A global research agenda on public health and social measures during emergencies.

The importance of strong coordination for research on public health and social measures was highlighted at the Seventy-fourth World Health Assembly in 2021. This article describes efforts undertaken by the World Health Organization (WHO) to develop a global research agenda on the use of public health and social measures during health emergencies. This work includes a multistep process that started with a global technical consultation convened by WHO in September 2021. The consultation included experts from around the world and from a wide range of disciplines, such as public health, education, tourism, finance and social sciences, and aimed to identify research and implementation approaches based on lessons learnt during the coronavirus disease 2019 pandemic. To prepare for future epidemics and pandemics, it is essential to adopt a more robust, comparable and systematic research approach to public health and social measures. Such comprehensive approach will better inform agile, balanced and context-specific implementation decisions during future emergencies. This article describes the methods used to develop global research priorities for public health and social measures and the next steps needed.

La soixante-quatorzième Assemblée mondiale de la Santé en 2021 a souligné l'importance d'une coordination solide pour la recherche sur la santé publique et les mesures sociales. Le présent article décrit les efforts entrepris par l'Organisation mondiale de la santé (OMS) pour élaborer un programme de recherche mondial sur l'utilisation des mesures de santé publique et des mesures sociales lors de situations d'urgence sanitaire. Ce travail comprend un processus en plusieurs étapes qui a commencé par une consultation technique mondiale organisée par l'OMS en septembre 2021. La consultation a réuni des experts du monde entier issus d'un large éventail de disciplines telles que la santé publique, l'éducation, le tourisme, la finance et les sciences sociales. Elle visait à identifier des approches de recherche et de mise en œuvre fondées sur les enseignements tirés de la pandémie de maladie à coronavirus de 2019. Pour se préparer aux futures épidémies et pandémies, il est essentiel d'adopter une approche de recherche plus solide, comparable et systématique en matière de santé publique et de mesures sociales. Cette approche globale permettra de mieux éclairer les décisions de mise en œuvre agiles, équilibrées et adaptées au contexte lors des futures situations d'urgence. Le présent article décrit les méthodes appliquées pour définir les priorités mondiales de recherche en matière de santé publique et de mesures sociales, ainsi que les prochaines étapes à franchir.

En la 74.ª Asamblea Mundial de la Salud, celebrada en 2021, se destacó la importancia de una sólida coordinación en la investigación sobre salud pública y medidas sociales. Este artículo describe los esfuerzos que ha emprendido la Organización Mundial de la Salud (OMS) para desarrollar un programa mundial de investigación sobre el uso de medidas sociales y de salud pública durante las emergencias sanitarias. Este trabajo incluye un proceso de varios pasos que comenzó con una consulta técnica mundial que convocó la OMS en septiembre de 2021. La consulta incluyó a expertos de todo el mundo y de una gran variedad de disciplinas, como la salud pública, la educación, el turismo, las finanzas y las ciencias sociales, y tuvo como objetivo identificar enfoques de investigación y aplicación basados en las lecciones aprendidas durante la pandemia de la enfermedad por coronavirus de 2019. Para prepararse ante futuras epidemias y pandemias, es esencial adoptar un enfoque de investigación más sólido, comparable y sistemático en materia de salud pública y medidas sociales. Este enfoque integral informará mejor las decisiones de aplicación ágiles, equilibradas y adaptadas al contexto durante futuras emergencias. En este artículo se describen los métodos utilizados para elaborar las prioridades mundiales de investigación sobre salud pública y medidas sociales, así como los próximos pasos necesarios.

VirulentPred 2.0: An improved method for prediction of virulent proteins in bacterial pathogens.

Virulence proteins in pathogens are essential for causing disease in a host. They enable the pathogen to invade, survive and multiply within the host, thus enhancing its potential to cause disease while also causing evasion of host defense mechanisms. Identifying these factors, especially potential vaccine candidates or drug targets, is critical for vaccine or drug development research. In this context, we present an improved version of VirulentPred 1.0 for rapidly identifying virulent proteins. The VirulentPred 2.0 is based on training machine learning models with experimentally validated virulent protein sequences. VirulentPred 2.0 achieved 84.71% accuracy with the validation dataset and 85.18% on an independent test dataset. The models are trained and evaluated with the latest sequence datasets of virulent proteins, which are three times greater in number than the proteins used in the earlier version of VirulentPred. Moreover, a significant improvement of 11% in the prediction accuracy over the earlier version is achieved with the best position-specific scoring matrix (PSSM)-based model for the latest test dataset. VirulentPred 2.0 is available as a user-friendly web interface at https://bioinfo.icgeb.res.in/virulent2/ and a standalone application suitable for bulk predictions. With higher efficiency and availability as a standalone tool, VirulentPred 2.0 holds immense potential for high throughput yet efficient identification of virulent proteins in bacterial pathogens.

