Relationships of the Location and Content of Rounds to Specialty, Institution, Patient-Census, and Team Size

OBJECTIVE: Existing observational data describing rounds in teaching hospitals are 15 years old, predate duty-hour regulations, are limited to one institution, and do not include pediatrics. We sought to evaluate the effect of medical specialty, institution, patient-census, and team participants upon time at the bedside and education occurring on rounds. METHODS AND PARTICIPANTS: Between December of 2007 and October of 2008 we performed 51 observations at Lucile Packard Children's Hospital, Seattle Children's Hospital, Stanford University Hospital, and the University of Washington Medical Center of 35 attending physicians. We recorded minutes spent on rounds in three location and seven activity categories, members of the care team, and patient-census. RESULTS: Results presented are means. Pediatric rounds had more participants (8.2 vs. 4.1 physicians, p<.001; 11.9 vs. 2.4 non-physicians, p<.001) who spent more minutes in hallways (96.9 min vs. 35.2 min, p<.001), fewer minutes at the bedside (14.6 vs. 38.2 min, p = .01) than internal medicine rounds. Multivariate regression modeling revealed that minutes at the bedside per patient was negatively associated with pediatrics (-2.77 adjusted bedside minutes; 95% CI -4.61 to -0.93; p<.001) but positively associated with the number of non-physician participants (0.12 adjusted bedside minutes per non physician participant; 95% CI 0.07 to 0.17; p = <.001). Education minutes on rounds was positively associated with the presence of an attending physician (2.70 adjusted education minutes; 95% CI 1.27 to 4.12; p<.001) and with one institution (1.39 adjusted education minutes; 95% CI 0.26 to 2.53; p = .02). CONCLUSIONS: Pediatricians spent less time at the bedside on rounds than internal medicine physicians due to reasons other than patient-census or the number of participants in rounds. Compared to historical data, internal medicine rounds were spent more at the bedside engaged in patient care and communication, and less upon educational activities

Treatment of Canine Osseous Tumors with Photodynamic Therapy: A Pilot Study

Photodynamic therapy uses nonthermal coherent light delivered via fiber optic cable to locally activate a photosensitive chemotherapeutic agent that ablates tumor tissue. Owing to the limitations of light penetration, it is unknown whether photodynamic therapy can treat large osseous tumors. We determined whether photodynamic therapy can induce necrosis in large osseous tumors, and if so, to quantify the volume of treated tissue. In a pilot study we treated seven dogs with spontaneous osteosarcomas of the distal radius. Tumors were imaged with MRI before and 48 hours after treatment, and the volumes of hypointense regions were compared. The treated limbs were amputated immediately after imaging at 48 hours and sectioned corresponding to the MR axial images. We identified tumor necrosis histologically; the regions of necrosis corresponded anatomically to hypointense tissue on MRI. The mean volume of necrotic tissue seen on MRI after photodynamic therapy was 21,305 mm3 compared with a pretreatment volume of 6108 mm3. These pilot data suggest photodynamic therapy penetrates relatively large canine osseous tumors and may be a useful adjunct for treatment of bone tumors

Classifying RNA-Binding Proteins Based on Electrostatic Properties

Protein structure can provide new insight into the biological function of a protein and can enable the design of better experiments to learn its biological roles. Moreover, deciphering the interactions of a protein with other molecules can contribute to the understanding of the protein's function within cellular processes. In this study, we apply a machine learning approach for classifying RNA-binding proteins based on their three-dimensional structures. The method is based on characterizing unique properties of electrostatic patches on the protein surface. Using an ensemble of general protein features and specific properties extracted from the electrostatic patches, we have trained a support vector machine (SVM) to distinguish RNA-binding proteins from other positively charged proteins that do not bind nucleic acids. Specifically, the method was applied on proteins possessing the RNA recognition motif (RRM) and successfully classified RNA-binding proteins from RRM domains involved in protein–protein interactions. Overall the method achieves 88% accuracy in classifying RNA-binding proteins, yet it cannot distinguish RNA from DNA binding proteins. Nevertheless, by applying a multiclass SVM approach we were able to classify the RNA-binding proteins based on their RNA targets, specifically, whether they bind a ribosomal RNA (rRNA), a transfer RNA (tRNA), or messenger RNA (mRNA). Finally, we present here an innovative approach that does not rely on sequence or structural homology and could be applied to identify novel RNA-binding proteins with unique folds and/or binding motifs

The Escherichia coli SOS Gene dinF Protects against Oxidative Stress and Bile Salts

DNA is constantly damaged by physical and chemical factors, including reactive oxygen species (ROS), such as superoxide radical (O2−), hydrogen peroxide (H2O2) and hydroxyl radical (•OH). Specific mechanisms to protect and repair DNA lesions produced by ROS have been developed in living beings. In Escherichia coli the SOS system, an inducible response activated to rescue cells from severe DNA damage, is a network that regulates the expression of more than 40 genes in response to this damage, many of them playing important roles in DNA damage tolerance mechanisms. Although the function of most of these genes has been elucidated, the activity of some others, such as dinF, remains unknown. The DinF deduced polypeptide sequence shows a high homology with membrane proteins of the multidrug and toxic compound extrusion (MATE) family. We describe here that expression of dinF protects against bile salts, probably by decreasing the effects of ROS, which is consistent with the observed decrease in H2O2-killing and protein carbonylation. These results, together with its ability to decrease the level of intracellular ROS, suggests that DinF can detoxify, either direct or indirectly, oxidizing molecules that can damage DNA and proteins from both the bacterial metabolism and the environment. Although the exact mechanism of DinF activity remains to be identified, we describe for the first time a role for dinF

wKinMut: An integrated tool for the analysis and interpretation of mutations in human protein kinases

