nucleoside hydrogels are found. Based on their self-assemble mechanisms, the cation-independent hydrogel is found to have potential applications in rapid visual detection of Ag+ and cysteine. Here, we show the machine learning model may provide a tool to predict nucleoside derivatives with hydrogel-forming ability.

Green finance, economic growth, and carbon emissions: a PVAR analysis.

The study examines the relationship between green finance development, carbon emission intensity, and economic development in China's 30 provinces. The entropy approach is used to calculate the green finance development index, and a panel vector autoregressive model is established using this index along with economic development and carbon emission intensity. The study finds that green finance can promote economic growth and help achieve emission reduction objectives, while increasing carbon emissions can also promote economic growth. The study also highlights regional differences, with the economic growth of the eastern and central regions negatively correlated with carbon emissions after surpassing the inflection point of the environmental Kuznets curve. Additionally, the study suggests that there is still room for growth in green finance in the western region. The findings have important policy implications for China in developing targeted development strategies.

Polo-like kinase (PLK) 5, a new member of the PLK family, serves as a biomarker to indicate anabatic tumor burden and poor prognosis for resectable non-small cell lung cancer.

Objective: A review argues that polo-like kinase 5 (PLK5) may be linked to unfavorable prognosis in non-small cell lung cancer (NSCLC) patients, which contradicts the discoveries from The Human Protein Atlas database (derived from TCGA analysis). This study intended to comprehensively confirm the association of PLK5 with clinical characteristics and prognosis in NSCLC patients. Methods: This two-center, retrospective, cohort study enrolled 210 NSCLC patients receiving surgical resection. PLK5 protein and mRNA were detected by immunohistochemistry and RT-qPCR in tumor and nontumor tissues. Moreover, RNA FPKM data for 994 lung cancer patients were obtained from The Human Protein Atlas database. Results: PLK5 protein was decreased in tumor tissue compared to nontumor tissue (P < 0.001). Additionally, decreased PLK5 protein was linked with increased pathological grade (P = 0.002), lymph node metastasis presence (P = 0.001), elevated tumor-node-metastasis (TNM) stage (P = 0.003), and abnormal cancer antigen 125 (CA125) (P = 0.002). Meanwhile, low PLK5 protein was correlated with shortened disease-free survival (DFS) (P = 0.007) and overall survival (OS) (P = 0.038); further multivariable Cox regression analysis revealed that low PLK5 protein independently predicted unfavorable DFS (hazard ratio = 0.573, P = 0.022). PLK5 mRNA was reduced in tumor tissue compared with nontumor tissue (P < 0.001); its decline was linked with enhanced pathological grade (P = 0.034), climbed TNM stage (P = 0.032), and abnormal CA125 (P = 0.002). Furthermore, low PLK5 mRNA was correlated with unfavorable DFS (P = 0.046). The Human Protein Atlas database also disclosed the link between low PLK5 mRNA and worse OS (P = 0.046). Conclusion: A PLK5 decrement reflects anabatic tumor burden and poor prognosis in NSCLC patients.

Low serum miR-98 as an unfavorable prognostic biomarker in patients with non-small cell lung cancer.

BACKGROUND: Blood-circulating miRNAs have been reported to act as potential biomarkers in various cancers including non-small cell lung cancer (NSCLC). OBJECTIVE: This study was to assess serum miR-98 levels in NSCLC patients and explore its potential prognostic value. METHODS: The relative expression levels of miR-98 were detected by quantitative RT-PCR in the sera of 127 NSCLC patients and 60 healthy controls. RESULTS: Our results showed that serum miR-98 expression was down-regulated in NSCLC patients compared with healthy controls. Receiver operating characteristic (ROC) curve analysis suggested that serum miR-98 could be used as a potential marker in the diagnosis of NSCLC. In addition, decreased serum miR-98 was positively correlated with worse TNM stage, lymph node metastasis, as well as unfavorable overall survival. Multivariate Cox regression analysis confirmed that serum miR-98 expression was an independent prognostic factor for NSCLC. CONCLUSIONS: Therefore, serum miR-98 might be useful as a promising biomarker for prognosis prediction of NSCLC.