Multitask Deep Learning Enabling a Synergy for Cadmium and Methane Mitigation with Biochar Amendments in Paddy Soils.

Biochar has demonstrated significant promise in addressing heavy metal contamination and methane (CH4) emissions in paddy soils; however, achieving a synergy between these two goals is challenging due to various variables, including the characteristics of biochar and soil properties that influence biochar's performance. Here, we successfully developed an interpretable multitask deep learning (MTDL) model by employing a tensor tracking paradigm to facilitate parameter sharing between two separate data sets, enabling a synergy between Cd and CH4 mitigation with biochar amendments. The characteristics of biochar contribute similar weightings of 67.9% and 62.5% to Cd and CH4 mitigation, respectively, but their relative importance in determining biochar's performance varies significantly. Notably, this MTDL model excels in custom-tailoring biochar to synergistically mitigate Cd and CH4 in paddy soils across a wide geographic range, surpassing traditional machine learning models. Our findings deepen our understanding of the interactive effects of Cd and CH4 mitigation with biochar amendments in paddy soils, and they also potentially extend the application of artificial intelligence in sustainable environmental remediation, especially when dealing with multiple objectives.

Violence on the front line: a qualitative comparative analysis of the causes of patient violence towards medical staff in China during the COVID-19 pandemic.

COVID-19, reduced funding and a shortage of healthcare workers has led to growing international concern about patient violence towards medical staff in medical settings. As the number of reported physical and verbal assaults increases, many medical staff are considering leaving their positions due to the resulting impact on their mental and physical wellbeing, creating a critical need to understand the causes for violence towards medical staff working on the front line. This study aims to examine the causes for patient violence towards medical staff in China during the COVID-19 pandemic. A case library was created containing twenty reported incidents of patient violence towards medical staff during the pandemic in China. Based on the Triadic Reciprocal Determinism (TRD) theory, we identify the personal, environmental, and behavioral factors, that cause incidents of violence towards medical staff. The outcome was set as 'Medical Staff Casualties', referring to whether, due to the violence experienced, the medical staff member was injured or died, or only experienced threatening or insulting behavior. Data was analyzed using Qualitative Comparative Analysis (QCA) to clarify the relationship between the different conditions and their relationship with the outcome. The study's results reveal that Relationship Closeness is a necessary condition for patient violence in the presence of outcome. Secondly, four distinct types of causes for patient violence towards medical staff were identified: Strong Relationship Oriented Violence, Healthcare Resources and Services Mismatched Violence, Violence caused by Ineffective Patient-Physician Communication, and Ineffective Communication Superimposed Low Patient Compliance Violence. Scientific guidance is provided for the creation of measures to prevent future violence towards medical staff from occurring. Strict precautions should be taken for preventing violence to protect a healthy society and harmonious medical environment, emphasizing the need for joint governance of multiple participants.

Lower serum levels of vitamin D in adults with urinary tract infection.

PURPOSE: We aimed to explore the association between urinary tract infection (UTI) in adults and serum 25-hydroxyvitamin D (25OHD), which was used to access vitamin D status. METHODS: Serum levels of 25OHD were retrospectively analyzed in 234 subjects (190 females and 44 males): 120 UTI patients (females = 103) and 114 age- and sex-matched healthy controls (females = 87). Serum 25OHD concentrations were categorized as follows: (1) < 20 ng/mL, 20 to < 30 ng/mL, and ≥ 30 ng/mL; (2) < 20 ng/mL and ≥ 20 ng/mL. RESULTS: Serum 25OHD levels were lower in patients with UTI (p < 0.01). Women with UTI presented significantly lower 25OHD concentrations than those without UTI (p < 0.01). No association between serum 25OHD levels and UTI in men was found (p > 0.05). The multivariable logistic regression models showed significant associations between UTI and 25OHD, female sex, neutrophilic lymphocyte ratio and C-reactive protein (p < 0.05). CONCLUSION: Lower 25OHD concentrations associated with UTI were most prominent among women. The associations between UTI and low serum 25OHD levels as well as female sex were independent of each other.

Bipartite graph-based collaborative matrix factorization method for predicting miRNA-disease associations.

