To study the effect of Vitamin D supplementation on reduction in level of HbA1 in patients recently diagnosed with diabetes mellitus Type II having asymptomatic Vitamin D deficiency.This randomized control trial was conducted at East Medical Ward Mayo Hospital Lahore for 6 months from January 01 2016 to June 30, 2016. 114 Patients were included through non probability purposive sampling technique. Informed consent and demographic information was collected. Patients were divided in two groups by randomization through tossing a coin. Group-A patients received Metformin tablet alone at 500 mg after dinner and Group-B patients were treated with same dosage of Metformin along with oral vitamin D at 200,000 IU monthly for three months. Blood sample was obtained at baseline, 3 months and 6 months of initiation of therapy. All samples were sent to the laboratory for complete blood count, blood sugar fasting, serum calcium, serum phosphorous, serum alkaline phosphatase, HbA1c and serum 25 Hyroxy Vitamin D (S-25(OH) D) levels and iPTH. Data entry and analysis was done by using SPSS 20.st and 2nd visit, in metformin group, mean HbA1c was 7.59±0.47%, 7.46±0.25% and 7.30±0.29%. At baseline, 1st and 2nd visit, in combination group, mean HbA1c was 7.71±0.19%, 7.57±0.21% and 7.43±0.26%. The difference was insignificant (p>0.05) at baseline while significant on later follow-ups (P<0.05).The mean age of patients in metformin group was 42.37±4.59 years while mean age of patients in combination group was 43.33±4.86years. Males were 45.6% and females were 54.4% in metformin group while in combination group, 36.8% were males’ and 63.2% were females’. At baseline, in metformin group, mean Vitamin D level was 17.09±1.73mg/dl and in combination group, mean Vitamin D level was 16.49±1.56mg/dl. The difference was insignificant (p>0.05). On 2nd visit, combination group mean Vitamin D was 29.04±3.96mg/dl. At baseline, 1Vitamin D supplementation improved the glycemic control but substantial reduction in HbA1c was statistically insignificant in both groups. Vitamin D deficiency is commonly due to reduced exposure to ultraviolet-B (UVB) radiation, and it is observed that there is reduced production of Vitamin D in the skin cells.2UVB emission is connected with a 162% increase in plasma 25 hydroxyvitamin D (25[OH] D) absorptions.5During last two decade, There are many Vitamin D alpha receptors have been developed in various tissues of our body and many new activities of Vitamin D have been studied ruminated by these receptors.9That is why adequate 25(OH)D levels are connected with a lesser possibility of confrontation of diabetes.12However, supplementation studies have not unambiguously found that Vitamin D favors an improvement in glucose homeostasis parameters.14After approval of synopsis from board of study and Institutional Review Board and Advanced Study Review Board of King Edward Medical University, all 114 patients were randomized to either Group A (Patients were treated with Metformin only at 500 mg after dinner) or B  after informed consent from patients or attendants. Patients with age 35-50 years of either gender who were diagnosed cases of type 2 diabetes mellitus having clinically asymptomatic serum vitamin D levels below 20ng/ml and taking metformin only for the treatment of type 2 diabetes mellitus with HbA1C level in between 7-9 were included in the study.All the patients who had fasting serum calcium >10.5 mg/dL, history of diseases like nephrolithiasis, hypercalciuria, malignancy, tuberculosis, sarcoidosis, Paget’s disease, malabsorption syndromes on medical records, patients of renal insufficiency diagnosed on the basis of renal profile, presence of proximal myopathy and pregnant patients were excluded.rd month (at first follow up), HbA1C levels were measured in all patients and S-25(OH) D levels were measured only in Group-B patients that received oral vitamin D. S-25(OH) D levels of over 150 ng/ml was not found in any patient at follow-up so no patient was dropped out of the study. At the end of 6th month, blood sample was obtained from each patient and was sent to the laboratory for HbA1c and S-25(OH)D levels were measured only in Group-B patients.Demographic information  was also obtained. Blood sample was obtained from each patient at baseline and was sent to the laboratory for complete blood count, blood sugar fasting, serum calcium, serum phosphorous, serum alkaline phosphatase, HbA1c and S-25(OH) D levels and iPTH. At the end of 3All this information was recorded on predesigned proforma. Data entry and analysis was done by using SPSS 20. Quantitative variables  D, CBC, RFT, and LFT) were presented by using mean± SD. Qualitative variables  were presented by using frequency table and percentages. Repeated measure ANOVA/Fried Man test was used to see the HbA1C level in both groups during course of follow up time period. Paired sample t-test/Wilcoxson signed ranked test was used to compared the HbA1C level before and after treatment. Efficacy of treatment was assessed in both treatment groups by using Chi-Square test. P-value < 0.05 was taken significant.2 and in combination group, mean BMI was 23.50±3.68 kg/m2. In metformin group, mean duration of diagnosis of diabetes was 2.63±1.26 years and in combination group, mean duration of diagnosis of diabetes was 3.25±1.18 years. At baseline, in metformin group, mean BSR was 219.88±40.40mg/dl and in combination group, mean BSR was 240.40±37.11mg/dl. The difference was significant (p 0.0065). At 1st visit, in metformin group, mean BSR was 184.37±34.08mg/dl and in combination group, mean BSR was 195.21±30.98mg/dl. The difference was insignificant (P 0.078).The mean age of patients in metformin group was 42.37±4.59 years while mean age of patients in combination group was 43.33±4.86years. There were 26 males (45.6%) and 31 females (54.4%) in metformin group while in combination group, there were 21 males (36.8%) and 36 females (63.2). At the baseline, in metformin group, there were 24 patients on anti-glycemic treatment while in combination group; there were 33 patients on anti-glycemic treatment. In metformin group, mean height was 1.68±0.08m and in combination group, mean height was 1.74±0.05m. In metformin group, mean weight was 72.08±11.10 kg and in combination group, mean weight was 70.61±9.65 kg. In metformin group, mean BMI was 25.49±3.99kg/mnd visit, in metformin group, mean BSR was 165.12±21.65mg/dl and in combination group, mean BSR was 168.42±19.89mg/dl. The difference was insignificant (P 0.399). Within groups, there was significant decrease in BSR (P 0.00). At baseline, in metformin group, mean calcium was 9.21±0.79mg/dl and in combination group, mean calcium was 9.15±0.74mg/dl. The difference was insignificant (P 0.668). At 1st visit, in metformin group, mean calcium was 9.45±0.80mg/dl and in combination group, mean calcium was 9.23±0.74mg/dl. The difference was insignificant (P 0.128). In 2nd visit, in metformin group, mean calcium was 9.79±0.81mg/dl and in combination group, mean calcium was 9.90±0.66mg/dl. The difference was insignificant (p 0.448). Within groups, there was significant increase in calcium (P 0.00). At baseline, in metformin group, mean phosphorus was 3.44±0.85mg/dl and in combination group, mean phosphorus was 3.37±0.91mg/dl. The difference was insignificant (P 0.663). At 1st visit, in metformin group, mean phosphorus was 3.79±0.91mg/dl and in combination group, mean phosphorus was 3.88±1.03mg/dl. The difference was insignificant (P 0.632).In 2nd visit, in metformin group, mean phosphorus was 4.16±1.09mg/dl and in combination group, mean phosphorus was 4.34±1.16mg/dl. The difference was insignificant (P 0.390). Within groups, there was significant increase in phosphorus (P 0.000). At baseline, in metformin group, mean AP was 98.26±25.09mg/dl and in combination group, mean AP was 98.47±30.59mg/dl. The difference was insignificant (P 0.968). At 1st visit, in metformin group, mean AP was 97.26±29.02mg/dl and in combination group, mean AP was 95.19±30.99mg/dl. The difference was insignificant (P 0.713). In 2nd visit, in metformin group, mean AP was 93.93±31.41mg/dl and in combination group, mean AP was 99.14±29.59mg/dl. The difference was insignificant (P 0.364). Within metformin group, there was insignificant decrease in AP (P 0.442), and in combination group, AP there was insignificant increase in AP (P 0.914).In 2Tables Despite the suggested role of vitamin D in the prevention of diabetes and cardiovascular disease or its risk factors, the evidence is not consistent and there is a paucity of randomized controlled trials in this field. The results of this study were very conflicting when compared with other studies. One reason for conflicting results in this study could be explained by the fact that in HbA1c was significantly higher in combination group as compared to metformin alone group, that is why at final follow-up the mean HbA1c was also low in metformin group alone.In Type II diabetes mellitus, significant improvements in HbA1c are obtained with enhanced Vitamin D supplementation as part of drug regimen over time. Investigation provides the first known evidence of a relationship between enhanced Vitamin D supplementation as part of a pre-existing medical regimen taken over long term and determinants of Type II diabetes mellitus in a group of overweight and obese patients with Type II diabetes mellitus.15Experimental data have indicated that Vitamin D insufficiency has an important influence on glucose metabolism. The majority of epidemiological studies have demonstrated an association between low Vitamin D and insulin resistance and/or Type II diabetes mellitus. Nevertheless evidence from randomized controlled trials remains inconclusive.16Nwosu et al showed that after three months of Vitamin D supplementation, there was a significant increase in 25(OH)D in Type II diabetes mellitus patients (p = 0.015). This study concluded that there was a clinically significant decrease in HbA1c from 8.5±2.9% at baseline to 7.7±2.5% in Type II diabetic patients. This study included both Type I and Type II diabetics and all the patients were given vitamin D and interestingly it also concluded that there was no clinically-significant decrease in HbA1c in Type I diabetics (8.5 ± 1.2 to 8.53 ± 1.1%).17Another study aimed to investigate the effect of 16-week daily vitamin D3 supplementation on HbA1C. Interestingly this double-blind, randomized, placebo-controlled trial showed that despite the fact that 25(OH) D increased from a mean of approximately 29nmol/L at baseline to 49 nmol/L after intervention but did not improve HbA1c.18The major contrast of this study with current one is that they included healthy subjects only and they replaced oral vitamin D daily in two dosage contraries to the present study, where vitamin D was given monthly.The results of the current study are comparable with the Krul-Poel et al. who showed that mean baseline serum 25-hydroxyVitamin D increased from 60.6 ± 23.3 to 101.4 ± 27.6 nmol/L and 59.1 ± 23.2 to 59.8 ± 23.2 nmol/L in Vitamin D plus metformin group and metformin alone group, respectively. Mean baseline HbA1c was 6.8 ± 0.5% in both groups. After 6 months, no effect was seen on HbA1c  and other indicators of glycemic control  in the entire study population. Subgroup analysis in patients with a serum 25(OH) D<50 nmol/L or anHbA1c level> 7% (53 mmol/mol) did not differ the results.19Prakash et al., results state otherwise that judicious supplementation of vitamin D amongst vitamin D deficient type 2 diabetic patients with near normal HbA1c can reduce the blood sugar level. HbA1c reduce to target levels if it is minimally elevated. These conflicting results can be explained by the fact that in this study conducted by Prakash et al. they did not alter the medicines in any group and most of the study subjects were taking more than one antidiabetic medicines. Moreover, the replacement oral vitamin D supplementation done was at dose of 60.000IU weekly for 3 months in one group as compared to present study where it was given monthly but at 200,000 IU.20Firstly, all the patients were one single drug therapy as contrast to international studies where patients were on multiple drugs including insulin. Moreover, the dosage of vitamin D was monthly which can explain the inadequate results of the study as the absorption status of the patients could vary depending upon the complications of diabetes especially autonomic neuropathy which was not evaluated in this study. But its negative results at least give an insight into the fact that prescribing vitamin D should be discouraged in diabetic patients for controlling blood sugar level until compelling study results are available world-wide.There is no doubt that further studies have to be conducted with different methodology and inclusion and exclusion criteria which could give more insight into the effectiveness of vitamin D in glycemic control of diabetic patients before final verdict can be made.Vitamin D supplementation can improve the level of Vitamin D in blood but did not have clinically significant impact on HbA1c reduction in Type II diabetic patients.FAR conceived, designed and did statistical analysis & editing of manuscript and final approval of manuscript.SM, DMK AND SH did data collection and manuscript writing.


