Role of the Microbiota and Antibiotics in Primary Sclerosing Cholangitis

Primary sclerosing cholangitis (PSC) is an idiopathic, progressive, cholestatic liver disease with considerable morbidity and mortality and no established pharmacotherapy. In addition to the long-recognized association between PSC and inflammatory bowel disease, several lines of preclinical and clinical evidence implicate the microbiota in the etiopathogenesis of PSC. Here we provide a concise review of these data which, taken together, support further investigation of the role of the microbiota and antibiotics in PSC as potential avenues toward elucidating safe and effective pharmacotherapy for patients afflicted by this illness

Modeling and analysis of spur structure of digital-to-time conversion based frequency synthesizers

Frequency synthesizers are critical components of all communication systems. This thesis considers the issue of undesirable frequency spurs of a relatively recent type of frequency synthesis architecture called digital-to-time conversion (DTC). The DTC-based frequency synthesis architecture has important performance benefits over older frequency synthesizers, such as fast frequency switching, large frequency range and fine frequency resolution. A DTC-based frequency synthesizer requires less power than a traditional direct synthesis based synthesizer with comparable frequency range, resolution and switching time. The DTC architecture is also easily scalable to newer low-cost digital complementary metal-oxide-semiconductor (CMOS) integrated circuit (IC) fabrication technologies. However, the DTC architecture suffers from an important undesirable characteristic: sub-harmonic spurious tones, hereafter, referred to as spurs. Spurs have undesirable effects in both the transmitter and the receiver. In a transmitter, spurs create an out-of-band emission of power that may breach the spectral emission mask set by regulatory agencies to enable co-existence of multiple transmitters in a crowded frequency spectrum. In a receiver, an inopportune-located spur in the local oscillator (LO) signal can mix an out-of-band strong interfering signal into the baseband on top of a mixed-down weak desirable signal. Unlike harmonic spurs that are known to be at multiples of the carrier frequency, sub-harmonic spurs are especially problematic as they have been difficult to predict as part of the design process. In fact, the spur patterns for most pairs of closely placed desired output frequencies for a DTC-based frequency synthesizer are seemingly unrelated. While one output frequency setting might have an output spectrum with only a few spurs, many other close-by output frequency settings might have output spectra with many weaker spurs. The primary contribution of this thesis is the development of spur creation models and analysis tools that can predict spur spectrum and spur power levels for a DTC-based frequency synthesizer. This is an important contribution for assuring achievable performance of frequency synthesizer during the design process. The modeling approach has been successful in accounting of more than 99% of spur spectral locations. Predicted power levels for more than 95% are within 10 dB of actual fabricated DTC-based frequency synthesizer ICs. The results developed in this thesis allow for an understanding of the relationship between spur patterns for different selected output frequencies. In the research reported in this thesis, the spur spectrum for a selected output frequency is shown to be due to periodic occurrences of errors in the locations of rising and falling edges of the output signal. Error sequences for different selected output frequencies are shown to be related in a way that can be exploited by application of the axis-scaling property of the Discrete Fourier Transform (DFT). The axis-scaling property of the DFT relates the transforms of two sets of sequences that are predictably permutated versions of each other. Their respective transforms are also (differently) permutated versions of each other. One key insight made in this thesis is the discovery that the time-domain errors for all output frequencies can be classified into a very small number of error sequence classes. All error sequences within a class are shown to be predictable permutations of each other. This insight along with the DFT axis-scaling property permits the respective spur spectra to be classified into error spectra classes. All error spectra within a spur spectra class are predictable permutations of each other. There are two sources of edge errors: quantization error and buffer delay errors. This classification of spur spectra to a few classes is shown to be possible for both sources of errors. In this thesis, the case of quantization-only error is considered first. The analysis is then extended to the case when both sources of error are present. As a result of the modeling and analytical techniques developed for spur spectra classification described in this thesis, design tools have been created to predict the spur spectra of DTC-based synthesizer designs for all possible selected output frequencies

Small Molecule Drug Release Form in Situ Forming Degradable Scaffolds Incorporating Hydrogels and Bioceramic Microparticles

The present invention relates to an injectable system combining a hydrogel, a bioceramic and a degradable matrix that provides for sustained drug delivery and structural support to recovering tissue, such as bone and the periodontium

On landmark selection and sampling in high-dimensional data analysis

In recent years, the spectral analysis of appropriately defined kernel matrices has emerged as a principled way to extract the low-dimensional structure often prevalent in high-dimensional data. Here we provide an introduction to spectral methods for linear and nonlinear dimension reduction, emphasizing ways to overcome the computational limitations currently faced by practitioners with massive datasets. In particular, a data subsampling or landmark selection process is often employed to construct a kernel based on partial information, followed by an approximate spectral analysis termed the Nystrom extension. We provide a quantitative framework to analyse this procedure, and use it to demonstrate algorithmic performance bounds on a range of practical approaches designed to optimize the landmark selection process. We compare the practical implications of these bounds by way of real-world examples drawn from the field of computer vision, whereby low-dimensional manifold structure is shown to emerge from high-dimensional video data streams.Comment: 18 pages, 6 figures, submitted for publicatio

