Cloud-Based Advanced Shuffled Frog Leaping Algorithm for Tasks Scheduling

In recent years, the world has seen incremental growth in online activities owing to which the volume of data in cloud servers has also been increasing exponentially. With rapidly increasing data, load on cloud servers has increased in the cloud computing environment. With rapidly evolving technology, various cloud-based systems were developed to enhance the user experience. But, the increased online activities around the globe have also increased data load on the cloud-based systems. To maintain the efficiency and performance of the applications hosted in cloud servers, task scheduling has become very important. The task scheduling process helps in reducing the makespan time and average cost by scheduling the tasks to virtual machines (VMs). The task scheduling depends on assigning tasks to VMs to process the incoming tasks. The task scheduling should follow some algorithm for assigning tasks to VMs. Many researchers have proposed different scheduling algorithms for task scheduling in the cloud computing environment. In this article, an advanced form of the shuffled frog optimization algorithm, which works on the nature and behavior of frogs searching for food, has been proposed. The authors have introduced a new algorithm to shuffle the position of frogs in memeplex to obtain the best result. By using this optimization technique, the cost function of the central processing unit, makespan, and fitness function were calculated. The fitness function is the sum of the budget cost function and the makespan time. The proposed method helps in reducing the makespan time as well as the average cost by scheduling the tasks to VMs effectively. Finally, the performance of the proposed advanced shuffled frog optimization method is compared with existing task scheduling methods such as whale optimization-based scheduler (W-Scheduler), sliced particle swarm optimization (SPSO-SA), inverted ant colony optimization algorithm, and static learning particle swarm optimization (SLPSO-SA) in terms of average cost and metric makespan. Experimentally, it was concluded that the proposed advanced frog optimization algorithm can schedule tasks to the VMs more effectively as compared with other scheduling methods with a makespan of 6, average cost of 4, and fitness of 10.

Chemical- and green-precursor-derived carbon dots for photocatalytic degradation of dyes

Rapid industrialization and untreated industrial effluents loaded with toxic and carcinogenic contaminants, especially dyes that discharge into environmental waters, have led to a rise in water pollution, with a substantial adverse impact on marine life and humankind. Photocatalytic techniques are one of the most successful methods that help in degradation and/or removal of such contaminants. In recent years, semiconductor quantum dots are being substituted by carbon dots (CDs) as photocatalysts, due to the ease of formation, cost-effectiveness, possible sustainability and scalability, much lower toxicity, and above all its high capacity to harvest sunlight (UV, visible, and near infrared) through electron transfer that enhances the lifetime of the photogenerated charge carriers. A better understanding between the properties of the CDs and their role in photocatalytic degradation of dyes and contaminants is required for the formation of controllable structures and adjustable outcomes. The focus of this review is on CDs and its composites as photocatalysts obtained from different sustainable green as well as chemical precursors. Apart from the synthesis, characterization, and properties of the CDs, the study also highlights the effect of different parameters on the photocatalytic properties of CDs and their composites for catalytic dye degradation mechanisms in detail. Besides the present research development in the field, potential challenges and future perspectives are also presented.

An Enhanced Version of Cat Swarm Optimization Algorithm for Cluster Analysis

Clustering is an unsupervised machine learning technique that optimally organizes the data objects in a group of clusters. In present work, a meta-heuristic algorithm based on cat intelligence is adopted for optimizing clustering problems. Further, to make the cat swarm algorithm (CSO) more robust for partitional clustering, some modifications are incorporated in it. These modifications include an improved solution search equation for balancing global and local searches, accelerated velocity equation for addressing diversity, especially in tracing mode. Furthermore, a neighborhood-based search strategy is introduced to handle the local optima and premature convergence problems. The performance of enhanced cat swarm optimization (ECSO) algorithm is tested on eight real-life datasets and compared with the well-known clustering algorithms. The simulation results confirm that the proposed algorithm attains the optimal results than other clustering algorithms.

