The ischemia-modified albumin in relation to pacemaker and defibrillator implantation

BACKGROUND: Ischemia-modified albumin (IMA) is considered a marker of myocardial ischemia whereas cardiac enzymes are released when cardiac necrosis occurs. It has previously been shown that permanent pacemaker-defibrillator insertion is associated with myocardial injury expressed as cardiac enzyme rise. OBJECTIVE: We assessed whether pacemaker-defibrillator implantation also induces changes in IMA plasma levels and whether, therefore, myocardial ischemia precedes necrosis. METHODS: We studied 64 consecutive patients undergoing pacemaker or defibrillator implantation; 43 were men and 21 women and their age was 70 +/- 11 years (range 23-84 years). Blood samples were collected at baseline, six hours and 48 hours following the procedure. IMA measured by the albumin cobalt binding test (ACB, Integra 800 analyzer), as well as creatine kinase (CK), the MB isoenzyme of creatine kinase (CK-MB) and cardiac troponin I (Tn-I) were evaluated. RESULTS: Data analysis showed that compared to baseline measurements, IMA increased at six hours (P = 0.015) and at 48 hours (P = 0.003)[97.6 +/- 10.2 vs 101.4 +/- 10.7 vs 102.1 +/- 9.2 U/mL at baseline, six hours and 48 hours, respectively]; similarly, CK increased at six hours (P = 0.0001) and remained high at 48 hours (P = 0.0001) [74.9 +/- 49.9 vs 136.1+/-186.7 vs 115.2 +/- 63.9 mIU/mL], while CK-MB increased at six hours (P = 0.0001), but returned to baseline values at 48 hours (P = 0.05) [0.90 +/- 0.89 vs 1.27 +/- 134 vs 0.71 +/- 0.63 ng/mL] and Tn-I increased at six hours (P = 0.0001) and returned to baseline levels at 48 hours (P = 0.32) [0.057 +/- 0.23 vs 0.16 +/- 0.36 vs 0.03 +/- 0.045 ng/mL]. CONCLUSION: Permanent pacemaker-defibrillator insertion is associated with myocardial ischemia and necrosis.     

Expression of hypoxia-inducible carbonic anhydrase-9 relates to angiogenic pathways and independently to poor outcome in non-small cell lung cancer.

Carbonic anhydrase-9 (CA9), a transmembrane enzyme with an extracellular active site, is involved in the reversible metabolism of the carbon dioxide to carbonic acid. Up-regulation of CA by hypoxia and the hypoxia-inducible factor (HIF) pathway has been recently postulated (Wykoff et al. Cancer Res., 60: 7075-7083, 2000). In the present study we examined the expression of this enzyme in non-small cell lung cancer. Of 107 cases analyzed, 39 (36.4%) had strong membrane/cytoplasmic expression of CA9 and were grouped as positive. The staining was confined around areas of necrosis, and a significant association of CA9 expression with the extent of necrosis was noted (P = 0.004). Nevertheless, 38 of 74 cases with focal or extensive necrosis did not express CA9. CA9 expression was more frequent in the squamous cell histology (P = 0.001) and with advanced T stage (P = 0.009). A significant coexpression of CA9 with platelet-derived endothelial cell growth factor and basic fibroblast growth factor receptor expression was noted. Double staining of CA9 with anti-CD31 monoclonal antibody revealed an overall higher microvessel density in the areas expressing CA9 than in negative areas (P = 0.0005). Thirty-one of 38 CA9-positive cases were positive for HIF1a/HIF2a, but HIF positivity was a more common event (68 of 107) and their patterns of expression were diffuse (not confined in the necrotic areas). A direct association of CA9 expression with epidermal growth factor receptor, c-erbB-2, and MUC1 expression was also noted (P < 0.04). Survival analysis showed that CA9 expression is related to poor prognosis. CA9 expression in tumors with low vascularization defined a prognosis similar to the one of patients with highly angiogenic tumors. Multivariate analysis revealed that CA9 expression is a significant prognostic factor independent of angiogenesis. We conclude that CA9 is an important molecule in non-small cell lung cancer, the up-regulation of which occurs in highly hypoxic/necrotic regions of the tumors. The expression of CA9 is linked to the expression of a constellation of proteins involved in angiogenesis, apoptosis inhibition, and cell-cell adhesion disruption, which explains the strong association of CA9 with poor outcome.     

