Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and lenvatinib; however, the rise of drug resistance and the high mortality rate necessitate the continuous discovery of effective targeting agents. To discover novel anti-HCC compounds, we first constructed a deep learning-based chemical representation model to screen more than 6 million compounds in the ZINC15 drug-like library. We successfully identified LGOd1 as a novel anticancer agent with a characteristic levoglucosenone (LGO) scaffold. The mechanistic studies revealed that LGOd1 treatment leads to HCC cell death by interfering with cellular copper homeostasis, which is similar to a recently reported copper-dependent cell death named cuproptosis. While the prototypical cuproptosis is brought on by copper ionophore-induced copper overload, mechanistic studies indicated that LGOd1 does not act as a copper ionophore, but most likely by interacting with the copper chaperone protein CCS, thus LGOd1 represents a potentially new class of compounds with unique cuproptosis-inducing property. In summary, our findings highlight the critical role of bioavailable copper in the regulation of cell death and represent a novel route of cuproptosis induction.

New insights into the oscillations of the nucleon electromagnetic form factors

The electromagnetic form factors of the proton and the neutron in the timelike region are investigated. Electron-positron annihilation into antinucleon-nucleon (N¯N) pairs is treated in distorted wave Born approximation, including the final-state interaction in the N¯N system. The latter is obtained by a Lippmann-Schwinger equation for N¯N potentials derived within SU(3) chiral effective field theory. By fitting to the phase shifts and (differential) cross section data, a high quality description is achieved. With these amplitudes, the oscillations of the electromagnetic form factors of the proton and the neutron are studied. It is found that each of them can be described by two fractional oscillators. One is characterized as "overdamped" and dominates near the threshold, while the other is "underdamped" and plays an important role in the high-energy region. These two oscillators are essential to understand the distributions of polarized electric charges induced by hard photons for the nucleons.

ARGLRR: A Sparse Low-Rank Representation Single-Cell RNA-Sequencing Data Clustering Method Combined with a New Graph Regularization

The development of single-cell transcriptome sequencing technologies has opened new ways to study biological phenomena at the cellular level. A key application of such technologies involves the employment of single-cell RNA sequencing (scRNA-seq) data to identify distinct cell types through clustering, which in turn provides evidence for revealing heterogeneity. Despite the promise of this approach, the inherent characteristics of scRNA-seq data, such as higher noise levels and lower coverage, pose major challenges to existing clustering methods and compromise their accuracy. In this study, we propose a method called Adjusted Random walk Graph regularization Sparse Low-Rank Representation (ARGLRR), a practical sparse subspace clustering method, to identify cell types. The fundamental low-rank representation (LRR) model is concerned with the global structure of data. To address the limited ability of the LRR method to capture local structure, we introduced adjusted random walk graph regularization in its framework. ARGLRR allows for the capture of both local and global structures in scRNA-seq data. Additionally, the imposition of similarity constraints into the LRR framework further improves the ability of the proposed model to estimate cell-to-cell similarity and capture global structural relationships between cells. ARGLRR surpasses other advanced comparison approaches on nine known scRNA-seq data sets judging by the results. In the normalized mutual information and Adjusted Rand Index metrics on the scRNA-seq data sets clustering experiments, ARGLRR outperforms the best-performing comparative method by 6.99% and 5.85%, respectively. In addition, we visualize the result using Uniform Manifold Approximation and Projection. Visualization results show that the usage of ARGLRR enhances the separation of different cell types within the similarity matrix.

[Two cases of airway dysfunction related to diacetyl exposure]

Occupational exposure to diacetyl can lead to bronchiolitis obliterans. In this paper, two patients with severe obstructive ventilation disorder who were exposed to diacetyl at a fragrance and flavours factory were analyzed. The clinical manifestations were cough and shortness of breath. One of them showed Mosaic shadows and uneven perfusion in both lungs on CT, while the other was normal. Field investigation found that 4 of the 8 workers in the factory were found to have obstructive ventilation disorder, and 2 had small airway dysfunction. This paper summarizes the diagnostic process of patients in order to improve the understanding of airway dysfunction caused by occupational exposure to diacetyl and promote the development of relevant standards.

Single-cell transcriptomic dissection of the cellular and molecular events underlying the triclosan-induced liver fibrosis in mice

BACKGROUND

Triclosan [5-chloro-2-(2,4-dichlorophenoxy) phenol, TCS], a common antimicrobial additive in many personal care and health care products, is frequently detected in human blood and urine. Therefore, it has been considered an emerging and potentially toxic pollutant in recent years. Long-term exposure to TCS has been suggested to exert endocrine disruption effects, and promote liver fibrogenesis and tumorigenesis. This study was aimed at clarifying the underlying cellular and molecular mechanisms of hepatotoxicity effect of TCS at the initiation stage.

METHODS

C57BL/6 mice were exposed to different dosages of TCS for 2 weeks and the organ toxicity was evaluated by various measurements including complete blood count, histological analysis and TCS quantification. Single cell RNA sequencing (scRNA-seq) was then carried out on TCS- or mock-treated mouse livers to delineate the TCS-induced hepatotoxicity. The acquired single-cell transcriptomic data were analyzed from different aspects including differential gene expression, transcription factor (TF) regulatory network, pseudotime trajectory, and cellular communication, to systematically dissect the molecular and cellular events after TCS exposure. To verify the TCS-induced liver fibrosis, the expression levels of key fibrogenic proteins were examined by Western blotting, immunofluorescence, Masson's trichrome and Sirius red staining. In addition, normal hepatocyte cell MIHA and hepatic stellate cell LX-2 were used as in vitro cell models to experimentally validate the effects of TCS by immunological, proteomic and metabolomic technologies.