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, which allows the prediction of Ki value of a new ligand molecule against DHDPS.

Influence of plant extracts mixed with endodontic sealers on the growth of oral pathogens in root canal: An in vitro study.

INTRODUCTION: Microbes are considered as the primary etiological agents in endodontic diseases. Ways of reducing these agents are root canal debridement and antibacterial filling materials. One of the factors in determining the success of endodontic treatment previously was sealing root canals with materials possessing potent bactericidal effect. Due to cytotoxic reactions of sealers and their inability to eliminate bacteria completely from dentinal tubules, trend to use natural plants extracts have been introduced. AIM: To compare antimicrobial activity of endodontic sealers added to herbal extracts. MATERIALS AND METHOD: Three sealers mixed with three herbal extracts were evaluated against seven strains of bacteria at various time intervals using Agar Diffusion Test. The mean zones of inhibition were measured. STATISTICAL ANALYSIS: All statistical analysis was performed using the SPSS 15 statistical software version, Chicago. Intergroup comparison was evaluated using Kruskal Walls test along with Mann Whitney U test. The Intragroup comparison was evaluatd using Friedman test along with Wilcoxon test. RESULTS: Statistically significant zones of bacterial growth inhibition were observed largest with Zinc Oxide Eugenol based sealer when mixed with Glycyrrhiza glabra (Licorice) followed in descending order by zinc oxide eugenol based sealer mixed with Tinospora cordifolia (Guduchi) and Mimusops elengi (Bakul) respectively. CONCLUSION: Zinc Oxide Eugenol based sealer with herbal extracts produced largest inhibitory zones followed in descending order by Resin based sealer and Calcium hydroxide along with three herbal extracts respectively.

VirulentPred: a SVM based prediction method for virulent proteins in bacterial pathogens.

BACKGROUND: Prediction of bacterial virulent protein sequences has implications for identification and characterization of novel virulence-associated factors, finding novel drug/vaccine targets against proteins indispensable to pathogenicity, and understanding the complex virulence mechanism in pathogens. RESULTS: In the present study we propose a bacterial virulent protein prediction method based on bi-layer cascade Support Vector Machine (SVM). The first layer SVM classifiers were trained and optimized with different individual protein sequence features like amino acid composition, dipeptide composition (occurrences of the possible pairs of ith and i+1th amino acid residues), higher order dipeptide composition (pairs of ith and i+2nd residues) and Position Specific Iterated BLAST (PSI-BLAST) generated Position Specific Scoring Matrices (PSSM). In addition, a similarity-search based module was also developed using a dataset of virulent and non-virulent proteins as BLAST database. A five-fold cross-validation technique was used for the evaluation of various prediction strategies in this study. The results from the first layer (SVM scores and PSI-BLAST result) were cascaded to the second layer SVM classifier to train and generate the final classifier. The cascade SVM classifier was able to accomplish an accuracy of 81.8%, covering 86% area in the Receiver Operator Characteristic (ROC) plot, better than that of either of the layer one SVM classifiers based on single or multiple sequence features. CONCLUSION: VirulentPred is a SVM based method to predict bacterial virulent proteins sequences, which can be used to screen virulent proteins in proteomes. Together with experimentally verified virulent proteins, several putative, non annotated and hypothetical protein sequences have been predicted to be high scoring virulent proteins by the prediction method. VirulentPred is available as a freely accessible World Wide Web server - VirulentPred, at http://bioinfo.icgeb.res.in/virulent/.

ESLpred2: improved method for predicting subcellular localization of eukaryotic proteins.

BACKGROUND: 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. RESULTS: 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. CONCLUSION: 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 http://www.imtech.res.in/raghava/eslpred2/.

DPROT: prediction of disordered proteins using evolutionary information.

The association of structurally disordered proteins with a number of diseases has engendered enormous interest and therefore demands a prediction method that would facilitate their expeditious study at molecular level. The present study describes the development of a computational method for predicting disordered proteins using sequence and profile compositions as input features for the training of SVM models. First, we developed the amino acid and dipeptide compositions based SVM modules which yielded sensitivities of 75.6 and 73.2% along with Matthew's Correlation Coefficient (MCC) values of 0.75 and 0.60, respectively. In addition, the use of predicted secondary structure content (coil, sheet and helices) in the form of composition values attained a sensitivity of 76.8% and MCC value of 0.77. Finally, the training of SVM models using evolutionary information hidden in the multiple sequence alignment profile improved the prediction performance by achieving a sensitivity value of 78% and MCC of 0.78. Furthermore, when evaluated on an independent dataset of partially disordered proteins, the same SVM module provided a correct prediction rate of 86.6%. Based on the above study, a web server ("DPROT") was developed for the prediction of disordered proteins, which is available at http://www.imtech.res.in/raghava/dprot/.