Artificial Bee Colony and Deep Neural Network-Based Diagnostic Model for Improving the Prediction Accuracy of Diabetes

A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to build automated diagnostic tool for helping the physician as well as monitor the health status of patients. These diagnostic tools are widely adopted in intensive care unit for life expectancy of patients. In this study, an effort is made to design an automated diagnostic model for the diagnosis and prediction of diabetes patients. The proposed diagnostic model is designed using artificial bee colony (ABC) algorithm and deep neural network (DNN) technique, called ABC-DNN-based diagnostic model. The ABC algorithm is applied to determine the relevant features for diabetes prediction and diagnosis while DNN technique is adopted for the prediction and diagnosis of diabetes affected patients. The performance of proposed diagnostic model is tested over Pima Indian Diabetes dataset and evaluated using accuracy, sensitivity, specificity, F-measure, Kappa, and area under curve (AUC) parameters. Further, 10-fold and 50-50% training-testing method are considered to assess the performance of proposed diagnostic model. The experimental results of proposed ABC-DNN model is compared with DNN technique and several existing diabetes studies. It is observed that proposed ABC-DNN model achieves 94.74% accuracy rate using 10-fold method.

A Block-Based Arithmetic Entropy Encoding Scheme for Medical Images

The digitization of human body, especially for treatment of diseases can generate a large volume of data. This generated medical data has a large resolution and bit depth. In the field of medical diagnosis, lossless compression techniques are widely adopted for the efficient archiving and transmission of medical images. This article presents an efficient coding solution based on a predictive coding technique. The proposed technique consists of Resolution Independent Gradient Edge Predictor16 (RIGED16) and Block Based Arithmetic Encoding (BAAE). The objective of this technique is to find universal threshold values for prediction and provide an optimum block size for encoding. The validity of the proposed technique is tested on some real images as well as standard images. The simulation results of the proposed technique are compared with some well-known and existing compression techniques. It is revealed that proposed technique gives a higher coding efficiency rate compared to other techniques.

A Rule-Based Monitoring System for Accurate Prediction of Diabetes

Diabetes is a chronic disease that can affect the life of people due to high sugar level in their blood. The sugar level is increased due to a lack of production of insulin in the human body. Large numbers of people are affected with diabetes and it can increase tremendously due life style behavior. Diabetes can also affect the other human organs, like kidneys, hearts, retinas and lead to the failure of these organs. This article presents a diabetic monitoring system to determine the risk of diabetes based on the personal health record of patients. In this work, several rules are designed based on the clinical as well as non-clinical symptoms. The effectiveness of the diabetes monitoring system is tested on a set of two hundred forty people. The simulation results are also compared with well-known techniques available for diabetes prediction. It is stated that proposed monitoring system obtains 90.41% accuracy rate as compared with other techniques.

AB0013 HLA ASSOCIATION WITH SYSTEMIC SCLEROSIS (SSc) IN NORTH INDIAN POPULATION AND FAMILIAL INHERITANCE PATTERNS

Background:

It is widely believed that SSc develops in an individual with a permissive genetic makeup.Genetic influences have long been suspected to impact SSc. In families with a history of SSc, the incidence of disease can range from 1.5 to 1.7% (1). There are several reports of familial occurrence and certain alleles of the HLA system have been associated with the disease (2).No Indian data pertaining to genetic basis of systemic sclerosis is present. Understanding the genetic basis of the disease will help us in defining the biomarkers of the disease in the population that can help in early diagnosis and prognosis.

Objectives:

To study HLA association with Systemic sclerosis (SSc) in North Indian Population and its genetic susceptibility to familial systemic sclerosis.

Methods:

A total of 150 SSc patients diagnosed by following ACR and EULAR criteria and 150 control subjects, were genotyped for HLA-A, B, DRB1, DQB1 loci by Luminex® 200 Instrument (USA). The association of alleles with disease susceptibility was tested by Chi-square test and Fisher’s exact test.