RESULTS

We established a relatively short term TCS exposure murine model and found the TCS mainly accumulated in the liver. The scRNA-seq performed on the livers of the TCS-treated and control group profiled the gene expressions of > 76,000 cells belonging to 13 major cell types. Among these types, hepatocytes and hepatic stellate cells (HSCs) were significantly increased in TCS-treated group. We found that TCS promoted fibrosis-associated proliferation of hepatocytes, in which Gata2 and Mef2c are the key driving TFs. Our data also suggested that TCS induced the proliferation and activation of HSCs, which was experimentally verified in both liver tissue and cell model. In addition, other changes including the dysfunction and capillarization of endothelial cells, an increase of fibrotic characteristics in B plasma cells, and M2 phenotype-skewing of macrophage cells, were also deduced from the scRNA-seq analysis, and these changes are likely to contribute to the progression of liver fibrosis. Lastly, the key differential ligand-receptor pairs involved in cellular communications were identified and we confirmed the role of GAS6_AXL interaction-mediated cellular communication in promoting liver fibrosis.

CONCLUSIONS

TCS modulates the cellular activities and fates of several specific cell types (including hepatocytes, HSCs, endothelial cells, B cells, Kupffer cells and liver capsular macrophages) in the liver, and regulates the ligand-receptor interactions between these cells, thereby promoting the proliferation and activation of HSCs, leading to liver fibrosis. Overall, we provide the first comprehensive single-cell atlas of mouse livers in response to TCS and delineate the key cellular and molecular processes involved in TCS-induced hepatotoxicity and fibrosis.

[Re-recognition of methotrexate monotherapy in rheumatoid arthritis]

With the in-depth research on treatment drugs of rheumatoid arthritis (RA), a variety of disease modifying anti-rheumatic drugs (DMARDs) have been approved for the treatment of RA. By now methotrexate (MTX) is still the anchor drug for RA treatment. The 2019 European League Against Rheumatism (EULAR) and 2021 American College of Rheumatology (ACR) recommendations for RA management both point out that for newly diagnosed RA patients, MTX monotherapy should be used as the first choice, which shows the importance of MTX monotherapy in DMARDs naïve RA. This paper mainly reviews the relevant researches on MTX in the treatment of RA in recent years, especially the relevant literature on the use of MTX monotherapy in initial treatment of RA. We summarize the development history, efficacy, treatment and adverse reactions of MTX to recognize the importance of MTX in the treatment of RA and the feasibility of MTX monotherapy.

Automatic Epileptic Seizure Detection Using Graph-Regularized Non-Negative Matrix Factorization and Kernel-Based Robust Probabilistic Collaborative Representation

Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development. An automatic detection of epileptic seizure method based on kernel-based robust probabilistic collaborative representation (ProCRC) combined with graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The raw EEG signals are pre-processed through the wavelet transform to obtain time-frequency distribution of EEG signals as preliminary feature information and GNMF is further employed for dimension reduction, retaining and enhancing the productive feature information of EEG signals. Then, the test sample is represented using robust ProCRC that can decide whether the testing sample belongs to each class (seizure or non-seizure) by jointly maximizing the likelihood. In addition, the kernel trick is applied to improve the separability of non-linear high dimensional EEG signals in robust ProCRC. Finally, post-processing techniques are introduced to generate more accurate and reliable results. The average epoch-based sensitivity of 96.48%, event-based sensitivity of 93.65% and specificity of 98.55% are acquired in this method, which is evaluated on the public Freiburg EEG database.

Identification of antimalarial targets of chloroquine by a combined deconvolution strategy of ABPP and MS-CETSA

BACKGROUND

Malaria is a devastating infectious disease that disproportionally threatens hundreds of millions of people in developing countries. In the history of anti-malaria campaign, chloroquine (CQ) has played an indispensable role, however, its mechanism of action (MoA) is not fully understood.

METHODS

We used the principle of photo-affinity labeling and click chemistry-based functionalization in the design of a CQ probe and developed a combined deconvolution strategy of activity-based protein profiling (ABPP) and mass spectrometry-coupled cellular thermal shift assay (MS-CETSA) that identified the protein targets of CQ in an unbiased manner in this study. The interactions between CQ and these identified potential protein hits were confirmed by biophysical and enzymatic assays.

RESULTS

We developed a novel clickable, photo-affinity chloroquine analog probe (CQP) which retains the antimalarial activity in the nanomole range, and identified a total of 40 proteins that specifically interacted and photo-crosslinked with CQP which was inhibited in the presence of excess CQ. Using MS-CETSA, we identified 83 candidate interacting proteins out of a total of 3375 measured parasite proteins. At the same time, we identified 8 proteins as the most potential hits which were commonly identified by both methods.

CONCLUSIONS

We found that CQ could disrupt glycolysis and energy metabolism of malarial parasites through direct binding with some of the key enzymes, a new mechanism that is different from its well-known inhibitory effect of hemozoin formation. This is the first report of identifying CQ antimalarial targets by a parallel usage of labeled (ABPP) and label-free (MS-CETSA) methods.

Celastrol mitigates inflammation in sepsis by inhibiting the PKM2-dependent Warburg effect

BACKGROUND

Sepsis involves life-threatening organ dysfunction and is caused by a dysregulated host response to infection. No specific therapies against sepsis have been reported. Celastrol (Cel) is a natural anti-inflammatory compound that shows potential against systemic inflammatory diseases. This study aimed to investigate the pharmacological activity and molecular mechanism of Cel in models of endotoxemia and sepsis.

METHODS

We evaluated the anti-inflammatory efficacy of Cel against endotoxemia and sepsis in mice and macrophage cultures treated with lipopolysaccharide (LPS). We screened for potential protein targets of Cel using activity-based protein profiling (ABPP). Potential targets were validated using biophysical methods such as cellular thermal shift assays (CETSA) and surface plasmon resonance (SPR). Residues involved in Cel binding to target proteins were identified through point mutagenesis, and the functional effects of such binding were explored through gene knockdown.

