Exploring neuron-specific steroid synthesis and DHEAS therapy in Alzheimer's disease.

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by cognitive decline and memory loss. Recent studies have suggested a potential role for steroid synthesis in AD pathology. This study investigated the co-localization of steroidogenic enzymes in neuronal cells, changes in enzyme expression in an AD mouse model, and steroid expressions in human AD samples. Additionally, we conducted a steroidomic metabolomics analysis and evaluated the effects of dehydroepiandrosterone sulfate (DHEAS) treatment in an AD mouse model. Immunofluorescence analysis revealed significant co-localization of cytochrome P450 family 17 subfamily A member 1 (CYP17A1) and steroidogenic acute regulatory protein (StAR) proteins with α-synuclein in presynaptic neurons, suggesting active steroid synthesis in these cells. Conversely, such co-localization was absent in astrocytes. In the AD mouse model, a marked decrease in the expression of steroidogenic enzymes (Cyp11a1, Cyp17a1, Star) was observed, especially in areas with amyloid beta plaque accumulation. Human AD and MS brain tissues showed similar reductions in StAR and CYP17A1 expressions. Steroidomic analysis indicated a downregulation of key steroids in the serum of AD patients. DHEAS treatment in AD mice resulted in improved cognitive function and reduced Aß accumulation. Our findings indicate a neuron-specific pathway for steroid synthesis, potentially playing a crucial role in AD pathology. The reduction in steroidogenic enzymes and key steroids in AD models and human samples suggests that impaired steroid synthesis is a feature of neurodegenerative diseases. The therapeutic potential of targeting steroid synthesis pathways, as indicated by the positive effects of DHEAS treatment, warrants further investigation.

Rapid Screening of New Psychoactive Substances Using pDART-QqQ-MS.

Drug abuse is a severe social problem worldwide. Particularly, the issue of new psychoactive substances (NPSs) have increasingly emerged. NPSs are structural or functional analogs of traditional illicit drugs, such as cocaine, cannabis, and amphetamine; these molecules provide the same or more severe neurological effects. Usually, immunoassays are utilized in the preliminary screening method. However, NPSs have poor detectability in commercially available immunoassay kits. Meanwhile, various chromatography combined with the mass spectrometry platform have been developed to quantify NPSs. Still, a significant amount of time and resources are required during these procedures. Therefore, we established a rapid analytical platform for NPSs employing paper-loaded direct analysis in real time triple quadrupole mass spectrometry (pDART-QqQ-MS). We implemented this platform for the semiquantitative analysis of forensic drug tests in urine. This platform significantly shrinks the analytical time of a single sample within 30 s and requires a low volume of the specimen. The platform can detect 21 NPSs in urine mixtures at a lower limit of qualification of concentration ranging from 20 to 75 nanograms per milliliter (ng mL-1) and is lower than the cutoff value of currently available immune-based devices for detecting multiple drugs (1000 ng mL-1). Urine samples from drug addicts have been collected to verify the platform's effectiveness. By combining efficiency and accuracy, our platform offers a promising solution for addressing the challenges posed by NPSs in drug abuse detection.

Impurities in over-the-counter pseudoephedrine leading to methcathinone detection in urine.

Methcathinone, a psychoactive substance with stimulant properties, has raised concerns in recent years due to its presence in urine screenings, even among individuals with no history of drug abuse. To prevent misjudgment, this work aims to explore the source of methcathinone in urine. A total of 58 urine samples tested positive for methcathinone in the National Taiwan University Hospital cohort, with 27 linked to illicit drug use and 31 from individuals with no drug use history. Co-occurrence analysis revealed a strong association between methcathinone and over-the-counter cold medications containing pseudoephedrine or ephedrine. In an in vivo experiment, participants who consumed pseudoephedrine-containing drugs showed the presence of methcathinone in their urine, suggesting a connection between these substances. Additionally, tests on pharmaceutical products containing pseudoephedrine detected small amounts of methcathinone as impurities. The findings suggest that the presence of methcathinone in nonillicit drug users may be attributed to impurities in over-the-counter pseudoephedrine-containing medications. This raises concerns about potential misinterpretations of drug screening results and underscores the need for more comprehensive criteria for assessing drug use. This study contributes to our understanding of the origin of methcathinone in urine, which has implications for legal justice and drug screening practices.

