Characterizing and TRAPing a Social Stress-Activated Neuronal Ensemble in the Ventral Tegmental Area

Social stress is a major contributor to neuropsychiatric issues such as depression, substance abuse and eating disorders. The ventral tegmental area (VTA) is involved in the effects of stress on cognitive and emotional processes perturbed in these disorders. However, the VTA is a cellularly heterogeneous brain area and it remains unclear which of its neuronal populations make up the social stress-sensitive ensemble. The current study characterizes the molecular, topographical and functional properties of VTA social stress-activated cells. First, we used immunohistochemical analysis of Fos protein, a marker of recent increased neuronal activity, to show that acute social stress activates a mainly neuronal ensemble in the VTA (VTA Social stress neurons). Topographical analysis showed that this ensemble, which comprises ∼11% of all VTA neurons, occurs across VTA subregions. Further analysis showed that approximately half of the VTA Social stress neurons express the dopamine synthesis rate-limiting enzyme tyrosine hydroxylase (TH). In a minority of cases this occurred with coexpression of vesicular glutamate transporter 2 (Vglut2). Also part of the ensemble were VTA cells expressing just Vglut2 without TH, and cells expressing the vesicular GABA transporter (VGAT) without TH. Next, using targeted recombination in active populations (TRAP2), we showed that VTA Social stress neurons can be permanently tagged and made tractable for future functional investigations. Using a combination of TRAP2 and patch-clamp electrophysiology we demonstrate that VTA Social stress neurons exhibit higher excitability than their non-TRAPed neighbor cells. Overall, this study shows that acute social stress activates an ensemble of neurons throughout the VTA, comprising distinct molecular identities, and with specific electrophysiological features. It also identifies TRAP2 as a tool to make this ensemble tractable for future functional studies

No effect of topical digoxin and furosemide for patients with actinic keratosis

Drug Delivery Technolog

⁸⁹Zr-Trastuzumab PET/CT Imaging of HER2-Positive Breast Cancer for Predicting Pathological Complete Response after Neoadjuvant Systemic Therapy:A Feasibility Study

Background: Approximately 20% of invasive ductal breast malignancies are human epidermal growth factor receptor 2 (HER2)-positive. These patients receive neoadjuvant systemic therapy (NAT) including HER2-targeting therapies. Up to 65% of patients achieve a pathological complete response (pCR). These patients might not have needed surgery. However, accurate preoperative identification of a pCR remains challenging. A radiologic complete response (rCR) on MRI corresponds to a pCR in only 73% of patients. The current feasibility study investigates if HER2-targeted PET/CT-imaging using Zirconium-89 (89Zr)-radiolabeled trastuzumab can be used for more accurate NAT response evaluation. Methods: HER2-positive breast cancer patients scheduled to undergo NAT and subsequent surgery received a 89Zr-trastuzumab PET/CT both before (PET/CT-1) and after (PET/CT-2) NAT. Qualitative and quantitative response evaluation was performed. Results: Six patients were enrolled. All primary tumors could be identified on PET/CT-1. Four patients had a pCR and two a pathological partial response (pPR) in the primary tumor. Qualitative assessment of PET/CT resulted in an accuracy of 66.7%, compared to 83.3% of the standard-of-care MRI. Quantitative assessment showed a difference between the SUVR on PET/CT-1 and PET/CT-2 (ΔSUVR) in patients with a pPR and pCR of −48% and −90% (p = 0.133), respectively. The difference in tumor-to-blood ratio on PET/CT-1 and PET/CT-2 (ΔTBR) in patients with pPR and pCR was −79% and −94% (p = 0.133), respectively. Three patients had metastatic lymph nodes at diagnosis that were all identified on PET/CT-1. All three patients achieved a nodal pCR. Qualitative assessment of the lymph nodes with PET/CT resulted in an accuracy of 66.7%, compared to 50% of the MRI. Conclusions: NAT response evaluation using 89Zr-trastuzumab PET/CT is feasible. In the current study, qualitative assessment of the PET/CT images is not superior to standard-of-care MRI. Our results suggest that quantitative assessment of 89Zr-trastuzumab PET/CT has potential for a more accurate response evaluation of the primary tumor after NAT in HER2-positive breast cancer.</p

Endogeneity in Panel Data Models with Time-Varying and Time-Fixed Regressors: To IV or Not IV?

