Learning-dependent structural plasticity of intracortical and sensory connections to functional domains of the olfactory tubercle

The olfactory tubercle (OT), which is a component of the olfactory cortex and ventral striatum, has functional domains that play a role in odor-guided motivated behaviors. Learning odor-guided attractive and aversive behavior activates the anteromedial (am) and lateral (l) domains of the OT, respectively. However, the mechanism driving learning-dependent activation of specific OT domains remains unknown. We hypothesized that the neuronal connectivity of OT domains is plastically altered through olfactory experience. To examine the plastic potential of synaptic connections to OT domains, we optogenetically stimulated intracortical inputs from the piriform cortex or sensory inputs from the olfactory bulb to the OT in mice in association with a food reward for attractive learning and electrical foot shock for aversive learning. For both intracortical and sensory connections, axon boutons that terminated in the OT domains were larger in the amOT than in the lOT for mice exhibiting attractive learning and larger in the lOT than in the amOT for mice exhibiting aversive learning. These results indicate that both intracortical and sensory connections to the OT domains have learning-dependent plastic potential, suggesting that this plasticity underlies learning-dependent activation of specific OT domains and the acquisition of appropriate motivated behaviors

Evaluation of Recently Developed Regression Equation with Direct Measurement of Low-density Lipoprotein Cholesterol in a Bangladeshi Population

Background: Meaningful underestimation of low-density lipoprotein (LDL) cholesterol is an important shortcoming of Friedewald’s formula (FF) at higher triglyceride (TG) levels. Recently a regression equation (RE) has been developed using lipid profiles in one setting and validated externally for the calculation of LDL cholesterol. This newly developed RE requires more studies in different settings. Objective: The aim of this study was to evaluate the performance of the regression equation against direct measurement. Materials and Methods: Lipid profiles of 600 subjects attending a tertiary healthcare center were included in this study. Specimens were collected and lipid profiles were measured by standard methods. Sixty two lipid profiles with TG above 400 mg/dL were excluded. Calculated LDL cholesterol values using FF and RE were compared with measured LDL cholesterol by Pearson’s correlation test, Passing & Bablok regression, accuracy within ±5% and ±12% of measured LDL cholesterol and two-tailed paired t test at various TG ranges. Results: The mean value of LDL cholesterol was 148.6 ± 37.2 mg/dL for direct measurement, 146.9 ± 42.4 mg/dL for FF and 148.6 ± 34.7 mg/dL for RE. The correlation coefficients of calculated LDL cholesterol values with measured LDL cholesterol were 0.949 (p<0.001) for FF and 0.959 (p<0.001) for RE. Passing & Bablok regression equation against measured LDL cholesterol was y = 0.897x + 16.2 for FF and y = 1.0842x – 13.1 for RE. Accuracy within ±5% of measured LDL cholesterol was 45% for FF, 57% for RE and within ±12% of measured LDL cholesterol was 84% for FF, 93% for RE. When calculated LDL cholesterol was compared with measured LDL cholesterol at different TG ranges, FF significantly underestimated LDL cholesterol at TG concentrations above 200 mg/dL whereas no significant difference was observed for RE. Conclusion: This study reveals that RE equation has similar performance to direct measurement for calculation of LDL cholestero