HLA Typing for HLA class I (A, B, C) and II(DR,DQ,DP) for familial study of systemic sclerosis in 2 families was performed by Next Generation Sequencing(NGS) with illumina MiniSeq using MIA FORA NGS Kits from IMMUCOR. Antinuclear patterns (ANA) and specific antibodies were detected by indirect Immunofluorescence and Immunoblot (Euroline, Germany).

Results:

Strong disease associations were observed for haplotypes A*24(OR=1.7;< 0.02), A*32(OR=2.8;< 0.02), B*35(OR=1.7;< 0.03), DRB1*11(OR=2.1;< 0.007). The reduced frequencies of haplotypes A*68(P< 0.05), DRB1*10(P< 0.05), DRB1*12 (P<0.00) among patients suggested a protective association. There was no statistical association found with HLA DQB*1.

Through NGS we observed that in the 1stfamily haplotypes HLA –A*11, 32, 24; B* 51, 55, 35; C*-14, 04; DRB1*15, 04; DQB1*05, 03; DPB1*04, 26 appears in affected family members with serological abnormalities.In the 2ndfamily both mother and daughter had same set of haplotypes except DQB1 with serological abnormalities. The haplotypes DPB1*04 was present in all the diseased individuals of both the families (Fig. 1 and table 1).

Table 1.

NGS HLA typing report

ABCDRB1DQB1DPB1F111 2435 1504 0415 1505 0502 26F211 3251 5514 0415 0405 0304 04F311 2435 5504 0415 1505 0526 04F432 1151 1514 0415 0405 0302 04F524 3335 4404 0715 0705 0226 14F611 2435 5504 0415 1505 0504 26F711 2435 5504 0415 1505 0504 26F824 3251 3514 0404 1503 0526 04F911 3251 5514 0415 0405 0304 04F1011 3344 5207 1211 0702 0304 13F1111 3344 5207 1211 0703 0304 13

Fig. 1

Conclusion:

The risk alleles A*24, 32; B*35; DRB1*11 were found to be associated with North Indian cohort of SSc, while the protecting alleles were A*68; DRB1*10, 12.These risk alleles were present in the SSc affected family members and the protective alleles were absent in the same. Surprisingly, even healthy members carried the same risk alleles but did not manifest the disease or have serological evidence of the same. We have not excluded occurrence of disease at a later age, as presently the healthy siblings are young. Thus our study indicates that though HLA association are found with SSc but many other factors like HLA (HLA *C, DPB1*) or non HLA genes as wells as epigenetic factors might also play a role in disease manifestation and severity.

References:

[1]Luo Y, Wang Y, Wang Q,et al. Systemic sclerosis: genetics and epigenetics. J Autoimmun.2013; 41:161–67.

[2]de Juan MD1, Belzunegui J, Belmonte I, Barado J, Figueroa M, Cancio J, Vidal S, Cuadrado E. An immunogenetic study of familial scleroderma. Ann Rheum Dis. 1994 Sep; 53(9):614-7.

Acknowledgments:

The technical help of Mr.Manoj Kumar and Mr.Vinkesh are hereby gratefully acknowledged Indian Council of Medical Research(Funding of Fellowship)

Disclosure of Interests:

None declared

Cellular Automata Based Model for E-Healthcare Data Analysis

E-healthcare is warm area of research and a number of algorithms have been applied to classify healthcare data. In the healthcare field, a large amount of clinical data is generated through MRI, CT scans, and other diagnostic tools. Healthcare analytics are used to analyze the clinical data of patient records, disease diagnosis, cost, hospital management, etc. Analytical techniques and data visualization are used to get the real time information. Further, this information can be used for decision making. Also, this information is useful for the better treatment of patients. In this work, an improved big bang-big crunch (BB-BC) based clustering algorithm is applied to analyze healthcare data. Cluster analysis is an important task in the field of data analysis and can be used to understand the organization of data. In this work, two healthcare datasets, CMC and cancer, are used and the proposed algorithm obtains better results when compared to MEBB-BC, BB-BC, GA, PSO and K-means algorithms. The performance of the improved BB-BC algorithm is also examined against benchmark clustering datasets. The simulation results showed that proposed algorithm improves the clustering results significantly when compared to other algorithms.

