Har bakterieidentifikasjon og resistensbestemmelse innvirkning på valg av antibiotikaregime ved bakteriemi? : en studie gjort ved Universitetssykehuset i Nord- Norge

Bakgrunn: Antibiotikaresistens er i dag blitt et signifikant problem på grunn av vårt misbruk – både overforbruk, underforbruk, feilbruk, manglende compliance, inadekvat dosering og ikke minst en likegyldighet til bruk. For alvorlige infeksjonssykdommer som sepsis kan dette gi dramatiske konsekvenser, da man er avhengig av effektive terapimidler da sykdommen er forbundet med høy morbiditet og mortalitet. Selv om Norge enda er et av de landene hvor resistensproblemet er minst så er det viktig at det tas på alvor, og at terapi følger gitte retningslinjer - blant annet ved sepsis hvor en initial bredspektret empirisk terapi raskest mulig bør endres til mer smalspektret terapi for å redusere risikoen for resistensutvikling. Formål: Formålet med denne studien var å undersøke om diagnostisk informasjon i form av mikrobiologisk bakterieidentifikasjon og resistensbestemmelse førte til endringer av bredspektret empirisk terapi ved bakteriemi. Karakterisering av endringer gjort og behandlingsregimer, og vurdere disse i forhold til gitte retningslinjer utarbeidet ved UNN i 2005 var også et mål med studien. Studien ble utført ved Avdeling for Mikrobiologi og Smittevern ved UNN, og sammenlignet med tilsvarende studie utført ved UNN i 2005 og ved St. Olavs Hospital i 2003. Metode: Studien er en deskriptiv observasjonsstudie basert på retrospektive data. Studiepopulasjonen var pasienter innlagt på UNN og med positive blodkulturer i 2006. Pasientjournaler og mikrobiologiske arbeidsskjemaer ble gjennomgått, og det ble registrert antibiotikabehandling og mikrobiologiske besvarelser for gitte registreringsperioder. Resultater: Av 276 episoder med reell bakteriemi ble det registrert endringer i terapiregimene i 34,1 % av besvarte episoder etter mikroskopifunn, 36,2 % etter preliminær resistensbestemmelse og 12,0 % etter definitiv resistensbestemmelse. Ved mistenkt bakteriemi var monoterapi med cefalosporiner eller kombinasjonsterapi med penicilliner og aminoglykosider mest fremtredende. Etter at mikrobiologiske data forelå bestod behandlingen av episoder med gram negative bakterier i hovedsak av monoterapi med cefalosporiner eller kombinasjonsterapi med enten penicilliner og aminoglykosider eller cefalosporiner og aminoglykosider. Behandlingen av episoder med gram positive bakterier bestod i hovedsak av monoterapi med penicilliner eller kombinasjonsterapi med penicilliner og aminoglykosider. Konklusjon: Resultatene tyder på at mikrobiologiske data hadde innvirkning på valg av antibiotikaregime ved bakteriemi. Det ble benyttet empirisk terapi anbefalt i antibiotikaveilederen i større grad ved UNN i 2006 enn ved UNN i 2002 og St. Olavs Hospital i 2002. Andelen monoterapi økte ved UNN fra 2002 til 2006. Resultatene av studien gir inntrykk av at terapien endret seg i henhold til bakterieidentifikasjon og resistensbestemmelse

A flow cytometry technique to study intracellular signals NF-κB and STAT3 in peripheral blood mononuclear cells-2

<p><b>Copyright information:</b></p><p>Taken from "A flow cytometry technique to study intracellular signals NF-κB and STAT3 in peripheral blood mononuclear cells"</p><p>http://www.biomedcentral.com/1471-2199/8/64</p><p>BMC Molecular Biology 2007;8():64-64.</p><p>Published online 31 Jul 2007</p><p>PMCID:PMC1949834.</p><p></p>) activation (versus untreated) from B-cells, T-lymphocytes and monocytes/macrophages. PBMCs were stimulated for the appropriate time and concentration of sCD40L (A) and IL10 (B) (as identified previously). The graphs represent the difference in percentage of phosphorylated nuclear factor between stimulated and untreated cells. Statistical significance (wilcoxon paired test; p < 0.05) was represented by an asterisk (*). Data represented the mean (± SD) of seven experiments

Identification of pathology markers.

<p>Expression of COL4A5 (collagen type IV, alpha-5), DCN (decorin), SERPINH1 (serpin peptidase inhibitor, H1), CTGF (connective tissue growth factor) and TRPC6 (transient receptor potential cation channel C6) in normal tissue (open columns C, n = 64) and pathological biopsies (hatched columns): DN, diabetic nephropathy (n = 7); MN, membranous nephropathy (n = 7); IgAN, IgA nephropathy (n = 5); LN, lupus nephropathy (n = 7). Data were standardized using either the reference gene RPLP1 (left, open columns) or SF (right, grey columns), and results are expressed as percent ± SE of normal group. Statistical comparisons with normal groups were performed by one way ANOVA followed by Holm-Sidak test: *, p<0.01; **, p<0.005; ***, p<0.001.</p

Occurrence in glomerular and tubular SAGE libraries from human kidneys of the specific tags of genes analyzed in this study.

