The relationship between object-based spatial ability and virtual navigation performance.

Spatial navigation is a multi-faceted behaviour drawing on many different aspects of cognition. Visuospatial abilities, such as mental rotation and visuospatial working memory, in particular, may be key factors. A range of tests have been developed to assess visuospatial processing and memory, but how such tests relate to navigation ability remains unclear. This understanding is important to advance tests of navigation for disease monitoring in various disorders (e.g., Alzheimer's disease) where spatial impairment is an early symptom. Here, we report the use of an established mobile gaming app, Sea Hero Quest (SHQ), as a measure of navigation ability in a sample of young, predominantly female university students (N = 78; 20; female = 74.3%; mean age = 20.33 years). We used three separate tests of navigation embedded in SHQ: wayfinding, path integration and spatial memory in a radial arm maze. In the same participants, we also collected measures of mental rotation (Mental Rotation Test), visuospatial processing (Design Organization Test) and visuospatial working memory (Digital Corsi). We found few strong correlations across our measures. Being good at wayfinding in a virtual navigation test does not mean an individual will also be good at path integration, have a superior memory in a radial arm maze, or rate themself as having a strong sense of direction. However, we observed that participants who were good in the wayfinding task of SHQ tended to perform well on the three visuospatial tasks examined here, and to also use a landmark strategy in the radial maze task. These findings help clarify the associations between different abilities involved in spatial navigation.

Using Virtual Reality to Study Spatial Mapping and Threat Learning.

Using spatial mapping processes to learn about threat and safety in an environment is crucial for survival. Research using conditioning paradigms has explored the effects of state (transient arousal) and trait anxiety (anxiety as an aspect of personality) on threat learning and acquisition. However, results are mixed, and little is known regarding why some individuals do not learn to discriminate between threat and safety during contextual conditioning. We used a virtual reality (VR) contextual threat conditioning paradigm to elucidate the effects of state and trait anxiety on contextual threat learning. 70 healthy participants (46 female) navigated and "picked" flowers in a VR environment. Flowers picked in the dangerous zone (half of the environment) were paired with an electric shock (or "bee sting") to the hand; flowers picked in the safe zone were never paired with a shock. Participants also collected and returned neutral objects as a measure of spatial memory. Galvanic skin response (GSR) was measured throughout the task and anxiety was assessed via the State Trait Anxiety Inventory (STAI). Participants were categorized as learners if they correctly identified the two zones after the task. Non-learners, compared to learners, performed significantly worse during the spatial memory task and demonstrated significantly higher state anxiety scores and GSR levels throughout the task. Learners showed higher skin conductance response (SCR) in the dangerous zone compared to the safe zone while non-learners showed no SCR differences between zones. These results indicate that state anxiety may impair spatial mapping, disrupting contextual threat learning.

An investigation into the effectiveness of Mood Lifters in the context of trauma exposure.

A large proportion of adults experiencing mental health problems do not receive care due to structural and attitudinal barriers. Mood Lifters (ML) is an evidence-based mental wellness program designed to reduce depression, anxiety, and stress symptoms. This study aims to extend the literature by examining whether ML reduces posttraumatic stress disorder (PTSD) symptoms, and if childhood trauma (CT) affects treatment outcomes. We hypothesized that ML will be effective for all symptoms. However, we predicted that those who endorse higher levels of CT would experience a diminished impact of ML on their symptoms. Graduate students and young professionals (N = 221), a demographic that is known to have greater mental health concerns, were randomly assigned to either the waitlist condition (n = 78) or the intervention condition (n = 143). Before and after ML participation, participants completed a series of questionnaires about their symptoms. ML reduced symptoms of PTSD for participants who received the intervention relative to the waitlist. Further, we also observed that while the overall regression models with CT predict posttreatment scores for all the clinical symptoms, the CT variable itself was predictive of only posttreatment anxiety. Unexpectedly, given that CT is often associated with less improvement, we found that those at all levels of CT experienced similar reductions in depression, anxiety and PTSD symptoms. Taken together, ML is a viable option to help people reduce depression, anxiety, stress, and PTSD regardless of the level of CT exposure. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

IoT-Enabled Framework for Early Detection and Prediction of COVID-19 Suspects by Leveraging Machine Learning in Cloud.

