Development of polymerase chain reaction for detection of predominant streptococcal isolates causing subclinical bovine mastitis

208-212Bovine mastitis is the most important source of loss for the growing dairy industry. Streptococci, with special reference to Streptococcus agalactiae, S. dysgalactiae and S. uberis, are the predominant pathogens causing bovine mastitis. A rapid, sensitive and specific test for the detection of these pathogens needs to be developed. To accomplish this, initially 163 milk samples were collected from various organized and unorganized sectors in and around Bangalore, India. These milk samples were screened for subclinical mastitis by somatic cell counting (SSC) and electro conduction (EC). Of these, 131 samples selected based on SCC and EC values were subjected for isolation of the organisms. Two sets of specific primers, targeting streptococcal 16S rRNA gene were designed for detection of S. agalactiae, S. dysgalactiae and S. uberis. The results of the study showed S. agalactiae as the predominant streptococci among the generally identified streptococcal species associated with subclinical bovine mastitis in dairy cattle in and around Bangalore. </span

Effectiveness of theobromine on enamel remineralization: a comparative in-vitro study

BackgroundRemineralizing agents demonstrate potential to reverse early carious lesions. Theobromine containing dentifrices claim to remineralize enamel lesions effectively. The aim of this in-vitro study was to evaluate and compare the remineralization potential of dentifrices containing theobromine, 0.21% sodium fluoride (NaF) with functionalized tricalcium phosphate (f-TCP) and amine fluoride on artificial enamel caries.Materials and methodsSound extracted human premolars were demineralized to produce deep artificial carious lesions. The teeth were sectioned longitudinally and allocated to three treatment groups with nine specimens in each group: Group A (NaF + f-TCP), Group B (amine fluoride), and Group C (theobromine). The specimens were then subjected to pH cycling for seven days. Confocal laser scanning microscopy (CLSM) was utilized to record the patterns of demineralization and remineralization. One-way ANOVA and paired t-test were used to analyze changes in lesion depth. The level of significance was set at

Application and Comparative Evaluation of Fluorescent Antibody, Immunohistochemistry and Reverse Transcription Polymerase Chain Reaction Tests for the Detection of Rabies Virus Antigen or Nucleic Acid in Brain Samples of Animals Suspected of Rabies in India

Accurate and early diagnosis of animal rabies is critical for undertaking public health measures. Whereas the direct fluorescent antibody (DFA) technique is the recommended test, the more convenient, direct rapid immunochemistry test (dRIT), as well as the more sensitive, reverse transcription polymerase chain reaction (RT-PCR), have recently been employed for the laboratory diagnosis of rabies. We compared the three methods on brain samples from domestic (dog, cat, cattle, buffalo, horse, pig and goat) and wild (leopard, wolf and jackal) animals from various parts of India. Of the 257 samples tested, 167 were positive by all the three tests; in addition, 35 of the 36 decomposed samples were positive by RT-PCR. This is the first study in which such large number of animal samples have been subjected to the three tests simultaneously. The results confirm 100% corroboration between DFA and dRIT, buttress the applicability of dRIT in the simple and rapid diagnosis of rabies in animals, and reaffirm the suitability of RT-PCR for samples unfit for testing either by DFA or dRIT

Not Available

Not AvailableSubclinical mastitis (SCM) represents a major proportion of the burden of mastitis. Determining somatic cell count (SCC) and electrical conductivity (EC) of milk are useful approaches to detect SCM. In order to correlate grades of SCM with the load of five major mastitis pathogens, 246 milk samples from a handful of organized and unorganized sectors were screened. SCC (>5 × 105/mL) and EC (>6.5 mS/cm) identified 110 (45 %) and 153 (62 %) samples, respectively, to be from SCM cases. Randomly selected SCM-negative samples as well as 186 samples positive by either SCC or EC were then evaluated for isolation of five major mastitis-associated bacteria. Of the 323 isolates obtained, 95 each were S. aureus and coagulase-negative staphylococci (CoNS), 48 were E. coli and 85 were streptococci. There was no association between the distribution of organisms and (a) the different groups of SCC, or (b) organised farms and unorganised sectors. By contrast, there was a significant difference in the distribution of CoNS, and not other species, between organized farms and unorganized sectors. In summary, bacteria were isolated irrespective of the density of somatic cells or the type of farm setting, and the frequency of isolation of CoNS was higher with organized farms. These results suggest the requirement for fine tuning SCC and EC limits and the higher probability for CoNS to be associated with SCM in organized diary sectors, and have implications for the identification, management and control of mastitis in India.Not Availabl

Polymerase chain reaction for the identification of bacteria.