Epstein-Barr virus latent membrane protein 1 activation of NF-kappaB through IRAK1 and TRAF6

Epstein-Barr virus latent membrane protein 1 (LMP1) activation of NF-kappaB is critical for Epstein-Barr virus-infected B lymphocyte survival. LMP1 activates the IkappaB kinase complex and NF-kappaB through two cytoplasmic signaling domains that engage tumor necrosis factor receptor-associated factor (TRAF)1/2/3/5 or TRADD and RIP. We now use cells lacking expression of TRAF2, TRAF5, TRAF6, IKKalpha, IKKbeta, IKKgamma, TAB2, IL-1 receptor-associated kinase (IRAK)1, or IRAK4 to assess their roles in LMP1-mediated NF-kappaB activation. LMP1-induced RelA nuclear translocation was similar in IKKalpha knockout (KO) and WT murine embryo fibroblasts (MEFs) but substantially deficient in IKKbeta KO MEFs. NF-kappaB-dependent promoter responses were also substantially deficient in IKKbeta KO MEFs but were hyperactive in IKKalpha KO MEFs. More surprisingly, NF-kappaB responses were near normal in TRAF2 and TRAF5 double-KO MEFs, IKKgamma KO MEFs, TAB2 KO MEFs, and IRAK4 KO MEFs but were highly deficient in TRAF6 KO MEFs and IRAK1 KO HEK293 cells. Consistent with the importance of TRAF6, LMP1-induced NF-kappaB activation in HEK293 cells was inhibited by expression of dominant-negative TAB2 and Ubc13 alleles. These data extend a role for IKKalpha in IKKbeta regulation, identify an unusual IKKbeta-dependent and IKKgamma-independent NF-kappaB activation, and indicate that IRAK1 and TRAF6 are essential for LMP1-induced NF-kappaB activation.     

From the Cover: Spatiotemporal variations of seismicity before major earthquakes in the Japanese area and their relation with the epicentral locations

Using the Japan Meteorological Agency earthquake catalog, we investigate the seismicity variations before major earthquakes in the Japanese region. We apply natural time, the new time frame, for calculating the fluctuations, termed β, of a certain parameter of seismicity, termed κ₁. In an earlier study, we found that β calculated for the entire Japanese region showed a minimum a few months before the shallow major earthquakes (magnitude larger than 7.6) that occurred in the region during the period from 1 January 1984 to 11 March 2011. In this study, by dividing the Japanese region into small areas, we carry out the β calculation on them. It was found that some small areas show β minimum almost simultaneously with the large area and such small areas clustered within a few hundred kilometers from the actual epicenter of the related main shocks. These results suggest that the present approach may help estimation of the epicentral location of forthcoming major earthquakes.In this study, we investigate the evolution of seismicity shortly before main shocks in the Japanese region, N2546E125148, using Japan Meteorological Agency (JMA) earthquake catalog as in ref 1. For this, we adopted the new time frame called natural time since our previous works using this time frame made the lead time of prediction as short as a few days (see below). For a time series comprising N earthquakes (EQs), the natural time χ_k is defined as χ_k = k/N, where k means the k^th EQ with energy Q_k (Fig. 1). Thus, the raw data for our investigation, to be read from the earthquake catalog, are χ_k = k/N and pk=Qk/∑n=1NQn, where p_k is the normalized energy. In natural time, we are interested in the order and energy of events but not in the time intervals between events.Open in a separate windowFig. 1.EQ sequence in (A) conventional time and (B) natural time. In B, Q_k is given in units of the energy ε corresponding to a 3.5M_JMA EQ.We first calculate a parameter called κ₁, which is defined as follows (2, 3), from the catalog.κ1=∑k=1Npkχk2−(∑k=1Npkχk)2=〈χ2〉−〈χ〉2.[1]We start the calculation of κ₁ at the time of initiation of Seismic Electric Signals (SES), the transient changes of the electric field of Earth that have long been successfully used for short-term EQ prediction (4, 5). The area to suffer a main shock is estimated on the basis of the selectivity map (4, 5) of the station that recorded the corresponding SES. Thus, we now have an area in which we count the small EQs of magnitude greater than or equal to a certain magnitude threshold that occur after the initiation of the SES. We then form time series of seismic events in natural time for this area each time a small EQ occurs, in other words, when the number of the events increases by one. The κ₁ value for each time series is computed for the pairs (χ_k,p_k) by considering that χ_k is “rescaled” to χ_k = k/(N +1) together with rescaling pk=Qk/∑n=1N+1Qn upon the occurrence of any additional event in the area. The resulting number of thus computed κ₁ values is usually of the order 10² to 10³ depending, of course, on the magnitude threshold adopted for the events that occurred after the SES initiation until the main shock occurrence. When we followed this procedure, it was found empirically that the values of κ₁ converge to 0.07 a few days before main shocks. Thus, by using the date of convergence to 0.07 for prediction, the lead times, which were a few months to a few weeks or so by SES data alone, were made, although empirically, as short as a few days (6, 7). In fact, the prominent seismic swarm activity in 2000 in the Izu Island region, Japan, was preceded by a pronounced SES activity 2 mo before it, and the approach of κ₁ to 0.07 was found a few days before the swarm onset (8). However, when SES data are not available, which is usually the case, it is not possible to follow the above procedure. To cope with this difficulty, in the previous work (1), we investigated the time change of the fluctuation of the κ₁ values during a few preseismic months for each EQ (which we call target EQ) over the large area N2546E125148 (Fig. 2A) for the period from 1 January 1984 to 11 March 2011, the day of M9.0 Tohoku EQ. Setting a threshold M_JMA = 3.5 to assure data completeness of JMA catalog, we were left with 47,204 EQs in the concerned period of about 326 mo: ∼150 EQs per month. For calculating the β values, we chose 200 EQs before target EQs to cover the seismicity in almost one and a half months.Open in a separate windowFig. 2.(A) The 47,204 EQs with M_JMA ≥ 3.5 that occurred during the period of our study. (B) Contours of the number of EQs per month within R = 250 km. Solid diamonds show the epicenters of six shallow EQs investigated in this study. (C) Contours of the natural time window W used in each of the 12,476 areas of radius R = 250 km with offset 0.1° from one another that have at least eight EQs per month.To obtain the fluctuation β of κ₁, we need many values of κ₁ for each target EQ. For this purpose, we first took an excerpt comprised of W successive EQs just before a target EQ from the seismic catalog. The number W was chosen to cover a period of a few months. For this excerpt, we form its subexcerpts Sj={Qj+k−1}k=1,2,…,N of consecutive N = 6 EQs (since at least six EQs are needed (2) for obtaining reliable κ₁) of energy Q_j+k?1 and natural time χ_k = k/N each. Further, pk=Qj+k−1/∑k=1NQj+k−1, and by sliding S_j over the excerpt of W EQs, j=1,2,…,W−N+1 (= W − 5), we calculate κ₁ using Eq. 1 for each j. We repeat this calculation for N=7,8,…,W, thus obtaining an ensemble of [(W − 4)(W − 5)]/2 (= 1 + 2 +…+ W − 5) κ₁ values. Then, we compute the average μ(κ₁) and the SD σ(κ₁) of thus obtained ensemble of [(W − 4)(W − 5)]/2 κ₁ values. The variability β of κ₁ for this excerpt W is defined to be β ≡ σ(κ₁)/μ(κ₁) and is assigned to the (W + 1)^th EQ, i.e., the target EQ.The time evolution of the β value can be pursued by sliding the excerpt through the EQ catalog. Namely, through the same process as above, β values assigned to (W + 2)^th, (W + 3)^th, … EQs in the catalog can be obtained.We found in ref. 1 that the fluctuation β of κ₁ values exhibited minimum a few months before all of the six shallow EQs of magnitude larger than 7.6 that occurred in the study period. A minimum of β ≡ σ(κ₁)/μ(κ₁) means large average and/or small deviation of κ₁ values (e.g., see ref. 9).In the present work, we calculate the β values for small areas before the six large EQs, which showed β minima of the large area.     

