Sci, Volume 6, Issue 2 (June 2024) – 7 articles

The present study investigates the utilization of ceramic coatings and insulation elements in the context of Cold Atmospheric Pressure Plasma (CAPP) surgery tools, highlighting how precise engineering modifications can influence surgical precision. The adoption of cold plasma in surgery can be reinforced by material advancements withstanding several specific challenges, including electrical and thermal protection. We explore the potential of alumina (Al₂O₃), renowned for its high dielectric strength and resistance, as a promising material solution for insulating electrodes. We evaluated the thermal performance of surgical tools concerning different insulation thicknesses. Our findings suggest that Al₂O₃–based coatings, with their superior characteristics, significantly enhance the usability of cold plasma technology, thus fostering its application in minimally invasive surgery. We examine the implications of these findings for the design of next-generation surgical instruments and propose avenues for future research. This work contributes to the field of biomedical engineering by showcasing the pivotal role of material science in advancing surgical technologies. Full article

The expression “twice-exceptionality” has been used to describe conditions in which giftedness and specific disorders coexist. Our study offers a retrospective analysis of clinical reports of gifted children evaluated for suspected specific learning disorders (SLD) or attention-deficit/hyperactivity disorder (ADHD). The initial sample included 456 school-aged children referred to our clinic for suspected SLD and/or ADHD over a two-year interval. The inclusion criteria were: a General Ability Index score above 120 in the cognitive assessment; age 6–18 years; and not satisfying diagnostic criteria for autism spectrum disorder. Forty children were selected for the study. We grouped patients according to the final diagnosis: neurodevelopmental disorder (SLD and/or ADHD) (n = 15), psychopathological disorder (n = 8), mixed neurodevelopmental and psychopathological (n = 13), no emerging disorder (n = 4). The study included 36 (90%) males. Mean age was 9.3 years (SD 1.62). Mean Full-Scale Intelligence Quotient was 121.7 (SD 7.77), mean General Ability Index was 130.2 (SD 6.79). Furthermore, the cognitive assessment of the different groups highlighted a non-homogeneous profile in all groups, with lower scores on working memory and processing speed indexes. Our results support the hypothesis that difficulties in gifted children’s adaptation to scholastic and social settings could be misinterpreted as a manifestation of a clear disease. Full article

This paper introduces a model to aid decision-makers in answering many of the important questions regarding how best to operate a cancer center. This study aims to allocate the available cancer center capacity to different cancer types to minimize the deviation in patient demand satisfied from desired supply targets across multiple cancer types. A stochastic chance-constrained model is proposed to consider uncertainties in new and returning patient demand. The proposed model determines the optimal specialization mix for oncologists based on the distribution of demand by cancer type, preventing potential mismatches. Additionally, it aims to balance workloads among oncologists and individual clinics and indirectly reduce support service costs by limiting their clinic days. Numerical results are presented using historical data collected from our collaborating cancer center to demonstrate the usefulness of the model. The results confirm that the ability to satisfy patient demand increases as oncologists become more flexible. In addition, the results show that even having a small number of highly flexible oncologists is sufficient to achieve strong patient demand satisfaction. Moreover, restricting the allowable workload difference among oncologists achieves an acceptable trade-off between workload balance and satisfying patient demand. Full article

Audio classification using deep learning models, which is essential for applications like voice assistants and music analysis, faces challenges when deployed on edge devices due to their limited computational resources and memory. Achieving a balance between performance, efficiency, and accuracy is a significant obstacle to optimizing these models for such constrained environments. In this investigation, we evaluate diverse deep learning architectures, including Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), for audio classification tasks on the ESC 50, UrbanSound8k, and Audio Set datasets. Our empirical findings indicate that Mel spectrograms outperform raw audio data, attributing this enhancement to their synergistic alignment with advanced image classification algorithms and their congruence with human auditory perception. To address the constraints of model size, we apply model-compression techniques, notably magnitude pruning, Taylor pruning, and 8-bit quantization. The research demonstrates that a hybrid pruned model achieves a commendable accuracy rate of 89 percent, which, although marginally lower than the 92 percent accuracy of the uncompressed CNN, strikingly illustrates an equilibrium between efficiency and performance. Subsequently, we deploy the optimized model on the Raspberry Pi 4 and NVIDIA Jetson Nano platforms for audio classification tasks. These findings highlight the significant potential of model-compression strategies in enabling effective deep learning applications on resource-limited devices, with minimal compromise on accuracy. Full article

