Association Between HLA Genotype and Cutaneous Adverse Reactions to Antiepileptic Drugs Among Epilepsy Patients in Northwest China

Mapping of spastic muscle activity after stroke: difference between passive stretch and active contraction

BACKGROUND

Investigating the spatial distribution of muscle activity would facilitate understanding the underlying mechanism of spasticity. The purpose of this study is to investigate the characteristics of spastic muscles during passive stretch and active contraction by high-density surface electromyography (HD-sEMG).

METHODS

Fourteen spastic hemiparetic subjects and ten healthy subjects were recruited. The biceps brachii (BB) muscle activity of each subject was recorded by HD-sEMG during passive stretch at four stretch velocities (10, 60, 120, 180˚/s) and active contraction at three submaximal contraction levels (20, 50, 80%MVC). The intensity and spatial distribution of the BB activity were compared by the means of two-way analysis of variance, independent sample t-test, and paired sample t-test.

RESULTS

Compared with healthy subjects, spastic hemiparetic subjects showed significantly higher intensity with velocity-dependent heterogeneous activation during passive stretch and more lateral and proximal activation distribution during active contraction. In addition, spastic hemiparetic subjects displayed almost non-overlapping activation areas during passive stretch and active contraction. The activation distribution of passive stretch was more distal when compared with the active contraction.

CONCLUSIONS

These alterations of the BB activity could be the consequence of deficits in the descending central control after stroke. The complementary spatial distribution of spastic BB activity reflected their opposite motor units (MUs) recruitment patterns between passive stretch and active contraction. This HD-sEMG study provides new neurophysiological evidence for the spatial relationship of spastic BB activity between passive stretch and active contraction, advancing our knowledge on the mechanism of spasticity.

TRIAL REGISTRATION

ChiCTR2000032245.

Effects of robotic rehabilitation using the Erigo® device on patients with neurological injury: a systematic review and meta-analysis of randomized clinical trials

OBJECTIVE

To systematically review the effects of robotic rehabilitation with the Erigo® device on patients with neurological injury on safety, spasticity, muscle strength, functionality, gait/balance, and changes in the level of consciousness.

METHODS

MEDLINE, SciELO, EMBASE, The Cochrane Library - CENTRAL and PEDro databases were consulted without the restriction of date and language. Randomized controlled trials that evaluated the robotic rehabilitation and compared it to conventional or placebo therapy, isolated or in association with other therapy, were selected. Studies in which the treatment time was less than 10 sessions were excluded. The risk of bias was assessed with the use of the RoB 2.0 tool.

RESULTS

Nine studies were included, totaling 347 patients. The robotic rehabilitation performed by the Erigo® device proved to be safe for neurological patients. The meta-analysis showed an improvement for spasticity (MD = 0.29; 95% CI = -0.49 to -0.08; I2 = 0%), but there was no significant increase in muscle strength in patients with stroke (MD = 0.25; CI 95% = -0.22 to -0,71; I2 = 0%). Erigo® showed inconclusive effects on functionality, gait/balance and level of consciousness in patients with severe acquired brain injury and vegetative or minimally conscious state. All studies present some concerns for the risk of bias.

CONCLUSION

Erigo® as a robotic rehabilitation strategy is safe for patients with acquired brain injury and appears to reduce spasticity in patients with stroke. The effects on muscle strength, functionality, gait and balance and level of consciousness remain uncertain and the methodological quality of the clinical trials included in this review is limited.

Environmental Enrichment in Stroke Research: an Update

Environmental enrichment (EE) refers to different forms of stimulation, where the environment is designed to improve the levels of sensory, cognitive, and motor stimuli, inducing stroke recovery in animal models. Stroke is a leading cause of mortality and neurological disability among older adults, hence the importance of developing strategies to improve recovery for such patients. This review provides an update on recent findings, compiling information regarding the parameters affected by EE exposure in both preclinical and clinical studies. During stroke recovery, EE exposure has been shown to improve both the cognitive and locomotor aspects, inducing important neuroplastic alterations, increased angiogenesis and neurogenesis, and modified gene expression, among other effects. There is a need for further research in this field, particularly in those aspects where the evidence is inconclusive. Moreover, it is necessary refine and adapt the EE paradigms for application in human patients.