BACKGROUND: With the rapid development of various advanced biotechnologies, researchers in related fields have realized that microRNAs (miRNAs) play critical roles in many serious human diseases. However, experimental identification of new miRNA-disease associations (MDAs) is expensive and time-consuming. Practitioners have shown growing interest in methods for predicting potential MDAs. In recent years, an increasing number of computational methods for predicting novel MDAs have been developed, making a huge contribution to the research of human diseases and saving considerable time. In this paper, we proposed an efficient computational method, named bipartite graph-based collaborative matrix factorization (BGCMF), which is highly advantageous for predicting novel MDAs. RESULTS: By combining two improved recommendation methods, a new model for predicting MDAs is generated. Based on the idea that some new miRNAs and diseases do not have any associations, we adopt the bipartite graph based on the collaborative matrix factorization method to complete the prediction. The BGCMF achieves a desirable result, with AUC of up to 0.9514 ± (0.0007) in the five-fold cross-validation experiments. CONCLUSIONS: Five-fold cross-validation is used to evaluate the capabilities of our method. Simulation experiments are implemented to predict new MDAs. More importantly, the AUC value of our method is higher than those of some state-of-the-art methods. Finally, many associations between new miRNAs and new diseases are successfully predicted by performing simulation experiments, indicating that BGCMF is a useful method to predict more potential miRNAs with roles in various diseases.

MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations.

BACKGROUND: MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations. RESULTS: The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005). CONCLUSIONS: The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

Puerarin Exerts a Delayed Inhibitory Effect on the Proliferation of Cardiomyocytes Derived from Murine ES Cells via Slowing Progression through G2/M Phase.

OBJECTIVE: Puerarin, which shows beneficial and protective effects on cardiovascular diseases, is the main isoflavone extracted from Pueraria lobata (kudzu) root. The aim of this study was to investigate the effects of puerarin on in vitro myocardial proliferation and its underlying mechanism. METHODS: Myocardial differentiation of transgenic embryonic stem (ES) cells was performed by embryoid body-based differentiation method. The proliferation assay of cardiomyocytes (CMs) derived from ES cells (ES-CMs) was performed by EdU (5-Ethynyl-2'-deoxyuridine) staining. Flow cytometry was employed to determine the cell cycle distribution and apoptosis of purified ES-CMs. Quantitative real-time PCR was utilized to study the transcription of genes related to cell cycle progression. Signaling pathways relating to proliferation were studied by western blot analysis and application of specific inhibitors. RESULTS: Puerarin exerted a delayed inhibitory effect on the proliferation of ES-CMs at the early-stage differentiation. Meanwhile, puerarin slowed progression through G2/M phase without inducing apoptosis of ES-CMs. Further assays showed that puerarin up-regulated the transcription of Cyclin A2, Cyclin B1 and Cdk1 in ES-CMs. The ERK1/2 specific inhibitor PD0325901 and the PI3K specific inhibitor Wortmannin successfully reversed puerarin-induced up-regulation of Cdk1 but not Cyclin A2 and B1. CONCLUSION: These findings suggest that puerarin inhibits CM proliferation via slowing progression through G2/M phase during early-stage differentiation.

The inconsistency of molecular subtypes between primary foci and metastatic axillary lymph nodes in Luminal A breast cancer patients among Chinese women, an indication for chemotherapy?

Patients with Luminal A breast cancer often have favorable prognosis, but some of these patients still have lymph node metastases, it remains unclear what the role of adjuvant chemotherapy is in Luminal A subtype with lymph node metastases. The aim of this study was to find a new method to distinguish which Luminal A patient can be benefited from chemotherapy. We retrospectively investigated the inconsistency of molecular subtypes between primary foci and metastatic axillary lymph nodes in Luminal A breast cancer patients, and analyzed the clinicopathologic characteristics, Recurrence score (RS), disease-free survival (DFS), and overall survival (OS) in 146 Luminal A breast cancer patients. The discordance of molecular subtypes between primary foci and metastatic lymph nodes were explored by univariate and multivariate logistic regression. The DFS and OS were calculated by the Kaplan-Meier survival curves, and the Cox regression analyses were performed to identify independent prognostic factors for DFS and OS. In our results, the inconsistency was found in 55 patients (55/146, 37.67 %). Lymphatic vascular invasion (OR 6.402, 95 % CI 2.371-17.287, P < 0.001), lymph node stage (OR 2.147, 95 % CI 1.095-4.209, P = 0.026), and histological grade (OR 3.319, 95 % CI 1.101-8.951, P = 0.032) were significantly related to the inconsistency. The inconsistent group (non-Luminal A variations) had a poor prognosis compared with the consistent group, the DFS between the two groups was significantly different (P = 0.022), but the OS did not have obvious difference (P = 0.140). Moreover, the inconsistency was associated with high RS (P = 0.036). In conclusion, more aggressive molecular subtypes in metastatic lymph nodes, which associated with poor prognosis, were observed in Luminal A breast cancer patients, which indicate that chemotherapy is necessary for these patients.

