Role of telemedicine in anesthesia: Are we ready yet?

Telemedicine is a modality which utilizes technology to provide and support health care across large distances. It has redefined the practices of medicine in many specialties and continues to be a boon for clinicians on many frontiers. Its role in the branch of anesthesia remains largely unexplored but has shown to be beneficial in all the three phases: pre-operative, intra-operative, and post-operative. Now time has come that anesthesiologists across the globe reassess their strategies and utilize the telemedicine facilities in the field of anesthesia.Copyright © 2021 EDP Sciences. All rights reserved.

Analysis of stress, attention, interest, and engagement in onsite and online higher education: A neurotechnological study

Este trabajo tiene como objetivo registrar y analizar, mediante el uso de neurotecnología, en un contexto formativo universitario presencial y online, el efecto que tiene en variables relevantes en el proceso de aprendizaje, lo cual supone una innovación en la literatura. En este estudio se ha empleado tecnología de neurociencia para medir el procesamiento cognitivo de los estímulos diseñados para una experiencia académica de una clase de máster universitario. Las neurotecnologías empleadas han sido la respuesta galvánica de la piel (GSR), la electroencefalografía (EEG) y el seguimiento ocular. Tras el análisis de los registros cerebrales, basados en la atención, interés, estrés y conexión emocional (engagement), en un contexto educativo presencial y su análisis comparativo con el seguimiento online, los resultados indicaron que los niveles de intensidad emocional de los alumnos que siguieron la clase de forma presencial son más elevados que aquellos que asistieron de forma online. A su vez, los valores de actividad cerebral positiva (atención, interés y engagement) son superiores en el grupo de asistencia presencial, siendo la variable negativa estrés también superior, pudiendo justificarse debido a que los alumnos conectados online no activaban la cámara. Los registros cerebrales de los alumnos que asisten a distancia muestran menor interés y atención, así como una menor intensidad emocional, por lo que el aprendizaje a distancia (online) es menos efectivo, a efectos de señales cerebrales, que la enseñanza en el aula, para una clase teórica de máster universitario.Alternate :The aim of this work is to register and analyse, using neurotechnology, in onsite onsite and online university educational context, the effect on relevant variables in the learning process. This represents an innovation in the current academic literature in this field. In this study, neuroscience technology has been used to measure the cognitive processing of stimuli designed for an academic experience in a university master's degree class. The neurotechnologies employed were galvanic skin response (GSR), electroencephalography (EEG) and eye tracking. After the analysis of the brain recordings, based on attention, interest, stress and engagement in an onsite educational context and their comparative analysis with the online monitoring, the results indicated that the levels of emotional intensity of the students who followed the class in person were higher than those who attended online. At the same time, the values of positive brain activity (attention, interest and engagement) were higher in the onsite group, and the negative variable stress was also higher, which could be explained by the fact that the online students did not activate the camera. The brain recordings of students who were distance learning show less interest and attention, as well as less emotional intensity, demonstrating that distance (online) learning is less effective than classroom learning, in terms of brain signals, for a theoretical university master's degree class.

Automatic Sleep Staging Using BiRNN with Data Augmentation and Label Redirection

Sleep staging has always been a hot topic in the field of sleep medicine, and it is the cornerstone of research on sleep problems. At present, sleep staging heavily relies on manual interpretation, which is a time-consuming and laborious task with subjective interpretation factors. In this paper, we propose an automatic sleep stage classification model based on the Bidirectional Recurrent Neural Network (BiRNN) with data bundling augmentation and label redirection for accurate sleep staging. Through extensive analysis, we discovered that the incorrect classification labels are primarily concentrated in the transition and nonrapid eye movement stage I (N1). Therefore, our model utilizes a sliding window input to enhance data bundling and an attention mechanism to improve feature enhancement after label redirection. This approach focuses on mining latent features during the N1 and transition periods, which can further improve the network model's classification performance. We evaluated on multiple public datasets and achieved an overall accuracy rate of 87.3%, with the highest accuracy rate reaching 93.5%. Additionally, the network model's macro F1 score reached 82.5%. Finally, we used the optimal network model to study the impact of different EEG channels on the accuracy of each sleep stage.