MLMSeg: A multi-view learning model for ultrasound thyroid nodule segmentation.

Accurate segmentation of the thyroid gland in ultrasound images is an essential initial step in distinguishing between benign and malignant nodules, thus facilitating early diagnosis. Most existing deep learning-based methods to segment thyroid nodules are learned from only a single view or two views, which limits the performance of segmenting nodules at different scales in complex ultrasound scanning environments. To address this limitation, this study proposes a multi-view learning model, abbreviated as MLMSeg. First, a deep convolutional neural network is introduced to encode the features of the local view. Second, a multi-channel transformer module is designed to capture long-range dependency correlations of global view between different nodules. Third, there are semantic relationships of structural view between features of different layers. For example, low-level features and high-level features are endowed with hidden relationships in the feature space. To this end, a cross-layer graph convolutional module is proposed to adaptively learn the correlations of high-level and low-level features by constructing graphs across different layers. In addition, in the view fusion, a channel-aware graph attention block is devised to fuse the features from the aforementioned views for accurate segmentation of thyroid nodules. To demonstrate the effectiveness of the proposed method, extensive comparative experiments were conducted with 14 baseline methods. MLMSeg achieved higher Dice coefficients (92.10% and 83.84%) and Intersection over Union scores (86.60% and 73.52%) on two different thyroid datasets. The exceptional segmentation capability of MLMSeg for thyroid nodules can greatly assist in localizing thyroid nodules and facilitating more precise measurements of their transverse and longitudinal diameters, which is of significant clinical relevance for the diagnosis of thyroid nodules.

RNA-binding proteins in breast cancer: Biological implications and therapeutic opportunities.

RNA-binding proteins (RBPs) refer to a class of proteins that participate in alternative splicing, RNA stability, polyadenylation, localization and translation of RNAs, thus regulating gene expression in post-transcriptional manner. Dysregulation of RNA-RBP interaction contributes to various diseases, including cancer. In breast cancer, disorders in RBP expression and function influence the biological characteristics of tumor cells. Targeting RBPs has fostered the development of innovative therapies for breast cancer. However, the RBP-related mechanisms in breast cancer are not completely clear. In this review, we summarize the regulatory mechanisms of RBPs and their signaling crosstalk in breast cancer. Specifically, we emphasize the potential of certain RBPs as prognostic factors due to their effects on proliferation, invasion, apoptosis, and therapy resistance of breast cancer cells. Most importantly, we present a comprehensive overview of the latest RBP-related therapeutic strategies and novel therapeutic targets that have proven to be useful in the treatment of breast cancer.

Hepatoviruses promote very-long-chain fatty acid and sphingolipid synthesis for viral RNA replication and quasi-enveloped virus release.

Virus-induced changes in host lipid metabolism are an important but poorly understood aspect of viral pathogenesis. By combining nontargeted lipidomics analyses of infected cells and purified extracellular quasi-enveloped virions with high-throughput RNA sequencing and genetic depletion studies, we show that hepatitis A virus, an hepatotropic picornavirus, broadly manipulates the host cell lipid environment, enhancing synthesis of ceramides and other sphingolipids and transcriptionally activating acyl-coenzyme A synthetases and fatty acid elongases to import and activate long-chain fatty acids for entry into the fatty acid elongation cycle. Phospholipids with very-long-chain acyl tails (>C22) are essential for genome replication, whereas increases in sphingolipids support assembly and release of quasi-enveloped virions wrapped in membranes highly enriched for sphingomyelin and very-long-chain ceramides. Our data provide insight into how a pathogenic virus alters lipid flux in infected hepatocytes and demonstrate a distinction between lipid species required for viral RNA synthesis versus nonlytic quasi-enveloped virus release.

Optimizing adrenal vein sampling in primary aldosteronism subtyping through LC-MS/MS and secretion ratios of aldosterone, 18-oxocortisol, and 18-hydroxycortisol.

