Neurology Papers & Publications

Background: Although stroke is a major problem in Hadhramout Governorate, there is a scarcity of reliable information on risk factors of stroke and predictors of in-hospital mortality. The aim of this study was to explore the risk factors and outcomes of stroke patients admitted to Ibn Sina Hospital, Hadhramout, Yemen, and to identify the predictors of in-hospital mortality. Methods and Materials: This retrospective cross-sectional study was conducted in Ibn Sina Hospital in Mukalla district, Hadhramout Governorate, over a 4-month period (from January 1, 2021, to April 30, 2021). Results: During the study period, we recruited 100 cases of stroke, of whom 77 (77%) were male and 23 (23%) were female. Their mean age was 65.42±12.78 years. Hypertension was the most common risk factor, occurring in 81 (81%) patients, while no risk factors were identified in 5 (5%) cases (Cryptogenic). Ischemic stroke was found in 70 (70%) cases, and hemorrhagic stroke was noticed in 30 (30%) patients. The in-hospital mortality was 29 (29%), and the univariate analysis found male sex, hypertension, and hemorrhagic stroke as probable predictors of in-hospital mortality. Only hemorrhagic stroke (adjusted odds ratio [OR]=2.053 and 95% confidence interval [CI]=0.822–1.599; p<0.001) and hypertension (adjusted OR= 1.677; 95% CI=0.555–1.495; p=0.011) were found to be independent predictors of mortality by multivariate logistic regression analysis. Conclusion: Stroke is a major problem in Hadhramaut Governorate with ischemic stroke being more than hemorrhagic. Men were more likely to have a stroke than women and majority of cases were elderly. Hypertension, diabetes mellitus, smoking, and dyslipidemia were the most commonly identified risk factors that were significantly associated with stroke. Hemorrhagic stroke and presence of hypertension were found to be risk factors for in-hospital mortality; therefore, hypertension should be well-controlled to reduce in-hospital mortality.

Karishma

Background and Objectives: The complications of type 2 diabetes mellitus (T2DM) can occur in some organs, such as the heart, blood vessels, eyes, kidneys, and nerves. Stroke, one of such complications, is increasing every year. This study aims to investigate the prevalence of and risk factors for stroke among T2DM patients in Qatar. Methods: This was a secondary post hoc analysis of collected data from our previous study titled “Association of Vitamin D deficiency with dyslipidemia, glycemic control, and microalbuminuria in patients with T2DM in Qatar.” Results: The prevalence of stroke among our patients was 3.8%. A comparison between stroke and no-stroke groups showed a significant association between stroke and other diseases, namely, chronic kidney diseases (CKD) (p=0.007), coronary artery disease (CAD) (p=0.010), peripheral vascular disease (PVD) (p<0.001), retinopathy (p=0.044), prolonged duration of diabetes mellitus (DM) (p=0.041), glycated hemoglobin (HbA1c) (p=0.006), and a high serum creatinine level (p=0.003). In the multivariate analysis, we identified the following variables as independent risk factors for stroke in patients with T2DM: male gender, CKD, CAD, PVD, high HbA1c, a high creatinine level, and prolonged duration of DM. Conclusion: The prevalence of stroke among T2DM patients in Qatar was around 3.8%. The main risk factors were male gender, CKD, CAD, PVD, high HbA1c, prolonged duration of DM, and a high level of creatinine.

Karishma

The coexistence of Moyamoya syndrome (MMS) and Graves’ disease (GD) is uncommon. Here, we report a case of a 41-year-old Filipino female, who presented with thyrotoxicosis and acute ischemic stroke. Based on her clinical presentation, cerebral computed tomography angiography, and thyroid function tests, she was diagnosed with MMS and GD. Her Burch-Wartofsky point scale score was 30, suggesting an impending thyroid storm. Antithyroid therapy was started with her neurological status deterioration initially, but after controlling the thyroid storm, the patient’s neurological status stabilized. She remained stable till she travelled to her country. We hypothesized that MMS in a patient with GD is mediated through anti-dsDNA antibodies, by altering key biological mechanisms, that is, inflammation, neutrophil extracellular traps, and apoptosis that drive a distinctive and coordinated immune and vascular activation. To the best of our knowledge, this is the first case of MMS associated with GD reported in Qatar.