Puerarin Suppresses the Self-Renewal of Murine Embryonic Stem Cells by Inhibition of REST-MiR-21 Regulatory Pathway.

BACKGROUND/AIMS: Puerarin shows a wide range of biological activities, including affecting the cardiac differentiation from murine embryonic stem (mES) cells. However, little is known about its effect and mechanism of action on the self-renewal of mES cells. This study aimed to determine the effect of puerarin on the self-renewal and pluripotency of mES cells and its underlying mechanisms. METHODS: RT-PCR and real-time PCR were used to detect the transcripts of core transcription factors, specific markers for multiple lineages, REST and microRNA-21 (miR-21). Colony-forming assay was performed to estimate the self-renewal capacity of mES cells. Western blotting and wortmannin were employed to explore the role of PI3K/Akt signaling pathway in the inhibitory action of puerarin on REST transcript. Transfected mES cells with antagomir21 were used to confirm the role of miR-21 in the action of puerarin on cell self-renewal. RESULTS: Puerarin significantly decreased the percentage of the self-renewal colonies, and suppressed the transcripts of Oct4, Nanog, Sox2, c-Myc and REST. Besides, PECAM, NCAM and miR-21 were up-regulated both under the self-renewal conditions and at day 4 of differentiation. The PI3K inhibitor wortmannin successfully reversed the mRNA expression changes of REST, Nanog and Sox2. Transfection of antagomir21 efficiently reversed the effects of puerarin on mES cells self-renewal. CONCLUSION: Inhibition of REST-miR-21 regulatory pathway may be the key mechanism of puerarin-induced suppression of mES cells self-renewal.

[Progress on PI3K/Akt signaling pathway regulating self-renewal and pluripotency of embryonic stem cells].

The phosphatidylinositol 3-kinase (PI3K) and its downstream target protein kinase B (Akt/PKB) can be activated by a variety of extracellular and intracellular signals. They are important signaling molecules and key survival factors involved in cell proliferation, differentiation, apoptosis and other cellular processes. Recently, many reports demonstrate that type I PI3K/Akt signaling pathway plays an important role in maintenance of self-renewal and pluripotency of embryonic stem (ES) cells. Further studies with regard to the self-renewal and pluripotency of ES cells and underlying molecular mechanisms are crucial to its application in cell replacement therapy, regenerative medicine and tissue engineering. The present review focuses on the recent progress on the mediation of PI3K/Akt signaling pathway on the maintenance of self-renewal and pluripotency of ES cells.

Genome-wide identification of SAMS gene family in Cucurbitaceae and the role of ClSAMS1 in watermelon tolerance to abiotic stress.

S-Adenosyl-L-methionine (SAM) is widely involved in plant growth, development, and abiotic stress response. SAM synthetase (SAMS) is the key enzyme that catalyzes the synthesis of SAM from methionine and ATP. However, the SAMS gene family has not been identified and their functions have not been characterized in most Cucurbitaceae plants. Here, a total of 30 SAMS genes were identified in nine Cucurbitaceae species and they were categorized into 3 subfamilies. Physicochemical properties and gene structure analysis showed that the SAMS protein members are tightly conserved. Further analysis of the cis-regulatory elements (CREs) of SAMS genes' promoter implied their potential roles in stress tolerance. To further understand the molecular functions of SAMS genes, watermelon SAMSs (ClSAMSs) were chosen to analyze the expression patterns in different tissues and under various abiotic stress and hormone responses. Among the investigated genes, ClSAMS1 expression was observed in all tissues and found to be up-regulated by abiotic stresses including salt, cold and drought treatments as well as exogenous hormone treatments including ETH, SA, MeJA and ABA. Furthermore, knockdown of ClSAMS1 via virus-induced gene silencing (VIGS) decreased SAM contents in watermelon seedings. The pTRSV2-ClSAMS1 plants showed reduced susceptibility to drought, cold and NaCl stress, indicating a positive role of ClSAMS1 in abiotic stresses tolerance. Those results provided candidate SAMS genes to regulate plant resistance against abiotic stresses in Cucurbitaceae plants.