Adrenal venous sampling (AVS) is the gold standard for identifying curable unilateral aldosterone excess in primary aldosteronism (PA). Studies have demonstrated the value of steroid profiling through liquid chromatography-tandem mass spectrometry (LC-MS/MS) in AVS interpretation. First, the performance of LC-MS/MS and immunoassay in assessing selectivity and lateralization was compared. Second, the utility of the proportion of individual steroids in adrenal veins in subtyping PA was analyzed. We enrolled 75 consecutive patients with PA who underwent AVS between 2020 and 2021. Fifteen adrenal steroids were analyzed in peripheral and adrenal veins through LC-MS/MS before and after adrenocorticotropic hormone (ACTH) stimulation. Through selectivity index that was based on cortisol and alternative steroids, LC-MS/MS rescued 45% and 66% of failed cases judged by immunoassay in unstimulated and stimulated AVS, respectively. LC-MS/MS identified more unilateral diseases than did immunoassay (76% vs. 45%, P < 0.05) and provided adrenalectomy opportunities to 69% of patients judged through immunoassay to have bilateral disease. The secretion ratios (individual steroid concentration/total steroid concentration) of aldosterone, 18-oxocortisol, and 18-hydroxycortisol were novel indicators for identifying unilateral PA. The 18-oxocortisol secretion ratio of ≥0.785‰ (sensitivity/specificity: 0.90/0.77) at pre-ACTH and aldosterone secretion ratio of ≤0.637‰ (sensitivity/specificity: 0.88/0.85) at post-ACTH enabled optimal accuracy for predicting ipsilateral and contralateral disease, respectively, in robust unilateral PA. LC-MS/MS improved the success rate of AVS and identified more unilateral diseases than immunoassay. The secretion ratios of steroids can be used to discriminate the broad PA spectrum.

Optical engineering of infrared PbS CQD photovoltaic cells for wireless optical power transfer systems.

Infrared photovoltaic cells (IRPCs) have attracted considerable attention for potential applications in wireless optical power transfer (WOPT) systems. As an efficient fiber-integrated WOPT system typically uses a 1550 nm laser beam, it is essential to tune the peak conversion efficiency of IRPCs to this wavelength. However, IRPCs based on lead sulfide (PbS) colloidal quantum dots (CQDs) with an excitonic peak of 1550 nm exhibit low short circuit current (Jsc) due to insufficient absorption under monochromatic light illumination. Here, we propose comprehensive optical engineering to optimize the device structure of IRPCs based on PbS CQDs, for 1550 nm WOPT systems. The absorption by the device is enhanced by improving the transmittance of tin-doped indium oxide (ITO) in the infrared region and by utilizing the optical resonance effect in the device. Therefore, the optimized device exhibited a high short circuit current density of 37.65 mA/cm2 under 1 sun (AM 1.5G) solar illumination and 11.91 mA/cm2 under 1550 nm illumination 17.3 mW/cm2. Furthermore, the champion device achieved a record high power conversion efficiency (PCE) of 7.17% under 1 sun illumination and 10.29% under 1550 nm illumination. The PbS CQDs IRPCs under 1550 nm illumination can even light up a liquid crystal display (LCD), demonstrating application prospects in the future.

MetaMOPE: a web service for mobile phase determination and fast chromatography peaks evaluation for metabolomics.

Motivation: Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used in metabolomics studies, while HILIC LC-MS is particularly suited for polar metabolites. Determining an optimized mobile phase and developing a proper liquid chromatography method tend to be laborious, time-consuming and empirical. Results: We developed a containerized web tool providing a workflow to quickly determine the optimized mobile phase by batch-evaluating chromatography peaks for metabolomics LC-MS studies. A mass chromatographic quality value, an asymmetric factor, and the local maximum intensity of the extracted ion chromatogram were calculated to determine the number of peaks and peak retention time. The optimal mobile phase can be quickly determined by selecting the mobile phase that produces the largest number of resolved peaks. Moreover, the workflow enables one to automatically process the repeats by evaluating chromatography peaks and determining the retention time of large standards. This workflow was validated with 20 chemical standards and successfully constructed a reference library of 571 metabolites for the HILIC LC-MS platform. Availability and implementation: MetaMOPE is freely available at https://metamope.cmdm.tw. Source code and installation instructions are available on GitHub: https://github.com/CMDM-Lab/MetaMOPE. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Determining plasma and cerebrospinal fluid concentrations of EGFR-TKI in lung cancer patients.

Epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are commonly used to treat advanced non-small cell lung cancer (NSCLC). A rapid and reliable method for measuring plasma and cerebrospinal fluid (CSF) concentrations of EGFR-TKIs is needed for therapeutic drug monitoring. By using UHPLC‒MS/MS with multiple reaction monitoring mode, we developed a method for rapidly determining the plasma and CSF concentrations of gefitinib, erlotinib, afatinib, and osimertinib. Protein precipitation was employed to remove protein interference for plasma and CSF matrix. The LC‒MS/MS assay was validated to be satisfactory in terms of linearity, precision, and accuracy. This method was successfully applied to measure plasma (n = 44) and CSF (n = 6) concentrations of EGFR-TKIs in NSCLC patients. The chromatographic separation was achieved by a Hypersil Gold aQ column within 3 min. The median plasma concentrations were 325.76, 1981.50, 42.62, 40.27, and 340.92 ng/ml for gefitinib erlotinib, afatinib 30 mg/day, afatinib 40 mg/day, and osimertinib, respectively. The CSF penetration rates were 2.15% for the patients receiving erlotinib therapy, 0.59% for afatinib, 0.08-1.12% for osimertinib 80 mg/day, and 2.18% for those receiving osimertinib 160 mg/day. This assay helps to predict the effectiveness and toxicities of EGFR-TKIs in the pursuit of precision medicine for lung cancer patients.

[Latest Findings on the Interaction Mechanism Between Depressive Disorder and Intestinal Permeability].

The intestinal barrier, a complex structure consisting of multiple layers of defense barriers, blocks the transfer of intestinal and foreign bacteria and their metabolites into the internal environment of the human body. Intestinal permeability can be used to evaluate the integrity of the intestinal barrier. Increased intestinal permeability has been observed in patients with depressive disorder. Some studies have reported an interaction between depressive disorder and intestinal barrier. Herein, we reviewed reported findings on the mechanisms of how systematic low-grade inflammation, vagal nerve dysfunction, and hypothalamic-pituitary-adrenal axis dysfunction cause changes in intestinal permeability in patients with depressive disorder and the pathogenic mechanism of how bacterial translocation caused by damaged intestinal barrier leads to depressive disorder. In addition, the potential mechanisms of how antidepressants improve intestinal permeability and how probiotics improve depressive disorder have been discussed.

An automated workflow on data processing (AutoDP) for semiquantitative analysis of urine organic acids with GC-MS to facilitate diagnosis of inborn errors of metabolism.

Determination of urine organic acids (UOAs) is essential to understand the disease progress of inborn errors of metabolism (IEM) and often relies on GC-MS analysis. However, the efficiency of analytical reports is sometimes restricted by data processing due to labor-intensive work if no proper tool is employed. Herein, we present a simple and rapid workflow with an R-based script for automated data processing (AutoDP) of GC-MS raw files to quantitatively analyze essential UOAs. AutoDP features automatic quality checks, compound identification and confirmation with specific fragment ions, retention time correction from analytical batches, and visualization of abnormal UOAs with age-matched references on chromatograms. Compared with manual processing, AutoDP greatly reduces analytical time and increases the number of identifications. Speeding up data processing is expected to shorten the waiting time for clinical diagnosis, which could greatly benefit clinicians and patients with IEM. In addition, with quantitative results obtained from AutoDP, it would be more feasible to perform retrospective analysis of specific UOAs in IEM and could provide new perspectives for studying IEM.

Development of the dried blood spot preparation protocol for comprehensive evaluation of the hematocrit effect.

The application of dried blood spots (DBS) has gradually increased in different fields because of its several advantages. The hematocrit (Hct) effect is one major analytical challenge that may affect the quantification accuracy of DBS samples and should be investigated when developing a novel DBS method. However, previous studies usually overlooked the Hct-related distribution bias when evaluating the Hct effect. This study aimed to propose an effective DBS preparation protocol for the comprehensive evaluation of the Hct effect. We selected voriconazole and posaconazole as the demonstration drugs. Fifteen microliters of the blood samples were spotted on DBS cards followed by whole spot extraction. An LC-MS/MS method was first developed to quantify voriconazole and posaconazole in DBS samples. The quantitation accuracy for both azole drugs was within 93.5%-111.7%, except for the accuracies of posaconazole at the LLOQ, which were less than 119%. The intra- and interday precision were below 11%. The validated LC-MS/MS method was used to develop the DBS preparation protocol for evaluating the Hct effect. Three critical parameters that may affect the observed Hct effect were investigated. The results showed that using the solid-state of the target analytes, spiking the target analytes before preparing different Hct levels, and allowing enough equilibrium time after spiking target analytes can provide a more holistic Hct effect evaluation. The validity of the proposed new protocol was verified by conversion factors obtained from 71 paired DBS and plasma samples. Conversion factors calculated by clinical samples were consistent with the Hct effect evaluated by manually prepared DBS samples. This new DBS preparation protocol eliminated the common pitfalls in studying the Hct effect and offered a comprehensive strategy to assess the Hct effect for further DBS studies.