In isolated, perfused hearts, energetics were measured using 31P magnetic resonance spectroscopy (MRS), and glycolysis and fatty acid oxidation were measured using [3H] labeling. Compared with a normoxic, chow-fed control mouse heart, hypoxia decreased PPARα expression, fatty acid oxidation, and mitochondrial uncoupling protein 3 (UCP3) levels, while increasing glycolysis, all of which served to maintain normal ATP concentrations ([ATP]) and thereby, ejection fractions. A high-fat diet increased cardiac PPARα expression, fatty acid oxidation, and UCP3 levels with decreased glycolysis. Hypoxia was unable to alter the high PPARα expression or reverse the metabolic changes caused by the high-fat diet, with the result that [ATP] and contractile function decreased significantly. The adaptive metabolic changes caused by hypoxia in control mouse hearts were found to have occurred already in PPARα-deficient (PPARα−/−) mouse hearts and sustained function in hypoxia despite an inability for further metabolic remodeling. We conclude that decreased cardiac PPARα expression is essential for adaptive metabolic remodeling in hypoxia, but is prevented by dietary fat.—Cole, M. A., Abd Jamil, A. H., Heather, L. C., Murray, A. J., Sutton, E. R., Slingo, M., Sebag-Montefiore, L., Tan, S. C., Aksentijević, D., Gildea, O. S., Stuckey, D. J., Yeoh, K. K., Carr, C. A., Evans, R. D., Aasum, E., Schofield, C. J., Ratcliffe, P. J., Neubauer, S., Robbins, P. A., Clarke, K. On the pivotal role of PPARα in adaptation of the heart to hypoxia and why fat in the diet increases hypoxic injury.The role of peroxisome proliferator-activated receptor α (PPARα)-mediated metabolic remodeling in cardiac adaptation to hypoxia has yet to be defined. Here, mice were housed in hypoxia for 3 wk before  In hypoxia, PHD activity is reduced, stabilizing HIF-α, which translocates to the nucleus and dimerizes with HIF-1β. The dimer transcriptionally activates ∼200 genes, including those involved in erythropoiesis, angiogenesis, and energy metabolism  and the HIF-2α subunit, respectively, associated with the PPARA gene in genome-wide scans  elevated in ischemic cardiac myocytes  and in itabolism . In hypode scans , and servia orchestrated changes in substrate metabolism and mitochondrial respiration, so that normoxic [ATP] and cardiac function can be maintained.Recent studies using cultured cells, knockout, or transgenic mice seemingly demonstrate that alterations in the HIF system, whether decreases or increases in aryl hydrocarbon nuclear translocator  , HIF-1α 22n = 106; 14 mo) and littermate PPARα−/− (n = 72) mice were housed on a 12 h light/dark cycle and fed ad libitum. The mice, a kind gift from Dr. Frank J. Gonzalez , were bred in-house on a pure 129Ev/Sv background with 10 backcrosses. All procedures conformed to ethical regulations of the United Kingdom Home Office.Male wild-type 129Ev/Sv  or a high-fat (55% fat) diet. To acclimatize mice to hypoxia, chamber O2 was reduced in daily steps over a 7 d period to be maintained at 11% (v:v) for a further 12 d. Following hypoxia, animals were reoxygenated for 1 h to ameliorate any short-term effects of reoxygenation on cardiac function.Mice were housed in a glass-fronted hypoxia chamber , in which NMRI was carried out on an 11.7 T (500 MHz) vertical bore (123-mm diameter) magnet , as previously described .−1 sodium pentobarbitone intraperitoneally and hearts were excised and arrested in ice-cold Krebs-Henseleit buffer. Hearts were perfused in Langendorff mode at 80 mmHg perfusion pressure and at 37°C with modified Krebs-Henseleit recirculating buffer gassed with 95% O2/5% CO2 containing 11 mM glucose and 0.4 mM palmitate prebound to 1.5% (w/v) albumin. A polyethylene balloon connected to a pressure transducer was inserted in the left ventricle (LV) and inflated to an end-diastolic pressure of 4–8 mmHg to measure pressures and heart rates.Mice were anesthetized using 60 mg ⋅ kg body weight3H]-glucose or -palmitate, respectively, in recirculating perfusion buffer, as previously described  was added to the buffer to measure the maximal insulin-stimulated response. Hearts were snap frozen in liquid N2. Cardiac lactate efflux was determined by measuring lactate concentrations, using lactate dehydrogenase, in timed buffer collections.Glycolytic flux and palmitate oxidation were measured in perfused hearts using 25 µCi 5-H-glucoseescribed . Buffer i peak areas. Intracellular pH, the cytosolic-free ADP ([ADP]free), and the free energy of ATP hydrolysis (ΔGATP) were calculated as previously described (Perfused hearts were inserted into the same NMR system as used for imaging (see Cardiac MRI section), but using a 10 mm diameter probe . Fully relaxed spectra were acquired by averaging 60 scans using a 90° pulse (60 μs) and a 10-s delay to give an acquisition time of 10 min. Data were analyzed using jMRUI software and 4 spectra were summed to determine ATP, PCr, and Pl-glutamine (4 mM). Cells were cultured on plates precoated with 5 μg/ml fibronectin and 0.02% gelatin and incubated in 5% CO2 at 37°C and 95% humidity. All experiments were performed on confluent, beating cells. Cells were exposed to 2% hypoxia or normoxia for 24 h. In separate experiments, normoxic HL-1 cells were incubated for 24 h with a specific PHD inhibitor, 50 µM 2-(1-chloro-4-hydroxyisoquinoline-3-carboxamido) acetic acid (IOX3) , were maintained in Claycomb Medium . Cells were supplemented with 10% fetal bovine serum, ascorbic acid (0.3 mM), norepinephrine (0.1 mM), and d (IOX3) , 30, whid (IOX3) . Cells w6 confluent HL-1 cells using a QiaAmp kit (Qiagen). cDNA was synthesized from the RNA template using a high-capacity transcriptase kit . Quantitative PCR amplification was performed using the Real-Time PCR System (Thermo Fisher Scientific Life Sciences). The PCR program had an initial heat-activation step of 95°C for 10 min. Forty cycles of thermocycling were performed with a denaturation step at 95°C for 15 s and an annealing and extension step at 60°C for 1 min. Fluorescence was measured following each extension step. After amplification, a melting curve was acquired and used to determine the PCR product specificity. Relative quantification of target gene expression was normalized to the housekeeping gene, β-actin, and performed using a 2−∆∆Ct method.Primers were designed using Primer3 software based on GenBank or EnsemblGenomes browser searches and obtained from Eurofins MWG Operon . References to primer sequences included the following: PPARα, NM_011144.6; PPARβ/δ, NM_011145.3; PPARγ, NM_011146.3; MCAD, NM_007382.4; UCP3, NM_009464.3; PDK4, NM_013743.2; VEGF, NM_009505.4; β-actin, NM_007393.3. Total RNA was extracted and purified from frozen heart and skeletal muscle tissues using a QiaAmp DNA Mini Kit . For cells, total RNA was extracted and purified from ≤5 × 10Powdered frozen cardiac or skeletal muscle was added to 300 µl lysis buffer and homogenized for 30 s, as previously described . ConflueEqual concentrations of protein from cardiac and skeletal muscle samples were loaded onto 12% SDS-PAGE gels, separated using electrophoresis, and transferred onto Immobilon-P membranes . Cardiac and skeletal muscle proteins were detected using the following polyclonal antibodies and dilutions in 5% milk: PGC-1α and MCAD , 1:500; GLUT1 , 1:1000; GLUT4 , 1:4000; PDK4 and UCP3 , 1:500 and 1:2500; MTE-1 , 1:2000; RXRα , 1:1000; HIF-1 and -2α , 1:2000 and 1:500.Secondary antibodies were horseradish peroxidase conjugate polyclonal with goat anti-rabbit specificity , all diluted to 1:3500, with the exception of GLUT1 and -4, HIF-1α (1:2000) and -2α (1:1000), and MCAD (donkey anti-goat). Consistent protein loading and transfer were confirmed by Ponceau staining and protein levels related to internal standards to ensure homogeneity between samples and gels. Bands were quantified using UN-SCAN-IT software , and all samples were run in duplicate on separate gels to confirm results.2, 0.1 mM EDTA, 0.1 mM NADH, 1 mM ADP, 4.5 U/ml lactate dehydrogenase, and 1 mM phosphoenolpyruvate. The PGK activity was recorded at 25°C, 340 nm, in an assay medium containing 50 mM imidazole buffer (pH 7.6), 2 mM MgCl2, 0.1 mM EDTA, 1 mM ATP, 5 mM 3-phosphoglycerate, and 0.2 mM NADH.The myocardial enzymatic activities of 3-phosphoglycerate kinase (PGK) and pyruvate kinase (PK) were measured using coupled enzyme assays . Frozen,Cardiac lipids were extracted from freeze-clamped tissue (25–50 mg) using 10 ml 2:1 chloroform:methanol solution as previously described , 28. Aftsem) were analyzed using 3-factor ANOVAs, with individual comparisons subsequently performed using Student's t tests. Results were considered significantly different at P < 0.05.Grouped data  and hematocrit by 33% (P < 0.001) but did not alter body weight. Established HIF-1α downstream targets, cardiac VEGF and PDK1 protein, increased 3.6-fold (P < 0.001) and 1.8-fold (P < 0.01), respectively. LV mass decreased by 19% (P < 0.001) following hypoxia, whereas right-ventricular (RV) mass was unaltered. Plasma metabolites in wild-type mice were unchanged by hypoxia .To investigate functional and metabolic responses of the P < 0.05), with PPARδ mRNA and PPARγ, RXR, and PGC-1α protein levels remaining unchanged . In parallel with lower PPARα mRNA levels, hypoxia decreased protein levels of downstream targets of PPARα: UCP3, MTE-1, and PDK4 (P < 0.05) lower, but proteins involved in mitochondrial respiration were unchanged following hypoxia .Hypoxia in chow-fed, wild-type mice decreased cardiac PPARα mRNA levels by 36% (and PDK4 B. MCAD pP < 0.01) and lactate efflux 2.4-fold (P < 0.01), measured in isolated, beating, perfused hearts using [3H] substrate labeling . Fatty acid oxidation was 26% (P < 0.02) lower in hearts from hypoxic mice compared with normoxic controls (P < 0.05) higher in hypoxic mice. In isolated, perfused hearts, [PCr] was 31% (P < 0.001) lower, but [ATP] was unaltered by hypoxia, measured using [31P] NMR spectroscopy and HPLC (P < 0.05) lower following hypoxia. Although [ADP]free increased 2-fold (P < 0.02), the calculated ΔGATP was preserved. In vivo cine MRI showed that 3 wk of hypoxia did not alter cardiac output, cardiac index, or ejection fraction, despite an 18% (P < 0.02) lower stroke volume (SV), resulting from a 17% (P < 0.05) lower end-diastolic volume (EDV) in hypoxic mice (Hypoxia increased cardiac glycolysis 2.3-fold (labeling C. HypoxiP < 0.01) loss of body weight and a 19% (P < 0.01) decrease in LV mass . High-fat feeding increased PPARα mRNA expression in both normoxia and hypoxia (P < 0.05), preventing any hypoxia-induced decrease (P < 0.001), and MCAD protein increased by 39% (P < 0.01) with high-fat feeding under both normoxia and hypoxia . High-fat feeding did not significantly increase cardiac PDK4 protein, although it prevented the decrease caused by hypoxia in chow-fed mice.The cardiac metabolic and energetic changes observed in hypoxic mice may have been caused by decreased PPARα expression. To investigate whether PPARα expression is central to hypoxic adaptation, we prevented PPARα down-regulation by increasing PPARα expression using high-fat (55% fat) feeding. The feeding of mice a high-fat diet for 3 wk in normoxia did not alter body weight, but with hypoxia, caused a 10% (P < 0.05), which remained unaltered by hypoxia , both unaffected by hypoxia. High-fat feeding decreased cardiac [PCr] by 25% (P < 0.05) in normoxic mice, but [PCr] did not decrease further with hypoxia (P < 0.05) lower in hearts from hypoxic high-fat–fed mice compared with chow-fed normoxic mice. Total Cr was unaltered in normoxic high-fat–fed