Comparison of single versus fractionated dose of stereotactic radiotherapy for salvaging local failures of nasopharyngeal carcinoma: a matched-cohort analysis

BACKGROUND: Local failure is an important cause of morbidity and mortality in nasopharyngeal carcinoma (NPC). Although surgery or brachytherapy may be feasible in selected cases, most patients with local failure require external beam re-irradiation. Stereotactic radiation using single or multiple fractions have been employed in re-irradiation of NPC, but the optimal fractionation scheme and dose are not clear. METHODS: Records of 125 NPC patients who received salvage stereotactic radiation were reviewed. A matched-pair design was used to select patients with similar prognostic factors who received stereotactic re-irradiation using single fraction (SRS) or multiple fractions (SRM). Eighty-six patients were selected with equal number in SRS and SRM groups. All patients were individually matched for failure type (persistent or recurrent), rT stage (rT1-2 or rT3-4), and tumor volume (5-10 cc, or >10 cc). Median dose was 12.5 Gy in single fraction by SRS, and 34 Gy in 2-6 fractions by SRM. RESULTS: Local control rate was better in SRM group although overall survival rates were similar. One- and 3-year local failure-free rates were 70% and 51% in SRS group compared with 91% and 83% in SRM group (p = 0.003). One- and 3-year overall survival rates were 98% and 66% in SRS group compared with 78% and 61% in SRM group (p = 0.31). The differences in local control were mainly observed in recurrent or rT2-4 disease. Incidence of severe late complications was 33% in SRS group vs. 21% in SRM group, including brain necrosis (16% vs. 12%) and hemorrhage (5% vs. 2%). CONCLUSION: Our study showed that SRM was superior to SRS in salvaging local failures of NPC, especially in the treatment of recurrent and rT2-4 disease. In patient with local failure of NPC suitable for stereotactic re-irradiation, use of fractionated treatment is preferred.link_to_subscribed_fulltex

Enriching for correct prediction of biological processes using a combination of diverse classifiers

<p>Abstract</p> <p>Background</p> <p>Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed.</p> <p>Results</p> <p>Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model.</p> <p>Conclusions</p> <p>This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models yielded incongruent results each of which were successfully combined into one superior model using the PIN measure we developed. Validation of the boosted predictions for gene functions showed the genes to be accurately predicted.</p

Current treatment options for recurrent nasopharyngeal cancer

Loco-regional control rate of nasopharyngeal carcinoma (NPC) has improved significantly in the past decade. However, local recurrence still represents a major cause of mortality and morbidity in advanced stages, and management of local failure remains a challenging issue in NPC. The best salvage treatment for local recurrent NPC remains to be determined. The options include brachytherapy, external radiotherapy, stereotactic radiosurgery, and nasopharyngectomy, either alone or in different combinations. In this article we will discuss the different options for salvage of locally recurrent NPC. Retreatment of locally recurrent NPC using radiotherapy, alone or in combination with other treatment modalities, as well as surgery, can result in long-term local control and survival in a substantial proportion of patients. For small-volume recurrent tumors (T1–T2) treated with external radiotherapy, brachytherapy or stereotactic radiosurgery, comparable results to those obtained with surgery have been reported. In contrast, treatment results of advanced-stage locally recurrent NPC are generally more satisfactory with surgery (with or without postoperative radiotherapy) than with reirradiation

