Interrelations of Clinical, Neurophysiological and Neuroimmunological Parameters in Depressive Patients after COVID-19

The aim of the study was to assess the impact of the coronavirus infection on clinical, neurophysiological and neuroimmunological parameters, as well as on their interrelations in young female depressive patients. Patients: a comparative analysis of quantitative clinical (according to the HDRS-17 scale), neurophysiological (EEG) and neuroimmunological (accordingto the "Neuro-immuno-test” technology) parameters was carried out in two groups of female depressive patients aged 16–25 years. The fi rst group included 46 patients who recovered from a mild or asymptomatic coronavirus infection ("COVID” group). The second group included 40 patients who were studied and treated before the start of the pandemic (i.e., those who did not have COVID — the "pre-COVID” group) and corresponding to patients of the fi rst group by gender, age, diagnoses, and syndrome structure of disorders. In all patients, prior to the start of the course of therapy, a multichannel EEG was recorded with the measurement of absolute spectral power and neuroimmunological parameters in blood plasma were determined. Methods: clinicalpsychopathological, psychometric, neurophysiological, neuroimmunological, statistical. Results: signifi cantly greater scores of somatic disorders cluster of HDRS-17 scale, and increased amount of slow-wave EEG activity (of delta, theta1 and theta2 subbands) were revealed in the "COVID” group in comparison to patients of "pre-COVID” group. Mean values of neuroimmunological parameters were not differed statistically between two groups, but the values of neuroplasticity markers (levels of autoantibodies to the S100b protein and to the basic myelin protein) in the "pre-COVID” group correlated positively with the spectral power values of the main EEG rhythm (alpha2 and alpha3 sub-bands), and in "COVID” group — with the values of the spectral power of slow-wave EEG activity, refl ecting a reduced brain functional state. Conclusion: the results obtained indicate that coronavirus infection, even in mild or asymptomatic forms, affects the clinical, neurophysiological and neuroimmunological parameters, as well as their interrelations in young female depressive patients. © 2023,Psychiatry (Moscow). All Rights Reserved.

Primacy Effect of Dynamic Multi-Sensory Covid ADV Influences Cognitive and Emotional EEG Responses.

Advertising uses sounds and dynamic images to provide visual, auditory, and tactile experiences, and to make the audience feel like the protagonist. During COVID-19, companies modified their communication by including pandemic references, but without penalizing multisensorial advertising. This study investigated how dynamic and emotional COVID-19-related advertising affects consumer cognitive and emotional responses. Nineteen participants, divided into two groups, watched three COVID-19-related and three non-COVID-19-related advertisements in two different orders (Order 1: COVID-19 and non-COVID-19; Order 2: non-COVID-19 and COVID-19), while electrophysiological data were collected. EEG showed theta activation in frontal and temporo-central areas when comparing Order 2 to Order 1, interpreted as cognitive control over salient emotional stimuli. An increase in alpha activity in parieto-occipital area was found in Order 2 compared to Order 1, suggesting an index of cognitive engagement. Higher beta activity in frontal area was observed for COVID-19 stimuli in Order 1 compared to Order 2, which can be defined as an indicator of high cognitive impact. Order 1 showed a greater beta activation in parieto-occipital area for non-COVID-19 stimuli compared to Order 2, as an index of reaction for painful images. This work suggests that order of exposure, more than advertising content, affects electrophysiological consumer responses, leading to a primacy effect.

Human Activity Recognition Using Machine Learning Technique

COVID has made education shift towards online mode. In online mode, instructors have a hard time keeping track of their students. Students' performance in online classes falls considerably below the level of learning due to a lack of attention. This initiative aids in the supervision of students during online classes. Artificial Intelligence (AI) models are being developed to better recognize student activities during online sessions. Many applications rely on determining an individual's mental state. When evaluating which subtask is the most challenging, a quantitative measure of human activity while executing a task can be helpful. Thus, the goal of this research is to create an algorithm that uses EEG data gathered with a Muse headset to measure the amount of cognitive intelligence of students during online classes. The data collected by the Muse headset is multidimensional, and it is pre-processed before being fed into machine learning algorithms. Using feature selection, the dataset's dimension is now reduced. The model's precision and recall were calculated, and a confusion matrix was created. The Support Vector Machine produces better outcomes in the experiment. © 2022 IEEE.