hearts but was 37% (P < 0.05) lower in hearts from high-fat–fed mice exposed to hypoxia. [ADP]free was 55% (P < 0.05) higher in hearts from normoxic high-fat–fed mice compared with normoxic chow-fed mice and increased by a further 38% with hypoxia (P < 0.05). High-fat feeding significantly lowered cardiac ΔGATP in all mouse hearts.High-fat feeding prevented the increase in cardiac glycolysis and lactate efflux that occurred in chow-fed mice in hypoxia, and decreased GLUT4 protein by 59% ( hypoxia C. High-f hypoxia E. [ATP] in vivo cardiac output by 24% compared with normoxic chow (P < 0.01) or high-fat-fed (P < 0.05) mice. This was a result of a 35% (P < 0.001) lower SV, accounted for by a 28% (P < 0.01) lower EDV and despite a significantly higher heart rate. The cardiac index was decreased by 15% (P < 0.05), and ejection fractions were 12% (P < 0.02) lower than in normoxic chow-fed mice. Hence, high-fat feeding prevented the adaptive molecular and metabolic responses to hypoxia that were observed in chow-fed mouse hearts, thereby causing cardiac dysfunction.High-fat feeding under normoxia had no effect on cardiac function F, wherea−/− mice were fed a chow diet and housed under hypoxia for 3 wk. PPARα−/− mice had 13% (P < 0.05) lower body weight and 18% (P < 0.05) lower LV mass than wild-type mice, with neither altered by hypoxia . Cardiac PPARα mRNA levels in PPARα−/− mice were negligible, although PPARδ mRNA and PPARγ, RXR, and PGC-1α protein levels were normal and unchanged by hypoxia . Cardiac UCP3 protein (P < 0.001) lower, and MTE-1 was 49% (P < 0.02) lower in PPARα−/− mouse hearts compared with wild-type mice, and both were unchanged by hypoxia. Other key mitochondrial proteins were unaltered relative to wild-type mice . PDK4 was 45% lower (P < 0.01), and MCAD protein was 43% (P < 0.001) lower in normoxic and hypoxic PPARα−/− mouse hearts.Having demonstrated that increased PPARα was associated with impaired function of the hypoxic heart, we next determined whether loss of PPARα altered cardiac metabolic and functional responses to hypoxia. PPARα protein B was 71%−/− mouse hearts had 3-fold (P < 0.01) higher glycolytic rates than normoxic wild-type mouse hearts and were unaffected by hypoxia . Lactate efflux from hypoxic PPARα−/− mouse hearts was 2-fold (P < 0.01) higher than from normoxic wild-type hearts and not different from normoxic PPARα−/− hearts. Cardiac GLUT4 protein was similar in normoxic and hypoxic PPARα−/− mice. Consistent with PPARα being a regulator of fatty acid metabolism, PPARα−/− mice had 24% lower (P < 0.05) cardiac fatty acid oxidation (P < 0.05) in normoxic and hypoxic PPARα−/− mouse hearts. Thus, the lack of PPARα prevented the metabolic changes that were observed in hypoxic wild-type mouse hearts.PPARα hypoxia C. Glycolxidation D than wi−/− mice, [PCr] was decreased by 43% (P < 0.001), although [ATP] was unaltered compared with normoxic wild-type mouse hearts (P < 0.001) and 34% (P < 0.05), respectively, in PPARα−/− mice. Total Cr was decreased by 20% (P < 0.001) in PPARα−/− mouse hearts with no effect of hypoxia. As a consequence, [ADP]free was 2.7-fold (P < 0.05) higher and ΔGATP significantly lower in all PPARα−/− mouse hearts, and unaltered by hypoxia.In PPARαe hearts E. Hypoxi−/− mice had normal cardiac function, but hypoxic PPARα−/− mice had 23% lower EDV (P < 0.01), 19% lower ESV (P < 0.05), 29% (P < 0.01) lower SV, and therefore, 20% lower cardiac output than normoxic wild-type mice . Cardiac PPARα and PPARδ mRNA levels and PPARγ and RXR protein concentrations were unaltered by high fat feeding, being the same as in the chow-fed PPARα−/− mice, with no effect of hypoxia  and 18% lower with hypoxia (P < 0.01), similar to chow-fed PPARα−/− mice exposed to chronic hypoxia . Ejection fraction was 6% lower in high-fat–fed PPARα−/− mice and unaltered by hypoxia (P < 0.05). Therefore, PPARα ablation nullified the metabolic flexibility seen in healthy mice fed a high-fat diet, resulting in impaired cardiac function and unaffected by hypoxia.To test whether high-fat feeding was operating  hypoxia B. Cardia without C, D. Car hypoxia E. CardiaSupplemental Table 1), energy intake was measured during the 3-wk protocol. Food consumption was not affected by hypoxia or by the high-fat diet (P < 0.05) higher energy intake on the high-fat diet, which again was unaffected by hypoxia. Cardiac levels of UCP3 and MTE-1 were elevated by high-fat feeding (P < 0.05) lower after 3 wk of hypoxia , a similar response to that in cardiac muscle (P < 0.001) and were not altered by hypoxia. Skeletal muscle MTE-1 protein levels showed identical responses to those of UCP3, being 34% (P < 0.05) lower in hypoxic, chow-fed mice and 71% (P < 0.001) higher after high-fat feeding with/without hypoxia. PDK4 protein levels increased by 55% (P < 0.01) during high-fat feeding, which again prevented the adaptive responses to hypoxia.To investigate the weight loss observed in wild-type hypoxic mice fed a high-fat diet  for 24 h, or the HIF system was chemically induced using 24 h incubation with the selective prolyl hydroxylase inhibitor, IOX3 (P < 0.001) and a 72% increase in HIF-2α (P < 0.05) protein levels . IOX3 was a more potent activator of the HIF system than hypoxia, with a 9-fold increase in HIF-1α (P < 0.001) and doubling of HIF-2α (P < 0.01) protein levels after 24 h incubation. Under HIF-1α control, VEGF mRNA increased by 37% (P < 0.05) in hypoxia and 59% (P < 0.001) with IOX3. PDK1 protein levels increased 34% in hypoxia (P < 0.05) and 86% with IOX3 treatment (P < 0.01). GLUT1 protein levels increased by 48% (P < 0.02) with hypoxia and 3-fold (P < 0.01) with IOX3.Having established that adaptation to cardiac hypoxia was regulated by PPARα, we used confluent, beating HL-1 cardiac cells to investigate whether such adaptations were directly linked to the HIF system. Cells were exposed to hypoxia  in HL-1 cells . PPARδ mRNA levels were unaffected by hypoxia or IOX3. PPARγ mRNA levels were not altered by hypoxia but were doubled (P < 0.001) after IOX3 incubation.Hypoxia decreased PPARα mRNA by 23%  by hypoxia but were not altered by IOX3. MCAD levels were 31% (P < 0.001) lower after hypoxia and 17% (P < 0.01) lower after IOX3. PDK4 levels were unaffected by hypoxia or IOX3.In cultured cardiac cells, PPARα-controlled mRNA expression was altered by hypoxia in a similar manner as the respective protein in the ic heart C. UCP3 lIn this study, we have demonstrated that normobaric hypoxia in mice decreased myocardial PPARα expression, fatty acid oxidation, and mitochondrial UCP3 and increased carbohydrate metabolism; such changes allowed normoxic [ATP] and contractile function to be maintained. Genetic ablation of PPARα in mice caused metabolic changes in the normoxic mouse heart that were already at the extreme of hypoxic adaptation and therefore, could not be altered by hypoxia. A high-fat diet increased mouse heart PPARα expression and prevented PPARα down-regulation by hypoxia. Thus, with high-fat feeding, the cardiac proteins under PPARα control  and fatty acid oxidation increased, whereas GLUT4 levels and glycolysis decreased; metabolic changes that remained in hypoxia. Consequently, the high-fat diet plus 3 wk exposure to hypoxia significantly decreased myocardial [ATP], cardiac index, and ejection fraction. To our knowledge, this is the first time that an increase in a dietary constituent, fat, has been shown to cause myocardial dysfunction in hypoxia, a finding that may have implications for altitude exposure  or heartin vivo hypoxia , low fatty acid oxidation, and low mitochondrial UCP3 and MTE-1 levels, all of which would have resulted in the most efficient use of any available oxygen. Thus, metabolism in the normoxic PPARα−/− mouse heart may be viewed as at the extreme of hypoxic adaptation, which increases resistance to ischemia  (−/−) (−/−) transgenic mice]  , increas−) (−/−) , and decic mice]  had thei−/− mouse heart can be viewed as at the extreme of hypoxic metabolic adaptation, in that they have no metabolic response to hypoxia.In summary, we propose that decreased myocardial PPARα expression is central to the metabolic changes required to maintain contractile function in hypoxia. A high-fat diet increased PPARα expression, fatty acid oxidation, and mitochondrial UCP levels—changes that were tolerated in normoxia, but decreased ATP and impaired cardiac function in hypoxia. Finally, the metabolic changes in normoxic PPARα


MAO-A), which influences the transcription rate of MAO-A. Examination of how MAO-A phenotypes impact behavioral and event-related potentials (ERPs) correlates of recognition memory may help explain individual differences in recognition memory performance. Therefore, the current study uses electroencephalography (EEG) in combination with genetic phenotyping of the MAO-A gene to determine how well-characterized ERP components of recognition memory, the early frontal old/new effect, left parietal old/new effect, late frontal old/new effect, and the late posterior negativity (LPN) are impacted by MAO-A phenotype during item and source memory. Our results show that individuals with the MAO-A phenotype leading to increased transcription have lower response sensitivity during both item and source memory. Additionally, during item memory the left parietal old/new effect is not present due to increased ERP amplitude for correct rejections. The results suggest that MAO-A phenotype changes EEG correlates of recognition memory and influences how well individuals differentiate between old and new items.A critical problem for developing personalized treatment plans for cognitive disruptions is the lack of understanding how individual differences influence cognition. Recognition memory is one cognitive ability that varies from person to person and that variation may be related to different genetic phenotypes. One gene that may impact recognition memory is the monoamine oxidase A gene ( MAO-A). The MAO-A gene has a well characterized functional polymorphism that affects transcription of the MAO-A protein leading to changes in enzymatic activity  showing increased transcription  perform better at spatial navigation in a virtual Morris water maze, a task used to assess episodic memory. The Mueller et al.  in combination with examination of MAO-A phenotype during performance of recognition memory tasks to determine how MAO-A may influence an individual’s memory performance.Genetic and pharmacological manipulation of MAO-A function in preclinical models, as well as different phenotypes of the r et al.  show thar et al.  study shMAO-A phenotype may reveal different strategies used to perform the memory tasks and may help resolve the conflicting findings for MAO-A activity levels from preclinical studies and human behavioral genetic studies. If memory processing is enhanced in the Low MAO-A group as suggested by the preclinical literature, then we would expect a larger FN400 and/or a larger left parietal old/new effect. However, if it is the High MAO-A group that leads to better memory as suggested by Mueller et al.  signals associated with recognition memory. There are three old/new effects, where the ERP amplitude is more positive for hits than correct rejections  were placed in the High MAO-A group, and 22 participants  were placed in the Low MAO-A group.This study was carried out in accordance with the recommendations of the Institutional Review Board of the University of Colorado with written informed consent from all subjects in accordance with the Declaration of Helsinki. The protocol was approved by the Institutional Review Board of