1,948 research outputs found
Characterization of bacteriophage communities and CRISPR profiles from dental plaque.
BackgroundDental plaque is home to a diverse and complex community of bacteria, but has generally been believed to be inhabited by relatively few viruses. We sampled the saliva and dental plaque from 4 healthy human subjects to determine whether plaque was populated by viral communities, and whether there were differences in viral communities specific to subject or sample type.ResultsWe found that the plaque was inhabited by a community of bacteriophage whose membership was mostly subject-specific. There was a significant proportion of viral homologues shared between plaque and salivary viromes within each subject, suggesting that some oral viruses were present in both sites. We also characterized Clustered Regularly Interspaced Short Palindromic Repeats (CRISPRs) in oral streptococci, as their profiles provide clues to the viruses that oral bacteria may be able to counteract. While there were some CRISPR spacers specific to each sample type, many more were shared across sites and were highly subject specific. Many CRISPR spacers matched viruses present in plaque, suggesting that the evolution of CRISPR loci may have been specific to plaque-derived viruses.ConclusionsOur findings of subject specificity to both plaque-derived viruses and CRISPR profiles suggest that human viral ecology may be highly personalized
Analysis of delivery pattern and neonatal outcome in the calendar year 2015 in urban field practice area of Kamineni Institute of Medical Sciences, Narketpally, India
Background: Neonatal mortality is the number of neonatal deaths in a given year per 1000 live births in that year. It is estimated that 5.9 million children under 5 years of age died in 2015, with a global under-five mortality rate of 42.5 per 1000 live births. Of those deaths, 45% were newborns, with a neonatal mortality rate of 19 per 1000 live births. The present study aimed at to assess the neonatal mortality in the study area and to study the socio demographic factors, obstetric and neonatal factors among study population.Methods: This is a Cross-sectional study conducted in the field practice area of Urban Health Centre (UHC) under department of Community Medicine, KIMS, Narketpally, Nalgonda district, Telangana from February 2016 to April 2016. A total of 240 women were included in the study. Information collected was their socio-demographic data, previous and present obstetric history and utilization of health facilities and condition of Baby after birth.Results: Neonatal mortality rate in the study area during the study period is 8.39/1000 live births. Out of 242 new born babies 129(53.3%) were males and 113 (46.7%) were females. Majority (94.2%) had a birth weight of more than 2.5 kgs. Out of 242 babies 7 babies were suffering from congenital anomalies (2), Sepsis (2) and skin infections (3).Conclusions: Most of the study population were in the age group of 19-25 years, literates, laborers by occupation, Hindu by religion, belong to nuclear families and belong to below poverty line. Most of the new born babies have birth weight >2.5 kgs
Transcriptome analysis of bacteriophage communities in periodontal health and disease.
BackgroundThe role of viruses as members of the human microbiome has gained broader attention with the discovery that human body surfaces are inhabited by sizeable viral communities. The majority of the viruses identified in these communities have been bacteriophages that predate upon cellular microbiota rather than the human host. Phages have the capacity to lyse their hosts or provide them with selective advantages through lysogenic conversion, which could help determine the structure of co-existing bacterial communities. Because conditions such as periodontitis are associated with altered bacterial biota, phage mediated perturbations of bacterial communities have been hypothesized to play a role in promoting periodontal disease. Oral phage communities also differ significantly between periodontal health and disease, but the gene expression of oral phage communities has not been previously examined.ResultsHere, we provide the first report of gene expression profiles from the oral bacteriophage community using RNA sequencing, and find that oral phages are more highly expressed in subjects with relative periodontal health. While lysins were highly expressed, the high proportion of integrases expressed suggests that prophages may account for a considerable proportion of oral phage gene expression. Many of the transcriptome reads matched phages found in the oral cavities of the subjects studied, indicating that phages may account for a substantial proportion of oral gene expression. Reads homologous to siphoviruses that infect Firmicutes were amongst the most prevalent transcriptome reads identified in both periodontal health and disease. Some genes from the phage lytic module were significantly more highly expressed in subjects with periodontal disease, suggesting that periodontitis may favor the expression of some lytic phages.ConclusionsAs we explore the contributions of viruses to the human microbiome, the data presented here suggest varying expression of bacteriophage communities in oral health and disease
Optimized DWT Based Digital Image Watermarking and Extraction Using RNN-LSTM
The rapid growth of Internet and the fast emergence of multi-media applications over the past decades have led to new problems such as illegal copying, digital plagiarism, distribution and use of copyrighted digital data. Watermarking digital data for copyright protection is a current need of the community. For embedding watermarks, robust algorithms in die media will resolve copyright infringements. Therefore, to enhance the robustness, optimization techniques and deep neural network concepts are utilized. In this paper, the optimized Discrete Wavelet Transform (DWT) is utilized for embedding the watermark. The optimization algorithm is a combination of Simulated Annealing (SA) and Tunicate Swarm Algorithm (TSA). After performing the embedding process, the extraction is processed by deep neural network concept of Recurrent Neural Network based Long Short-Term Memory (RNN-LSTM). From the extraction process, the original image is obtained by this RNN-LSTM method. The experimental set up is carried out in the MATLAB platform. The performance metrics of PSNR, NC and SSIM are determined and compared with existing optimization and machine learning approaches. The results are achieved under various attacks to show the robustness of the proposed work
POLYPHARMACY INDUCED DRUG INTERACTIONS, ADVERSE DRUG REACTIONS (ADR) AND MEDICATION ERRORS IN TERTIARY CARE SOUTH INDIAN HOSPITAL
Objective: To study the pattern of drug interactions (DI) in our hospital and to identify whether it is associated with polypharmacy. To determine the level of severity of potential drug-drug interactions (PDDI), to detect, monitor and prevention of ADRs in the hospitalized patients and to identify the medication errors (ME).
Methods: A prospective interventional study was conducted in a 300 bedded tertiary care South Indian hospital for a period of 6 mo. Prescriptions were analysed for PDDI using Micromedex software 2.2. The causality and severity of ADRs were assessed by using Naranjo’s, WHO UMC Scales and Hart wigs severity scales. ME was identified by review of patient drug charts.
Results: Total 190 prescriptions were analyzed, in which 1028 drug interactions were seen. Out of which 718 were DDI, 198 DFI, 100 DEI, and 12 DTI were observed. More number of DI was seen in cardiovascular drugs, antibiotics followed by antacids and antiulcer agents. A total of 52 ADRs were identified in 43 patients. Diuretics, cardiovascular drugs were associated with a higher incidence of ADRs followed by Anti-Diabetic agents. 58 ME was seen in 190 prescriptions, among them omission error, prescribing errors and Wrong dose error was seen.
Conclusion: Clinical pharmacist plays a potential role in the health care system in assisting the physician i.e. modifying the number of drugs taken, number of doses taken, medication adherence, identification of drug interactions, preventing, monitoring and detection of ADRs and identifying the medication errors
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