Anxiolytic-like Effects and Quantitative EEG Profile of Palmitone Induces Responses Like Buspirone Rather Than Diazepam as Clinical Drugs.

Anxiety is a mental disorder with a growing worldwide incidence due to the SARS-CoV-2 virus pandemic. Pharmacological therapy includes drugs such as benzodiazepines (BDZs) or azapirones like buspirone (BUSP) or analogs, which unfortunately produce severe adverse effects or no immediate response, respectively. Medicinal plants or their bioactive metabolites are a shared global alternative to treat anxiety. Palmitone is one active compound isolated from Annona species due to its tranquilizing activity. However, its influence on neural activity and possible mechanism of action are unknown. In this study, an electroencephalographic (EEG) spectral power analysis was used to corroborate its depressant activity in comparison with the anxiolytic-like effects of reference drugs such as diazepam (DZP, 1 mg/kg) and BUSP (4 mg/kg) or 8-OH-DPAT (1 mg/kg), alone or in the presence of the GABAA (picrotoxin, PTX, 1 mg/kg) or serotonin 5-HT1A receptor antagonists (WAY100634, WAY, 1 mg/kg). The anxiolytic-like activity was assayed using the behavioral response of mice employing open-field, hole-board, and plus-maze tests. EEG activity was registered in both the frontal and parietal cortex, performing a 10 min baseline and 30 min recording after the treatments. As a result, anxiety-like behavior was significantly decreased in mice administered with palmitone, DZP, BUSP, or 8-OH-DPAT. The effect of palmitone was equivalent to that produced by 5-HT1A receptor agonists but 50% less effective than DZP. The presence of PTX and WAY prevented the anxiolytic-like response of DZP and 8-OH-DPAT, respectively. Whereas only the antagonist of the 5-HT1A receptor (WAY) inhibited the palmitone effects. Palmitone and BUSP exhibited similar changes in the relative power bands after the spectral power analysis. This response was different to the changes induced by DZP. In conclusion, brain electrical activity was associated with the anxiolytic-like effects of palmitone implying a serotoninergic rather than a GABAergic mechanism of action.

Autoantibody-negative but probable pediatric autoimmune encephalitis following COVID-19 vaccination: A case report

Background: Large-scale vaccination against the novel coronavirus (COVID-19) occurred globally at an unprecedented pace. Sporadic cases of autoimmune encephalitis (AE) have been reported following COVID-19 vaccination, mainly in adults. Case report: A 14-year-old girl developed altered mental status and was brought to our emergency department because of a seizure 19 days after receiving the third dose of COVID-19 vaccination. She was treated with steroid pulse therapy and fully recovered. The diagnosis of probable autoantibody-negative AE was finally made. Conclusion(s): This case met the criteria for probable autoantibody-negative AE in children, as well as adults. Because of the temporal association and absence of another identifiable cause, her conditions may have been triggered by the COVID-19 vaccination. To our knowledge, this is the first published pediatric case of autoantibody-negative but probable AE following COVID-19 vaccination.Copyright © 2023 The Authors

II International Convention on Science and Consciousness, Santiago de Cuba, Cuba, December 8-10, 2021

This conference proceedings contains 15 articles that discuss various topics in the fields of medicine, psychology, and technology. The articles focus on the adaptation and validation of psychological scales, the effects of COVID-19 on physical and psychological health, the development of biomedical applications, and the evaluation of obstetric risks during the pandemic. It also covers topics related to family influence on child development, coping strategies for infertile couples, and the antioxidant potential of natural products. The pedagogical works included in the proceedings focus on neuropsychological interventions and vulnerability to successful aging and mental health. A literature review delves into the theoretical considerations regarding the study of family, self-determination, and disability in health contexts.

