Non-clinical Applications Research Articles

CRISPR-directed gene-editing induces genetic rearrangement within the human globin gene locus

CRISPR-Cas is a revolutionary technology but has already demonstrated significant feasibility for clinical and non-clinical applications. While the efficiency and precision of this remarkable genetic tool is unprecedented, unfortunately, a series of collateral genetic rearrangement have been reported in response to double-stranded DNA breakage. Once these molecular scissions occur, the cascade of DNA repair reactions can lead to genomic rearrangements especially if breakage takes place within a family of sequence related genes. Here, we demonstrate that CRISPR- directed gene editing near the sickle cell mutation site generates a curious genetic outcome; a footprint of the δ globin gene proximal to the CRISPR/Cas cut site(s). This rearrangement is not dependent on the presence of an exogenously added DNA template but is apparently dependent on a double strand break. Our results the highlight recombinational capacity of double strand breaks in human chromosomes where the aim is to edit a human gene.

Electrodermal activity in pain assessment and its clinical applications

Electrodermal activity (EDA) measures skin conductivity, reflecting sweat gland activity, and is considered a noninvasive measure of the sympathetic nervous system (SNS). Consequently, EDA has emerged as an informative physiomarker in clinical and nonclinical applications in assessing dynamics of SNS functions. With recent proliferation of the abuse of pain medications, there is a pressing need for objective pain assessment given that a self-pain rating is the only metric doctors use for prescribing medications. To overcome this limitation, there has been increased attention on the use of EDA due to close association between pain and the SNS. With advancements in wearable sensors combined with signal processing and machine learning, it has become more feasible to objectively assess pain using EDA. This paper provides a comprehensive review of recent research related to the use of EDA for objective pain assessment and its clinical applications. Furthermore, this paper discusses the use of recent new developments in signal processing and machine learning techniques, and examines current challenges and future directions that can enable better quantitative assessment of pain using EDA.

Advancing orthopaedic trauma care through WhatsApp: An analysis of clinical and non-clinical applications, challenges, and future directions.

The integration of WhatsApp, a widely-used instant messaging application (IMA), into the realm of orthopaedics and trauma surgery has emerged as a significant development in recent years. This paper explores the multifaceted role of WhatsApp in orthopaedics, focusing on its clinical and non-clinical applications, advantages, disadvantages, and future prospects. The study synthesizes findings from various research papers, emphasizing the growing reliance on mobile technology in healthcare. WhatsApp's role in orthopaedics is notable for its ease of use, real-time communication, and accessibility. Clinically, it facilitates triage, teleconsultation, diagnosis, treatment, patient advice, and post-operative monitoring. Non-clinically, it supports telemedicine, teleradiology, virtual fracture clinics, research, and education in orthopaedic surgery. The application has proven beneficial in enhancing communication among healthcare teams, providing quick responses, and motivating junior physicians. Its use in educational settings has been shown to improve learner's understanding and patient care. However, the use of WhatsApp in orthopaedics is not without challenges. Risks include the potential spread of misleading information, privacy concerns, and issues with image quality affecting diagnosis and treatment decisions. The paper acknowledges the importance of maintaining professional boundaries and the need for oversight measures to ensure content accuracy. Looking forward, the potential of WhatsApp and similar IMAs in orthopaedics lies in their ability to streamline data collection and retrieval, improve doctor-patient communication, and address challenges like bureaucratic red tape and limited resources. The paper suggests that future orthopaedic practice, particularly in emergency departments, will increasingly rely on such technologies for efficient patient management. This shift, however, must be approached with an understanding of the ethical, legal, and practical implications of integrating social media and mobile technology in healthcare.

Application of microphysiological systems for nonclinical evaluation of cell therapies

Microphysiological systems (MPS) are gaining broader application in the pharmaceutical industry but have primarily been leveraged in early discovery toxicology and pharmacology studies with small molecules. The adoption of MPS offers a promising avenue to reduce animal use, improve in-vitro-to-in-vivo translation of pharmacokinetics/pharmacodynamics and toxicity correlation, and provide mechanistic understanding of model species suitability. While MPS have demonstrated utility in these areas with small molecules and biologics, MPS models in cell therapy development have not been fully explored, let alone validated. Distinguishing features of MPS, including long-term viability and physiologically relevant expression of functional enzymes, receptors, and pharmacological targets make them attractive tools for nonclinical characterization. However, there is currently limited published evidence of MPS being utilized to study the disposition, metabolism, pharmacology, and toxicity profiles of cell therapies. This review provides an industry perspective on the nonclinical application of MPS on cell therapies, first with a focus on oncology applications followed by examples in regenerative medicine.

