Cryptogenic stroke (CS) is an ischemic stroke of unknown cause with increasing incidence in India. Common and rare genetic variants have been associated with the risk of stroke. We carried out targeted analysis of whole exome sequencing on a small cohort of 16 CS patients compared to 16 healthy unaffected relatives to determine whether rare coding variants in genes previously associated with stroke could play a role in India. Variants were filtered for coverage (≥20x) and minor allele frequency (≤0.01). Putative deleterious variants were identified using a range of bioinformatic tools. Targeted analysis was performed by filtering for those variants present in a panel of 220 stroke-related genes. Phenotypes, pathways and cell compartments to which genes carrying putative deleterious (PHRED-scaled CADD scores ≥15) variants belonged were determined using Enrichr. STRING was employed to identify interacting proteins. We identified 17 potentially damaging variants specific to Indian CS patients in 15 genes contributing to phenotypes (e.g., hemorrhage; abnormal blood coagulation; dilated aorta, increased heart weight) and pathways (e.g., platelet degranulation, common pathway of fibrin clot formation; response to elevated platelet cytosolic Ca2+) that were not observed in unaffected relatives. STRING analysis identified 6 genes (ITGA2B, F13A1, F5, ATP7A, GLA, ABCC6) encoding interacting proteins that could be prioritised for follow-up studies. This should include secondary sequence validation, as well as extended pedigree and functional laboratory-based gene-editing studies to validate the clinical relevance of specific variants to CS. Although limited by small sample size, our study provides novel data on CS in a geographical region and ethnic group not well studied to date.

Background and ObjectivesThe most effective antiseizure medications (ASMs) for poststroke seizures (PSSs) remain unclear. We aimed to determine outcomes associated with ASMs in people with PSS.MethodsWe systematically searched electronic databases for studies on patients with PSS on ASMs. Our outcomes were seizure recurrence, adverse events, drug discontinuation rate, and mortality. We assessed the risk of bias using Cochrane Risk of Bias tool for randomized controlled trials and Risk Of Bias In Non-randomized Studies of Interventions tools. Using levetiracetam as the reference treatment, we conducted a frequentist network meta-analysis and determined the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation methodology.ResultsOur search yielded 15 studies (3 randomized, 12 nonrandomized, N = 18,676 patients (121 early and 18,547 late seizures), 60% male, mean age 69 years) comparing 13 ASMs. Three studies had moderate and 12 had high risk of bias. Seizure recurrence was 24.8%. Compared with levetiracetam, very low-certainty evidence suggested that phenytoin was associated with higher seizure recurrences (odds ratio [OR] 7.3, 95% CI 3.7-14.5) and more adverse events (OR 5.2, 95% CI 1.2-22.9). Low-certainty evidence suggested that carbamazepine (OR 1.8, 95% CI 1.5-2.2) and phenytoin (OR 1.9, 95% CI 1.4-2.8) were associated with high drug discontinuation rates. Moderate to high-certainty evidence suggested that valproic acid (OR 4.7, 95% CI 3.6-6.3) and phenytoin (OR 8.3, 95% CI 5.7-11.9) were associated with higher mortality rates. Considering all treatments and using the GRADE approach for treatment ranking, very low-certainty evidence suggested that eslicarbazepine, lacosamide, and levetiracetam had the fewest seizure recurrences. Low to very low-certainty evidence suggested that lamotrigine had the fewest adverse events and drug discontinuations, whereas lamotrigine and levetiracetam exhibited low mortality rates with moderate-certainty evidence.DiscussionWe found that levetiracetam and lamotrigine may be safe and tolerable ASMs for PSS. Despite ASM use, the seizure recurrence rate remains high in the PSS population. Owing to bias and confounding risks, these findings should be interpreted cautiously.Trial Registration InformationPROSPERO: CRD42022363844.

Situations are often encountered, especially in the medical sciences, where observing each stage of an event is necessary and overlooking it might be risky for the well-being of an individual. Keeping the same very viewpoint, this article presents the analysis of real Modified Rankin score data with multiple responses from a Bayesian perspective using a polytomous logistic regression model. The study involves utilizing the Markov chain Monte Carlo technique for acquiring samples from the resulting posterior distribution. Finally, to check the scope of the model simplification, several covariates are tested against zero and then a comparison between the full model and the simplified model is proposed based on the deviance information criterion.