Successes and challenges of expansion of environmental poliovirus surveillance in the WHO South-East Asia Region.

The last decade has witnessed an exponential expansion of environmental surveillance (ES) of poliovirus in sewage samples in the World Health Organization (WHO) South-East Asia Region. This has grown from only three sites in Mumbai, India in 2001 to 56 sites in 2017 in Bangladesh, India, Indonesia, Myanmar, Nepal and Thailand. ES is critical to the region in providing evidence of silent transmission of vaccine-derived poliovirus and Sabin-like poliovirus type 2 - especially since the global "switch" to cease use of oral polio vaccine type 2 - and for monitoring the effectiveness of containment activities. This targeted expansion of ES to supplement surveillance for acute flaccid paralysis (AFP) required quality assurance in ES procedures, improvements in the sensitivity of the laboratory-based surveillance system, and establishment of real-time data analysis for evidence-based programmes. ES in the region has provided documentary evidence for the absence of indigenous wild poliovirus in circulation and no importations via international travellers. Post-switch, while no vaccine-derived poliovirus was detected from AFP cases, ES identified five ambiguous vaccine-derived polioviruses in 2016 and early 2017, with no evidence of circulation. Future challenges include monitoring for vaccine-derived poliovirus strains shed for a prolonged time by immunodeficient individuals, and expanding ES to areas lacking sewage networks. To maintain the polio-free status of the WHO South-East Asia Region and achieve a world free of poliomyelitis, critical evaluation of immunization coverage, continued performance of surveillance for acute flaccid paralysis, and enhanced analysis of sewage samples to detect any breach in containment are essential.

PEPstr: a de novo method for tertiary structure prediction of small bioactive peptides.

Among secondary structure elements, beta-turns are ubiquitous and major feature of bioactive peptides. We analyzed 77 biologically active peptides with length varying from 9 to 20 residues. Out of 77 peptides, 58 peptides were found to contain at least one beta-turn. Further, at the residue level, 34.9% of total peptide residues were found to be in beta-turns, higher than the number of helical (32.3%) and beta-sheet residues (6.9%). So, we utilized the predicted beta-turns information to develop an improved method for predicting the three-dimensional (3D) structure of small peptides. In principle, we built four different structural models for each peptide. The first 'model I' was built by assigning all the peptide residues an extended conformation (phi = Psi = 180 degrees ). Second 'model II' was built using the information of regular secondary structures (helices, beta-strands and coil) predicted from PSIPRED. In third 'model III', secondary structure information including beta-turn types predicted from BetaTurns method was used. The fourth 'model IV' had main-chain phi, Psi angles of model III and side chain angles assigned using standard Dunbrack backbone dependent rotamer library. These models were further refined using AMBER package and the resultant C(alpha) rmsd values were calculated. It was found that adding the beta-turns to the regular secondary structures greatly reduces the rmsd values both before and after the energy minimization. Hence, the results indicate that regular and irregular secondary structures, particularly beta-turns information can provide valuable and vital information in the tertiary structure prediction of small bioactive peptides. Based on the above study, a web server PEPstr (http://www.imtech.res.in/raghava/pepstr/) was developed for predicting the tertiary structure of small bioactive peptides.

Oxypred: prediction and classification of oxygen-binding proteins.

This study describes a method for predicting and classifying oxygen-binding proteins. Firstly, support vector machine (SVM) modules were developed using amino acid composition and dipeptide composition for predicting oxygen-binding proteins, and achieved maximum accuracy of 85.5% and 87.8%, respectively. Secondly, an SVM module was developed based on amino acid composition, classifying the predicted oxygen-binding proteins into six classes with accuracy of 95.8%, 97.5%, 97.5%, 96.9%, 99.4%, and 96.0% for erythrocruorin, hemerythrin, hemocyanin, hemoglobin, leghemoglobin, and myoglobin proteins, respectively. Finally, an SVM module was developed using dipeptide composition for classifying the oxygen-binding proteins, and achieved maximum accuracy of 96.1%, 98.7%, 98.7%, 85.6%, 99.6%, and 93.3% for the above six classes, respectively. All modules were trained and tested by five-fold cross validation. Based on the above approach, a web server Oxypred was developed for predicting and classifying oxygen-binding proteins (available from http://www.imtech.res.in/raghava/oxypred/).

