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.

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.

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.

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.

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.

Venous thromboembolism in children with acute lymphoblastic leukemia in China: a report from the Chinese Children's Cancer Group-ALL-2015.

Venous thromboembolism (VTE) is a complication in children with acute lymphoblastic leukemia (ALL). The Chinese Children's Cancer Group-ALL-2015 protocol was carried out in China, and epidemiology, clinical characteristics, and risk factors associated with VTE were analyzed. We collected data on VTE in a multi-institutional clinical study of 7640 patients with ALL diagnosed in 20 hospitals from January 2015 to December 2019. First, VTE occurred in 159 (2.08%) patients, including 90 (56.6%) during induction therapy and 108 (67.92%) in the upper extremities. T-ALL had a 1.74-fold increased risk of VTE (95% CI 1.08-2.8, P = 0.022). Septicemia, as an adverse event of ALL treatment, can significantly promote the occurrence of VTE (P < 0.001). Catheter-related thrombosis (CRT) accounted for 75.47% (n = 120); and, symptomatic VTE, 58.49% (n = 93), which was more common in patients aged 12-18 years (P = 0.023), non-CRT patients (P < 0.001), or patients with cerebral thrombosis (P < 0.001). Of the patients with VTE treated with anticoagulation therapy (n = 147), 4.08% (n = 6) had bleeding. The VTE recurrence rate was 5.03% (n = 8). Patients with VTE treated by non-ultrasound-guided venous cannulation (P = 0.02), with residual thrombus (P = 0.006), or with short anticoagulation period (P = 0.026) had high recurrence rates. Thus, preventing repeated venous puncture and appropriately prolonged anticoagulation time can reduce the risk of VTE recurrence.

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.

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.

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.

Specific bio-functional CBD-PR1P peptide binding VEGF to collagen hydrogels promotes the recovery of cerebral ischemia in rats.

Ischemic stroke was a leading cause of death and long-term disability. It was an effective way to improve cerebral ischemia injury by promoting angiogenesis and neuroprotection. Vascular endothelial growth factor (VEGF) was a potent pro-angiogenic factor, and had neuroprotective effect. A short peptide (PR1P) derived from the extracellular VEGF-binding glycoprotein-Prominin-1 was reported to specifically bind to VEGF. In order to realize sustained release of VEGF, a bio-functional peptide-CBD-PR1P was constructed, which target VEGF to collagen hydrogels to limit the diffusion of VEGF. When the collagen hydrogels loading with CBD-PR1P and VEGF were injected into the cerebral ischemic cortex, increased angiogenesis, decreased apoptosis and enhanced neurons survival were observed in the ischemic area, that promoted the motor functional recovery of cerebral ischemic injury. Thus, this targeting delivery system of VEGF provided a promising therapeutic strategy for cerebral ischemia.

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.

NCPLP: A Novel Approach for Predicting Microbe-Associated Diseases With Network Consistency Projection and Label Propagation.

A growing number of clinical studies have provided substantial evidence of a close relationship between the microbe and the disease. Thus, it is necessary to infer potential microbe-disease associations. But traditional approaches use experiments to validate these associations that often spend a lot of materials and time. Hence, more reliable computational methods are expected to be applied to predict disease-associated microbes. In this article, an innovative mean for predicting microbe-disease associations is proposed, which is based on network consistency projection and label propagation (NCPLP). Given that most existing algorithms use the Gaussian interaction profile (GIP) kernel similarity as the similarity criterion between microbe pairs and disease pairs, in this model, Medical Subject Headings descriptors are considered to calculate disease semantic similarity. In addition, 16S rRNA gene sequences are borrowed for the calculation of microbe functional similarity. In view of the gene-based sequence information, we use two conventional methods (BLAST+ and MEGA7) to assess the similarity between each pair of microbes from different perspectives. Especially, network consistency projection is added to obtain network projection scores from the microbe space and the disease space. Ultimately, label propagation is utilized to reliably predict microbes related to diseases. NCPLP achieves better performance in various evaluation indicators and discovers a greater number of potential associations between microbes and diseases. Also, case studies further confirm the reliable prediction performance of NCPLP. To conclude, our algorithm NCPLP has the ability to discover these underlying microbe-disease associations and can provide help for biological study.

Cytogenetic Characteristics of Childhood Acute Lymphoblastic Leukemia: A Study of 1541 Chinese Patients Newly Diagnosed between 2001 and 2014.