Background Platelet-monocyte (PMA) and platelet-neutrophil aggregations (PNA) are critical in causing acute inflammatory reactions favoring vascular dysfunction. However, the precise pathophysiological link between Platelet-leukocyte aggregates and Vascular Dementia (VaD) remains undetermined. Our study aimed to investigate whether platelet hyperresponsiveness is independently associated with a predictor of VaD. Methods Platelet from 19 VaD patients and 18 age-matched healthy controls were subjected to different investigations. Result PMA, PNA, P-selectin externalization, and intracellular free Ca +2 ([Ca +2 i ]) flux were evaluated either in whole blood or in platelet-rich plasma. The result revealed that PMA, PNA, P-selectin, and [Ca +2 ] i were found to be significantly outnumbered in the VaD group (4.1, 2.8, 2.7, and 2.5 times higher) compared to the control group with p-value <0.001, <0.001, <0.001, and 0.001 at 95% CI = 31.164 to 54.855, 8.653 to 22.793, 35.064 to 94.369 and 8747.015 to 28829.618 respectively. Conclusion Patients with Vascular Dementia have increased platelet leucocyte interaction, and PMA has the most significant prediction of vascular dementia than in subjects of healthy control. Thus, platelets in VaD patients switch to a ‘hyperactive’ phenotype.

BACKGROUND AND OBJECTIVES:The most effective antiseizure medications (ASMs) for poststroke seizures (PSSs) remain unclear. We aimed to determine outcomes associated with ASMs in people with PSS. METHODS:We systematically searched electronic databases for studies on patients with PSS on ASMs. Our outcomes were seizure recurrence, adverse events, drug discontinuation rate, and mortality. We assessed the risk of bias using Cochrane Risk of Bias tool for randomized controlled trials and Risk Of Bias In Non-randomized Studies of Interventions tools. Using levetiracetam as the reference treatment, we conducted a frequentist network meta-analysis and determined the certainty of evidence using the Grading of Recommendations Assessment, Development, and Evaluation methodology. RESULTS:Our search yielded 15 studies (3 randomized, 12 nonrandomized, N = 18,676 patients (121 early and 18,547 late seizures), 60% male, mean age 69 years) comparing 13 ASMs. Three studies had moderate and 12 had high risk of bias. Seizure recurrence was 24.8%. Compared with levetiracetam, very low-certainty evidence suggested that phenytoin was associated with higher seizure recurrences (odds ratio [OR] 7.3, 95% CI 3.7-14.5) and more adverse events (OR 5.2, 95% CI 1.2-22.9). Low-certainty evidence suggested that carbamazepine (OR 1.8, 95% CI 1.5-2.2) and phenytoin (OR 1.9, 95% CI 1.4-2.8) were associated with high drug discontinuation rates. Moderate to high-certainty evidence suggested that valproic acid (OR 4.7, 95% CI 3.6-6.3) and phenytoin (OR 8.3, 95% CI 5.7-11.9) were associated with higher mortality rates. Considering all treatments and using the GRADE approach for treatment ranking, very low-certainty evidence suggested that eslicarbazepine, lacosamide, and levetiracetam had the fewest seizure recurrences. Low to very low-certainty evidence suggested that lamotrigine had the fewest adverse events and drug discontinuations, whereas lamotrigine and levetiracetam exhibited low mortality rates with moderate-certainty evidence. DISCUSSION:We found that levetiracetam and lamotrigine may be safe and tolerable ASMs for PSS. Despite ASM use, the seizure recurrence rate remains high in the PSS population. Owing to bias and confounding risks, these findings should be interpreted cautiously. TRIAL REGISTRATION INFORMATION:PROSPERO: CRD42022363844.

