Induction Chemotherapy in Technically Unresectable Locally Advanced Oral Cavity Cancers: Does It Make a Difference?

Induction chemotherapy in technically unresectable locally advanced oral cavity cancers: Does it make a difference?

Indian J Cancer. 2013 Jan-Mar; 50(1): 1-8
Patil VM, Noronha V, Muddu VK, Gulia S, Bhosale B, Arya S, Juvekar S, Chatturvedi P, Chaukar DA, Pai P, D’cruz A, Prabhash K

Background: Locally advanced and unresectable oral cavity cancers have a poor prognosis. Induction might be beneficial in this setting by reducing tumor bulk and allowing definitive surgery. Aim: To analyze the impact of induction chemotherapy on locally advanced, technically unresectable oral cavity cancers. Materials and Methods: Retrospective analysis of patients with locally advanced oral cavity cancers, who were treated with neoadjuvant chemotherapy (NACT) during the period between June 2009 and December 2010. Data from a prospectively filled database were analyzed for information on patient characteristics, chemotherapy received, toxicity, response rates, local treatment offered, patterns of failure, and overall survival. The statistical analysis was performed with SPSS version 16. Results: 123 patients, with a median age of 42 years were analyzed. Buccal mucosa was the most common subsite (68.30%). Three drug regimen was utilized in 26 patients (21.10%) and the rest received two drug regimen. Resectability was achieved in 17 patients treated with 3 drug regimen (68.00%) and 36 patients receiving 2 drug regimen. Febrile neutropenia was seen in 3 patients (3.09%) receiving 2 drug regimen and in 9 patients (34.62%) receiving 3 drug regimen. The estimated median OS was not reached in patients who had clinical response and underwent surgery as opposed to 8 months in patients treated with non-surgical modality post NACT (P = 0.0001). Conclusion: Induction chemotherapy was effective in converting technically unresectable oral cavity cancers to operable disease in approximately 40% of patients and was associated with significantly improved overall survival in comparison to nonsurgical treatment. HubMed – drug

Muscle metaboreflex and autonomic regulation of heart rate in humans.

J Physiol. 2013 May 27;
Fisher JP, Adlan AM, Shantsila A, Secher JF, Sørensen H, Secher NH

We elucidated the autonomic mechanisms whereby heart rate (HR) is regulated by the muscle metaboreflex. Eight male participants (22±3 yr) performed 3 exercise protocols: 1) enhanced metaboreflex activation with partial flow restriction (bi-lateral thigh cuff inflation) during leg cycling exercise, 2) isolated muscle metaboreflex activation (post-exercise ischemia; PEI) following leg cycling exercise, 3) isometric handgrip followed by PEI. Trials were undertaken under control (no drug), ?1-adrenergic blockade (metoprolol) and parasympathetic blockade (glycopyrrolate) conditions. HR increased with partial flow restriction during leg cycling in the control condition (?11± 2 beats·min(-1); P < 0.05). The magnitude of this increase in HR was similar with parasympathetic blockade (?11±2 beats·min(-1)), but attenuated with ?-adrenergic blockade (?4±1 beats·min(-1); P < 0.05 vs. control and parasympathetic blockade). During PEI following leg cycling exercise, HR remained similarly elevated above rest under all conditions (?11±2, ?13±3 and ?9±4 beats·min(-1), for control, ?-adrenergic and parasympathetic blockade; P > 0.05 between conditions). During PEI following handgrip, HR was similarly elevated from rest under control and parasympathetic blockade (?4±1 vs. ?4±2 beats·min(-1); P > 0.05 between conditions) conditions, but attenuated with ?-adrenergic blockade (?0.2±1 beats·min(-1); P > 0.05 vs. rest). Thus muscle metaboreflex activation mediated increases in HR are principally attributable to increased cardiac sympathetic activity, and only following exercise with a large muscle mass (PEI following leg cycling) is there a contribution from the partial withdrawal of cardiac parasympathetic tone. HubMed – drug

Operon Prediction using Chaos Embedded Particle Swarm Optimization.

IEEE/ACM Trans Comput Biol Bioinform. 2013 May 20;
Chuang LY, Yang CH, Tsai JH, Yang CH

Operons contain valuable information for drug design and determining protein functions. Genes within an operon are co-transcribed to a single-strand mRNA and must be co-regulated. The identification of operons is thus critical for a detailed understanding of the gene regulations. However, currently used experimental methods for operon detection are generally difficult to implement and time-consuming. In this paper, we propose a chaotic binary particle swarm optimization (CBPSO) to predict operons in bacterial genomes. The intergenic distance, participation in the same metabolic pathway and the cluster of orthologous groups (COG) properties of the Escherichia coli genome are used to design a fitness function. Furthermore, the Bacillus subtilis, Pseudomonas aeruginosa PA01, Staphylococcus aureus and Mycobacterium tuberculosis genomes are tested and evaluated for accuracy, sensitivity, and specificity. The computational results indicate that the proposed method works effectively in terms of enhancing the performance of the operon prediction. The proposed method also achieved a good balance between sensitivity and specificity when compared to methods from the literature. HubMed – drug

A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction Problem.

IEEE/ACM Trans Comput Biol Bioinform. 2013 May 24;
Dehzangi A, Paliwal K, Sharma A, Dehzangi O, Sattar A

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics which demands more attention and exploration. In this study, we propose a novel feature extraction model which incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, Naive Bayes, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks. HubMed – drug