A Medical Data Cleaner

This report describes medical-data cleaning tool, called MedDataCleaner that can detect outliers in medical data and assistant Database Administrators in resolving data-related problem. Specifically, MedDataCleaner, enables the users to define cleaning rules and offers the ability to choose classification methods that help determine if the data is good or bad. MedDataClearer uses Vitruvian DB objects for object-relation mapping (ORM) support and Vitruvian alignment links for designing the GUI. My contribution towards this work includes designing the user interfaces using Vitruvian Alignment links, design and implement mean, standard deviation and neural classification methods using Vitruvian DB objects

Towards User Guided Actionable Recourse

Machine Learning's proliferation in critical fields such as healthcare, banking, and criminal justice has motivated the creation of tools which ensure trust and transparency in ML models. One such tool is Actionable Recourse (AR) for negatively impacted users. AR describes recommendations of cost-efficient changes to a user's actionable features to help them obtain favorable outcomes. Existing approaches for providing recourse optimize for properties such as proximity, sparsity, validity, and distance-based costs. However, an often-overlooked but crucial requirement for actionability is a consideration of User Preference to guide the recourse generation process. In this work, we attempt to capture user preferences via soft constraints in three simple forms: i) scoring continuous features, ii) bounding feature values and iii) ranking categorical features. Finally, we propose a gradient-based approach to identify User Preferred Actionable Recourse (UP-AR). We carried out extensive experiments to verify the effectiveness of our approach

Adaptive Adversarial Training Does Not Increase Recourse Costs

Recent work has connected adversarial attack methods and algorithmic recourse methods: both seek minimal changes to an input instance which alter a model's classification decision. It has been shown that traditional adversarial training, which seeks to minimize a classifier's susceptibility to malicious perturbations, increases the cost of generated recourse; with larger adversarial training radii correlating with higher recourse costs. From the perspective of algorithmic recourse, however, the appropriate adversarial training radius has always been unknown. Another recent line of work has motivated adversarial training with adaptive training radii to address the issue of instance-wise variable adversarial vulnerability, showing success in domains with unknown attack radii. This work studies the effects of adaptive adversarial training on algorithmic recourse costs. We establish that the improvements in model robustness induced by adaptive adversarial training show little effect on algorithmic recourse costs, providing a potential avenue for affordable robustness in domains where recoursability is critical

Formulation and Evaluation of Nizatiding Floatince Table.

This study proves that GFDDS of furosemide can be designed using HPMC K4M as matrix polymer, which provides nearly zero order release kinetics and thus possienhancement of oral bioavailability of the drug. This study was done as per the method described in section 2.2.2.3. Absorbance values at max314 nm at different time points were tabulated. Nizatidine floating tablets were successfully prepared with hydrophilic polymers like HPMC K4M and Poly Ethylene Oxide WSR 1105. All formulations were evaluated for Compressibility Index, Angle of repose and Hausner ratio. The results indicated that the final blend had good flow and suited for direct compression technique. From the pre-formulation studies for drug excipient compatibility it was observed that Nizatidine had interaction with citric acid. No physical or chemical incompatibility existed between the drug and other excipients. All formulations were tested for post compression parameters like hardness, thickness, weight variation, friability and drug content. All estimated parameters were found to be within the limits. This indicated that all the prepared formulations were good. All formulations were tested for buoyancy properties like floating lag time & total floating time. Almost all the formulations showed satisfactory results. All formulations were tested for in vitro drug release. The optimized formulations among HPMC K4M and Poly Ethylene Oxide WSR 1105 are F2 and F6. These were chosen because of their close similarity with predicted theoretical release profile. The F6 formulation was chosen as the best formulation among all the other formulations.So stability studies are performed after one month also the formulation is stable

