Deep Learning-Based Method for Compound Identification in NMR Spectra of Mixtures.

Nuclear magnetic resonance (NMR) spectroscopy is highly unbiased and reproducible, which provides us a powerful tool to analyze mixtures consisting of small molecules. However, the compound identification in NMR spectra of mixtures is highly challenging because of chemical shift variations of the same compound in different mixtures and peak overlapping among molecules. Here, we present a pseudo-Siamese convolutional neural network method (pSCNN) to identify compounds in mixtures for NMR spectroscopy. A data augmentation method was implemented for the superposition of several NMR spectra sampled from a spectral database with random noises. The augmented dataset was split and used to train, validate and test the pSCNN model. Two experimental NMR datasets (flavor mixtures and additional flavor mixture) were acquired to benchmark its performance in real applications. The results show that the proposed method can achieve good performances in the augmented test set (ACC = 99.80%, TPR = 99.70% and FPR = 0.10%), the flavor mixtures dataset (ACC = 97.62%, TPR = 96.44% and FPR = 2.29%) and the additional flavor mixture dataset (ACC = 91.67%, TPR = 100.00% and FPR = 10.53%). We have demonstrated that the translational invariance of convolutional neural networks can solve the chemical shift variation problem in NMR spectra. In summary, pSCNN is an off-the-shelf method to identify compounds in mixtures for NMR spectroscopy because of its accuracy in compound identification and robustness to chemical shift variation.

RNase H meets molecular beacons: an ultrasensitive fluorometric assay for nucleic acids.

An innovative signal amplification strategy assisted by RNase H is described here for the detection of DNA targets in a universal fashion. A tailor-made RNA molecular beacon (RMB) conjugated with a fluorescence resonance energy transfer (FRET) pair (fluorophore and quencher) was designed, characterized, and combined with the employment of RNase H. Its performance is compared to that of other nucleases including Exonuclease III and T7 exonuclease. Fluorometry, performed best at excitation/emission wavelengths of 490/520 nm, gives an amazingly low detection limit of 23 fM for target DNA. The method was verified by the determination of human hemochromatosis (HFE) gene. It is perceived that the method represents a versatile tool for the detection of a wide range of targets. Graphical Abstract An RNase H-assisted signal amplification (RASA) method for the fluorometric assay of nucleic acids has been developed by using a unique RNA molecular beacon (RNA MB) conjugated with a fluorophore (F) and quencher (Q) pair for signal generation.

Simultaneous targeting PI3K and PERK pathways promotes cell death and improves the clinical prognosis in esophageal squamous carcinoma.

PI3K pathway is an important anti-tumor target, but its effect and mechanism is not clear in esophageal squamous cell carcinoma (ESCC). By analysis of the Cancer Genome Atlas (TCGA) datasets, we found that PI3Ks level were significantly upregulated in human esophageal cancer tissues compared with that in non-cancer tissues. The alteration of PI3K can significantly affect the overall patient survival in ESCC but not in esophageal adenocarcinoma (EAC). We found that the classic PI3K inhibitor LY294002 obviously inhibited the canonical mammalian target of rapamycin (mTOR) pathway and restrained the growth of ESCC with less toxicity to normal cells. Besides, LY294002 inhibited noncanonical PKR-like ER kinase (PERK)/elF2α/ATF4 pathway as well. Both siRNA and the small molecule inhibitor GSK2656157 against PERK/elF2α/ATF4 pathway can significantly inhibit the growth of ESCC. More importantly, GSK2656157 aggravated the inhibitory effect of LY294002 on cell growth, colony formation, and apoptosis induction of ESCC. In addition of dual high expression of PI3K and PERK pathways in the ESCC patients, the difference of overall survival (OS) was more significant than using PI3K alone. These results indicated that dual targeting of PI3K and PERK pathways might improve clinical prognosis and enhance the treatment of ESCC patients.

A Quantum Dot Fluorescence Sensor System Design for Hg²âº Trace Detection.

