Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological ...experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important.
In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively.
The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.
Based on the official data modeling, this paper studies the transmission process of the Corona Virus Disease 2019 (COVID-19). The error between the model and the official data curve is quite small. ...At the same time, it realized forward prediction and backward inference of the epidemic situation, and the relevant analysis help relevant countries to make decisions.
Sepsis is considered to be a systemic inflammatory response, which results in organ dysfunction. LncRNA nuclear-enriched abundant transcript 1 (NEAT1) involved in sepsis progression has been ...reported. However, the underlying mechanism of NEAT1 in sepsis-induced inflammatory response remains to be revealed. In this study, NEAT1 and POU domain class 2 transcription factor 1 (POU2F1) were highly expressed in LPS-induced septic RAW264.7 cells, opposite to miR-31-5p expression. Furthermore, we found that NEAT1 silencing inhibited LPS-induced inflammatory response and cell proliferation, and promoted cell apoptosis. Subsequently, we found that miR-31-5p interacted with NEAT1 and targeted the 3′UTR of POU2F1, and in LPS-induced RAW264.7 cells, the inhibition of NEAT1 silencing was reversed by miR-31-5p knockdown, while POU2F1 downregulation could cover the functions of miR-31-5p knockdown. In a word, this study indicates that NEAT1 inhibits the LPS-induced progression of sepsis in RAW264.7 cells by modulating miR-31-5p/POU2F1 axis, suggesting that NEAT1 will be the potential therapeutic target for sepsis.
When coupled online with mass spectrometry (MS), widely applied water-in-oil droplet-based microfluidics for single cell analysis met problems. For example, the oil phase rumpled the stability, ...efficiency, and accuracy of MS, the conventional interface between MS and the microfluidic chip suffered the low sample introduction efficiency, and the transportation rates sometimes unmatched the readout dwell times for transient signal acquisition. Considering cells are already “droplets” with hydrophilic surface and elastic hydrophobic membrane, we developed an oil-free passive microfluidic system (OFPMS) that consists of alternating straight-curved-straight microchannels and a direct infusion (dI) micronebulizer for inductively coupled plasma quadrupole-based mass spectrometry (ICP-qMS) of lined-up single-cell. OFPMS guarantees exact single cell isolation one by one just using a thermo-decomposable NH4HCO3 buffer, eliminating the use of any oil and incompatible polymer carriers. It is more flexible and facile to adapt to the dwell time of ICP-qMS owing to the adjustable throughput of 400 to 25000 cells/min and the controllable interval time of at least 20 ms between the lined-up adjacent single cells. Quantitative single-cell transportation and high detection efficiency of more than 70% was realized using OFPMS-dI-ICP-qMS exemplified here. Thus, cell-to-cell heterogeneity can be simply uncovered via the determination of metals in the individual cells.
Increasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs. Identifying circRNA-disease associations is of great ...significance to precise diagnosis and treatment of diseases. However, the traditional biological experiment is usually time-consuming and expensive. Hence, it is necessary to develop a computational framework to infer unknown associations between circRNA and disease. In this work, we propose an efficient framework called MSPCD to infer unknown circRNA-disease associations. To obtain circRNA similarity and disease similarity accurately, MSPCD first integrates more biological information such as circRNA-miRNA associations, circRNA-gene ontology associations, then extracts circRNA and disease high-order features by the neural network. Finally, MSPCD employs DNN to predict unknown circRNA-disease associations. Experiment results show that MSPCD achieves a significantly more accurate performance compared with previous state-of-the-art methods on the circFunBase dataset. The case study also demonstrates that MSPCD is a promising tool that can effectively infer unknown circRNA-disease associations.
Hepatitis B virus (HBV) is considered a "metabolic virus" and affects many hepatic metabolic pathways. However, how HBV affects lipid metabolism in hepatocytes remains uncertain yet. Accumulating ...clinical studies suggested that compared to non-HBV-infected controls, chronic HBV infection was associated with lower levels of serum total cholesterol and triglycerides and a lower prevalence of hepatic steatosis. In patients with chronic HBV infection, high ALT level, high body mass index, male gender, or old age was found to be positively correlated with hepatic steatosis. Furthermore, mechanisms of how HBV infection affected hepatic lipid metabolism had also been explored in a number of studies based on cell lines and mouse models. These results demonstrated that HBV replication or expression induced extensive and diverse changes in hepatic lipid metabolism, by not only activating expression of some critical lipogenesis and cholesterolgenesis-related proteins but also upregulating fatty acid oxidation and bile acid synthesis. Moreover, increasing studies found some potential targets to inhibit HBV replication or expression by decreasing or enhancing certain lipid metabolism-related proteins or metabolites. Therefore, in this article, we comprehensively reviewed these publications and revealed the connections between clinical observations and experimental findings to better understand the interaction between hepatic lipid metabolism and HBV infection. However, the available data are far from conclusive, and there is still a long way to go before clarifying the complex interaction between HBV infection and hepatic lipid metabolism.