Acid-base and electrolyte disorders in patients with diabetes mellitus

Diabetes mellitus is the most common metabolic disorder in the community. The diabetics may suffer from acid-base and electrolyte disorders due to complications of diabetes mellitus and the medication they receive. In this study, acid-base and electrolyte disorders were evaluated among outpatient diabetics in our hospital. The study consisted of patients with diabetes mellitus who visited the hospital as outpatients between the period January 1, 2004 to December 31, 2006. The patients' medical history, age and type of diabetes were noted, including whether they were taking diuretics and calcium channel blockers or not. Serum creatinine, proteins, sodium, potassium and chloride and blood gases were measured in all patients. Proteinuria was measured by 24-h urine collection. Two hundred and ten patients were divided in three groups based on the serum creatinine. Group A consisted of 114 patients that had serum creatinine < 1.2 mg/dL, group B consisted of 69 patients that had serum creatinine ranging from 1.3 to 3 mg/dL and group C consisted of 27 patients with serum creatinine > 3.1 mg/dL. Of the 210 patients, 176 had an acid-base disorder. The most common disorder noted in group A was metabolic alkalosis. In groups B and C, the common disorders were metabolic acidosis and alkalosis, and metabolic acidosis, respectively. The most common electrolyte disorders were hypernatremia (especially in groups A and B), hyponatremia (group C) and hyperkalemia (especially in groups B and C). It is concluded that: (a) in diabetic outpatients, acid-base and electrolyte disorders occurred often even if the renal function is normal, (b) the most common disorders are metabolic alkalosis and metabolic acidosis (the frequency increases with the deterioration of the renal function) and (c) the common electrolyte disorders are hypernatremia and hypokalemia.