Computational identification of ubiquitylation sites from protein sequences

<p>Abstract</p> <p>Background</p> <p>Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites.</p> <p>Results</p> <p>We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, <it>k</it>-nearest neighbor, and NaïveBayes). Results show that the set of used 531 physicochemical properties and support vector machine (SVM) are the best kind of features and classifier respectively that their combination has a prediction accuracy of 72.19% using leave-one-out cross-validation.</p> <p>Consequently, an informative physicochemical property mining algorithm (IPMA) is proposed to select an informative subset of 531 physicochemical properties. A prediction system UbiPred was implemented by using an SVM with the feature set of 31 informative physicochemical properties selected by IPMA, which can improve the accuracy from 72.19% to 84.44%. To further analyze the informative physicochemical properties, a decision tree method C5.0 was used to acquire if-then rule-based knowledge of predicting ubiquitylation sites. UbiPred can screen promising ubiquitylation sites from putative non-ubiquitylation sites using prediction scores. By applying UbiPred, 23 promising ubiquitylation sites were identified from an independent dataset of 3424 putative non-ubiquitylation sites, which were also validated by using the obtained prediction rules.</p> <p>Conclusion</p> <p>We have proposed an algorithm IPMA for mining informative physicochemical properties from protein sequences to build an SVM-based prediction system UbiPred. UbiPred can predict ubiquitylation sites accompanied with a prediction score each to help biologists in identifying promising sites for experimental verification. UbiPred has been implemented as a web server and is available at <url>http://iclab.life.nctu.edu.tw/ubipred</url>.</p

B Cell Activating Factor (BAFF) and T Cells Cooperate to Breach B Cell Tolerance in Lupus-Prone New Zealand Black (NZB) Mice

The presence of autoantibodies in New Zealand Black (NZB) mice suggests a B cell tolerance defect however the nature of this defect is unknown. To determine whether defects in B cell anergy contribute to the autoimmune phenotype in NZB mice, soluble hen egg lysozyme (sHEL) and anti-HEL Ig transgenes were bred onto the NZB background to generate double transgenic (dTg) mice. NZB dTg mice had elevated levels of anti-HEL antibodies, despite apparently normal B cell functional anergy in-vitro. NZB dTg B cells also demonstrated increased survival and abnormal entry into the follicular compartment following transfer into sHEL mice. Since this process is dependent on BAFF, BAFF serum and mRNA levels were assessed and were found to be significantly elevated in NZB dTg mice. Treatment of NZB sHEL recipient mice with TACI-Ig reduced NZB dTg B cell survival following adoptive transfer, confirming the role of BAFF in this process. Although NZB mice had modestly elevated BAFF, the enhanced NZB B cell survival response appeared to result from an altered response to BAFF. In contrast, T cell blockade had a minimal effect on B cell survival, but inhibited anti-HEL antibody production. The findings suggest that the modest BAFF elevations in NZB mice are sufficient to perturb B cell tolerance, particularly when acting in concert with B cell functional abnormalities and T cell help

Prognostic value of gross tumor volume delineated by FDG-PET-CT based radiotherapy treatment planning in patients with locally advanced pancreatic cancer treated with chemoradiotherapy

<p>Abstract</p> <p>Background</p> <p>We aimed to assess whether gross tumor volume (GTV) determined by fusion of contrast-enhanced computerized tomography (CT) and 18F-fluoro-deoxy-D-glucose positron emission tomography-CT (FDG-PET-CT) based radiotherapy planning could predict outcomes, namely overall survival (OS), local-regional progression-free survival (LRPFS), and progression-free survival (PFS) in cases with locally advanced pancreas cancer (LAPC) treated with definitive concurrent chemoradiotherapy.</p> <p>Methods</p> <p>A total of 30 patients with histological proof of LAPC underwent 50.4 Gy (1.8 Gy/28 fractions) of radiotherapy concurrent with continuously infused 5-FU followed by 4 to 6 courses of maintenance gemcitabine. Target volume delineations were performed on FDG-PET-CT-based RTP. Patients were stratified into 2 groups: GTV lesser (GTV_L) versus greater (GTV_G) than cut off value determined by receiver operating characteristic (ROC) analysis, and compared in terms of OS, LRPFS and PFS.</p> <p>Results</p> <p>Median GTV delineated according to the FDG-PET-CT data was 100.0 cm³. Cut off GTV value determined from ROC curves was 91.1 cm³. At a median follow up of 11.2 months, median OS, LRPFS and PFS for the entire population were 10.3, 7.8 and 5.7 months, respectively. Median OS, LRPFS and PFS for GTV_Land GTV_Gcohorts were 16.3 vs. 9.5 (<it>p </it>= 0.005), 11.0 vs. 6.0 (<it>p </it>= 0.013), and 9.0 vs. 4.8 months (<it>p </it>= 0.008), respectively.</p> <p>Conclusions</p> <p>The superior OS, LRPFS and PFS observed in GTV_Lpatients over GTV_Gones suggests a potential for FDG-PET-CT-defined GTV size in predicting outcomes of LAPC patients treated with definitive C-CRT, which needs to be validated by further studies with larger cohorts.</p