Mental illnesses, such as clinical depression, have taken an unprecedented toll on society and the economy on a global scale. The relationship between stress management and mental health decline is of utmost significance, especially as most avenues of mental health management remain inaccessible for the majority of the general public, i.e., interview-based, and face-to-face interventions or costly drug-based therapies. Cortisol, the primary stress hormone, regulates the stress response in the human body and, through persistent activation, can lead to chronic stress and mental health deterioration. Thereby, the measurement and evaluation of cortisol within saliva could harness potential developments in management and diagnostic tools to monitor physiological and psychological stress in simple point-of-care applications. The current study aims to determine the concentration of salivary cortisol in spiked artificial saliva samples using blue tetrazolium (BT) dye as a colorimetric indicator. The proposed method showcases the use of the BT dye as an effective method for the rapid measurement of salivary cortisol, with accuracy comparable to the gold-standard method for salivary cortisol analysis, enzyme-linked immunoassays (ELISAs). Finally, a prototype colorimetric sensor has been developed for point-of-care applications of stress monitoring via salivary cortisol measurement. Full article

(1) Background: Anxiety disorders are among the most common psychiatric conditions and have a rising prevalence. Patients with anxiety disorders can, however, be deterred from seeking treatment due to associated stigmas and medication side effects. Evidence indicates that promising digital health solutions to address those concerns reside in the growing field of extended reality (XR). The limited literature synthesis from the perspectives of patients and healthcare professionals (HCPs) regarding the experiences and effectiveness of XR-based anxiety disorder therapies motivated the undertaking of this systematic review. (2) Methods: A systematic search of the literature was conducted according to the PRISMA 2020 guidelines on the following databases: CINAHL, APA PsycNet and PubMed. The search was completed on 23 January 2024 with no restriction on the time of publication. Studies were screened based on a predetermined selection criteria relevant to the research aims. (3) Results: Five studies fulfilled the inclusion requirements. The majority investigated the use of XR tools for individual therapy and indicated that they can be as effective for patients as traditional methods and can aid in HCPs’ therapeutic tasks. (4) Conclusions: XR-based anxiety disorder therapies are generally perceived as immersive and with minimal side effects by patients, while HCPs mostly consider XR tools as practical and assistive. However, refinements with the XR setup could further improve the experience. Such modalities represent potent drug-free alternatives or supplements to traditional therapy and could be considered for remote, individual care. The findings’ generalisability requires further research into more conditions within the anxiety disorder group, as well as larger sample sizes. Full article

Mood profile clusters have previously been identified in several cultural contexts. In the present study, six mood profile clusters referred to as the iceberg, inverse Everest, inverse iceberg, shark fin, submerged, and surface profiles, were investigated in a Greek population. The names of the mood profiles reflect how they appear after raw scores for Tension, Depression, Anger, Vigor, Fatigue, and Confusion (in that order), are converted to T-scores and depicted graphically. A Greek translation of the Brunel Mood Scale (BRUMS-Greek) was completed by 1786 adults, comprising 1417 exercise participants and 369 physically inactive adults (male = 578, female = 1208) aged 18–64 years (M = 34.73 ± 11.81 years). Although the male–female ratio emphasized females, sample sizes of over 500 suggest some degree of representativeness. Seeded k-means cluster analysis clearly identified the six hypothesized mood profiles. Men were over-represented for the iceberg profile. For age, the 18–25 years group were under-represented for the iceberg profile, whereas the 46–55 and 56+ years groups were over-represented. The 56+ years group were under-represented for the inverse Everest, and the 18–25 years group were over-represented for the shark fin profile. For body mass index (BMI), participants in the obese weight category were over-represented for the inverse iceberg and shark fin profiles and under-represented for the submerged profile. Active participants were over-represented for the iceberg and submerged profiles, and under-represented for the inverse Everest, inverse iceberg, and surface profiles. Findings supported the cross-cultural equivalence of the mood profile clusters and confirmed the link between physical inactivity, obesity, and negative mood profiles. Full article