Role of Negative Pressure Wound Therapy in Healing of Diabetic Foot Ulcers

Introduction: Foot disorders such as ulceration, infection and gangrene are the most common, complex and costly sequelae of diabetes mellitus.[1-3] Even for the most superficial wounds, treatment is often difficult with poor healing responses and high rates of complications. The purpose of this study is to compare the rate of ulcer healing with the negative pressure dressing technique to conventional moist dressings in the treatment of diabetic foot ulcers. Materials and Methods: The study was conducted on 30 patients, which were divided into two groups. One group received negative pressure dressing while other group received conventional saline moistened gauze dressing. Results were compared for rate of wound healing. Results: There was a statistically significant difference in the rate of appearance of granulation tissue between the two groups; with granulation tissue appearing earlier in the study group. The study group promised a better outcome (80% complete responders) as compared to the control group (60% complete responders). Conclusions: Negative pressure wound therapy has a definitive role in healing of diabetic foot ulcers

Self-nanoemulsifying Drug Delivery Systems of Valsartan: Preparation and In-Vitro Characterization

The main objective this study is to prepare and evaluate the selfnanoemulsifying drug delivery (SNEDDS) system in order to achieve a better dissolution rate of a poorly water soluble drug valsartan.  The present research work describes a SNEDDS of valsartan using labrasol, Tween 20 and Polyethylene glycol (PEG) 400. The pseudo-ternary phase diagrams with presence and absence of drug were plotted to check for the emulsification range and also to evaluate the effect of valsartan on the emulsification behavior of the phases. The mixtures consisting of oil (labrasol ) with surfactant (tween20), co-surfactant (PEG 400) were found to be optimum formulations. Prepared formulations were evaluated for its particle size distribution, nanoemulsifying properties, robustness to dilution, self emulsication time, turbidity measurement, drug content and in-vitro dissolution. The optimized formulations  are further evaluated for heating cooling cycle, centrifugation studies, freeze thaw cycling, particle size distribution and zeta potential were carried out to confirm the stability of the formed SNEDDS formulations. The prepared formulation has a significant improvement in terms of the drug solubility as compared with marketed tablet and pure drug, thus, this greater dissolution of valsartan from formulations could lead to higher absorption and higher oral bioavailability

Virtual Screening of potential drug-like inhibitors against Lysine/DAP pathway of Mycobacterium tuberculosis

Background: An explosive global spreading of multidrug resistant Mycobacterium tuberculosis (Mtb) is a catastrophe, which demands an urgent need to design or develop novel/potent antitubercular agents. The Lysine/DAP biosynthetic pathway is a promising target due its specific role in cell wall and amino acid biosynthesis. Here, we report identification of potential antitubercular candidates targeting Mtb dihydrodipicolinate synthase (DHDPS) enzyme of the pathway using virtual screening protocols. Results: In the present study, we generated three sets of drug-like molecules in order to screen potential inhibitors against Mtb drug target DHDPS. The first set of compounds was a combinatorial library, which comprised analogues of pyruvate (substrate of DHDPS). The second set of compounds consisted of pyruvate-like molecules i.e. structurally similar to pyruvate, obtained using 3D flexible similarity search against NCI and PubChem database. The third set constituted 3847 anti-infective molecules obtained from PubChem. These compounds were subjected to Lipinski's rule of drug-like five filters. Finally, three sets of drug-like compounds i.e. 4088 pyruvate analogues, 2640 pyruvate-like molecules and 1750 anti-infective molecules were docked at the active site of Mtb DHDPS (PDB code: 1XXX used in the molecular docking calculations) to select inhibitors establishing favorable interactions. Conclusion: The above-mentioned virtual screening procedures helped in the identification of several potent candidates that possess inhibitory activity against Mtb DHDPS. Therefore, these novel scaffolds/candidates which could have the potential to inhibit Mtb DHDPS enzyme would represent promising starting points as lead compounds and certainly aid the experimental designing of antituberculars in lesser time

KiDoQ: using docking based energy scores to develop ligand based model for predicting antibacterials

