Does the extent of neck surgery based on preoperative calcitonin level influence survival in medullary thyroid carcinoma: a retrospective tertiary centre experience

INTRODUCTION

Medullary thyroid carcinoma (MTC) is a rare thyroid cancer arising from the calcitonin-secreting parafollicular cells. Previous studies suggested a preoperative calcitonin level >200ng/l is an indication for prophylactic lateral neck dissection (LND) to remove micrometastases and improve survival outcomes.

METHODS

This retrospective single-centre study assessed the efficacy of preoperative calcitonin levels as a marker for determining need for prophylactic LND in MTC. Data were obtained on demographics, preoperative calcitonin levels, size and laterality of index tumour, type of neck dissection (central neck dissection (CND), LND), nodes removed, levels with involved nodes, number of nodes histologically involved, mortality, adjuvant therapy and locoregional recurrence.

RESULTS

A total of 33 patients were identified from St George's University Hospitals NHS Foundation Trust between 1 January 2001 and 19 August 2021; 8 were excluded for data quality issues. Of the 18 classified with a high preoperative calcitonin level (>200ng/l), 10 (56%) had a LND and 8 (44%) had a CND. In the low-calcitonin group, three (43%) patients had a CND only and four (57%) had a LND. There was no difference in absolute or disease-free survival between the low and high groups (p=0.960, p=0.817), or between those who had a CND and LND in the high-calcitonin group (p=0.607, hazard ratio (HR) 0.55; p=0.129, HR 8.78).

CONCLUSION

There was no statistically significant difference in outcomes between high and low calcitonin groups. A selective approach to performing LND in MTC patients based on clinical and imaging findings suggesting disease presence in the lateral neck should be explored further.

Unraveling the molecular basis of cannabidiolic acid methyl Ester's anti-depressive effects in a rat model of treatment-resistant depression

Major depressive disorder (MDD) stands as a significant cause of disability globally. Cannabidiolic Acid-Methyl Ester (CBDA-ME) (EPM-301, HU-580), a derivative of Cannabidiol, demonstrates immediate antidepressant-like effects, yet it has undergone only minimal evaluation in psychopharmacology. Our goal was to investigate the behavioral and potential molecular mechanisms associated with the chronic oral administration of this compound in the Wistar Kyoto (WKY) genetic model of treatment-resistant depression. Male WKY rats were subjected to behavioral assessments before and after receiving chronic (14-day) oral doses of CBDA-ME (0.5 mg/kg), 15 mg/kg of imipramine or vehicle. At the end of the study, plasma corticosterone levels and mRNA expression of various genes in the medial Prefrontal Cortex and Hippocampus were measured. Behavioral outcomes from CBDA-ME treatment indicated an antidepressant-like effect similar to imipramine, as oral ingestion reduced immobility and increased swimming duration in the Forced Swim Test. Neither treatment influenced locomotion in the Open Field Test nor preference in the Saccharin Preference Test. The behavioral impact in WKY rats coincided with reduced corticosterone serum levels, upregulated mRNA expression of Cannabinoid receptor 1, Fatty Acid Amide Hydrolase, and Corticotropin-Releasing Hormone Receptor 1, alongside downregulation of the Serotonin Transporter in the hippocampus. Additionally, there was an upregulation of CB1 mRNA expression and downregulation of Brain-Derived Neurotrophic Factor in the mPFC. These findings contribute to our limited understanding of the antidepressant effects of CBDA-ME and shed light on its potential psychopharmacological mechanisms. This discovery opens up possibilities for utilizing cannabinoids in the treatment of major depressive disorder and related conditions.

Perspectives on incorporating expert feedback into model updates

Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficient consideration of how practitioners should translate domain expertise into ML updates. In this review, we consider how to capture interactions between practitioners and experts systematically. We devise a taxonomy to match expert feedback types with practitioner updates. A practitioner may receive feedback from an expert at the observation or domain level and then convert this feedback into updates to the dataset, loss function, or parameter space. We review existing work from ML and human-computer interaction to describe this feedback-update taxonomy and highlight the insufficient consideration given to incorporating feedback from non-technical experts. We end with a set of open questions that naturally arise from our proposed taxonomy and subsequent survey.