RESULTS

Cel protected mice from lethal endotoxemia and improved their survival with sepsis, and it significantly decreased the levels of pro-inflammatory cytokines in mice and macrophages treated with LPS (P < 0.05). Cel bound to Cys424 of pyruvate kinase M2 (PKM2), inhibiting the enzyme and thereby suppressing aerobic glycolysis (Warburg effect). Cel also bound to Cys106 in high mobility group box 1 (HMGB1) protein, reducing the secretion of inflammatory cytokine interleukin (IL)-1β. Cel bound to the Cys residues in lactate dehydrogenase A (LDHA).

CONCLUSION

Cel inhibits inflammation and the Warburg effect in sepsis via targeting PKM2 and HMGB1 protein.

Predicting miRNA-disease associations via layer attention graph convolutional network model

Background

MiRNA is a class of non-coding single-stranded RNA molecules with a length of approximately 22 nucleotides encoded by endogenous genes, which can regulate the expression of other genes. Therefore, it is very important to predict the associations between miRNA and disease. Predecessors developed a new prediction method of drug-disease association, and it achieved good results.

Methods

In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. First, we integrate three associations into a heterogeneous network, such as the known miRNA-disease association, miRNA-miRNA similarities and disease-disease similarities, next we apply graph convolution network to learn the embedding of miRNA and disease. We use an attention mechanism to combine embedding from multiple convolution layers. Unobserved miRNA-disease associations are scored based on integrated embedding.

Results

After fivefold cross-validations, the value of AUC is reached 0.9091, which is higher than other prediction methods and baseline methods.

Conclusions

In this paper, we introduced the method of LAGCN to identify potential miRNA-disease associations. LAGCN has achieved good performance in predicting miRNA-disease associations, and it is superior to other association prediction methods and baseline methods.

[Clinical analysis of 10 cases with extramedullary plasmacytoma of the head and neck]

Objective: To explore the clinical characteristics, treatment methods and outcomes of extramedullary plasmacytoma of the head and neck. Methods: A retrospective analysis was conducted on 10 cases with extramedullary plasmacytoma of the head and neck who were admitted to Henan Tumor Hospital from January 2005 to January 2020. Among the 10 patients, 6 were male and 4 were female. The average age at diagnosis was 56.3 years old (34-74 years old). Among them, 3 cases were located in the nasal cavity, 2 cases in the nasopharynx, 1 case in the sinuses, 2 cases in the larynx, 1 case in the oropharynx, and 1 case in the cervical lymph nodes. Treatments were administered according to tumor size and resection extent. Complete surgical excision (negative margins) was preferred, followed by adjuvant radiotherapy or radiotherapy alone. The clinical characteristics, diagnosis, treatment and prognosis of EMP were analyzed. Results: The patients' symptoms were not specific, frequently with local obstruction symptom and localized masses. All patients were confirmed pathologically as suffering from monoclonal plasmacytoma, with negative bone marrow biopsy and negative skeletal survey. Five patients received surgery, 3 received radiotherapy, and 2 received surgery with additional radiation. The follow-up time was 16-125 months, with a median of 92 months. Two patients developed into multiple myeloma. One patient who received radiotherapy after surgery relapsed after 7 years of follow-up and again received surgical treatment, with no evidence of second recurrence. The remaining patients had no recurrence or progression. Conclusion: Extramedullary plasmacytoma of the head and neck has a good prognosis. Surgical treatment can be considered for completely resectable lesions.

Multi-view manifold regularized compact low-rank representation for cancer samples clustering on multi-omics data

BACKGROUND

The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method.

RESULTS

In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method.

CONCLUSIONS

Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods.

A review on partial-wave dynamics with chiral effective field theory and dispersion relation

The description of strong interaction physics of low-lying resonances is out of the valid range of perturbative QCD. Chiral effective field theories (EFTs) have been developed to tackle the issue. Partial wave dynamics is the systematic tool to decode the underlying physics and reveal the properties of those resonances. It is extremely powerful and helpful for our understanding of the non-perturbative regime, especially when dispersion techniques are utilized simultaneously. Recently, plenty of exotic/ordinary hadrons have been reported by experiment collaborations, e.g. LHCb, Belle, and BESIII, etc. In this review, we summarize the recent progress on the applications of partial wave dynamics combined with chiral EFTs and dispersion relations, on related topics, with emphasis onππ,πK,πNandK̄Nscatterings.

IPCARF: improving lncRNA-disease association prediction using incremental principal component analysis feature selection and a random forest classifier

Background

Identifying lncRNA-disease associations not only helps to better comprehend the underlying mechanisms of various human diseases at the lncRNA level but also speeds up the identification of potential biomarkers for disease diagnoses, treatments, prognoses, and drug response predictions. However, as the amount of archived biological data continues to grow, it has become increasingly difficult to detect potential human lncRNA-disease associations from these enormous biological datasets using traditional biological experimental methods. Consequently, developing new and effective computational methods to predict potential human lncRNA diseases is essential.

Results

Using a combination of incremental principal component analysis (IPCA) and random forest (RF) algorithms and by integrating multiple similarity matrices, we propose a new algorithm (IPCARF) based on integrated machine learning technology for predicting lncRNA-disease associations. First, we used two different models to compute a semantic similarity matrix of diseases from a directed acyclic graph of diseases. Second, a characteristic vector for each lncRNA-disease pair is obtained by integrating disease similarity, lncRNA similarity, and Gaussian nuclear similarity. Then, the best feature subspace is obtained by applying IPCA to decrease the dimension of the original feature set. Finally, we train an RF model to predict potential lncRNA-disease associations. The experimental results show that the IPCARF algorithm effectively improves the AUC metric when predicting potential lncRNA-disease associations. Before the parameter optimization procedure, the AUC value predicted by the IPCARF algorithm under 10-fold cross-validation reached 0.8529; after selecting the optimal parameters using the grid search algorithm, the predicted AUC of the IPCARF algorithm reached 0.8611.