COVID-19 is the repugnant but the most searched word since its outbreak in November 2019 across the globe. The world has to battle with it until an effective solution is developed. Due to the advancement in mobile and sensor technology, it is possible to come up with Internet of things-based healthcare systems. These novel healthcare systems can be proactive and preventive rather than traditional reactive healthcare systems. This article proposes a real-time IoT-enabled framework for the detection and prediction of COVID-19 suspects in early stages, by collecting symptomatic data and analyzing the nature of the virus in a better manner. The framework computes the presence of COVID-19 virus by mining the health parameters collected in real time from sensors and other IoT devices. The framework is comprised of four main components: user system or data collection center, data analytic center, diagnostic system, and cloud system. To point out and detect the COVID-19 suspected in real time, this work proposes the five machine learning techniques, namely support vector machine (SVM), decision tree, naïve Bayes, logistic regression, and neural network. In our proposed framework, the real and primary dataset collected from SKIMS, Srinagar, is used to validate our work. The experiment on the primary dataset was conducted using different machine learning techniques on selected symptoms. The efficiency of algorithms is calculated by computing the results of performance metrics such as accuracy, precision, recall, F1 score, root-mean-square error, and area under the curve score. The employed machine learning techniques have shown the accuracy of above 95% on the primary symptomatic data. Based on the experiment conducted, the proposed framework would be effective in the early identification and prediction of COVID-19 suspect realizing the nature of the disease in better way.

α-Tocopherol as functional excipient for resveratrol and coenzyme Q10-loaded SNEDDS for improved bioavailability and prophylaxis of breast cancer.

The present study evaluates the prophylactic efficacy of α-tocopherol (α-TOH), resveratrol (RES), and coenzyme Q10 (CoQ10) co-loaded self-nanoemulsifying drug delivery system (α-TOH-RES-CoQ10 SNEDDS) in 7,12-Dimethylbenz[a]anthracene (DMBA) induced breast cancer model. SNEDDS formulation components were rationally selected and optimized for maximum drug loading by applying the design of experiments and further evaluated for stability in simulated gastrointestinal fluids, functional stability of antioxidants, in vitro release, Caco-2 cell uptake, oral bioavailability and prophylactic anticancer activity. The SNEDDS demonstrated excellent stability in stimulated gastrointestinal fluids. The functional activity of antioxidants was confirmed by 2,2-diphenylpicrylhydrazyl (DPPH) scavenging assay wherein significantly (p > .05) higher antioxidant activity was observed in case of SNEDDS as compared with free antioxidants. Coumarin 6 (C-6)-loaded SNEDDS formulation demonstrated remarkably higher Caco-2 cell uptake in comparison with free C-6, indicative of efficient internalization of sub-micron SNEDDS droplets by Caco-2 cells. In line with Caco-2 cell uptake observations, α-TOH-RES-CoQ10-SNEDDS showed ∼2.30- and ∼3.64-fold increase in the AUC0-∞ values of RES and CoQ10 in comparison with free antioxidants. Significantly lower (p < .001) tumor volume (∼327 mm3) was found in case of animals treated with α-TOH-RES-CoQ10-SNEDDS in comparison with free antioxidant combination (∼1070 mm3) and DMBA control (∼1540 mm3) groups. Conclusively, the proposed strategy posed great potential in improving the prophylactic activity of antioxidants and hold promise for further exploration.

Role of KCa3.1 Channels in CNS Diseases: A Concise Review.

KCa3.1 protein is part of a heterotetrameric voltage-independent potassium channel, the activity of which depends on the intracellular calcium binding to calmodulin. KCa3.1 is immensely significant in regulating immune responses and primarily expressed in cells of hematopoietic lineage. It is one of the attractive pharmacological targets that are known to inhibit neuroinflammation. KCa3.1 blockers mediate neuroprotection through multiple mechanisms, such as by targeting microglia-mediated neuronal killing. KCa3.1 modulators may provide alternative treatment options for neurological disorders like ischemic stroke, Alzheimer disease, glioblastoma multiforme, multiple sclerosis and spinal cord injury. This review is an attempt to draw attention towards KCa3.1 channel, which was never exploited to its full potential as a viable therapeutic candidate against various neurological disorders.