Tri-model classifiers for EEG based mental task classification: hybrid optimization assisted framework

The commercial adoption of BCI technologies for both clinical and non-clinical applications is drawing scientists to the creation of wearable devices for daily living. Emotions are essential to human existence and have a significant impact on thinking. Emotion is frequently linked to rational decision-making, perception, interpersonal interaction, and even basic human intellect. The requirement for trustworthy and implementable methods for the detection of individual emotional responses is needed with rising attention of the scientific community towards the establishment of some significant emotional connections among people and computers. This work introduces EEG recognition model, where the input signal is pre-processed using band pass filter. Then, the features like discrete wavelet transform (DWT), band power, spectral flatness, and improved Entropy are extracted. Further, for recognition, tri-classifiers like long short term memory (LSTM), improved deep belief network (DBN) and recurrent neural network (RNN) are used. Also to enhance tri-model classifier performance, the weights of LSTM, improved DBN, and RNN are tuned by model named as shark smell updated BES optimization (SSU-BES). Finally, the perfection of SSU-BES is demonstrated over diverse metrics.

Reliability and validity of the Chinese version of the Athens insomnia scale for non-clinical application in Chinese athletes.

This study aimed to revise and examine the reliability and validity of the Chinese version of the Athens Insomnia Scale for Non-clinical Application (AIS-NCA) among Chinese athletes. Additionally, the study tested the scale in non-athlete individuals with similar sleep management practices to further analyze its cultural specificity among Chinese athletes and make preliminary inferences about its applicability in other Chinese populations. Four hundred twenty-six Chinese professional athletes and 779 high school students participated in this research. Both athletes and students were divided into two parallel groups for exploratory and confirmatory factor analyses. Additionally, three athlete samples and one student sample were established for reliability and validity assessments. Among athletes, the Pittsburgh Sleep Quality Index, the Epworth Sleepiness Scale, the Athlete Sleep Screening Questionnaire, and the Warwick-Edinburgh Mental Well-Being Scale were employed to evaluate convergent and discriminant validity. Re-test reliability was evaluated at intervals of 1 and 2 weeks. In the case of students, convergent and discriminant validity were tested using the Pittsburgh Sleep Quality Index and the General Self-Efficacy Scale, with re-test reliability assessed at two-week intervals. The Chinese version of the AIS-NCA consists of six items, categorized into two dimensions: sleep problems and daytime functioning. This structure explained 65.08% (athletes) and 66.22% (students) of the variance. Confirmatory factor analysis revealed good model fit, with values of χ2/df = 2.217, CFI = 0.975, AGFI = 0.929, TLI = 0.953, and RMSEA = 0.076 among athletes, and χ2/df = 3.037, CFI = 0.979, AGFI = 0.947, TLI = 0.961, and RMSEA = 0.072 among students. The scale demonstrated a reasonable degree of measurement invariance. The overall scale and two subscales exhibited strong reliability and validity among athletes. Similar results in terms of reliability and validity were also observed within the student sample. The Chinese version of the AIS-NCA shows promise as an assessment tool for evaluating the sleep quality of Chinese athletes. It effectively captures both sleep-related concerns and daytime functionality within the athlete population. The scale demonstrates solid reliability and validity in professional athletes and holds potential for application across various other demographic groups in China.

Psychological changes in athletes infected with Omicron after return to training: fatigue, sleep, and mood.

This study aims to analyze the changes of approximately 1 month in fatigue, sleep, and mood in athletes after returning to training following infection with the COVID-19 Omicron strain and provide recommendations for returning to training after infection. Two hundred and thirty professional athletes who had returned to training after being infected with COVID-19 in December 2022 were recruited to participate in three tests conducted from early January 2023. The second test was completed approximately 1 week after the first, and the third was completed about 2 weeks after the second. Each test consisted of completing scales and the exercise-induced fatigue measure. The scales included a visual analog scale, the Athens Insomnia Scale for non-clinical application, and the Depression-Anxiety-Stress scale. The exercise task was a six-minute stair climb test, and athletes evaluated subjective fatigue levels before and after exercise using another Visual Analog Scale and the Karolinska Sleepiness Scale. After returning to training, athletes' physical fatigue decreased initially but increased as training progressed. Cognitive fatigue did not change significantly. The exercise task led to elevated levels of physical fatigue after a longer duration of training. Sleep quality problems decreased rapidly after the start of training but remained stable with prolonged training. Depression levels continued to decline, while anxiety levels only reduced after a longer duration of training. Stress levels decreased rapidly after the start of training but did not change with prolonged training. Athletes who return to training after recovering from COVID-19 experience positive effects on their fatigue, sleep, and mood. It is important to prioritize anxiety assessment and interventions during the short period after returning and to continue monitoring fatigue levels and implementing recovery interventions over a longer period of time.

Lemniscate of Bernoulli's map quantifiers: innovative measures for EEG emotion recognition.

Thanks to the advent of affective computing, designing an automatic human emotion recognition system for clinical and non-clinical applications has attracted the attention of many researchers. Currently, multi-channel electroencephalogram (EEG)-based emotion recognition is a fundamental but challenging issue. This experiment envisioned developing a new scheme for automated EEG affect recognition. An innovative nonlinear feature engineering approach was presented based on Lemniscate of Bernoulli's Map (LBM), which belongs to the family of chaotic maps, in line with the EEG's nonlinear nature. As far as the authors know, LBM has not been utilized for biological signal analysis. Next, the map was characterized using several graphical indices. The feature vector was imposed on the feature selection algorithm while evaluating the role of the feature vector dimension on emotion recognition rates. Finally, the efficiency of the features on emotion recognition was appraised using two conventional classifiers and validated using the Database for Emotion Analysis using Physiological signals (DEAP) and SJTU Emotion EEG Dataset-IV (SEED-IV) benchmark databases. The experimental results showed a maximum accuracy of 92.16% for DEAP and 90.7% for SEED-IV. Achieving higher recognition rates compared to the state-of-art EEG emotion recognition systems suggest the proposed method based on LBM could have potential both in characterizing bio-signal dynamics and detecting affect-deficit disorders.

The Current and Future Perspectives of Postbiotics.

With the emphasis on intestinal health, probiotics have exploded into a vast market potential. However, new scientific evidence points out that the beneficial health benefits of probiotics are not necessarily directly related to viable bacteria. However, the metabolites or bacterial components of the live bacteria are the driving force behind health promotion. Therefore, scientists gradually noticed that the beneficial effects of probiotics are based on bacteria itself, metabolites, or cell lysates, and these factors are officially named "postbiotics" by the ISAPP. Postbiotic components are diverse and outperform live probiotics in terms of technology, safety, and cost due to their good absorption, metabolism, and organismal distribution. Postbiotics have been shown to have bioactivities such as antimicrobial, antioxidant, anti-inflammatory, anti-proliferative, and immunomodulation. Moreover, numerous studies have revealed the significant potential of postbiotics for disease treatment. This paper first presents the production and classification of postbiotics with examples from lactic acid bacteria (LAB), followed by the mechanisms of action with the most recent pre-clinical and clinical studies and the wide range of non-clinical and clinical applications of postbiotics. Furthermore, the current and future prospects of the postbiotic market with commercialavailable products are discussed. Finally, we comment on the knowledge gaps and future clinical applications with several examples.

Modeling the acceptability of BCIs for motor rehabilitation after stroke: A large scale study on the general public.