Conclusions

We compared IPCARF with the existing LRLSLDA, LRLSLDA-LNCSIM, TPGLDA, NPCMF, and ncPred prediction methods, which have shown excellent performance in predicting lncRNA-disease associations. The compared results of 10-fold cross-validation procedures show that the predictions of the IPCARF method are better than those of the other compared methods.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04104-9.

Joint Lp-Norm and L2,1-Norm Constrained Graph Laplacian PCA for Robust Tumor Sample Clustering and Gene Network Module Discovery

The dimensionality reduction method accompanied by different norm constraints plays an important role in mining useful information from large-scale gene expression data. In this article, a novel method named Lp-norm and L2,1-norm constrained graph Laplacian principal component analysis (PL21GPCA) based on traditional principal component analysis (PCA) is proposed for robust tumor sample clustering and gene network module discovery. Three aspects are highlighted in the PL21GPCA method. First, to degrade the high sensitivity to outliers and noise, the non-convex proximal Lp-norm (0 < p < 1)constraint is applied on the loss function. Second, to enhance the sparsity of gene expression in cancer samples, the L2,1-norm constraint is used on one of the regularization terms. Third, to retain the geometric structure of the data, we introduce the graph Laplacian regularization item to the PL21GPCA optimization model. Extensive experiments on five gene expression datasets, including one benchmark dataset, two single-cancer datasets from The Cancer Genome Atlas (TCGA), and two integrated datasets of multiple cancers from TCGA, are performed to validate the effectiveness of our method. The experimental results demonstrate that the PL21GPCA method performs better than many other methods in terms of tumor sample clustering. Additionally, this method is used to discover the gene network modules for the purpose of finding key genes that may be associated with some cancers.

Sparse robust graph-regularized non-negative matrix factorization based on correntropy

Non-negative Matrix Factorization (NMF) is a popular data dimension reduction method in recent years. The traditional NMF method has high sensitivity to data noise. In the paper, we propose a model called Sparse Robust Graph-regularized Non-negative Matrix Factorization based on Correntropy (SGNMFC). The maximized correntropy replaces the traditional minimized Euclidean distance to improve the robustness of the algorithm. Through the kernel function, correntropy can give less weight to outliers and noise in data but give greater weight to meaningful data. Meanwhile, the geometry structure of the high-dimensional data is completely preserved in the low-dimensional manifold through the graph regularization. Feature selection and sample clustering are commonly used methods for analyzing genes. Sparse constraints are applied to the loss function to reduce matrix complexity and analysis difficulty. Comparing the other five similar methods, the effectiveness of the SGNMFC model is proved by selection of differentially expressed genes and sample clustering experiments in three The Cancer Genome Atlas (TCGA) datasets.

Multi-cancer samples clustering via graph regularized low-rank representation method under sparse and symmetric constraints

Background

Identifying different types of cancer based on gene expression data has become hotspot in bioinformatics research. Clustering cancer gene expression data from multiple cancers to their own class is a significance solution. However, the characteristics of high-dimensional and small samples of gene expression data and the noise of the data make data mining and research difficult. Although there are many effective and feasible methods to deal with this problem, the possibility remains that these methods are flawed.

Results

In this paper, we propose the graph regularized low-rank representation under symmetric and sparse constraints (sgLRR) method in which we introduce graph regularization based on manifold learning and symmetric sparse constraints into the traditional low-rank representation (LRR). For the sgLRR method, by means of symmetric constraint and sparse constraint, the effect of raw data noise on low-rank representation is alleviated. Further, sgLRR method preserves the important intrinsic local geometrical structures of the raw data by introducing graph regularization. We apply this method to cluster multi-cancer samples based on gene expression data, which improves the clustering quality. First, the gene expression data are decomposed by sgLRR method. And, a lowest rank representation matrix is obtained, which is symmetric and sparse. Then, an affinity matrix is constructed to perform the multi-cancer sample clustering by using a spectral clustering algorithm, i.e., normalized cuts (Ncuts). Finally, the multi-cancer samples clustering is completed.

Conclusions

A series of comparative experiments demonstrate that the sgLRR method based on low rank representation has a great advantage and remarkable performance in the clustering of multi-cancer samples.

A Low-Rank Representation Method Regularized by Dual-Hypergraph Laplacian for Selecting Differentially Expressed Genes

Differentially expressed genes selection becomes a hotspot and difficulty in recent molecular biology. Low-rank representation (LRR) uniting graph Laplacian regularization has gained good achievement in the above field. However, the co-expression information of data cannot be captured well by graph regularization. Therefore, a novel low-rank representation method regularized by dual-hypergraph Laplacian is proposed to reveal the intrinsic geometrical structures hidden in the samples and genes direction simultaneously, which is called dual-hypergraph Laplacian regularized LRR (DHLRR). Finally, a low-rank matrix and a sparse perturbation matrix can be recovered from genomic data by DHLRR. Based on the sparsity of differentially expressed genes, the sparse disturbance matrix can be applied to extracting differentially expressed genes. In our experiments, two gene analysis tools are used to discuss the experimental results. The results on two real genomic data and an integrated dataset prove that DHLRR is efficient and effective in finding differentially expressed genes.

L2,1-GRMF: an improved graph regularized matrix factorization method to predict drug-target interactions

Background

Predicting drug-target interactions is time-consuming and expensive. It is important to present the accuracy of the calculation method. There are many algorithms to predict global interactions, some of which use drug-target networks for prediction (ie, a bipartite graph of bound drug pairs and targets known to interact). Although these algorithms can predict some drug-target interactions to some extent, there is little effect for some new drugs or targets that have no known interaction.

