Bicycling injuries and mortality in Victoria, 2001-2006

OBJECTIVE: To investigate the incidence of bicycling injuries and bicycle injury characteristics in the Victorian population. DESIGN: Review of prospectively collected data. SETTING: Bicycling injury data were extracted from four datasets for the period July 2001 to June 2006: (i) emergency department (ED) presentations from the Victorian Emergency Minimum Dataset; (ii) hospital admissions from the Victorian Admitted Episodes Data Set; (iii) major trauma cases from the Victorian State Trauma Registry (VSTR); and (iv) deaths from the National Coroners Information System. MAIN OUTCOME MEASURES: The profile and incidence of bicycling injuries across the datasets and years. RESULTS: In the 5 years, 25 920 bicycle-related ED presentations were recorded, 10 552 bicyclists were admitted to hospital, 298 bicycling injuries were classified as major trauma (VSTR), and there were 47 bicycling fatalities. From 2001 to 2006, the incidence of bicycle-related ED presentations (incidence rate ratio [IRR] = 1.42; 95% CI, 1.37-1.48), hospital admissions (IRR = 1.16; 95% CI, 1.09-1.23) and major trauma (IRR = 1.76; 95% CI, 1.22-2.55) increased significantly. Most of those injured were males, aged < 35 years, with road-related injuries. Patients classified as having major trauma had a significantly higher incidence of trunk and head/face/neck injuries compared with those presenting to an ED or admitted to hospital. CONCLUSION: The incidence of serious bicycling injury has risen over recent years, highlighting the need for targeted prevention programs. Accurate data on cycling participation, use of injury prevention strategies, and injury profiles would assist in reducing bicycle-related injury

A method for detecting and correcting feature misidentification on expression microarrays

BACKGROUND: Much of the microarray data published at Stanford is based on mouse and human arrays produced under controlled and monitored conditions at the Brown and Botstein laboratories and at the Stanford Functional Genomics Facility (SFGF). Nevertheless, as large datasets based on the Stanford Human array began to accumulate, a small but significant number of discrepancies were detected that required a serious attempt to track down the original source of error. Due to a controlled process environment, sufficient data was available to accurately track the entire process leading to up to the final expression data. In this paper, we describe our statistical methods to detect the inconsistencies in microarray data that arise from process errors, and discuss our technique to locate and fix these errors. RESULTS: To date, the Brown and Botstein laboratories and the Stanford Functional Genomics Facility have together produced 40,000 large-scale (10–50,000 feature) cDNA microarrays. By applying the heuristic described here, we have been able to check most of these arrays for misidentified features, and have been able to confidently apply fixes to the data where needed. Out of the 265 million features checked in our database, problems were detected and corrected on 1.3 million of them. CONCLUSION: Process errors in any genome scale high throughput production regime can lead to subsequent errors in data analysis. We show the value of tracking multi-step high throughput operations by using this knowledge to detect and correct misidentified data on gene expression microarrays

Safety and activity of varlilumab, a novel and first-in-class agonist anti-CD27 antibody, for hematologic malignancies.

CD27, a costimulatory molecule on T cells, induces intracellular signals mediating cellular activation, proliferation, effector function, and cell survival on binding to its ligand, CD70. Varlilumab, a novel, first-in-class, agonist immunoglobulin G1 anti-CD27 antibody, mediates antitumor immunity and direct killing of CD27+ tumor cells in animal models. This first-in-human, dose-escalation, and expansion study evaluated varlilumab in patients with hematologic malignancies. Primary objectives were to assess safety and the maximum tolerated and optimal biologic doses of varlilumab. Secondary objectives were to evaluate pharmacokinetics, pharmacodynamics, immunogenicity, and antitumor activity. In a 3 + 3 dose-escalation design, 30 patients with B-cell (n = 25) or T-cell (n = 5) malignancies received varlilumab (0.1, 0.3, 1, 3, or 10 mg/kg IV) as a single dose with a 28-day observation period, followed by weekly dosing (4 doses per cycle, up to 5 cycles, depending on tumor response). In an expansion cohort, 4 additional patients with Hodgkin lymphoma received varlilumab at 0.3 mg/kg every 3 weeks (4 doses per cycle, up to 5 cycles). No dose-limiting toxicities were observed. Treatment-related adverse events, generally grade 1 to 2, included fatigue, decreased appetite, anemia, diarrhea, and headache. Exposure was linear and dose-proportional across dose groups and resulted in increases in proinflammatory cytokines and soluble CD27. One patient with stage IV Hodgkin lymphoma experienced a complete response and remained in remission at \u3e33 months with no further anticancer therapy. These data support further investigation of varlilumab for hematologic malignancies, particularly in combination approaches targeting nonredundant immune regulating pathways. This trial was registered at www.clinicaltrials.gov as #NCT01460134

Rice_Phospho 1.0: a new rice-specific SVM predictor for protein phosphorylation sites

Experimentally-determined or computationally-predicted protein phosphorylation sites for distinctive species are becoming increasingly common. In this paper, we compare the predictive performance of a novel classification algorithm with different encoding schemes to develop a rice-specific protein phosphorylation site predictor. Our results imply that the combination of Amino acid occurrence Frequency with Composition of K-Spaced Amino Acid Pairs (AF-CKSAAP) provides the best description of relevant sequence features that surround a phosphorylation site. A support vector machine (SVM) using AF-CKSAAP achieves the best performance in classifying rice protein phophorylation sites when compared to the other algorithms. We have used SVM with AF-CKSAAP to construct a rice-specific protein phosphorylation sites predictor, Rice-Phospho 1.0 (http://bioinformatics.fafu.edu.cn/rice-phospho1.0). We measure the Accuracy (ACC) and Matthews Correlation Coefficient (MCC) of Rice-Phospho 1.0 to be 82.0% and 0.64, significantly higher than those measures for other predictors such as Scansite, Musite, PlantPhos and PhosphoRice. Rice-Phospho 1.0 also successfully predicted the experimentally identified phosphorylation sites in LOC-Os03g51600.1, a protein sequence which did not appear in the training dataset. In summary, Rice-phospho 1.0 outputs reliable predictions of protein phosphorylation sites in rice, and will serve as a useful tool to the community

Accuracy of Protein-Protein Binding Sites in High-Throughput Template-Based Modeling

The accuracy of protein structures, particularly their binding sites, is essential for the success of modeling protein complexes. Computationally inexpensive methodology is required for genome-wide modeling of such structures. For systematic evaluation of potential accuracy in high-throughput modeling of binding sites, a statistical analysis of target-template sequence alignments was performed for a representative set of protein complexes. For most of the complexes, alignments containing all residues of the interface were found. The full interface alignments were obtained even in the case of poor alignments where a relatively small part of the target sequence (as low as 40%) aligned to the template sequence, with a low overall alignment identity (<30%). Although such poor overall alignments might be considered inadequate for modeling of whole proteins, the alignment of the interfaces was strong enough for docking. In the set of homology models built on these alignments, one third of those ranked 1 by a simple sequence identity criteria had RMSD<5 Å, the accuracy suitable for low-resolution template free docking. Such models corresponded to multi-domain target proteins, whereas for single-domain proteins the best models had 5 Å<RMSD<10 Å, the accuracy suitable for less sensitive structure-alignment methods. Overall, ∼50% of complexes with the interfaces modeled by high-throughput techniques had accuracy suitable for meaningful docking experiments. This percentage will grow with the increasing availability of co-crystallized protein-protein complexes

Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

<p>Abstract</p> <p>Background</p> <p>Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone.</p> <p>Results</p> <p>We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods.</p> <p>Conclusions</p> <p>The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.</p> <p>Availability</p> <p>Datasets and software are available at <url>http://mail.ustc.edu.cn/~bigeagle/BMCBioinfo2010/index.htm</url>.</p

NSC23925, Identified in a High-Throughput Cell-Based Screen, Reverses Multidrug Resistance