A Feasibility Study of LORETA Z-Score Neurofeedback Training in Adults with Schizophrenia-Spectrum Disorder Experiencing Treatment-Resistant Auditory Verbal Hallucinations

Introduction: The incomplete effectiveness of interventions demands new ways to help people diagnosed with schizophrenia who experience auditory verbal hallucinations (SZ-AVH). We aimed to perform a feasibility study of low-resolution electromagnetic tomography analysis (LORETA) neurofeedback with people exhibiting treatment-resistant SZ-AVH. Method(s): We examined changes in resting-state quantitative electroencephalogram (qEEG) in four people with SZ-AVH (three male, one female) after LORETA Z-score neurofeedback training. Result(s): The study design had to be amended due to a national COVID-19 lockdown. Neurofeedback was well tolerated and no participants dropped out. Recruitment was the main feasibility issue. Barriers included a lack of knowledge of neurofeedback by patients and mental health teams, as well as the travel and time commitment involved. For the only patient who completed all 20 sessions, elevated frontal, central, and temporal theta absolute power measured at baseline normalized after treatment, but decreased temporal delta and an increase in coherence for all frequency bands were also found. Conclusion(s): Two key lessons were drawn for the feasibility of trials of EEG neurofeedback in this population. First, significant effort is needed to educate mental health professionals and patients about neurofeedback. Second, the equipment employed for neurofeedback training needs to be physically based at a site where patients routinely attend.Copyright © 2022. Amico et al.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review.

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.

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.

Modeling of Brain Cortical Activity during Relaxation and Mental Workload Tasks Based on EEG Signal Collection

Coronavirus disease 2019 (COVID-19) has caused everything from daily hassles, relationship issues, and work pressures to health concerns and debilitating phobias. Relaxation techniques are one example of the many methods used to address stress, and they have been investigated for decades. In this study, we aimed to check whether there are differences in the brain cortical activity of participants during relaxation or mental workload tasks, as observed using dense array electroencephalography, and whether these differences can be modeled and then classified using a machine learning classifier. In this study, guided imagery as a relaxation technique was used in a randomized trial design. Two groups of thirty randomly selected participants underwent a guided imagery session;other randomly selected participants performed a mental task. Participants were recruited among male computer science students. During the guided imagery session, the electroencephalographic activity of each student's brain was recorded using a dense array amplifier. This activity was compared with that of a group of another 30 computer science students who performed a mental task. Power activity maps were generated for each participant, and examples are presented and discussed to some extent. These types of maps cannot be easily interpreted by therapists due to their complexity and the fact that they vary over time. However, the recorded signal can be classified using general linear models. The classification results as well as a discussion of prospective applications are presented. © 2023 by the authors.

Desflurane is risk factor for postoperative delirium in older patients' independent from intraoperative burst suppression duration.