Results

Since the datasets are usually located at or near low-dimensional nonlinear manifolds, we propose an improved GRMF (graph regularized matrix factorization) method to learn these flow patterns in combination with the previous matrix-decomposition method. In addition, we use one of the pre-processing steps previously proposed to improve the accuracy of the prediction.

Conclusions

Cross-validation is used to evaluate our method, and simulation experiments are used to predict new interactions. In most cases, our method is superior to other methods. Finally, some examples of new drugs and new targets are predicted by performing simulation experiments. And the improved GRMF method can better predict the remaining drug-target interactions.

Microstructure Evolution and Nanotribological Properties of Different Heat-Treated AISI 420 Stainless Steels after Proton Irradiation

In this paper, low-energy proton irradiation experiments with different cumulative fluences were performed on samples of AISI 420 stainless steel that were either annealed or tempered at 600 or 700 °C. The effects of the cumulative proton irradiation fluence on the evolution of the microstructure of AISI 420 were studied by transmission electron microscopy (TEM). Scratch tests were performed using a Tribo Indenter nanomechanical tester, in order to investigate the effects of the cumulative fluence on the tribological properties of the AISI 420 stainless steel. The results indicate that the dislocation density of the microstructure near the surface of the AISI 420 stainless steel increases with higher cumulative proton irradiation fluences. Under the same load, the nanoscale friction coefficient and wear rate both decreased with increasing cumulative proton irradiation fluence. This indicates that the surface hardening effect induced by proton irradiation can diminish the nanoscale friction coefficient and wear rate.

ACCBN: ant-Colony-clustering-based bipartite network method for predicting long non-coding RNA-protein interactions

BACKGROUND

Long non-coding RNA (lncRNA) studies play an important role in the development, invasion, and metastasis of the tumor. The analysis and screening of the differential expression of lncRNAs in cancer and corresponding paracancerous tissues provides new clues for finding new cancer diagnostic indicators and improving the treatment. Predicting lncRNA-protein interactions is very important in the analysis of lncRNAs. This article proposes an Ant-Colony-Clustering-Based Bipartite Network (ACCBN) method and predicts lncRNA-protein interactions. The ACCBN method combines ant colony clustering and bipartite network inference to predict lncRNA-protein interactions.

RESULTS

A five-fold cross-validation method was used in the experimental test. The results show that the values of the evaluation indicators of ACCBN on the test set are significantly better after comparing the predictive ability of ACCBN with RWR, ProCF, LPIHN, and LPBNI method.

CONCLUSIONS

With the continuous development of biology, besides the research on the cellular process, the research on the interaction function between proteins becomes a new key topic of biology. The studies on protein-protein interactions had important implications for bioinformatics, clinical medicine, and pharmacology. However, there are many kinds of proteins, and their functions of interactions are complicated. Moreover, the experimental methods require time to be confirmed because it is difficult to estimate. Therefore, a viable solution is to predict protein-protein interactions efficiently with computers. The ACCBN method has a good effect on the prediction of protein-protein interactions in terms of sensitivity, precision, accuracy, and F1-score.

The computational prediction of drug-disease interactions using the dual-network L2,1-CMF method

Background

Predicting drug-disease interactions (DDIs) is time-consuming and expensive. Improving the accuracy of prediction results is necessary, and it is crucial to develop a novel computing technology to predict new DDIs. The existing methods mostly use the construction of heterogeneous networks to predict new DDIs. However, the number of known interacting drug-disease pairs is small, so there will be many errors in this heterogeneous network that will interfere with the final results.

Results

A novel method, known as the dual-network L_2,1-collaborative matrix factorization, is proposed to predict novel DDIs. The Gaussian interaction profile kernels and L_2,1-norm are introduced in our method to achieve better results than other advanced methods. The network similarities of drugs and diseases with their chemical and semantic similarities are combined in this method.

Conclusions

Cross validation is used to evaluate our method, and simulation experiments are used to predict new interactions using two different datasets. Finally, our prediction accuracy is better than other existing methods. This proves that our method is feasible and effective.

Network analysis based on low-rank method for mining information on integrated data of multi-cancers

The noise problem of cancer sequencing data has been a problem that can't be ignored. Utilizing considerable way to reduce noise of these cancer data is an important issue in the analysis of gene co-expression network. In this paper, we apply a sparse and low-rank method which is Robust Principal Component Analysis (RPCA) to solve the noise problem for integrated data of multi-cancers from The Cancer Genome Atlas (TCGA). And then we build the gene co-expression network based on the integrated data after noise reduction. Finally, we perform nodes and pathways mining on the denoising networks. Experiments in this paper show that after denoising by RPCA, the gene expression data tend to be orderly and neat than before, and the constructed networks contain more pathway enrichment information than unprocessed data. Moreover, learning from the betweenness centrality of the nodes in the network, we find some abnormally expressed genes and pathways proven that are associated with many cancers from the denoised network. The experimental results indicate that our method is reasonable and effective, and we also find some candidate suspicious genes that may be linked to multi-cancers.

Co-differential Gene Selection and Clustering Based on Graph Regularized Multi-View NMF in Cancer Genomic Data

Cancer genomic data contain views from different sources that provide complementary information about genetic activity. This provides a new way for cancer research. Feature selection and multi-view clustering are hot topics in bioinformatics, and they can make full use of complementary information to improve the effect. In this paper, a novel integrated model called Multi-view Non-negative Matrix Factorization (MvNMF) is proposed for the selection of common differential genes (co-differential genes) and multi-view clustering. In order to encode the geometric information in the multi-view genomic data, graph regularized MvNMF (GMvNMF) is further proposed by applying the graph regularization constraint in the objective function. GMvNMF can not only obtain the potential shared feature structure and shared cluster group structure, but also capture the manifold structure of multi-view data. The validity of the proposed GMvNMF method was tested in four multi-view genomic data. Experimental results showed that the GMvNMF method has better performance than other representative methods.