Self-Guided Belief Propagation - A Homotopy Continuation Method

Belief propagation (BP) is a popular method for performing probabilistic inference on graphical models. In this work, we enhance BP and propose self-guided belief propagation (SBP) that incorporates the pairwise potentials only gradually. This homotopy continuation method converges to a unique solution and increases the accuracy without increasing the computational burden. We provide a formal analysis to demonstrate that SBP finds the global optimum of the Bethe approximation for attractive models where all variables favor the same state. Moreover, we apply SBP to various graphs with random potentials and empirically show that: (i) SBP is superior in terms of accuracy whenever BP converges, and (ii) SBP obtains a unique, stable, and accurate solution whenever BP does not converge.

SphereFace Revived: Unifying Hyperspherical Face Recognition

This paper addresses the deep face recognition problem under an open-set protocol, where ideal face features are expected to have smaller maximal intra-class distance than minimal inter-class distance under a suitably chosen metric space. To this end, hyperspherical face recognition, as a promising line of research, has attracted increasing attention and gradually become a major focus in face recognition research. As one of the earliest works in hyperspherical face recognition, SphereFace explicitly proposed to learn face embeddings with large inter-class angular margin. However, SphereFace still suffers from severe training instability which limits its application in practice. In order to address this problem, we introduce a unified framework to understand large angular margin in hyperspherical face recognition. Under this framework, we extend the study of SphereFace and propose an improved variant with substantially better training stability - SphereFace-R. Specifically, we propose two novel ways to implement the multiplicative margin, and study SphereFace-R under three different feature normalization schemes (no feature normalization, hard feature normalization and soft feature normalization). We also propose an implementation strategy - "characteristic gradient detachment" - to stabilize training. Extensive experiments on SphereFace-R show that it is consistently better than or competitive with state-of-the-art methods.

How transparency modulates trust in artificial intelligence

The study of human-machine systems is central to a variety of behavioral and engineering disciplines, including management science, human factors, robotics, and human-computer interaction. Recent advances in artificial intelligence (AI) and machine learning have brought the study of human-AI teams into sharper focus. An important set of questions for those designing human-AI interfaces concerns trust, transparency, and error tolerance. Here, we review the emerging literature on this important topic, identify open questions, and discuss some of the pitfalls of human-AI team research. We present opposition (extreme algorithm aversion or distrust) and loafing (extreme automation complacency or bias) as lying at opposite ends of a spectrum, with algorithmic vigilance representing an ideal mid-point. We suggest that, while transparency may be crucial for facilitating appropriate levels of trust in AI and thus for counteracting aversive behaviors and promoting vigilance, transparency should not be conceived solely in terms of the explainability of an algorithm. Dynamic task allocation, as well as the communication of confidence and performance metrics—among other strategies—may ultimately prove more useful to users than explanations from algorithms and significantly more effective in promoting vigilance. We further suggest that, while both aversive and appreciative attitudes are detrimental to optimal human-AI team performance, strategies to curb aversion are likely to be more important in the longer term than those attempting to mitigate appreciation. Our wider aim is to channel disparate efforts in human-AI team research into a common framework and to draw attention to the ecological validity of results in this field.

Recent advances in artificial intelligence (AI) and machine learning have brought the study of human-AI (HAI) teams into sharper focus. An important set of questions for those designing HAI interfaces concerns trust—specifically, human trust in the AI systems with which they form teams. We review the literature on how perceiving an AI making mistakes violates trust and how such violations might be repaired. In doing so, we discuss the role played by various forms of algorithmic transparency in the process of trust repair, including explanations of algorithms, uncertainty estimates, and performance metrics.

Erratum: Author Correction: Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

[This corrects the article DOI: 10.1038/s42256-021-00421-z.].

Filling gaps in trustworthy development of AI

[Figure: see text].

Advancing COVID-19 diagnosis with privacy-preserving collaboration in artificial intelligence

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses; however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health.

Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

Leveraging Data Science to Combat COVID-19: A Comprehensive Review

COVID-19, an infectious disease caused by the SARS-CoV-2 virus, was declared a pandemic by the World Health Organisation (WHO) in March 2020. By mid-August 2020, more than 21 million people have tested positive worldwide. Infections have been growing rapidly and tremendous efforts are being made to fight the disease. In this paper, we attempt to systematise the various COVID-19 research activities leveraging data science, where we define data science broadly to encompass the various methods and tools-including those from artificial intelligence (AI), machine learning (ML), statistics, modeling, simulation, and data visualization-that can be used to store, process, and extract insights from data. In addition to reviewing the rapidly growing body of recent research, we survey public datasets and repositories that can be used for further work to track COVID-19 spread and mitigation strategies. As part of this, we present a bibliometric analysis of the papers produced in this short span of time. Finally, building on these insights, we highlight common challenges and pitfalls observed across the surveyed works. We also created a live resource repository at https://github.com/Data-Science-and-COVID-19/Leveraging-Data-Science-To-Combat-COVID-19-A-Comprehensive-Review that we intend to keep updated with the latest resources including new papers and datasets.

Rare case of venous tumour thrombus from acinic cell carcinoma of the parotid gland

Head and neck tumour thrombus is a rare pathology and at present there are no reported cases of tumour thrombus secondary to acinic cell carcinoma of the parotid gland. We report a case of an 81-year-old man with an acinic cell carcinoma of the left parotid and an intravenous tumour thrombus extending from the retromandibular vein into the internal jugular vein. This case also highlights the importance of radiological imaging in the management of tumour thrombus.

Implementing diffusion-weighted MRI for body imaging in prospective multicentre trials: current considerations and future perspectives

For body imaging, diffusion-weighted MRI may be used for tumour detection, staging, prognostic information, assessing response and follow-up. Disease detection and staging involve qualitative, subjective assessment of images, whereas for prognosis, progression or response, quantitative evaluation of the apparent diffusion coefficient (ADC) is required. Validation and qualification of ADC in multicentre trials involves examination of i) technical performance to determine biomarker bias and reproducibility and ii) biological performance to interrogate a specific aspect of biology or to forecast outcome. Unfortunately, the variety of acquisition and analysis methodologies employed at different centres make ADC values non-comparable between them. This invalidates implementation in multicentre trials and limits utility of ADC as a biomarker. This article reviews the factors contributing to ADC variability in terms of data acquisition and analysis. Hardware and software considerations are discussed when implementing standardised protocols across multi-vendor platforms together with methods for quality assurance and quality control. Processes of data collection, archiving, curation, analysis, central reading and handling incidental findings are considered in the conduct of multicentre trials. Data protection and good clinical practice are essential prerequisites. Developing international consensus of procedures is critical to successful validation if ADC is to become a useful biomarker in oncology.

KEY POINTS

• Standardised acquisition/analysis allows quantification of imaging biomarkers in multicentre trials. • Establishing "precision" of the measurement in the multicentre context is essential. • A repository with traceable data of known provenance promotes further research.

Mechanism and non-mechanism based imaging biomarkers for assessing biological response to treatment in non-small cell lung cancer

Therapeutic options in locally advanced non-small cell lung cancer (NSCLC) have expanded in the past decade to include a palate of targeted interventions such as high dose targeted thermal ablations, radiotherapy and growing platform of antibody and small molecule therapies and immunotherapies. Although these therapies have varied mechanisms of action, they often induce changes in tumour architecture and microenvironment such that response is not always accompanied by early reduction in tumour mass, and evaluation by criteria other than size is needed to report more effectively on response. Functional imaging techniques, which probe the tumour and its microenvironment through novel positron emission tomography and magnetic resonance imaging techniques, offer more detailed insights into and quantitation of tumour response than is available on anatomical imaging alone. Use of these biomarkers, or other rational combinations as readouts of pathological response in NSCLC have potential to provide more accurate predictors of treatment outcomes. In this article, the robustness of the more commonly available positron emission tomography and magnetic resonance imaging biomarker indices is examined and the evidence for their application in NSCLC is reviewed.

