PD-1/PD-L1 blockade is a potent adjuvant in treatment of Staphylococcus aureus osteomyelitis in mice.

There is no effective therapy for implant-associated Staphylococcus aureus osteomyelitis, a devastating complication after orthopedic surgery. An immune-suppressive profile with up-regulated programmed cell death 1/programmed death ligand 1 (PD-1/PD-L1) was identified based on our transcriptional data (GEO: GSE166522) from a mouse model of S. aureus osteomyelitis. PD-1/PD-L1 expression was up-regulated mainly in F4/80+ macrophages surrounding the abscess in S. aureus-infected bone. Mechanistically, PD-1/PD-L1 activated mitophagy to suppress production of mitochondrial reactive oxygen species (ROS), suppressing the bactericidal function of macrophages. Using neutralizing antibodies for PD-L1 or PD-1, or knockout of PD-L1 adjuvant to gentamicin markedly reduced mitophagy in bone marrow F4/80+ cells, enhanced bacterial clearance in bone tissue and implants, and reduced bone destruction in mice. PD-1/PD-L1 expression was also increased in the bone marrow from individuals with S. aureus osteomyelitis. These findings uncover a so far unknown function of PD-1/PD-L1-mediated mitophagy in suppressing the bactericidal function of bone marrow macrophages.

Tethered cord syndrome from pediatric and adult perspectives: a comprehensive systematic review of 6135 cases.

OBJECTIVE: This study aimed to investigate the differences in clinical features, diagnostic examination, treatment, and pathological results between adult-onset and pediatric-onset tethered cord syndrome (TCS). METHODS: The authors searched the PubMed, Embase, and Cochrane Library databases through January 2023 for reports on TCS, extracting information on clinical features, imaging data, treatment modalities, prognosis, and pathological research results. A total of 6135 cases from 246 articles were included in the analysis. This review was conducted in accordance with the 2020 PRISMA guidelines and registered on PROSPERO. RESULTS: The most common adult clinical manifestations were pain, urinary symptoms, and numbness; in children, they were urinary symptoms, skin lesions, bowel symptoms, and unspecific motor deficits. Surgical treatment was the primary approach for both adults and children, with a higher clinical improvement rate observed in adults. However, adults also had a higher rate of surgical complications than children. TCS pathological studies have not yet identified the differences between adults and children, and the pathogenesis of adult-onset TCS requires further investigation. CONCLUSIONS: Adult-onset and pediatric-onset TCS exhibit certain differences in clinical characteristics, diagnostic examinations, and treatments. However, significant differences have not been found in current pathological studies between adults and children. Systematic review registration no.: CRD42023479450 (www.crd.york.ac.uk/prospero).

Characterization of sidebands in fiber lasers based on nonlinear Fourier transformation.

Phase evolution of soliton and that of first-order sidebands in a fiber laser are investigated by using nonlinear Fourier transform (NFT). Development from dip-type sidebands to peak-type (Kelly) sidebands is presented. The phase relationship between the soliton and the sidebands calculated by the NFT are in good agreement with the average soliton theory. Our results suggest that NFT can be an effective tool for the analysis of laser pulses.

Blind SOP recovery of eigenvalue communication system based on a nonlinear Fourier transform.

Owing to the random birefringence of optical fibers, the recovery of the state of polarization (SOP) is urgently needed, especially in the nonlinear spectrum division multiplexing transmissions. Based on the variance of the polarization power ratio among symbols as the cost function, we propose a novel algorithm for the blind SOP recovery of eigenvalue communications. In the single eigenvalue transmissions with phase-shift keying or 16-ary amplitude and phase-shift keying constellations, at least 25.3 dB polarization extinction ratio can be achieved by using a block length of 30, even under 7 dB OSNR condition. It also shows that the proposed algorithm can be employed in multi-eigenvalue NFDM transmissions and full-spectrum modulated NFDM system. In the experiment, our proposed algorithm performs the same as the training symbol based method in back-to-back and less than 3000 km fiber link conditions; a maximum performance gain of 1.6 dB was obtained in ultra-long-haul condition (4300 km). It also shows that the impact of the polarization mode dispersion of a single-mode fiber on the algorithm is negligible.

Polarization dynamics of vector solitons in a fiber laser.

We investigate the polarization dynamics of vector solitons in a fiber laser mode-locked by a saturable absorber (SA). Three types of vector solitons were obtained in the laser, including group velocity locked vector solitons (GVLVS), polarization locked vector solitons (PLVS), and polarization rotation locked vector solitons (PRLVS). Their polarization evolution during intracavity propagation is discussed. Pure vector solitons are obtained from the continuous wave (CW) background by soliton distillation, and the characteristics of the vector solitons without and with distillation are analyzed, respectively. Numerical simulations suggest that the features of vector solitons in a fiber laser could be assemble to those generated in fibers.

