Denaturation and unfolding of human anaphylatoxin C3a: an unusually low covalent stability of its native disulfide bonds.

The complement C3a anaphylatoxin is a major molecular mediator of innate immunity. It is a potent activator of mast cells, basophils and eosinophils and causes smooth muscle contraction. Structurally, C3a is a relatively small protein (77 amino acids) comprising a N-terminal domain connected by 3 native disulfide bonds and a helical C-terminal segment. The structural stability of C3a has been investigated here using three different methods: Disulfide scrambling; Differential CD spectroscopy; and Reductive unfolding. Two uncommon features regarding the stability of C3a and the structure of denatured C3a have been observed in this study. (a) There is an unusual disconnection between the conformational stability of C3a and the covalent stability of its three native disulfide bonds that is not seen with other disulfide proteins. As measured by both methods of disulfide scrambling and differential CD spectroscopy, the native C3a exhibits a global conformational stability that is comparable to numerous proteins with similar size and disulfide content, all with mid-point denaturation of [GdmCl](1/2) at 3.4-5M. These proteins include hirudin, tick anticoagulant protein and leech carboxypeptidase inhibitor. However, the native disulfide bonds of C3a is 150-1000 fold less stable than those proteins as evaluated by the method of reductive unfolding. The 3 native disulfide bonds of C3a can be collectively and quantitatively reduced with as low as 1mM of dithiothreitol within 5 min. The fragility of the native disulfide bonds of C3a has not yet been observed with other native disulfide proteins. (b) Using the method of disulfide scrambling, denatured C3a was shown to consist of diverse isomers adopting varied extent of unfolding. Among them, the most extensively unfolded isomer of denatured C3a is found to assume beads-form disulfide pattern, comprising Cys(36)-Cys(49) and two disulfide bonds formed by two pair of consecutive cysteines, Cys(22)-Cys(23) and Cys(56)-Cys(57), a unique disulfide structure of polypeptide that has not been documented previously.

Hepatocellular carcinoma survival in uninsured and underinsured patients.

BACKGROUND: The incidence of hepatitis C virus (HCV) and hepatocellular carcinoma (HCC) is increasing. The purpose of this study is to establish baseline survival in a medically-underserved population and to evaluate the effect of HCV seropositivity on our patient population. MATERIALS AND METHODS: We reviewed clinicopathologic parameters from a prospective tumor registry and medical records from the Harris County Hospital District (HCHD). Outcomes were compared using Kaplan-Meier survival analysis and log-rank tests. RESULTS: A total of 298 HCC patients were identified. The median survival for the entire cohort was 3.4 mo. There was no difference in survival between the HCV seropositive and the HCV seronegative groups (3.6 mo versus 2.6 mo, P = 0.7). Patients with a survival <1 mo had a significant increase in>αfetoprotein (AFP), international normalized ratio (INR), model for end-stage liver disease (MELD) score, and total bilirubin and decrease in albumin compared with patients with a survival ≥ 1 mo. CONCLUSIONS: Survival for HCC patients in the HCHD is extremely poor compared with an anticipated median survival of 7 mo reported in other studies. HCV seropositive patients have no survival advantage over HCV seronegative patients. Poorer liver function at diagnosis appears to be related to shorter survival. Further analysis into variables contributing to decreased survival is needed.

Syntaxin 3B is essential for the exocytosis of synaptic vesicles in ribbon synapses of the retina.

Ribbon synapses of the vertebrate retina are specialized synapses that release neurotransmitter by synaptic vesicle exocytosis in a manner that is proportional to the level of depolarization of the cell. This release property is different from conventional neurons, in which the release of neurotransmitter occurs as a short-lived burst triggered by an action potential. Synaptic vesicle exocytosis is a calcium regulated process that is dependent on a set of interacting synaptic proteins that form the so-called SNARE (soluble N-ethylmaleimide sensitive factor attachment protein receptor) complex. Syntaxin 3B has been identified as a specialized SNARE molecule in ribbon synapses of the rodent retina. However, the best physiologically-characterized neuron that forms ribbon-style synapses is the rod-dominant or Mb1 bipolar cell of the goldfish retina. We report here the molecular characterization of syntaxin 3B from the goldfish retina. Using a combination of reverse transcription (RT) polymerase chain reaction (PCR) and immunostaining with a specific antibody, we show that syntaxin 3B is highly enriched in the plasma membrane of bipolar cell synaptic terminals of the goldfish retina. Using membrane capacitance measurements we demonstrate that a peptide derived from goldfish syntaxin 3B inhibits synaptic vesicle exocytosis. These experiments demonstrate that syntaxin 3B is an important factor for synaptic vesicle exocytosis in ribbon synapses of the vertebrate retina.

Syntaxin 3b is a t-SNARE specific for ribbon synapses of the retina.