Brain hemorrhage and strokes are serious medical conditions that can have devastating effects on a person's overall well-being and are influenced by several factors. We often encounter such scenarios specially in medical field where a single variable is associated with several other features. Visualizing such datasets with a higher number of features poses a challenge due to their complexity. Additionally, the presence of a strong correlation structure among the features makes it hard to determine the impactful variables with the usual statistical procedure. The present paper deals with analysing real life wide Modified Rankin Score dataset within a Bayesian framework using a logistic regression model by employing Markov chain Monte Carlo simulation. Latterly, multiple covariates in the model are subject to testing against zero in order to simplify the model by utilizing a model comparison tool based on Bayes Information Criterion.

BackgroundPost-stroke pain is common after a stroke and might be underreported. We describe Persistent Facial Pain (PFP) developed in post-stroke patients.Methodology: This was a prospective hospital-based cohort study of stroke patients, and patients were followed up. Out of 415 stroke patients, 26 developed PFP.ResultOut of all PFP patients, six patients had an ischemic stroke, and 20 had a hemorrhagic stroke. 57.7% of patients had hypertension, while 34.6 patients had diabetes. The stroke location was left-sided in 12 patients and right-sided in 14 patients. 46.15% of patients responded to venlafaxine, 30.77% responded to amitriptyline, and 23.08% responded to pregabalin.ConclusionPersistent facial pain is a pain syndrome that might be missed in patients post-stroke. It might be more common in hemorrhagic stroke patients than in ischemic stroke patients. It responds adequately to antidepressants. A high index of suspicion is required to diagnose and appropriately manage these patients.

We propose a hybrid machine learning algorithm (i.e., P2CA−PSO−ANN) to model malaria outbreak in three districts (Barmer, Bikaner, and Jodhpur) of Rajasthan in the Western India. We have used different meteorological variables (i.e., relative humidity, temperature, and rainfall) as input features to predict malaria. We have also considered the combined impact of these variables through a linear data fusion. We then extract the uncorrelated information from the feature set by applying Probabilistic Principal Component Analysis (P2CA). We trained the fully connected feed-forward Artificial Neural Network (ANN) by optimising its hyperparameters iteratively through a bio-inspired optimisation algorithm (Particle Swarm Optimisation). We train and evaluate the performance of this algorithm using monthly meteorological variables from 2009 - 2012. This accurately predicts the malaria cases with the coefficient of correlation (R = 0.99), and Root Mean Square Error (RMSE = 1.76). Finally, we compare our model with different benchmark algorithms (Generalised Regression Neural Network (GRNN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest, and Radial Basis Neural Networks (RBNN)) in terms of accuracy. We observed the performance of hybrid machine learning model relatively high. This study can be used as an early warning intelligent system to predict the malaria outbreaks solely from meteorological data.

The role of environmental contaminants and their association with stroke is still being determined. Association has been shown with air pollution, noise, and water pollution; however, the results are inconsistent across studies. A systematic review and meta-analysis of the effect of persistent organic pollutants (POP) in ischemic stroke patients were conducted; a comprehensive literature search was carried out until 30th June 2021 from different databases. The quality of all the articles which met our inclusion criteria was assessed using NewcastleOttawa scaling; five eligible studies were included in our systematic review. The most studied POP in ischemic stroke was polychlorinated biphenyls (PCBs), and they have shown a trend for association with ischemic stroke. The study also revealed that living near a source of POPs contamination constitutes a risk of exposure and an increased risk of ischemic stroke. Although our study provides a strong positive association of POPs with ischemic stroke, more extensive studies must be conducted to prove the association.

In most situations, cognitive deterioration is gradual and sneaky, but it may sometimes be quick. The recent emphasis is on the early stages of cognitive abnormalities, when executive and visuospatial abnormalities are common and can coexist with memory deficits, raising the possibility of dementia developing early. Visual hallucinations, advanced age, and biomarker alterations – including cortical atrophy, Alzheimer's-like changes in functional MRI and in cerebrospinal fluid, as well as slowness and frequency variation in EEG – are additional risk factors for the early development of dementia. The current status of different cognitive disorders, such as Parkinson's disease, frontotemporal dementia, Lewy body, and Alzheimer's-like disorders, are, however, still completely unknown. Finding disease-modifying treatments and biomarkers that more accurately predict cognitive decline and identify individuals at high risk of early and fast cognitive impairment are difficult tasks. But firstly in depth knowledge of the current scenario of cognitive disorders dealing with its aetiology, pathogenesis, and diagnosis are important for the treatment of patients. Thus, this chapter emphasizes the recent status of different cognitive diseases.