The Diagnosis of Dengue Disease

This article describes how Dengue fever is a fatal and hazardous disease resulting from the bite of several species of the female mosquito (principally, Aedesaegypti). Symptoms of the dengue fever mimic those of a number of other infectious and/or mosquito-borne tropical diseases such as Viral flu, Chikungunya, and Zika fever. Yet, with dengue fever, human life can be more at risk due to severe depletion of blood platelets. Thus, early detection of dengue disease can ensure saving lives; furthermore, it can help in making a preventive move before the disease progresses to epidemic proportion. Hence, the target of this article is to propose a model for an early detection and precise diagnosis of dengue disease. In this article, three prevalent machine learning methodologies, including, Artificial Neural Network (ANN), Decision Tree (DT) and Naive Bayes (NB) are evaluated for designing a diagnostic model. The performance of these models is assessed utilizing available dengue datasets. Results comparing and contrasting performance of diagnostic models utilizing accuracy, sensitivity, specificity and error rate parameters showed that ANN-based diagnostic model appears to yield better performance measures over both the DT and NB models.

Critical Review of "Family Health Advisory Services" Assessment in MBBS Training Program in Community Medicine

Context:

Family Health Advisory Services (FHAS) posting as well as its assessment is resource demanding but fails to enjoy priority. Study focuses on a holistic overview of the assessment process to understand need for change.

Aims:

The aim of this study is to identify perceived gaps in current assessment practices related to FHAS posting.

Settings and Design:

A cross-sectional mixed method study among all the V semester students currently undergoing assessment for the posting, past students (selected VII semester students and interns), preceptors (supervising residents – postgraduate students in department and senior resident, health assistants, medical social service officer), and involved faculty.

Subject and Methods:

Self-administered questionnaire, in-depth interview, focus group discussions (two) as well as observations using checklist were used for data collection and triangulation.

Statistical Analysis Used:

Quantitative data used in this study were statistical measures of central tendency and dispersion. Qualitative data transcript repeatedly read to identify underlying common themes, compared to draw inference.

Results:

There was a lack of guidelines and communication regarding assessment. Formative assessment was not performed and replaced by one time end assessment. All components of learning were not assessed. End-posting assessment was not standardized and unrelated to learning objectives. Award of scores was skewed toward right for intervention and toward left for analysis and community diagnosis.

Conclusions:

There is a need to focus on proper implementation of programme to strengthen formative assessment. Assessment should be relevant to learning objectives of posting. Faculty has to lead by example.

Distribution trends & antibiogram pattern of Salmonella enterica serovar Newport in India

Background & objectives:

Salmonellosis is a major public health concern worldwide. Besides typhoidal salmonellae, infections due to non-typhoidal serovars of Salmonella are also associated with high morbidity and mortality leading to huge economic losses. Among non-typhoidal serovars, Salmonella Newport has been reported as a major cause of foodborne infections resulting in outbreaks due to consumption of contaminated food items. Little data related to this serovar are available from India leading to the scarcity of information on the distribution trends of this important serovar in the country. Therefore, an effort was made in the present study to generate data on distribution trends and antibiogram of S. Newport in the country.

Methods:

S. Newport isolates received at the National Salmonella and Escherichia Centre at Kasauli, India, during January 2010 to December 2013 were analysed for their distribution trends and antibiogram data were also generated using standard methods.

