Studies of a Novel Phage Lytic Enzyme, PlySs2

Streptococcus suis infects pigs worldwide and may be zoonotically transmitted to humans with a mortality rate of up to 20%. Methicillin-resistant Staphylococcus aureus (MRSA) and Streptococcus pyogenes (group A streptococci – GrAS) cause potentially fatal human diseases. These are just three of the many Gram-positive pathogens for which resistance to leading antibiotics has emerged. The goal of this work was to develop a novel antimicrobial treatment to combat these and other antibiotic-resistant pathogens. We identified a novel bacteriophage lysin, derived from an S. suis phage termed PlySs2 (phage lysin from S. suis 2). This thesis is divided into four main sections detailing PlySs2: characterization (chapter 2); activity against S. suis (chapter 3); broad lytic activity (chapter 4); and efficacy in vivo (chapter 5). PlySs2 has an N-terminal CHAP catalytic domain and a C-terminal SH3b binding domain. It is stable at 50°C for 30 min, 37°C for \u3e24 h, 4°C for 15 days, and -80°C for \u3e7 months; it maintained full activity after 10 freeze-thaw cycles. PlySs2 displays potent lytic activity against most strains of S. suis including the type strain S735, the pathogenic serotype 2, strain 10, and the pathogenic serotype 9 strain 7997. At 64 μg/ml, PlySs2 reduced multiple strains of S. suis by 6-logs within 1 hour in vitro. PlySs2 exhibited a minimum inhibitory concentration (MIC) of 32 μg/ml for S. suis strain S735 and 64 μg/ml for strain 7997. While resistance to gentamicin was observed after systematically increasing levels of gentamicin in an S. suis culture, the same protocol resulted in no observable resistance to PlySs2. The bacteriophage lysin PySs2 also exhibits broad lytic activity against MRSA, vancomycin-intermediate S. aureus (VISA), Streptococcus suis, Listeria, Staphylococcus simulans, Staphylococcus epidermidis, Streptococcus equi, Streptococcus agalactiae (group B streptococci – GBS), S. pyogenes, Streptococcus sanguinis, group G streptococci (GGS), group E streptococci (GES), and Streptococcus pneumoniae. PlySs2 at 128 μg/ml in vitro reduced MRSA and S. pyogenes by 5-logs and 3-logs within 1 hour respectively, and exhibited a minimum inhibitory concentration (MIC) of 16 μg/ml for MRSA. Serially increasing exposure of MRSA and S. pyogenes to PlySs2 or mupirocin resulted in no observed resistance to PlySs2 and resistance to mupirocin. The relevance of our in vitro work was confirmed with multiple in vivo experiments. Using a single 0.1-mg dose of PlySs2, the colonizing S. suis strain 7997 was reduced from the murine intranasal mucosa by \u3e4 logs; a 0.1-mg dose of gentamicin reduced S. suis by \u3c3-logs. A combination of 0.05 mg PlySs2 + 0.05 mg gentamicin reduced S. suis by \u3e5-logs. In protecting against mixed infections, a single, 2-mg dose of PlySs2 protected 92% (22/24) of the mice in a bacteremia model of dual MRSA and S. pyogenes infection. This is the first known lysin with broad activity against multiple serotypes and strains of S. suis, making it a vital tool in the treatment and prevention of S. suis infections in pigs and humans. To date, no other lysin has shown such notable broad lytic activity, stability, and efficacy against multiple, leading, human bacterial pathogens; PlySs2 has all the characteristics to be an effective therapeutic

Continuum Theory of Polymer Crystallization

We present a kinetic model of crystal growth of polymers of finite molecular weight. Experiments help to classify polymer crystallization broadly into two kinetic regimes. One is observed in melts or in high molar mass polymer solutions and is dominated by nucleation control with $G \sim \exp(1/T \Delta T)$, where $G$ is the growth rate and $\Delta T$ is the super-cooling. The other is observed in low molar mass solutions (as well as for small molecules) and is diffusion controlled with $G \sim \Delta T$, for small $\Delta T$. Our model unifies these two regimes in a single formalism. The model accounts for the accumulation of polymer chains near the growth front and invokes an entropic barrier theory to recover both limits of nucleation and diffusion control. The basic theory applies to both melts and solutions, and we numerically calculate the growth details of a single crystal in a dilute solution. The effects of molecular weight and concentration are also determined considering conventional polymer dynamics. Our theory shows that entropic considerations, in addition to the traditional energetic arguments, can capture general trends of a vast range of phenomenology. Unifying ideas on crystallization from small molecules and from flexible polymer chains emerge from our theory.Comment: 37 double-spaced pages including 8 figures, submitted to the Journal of Chemical Physic

Aircraft aerodynamic prediction method for V/STOL transition including flow separation

A numerical procedure was developed for the aerodynamic force and moment analysis of V/STOL aircraft operating in the transition regime between hover and conventional forward flight. The trajectories, cross sectional area variations, and mass entrainment rates of the jets are calculated by the Adler-Baron Jet-in-Crossflow Program. The inviscid effects of the interaction between the jets and airframe on the aerodynamic properties are determined by use of the MCAIR 3-D Subsonic properties are determined by use of the MCAIR 3-D Subsonic Potential Flow Program, a surface panel method. In addition, the MCAIR 3-D Geometry influence Coefficient Program is used to calculate a matrix of partial derivatives that represent the rate of change of the inviscid aerodynamic properties with respect to arbitrary changes in the effective wing shape

A study of beryllium and beryllium-lithium complexes in single crystal silicon

When beryllium is thermally diffused into silicon, it gives rise to acceptor levels 191 MeV and 145 meV above the valence band. Quenching and annealing studies indicate that the 145-MeV level is due to a more complex beryllium configuration than the 191-MeV level. When lithium is thermally diffused into a beryllium-doped silicon sample, it produces two acceptor levels at 106 MeV and 81 MeV. Quenching and annealing studies indicate that these levels are due to lithium forming a complex with the defects responsible for the 191-MeV and 145-MeV beryllium levels, respectively. Electrical measurements imply that the lithium impurity ions are physically close to the beryllium impurity atoms. The ground state of the 106-MeV beryllium level is split into two levels, presumably by internal strains. Tentative models are proposed

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis