Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a ...formidable challenge, especially amidst constrained participant numbers. The challenge escalates when dealing with small datasets, a common scenario in preliminary medical research. While recent advances have ushered in few-shot learning algorithms adept at handling sparse data, this paper pioneers a distinctive methodology involving a visualization-centric approach to navigating the small-data challenge in diagnosing stroke survivors based on gait-analysis-derived biomechanical data. Employing Siamese neural networks (SNNs), our method transforms a biomechanical time series into visually intuitive images, facilitating a unique analytical lens. The kinematic data encapsulated comprise a spectrum of gait metrics, including movements of the ankle, knee, hip, and center of mass in three dimensions for both paretic and non-paretic legs. Following the visual transformation, the SNN serves as a potent feature extractor, mapping the data into a high-dimensional feature space conducive to classification. The extracted features are subsequently fed into various machine learning (ML) models like support vector machines (SVMs), Random Forest (RF), or neural networks (NN) for classification. In pursuit of heightened interpretability, a cornerstone in medical AI applications, we employ the Grad-CAM (Class Activation Map) tool to visually highlight the critical regions influencing the model’s decision. Our methodology, though exploratory, showcases a promising avenue for leveraging visualized biomechanical data in stroke diagnosis, achieving a perfect classification rate in our preliminary dataset. The visual inspection of generated images elucidates a clear separation of classes (100%), underscoring the potential of this visualization-driven approach in the realm of small data. This proof-of-concept study accentuates the novelty of visual data transformation in enhancing both interpretability and performance in stroke diagnosis using limited data, laying a robust foundation for future research in larger-scale evaluations.
Wet media milling, coupled with spay drying, is a commonly proposed formulation strategy for the production and solidification of nanosuspensions in order to overcome the solubility barrier of BCS ...Class II substances. However, the application of mechanically and thermally intensive processes is not straightforward in the cases of ductile and/or low melting point substances that may additionally be susceptible to eutectic formation. Using ibuprofen (IBU) as a model drug with non-favorable mechanical and melting properties, we attempt to rationalize nanocrystal formulation and manufacturing in an integrated approach by implementing Quality by Design (QbD) methodology, particle informatics techniques and computationally assisted process design. Wet media milling was performed in the presence of different stabilizers and co-milling agents, and the nanosuspensions were solidified by spray-drying. The effects of key process parameters (bead diameter, milling time and rotational speed) and formulation variables (stabilizer type and drug/stabilizer ratio) on the critical quality attributes (CQAs), i.e., Z-average size, polydispersity index (PDI), ζ-potential and redispersibility of spray-dried nanosuspensions were evaluated, while possible correlations between IBU free surface energy and stabilizer effectiveness were studied. The fracture mechanism and surface stabilization of IBU were investigated by computer simulation of the molecular interactions at the crystal lattice level. As a further step, process design accounting for mass-energy balances and predictive thermodynamic models were constructed to scale-up and optimize the design space. Contemplating several limitations, our multilevel approach offers insights on the mechanistic pathway applicable to the substances featuring thermosensitivity and eutectic tendency.
Frontotemporal dementia (FTD) is a neurodegenerative disorder characterized by progressive impairments in behavior, executive function, and language, primarily affecting individuals under the age of ...65. This disorder is associated with expressive and receptive anomia, word comprehension deficits, and behavioral symptoms such as apathy, loss of empathy, and disinhibition, all of which closely correlate with functional impairment in daily activities. Despite substantial efforts, research on occupational therapy (OT) interventions has yet to demonstrate clear benefits in managing the disease. The aim of this study is to investigate OT interventions and assess their efficacy, with a specific focus on individuals suffering from FTD. We systematically conducted searches on two databases, namely Medline and Science Direct, spanning a ten-year period from 2003 to 2023, in accordance with the PRISMA guidelines. Eleven studies met the inclusion criteria. OT interventions targeted both patients and caregivers and yielded significant positive improvements in their lives. A key focus of these interventions was to teach acceptable alternatives to the behaviors exhibited by FTD patients, as these behaviors are strongly influenced by the disease itself. OT contributes positively to enhancing the quality of life of FTD patients and alleviating the caregiving burden experienced by those providing long-term care to these patients.