Background: Postoperative Delirium (POD) is the most frequent neurocognitive complication after general anesthesia in older patients. The development of POD is associated with prolonged periods of burst suppression activity in the intraoperative electroencephalogram (EEG). The risk to present burst suppression activity depends not only on the age of the patient but is also more frequent during propofol anesthesia as compared to inhalative anesthesia. The aim of our study is to determine, if the risk to develop POD differs depending on the anesthetic agent given and if this correlates with a longer duration of intraoperative burst suppression. Methods: In this secondary analysis of the SuDoCo trail [ISRCTN 36437985] 1277 patients, older than 60 years undergoing general anesthesia were included. We preprocessed and analyzed the raw EEG files from each patient and evaluated the intraoperative burst suppression duration. In a logistic regression analysis, we assessed the impact of burst suppression duration and anesthetic agent used for maintenance on the risk to develop POD. Results: 18.7% of patients developed POD. Burst suppression duration was prolonged in POD patients (POD 27.5 min ± 21.3 min vs. NoPOD 21.4 ± 16.2 min, p < 0.001), for each minute of prolonged intraoperative burst suppression activity the risk to develop POD increased by 1.1% (OR 1.011, CI 95% 1.000-1.022, p = 0.046). Burst suppression duration was prolonged under propofol anesthesia as compared to sevoflurane and desflurane anesthesia (propofol 32.5 ± 20.3 min, sevoflurane 17.1 ± 12.6 min and desflurane 20.1 ± 16.0 min, p < 0.001). However, patients receiving desflurane anesthesia had a 1.8fold higher risk to develop POD, as compared to propofol anesthesia (OR 1.766, CI 95% 1.049-2.974, p = 0.032). Conclusion: We found a significantly increased risk to develop POD after desflurane anesthesia in older patients, even though burst suppression duration was shorter under desflurane anesthesia as compared to propofol anesthesia. Our finding might help to explain some discrepancies in studies analyzing the impact of burst suppression duration and EEG-guided anesthesia on the risk to develop POD.

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.

Comparing online cognitive load on mobile versus PC-based devices.

Navigating the web represents a complex cognitive activity that requires effective integration of different stimuli and the correct functioning of numerous cognitive abilities (including attention, perception, and working memory). Despite the potential relevance of the topic, numerous limitations are present throughout the literature about the cognitive load during online activities. The main aim of this study is to investigate cognitive load during comprehension and information-seeking tasks. In particular, we here focus on the comparison of the cognitive load required while performing those tasks using mobile or PC-based devices. This topic has become even more crucial due to the massive adoption of smart working and distance learning during the COVID-19 pandemic. A great effort is nowadays devoted to the detection and quantification of stressful states induced by working and learning activities. Continuous stress and excessive cognitive load are two of the main causes of mental and physical illnesses such as depression or anxiety. Cognitive load was measured through electroencephalography (EEG), acquired via a low-cost wireless EEG headset. Two different tasks were considered: reading comprehension (CO) of online text and online information-seeking (IS). Moreover, two experimental conditions were compared, administering the two tasks using mobile (MB) and desktop (PC) devices. Eleven participants were involved in each experimental condition, MB and PC, performing both the tasks on the same device, for a total of twenty-two people, recruited from students, researchers, and employees of the university. The following two research questions were investigated: Q1: Is there a difference in the cognitive load while performing the comprehension and the information-seeking tasks? Q2: Does the adopted device influence the cognitive load? The results obtained show that the baseline (BL) requires the lower cognitive load in both the conditions, while in IS task, the requirement reaches its highest value, especially using a mobile phone. In general, the power of all the brain wave bands increased in all conditions (MB and PC) during the two tasks (CO and IS), except for alpha, which is usually high in a state of relaxation and low cognitive load. People include website navigation into their daily routines, and for this, it is important to create an interaction that is as easy and barrier-free as possible. An effective design allows a user to focus on interesting information: many website architectures, instead, are an obstacle to be overcome; they impose a high cognitive load and poor user experience. All these aspects draw cognitive resources away from the user's primary task of finding and comprehending the site's information. Having information about how the cognitive load varies based on the device adopted and the considered task can provide useful indicators in this direction. This work suggests that using an EEG low-cost wearable device could be useful to quantify the cognitive load induced, allowing the development of new experiments to analyse these dependencies deeper, and to provide suggestions for better interaction with the web.