Previous studies have demonstrated that ribbon synapses in the retina do not contain the t-SNARE (target-soluble N-ethylmaleimide-sensitive factor attachment protein receptor) syntaxin 1A that is found in conventional synapses of the nervous system. In contrast, ribbon synapses of the retina contain the related isoform syntaxin 3. In addition to its localization in ribbon synapses, syntaxin 3 is also found in nonneuronal cells, where it has been implicated in the trafficking of transport vesicles to the apical plasma membrane of polarized cells. The syntaxin 3 gene codes for four different splice forms, syntaxins 3A, 3B, 3C, and 3D. We demonstrate here by using analysis of EST databases, RT-PCR, in situ hybridization, and Northern blot analysis that cells in the mouse retina express only syntaxin 3B. In contrast, nonneuronal tissues, such as kidney, express only syntaxin 3A. The two major syntaxin isoforms (3A and 3B) have an identical N-terminal domain but differ in the C-terminal half of the SNARE domain and the C-terminal transmembrane domain. These two domains are thought to be directly involved in synaptic vesicle fusion. The interaction of syntaxin 1A and syntaxin 3B with other synaptic proteins was examined. We found that both proteins bind Munc18/N-sec1 with similar affinity. In contrast, syntaxin 3B had a much lower binding affinity for the t-SNARE SNAP25 compared with syntaxin 1A. By using an in vitro fusion assay, we could demonstrate that vesicles containing syntaxin 3B and SNAP25 could fuse with vesicles containing synaptobrevin2/VAMP2, demonstrating that syntaxin 3B can function as a t-SNARE.

Pancreatic adenocarcinoma in a patient with situs inversus: a case report of this rare coincidence.

BACKGROUND: Situs inversus (SI) is a relatively rare occurrence in patients with pancreatic adenocarcinoma. Pancreatic resection in these patients has rarely been described. CT scan imaging is a principle modality for detecting pancreatic cancer and its use in SI patients is seldom reported. CASE PRESENTATION: We report a 48 year old woman with SI who, despite normal CT scan 8 months earlier, presented with obstructive jaundice and a pancreatic head mass requiring a pancreaticoduodenectomy. The surgical pathology report demonstrated pancreatic adenocarcinoma. CONCLUSION: SI is a rare condition with concurrent pancreatic cancer being even rarer. Despite the rarity, pancreaticoduodenectomy in these patients for resectable lesions is safe as long as special consideration to the anatomy is taken. Additionally, radiographic imaging has significantly improved detection of early pancreatic cancer; however, there continues to be a need for improved detection of small neoplasms.

AVIAN ROTAVIRUSES

The prevalence of antirotavirus antibodies in chickens and turkeys in the Gonzales, Texas and Llano, Texas areas was studied. Caged layer chicken flocks were found to have a prevalence of 64% when samples were taken randomly. This compares to 45% in chicken broiler breeder flocks and 92% in turkey breeding flocks. The natural occurrence of turkey rotavirus infection in two separate field studies showed an increase in mortality varying from 9% to 45% above expected death losses. Clinically, pasted vents, lacitude, and general malaise were noted in affected poults. Lesions noted on post mortem examination were; slight ballooning of the small intestine, excessively large ceca, and mild hyperemia of the small and large intestines.^ The use of maternal antibody from simian rotavirus immunized chickens' eggs for preventing murine rotavirus infection in infant mice was investigated. There was a reduction from 91% to 15% incidence when infant mice were treated twice daily with egg yolk immunoglobulin.^ The need for a convenient, easily grown and rapidly reproducing model for avian and mammalian rotaviruses led to the use of coturnix chicks. The turkey rotavirus was adapted to the quail chicks be serial passage. Transmission and scanning electron microscopy as well as micropathological methods were used in the study of the pathogenesis of rotavirus infection in quail and infant mice. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Targeting Special Populations for Family Preservation: The Influence of Worker Competence and Organizational Culture

Today there are approximately 581,000 children in the United States foster care system. Children of color, one special population group, are disproportionately represented in the foster care system. Family preservation, a program that aims to improve family functioning and thus decrease the need for foster care, has been examined closely. Some researchers believe that family preservation programs have failed partly due to practitioners' inability to target appropriate families (Feldman, 1990; Schuerman, Rzepnicki & Littell, 1994). Additionally, research confirms that children of color are not the target of family preservation services (Denby, Curtis, & Alford, 1998). Improvements in the effectiveness of family preservation will require many types of reform both internal and external to the program. Among the types of internal reform needed is accurate "targeting of services. " Given the overrepresentation of children of color in the foster care system, this group must be among those who are targeted for services. The results of a national survey of 254 family preservation workers reveal a "profile" of the worker who is likely to target special populations, including children of color, for family preservation services. A case is made for service improvements and training to facilitate the "profiled" workers' competencies.

Family Preservation Journal, 2003, Volume 7. (Entire issue)

Entire issue (large pdf file) Articles include: Social Workers' Perceptions of Family Preservation Programs. Elaine M Maccio, David Skiba, Howard J Doueck, Karen A. Randolph, Elisabeth A. Weston, and Lorie E. Anderson Targeting Special Populations for Family Preservation: The Influence of Worker Competence and Organizational Culture. Ramona W: Denby, Keith A. Alford, and Carla M Curtis Understanding and Fostering Family Resilience. Robert G. Blair Walking Our Talk in the Neighborhoods: Building Professional/Natural Helper Partnerships. Jill Kinney and Margaret Trent Intersystem Collaboration: A Statewide Initiative to Support Families. Elizabeth M Tracy, David E. Biegel, Ann C. Rebeck, and Jeffrey A. Johnsen