In vitro influence of stem surface finish and mantle conformity on pressure generation in cemented hip arthroplasty

Background and purpose Under physiological loads, debonded cemented femoral stems have been shown to move within their cement mantle and generate a fluid pump that may facilitate peri-prosthetic osteolysis by pressurizing fluid and circulating wear debris. The long-term physiological loading of rough and polished tapered stems in vitro has shown differences in performance, with greater interface pressures generated by the rough stems. In this study we investigated the individual effects of stem surface finish, degree of mantle wear, and mode of loading on the stem pump mechanism

In vitro comparison of the effects of rough and polished stem surface finish on pressure generation in cemented hip arthroplasty

Background and purpose High pressures around implants can cause bone lysis and loosening. We investigated how pressures are generated around cemented femoral stems

Poor survival outcomes in HER2 positive breast cancer patients with low grade, node negative tumours

We present a retrospective analysis on a cohort of low-grade, node-negative patients showing that human epidermal growth factor receptor 2 (HER2) status significantly affects the survival in this otherwise very good prognostic group. Our results provide support for the use of adjuvant trastuzumab in patients who are typically classified as having very good prognosis, not routinely offered standard chemotherapy, and who as such do not fit current UK prescribing guidelines for trastuzumab

Limited effect of patient and disease characteristics on compliance with hospital antimicrobial guidelines

Objective: Physicians frequently deviate from guidelines that promote prudent use of antimicrobials. We explored to what extent patient and disease characteristics were associated with compliance with guideline recommendations for three common infections. Methods: In a 1-year prospective observational study, 1,125 antimicrobial prescriptions were analysed for compliance with university hospital guidelines. Results: Compliance varied significantly between and within the groups of infections studied. Compliance was much higher for lower respiratory tract infections (LRTIs; 79%) than for sepsis (53%) and urinary tract infections (UTIs; 40%). Only predisposing illnesses and active malignancies were associated with more compliant prescribing, whereas alcohol/ intravenous drug abuse and serum creatinine levels > 130 mu mol/l were associated with less compliant prescribing. Availability of culture results had no impact on compliance with guidelines for sepsis but was associated with more compliance in UTIs and less in LRTIs. Narrowing initial broad-spectrum antimicrobial therapy to cultured pathogens was seldom practised. Most noncompliant prescribing concerned a too broad spectrum of activity when compared with guideline-recommended therapy. Conclusion: Patient characteristics had only a limited impact on compliant prescribing for a variety of reasons. Physicians seemed to practise defensive prescribing behaviour, favouring treatment success in current patients over loss of effectiveness due to resistance in future patients

The role of Herceptin in early breast cancer

Herceptin is widely regarded as the most important development in the treatment of breast cancer since Tamoxifen and the development of the multidisciplinary team (MDT). It is particularly exciting from an oncological polint of view as it represents success in the emerging field of specific targeted therapies to specific molecular abnormalities in tumour cells. This review will focus on the nature of the Her2 overexpression and the role of herceptin in the treatment of early breast cancer

Predictors of inhospital mortality and re-hospitalization in older adults with community-acquired pneumonia: a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>A better understanding of potentially modifiable predictors of in-hospital mortality and re-admission to the hospital following discharge may help to improve management of community-acquired pneumonia in older adults. We aimed to assess the associations of potentially modifiable factors with mortality and re-hospitalization in older adults hospitalized with community-acquired pneumonia.</p> <p>Methods</p> <p>A prospective cohort study was conducted from July 2003 to April 2005 in two Canadian cities. Patients aged 65 years or older hospitalized for community-acquired pneumonia were followed up for up to 30 days from initial hospitalization for mortality and these patients who were discharged alive within 30 days of initial hospitalization were followed up to 90 days of initial hospitalization for re-hospitalization. Separate logistic regression analyses were performed identify the predictors of mortality and re-hospitalization.</p> <p>Results</p> <p>Of 717 enrolled patients hospitalized for community-acquired pneumonia, 49 (6.8%) died within 30 days of hospital admission. Among these patients, 526 were discharged alive within 30 days of hospitalization of whom 58 (11.2%) were re-hospitalized within 90 days of initial hospitalization. History of hip fracture (odds ratio (OR) = 4.00, 95% confidence interval (CI) = (1.46, 10.96), P = .007), chronic obstructive pulmonary disease (OR = 2.31, 95% CI = (1.18, 4.50), P = .014), cerebrovascular disease (OR = 2.11, 95% CI = (1.03, 4.31), P = .040) were associated with mortality. Male sex (OR = 2.35, 95% CI = (1.13, 4.85), P = .022) was associated with re-hospitalization while vitamin E supplementation was protective (OR = 0.37 (0.16, 0.90), P = .028). Lower socioeconomic status, prior influenza and pneumococcal vaccinations, appropriate antibiotic prescription upon admission, and lower nutrition risk were not significantly associated with mortality or re-hospitalization.</p> <p>Conclusion</p> <p>Chronic comorbidities appear to be the most important predictors of death and re-hospitalization in older adults hospitalized with community-acquired pneumonia while vitamin E supplementation was protective.</p

How accurate and statistically robust are catalytic site predictions based on closeness centrality?