Multidrug resistance (MDR) is a major factor which contributes to the failure of cancer chemotherapy, and numerous efforts have been attempted to overcome MDR. To date, none of these attempts have yielded a tolerable and effective therapy to reverse MDR; thus, identification of new agents would be useful both clinically and scientifically.To identify small molecule compounds that can reverse chemoresistance, we developed a 96-well plate high-throughput cell-based screening assay in a paclitaxel resistant ovarian cancer cell line. Coincubating cells with a sublethal concentration of paclitaxel in combination with each of 2,000 small molecule compounds from the National Cancer Institute Diversity Set Library, we identified a previously uncharacterized molecule, NSC23925, that inhibits Pgp1 and reverses MDR1 (Pgp1) but does not inhibit MRP or BCRP-mediated MDR. The cytotoxic activity of NSC23925 was further evaluated using a panel of cancer cell lines expressing Pgp1, MRP, and BCRP. We found that at a concentration of >10 microM NSC23925 moderately inhibits the proliferation of both sensitive and resistant cell lines with almost equal activity, but its inhibitory effect was not altered by co-incubation with the Pgp1 inhibitor, verapamil, suggesting that NSC23925 itself is not a substrate of Pgp1. Additionally, NSC23925 increases the intracellular accumulation of Pgp1 substrates: calcein AM, Rhodamine-123, paclitaxel, mitoxantrone, and doxorubicin. Interestingly, we further observed that, although NSC23925 directly inhibits the function of Pgp1 in a dose-dependent manner without altering the total expression level of Pgp1, NSC23925 actually stimulates ATPase activity of Pgp, a phenomenon seen in other Pgp inhibitors.The ability of NSC23925 to restore sensitivity to the cytotoxic effects of chemotherapy or to prevent resistance could significantly benefit cancer patients

Phase I study of pegylated liposomal doxorubicin and the multidrug-resistance modulator, valspodar

Valspodar, a P-glycoprotein modulator, affects pharmacokinetics of doxorubicin when administered in combination, resulting in doxorubicin dose reduction. In animal models, valspodar has minimal interaction with pegylated liposomal doxorubicin (PEG-LD). To determine any pharmacokinetic interaction in humans, we designed a study to determine maximum tolerated dose, dose-limiting toxicity (DLT), and pharmacokinetics of total doxorubicin, in PEG-LD and valspodar combination therapy in patients with advanced malignancies. Patients received PEG-LD 20–25 mg m−2 intravenously over 1 h for cycle one. In subsequent 2-week cycles, valspodar was administered as 72 h continuous intravenous infusion with PEG-LD beginning at 8 mg m−2 and escalated in an accelerated titration design to 25 mg m−2. Pharmacokinetic data were collected with and without valspodar. A total of 14 patients completed at least two cycles of therapy. No DLTs were observed in six patients treated at the highest level of PEG-LD 25 mg m−2. The most common toxicities were fatigue, nausea, vomiting, mucositis, palmar plantar erythrodysesthesia, diarrhoea, and ataxia. Partial responses were observed in patients with breast and ovarian carcinoma. The mean (range) total doxorubicin clearance decreased from 27 (10–73) ml h−1 m−2 in cycle 1 to 18 (3–37) ml h−1 m−2 with the addition of valspodar in cycle 2 (P=0.009). Treatment with PEG-LD 25 mg m−2 in combination with valspodar results in a moderate prolongation of total doxorubicin clearance and half-life but did not increase the toxicity of this agent

Doxorubicin loaded Polymeric Nanoparticulate Delivery System to overcome drug resistance in osteosarcoma

<p>Abstract</p> <p>Background</p> <p>Drug resistance is a primary hindrance for the efficiency of chemotherapy against osteosarcoma. Although chemotherapy has improved the prognosis of osteosarcoma patients dramatically after introduction of neo-adjuvant therapy in the early 1980's, the outcome has since reached plateau at approximately 70% for 5 year survival. The remaining 30% of the patients eventually develop resistance to multiple types of chemotherapy. In order to overcome both the dose-limiting side effects of conventional chemotherapeutic agents and the therapeutic failure incurred from multidrug resistant (MDR) tumor cells, we explored the possibility of loading doxorubicin onto biocompatible, lipid-modified dextran-based polymeric nanoparticles and evaluated the efficacy.</p> <p>Methods</p> <p>Doxorubicin was loaded onto a lipid-modified dextran based polymeric nano-system. The effect of various concentrations of doxorubicin alone or nanoparticle loaded doxorubicin on KHOS, KHOS_R2, U-2OS, and U-2OS_R2cells was analyzed. Effects on drug retention, immunofluorescence, Pgp expression, and induction of apoptosis were also analyzed.</p> <p>Results</p> <p>Dextran nanoparticles loaded with doxorubicin had a curative effect on multidrug resistant osteosarcoma cell lines by increasing the amount of drug accumulation in the nucleus via Pgp independent pathway. Nanoparticles loaded with doxorubicin also showed increased apoptosis in osteosarcoma cells as compared with doxorubicin alone.</p> <p>Conclusion</p> <p>Lipid-modified dextran nanoparticles loaded with doxorubicin showed pronounced anti-proliferative effects against osteosarcoma cell lines. These findings may lead to new treatment options for MDR osteosarcoma.</p