the University of Colorado. Participants for the study were recruited from the University of Colorado Boulder community. Data from these same participants examining how the 5HTTLPR serotonin transporter polymorphism affects ERP’s and how dopamine transporter polymorphisms affect ERP’s and oscillatory power during recognition memory has been reported elsewhere . During the encoding task, participants encoded stimuli as they carried out a Place task and a Pleasantness task  was used to collect scalp voltages from participants during the testing phase of the experiment. Individual sensors were adjusted until impedances were less than 50 kΩ, and amplified analog voltages (0.1–100 Hz bandpass) were digitized at 250 Hz.n = 2). The data was filtered from 0.1 Hz to 40 Hz and re-referenced to the average signal. Then the data was epoched from 800 ms before stimulus presentation to 1500 ms after stimulus presentation (−800 ms to 1500 ms) for hit and correct rejection trials. A baseline of −800 ms to 0 ms was used for each epoch. After the data was split into epochs, participant data was put through a moving window artifact rejection where channel voltages registering a 100 mV change were rejected in 50 ms bins of 100 ms length. After artifact rejection, participants were required to have 20 artifact free epochs in each of the hit and correct rejection bins. Across participants, there was an average of 112.71 ± 38.13 (mean ± SD) hit trials and 106.07 ± 34.52 correct rejection trials in the item memory task and 113.48 ± 35.8 hit trials and 106.1 ± 38.86 correct rejection trials in the source memory task.EEGLAB  were placed in the High MAO-A group (n = 37). Males with a 3R variant and females heterozygous (3R/4R) or homozygous for the three repeat version (3R/3R) of the MAO-A gene were placed in the Low MAO-A group (n = 22). In females (n = 22), the genotypes were distributed according to Hardy-Weinberg equilibrium . One participant was left out of the grouping as they had a 4R/5R allelic variant.Saliva samples were collected from subjects using a commercial product  and allelic variants of the p < 0.05, 2 (hit vs. correct rejection) × 2 (High vs. Low MAO-A group) repeated measures analysis of variances (ANOVAs) were performed for item and source memory tasks independently, in order to determine any differences in reaction time and accuracy between genotype groups. Additionally, response sensitivity measured using da, and response bias measures with ca were compared between the High and Low MAO-A groups with independent samples t-tests.Reaction times and proportion correctly identified were calculated for the item memory and source memory task conditions. In the item memory task, hits were defined as old items successfully identified as old while correct rejections were new items correctly identified as new. For the source memory task analysis, source hits were used, which were defined as stimuli where the correct source was identified. Using a significance of MAO-A group as factors. The final ANOVA was a 2 × 2 × 2 ANOVA assessing the LPN in the LPS and RPS 1000–1500 ms ROI using hemisphere, condition, and MAO-A group as factors.We analyzed the item and source data using groupings of seven electrodes for different scalp locations  within specific timeframes Figure , similart-test examining response sensitivity (da) during item memory revealed a significant difference (t(57) = 2.4, p = 0.02, Cohen’s d = 0.71) between the High MAO-A  and Low MAO-A  between the two groups  = 1.76, p = 0.19, partial η2 = 0.03), a non-significant main effect of group  = 3.04, p = 0.087, partial η2 = 0.051), and a non-significant condition × group interaction  = 0.213, p = 0.646, partial η2 = 0.004; Figure F = 0.005, p = 0.943, partial η2 < 0.001) or a main effect of group  = 0.168, p = 0.683, partial η2 = 0.003). There was a significant main effect of condition  = 40.415, p < 0.001, partial η2 = 0.415; Figure The independent s Figure . The 2 (t-test examining response sensitivity (da) during source memory revealed decreased response sensitivity in the High MAO-A (mean ± SEM = 1.27 ± 0.10) compared to the Low MAO-A group  = 2.05, p = 0.045, Cohen’s d = 0.57) with no difference in response bias  = 15.42, p < 0.001, partial η2 = 0.21), where accuracy for correct rejections was better than for hits across groups. However, there was no main effect of group and no condition × group interaction  = 1.57, p = 0.087, Cohen’s d = 0.45), where those in the High-MAO-A group made more source misattributions (35.86 ± 1.4%) than the Low-MAO-A group (31.26 ± 2.5%). The examination of reaction time during source memory revealed a significant main effect of condition  = 53.14, p < 0.001, partial η2 = 0.48), where hits took significantly longer than correct rejections. There was also a significant main effect of group  = 5.63, p = 0.021, partial η2 = 0.09) with those in the High-MAO-A group being faster across both hits and correct rejections , MAO-A group , and the condition × MAO-A group interaction  were not significant. There was a main effect of hemisphere  = 6.97, p = 0.011, partial η2 = 0.11), with LAS showing significant higher mean amplitudes than RAS across both hits and correct rejections. No other interactions were significant  × 2 (hits vs. correct rejection) × 2 (MAO-A group) examining mean ERP amplitude in the LAS and RAS ROIs during item memory did not reveal any interaction between ERP amplitude and MAO-A genetic variant. The main effect of condition  = 4.89, p = 0.031, partial η2 = 0.08) and a significant condition × MAO-A group interaction  = 4.20, p = 0.045, partial η2 = 0.069). The main effect of group was not significant  = 1.51, p = 0.224, partial η2 = 0.03). A post hoc paired samples t-test comparing mean hit (0.85 ± 0.26 μV) to correct rejection (0.33 ± 0.28 μV) amplitude within the MAO-A Low group showed a significant difference (t(21) = 3.28, p = 0.004, Cohen’s d = 0.41), revealing the presence of the parietal old/new effect. Critically, there was no significant difference between mean hit (1.11 ± 0.31 μV) and correct rejection (1.09 ± 0.28 μV) amplitudes in the MAO-A High group (t(36) = 0.133, p = 0.90, Cohen’s d = 0.01) suggesting the parietal old/new effect is not present in individuals with high MAO-A transcription efficiency  = 0.59, p = 0.56, Cohen’s d = 0.16) and marginal difference in mean CR amplitude (t(57) = 1.8, p = 0.07, Cohen’s d = 0.50) between groups. These results suggest that the lack of a parietal old/new effect in the High MAO-A group may be due to an increase in mean CR amplitude.The 2 (condition) × 2 (MAO-A) group repeated measures ANOVA examining mean ERP amplitude in the LPS ROI 500–800 ms post-stimulus presentation revealed a significant main effect of condition  = 6.59, p = 0.013, partial η2 = 0.104) with mean hit amplitude across groups being higher than mean CR amplitude. There was no main effect of group  and no significant condition × MAO-A group interaction  × 2 (MAO-A group) ANOVA examining mean ERP amplitudes in RAS 1000–1500 ms post-stimulus presentation revealed a significant main effect of condition  = 22.52, p < 0.001, partial η2 = 0.28) with mean hit amplitude being significantly lower than mean correct rejection amplitude  × 2 (hemisphere) × 2 (MAO-A group) repeated measures ANOVA in the LPS and RPS ROIs 1000–1500 ms post-stimulus presentation. The ANOVA revealed a significant main effect of condition  = 6.51, p < 0.01, partial η2 = 0.10) with mean hit amplitude being greater than mean correct rejection amplitude  = 6.03, p = 0.017, partial η2 = 0.1) where the left hemisphere had higher ERP amplitudes. However, there were no significant interactions with MAO-A group nor was there a main effect of MAO-A group suggesting that the FN400 during source memory is not affected by MAO-A transcription efficiency.The 2 (hemisphere) × 2 (hits vs. CR) × 2 (MAO-A group) examining mean ERP amplitude in the LAS and RAS ROIs revealed a significant main effect of condition  = 10.45, p < 0.01, partial η2 = 0.16; Figures post hoc tests to verify that the parietal old/new effect during source memory was present in both groups. The post hoc paired samples t-tests revealed the presence of the parietal old/new effect in both the High MAO-A (t(36) = 2.649, p = 0.012, Cohen’s d = 0.44) and Low MAO-A groups (t(21) = 2.496, p = 0.02, Cohen’s d = 0.53). However, there was no main effect of group or a condition × group interaction suggesting that MAO-A phenotype does not influence the left parietal old/new effect during source memory.The 2 (hits vs. CR) × 2 (MAO-A group) ANOVA comparing mean ERP amplitudes in the LPS ROI 500–800 ms post-stimulus presentation revealed a main effect of condition  = 11.27, p < 0.01, partial η2 = 0.17). There was also a main effect of group  = 7.36, p < 0.01, partial η2 = 0.11) where mean ERP amplitudes in the Low MAO-A group (0.91 μV ± 0.21) were higher than in the High-MAO-A group (0.20 μV ± 0.16). However, there was no condition × MAO-A group interaction  = 0.066, p = 0.799, partial η2 = 0.001; Figures The 2 (condition) × 2 (MAO-A group) ANOVA comparing mean ERP amplitudes 1000–1500 ms post-stimulus presentation in the RAS ROI revealed a significant main effect of condition  = 26.382, p < 0.001, partial η2 = 0.32) with hits significantly more negative than CR  × 2 (hemisphere) × 2 (MAO-A group) ANOVA comparing mean ERP amplitudes in the LPS and RPS ROI’s 1000–1500 ms post-stimulus presentation revealed a significant main effect of condition  which may explain the lack of differences in proportion of correct responses and source misattribution differences between the High and Low MAO-A groups. However, our study relies on examining how ERP correlates of recognition memory change and the effect size observed for the left parietal old/new effect in the Low MAO-A group is moderate (Cohen’s d = 0.41). Additionally, the effect size observed for response sensitivity differences between groups was moderate (Cohen’s d = 0.57) during source memory and large (Cohen’s d = 0.71) during item memory. The medium to large effect sizes combined with the finding that on two different test days for two different memory tasks, item and source response sensitivity was lower for High MAO-A participants gives us confidence that MAO-A phenotype influences response selectivity and the left parietal old/new effect.Studies examining the relationship between SNP’s and behavioral changes in cognitive abilities have small effect sizes (Chabris et al., MAO-A gene impact EEG correlates of both item and source recognition memory. Pre-clinical studies that either knock-out the MAO-A gene (Kim et al., MAO-A gene or inhibition of MAO-A activity, have higher response sensitivity scores and a left parietal old/new effect, suggesting the presence of a stronger memory signal. In animals, this stronger memory signal resulting from lower MAO-A enzymatic activity may lead to increased performance. In contrast, individuals with the MAO-A phenotype leading to high levels of MAO-A enzymatic activity may have increased physiological noise and may response more carelessly leading to a decreased ability to differentiate old and new items.One potential path to understanding individual differences in cognitive function is to examine how different genetic phenotypes impact the neurophysiology supporting cognition. In our study, we examined how phenotypes of the RSR analyzed the behavioral and EEG data, interpreted the data and wrote the manuscript. AS conducted all genetic testing and edited the manuscript. TC helped conceptualize the project and edited the manuscript. EN conceptualized