We demonstrated that the exercise training improved urinary NAG levels as well as the change rate of urinary ACR, independent of body weight and glycemic selleck chemical Volasertib status in the kidneys of KK-Ay mice, although moderate-intensity exercise increased expression of HIF-1�� in the kidneys. In our study, no significant changes were observed in the levels of Ccr between sedentary KK-Ay and exercised KK-Ay mice. Therefore, it is indicated that the decrease of urinary ACR was not due to the reduction of renal blood flow/glomerular filtration rate, but more likely to the effect of exercise. It is thought that appropriate exercise increases antioxidant enzymes, although excessive exercise causes inflammation, increases oxidative stress associated with ROS, and decreases the renal blood flow and glomerular filtration rate.

In our study, both exercises decreased urinary 8-OHdG levels, an oxidative stress marker. However, contrary to our expectation, low-intensity exercise was more effective than moderate-intensity exercise in terms of renal function. Further investigation is required to determine appropriate exercise intensity. It appears that low-intensity exercise attenuates the progression of early DN without affecting marked renal ischemia. Thus, attention should be paid to renal ischemia even though albuminuria has improved. Reductions in the rate of urinary ACR change, urinary NAG, and maintained podocyte numbers, with parallel improvements in oxidative damage and chronic inflammation, might be related to beneficial effects of exercise in DN [76].4.

ConclusionDespite the successful use of lifestyle changes, metabolic control, and blood pressure control, including ACE inhibitors and ARB therapy, residual renal risk remains very high, leaving the diabetic population with a clear unmet need for novel treatment options. As outlined in this review, various drugs are in development. It is anticipated that some of the newer agents that are currently the focus of clinical trials will ultimately lead to improvements in slowing the progression and eventually improving the prognosis of this devastating disease.Conflict of Interests The authors declare no conflict of interests.AcknowledgmentsThe authors thank S. Horikoshi, Ph.D., K. Funabiki, Ph.D., Y. Makita, Ph.D., T. Ito, Ph.D., S. Hagiwara, Ph.D., T. Yamazaki, Ph.D., I. Ohara, Ph.D., M. Murakoshi, Ph.D., M. Matsumoto, Ph.

D., T. Aoki, Ph.D., Y. Ishikawa, Ph.D., and J. Y. Moon, Ph.D., for their support.
Survival analysis is a branch of statistics that is of interest to researchers in when patients’ death will occur after some therapies [1]. So far there are many methods to analyze survival data, for example, Kaplan-Meier curve, logrank test, Cox proportional hazards Cilengitide model, and so on. We often have information about patients’ survival status and survival time.

Therefore, the solid-liquid ratio of 1:20 was chosen for further optimization studies.Figure selleck kinase inhibitor 2Effect of extraction solvent on TPC and chlorogenic acid yield (solid-liquid ratio, 1:15; extraction time, 30min; number of extractions, 1; n = 5).2.3. Effect of Ultrasonic Time on Yield of TPC and Chlorogenic AcidIn order to obtain the maximum yield of TPC and chlorogenic acid from the root of I. helenium, ultrasound-assisted extractions were performed at five extraction time (20, 30, 40, 50 and 60min). The effect of different extraction time on yield of TPC and chlorogenic acid is shown in Figure 3. It was reported that long period of extraction time favors the phenolic compounds production [20]. Likewise, at constant ethanol concentration and solid-liquid ratio, increasing the extraction time significantly increased the yield at the initial stage.

But further increased in the ultrasonic time did not show any increase in the total phenolic content when the extraction was more than 40min. Accordingly, 40min was chosen as the extraction time in succeeding experiments.Figure 3Effect of solid-liquid ratio on TPC and chlorogenic acid yield (ethanol concentration, 25%; extraction time, 30min; number of extractions, 1; n = 5).2.4. Effect of Number of Extractions on Yield of TPC and Chlorogenic AcidIn order to evaluate the number of extractions on yield of TPC and chlorogenic acid, four different numbers of extractions were applied to the extraction experiments, respectively.

It can be seen from Figure 4 that the yield of TPC and chlorogenic acid was first increased with increasing extraction times, and a relatively high yield of TPC and chlorogenic acid was achieved when the samples were extracted for 3 times. Figure 4Effect of ultrasonic time on TPC and chlorogenic acid yield (ethanol concentration, 25%; solid-liquid ratio, 1:20; number of extractions, 1; n = 5).2.5. Orthogonal Design Experiment An orthogonal array of four factors and three levels was constructed to optimize UAE conditions. The experimental design and data analysis are shown in Table 1. The Km (m = 1�C3) values are the averages of TPC or chlorogenic acid of every factor at each level. R value is the range of K value. According to the R value, it can be observed that there were great differences between each factor.

