Development and application of bioinformatics tools for discovery disease markers and disease targeting antibodies

Ph.DDOCTOR OF PHILOSOPH

A Balanced Feed Filtering Antenna With Novel Coupling Structure for Low-Sidelobe Radar Applications

A fourth-order filtering patch antenna with a novel coupling structure is presented in this paper. Using the proposed coupling structure, both the balanced coupling feed and cross-coupling are realized. Two identical slots etched on the ground plane are utilized to excite the radiating patch with the reduced cross-polarization level. A short slot etched on the ground plane is employed for cross-coupling, which introduces two controllable radiation nulls with a steep roll-off rate. In addition, owing to the split-ring resonators and hairpin resonators, the improved impedance bandwidth is achieved with the fourth-order filtering response. To demonstrate the proposed design techniques, both the filtering antenna element and the low-sidelobe array are designed, fabricated, and measured. The measured results show that the proposed antenna has the impedance bandwidth of 12% (4.78–5.39 GHz) with the total height of 0.06?0 , the cross-polarization level lower than ?31 dB, and two radiation nulls with the suppression higher than 31 dB. For the low-sidelobe antenna array, wide impedance bandwidth is also obtained with the sidelobe level below ?28.7 dB, the cross-polarization level below ?34 dB, and the out-of-band suppression better than 25 dB

Increased CK5/CK8-Positive Intermediate Cells with Stromal Smooth Muscle Cell Atrophy in the Mice Lacking Prostate Epithelial Androgen Receptor

Results from tissue recombination experiments documented well that stromal androgen receptor (AR) plays essential roles in prostate development, but epithelial AR has little roles in prostate development. Using cell specific knockout AR strategy, we generated pes-ARKO mouse with knock out of AR only in the prostate epithelial cells and demonstrated that epithelial AR might also play important roles in the development of prostate gland. We found mice lacking the prostate epithelial AR have increased apoptosis in epithelial CK8-positive luminal cells and increased proliferation in epithelial CK5-positive basal cells. The consequences of these two contrasting results could then lead to the expansion of CK5/CK8-positive intermediate cells, accompanied by stromal atrophy and impaired ductal morphogenesis. Molecular mechanism dissection found AR target gene, TGF-β1, might play important roles in this epithelial AR-to-stromal morphogenesis modulation. Collectively, these results provided novel information relevant to epithelial AR functions in epithelial-stromal interactions during the development of normal prostate, and suggested AR could also function as suppressor in selective cells within prostate

Neural protection by naturopathic compounds—an example of tetramethylpyrazine from retina to brain

Given the advantages of being stable in the ambient environment, being permeable to the blood–brain and/or blood–eye barriers and being convenient for administration, naturopathic compounds have growingly become promising therapeutic candidates for neural protection. Extracted from one of the most common Chinese herbal medicines, tetramethylpyrazine (TMP), also designated as ligustrazine, has been suggested to be neuroprotective in the central nervous system as well as the peripheral nerve network. Although the detailed molecular mechanisms of its efficacy for neural protection are understood limitedly, accumulating evidence suggests that antioxidative stress, antagonism for calcium, and suppression of pro-inflammatory factors contribute significantly to its neuroprotection. In animal studies, systemic administration of TMP (subcutaneous injection, 50 mg/kg) significantly blocked neuronal degeneration in hippocampus as well as the other vulnerable regions in brains of Sprague–Dawley rats following kainate-induced prolonged seizures. Results from us and others also demonstrated potent neuroprotective efficacy of TMP for retinal cells and robust benefits for brain in Alzheimer’s disease or other brain injury. These results suggest a promising prospect for TMP to be used as a treatment of specific neurodegenerative diseases. Given the assessment of the distribution, metabolism, excretion, and toxicity information that is already available on most neuroprotective naturopathic compounds such as TMP, it would not take much preclinical data to justify bringing such therapeutic compounds to clinical trials in humans