the project, collected the data and edited the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Dear Editor,2-microglubulin (β2m). Optimizing FcRn-IgG interaction through Fc engineering is an effective strategy to improve pharmacokinetic (PK) or pharmacodynamics (PD) properties of therapeutic antibodies  of antibodies in vivo , a novel therapeutic mAb against HBV. This mAb binds to an unique epitope on HBsAg and potently suppress levels of HBsAg and HBV DNA for several weeks in HBV transgenic mice  binding with mutations in the CH2-CH3 interface, which have been reported to modulate binding to hFcRn, transplacental transport, and serum half-life. Our preliminary results suggested that only these five Fc mutants, T307A/E380A/N434A (AAA) . The binding of Fc variants at pH 7.4 were comparably improved with the same ranking of binding affinity as that at pH 6.0  at the lower pH of the early endosome (pH 6.0–6.5) and dissociates at the neutral pH of blood (pH 7.4), we established a CLEIA assay to screen antibodies for hFcRn binding at pH 6.0 and pH 7.4, respectively. As expected, hFcRn was shown to bind WT huE6F6 and Fc mutants in a strictly pH-dependent manner, with strong binding at pH 6.0 but reduced interaction at pH 7.4 Fig. B. Furthe6.0 Fig. C.50 values indicated that the YTE variant with IC50 values of 24.7 μg/mL performed about 40-fold better than did WT huE6F6 in competitive binding to hFcRn at pH 6.0 , which was used in analyzing the PK behavior in vivo. Other Fc variants showed comparatively lower IC50 values of 100–500 μg/mL  epithelial cell line. Dylight-594 labeled human IgG was used as competitor. Comparison of the ICn = 6 animals per antibody). The relevant PK parameters and average serum concentration time profiles were shown in Table 1/2 = 20.1 ± 7.5 h; YTE, t1/2 = 30.9 ± 10.8 h; P < 0.05; Table P < 0.05; Table inf) was ~1.2-fold higher for the YTE variant  than for WT huE6F6  dose of 10 mg/kg antibody . Following a single i.v. dose of 20 mg/kg antibody, the PK profile of the YTE variant was found to be distinct from that of the WT. YTE variant exhibited 2.0-fold increased AUCinf , 2.5-fold prolonged t1/2  and 2.1-fold reduced serum clearance  when compared with WT huE6F6 in cynomolgus monkeys  . A remarkable increase in AUCinf, 1.8-fold  and a significant, 2-fold, decrease in CL  was observed for the YTE variant as compared to WT    dose of antibody . But for chronic viral infection, the antibody infusion requires a high-level dose  and more frequent dosing to effectively eradicate the circulating virus, which may induce severe adverse events in patients. Therefore, engineering of huE6F6 to increase its serum half-life offers the potential benefits of greater efficacy, reduce cost, lower dosage and less frequent dosing. It is of great significance for the development of Fc-engineered E6F6-based therapeutics used in CHB treatment.P > 0.05)  into the Fc portion of humanized anti-VEGF antibody  Fig. G. We proin vivo pharmacokinetic study has important implications for IgG variants with long half-lives in the CHB clinical setting. Though the pharmacokinetics, safety, and efficacy of this molecule have yet to be studied in humans, our work so far demonstrates its potential benefits for improving compliance with more convenient, long-term dosing. This may ultimately improve the clinical outcome of treatment.This is the first preclinical study to evaluate the pharmacokinetics of an anti-HBV humanized and Fc-modified monoclonal antibody in mice and nonhuman primates, demonstrating a significant increase in serum half-life of up to 300 h with the Fc YTE triple mutation in cynomolgus monkeys. Our Supplementary material 1 (PDF 344 kb)Below is the link to the electronic supplementary material.


The aim of the present study was to evaluate the different doses of Butyric acid (BA) and Arsenic (As) in liver mitochondria oxidative stress and pancreatic islet insulin secretion of male mouse.BA is found in many foods and As as a toxic metal is present in drinking water. They can induce oxidative stress in tissues. In this experimental study, Liver mitochondria were isolated by administration of the different centrifugation method and pancreatic islets were isolated by collagenase method. Mitochondria were incubated by BA  and As  as the islets were incubated by BA  and As  for 1 hour. At the end of the experiment, mitochondrial viability and membrane potential, ROS, MDA, GSH and islets insulin secretion were measured by their specific methods.p<0.05). The doses of BA 75μM and As 100μM have been revealed the most mitochondria toxic concentrations. Also, the doses of 1000μM for BA and 100μM for As were considered as reducing concentrations for islets insulin secretion. Additionally, co administration of them intensified more these effects BA and As administration increased mitochondrial levels of ROS, MDA and decreased GSH and pancreatic islet insulin secretion in a dose dependent manner (Alone or in combination administration of BA and As induced oxidative stress in liver mitochondria and decreased insulin secretion of pancreatic islets. Butyric acid (BA) as an anaerobic fermentation product has been found in milk, butter, parmesan cheese. Milk fatty acid contents such as BA immediately absorbed in the upper intestine and taken up by the liver immediately. The effective concentration of BA depends on the rate of hydrolysis, volume of fluid transit, and the balance of material in the meal. So, it is highly possible that several tissues are exposed to millimolar levels of BA followed by administration of milk fat . As the Arsenic (As) is an Earth’s crust toxic metal and distributed in the soil, drinking water and food . BetweenIn our previous study it was revealed that concomitant administration of As and high fat diet (HFD) induced a different form of type2 diabetes that characterized by deficiencies of oral glucose tolerance test (OGTT) and islet insulin secretion through the mitochondrial oxidative stress enhancement . So, sinStudy design Experimental Animals Adult male NMRI mice (30-35 g) were obtained from the animal facility of the Ahvaz Jundishapur University of Medical Science (AJUMS), which is completelyattributed by AJUMS animal care guidelines with an ethics committee grantee No. IR.AJUMS.REC.1394.604.Theyhoused in an air-conditioned room with controlled temperature of 20±4°C, humidity of 70-80% and maintained on a 12:12 h light cycle with free access to food and water. ChemicalsSodium arsenite (NaAsO2),Butyric acid(short chain fatty acid), 4-2-hydroxyethyl-1-piperazineethanesulfonic acid (HEPES), DMSO, D-mannitol, thiobarbutiric acid (TBA), MTT , dithiobis-2-nitrobenzoic acid (DTNB), reduced glutathione (GSH), 2′,7′-dichlorofluorescein diacetate (DCFH-DA), sodium succinate, sulfuric acid, Tetramethoxypropane (TEP), sucrose, KCl, Na2HPO4, MgCl2, MnCl2,potassium phosphate,Rhodamine 123 (Rh 123), Coomassie blue, ethylenediaminetetraacetic acid(EDTA) and bovine serum albumin (BSA) were purchased from SigmaChemical Co. . Experimental procedure Mitochondrial isolationLiver mitochondrial isolation was performed using centrifugation method as described previously . The aniIslet isolation22.56mM, MgCl21mM, NaHCO310mM, HEPES 15mM, supplemented with 0.5% bovine serum albumin (BSA) and stabled with a mixture of 95% oxygen, 5% carbon dioxide, pH 7.4) and centrifuged at 100×g for 5 min complex II to formazan. At the first, 100 μL of mitochondrial suspension (0.5 mg protein/mL) was incubated with different concentrations of As or BA for 1 hour. Then, 50 μL MTT 0.4% was added to this medium and incubated at 37°Cfor 30 min. Finally, the product of purple formazan crystals was dissolvedin100 μL dimethyl sulfoxide (DMSO), and the absorbance was measured at 570 nm by a spectrophotometer (UV-1650PCShimadzu) .Mitochondrial Membrane Potential AssessmentMitochondrial membrane potential (MMP) was measured by mitochondrial uptake of rhodamine 123 with fluorescence spectrophotometer. The mitochondrial fractions (0.5 mg protein/mL) were incubated with various concentrations of As and BA for 1 h. Then, 10 μM of rhodamine 123 (Rh123) was added to the mitochondrial solution. The capacity of mitochondria for Rh123 uptake the was calculated as a difference between control and treated mitochondria by the measurement of Fluorescence intensity spectrofluorometerically  , 33.Mitochondrial ROS AssessmentThe levels of ROS were measured by adding 1 mL of dichloro-dihydro-fluorescein diacetate (DCFH-DA) 3.32M to the 1 mL of isolated mitochondria sample. DCFH-DA enters into mitochondria and hydrolyzes to non fluorescent DCFH. Then, oxidized to form highly fluorescent 2,7-dichlorofluorescein through the reaction with ROS. Further, the fluorescence intensity was measured by a fluorescence spectrophotometer  .Lipid Peroxidation MeasurementThe content of MDA was determined using thiobarbituric acid reactive substances (TBARS) formation. The mitochondrial fractions (0.5 mg protein/mL) were incubated with various concentrations of As and BA for 1 h at 30 °C. Then, 1mL of the separated mitochondrial solution was mixed with 250µL trichloroacetic acid (70%) and centrifuged at 3000×g for 15 min. The supernatant was added to 1mL TBA (0.8%) and placed in a boiling water bath for 30 min. The sample absorbance was read at 412 nm on a spectrophotometer. Values were expressed as µg/mg protein. Since 99% of the TBARS were malondialdehyde (MDA), TBARS concentrations of the samples were calculated from a standard curve using 1, 1, 3,3-tetramethoxypropane ,36.Mitochondrial GSH MeasurementReduced glutathione (GSH) was analyzed using DTNB  as an indicator. The mitochondrial sample (0.5 mg protein/mL) was incubated with various concentrations of As and BA for 1 h at 30°C. Then, 0.1 mL of mitochondrial fractions was added into the 0.1 mol/L phosphate buffers with 0.04% DTNB in a total volume of 3.0 mL (pH 7.4). The developed yellow color was spectrophotometrically read at 412 nm  .Islet’s insulin secretion Ten manually dissected islets were transferred into the 2mL micro tubes containing Krebs-bicarbonate buffer with 5.6mM of d-Glucose concentrations (similar to fasting blood glucose).The diffEthical statementPrior to the experiment, all protocols were confirmed to be in accordance with the Guidelines of Animal Ethics Committee of Ahvaz Jundishapur University of Medical Sciences.This research was approved by the ethical committee of the Ahvaz Jundishapur University of Medical Sciences with an ethics committee number (IR.AJUMS.REC.1394.604).Statistical Assessmentp<0.05.The obtained data were statistically analyzed by using SPSS software as mean ± standard error of mean (SEM) with one-way analysis of variance (ANOVA) and, post hoc least significant difference (LSD) tests. Further, the differences were considered statistically significant at Effects of BA and As exposure on liver mitochondria viabilityp<0.05), 75µM (p<0.001), 150µM (p<0.01) and 300µM (p<0.001) compared to BA 0µM. Further, BA 300 µM utilization reduced this variable when compared to BA 35 µM (p<0.05) (p<0.05) and 100, 200µM (p<0.01) in comparison with As 0µM (p<0.001) . The results of BA administration showed a significant decrease in the MTT level at a dose of 35µM ((p<0.05) . Also, th As 0µM . Furtherp<0.001) .Effect of BA and As exposure on mitochondrial membrane damagep<0.01). This variable increased in BA 75 µM group compared to BA 35 µM (p<0.05). Also, As 100, 200 µM administration increased the levels of ΔΨ in comparison with As 20, 50 µM (p<0.05) (p<0.01)  . Finally(p<0.05)  revealed(p<0.01) . Effect of BA and As exposure on mitochondrial