Results:

In the present study, S. Newport isolates were received from eight States and one union territory of the country and highest proportion of S. Newport isolates were found to be from humans (53.61%) followed by animals (27.84%) and food (18.56%). S. Newport isolates exhibited resistance to all drugs used in the present study except chloramphenicol, ciprofloxacin and cefuroxime.

Interpretation & conclusions:

Considering distribution of this important serovar of Salmonalla and its wide range of reservoirs, steps towards formulation and execution of efficient surveillance programmes should be taken.

S2'-subsite variations between human and mouse enzymes (plasmin, factor XIa, kallikrein) elucidate inhibition differences by tissue factor pathway inhibitor -2 domain1-wild-type, Leu17Arg-mutant and aprotinin

Essentials Current antifibrinolytics - aminocaproic acid and tranexamic acid-can cause seizures or renal injury. KD1L17R -KT , aprotinin and tranexamic acid were tested in a modified mouse tail-amputation model. S2'-subsite variations between human and mouse factor XIa result in vastly different inhibition profiles. KD1L17R -KT reduces blood loss and D-dimer levels in mouse with unobserved seizures or renal injury.

SUMMARY

Background Using tissue factor pathway inhibitor (TFPI)-2 Kunitz domain1 (KD1), we obtained a bifunctional antifibrinolytic molecule (KD1L17R -KT ) with C-terminal lysine (kringle domain binding) and P2'-residue arginine (improved specificity towards plasmin). KD1L17R -KT strongly inhibited human plasmin (hPm), with no inhibition of human kallikrein (hKLK) or factor XIa (hXIa). Furthermore, KD1L17R -KT reduced blood loss comparable to aprotinin in a mouse liver-laceration model of organ hemorrhage. However, effectiveness of these antifibrinolytic agents in a model of hemorrhage mimicking extremity trauma and their inhibition efficiencies for mouse enzymes (mPm, mKLK or mXIa) remain to be determined. Objective To determine potential differences in inhibition constants of various antifibrinolytic agents against mouse and human enzymes and test their effectiveness in a modified mouse tail-amputation hemorrhage model. Methods/Results Unexpectedly, mXIa was inhibited with ~ 17-fold increased affinity by aprotinin (Ki ~ 20 nm) and with measurable affinity for KD1L17R -KT (Ki ~ 3 μm); in contrast, KD1WT -VT inhibited hXIa or mXIa with similar affinity. Compared with hPm, mPm had ~ 3-fold reduced affinity, whereas species specificity for hKLK and mKLK was comparable for each inhibitor. S2'-subsite variations largely accounted for the observed differences. KD1L17R -KT and aprotinin were more effective than KD1WT -VT or tranexamic acid in inhibiting tPA-induced mouse plasma clot lysis. Further, KD1L17R -KT was more effective than KD1WT -VT and was comparable to aprotinin and tranexamic acid in reducing blood loss and D-dimer levels in the mouse tail-amputation model. Conclusions Inhibitor potencies differ between antifibrinolytic agents against human and mouse enzymes. KD1L17R -KT is effective in reducing blood loss in a tail-amputation model that mimics extremity injury.

Role of Soft Computing Approaches in HealthCare Domain: A Mini Review

In the present era, soft computing approaches play a vital role in solving the different kinds of problems and provide promising solutions. Due to popularity of soft computing approaches, these approaches have also been applied in healthcare data for effectively diagnosing the diseases and obtaining better results in comparison to traditional approaches. Soft computing approaches have the ability to adapt itself according to problem domain. Another aspect is a good balance between exploration and exploitation processes. These aspects make soft computing approaches more powerful, reliable and efficient. The above mentioned characteristics make the soft computing approaches more suitable and competent for health care data. The first objective of this review paper is to identify the various soft computing approaches which are used for diagnosing and predicting the diseases. Second objective is to identify various diseases for which these approaches are applied. Third objective is to categories the soft computing approaches for clinical support system. In literature, it is found that large number of soft computing approaches have been applied for effectively diagnosing and predicting the diseases from healthcare data. Some of these are particle swarm optimization, genetic algorithm, artificial neural network, support vector machine etc. A detailed discussion on these approaches are presented in literature section. This work summarizes various soft computing approaches used in healthcare domain in last one decade. These approaches are categorized in five different categories based on the methodology, these are classification model based system, expert system, fuzzy and neuro fuzzy system, rule based system and case based system. Lot of techniques are discussed in above mentioned categories and all discussed techniques are summarized in the form of tables also. This work also focuses on accuracy rate of soft computing technique and tabular information is provided for each category including author details, technique, disease and utility/accuracy.