From pre-processing to advanced dynamic modeling of pupil data

The pupil of the eye provides a rich source of information for cognitive scientists, as it can index a variety of bodily states (e.g., arousal, fatigue) and cognitive processes (e.g., attention, decision-making). As pupillometry becomes a more accessible and popular methodology, researchers have proposed a variety of techniques for analyzing pupil data. Here, we focus on time series-based, signal-to-signal approaches that enable one to relate dynamic changes in pupil size over time with dynamic changes in a stimulus time series, continuous behavioral outcome measures, or other participants' pupil traces. We first introduce pupillometry, its neural underpinnings, and the relation between pupil measurements and other oculomotor behaviors (e.g., blinks, saccades), to stress the importance of understanding what is being measured and what can be inferred from changes in pupillary activity. Next, we discuss possible pre-processing steps, and the contexts in which they may be necessary. Finally, we turn to signal-to-signal analytic techniques, including regression-based approaches, dynamic time-warping, phase clustering, detrended fluctuation analysis, and recurrence quantification analysis. Assumptions of these techniques, and examples of the scientific questions each can address, are outlined, with references to key papers and software packages. Additionally, we provide a detailed code tutorial that steps through the key examples and figures in this paper. Ultimately, we contend that the insights gained from pupillometry are constrained by the analysis techniques used, and that signal-to-signal approaches offer a means to generate novel scientific insights by taking into account understudied spectro-temporal relationships between the pupil signal and other signals of interest.

Robotic exoskeleton embodiment in post-stroke hemiparetic patients: an experimental study about the integration of the assistance provided by the REFLEX knee exoskeleton

Hemiparetic gait is the most common motor-disorder after stroke and, in spite of rehabilitation efforts, it is persistent in 50% of community dwelling stroke-survivors. Robotic exoskeletons have been proposed as assistive devices to support impaired joints. An example of these devices is the REFLEX knee exoskeleton, which assists the gait of hemiparetic subjects and whose action seems to be properly embodied by stroke survivors, who were able to adapt the motion of their non-assisted limbs and, therefore, reduce their compensation mechanisms. This paper presents an experimental validation carried out to deepen into the effects of REFLEX's assistance in hemiparetic subjects. Special attention was paid to the effect produced in the muscular activity as a metric to evaluate the embodiment of this technology. Significant differences were obtained at the subject level due to the assistance; however, the high dispersion of the measured outcomes avoided extracting global effects at the group level. These results highlight the need of individually tailoring the action of the robot to the individual needs of each patient to maximize the beneficial outcomes. Extra research effort should be done to elucidate the neural mechanisms involved in the embodiment of external devices by stroke survivors.

Role of multi-targeted bioactive natural molecules and their derivatives in the treatment of Alzheimer's disease: an insight into structure-activity relationship

Alzheimer's disease (AD) is a complex neurodegenerative disorder involving cognitive dysfunction like short-term memory and behavioral changes as the disease progresses due to other unaltered physiological factors. The solution for this problem is Multi-targeted Drugs (MTDs), which can affect multiple determinants to realize the multifunctional effects. Acetylcholinesterase (AChE) inhibitors donepezil, rivastigmine, galantamine, and N-methyl-D-aspartate (NMDA) receptor antagonist memantine are FDA-approved drugs used to treat AD symptomatically. The key objective of this review is to understand multitargeted bioactive natural molecules that could be considered as leads for further development as effective drugs for treating AD, along with understanding its pharmacology and structure-activity relationship (SAR). Understanding the molecular mechanism of the AD pathophysiology, the role of existing drugs, treatment of AD via amyloid beta (Aβ) plaque, and neurofibrillary tangle (NFT) inhibition by natural bioactive molecules were also discussed in the review. The current quest and recent advancements with natural bioactive compounds like physostigmine, resveratrol, curcumin, and catechins, along with the study of in silico SAR, were reported in the present study. This review summarises the structural properties required for bioactive natural molecules to show anti-Alzheimer's activity by emphasizing on SAR of several bioactive natural molecules targeting various AD pathologies, their key molecular interactions that are critical for target specificity, their role as multitargeted ligands, used with adjunctive therapy for AD followed by related US patents granted recently. This article highlights the significance of the structural features of natural bioactive molecules in the treatment of AD and establishes a connection between them.Communicated by Ramaswamy H. Sarma.

Visual imagery deficits in posterior cortical atrophy

Visual imagery has a close overlapping relationship with visual perception. Posterior cortical atrophy (PCA) is a neurodegenerative syndrome marked by early impairments in visuospatial processing and visual object recognition. We asked whether PCA would therefore also be marked by deficits in visual imagery, tested using objective forced-choice questionnaires, and whether imagery deficits would be selective for certain properties. We recruited four patients with PCA and a patient with integrative visual agnosia due to bilateral occipitotemporal strokes for comparison. We administered a test battery probing imagery for object shape, size, colour lightness, hue, upper-case letters, lower-case letters, word shape, letter construction, and faces. All subjects showed significant impairments in visual imagery, with imagery for lower-case letters most likely to be spared. We conclude that PCA subjects can show severe deficits in visual imagery. Further work is needed to establish how frequently this occurs and how early it can be found.