Acute Transient Encephalopathy after Moderna COVID-19 Vaccine

Although mRNA vaccine responses following previous coronavirus disease 2019 (COVID-19) infection have not been assessed in trials, it has been shown that serological evidence of previous COVID-19 generates strong humoral and cellular responses to one dose of mRNA vaccine. We describe a patient with prior COVID-19 infection who developed acute transient encephalopathy with elevated inflammatory markers within 24 h of her first injection of Moderna COVID-19 vaccine. A 69-year-old cognitively normal woman presented with intermittent inattention, disorientation, left/right confusion, weakness, gait instability, and decreased speech. Head CT, brain MRI and MRA, complete blood count, liver enzymes, hepatitis B serology, ammonia, thyroid function, vitamin B12, and pulse oximetry were normal. Electroencephalography performed 48 h after symptom onset showed diffuse triphasic waves, diffuse theta and delta slowing, and no posterior dominant rhythm. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) IgG was positive and inflammatory markers were elevated. On day 5 post-vaccine, she returned to her baseline, without neurological sequelae. The reported patient likely developed a transient inflammatory encephalopathy associated with an abnormal immunologic reaction to one dose of COVID-19 vaccine, in the setting of remote COVID-19 infection (1 year prior), SARS-CoV-2 IgG-positivity, and multiple comorbidities. Physicians should be alert to possible postvaccination reactogenicity in individuals with SARS-CoV-2 IgG-positivity, including risk of neuro-inflammation.

Development of Personalized Sleep Induction System based on Mental States

Sleep is an essential behavior to prevent the decrement of cognitive, motor, and emotional performance and various diseases. However, it is not easy to fall asleep when people want to sleep. There are various sleep-disturbing factors such as the COVID-19 situation, noise from outside, and light during the night. We aim to develop a personalized sleep induction system based on mental states using electroencephalogram and auditory stimulation. Our system analyzes users' mental states using an electroencephalogram and results of the Pittsburgh sleep quality index and Brunel mood scale. According to mental states, the system plays sleep induction sound among five auditory stimulation: white noise, repetitive beep sounds, rainy sound, binaural beat, and sham sound. Finally, the sleep-inducing system classified the sleep stage of participants with 94.7% and stop auditory stimulation if participants showed non-rapid eye movement sleep. Our system makes 18 participants fall asleep among 20 participants. © 2023 IEEE.

Infantile spasms: guidance for pediatricians

In a cross-sectional study of 100 parents of children with infantile spasms, the median time from spasm onset to first visit with any health care provider was 5 days, but the median time from onset to first visit with an "effective provider" (one who provided both accurate diagnosis and prescription for appropriate first-line treatment) was 24 days,5 a delay attributed at least in part to poor awareness of the condition among providers.5 Given that worse outcomes may be associated with even a 1-week delay in treatment from onset, it is critical that pediatric health care providers are proficient in recognizing this condition.67 Clinical features Infantile spasms An infantile spasm is brief and abrupt, generally 1 to 3 seconds, with muscle contraction that can include the head, neck, trunk, and/or extremities. Home video recording, first advised by the Child Neurology Society to streamline IESS management at the onset of the COVID-19 pandemic, has since been endorsed as a continued recommendation toward timely intervention.4 In preparing to evaluate a patient with possible IESS, pediatricians should ask caregivers to record suspected events. The EEG pattern during the spasm itself is a high-amplitude sharp or slow wave followed by a relative electrodecrement. Because the interictal EEG is generally abnormal, it is not necessary to capture a spasm during the EEG recording to support the diagnosis. Workup for an underlying etiology if not known is important not only because some are associated with other health concerns requiring monitoring and intervention, but also because it can guide management, as some etiologies may respond better to different treatment approaches.9 Identification may also guide appropriate counseling of families, including prognostication and possible genetic counseling.