CMB Telescopes and Optical Systems

The cosmic microwave background radiation (CMB) is now firmly established as a fundamental and essential probe of the geometry, constituents, and birth of the Universe. The CMB is a potent observable because it can be measured with precision and accuracy. Just as importantly, theoretical models of the Universe can predict the characteristics of the CMB to high accuracy, and those predictions can be directly compared to observations. There are multiple aspects associated with making a precise measurement. In this review, we focus on optical components for the instrumentation used to measure the CMB polarization and temperature anisotropy. We begin with an overview of general considerations for CMB observations and discuss common concepts used in the community. We next consider a variety of alternatives available for a designer of a CMB telescope. Our discussion is guided by the ground and balloon-based instruments that have been implemented over the years. In the same vein, we compare the arc-minute resolution Atacama Cosmology Telescope (ACT) and the South Pole Telescope (SPT). CMB interferometers are presented briefly. We conclude with a comparison of the four CMB satellites, Relikt, COBE, WMAP, and Planck, to demonstrate a remarkable evolution in design, sensitivity, resolution, and complexity over the past thirty years.Comment: To appear in: Planets, Stars and Stellar Systems (PSSS), Volume 1: Telescopes and Instrumentatio

Protein-Protein Interaction Site Predictions with Three-Dimensional Probability Distributions of Interacting Atoms on Protein Surfaces

Protein-protein interactions are key to many biological processes. Computational methodologies devised to predict protein-protein interaction (PPI) sites on protein surfaces are important tools in providing insights into the biological functions of proteins and in developing therapeutics targeting the protein-protein interaction sites. One of the general features of PPI sites is that the core regions from the two interacting protein surfaces are complementary to each other, similar to the interior of proteins in packing density and in the physicochemical nature of the amino acid composition. In this work, we simulated the physicochemical complementarities by constructing three-dimensional probability density maps of non-covalent interacting atoms on the protein surfaces. The interacting probabilities were derived from the interior of known structures. Machine learning algorithms were applied to learn the characteristic patterns of the probability density maps specific to the PPI sites. The trained predictors for PPI sites were cross-validated with the training cases (consisting of 432 proteins) and were tested on an independent dataset (consisting of 142 proteins). The residue-based Matthews correlation coefficient for the independent test set was 0.423; the accuracy, precision, sensitivity, specificity were 0.753, 0.519, 0.677, and 0.779 respectively. The benchmark results indicate that the optimized machine learning models are among the best predictors in identifying PPI sites on protein surfaces. In particular, the PPI site prediction accuracy increases with increasing size of the PPI site and with increasing hydrophobicity in amino acid composition of the PPI interface; the core interface regions are more likely to be recognized with high prediction confidence. The results indicate that the physicochemical complementarity patterns on protein surfaces are important determinants in PPIs, and a substantial portion of the PPI sites can be predicted correctly with the physicochemical complementarity features based on the non-covalent interaction data derived from protein interiors

Prediction of backbone dihedral angles and protein secondary structure using support vector machines

<p>Abstract</p> <p>Background</p> <p>The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.</p> <p>Results</p> <p>We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.</p> <p>Conclusions</p> <p>We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at <url>http://comp.chem.nottingham.ac.uk/disspred/</url>.</p