Functional integrity of the retrosplenial cortex is essential for rapid consolidation and recall of fear memory

Memory storage is a temporally graded process involving different phases and different structures in the mammalian brain. Cortical plasticity is essential to store stable memories, but little is known regarding its involvement in memory processing. Here we show that fear memory consolidation requires early post-training macromolecular synthesis in the anterior part of the retrosplenial cortex (aRSC), and that reversible pharmacological inactivation of this cortical region impairs recall of recent as well as of remote memories. These results challenge the generally accepted idea that neocortical areas are slow encoding systems that participate in the retrieval of remote memories only.Fil: Katche, Cynthia Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Dorman, Guido. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencias; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Slipczuk, Leandro. Einstein Medical Center; Estados UnidosFil: Cammarota, Martin Pablo. Pontificia Universidade Catolica Do Rio Grande Do Sul; Brasil. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Medina, Jorge Horacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; Argentina. Universidad de Buenos Aires. Facultad de Medicina. Departamento de Ciencias Fisiológicas; Argentin

Monoclonal antibodies, gene silencing and gene editing (CRISPR) therapies for the treatment of hyperlipidemia-The future is here

Hyperlipidemia is a significant risk factor for atherosclerotic cardiovascular disease. Undertreatment of elevated lipids persists despite existing therapies. Here, we provide an update on monoclonal antibodies, gene silencing therapies, and gene editing techniques for the management of hyperlipidemia. The current era of cutting-edge pharmaceuticals targeting low density lipoprotein cholesterol, PCSK9, lipoprotein (a), angiopoietin-like 3, and apolipoprotein C3 are reviewed. We outline what is known, studies in progress, and futuristic goals. This review of available and upcoming biotechnological lipid therapies is presented for clinicians managing patients with familial hyperlipidemia, statin intolerance, hypertriglyceridemia, or elevated lipoprotein (a) levels

BDNF Activates mTOR to Regulate GluR1 Expression Required for Memory Formation

Background: The mammalian target of Rapamycin (mTOR) kinase plays a key role in translational control of a subset of mRNAs through regulation of its initiation step. In neurons, mTOR is present at the synaptic region, where it modulates the activity-dependent expression of locally-translated proteins independently of mRNA synthesis. Indeed, mTOR is necessary for different forms of synaptic plasticity and long-term memory (LTM) formation. However, little is known about the time course of mTOR activation and the extracellular signals governing this process or the identity of the proteins whose translation is regulated by this kinase, during mnemonic processing. Methodology/Principal Findings: Here we show that consolidation of inhibitory avoidance (IA) LTM entails mTOR activation in the dorsal hippocampus at the moment of and 3 h after training and is associated with a rapid and rapamycinsensitive increase in AMPA receptor GluR1 subunit expression, which was also blocked by intra-hippocampal delivery of GluR1 antisense oligonucleotides (ASO). In addition, we found that pre- or post-training administration of function-blocking anti-BDNF antibodies into dorsal CA1 hampered IA LTM retention, abolished the learning-induced biphasic activation of mTOR and its readout, p70S6K and blocked GluR1 expression, indicating that BDNF is an upstream factor controlling mTOR signaling during fear-memory consolidation. Interestingly, BDNF ASO hindered LTM retention only when given into dorsal CA1 1 h after but not 2 h before training, suggesting that BDNF controls the biphasic requirement of mTOR during LT