<p>Data, from Chabardès-Garonne <i>et al</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007779#pone.0007779-ChabardesGaronne1" target="_blank">[11]</a>. correspond to tag counts normalized to 50,000 total tags per library. Human Genome Organization (HUGO) gene symbol is followed by a usual name and the RefSeq identification. SAGE data are available at GEO (<a href="http://www.ncbi.nlm.nih.gov/geo/" target="_blank">www.ncbi.nlm.nih.gov/geo/</a>) (GSM10419, GSM10423, GSM10426 and GSM10428). Tag sequences are also provided.</p

Localization of gene expression.

<p>Determination of the composition of kidney samples may serve to localize gene expression along the nephron through comparison of expression levels in subgroups of samples with different enrichment in a given structure. This is illustrated for DUSP9 (Dual-specificity phosphatase 9), MUC1 (Mucin 1), GSTA1 (Glutathione S-transferase α1) and DCN (decorin), by comparing (<b>A</b>) eight subgroups made of the ten samples with the lowest (hatched columns) and the highest (full columns) proportions of G (yellow), PCT (blue), cTAL (green) and CCD (red), (<b>B</b>) subgroups with similar cTAL content but different CCD content (groups 1 (n = 5) and 2 (n = 5)) or with similar CCD content but different cTAL content (groups 2 and 3 (n = 5)), and (<b>C</b>) subgroups with similar G content but different PCT content (groups 1(n = 6) and 2 (n = 4)) or with similar PCT content but different G content (groups 2 and 3 (n = 8)). Results indicate that <b>A</b>: DUSP9 and MUC1 were preferentially expressed in cTAL- and CCD-rich samples whereas GSTA1 and DCN were preferentially expressed in G- and PCT-rich samples. Statistical differences between groups: *, p<0.05; **, p<0.001; <b>B</b>: DUSP9 was preferentially expressed in cTAL whereas MUC1 was expressed in both cTAL and CCD because DUSP9 expression increased with cTAL content but not with CCD content, whereas MUC1 expression increased with both cTAL and CCD contents. Statistical comparison was performed between groups 1 and 2 and groups 2 and 3: *, p<0.01; **p<0.005; and <b>C</b>: GSTA1 and DCN were preferentially expressed in PCT and G respectively. Statistical comparison was performed between groups 1 and 2 and groups 2 and 3: **p<0.005. These conclusions are consistent with known expression profiles of these four genes (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007779#pone-0007779-t001" target="_blank">table 1</a>).</p

Expression of structure-specific markers in human kidney.

<p>Relationship between the expression levels of specific markers of glomerulus (PODXL and WT1), proximal tubule (ALDOB and SLC13A3), cortical thick ascending limb of Henle's loop (SLC12A1 and UMOD) and CCD (AQP2 and FXYD4) in >60 normal (closed symbols) and >40 pathological kidney samples (open symbols). For each structure, the slopes of the regression lines corresponding to normal (full line, equation and R² at bottom right) and pathological samples (dotted line, equation and R² value at top left) were not statistically different (t test).</p

Structural heterogeneity of samples.

<p>TCA-computed fractional volumes of proximal convoluted tubules (PCT, blue), glomeruli (G, yellow), cortical thick ascending limbs of Henle's loop (cTAL, green) and aldosterone-sensitive distal nephron (CCD, red) in 94 normal samples (A), 36 pathological needle biopsies (B) and twenty fragments of normal tissue from a same patient (C). Samples are ranged according to increasing fractional volume of PCTs.</p

Data standardization.

<p>Expression of NPHS2 (podocin), GSTA1 (Glutathione S-transferase α1), DUSP9 (Dual-specificity phosphatase 9), PTGER1 (Prostaglandin E receptor 1), KCNJ1 (Renal outer medullary potassium channel, ROMK1) and MUC1 (Mucin 1) in 60 normal samples was standardized either by the reference gene RPLP1 (blue points) or by SF (pink points). Similar results were obtained when using RPL19 or PPIA as reference genes. Data are expressed as fold of the lowest value, and samples are ranged according to increasing SF-standardized values. Values in the graphs are the variances for RPL1-normalized (blue) and SF-normalized data (pink).</p

Validation of TCA by histo-morphometric analysis of kidney tissue composition.

<p><b>A</b>. Overview of three kidney serial sections stained with anti-uromodulin antibody (I), anti-AQP2 antibody (II), and toluidine blue (III, micrographs after LCM). The zones (1 to 4) used for analysis are delineated. <b>B</b>. Higher magnification images of zones 4 (before LCM for section III). <b>C</b>. Image analysis of zone 4 allowing the construction of the color-coded image (G, yellow; PCTs, blue; cTAL, green; CCDs, red; grey, remaining tissue) used for determination of the surface area of the different compartments. <b>D</b>. Relationship between TCA-computed and measured fractional volumes of the 4 compartments in 8 samples analyzed as described in C (same color code as in C). The slope of the regression line was not statistically different from 1 (t test).</p

Sequence of nucleotide primers used for PCR.

<p>Specific primers were designed using the Light Cycler Probe Design software. Gene symbols are from HUGO.</p