Uncovering the Correlation between COVID-19 and Neurodegenerative Processes: Toward a New Approach Based on EEG Entropic Analysis.

COVID-19 is an ongoing global pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Although it primarily attacks the respiratory tract, inflammation can also affect the central nervous system (CNS), leading to chemo-sensory deficits such as anosmia and serious cognitive problems. Recent studies have shown a connection between COVID-19 and neurodegenerative diseases, particularly Alzheimer's disease (AD). In fact, AD appears to exhibit neurological mechanisms of protein interactions similar to those that occur during COVID-19. Starting from these considerations, this perspective paper outlines a new approach based on the analysis of the complexity of brain signals to identify and quantify common features between COVID-19 and neurodegenerative disorders. Considering the relation between olfactory deficits, AD, and COVID-19, we present an experimental design involving olfactory tasks using multiscale fuzzy entropy (MFE) for electroencephalographic (EEG) signal analysis. Additionally, we present the open challenges and future perspectives. More specifically, the challenges are related to the lack of clinical standards regarding EEG signal entropy and public data that can be exploited in the experimental phase. Furthermore, the integration of EEG analysis with machine learning still requires further investigation.

Sensitivity of the reward system in post-COVID-fatigue

Background: The health consequences of the SARS-CoV-2 pandemic are dominating the international healthcare systems. More than 15% of patients with supposedly mild SARS-CoV-II disease develop persisting symptoms (Sudre et al., 2021). In addition to known internal limitations, such as respiratory distress or tachycardia, severe neurological deficits are prominent. For example, fatigue persisting for months, cognitive impairment, and a marked increase in daytime sleepiness, sometimes accompanied by an inability to work, are described (Taquet et al., 2021). Previous research indicates that hospitalized patients suffering from COVID-19 often develop fatigue or muscle weakness (63%), difficulties in sleep (26%) and psychiatric disorders, such as anxiety and depression (23%) (Taquet et al., 2021). This constellation of symptoms can lead to severe limitations in the everyday lives of the people concerned. The pathophysiology of this multifaceted neurological and dysautonomic symptom complex is not yet understood but now becoming the focus of interdisciplinary research in the context of the global pandemic. A similar disease is chronic fatigue syndrome (CFS). Affected patients suffer from very comparable limitations, especially persistent fatigue. Evidence suggests an alteration of the specific cerebral reward system in CFS, an important modulator of learning processes involved in various homeostatic regulatory processes (Wylie and Flashman, 2017). Objective(s): Based on the similarity of symptoms in CFS and Post-COVID fatigue this study aims to investigate whether a reduced sensitivity of the reward system in the context of postviral fatigue syndrome is present. We hypothesize that the sensitivity of the reward system in patients with Post-COVID syndrome is reduced compared to healthy adults. Method(s): 24 subjects with a diagnosed Post-COVID syndrome and 20 healthy individuals between the age of 18-55 without relevant neurological or psychiatric disorders in the medical record participated in the study. Magnetoencephalography and electroencephalography were used for the characterization of the reward system during the monetary incentive delay task, a classic paradigm used in existing publications (Frank et al., 2004;Opitz et al., 2022). In addition, standardized questionnaires were used to obtain further information about the included individuals' living conditions and the severity of symptoms. Result(s) and Conclusion(s): Results of the study will help to better characterize reward network changes in the context of fatigue symptoms to open up therapeutic options for medication or psychotherapeutic interventions. Data analysis will be completed by the start of the conference.Copyright © 2023

