Microbial spectrum of urinary tract infections and its antibiogram in a tertiary care hospital

Background: Urinary tract infections are one of the major health problem effecting both sexes of all age group. UTIs are often treated with different broad-spectrum antibiotics. The aim of this study was to determine the prevalence of bacteria causing urinary tract infections and their susceptibility pattern from patients reporting in RIMS Hospital.Methods: Mid stream urine (MSU) specimens sent to the laboratory from October 2014 to September 2016 were collected and inoculated onto blood agar and MacConkey agar and incubated at 37ºC for 24 hours. Identification and antibiotic susceptibility test was done following standard operative procedures.Results: 25.66% (1142/4450) samples showed a significant growth out of which 42% (479/1142) were male and 58% (663/1142) were female. E. coli has been found to be the major pathogen causing UTI which account for 61% (696/1142) followed by Staphylococcus aureus 12% (137/1142), CONS 7% (79), Enterococcus spp. 6% (67), Klebsiella spp. 5% (57), Proteus spp. 2% (22), Pseudomonas spp. 2%, Acinetobacter spp. 2% and Candida spp. 3%. Imipenem was the most susceptible antibiotic for Enterobacteriaceae, E. coli (85.9%), Klebsiella spp. (89.4%) and Proteus spp. (95.4%). Vancomycin is 100% sensitive while Linezolid, Nitrofurantoin and Gentamicin are also highly sensitive for both Staphylococcus aureus and CONS.Conclusions: These data may be used to determine trends in antimicrobial susceptibilities, to formulate local antibiotic policies in order to assist clinicians in the rational choice of antibiotic therapy to prevent misuse, or overuse, of antibiotics

Microbiological surveillance of operation theatre in a tertiary care hospital in North East India

Background: Good hospital hygiene is an integral part of infection control programme. “Microbiological surveillance” provides data about the factors contributing to infection. Bacterial counts in operation theatres are influenced by number of individual present, ventilation and air flow methods. Purpose of the study is to find out prevalence rate of microorganisms in Operation Theatre, to find out the frequency of contamination from various sites in operation theatre.Methods: The study was conducted in the department of microbiology, Regional institute of medical sciences, Imphal, Manipur, India. Air samples were taken by settle plate method in petri dishes containing blood agar and surface samples were taken by a sterile swab soaked in nutrient broth from all operation theatres. The samples were processed according to standard operative procedures.Results: Least bacterial colony forming unit (CFU) was shown by ophthalmology OT 17 CFU/mm3 and highest was shown by emergency OT 200 CFU/mm3. Isolated organism was divided into normal flora (CONS, micrococci), contaminant (bacillus species) and pathogenic organism e.g. Staphylococcus aureus, Acinetobacter spp., Pseudomonas spp. 15 (23.4%) swab samples out of a total of 64 swab samples were found to be growth positive. Out of that 4 CONS, 4 micrococci, 3 Bacillus spp, 2 Acinetobacter spp, 1 Enterobacter spp, 1 Pseudomonas spp. were isolated.Conclusions: Strengthening surveillance and laboratory capacity will surely enhance infection prevention and control. Routine sampling is strongly recommended for increasing awareness to identify and control all possible sources and types of infections

Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders

Electrocardiography (ECG) has been a reliable method for monitoring the proper functioning of the cardiovascular system for decades. Recently, there has been a lot of research focusing on accurately analyzing the heart condition through ECG. In recent days, numerous attempts are being made to analyze these signals using deep learning algorithms, including the implementation of artificial neural networks like convolutional neural networks, recurrent neural networks, and the like. In this context, this chapter intends to present some important techniques for classifying heartbeats based on deep neural networks with 1D CNN. Five ECG signals (N, S, V, F, and Q) standardization are based on the AAMI EC57 standard. The primary focus of this chapter is to discuss the techniques to classify ECG signals in those classes with promising accuracy and draw a clear picture of the current state-of-the-art in this sphere of study

Genome-wide association studies for diabetic macular edema and proliferative diabetic retinopathy