Orientation and tropism of lumbar facet joints in degenerative spondylolisthesis

The orientation and tropism of the lumbar facet joints at L4-5 level was assessed by magnetic resonance imaging (MRI) in 53 patients with degenerative L4-5 spondylolisthesis and 53 age- and sex-matched normal control subjects. The degree of disc degeneration at the L4-5 level and of vertebral slip on lateral radiographs was also evaluated. Patients with degenerative spondylolisthesis had more sagittally orientated facet joints (P < 0.01) and more significant facet joint tropism (P < 0.05) than normal control subjects. For patients with degenerative spondylolisthesis, the facet joint tropism was significantly correlated with the degree of disc degeneration (P < 0.05). The results suggest that morphological abnormalities of the lumbar facet joints are a predisposing factor in the development of degenerative spondylolisthesis.

Acute central cervical cord injury: the effect of age upon prognosis

In this clinical study, a total of 89 consecutive patients with acute cervical cord injury was analysed retrospectively. There were 29 patients aged 60 years and over, accounting for 32.6% of all the cases. All the patients were followed up from 1 to 15 years, with an average of 6 years and 4 months. Among the patients of 60 years and over, 27 (93.1%) had both upper and lower extremities involved, compared with 40 (66.7%) of those under 60 years (P<0.05). Also among the older group, 24 had radiographic findings of severe cervical spinal degeneration, a higher incidence than in the patients under 60 years (P<0.05). There were two deaths in patients of 60 years and older. The average American Spinal Injury Association (ASIA) scores in both the younger and older groups at final follow-up were significantly larger (P<0.01) than on admission, but the age of the patients was negatively correlated with the ASIA scores both on admission (P<0.05) and at final follow-up (P<0.01). The ASIA score was significantly lower (P<0.01) in patients aged 60 years and over than in those younger than 60 years both on admission and at final follow-up. Patients aged 60 years and over with acute central cervical cord injury have a poorer prognosis although they may experience significant neurological improvement.

Direct repair of defect in lumbar spondylolysis and mild isthmic spondylolisthesis by bone grafting, with or without facet joint fusion

Forty-six patients with lumbar spondylolysis and mild isthmic spondylolisthesis were managed with direct repair of the defect with or without facet joint fusion in the affected segment. There were 24 males and 22 females, ranging in age from 15 to 56 years (average, 38.2 years). These patients had experienced clinical symptoms due to spondylolysis for between 4 months and 20 years (average, 5.3 years). Of 46 patients, 28 had no spondylolisthesis, 11 had Meyerding grade I vertebral slippage and 7 had grade II. Direct repair of 98 defects was performed on these patients. Twenty-six patients, in whom the disc adjacent to the defect was determined as degenerative by magnetic resonance imaging (MRI), simultaneously underwent facet joint fusion; 17 in one segment and 9 in two segments. The average period of follow-up was 50 months (24-92 months). Ninety-four defects achieved bony healing. As a result, 28 patients were graded as having an excellent outcome, 15 good, and 3 fair. Bone grafting in the defects achieves union between the loose lamina and the anterior element of lumbar vertebrae, and reconstructs the anatomic structure and physiologic functions of the lumbar vertebrae. There was no significant difference in outcome between the spondylolytic/spondylolisthetic patients with non-degenerative disc, who were treated with direct repair of defect only, and those with degenerative disc, who additionally underwent a fusion procedure (P > 0.05). The present series demonstrates a satisfactory result and a high rate of bony healing of the pars defect by this operative procedure in patients with lumbar spondylolysis and mild isthmic spondylolisthesis. Preoperative assessment of the disc degeneration with MRI is of great assistance in making the protocol choice of whether to opt for fusion.

Remodeling of the spinal canal after thoracolumbar burst fractures

Thirty-one patients with thoracolumbar burst fractures, seven untreated, 16 treated nonoperatively, and eight treated operatively, were analyzed retrospectively and followed up for 3 to 7 years. The initial and final degrees of neurologic deficit and the stenotic ratio of the spinal canal were recorded. Stenotic ratio significantly decreased from the first examination (range, 12.3%-74.5%; average, 26.2%) to the final followup (range, 5.4%-46.5%; average, 19.2%), but there were no differences of the percentage of remodeling between patients who were untreated and those treated nonoperatively and operatively. The recovery rate was highly significantly related to the stenotic ratio at first examination. Nonoperative management may be considered for treatment of patients who are neurologically intact or only slightly impaired with thoracolumbar burst fractures.

Disc degeneration in patients with lumbar spondylolysis

The grade of intervertebral disc degeneration in 73 symptomatic patients with lumbar spondylolysis was evaluated with magnetic resonance imaging to assess the prevalence of disc degeneration. The results were compared with those of age- and sex-matched asymptomatic control populations and with patients with clinical back pain symptoms but without spondylolysis. The relationship between disc degeneration and age of patients, the duration and severity of clinical symptoms, and the grade of vertebral slip was evaluated. A five-point grading scale based on T2 signal intensity recommended by Gibson et al. was used to evaluate the degree of disc degeneration. The grade of disc degeneration below the spondylolytic defect in the patients was highly significantly related to the age of patients (p < 0.01) and was more severe than in the controls (p < 0.01), but there was no difference of disc degeneration above the defect between the patients and the controls (p > 0.05). Furthermore, the grade of disc degeneration below the spondylolytic defect in the patients was highly significantly correlated with the duration of clinical symptoms (p < 0.01), although it was not associated with the severity of clinical symptoms or the grade of vertebral slip (p > 0.05). The results suggest that the degree of disc degeneration at the spondylolytic level is a more important determinant for choice of the fusion procedure than the age of the patients or the grade of vertebral slip, and that preoperative magnetic resonance examination is of great importance to evaluation of disc degeneration.