An unusually fulminant case of encephalomyelitis in an 80 year old

Background: There have been reports of demyelinating syndromes in association with COVID-19 and to a much lesser extent COVID 19 vaccines. The association between demyelination and vaccines, in general, remains controversial. We review a presentation of fulminant demyelination, and discuss antecedent COVID-19 vaccination, the formulation of a broader differential diagnosis and ultimately the pathologic diagnosis. Case presentation: An 80-year-old woman presented with seizure, encephalopathy, quadriparesis and ultimately expired. She received a SARS-CoV-2 vaccine one day prior. Imaging revealed contrast enhancing cerebral lesions, longitudinally extensive transverse myelitis. CSF was markedly inflammatory. Pathologic examination of the CNS lesions revealed demyelination and inflammation beyond white matter, not restricted to a perivenular distribution. Conclusion(s): This case depicts a seemingly fulminant course of a diffuse demyelinating syndrome characterized clinicopathologically as Marburg's variant of multiple sclerosis. There are several unique aspects of this case including the extremely rapid course, the unusual evolution of CSF abnormalities, with hypoglycorrhachia and markedly elevated protein. The proximity to vaccination is a pertinent association to document, though we cannot unequivocally prove causation.Copyright © 2022 The Authors

Attention impairment in depression and anxiety

Our experience of the world is defined not only by what surrounds us, but also by what we pay attention to. Because goal-directed attention is essential for so many aspects of cognition, from perception to learning to decision-making, impairments of attention in the context of mental illness can be severely debilitating. Despite this impact, we know relatively little from human neuroscience about the specific attention impairments that comprise "concentration difficulties," a symptom and diagnostic criterion of mood and anxiety disorders that is often not alleviated with current first-line treatments. In this dissertation, I aim to better understand mechanisms of goal-directed attention in healthy adults and characterize various forms of attention impairment in individuals with depression and anxiety using multimodal human neuroscience methods.First, I review the state of the field regarding attention impairments in depression and anxiety (Chapter 1). I highlight both the key advances in cognitive neuroscience regarding the neural correlates of subtypes of attention and the ways in which these findings might inform precision psychiatry. Next, I investigate a potential neural correlate of selective attention in a sample of healthy adults using functional magnetic resonance imaging (fMRI) (Chapter 2). Using statistical analysis tools to disentangle ongoing neural activity from stimulus-driven activity, I demonstrate that stimulus-independent neural signals are associated with the sharing of attended visual information across the cortex. Leveraging these findings, I then characterize selective attention impairments in adults with Major Depressive Disorder using fMRI and electro-encephalography (EEG) (Chapter 3). I find that feature-based selective attention impairments are severe in a subset of depressed individuals and are specifically associated with fronto-parietal hypo-connectivity and decreased posterior alpha oscillations, consistent with my prior observations of selective attention correlates in healthy adults.I then develop a machine-learning algorithm that can successfully predict changes in selective attention with antidepressant pharmacotherapy and show that stressors occurring in childhood are associated with poorer selective attention in depressed adults (Chapter 4). In a study of individuals with a range of mood and anxiety symptoms, I develop novel behavioral paradigms to assess transdiagnostic sub-domains of attention impairment (Chapter 5). These data reveal that spatial attention impairments partially mediate the association between early life stress and anxiety and are associated with increased anxiety and concentration problems during the COVID-19 pandemic. Finally, I put forward a theoretical model for how attention may become impaired in depression and anxiety and detail important directions for future research (Chapter 6).Together, these findings provide insight into the neural mechanisms underlying different subdomains of attention, clarify our understanding of attention impairments as a trans-diagnostic symptom dimension, and identify neural targets for the development of more personalized treatment, setting the stage for future studies in both basic and clinical neuroscience. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

The modulatory effects of visual speech on auditory speech perception: A multi-modal investigation of how vision alters the temporal, spatial and spectral components of speech

