22 research outputs found

    ANTIMICROBIAL SUSCEPTIBILITY AND PREVALENCE OF EXTENDED SPECTRUM BETALACTAMASE (ESBL) AND METALLO BETALACTAMASE (MBL) AND ITS CO-EXISTENCE AMONG PSEUDOMONAS AERUGINOSA RECOVERED FROM OCULAR INFECTIONS

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    Objectives: To evaluate the changing trends in antimicrobial susceptibility rate and detection of ESBL and MBL among Pseudomonas aeruginosa isolated from various ocular infections over a 2 year periods with special reference to detection of ESBL and MBL co-existence among P aeruginosa recovered from ocular infections.Methods: All ocular specimens, culture positive for P aeruginosa (n=110) isolated from clinically suspected patients were submitted to L &T Microbiology Research Centre, Chennai, Tamil Nadu, India. Culture, antimicrobial susceptibility testing and ESBL detection was performed by Standard methods. MBL production was screened by Carbapenem-EDTA combination disk method.Results: Of the 3247 samples subjected to culture from August 2012– July 2014 by standard method 276 were positive for bacterial growth, thereby 8.5% of ocular infections mediated by bacterial pathogens. Out of 276 culture positives 110 (39.8%) Pseudomonas aeruginosa isolates recovered from ocular infections. The resistance rate for commonly used drugs against ocular infection includes Gentamycin [23.63%], Gatifloxacin [20.9%], Moxifloxacin [20%], Tobramycin [20%], ciprofloxacin [19.09%]. Totally 15 (13.63%) out of 110 isolates were identified as ESBL producer and 11 (10%) out of 110 isolates were identified as MBL producer by screening test, including 7 isolates have co-produced both ESBL and MBL enzymes and 4 isolates were only positive for MBL production.Conclusion: Though fluoroquinolones remains a good choice for ocular Pseudomonal infection. Gradual emergence of resistance to fluoroquinolones and aminoglycosides also noted from this study. The emergence of ESBL, MBL and pandrug resistance among P aeruginosa from ocular infections is an alarm rational finding which necessitates the earlier detection of both ESBL and MBL production as individual or co-existence in ocular isolates, which may pave the way for appropriate therapy for sight threatening conditions like endophthalmitis.Â

    Application of PCR-based DNA sequencing technique for the detection of Leptospira in peripheral blood of septicemia patients

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    ABSTRACT Aim: Isolation, dark field detection and microscopic agglutination test (MAT) are considered -gold standard‖ tests for diagnosis of Leptospirosis. Several PCR assays are reported but very few have been evaluated for detection of Leptospirosis. Therefore, this study was undertaken. This study aims to design and standardize polymerase chain reaction (PCR) -based DNA sequencing technique for the detection of pathogenic Leptospira from peripheral blood of patients clinically diagnosed with septicemia. Methodology and Results: Two hundred and seven (207) blood samples from patients were diagnosed with septicemia which includes 100 bacterial (other than Leptospira) culture positive and 107 bacterial culture negative samples were studied. Primers for Nested PCR targeting LipL32 gene of Leptospira interrogans were designed and the specificity of primers was tested against serum samples positive/negative by either MAT or dark field microscopy. PCR amplified products were further confirmed by DNA sequencing. The standardized nPCR was sensitive and specific to Leptospira interrogans. Twenty-one (21%) out of 100 culture positive blood samples, three (2.8%) out of 107 culture negative samples showed nPCR positivity and were confirmed as Leptospira interrogans by DNA sequencing (p<0.001). A sensitive nPCR specific to Leptospira interrogans was developed. Conclusion, significance and impact of study: The p value (<0.001) signifies that Leptospira is commonly associated with other bacteria circulating in blood indicating that a decreased immune status is created primarily by a bacterium with enhanced possibility of development of Leptospiral infection probably be of an endogenous origin

    Hepatitis C Virus NS3 Mediated Microglial Inflammation via TLR2/TLR6 MyD88/NF-κB Pathway and Toll Like Receptor Ligand Treatment Furnished Immune Tolerance

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    <div><p>Background</p><p>Recent evidence suggests the neurotrophic potential of hepatitis C virus (HCV). HCV NS3 protein is one of the potent antigens of this virus mediating inflammatory response in different cell types. Microglia being the immune surveillance cells in the central nervous system (CNS), the inflammatory potential of NS3 on microglia was studied. Role of toll like receptor (TLR) ligands Pam2CSK3 and Pam3CSK4 in controlling the NS3 mediated microglial inflammation was studied using microglial cell line CHME3.</p><p>Methods</p><p>IL (Interleukin)-8, IL-6, TNF-α (Tumor nicrosis factor alpha) and IL-1β gene expressions were measured by semi quantitative RT-PCR (reverse transcription-PCR). ELISA was performed to detect IL-8, IL-6, TNF-α, IL-1β and IL-10 secretion. FACS (Flourescent activated cell sorting) was performed to quantify TLR1, TLR2, TLR6, MyD88 (Myeloid differntiation factor 88), IkB-α (I kappaB alpha) and pNF-κB (phosphorylated nuclear factor kappaB) expression. Immunofluorescence staining was performed for MyD88, TLR6 and NF-κB (Nuclear factor kappaB). Student's t-test or One way analysis of variance with Bonferoni <i>post hoc</i> test was performed and p < 0.05 was considered significant.</p><p>Results</p><p>Microglia responded to NS3 by secreting IL-8, IL-6, TNF-α and IL-1β via TLR2 or TLR6 mediated MyD88/NF-κB pathway. Transcription factor NF-κB was involved in activating the cytokine gene expression and the resultant inflammatory response was controlled by NF-κB inhibitor, Ro106-9920, which is known to down regulate pro-inflammatory cytokine secretion. Activation of the microglia by TLR agonists Pam3CSK4 and Pam2CSK4 induced immune tolerance against NS3. TLR ligand treatment significantly down regulated pro-inflammatory cytokine secretion in the microglia. IL-10 secretion was suggested as the possible mechanism by which TLR agonists induced immune tolerance. NS3 as such was not capable of self-inducing immune tolerance in microglia.</p><p>Conclusion</p><p>In conclusion, NS3 protein was capable of activating microglia and the inflammatory response could be controlled via blocking the transcription factor NF-κB, or by treating the microglia with TLR ligands which likely function via secreting anti-inflammatory cytokines such as IL-10. This can have therapeutic potential in controlling HCV mediated neuroinflammation.</p></div

