COVID-19 pandemic has undoubtedly disrupted the well-established, traditional structure of medical education. Τhe new limitations of physical presence have accelerated the development of an online ...learning environment, comprising both of asynchronous and synchronous distance education, and the introduction of novel ways of student assessment. At the same time, this prolonged crisis had serious implications on the lives of medical students including their psychological well-being and the impact on their academic trajectories. The new reality has, on many occasions, triggered the 'acting up' of medical students as frontline healthcare staff, which has been perceived by many of them as a positive learning and contributing experience, and has led to a variety of responses from the educational institutions. All things considered, the urgency for rapid and novel adaptations to the new circumstances has functioned as a springboard for remarkable innovations in medical education,including the promotion of a more "evidence-based" approach.
Introduction
Stroke represents a major cause of functional disability with increasing prevalence. Thus, it is imperative that stroke prognosis be both timely and valid. Up to today, several ...biomarkers have been investigated in an attempt to forecast stroke survivors’ potential for motor recovery, transcranial magnetic stimulation (TMS) being among them.
Methods
A literature research of two databases (MEDLINE and Scopus) was conducted in order to trace all relevant studies published between 1990 and 2021 that focused on the potential utility of TMS implementation on stroke prognosis. Only full-text articles published in the English language were included.
Results
Thirty-nine articles have been traced and included in this review.
Discussion
Motor evoked potentials (MEPs) recording is indicative of a favorable prognosis concerning the motor recovery of upper and lower extremities’ weakness, swallowing and speech difficulties, and the patient’s general functional outcome. On the contrary, MEP absence is usually associated with poor prognosis. Relative correlations have also been made among other TMS variants (motor threshold, MEP amplitude, central motor conduction time) and the expected recovery rate. Overall, TMS represents a non-invasive, fast, safe, and reproducible prognostic tool poststroke that could resolve prognostic uncertainties in cases of stroke.
Aim
This study aimed at evaluating the cholinergic hypothesis in Alzheimer’s disease (AD) patients utilizing the pupillometry method, cognitive tests and Hamilton Depression Rating Scale (HAM-D), as ...well as to examine whether a correlation between cognitive tests and pupillometry exists.
Methods
Forty-two patients with mean age 69.2 ± 7.0 years and documented AD volunteered to participate in this study, while 33 healthy matched subjects served as controls. All subjects underwent a pupillometric measurement and performed the Wechsler Memory Scale (WMS) and Mini Mental State Examination (MMSE). Also, HAM-D was used to assess the severity of depressive symptoms. The pupillometric parameters studied were (1) latency for the onset of constriction (T1), (2) maximum constriction velocity (VCmax), and (3) maximum constriction acceleration (ACmax).
Results
In AD patients MMSE and WMS score were correlated with ACmax (
r
= −0.409,
p
< 0.05 and
r
= −0.513,
p
< 0.05, respectively) and VCmax (
r
= −0.664,
p
< 0.05 and
r
= −0.771,
p
< 0.05), respectively. Moreover, T1 was found to be significantly increased by 23 % (
p
< 0.05) in AD patients compared to healthy subjects. Conversely, the mean scores of VCmax and ACmax were significantly decreased in AD patients by 46 % (
p
< 0.05) and by 47 % (
p
< 0.05), respectively, as compared to healthy subjects. There was no significant difference between the two groups for HAM-D. Additionally, AD patients showed decreased score in WMS by 40 % (
p
< 0.05) and in MMSE by 28.5 % (
p
< 0.05) compared to healthy subjects. Of the indices that were studied VCmax and ACmax are governed mainly by the action of the Parasympathetic Nervous System.
Conclusions
The results of this study demonstrated that there is a correlation between cognitive tests and pupillometry in AD patients. Thus, pupillometry could be considered as a sensitive technique for the investigation of cholinergic deficits, which indirectly lead to memory and cognitive disorders in AD patients.
Background
The spectrum of reported neurological sequelae associated with SARS-CoV-2 is continuously expanding, immune mediated neuropathies like Guillain–Barre syndrome (GBS) and exacerbations of ...preexisting chronic inflammatory demyelinating polyneuropathy (CIDP) being among them. However, respective cases of acute onset CIDP (A-CIDP) are rare.
Case presentation
We hereby report two cases of A-CIDP after COVID-19 infection and Ad26.COV2.S vaccination that presented with flaccid paraparesis and acroparesthesias (Case presentation 1; female, 52) and facial diplegia accompanied by acroparesthesias (Case presentation 2; male, 62), respectively. In both instances clinical, neurophysiological and CSF findings were indicative of acute inflammatory demyelinating polyneuropathy, thus both patients were initially treated with intravenous immunoglobulins resulting in clinical improvement. Nevertheless, the first patient relapsed 5 weeks after the initial episode, thus was diagnosed with GBS with treatment related fluctuations (GBS-TRF) and treated successfully with seven plasma exchange (PLEX) sessions. However, 11 weeks from symptom onset she relapsed again. Taking into account that the second relapse occurred more than 8 weeks after the first episode, the potential diagnosis of A-CIDP was reached and oral dexamethasone 40 mg/d for 4 consecutive days every 4 weeks was administered. With regards to the second patient, he relapsed > 8 weeks after the initial episode, thus was also diagnosed with A-CIDP and treated with 7 PLEX sessions followed by similar to the aforementioned corticosteroid therapy. On 2 month follow-up both patients exhibited remarkable clinical improvement.
Conclusions
Close surveillance of patients presenting with immune neuropathies in the context of SARS-CoV-2 infection or immunization is crucial for timely implementation of appropriate treatment. Prompt A-CIDP distinction from GBS-TRF is of paramount importance as treatment approach and prognosis between these two entities differ.
In many instances, the differential diagnosis between Guillain–Barre syndrome and chronic inflammatory demyelinating polyneuropathy (CIDP) may be challenging. The aim of this letter to the editor is ...to elucidate comments and concerns raised, regarding our latest published article dealing with two patients that developed acute-onset CIDP after SARS-CoV-2 infection and Ad26.COV2.S vaccination, respectively.
Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with ...premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients’ class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments.
Stroke constitutes the primary source of adult functional disability, exhibiting a paramount socioeconomic burden. Thus, it is of great importance that the prediction of stroke outcome be both prompt ...and accurate. Although modern neuroimaging and neurophysiological techniques are accessible, easily available blood biomarkers reflecting underlying stroke-related pathophysiological processes, including glial and/or neuronal death, neuroendocrine responses, inflammation, increased oxidative stress, blood–brain barrier disruption, endothelial dysfunction, and hemostasis, are required in order to facilitate stroke prognosis. A literature search of two databases (MEDLINE and Science Direct) was conducted in order to trace all relevant studies published between 1 January 2010 and 31 December 2021 that focused on the clinical utility of brain natriuretic peptide, glial fibrillary acidic protein, the red cell distribution width, the neutrophil-to-lymphocyte ratio, matrix metalloproteinase-9, and aquaporin-4 as prognostic tools in stroke survivors. Only full-text articles published in English were included. Twenty-eight articles were identified and are included in this review. All studied blood-derived biomarkers proved to be valuable prognostic tools poststroke, the clinical implementation of which may accurately predict the survivors’ functional outcomes, thus significantly enhancing the rehabilitation efficiency of stroke patients. Along with already utilized clinical, neurophysiological, and neuroimaging biomarkers, a blood-derived multi-biomarker panel is proposed as a reasonable approach to enhance the predictive power of stroke prognostic models.
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.
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