A new compact solid-state neutral particle analyser at ASDEX Upgrade: Setup and physics modeling

At ASDEX Upgrade (AUG), a new compact solid-state detector has been installed to measure the energy spectrum of fast neutrals based on the principle described by Shinohara et al. [Rev. Sci. Instrum. 75, 3640 (2004)]. The diagnostic relies on the usual charge exchange of supra-thermal fast-ions with neutrals in the plasma. Therefore, the measured energy spectra directly correspond to those of confined fast-ions with a pitch angle defined by the line of sight of the detector. Experiments in AUG showed the good signal to noise characteristics of the detector. It is energy calibrated and can measure energies of 40-200 keV with count rates of up to 140 kcps. The detector has an active view on one of the heating beams. The heating beam increases the neutral density locally; thereby, information about the central fast-ion velocity distribution is obtained. The measured fluxes are modeled with a newly developed module for the 3D Monte Carlo code F90FIDASIM [Geiger et al., Plasma Phys. Controlled Fusion 53, 65010 (2011)]. The modeling allows to distinguish between the active (beam) and passive contributions to the signal. Thereby, the birth profile of the measured fast neutrals can be reconstructed. This model reproduces the measured energy spectra with good accuracy when the passive contribution is taken into account.

Response of LaBr3(Ce) scintillators to 2.5 MeV fusion neutrons

Measurements of the response of LaBr3(Ce) to 2.5 MeV neutrons have been carried out at the Frascati Neutron Generator and at tokamak facilities with deuterium plasmas. The observed spectrum has been interpreted by means of a Monte Carlo model. It is found that the main contributor to the measured response is neutron inelastic scattering on (79)Br, (81)Br, and (139)La. An extrapolation of the count rate response to 14 MeV neutrons from deuterium-tritium plasmas is also presented. The results are of relevance for the design of γ-ray diagnostics of fusion burning plasmas.

Endocannabinoid receptor deficiency affects maternal care and alters the dam's hippocampal oxytocin receptor and brain-derived neurotrophic factor expression

Maternal care is the newborn's first experience of social interaction, and this influences infant survival, development and social competences throughout life. We recently found that postpartum blocking of the endocannabinoid receptor-1 (CB1R) altered maternal behaviour. In the present study, maternal care was assessed by the time taken to retrieve pups, pups' ultrasonic vocalisations (USVs) and pup body weight, comparing CB1R deleted (CB1R KO) versus wild-type (WT) mice. After culling on postpartum day 8, hippocampal expression of oxytocin receptor (OXTR), brain-derived neurotrophic factor (BDNF) and stress-mediating factors were evaluated in CB1R KO and WT dams. Comparisons were also performed with nulliparous (NP) CB1R KO and WT mice. Compared to WT, CB1R KO dams were slower to retrieve their pups. Although the body weight of the KO pups did not differ from the weight of WT pups, they emitted fewer USVs. This impairment of the dam-pup relationship correlated with a significant reduction of OXTR mRNA and protein levels among CB1R KO dams compared to WT dams. Furthermore, WT dams exhibited elevated OXTR mRNA expression, as well as increased levels of mineralocorticoid and glucocorticoid receptors, compared to WT NP mice. By contrast, CB1R KO dams showed no such elevation of OXTR expression, alongside lower BDNF and mineralocorticoid receptors, as well as elevated corticotrophin-releasing hormone mRNA levels, when compared to CB1R KO NP. Thus, it appears that the disruption of endocannabinoid signalling by CB1R deletion alters expression of the OXTR, apparently leading to deleterious effects upon maternal behaviour.

Diagnostics development for quasi-steady-state operation of the Wendelstein 7-X stellarator (invited)

The critical issues in the development of diagnostics, which need to work robust and reliable under quasi-steady state conditions for the discharge durations of 30 min and which cannot be maintained throughout the one week duration of each operation phase of the Wendelstein 7-X stellarator, are being discussed.