Conductivity fluctuations in proton-implanted ZnO microwires

Electric noise can be an important limitation for applications of conducting elements in the nanometer size range. The intrinsic electrical noise of prospective materials for opto-spintronics applications like ZnO has not yet been characterized. In this study, we have investigated the conductivity fluctuations in 10 nm thick current paths produced by proton implantation of ZnO microwires at room temperature. The voltage noise under a constant dc current bias in undoped, as well as in Li-doped microwires, is characterized by [Formula: see text] power spectra with [Formula: see text]. The noise intensity scales with the square of the bias current pointing to bias-independent resistivity fluctuations as a source of the observed noise. The normalized power spectral density appears inversely proportional to the number of carriers in the probed sample volume, in agreement with the phenomenological Hooge law. For the proton-implanted ZnO microwire and at 1 Hz we obtain a normalized power spectral density as low as [Formula: see text] Hz(-1).

Prediction of different types of liver diseases using rule based classification model

BACKGROUND

Diagnosing different types of liver diseases clinically is a quite hectic process because patients have to undergo large numbers of independent laboratory tests. On the basis of results and analysis of laboratory test, different liver diseases are classified. Hence to simplify this complex process, we have developed a Rule Base Classification Model (RBCM) to predict different types of liver diseases. The proposed model is the combination of rules and different data mining techniques.

OBJECTIVE

The objective of this paper is to propose a rule based classification model with machine learning techniques for the prediction of different types of Liver diseases.

METHOD

A dataset was developed with twelve attributes that include the records of 583 patients in which 441 patients were male and rests were female. Support Vector Machine (SVM), Rule Induction (RI), Decision Tree (DT), Naive Bayes (NB) and Artificial Neural Network (ANN) data mining techniques with K-cross fold technique are used with the proposed model for the prediction of liver diseases. The performance of these data mining techniques are evaluated with accuracy, sensitivity, specificity and kappa parameters as well as statistical techniques (ANOVA and Chi square test) are used to analyze the liver disease dataset and independence of attributes.

RESULT

Out of 583 patients, 416 patients are liver diseases affected and rests of 167 patients are healthy. The proposed model with decision tree (DT) technique provides the better result among all techniques (RI, SVM, ANN and NB) with all parameters (Accuracy 98.46%, Sensitivity 95.7%, Specificity 95.28% and Kappa 0.983) while the SVM exhibits poor performance (Accuracy 82.33%, Sensitivity 68.03%, Specificity 91.28% and Kappa 0.801). It is also found that the best performance of the model without rules (RI, Accuracy 82.68%, Sensitivity 86.34%, Specificity 90.51% and Kappa 0.619) is almost similar to the worst performance of the rule based classification model (SVM, Accuracy 82.33%, Sensitivity 68.03%, Specificity 91.28% and Kappa 0.801 and the accuracy of chi square test is 76.67%.

CONCLUSION

This study demonstrates that there is a significant difference between the proposed rules based classification model and the model without rules for the liver diseases prediction and the rule based classification model with decision tree (DT) technique provides most accurate result. This model can be used as a valuable tool for medical decision making.

