Combining Transfer of TTF-1 and Pax-8 Gene: a Potential Strategy to Promote Radioiodine Therapy of Thyroid Carcinoma

Cotransfer of TTF-1 and Pax-8 gene to tumor cells, resulting in the reexpression of iodide metabolism-associated proteins, such as sodium iodide symporter (NIS), thyroglobulin (Tg), thyroperoxidase (TPO), offers the possibility of radioiodine therapy to non-iodide-concentrating tumor because the expression of iodide metabolism-associated proteins in thyroid are mediated by the thyroid transcription factors TTF-1 and Pax-8. The human TTF-1 and Pax-8 gene were transducted into the human thyroid carcinoma (K1 and F133) cells by the recombinant adenovirus, AdTTF-1 and AdPax-8. Reexpression of NIS mRNA and protein, but not TPO and Tg mRNA and protein, was detected in AdTTF-1-infected F133 cells, following with increasing radioiodine uptake (6.1~7.4 times), scarcely iodide organification and rapid iodide efflux (t1/2≈8 min in vitro, t1/2≈4.7 h in vivo).
In contrast, all of the reexpression of NIS, TPO and Tg mRNA and proteins in F133 cells were induced by the synergetic effect of TTF-1 and Pax-8. AdTTF-1 and AdPax-8 coinfected K1 and F133 cells could effectively accumulate radioiodine (6.6-7.5 times) and obviously retarded radioiodine retention (t1/2≈25-30 min in vitro, t1/2≈12 h in vivo) (p<0.05).
Accordingly, the effect of radioiodine therapy of TTF-1 and Pax-8 cotransducted K1 and
F133 cells (21-25% survival rate in vitro) was better than that of TTF-1-transducted cells
(40% survival rate in vitro) (p<0.05). These results indicate that single TTF-1 gene transfer may have limited efficacy of radioiodine therapy because of rapid radioiodine efflux. The cotransduction of TTF-1 and Pax-8 gene, with resulting NIS-mediated radioiodine accumulation and TPO and Tg-mediated radioiodine organification and intracellular retention, may lead to effective radioiodine therapy of thyroid carcinoma

Proteases in Malaria Parasites - A Phylogenomic Perspective

Malaria continues to be one of the most devastating global health problems due to the high morbidity and mortality it causes in endemic regions. The search for new antimalarial targets is of high priority because of the increasing prevalence of drug resistance in malaria parasites. Malarial proteases constitute a class of promising therapeutic targets as they play important roles in the parasite life cycle and it is possible to design and screen for specific protease inhibitors. In this mini-review, we provide a phylogenomic overview of malarial proteases. An evolutionary perspective on the origin and divergence of these proteases will provide insights into the adaptive mechanisms of parasite growth, development, infection, and pathogenesis.

LSSANet: A Long Short Slice-Aware Network for Pulmonary Nodule Detection

Convolutional neural networks (CNNs) have been demonstrated to be highly effective in the field of pulmonary nodule detection. However, existing CNN based pulmonary nodule detection methods lack the ability to capture long-range dependencies, which is vital for global information extraction. In computer vision tasks, non-local operations have been widely utilized, but the computational cost could be very high for 3D computed tomography (CT) images. To address this issue, we propose a long short slice-aware network (LSSANet) for the detection of pulmonary nodules. In particular, we develop a new non-local mechanism termed long short slice grouping (LSSG), which splits the compact non-local embeddings into a short-distance slice grouped one and a long-distance slice grouped counterpart. This not only reduces the computational burden, but also keeps long-range dependencies among any elements across slices and in the whole feature map. The proposed LSSG is easy-to-use and can be plugged into many pulmonary nodule detection networks. To verify the performance of LSSANet, we compare with several recently proposed and competitive detection approaches based on 2D/3D CNN. Promising evaluation results on the large-scale PN9 dataset demonstrate the effectiveness of our method. Code is at https://github.com/Ruixxxx/LSSANet.Comment: MICCAI 202

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

Background: Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Much recent work has focused on developing new representations for protein sequences, called string kernels, for use with support vector machine (SVM) classifiers. However, while some of these approaches exhibit state-of-the-art performance at the binary protein classification problem, i.e. discriminating between a particular protein class and all other classes, few of these studies have addressed the real problem of multi-class superfamily or fold recognition. Moreover, there are only limited software tools and systems for SVM-based protein classification available to the bioinformatics community. Results: We present a new multi-class SVM-based protein fold and superfamily recognition system and web server called SVM-Fold, which can be found at http://svm-fold.c2b2.columbia.edu. Our system uses an efficient implementation of a state-of-the-art string kernel for sequence profiles, called the profile kernel, where the underlying feature representation is a histogram of inexact matching k-mer frequencies. We also employ a novel machine learning approach to solve the difficult multi-class problem of classifying a sequence of amino acids into one of many known protein structural classes. Binary one-vs-the-rest SVM classifiers that are trained to recognize individual structural classes yield prediction scores that are not comparable, so that standard "one-vs-all" classification fails to perform well. Moreover, SVMs for classes at different levels of the protein structural hierarchy may make useful predictions, but one-vs-all does not try to combine these multiple predictions. To deal with these problems, our method learns relative weights between one-vs-the-rest classifiers and encodes information about the protein structural hierarchy for multi-class prediction. In large-scale benchmark results based on the SCOP database, our code weighting approach significantly improves on the standard one-vs-all method for both the superfamily and fold prediction in the remote homology setting and on the fold recognition problem. Moreover, our code weight learning algorithm strongly outperforms nearest-neighbor methods based on PSI-BLAST in terms of prediction accuracy on every structure classification problem we consider. Conclusion: By combining state-of-the-art SVM kernel methods with a novel multi-class algorithm, the SVM-Fold system delivers efficient and accurate protein fold and superfamily recognition