The number of extractions Carfilzomib was found to be the most important factor, afterward followed by ethanol concentration, ultrasonic time, and solid-liquid ratio. Therefore the maximum yield of TPC and chlorogenic acid was obtained when the conditions were C2A3D2B2, namely, number of extractions 2 times, 30% ethanol as the solvent, ultrasonic time of 40min, and solid-liquid ratio of 1:20, respectively. Through confirmatory test, the yield of TPC and chlorogenic acid was 6.13 �� 0.58 and 1.32 �� 0.17mg/g, respectively.

selleck compound Conductivity of a steel-wire mesh is studied during the procedure of cutting wires (bonds) in the mesh. The goal is to find an average number of bonds which should be removed …In each iteration of the experiment, a steel wire (a ��bond��) is cut (Watson and Leath actually studied ��site�� percolation; i.e., in each iteration they cut the four wires coming to a junction). The electric conductance of the lattice gradually decreases by cutting the wires. The idea is to cut steel wires randomly until no electrical current can pass through the mesh.Let P be the ratio of unblocked bonds to the total number of bonds. On average, when bonds are cut, at a critical value, say PC, conductivity of the lattice vanishes to zero [35]. Therefore, PC is a random variable which can be estimated by repeating the experiment several times.

The method used in present study is based on solving a sequence of linear programming (LP) problems. In our algorithm, we used F2C2 [34] to study reactions deletions and their consequences on the activity of metabolic fluxes. The algorithm starts by correcting reversibility of reactions in a metabolic network and deleting all dead-end reactions. Then, in each iteration, one column of the stoichiometric matrix of the metabolic network (or equivalently, a reaction in metabolic network) is randomly deleted (Figure 1(b)). The procedure continues until all reactions become blocked based on the F2C2 program. Finally, the critical ratio is computed as follows:PC=Number??of??deleted??reactionsNumber??of??unblocked??reactions??in??the??original??network.

(2)The experiment is repeated 100 times for each network, and average PC values were computed for each of the metabolic network models.We also compared our results with a classical measure of metabolic network robustness [38] based on flux balance analysis (FBA) [39]. This approach is based on in silico deletion of reactions. In each iteration, a reaction is deleted from the network and the sensitivity of the growth rate to the reaction deletion is modeled. We used the core reductive algorithm [8, 9, 40] for this purpose. In each iteration, we find a (randomly selected) minimal reaction set which can be used to produce biomass from a minimal growth medium in steady-state conditions. In a highly robust network, a considerable number of reactions can be deleted without influencing growth, while in a sensitive network deletion of a few reactions can result in no biomass production.

Therefore, the average ratio of ��unnecessary�� reactions to the total number of reactions can be used as a measure of network robustness. For each metabolic network, the experiment was repeated 1000 times to have a good estimation of this ratio.2.4. Batimastat Statistical AnalysisThe R package (http://www.r-project.org/) was used for statistical analyses. In order to compare the PC distributions in two organisms, one-sided two-sample t-test was used.

Inc., Santa Clara, California, USA) by using a 27-plex Bioplex Human Cytokine kit, which includes IL-1��, IL-1ra, IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-9, IL-10, IL-12 (p70), IL-13, IL-15, IL-17, eotaxin, basic FGF, G-CSF, GM-CSF, IFN-��, IP-10, MCP-1 (MCAF), MIP-1��, MIP-1��, PDGF-BB, RANTES, TNF-��, and selleck VEGF (Bio-Rad Laboratories), according to the manufacturer’s instructions. In 50 children with bacterial meningitis, in whom sufficient CSF existed for analysis, CSF growth factors were determined on admission. Plasma Ang-1 and Ang-2 were determined by using a commercial ELISA assay (R&D Systems Europe, Ltd., Abingdon, UK). We have previously reported the analysis of chemokines and pro-and antiinflammatory cytokines in this cohort [33,35].