Predication of Parkinson's disease using data mining methods: a comparative analysis of tree, statistical, and support vector machine classifiers

The prediction of Parkinson's disease in early age has been challenging task among researchers, because the symptoms of disease came into existence in middle and late middle age. There are lots of symptoms that lead to Parkinson's disease. But this article focuses on the speech articulation difficulty symptoms of PD affected people and try to formulate the model on the behalf of three data mining methods. These three data mining methods are taken from three different domains of data mining i.e., from tree classifier, statistical classifier, and support vector machine classifier. Performance of these three classifiers is measured with three performance matrices i.e., accuracy, sensitivity, and specificity. Hence, the main task of this article is tried to find out which model identified the PD affected people more accurately.

Hypothyroidism Complicated by Angina Pectoris: Therapeutic Approaches

The association of hypothyroidism and coronary artery disease is not uncommon. The precipitation of angina pectoris, cardiac arrhythmia, and even myocardial infarction may occur in patients when initiating rapid replacement therapy for hypothyroidism. This is particularly true when replacement therapy is instituted in elderly persons or in patients with preexisting coronary artery disease. A starting daily dose of 12.5 to 25 micrograms and increments of 25 micrograms every 2 to 3 weeks is recommended. Close monitoring of cardiac symptoms is essential to avoid side effects. Medical management of angina pectoris includes administration of beta-blockers, nitrates, or at times combination antianginal therapy may be most effective. Persistence of angina in these patients may require coronary angiography with subsequent angioplasty or coronary artery bypass surgery.

Detection of Shiga toxin variants among Shiga toxin-forming Escherichia coli isolates from animal stool, meat and human stool samples in India

AIM

To study the prevalence and distribution of various variants in the stx gene of Shiga toxin-producing Escherichia coli (STEC) isolated from diverse environmental sources (animal stool, meat) and human illness, from a large geographic area in India, and to understand the association between variants, serotype distribution and human disease.

METHODS AND RESULTS

A surveillance for STEC was conducted in the semi-urban and rural areas of Punjab, Himachal, Haryana and Chandigarh. Shiga toxin-producing Escherichia coli isolates (80 animal stool, 39 meat, 21 human stool from diarrhoea and HUS cases) were characterized for stx variants by PCR. Shiga-like toxin (Stx) was detected using Ridascreen-EIA assay. Variant stx2c was the most common (25·1%), followed by stx1d (13%), stx1c (10·7%) and stx2d (9·2%), whereas stx2e, stx2f and stx2g were absent. Only 8/21 (38%) human isolates harboured stx variants, of which stx2c and stx2d were found in 2 and 1 isolates, respectively. The low frequency of carriage of these potentially more pathogenic variants may explain the low severity of human illness seen in India. Shiga-like toxin was detected in only 42 of the isolates positive for the stx genes probably due to the low levels of toxins produced. Serogroup distribution was found to be diverse, suggesting the lack of any predominant circulating type.

CONCLUSIONS

The presence of stx variants 1c, 1d, 2c and stx2d in diverse environmental and human sources in India was demonstrated. The prevalence of the most common subtype stx2c found in this study in animal isolates may pose a threat to the public health. We report the subtyping of human STEC isolates and report the presence of stx1d subtype for the first time from India.

SIGNIFICANCE AND IMPACT OF THE STUDY

We demonstrated the presence of potentially pathogenic subtypes in the environmental specimens which may act as a reservoir for human infections. Serogroups new to India were also reported.

Enucleation of the solitary epithelial cyst of pancreatic head in an adult: a case report and review of the literature

Solitary true pancreatic cyst is a rare entity, and only a few cases are reported in the literature. We report a case of a 35-year-old woman who had a cyst in the head of the pancreas and gall stones and presented with complaints of pain in the epigastric region. The patient underwent open cholecystectomy with aspiration of the pancreatic cyst at some other private hospital. After 4 months, she presented to us with no relief in pain. Repeat contrast-enhanced computed tomography of the abdomen showed recurrence of the cyst. The patient underwent enucleation of the cyst at our hospital. During a 2-year follow-up after the enucleation, she remained asymptomatic.