oxidative stressp<0.01), 150 and 300µM (p<0.05) increased ROS levels in comparison with BA 0µM (p<0.01) (p<0.01)  . Moreove(p<0.01) .Effect of BA and As exposure on mitochondrial MDA levelp<0.05), 75µM (p<0.001), 150µM (p<0.01) and 300µM (p<0.001) when compared to BA 0µM. Also, the same effect was observed after BA 75 µM administration compared to BA 35, 150 µM (p<0.05) (p<0.05), 100 and 200µM (p<0.001) compared to As 0µM,. Further, MDA showed a significant increase in As 20, 50 µM group compared to As 200 µM (p<0.01) (p<0.01) compared to BA 0µM + As 0µM (MDA levels increased in BA 35µM ((p<0.05) . This pe(p<0.05)  mitochon(p<0.01)  and this+ As 0µM .Effect of BA and As exposure on mitochondrial GSH levelp<0.05), 150 and 300µM (p<0.01) versus to BA 0µM. This antioxidant decreased in BA 150, 300 µM when compared to BA 35 µM (p<0.05) (p<0.05) and 200µM (p<0.01) incubation (p<0.001)  . Furthercubation . Moreovep<0.001) . Effect of BA and As exposure onislets insulin secretionp<0.05). Also, BA 1000, 1500 µM administration showed a significant decreased in insulin secretion of pancreatic islets when compared to BA 250 µM (p<0.01) and 500 µM  (p<0.05)  . Moreove(p<0.05) .Present data indicated that alone BA and As administration induced liver mitochondrial oxidative stress via increase MDA, ROS, and decrease GSH levels in a concentration dependent manner. Further, alone administration of these agents reduced pancreatic islet insulin secretion dose dependently. Dose responses of BA and As revealed that the maximum mitochondrial oxidative stress-induced concentrations are 75µM for BA and 100µM for As. Moreover, the maximum doses of these agents that able to reduce islet insulin secretion were 1000µM for BA and 100µM for As. However, the results of BA and As co administration indicated that the combination of them produced a more tendency to increase mitochondrial oxidative stress and decrease pancreatic islet insulin secretion, but, these effects were not statistically significant in alone administration of them.et al. showed that BA induced oxidative stress via over generation of ROS, decreased cell viability and mitochondrial membrane potential in primary human fibroblasts of gingival tissue(41).However, in vivo study of Cummings, et al. revealed a 375µM concentration of BA in the liver of sudden death victims(The hepatic cell mitochondrial is exposing to ROS damage via fatty acid oxidation. Serum MDA level increased through the severity of liver disease  and Gluth victims, but preet al. showed that As utilization induced liver tissue damage through the increase oxidative stress, lipid peroxidation and decrease GSH levels. So, it could be suggested that animals treated by As aggravate liver mitochondrial damage and viability through the oxidative stress and reduce the GSH level (Several studies have been demonstrated that As exposure could induce severe toxicity in the liver by generating free radicals. Also, during As administration oxidative stress-mediated mitochondrial damage has been occurring . So, theSH level .Increased metabolism of free fatty acids through the mitochondrial oxidation can cause to mitochondrial damage. It is certainly revealed that the islets are more susceptible to lipotoxic damage at normal glucose levels . Low subet al. study demonstrated that the islet insulin secretion did not change at a dose of BA 10µM (However, Itoh,  BA 10µM , but preThe main limitations in this work are the liver crucial homogenization step and protocols, because less force is required compared to hard tissues such as muscle. Also, since many liver samples were prepared simultaneously, the centrifugation steps of them become difficult and time-consuming. Moreover, the loss of some healthy islets has been occurred through the inevitable time consuming of islet isolation procedure.In conclusion, present data established the highest in vitro dose of BA and As that decreased islets insulin secretion and induced mitochondrial oxidative stress in liver through the imbalance between oxidation factors formation and antioxidant defenses. So, according to the presence of BA in dairy food such as milk, butter and parmesan cheese, the consumption of them should be accompanied with more caution. Finally, since specific tissue oxidative damage may be occurred through the As administration, further studies are required to remove this metal from drinking water.


Although the impact of dopamine on reward learning is well documented, its influence on other aspects of behavior remains the subject of much ongoing work. Dopaminergic drugs are known to increase risk-taking behavior, but the underlying mechanisms for this effect are not clear. We probed dopamine's role by examining the effect of its precursor L-DOPA on the choices of healthy human participants in an experimental paradigm that allowed particular components of risk to be distinguished. We show that choice behavior depended on a baseline  gambling propensity, a gambling preference scaling with the amount/variance, and a value normalization factor. Boosting dopamine levels specifically increased just the value-independent baseline gambling propensity, leaving the other components unaffected. Our results indicate that the influence of dopamine on choice behavior involves a specific modulation of the attractiveness of risky options—a finding with implications for understanding a range of reward-related psychopathologies including addiction. Dopamine has a fundamental role in adaptive behavior, having a well-established influence on reward learning. Phasic dopaminergic bursts encode a prediction error signal  or placebo across different sessions. We analyzed participants' choice behavior in a gambling paradigm  or placebo (500 mg ascorbic acid). Drug/placebo administration order was counterbalanced with 14 individuals receiving L-DOPA on the second session. Following drug/placebo administration and a 45-min break, participants performed the same task as in the first session. A subjective state questionnaire was filled three times, namely before drug/placebo administration and before and after the task. Comparison between the different scores did not show statistically significant differences when correcting for multiple comparisons . Drug/plIn each session, participants performed a 40-min gambling task  where onAt the beginning of each block, the range of the upcoming reward distribution was displayed for 8 s. For each trial, after a 1.5-s inter-trial interval, the certain monetary amount and gamble were displayed on the left and right sides of the screen, with their positions pseudorandomized. Participants chose the left or right option by pressing the corresponding button on a keypad. Immediately after choice, the chosen option was displayed for 300 ms followed by the outcome for 1 s. Participants had 3 s to respond, and late responses were met with a statement ‘too late' and a zero outcome. On average, participants missed two trials in the first session, and one trial in the second and third sessions. At the end of each session, one outcome was randomly selected and added to an initial participation payment of £30.For each session, before the task, participants were fully instructed on task contingencies and on the rules determining the participation payment. The monetary amounts selected for each session as additional payment were revealed only after the final session, when payment was carried out.x is V(x)=mean normalization; α is a value function parameter, which determines whether reward variance increases (α>0) or decreases (α<0) the subjective value of risky options; and μ is a gambling bias parameter, which determines a baseline propensity to gamble regardless of which options are presented. Taking A as the sure monetary amount and χ as an indicator of the low-value (χ=0) or high-value context ((χ=1), then the subjective value of the certain option is VCERT=A−χτ so that a positive context parameter τ reduces this value in the high-value context. The subjective value of the risky gamble is VGAMB=A−χτ+α (A−χτ)2+μ. The probability of choosing the gamble is given by a sigmoidal choice rule σ(VGAMB−VCERT)=1/(1+(−(VGAMB−VCERT)).We characterized choice behavior by implementing the same computational model as in our previous study  larger gambling bias parameter μ increases the overall propensity to gamble, (iii) the context parameter τ determines whether, in the high compared with low value context, the subjective values attributed to EV increases (τ<0) or—as predicted by a value normalization hypothesis—decreases (τ>0), impacting on gambling propensity.The effects postulated by the model in determining gambling probability as a function of different trial EV and different parameter sets are represented in fminsearch in Matlab, without enforcing any constraint. We took the best fitting out of 50 locally optimal parameter sets, sampling initial values randomly from Gaussian distributions with mean 0 and SD 2. For estimation, trials with RTs longer than 3 s  were discarded . We used Bayesian model comparison to compare our model with simpler versions in which one or two parameters were fixed, and with more complex versions in which one or more parameters were each replaced by separate parameters for each of the three sessions. Bayesian Information Criterion (BIC) scores were computed for each model fit and summed across subjects. BIC scores penalize for parameter number and the model with the lowest BIC score is preferred.We estimated the values of the parameters using function p=0.22; session 2 (S2): Mean=45.93, SD=22.49, t(31)=1.0, p=0.31; session 3 (S3): Mean=47.40, SD=23.23, t(31)=0.6, p=0.53; two-tailed p=0.05 is used as significant threshold throughout). We estimated a logistic regression model of choice including the trial EV as predictor . The EV-related beta weight (a measure we refer to as gambling slope) did not differ significantly from zero, indicating that participants did not gamble more with large or small EVs (S1: t(31)=0.2, p=0.84; S2: t(31)=0.3, p=0.80; S3: t(31)=0.3, p=0.77). We found no difference in gambling percentage between low- and high-value contexts (S1: t(31)=−0.306, p=0.762; S2: t(31)=−0.891, p=0.380; S3: t(31)=−0.968, p=0.341) even when considering gambles for EVs that overlapped in the two contexts (S1: t(31)=0.127, p=0.900; S2: t(31)=−0.574, p=0.570; S3: t(31)=−0.403, p=0.689).We first sought to replicate previous findings from a study using the identical task =−0.962, p<0.001; S3: r(32)=−0.976, p<0.001; note that estimation of the latter is unaffected by which intercept is assumed), whereas there was no correlation between average gambling percentage and gambling slope =0.063, p=0.732; S3: r(32)=−0.077, p=0.676). This suggests that average gambling percentage and tendency to gamble with large or small EV (captured by the gambling slope) are two different dimensions.There was a correlation between the intercept of the logistic regression and gambling slope (S1: r(32)=−0.974, p=0.010; S2: r(29)=0.511, p=0.003; S3: r(29)=0.484, p=0.006). Altogether, these data replicate previous findings  are relatively larger, namely in the low-value context. Conversely, participants with a gambling preference for smaller EVs would gamble more when overlapping EVs are relatively smaller, namely in the high-value context. Therefore, we predicted that the individual gambling slope correlated with the difference in gambling for overlapping EVs across contexts. This was confirmed in all sessions by partial correlations between gambling slope and gambling percentage in the low-value context controlling