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"Jian, Shi"
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Machine learning and bioinformatics approaches for classification and clinical detection of bevacizumab responsive glioblastoma subtypes based on miRNA expression
For the precise treatment of patients with glioblastoma multiforme (GBM), we classified and detected bevacizumab (BVZ)-responsive subtypes of GBM and found their differential expression (DE) of miRNAs and mRNAs, clinical characteristics, and related functional pathways. Based on miR-21 and miR-10b expression z-scores, approximately 30% of GBM patients were classified as having the GBM BVZ-responsive subtype. For this subtype, GBM patients had a significantly shorter survival time than other GBM patients (
p
= 0.014), and vascular endothelial growth factor A (VEGF) methylation was significantly lower than that in other GBM patients (
p
= 0.005). It also revealed 14 DE miRNAs and 7 DE mRNAs and revealed functional characteristics between GBM BVZ subgroups. After comparing several machine learning algorithms, the construction and cross-validation of the SVM classifier were performed. For clinical use, miR-197 was optimized and added to the miRNA panel for better classification. Afterwards, we validated the classifier with several GBM datasets and discovered some key related issues. According to this study, GBM BVZ subtypes can be classified and detected by a combination of SVM classifiers and miRNA panels in existing tissue GBM datasets. With certain modifications, the classifier may be used for the classification and detection of GBM BVZ subtypes for future clinical use.
Journal Article
إدارة الأزمات في زمن الأوبئة : مقالات لـ 56 عالما في الإدارة
يضم هذا الكتاب خلاصة تجارب وعصارة أفكار 56 عالما، هم من أبرز علماء الإدارة في الصين، وقد حملوا على عاتقهم مسؤولية قيادة المؤسسات الصينية لسنوات عديدة، وهو كتاب مرجعي لكل المؤسسات على المستوى العالمي والتي إن حدث لها ضرر في فترات الأزمات أو الأوبئة، فلن يتوقف هذا الضرر عند ملاكها أو المنتفعين منها، بل سيمتد أثره إلى قطاعات عريضة من العمالة، وسيضرب القوة الإنتاجية ولا سيما الصادرات والواردات وغيرها من الموارد. ومن ثم فهم محاربون على الخطوط الأولى، مثلهم مثل الأطباء في أزمة انتشار فيروس كورونا المستجد، وإن كان مجال تخصص كل مختلفا منهم عن الآخر، ففريق منهم ينقذ حياة الناس، بينما الفريق الآخر ينقذ أقواتهم. ولغة الكتاب لغة سهلة وبسيطة، تنطلق أفكاره من مواقف عامة وليست من مواقف خاصة محددة، ومن هنا تعد أفكاره صالحة للتطبيق على المؤسسات الصغيرة والمتوسطة في كل مكان، والتي أصبح لزاما عليها أن تلجأ للابتكار والإبداع إن أرادت الاستمرار على قيد الحياة، وبات عليها أن تبحث وسط ركام الأزمة عن الإيجابيات التي يمكن أن تهب لها حياة جديدة وسبلا مبتكرة للخلاص.
Book
Tumor Necrosis Factor Receptor-Associated Factor Regulation of Nuclear Factor κB and Mitogen-Activated Protein Kinase Pathways
Tumor necrosis factor receptor (TNFR)-associated factors (TRAFs) are a family of structurally related proteins that transduces signals from members of TNFR superfamily and various other immune receptors. Major downstream signaling events mediated by the TRAF molecules include activation of the transcription factor nuclear factor κB (NF-κB) and the mitogen-activated protein kinases (MAPKs). In addition, some TRAF family members, particularly TRAF2 and TRAF3, serve as negative regulators of specific signaling pathways, such as the noncanonical NF-κB and proinflammatory toll-like receptor pathways. Thus, TRAFs possess important and complex signaling functions in the immune system and play an important role in regulating immune and inflammatory responses. This review will focus on the role of TRAF proteins in the regulation of NF-κB and MAPK signaling pathways.
Journal Article
DPDDI: a deep predictor for drug-drug interactions
Background
The treatment of complex diseases by taking multiple drugs becomes increasingly popular. However, drug-drug interactions (DDIs) may give rise to the risk of unanticipated adverse effects and even unknown toxicity. DDI detection in the wet lab is expensive and time-consuming. Thus, it is highly desired to develop the computational methods for predicting DDIs. Generally, most of the existing computational methods predict DDIs by extracting the chemical and biological features of drugs from diverse drug-related properties, however some drug properties are costly to obtain and not available in many cases.
