BACKGROUND: As the SARS-CoV-2 virus created a global pandemic and rapidly became an imminent threat to the health and lives of people worldwide, the need for a vaccine and its quick distribution among the population was evident. Due to the urgency, and on the back of international collaboration, vaccines were developed rapidly. However, vaccination rollouts showed different success rates in different countries and some also led to increased vaccine hesitancy. OBJECTIVE: The aim of this study was to identify the role of information sharing and context sensitivity in various vaccination programs throughout the initial COVID-19 vaccination rollout in different countries. Moreover, we aimed to identify factors in national vaccination programs related to COVID-19 vaccine hesitancy, safety, and effectiveness. Toward this end, multidisciplinary and multinational opinions from members of the Navigating Knowledge Landscape (NKL) network were analyzed. METHODS: From May to July 2021, 25 completed questionnaires from 27 NKL network members were collected. These contributors were from 17 different countries. The responses reflected the contributors' subjective viewpoints on the status and details of the COVID-19 vaccination rollout in their countries. Contributors were asked to identify strengths, weaknesses, opportunities, and threats (ie, SWOT) of the respective vaccination programs. The responses were analyzed using reflexive thematic analysis, followed by frequency analysis of identified themes according to the represented countries. RESULTS: The perspectives of NKL network members showed a link between organizational elements of the vaccination rollout and the accompanying societal response, both of which were related to strengths and weaknesses of the process. External sociocultural variables, improved public communication around vaccination-related issues, ethical controversies, and the spread of disinformation were the dominant themes related to opportunities and challenges. In the SWOT 2×2 matrix, Availability and Barriers emerged as internal categories, whereas Transparent communication and promotion and Societal divide emerged as key external categories. CONCLUSIONS: Inventory of themes and categories inspired by elements of the SWOT framework provides an informative multidisciplinary perspective for effective implementation of public health strategies in the battle against COVID-19 or any future pandemics of a similar nature.
- MeSH
- COVID-19 * epidemiologie MeSH
- komunikace MeSH
- lidé MeSH
- SARS-CoV-2 MeSH
- vakcinace MeSH
- vakcíny proti COVID-19 * terapeutické užití MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
OBJECTIVE: The International Consortium on Manual Therapies (ICMT) is a grassroots interprofessional association open to any formally trained practitioner of manual therapy (MT) and basic scientists promoting research related to the practice of MT. Currently, MT research is impeded by professions' lack of communication with other MT professions, biases, and vernacular. Current ICMT goals are to minimize these barriers, compare MT techniques, and establish an interprofessional MT glossary. METHODS: Practitioners from all professions with training in manual therapies were encouraged by e-mail and website to participate (www.ICMTConferene.org). Video conferences were conducted at least bimonthly for 2.5 years by profession-specific and interprofessional focus groups (FGs). Members summarized scopes of practice, technique descriptions, associated mechanisms of action (MOA), and glossary terms. Each profession presented their work to the interprofessional FG to promote dialogue, understanding and consensus. Outcomes were reported and refined at numerous public events. RESULTS: Focus groups with representatives from 5 MT professions, chiropractic, massage therapy, osteopathic, physical therapy and structural integration identified 17 targeting osseous structures and 49 targeting nonosseous structures. Thirty-two techniques appeared distinct to a specific profession, and 13 were used by more than 1. Comparing descriptions identified additional commonalities. All professions agreed on 4 MOA categories for MT. A glossary of 280 terms and definitions was consolidated, representing key concepts in MT. Twenty-one terms were used by all MT professions and basic scientists. Five terms were used by MT professions exclusive of basic scientists. CONCLUSION: Outcomes suggested a third to a half of techniques used in MT are similar across professions. Additional research is needed to better define the extent of similarity and how to consistently identify those approaches. Ongoing expansion and refinement of the glossary is necessary to promote descriptive clarity and facilitate communication between practitioners and basic scientists.