We analyse the problem of parameter inconsistency in panel data econometrics due to the correlation of exogenous variables with the error term. A common solution in this setting is to use Instrumental-Variable (IV) estimation in the spirit of Hausman-Taylor (1981). However, some potential shortcomings of the latter approach recently gave rise to the use of non-IV two-step estimators. Given their growing number of empirical applications, we aim to systematically compare the performance of IV and non-IV approaches in the presence of time-fixed variables and right hand side endogeneity using Monte Carlo simulations, where we explicitly control for the problem of IV selection in the Hausman-Taylor case. The simulation results show that the Hausman- Taylor model with perfect-knowledge about the underlying data structure (instrument orthogonality) has on average the smallest bias. However, compared to the empirically relevant specification with imperfect-knowledge and instruments chosen by statistical criteria, the non-IV rival performs equally well or even better especially in terms of estimating variable coefficients for time- fixed regressors. Moreover, the non-IV method tends to have a smaller root mean square error (rmse) than both Hausman-Taylor models with perfect and imperfect knowledge about the underlying correlation between r.h.s variables and residual term. This indicates that it is generally more efficient. The results are roughly robust for various combinations in the time and cross-section dimension of the data

Cross-cohort generalizability of deep and conventional machine learning for MRI-based diagnosis and prediction of Alzheimer's disease

This work validates the generalizability of MRI-based classification of Alzheimer’s disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI).We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the Alzheimer’s Disease Neuroimaging Initiative (ADNI; 334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer Neurodegenerative Diseases Biobank data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia.AD-CN classification based on modulated GM maps resulted in a similar area-under-the-curve (AUC) for SVM (0.940; 95%CI: 0.924–0.955) and CNN (0.933; 95%CI: 0.918–0.948). Application to conversion prediction in MCI yielded significantly higher performance for SVM (AUC = 0.756; 95%CI: 0.720-0.788) than for CNN (AUC = 0.742; 95%CI: 0.709-0.776) (p<0.01 for McNemar’s test). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896; 95%CI: 0.855–0.932) and CNN (0.876; 95%CI: 0.836–0.913). For prediction in MCI, performances decreased for both SVM (AUC = 0.665; 95%CI: 0.576-0.760) and CNN (AUC = 0.702; 95%CI: 0.624-0.786). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images (p=0.01).Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice

Stress-driven potentiation of lateral hypothalamic synapses onto ventral tegmental area dopamine neurons causes increased consumption of palatable food

Stress can cause overconsumption of palatable high caloric food. Despite the important role of stress eating in obesity and (binge) eating disorders, its underlying neural mechanisms remain unclear. Here we demonstrate in mice that stress alters lateral hypothalamic area (LHA) control over the ventral tegmental area (VTA), thereby promoting overconsumption of palatable food. Specifically, we show that glutamatergic LHA neurons projecting to the VTA are activated by social stress, after which their synapses onto dopamine neurons are potentiated via AMPA receptor subunit alterations. We find that stress-driven strengthening of these specific synapses increases LHA control over dopamine output in key target areas like the prefrontal cortex. Finally, we demonstrate that while inducing LHA-VTA glutamatergic potentiation increases palatable fat intake, reducing stress-driven potentiation of this connection prevents such stress eating. Overall, this study provides insights in the neural circuit adaptations caused by stress that drive overconsumption of palatable food

Response of a Specialist Bat to the Loss of a Critical Resource

Human activities have negatively impacted many species, particularly those with unique traits that restrict their use of resources and conditions to specific habitats. Unfortunately, few studies have been able to isolate the individual and combined effects of different threats on population persistence in a natural setting, since not all organisms can be associated with discrete habitat features occurring over limited spatial scales. We present the results of a field study that examines the short-term effects of roost loss in a specialist bat using a conspicuous, easily modified resource. We mimicked roost loss in the natural habitat and monitored individuals before and after the perturbation to determine patterns of resource use, spatial movements, and group stability. Our study focused on the disc-winged bat Thyroptera tricolor, a species highly morphologically specialized for roosting in the developing furled leaves of members of the order Zingiberales. We found that the number of species used for roosting increased, that home range size increased (before: mean 0.14±SD 0.08 ha; after: 0.73±0.68 ha), and that mean association indices decreased (before: 0.95±0.10; after: 0.77±0.18) once the roosting habitat was removed. These results demonstrate that the removal of roosting resources is associated with a decrease in roost-site preferences or selectivity, an increase in mobility of individuals, and a decrease in social cohesion. These responses may reduce fitness by potentially increasing energetic expenditure, predator exposure, and a decrease in cooperative interactions. Despite these potential risks, individuals never used roost-sites other than developing furled leaves, suggesting an extreme specialization that could ultimately jeopardize the long-term persistence of this species' local populations