<p>Genomic DNA was isolated from the obtained isolates as well as reference strains, and subjected to mono- or multi-plex PCR as described in the Materials and Methods and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0142717#pone.0142717.t001" target="_blank">Table 1</a>. The experiments were repeated at least three times and representative gel pictures are shown. Note that each panel is composed from two separate gels since all the samples could not be accommodated in a single gel. <b>(A) PCR for genus-specific <i>tuf</i> genes of streptococci and staphylococci.</b> Lane designation: M, 100 bp ladder; 1–5, <i>Streptococcus</i> spp. isolates; 6, Reference strain Streptococcus AD1; 7, No template control for streptococcus; 8, Negative control (<i>S</i>. <i>aureus</i>, <i>E</i>. <i>coli</i>); 9, Reagent control; 10, Reference strain <i>S</i>. <i>aureus</i> 96; 11, No template control for staphylococcus; 12–18: <i>Staphylococcus</i> spp. isolates. <b>PCR for <i>S</i>. <i>aureus nuc</i> (lanes 1–11) and <i>E</i>. <i>coli alr</i> (lanes 12–21) genes.</b> Lane designation: M, 100 bp ladder; 1–8, <i>S</i>. <i>aureus</i> test isolates; 9, Reference strain SAU-3; 10, Negative control (<i>E</i>. <i>coli</i>); 11, No template control; 12, Negative control (<i>S</i>. <i>aureus</i>); 13, Reference strain EC11 (<i>E</i>. <i>coli</i>); 14–16, Test isolates of <i>E</i>. <i>coli</i>; 17, No template control; 18–20, Test isolates; 21, Negative control (streptococcus). <b>(B) PCR for the identification of CoNS species.</b> Lane designation: M, 100 bp ladder; 1, <i>S</i>. <i>haemolyticus</i> (MTCC 3383) control; 2, <i>S</i>. <i>sciuri</i> (MTCC 6154) control; 3, <i>S</i>. <i>saprophyticus</i> (MTCC 6155) control; 4, <i>S</i>. <i>arlettae</i> (JQ764624) control; 5, <i>S</i>. <i>chromogenes</i> (MTCC 3545) control; 6, <i>S</i>. <i>sciuri</i> (MTCC 6154) control; 7, <i>S</i>. <i>xylosus</i> (FJ90627.1) control; 8, <i>S</i>. <i>simulans</i> (AF495498.1) control; 9, <i>S</i>. <i>epidermidis</i> (MTCC 3615) control; 10, <i>S</i>. <i>haemolyticus</i> (MTCC 3383) control; 11, <i>S</i>. <i>sciuri</i> (MTCC 6154) control; 12, <i>S</i>. <i>saprophyticus</i> (MTCC 6155) control; 13, <i>S</i>. <i>arlettae</i> (JQ764624) control; 14, <i>S</i>. <i>chromogenes</i> (MTCC 3545) control; 15, <i>S</i>. <i>sciuri</i> (MTCC 6154) control; 16, <i>S</i>. <i>simulans</i> (AF495498.1) control; 17, <i>S</i>. <i>xylosus</i> (FJ90627.1) control; 18, <i>S</i>. <i>epidermidis</i> (MTCC 3615) control. This Panel represents two mutually exclusive pictures depicting the results of the standardization of one tube each of the two-tube multiplex PCR. In the left panel, primers for <i>S</i>. <i>arlettae</i>, <i>S</i>. <i>chromogenes</i>, <i>S</i>. <i>sciuri</i>, <i>S</i>. <i>epidermidis</i> and <i>S</i>. <i>saprophyticus</i> were used, and <i>S</i>. <i>haemolyticus</i>, <i>S</i>. <i>xylosus</i> and <i>S</i>. <i>simulans</i> DNA served as negative controls. In the right panel, primers for <i>S</i>. <i>equorum</i>, <i>S</i>. <i>haemolyticus</i>, <i>S</i>. <i>xylosus</i>, <i>S</i>. <i>simulans</i> and <i>S</i>. <i>fluerettii</i> were used, and <i>S</i>. <i>sciuri</i>, <i>S</i>. <i>sapryphyticus</i>, <i>S</i>. <i>arlettae</i>, <i>S</i>. <i>chromogenes</i> and <i>S</i>. <i>epidermidis</i> DNA served as negative controls. Numbers in parentheses indicate the GenBank Accession numbers or the MTCC culture designations. <b>(C) PCR for the identification of <i>Streptococcus</i> species.</b> Lane designation: M, 100 bp ladder; 1–20, Test streptococcal isolates streptococci (no amplification); 21, Negative control (<i>S</i>. <i>aureus</i>); 22, Negative control (<i>E</i>. <i>coli</i>); 23 & 24, No template control; 25, Tube 2 positive control (<i>Streptococcus</i> reference strain AD3); 26, Tube 1 positive controls (<i>Streptococcus</i> reference strains AD1 and AD6).</p

Isolation, biochemical and molecular identification, and in-vitro antimicrobial resistance patterns of bacteria isolated from bubaline subclinical mastitis in South India

Buffaloes are the second largest source of milk. Mastitis is a major impediment for milk production, but not much information is available about bubaline mastitis, especially subclinical mastitis. The aim of this study was to (a) investigate the application of various tests for the diagnosis of bubaline subclinical mastitis, (b) identify the major bacteria associated with it, and (c) evaluate the antibiotic resistance pattern of the bacteria. To this end, 190 quarter milk samples were collected from 57 domesticated dairy buffaloes from organized (64 samples) and unorganized (126 samples) sectors. Of these, 48.4%, 40.0%, 45.8%, 61.1%, and 61.6% were positive for subclinical mastitis by somatic cell count, electrical conductivity, California mastitis test, bromothymol blue test, and N-acetyl glucosaminidase test, respectively. As compared to the gold standard of somatic cell count, California mastitis test performed the best. However, a combination of the two methods was found to be the best option. Microbiological evaluation, both by biochemical methods as well as by monoplex and multiplex polymerase chain reaction, revealed that coagulase-negative staphylococci were the most predominant (64.8%) bacteria, followed by streptococci (18.1%), Escherichia coli (9.8%) and Staphylococcus aureus (7.3%). Most of the pathogens were resistant to multiple antibiotics, especially to β-lactam antibiotics. We propose that California mastitis test be combined with somatic cell count for diagnosis of subclinical mastitis in domestic dairy buffaloes. Further, our results reveal high resistance of the associated bacteria to the β-lactam class of antibiotics, and a possible major role of coagulase-negative staphylococci in causing the disease in India

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

BackgroundFuture trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050.MethodsUsing forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline.FindingsIn the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]).InterpretationGlobally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions.FundingBill & Melinda Gates Foundation.</p