for the high-value context (S1: r(29)=0.454, p=0.671) nor in the difference in gambling percentage between contexts =0.330, p=0.720; for overlapping EVs: F=0.304, p=0.739). Also, we observed a correlation across sessions =0.834, p<0.001; S2 and S3: r(32)=0.843, p<0.001), the gambling slope (S1 and S2: r(32)=0.726, p<0.001; S1 and S3: r(32)=0.833, p<0.001; S2 and S3: r(32)=0.849, p<0.001), and the contextual differences for overlapping EVs (S1 and S2: r(32)=0.505, p=0.003; S1 and S3: r(32)=0.594, p<0.001; S2 and S3: r(32)=0.682, p<0.001), suggesting these measures underpin stable traits.Next, we investigated cross-section stability in choice behavior. We did not observe any systematic difference in gambling percentage across sessions =0.4, sessions  for the p<0.001; S1 and S3: r(32)=0.760, p<0.001; S2 and S3: r(32)=0.707, p<0.001; baseline choice precision: S1 and S2: r(32)=0.688, p<0.001; S1 and S3: r(32)=0.643, p<0.001; S2 and S3: r(32)=0.743, p<0.001). The absolute gambling slope changed across sessions =7.249, p=0.001), an effect driven by a smaller absolute gambling slope in the first session (S1 vs S2: t(31)=−2.95, p=0.006; S1 vs S3: t(31)=−3.722, p=0.001; S2 vs S3: t(31)=−0.681, p=0.501). An increased absolute gambling slope after the first session indicates that choice dependency on EV is enhanced after a first exposure to the task. This may be connected to evidence showing a tendency to rely more on a strategy after this strategy is repeatedly selected (p=0.345).To investigate whether dopamine is linked with choice precision , the gambling bias parameter  did not . To asseτ was significantly positive (t(31)=3.03, p=0.005), consistent with value normalization in which subjective values were rescaled to the contextual reward distribution. Note that in the model, VGAMB−VCERT=α (A−χτ)2+μ. This is similar to a simple logistic regression, where the value function parameter α corresponds to a slope parameter and the gambling bias parameter μ corresponds to an intercept. According to this, the value function parameter α was correlated with the gambling slope, suggesting that both capture a preference to gamble with large or small trial amount (S1: r(32)=0.990, p<0.001; S2: r(32)=0.985, p<0.001; S3: r(32)=0.957, p<0.001), but it was not correlated with the average gambling percentage (S1: r(32)=−0.062, p=0.735; S2: r(32)=0.133, p=0.470; S3: r(32)=0.019, p=0.917). The gambling bias parameter μ correlated with the intercept of the logistic regression model of choice having the EV as predictor (S1: r(32)=0.981, p<0.001; S2: r(32)=0.976, p<0.001; S3: r(32)=0.941, p<0.001). Similarly to the logistic regression, the gambling bias parameter μ and the value function parameter α were inversely correlated (S1: r(32)=−0.892, p<0.001; S2: r(32)=−0.879, p<0.001; S3: r(32)=−0.871, p<0.001). However, note that a correlation between intercept and slope does not affect the estimate of the slope and maintains the two parameters identifiable during model fitting .The parameters in the model are expected to provide quantitative characterizations of elements of behavior. As predicted, the context parameter α, μ, and τ against models where one of these parameters was fixed to zero. Consistent with real data, the full model replicated the lack of correlation between average gambling and the gambling slope =−0.091, p=0.624), while a correlation emerged when data were simulated using a model without the gambling bias parameter μ (r(32)=0.78, p<0.001). Moreover, again consistent with empirical data, the full model replicated the partial correlation between the gambling slope and the gambling for choices that overlapped across context in the low-value context controlling for the high value-context (r(29)=0.490, p=0.007). This was not obtained when data were simulated using a model without the value function parameter α (r(32)=0.03, p=0.89) or without the context parameter τ (r(32)=−0.08, p=0.663). We also inspected that the model which best explained the data  could replicate the raw behavioral analyses. Hence, we simulated data with this model and found a lack of correlation between average gambling and the gambling slope (S1: r(32)=−0.05, p=0.786; S2: r(32)=−0.02, p=0.913; S3: r(32)=−0.09, p=0.624), and a partial correlation between the gambling slope and the gambling for choices that overlapped across context in the low-value context controlling for the high value-context (S1: r(29)=0.40, p=0.032; S2: r(29)=0.52, p=0.003; S3: r(29)=0.53, p=0.003). Next, we fitted the model to the simulated data and the estimated parameters were correlated with the true parameters across individuals (τ: r(29)=0.85, p<0.001; S1: α: r(29)=0.97, p<0.001; μ: r(29)=0.92, p<0.001; S2: α: r(29)=0.98, p<0.001; μ: r(29)=0.91, p<0.001; S3: α: r(29)=0.95, p<0.001; μ: r(29)=0.94, p=0.001). Collectively, these analyses validate the mean-return model and show that it is able to reproduce raw behavioral analyses and that parameters can be estimated reliably.To analyze whether the model is able to explain raw behavioral analyses, we used the model together with subject-specific parameters estimates to generate simulated data  and performed exactly the same behavioral analyses on the simulated data that we had used for the experimental data. For this analysis, we focused on the first session alone and compared a model with the three parameters μ were correlated across sessions (S1 and S2: r(32)=0.797, p<0.001; S1 and S3: r(32)=0.854, p<0.001; S2 and S3: r(32)=0.882, p<0.001) as well as the value function coefficients α (S1 and S2: r(32)=0.667, p<0.001; S1 and S3: r(32)=0.775, p<0.001; S2 and S3: r(32)=0.813, p<0.001), consistent with these measures underlying stable individual risk attitudes.Next, we assessed cross-session stability of parameters. The individual gambling bias parameters vs BIC=63491), suggesting that participants' choices were not affected by previous outcomes.Recent evidence from rodent data suggests that dopamine mediates the effect of recent reward history on risky choice =3.45, p=0.002; S3: t(31)=3.39, p=0.002). This index was expected to be affected by our drug manipulation according to a hypothesis that different levels of dopamine alter reward contextual normalization. A final prediction we considered is that boosting dopamine levels would imply that all rewards would appear subjectively less or more valuable, resulting in changes of average gambling in participants with convex and concave subjective value functions (but in opposite directions).Our main research goal was to investigate effects of L-DOPA compared with placebo on risk-taking behavior. On the basis of a hypothesis that dopamine boosts overall risk-taking, an increased average gambling percentage is predicted under L-DOPA (p=0.034). More detailed analyses showed that the drug–weight interaction was driven by a larger average gambling percentage under L-DOPA compared with placebo in low-weight (p=0.044) but not high-weight participants (p=0.625). This was confirmed by a similar analysis where we observed a significant inverse partial correlation between average gambling percentage under L-DOPA and body, controlling for average gambling percentage under placebo (p=0.043). This supports the idea that L-DOPA boosts risk-taking behavior. The role of weight is not surprising given the well-established link between this variable and effective drug dose.Given the same dosage (150 mg) was administered to all participants and given previous reports that the effect of L-DOPA can depend on body weight (p=0.688). However, a similar analysis having gender as between-subjects factor showed a significant interaction effect =4.54, p=0.041), and we observed higher weight in males than females (t(30)=6.21, p<0.001). This raises the possibility that gender might explain the weight–drug interaction effect on average gambling percentage. If this was the case, we would expect no relationship between weight and effect of L-DOPA (relative to placebo) on average gambling percentage within subgroups, namely within males and females. Contrary to this prediction, we found a significant inverse partial correlation in males between average gambling percentage under L-DOPA and body weight, controlling for average gambling percentage under placebo (r(13)=−0.516, p=0.049). No partial correlation was found in females (r(13)=−0.078, p=0.782), possibly owing to a ceiling effect, as females were substantially lighter than males.To assess whether the role of weight as mediator was confounded by age, participants were assigned to young/old groups (based on a median split) and a mixed-effect ANOVA on average gambling percentage was run with drug as within-subjects factor and weight grouping as between-subjects factor, but we found no interaction effect =0.16, p=0.026) and excluded gender (t(31)=−0.60, p=0.55) as predictor. These analyses are more consistent with the possibility that weight and not gender mediated the effect of drug on average gambling percentage, also in line with previous findings , the regression model always included weight (t(31)=2.34, p=0.034) plus, unsurprisingly, a gambling slope–EV bin interaction effect =46.16, p<0.001); all other effects were not statistically significant.Contingent on whether participants' gambling slope was positive or negative in the first session, subjects were also assigned to positive/negative gambling slope groups. To test for specific effects of EV, we grouped the different EVs in two bins, one for EVs that are negative relative to the contextual mean EV  and the other for EVs that are positive relative to the contextual mean EV . We implemented a mixed-effect ANOVA with drug and EV bins as within-subjects factors, and weight and gambling slope grouping as between-subjects factors. We observed a significant drug–weight interaction effect =4.96, Overall, these data suggest that boosting dopamine levels with L-DOPA enhances an average gambling propensity and this effect does not interact with EV or with participants' stable preferences to gamble more with small or large EVs.et al, 2010; Investigating choice under risk is an important probe of the role of dopamine in decision-making. In general, drugs that boost dopamine levels enhance the propensity to take risks  exhibited increased mean gambling preference. The interaction with body weight was not surprising given the use of a fixed drug dosage and the well-established relationship between body weight and drug effects . However, our findings also did not support this prediction, suggesting that dopaminergic levels might be unrelated to reward normalization.That dopamine and dopaminergic manipulations affect learning is well established , raisingIn conclusion, we studied the role of dopamine in decision-making using a gambling task that allowed us to separate several aspects of choice behavior. Boosting dopamine levels enhanced a baseline propensity to take risks, but had no effect on any other aspect of choice behavior. This raises the possibility that dopamine modulates the attractiveness of surprising outcomes or increases an optimism bias. Such a finding is of potential interest in clinical populations, and suggests the possibility that patients with abnormal dopaminergic functioning or under dopaminergic treatment  are differently attracted by surprising outcomes. This may help not only in understanding psychopathologies such as gambling and drug abuse, with well-known links with dopamine and risk behavior, but also other conditions in which dopamine is implicated in connection with aberrant salience , such asThe authors declare no conflict of interest.