Bioinformatics approaches for the analysis of lipidomics data

The potential impact of lipid research has been increasingly realised both in disease treatment and prevention. Recent advances in soft ionization mass spectrometry (MS) such as electrospray ionization (ESI) have permitted parallel monitoring of several hundreds of lipids in a single experiment and thus facilitated lipidomics level studies. These advances, however, pose a greater challenge for bioinformaticians to handle massive amounts of information-rich MS data from modern analytical instruments in order to understand complex functions of lipids. The main aims of this thesis were to 1) develop bioinformatics approaches for lipid identification based on ultra performance liquid chromatography coupled to mass spectrometry (UPLC/MS) data, 2) predict the functional annotations for unidentified lipids, 3) understand the omics data in the context of pathways and 4) apply existing chemometric methods for exploratory data analysis as well as biomarker discovery. A bioinformatics strategy for the construction of lipid database for major classes of lipids is presented using simplified molecular input line entry system (SMILES) approach. The database was annotated with relevant information such as lipid names including short names, SMILES information, scores, molecular weight, monoisotopic mass, and isotope distribution. The database was tailored for UPLC/MS experiments by incorporating the information such as retention time range, adduct information and main fragments to screen for the potential lipids. This database information facilitated building experimental tandem mass spectrometry libraries for different biological tissues. Non-targeted metabolomics screening is often get plagued by the presence of unknown peaks and thus present an additional challenge for data interpretation. Multiple supervised classification methods were employed and compared for the functional prediction of class labels for unidentified lipids to facilitate exploratory analysis further as well as ease the identification process. As lipidomics goes beyond complete characterization of lipids, new strategies were developed to understand lipids in the context of pathways and thereby providing insights for the phenotype characterization. Chemometric methods such as principal component analysis (PCA) and partial least squares and discriminant analysis (PLS/DA) were utilised for exploratory analysis as well as biomarker discovery in the context of different disease phenotypes

Bioinformatics strategies for lipidomics analysis: characterization of obesity related hepatic steatosis.

BACKGROUND: Lipids are an important and highly diverse class of molecules having structural, energy storage and signaling roles. Modern analytical technologies afford screening of many lipid molecular species in parallel. One of the biggest challenges of lipidomics is elucidation of important pathobiological phenomena from the integration of the large amounts of new data becoming available. RESULTS: We present computational and informatics approaches to study lipid molecular profiles in the context of known metabolic pathways and established pathophysiological responses, utilizing information obtained from modern analytical technologies. In order to facilitate identification of lipids, we compute the scaffold of theoretically possible lipids based on known lipid building blocks such as polar head groups and fatty acids. Each compound entry is linked to the available information on lipid pathways and contains the information that can be utilized for its automated identification from high-throughput UPLC/MS-based lipidomics experiments. The utility of our approach is demonstrated by its application to the lipidomic characterization of the fatty liver of the genetically obese insulin resistant ob/ob mouse model. We investigate the changes of correlation structure of the lipidome using multivariate analysis, as well as reconstruct the pathways for specific molecular species of interest using available lipidomic and gene expression data. CONCLUSION: The methodology presented herein facilitates identification and interpretation of high-throughput lipidomics data. In the context of the ob/ob mouse liver profiling, we have identified the parallel associations between the elevated triacylglycerol levels and the ceramides, as well as the putative activated ceramide-synthesis pathways.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Lipidome as a predictive tool in progression to type 2 diabetes in Finnish men

Background. There is a need for early markers to track and predict the development of type 2 diabetes mellitus (T2DM) from the state of normal glucose tolerance through prediabetes. In this study we tested whether the plasma molecular lipidome has biomarker potential to predicting the onset of T2DM. Methods. We applied global lipidomic profiling on plasma samples from well-phenotyped men (107 cases, 216 controls) participating in the longitudinal METSIM study at baseline and at five-year follow-up. To validate the lipid markers, an additional study with a representative sample of adult male population (n = 631) was also conducted. A total of 277 plasma lipids were analyzed using the lipidomics platform based on ultra performance liquid chromatography coupled to time-of-flight mass spectrometry. Lipids with the highest predictive power for the development of T2DM were computationally selected, validated and compared to standard risk models without lipids. Results. A persistent lipid signature with higher levels of triacylglycerols and diacyl-phospholipids as well as lowerlevels of alkylacyl phosphatidylcholines was observed in progressors to T2DM. Lysophosphatidylcholine acyl C18:2 (LysoPC(18:2)), phosphatidylcholines PC(32:1), PC(34:2e) and PC(36:1), and triacylglycerol TG(17:1/18:1/18:2) were selected to the full model that included metabolic risk factors and FINDRISC variables. When further adjusting for BM and age, these lipids had respective odds ratios of 0.32, 2.4, 0.50, 2.2 and 0.31 (all p 0; p <0.05). Notably, the lipid models remained predictive of the development of T2DM in the fasting plasma glucose-matched subset of the validation study. Conclusion. This study indicates that a lipid signature characteristic of T2DM is present years before the diagnosis and improves prediction of progression to T2DM. Molecular lipid biomarkers were shown to have predictive power also in a high-risk group, where standard risk factors are not helpful at distinguishing progressors from non-progressors. (C) 2017 The Authors. Published by Elsevier Inc.Peer reviewe