Diagnostics design for steady-state operation of the Wendelstein 7-X stellarator

The status of the diagnostic developments for the quasistationary operable stellarator Wendelstein 7-X (maximum pulse length of 30 min at 10 MW ECRH heating at 140 GHz) will be reported on. Significant emphasis is being given to the issue of ECRH stray radiation shielding of in-vessel diagnostic components, which will be critical at high density operation requiring O2 and OXB heating.

Behavioral Effects of Gut Hormones in Neonatal Rats: I. Somatostatin Administration During the First Postnatal Week

This exploratory study attempted to uncover behavioral and physical outcomes of changes in the peripheral SMS system in the first postnatal week. On postnatal days 1-7, Sprague-Dawley rat pups received daily s.c. injections of Somatostatin (SMS; 8 or 40 micrograms/kg), saline, or CPP-1 (8 or 40 micrograms/kg), a putative SMS receptor antagonist. Physical growth and neurobehavioral development of the pups, assessed on days 3, 6, 9 and 12, were not affected, in 3 separate replications (n = 11/treatment/replication). In contrast, neonatal CPP-1 (40 micrograms/kg) reduced separation distress on day 14, as measured by ultrasonic vocalization and activity. In addition, neonatal SMS (40 micrograms/kg) tended to impair learning on a milk-rewarded Y-maze on days 15-16. These findings support further examination of the potential role of SMS in behavioral development.

Diagnostic developments for quasicontinuous operation of the Wendelstein 7-X stellarator

The stellarator Wendelstein 7-X will allow for quasicontinuous operation with the duration only being limited to two 30 min discharges per day, at a continuous heating power of 10 MW electron cyclotron resonance heating (ECRH) at 140 GHz, by the capacity of the cooling water reservoir. This will result in high thermal loads on all plasma facing components of 50-100 kW/m(2) from radiation alone and of up to about 500 kW/m(2) on components additionally exposed to convective loads. In high density scenarios toroidally varying ECRH stray radiation levels of 50-200 kW/m(2) need to be coped with, requiring careful material selection and different shielding and hardening techniques. Furthermore, a gradual buildup of coatings on plasma facing optical components, which without any measures being taken, would lead to high transmission losses already within a few days of long pulse operation (equivalent to about 1 year of operation in pulsed devices like JET or ASDEX-upgrade) and therefore needs to be prevented as much as possible. In addition in situ cleaning as well as absolute calibration techniques need to be developed for all plasma facing optical systems. Here we report about some of our efforts to find, for various types of diagnostics, ways to cope with these adverse effects. Moreover, we give a few examples for individual diagnostic specific issues with respect to quasicontinuous operation, such as the development of a special integrator for the magnetic diagnostics as well as special interferometer types which can cope with unavoidable vibrations and slow path length changes due to, e.g., thermal expansion of the plasma vessel.

Experimental observation of oscillating and interacting matter wave dark solitons

We report on the generation, subsequent oscillation and interaction of a pair of matter-wave dark solitons. These are created by releasing a Bose-Einstein condensate from a double well potential into a harmonic trap in the crossover regime between one dimension and three dimensions. Multiple oscillations and collisions of the solitons are observed, in quantitative agreement with simulations of the Gross-Pitaevskii equation. An effective particle picture is developed and confirms that the deviation of the observed oscillation frequencies from the asymptotic prediction nu(z)/sqrt 2, where nu(z) is the longitudinal trapping frequency, results from the dimensionality of the system and the soliton interactions.

Adaptation to lactation in OLETF rats lacking CCK-1 receptors: body weight, fat tissues, leptin and oxytocin

OBJECTIVE

To understand the adaptation to lactation of obese rats, by studying the interplay among the gut hormone cholecystokinin (CCK), the adiposity hormone leptin and the affiliation hormone oxytocin in modulating body mass and fat storage.

DESIGN

Strain differences were examined between Otsuka Long Evans Tokushima Fatty (OLETF) rats lacking expression of functional CCK-1 receptors and Long Evans Tokushima Otsuka (LETO) controls, tested as nulliparous dams, at the 7 and 15th lactation day, at weaning (lactation day 22) or 8 weeks postweaning.