Effects of puerarin on cardiac differentiation and ventricular specialization of murine embryonic stem cells.

AIMS: It is important to screen and identify chemical compounds to improve the efficiency of cardiac differentiation and specialization of embryonic stem (ES) cells. The objective of this study was to investigate the effect of puerarin, a natural phytoestrogen, on the in vitro cardiac differentiation and ventricular specialization of murine ES cells. METHODS: Cardiac differentiation of murine ES cells was performed by embryoid body (EB)-based differentiation method. Quantitative RT-PCR, flow cytometry and immunofluorescence were employed to identify cardiomyocytes (CMs) derived from murine ES cells (mES-CMs). Patch clamp was used to study the electrophysiological properties of CMs. RESULTS: We found that continuous puerarin treatment significantly increased the population of ES-CMs which express typical cardiac markers and are electrophysiological intact. Puerarin treatment shifted the cardiac phenotype from pacemaker-like cells to ventricular-like cells, which were Mlc2v-positive and present typical ventricular-like AP. Puerarin up-regulated transcripts involved in cardiac differentiation and ventricular specialization of ES cells. CONCLUSION: Our results suggest that puerarin promotes cardiac differentiation, and significantly enhances the specialization of mES cells into ventricular-like CMs. Puerarin may be used to increase the yield of ventricular mES-CMs during in vitro differentiation.

Baicalin maintains late-stage functional cardiomyocytes in embryoid bodies derived from murine embryonic stem cells.

BACKGROUND/AIMS: Low efficiency of cardiomyocyte (CM) differentiation from embryonic stem (ES) cells limits their therapeutic use. The objective of this study was to investigate the effect of baicalin, a natural flavonoid compound, on the in vitro cardiac differentiation of murine ES cells. METHODS: The induction of ES cells into cardiac-like cells was performed by embryoid body (EB)-based differentiation method. The electrophysiological properties of the ES cell-derived CMs (ES-CMs) were measured by patch-clamp. The biomarkers of ES-CMs were determined by quantitative RT-PCR and immunofluorescence. RESULTS: Continuous baicalin treatment decreased the size of EBs, and increased the proportion of α-actinin-positive CMs and transcript level of cardiac specific markers in beating EBs by inducing cell death of non-CMs. Baicalin increased the percentage of working ES-CMs which had typical responses to ß-adrenergic and muscarinic stimulations. CONCLUSION: Baicalin maintains the late-stage functional CMs in EBs derived from murine ES cells. This study describes a new insight into the various biological effects of baicalin on cardiac differentiation of pluripotent stem cells.

Change of urban and rural construction land and driving factors of arable land occupation.

Under the background of global urbanization, the continuous expansion and extensive utilization of urban and rural construction land has caused a large amount of arable land to be occupied, which seriously threatens national food security. This paper describes the spatio-temporal patterns of urban and rural construction land expansion and its occupation of arable land by using the urban and rural construction land expansion intensity, the urban and rural construction land expansion intensity difference index, and geo-detector model. It also explores the mechanisms through which the arable land was occupied. Results showed that construction land in both urban and rural areas expanded over the period 2009-2018 despite a large number of rural and urban migrants, and the major contributor to the rapid urbanization in China. This dual expansion could mainly be attributed to the tendency of these migrants to keep or even enlarge their rural construction land, which also resulted in a severer arable land loss than that caused by the expansion of urban construction land. Second, the rate of arable land occupied by urban and rural construction land in Henan province has been gradually slowing down, whereas the expansion of rural construction land is most dependent on arable land occupation. Third, according to the geo-detector model, the relationship between urbanization level and arable land occupied by urban and rural construction was the strongest, followed by the growth rate of fixed asset investment and the proportion of secondary and tertiary industries in GDP.

Research on Rural Population/Arable Land/Rural Settlements Association Model and Coordinated Development Path: A Case Analysis of the Yellow River Basin (Henan Section).