Verbena officinalis crude extract (Vo.Cr). Pentylenetetrazole (PTZ)-induced seizures, elevated plus maze, light–dark box (LDB), open field and thiopental-induced sleeping test models were employed to evaluate Vo.Cr actions in mice. Vo.Cr dose-dependently (100–500 mg/Kg) delayed onset time of myoclonic jerks and tonic-clonic seizures, while decreased duration of tonic-clonic seizures . Vo.Cr at 100 and 300–500 mg/Kg doses reduced animals’ mortality in PTZ-induced seizures test to 75 and 0%, respectively. Vo.Cr (50–300 mg/Kg) significantly increased time spent and number of entries into open arms, while decreased time spent and number of entries into closed arms , measured in elevated plus maze. Vo.Cr (50–300 mg/Kg) increased time spent in light compartment, while decreased time spent in dark compartment  in LDB, like caused by diazepam. In open field test, Vo.Cr decreased number of ambulations and rearings frequencies, while increased the number of central squares crossings. In thiopental-induced sleeping test, Vo.Cr (50–300 mg/Kg) decreased onset time of sleep, while increased the duration of sleep . These results indicate that Verbena officinalis possess anticonvulsant, anxiolytic and sedative activities, which provides scientific background for its medicinal application in various neurological ailments, such as epilepsy, anxiety, and insomnia.We describe different neuropharmacological effects of  Verbena officinalis is a perennial herb, belongs to family Verbenaceae, commonly known as “Vervian,” “Herb of grace,” “Pigeon’s grass” and localy “Karenta” or “Pamukh.” It mostly grows in Europe and Asia, commonly found in cultivated fields and wastelands near water and cultivated in Northern and Western areas of Pakistan  was collected from National Industrial State, Rawat, Islamabad, Pakistan and was verified by Dr. Mushtaq Ahmad, a Plant Taxonomist, Department of Plant Science, Quaid-e-Azam University, Islamabad, Pakistan. A voucher specimen number 270 was deposited at the same Department for future reference. The plant material was shade dried until water evaporated completely and then grinded. The coarse powder (2.7 Kg) was macerated in 70% aqueous-methanol under room conditions for 2-weeks with gentle occasional shaking. The extract was filtered and then concentrated in vacuum under reduced pressure using rotary evaporator to obtain a blackish thick paste of Verbena officinalis crude extract (Vo.Cr), which was completely solublized in normal saline (0.9% w/v). The percentage yield of extract was found to be 18.66% w/w.ad libitum. Experiments performed complied with rules of Institute of Laboratory Animal Resources, Commission on Life Sciences University, National Research Council (1996), approved by Ethical Committee of Riphah Institute of Pharmaceutical Sciences (Ref. No: REC/RIPS/2015/006).Swiss albino mice (25–35 g) of either sex were used for this study, housed in Animal House of Riphah Institute of Pharmaceutical Sciences in polypropylene (22 cm × 37 cm) cages under standard laboratory environment; 25 ± 2°C, light and darkness duration were for 12 h each and had free access to standard diet and water Diazepam , pentylenetetrazole (PTZ) and thiopental sodium  were obtained from Roche Pharmaceuticals, Pakistan, Sigma-Aldrich, Co. LLC, USA and Abbot Laboratories, Pakistan, respectively.Mice were divided into five groups  and injected (i.p) with normal saline (10 mL/Kg), Vo.Cr (100–500 mg/Kg), and diazepam (1 mg/Kg). Thirty min later to saline, Vo.Cr and diazepam treatment, an i.p. dose of PTZ (90 mg/Kg) was given to all animals and each animal was observed for onset time of myoclonic jerks and tonic-clonic seizures, as well as duration of tonic-clonic seizures for 30 min. Drugs that delayed onset of myoclonic jerks, tonic-clonic seizures and/or shortened duration of tonic-clonic seizures are considered to exhibit anticonvulsant effect . The aniTest EPM model consist of a wooden apparatus having four arms (two open arms of 30 cm × 5 cm and two closed arms of 30 cm × 5 cm × 15 cm) connected via a central opened area (5 cm × 5 cm), elevated up to 40 cm from the floor, as previously described . The edgLight–dark box (LDB) model contains wooden box of dimensions 44 cm × 21 cm × 21 cm and divided into a small compartment (1/3rd), painted black inside and covered with a wooden lid and a large compartment (2/3rd) which was painted white, covered by a transparent glass and illuminated by a 60 watt bulb. The two compartments were separated by a wooden blank, having a hole of 7 cm × 7 cm in center at surface of floor. Mice were divided into five groups , received an i.p. dose of normal saline (10 mL/Kg) and Vo.Cr (50–300 mg/Kg) once/day for 7 days, while one group was treated with diazepam (2 mg/Kg), injected once time 30 min prior to test. After 30 min of saline, Vo.Cr and diazepam treatment, a 5 min test was performed by placing each mouse in center of lighted box, keeping face away of opening hole and time spent in each compartment was recorded via a digital video camera .The apparatus is made of a wooden base divided into 12 equal squares with glass walls of dimensions (50 cm × 25 cm × 50 cm). Mice were divided into five groups , received an i.p. dose of normal saline (10 mL/Kg) and Vo.Cr (50–300 mg/Kg) once/day for 7 days, while one group was treated with diazepam (2 mg/Kg), injected once time 30 min prior to test. After 30 min of saline, Vo.Cr and diazepam treatment, a 5 min test was performed by placing each mouse in corner square to explore the arena and recorded numbers of ambulations, rearings, and central squares crossings , via digThiopental-induced sleeping test was used for determining sedative activity . Mice weMice in two groups were administered with Vo.Cr high doses of 3 and 5 g/Kg, i.p, then kept under observation for 24 h and observed for toxicity symptoms or death.n = number of experiments). The statistical parameter applied is one-way analysis of variance (ANOVA) with Tukey post hoc test. P < 0.05 was noted as significantly different. The bar-graphs were analyzed using Graph Pad program .Data expressed are mean ± standard error of mean . In control saline group, onset times of myoclonic jerks, tonic-clonic seizures and duration of tonic-clonic seizures were 35.25 ± 5.39, 42 ± 5.05, and 45 ± 2.74 s, respectively. In Vo.Cr (100 mg/Kg) treated group, onset times of myoclonic jerks and tonic-clonic seizures increased to 55.25 ± 6.09 and 62 ± 4.97 s, respectively , while duration time of tonic-clonic seizures reduced to 35.75 ± 3.59 s. In Vo.Cr (300 mg/Kg) treated group, onset times of myoclonic jerks and tonic-clonic seizures increased to 75 ± 1.58 and 79.25 ± 2.56 s, respectively , while duration time of tonic-clonic seizures reduced to 30.25 ± 2.32 s . In Vo.Cr (500 mg/Kg) treated group, onset times of myoclonic jerks and tonic-clonic seizures increased to 87.25 ± 4.03 and 118.25 ± 3.79 s, respectively, while duration time of tonic-clonic seizures reduced to 16.25 ± 2.39 s . In diazepam (1 mg/Kg) treated group, onset times of myoclonic jerks and tonic-clonic seizures increased to 90.5 ± 2.53 and 130 ± 1.87 s, respectively, while duration time of tonic-clonic seizures reduced to 12.5 ± 2.33 s . The saline treated group animals showed 100% mortality and immediately died. Vo.Cr at 100 mg/Kg and 300–500 mg/Kg doses reduced PTZ-induced seizures mortality rate to 75 and 0%  respectively, while diazepam at test dose of 1 mg/Kg, reduced mortality to 0%  as shown in Table 1.Vo.Cr dose-dependently (100–500 mg/Kg) delayed onset time of PTZ (90 mg/Kg) mediated both myclonic jerks and tonic-clonic seizures, while decreased duration time of tonic-clonic seizures . In control saline group, time spent in open and closed arms were 23 ± 3.11 and 221 ± 9.57 s, respectively. In Vo.Cr (50 mg/Kg) treated group, time spent in open arms increased to 51.75 ± 4.72 s while time spent in closed arms reduced to 176 ± 7.52 s . In Vo.Cr (100 mg/Kg) treated group, time spent in open arms increased to 113 ± 5.31 s while time spent in closed arms reduced to 129.75 ± 5.59 s . In Vo.Cr (300 mg/Kg) treated group, time spent in open arms was 58 ± 5.58 s while time spent in closed arms was 169 ± 7.04 s . In diazepam (2 mg/Kg) treated group, time spent in open arms was increased to 94.5 ± 5.06 s while time spent in closed arms reduced to 124.25 ± 9.41 s .Vo.Cr at 50–300 mg/Kg increased time spent by animals in open arms, while decreased time spent in closed arms . In control saline group, numbers of entries into open and closed arms were 5.75 ± 1.32 and 17.25 ± 1.75, respectively. In Vo.Cr (50 mg/Kg) treated group, number of entries into open arms increased to 12.5 ± 1.32, while number of entries in closed arms reduced to 10.75 ± 0.63 . In Vo.Cr (100 mg/Kg) treated group, number of entries into open arms increased to 13.75 ± 1.11, while number of entries in closed arms reduced to 8.0 ± 1.29 . In Vo.Cr (300 mg/Kg) treated group, number of entries into open arms were 12.25 ± 1.38 , while number of entries in closed arms reduced to 6.25 ± 1.32 . In diazepam (2 mg/Kg) treated group, number of entries into open arms increased to 14.25 ± 1.25 , while number of entries in closed arms reduced to 6.5 ± 1.94 .Vo.Cr at 50–300 mg/Kg increased number of animals’ entries into open arms, while decreased number of entries into closed arms . In control saline group, times spent in light and dark compartments were 88.5 ± 5.25 and 194.75 ± 7.25 s, respectively. In Vo.Cr (50 mg/Kg) treated group, time spent in light compartment increased to 138.75 ± 12.13 s, while time spent in dark compartment reduced to 145 ± 9.25 s . In Vo.Cr (100 mg/Kg) treated group, time spent in light compartment increased to 158 ± 9.46 s, while time spent in dark compartment reduced to 134 ± 8.27 s . In Vo.Cr (300 mg/Kg) treated group, time spent in light compartment was 142 ± 7.22 s, while time spent in dark compartment was 151 ± 4.53 s . In diazepam (2 mg/Kg) treated group, time spent in light compartment was 140.5 ± 4.74 s, while time spent in dark compartment was 144.5 ± 5.12 s .Vo.Cr at 50–300 mg/Kg increased time spent by animals in light compartment, while decreased time spent in dark compartment  treated group, numbers of ambulations and rearings were 109.5 ± 8.06 and 42.5 ± 2.22  respectively, while number of central squares crossings increased to 11.25 ± 0.85 . In Vo.Cr (100 mg/Kg) treated group, numbers of ambulations and rearings were 106.5 ± 4.03 and 39.5 ± 2.9  respectively, while number of central squares crossings increased to 15 ± 0.41 . In Vo.Cr (300 mg/Kg) treated group, numbers of ambulations and rearings reduced to 74.25 ± 3.30 and 24.25 ± 1.97 respectively , while number of central squares crossings were 10.75 ± 0.85 . In diazepam (2 mg/Kg) treated group, numbers of ambulations and rearings decreased to 70 ± 4.66 and 15.25 ± 2.96 respectively , while number of central squares crossings increased to 11 ± 0.91 .Vo.Cr at highest tested dose of 300 mg/Kg, significantly decreased numbers of animals’ ambulations and rearings frequencies, while increased the number of central squares crossings at all tested doses (50–300 mg/Kg) compared to control saline group, as presented in Figures 6A,B). In control saline group, onset and duration of sleep times were 3.53 ± 0.27 and 8.25 ± 1.16 min, respectively. In Vo.Cr (50 mg/Kg) treated group, onset time of sleep decreased to 2.61 ± 0.21 min , while duration of sleep time was increased to 14.50 ± 1.57 min. In Vo.Cr (100 mg/Kg) treated group, onset time of sleep was decreased to 2.39 ± 0.04 min, while duration of sleep time was increased to 66.45 ± 6.13 min . In Vo.Cr (300 mg/Kg) treated group, onset time of sleep was further decreased to 1.6 ± 0.14 min, while duration of sleep time was increased to 523.65 ± 13.03 min . In diazepam (3 mg/Kg) treated group, onset time of sleep was 1.32 ± 0.05 min, while duration of sleep time was 571.8 ± 12.17 min .Vo.Cr dose-dependently (50–300 mg/Kg) decreased animals’ onset time of sleep, while increased sleep duration -benzylic alcohol safranal  and tannins  cannot be ignored. In open field test principle, increased number of ambulations, rearings and central squares crossings reveal anxiolytic activity, while reduction in locomotion showed sedative effect. Vo.Cr at lower doses of 50–100 mg/Kg did not produced statistically any prominent difference in ambulation and rearing frequencies, compared to saline group (P > 0.05), but both ambulation and rearing frequencies were significantly reduced at highest dose of 300 mg/Kg . The decreased locomotor activity at higher dose of Vo.Cr might be due to sedative effect of the plant. Vo.Cr, at all tested doses (50–300 mg/Kg) significantly increased numbers of central squares crossings, showing its anxiolytic activity. Diazepam, a reference drug also reduced both ambulation and rearing frequencies, because of its sedative property, while increased the number of central squares crossings, due to owning anxiolytic profile (A receptor. The authors suggest that they could induce CNS activities through different mechanism like inhibition of the N-methyl-D-aspartate or serotonin receptors and the type of sugar linkage with the aglycone should be an important factor for their sedative activities (Verbena officinalis (Verbena officinalis may be due to these constituents possibly through GABA pathways by acting on sites other than benzodiazepine ones or through pathways other than GABAergic system.We employed different pharmacological techniques, to investigate anticonvulsant, anxiolytic and sedative effects of re drugs . Vo.Cr ceceptors . Dr. Thoand dull . Verbeniicinalis  which misafranal  and safrmulation , suggestic drugs . Agents,ic drugs . Vo.Cr sic drugs . At the  tannins , which a tannins . Several effects , evidenc profile . Benzodi profile . Substan profile . In thiotivities . Scutellicinalis , so sedaVerbena officinalis exhibits anticonvulsant, anxiolytic and sedative effects, which validates its folk use in neurological disorders and a step forward toward exploration of evidence-based alternative medicines. Further in-depth advance molecular studies are warranted to elucidate pharmacodynamics basis of the pharmacological actions.The present study reveals that AK performed this research work under the supervision of A-uK and co-supervision TA.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.