<p>Abstract</p> <p>Background</p> <p>We examine the accuracy of enzyme catalytic residue predictions from a network representation of protein structure. In this model, amino acid α-carbons specify vertices within a graph and edges connect vertices that are proximal in structure. Closeness centrality, which has shown promise in previous investigations, is used to identify important positions within the network. Closeness centrality, a global measure of network centrality, is calculated as the reciprocal of the average distance between vertex <it>i </it>and all other vertices.</p> <p>Results</p> <p>We benchmark the approach against 283 structurally unique proteins within the Catalytic Site Atlas. Our results, which are inline with previous investigations of smaller datasets, indicate closeness centrality predictions are statistically significant. However, unlike previous approaches, we specifically focus on residues with the very best scores. Over the top five closeness centrality scores, we observe an average true to false positive rate ratio of 6.8 to 1. As demonstrated previously, adding a solvent accessibility filter significantly improves predictive power; the average ratio is increased to 15.3 to 1. We also demonstrate (for the first time) that filtering the predictions by residue identity improves the results even more than accessibility filtering. Here, we simply eliminate residues with physiochemical properties unlikely to be compatible with catalytic requirements from consideration. Residue identity filtering improves the average true to false positive rate ratio to 26.3 to 1. Combining the two filters together has little affect on the results. Calculated p-values for the three prediction schemes range from 2.7E-9 to less than 8.8E-134. Finally, the sensitivity of the predictions to structure choice and slight perturbations is examined.</p> <p>Conclusion</p> <p>Our results resolutely confirm that closeness centrality is a viable prediction scheme whose predictions are statistically significant. Simple filtering schemes substantially improve the method's predicted power. Moreover, no clear effect on performance is observed when comparing ligated and unligated structures. Similarly, the CC prediction results are robust to slight structural perturbations from molecular dynamics simulation.</p

Prediction of catalytic residues using Support Vector Machine with selected protein sequence and structural properties

BACKGROUND: The number of protein sequences deriving from genome sequencing projects is outpacing our knowledge about the function of these proteins. With the gap between experimentally characterized and uncharacterized proteins continuing to widen, it is necessary to develop new computational methods and tools for functional prediction. Knowledge of catalytic sites provides a valuable insight into protein function. Although many computational methods have been developed to predict catalytic residues and active sites, their accuracy remains low, with a significant number of false positives. In this paper, we present a novel method for the prediction of catalytic sites, using a carefully selected, supervised machine learning algorithm coupled with an optimal discriminative set of protein sequence conservation and structural properties. RESULTS: To determine the best machine learning algorithm, 26 classifiers in the WEKA software package were compared using a benchmarking dataset of 79 enzymes with 254 catalytic residues in a 10-fold cross-validation analysis. Each residue of the dataset was represented by a set of 24 residue properties previously shown to be of functional relevance, as well as a label {+1/-1} to indicate catalytic/non-catalytic residue. The best-performing algorithm was the Sequential Minimal Optimization (SMO) algorithm, which is a Support Vector Machine (SVM). The Wrapper Subset Selection algorithm further selected seven of the 24 attributes as an optimal subset of residue properties, with sequence conservation, catalytic propensities of amino acids, and relative position on protein surface being the most important features. CONCLUSION: The SMO algorithm with 7 selected attributes correctly predicted 228 of the 254 catalytic residues, with an overall predictive accuracy of more than 86%. Missing only 10.2% of the catalytic residues, the method captures the fundamental features of catalytic residues and can be used as a "catalytic residue filter" to facilitate experimental identification of catalytic residues for proteins with known structure but unknown function

Combination antibiotic therapy for community-acquired pneumonia

Community-acquired pneumonia (CAP) is a common and potentially serious illness that is associated with morbidity and mortality. Although medical care has improved during the past decades, it is still potentially lethal. Streptococcus pneumoniae is the most frequent microorganism isolated. Treatment includes mandatory antibiotic therapy and organ support as needed. There are several antibiotic therapy regimens that include β-lactams or macrolides or fluoroquinolones alone or in combination. Combination antibiotic therapy achieves a better outcome compared with monotherapy and it should be given in the following subset of patients with CAP: outpatients with comorbidities and previous antibiotic therapy, nursing home patients with CAP, hospitalized patients with severe CAP, bacteremic pneumococcal CAP, presence of shock, and necessity of mechanical ventilation. Better outcome is associated with combination therapy that includes a macrolide for wide coverage of atypical pneumonia, polymicrobial pneumonia, or resistant Streptococcus pneumoniae. Macrolides have shown different properties other than antimicrobial activity, such as anti-inflammatory properties. Although this evidence comes from observational, most of them retrospective and nonblinded studies, the findings are consistent. Ideally, a prospective, multicenter, randomized trial should be performed to confirm these findings