Karishma

We reported a case of cyclophosphamide (CYP)-induced posterior reversible encephalopathy syndrome (PRES) in a 26-year-old previously healthy male patient who was presented to the emergency department with a history of fever, shortness of breath, and hemoptysis. After extensive investigations, including bronchoscopy and autoimmune screening, he was diagnosed with renalpulmonary syndrome. The diagnosis of CYP-related PRES was based on the development of neurological clinical picture supported by magnetic resonance imaging findings. The dose of CYP was decreased to 75 mg/day, and the patient’s symptoms improved after 3 days.

Karishma

Background: Intracerebral hemorrhage (ICH) has not been widely investigated in young adults. This study aims to describe the risk factors of ICH with a focus on the possible effect of non-modifiable risk factors, such as genetic factors, to assess the ICH outcomes, and to identify the prognostic factors after ICH among young adult patients. Methods: This prospective and observational study was conducted at two hospitals at Hamad Medical Corporation, Qatar, namely Hamad General Hospital and Alkhor Hospital. The study included young patients (16–45 years old) admitted with ICH between January 1, 2015, and December 31, 2018. Results: We examined 238 consecutive young patients with ICH consisting of 212 (89.1%) males and 26 (10.9%) females. The mean age was 37.8±6.23 years. The most common risk factor found in 187 (78.6%) patients was hypertension (HTN), while 19 (8.0%) patients had no obvious risk factors (cryptogenic). The primary site of bleeding was cerebral cortex (lobar) in 107 (44.96%) patients and then basal ganglia in 97 (40.76%) patients. The in-hospital mortality was 19 (8.0%); the National Institutes of Health Stroke Scale >14 on admission (adjusted OR=2.06; 95% CI=1.448–2.938; p<0.001), Barthel index score ≤40 on admission (adjusted OR=1.09; 95% CI=1.015–1.178; p=0.019), and HTN (adjusted OR=0.075; 95% CI=0.008–0.724; p=0.025) were found to be independent predictors of in-hospital mortality by multivariate analysis. A 1-year follow-up showed mortality in 7 (3.2%) patients and no new events in 139 (63.8%) cases. Conclusion: HTN, smoking, and excessive alcohol consumption are important modifiable risk factors for ICH among young patients in Qatar, requiring early identification and treatment to prevent this dangerous type of stroke. In addition, we recommend conducting further studies focusing on the genetic risk factors of ICH among young adults, particularly those with cryptogenic ICH, to identify whether genetic risk factors are involved.

Karishma

Background and Objective: The COVID-19 pandemic affected medical care systems including stroke care, globally. In this study, we investigated the impact of the COVID-19 outbreak on stroke care in Hadramout, in terms of rate of admission, access to care, risk factors, clinical presentation, and outcome. Materials and Methods: A hospital-based cross-sectional study comparing all stroke patients admitted to Ibn-seena University Hospital (ISTH), Mukalla, Hadramout, during two periods, May 1–October 31, 2020, during the pandemic of COVID-19 (COVID-19 group), and from May 1 to October 31, 2019 (pre-COVID-19 group). Data collected from patients’ medical record files into a master sheet, and were processed by the Statistical Package for Social Sciences software. Results: There were 117 stroke patients admitted in COVID-19 pandemic in 2020, and 213 patients admitted in the same period in 2019. Stroke admission declined by 45.1% (Odds Ratio [OR]=0.30, 95% confidence interval [CI] [95% CI]: 0.22–0.42, p<0.0001) with no age and sex differences. Hypertension (HTN) and diabetes mellitus were more frequent in COVID-19 group than the pre-COVID-19 group (OR=1.74, 95% CI: 1.08–2.80, p=0.02) and (OR=1.81, 95% CI: 1.14–2, 88, p=0.01), respectively. No significant difference in other risk factors was found. Patients in COVID-19 group arrived the hospital more late than the patients in pre-COVID-19 group (OR=2.63, 95% CI: 1.64–4.21, p<0.0001). Dysphasia and altered consciousness including coma were more common in COVID-19 group compared with pre-COVID-19 group (OR=4.5, 95% CI: 2.18–9.08, p<0.0001) and (OR=3.2, 95% CI: 2.00–5.12, p<0.0001), respectively. Hospital stay was greatly reduced among COVID-19 group as compared with pre-COVID-19 group (02.9±0.31 days vs. 8.6±0.92 days, p<0.0001) Mortality rate was higher among COVID-19 group than the pre-COVID-19 group (41.9% vs. 27.2%, p=007). Conclusion: The number of stroke patients admitted during the COVID-19 pandemic decreased, they arrived late and spent shorter hospital stays while having higher rates of HTN, diabetes, and impaired consciousness with a high mortality rate.