MEASUREMENTS

We measured body mass, fat pads (brown, retroperitoneal and inguinal) and inguinal adipocytes. Plasma levels of leptin and oxytocin were determined.

RESULTS

Fat depots of LETO female rats were larger during lactation compared to the levels found in postweaning and nulliparous female rats. LETO female rats gained weight and accumulated fat during pregnancy and lactation, returning to their normal fat levels postweaning. In contrast, OLETF female rats presented lower body weight and fat depots during the lactation period than nulliparous dams, and regained the weight and fat postweaning. Plasma leptin and oxytocin were highly correlated and followed the same pattern. OLETF leptin levels were highly correlated with fat depot and inguinal cell surface. No significant correlation was found for LETO parameters.

CONCLUSIONS

Pregnancy and lactation are energy-consuming events, which naturally induce female rats to increase food intake and accumulate fat. When challenged by the demands of rapidly growing preobese OLETF pups, OLETF dams' fat stores are reduced to lean, LETO levels. During lactation, sensitivity of the oxytocinergic neurons descending from the paraventricular nuclei to the nucleus of the solitary tract to CCK is reduced. We theorized that this pathway is not available to OLETF female rats that lack functional CCK-1 receptors to mediate the signal. The current study contributes to the understanding of the female body's adaptation to lactation.

Dehydroepiandrosterone and monoamines in the limbic system of a genetic animal model of childhood depression

Monoamines and dehydroepiandrosterone (DHEA) levels were measured in a genetic animal model for childhood depression in four subcortical structures: nucleus accumbens (Nac), ventral tegmental area (VTA), amygdala and hypothalamus. The "depressive-like" strain was the Flinders Sensitive Line (FSL), compared to their controls, Sprague-Dawley (SD) rats. Prepubertal FSL rats showed abnormal levels of only a few monoamines and their metabolites in these brain regions. This is in contrast to former studies, in which adult FSL rats exhibited significantly higher levels of all the monoamines and their metabolites measured. These different abnormal monoamine patterns between the "depressed" prepubertal rats and their adults, may help to explain why depressed children and adolescents fail to respond to antidepressant treatment as well as adults do. On the other hand, FSL prepubertal rats exhibited the same pattern of abnormal DHEA basal levels as was found in adults in previous experiments. The results from the current study may imply that treatment with DHEA could be a promising novel therapeutic option for depressed children and adolescents that fail to respond to common (monoaminergic) antidepressant treatments.

Energy-confinement scaling for high-beta plasmas in the W7-AS stellarator

High-beta energy-confinement data are subjected to comparisons of scaling invariant, first-principles physical models. The models differ in the inclusion of basic equations indicating the nature of transport. The result for high-beta data of the W7-AS stellarator is that global transport is described best with a collisional high-beta model, which is different from previous outcomes for low-beta data. Model predictive calculations indicate the validation of energy-confinement prediction with respect to plasma beta and collisionality nu*. The finding of different transport behaviors in distinct beta regimes is important for the development of fusion energy based on magnetic confinement and for the assessment of different confinement concepts.

Assessment of antidepressant and anxiolytic properties of NK1 antagonists and substance P in Wistar Kyoto rats

In an attempt to explore the involvement of substance P in depression and anxiety and its' potential therapeutic effects, we measured basal plasma and hypothalamic levels of substance P in a well-studied animal model of depression--adult male Wistar Kyoto (WKY) rats and their controls, Wistar rats. We also studied the influence of a substance P receptor (NK1) antagonist (SPA) on "anxiety-like" and "depressive-like" behaviors exhibited by the WKY rats in the open field and swim test paradigms, compared to controls. WKY rats exhibited lower levels of substance P compared to controls in the hypothalamus. Though the WKY strain exhibited less rearing behavior in the open field compared to controls, SPA did not influence this pattern of behavior. In contrast, SPA had a significant effect on a depressive-like behavior exhibited by the WKY strain--it reduced significantly the immobility duration of WKY rats in the swim test. Thus it seems that depression involves alterations in levels of substance P, and that NK1 antagonists may be effective in the relief of depressive, but not anxiety symptoms.