This systematic review explores the multifaceted challenges faced by caregivers of stroke survivors, addressing the global impact of strokes and the anticipated rise in survivors over the coming ...decades. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a thorough literature search identified 34 relevant studies published between 2018 and 2023. The review categorizes caregiver burden into four domains: physical health, social functioning, financial issues, and psychological health. Caregivers often experience a decline in physical health, marked by chronic fatigue, sleep disturbances, and pain. Emotional distress is prevalent, leading to anxiety and depression, especially in cases of high burden. Financial strains arise from medical expenses and employment changes, exacerbating the overall burden. Contextual factors, such as cultural norms and resource availability, influence the caregiver experience. The Newcastle-Ottawa scale assessed the methodological quality of studies. The conclusion emphasizes tailored interventions and support systems for caregivers, with practical recommendations for healthcare professionals, therapists, mental health professionals, financial counselors, and policymakers. This comprehensive review enhances the understanding of caregiver experiences and provides actionable insights to improve stroke care and rehabilitation The study's novelty lies in its holistic examination of caregiver burden in stroke care, its focus on the recent literature, and its emphasis on forecasting caregiver outcomes, contributing valuable insights for proactive intervention strategies.
There is a growing body of evidence highlighting the role of gut microbiota as a biological basis of psychiatric disorders. The existing literature suggest that cognitive and emotional activities can ...be influenced by microbes through the microbiota–gut–brain axis and implies an association between alterations in the gut microbiome and several psychiatric conditions, such as autism, depression, bipolar disorder and psychosis. The aim of this review is to summarise recent findings and provide concise updates on the latest progress of the role of gut microbiota in the development and maintenance of psychiatric symptoms in schizophrenia and the first episode of psychosis. Despite the lack of consistent findings in regard to specific microbiome changes related to psychosis, the emerging literature reports significant differences in the gut microbiome of schizophrenic subjects compared to healthy controls and increasingly outlines the significance of an altered microbiome composition in the pathogenesis, development, symptom severity and prognosis of psychosis. Further human studies are, however, required, which should focus on identifying the drivers of microbiota changes in psychosis and establish the direction of causality between psychosis and microbiome alterations.
Despite therapeutic advancements, stroke remains a leading cause of death and long-term disability. The quality of current stroke prognostic models varies considerably, whereas prediction models of ...post-stroke disability and mortality are restricted by the sample size, the range of clinical and risk factors and the clinical applicability in general. Accurate prognostication can ease post-stroke discharge planning and help healthcare practitioners individualize aggressive treatment or palliative care, based on projected life expectancy and clinical course. In this study, we aimed to develop an explainable machine learning methodology to predict functional outcomes of stroke patients at discharge, using the Modified Rankin Scale (mRS) as a binary classification problem. We identified 35 parameters from the admission, the first 72 h, as well as the medical history of stroke patients, and used them to train the model. We divided the patients into two classes in two approaches: "Independent" vs. "Non-Independent" and "Non-Disability" vs. "Disability". Using various classifiers, we found that the best models in both approaches had an upward trend, with respect to the selected biomarkers, and achieved a maximum accuracy of 88.57% and 89.29%, respectively. The common features in both approaches included: age, hemispheric stroke localization, stroke localization based on blood supply, development of respiratory infection, National Institutes of Health Stroke Scale (NIHSS) upon admission and systolic blood pressure levels upon admission. Intubation and C-reactive protein (CRP) levels upon admission are additional features for the first approach and Erythrocyte Sedimentation Rate (ESR) levels upon admission for the second. Our results suggest that the said factors may be important predictors of functional outcomes in stroke patients.
To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.
A comprehensive ...literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ‘‘endourology’’, ‘‘artificial intelligence’’, ‘‘machine learning’’, and ‘‘urolithiasis'’ were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language.
A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors.
AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.