- MeSH
- chiropraxe * MeSH
- fyzioterapie (techniky) MeSH
- lidé MeSH
- muskuloskeletální manipulace * MeSH
- osteopaté * MeSH
- osteopatické lékařství * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
BACKGROUND: Refractory out-of-hospital cardiac arrest (OHCA) has a poor outcome. In patients, who cannot be rescued despite using advanced techniques like extracorporeal cardiopulmonary resuscitation (ECPR), organ donation may be considered. This study aims to evaluate, in refractory OHCA, how ECPR versus a standard-based approach allows organ donorship. METHODS: The Prague OHCA trial randomized adults with a witnessed refractory OHCA of presumed cardiac origin to either an ECPR-based or standard approach. Patients who died of brain death or those who died of primary circulatory reasons and were not candidates for cardiac transplantation or durable ventricle assist device were evaluated as potential organ donors by a transplant center. In this post-hoc analysis, the effect on organ donation rates and one-year organ survival in recipients was examined. RESULTS: Out of 256 enrolled patients, 75 (29%) died prehospitally or within 1 hour after admission and 107 (42%) during the hospital stay. From a total of 24 considered donors, 21 and 3 (p = 0.01) were recruited from the ECPR vs standard approach arm, respectively. Fifteen brain-dead and none cardiac-dead subjects were ultimately accepted, 13 from the ECPR and two from the standard strategy group. A total of 36 organs were harvested. The organs were successfully transplanted into 34 recipients. All transplanted organs were fully functional, and none of the recipients died due to graft failure within the one-year period post-transplant. CONCLUSION: The ECPR-based approach in the refractory OHCA trial is associated with increased organ donorship and an excellent outcome of transplanted organs. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01511666. Registered January 19, 2012.
- MeSH
- dospělí MeSH
- kardiopulmonální resuscitace * metody MeSH
- lidé MeSH
- mimotělní membránová oxygenace * metody MeSH
- retrospektivní studie MeSH
- transplantace orgánů * MeSH
- zástava srdce mimo nemocnici * terapie MeSH
- získávání tkání a orgánů * MeSH
- Check Tag
- dospělí MeSH
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- randomizované kontrolované studie MeSH
- MeSH
- posudkové řízení MeSH
- psaní * MeSH
- umělá inteligence * MeSH
- Publikační typ
- časopisecké články MeSH
- komentáře MeSH
Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.
- MeSH
- bdění fyziologie MeSH
- elektroencefalografie metody MeSH
- elektrokortikografie MeSH
- psi MeSH
- spánek REM fyziologie MeSH
- spánek * fyziologie MeSH
- stadia spánku * fyziologie MeSH
- zvířata MeSH
- Check Tag
- psi MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.
BACKGROUND: Survival rates in refractory out-of-hospital cardiac arrest (OHCA) remain low with conventional advanced cardiac life support (ACLS). Extracorporeal life support (ECLS) implantation during ongoing resuscitation, a method called extracorporeal cardiopulmonary resuscitation (ECPR), may increase survival. This study examined whether ECPR is associated with improved outcomes. METHODS: Prague OHCA trial enrolled adults with a witnessed refractory OHCA of presumed cardiac origin. In this secondary analysis, the effect of ECPR on 180-day survival using Kaplan-Meier estimates and Cox proportional hazard model was examined. RESULTS: Among 256 patients (median age 58 years, 83% male) with median duration of resuscitation 52.5 min (36.5-68), 83 (32%) patients achieved prehospital ROSC during ongoing conventional ACLS prehospitally, 81 (32%) patients did not achieve prehospital ROSC with prolonged conventional ACLS, and 92 (36%) patients did not achieve prehospital ROSC and received ECPR. The overall 180-day survival was 51/83 (61.5%) in patients with prehospital ROSC, 1/81 (1.2%) in patients without prehospital ROSC treated with conventional ACLS and 22/92 (23.9%) in patients without prehospital ROSC treated with ECPR (log-rank p < 0.001). After adjustment for covariates (age, sex, initial rhythm, prehospital ROSC status, time of emergency medical service arrival, resuscitation time, place of cardiac arrest, percutaneous coronary intervention status), ECPR was associated with a lower risk of 180-day death (HR 0.21, 95% CI 0.14-0.31; P < 0.001). CONCLUSIONS: In this secondary analysis of the randomized refractory OHCA trial, ECPR was associated with improved 180-day survival in patients without prehospital ROSC. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT01511666, Registered 19 January 2012.