Background: Identification of novel drug targets and their inhibitors is a major challenge in the field of drug designing and development. Diaminopimelic acid (DAP) pathway is a unique lysine biosynthetic pathway present in bacteria, however absent in mammals. This pathway is vital for bacteria due to its critical role in cell wall biosynthesis. One of the essential enzymes of this pathway is dihydrodipicolinate synthase (DHDPS), considered to be crucial for the bacterial survival. In view of its importance, the development and prediction of potent inhibitors against DHDPS may be valuable to design effective drugs against bacteria, in general. Results: This paper describes a methodology for predicting novel/potent inhibitors against DHDPS. Here, quantitative structure activity relationship (QSAR) models were trained and tested on experimentally verified 23 enzyme's inhibitors having inhibitory value (Ki) in the range of 0.005-22(mM). These inhibitors were docked at the active site of DHDPS (1YXD) using AutoDock software, which resulted in 11 energy-based descriptors. For QSAR modeling, Multiple Linear Regression (MLR) model was engendered using best four energy-based descriptors yielding correlation values R/q2 of 0.82/0.67 and MAE of 2.43. Additionally, Support Vector Machine (SVM) based model was developed with three crucial descriptors selected using F-stepping remove-one approach, which enhanced the performance by attaining R/q2 values of 0.93/0.80 and MAE of 1.89. To validate the performance of QSAR models, external cross-validation procedure was adopted which accomplished high training/testing correlation values (q2/r2) in the range of 0.78-0.83/0.93-0.95. Conclusions: Our results suggests that ligand-receptor binding interactions for DHDPS employing QSAR modeling seems to be a promising approach for prediction of antibacterial agents. To serve the experimentalist to develop novel/potent inhibitors, a webserver "KiDoQ" has been developed http://crdd.osdd.net/raghava/kidoq webcite, which allows the prediction of Ki value of a new ligand molecule against DHDPS

Self-nanoemulsifying Drug Delivery Systems of Valsartan: Preparation and In-Vitro Characterization

The main objective this study is to prepare and evaluate the selfnanoemulsifying drug delivery (SNEDDS) system in order to achieve a better dissolution rate of a poorly water soluble drug valsartan.  The present research work describes a SNEDDS of valsartan using labrasol, Tween 20 and Polyethylene glycol (PEG) 400. The pseudo-ternary phase diagrams with presence and absence of drug were plotted to check for the emulsification range and also to evaluate the effect of valsartan on the emulsification behavior of the phases. The mixtures consisting of oil (labrasol ) with surfactant (tween20), co-surfactant (PEG 400) were found to be optimum formulations. Prepared formulations were evaluated for its particle size distribution, nanoemulsifying properties, robustness to dilution, self emulsication time, turbidity measurement, drug content and in-vitro dissolution. The optimized formulations  are further evaluated for heating cooling cycle, centrifugation studies, freeze thaw cycling, particle size distribution and zeta potential were carried out to confirm the stability of the formed SNEDDS formulations. The prepared formulation has a significant improvement in terms of the drug solubility as compared with marketed tablet and pure drug, thus, this greater dissolution of valsartan from formulations could lead to higher absorption and higher oral bioavailability

ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins

<p>Abstract</p> <p>Background</p> <p>The expansion of raw protein sequence databases in the post genomic era and availability of fresh annotated sequences for major localizations particularly motivated us to introduce a new improved version of our previously forged eukaryotic subcellular localizations prediction method namely "ESLpred". Since, subcellular localization of a protein offers essential clues about its functioning, hence, availability of localization predictor would definitely aid and expedite the protein deciphering studies. However, robustness of a predictor is highly dependent on the superiority of dataset and extracted protein attributes; hence, it becomes imperative to improve the performance of presently available method using latest dataset and crucial input features.</p> <p>Results</p> <p>Here, we describe augmentation in the prediction performance obtained for our most popular ESLpred method using new crucial features as an input to Support Vector Machine (SVM). In addition, recently available, highly non-redundant dataset encompassing three kingdoms specific protein sequence sets; 1198 fungi sequences, 2597 from animal and 491 plant sequences were also included in the present study. First, using the evolutionary information in the form of profile composition along with whole and N-terminal sequence composition as an input feature vector of 440 dimensions, overall accuracies of 72.7, 75.8 and 74.5% were achieved respectively after five-fold cross-validation. Further, enhancement in performance was observed when similarity search based results were coupled with whole and N-terminal sequence composition along with profile composition by yielding overall accuracies of 75.9, 80.8, 76.6% respectively; best accuracies reported till date on the same datasets.</p> <p>Conclusion</p> <p>These results provide confidence about the reliability and accurate prediction of SVM modules generated in the present study using sequence and profile compositions along with similarity search based results. The presently developed modules are implemented as web server "ESLpred2" available at <url>http://www.imtech.res.in/raghava/eslpred2/</url>.</p