Visual speech information, especially that provided by the mouth and lips, is important during face-to-face communication. This has been made more evident by the increased difficulty of speech perception because mask usage has become commonplace in response to the COVID-19 pandemic. Masking obscures the mouth and lips, thus eliminating meaningful information from visual cues that are used to perceive speech correctly. To fully understand the perceptual benefits afforded by visual information during audiovisual speech perception, it is necessary to explore the underlying neural mechanisms involved. While several studies have shown neural activation of auditory regions in response to visual speech, the information represented by these activations remain poorly understood. The objective of this dissertation is to investigate the neural bases for how visual speech modulates the temporal, spatial, and spectral components of audiovisual speech perception, and the type of information encoded by these signals.Most studies approach this question by using techniques sensitive to one or two important dimensions (temporal, spatial, or spectral). Even in studies that have used intracranial electroencephalography (iEEG), which is sensitive to all three dimensions, research conventionally quantifies effects using single-subject statistics, leaving group-level variance unexplained. In Study 1, I overcome these shortcomings by investigating how vision modulates auditory speech processes across spatial, temporal and spectral dimensions in a large group of epilepsy patients with intracranial electrodes implanted (n = 21). The results of this study demonstrate that visual speech produced multiple spatiotemporally distinct patterns of theta, beta, and high-gamma power changes in auditory regions in the superior temporal gyrus (STG).While study 1 showed that visual speech evoked activity in auditory areas, it is not clear what, if any, information is encoded by these activations. In Study 2, I investigated whether these distinct patterns of activity in the STG, produced by visual speech, contain information about what word is being said. To address this question, I utilized a support-vector machine classifier to decode the identities of four word types (consonants beginning with 'b', 'd', 'g', and 'f') from activity in the STG recorded during spoken (phonemes: basic units of speech) or silent visual speech (visemes: basic units of lipreading information). Results from this study indicated that visual speech indeed encodes lipreading information in auditory regions.Studies 1 and 2 provided evidence from iEEG data obtained from patients with epilepsy. In order to replicate these results in a normative population and to leverage improved spatial resolution, in Study 3 I acquired data from a large cohort of normative subjects (n = 64) during a randomized event-related functional magnetic resonance imaging (fMRI) experiment. Similar to that of Study 2, I used machine learning to test for classification of phonemes and visemes (/fafa/, /kaka/, /mama/) from auditory, auditory-visual, and visual regions in the brain. Results conceptually replicated the results of Study 2, such that phoneme and viseme identities could both be classified from the STG, revealing that this information is encoded through distributed representations. Further analyses revealed similar spatial patterns in the STG between phonemes and visemes, consistent with the model that viseme information is used to target corresponding phoneme populations in auditory regions. Taken together, the findings from this dissertation advance our understanding of the neural mechanisms that underlie the multiple ways in which vision alters the temporal, spatial and spectral components of audiovisual speech perception. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Framing, risk perception and social health campaigns: A neuroscientific analysis

Health authorities have widely used social health campaigns to improve the attitudes and healthy behaviours of the population. During the COVID-19 pandemic, they became an essential tool in increasing compliance with health measures, especially among the young population, a particularly reluctant group. The aim of this study was to analyse the effectiveness of different campaigns in improving young people's intention to change their behaviour towards compliance with health measures. For this purpose, an experimental study was conducted using neurophysiological tools (electroencephalogram and galvanic skin response) as well as self-reported data from a questionnaire. The experiment analysed three health campaigns with different narrative frames and emotions in the messages. The results showed different degrees of persuasive effectiveness depending on the framing, emotions used, and level of intensity of such emotions. Overall, it was concluded that negative framing strategies and high levels of intensity worked effectively. The influence of the perceived risk declared by the participants on the impact of the different campaigns was also analysed. In this case, for the most difficult target to activate, subjects with low perceived risk, negative and low-intensity framing strategies were revealed to be the most effective. Implications for the design of campaigns were derived, and limitations and future lines of research were addressed. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Sleep staging performance of a signal-agnostic cloud-based real-time sleep analytics platform