Strokes leave around 40% of survivors dependent in their activities of daily living, notably due to severe motor disabilities. Brain-computer interfaces (BCIs) have been shown to be efficiency for improving motor recovery after stroke, but this efficiency is still far from the level required to achieve the clinical breakthrough expected by both clinicians and patients. While technical levers of improvement have been identified (e.g., sensors and signal processing), fully optimized BCIs are pointless if patients and clinicians cannot or do not want to use them. We hypothesize that improving BCI acceptability will reduce patients' anxiety levels, while increasing their motivation and engagement in the procedure, thereby favoring learning, ultimately, and motor recovery. In other terms, acceptability could be used as a lever to improve BCI efficiency. Yet, studies on BCI based on acceptability/acceptance literature are missing. Thus, our goal was to model BCI acceptability in the context of motor rehabilitation after stroke, and to identify its determinants. The main outcomes of this paper are the following: i) we designed the first model of acceptability of BCIs for motor rehabilitation after stroke, ii) we created a questionnaire to assess acceptability based on that model and distributed it on a sample representative of the general public in France (N = 753, this high response rate strengthens the reliability of our results), iii) we validated the structure of this model and iv) quantified the impact of the different factors on this population. Results show that BCIs are associated with high levels of acceptability in the context of motor rehabilitation after stroke and that the intention to use them in that context is mainly driven by the perceived usefulness of the system. In addition, providing people with clear information regarding BCI functioning and scientific relevance had a positive influence on acceptability factors and behavioral intention. With this paper we propose a basis (model) and a methodology that could be adapted in the future in order to study and compare the results obtained with: i) different stakeholders, i.e., patients and caregivers; ii) different populations of different cultures around the world; and iii) different targets, i.e., other clinical and non-clinical BCI applications.

Camera Measurement of Physiological Vital Signs

The need for remote tools for healthcare monitoring has never been more apparent. Camera measurement of vital signs leverages imaging devices to compute physiological changes by analyzing images of the human body. Building on advances in optics, machine learning, computer vision, and medicine, these techniques have progressed significantly since the invention of digital cameras. This article presents a comprehensive survey of camera measurement of physiological vital signs, describing the vital signs that can be measured and the computational techniques for doing so. I cover both clinical and non-clinical applications and the challenges that need to be overcome for these applications to advance from proofs of concept. Finally, I describe the current resources (datasets and code) available to the research community and provide a comprehensive webpage (https://cameravitals.github.io/) with links to these resource and a categorized list of all papers referenced in this article.

Cognitive Neuroscience of Conscious Dreaming: Investigating Methodological Approaches to Detect Dream Lucidity in Sleep Studies

Background: It is uncommon for dreamers to be aware of their dreaming status while being inside their own dream. Yet, it is possible to become mindful and conscious of the fact that one is dreaming. This fairly rare phenomenon is called lucid dreaming. Cognitive functions are preserved while sleeping, and in the case of trained and experienced lucid dreamers, this also permits them to manage and influence their own dream environment, characters present, own actions, and the progression and plot of the dream itself. The neuroscience of lucid dreaming is novel but started to gain interest across a broad spectrum of interdisciplinary fields, including neurology, psychotherapy, philosophy, and sports sciences. Aim of the Study: To evaluate approaches currently practiced during dream lucidity sleep experiments, as well as to suggest new methodologies and protocols that can be adopted to detect the emergence of self-awareness during sleep. Materials and Methods: Analysis and evaluation of some of the existing methods was performed and combined with their own methodological and theoretical insights. Analysed were selected sleep studies and medical literature. Results: Neuroimaging employing electroencephalography, electrooculogram and sleep polysomnography simultaneously can be effective in objectively measuring and validating dream lucidity. Agreed-upon sequence patterns involving a left-right-left-right eye movement as a cue from a lucid dreamer signaling, they have entered an awareness state in parallel with polysomnography data can be effectively employed. Conclusion: Further studies are vital for investigating the clinical and non-clinical applications of lucid dreaming. Dream lucidity, becoming conscious of dreaming while dreaming, might be therapeutically beneficial in various biological and psychological applications, including as a therapy for nightmares or narcolepsy. Diagnostic indications of emerging self-awareness based on more accurate neuroimaging protocols could also be therapeutically applicable to conditions like anaesthesia awareness and locked-in syndrome.