Prediction of nuclear proteins using SVM and HMM models

<p>Abstract</p> <p>Background</p> <p>The nucleus, a highly organized organelle, plays important role in cellular homeostasis. The nuclear proteins are crucial for chromosomal maintenance/segregation, gene expression, RNA processing/export, and many other processes. Several methods have been developed for predicting the nuclear proteins in the past. The aim of the present study is to develop a new method for predicting nuclear proteins with higher accuracy.</p> <p>Results</p> <p>All modules were trained and tested on a non-redundant dataset and evaluated using five-fold cross-validation technique. Firstly, Support Vector Machines (SVM) based modules have been developed using amino acid and dipeptide compositions and achieved a Mathews correlation coefficient (MCC) of 0.59 and 0.61 respectively. Secondly, we have developed SVM modules using split amino acid compositions (SAAC) and achieved the maximum MCC of 0.66. Thirdly, a hidden Markov model (HMM) based module/profile was developed for searching exclusively nuclear and non-nuclear domains in a protein. Finally, a hybrid module was developed by combining SVM module and HMM profile and achieved a MCC of 0.87 with an accuracy of 94.61%. This method performs better than the existing methods when evaluated on blind/independent datasets. Our method estimated 31.51%, 21.89%, 26.31%, 25.72% and 24.95% of the proteins as nuclear proteins in <it>Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster</it>, mouse and human proteomes respectively. Based on the above modules, we have developed a web server NpPred for predicting nuclear proteins <url>http://www.imtech.res.in/raghava/nppred/</url>.</p> <p>Conclusion</p> <p>This study describes a highly accurate method for predicting nuclear proteins. SVM module has been developed for the first time using SAAC for predicting nuclear proteins, where amino acid composition of N-terminus and the remaining protein were computed separately. In addition, our study is a first documentation where exclusively nuclear and non-nuclear domains have been identified and used for predicting nuclear proteins. The performance of the method improved further by combining both approaches together.</p

Dose reduction to normal tissues as compared to the gross tumor by using intensity modulated radiotherapy in thoracic malignancies

BACKGROUND AND PURPOSE: Intensity modulated radiotherapy (IMRT) is a powerful tool, which might go a long way in reducing radiation doses to critical structures and thereby reduce long term morbidities. The purpose of this paper is to evaluate the impact of IMRT in reducing the dose to the critical normal tissues while maintaining the desired dose to the volume of interest for thoracic malignancies. MATERIALS AND METHODS: During the period January 2002 to March 2004, 12 patients of various sites of malignancies in the thoracic region were treated using physical intensity modulator based IMRT. Plans of these patients treated with IMRT were analyzed using dose volume histograms. RESULTS: An average dose reduction of the mean values by 73% to the heart, 69% to the right lung and 74% to the left lung, with respect to the GTV could be achieved with IMRT. The 2 year disease free survival was 59% and 2 year overall survival was 59%. The average number of IMRT fields used was 6. CONCLUSION: IMRT with inverse planning enabled us to achieve desired dose distribution, due to its ability to provide sharp dose gradients at the junction of tumor and the adjacent critical organs

Design and testing of hydrophobic core/hydrophilic shell nano/micro particles for drug-eluting stent coating

In this study, we designed a novel drug-eluting coating for vascular implants consisting of a core coating of the anti-proliferative drug docetaxel (DTX) and a shell coating of the platelet glycoprotein IIb/IIIa receptor monoclonal antibody SZ-21. The core/shell structure was sprayed onto the surface of 316L stainless steel stents using a coaxial electrospray process with the aim of creating a coating that exhibited a differential release of the two drugs. The prepared stents displayed a uniform coating consisting of nano/micro particles. In vitro drug release experiments were performed, and we demonstrated that a biphasic mathematical model was capable of capturing the data, indicating that the release of the two drugs conformed to a diffusion-controlled release system. We demonstrated that our coating was capable of inhibiting the adhesion and activation of platelets, as well as the proliferation and migration of smooth muscle cells (SMCs), indicating its good biocompatibility and anti-proliferation qualities. In an in vivo porcine coronary artery model, the SZ-21/DTX drug-loaded hydrophobic core/hydrophilic shell particle coating stents were observed to promote re-endothelialization and inhibit neointimal hyperplasia. This core/shell particle-coated stent may serve as part of a new strategy for the differential release of different functional drugs to sequentially target thrombosis and in-stent restenosis during the vascular repair process and ensure rapid re-endothelialization in the field of cardiovascular disease

Failure to obtain adequate anaesthesia associated with a bifid mandibular canal: a case report

The document attached has been archived with permission from the Australian Dental Association. An external link to the publisher’s copy is included.The inferior alveolar nerve (IAN) block is the most common method for obtaining mandibular anaesthesia in dental practice but it is estimated to have a success rate of only 80 to 85 per cent. Causes of failure include problems with operator technique and anatomical variation between individuals. This case report involves a patient who received IAN blocks on two separate occasions that resulted in only partial anaesthesia of the ipsilateral side of the mandible. Radiographic assessment disclosed the presence of bifid mandibular canals that were present bilaterally and that may have affected the outcomes of the local anaesthetic procedures. Previous studies of bifid mandibular canals are reviewed and suggestions provided that should enable clinicians to differentially diagnose, and then manage, cases where IAN blocks result in inadequate mandibular anaesthesia.K Lew, G Townsen