Sleep is one of the most important biological processes acknowledged as a vital determinant of human performance and health. Sleep has been acknowledged to promote healing, restore energy, improve the immune system through interactions, and affect human behaviour and brain functions. To this end, even the transient alteration of sleeping patterns, including severe sleep deprivation, can impair one's cognitive performance and judgment, even as prolonged aberrations have been associated with the development of disease. The existing global sleep trends indicate a decrement in average sleep durations. Owing to such trends and the various implications of sleep on human well-being and health, enhanced characterisation of the sleep attributes indicates a public health priority. Further, the advancement and use of multi-modal sensors with technologies to monitor physical activity, sleep, and circadian rhythms have increased dramatically in recent years. For the first time, accurate sleep monitoring on a large scale is now possible. However, there is a need to overcome several significant challenges to realise the full potential of these technologies for individuals, medicine, and research. In this chapter, a review of the present levels of the sleep-monitoring technologies in patients with cognitive impairments, in addition to assessing the difficulties and potentials lying ahead, from data gathering through the ultimate execution of findings within the consumer and clinical contexts.. Further, the chapter will review the advantages and disadvantages of the extant and novel sensing technologies, focusing on new data driven technologies that include Artificial Intelligence.

Mental illnesses refer to the broader array of mental health disorders affecting the emotions, behaviours, thinking, and moods of individuals. Among the notable mental illnesses are included bipolar disorders, depression, dementia, psychiatric disorders, and schizophrenia. The prevalence of mental illness was dramatically increased during the COVID-19 pandemic and, post-pandemic, neuropsychiatric abnormalities were also reported. Consequently, deep learning refers to the unique symbolic artificial intelligence techniques that have become popular in academia and realized as benchmarks in different fields including computational linguistics and image processing. Owing to the multidirectional data approach alongside the superior performance of deep learning, it has been extensively applied in neuroimaging research in relation to several psychiatric disorders in addition to other applications in the field of healthcare. This chapter will, therefore, offer an in-depth insight into deep learning and its functions in enhancing the knowledge on mental illnesses alongside its promising future with regard to mental illness diagnosis and treatment.

INTRODUCTION:Migraine is a common headache syndrome associated with various other comorbidities. Thyroid replacement in migraine patients with hypothyroidism improves headaches; however, thyroid hormone replacement in subclinical hypothyroidism is debatable, and its efficacy is not known.OBJECTIVE AND METHODOLOGY:This prospective, single-centre, quasi-randomised interventional study was conducted on patients visiting the General Medicine and Neurology outpatient department at a tertiary centre to look at the efficacy of thyroxine in subclinical hypothyroidism.RESULTS:We assessed 87 patients for analysis; no patients were lost to follow-up. There was a decrease in all parameters evaluated (headache frequency, severity, duration, MIDAS score, MIDAS grade) at three months of follow-up in the treatment group compared to placebo group. There was a significant decrease in headache frequency and severity in the levothyroxine group compared to the placebo group at three months of follow-up. Also, the follow-up MIDAS score (mean ± SD: 6.30 ± 2.455 scores vs. 8.45 ± 5.757 scores) was significantly decreased by treatment at three months follow-up.CONCLUSION:Treatment of subclinical hypothyroidism effectively reduces migraine headaches, and it is logical to check thyroid function status in patients presenting with migraine headaches. However, a larger randomised controlled trial is required to prove the efficacy of levothyroxine in migraine with subclinical hypothyroidism.