Defective determination of synthetic cathinones in blood for forensic investigation.

Antemortem specimens are sometimes the sole sources available for forensic investigation, and samples collected in nonideal ways are inevitably employed to achieve toxicological analysis. It is essential to assess the effects of blood collection tubes on the recoveries of emerging synthetic cathinones (SC) to estimate actual drug concentrations, and no such systematic investigations have been previously carried out. Seventy-one SC with various LogP values were employed to examine commonly used blood collection tubes, including plasma tubes, serum tubes and gel-containing tubes in recoveries which determined by a reliable LC-MS/MS method. Significantly poor recoveries for hydrophobic SC were obtained using serum separating tubes (SST). Notably, the suppressed recoveries in SST can be reversed by adding anticoagulants. Adding a procoagulant to a plasma separating tube (PST) considerably reduced recoveries, which indicated that clotting processes in the presence of polymeric gels contributed to poor recoveries of these hydrophobic drugs. In this study, we find that clotting formation in the presence of polymeric gels could significantly affect the determination of hydrophobic drugs. However, in real-world scenarios, nonideal collection methods are inevitably employed for antemortem specimens. Thus, it is important to rigorously interpret forensic toxicological results, especially for susceptible species.

MAGCN: A Multiple Attention Graph Convolution Networks for Predicting Synthetic Lethality.

Synthetic lethality (SL) is a potential cancer therapeutic strategy and drug discovery. Computational approaches to identify synthetic lethality genes have become an effective complement to wet experiments which are time consuming and costly. Graph convolutional networks (GCN) has been utilized to such prediction task as be good at capturing the neighborhood dependency in a graph. However, it is still a lack of the mechanism of aggregating the complementary neighboring information from various heterogeneous graphs. Here, we propose the Multiple Attention Graph Convolution Networks for predicting synthetic lethality (MAGCN). First, we obtain the functional similarity features and topological structure features of genes from different data sources respectively, such as Gene Ontology data and Protein-Protein Interaction. Then, graph convolutional network is utilized to accumulate the knowledge from neighbor nodes according to synthetic lethal associations. Meanwhile, we propose a multiple graphs attention model and construct a multiple graphs attention network to learn the contribution factors of different graphs to generate embedded representation by aggregating these graphs. Finally, the generated feature matrix is decoded to predict potential synthetic lethal interaction. Experimental results show that MAGCN is superior to other baseline methods. Case study demonstrates the ability of MAGCN to predict human SL gene pairs.

Development of an enrichment-free one-pot sample preparation and ultra-high performance liquid chromatography-tandem mass spectrometry method to identify Immunoglobulin A1 hinge region O-glycoforms for Immunoglobulin A nephropathy.

Immunoglobulin A nephropathy (IgAN) is a highly prevalent autoimmune renal disease. Human IgA1 with galactose deficiency in the hinge region (HR) has been identified as an autoantigen for this disease. Therefore, analyzing IgA1 HR glycoforms in biofluids is important for biomarker discovery. Herein, an analytical method that includes one-pot sample preparation with unbiased plasma IgA purification, dual internal standard addition, and sensitive ultra-high-performance liquid chromatography-triple quadrupole tandem mass spectroscopy (UHPLC-QqQ-MS/MS) was developed. Targeted O-glycopeptides detection was performed in pooled plasma with the validation of theoretical retention times, enzymatic treatment outcomes, product ion scans, and signal repeatability. A total of 42 IgA1 O-glycopeptides with N-acetylgalactosamines, galactoses, and sialic acids were determined from 8 µL of plasma. The newly developed method was applied to plasma samples from 16 non-IgAN controls and 19 IgAN patients. Comparing the 42 targets, 16 IgA1 HR O-glycopeptides were statistically different between the two groups (p<0.05). Decreased sialylation was identified in the IgA1 hinge region of IgAN patients, which was also correlated with the estimated glomerular filtration rate (eGFR). The developed method is sensitive and precise and can be used to identify plasma biomarkers for IgA nephropathy.