Molecular subtyping based on immune cell marker genes predicts prognosis and therapeutic response in patients with lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LA) is one of the most common malignancies and is responsible for the greatest number of tumor-related deaths. Our research aimed to explore the molecular subtype signatures of LA to clarify the correlation among the immune microenvironment, clinical outcomes, and therapeutic response. METHODS: The LA immune cell marker genes (LICMGs) identified by single-cell RNA sequencing (scRNA-seq) analysis were used to discriminate the molecular subtypes and homologous immune and metabolic traits of GSE72094 LA cases. In addition, the model-building genes were identified from 1441 LICMGs by Cox-regression analysis, and a LA immune difference score (LIDscore) was developed to quantify individual differences in each patient, thereby predicting prognosis and susceptibility to immunotherapy and chemotherapy of LA patients. RESULTS: Patients of the GSE72094 cohort were divided into two distinct molecular subtypes based on LICMGs: immune activating subtype (Cluster-C1) and metabolically activating subtype (cluster-C2). The two molecular subtypes have distinct characteristics regarding prognosis, clinicopathology, genomics, immune microenvironment, and response to immunotherapy. Among the LICMGs, LGR4, GOLM1, CYP24A1, SFTPB, COL1A1, HLA-DQA1, MS4A7, PPARG, and IL7R were enrolled to construct a LIDscore model. Low-LIDscore patients had a higher survival rate due to abundant immune cell infiltration, activated immunity, and lower genetic variation, but probably the higher levels of Treg cells in the immune microenvironment lead to immune cell dysfunction and promote tumor immune escape, thus decreasing the responsiveness to immunotherapy compared with that of the high-LIDscore patients. Overall, high-LIDscore patients had a higher responsiveness to immunotherapy and a higher sensitivity to chemotherapy than the low-LIDscore group. CONCLUSIONS: Molecular subtypes based on LICMGs provided a promising strategy for predicting patient prognosis, biological characteristics, and immune microenvironment features. In addition, they helped identify the patients most likely to benefit from immunotherapy and chemotherapy.

Miniature mass spectrometer signal processing based on ensemble empirical mode decomposition feature enhancement.

RATIONALE: The high sensitivity of the miniature mass spectrometer plays an irreplaceable role in rapid on-site detection. However, its analysis accuracy and stability should be improved due to the influence of sample pretreatment and use environment. The present study investigates the processing effects of ensemble empirical mode decomposition (EEMD) feature enhancement methods on the determination coefficient (R2 ) and relative standard deviation (RSD) of caffeine mass spectrometry (MS) signals. METHODS: This paper employs the EEMD method combined with polynomial curve fitting to enhance the characteristics of seven caffeine mass spectrum signals with different concentrations and 15 groups of caffeine mass spectrum signals with the same concentration, and the wavelet analysis method was used for comparative verification. The determination coefficient and RSD of the two methods were compared. RESULTS: We found the EEMD method's capability in adaptively decomposing caffeine mass spectrum signals is better than wavelet analysis method. The determination coefficient of the EEMD enhanced feature is better than 0.999, and the RSD is better than 2%, and both are better than wavelet analysis methods. CONCLUSIONS: The feature enhancement processing using the EEMD method has significantly improved the determination coefficient and RSD of the sample curve, improving the accuracy and stability of the data and providing a new way for miniature mass spectrometer signal processing.

COVID-19's effect on the spatial integration of fish markets: Evidence from carp price in China.

While curbing the spread of Coronavirus Disease 2019 (COVID-19), lockdown policies and "stay-at-home" restrictions caused massive supply chain disruptions worldwide. This led to breaks in spatial market integration, which could further lead to market inefficiency and resource misallocation. Taking daily price data from 2016 to 2021, this study investigates COVID-19's effect on the spatial market integration of fish in China using cointegration tests. We find a high degree of spatial market integration for fish in China before the COVID-19 pandemic. Further, our results show that COVID-19's effect on the spatial market integration of fish varies spatially in China. Specifically, COVID-19 reduces the degree of spatial market integration in most provinces, especially those with high infection rates. Meanwhile, the degree of spatial market integration in provinces with low infection rates remains high. Therefore, the government should be regionally specific when formulating market recovery policies.

Glycyrrhetinic Acid Receptor-Mediated Zeolitic Imidazolate Framework-8 Loaded Doxorubicin as a Nanotherapeutic System for Liver Cancer Treatment.