Big data and computational biology strategy for personalized prognosis

The era of big data and precision medicine has led to accumulation of massive datasets of gene expression data and clinical information of patients. For a new patient, we propose that identification of a highly similar reference patient from an existing patient database via similarity matching of both clinical and expression data could be useful for predicting the prognostic risk or therapeutic efficacy.Here, we propose a novel methodology to predict disease/treatment outcome via analysis of the similarity between any pair of patients who are each characterized by a certain set of pre-defined biological variables (biomarkers or clinical features) represented initially as a prognostic binary variable vector (PBVV) and subsequently transformed to a prognostic signature vector (PSV). Our analyses revealed that Euclidean distance rather correlation distance measure was effective in defining an unbiased similarity measure calculated between two PSVs.We implemented our methods to high-grade serous ovarian cancer (HGSC) based on a 36-mRNA predictor that was previously shown to stratify patients into 3 distinct prognostic subgroups. We studied and revealed that patient's age, when converted into binary variable, was positively correlated with the overall risk of succumbing to the disease. When applied to an independent testing dataset, the inclusion of age into the molecular predictor provided more robust personalized prognosis of overall survival correlated with the therapeutic response of HGSC and provided benefit for treatment targeting of the tumors in HGSC patients.Finally, our method can be generalized and implemented in many other diseases to accurately predict personalized patients' outcomes.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio

Additional file 2: Table S1. of Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets

The 13-miRNA subset from K-means. Table S2. The 100 survival significant miRNAs and results of the DDSS-1D, DDSS-2D and SWVg analyses. TCGA dataset was analysed. The 10 survival significant miRNAs belonging to the 13 miRNA subset selected by K-means analysis are highlighted. Table S3. The 25-miR subset selected by DDSS-1D with ten-fold cross validation. Table S4. The frequency of miRs in DDSS-2D miR pairs. The miRNAs which was reported in 13 miRNAs selected by K-means analysis were highlighted in yellow cell. Table S5. 28 miRNAs which display the patterns with three groups in DDSS-1D. Table S6. Analysis of 19-miRNA prognostic signature. Significant clusters generated from DAVID functional annotation tools. Each DAVID annotation cluster represents one biological theme by grouping similar annotation terms according to the common genes shared by them. Table S7. Association analysis of clinical indicators with groups separated by 19 miRNAs from SWV based on DDSS-1D. Table S8. Seventeen survival-significant miRNAs in Shih et al signature [5]. Table S9. Wald P-values of 4 significant miRNA in TCGA dataset, supported by independent dataset [5]. Table S10. Three miRNA survival prediction signatures of HG-SOC. Table S11. Comparison of the 19-miRNA survival prediction signature with other miRNA-based survival prediction signatures of HG-SOC. Table S12. Concensus subset of survival significant miRNAs. Table S13.  DDSS-1D-based  selection of the 31 miRNAs  and SWVg analysis. Table S14.  Interactions between miRNAs and predicted direct  target  mRNAs found  in 36 mRNA HG-EOC prognostic classifier. Table S15. Significant signaling pathways (by DIANA-mirPath v.2 software) targeting by the miRNA subsets belonging to five miRNA signatures. The common pathways shared by miRNA signatures from K-means, DDSS-D1, DDSS-D2 and SWVg analyses are marked in boldface. Table S16. Initial data lists  for for miRPath-7.3 analysis,  the number target genes and the target identification methods. Table S17. Common signaling pathways. Table S18. Neurotrophin signalling pathway: Updated lists of the miRNAs refereeing to the 19-miRNA (19_mir_N), 21-miRNA (21_mir_N) and 31miRNA (31_mir_N) prognostic signatures and their characteristics. Table S19. Neurotrophin signaling pathway data (miRNAs, mRNA). Table S20. Number of miRNAs interacting with a target mRNA associated with gene encoding neurophilin signaling pathway. Table S21. Tarbase Experimentally Supported Interactions for miRNAs. (XLS 400 kb

Additional file 1: of Identification of common oncogenic and early developmental pathways in the ovarian carcinomas controlling by distinct prognostically significant microRNA subsets

Methods. Additional Methods. (DOCX 39 kb

Deep Learning for Retinal Image Quality Assessment of Optic Nerve Head Disorders

10.1097/APO.0000000000000404ASIA-PACIFIC JOURNAL OF OPHTHALMOLOGY103282-28