HIV determinationHIV status was assessed in children 18 months or older by using at least two of the following tests; Unigold and Serocard (Trinity Biotech, Wicklow, Ireland), or Determine-HIV (Abbott Laboratories, Springfield, IL, USA). At least two tests were required to be positive for a subject to be classified as HIV infected. In children younger than 18 months, and in those with discordant antibody tests, HIV status was determined by using Amplicor HIV-1 DNA Test version 1.5 (Roche Diagnostics, South San Francisco, CA, USA).Statistical analysisThe growth factors and angiopoietins determined were summarized by using geometric means and interquartile ranges (IQRs). Two-sample t tests were used to compare growth-factor concentrations between groups, by using log-transformed data.

Multiway analyses of variance were used to obtain adjusted comparisons for each factor of interest (main effects: SBI/NBI, pneumonia/meningitis, HIV status, survivor/nonsurvivor, and gram positive/negative infection). Correlations between growth factors and other variables were estimated by using Spearman’s rho correlation coefficient. Fisher’s Exact test was used to compare proportions. Univariable and multivariable logistic regression analyses were performed to develop a prognostic model of the influence of confounding factors (HIV status, age, sex, diagnosis, and previous antibiotics) on the primary outcome measure, inpatient mortality. CSF and plasma growth factors in children with bacterial meningitis were analyzed by using Wilcoxon’s Signed Ranks test. Adjusted odds ratios (ORs) were obtained by using logistic regression. All tests were two-tailed, and a P value of < 0.05 was considered significant.ResultsPatient characteristicsWe studied 293 children (57% boys), of whom 64 (22%) died. The median age was 2.4 years, and the IQR, 0.7 to Drug_discovery 6.0 years.

In Figure 3, the spectra in www.selleckchem.com/products/FTY720.html the region 1300�C750/cm collected on some of the samples analyzed in this work are reported. In particular, in the left plot of Figure 3 the spectra of two samples solidified at two cooling rates (the lowest, 0.3K/s, and the fastest, 110K/s) are compared. For i-PP, several absorption bands of the crystalline and amorphous fraction have been identified [17], and the commonly adopted ones are highlighted in Figure 3. The most defined and isolated one is at 841/cm due to CH2 rocking and CH axial bending. Another band partially overlapping the first one is the band at 998/cm, due to CH3 equatorial rocking, C�CCH3 stretching, CH, bending and CH2 twisting. All the mentioned bands are sensitive to the order of long helicoidal chains, and then they measure the contribution to order of �� phase as well as of �� phase and of mesomorphic structures.

Thus crystallinity degree as measured by IR is an average crystallinity degree: it is not possible to discriminate between contribution of different phases. In plot (a) of Figure 3 it can be noticed that the peaks corresponding to the amorphous phase are slightly more pronounced, and conversely the peaks corresponding to the crystalline phase are slightly less pronounced, for the sample solidified at the highest cooling rate. The effect is more evident on zooming in a narrower region, as done in inset of the plot (a) of Figure 3. This indicates that, as expected, crystallinity slightly decreases on increasing cooling rate.

The effect of recycling steps is analyzed in plot (b) of Figure 3, where samples undergoing different recycling steps and solidified at the fastest applied cooling rate (of the order of 100K/s) are compared. The spectra look quite similar with some differences for the shoulder at 1158/cm (assigned to the amorphous phase [18]) which is more pronounced for the sample which underwent 10 steps of recycling and for the peak at 888/cm which increases on increasing the recycling steps. This latter peak Carfilzomib is attributed to the external vinylidene groups, which are formed by disproportionation between free radicals formed by rupture of the polymer backbone and are an index of thermal oxidation [19]. The increase of the peak at 888/cm is a clear indication of thermal degradation of the material on increasing the recycling steps.Figure 3FTIR spectra collected on some of the samples analyzed in this work. In plot (a) spectra of virgin samples solidified at two cooling rates; in plot (b) spectra of samples undergone different recycling steps and solidified at the fastest applied cooling …For a quantitative determination of the crystallinity degree, the FTIR absorbance spectra were analyzed applying Lambert and Beer’s law to selected peaks [20].