In this study, we designed and developed a DOX nanodrug delivery system (PEG-GA@ZIF-8@DOX) using ZIF-8 as the carrier and glycyrrhetinic acid (GA) as the targeting ligand. We confirmed that DOX was loaded and PEG-GA was successfully modified on the surface of the nanoparticles. The in vitro release profile of the system was investigated at pH 5.0 and 7.4. The cellular uptake, in vitro cytotoxicity, and lysosomal escape characteristics were examined using HepG2 cells. We established an H22 tumor-bearing mouse model and evaluated the in vivo antitumor activity. The results showed that the system had a uniform nanomorphology. The drug loading capacity was 11.22 ± 0.87%. In acidic conditions (pH 5.0), the final release rate of DOX was 57.73%, while at pH 7.4, it was 25.12%. GA-mediated targeting facilitated the uptake of DOX by the HepG2 cells. PEG-GA@ZIF-8@DOX could escape from the lysosomes and release the drug in the cytoplasm, thus exerting its antitumor effect. When the in vivo efficacy was analyzed, we found that the tumor inhibition rate of PEG-GA@ZIF-8@DOX was 67.64%; it also alleviated the loss of the body weight of the treated mice. This drug delivery system significantly enhanced the antitumor effect of doxorubicin in vitro and in vivo, while mitigating its toxic side effects.

TA-MSCs, TA-MSCs-EVs, MIF: their crosstalk in immunosuppressive tumor microenvironment.

As an important component of the immunosuppressive tumor microenvironment (TME), it has been established that mesenchymal stem cells (MSCs) promote the progression of tumor cells. MSCs can directly promote the proliferation, migration, and invasion of tumor cells via cytokines and chemokines, as well as promote tumor progression by regulating the functions of anti-tumor immune and immunosuppressive cells. MSCs-derived extracellular vesicles (MSCs-EVs) contain part of the plasma membrane and signaling factors from MSCs; therefore, they display similar effects on tumors in the immunosuppressive TME. The tumor-promoting role of macrophage migration inhibitory factor (MIF) in the immunosuppressive TME has also been revealed. Interestingly, MIF exerts similar effects to those of MSCs in the immunosuppressive TME. In this review, we summarized the main effects and related mechanisms of tumor-associated MSCs (TA-MSCs), TA-MSCs-EVs, and MIF on tumors, and described their relationships. On this basis, we hypothesized that TA-MSCs-EVs, the MIF axis, and TA-MSCs form a positive feedback loop with tumor cells, influencing the occurrence and development of tumors. The functions of these three factors in the TME may undergo dynamic changes with tumor growth and continuously affect tumor development. This provides a new idea for the targeted treatment of tumors with EVs carrying MIF inhibitors.

Linear optical sampling enabled soliton nonlinear frequency spectrum classification.

Nonlinear Fourier transform (NFT) is a powerful tool for characterizing optical soliton dynamics, which, however, suffers from fundamental limitations that ultra-wide bandwidth photodetectors and ultra-high sampling rate analog-to-digital converters should be used when accessing the full-field information of an ultrafast optical pulse. Herein, we report on the experimental demonstration of the linear optical sampling (LOS) enabled nonlinear frequency spectrum classification of ultrashort optical pulses, which could break this limitation. Instead of traditional coherent detection, the LOS overcomes the ultra-wide bandwidth constraint of commercially available optoelectrical devices. By finely adjusting the repetition rate difference between the soliton to be characterized and the sampling pulsed source, a 55.56-TSa/s equivalent sampling rate arising in the LOS can be secured, where only 400-MHz balanced photodetectors and 5-GSa/s analog-to-digital converter are used. Meanwhile, according to the nonlinear frequency spectrum calculated from the accurate full-field information, the promising concept of soliton distillation has been experimentally verified for the first time. The LOS-enabled NFT technique provides an alternative and efficient characterization tool for ultrafast fiber lasers, which facilities comprehensive insight into soliton dynamics.

GCAEMDA: Predicting miRNA-disease associations via graph convolutional autoencoder.