Surgical treatment of nonunited fractures of the odontoid process, with special reference to occipitocervical fusion for unreducible atlantoaxial subluxation or instability

Fifty-seven consecutive patients treated surgically for nonunited fractures of the odontoid process were reviewed. All patients presented late, exhibiting neurological deficits subsequent to nonunion. Delay in presentation was between 6 and 120 months (mean 32 months) after the original injury, due to missed diagnosis or inappropriate management. Seven patients who were reduced in traction underwent a Gallie atlantoaxial fusion. In the remaining 50 patients who were unreducible, an occipitocervical arthrodesis was performed. They were followed up for a minimum of 2 years, except one who died from postoperative respiratory failure. All patients obtained a solid bony union, including two in whom nonunion occurred following atlantoaxial fusion, and occipitocervical fusion was added as a rescue. Thirty-eight patients achieved excellent neurological recovery, nine still had some disability, five retained their neurological deficits and two reported a deterioration. In two patients, a recurrence in a traumatic episode was experienced long after a resolution. Our findings demonstrate that occipitocervical arthrodesis is preferable for unreducible subluxation or instability of atlantoaxial articulation in nonunion of odontoid fractures.

Immediate early gene 2 of human cytomegalovirus increases interleukin 2 receptor-alpha gene expression

Previously, we demonstrated that the cytomegalovirus (CMV) immediate early 2 (IE2) gene product upregulates interleukin-2 receptor-alpha (IL-2R alpha) gene expression. To further define this effect, we used a series of IL-2R alpha promoter truncations to identify that the primary site of CMV IE2 activity was in the region between -281 and -273 of the IL-2R alpha promoter, an area without known transcription factor activity. Deletion of known transcription factor enhancer elements resulted in a similar decrease in IE2-driven promoter activation. This unique sequence from the IL-2R alpha promoter was shown to drive a minimal promoter in the presence of IE2. These studies identify a new transcription factor binding site in the IL-2R alpha promoter, which may be specifically responsive to the CMV IE2 gene product. These studies also suggest that one mechanism whereby CMV infection may result in organ transplant rejection is through increased IL-2R alpha expression.

Fractures of the patella in children

Fractures of the patella are relatively rare injuries for children. Fourteen patients with patellar fractures were reviewed. There were 12 boys and two girls with the age ranging between 9 and 15 years and an average of 11.4 years. Sleeve fractures were the most common type of patellar fractures observed (eight cases), followed by transverse fractures (four cases) and comminuted fractures (two cases). All the fractures were treated with open reduction and followed up for 2 to 20 years. Overall results were good in 13 patients, and flexion limitation of the knee was seen in one. Fractures of the patella in children are characterised by sleeve fractures. The prognosis of the patients treated with open reduction is generally good.

The relationship between osteoarthritis and osteoporosis in the spine

The degree of osteoarthritis and osteoporosis in the lumbar spine has been compared in patients with spinal osteoarthritis and osteoporotic fractures and in a normal control group of 252 elderly subjects, to investigate the relationship between osteoarthritis and osteoporosis in the spine. The two indices were highly significantly different in the three groups (p<0.01). The degree of osteoarthritis in the osteoarthritic patients was higher (p<0.01) than in the fracture patients and the normal control subjects, while the degree of osteoporosis in the fracture patients was higher than in osteoarthritic patients and normal controls (p<0.01). These data support the conclusion that osteoarthritis and osteoporosis in the spine are inversely related.

Osteoporosis in Colles fracture

Previous studies of bone density in patients with Colles fracture have been inconclusive. We measured bone density at the second metacarpal in 36 postmenopausal women with Colles fracture. The patients were found to have highly significant (P < 0.01) or significant (P < 0.05) differences in the parameters of bone density compared with controls matched for age. The results suggest that osteoporosis is of definite relevance to the etiology of Colles fracture in postmenopausal women.

Cytomegalovirus modulates transcription factors necessary for the activation of the tumor necrosis factor-alpha promoter

Several studies have demonstrated that cytomegalovirus (CMV) infection increases expression of the tumor necrosis factor (TNF) gene. This effect is mediated, in part, by an effect of the CMV immediate early 1 (IE1) gene product on the TNF promoter. To further analyze these interactions, we used plasmids with TNF promoter truncations to determine the site within the promoter where the CMV IE1 gene product mediates its effect. The site was localized to a 40-base pair segment that contains a cAMP response element (CREB). Deletion of the cAMP response element increased basal promoter activation but had little effect on IE1-induced activation. Additional studies demonstrated that the cAMP element is flanked 5' by a PU.1 site and 3' by an NF-kappa B site, both of which increase expression of the TNF promoter. These sequences demonstrated IE1 responsiveness. We next determined the relevance of these observations for normal human cells by infecting human alveolar macrophages with CMV. In these studies we evaluated expression of NF-kappa B, PU.1 and CREB by gel shift assay at immediate early times after infection. We found that CMV infection increased the binding activity of NF-kappa B and PU.1 and decreased the binding activity of CREB. CMV infection also increased expression of the TNF gene in alveolar macrophages. These observations suggest that CMV increases TNF gene expression, in part, by altering the binding activity of transcription factors that regulate gene expression.

Multiple non-contiguous injuries of the spine

Forty-seven patients with multiple non-contiguous injuries of the spine were reviewed retrospectively, giving an incidence of 5.4 per cent of 875 spinal injuries. Thirty-three were men and 14 women with an age range of between 16 and 78 years. Twenty-three were treated operatively. The multiple non-contiguous injuries had a more complicated mechanism of injury and tended to be more severe injuries than single-level injuries as they were combined more frequently with injuries of other parts of the body. Being aware of this combination of injuries leads to an earlier diagnosis. Operative reconstruction is recommended.