Karishma

The subjective experience of consciousness, a cornerstone of human existence, is profoundly disrupted in disorders of consciousness (DOC) arising from severe brain injuries, spanning-states from coma to the minimally conscious state. A significant challenge in clinical practice is the phenomenon of covert consciousness, in which individuals may retain awareness despite the absence of overt behavioral responsiveness. Diagnosis based solely on observable behavior is inherently limited by factors such as co-occurring motor impairments, the fluctuating nature of consciousness, and subjective interpretation, potentially leading to misclassification. To overcome these limitations, neuroscientific methodologies have advanced significantly. To address these limitations, neuroscientific methods have advanced considerably. Functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) provide objective evidence of preserved brain activity and cognitive processing, enabling detection of willful modulation and offering prognostic insight. Electrophysiological techniques—including electroencephalography (EEG), event-related potentials (ERPs), transcranial magnetic stimulation combined with EEG (TMS-EEG), and advanced downstate analysis—further reveal dynamic neural patterns indicative of residual awareness. The detection of covert consciousness has profound ethical, clinical, and societal implications. It necessitates a re-examination of patient rights, end-of-life decision-making, the use of brain-computer interfaces, and societal conceptions of personhood. This evolving understanding mandates a shift towards integrating objective neuroscientific assessments with compassionate, person-centered care, aiming to preserve dignity and navigate the complex ethical landscape of severe brain injury.

Mostafa Eissa

This paper proposes EPyNet, a deep learning architecture designed for energy reduced audio emotion recognition.In the domain of audio based emotion recognition, where discerning emotional cues from audio input is crucial, the integration of artificial intelligence techniques has sparked a transformative shift in accuracy and performance.Deep learning , renowned for its ability to decipher intricate patterns, spearheads this evolution. However, the energy efficiency of deep learning models, particularly in resource-constrained environments, remains a pressing concern. Convolutional operations serve as the cornerstone of deep learning systems. However, their extensive computational demands leading to energy-inefficient computations render them as not ideal for deployment in scenarios with limited resources. Addressing these challenges, researchers came up with one-dimensional convolutional neural network (1D CNN) array convolutions, offering an alternative to traditional two-dimensional CNNs, with reduced resource requirements. However , this array-based operation reduced the resource requirement, but the energy-consumption impact was not studied. To bridge this gap, we introduce EPyNet, a deep learning architecture crafted for energy efficiency with a particular emphasis on neuron reduction. Focusing on the task of audio emotion recognition, We evaluate EPyNet on five public audio corpora-RAVDESS, TESS, EMO DB, CREMA D, and SAVEE.We propose three versions of EPyNet, a lightweight neural network designed for efficient emotion recognition, each optimized for different trade-offs between accuracy and energy efficiency. Experimental results demonstrated that the 0.06M EPyNet reduced energy consumed by 76.5% while improving accuracy by 5% on RAVDESS, 25% on TESS, and 9.75% on SAVEE. The 0.2M and 0.9M models reduced energy consumed by 64.9% and 70.3%, respectively. Additionally, we compared our Proposed 0.06M system with the MobileNet models on the CIFAR-10 dataset and achieved significant improvements. The 1035 proposed system reduces energy by 86.2% and memory by 95.7% compared to MobileNet, with a slightly lower accuracy of 0.8%. Compared to MobileNetV2, it improves accuracy by 99.2% and reduces memory by 93.8%. When compared to MobileNetV3, it achieves 57.2% energy reduction, 85.1% memory reduction, and a 24.9% accuracy improvement. We further test the scalability and robustness of the proposed solution on different data dimensions and frameworks.

Jiby Mariya Jose

s deep learning models continue to grow in complexity, the computational and energy demands associated with their training and deployment are becomingincreasingly significant, particularly for convolutional neural networks (CNNs) deployed on CPU-bound and resource- limited devices. Fully connected (FC)layers, while vital, are energy-intensive, accounting for 85.7% of a network’s parameters but contributing only 1% of the computations. This research proposes anovel approach to optimising these layers for greater energy efficiency by integrating low-rank factorisation with differential partial differential equations (PDEs).The introduction of the LowRankDense layer, which combines low-rank matrix factorisation with a differential PDE solver, aims to reduce both the parametercount and energy consumption of FC layers. Experiments conducted on the MNIST, Fashion MNIST, and CIFAR-10 datasets demonstrate the effectiveness ofthis approach, yielding promising results in terms of reduced energy usage and maintaining comparable performance, thereby enhancing the practicality andsustainability of CNNs for widespread use in environments with limited computational resources

Jiby Mariya Jose