microRNAs (miRNAs) are small non-coding RNAs related to a number of complicated biological processes. A growing body of studies have suggested that miRNAs are closely associated with many human diseases. It is meaningful to consider disease-related miRNAs as potential biomarkers, which could greatly contribute to understanding the mechanisms of complex diseases and benefit the prevention, detection, diagnosis and treatment of extraordinary diseases. In this study, we presented a novel model named Graph Convolutional Autoencoder for miRNA-Disease Association Prediction (GCAEMDA). In the proposed model, we utilized miRNA-miRNA similarities, disease-disease similarities and verified miRNA-disease associations to construct a heterogeneous network, which is applied to learn the embeddings of miRNAs and diseases. In addition, we separately constructed miRNA-based and disease-based sub-networks. Combining the embeddings of miRNAs and diseases, graph convolutional autoencoder (GCAE) was utilized to calculate association scores of miRNA-disease on two sub-networks, respectively. Furthermore, we obtained final prediction scores between miRNAs and diseases by adopting an average ensemble way to integrate the prediction scores from two types of subnetworks. To indicate the accuracy of GCAEMDA, we applied different cross validation methods to evaluate our model whose performances were better than the state-of-the-art models. Case studies on a common human diseases were also implemented to prove the effectiveness of GCAEMDA. The results demonstrated that GCAEMDA was beneficial to infer potential associations of miRNA-disease.

SCMFMDA: Predicting microRNA-disease associations based on similarity constrained matrix factorization.

miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.

mRNA Transcriptome Analysis of Bone in a Mouse Model of Implant-Associated Staphylococcus aureus Osteomyelitis.

To investigate the molecular pathogenesis of bone with osteomyelitis, we developed implant-associated osteomyelitis (IAOM) models in mice. An orthopedic stainless pin was surgically placed in the right femoral midshaft of mice, followed by an inoculation of Staphylococcus aureus into the medullary cavity. Typical characteristics of IAOM, like periosteal reaction and intraosseous abscess, occurred by day 14 postinfection. By day 28 postinfection, necrotic abscess, sequestrum formation, and deformity of the whole femur were observed. Transcriptional analysis identified 101 and 1,702 differentially expressed genes (DEGs) between groups by days 3 and 14 postinfection, respectively. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed the enrichment of pathways in response to the bacterium, receptor-ligand activity, and chemokine signaling by day 3 postinfection. However, by day 14 postinfection, the enrichment switched to angiogenesis, positive regulation of cell motility and migration, skeletal system development, and cytokine-cytokine receptor interaction. Furthermore, protein-protein interaction network analysis identified 4 cytokines (interleukin 6 [IL-6], Cxcl10, gamma interferon [IFN-γ], and Cxcl9) associated with IAOM at an early stage of infection. Overall, as the pathological changes in this mouse model were consistent with those in human IAOM, our model may be used to investigate the mechanism and treatment of IAOM. Furthermore, the data for transcriptome sequencing and bioinformatic analysis will be an important resource for dissecting the molecular pathogenesis of bone with IAOM.

Amino acid sites related to the PB2 subunits of IDV affect polymerase activity.

BACKGROUND: In 2011, a new influenza virus, named Influenza D Virus (IDV), was isolated from pigs, and then cattle, presenting influenza-like symptoms. IDV is one of the causative agents of Bovine Respiratory Disease (BRD), which causes high morbidity and mortality in feedlot cattle worldwide. To date, the molecular mechanisms of IDV pathogenicity are unknown. Recent IDV outbreaks in cattle, along with serological and genetic evidence of IDV infection in humans, have raised concerns regarding the zoonotic potential of this virus. Influenza virus polymerase is a determining factor of viral pathogenicity to mammals. METHODS: Here we take a prospective approach to this question by creating a random mutation library about PB2 subunit of the IDV viral polymerase to test which amino acid point mutations will increase viral polymerase activity, leading to increased pathogenicity of the virus. RESULTS: Our work shows some exact sites that could affect polymerase activities in influenza D viruses. For example, two single-site mutations, PB2-D533S and PB2-G603Y, can independently increase polymerase activity. The PB2-D533S mutation alone can increase the polymerase activity by 9.92 times, while the PB2-G603Y mutation increments the activity by 8.22 times. CONCLUSION: Taken together, our findings provide important insight into IDV replication fitness mediated by the PB2 protein, increasing our understanding of IDV replication and pathogenicity and facilitating future studies.

Synthesis and biological evaluation of isatin derivatives containing 1,3,4-thiadiazole as potent a-glucosidase inhibitors.

A series of (Z)-3-(2-(1,3,4-thiadiazol-2-yl)hydrazono)-1-substituted indolin-2-ones derivatives (3a-3m) were designed and synthesized. All newly synthesized compounds were evaluated for their a-glucosidase inhibitory activity with resveratrol as positive control in vitro. Except for 3i and 3j, all of the compounds showed a potent inhibitory activity against a-glucosidase with IC50 values in the range of 3.12 ± 1.25 to 45.95 ± 1.26 µM and the purity of these compounds was greater than 95%. The IC50 values were being compared to the standard resveratrol (IC50 = 22.00 ± 1.15 µM) and it was found that compounds 3b, 3d-3h were found to be more active than resveratrol. Specifically, (Z)-3-(2-(1,3,4-thiadiazol-2-yl)hydrazono)-1-(4-chlorobenzyl)indolin-2-one (3d) exhibited the most potent a-glucosidase inhibitory activity with IC50 value of 3.12 ± 1.25 µM. The kinetic analysis revealed that compound (3d) is noncompetitive inhibitor. Structure activity relationship has been established for all compounds. Furthermore, the binding interactions of compound 3d with the active site of a-glucosidase were confirmed through molecular docking. This study has identified a new class of potent a-glucosidase inhibitors for further investigation.

