Enhancing poststroke hand movement recovery: Efficacy of RehabSwift, a personalized brain-computer interface system.

This study explores the efficacy of our novel and personalized brain-computer interface (BCI) therapy, in enhancing hand movement recovery among stroke survivors. Stroke often results in impaired motor function, posing significant challenges in daily activities and leading to considerable societal and economic burdens. Traditional physical and occupational therapies have shown limitations in facilitating satisfactory recovery for many patients.

Effect of Octamer-Binding Transcription Factor 4 Overexpression on the Neural Induction of Human Dental Pulp Stem Cells.

Stem cell-based therapy is a potential alternative strategy for brain repair, with neural stem cells (NSC) presenting as the most promising candidates. Obtaining sufficient quantities of NSC for clinical applications is challenging, therefore alternative cell types, such as neural crest-derived dental pulp stem cells (DPSC), may be considered. Human DPSC possess neurogenic potential, exerting positive effects in the damaged brain through paracrine effects.

Developing and Planning a Protocol for Implementing Health Promoting Animal Assisted Interventions (AAI) in a Tertiary Health Setting.

The Ottawa Charter identifies that multiple levels of government, non-government, community, and other organizations should work together to facilitate health promotion, including in acute settings such as hospitals. We outline a method and protocol to achieve this, namely an Action Research (AR) framework for an Animal Assisted Intervention (AAI) in a tertiary health setting. Dogs Offering Support after Stroke (DOgSS) is an AR study at a major tertiary referral hospital.

FAST-IT: ind imple est - n IA (transient ischaemic attack): a prospective cohort study to develop a multivariable prediction model for diagnosis of TIA through proteomic discovery and candidate lipid mass spectrometry, neuroimaging and machine learning-study protocol.

Introduction: Transient ischaemic attack (TIA) may be a warning sign of stroke and difficult to differentiate from minor stroke and TIA-mimics. Urgent evaluation and diagnosis is important as treating TIA early can prevent subsequent strokes. Recent improvements in mass spectrometer technology allow quantification of hundreds of plasma proteins and lipids, yielding large datasets that would benefit from different approaches including machine learning.

Automated information extraction from free-text medical documents for stroke key performance indicators: a pilot study.

Automated information extraction might be able to assist with the collection of stroke key performance indicators (KPI). The feasibility of using natural language processing for classification-based KPI and datetime field extraction was assessed. Using free-text discharge summaries, random forest models achieved high levels of performance in classification tasks (area under the receiver operator curve 0.

Prospective and external validation of stroke discharge planning machine learning models.

Machine learning may be able to help with predicting factors that aid in discharge planning for stroke patients. This study aims to validate previously derived models, on external and prospective datasets, for the prediction of discharge modified Rankin scale (mRS), discharge destination, survival to discharge and length of stay. Data were collected from consecutive patients admitted with ischaemic or haemorrhagic stroke at the Royal Adelaide Hospital from September 2019 to January 2020, and at the Lyell McEwin Hospital from January 2017 to January 2020.

Correction: Two-year efficacy of varenicline tartrate and counselling for inpatient smoking cessation (STOP study): A randomized controlled clinical trial.

[This corrects the article DOI: 10.1371/journal.pone.

Normal aging, motor neurone disease, and Alzheimer's disease are characterized by cortical changes in inflammatory cytokines.

The role of increased brain inflammation in the development of neurodegenerative diseases is unclear. Here, we have compared cytokine changes in normal aging, motor neurone disease (MND), and Alzheimer's disease (AD). After an initial analysis, six candidate cytokines, interleukin (IL)- 4, 5, 6, 10, macrophage inhibitory protein (MIP)-1α, and fibroblast growth factor (FGF)-2, showing greatest changes were assayed in postmortem frozen human superior frontal gyri (n = 12) of AD patients, aging and young adult controls along with the precentral gyrus (n = 12) of MND patients.

Improving the accuracy of stroke clinical coding with open-source software and natural language processing.

Clinical coding is an important task, which is required for accurate activity-based funding. Natural language processing may be able to assist with improving the efficiency and accuracy of clinical coding. The aims of this study were to explore the feasibility of using natural language processing for stroke hospital admissions, employed with open-source software libraries, to aid in the identification of potentially misclassified (1) category of Adjacent Diagnosis Related Groups (ADRG), (2) the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) diagnoses, and (3) Diagnosis Related Groups (DRG).

Daily estimates of individual discharge likelihood with deep learning natural language processing in general medicine: a prospective and external validation study.

Machine learning, in particular deep learning, may be able to assist in the prediction of the length of stay and timing of discharge for individual patients. Artificial neural networks applied to medical text have previously shown promise in this area. In this study, a previously derived artificial neural network was applied to prospective and external validation datasets.

Mixed-data deep learning in repeated predictions of general medicine length of stay: a derivation study.

The accurate prediction of likely discharges and estimates of length of stay (LOS) aid in effective hospital administration and help to prevent access block. Machine learning (ML) may be able to help with these tasks. For consecutive patients admitted under General Medicine at the Royal Adelaide Hospital over an 8-month period, daily ward round notes and relevant discrete data fields were collected from the electronic medical record.

Clinical Translation of Cell Therapies in Stroke (CT2S) Checklist-a pragmatic tool to accelerate development of cell therapy products.