To Each Imaging Modality, Their Own MAD

PURPOSE: The clinical significance of mitral annular disjunction (MAD) is uncertain. Imaging modality might impact the prevalence of MAD. We aimed to assess MAD prevalence at transthoracic echocardiography (TTE) and cardiac magnetic resonance (CMR) as well as their inter-modality agreement.METHODS: This observational retrospective study included patients undergoing TTE and CMR within 6 months. MAD was defined as ≥1 mm systolic separation between the left atrial wall-mitral leaflet and the left ventricular (LV) wall. The maximum MAD longitudinal extent was measured. The inter-modality agreement for MAD diagnosis was evaluated.RESULTS: One hundred twenty four patients (59 ± 17 years; 62% male) were included. MAD was detected in 60 (48%) using CMR and in 10 (8%) using TTE. All patients with MAD on TTE had MAD on CMR. The inter-modality agreement was low (Cohen's kappa = 0.17) but improved when the diagnostic cut-off was increased from 1 to 5 mm (Cohen's kappa = 0.66). The median longitudinal length of MAD was 2.0 mm (25th-75th percentiles: 1.5-3.0) by CMR and 4.0 mm (25th-75th percentiles: 2.7-4.5) by TTE with moderate agreement (intraclass correlation coefficient = 0.66).CONCLUSION: MAD of limited extent is common on CMR and more than two thirds of patients showing MAD on CMR did not have MAD on TTE. The inter-modality agreement between TTE and CMR increased when the diagnostic threshold for MAD was increased from 1 to 5 mm. Methodological discrepancies impact MAD assessment and contribute to the discordant prevalence and clinical significance reported in the literature.</p

Automatic valve plane localization in myocardial perfusion SPECT/CT by machine learning: anatomic and clinical validation

Precise definition of the mitral valve plane (VP) during segmentation of the left ventricle for SPECT myocardial perfusion imaging (MPI) quantification often requires manual adjustment, which affects the quantification of perfusion. We developed a machine learning approach using support vector machines (SVM) for automatic VP placement. Methods: A total of 392 consecutive patients undergoing 99mTc-tetrofosmin stress (5 min; mean ± SD, 350 ± 54 MBq) and rest (5 min; 1,024 ± 153 MBq) fast SPECT MPI attenuation corrected (AC) by CT and same-day coronary CT angiography were studied; included in the 392 patients were 48 patients who underwent invasive coronary angiography and had no known coronary artery disease. The left ventricle was segmented with standard clinical software (quantitative perfusion SPECT) by 2 experts, adjusting the VP if needed. Two-class SVM models were computed from the expert placements with 10-fold cross validation to separate the patients used for training and those used for validation. SVM probability estimates were used to compute the best VP position. Automatic VP localizations on AC and non-AC images were compared with expert placement on coronary CT angiography. Stress and rest total perfusion deficits and detection of per-vessel obstructive stenosis by invasive coronary angiography were also compared. Results: Bland-Altman 95% confidence intervals (CIs) for VP localization by SVM and experts for AC stress images (bias, 1; 95% CI, -5 to 7 mm) and AC rest images (bias, 1; 95% CI, -7 to 10 mm) were narrower than interexpert 95% CIs for AC stress images (bias, 0; 95% CI, -8 to 8 mm) and AC rest images (bias, 0; 95% CI, -10 to 10 mm) (P < 0.01). Bland-Altman 95% CIs for VP localization by SVM and experts for non-AC stress images (bias, 1; 95% CI, -4 to 6 mm) and non-AC rest images (bias, 2; 95% CI, -7 to 10 mm) were similar to interexpert 95% CIs for non-AC stress images (bias, 0; 95% CI, -6 to 5 mm) and non-AC rest images (bias, -1; 95% CI, -9 to 7 mm) (P was not significant [NS]). For regional detection of obstructive stenosis, ischemic total perfusion deficit areas under the receiver operating characteristic curve for the 2 experts (AUC, 0.79 [95% CI, 0.7-0.87]; AUC, 0.81 [95% CI, 0.73-0.89]) and the SVM (0.82 [0.74-0.9]) for AC data were the same (P = NS) and were higher than those for the unadjusted VP (0.63 [0.53-0.73]) (P < 0.01). Similarly, for non-AC data, areas under the receiver operating characteristic curve for the experts (AUC, 0.77 [95% CI, 0.69-0.89]; AUC, 0.8 [95% CI, 0.72-0.88]) and the SVM (0.79 [0.71-0.87]) were the same (P = NS) and were higher than those for the unadjusted VP (0.65 [0.56-0.75]) (P < 0.01). Conclusion: Machine learning with SVM allows automatic and accurate VP localization, decreasing user dependence in SPECT MPI quantification