Rapalogs can promote cancer cell stemness in vitro in a Galectin-1 and H-ras-dependent manner

Currently several combination treatments of mTor- and Ras-pathway inhibitors are being tested in cancer therapy. While multiple feedback loops render these central signaling pathways robust, they complicate drug targeting. Here, we describe a novel H-ras specific feedback, which leads to an inadvertent rapalog induced activation of tumorigenicity in Ras transformed cells. We find that rapalogs specifically increase nanoscale clustering (nanoclustering) of oncogenic H-ras but not K-ras on the plasma membrane. This increases H-ras signaling output, promotes mammosphere numbers in a H-ras-dependent manner and tumor growth in ovo. Surprisingly, also other FKBP12 binders, but not mTor- inhibitors, robustly decrease FKBP12 levels after prolonged (> 2 days) exposure. This leads to an upregulation of the nanocluster scaffold galectin-1 (Gal-1), which is responsible for the rapamycin-induced increase in H-ras nanoclustering and signaling output. We provide evidence that Gal-1 promotes stemness features in tumorigenic cells. Therefore, it may be necessary to block inadvertent induction of stemness traits in H-ras transformed cells by specific Gal-1 inhibitors that abrogate its effect on H-ras nanocluster. On a more general level, our findings may add an important mechanistic explanation to the pleiotropic physiological effects that are observed with rapalogs.Peer reviewe

Development of actionable targets of multi-kinase inhibitors (AToMI) screening platform to dissect kinase targets of staurosporines in glioblastoma cells

Therapeutic resistance to kinase inhibitors constitutes a major unresolved clinical challenge in cancer and especially in glioblastoma. Multi-kinase inhibitors may be used for simultaneous targeting of multiple target kinases and thereby potentially overcome kinase inhibitor resistance. However, in most cases the identification of the target kinases mediating therapeutic effects of multi-kinase inhibitors has been challenging. To tackle this important problem, we developed an actionable targets of multi-kinase inhibitors (AToMI) strategy and used it for characterization of glioblastoma target kinases of staurosporine derivatives displaying synergy with protein phosphatase 2A (PP2A) reactivation. AToMI consists of interchangeable modules combining drug-kinase interaction assay, siRNA high-throughput screening, bioinformatics analysis, and validation screening with more selective target kinase inhibitors. As a result, AToMI analysis revealed AKT and mitochondrial pyruvate dehydrogenase kinase PDK1 and PDK4 as kinase targets of staurosporine derivatives UCN-01, CEP-701, and K252a that synergized with PP2A activation across heterogeneous glioblastoma cells. Based on these proof-of-principle results, we propose that the application and further development of AToMI for clinically applicable multi-kinase inhibitors could provide significant benefits in overcoming the challenge of lack of knowledge of the target specificity of multi-kinase inhibitors.Peer reviewe

Genomic prediction of coronary heart disease

Aims Genetics plays an important role in coronary heart disease (CHD) but the clinical utility of genomic risk scores (GRSs) relative to clinical risk scores, such as the Framingham Risk Score (FRS), is unclear. Our aim was to construct and externally validate a CHD GRS, in terms of lifetime CHD risk and relative to traditional clinical risk scores. Methods and results We generated a GRS of 49 310 SNPs based on a CARDIoGRAMplusC4D Consortium meta-analysis of CHD, then independently tested it using five prospective population cohorts (three FINRISK cohorts, combined n = 12 676, 757 incident CHD events; two Framingham Heart Study cohorts (FHS), combined n = 3406, 587 incident CHD events). The GRS was associated with incident CHD (FINRISK HR = 1.74, 95% confidence interval (CI) 1.61-1.86 per S.D. of GRS; Framingham HR = 1.28, 95% CI 1.18-1.38), and was largely unchanged by adjustment for known risk factors, including family history. Integration of the GRS with the FRS or ACC/AHA13 scores improved the 10 years risk prediction (meta-analysis C-index: +1.5-1.6%, P = 60 years old (meta-analysis C-index: +4.6-5.1%, P <0.001). Importantly, the GRS captured substantially different trajectories of absolute risk, with men in the top 20% of attaining 10% cumulative CHD risk 12-18 y earlier than those in the bottom 20%. High genomic risk was partially compensated for by low systolic blood pressure, low cholesterol level, and non-smoking. Conclusions A GRS based on a large number of SNPs improves CHD risk prediction and encodes different trajectories of lifetime risk not captured by traditional clinical risk scores.Peer reviewe