Trastuzumab resistance in HER2-positive breast cancer: Mechanisms, emerging biomarkers and targeting agents.

Trastuzumab is a standard molecular targeted therapy for human epidermal growth factor receptor 2(HER2) -positive breast cancer, which can significantly improve the survival of patients with this molecular subtype of breast cancer. However, the clinical problem of onset or secondary resistance to trastuzumab has limited its efficacy. Therefore, it is very important to explore the mechanism of trastuzumab resistance and formulate countermeasures. Our study described the underlying molecular mechanism of trastuzumab resistance including ERBB2 mutations and nuclear localization, transcriptional and post-translational alterations of ERBB2, over-activation of bypass signaling pathways activation and so on. Then summarize the potential emerging predicting biomarkers and therapeutic strategies for trastuzumab resistance, in order to provide research direction for reversing trastuzumab resistance.

Comparison of clinical characteristics between meth/amphetamine and synthetic cathinone users presented to the emergency department.

BACKGROUND: Synthetic cathinones (SC) are popular new psychoactive substances that produce sympathomimetic toxicity. Meth/amphetamine and SC have similar chemical structures and pharmacological effects. We aimed to compare the clinical characteristics between meth/amphetamine and SC users presenting to the emergency department (ED). METHODS: This retrospective observational cohort study included patients who presented to six EDs from May 2017 to April 2021 with symptoms that related to recreational drug use and whose urine toxicology tests were positive only for meth/amphetamine or SC through liquid chromatography-tandem mass spectrometry. RESULTS: There were 379 patients who tested positive only for meth/amphetamine (MA group), and 87 patients tested positive only for SC (SC groups). Patients in the MA group were older than those in the SC group (median (IQR); MA: 37.0 (30-43.7), SC: 25.0 (21.0-32.7), p < 0.001). There were no significant between-group differences in the sex distribution and initial chief complaints. Compared with the MA group, the SC group had more cases of tachycardia (≥ 135/min; MA: 29 (8.2%), SC:16 (19.0%), p = 0.0031) and hyperthermia (≥ 38 °C; MA: 31 (8.2%), SC:18 (20.7%), p = 0.001). Besides, the SC group had significantly higher levels of creatinine kinase (CK, IU/L; MA: 263 (115-601), SC: 497 (206-9216), p = 0.008) as well as a higher risk of rhabdomyolysis (CK > 1000; MA:32 (8.4%), SC: 16 (18.4%), p = 0.006) and severe rhabdomyolysis (CK > 10,000; MA:10 (2.6%), SC:10 (11.5%), p = 001). Multivariable logistic regression analyses indicated SC group in comparison with the MA group (adjusted odds ratio: 2.732, 95% confidence interval: 1. 250-5.972, p = 0.012) was an association with the risk of rhabdomyolysis. CONCLUSION: Our findings demonstrate that tachycardia, hyperthermia, and rhabdomyolysis were more common among cathinone users than among meth/amphetamine users presented to EDs.

Predicting miRNA-Disease Associations via Combining Probability Matrix Feature Decomposition With Neighbor Learning.

Predicting the associations of miRNAs and diseases may uncover the causation of various diseases. Many methods are emerging to tackle the sparse and unbalanced disease related miRNA prediction. Here, we propose a Probabilistic matrix decomposition combined with neighbor learning to identify MiRNA-Disease Associations utilizing heterogeneous data(PMDA). First, we build similarity networks for diseases and miRNAs, respectively, by integrating semantic information and functional interactions. Second, we construct a neighbor learning model in which the neighbor information of individual miRNA or disease is utilized to enhance the association relationship to tackle the spare problem. Third, we predict the potential association between miRNAs and diseases via probability matrix decomposition. The experimental results show that PMDA is superior to other five methods in sparse and unbalanced data. The case study shows that the new miRNA-disease interactions predicted by the PMDA are effective and the performance of the PMDA is superior to other methods.