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)

Extenics in Face Recognition

Face recognition is facing a new challenge, which was resulted from the contradiction between face recognition and the necessity to wear masks during the COVID-19. This article introduces extenics in face recognition (EFR) for solving the contradiction and constructs a facial model for psychology analysis to help the patients suffering from mental disorders. Moreover, a 2D-3D scene transformation model is integrated with EFR to fuse the virtual scene and real scene. Perspective fusions of EFR with EEG are also explained utilizing the five-layer intelligence theory for fighting against the COVID-19. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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

Developing an Application in the Forest for New Tourism Post COVID-19 -Experiments in Oku-Nikko National Park

Tourism, which has developed in line with the development of transport, has had to undergo major changes. As the push for SDGs spreads across the world, and for safe travel post-COVID-16, environmentally friendly smallgroup tourism is being promoted. It would be beneficial if the smartphones, which is used daily lives, could be useful in the nature for small groups of novice walkers to walk safety and knowing some new information about the area. However, the signal conditions are not always perfect in forests. Therefore, we have developed a smartphone application using Bluetooth Low Energy (BLE) beacons equipped with solar panels in Nikko National Park in Japan. Japan has long had the concept of forest bathing. Walking in the forest is told to have positive effects on the body and in the mind. We tried to clarify one of effects of forest bathing by measuring brain waves. We measured the effects of walking in nature by conducting simple EEG measurements while walking and measuring the degree of relaxation in the forest in 2021. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Depression screening using hybrid neural network.

Depression is a common cause of increased suicides worldwide, and studies have shown that the number of patients suffering from major depressive disorder (MDD) increased several-fold during the COVID-19 pandemic, highlighting the importance of disease detection and depression management, while increasing the need for effective diagnostic tools. In recent years, machine learning and deep learning methods based on electroencephalography (EEG) have achieved significant results in the field of automatic depression detection. However, most current studies have focused on a small number of EEG signal channels, and experimental data require special processing by professionals. In this study, 128 channels of EEG signals were simply filtered and 24-fold leave-one-out cross-validation experiments were performed using 2DCNN-LSTM classifier, support vector machine, K-nearest neighbor and decision tree. The current results show that the proposed 2DCNN-LSTM model has an average classification accuracy of 95.1% with an AUC of 0.98 for depression detection of 6-second participant EEG signals, and the model is much better than 72.05%, 79.7% and 79.49% for support vector machine, K nearest neighbor and decision tree. In addition, we found that the model achieved a 100% probability of correctly classifying the EEG signals of 300-second participants.

Investigating brain cortical activity in patients with post-COVID-19 brain fog.

Brain fog is a kind of mental problem, similar to chronic fatigue syndrome, and appears about 3 months after the infection with COVID-19 and lasts up to 9 months. The maximum magnitude of the third wave of COVID-19 in Poland was in April 2021. The research referred here aimed at carrying out the investigation comprising the electrophysiological analysis of the patients who suffered from COVID-19 and had symptoms of brain fog (sub-cohort A), suffered from COVID-19 and did not have symptoms of brain fog (sub-cohort B), and the control group that had no COVID-19 and no symptoms (sub-cohort C). The aim of this article was to examine whether there are differences in the brain cortical activity of these three sub-cohorts and, if possible differentiate and classify them using the machine-learning tools. he dense array electroencephalographic amplifier with 256 electrodes was used for recordings. The event-related potentials were chosen as we expected to find the differences in the patients' responses to three different mental tasks arranged in the experiments commonly known in experimental psychology: face recognition, digit span, and task switching. These potentials were plotted for all three patients' sub-cohorts and all three experiments. The cross-correlation method was used to find differences, and, in fact, such differences manifested themselves in the shape of event-related potentials on the cognitive electrodes. The discussion of such differences will be presented; however, an explanation of such differences would require the recruitment of a much larger cohort. In the classification problem, the avalanche analysis for feature extractions from the resting state signal and linear discriminant analysis for classification were used. The differences between sub-cohorts in such signals were expected to be found. Machine-learning tools were used, as finding the differences with eyes seemed impossible. Indeed, the A&B vs. C, B&C vs. A, A vs. B, A vs. C, and B vs. C classification tasks were performed, and the efficiency of around 60-70% was achieved. In future, probably there will be pandemics again due to the imbalance in the natural environment, resulting in the decreasing number of species, temperature increase, and climate change-generated migrations. The research can help to predict brain fog after the COVID-19 recovery and prepare the patients for better convalescence. Shortening the time of brain fog recovery will be beneficial not only for the patients but also for social conditions.

