Molecular characterization of Listeria monocytogenes in bovine milk and evaluating the sensitivity of PCR for direct detection in milk

Food-borne listeriosis, recognized as an emerging bacterial disease of humans and animals worldwide, is caused by L. monocytogenes with at least 95% of the strains isolated from foods and patients belonging to serovars 1/2a, 1/ 2b and 4b. Milk and dairy products were implicated as sources of listeriae in several widely publicized incidents, thus suggesting that the mammary glands of mastitic cattle may be an important reservoir of Listeria. In the present study, 350 bovine milk samples were collected for prevalence and molecular characterization studies of Listeria spp. The isolates were phenotypically and genotypically characterized by biochemical tests, haemolysis on sheep blood agar, CAMP test, PI-PLC assay and multiplex PCR targeting virulence cluster genes namely haemolysin (hlyA), PI-PLC (plcA), actin (actA), p60 (iap) and regulatory (prfA); along with multiplex PCR for typing major serovars targeting lmo0737, ORF2819, ORF2110 and prs genes. Four pathogenic L. monocytogenes were recovered indicating prevalence rate of 1.14% in milk while the overall prevalence rate of Listeria spp. was 1.42%. All the four pathogenic isolates were characterized as L. monocytogenes serotype 4b. Antibiogram of the pathogenic L. monocytogenes isolates revealed sensitivity for amikacin, gentamycin, norfloxacin and doxycyclin. Animal sera (169) screened by indirect ELISA for antibodies against listeriolysin O showed sero-positivity of 7.1%. Sensitivity of PCR for direct detection from milk was evaluated to be 8.8 × 105 L. monocytogenes cells/ml of milk. Thus, the presence of pathogenic strains of L. monocytogenes in raw milk appeared to be a cause for concern with profound public health implications

Search for Doubly-Charged Higgs Bosons at LEP

Doubly-charged Higgs bosons are searched for in e^+e^- collision data collected with the L3 detector at LEP at centre-of-mass energies up to 209 GeV. Final states with four leptons are analysed to tag the pair-production of doubly charged Higgs bosons. No significant excess is found and lower limits at 95% confidence level on the doubly-charged Higgs boson mass are derived. They vary from 95.5 GeV to 100.2 GeV, depending on the decay mode. Doubly-charged Higgs bosons which couple to electrons would modify the cross section and forward-backward asymmetry of the e^+e^- -> e^+e^- process. The measurements of these quantities do not deviate from the Standard Model expectations and doubly-charged Higgs bosons with masses up to the order of a TeV are excluded

Search for R-parity Violating Decays of Supersymmetric Particles in e+e- Collisions at LEP

A search, in e^+e^- collisions, for chargino, neutralino, scalar lepton and scalar quark pair-production is performed, without assuming R-parity conservation in decays, in the case that only one of the coupling constants lambda_ijk or lambda''_ijk is non-negligible. No signal is found in data up to a centre-of-mass energy of 208GeV. Limits on the production cross sections and on the masses of supersymmetric particles are derived

A proteomic survival predictor for COVID-19 patients in intensive care

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

A time-resolved proteomic and prognostic map of COVID-19

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease

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Not AvailableSuggested cropping systems (based on testing under NPOF) 1. Maize-Potato-Summer moong 2. Turmeric-Onion 3. Basmati rice-Wheat-Green manure 4. Maize-Durum wheat-Cowpea (Fodder) 5. Maize-Berseem-Bajra fodder cropping system 6. Maize-Berseem-Maize+cowpea fodder cropping systemNot Availabl

Evaluation of pesticide residues in human blood samples from Punjab (India)

Aim: The present study was undertaken to estimate the current status of residues of organochlorine pesticides (OCPs), organophosphates (OPs) and synthetic pyrethroids (SPs) pesticides in human blood. Materials and Methods: Human blood samples were analyzed by gas chromatography and confirmed by gas chromatographymass spectrometry in selective ion monitoring mode. Results: The gas chromatographic analysis of human blood samples collected from Punjab revealed the presence of p,p’-dichlorodiphenyl dichloroethylene (DDE), p,p’ dichlorodiphenyl dichloroethane (DDD), o,p’ DDE and β-endosulfan at mean levels of 15.26, 2.71, 5.62 and 4.02 ng/ml respectively. p,p’ DDE residue was observed in 18.0% blood samples, and it contributes 55% of the total pesticide burden in human blood. The difference of total dichlorordiphenyl trichloroethane (DDT) between different age groups of humans was found to be statistically significant (p<0.05). The difference of DDT and endosulfan between dietary habits, gender and spraying of pesticides was found statistically non-significant, however endosulfan residues were observed only in pesticide sprayer’s population. Conclusion: Occurrence of p,p’ DDE, p,p’ DDD, o,p’ DDE in human blood indicated restricted use of DDT. However, presence of endosulfan residues in occupationally exposed population is a matter of public health concern