3. Compared to patients with PSPA-VAP, patients with PRPA-VAP were Crizotinib order significantly more likely to have received broad-spectrum antimicrobials during their admission in the ICU (77.1% (54 of 70 patients) vs. 60.1% (92 of 153 patients); P = 0.03). Before VAP diagnosis, patients with PRPA-VAP were more likely to have received tazobactam therapy (18.6% (13 of 70 patients) vs. 9.2% (14 of 153 patients); P = 0.01), and to have received ureidopenicillins or carboxypenicillins therapy (31.4% (22 of 70 patients) vs. 13.1% (20 of 153 patients); P = 0.0004). Differences in the use of fluoroquinolones therapy did not reach statistical significance (24.3% in PRPA group (7 of 70 patients) vs. 13.1% in PSPA group (20 of 153 patients); P = 0.058).

Table 3Antimicrobials received in the ICU within the seven days prior to VAP onsetPercentage of antibiotic-free days was different between the two groups for tazobactam, and ureidopenicillins-carboxypenicillins. Compared with patients with PRPA-VAP, patients with PSPA-VAP had more tazobactam-free days (P = 0.019), and less ureidopenicillins-carboxypenicillins-free days (P = 0.007).Adequate antibiotic therapy was started within 24 h after the diagnosis of VAP for 36 patients (51.4%) in the PRPA-VAP group, versus 117 patients (76.5%) in the PSPA-VAP group (P = 0.001). Adequate antibiotic therapy was started at least two days after PA VAP diagnosis for 25 patients (35.7%) in the PRPA-VAP group, versus for 26 patients (17%) in the PSPA-VAP group (P = 0.007). Use of bi- or tri-antimicrobial-therapy was similar between groups.

Antibiotic therapy before ICU discharge was not adequate for 9 patients (12.9%) in the PRPA-VAP group, versus 10 patients (6.5%) in the PSPA-VAP group (P = 0.054).The rate of recurrence was not influenced by resistance (PSPA-VAP 28 (18.3%) vs PRPA-VAP 11 (15.7%), P = 0.83).Compared with patients with PSPA-VAP, PRPA-VAP patients had similar lengths of ICU stay prior to VAP (11 days (range, 6 to 18 days) vs. 9 days (range, 6 to 17 days); P = 0.53). PRPA-VAP and PSPA-VAP were associated with similar crude ICU mortality (38% (25 of 70 patients) vs. 41% (62 of 153 patients); P = 0.56) as well as in hospital mortality (43% (30 of 70 patients) vs. 44% (68 of 153 patients); P = 0.85).Risk factors for deathRisk factors for ICU death are listed in Table Table1.1.

Risk factors found for ICU death at admission were: age, at least one chronic illness, admission for cardiac illness or septic shock, Simplified Acute Physiology Score version II (SAPS II), organ dysfunction scores (LOD, SOFA). Two days before PA-VAP, risk factors for ICU death were: GSK-3 treatment with vasopressors, treatment with steroids, SAPS II, and organ dysfunction scores (LOD, SOFA).Resistance to Ureido/carboxypenicillin was not a risk factor for ICU death.

Step 5 ��When the number of iteration reaches a predefined maximum number, output the optimal results; otherwise, repeat Steps 2�C4.4.2. Multiobjective Evolution Algorithm (MOEA) Approach for the MSJRDIn this section, a brief introduction blog of sinaling pathways of MOP is given. Then, an HDE-based procedure to handle the MSJRD using noninferior and crowding distance is designed.4.2.1. Some Definitions of MOP Definition 1 (multiobjectiveoptimizationproblems(MOP)) i=1,2,��,m.(20)A general?gi(x)��0,?��Min?F(x)=f1(x),f2(x),��,fk(x)Subject??to: MOP consists of n decision variables, k objective functions, and m constrains. In Definition 1, x refers to the decision space and gi(x) are constrains of MOP.Definition 2 (Pareto optimal solution) ��The optimal solution of MOP is often referred to as the Pareto optimal solution.

Let vector a belong to x and suppose that x* is a subset of x. If there does not exist any vector in x* that is better than a, then a is called noninferior solution (or Pareto optimal solution) of x*. Moreover, if vector a is the noninferior solution of x, then vector a is the Pareto optimal of the MOP.4.2.2. HDE-Based Procedures for MSJRD Using MOEA Approach There exist many difficulties when applying DE to solve an MOP compared with single objective problem. The main challenges for solving MOP are as follows: how to generate offspring and how to keep Pareto solutions uniformly distributed. The classical DE is not suitable for an MOP since many good solutions may be abandoned due to its one-to-one competing mechanism. This will also be confirmed by a numerical example.