Background: Cell therapies present an exciting potential but there is a long history of expensive translational failures in stroke research. Researchers engaged in cell therapy research would benefit from a practical framework that can help in planning research and development of investigational cell therapies into viable medical products.

Methods: We developed a checklist using a mixed methodology approach to evaluate the impact of study design, regulatory policy, ethical, and health economic considerations for efficient implementation of early phase cell therapy studies.

Delayed Treatment with Human Dental Pulp Stem Cells Accelerates Functional Recovery and Modifies Responses of Peri-Infarct Astrocytes Following Photothrombotic Stroke in Rats.

Dental pulp contains multipotent mesenchymal stem cells that improve outcomes when administered early after temporary middle cerebral artery occlusion in rats. To further assess the therapeutic potential of these cells, we tested whether functional recovery following stroke induced by photothrombosis could be modified by a delayed treatment that was initiated after the infarct attained maximal volume. Photothrombosis induces permanent focal ischemia resulting in tissue changes that better reflect key aspects of the many human strokes in which early restoration of blood flow does not occur.

Plasmin Generation Potential and Recanalization in Acute Ischaemic Stroke; an Observational Cohort Study of Stroke Biobank Samples.

More than half of patients who receive thrombolysis for acute ischaemic stroke fail to recanalize. Elucidating biological factors which predict recanalization could identify therapeutic targets for increasing thrombolysis success. We hypothesize that individual patient plasmin potential, as measured by response to recombinant tissue-type plasminogen activator (rt-PA), is a biomarker of rt-PA response, and that patients with greater plasmin response are more likely to recanalize early.

Machine learning in the prediction of medical inpatient length of stay.

Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be difficult for medical patients. This review was conducted with the aim of identifying and assessing previous studies on the application of machine learning to the prediction of total hospital inpatient LOS for medical patients.

Stroke prognostication for discharge planning with machine learning: A derivation study.

Post-stroke discharge planning may be aided by accurate early prognostication. Machine learning may be able to assist with such prognostication. The study's primary aim was to evaluate the performance of machine learning models using admission data to predict the likely length of stay (LOS) for patients admitted with stroke.

Stroke incidence and subtypes in Aboriginal people in remote Australia: a healthcare network population-based study.

Objectives: We aimed to compare the incidence, subtypes and aetiology of stroke, and in-hospital death due to stroke, between Aboriginal and non-Aboriginal people in Central Australia, a remote region of Australia where a high proportion Aboriginal people reside (40% of the population). We hypothesised that the rates of stroke, particularly in younger adults, would be greater in the Aboriginal population, compared with the non-Aboriginal population; we aimed to elucidate causes for any identified disparities.

Design: A retrospective population-based study of patients hospitalised with stroke within a defined region from 1 January 2011 to 31 December 2014.

Two-year efficacy of varenicline tartrate and counselling for inpatient smoking cessation (STOP study): A randomized controlled clinical trial.

Introduction: Varenicline tartrate is superior for smoking cessation to other tobacco cessation therapies by 52 weeks, in the outpatient setting. We aimed to evaluate the long-term (104 week) efficacy following a standard course of inpatient-initiated varenicline tartrate plus Quitline-counselling compared to Quitline-counselling alone.

Methods: Adult patients (n = 392, 20-75 years) admitted with a smoking-related illnesses to one of three hospitals, were randomised to receive either 12-weeks of varenicline tartrate (titrated from 0.

Developing a multivariable prediction model for functional outcome after reperfusion therapy for acute ischaemic stroke: study protocol for the Targeting Optimal Thrombolysis Outcomes (TOTO) multicentre cohort study.

Introduction: Intravenous thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) is the only approved pharmacological reperfusion therapy for acute ischaemic stroke. Despite population benefit, IVT is not equally effective in all patients, nor is it without significant risk. Uncertain treatment outcome prediction complicates patient treatment selection.

Cognitive Reserve as an Emerging Concept in Stroke Recovery.

Stroke is a leading cause of death and disability. It is a complex and largely heterogeneous condition. Prognosis for variations in impairment and recovery following stroke continues to be challenging and inaccurate, highlighting the need to examine the influence of other currently unknown variables to better predict and understand interindividual differences in stroke impairment and recovery.

Are patient educational resources effective at deterring stroke survivors from considering experimental stem cell treatments? A randomized controlled trial.

Objective: To evaluate whether online resources developed to educate people about the risks associated with experimental stem cell (SC) treatments influence stroke survivors' attitudes about the safety and effectiveness of these treatments.

Methods: Adult stroke survivors who had not previously received SC treatments (N = 112) were recruited from international stroke advocacy/support groups for a prospective, parallel-group randomized controlled trial. Participants indicated whether they were considering SC treatments (yes/no) prior to, immediately following, and 30-days after reading/viewing the International Society for Stem Cell Research booklet or Stem Cell Network video.

Prediction of general medical admission length of stay with natural language processing and deep learning: a pilot study.

Length of stay (LOS) and discharge destination predictions are key parts of the discharge planning process for general medical hospital inpatients. It is possible that machine learning, using natural language processing, may be able to assist with accurate LOS and discharge destination prediction for this patient group. Emergency department triage and doctor notes were retrospectively collected on consecutive general medical and acute medical unit admissions to a single tertiary hospital from a 2-month period in 2019.