Lethal infection caused by Tetratrichomonas gallinarum in black swans (Cygnus atratus).

BACKGROUND: Tetratrichomonas gallinarum is parasitic protozoa with a wide host range. However, its lethal infection is rare reported. CASE PRESENTATION: Here, we described the first lethal cases of T. gallinarum infection in black swans in China. Five black swans died within a week in succession without obvious symptoms except mild diarrhea. At necropsy, severe lesions were observed in caeca with thickened caecal walls and hemorrhages in the mucosa. A large number of moving trophozoites were found in the contents of the cecum by microscopic examination. The livers were enlarged with multiple bleeding spots on the surface. Histopathology of the livers showed mononuclear cell infiltration and moderate hyperplasia of fibrous tissue. The histopathology of the cecum showed that the villi of the cecum were edematous. Finally, the presence of T. gallinarum was determined by specific PCR andin-situ hybridization assay. Additionally, common pathogens that can cause similar symptoms were excluded. CONCLUSIONS: The death of the black swan was caused by T. gallinarum, suggesting that the parasite might be a new threat to the Cygnus birds.

MiRNA-disease association prediction via hypergraph learning based on high-dimensionality features.

BACKGROUND: MicroRNAs (miRNAs) have been confirmed to have close relationship with various human complex diseases. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases. However, it is still a big challenge to identify which miRNAs are related to diseases. As experimental methods are in general expensive and time-consuming, it is important to develop efficient computational models to discover potential miRNA-disease associations. METHODS: This study presents a novel prediction method called HFHLMDA, which is based on high-dimensionality features and hypergraph learning, to reveal the association between diseases and miRNAs. Firstly, the miRNA functional similarity and the disease semantic similarity are integrated to form an informative high-dimensionality feature vector. Then, a hypergraph is constructed by the K-Nearest-Neighbor (KNN) method, in which each miRNA-disease pair and its k most relevant neighbors are linked as one hyperedge to represent the complex relationships among miRNA-disease pairs. Finally, the hypergraph learning model is designed to learn the projection matrix which is used to calculate uncertain miRNA-disease association score. RESULT: Compared with four state-of-the-art computational models, HFHLMDA achieved best results of 92.09% and 91.87% in leave-one-out cross validation and fivefold cross validation, respectively. Moreover, in case studies on Esophageal neoplasms, Hepatocellular Carcinoma, Breast Neoplasms, 90%, 98%, and 96% of the top 50 predictions have been manually confirmed by previous experimental studies. CONCLUSION: MiRNAs have complex connections with many human diseases. In this study, we proposed a novel computational model to predict the underlying miRNA-disease associations. All results show that the proposed method is effective for miRNA-disease association predication.

GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations.

Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA-disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA-disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA-disease associations.

Graph regularized L2,1-nonnegative matrix factorization for miRNA-disease association prediction.

BACKGROUND: The aberrant expression of microRNAs is closely connected to the occurrence and development of a great deal of human diseases. To study human diseases, numerous effective computational models that are valuable and meaningful have been presented by researchers. RESULTS: Here, we present a computational framework based on graph Laplacian regularized L2, 1-nonnegative matrix factorization (GRL2, 1-NMF) for inferring possible human disease-connected miRNAs. First, manually validated disease-connected microRNAs were integrated, and microRNA functional similarity information along with two kinds of disease semantic similarities were calculated. Next, we measured Gaussian interaction profile (GIP) kernel similarities for both diseases and microRNAs. Then, we adopted a preprocessing step, namely, weighted K nearest known neighbours (WKNKN), to decrease the sparsity of the miRNA-disease association matrix network. Finally, the GRL2,1-NMF framework was used to predict links between microRNAs and diseases. CONCLUSIONS: The new method (GRL2, 1-NMF) achieved AUC values of 0.9280 and 0.9276 in global leave-one-out cross validation (global LOOCV) and five-fold cross validation (5-CV), respectively, showing that GRL2, 1-NMF can powerfully discover potential disease-related miRNAs, even if there is no known associated disease.