Introduction The coronavirus pandemic has brought unprecedented changes to the health care system, including sleep medicine. Remote monitoring and telemedicine played a significant role in this shift. We anticipate these changes to continue in the future with internet-connected wearables (ICWs) playing an important role in measuring and managing sleep remotely. As these ICWs measures a small subset of signals traditionally measured during polysomnography (PSG), manual sleep staging becomes non-trivial and sometimes impossible. The ability to do accurate and reliable automatic sleep staging using different modalities of physiological signals remotely is becoming ever more important. Methods The current work seeks to quantify the sleep staging performance of Z3Score-Neo (https://z3score.com, Neurobit Technologies, Singapore), a signal agnostic, cloud-based real-time sleep analytics platform. We tested its staging performance on the CINC open dataset with N=994 subjects using various combinations of signals including Electroencephalogram (EEG), Electrooculogram (EOG), Electromyogram (EMG), and Instantaneous Heart Rate (IHR) derived from Electrocardiogram (ECG). The staging was compared against manual scoring based on PSG. For IHR based staging, N1 and N2 were combined. Results We achieved substantial agreement (all Cohen's Kappa > 0.7) between automatic and manual staging using various combinations of EEG, EOG and EMG channels with accuracies varying between 81.76% (two central EEGs, one EOG, one EMG), 79.31% (EEG+EOG), 78.73% (EEG only) and 78.09% (one EOG). We achieved moderate agreement (accuracy: 72.8% κ=0.54) with IHR derived from ECG. Conclusion Our results demonstrated the accuracy of a cloud-based sleep analytics platform on an open dataset, using various combinations of ecologically valid physiological signals. EOG and EMG channels can be easily self-administered using sticker-based electrodes and can be added to existing home sleep apnea test (HSAT) kits significantly improving their utility. ICWs are already capable of accurately measuring EEG/EOG (Muse, InteraXon Inc., Toronto, Canada;Dreem band, Dreem, USA) and IHR derived from ECG (Movesense, Suunto, Finland) or photoplethysmogram (Oura Ring, Oura Health Oy, Finland) or through non-contact ballistocardiogram/radio-based measurements (Dozee, Turtle Shell Technologies, India;Sleepiz, Sleepiz AG, Switzerland). Therefore, a well-validated cloud-based staging platform solves a major technological hurdle towards the proliferation of remote monitoring and telehealth in sleep medicine. Support (if any):

Possible autoimmune pediatric encephalitis following COVID-19 infection with focal cerebral dysfunction in a young female: A case report

Introduction: Post-COVID-19 autoimmune encephalitis is a rare manifestation following COVID-19. Most cases have not demonstrated solid evidence regarding their pathogenesis. Some believe it to be an immune process. Case presentation: In this case report, we present a case of a young female who presented to our emergency department with visual, auditory, and olfactory hallucinations after successfully treating COVID-19 two weeks prior to this visit. On examination, her vital signs were stable, but she was agitated, distressed, and hallucinating. Neurological examinations were normal. Laboratory investigations, including autoimmune profiles, were all negative. Magnetic resonance imaging of the brain showed non-specific changes in the bilateral frontal area. Electroencephalography (EEG) showed lateralized rhythmic delta activity (LRDA) arising more from the right occipital lobes. Autoimmune psychosis was suspected due to psychosis, abnormal imaging, and abnormal EEG findings. She was given corticosteroids and antipsychotic medication. Her symptoms improved within ten days. On follow-up, she remained well without any return of psychosis. Conclusion(s): Possible autoimmune pediatric encephalitis following COVID-19 is a rare entity that has scarcely been reported. The majority of the cases were reported to have been related to stress following the infection. To establish the correct diagnosis, an extensive workup, including an autoimmune profile, lumbar puncture, magnetic resonance imaging, and electroencephalography, is recommended.Copyright © 2022 The Author(s)

The Power of Touch: The Effects of Havening Touch on Subjective Distress, Mood, Brain Function, and Psychological Health