- MeSH
- dospělí MeSH
- kardiopulmonální resuscitace * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- mimotělní membránová oxygenace * metody MeSH
- rozšířená kardiopulmonální recuscitace MeSH
- urgentní zdravotnické služby * metody MeSH
- zástava srdce mimo nemocnici * terapie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- randomizované kontrolované studie MeSH
BACKGROUND: Treating memory and cognitive deficits requires knowledge about anatomical sites and neural activities to be targeted with particular therapies. Emerging technologies for local brain stimulation offer attractive therapeutic options but need to be applied to target specific neural activities, at distinct times, and in specific brain regions that are critical for memory formation. METHODS: The areas that are critical for successful encoding of verbal memory as well as the underlying neural activities were determined directly in the human brain with intracranial electrophysiological recordings in epilepsy patients. We recorded a broad range of spectral activities across the cortex of 135 patients as they memorised word lists for subsequent free recall. FINDINGS: The greatest differences in the spectral power between encoding subsequently recalled and forgotten words were found in low theta frequency (3-5 Hz) activities of the left anterior prefrontal cortex. This subsequent memory effect was proportionally greater in the lower frequency bands and in the more anterior cortical regions. We found the peak of this memory signal in a distinct part of the prefrontal cortex at the junction between the Broca's area and the frontal pole. The memory effect in this confined area was significantly higher (Tukey-Kramer test, p<0.05) than in other anatomically distinct areas. INTERPRETATION: Our results suggest a focal hotspot of human verbal memory encoding located in the higher-order processing region of the prefrontal cortex, which presents a prospective target for modulating cognitive functions in the human patients. The memory effect provides an electrophysiological biomarker of low frequency neural activities, at distinct times of memory encoding, and in one hotspot location in the human brain. FUNDING: Open-access datasets were originally collected as part of a BRAIN Initiative project called Restoring Active Memory (RAM) funded by the Defence Advanced Research Project Agency (DARPA). CT, ML, MTK and this research were supported from the First Team grant of the Foundation for Polish Science co-financed by the European Union under the European Regional Development Fund.
- MeSH
- lidé MeSH
- magnetická rezonanční tomografie MeSH
- mapování mozku MeSH
- mozek fyziologie MeSH
- paměť * fyziologie MeSH
- prefrontální mozková kůra * fyziologie MeSH
- rozpomínání fyziologie MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
Objective.Electrical deep brain stimulation (DBS) is an established treatment for patients with drug-resistant epilepsy. Sleep disorders are common in people with epilepsy, and DBS may actually further disturb normal sleep patterns and sleep quality. Novel implantable devices capable of DBS and streaming of continuous intracranial electroencephalography (iEEG) signals enable detailed assessments of therapy efficacy and tracking of sleep related comorbidities. Here, we investigate the feasibility of automated sleep classification using continuous iEEG data recorded from Papez's circuit in four patients with drug resistant mesial temporal lobe epilepsy using an investigational implantable sensing and stimulation device with electrodes implanted in bilateral hippocampus (HPC) and anterior nucleus of thalamus (ANT).Approach.The iEEG recorded from HPC is used to classify sleep during concurrent DBS targeting ANT. Simultaneous polysomnography (PSG) and sensing from HPC were used to train, validate and test an automated classifier for a range of ANT DBS frequencies: no stimulation, 2 Hz, 7 Hz, and high frequency (>100 Hz).Main results.We show that it is possible to build a patient specific automated sleep staging classifier using power in band features extracted from one HPC iEEG sensing channel. The patient specific classifiers performed well under all thalamic DBS frequencies with an average F1-score 0.894, and provided viable classification into awake and major sleep categories, rapid eye movement (REM) and non-REM. We retrospectively analyzed classification performance with gold-standard PSG annotations, and then prospectively deployed the classifier on chronic continuous iEEG data spanning multiple months to characterize sleep patterns in ambulatory patients living in their home environment.Significance.The ability to continuously track behavioral state and fully characterize sleep should prove useful for optimizing DBS for epilepsy and associated sleep, cognitive and mood comorbidities.
- MeSH
- epilepsie komplikace MeSH
- hipokampus MeSH
- hluboká mozková stimulace * metody MeSH
- lidé MeSH
- mozek MeSH
- nuclei anteriores thalami * MeSH
- poruchy spánku a bdění * komplikace diagnóza terapie MeSH
- retrospektivní studie MeSH
- thalamus MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.
- MeSH
- databáze faktografické MeSH
- lidé MeSH
- neuronové sítě (počítačové) * MeSH
- pohyb * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