Therefore, we also use an HDE which uses truncation selection to choose next generation based on front rank and crowding distance adopted by Qian and li [27]. The steps of calculating crowding distance are presented in Algorithm 1.Algorithm 1Steps of calculating crowding distance.In this algorithm, the low front rank corresponds to the high quality of a solution. As to the those individuals with the same front rank, the larger crowding distance means better distribution. Therefore, individuals with lower front rank and larger crowding distance are selected to the next generation.The first target can be divided into an inventory problem and a delivery problem. When all ki are determined, the optimal delivery cost can be calculated by solving a TSP.

In addition, for a stochastic JRP with normal distributed demand, when ki, zi, and T are known, the stochastic JRP can then be solved. With the same value of ki, zi, and T in the second target, we can obtain the corresponding value of the second target. Then change ki, zi, and T with the following steps until the termination condition is satisfied. The steps of HDE-based Brefeldin_A approach are described as follows.Step 1 ��Initialization: set related parameters (CR, F, and NP) for the HDE. Set the lower bound and the upper bound of ki, respectively; that is, kiLB = 1 and kijUB = 100.

In a secondary analysis, we evaluated whether hemofiltration improved global organ dysfunction.Materials selleck chem inhibitor and methodsStudy designWe conducted an unblinded RCT of continuous venovenous hemofiltration (CVVH) vs. continuous venovenous hemodialysis (CVVHD) with concealed allocation (http://Clinicaltrials.gov registration number NCT00675818). Our reporting follows the updated CONSORT statement [8].SettingParticipants were recruited from ICUs at six academic hospitals: Mt. Sinai Hospital (Medical-Surgical ICU), Sunnybrook Health Sciences Centre (Critical Care Unit and Cardiovascular ICU) and St. Michael’s Hospital (Medical-Surgical and Cardiovascular ICUs), all in Toronto, Canada; Victoria Hospital (Critical Care Trauma Centre), and University Hospital (Medical/Surgical Intensive Care Unit and Cardiac Surgery Recovery Unit), both in London, Canada, and University of Alberta Hospital (General Systems Intensive Care Unit) in Edmonton, Canada.

The Research Ethics Boards of Mt. Sinai Hospital, St. Michael’s Hospital, Sunnybrook Health Sciences Centre, London Health Sciences Centre and the University of Alberta approved the protocol. The Applied Health Research Centre at St. Michael’s Hospital (Toronto, Ontario, Canada) was the trial coordinating center.PopulationWe enrolled critically ill adults (�� 16 years of age) with AKI, defined as a serum creatinine increase �� 50% from baseline (defined as the last known pre-morbid serum creatinine or earliest value available from the current admission).

At the time of screening, at least one of the following indications for RRT initiation needed to be present: (i) oliguria (defined as urine output < 100 mL in the preceding 4 hours); (ii) metabolic acidosis (serum bicarbonate < 15 mmol/L and pH < 7.25); (iii) refractory hyperkalemia (serum potassium > 6 mmol/L despite medical efforts at potassium removal); (iv) serum urea > 50 mmol/L, or (v) suspected uremic organ involvement (pericarditis, encephalopathy, neuropathy or myopathy). Finally, participants needed to be hemodynamically unstable, defined as Sequential Organ Failure Assessment (SOFA)- Cardiovascular Carfilzomib score �� 1 on the day of screening (see Additional file 1 for the modified SOFA score used in this study). This required the patient to have mean arterial pressure < 70 mmHg or receipt of at least one vasopressor or inotrope [9].

Importantly, the pathophysiology of SSAKI may be unique from that of ischemic or nephrotoxic AKI [18]. Therapies aiming to restore renal perfusion in ischemic AKI [19-21] have not been selleck chem inhibitor demonstrated to be particularly effective and may be even less effective in SSAKI, a process that may not be secondary to impaired glomerular preload. Persistent SSAKI may fall into the class of fluid-unresponsive AKI [22]. Renal replacement therapy has been used as therapy for AKI and data exist demonstrating that initiation prior to accumulation of excessive fluid overload may improve outcomes [23,24]. The aggregate data, however, show that patients with SSAKI have consistently increased mortality, even with early renal replacement therapy initiation [25,26].