Stroke has become the first cause of functional disability and one of the leading causes of mortality worldwide. Therefore, it is of crucial importance to develop accurate biomarkers to assess stroke ...risk and prognosis. Emerging evidence suggests that neutrophil extracellular trap (NET) levels may serve as a valuable biomarker to predict stroke occurrence and functional outcome. NETs are known to create a procoagulant state by serving as a scaffold for tissue factor (TF) and platelets inducing thrombosis by activating coagulation pathways and endothelium. A literature search was conducted in two databases (MEDLINE and Scopus) to trace all relevant studies published between 1 January 2016 and 31 December 2022, addressing the potential utility of NETs as a stroke biomarker. Only full-text articles in English were included. The current review includes thirty-three papers. Elevated NET levels in plasma and thrombi seem to be associated with increased mortality and worse functional outcomes in stroke, with all acute ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage included. Additionally, higher NET levels seem to correlate with worse outcomes after recanalization therapies and are more frequently found in strokes of cardioembolic or cryptogenic origin. Additionally, total neutrophil count in plasma seems also to correlate with stroke severity. Overall, NETs may be a promising predictive tool to assess stroke severity, functional outcome, and response to recanalization therapies.
As a result of social progress and improved living conditions, which have contributed to a prolonged life expectancy, the prevalence of strokes has increased and has become a significant phenomenon. ...Despite the available stroke treatment options, patients frequently suffer from significant disability after a stroke. Initial stroke severity is a significant predictor of functional dependence and mortality following an acute stroke. The current study aims to collect and analyze data from the hyperacute and acute phases of stroke, as well as from the medical history of the patients, in order to develop an explainable machine learning model for predicting stroke-related neurological deficits at discharge, as measured by the National Institutes of Health Stroke Scale (NIHSS). More specifically, we approached the data as a binary task problem: improvement of NIHSS progression vs. worsening of NIHSS progression at discharge, using baseline data within the first 72 h. For feature selection, a genetic algorithm was applied. Using various classifiers, we found that the best scores were achieved from the Random Forest (RF) classifier at the 15 most informative biomarkers and parameters for the binary task of the prediction of NIHSS score progression. RF achieved 91.13% accuracy, 91.13% recall, 90.89% precision, 91.00% f1-score, 8.87% FNrate and 4.59% FPrate. Those biomarkers are: age, gender, NIHSS upon admission, intubation, history of hypertension and smoking, the initial diagnosis of hypertension, diabetes, dyslipidemia and atrial fibrillation, high-density lipoprotein (HDL) levels, stroke localization, systolic blood pressure levels, as well as erythrocyte sedimentation rate (ESR) levels upon admission and the onset of respiratory infection. The SHapley Additive exPlanations (SHAP) model interpreted the impact of the selected features on the model output. Our findings suggest that the aforementioned variables may play a significant role in determining stroke patients’ NIHSS progression from the time of admission until their discharge.
Stroke is one of the leading causes of long-term disabilities in motor and cognition functionality. An early and accurate prediction of rehabilitation outcomes can lead to a tailor-made treatment ...that can significantly improve the post-stroke quality of life of a person. This scoping review aimed to summarize studies that use Artificial Intelligence (AI) for the prediction of language and cognition rehabilitation outcomes and the need to use AI in this domain. This study followed the PRISMA-ScR guidelines for two databases, Scopus and PubMed. The results, which are measured with several metrics depending on the task, regression, or classification, present encouraging outcomes as they can predict the cognitive functionality of post-stroke patients with relative precision. Among the results of the paper are the identification of the most effective Machine Learning (ML) algorithms, and the identification of the key factors that influence rehabilitation outcomes. The majority of studies focus on aphasia and present high performance achieving up to 97% recall and 91.4% precision. The main limitations of the studies were the small subject population and the lack of an external dataset. However, effective ML algorithms along with explainability are expected to become among the most prominent solutions for precision medicine due to their ability to overcome non-linearities on data and provide insights and transparent predictions that can help healthcare professionals make more informed and accurate decisions.
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