Effects of dopamine D4 receptor antagonist on spontaneous alternation in rats

<p>Abstract</p> <p>Background</p> <p>The present study was a component of a series of studies scrutinising the neuroreceptor substrate of behavioural flexibility in a rat model. Spontaneous alternation paradigms model the natural tendency of rodents to spontaneously and flexibly shift between alternative spatial responses. In the study it was tested for the first time if the neurochemical substrate mediating spontaneous alternation behaviour includes the dopamine D₄receptor.</p> <p>Methods</p> <p>The acute effects of the highly selective dopamine D₄receptor antagonist L-745,870 on rats' performance in a spontaneous alternation paradigm in a T-maze were examined. The paradigm was a food-rewarded continuous trial procedure performed for 20 trials.</p> <p>Results</p> <p>The spontaneous alternation rate was not affected by the doses of the drug administered (0.02 mg/kg; 0.2 mg/kg; 2 mg/kg), but the position bias of the group receiving the highest L-745,870 dose (2 mg/kg) was significantly increased compared to the group that received the lowest dose (0.02 mg/kg). No significant effects on position bias were found compared to saline. The drug did not increase response perseveration.</p> <p>Conclusion</p> <p>The results show that the neural substrate mediating the spatial distribution of responses in the spontaneous alternation paradigm includes the D₄receptor. However, the statistically significant effect of L-745,870 on position bias was found comparing a high drug dose with a low drug dose, and not comparing the drug doses with saline. For the tested doses of L-745,870 the effect on position bias was not large enough to affect the alternation rate.</p

A Comparative Study of Federated Learning Models for COVID-19 Detection

Deep learning is effective in diagnosing COVID-19 and requires a large amount of data to be effectively trained. Due to data and privacy regulations, hospitals generally have no access to data from other hospitals. Federated learning (FL) has been used to solve this problem, where it utilizes a distributed setting to train models in hospitals in a privacy-preserving manner. Deploying FL is not always feasible as it requires high computation and network communication resources. This paper evaluates five FL algorithms' performance and resource efficiency for Covid-19 detection. A decentralized setting with CNN networks is set up, and the performance of FL algorithms is compared with a centralized environment. We examined the algorithms with varying numbers of participants, federated rounds, and selection algorithms. Our results show that cyclic weight transfer can have better overall performance, and results are better with fewer participating hospitals. Our results demonstrate good performance for detecting COVID-19 patients and might be useful in deploying FL algorithms for covid-19 detection and medical image analysis in general

Fed-Safe: Securing Federated Learning in Healthcare Against Adversarial Attacks

This paper explores the security aspects of federated learning applications in medical image analysis. Current robustness-oriented methods like adversarial training, secure aggregation, and homomorphic encryption often risk privacy compromises. The central aim is to defend the network against potential privacy breaches while maintaining model robustness against adversarial manipulations. We show that incorporating distributed noise, grounded in the privacy guarantees in federated settings, enables the development of a adversarially robust model that also meets federated privacy standards. We conducted comprehensive evaluations across diverse attack scenarios, parameters, and use cases in cancer imaging, concentrating on pathology, meningioma, and glioma. The results reveal that the incorporation of distributed noise allows for the attainment of security levels comparable to those of conventional adversarial training while requiring fewer retraining samples to establish a robust model

Trigeminal neuralgia: new classification and diagnostic grading for practice and research

Trigeminal neuralgia (TN) is an exemplary condition of neuropathic facial pain. However, formally classifying TN as neuropathic pain based on the grading system of the International Association for the Study of Pain is complicated by the requirement of objective signs confirming an underlying lesion or disease of the somatosensory system. The latest version of the International Classification of Headache Disorders created similar difficulties by abandoning the term symptomatic TN for manifestations caused by major neurologic disease, such as tumors or multiple sclerosis. These diagnostic challenges hinder the triage of TN patients for therapy and clinical trials, and hamper the design of treatment guidelines. In response to these shortcomings, we have developed a classification of TN that aligns with the nosology of other neurologic disorders and neuropathic pain. We propose 3 diagnostic categories. Classical TN requires demonstration of morphologic changes in the trigeminal nerve root from vascular compression. Secondary TN is due to an identifiable underlying neurologic disease. TN of unknown etiology is labeled idiopathic. Diagnostic certainty is graded possible when pain paroxysms occur in the distribution of the trigeminal nerve branches. Triggered paroxysms permit the designation of clinically established TN and probable neuropathic pain. Imaging and neurophysiologic tests that establish the etiology of classical or secondary TN determine definite neuropathic pain

Does chronic pain hinder physical activity among older adults with type 2 diabetes? : Health Psychology and Behavioral Medicine

ABSTRACT Background: Physical activity (PA) is a key component in management of type 2 diabetes (T2D). Pain might be a barrier to PA especially among older adults with T2D, but surprisingly few studies have investigated the association between chronic pain and PA. Our aim was to evaluate the prevalence of chronic pain among older adults with T2D and to examine the association between chronic pain and PA while taking important life-contextual factors into account. Methods: Data of this register-based, cross-sectional study were collected in a survey among adults with T2D (n=2866). In the current study, only respondents aged 65?75 years were included (response rate 63%, n=1386). Data were analysed by means of descriptive statistics and multivariate logistic regression analysis. Results: In total, 64% reported chronic pain. In specific groups, e.g. women and those who were obese, the prevalence was even higher. Among respondents experiencing chronic pain, frequent pain among women and severe pain among both genders were independently associated with decreased likelihood of being physically active. Moreover, the likelihood of being physically active decreased with higher age and BMI, whereas it increased with higher autonomous motivation and feelings of energy. Among physically active respondents suffering from chronic pain, neither intensity nor frequency of pain explained engagement in exercise (as compared with incidental PA). Instead, men were more likely to exercise regularly as were those with good perceived health and higher autonomous motivation. Conclusions: The prevalence of chronic pain is high among older adults with T2D. This study shows that among those suffering from chronic pain, severe pain is independently and inversely associated with being physically active, as is frequent pain, but only among women. Moreover, the findings show the importance of autonomous motivation and health variables for both incidental PA and exercise among older adults with T2D experiencing chronic pain.Peer reviewe

The Hidden Adversarial Vulnerabilities of Medical Federated Learning

In this paper, we delve into the susceptibility of federated medical image analysis systems to adversarial attacks. Our analysis uncovers a novel exploitation avenue: using gradient information from prior global model updates, adversaries can enhance the efficiency and transferability of their attacks. Specifically, we demonstrate that single-step attacks (e.g. FGSM), when aptly initialized, can outperform the efficiency of their iterative counterparts but with reduced computational demand. Our findings underscore the need to revisit our understanding of AI security in federated healthcare settings

Optimal combinations of acute phase proteins for detecting infectious disease in pigs

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