The modest efficacy of biomarkers at identifying SSAKI also underscores the notion that the pathophysiology of SSAKI is unique from other etiologies of AKI. There is a need to identify novel candidate biomarkers of SSAKI, which would expedite early treatment aimed at preventing the effects of this highly morbid complication of sepsis.We have generated an extensive genome-wide expression database from children with septic shock by way of microarray technology and have now leveraged this database to identify candidate biomarkers for SSAKI [27-32]. Herein we report the identification of 21 unique gene probes upregulated in patients with SSAKI, within the first 24 hours of admission to the pediatric intensive care unit (PICU), and their ability to robustly predict SSAKI.

Two readily measurable gene products from this list, matrix metalloproteinase-8 (MMP-8) and neutrophil elastase-2, show high sensitivity for SSAKI in a cohort of patients with septic shock.Materials and methodsPatients and data collectionThe study protocol was approved by the Institutional Review Boards of each participating institution (n = 11). Children ��10 years of age admitted to the PICU and meeting pediatric-specific criteria for septic shock were eligible for enrollment [33]. Controls, used to normalize the microarray data across the patients with septic shock and to conduct the three-group analysis of variance in the first derivation analysis, were recruited from the ambulatory departments of participating institutions using published inclusion and exclusion criteria [28-32]. These controls were required to reliably compare data across different batches of samples.

All patients and controls in the derivation cohort were previously reported in microarray-based studies addressing hypotheses entirely different from that of the current report [28-32]. All microarray data have been deposited in the NCBI Gene Expression Omnibus (GEO:”type”:”entrez-geo”,”attrs”:”text”:”GSE26440″,”term_id”:”26440″GSE26440 Brefeldin_A and GEO:”type”:”entrez-geo”,”attrs”:”text”:”GSE26378″,”term_id”:”26378″GSE26378). The patients in the validation cohort have not been previously reported in any manner.

In a third set, PRP was incubated for 72 hours with lactic acid (30% in water) (Sigma Aldrich) or metformin (16,600 mg/L) plus sodium mainly bicarbonate. Lactic acid was added to PRP every 24 hours so as to reach the same lactate level as the samples incubated with metformin (16,600 mg/L). Sodium bicarbonate was added every 24 hours to PRP already treated with metformin (16,600 mg/L) to maintain bicarbonate at the same level as the samples incubated with saline. Plasma pH, lactate levels and platelet oxygen consumption were measured at 72 hours.Finally, we incubated human red blood cells, instead of platelets, with saline or metformin (16,600 mg/L) and measured pH and lactate levels every 24 hours, up to 72 hours.ex vivo experimentWe enrolled ten consecutive patients admitted since 2008 to one Hospital in Milan (Italy) with lactic acidosis (arterial pH < 7.

30 and lactate concentration > 5 mmol/L), serum metformin concentration > 10 mg/L (therapeutic level is < 4 mg/L) and no other primary explanation for lactic acidosis (such as, for instance, overt respiratory, heart or liver failure). Exclusion criteria were pre-existing mitochondrial disease and hemoglobin < 8 g/dl (< 10 g/dl in the case of ischemic cardiomyopathy). Platelet mitochondrial function was studied within 48 hours of diagnosis. Blood was anticoagulated with ethylenediamine tetraacetic acid (EDTA) (30 ml) (for measuring platelet mitochondrial respiratory chain complex activities, always done) or citrate (20 ml) (for measuring platelet mitochondrial membrane potential, only performed since the beginning of 2010).

It was then sedimented and centrifuged (2,500 g for 10 min) and PRP collected for further analysis (see below). Ten healthy volunteers (similar in sex and age to intoxicated patients) acted as controls.Mitochondrial respiratory chain complex enzyme activitiesPRP (either from in vitro or ex vivo experiments) was washed with distilled water, centrifuged at 5,000 g for 10 min (14,500 g from the second cycle on) and then GSK-3 washed again with PBS until a clear platelet pellet could be stored at -80��C (two or three cycles were usually required). At the time of analysis, the platelet pellet was diluted in buffer (KCl 120 mM, HEPES 20 mM, MgCl2 5 mM and EGTA 1 mM; pH 7.2, 300 to 400 ��l), sonicated (two cycles at 60 W for 10 seconds) and centrifuged (750 g for 10 min) while kept at 4��C. Supernatant was then analyzed using spectrophotometry (at 30��C). We measured the activity of respiratory chain NADH-ubiquinone 1 reductase (complex I), succinate-cytochrome c reductase (complex II+III) and cytochrome c oxidase (complex IV) and expressed it relative to that of citrate synthase (a marker of mitochondrial density) [26]. Proteins were measured using Lowry’s method.