Results
In this work, we presented a novel method (namely DPDDI) to predict DDIs by extracting the network structure features of drugs from DDI network with graph convolution network (GCN), and the deep neural network (DNN) model as a predictor. GCN learns the low-dimensional feature representations of drugs by capturing the topological relationship of drugs in DDI network. DNN predictor concatenates the latent feature vectors of any two drugs as the feature vector of the corresponding drug pairs to train a DNN for predicting the potential drug-drug interactions. Experiment results show that, the newly proposed DPDDI method outperforms four other state-of-the-art methods; the GCN-derived latent features include more DDI information than other features derived from chemical, biological or anatomical properties of drugs; and the concatenation feature aggregation operator is better than two other feature aggregation operators (i.e., inner product and summation). The results in case studies confirm that DPDDI achieves reasonable performance in predicting new DDIs.
Conclusion
We proposed an effective and robust method DPDDI to predict the potential DDIs by utilizing the DDI network information without considering the drug properties (i.e., drug chemical and biological properties). The method should also be useful in other DDI-related scenarios, such as the detection of unexpected side effects, and the guidance of drug combination.
Journal Article
Considering Exosomal miR-21 as a Biomarker for Cancer
Cancer is a fatal human disease. Early diagnosis of cancer is the most effective method to prevent cancer development and to achieve higher survival rates for patients. Many traditional diagnostic methods for cancer are still not sufficient for early, more convenient and accurate, and noninvasive diagnosis. Recently, the use of microRNAs (miRNAs), such as exosomal microRNA-21(miR-21), as potential biomarkers was widely reported. This initial systematic review analyzes the potential role of exosomal miR-21 as a general biomarker for cancers. A total of 10 studies involving 318 patients and 215 healthy controls have covered 10 types of cancers. The sensitivity and specificity of pooled studies were 75% (0.70–0.80) and 85% (0.81–0.91), with their 95% confidence intervals (CIs), while the area under the summary receiver operating characteristic curve (AUC) was 0.93. Additionally, we examined and evaluated almost all other issues about biomarkers, including cutoff points, internal controls and detection methods, from the literature. This initial meta-analysis indicates that exosomal miR-21 has a strong potential to be used as a universal biomarker to identify cancers, although as a general biomarker the case number for each cancer type is small. Based on the literature, a combination of miRNA panels and other cancer antigens, as well as a selection of appropriate internal controls, has the potential to serve as a more sensitive and accurate cancer diagnosis tool. Additional information on miR-21 would further support its use as a biomarker in cancer.
Journal Article
Regulatory networks between neurotrophins and miRNAs in brain diseases and cancers
Neurotrophins are involved in many physiological and pathological processes in the nervous system. They regulate and modify signal transduction, transcription and translation in neurons. It is recently demonstrated that the neurotrophin expression is regulated by microRNAs （miRNAs）, changing our views on neurotrophins and miRNAs. Generally, miRNAs regulate neurotrophins and their receptors in at least two ways： （1） miRNAs bind directly to the 3＇ untranslated region （UTR） of isoform-specific mRNAs and post-transcriptionally regulate their expression; （2） miRNAs bind to the 3＇ UTR of the regulatory factors of neurotrophins and regulate their expression. On the other hand, neurotrophins can regulate miRNAs. The results of BNDF research show that neurotrophins regulate miRNAs in at least three ways： （1） ERK stimulation enhances the activation of TRBP （HIV-1 TAR RNA-binding protein） and Dicer, leading to the upregulation of miRNA biogenesis; （2） ERK-dependent upregulation of Lin28a （RNA-binding proteins） blocks select miRNA biogenesis; （3） transcriptional regulation of miRNA expression through activation of transcription factors, including CREB and NF-KB. These regulatory processes integrate positive and negative regulatory loops in neurotrophin and miRNA signaling pathways, and also expand the function of neurotrophins and miRNAs. In this review, we summarize the current knowledge of the regulatory networks between neurotrophins and miRNAs in brain diseases and cancers, for which novel cutting edge therapeutic, delivery and diagnostic approaches are emerging.
Journal Article