Further evidence of centrophenoxine mediated protection in aluminium exposed rats by biochemical and light microscopy analysis.

The environmental agent aluminium has been intensively investigated in the initiation and progression of various neurological disorders and the role of oxidative stress in these disorders is a widely discussed phenomenon. In this light, the present study is focused on the role of aluminium in mediating oxidative stress, which may help in better understanding its role in neuronal degeneration. Further, we have exploited a known anti-aging drug centrophenoxine to explore its potential in the conditions of metal induced oxidative damage. Aluminium was administered orally at a dose level of 100 mg/kg b.wt./day for a period of 6 weeks followed by a post treatment of centrophenoxine at a dose level of 100 mg/kg b.wt./day for another 6 weeks. Following aluminium exposure, a significant increase in lipid peroxidation levels (estimated by MDA) were observed which was accompanied by a decrease in reduced glutathione content in both cerebrum and cerebellum of rat brain. Post treatment of centrophenoxine significantly reduced the lipid peroxidation levels and also increased the reduced glutathione content in both the regions. Histologically observed marked deteriorations in the organization of various cellular layers in both cerebrum and cerebellum were observed after aluminium administration. Centrophenoxine treated animals showed an appreciable improvement in the histoarchitecture of the cellular layers. Our results indicate that centrophenoxine has an antioxidant potential and should be examined further in aluminium toxic conditions.

Periodontal Intervention in Speedy Orthodontics-A Case Report.

The use of orthodontic treatment in adult patients is becoming more common and these patients have more specific objectives and concerns related to facial and dental aesthetics, specially regarding duration of treatment. Dentists are on the lookout for techniques for increased efficiency in orthodontic treatment. Alveolar Corticotomy-assisted orthodontic treatment is a recent orthodontic technique that is recently gaining wide acceptance and is recorded as effective means of accelerating orthodontic treatment. A 17-year-old female patient was undergoing orthodontic treatment for the past one year but during her space closure, a visual examination confirmed a buccal thickening that was encountered in the buccal plate between premolars and canine. Periodontal intervention involved elective alveolar decortication in the form of dots performed around the teeth that were to be moved. This was carried out to induce a state of increased tissue turnover and a transient osteopenia, which further helps in faster rate of orthodontic tooth movement. Its main advantages are reduction of treatment time and post-orthodontic stability.

Test Tube Tooth: The Next Big Thing.

Unlike some vertebrates and fishes, humans do not have the capacity for tooth regeneration after the loss of permanent teeth. Although artificial replacement with removable dentures, fixed prosthesis and implants is possible through advances in the field of prosthetic dentistry, it would be ideal to recreate a third set of natural teeth to replace lost dentition. For many years now, researchers in the field of tissue engineering have been trying to bioengineer dental tissues as well as whole teeth. In order to attain a whole tooth through dental engineering, that has the same or nearly same biological, mechanical and physical properties of a natural tooth, it's necessary to deal with all the cells and tissues which are concerned with the formation, maintenance and repair of the tooth. In this article we review the steps involved in odontogenesis or organogenesis of a tooth and progress in the bioengineering of a whole tooth.

Diagnostic Methods for Detection of Cotinine Level in Tobacco Users: A Review.

The greatest disease-producing product known to man is tobacco. It is a cause of many oral diseases and adverse oral conditions. In India, tobacco is available in smokeless and smoking form. Tobacco contains nicotine which metabolises to form a toxic alkaloid i.e. cotinine. It stimulates autonomic ganglia and central nervous system. Cotinine is the best indicator of tobacco smoke exposure. Various methods are used to measure cotinine level in blood, saliva and urine such as high performance liquid chromatography, colorimetric assay, gas chromatography, NicAlert saliva test, etc. Thus such wide range of methods for cotinine detection in tobacco users requires a detailed discussion regarding their utility. This review will help readers to compare various methods for cotinine detection and enable them to make scientifically informative decision.

Oral Health Status of Rural and Urban Population of Gurgaon Block, Gurgaon District Using WHO Assessment Form through Multistage Sampling Technique.

