New G-Triplex DNA Dramatically Activates the Fluorescence of Thioflavin T and Acts as a Turn-On Fluorescent Sensor for Uracil-DNA Glycosylase Activity Detection.

G-triplexes are G-rich oligonucleotides composed of three G-tracts and have absorbed much attention due to their potential biological functions and attractive performance in biosensing. Through the optimization of loop compositions, DNA lengths, and 5'-flanking bases of G-rich sequences, a new stable G-triplex sequence with 14 bases (G3-F15) was discovered to dramatically activate the fluorescence of Thioflavin T (ThT), a water-soluble fluorogenic dye. The fluorescence enhancement of ThT after binding with G3-F15 reached 3200 times, which was the strongest one by far among all of the G-rich sequences. The conformations of G3-F15 and G3-F15/ThT were studied by circular dichroism. The thermal stability measurements indicated that G3-F15 was a highly stable G-triplex structure. The conformations of G3-F15 and G3-F15/ThT in the presence of different metal cations were studied thoroughly by fluorescent spectroscopy, circular dichroism, and nuclear magnetic resonance. Furthermore, using the G3-F15/ThT complex as a fluorescent probe, a robust and simple turn-on fluorescent sensor for uracil-DNA glycosylase activity was developed. This study proposes a new systematic strategy to explore new functional G-rich sequences and their ligands, which will promote their applications in diagnosis, therapy, and biosensing.

Bioinformatics Analysis Reveals a Novel Prognostic Model for Esophageal Squamous Cell Carcinoma.

Background: Esophageal squamous cell carcinoma (ESCC), a gastrointestinal cancer, is associated with poor prognosis. Prognostic models predict the likelihood of disease progression and are important for the management of patients with ESCC. The objective of this study was to develop a prognostic model for ESCC using bioinformatics analysis. Methods: Two transcriptome microarray Gene Expression Omnibus ESCC datasets (GSE53624 and GSE53622) were analyzed using bioinformatics methods. Differentially expressed genes (DEGs) were identified using the R package limma, and genes associated with survival outcomes in both datasets were identified by Kaplan-Meier analysis. Genes with diagnostic or prognostic value were selected for further analysis, and hazard ratios and their relationship with pathological TNM (pTNM) staging were investigated using univariate and multivariate Cox analysis. After selecting the independent factors from pTNM staging, Cox analysis and nomogram plotting were performed. The ability of the model to stratify risk and predict survival was evaluated and compared with the pTNM staging system to determine its potential clinical value. Key genes were analyzed by immunohistochemistry and RT-PCR. Results: Four candidate genes (B3GNT3, MACC1, NELL2, and USH1G) with prognostic value were identified from the two transcriptome microarray datasets. Age, pTNM stage, and B3GNT3, MACC1, and NELL2 were identified as independent factors associated with survival in the multivariate Cox analysis and used to establish a prognostic model. The model demonstrated significantly higher accuracy in predicting 3-year survival than the pTNM staging system and was useful for further risk stratification in patients with ESCC. B3GNT3 was significantly downregulated in ESCC tumor tissues and negatively associated with lymph node metastasis. Bioinformatics analysis indicated that B3GNT3 may play a role in immune regulation by regulating M2 macrophages. Conclusion: This study developed a new prognostic model for ESCC and identified B3GNT3 as a potential biomarker negatively associated with lymph node metastasis, which warrants further validation.

Association between sleep quality and dry eye disease: a literature review and meta-analysis.

OBJECTIVE: The purpose of this article is to systematically review the association between dry eye and sleep quality. METHODS: PubMed, EMBASE, Cochrane, Web of Science, and grey literature databases were searched for observational studies published before April 2023. Meta-analysis was performed using STAT15 software. RESULTS: A total of 21 studies with 419,218 participants were included. The results showed that the dry eye subjects had a worse sleep quality than the healthy population, with poorer subjective sleep quality, longer sleep latency, and a higher risk of unhealthy sleep duration such as insufficient sleep or excessive sleep. The Pittsburgh Sleep Quality Index (PSQI) scores of the dry eye subjects were significantly higher than those of the control subjects (WMD = 1.78, 95%CI: 1.06, 2.50, P < 0.001). The dry eye subjects scored higher than the control subjects in sleep quality, sleep latency, and sleep disturbance in PSQI; there was no difference between the dry eye individuals and control subjects in sleep duration, sleep efficiency, daytime dysfunction, and sleep medication scores. The risk of sleep disorders in the dry eye subjects was significantly higher than that in the non-dry eye subjects (RR = 2.20, 95%CI: 1.78, 2.72, P < 0.001); the risk of insufficient sleep in the dry eye subjects was higher than that in the control subjects (RR = 3.76, 95%CI: 3.15, 4.48, P < 0.001), and the prevalence of excessive sleepiness in dry eye subjects was higher than that in the control subjects (RR = 5.53, 95%CI: 3.83, 7.18, P < 0.001). The ESS scores of the dry eye subjects were significantly higher than those of the control subjects (WMD = 3.02, 95%CI: 2.43, 3.60, P < 0.01). CONCLUSION: Our meta-analysis suggests that individuals with dry eye have a worse sleep quality than the healthy population, with poorer subjective sleep quality, longer sleep latency, and higher risk of unhealthy sleep duration such as insufficient sleep or excessive sleepiness.

Qualitative and Quantitative Analytical Techniques of Nucleic Acid Modification Based on Mass Spectrometry for Biomarker Discovery.

Nucleic acid modifications play important roles in biological activities and disease occurrences, and have been considered as cancer biomarkers. Due to the relatively low amount of nucleic acid modifications in biological samples, it is necessary to develop sensitive and reliable qualitative and quantitative methods to reveal the content of any modifications. In this review, the key processes affecting the qualitative and quantitative analyses are discussed, such as sample digestion, nucleoside extraction, chemical labeling, chromatographic separation, mass spectrometry detection, and data processing. The improvement of the detection sensitivity and specificity of analytical methods based on mass spectrometry makes it possible to study low-abundance modifications and their biological functions. Some typical nucleic acid modifications and their potential as biomarkers are displayed, and efforts to improve diagnostic accuracy are discussed. Future perspectives are raised for this research field.

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study.

Background: Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms. Methods: Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software. Results: A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.