The countryside is a complex regional system with population and land as the core elements, and it is of great significance to study the coordination of the rural human-land relationship for promoting rural ecological protection and high-quality development. The Yellow River Basin (Henan section) is an important grain-producing area with dense population, fertile soil, and rich water resources. Based on the rate of change index and Tapio decoupling model, this study took county-level administrative region as the evaluation unit to explore the characteristics of the spatio-temporal correlation model of rural population/arable land/rural settlements in the Yellow River Basin (Henan section) from 2009 to 2018 and the optimal path of coordinated development. The results show the following: (1) The decrease of rural population, the increase of arable land in a relatively large part of non-central cities, the decrease of arable land in central cities, and the general increase in the area of rural settlements are the most important characteristics of the Yellow River Basin (Henan section) for the change of rural population/arable land/rural settlements. (2) There are spatial agglomeration characteristics of rural population changes, arable land changes, and rural settlements changes. Areas with a high degree of change in arable land have a certain degree of spatial consistency with areas with a high degree of change in rural settlements. (3) The type of T3 (rural population and arable land)/T3 (rural population and rural settlement) is the most important temporal and spatial combination mode, and rural population outflow is serious. In general, the spatio-temporal correlation model of rural population/arable land/rural settlements in the eastern and western sections of the Yellow River Basin (Henan section) is better than that in the middle section. The research results are helpful to deeply understand the relationship between rural population and land in the period of rapid urbanization and can provide reference for the classification and sub-standard policies of rural revitalization. It is urgent to establish sustainable rural development strategies for improving the human-land relationship, narrowing the rural-urban disparity, innovating rural residential land area policies, and revitalizing the rural area.

NTBiRW: A Novel Neighbor Model based on Two-tier Bi-Random Walk for Predicting Potential Disease-related Microbes.

Studies have revealed that microbes have an important effect on numerous physiological processes, and further research on the links between diseases and microbes is significant. Given that laboratory methods are expensive and not optimized, computational models are increasingly used for discovering disease-related microbes. Here, a new neighbor approach based on two-tier Bi-Random Walk is proposed for potential disease-related microbes, known as NTBiRW. In this method, the first step is to construct multiple microbe similarities and disease similarities. Then, three kinds of microbe/disease similarity are integrated through two-tier Bi-Random Walk to obtain the final integrated microbe/disease similarity network with different weights. Finally, Weighted K Nearest Known Neighbors (WKNKN) is used for prediction based on the final similarity network. In addition, leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold CV) are applied for evaluating the performance of NTBiRW. Multiple evaluating indicators are taken to show the performance from multiple perspectives. And most of the evaluation index values of NTBiRW are better than those of the compared methods. Moreover, in case studies on atopic dermatitis and psoriasis, most of the first 10 candidates in the final result can be proven. This also demonstrates the capability of NTBiRW for discovering new associations. Therefore, this method can contribute to the discovery of disease-related microbes and thus offer new thoughts for further understanding the pathogenesis of diseases.

A Method Based On Dual-Network Information Fusion to Predict MiRNA-Disease Associations.

MicroRNAs (miRNAs) are single-stranded small RNAs. An increasing number of studies have shown that miRNAs play a vital role in many important biological processes. However, some experimental methods to predict unknown miRNA-disease associations (MDAs) are time-consuming and costly. Only a small percentage of MDAs are verified by researchers. Therefore, there is a great need for high-speed and efficient methods to predict novel MDAs. In this paper, a new computational method based on Dual-Network Information Fusion (DNIF) is developed to predict potential MDAs. Specifically, on the one hand, two enhanced sub-models are integrated to reconstruct an effective prediction framework; on the other hand, the prediction performance of the algorithm is improved by fully fusing multiple omics data information, including validated miRNA-disease associations network, miRNA functional similarity, disease semantic similarity and Gaussian interaction profile (GIP) kernel network associations. As a result, DNIF achieves the excellent performance under situation of 5-fold cross validation (average AUC of 0.9571). In the cases study of three important human diseases, our model has achieved satisfactory performance in predicting potential miRNAs for certain diseases. The reliable experimental results demonstrate that DNIF could serve as an effective calculation method to accelerate the identification of MDAs.

MSF-LRR: Multi-Similarity Information Fusion Through Low-Rank Representation to Predict Disease-Associated Microbes.