Exploratory Study on Chemosensory Event-Related Potentials in Long COVID-19 and Mild Cognitive Impairment: A Common Pathway?

People affected by the Long COVID-19 (LC) syndrome often show clinical manifestations that are similar to those observed in patients with mild cognitive impairments (MCI), such as olfactory dysfunction (OD), brain fog, and cognitive and attentional diseases. This study aimed to investigate the chemosensory-evoked related potentials (CSERP) in LC and MCI to understand if there is a common pathway for the similarity of symptoms associated with these disorders. Eighteen LC patients (mean age 53; s.d. = 7), 12 patients diagnosed with MCI (mean age 67; s.d. = 6), and 10 healthy control subjects (mean age 66; s.d. = 5, 7) were recruited for this exploratory study. All of them performed a chemosensory event-related potentials (CSERP) task with the administration of trigeminal stimulations (e.g., the odorants cinnamaldehyde and eucalyptus). Study results highlighted that MCI and LC showed reduced N1 amplitude, particularly in the left frontoparietal network, involved in working memory and attentional deficits, and a reduction of P3 latency in LC. This study lays the foundations for evaluating aspects of LC as a process that could trigger long-term functional alterations, and CSERPs could be considered valid biomarkers for assessing the progress of OD and an indicator of other impairments (e.g., attentional and cognitive impairments), as they occur in MCI.

Patient characteristics, anaesthetic workload and techniques in the UK: an analysis from the 7th National Audit Project (NAP7) activity survey.

Detailed contemporary knowledge of the characteristics of the surgical population, national anaesthetic workload, anaesthetic techniques and behaviours are essential to monitor productivity, inform policy and direct research themes. Every 3-4 years, the Royal College of Anaesthetists, as part of its National Audit Projects (NAP), performs a snapshot activity survey in all UK hospitals delivering anaesthesia, collecting patient-level encounter data from all cases under the care of an anaesthetist. During November 2021, as part of NAP7, anaesthetists recorded details of all cases undertaken over 4 days at their site through an online survey capturing anonymous patient characteristics and anaesthetic details. Of 416 hospital sites invited to participate, 352 (85%) completed the activity survey. From these, 24,177 reports were returned, of which 24,172 (99%) were included in the final dataset. The work patterns by day of the week, time of day and surgical specialty were similar to previous NAP activity surveys. However, in non-obstetric patients, between NAP5 (2013) and NAP7 (2021) activity surveys, the estimated median age of patients increased by 2.3 years from median (IQR) of 50.5 (28.4-69.1) to 52.8 (32.1-69.2) years. The median (IQR) BMI increased from 24.9 (21.5-29.5) to 26.7 (22.3-31.7) kg.m-2 . The proportion of patients who scored as ASA physical status 1 decreased from 37% in NAP5 to 24% in NAP7. The use of total intravenous anaesthesia increased from 8% of general anaesthesia cases to 26% between NAP5 and NAP7. Some changes may reflect the impact of the COVID-19 pandemic on the anaesthetic population, though patients with confirmed COVID-19 accounted for only 149 (1%) cases. These data show a rising burden of age, obesity and comorbidity in patients requiring anaesthesia care, likely to impact UK peri-operative services significantly.

Telemedicine and Epilepsy Care.

Telemedicine is a method of health care delivery well suited for epilepsy care, where there is an insufficient supply of trained specialists. The telemedicine "Hub and Spoke" approach allows patients to visit their local health clinic ('Spokes') to establish appropriate care and monitoring for their seizure disorder or epilepsy, and remotely connect with epileptologists or neurologists at centralized centers of expertise ('Hubs'). The COVID-19 pandemic resulted in an expansion of telemedicine capabilities and use, with favorable patient and provider experience and outcomes, allowing for its wide scale adoption beyond COVID-19.