Lumbar disc herniation in patients with developmental spinal stenosis

Lumbar spinal stenosis and lumbar disc herniation are usually regarded as two pathogenetically different conditions, but in the literature lumbar disc herniation in patients with developmental spinal stenosis has been rarely documented. In a clinical retrospective study, 42 lumbar disc herniations with developmental spinal stenosis were reported and analyzed. Discectomy was performed after laminotomy. The patients were followed-up for an average of 4.4 years (range 2-7 years). The preoperative symptoms disappeared completely in 28 patients, in 13 patients some degree of backache remained although their nerve root pain had been relieved, and in 1 patient intermittent claudication reappeared after 6 years resolution of their preoperative pain. We conclude that when developmental spinal stenosis is combined with disc herniation, discectomy through laminotomy rather than laminectomy is usually sufficient for decompression.

Cytomegalovirus modulates interleukin-6 gene expression

Complications after lung transplantation include the development of rejection and an increased incidence of infection, particularly with cytomegalovirus (CMV). Several recent studies have suggested that interleukin (IL)-6 may be used to detect both infection and rejection after lung transplantation. In addition, IL-6 may play a role in the development of bronchiolitis obliterans after transplantation. Because CMV is also associated with the development of bronchiolitis obliterans after transplantation, we determined whether CMV induces IL-6 gene expression. We demonstrated that CMV infection increased both IL-6 protein and mRNA in peripheral blood mononuclear cells. We also demonstrated that the CMV immediate early 1 gene product increased expression of the IL-6 promoter. This effect of the CMV immediate early 1 gene product was dependent upon the presence of specific transcription factor binding sites in the IL-6 promoter. These studies demonstrate that CMV may be an important cofactor in the development of rejection and infection after transplantation through its effects on IL-6.

[Histological investigation of the ligamentum flavum]

The micro- and ultrastructures of the human ligamentum flavum were investigated. It was found that the ligamentum flavum is made of chiefly of elastic fibers. This kind of structure provides a basis for the important physiological functions of the ligamentum flavum. So the load is transmitted between the vertebrae and spinal stability is maintained by means of this ligament. Then clinical relevance was discussed.

Radiographic measurement of the prevertebral soft tissue of cervical vertebrae

The radiographic measurement of the prevertebral soft tissue of cervical vertebrae was performed in 87 normal adults. According to the results of the measurement, 10 mm and 20 mm were used as the upper limit normal values of the retropharyngeal and retrotracheal space respectively. We conclude that although the widened soft tissue space is of diagnostic significance, diagnosis should be made on the basis of analysis of the injury history, clinical manifestation and imaging examination.

[Long-term results of meniscectomy]

The long-term results after total meniscectomy in 60 patients have been reviewed with an average follow-up period of 16.1 years (10 to 33 years) and 58.3% excellent and good results were demonstrated. Statistical analysis showed that the late results were related to the time of follow-up and not associated with the length of history before operation. Radiological degeneration of the knee was seen in 87.5% of the patients, while no relationship between the degeneration and the clinical results was found. It is indicated from this retrospective study that the menisci are of so great importance in biomechanics that total meniscectomy should be as far as possible avoided.

Effects of discectomy on the stress distribution in the lumbar spine

The change of the stress distribution of the lumbar spine after discectomy was analysed by the three-dimensional finite element method. It was shown that the stress level in the posterior element was higher, but that in the anterior element was lower than before disc excision. The most significant change of the stress distribution was found in the trabecular bone of the vertebral body. It is considered that in discectomy the normal disc tissue should be preserved as much as possible to maintain good function of the spine.

[Comparison of the stress distribution with bone density in the spine]

The results of study on stress distribution of the lumbar spine analyzed with a mechanical model of the lumbar motion segment constructed by a three-dimensional finite element method, and the comparison of resulting values with the corresponding bone density represented by the attenuation value of CT were reported. The results indicated that the stress levels in various parts of the lumbar spine were closely related to the CT values. The authors conclude that the mechanical stress is an important factor in maintaining the dynamic balance of bone growth.

Influence of flexion-extension motion of lumbar spine on lumbosacral dural sac. An experimental study

To analyze the effects of flexion-extension motion of lumbar spine on the position and shape of lumbosacral dural sac, 10 flexion/extension lateral myelograms of cadaver specimens of normal lumbar spine were measured. The results showed that moving from flexion to extension both sagittal diameter and length of the dural sac decreased significantly, the dural sac moved caudally, and the sagittal diameter of the anterior epidural space decreased in the mid-vertebral level but increased in the intervertebral level. The dynamic changes of the dural sac and their influence factors and clinical relevance were discussed.

[Effect of discectomy on the stress distribution in lumbar spine]

The change of stress distribution of lumbar spine after discectomy was analysed with three-dimensional finite element method. It was showed that the stress level in posterior element was elevated, but the stress level in anterior element was lower than before. The most significant change of stress distribution was found in trabecular bone of vertebral bodies. The clinical relevance was discussed.

The effect of flexion-extension motion of the lumbar spine on the capacity of the spinal canal. An experimental study

The symptoms of lumbar spinal stenosis are often posture-dependent, and it is generally accepted that the capacity of the spinal canal is affected by flexion-extension motions of the lumbar spine. A study of spinal capacity in flexion-extension of ten cadaver specimens on the lumbar spine was done and measurements were obtained from the flexion-extension lateral myelograms. There proved to be a larger capacity of dural sac of 3.5 to 6.0 ml (4.85 +/- 0.75 ml) in flexion than in extension, and the differences were highly significant (P less than 0.001). In addition, the sagittal diameter of the dural sac and the length of the spinal canal increased from extension to flexion. Because of the effect of flexion-extension motion of the lumbar spine on the spinal capacity, we suggest that maintaining the lumbar spine in flexion is more suitable since spinal canal capacity is enlarged and symptoms may be mitigated.