BACKGROUND: Protein kinases are involved in relevant physiological functions and a broad number of mutations in this superfamily have been reported in the literature to affect protein function and stability. Unfortunately, the exploration of the consequences on the phenotypes of each individual mutation remains a considerable challenge. RESULTS: The wKinMut web-server offers direct prediction of the potential pathogenicity of the mutations from a number of methods, including our recently developed prediction method based on the combination of information from a range of diverse sources, including physicochemical properties and functional annotations from FireDB and Swissprot and kinase-specific characteristics such as the membership to specific kinase groups, the annotation with disease-associated GO terms or the occurrence of the mutation in PFAM domains, and the relevance of the residues in determining kinase subfamily specificity from S3Det. This predictor yields interesting results that compare favourably with other methods in the field when applied to protein kinases. Together with the predictions, wKinMut offers a number of integrated services for the analysis of mutations. These include: the classification of the kinase, information about associations of the kinase with other proteins extracted from iHop, the mapping of the mutations onto PDB structures, pathogenicity records from a number of databases and the classification of mutations in large-scale cancer studies. Importantly, wKinMut is connected with the SNP2L system that extracts mentions of mutations directly from the literature, and therefore increases the possibilities of finding interesting functional information associated to the studied mutations. CONCLUSIONS: wKinMut facilitates the exploration of the information available about individual mutations by integrating prediction approaches with the automatic extraction of information from the literature (text mining) and several state-of-the-art databases. wKinMut has been used during the last year for the analysis of the consequences of mutations in the context of a number of cancer genome projects, including the recent analysis of Chronic Lymphocytic Leukemia cases and is publicly available at http://wkinmut.bioinfo.cnio.es

Exploring Fold Space Preferences of New-born and Ancient Protein Superfamilies

The evolution of proteins is one of the fundamental processes that has delivered the diversity and complexity of life we see around ourselves today. While we tend to define protein evolution in terms of sequence level mutations, insertions and deletions, it is hard to translate these processes to a more complete picture incorporating a polypeptide's structure and function. By considering how protein structures change over time we can gain an entirely new appreciation of their long-term evolutionary dynamics. In this work we seek to identify how populations of proteins at different stages of evolution explore their possible structure space. We use an annotation of superfamily age to this space and explore the relationship between these ages and a diverse set of properties pertaining to a superfamily's sequence, structure and function. We note several marked differences between the populations of newly evolved and ancient structures, such as in their length distributions, secondary structure content and tertiary packing arrangements. In particular, many of these differences suggest a less elaborate structure for newly evolved superfamilies when compared with their ancient counterparts. We show that the structural preferences we report are not a residual effect of a more fundamental relationship with function. Furthermore, we demonstrate the robustness of our results, using significant variation in the algorithm used to estimate the ages. We present these age estimates as a useful tool to analyse protein populations. In particularly, we apply this in a comparison of domains containing greek key or jelly roll motifs

A new approach to assess and predict the functional roles of proteins across all known structures

The three dimensional atomic structures of proteins provide information regarding their function; and codified relationships between structure and function enable the assessment of function from structure. In the current study, a new data mining tool was implemented that checks current gene ontology (GO) annotations and predicts new ones across all the protein structures available in the Protein Data Bank (PDB). The tool overcomes some of the challenges of utilizing large amounts of protein annotation and measurement information to form correspondences between protein structure and function. Protein attributes were extracted from the Structural Biology Knowledgebase and open source biological databases. Based on the presence or absence of a given set of attributes, a given protein’s functional annotations were inferred. The results show that attributes derived from the three dimensional structures of proteins enhanced predictions over that using attributes only derived from primary amino acid sequence. Some predictions reflected known but not completely documented GO annotations. For example, predictions for the GO term for copper ion binding reflected used information a copper ion was known to interact with the protein based on information in a ligand interaction database. Other predictions were novel and require further experimental validation. These include predictions for proteins labeled as unknown function in the PDB. Two examples are a role in the regulation of transcription for the protein AF1396 from Archaeoglobus fulgidus and a role in RNA metabolism for the protein psuG from Thermotoga maritima

Contributions of Histone H3 Nucleosome Core Surface Mutations to Chromatin Structures, Silencing and DNA Repair

Histone H3 mutations in residues that cluster in a discrete region on the nucleosome surface around lysine 79 of H3 affect H3-K79 methylation, impair transcriptional silencing in subtelomeric chromatin, and reveal distinct contributions of histone H3 to various DNA-damage response and repair pathways. These residues might act by recruitment of silencing and DNA-damage response factors. Alternatively, their location on the nucleosome surface suggests a possible involvement in nucleosome positioning, stability and nucleosome interactions. Here, we show that the yeast H3 mutants hht2-T80A, hht2-K79E, hht2-L70S, and hht2-E73D show normal nucleosome positioning and stability in minichromosomes. However, loss of silencing in a subtelomeric URA3 gene correlates with a shift of the promoter nucleosome, while nucleosome positions and stability in the coding region are maintained. Moreover, the H3 mutants show normal repair of UV lesions by photolyase and nucleotide excision repair in minichromosomes and slightly enhanced repair in the subtelomeric region. Thus, these results support a role of those residues in the recruitment of silencing proteins and argue against a general role in nucleosome organization