    TLR6 was involved in NS3 mediated microglial activation.

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    <p>Microglia was exposed to 20 ng/ml of NS3 for different time points and cells were stained for TLR6 protein expression. <b>(A-B)</b> Flow cytometry results indicates that TLR6 expression was significantly high from 1 hr to 3 hr time point. <b>(C)</b> Also NS3 exposed microglia stained brighter for TLR6 at 3 hr time point compared to the control cells. <b>(D-F)</b> TLR6 expression was silenced by TLR6 specific siRNA and the <b>(G)</b> TLR6 silenced cells secreted significantly less IL-6. Data is expressed as mean (n = 3) ± SE. * p < 0.05. Scale bar corresponds to 50 micron.</p

    TLRs signalled via MyD88/NF-kB pathway.

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    <p><b>(A-B)</b> MyD88 was up regulated only during 1 hr time point and there was no significant difference observed at later time points. <b>(C)</b> Fluorescent signal for MyD88 was visually high in NS3 exposed microglia at 1 hr time point. <b>(D)</b> Nuclear translocation of NF-kB was observed at 1 hr time point for NS3 exposed cells. Flow cytometry data shows that <b>(E-F)</b> pNF-kB protein expression was significantly upregulated and <b>(G-H)</b> IkB-α protein expression was significantly downregulated from 1 hr to 24 hr exposure to NS3. <b>(I-J)</b> CHME3 cells were pre-treated with NF-kB inhibitor, Ro 106–9920 for 16 hrs before being exposed to 20 ng/ml of NS3. Flow cytometry data shows that pNF-kB expression in 10 μM and 100 μM NF-kB inhibitor treated microglia were significantly less during NS3 exposure at 6 hr time point compared to NS3 alone exposed cells. Immunofluorescence data is representative of 3 independent experiments, scale bar represents 50 micron. The data is expressed as mean (n = 3) ± SE, * p < 0.05, ^ p < 0.05.</p

    HCV NS3 induced pro inflammatory cytokine gene expression in microglia.

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    <p>CHME3 cells were exposed to 20 ng/ml of HCV NS3 for different time points and RT-PCR was performed to detect the cytokine gene expression at various time points. <b>(A)</b> IL-8, IL-6, TNF-α, IL-1β mRNA was expressed in the microglial cells exposed to NS3. The gel OD values were measured by image J and were expressed as cytokine OD values normalized to that of the corresponding GAPDH. <b>(B-D)</b> Microglia expressed IL-8 mRNA from 3 hr to 24 hr, <b>(C)</b> IL-6 mRNA was expressed from 3 hr to 24 hr, <b>(D)</b> TNF-α was expressed from 6 hr to 24 hr and <b>(D)</b> IL-1β was expressed from 3 hr to 24 hr. Lanes 1–8 represents control, NS3 treated cells at 1 hr, 3 hr, 6 hr, 12 hr, 24 hr, negative control (NC), 100 bp ladder in that order. The data is expressed as mean (n = 3) ± SE, * p < 0.05, ** p < 0.01.</p

    Pam3CSK4 induced immune tolerance against HCV NS3 mediated inflammation.

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    <p>Microglia was pre treated with 50 ng/ml of Pam3CSK4 for 16 hrs. RT-PCR and ELISA was performed to study the immune tolerance mediated by Pam3CSK4. <b>(A, B, C and D)</b> Pam3CSK4 treatment completely blocked IL-8, IL-6 and TNF-α gene expression and IL-1β gene expression was down regulated compared to positive control (PC NC represents the PCR reagent negative control. <b>(E, F, G and H)</b> TNF-α and IL-1β were secreted at a significant level in the Pam3CSK4 treated cells even after replacing the cells with fresh growth medium without agonist (## p< 0.01). TNF-α and IL-1β were significantly upregulated in Pam3CSK4 + NS3 treated cells compared to Pam3CSK4 exposed cells (^ p < 0.05, ^^ p < 0.01). There was a significant down regulation for IL-8 and other 3 cytokines in the Pam3CSK4 + NS3 treated cells compared to NS3 alone treated cells (** p < 0.01). The data is expressed as mean (n = 3) ± SE.</p
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