The detection of Hg²âº ions usually requires large laboratory equipment, which encounters difficulties for rapid field test in most applications. In this paper, we design a reflective sensor for trace Hg²âº analysis based on the fluorescent quenching of Quantum dots, which contains two major modules, i. e. the fluorescent sensing module and the signal processing module. The fluorescence sensing module is composed of a laser source, a light collimated system and a photo-detector, which enables the realization of the fluorescence excitation as well as its detection. The signal processing module realized the further amplification of the detected signal and hereafter the filtering of noises. Furthermore, the Hg²âº concentration will displayed on the QT interface using a Linux embedded system. The sensor system is low cost and small, which makes it available for rapid field test or portable applications. Experimental results show that the sensor has a good linear relationship for the Hg²âº concentration range from 15.0 x 10⁻9 to 1.8 x 10⁻6 mol · L⁻¹. The regression equation is V0/V = 1.309 13 + 3.37c, where c is Hg²âº concentration, and V0 is the voltage value for the blank case. In our work, the linearity is determined as 0. 989 26. The experiments exhibit that Ca²âº, Mn²âº and Pb²âº ions have small influence on the Hg²âº detection, and the interfere of other common ions can be neglected, which indicates a good selectivity of the sensor. Finally, it shows that our sensor has a rapid response time of 35 s and a good repeatability, thus it is potential for field test of trace Hg²âº.

Dihydroartemisinin Modulates Enteric Glial Cell Heterogeneity to Alleviate Colitis.

The precise mechanism underlying the therapeutic effects of dihydroartemisinin (DHA) in alleviating colitis remains incompletely understood. A strong correlation existed between the elevation of glial fibrillary acidic protein (GFAP)+/S100 calcium binding protein B (S100ß)+ enteric glial cells (EGCs) in inflamed colonic tissues and the disruption of the intestinal epithelial barrier (IEB) and gut vascular barrier (GVB) observed in chronic colitis. DHA demonstrated efficacy in restoring the functionality of the dual gut barrier while concurrently attenuating intestinal inflammation. Mechanistically, DHA inhibited the transformation of GFAP+ EGCs into GFAP+/S100ß+ EGCs while promoting the differentiation of GFAP+/S100ß+ EGCs back into GFAP+ EGCs. Furthermore, DHA induced apoptosis in GFAP+/S100ß+ EGCs by inducing cell cycle arrest at the G0/G1 phase. The initial mechanism is further validated that DHA regulates EGC heterogeneity by improving dysbiosis in colitis. These findings underscore the multifaceted therapeutic potential of DHA in ameliorating colitis by improving dysbiosis, modulating EGC heterogeneity, and preserving gut barrier integrity, thus offering promising avenues for novel therapeutic strategies for inflammatory bowel diseases.

Clinical and Gene Features of SARS-CoV-2-Positive Recurrence in Patients Recovered From COVID-19.

There are still frequent reports that a number of recovered coronavirus disease 2019 (COVID-19) patients following discharge have re-detectable positive (RP) results by RT-PCR. Understanding the clinical and molecular characteristics of RP patients may have implications for curbing the COVID-19 pandemic. In this study, 318 COVID-19 convalescent patients, including 59 RP patients and 259 non-RP (NRP) patients, were enrolled. Among RP patients, women accounted for a significantly high proportion (67.8%), and the titers of IgG and IgM antibodies in this group were also significantly high. Differentially expressed genes (DEGs), including 692 upregulated and 383 downregulated genes, overlapped in two public GEO datasets containing RP and NRP blood cell samples. Enrichment analysis indicated that these DEGs were related to several key signaling pathways, such as viral infection, immune activation, and inflammatory responses. Importantly, 59 indicator genes constituting the core network exhibited high diagnostic values and were correlated with markers of different immune cells. Among these, 12 drug-related genes were associated with the RP results. Our work suggests that, in addition to clinically available features, blood cell transcriptome sequencing can be performed to obtain gene signatures for diagnosis of RP patients.

Upregulation of Centromere Proteins as Potential Biomarkers for Esophageal Squamous Cell Carcinoma Diagnosis and Prognosis.