Accurate measurements of water vapor transmission through high-performance barrier layers

We report a new approach to measuring very low rates of water vapor transmission through high-performance barrier layers, based on detection of the water vapor by cavity ring-down infrared spectroscopy. It provides accurate and traceable measurements with a detection limit for water vapor transmission significantly below 1 × 10(-4) g/m(2)/day. The system is underpinned by dynamic reference standards of water vapor generated between 5 and 2000 nmol∕mol with an estimated relative expanded uncertainty of ±2%. It has been compared with other methods and demonstrates good comparability.

Antimicrobial evaluation of mangiferin analogues

The naturally occurring xanthone glycoside mangiferin has been isolated by column chromatography from the ethanol extract of stem bark of Mangifera indica. Mangiferin was further converted to 5-(N-phenylaminomethyleno)mangiferin, 5-(N-p-chlorophenylaminomethyleno) mangiferin, 5-(N-2-methylphenylaminomethyleno) mangiferin, 5-(N-p-methoxyphenylaminomethyleno) mangiferin, 5-(N, N-diphenylaminomethyleno) mangiferin, 5-(N--napthylaminomethyleno) mangiferin and 5-(N-4-methylphenylaminomethyleno) mangiferin. Mangiferin and its analogues were characterized by melting point and R(f) value determination and through spectral technique like UV, IR, and NMR spectral analysis. The synthesized compounds were screened for antimicrobial activity.

Antihistaminic and antiviral activities of steroids of Turbinaria conoides

The steroids 3,6,17-trihydroxy-stigmasta-4,7,24(28)-triene (A) and 14,15,18,20-diepoxyturbinarin (B) were isolated from the cyclohexane extract of brown alga, Turbinaria conoides (J. Agardh) Kutzing, and have been reported for their antimicrobial activity by us. In this study, the isolated compounds were evaluated for comprehensive antihistaminic, antiviral and cytotoxicity screening. The antihistaminic study was performed using in vitro standard animal models. Evaluation of the potency (EC(50)), affinity (pA(2)) and the maximal response (E(max)) of the histamine alone and in the presence of the compounds were determined. Antiviral activity and cytotoxicity were performed in Crandell-Rees feline kidney (CRFK) cells by a colorimetric formazan-based MTS assay. No significant antiviral activity or cytotoxicity were observed for the compounds in the CRFK cells. Compound A inhibited the histamine-induced concentration at 20 µg mL(-1)(p < 0.05). The most significant inhibition (97%) was observed for compound B (p < 0.01) at the same concentration, which was comparable to that of the positive control chlorpheniramine maleate (10 µg mL(-1)). This potentiality suggests that 14,15,18,20-diepoxyturbinarin (B) can be developed as a new lead antihistaminic agent.

Menthone semicarbazides and thiosemicarbazides as anticonvulsant agents

A series of novel (+/-) 3-menthone semi carbazides (1-7) and thiosemicarbazides synthesized using an appropriate synthetic route and characterized by thin layer chromatography and spectral analysis. The anticonvulsant activity of synthesized compounds was established after intraperitoneal administration in three seizure models in mice which include maximal electroshock seizure (MES), subcutaneous pentylene tetrazole (scPTZ) induced seizure and minimal neurotoxicity test. Seven compounds exhibited protection in both models and N(1) - (4-fluorophenyl) - N(4)- (menth-3-one) semicarbazide (4) emerged as the most active compound with MES ED(50) of 44.15mg/kg and scPTZ ED(50) of 38.68mg/kg at 0.25h duration. These compounds were found to elevate gamma-amino butyric acid (GABA) levels in the midbrain region, thus indicating that (+/-) 3-menthone semicarbazides could be considered as a lead molecule in designing of a potent anticonvulsant drug.