OBJECTIVE: Cytogenetic abnormalities have been proven to be the most valuable parameter for risk stratification of childhood acute lymphoblastic leukemia (ALL). However, studies on the prevalence of cytogenetic abnormalities and their correlation to clinical features in Chinese pediatric patients are limited, especially large-scale studies. METHODS: We collected the cytogenetics and clinical data of 1541 children newly diagnosed with ALL between 2001 and 2014 in four Chinese hospitals, and retrospectively analyzed their clinical features, prognosis and risk factors associated with pediatric ALL. RESULTS: All of these patients had karyotyping results, and some of them were tested for fusion genes by fluorescence in situ hybridization or reverse-transcription polymerase chain reaction. Overall, 930 cases (60.4%) had abnormal cytogenetics in this study, mainly including high hyperdiploidy (HHD, n=276, 17.9%), hypodiploidy (n=74, 4.8%), t(12;21)/TEL-AML1 (n=260, 16.9%), t(1;19)/E2A-PBX1 (n=72, 4.7%), t(9;22)/BCR-ABL (n=64, 4.2%), and t(v;11q23)/MLL rearrangements (n=40, 2.6%). The distribution of each cytogenetic abnormality was correlated with gender, age, white blood cell count at diagnosis, and immunophenotype. In addition, multivariate analysis suggested that t(v;11q23)/MLL rearrangements (OR: 2.317, 95%CI: 1.219-3.748, P=0.008) and t(9;22)/BCR-ABL (OR: 2.519, 95%CI: 1.59-3.992, P<0.001) were independent risk factors for a lower event-free survival (EFS) rate in children with ALL, while HHD (OR: 0.638, 95%CI: 0.455-0.894, P=0.009) and t(12;21)/TEL-AML1 (OR: 0.486, 95%CI: 0.333-0.707, P<0.001) were independent factors of a favorable EFS. CONCLUSION: The cytogenetic characteristics presented in our study resembled other research groups, emphasizing the important role of cytogenetic and molecular genetic classification in ALL, especially in B-ALL.

Humulus scandens-Derived Biochars for the Effective Removal of Heavy Metal Ions: Isotherm/Kinetic Study, Column Adsorption and Mechanism Investigation.

Humulus scandens was first adopted as a biomass precursor to prepare biochars by means of a facile molten salt method. The optimized biochar exhibits a high specific surface area of ~450 m2/g, a rich porous structure and abundant oxygen functional groups, which demonstrate excellent adsorption performance for heavy metal ions. The isotherm curves fit well with the Langmuir models, indicating that the process is governed by the chemical adsorption, and that the maximum adsorption capacity can reach 748 and 221 mg/g for Pb2+ and Cu2+, respectively. In addition, the optimized biochar demonstrates good anti-interference ability and outstanding removal efficiency for Cu2+ and Pb2+ in simulated wastewater. The mechanism investigation and DFT calculation suggest that the oxygen functional groups play dominant roles in the adsorption process by enhancing the binding energy towards the heavy metal ions. Meanwhile, ion exchange also serves as the main reason for the effective removal.

Serum potassium, albumin and vitamin B12 as potential oxidative stress markers of fungal peritonitis.

BACKGROUND: Biomarkers of oxidative stress (OS) have been poorly explored in fungal peritonitis (FP). Potassium is a regulator of pro-oxidants and antioxidants. Albumin and vitamin B12 (B12) are vital antioxidant agents in the circulatory system. This study aimed to investigate the antioxidative role of serum potassium, albumin and B12 in FP. METHODS: Serum levels of potassium, albumin and B12 were retrospectively analyzed in 21 patients with a confirmed diagnosis of FP, 105 bacterial peritonitis (BP) patients and 210 patients receiving peritoneal dialysis without peritonitis. RESULTS: Serum levels of potassium, albumin and B12 were lower in FP patients than in BP patients. Serum potassium concentration was statistically related to albumin concentration in peritonitis patients. Univariate and multivariate binary logistic regression analysis suggested that serum level of potassium and albumin were independent risk factors of FP when compared with BP. Lower potassium and B12 levels were independently associated with higher rates of technique failure in peritonitis. CONCLUSION: These findings suggest lower serum potassium, albumin and B12 as potential oxidative stress markers of FP and raise the hypothesis that an increased level of OS could contribute to FP.KEY MESSAGESFP remains a serious complication of peritoneal dialysis (PD), with higher morbidity (1-23.8%) and mortality (2-25%), and oxidative stress plays a role in it.Our study suggested serum potassium, albumin and vitamin B12 as potential oxidative stress markers of fungal peritonitis.

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.

Epidemiology of Takayasu arteritis in Shanghai: A hospital-based study and systematic review.

BACKGROUND: Takayasu arteritis (TAK) is a rare large vessel vasculitis, and epidemiological data on TAK are lacking in China. Thus, we designed this study to estimate the TAK prevalence and incidence in residential Shanghai, China. METHODS: Data on diagnosed TAK cases aged over 16 years were retrieved from 22 tertiary hospitals in Shanghai through hospital electronic medical record systems between January 1, 2015 and December 31, 2017 to estimate the prevalence and incidence. A systematic literature review based on searches in PubMed, Ovid-Medline, Excerpta Medica Database (EMBASE), Web of Science, and China National Knowledge Infrastructure (CNKI) was performed to summarize TAK distribution across the world. RESULTS: In total 102 TAK patients, with 64% female, were identified. The point prevalence (2015-2017) was 7.01 (95% CI 5.65-8.37) cases per million, and the mean annual incidence was 2.33 (1.97-3.21) cases per million. The average age of TAK patients was 44 ± 16 years, with the highest prevalence (11.59 [9.23-19.50] cases per million) and incidence (3.55 [0.72 3.74] cases per million) in the 16 to 34 years population. Seventeen reports were included in the system review, showing that the epidemiology of TAK varied greatly across the world. The incidence and prevalence were both relatively higher in Asian countries, with the prevalence ranging 3.3-40 cases per million and annual incidence ranging 0.34-2.4 cases per million. CONCLUSIONS: The prevalence and incidence of TAK in Shanghai was at moderate to high levels among the previous reports. The disease burden varied globally among racial populations.