Role of the Microbiota and Antibiotics in Primary Sclerosing Cholangitis

Primary sclerosing cholangitis (PSC) is an idiopathic, progressive, cholestatic liver disease with considerable morbidity and mortality and no established pharmacotherapy. In addition to the long-recognized association between PSC and inflammatory bowel disease, several lines of preclinical and clinical evidence implicate the microbiota in the etiopathogenesis of PSC. Here we provide a concise review of these data which, taken together, support further investigation of the role of the microbiota and antibiotics in PSC as potential avenues toward elucidating safe and effective pharmacotherapy for patients afflicted by this illness

Reliability of causality assessment for drug, herbal and dietary supplement hepatotoxicity in the Drug‐Induced Liver Injury Network (DILIN)

Background & AimsBecause of the lack of objective tests to diagnose drug‐induced liver injury (DILI), causality assessment is a matter of debate. Expert opinion is often used in research and industry, but its test–retest reliability is unknown. To determine the test–retest reliability of the expert opinion process used by the Drug‐Induced Liver Injury Network (DILIN).MethodsThree DILIN hepatologists adjudicate suspected hepatotoxicity cases to one of five categories representing levels of likelihood of DILI. Adjudication is based on retrospective assessment of gathered case data that include prospective follow‐up information. One hundred randomly selected DILIN cases were re‐assessed using the same processes for initial assessment but by three different reviewers in 92% of cases.ResultsThe median time between assessments was 938 days (range 140–2352). Thirty‐one cases involved >1 agent. Weighted kappa statistics for overall case and individual agent category agreement were 0.60 (95% CI: 0.50–0.71) and 0.60 (0.52–0.68) respectively. Overall case adjudications were within one category of each other 93% of the time, while 5% differed by two categories and 2% differed by three categories. Fourteen per cent crossed the 50% threshold of likelihood owing to competing diagnoses or atypical timing between drug exposure and injury.ConclusionsThe DILIN expert opinion causality assessment method has moderate interobserver reliability but very good agreement within one category. A small but important proportion of cases could not be reliably diagnosed as ≥50% likely to be DILI.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111130/1/liv12540.pd

Usage of Glimepiride/Metformin Fixed-dose Combination with Insulin in Management of Type 2 Diabetes Mellitus: An Indian Experience

Background: Type 2 diabetes mellitus (T2DM) poses a major public health burden. The present case-based questionnaire survey evaluated the treatment pattern and clinical experience of healthcare professionals (HCPs) in prescribing glimepiride/metformin fixed-dose combination (FDC) with insulin, with or without other oral hypoglycemic agents (OHAs), to patients with T2DM in the Indian setting. Material and methods: A retrospective, multicenter, observational, case-based questionnaire survey was conducted at several healthcare centers in India with the help of medical records of patients having T2DM, who were prescribed different strengths of glimepiride/metformin FDC. Data was collected from the patients’ medical records and were analyzed using statistical tests. Results: A total of 1,013 patients with T2DM were included in this study. The mean (± standard deviation [SD]) age of patients was 53.5 ± 13.9 years. Mean duration of diabetes was 6.3 ± 4.8 years. About 70.1% of the patients received glimepiride/metformin FDC as first-line therapy and 29.9% received it as second-line therapy. Around 66.3% of the patients in first-line glimepiride/metformin FDC group received insulin once a day, and the proportion increased to 86.8% of the patients in second-line therapy group. Other OHAs were used in 754 (74.4%) patients. About 18.2% (n = 185) patients reported change in weight, with a slightly larger number of patients having reduction in weight. There was considerable reduction in HbA1c, FPG and PPG in patients receiving glimepiride/metformin FDC with insulin, irrespective of OHA use. Efficacy and tolerability were reported as good to excellent for 96.2% and 94.8% patients, respectively. Conclusion: This case-based questionnaire survey shows the usage pattern of various strengths of glimepiride/metformin FDC with insulin and the HCPs’ practice approach regarding early initiation of this combination in Indian patients with T2DM

Low-Rank Subspace Override for Unsupervised Domain Adaptation

Current supervised learning models cannot generalize well across domain boundaries, which is a known problem in many applications, such as robotics or visual classification. Domain adaptation methods are used to improve these generalization properties. However, these techniques suffer either from being restricted to a particular task, such as visual adaptation, require a lot of computational time and data, which is not always guaranteed, have complex parameterization, or expensive optimization procedures. In this work, we present an approach that requires only a well-chosen snapshot of data to find a single domain invariant subspace. The subspace is calculated in closed form and overrides domain structures, which makes it fast and stable in parameterization. By employing low-rank techniques, we emphasize on descriptive characteristics of data. The presented idea is evaluated on various domain adaptation tasks such as text and image classification against state of the art domain adaptation approaches and achieves remarkable performance across all tasks