Biomedical signals are biological signals that convey information about the condition or behaviour of the living body. The concept of biological signals has been widely used in the diagnosis of various diseases in clinical practice and also used in research aspects for implementation in biomedical technology. This chapter focuses on the in-depth knowledge of various biomedical signals such as in the electroencephalogram, electromyogram, magnetoencephalogram, electrocardiogram, and heart rate, as well as their application to collect pathophysiological information, as well as obtaining and diagnosing various neurological disorders, such as Parkinson's disease, Alzheimer's disease, frontotemporal dementia, Lewy body dementia, multiple sclerosis, and amyotrophic lateral sclerosis.Electrophysiological techniques can provide us with quantifiable assessments of movement disorders. Several reflex circuits, including the startle reflex, blink reflex, and long latency reflex, can be activated by particular types of external stimuli and are important in the diagnosis of myoclonus, excessive startle, and stiff person syndrome. The interactions of many signals with modalities, as well as different algorithms and methodologies, have a considerable impact on biomedical applications. Thus, the computer-aided diagnosis presented in this chapter will help neurologists, neurosurgeons, radiologists, and other healthcare providers to make better clinical decisions.

Platelet-monocyte (PMA) and platelet-neutrophil aggregations (PNA) play critical roles in the evolution of acute ischemic stroke (AIS). The present study investigates the mechanistic basis of platelet responsiveness in cryptogenic stroke compared with cardioembolic stroke. Platelet from 16 subjects, each from cryptogenic and cardioembolic stroke groups and 18 age-matched healthy controls were subjected to different investigations. Compared to healthy controls, platelet-monocyte and platelet-neutrophil interactions were significantly elevated in cryptogenic (2.7 and 2.1 times) and cardioembolic stroke (3.9 and 2.4 times). P-selectin expression on platelet surface was 1.89 and 2.59 times higher in cryptogenic and cardioembolic strokes, respectively, compared to healthy control. Cell population with [Ca2+i] in either stroke group was significantly outnumbered (by 83% and 72%, respectively, in cryptogenic and cardioembolic stroke) in comparison to healthy controls. Noteworthy, TEG experiment revealed that the cryptogenic stroke exhibited significant decline in Reaction Time (R) and amplitude of 20 mm (K) (by 32% and 33%, respectively) while thrombin burst (α-angle) was augmented by 12%, which reflected substantial boost in thrombus formation in cryptogenic stroke. Although TEG analysis reveals a state of hypercoagulability in patients with cryptogenic stroke. However, platelets from both stroke subtypes switch to a ‘hyperactive’ phenotype.

Background Spontaneous intracerebral hemorrhage (SICH) accounts for 7.5%-30% of all strokes and carries higher morbidity and mortality. Raised blood urea nitrogen and creatinine ratio (BUNR) is a marker of dehydration and related to poor outcome in stroke patients. However, the ratio varies between 15 and 80 in different studies. The aim of the present study was to assess BUNR as an independent predictor of mortality and its sensitivity and specificity in predicting outcome in the SICH population. Materials and Methods Patients above the age of 18 years with SICH who were admitted in the Department of Neurology at Sir Sunderlal Hospital, Banaras Hindu University between January 2018 and July 2020 were enrolled in the study and prospectively followed up. Demographic, clinical, radiological, and outcome parameters were recorded. Results A total of 217 patients were included. Of these, 137 (63%) were males. Seventy-one patients died during the initial 30 days. Number of patients with intraventricular hemorrhage (IVH; P = 0.003), higher mean intracerebral hemorrhage (ICH) volume (P < 0.001) and midline shift (P = 0.021), and poor Glasgow Coma Scale (GCS) score (<9) (P = 0.040) was more in the group which did not survive. Mean level of urea was significantly lower among survivors than in those who died (P = 0.001). BUNR was also significantly higher in those who died than in those who survived (P = 0.001). BUNR with a cutoff value of 39.17 was significantly associated with mortality at 30 days with a sensitivity and specificity of 61.97% and 62.33%, respectively. On performing two different multivariable logistic studies, it was found that model B with BUNR ratio as a predictor of mortality out performed model A (without BUNR). Conclusions The study showed that SICH was associated with significant mortality. Independent predictors of death at 30 days were lower GCS on admission, larger hematoma volume, and BUNR of more than 39.17.