Effects of three oral analgesics on postoperative pain following root canal preparation: a controlled clinical trial

Aim  To compare the effects of single doses of three oral medications on postoperative pain following instrumentation of root canals in teeth with irreversible pulpitis. Methodology  In this double‐blind clinical trial, 100 patients who had anterior or premolar teeth with irreversible pulpitis without any signs and symptoms of acute or chronic apical periodontitis and moderate to severe pain were divided by balanced block random allocation into four groups of 25 each, a control group receiving a placebo medication, and three experimental groups receiving a single dose of either Tramadol (100 mg), Novafen (325 mg of paracetamol, 200 mg ibuprofen and 40 mg caffeine anhydrous) or Naproxen (500 mg) immediately after the first appointment where the pulp was removed, and the canals were fully prepared. The intensity of pain was scored based on 10‐point VAS before and after treatment for up to 24 h postoperatively. Data were submitted to repeated analysis of variance. Results  At the 6, 12 and 24 h postoperative intervals after drug administration, the intensity of pain was significantly lower in the experimental groups than in the placebo group ( P   0.05). Conclusion  A single oral dose of Naproxen, Novafen and Tramadol taken immediately after treatment reduced postoperative pain following pulpectomy and root canal preparation of teeth with irreversible pulpitis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89451/1/j.1365-2591.2011.01950.x.pd

Computational design and sensing algorithms for nanopore-based molecular tagging and peptide detection

Thesis (Ph.D.)--University of Washington, 2021Molecular sensing provides a window into the complex world of otherwise invisible molecules, allowing us to measure protein abundance or sequence DNA, for example. Commercially available nanopore arrays have already made DNA sequencing less expensive and more portable than existing platforms, and they have recently emerged as potential tools for general purpose molecular sensing. Nanopore arrays record a time series of ionic current observations and do not intrinsically detect any particular types of molecules; any molecule that can physically flow through the pore will partially block the ionic current flow in unique ways depending on its physical properties, producing a characteristic current trace. Since only DNA and RNA sequencing are officially supported, any applications beyond straightforward DNA sequencing require developing novel computational pipelines and algorithms to extract biologically relevant information. Here I present computational methods for three novel uses of commercial nanopore devices: (1) Porcupine, a molecular tagging system using custom designed nanopore-orthogonal DNA molecular bits (molbits); (2) Big Bits, a DNA data storage implementation using sequentially encoded molbits; and (3) Poretitioner, a pipeline for identifying NanoporeTERs (NTERs, Nanopore-addressable protein Tags Engineered as Reporters) and other engineered molecules. In each chapter, I present my contributions to novel computational analysis of nanopore data for these applications. Briefly, Porcupine labels physical objects using molecular tags. These tags encode digital information via the presence and absence of molbits, which I algorithmically designed to produce visually unique nanopore signals. The tags are later read back and decoded directly from the nanopore ionic current trace using a convolutional neural network (CNN). Big Bits extends upon this, using design principles from Porcupine to encode even more information for DNA data storage. Instead of using presence or absence to encode information, molbits in Big Bits are encoded sequentially. In the Poretitioner pipeline, I extract ionic current for captured peptides, then filter, classify, and quantify them using components built by both myself and others that can be tuned for various molecules

Histoimmunogenetics Markup Language 1.0: Reporting next generation sequencing-based HLA and KIR genotyping

We present an electronic format for exchanging data for HLA and KIR genotyping with extensions for next-generation sequencing (NGS). This format addresses NGS data exchange by refining the Histoimmunogenetics Markup Language (HML) to conform to the proposed Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING) reporting guidelines (miring.immunogenomics.org). Our refinements of HML include two major additions. First, NGS is supported by new XML structures to capture additional NGS data and metadata required to produce a genotyping result, including analysis-dependent (dynamic) and method-dependent (static) components. A full genotype, consensus sequence, and the surrounding metadata are included directly, while the raw sequence reads and platform documentation are externally referenced. Second, genotype ambiguity is fully represented by integrating Genotype List Strings, which use a hierarchical set of delimiters to represent allele and genotype ambiguity in a complete and accurate fashion. HML also continues to enable the transmission of legacy methods (e.g. site-specific oligonucleotide, sequence-specific priming, and Sequence Based Typing (SBT)), adding features such as allowing multiple group-specific sequencing primers, and fully leveraging techniques that combine multiple methods to obtain a single result, such as SBT integrated with NGS

Histoimmunogenetics Markup Language 1.0: Reporting Next Generation Sequencing-based HLA and KIR Genotyping

AbstractWe present an electronic format for exchanging data for HLA and KIR genotyping with extensions for next-generation sequencing (NGS). This format addresses NGS data exchange by refining the Histoimmunogenetics Markup Language (HML) to conform to the proposed Minimum Information for Reporting Immunogenomic NGS Genotyping (MIRING) reporting guidelines (miring.immunogenomics.org). Our refinements of HML include two major additions. First, NGS is supported by new XML structures to capture additional NGS data and metadata required to produce a genotyping result, including analysis-dependent (dynamic) and method-dependent (static) components. A full genotype, consensus sequence, and the surrounding metadata are included directly, while the raw sequence reads and platform documentation are externally referenced. Second, genotype ambiguity is fully represented by integrating Genotype List Strings, which use a hierarchical set of delimiters to represent allele and genotype ambiguity in a complete and accurate fashion. HML also continues to enable the transmission of legacy methods (e.g. site-specific oligonucleotide, sequence-specific priming, and Sequence Based Typing (SBT)), adding features such as allowing multiple group-specific sequencing primers, and fully leveraging techniques that combine multiple methods to obtain a single result, such as SBT integrated with NGS