An Increase in microbial activity is shown to be intimately connected with the pathogenesis of diseases. Considering the expense of traditional verification methods, researchers are working to develop high-efficiency methods for detecting potential disease-related microbes. In this article, a new prediction method, MSF-LRR, is established, which uses Low-Rank Representation (LRR) to perform multi-similarity information fusion to predict disease-related microbes. Considering that most existing methods only use one class of similarity, three classes of microbe and disease similarity are added. Then, LRR is used to obtain low-rank structural similarity information. Additionally, the method adaptively extracts the local low-rank structure of the data from a global perspective, to make the information used for the prediction more effective. Finally, a neighbor-based prediction method that utilizes the concept of collaborative filtering is applied to predict unknown microbe-disease pairs. As a result, the AUC value of MSF-LRR is superior to other existing algorithms under 5-fold cross-validation. Furthermore, in case studies, excluding originally known associations, 16 and 19 of the top 20 microbes associated with Bacterial Vaginosis and Irritable Bowel Syndrome, respectively, have been confirmed by the recent literature. In summary, MSF-LRR is a good predictor of potential microbe-disease associations and can contribute to drug discovery and biological research.

BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix.

With the high-quality development of bioinformatics technology, miRNA-disease associations (MDAs) are gradually being uncovered. At present, convenient and efficient prediction methods, which solve the problem of resource-consuming in traditional wet experiments, need to be further put forward. In this study, a space projection model based on block matrix is presented for predicting MDAs (BMPMDA). Specifically, two block matrices are first composed of the known association matrix and similarity to increase comprehensiveness. For the integrity of information in the heterogeneous network, matrix completion (MC) is utilized to mine potential MDAs. Considering the neighborhood information of data points, linear neighborhood similarity (LNS) is regarded as a measure of similarity. Next, LNS is projected onto the corresponding completed association matrix to derive the projection score. Finally, the AUC and AUPR values for BMPMDA reach 0.9691 and 0.6231, respectively. Additionally, the majority of novel MDAs in three disease cases are identified in existing databases and literature. It suggests that BMPMDA can serve as a reliable prediction model for biological research.

Predicting miRNA-Disease Associations Through Deep Autoencoder With Multiple Kernel Learning.

Determining microRNA (miRNA)-disease associations (MDAs) is an integral part in the prevention, diagnosis, and treatment of complex diseases. However, wet experiments to discern MDAs are inefficient and expensive. Hence, the development of reliable and efficient data integrative models for predicting MDAs is of significant meaning. In the present work, a novel deep learning method for predicting MDAs through deep autoencoder with multiple kernel learning (DAEMKL) is presented. Above all, DAEMKL applies multiple kernel learning (MKL) in miRNA space and disease space to construct miRNA similarity network and disease similarity network, respectively. Then, for each disease or miRNA, its feature representation is learned from the miRNA similarity network and disease similarity network via the regression model. After that, the integrated miRNA feature representation and disease feature representation are input into deep autoencoder (DAE). Furthermore, the novel MDAs are predicted through reconstruction error. Ultimately, the AUC results show that DAEMKL achieves outstanding performance. In addition, case studies of three complex diseases further prove that DAEMKL has excellent predictive performance and can discover a large number of underlying MDAs. On the whole, our method DAEMKL is an effective method to identify MDAs.

Modified CFBP-bFGF targeting to ischemic brain promoted the functional recovery of cerebral ischemia.

The cerebral ischemia was one of the most common causes of disability and death worldwide. Basic fibroblast growth factor (bFGF) was reported to have neuroprotective function as well as promoting angiogenesis in the ischemic brain, but the targeting delivery of bFGF to ischemic brain was still difficult. In present study, a specific peptide was used to modify bFGF to construct recombinant CFBP-bFGF, and CFBP-bFGF could specifically deliver to ischemic brain through binding with the upregulated protein-connective tissue growth factor (CTGF). When CFBP-bFGF was used in rats with cerebral ischemia by intravenous injection, local concentration of the bFGF in ischemic brain was significantly increased. In addition, enhanced neurons survival, increased angiogenesis, decreased neuroinflammation were observed, that improved the motor functional recovery of cerebral ischemic injury. These results demonstrated that the targeting delivery of CFBP-bFGF would be a potential therapeutic approach for cerebral ischemia.