Esophageal squamous cell carcinoma (ESCC) has a high incidence and low survival rate, necessitating the identification of novel specific biomarkers. Centromere-associated proteins (CENPs) have been reported to be biomarkers for many cancers, but their roles in ESCC have seldom been investigated. Here, the potential clinical roles of CENPs in ESCC patients were demonstrated by a systematic bioinformatics analysis. Most CENP-encoding genes were differentially expressed between tumor and normal tissues. CENPA, CENPE, CENPF, CENPI, CENPM, CENPN, CENPQ, and CENPR were upregulated universally in the three datasets. Survival analysis demonstrated that high expression of CENPE and CENPQ was positively correlated with the outcomes of ESCC patients. The CENPE-based forecast model was more accurate than the tumor-node-metastasis (TNM) staging-based model, which was classified as stage I/II vs. III/IV. More importantly, the forecast model based on the commonly upregulated CENPs exhibited a much higher area under the curve (AUC) value (0.855) than the currently known TTL, ZNF750, AC016205.1, and BOLA3 biomarkers. The nomogram model integrating the CENPs, TNM stage, and sex was highly accurate in the prognosis of ESCC patients (AUC = 0.906). Besides, gene set enrichment analysis (GSEA) demonstrated that CENPE expression is significantly correlated with cell cycle, G2/M checkpoint, mitotic spindle, p53, etc. Finally, in validation experiments, we also found that CENPE and CENPQ were significantly overexpressed in esophageal cancer cells. Taken together, these results clearly suggest that CENPs are clinically promising diagnostic and prognostic biomarkers for ESCC patients.

Upregulated expression of MMP family genes is associated with poor survival in patients with esophageal squamous cell carcinoma via regulation of proliferation and epithelial­mesenchymal transition.

Matrix metalloproteinases (MMPs) are involved in the cleavage of several components of the extracellular matrix and serve important roles in tumor growth, metastasis and invasion. Previous studies have focused on the expression of one or several MMPs in esophageal squamous cell carcinoma (ESCC); however, in the present study, the transcriptomics of all 23 MMPs were systematically investigated with a focus on the prognostic value of the combination of MMPs. In this study, 8 overlapping differentially expressed genes of the MMP family were identified based on data obtained from Gene Expression Omnibus and The Cancer Genome Atlas. The prognostic value of these MMPs were investigated; the receiver operating characteristic curves, survival curves and nomograms showed that the combination of 6 selected MMPs possessed a good predictive ability, which was more accurate than the prediction model based on Tumor­Node­Metastasis stage. Gene set enrichment analysis and gene co­expression analysis were performed to investigate the potential mechanism of action of MMPs in ESCC. The MMP family was associated with several signaling pathways, such as epithelial­mesenchymal transition (EMT), Notch, TGF­ß, mTOR and P53. Cell Counting Kit­8, colony formation, wound healing assays and western blotting were used to determine the effect of BB­94, a pan­MMP inhibitor, on proliferation and migration of ESCC cells. BB­94 treatment decreased ESCC cell growth, migration and EMT. Therefore, MMPs may serve both as diagnostic and prognostic biomarkers of ESCC, and MMP inhibition may be a promising preventive and therapeutic strategy for patients with ESCC.

The clinical significance of collagen family gene expression in esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a subtype of esophageal cancer with high incidence and mortality. Due to the poor 5-year survival rates of patients with ESCC, exploring novel diagnostic markers for early ESCC is emergent. Collagen, the abundant constituent of extracellular matrix, plays a critical role in tumor growth and epithelial-mesenchymal transition. However, the clinical significance of collagen genes in ESCC has been rarely studied. In this work, we systematically analyzed the gene expression of whole collagen family in ESCC, aiming to search for ideal biomarkers. METHODS: Clinical data and gene expression profiles of ESCC patients were collected from The Cancer Genome Atlas and the gene expression omnibus databases. Bioinformatics methods, including differential expression analysis, survival analysis, gene sets enrichment analysis (GSEA) and co-expression network analysis, were performed to investigate the correlation between the expression patterns of 44 collagen family genes and the development of ESCC. RESULTS: A total of 22 genes of collagen family were identified as differentially expressed genes in both the two datasets. Among them, COL1A1, COL10A1 and COL11A1 were particularly up-regulated in ESCC tissues compared to normal controls, while COL4A4, COL6A5 and COL14A1 were notably down-regulated. Besides, patients with low COL6A5 expression or high COL18A1 expression showed poor survival. In addition, a 7-gene prediction model was established based on collagen gene expression to predict patient survival, which had better predictive accuracy than the tumor-node-metastasis staging based model. Finally, GSEA results suggested that collagen genes might be tightly associated with PI3K/Akt/mTOR pathway, p53 pathway, apoptosis, cell cycle, etc. CONCLUSION: Several collagen genes could be potential diagnostic and prognostic biomarkers for ESCC. Moreover, a novel 7-gene prediction model is probably useful for predicting survival outcomes of ESCC patients. These findings may facilitate early detection of ESCC and help improves prognosis of the patients.