Outlier detection is of great significance in the domain of data mining. Its task is to find those target points that are not identical to most of the object generation mechanisms. The existing ...algorithms are mainly divided into density-based algorithms and distance-based algorithms. However, both approaches have some drawbacks. The former struggles to handle low-density modes, while the latter cannot detect local outliers. Moreover, the outlier detection algorithm is very sensitive to parameter settings. This paper proposes a new two-parameter outlier detection (TPOD) algorithm. The method proposed in this paper does not need to manually define the number of neighbors, and the introduction of relative distance can also solve the problem of low density and further accurately detect outliers. This is a combinatorial optimization problem. Firstly, the number of natural neighbors is iteratively calculated, and then the local density of the target object is calculated by adaptive kernel density estimation. Secondly, the relative distance of the target points is computed through natural neighbors. Finally, these two parameters are combined to obtain the outlier factor. This eliminates the influence of parameters that require users to determine the number of outliers themselves, namely, the top-n effect. Two synthetic datasets and 17 real datasets were used to test the effectiveness of this method; a comparison with another five algorithms is also provided. The AUC value and F1 score on multiple datasets are higher than other algorithms, indicating that outliers can be found accurately, which proves that the algorithm is effective.
Purpose
The purpose of the study is to demonstrate the value of quantitative amide proton transfer (APT) imaging for differentiating glioma grades and detecting tumor proliferation.
Procedures
This ...study included 32 subjects with 16 low-grade gliomas (LGG) and 16 high-grade gliomas (HGG) confirmed by histopathology. Chemical exchange saturation transfer (CEST) magnetic resonance imaging with APT weighting was performed on a 3 T scanner. After B
0
correction, Z-spectra were fitted with Lorentzian functions corresponding to the upfield semi-solid magnetization transfer and nuclear overhauser enhancement (MT&NOE) effect, the direct saturation (DS) effect, and the downfield APT effect centered at around − 1.5, 0, and + 3.5 ppm, respectively. To compute the Z-spectral fitted APT (fitted_APT) in solid tumor tissue, double-peak histogram fitting of pixel MT&NOE effect from the whole tumor was used to remove necrosis regions. The fitted APT was then compared with the conventional APT based on magnetization transfer ratio asymmetry. Receiver operating characteristic (ROC) analysis was used to compare the performance between Z-spectral fitted contrasts and the con_APT for LGG
versus
HGG differentiation. Additionally, the correlations between the imaging contrasts (fitted_APT, con_APT, and fitted_MT&NOE) and Ki-67 labeling index for tumor proliferation were also evaluated.
Results
Z-spectral fitted_APT shows improved statistical power for differentiating HGG and LGG (7.58 ± 0.99
vs.
6.79 ± 1.05 %,
p
< 0.05) than con_APT (4.34 ± 0.95
vs.
4.05 ± 2.02 %,
p
> 0.05) in solid tumor tissues. Analyses of whole tumor, on the other hand, have less differentiating power for both fitted_APT (
p
from 0.032 to 0.08) and con_APT (
p
from 0.696 to 0.809). Similarly, based on ROC analyses, fitted_APT shows larger area under the curve (AUC = 0.723) than con_APT (AUC = 0.543). The combination of fitted APT, DS, and MT&NOE further improved the specificity (75 %), diagnostic accuracy (78.2 %), and area under the curve (0.758) in differentiating LGG and HGG. Consistently, fitted_APT (
r
= 0.451,
p
= 0.018) is better correlated with Ki-67 than con_APT (
r
= 0.331,
p
= 0.092).
Conclusions
Fitted APT from Z-spectrum improves differentiation of low- and high-grade gliomas and better correlated with tumor proliferation than conventional APT.
In this paper, we consider the clustered path travelling salesman problem. In this problem, we are given a complete graph
G
=
(
V
,
E
)
with an edge weight function
w
satisfying the triangle ...inequality. In addition, the vertex set
V
is partitioned into clusters
V
1
,
…
,
V
k
and
s
,
t
are two given vertices of
G
with
s
∈
V
1
and
t
∈
V
k
. The objective of the problem is to find a minimum Hamiltonian path of
G
from
s
to
t
, where all vertices of each cluster are visited consecutively. In this paper, we deal with the case that the start-vertex and the end-vertex of the path on each cluster are both specified, and for it we provide a polynomial-time approximation algorithm.
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