Objective: Havening is a psychosensory therapeutic technique that purportedly harnesses the power of touch to stimulate oxytocin release and facilitate adaptive processing of distressing thoughts/memories. Although Havening is used in clinics worldwide, with anecdotal evidence, very few empirical studies exist to support its efficacy or mechanism of action. The present study is the first to investigate the effects of Havening Touch on subjective distress, mood, brain function, and well-being. Method(s): Participants (n = 24) underwent a single session of Havening, in response to a self-reported distressing event. Mood and resting-state electroencephalography were assessed prior to, and immediately following, the session. Psychological health was assessed at baseline and 2 weeks followup via an online self-report questionnaire. Result(s): There was a greater reduction in subjective units of distress during sessions that included Havening Touch (H+) than sessions that did not include Havening Touch (H-). Electroencephalography results showed an increase in beta and a reduction in gamma activity in H+. Both groups showed reduction in negative mood states immediately following the session and better psychological health at follow-up. Conclusion(s): Findings suggest both touch and nontouch components of the intervention have therapeutic potential, and that Havening Touch may accelerate a reduction in distress during a single Havening session.Copyright © 2022 American Psychological Association

Can NeuroIS improve executive employee recruitment? Classifying levels of executive functions using resting state EEG and data science methods

Based on the assumption that the success of an organization is largely determined by the knowledge and skills of its employees, human resource (HR) departments invest considerable resources in the employee recruitment process with the aim of selecting the best, most suitable employees. Due to the high cost of the recruitment process along with its high rate of uncertainty, HR recruiters utilize a variety of methods and instruments to improve the efficiency and effectiveness of this process. Thus far, however, neurological methods, in which neurobiological signals from an examined person are analyzed, have not been utilized for this purpose. This study is the first to propose a neuro-based decision support system to classify cognitive functions into levels, whose target is to enrich the information and indications regarding the candidate along the employee recruitment processes. We first measured relevant functional and cognitive abilities of 142 adult participants using traditional computer-based assessment, which included a battery of four tests regarding executive functions and intelligence score, consistent with actual recruitment processes. Second, using electroencephalogram (EEG) technology, which is one of the dominant measurement tools in NeuroIS research, we collected the participants' brain signals by administering a resting state EEG (rsEEG) on each participant. Finally, using advanced machine and deep learning algorithms, we leveraged the collected rsEEG to classify participants' levels of executive functions and intelligence score. Our empirical analyses show encouraging results of up to 72.6% accuracy for the executive functions and up to 71.2% accuracy for the intelligence score. Therefore, this study lays the groundwork for a novel, generic (non-stimuli based) system that supports the current employee recruitment processes, that is based on psychological theories of assessing executive functions. The proposed decision support system could contribute to the development of additional medium of assessing employees remotely which is especially relevant in the current Covid-19 pandemic. While our method aims at classification rather than at explanation, our intriguing findings have the potential to push forward NeuroIS research and practice. © 2023 Elsevier B.V.

Internet of Medical Things - The Future of Healthcare

Background: The Internet of Medical Things (IoMT) is now being connected to medical equipment to make patients more comfortable, offer better and more affordable health care options, and make it easier for people to get good care in the comfort of their own homes. Objective(s): The primary purpose of this study is to highlight the architecture and use of IoMT (Internet of Medical Things) technology in the healthcare system. Method(s): Several sources were used to acquire the material, including review articles published in various journals that had keywords such as, Internet of Medical Things, Wireless Fidelity, Remote Healthcare Monitoring (RHM), Point-of-care testing (POCT), and Sensors. Result(s): IoMT has succeeded in lowering both the cost of digital healthcare systems and the amount of energy they use. Sensors are used to measure a wide range of things, from physiological to emotional responses. They can be used to predict illness before it happens. Conclusion(s): The term "Internet of Medical Things" refers to the broad adoption of healthcare solutions that may be provided in the home. Making such systems intelligent and efficient for timely prediction of important illnesses has the potential to save millions of lives while decreasing the burden on conventional healthcare institutions, such as hospitals. patients and physicians may now access real-time data due to advancements in IoM. Copyright © 2022 Wal et al.