NEUROSCIENCE OF SLEEP AND COGNITION: INVESTIGATING THE EFFECTIVENESS OF THE NEW DREAM LUCIDITY INDUCTION PROTOCOL – A PILOT STUDY

The lack of standardized, efficient, and dependable lucid dream induction methods is one of the major barriers to studying the clinical and non-clinical applications of dream lucidity. Although the interest in therapeutic usage of lucid dreaming strategies in neuroscience of consciousness studies has increased in recent decades, many of the current induction approaches appear to not be replicable reliably. In this study, a new protocol for bringing on lucid dreaming was created, implemented, analyzed, and contrasted with two distinct other approaches. Techniques to induce dream lucidity include reality testing methods, the wake-and-go-to-bed technique, mnemonic initiation, senses-initiation, and a combination of the above. Before beginning their 2 weeks of lucid dream induction new protocol study, a total of 36 participants filled out a pre-test survey, maintained a dream recall diary and sleep log for the duration of the experiment, and completed a final survey. The findings showed that both the mnemonic induction and senses-initiation techniques were mildly effective in bringing about lucid dreaming. The new hybrid protocol, combining the reality check stra- tegy with dream recall amplification, performed significantly better. For lucid dream induction to work optimally, participants needed to become more proficient at remembering their dreams after waking up, so they were requested to maintain a sleep journal for 7 days before the experiment and throughout the study to enhance their ability to dream recall. There was no correlation found between the quality of one's sleep and the frequency of dream lucidity. The results demonstrated that the new induction protocol was more successful than the other two investigated methods. There are many clinical applications for dream lucidity, such as treatment of recurring and debilitating nightmares, narcolepsy, and PTSD. Therapeu- tic potential also includes future usage for anesthesia awareness and locked-in syndrome. As such, further dream lucidity research remains important.

Impact of Sleep and Brain Science of Lucid Dreaming: Insights into Therapeutic Applications

Lucid dreaming is being conscious within one’s own dream. In recent years, dream lucidity has started to attract more attention not only from the broad neuroscience field but also from applied neurology, psychiatry, sports performance science, or art therapy areas. The aim of this research is to investigate and evaluate some of the therapeutic potentials of dream lucidity. The findings propose that the emergence of self-awareness during sleep could have various health and wellness benefits and applications. Analysis and evaluation of existing neuroscientific data on lucid dreaming was performed and combined with own investigations and methodological insights. In addition, virtual reality training and transcranial direct current stimulation areas of research were also investigated in the context of potential applications for inducing dream lucidity. Results show that various clinical and non-clinical applications of lucid dreaming techniques might be effectively employed in reducing nightmares, increasing mindfulness, or as an experimental treatment for individuals suffering from certain mental health disorders like narcolepsy or post-traumatic stress disorder. It’s further suggested that dream lucidity training might help with sleep quality while reducing daytime stress and anxiety. Nevertheless, further research and caution in this area are required to fully understand both the therapeutic benefits and potentially negative long term consequences of cultivating lucid dreaming.

Dentists’ Self-evaluated Ability in Diagnosing and Updating About Pulpotomy

This study aimed to self-evaluate the knowledge of different dental professionals' profiles in Brazil on diagnosing and indicating pulpotomy in primary teeth and how these groups updated on this theme. Three groups (G1: professors, G2: specialists, and G3: professionals) answered an online questionnaire containing 20 questions and returned 416 questionnaires. Data were subjected to Chi-square associative tests and t tests, with P < .05. G1 and G2 reported to indicate pulpotomy, highlighting the tendency of these groups towards seeking updates on this subject. All 3 groups used academic materials for updating. However, G2 had a high demand for updates through congresses, whilst G3 had a high demand for updates through social media (Google and Google Scholar). The most cited indication by G1 and G2 was "accidental pulp exposure" and by G3 was "teeth with extensive carious lesions that have involved the pulp with radiographic confirmation." All 3 groups opted for pulpotomy to keep the tooth in the arch. Thus, G1 and G2 are the groups who are more updated on the subject, using mainly academic materials, which can be associated with the fact that such groups indicated pulpotomy in primary teeth. The lower interest in searching about pulpotomy in G3 can be understood as a nonclinical application of the topic for this group. Professionals who work directly with paediatric dentistry (professors or specialists) felt more capable of diagnosing and treating cases of pulpotomy. In addition, most of the professionals used scientifically based sources for getting information and actualisation on the subject of pulp therapies.