INTRODUCTION: Oral health is an integral part of general health and well being. Poor oral health can affect a person physiologically and psychologically irrespective of age group. AIM: To assess the oral health status and treatment needs of urban and rural population of Gurgaon Block, Gurgaon District, Haryana, India. MATERIALS AND METHODS: A descriptive cross-sectional study was conducted among 810 urban and rural subjects belonging to index age groups of 5, 12, 15, 35-44 and 65-74 years as recommended by WHO, in the city of Gurgaon, Haryana. The World Health Organization Oral Health Assessment Form (1997) was used for data collection in which clinical examination, soft and hard tissue findings as well as dentofacial anomalies were recorded. The subjects were selected by multistage random sampling and examined throughout the area by a house to house survey. STATISTICAL ANALYSIS: The data was collected and subjected to analysis through SPSS 21. Chi-square was used for compilation of results. RESULTS: Of the total population 44.9% had dental caries with a mean DMFT of 1.61. Prevalence of periodontal diseases was 65%; 46% of the population suffered from malocclusions of which 21.19 % had the severe type. Dental fluorosis was found to be highly prevalent (46%) out of which 11.23% had moderate and 9.6% had severe type of fluorosis. Treatment was found to be required among 83% of population. CONCLUSION: The dental health care needs are very high both in rural and urban areas in spite of basic facilities available in urban areas. Hence professional and administrative attention is required both in urban and rural areas. Gurgaon Block can be used as a model district to find the effectiveness of programs in bringing down the oral diseases and maintenance of the oral health of the people on a long term basis.

Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure.

The present study is an attempt to develop a neural network-based method for predicting the real value of solvent accessibility from the sequence using evolutionary information in the form of multiple sequence alignment. In this method, two feed-forward networks with a single hidden layer have been trained with standard back-propagation as a learning algorithm. The Pearson's correlation coefficient increases from 0.53 to 0.63, and mean absolute error decreases from 18.2 to 16% when multiple-sequence alignment obtained from PSI-BLAST is used as input instead of a single sequence. The performance of the method further improves from a correlation coefficient of 0.63 to 0.67 when secondary structure information predicted by PSIPRED is incorporated in the prediction. The final network yields a mean absolute error value of 15.2% between the experimental and predicted values, when tested on two different nonhomologous and nonredundant datasets of varying sizes. The method consists of two steps: (1) in the first step, a sequence-to-structure network is trained with the multiple alignment profiles in the form of PSI-BLAST-generated position-specific scoring matrices, and (2) in the second step, the output obtained from the first network and PSIPRED-predicted secondary structure information is used as an input to the second structure-to-structure network. Based on the present study, a server SARpred (http://www.imtech.res.in/raghava/sarpred/) has been developed that predicts the real value of solvent accessibility of residues for a given protein sequence. We have also evaluated the performance of SARpred on 47 proteins used in CASP6 and achieved a correlation coefficient of 0.68 and a MAE of 15.9% between predicted and observed values.

"Why We Say No! A Look Through the Editor's Eye".

BACKGROUND: The rapidly flourishing health science has provided a ground to perform research work and contribute to the field of science. On the other hand, reporting the research is equally important as carrying out research. Many such researches and their ground breaking work remain unreported or do not reach the guild, because of poor drafting skills. In nine years since Journal of Clinical and Diagnostic Research (JCDR) inception, editorial have come across many manuscripts which are clinically and socially relevant in their message, but lack legible drafting. It was felt that an objective analysis of the reasons for rejection, of manuscripts, is required. AIM: The present study was conducted with the aim to determine the reasons for rejection of medical and dental manuscript submitted in JCDR. MATERIALS AND METHODS: Retrospective analysis of 1000 consecutive medical and dental articles submitted to JCDR since 1(st) August 2014 was done. Only those articles (902) that reached the end point on decision were considered. The reasons of rejection for medical and dental articles were enlisted and analyzed. When there were multiple reasons of rejection and all of them were critical, then they were counted in all the categories. RESULTS: Out of the 902 consecutive articles 522 articles underwent rejection. Among the rejected ones, dental specialty comprised of 43.5% and medical articles contributed 56.5%. The most frequent reasons for rejection were commonality (44.6%), non compliance by authors (17.8%), methodological issues (17.3%), plagiarism (11.1%), received same topic and published (7.66%), poor draft (6.70%), data inconsistency (5.77%), mismanagement (1.72%), blacklisted author (1.14%), ethical and out of scope were 0.57% each. CONCLUSION: Based on our findings, it can be concluded that manuscript rejection can be avoided by the authors, if the topic is well choosen and communication is maintained with the journal editorial.

Hirudotherapy in Medicine and Dentistry.

The concept of Unani medicine is based on balancing body humours, the imbalance of which causes diseases. The application of leech therapy in medical and dental science is well recognized. Although easy and non-invasive, complications also exist. The article aims to presents a brief review on the applications of leech therapy. The physiological effect, along with its therapeutic role in cancer, diabetes and dentistry have been underlined. Complications of leech therapy have also been dealt with.