Background: Diabetic macular edema (DME) and proliferative diabetic retinopathy (PDR) are sight threatening complications of diabetes mellitus and leading causes of adult onset blindness worldwide. Genetic risk factors for diabetic retinopathy (DR) have been described previously, but have been difficult to replicate between studies, which have often used composite phenotypes and been conducted in different populations. This study aims to identify genetic risk factors for DME and PDR as separate complications in Australians of European descent with type 2 diabetes. Methods: Caucasian Australians with type 2 diabetes were evaluated in a genome wide association study (GWAS) to compare 270 DME cases and 176 PDR cases with 435 non retinopathy controls. All participants were genotyped by SNP array and after data cleaning, cases were compared to controls using logistic regression adjusting for relevant covariates. Results: The top ranked SNP for DME was rs1990145 (p = 4.10 x 10(-6), OR = 2.02 95%CI [1.50, 2.72]) on chromosome 2. The top-ranked SNP for PDR was rs918519 (p = 3.87 x 10(-6), OR = 0.35 95%CI [0.22, 0.54]) on chromosome 5. A trend towards association was also detected at two SNPs reported in the only other reported GWAS of DR in Caucasians; rs12267418 near MALRD1 (p = 0.008) in the DME cohort and rs16999051 in the diabetes gene PCSK.2 (p = 0.007) in the PDR cohort. Conclusion: This study has identified loci of interest for DME and PDR, two common ocular complications of diabetes. These findings require replication in other Caucasian cohorts with type 2 diabetes and larger cohorts will be required to identify genetic loci with statistical confidence. There is considerable overlap in the patient cohorts with each retinopathy subtype, complicating the search for genes that contribute to PDR and DME biology

Genetic study of Diabetic Retinopathy: recruitment methodology and analysis of baseline characteristics

ARC and NHMRC funded authors may self-archive the author accepted version of their paper (authors manuscript) after a 12-month embargo period from publication in an open access institutional repository.BACKGROUND: Diabetic retinopathy (DR) is a blinding disease of increasing prevalence, caused by a complex interplay of genetic and environmental factors. Here we describe the patient recruitment methodology, case and control definitions, and clinical characteristics of a study sample to be used for genome-wide association (GWAS) analysis to detect genetic risk variants of DR. METHODS: 1669 participants with either type 1 (T1) or type 2 (T2) diabetes mellitus (DM) aged 18 to 95 years were recruited in Australian hospital clinics. Individuals with T2DM had disease duration of at least 5 years, and were taking oral hypoglycemic medication, and/or insulin therapy. Participants underwent ophthalmic examination. Medical history and biochemistry results were collected. Venous blood was obtained for genetic analysis. RESULTS: 683 diabetic cases (178 T1DM and 505 T2DM participants) with sight-threatening DR, defined as severe non-proliferative DR (NPDR), proliferative DR (PDR) or diabetic macular edema (DME) were included in this analysis. 812 individuals with DM but no DR or minimal NPDR were recruited as controls (191 with T1DM and 621 with T2DM). The presence of sight-threatening DR was significantly correlated with DM duration, hypertension, nephropathy, neuropathy, HbA1C and BMI. DME was associated with T2DM (p<0.001), whereas PDR was associated with T1DM (p<0.001). CONCLUSIONS: Adoption of a case-control study design involving extremes of the DR phenotype makes this a suitable cohort, for a well-powered GWAS to detect genetic risk variants for DR.This work was funded by a grant from the Ophthalmic Research Institute of Australia, and Project Grant #595918 from the National Health and Medical Research Council (NHMRC) of Australia. JEC is supported in part by a NHMRC Practitioner Fellowship and KPB by a Career Development Fellowship. Research conducted at Moorfields Eye Hospital was funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology

Erratum: Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

Interpretation: By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017

Background The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk–outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk–outcome pairs, and new data on risk exposure levels and risk–outcome associations. Methods We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk–outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017. Findings In 2017, 34·1 million (95% uncertainty interval [UI] 33·3–35·0) deaths and 1·21 billion (1·14–1·28) DALYs were attributable to GBD risk factors. Globally, 61·0% (59·6–62·4) of deaths and 48·3% (46·3–50·2) of DALYs were attributed to the GBD 2017 risk factors. When ranked by risk-attributable DALYs, high systolic blood pressure (SBP) was the leading risk factor, accounting for 10·4 million (9·39–11·5) deaths and 218 million (198–237) DALYs, followed by smoking (7·10 million [6·83–7·37] deaths and 182 million [173–193] DALYs), high fasting plasma glucose (6·53 million [5·23–8·23] deaths and 171 million [144–201] DALYs), high body-mass index (BMI; 4·72 million [2·99–6·70] deaths and 148 million [98·6–202] DALYs), and short gestation for birthweight (1·43 million [1·36–1·51] deaths and 139 million [131–147] DALYs). In total, risk-attributable DALYs declined by 4·9% (3·3–6·5) between 2007 and 2017. In the absence of demographic changes (ie, population growth and ageing), changes in risk exposure and risk-deleted DALYs would have led to a 23·5% decline in DALYs during that period. Conversely, in the absence of changes in risk exposure and risk-deleted DALYs, demographic changes would have led to an 18·6% increase in DALYs during that period. The ratios of observed risk exposure levels to exposure levels expected based on SDI (O/E ratios) increased globally for unsafe drinking water and household air pollution between 1990 and 2017. This result suggests that development is occurring more rapidly than are changes in the underlying risk structure in a population. Conversely, nearly universal declines in O/E ratios for smoking and alcohol use indicate that, for a given SDI, exposure to these risks is declining. In 2017, the leading Level 4 risk factor for age-standardised DALY rates was high SBP in four super-regions: central Europe, eastern Europe, and central Asia; north Africa and Middle East; south Asia; and southeast Asia, east Asia, and Oceania. The leading risk factor in the high-income super-region was smoking, in Latin America and Caribbean was high BMI, and in sub-Saharan Africa was unsafe sex. O/E ratios for unsafe sex in sub-Saharan Africa were notably high, and those for alcohol use in north Africa and the Middle East were notably low. Interpretation By quantifying levels and trends in exposures to risk factors and the resulting disease burden, this assessment offers insight into where past policy and programme efforts might have been successful and highlights current priorities for public health action. Decreases in behavioural, environmental, and occupational risks have largely offset the effects of population growth and ageing, in relation to trends in absolute burden. Conversely, the combination of increasing metabolic risks and population ageing will probably continue to drive the increasing trends in non-communicable diseases at the global level, which presents both a public health challenge and opportunity. We see considerable spatiotemporal heterogeneity in levels of risk exposure and risk-attributable burden. Although levels of development underlie some of this heterogeneity, O/E ratios show risks for which countries are overperforming or underperforming relative to their level of development. As such, these ratios provide a benchmarking tool to help to focus local decision making. Our findings reinforce the importance of both risk exposure monitoring and epidemiological research to assess causal connections between risks and health outcomes, and they highlight the usefulness of the GBD study in synthesising data to draw comprehensive and robust conclusions that help to inform good policy and strategic health planning

Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: Global development goals increasingly rely on country-specific estimates for benchmarking a nation's progress. To meet this need, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2016 estimated global, regional, national, and, for selected locations, subnational cause-specific mortality beginning in the year 1980. Here we report an update to that study, making use of newly available data and improved methods. GBD 2017 provides a comprehensive assessment of cause-specific mortality for 282 causes in 195 countries and territories from 1980 to 2017. METHODS: The causes of death database is composed of vital registration (VR), verbal autopsy (VA), registry, survey, police, and surveillance data. GBD 2017 added ten VA studies, 127 country-years of VR data, 502 cancer-registry country-years, and an additional surveillance country-year. Expansions of the GBD cause of death hierarchy resulted in 18 additional causes estimated for GBD 2017. Newly available data led to subnational estimates for five additional countries-Ethiopia, Iran, New Zealand, Norway, and Russia. Deaths assigned International Classification of Diseases (ICD) codes for non-specific, implausible, or intermediate causes of death were reassigned to underlying causes by redistribution algorithms that were incorporated into uncertainty estimation. We used statistical modelling tools developed for GBD, including the Cause of Death Ensemble model (CODEm), to generate cause fractions and cause-specific death rates for each location, year, age, and sex. Instead of using UN estimates as in previous versions, GBD 2017 independently estimated population size and fertility rate for all locations. Years of life lost (YLLs) were then calculated as the sum of each death multiplied by the standard life expectancy at each age. All rates reported here are age-standardised

Global, regional, and national comparative risk assessment of 84 behavioural, environmental and occupational, and metabolic risks or clusters of risks for 195 countries and territories, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017.

BACKGROUND: The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 comparative risk assessment (CRA) is a comprehensive approach to risk factor quantification that offers a useful tool for synthesising evidence on risks and risk-outcome associations. With each annual GBD study, we update the GBD CRA to incorporate improved methods, new risks and risk-outcome pairs, and new data on risk exposure levels and risk-outcome associations. METHODS: We used the CRA framework developed for previous iterations of GBD to estimate levels and trends in exposure, attributable deaths, and attributable disability-adjusted life-years (DALYs), by age group, sex, year, and location for 84 behavioural, environmental and occupational, and metabolic risks or groups of risks from 1990 to 2017. This study included 476 risk-outcome pairs that met the GBD study criteria for convincing or probable evidence of causation. We extracted relative risk and exposure estimates from 46 749 randomised controlled trials, cohort studies, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. Using the counterfactual scenario of theoretical minimum risk exposure level (TMREL), we estimated the portion of deaths and DALYs that could be attributed to a given risk. We explored the relationship between development and risk exposure by modelling the relationship between the Socio-demographic Index (SDI) and risk-weighted exposure prevalence and estimated expected levels of exposure and risk-attributable burden by SDI. Finally, we explored temporal changes in risk-attributable DALYs by decomposing those changes into six main component drivers of change as follows: (1) population growth; (2) changes in population age structures; (3) changes in exposure to environmental and occupational risks; (4) changes in exposure to behavioural risks; (5) changes in exposure to metabolic risks; and (6) changes due to all other factors, approximated as the risk-deleted death and DALY rates, where the risk-deleted rate is the rate that would be observed had we reduced the exposure levels to the TMREL for all risk factors included in GBD 2017