Study of effect of gluten-free diet on vitamin D levels and bone mineral density in celiac disease patients.

Objective: Celiac disease (CD) is a multifactorial immune-mediated enteropathy caused by a response to ingested gluten. The current available treatment for CD is lifelong gluten-free diet (GFD). This study was done to see the effect of GFD on Vitamin D levels and bone mass density in celiac patients. Methods: A prospective interventional study on newly diagnosed celiac patients was conducted in the Pediatrics department of a tertiary care teaching institute in 2 stages viz. on presentation and after 6 months of GFD. Anthropometric measurements, biochemical investigations, Vitamin D levels, and DEXA scan was done at recruitment and after 6 months of GFD and was analyzed. Results: In newly diagnosed 60 pediatric celiac patients, positive effect of GFD on anthropometry, hemoglobin, Vitamin D levels, DEXA scan parameters was observed. Significant difference was found in Vitamin D levels which increased from baseline 14.85 ± 5.39 to 18.22 ± 5.67 ng/ml after 6 months of GFD (P < 0.05). Significant difference was found in BMD (mean Z-score) which increased from -0.941 ± 0.738 to -0.640 ± 0.60 after 6 months of GFD (P < 0.001). Conclusion: Our study concluded that there is significant increase in vitamin D levels as well as Z-score, bone mass density (BMD) and bone Mass Content (BMC) after 6 months of GFD.

Phenotypic and genotypic antimicrobial resistance patterns of Escherichia coli and Klebsiella isolated from dairy farm milk, farm slurry and water in Punjab, India.

Antibiotic resistance is a mushrooming pandemic at national and international levels which if not controlled at this very moment, can lead to global problems. Main reason for emerging bacterial resistance is repeated exposure of bacteria to antimicrobial agents and access of bacteria to increasingly large pools of antimicrobial resistance genes in mixed bacterial populations. A total of 51 villages were sampled in the current study contributing to a total of 153 farms. A total of 612 samples comprising 153 each of raw pooled milk samples, slurry, animal drinking water and human drinking water were gathered from small, medium and large farms located in all seven tehsils of Ludhiana district of Punjab. In addition to that, 37 samples of village pond water were also collected from the targeted villages. Out of total 153 slurry, raw pooled milk samples, animal drinking water and human drinking water samples (each), the prevalence of 24.8%, 60%, 26.7% and 16.3% was found for E. coli respectively. On the other hand, for Klebsiella, the overall prevalence of 19.6%, 51%, 20.2% and 5.8% was found from slurry, raw pooled milk samples, animal drinking water and human drinking water respectively. In all matrices, the comparative frequency of resistance genes in positive isolates of E. coli and K. pneumoniae was: tetA > tetB > tetC, qnrS > qnrB > qnrA, sulII > sulI > sulIII. The highest proportion of resistance genes was found in slurry (193 genes) followed by milk (71 genes). The overall pattern of resistant genes was tetA > sulII > qnrS. In conclusion, data from the present study suggested that commensal E. coli and Klebsiella may act as reservoirs of antimicrobial drug resistance genes which may be mobilised into human populations and untreated animal waste may be considered an important source of resistant bacteria leading to environmental pollution.

Epidemiological assessment of antibiotic residues in dairy farm milk and farm waste and water in northern India.

Antibiotics are frequently used in the dairy sector for prophylactic uses along with therapeutic purposes. Throughout the globe, antibiotic resistance has turned out as one of the greatest public health issue with greater concern in developing countries, such as India. On the matrix wise comparison of the study, slurry samples in all three farming systems show the highest proportion of positive samples. Out of 153 slurry samples, 15.6% samples showed the presence of antibiotic residues. Eighteen milk samples (11.7%) showed the presence of residues following this trend. Only one sample (0.65%) was positive of animal drinking water in the study. None of the targeted residues were found in any sample of human drinking water. The four pond water samples showed the prevalence of residues of oxytetracycline (2 samples) and enrofloxacin (2 samples). Medium size farms (10-30 animals) comprised comparative higher levels than small (< 10) and large farms (> 30). The excretion mass modelling of antibiotics released in the environment indicated 8325.41 kg of oxytetracycline and 12,498.89 kg of enrofloxacin per year. The study helps in providing understanding in the relation between antibiotics usage and dissemination of their residues to the environment which may result in likely ecotoxicological consequences.

The Clean India Mission: Public and animal health benefits.

The Clean India Mission is a national campaign that aims for complete elimination of open defecation from the country. In India, 564 million people do not have access to toilets and defecate in the open environment. The 'Millennium development goals' have given increased weightage to elimination of open defecation for improving health, nutrition and productivity of developing country populations. The Indian economy bears an estimated annual total loss (in terms of health, education, access time and tourism) of US$ 54 billion due to lack of toilets, poor hygiene and over US$ 38.5 billion in treatment costs for diseases occurring due to poor hygiene. Out of 1415 human pathogens, at least more than 10% of pathogens are transmitted through the faecal-oral route. The practice of open defecation helps pathogens persist in the environment and cause diseases. This review focuses on the current status and harms of open defecation, as well as the public and animal health benefits of implementing 'The Clean India Mission' in India.

Extraction of events and temporal expressions from clinical narratives.

This paper addresses an important task of event and timex extraction from clinical narratives in context of the i2b2 2012 challenge. State-of-the-art approaches for event extraction use a multi-class classifier for finding the event types. However, such approaches consider each event in isolation. In this paper, we present a sentence-level inference strategy which enforces consistency constraints on attributes of those events which appear close to one another. Our approach is general and can be used for other tasks as well. We also design novel features like clinical descriptors (from medical ontologies) which encode a lot of useful information about the concepts. For timex extraction, we adapt a state-of-the-art system, HeidelTime, for use in clinical narratives and also develop several rules which complement HeidelTime. We also give a robust algorithm for date extraction. For the event extraction task, we achieved an overall F1 score of 0.71 for determining span of the events along with their attributes. For the timex extraction task, we achieved an F1 score of 0.79 for determining span of the temporal expressions. We present detailed error analysis of our system and also point out some factors which can help to improve its accuracy.

Using domain knowledge and domain-inspired discourse model for coreference resolution for clinical narratives.

OBJECTIVE: This paper presents a coreference resolution system for clinical narratives. Coreference resolution aims at clustering all mentions in a single document to coherent entities. MATERIALS AND METHODS: A knowledge-intensive approach for coreference resolution is employed. The domain knowledge used includes several domain-specific lists, a knowledge intensive mention parsing, and task informed discourse model. Mention parsing allows us to abstract over the surface form of the mention and represent each mention using a higher-level representation, which we call the mention's semantic representation (SR). SR reduces the mention to a standard form and hence provides better support for comparing and matching. Existing coreference resolution systems tend to ignore discourse aspects and rely heavily on lexical and structural cues in the text. The authors break from this tradition and present a discourse model for "person" type mentions in clinical narratives, which greatly simplifies the coreference resolution. RESULTS: This system was evaluated on four different datasets which were made available in the 2011 i2b2/VA coreference challenge. The unweighted average of F1 scores (over B-cubed, MUC and CEAF) varied from 84.2% to 88.1%. These experiments show that domain knowledge is effective for different mention types for all the datasets. DISCUSSION: Error analysis shows that most of the recall errors made by the system can be handled by further addition of domain knowledge. The precision errors, on the other hand, are more subtle and indicate the need to understand the relations in which mentions participate for building a robust coreference system. CONCLUSION: This paper presents an approach that makes an extensive use of domain knowledge to significantly improve coreference resolution. The authors state that their system and the knowledge sources developed will be made publicly available.