Editorial: Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-Being) Applications

Editorial: Brain-Computer Interfaces for Non-clinical (Home, Sports, Art, Entertainment, Education, Well-Being) Applications

Survey and Recommendations on the Use of P-Values Driving Decisions in Nonclinical Pharmaceutical Applications

Survey and Recommendations on the Use of P-Values Driving Decisions in Nonclinical Pharmaceutical Applications

Review on Gold Nanoparticles-Based Biosensors in Clinical and Non-Clinical Applications

Gold nanoparticles (GNP) acquire unique properties that have made significant contributions to clinical and non-clinical fields, specifically in the application of GNP’s for designing biosensor devices in which exhibit novel functional properties. Many properties of GNP’s are reviewed in this literature including optical properties, biocompatibility, conductivity, catalytic properties, high surface-to-volume ratio, and high density of the GNPs, that make them excellent in the application of constructing GNP-based biosensors. This literature review covers a specific comparison between the optical, electrochemical, and piezoelectric biosensors, as these are the three most common GNP-based biosensors. Optical biosensors are optimal due to their ability to cater to surface modification, which then leads to the ability for selective bonding. Furthermore, with the use of GNP and the sensor's non-invasive and non-toxic method of use, high-resolution images and signals can be formed. The sensitivity and specificity of electrochemical biosensors with the conductivity of GNPs, the electrodes of this stable biosensor can detect tumour markers in the human body. Piezoelectric biosensors are mass sensitive sensors and with the use of GNP, it amplifies the changes in mass. Through this, these sensors progress to be immunosensors which determine microorganisms and macromolecular compounds. As well, this review will conclude with an outline of present and future research recommendations for real-world application of the three GNP-based biosensors discussed.

Safe magnetic resonance imaging on biocompatible nanoformulations.

Magnetic resonance imaging (MRI) holds promise for the early clinical diagnosis of various diseases, but most clinical MR techniques require the use of a contrast medium. Several nanomaterial (NM) mediated contrast agents (CAs) are widely used as T1- and T2-based MR contrast agents for clinical and non-clinical applications. Unfortunately, most NM-based CAs are toxic or non-biocompatible, restricting their practical/clinical applications. Therefore, the development of nontoxic and biocompatible CAs for clinical MRI diagnosis is highly desired. To this end, several biocompatible and biomimetic strategies have been developed to offer long blood circulation time, significant biocompatibility, in vivo biodistribution and high contrast ability for efficient imaging. However, detailed review reports on biocompatible NMs, specifically for MR imaging have not yet been summarized. Thus, in the present review we summarize various surface coating strategies (such as polymers, proteins, cell membranes, etc.) to achieve biocompatible NPs, providing a detailed discussion of advances and future prospects for safe MRI imaging.

A Survey on EEG-Based Solutions for Emotion Recognition With a Low Number of Channels

The market uptake of Brain-Computer Interface technologies for clinical and non-clinical applications is attracting the scientific world towards the development of daily-life wearable systems. Beyond the use of dry electrodes and wireless technology, reducing the number of channels is crucial to enhance the ergonomics of devices. This paper presents a review of the studies exploiting a number of channels less than 16 for electroencephalographic (EEG) based-emotion recognition. The main findings of this review concern: (i) the criteria to select the most promising scalp areas for EEG acquisitions; (ii) the attention to prior neurophysiological knowledge; and (iii) the convergences among different studies with respect to preferable areas of the scalp for signal acquisition. Three main approaches emerge for channel selection: data-driven, prior knowledge-based, and based on commercially-available wearable solutions. The most spread is the data-driven, but the neurophysiology of emotions is rarely taken into account. Furthermore, commercial EEG devices usually do not provide electrodes purposefully chosen to assess emotions. Considerable convergences emerge for some electrodes: Fp1, Fp2, F3 and F4 resulted the most informative channels for the valence dimension, according to both data-driven and neurophysiological prior knowledge approaches. The P3 and P4 resulted in being significant for the arousal dimension.