LoveYourBrain Mindset: Feasibility, Acceptability, Usability, and Effectiveness of an Online Yoga, Mindfulness, and Psychoeducation Intervention for People with Traumatic Brain Injury

OBJECTIVE

Despite the benefits of yoga, mindfulness, and psychoeducation after traumatic brain injury (TBI), no online programming exists. This study investigated the feasibility, acceptability, usability, and effectiveness of the LoveYourBrain Mindset online program for people with TBI.

RESEARCH DESIGN

Pre-post, retrospective intervention.

METHODS

LoveYourBrain Mindset is a six-week online yoga, mindfulness, and psychoeducation program with weekly interactive Zoom classes and prerecorded mindfulness tools. Two interactive class types (45-minute group discussion, 75-minute group discussion and gentle yoga) are offered to enhance accessibility. People were eligible if they experienced TBI, were a caregiver, or clinician; ≥15 years old; and capable of gentle exercise and group discussion. We analyzed attendance, program ratings, mindfulness tools utilization, and pre/post-intervention differences in QOL, resilience, emotional/behavioral dysregulation, cognition, and positive affect/well-being.

RESULTS

Overall, 1539 individuals signed up for LoveYourBrain Mindset with 1093 (71.02%) participating in one/more classes. The mean program rating was 9.09 (SD=1.28). Majority (62.99%) used the mindfulness tools each week. Multiple linear regression models controlling for age, TBI severity, and gender indicated significant improvements in QOL, resilience, emotional/behavioral dysregulation, cognition, and positive affect/well-being (p<0.001).

CONCLUSION

LoveYourBrain Mindset is feasible, acceptable, usable, and may improve outcomes among people with TBI.

Predictors of Social Participation Outcome after Traumatic Brain Injury Differ According to Rehabilitation Pathways

Social participation (SP) is one of many objectives in the rehabilitation of patients with traumatic brain injury (TBI). Studies on predictors of SP specific to post-acute universally accessible specialized rehabilitation pathways following TBI are scarce. Our objectives were to: 1) characterize SP, as well as a set of pre-injury, injury-related, and post-injury variables in individuals participating in inpatient-outpatient or outpatient rehabilitation pathways within a universally accessible and organized trauma continuum of care; and 2) examine the ability of pre-injury, injury-related, and post-injury variables in predicting SP outcome after TBI according to rehabilitation path. Participants (N = 372) were adults admitted to an inpatient-outpatient rehabilitation pathway or an outpatient rehabilitation pathway after sustaining a TBI between 2016 and 2020, and for whom Mayo-Portland Adaptability Intentory-4 (MPAI-4) outcomes were prospectively obtained at the start and end of rehabilitation. Additional data was collected from medical files. For both rehabilitation pathways, predicted SP outcome was MPAI-4 Participation score at discharge from outpatient rehabilitation. Multiple regression models investigated the predictive value of each variable for SP outcome, separately for each care pathway. Main findings show that for the inpatient-outpatient sample, three variables (education years, MPAI-4 Ability and Adjustment scores at rehabilitation intake) significantly predicted SP outcome, with the regression model accounting for 49% of the variance. For the outpatient sample, five variables (pre-morbid hypertension and mental health diagnosis, total indirect rehabilitation hours received, MPAI-4 Abilities and Adjustment scores at rehabilitation intake) significantly predicted SP outcome, with the regression model accounting for 47% of the variance. In conclusion, different pre-morbid and post-injury variables are involved in predicting SP, depending on the rehabilitation path followed. The predictive value of those variables could help clinicians identify patients more likely of showing poorer SP at discharge and who may require additional or different interventions.

Bioimaging Probes Based on Magneto-Fluorescent Nanoparticles

Novel nanomaterials are of interest in biology, medicine, and imaging applications. Multimodal fluorescent-magnetic nanoparticles demand special attention because they have the potential to be employed as diagnostic and medication-delivery tools, which, in turn, might make it easier to diagnose and treat cancer, as well as a wide variety of other disorders. The most recent advancements in the development of magneto-fluorescent nanocomposites and their applications in the biomedical field are the primary focus of this review. We describe the most current developments in synthetic methodologies and methods for the fabrication of magneto-fluorescent nanocomposites. The primary applications of multimodal magneto-fluorescent nanoparticles in biomedicine, including biological imaging, cancer treatment, and drug administration, are covered in this article, and an overview of the future possibilities for these technologies is provided.

Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG

Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.

Atrial Fibrillation, thromboembolic risk, and the potential role of the natriuretic peptides, a focus on BNP and NT-proBNP - A narrative review

Atrial fibrillation (AF) is one of the most commonly encountered arrythmia in clinical practice. AF itself can be driven by genetic predisposition, ectopic electrical activity, and abnormal atrial tissue substrates. Often there is no single etiological mechanism, but rather a combination of factors that feed back to remodel and worsen tissue substrate, "AF begets AF". The clinical consequences of AF can often include emboli, heart failure, and early mortality. The classical AF cardioembolic (CE) concept requires thrombus formation in the left atrial appendage, with subsequent embolization. The temporal dissociation between AF occurrence and CE events has thrown doubt on AF as the driver of this mechanism. Instead, there has been a resurgence of the "atrial cardiomyopathy" (ACM) concept. An ACM is proposed as a potential mechanism of embolic disease through promotion of prothrombotic mechanisms, with AF instead reflecting atrial disease severity. Regardless, AF has been implicated in 25% to 30% of cryptogenic strokes. Natriuretic peptide(NP)s have been shown to be elevated in AF, with higher levels of both NT-proBNP and BNP being predictive of incidental AF. NPs potentially reflect the atrial environment and could be used to identify an underlying ACM. Therefore, this narrative review examines this evidence and mechanisms that may underpin the role of NPs in identifying atrial dysfunction, with focus on both, BNP and NTproBNP. We explore their potential role in the prediction and screening for both, ACM and AF. Moreover, we compare both NPs directly to ascertain a superior biomarker.

Exploring biomarkers of processing speed and executive function: The role of the anterior thalamic radiations

INTRODUCTION

Processing speed and executive function are often impaired after stroke and in typical aging. However, there are no reliable neurological markers of these cognitive impairments. The trail making test (TMT) is a common index of processing speed and executive function. Here, we tested candidate MRI markers of TMT performance in a cohort of older adults and individuals with chronic stroke.

METHODS

In 61 older adults and 32 individuals with chronic stroke, we indexed white matter structure with region-specific lesion load (of white matter hyperintensities (WMHs) and stroke lesions) and diffusion tensor imaging (DTI) from four regions related to TMT performance: the anterior thalamic radiations (ATR), superior longitudinal fasciculus (SLF), forceps minor, and cholinergic pathways. Regression modelling was used to identify the marker(s) that explained the most variance in TMT performance.

RESULTS

DTI metrics of the ATR related to processing speed in both the older adult (TMT A: β = -3.431, p < 0.001) and chronic stroke (TMT A: β = 11.282, p < 0.001) groups. In the chronic stroke group executive function was best predicted by a combination of ATR and forceps minor DTI metrics (TMT B: _adjustedR² = 0.438, p < 0.001); no significant predictors of executive function (TMT B) emerged in the older adult group. No imaging metrics related to set shifting (TMT B-A). Regional DTI metrics predicted TMT performance above and beyond whole-brain stroke and WMH volumes and removing whole-brain lesion volumes improved model fits.

CONCLUSIONS

In this comprehensive assessment of candidate imaging markers, we demonstrate an association between ATR microstructure and processing speed and executive function performance. Regional DTI metrics provided better predictors of cognitive performance than whole-brain lesion volumes or regional lesion load, emphasizing the importance of lesion location in understanding cognition. We propose ATR DTI metrics as novel candidate imaging biomarker of post-stroke cognitive impairment.

Ameliorating effect of ketogenic diet on acute status epilepticus: Insights into biochemical and histological changes in rat hippocampus

This study aimed to evaluate the potential neuroprotective effects of ketogenic diet (KD) against the neuronal disruptions induced by SE in lithium-pilocarpine rat model of status epilepticus (SE). Four groups of female rats include; groups I and III received standard diet and groups II and IV received KD for 3 weeks. Groups I and II were left untreated, while groups III and IV were injected with LiCl (127 mg/kg, i.p.) followed by pilocarpine HCl (10 mg/kg, i.p.) 18-24 h later, repeatedly, till induction of SE. 72 h post-SE, KD effectively ameliorated the balance between excitatory (glutamate) and inhibitory (GABA) neurotransmitters and the oxidative stress indices, increased adenine nucleotides and decreased immunoreactivity of iNOS, TNFα, glial fibrillary acidic protein, and synaptophysin. Thiswas in association with improvement in inflammatory response and neuronal tissue characteristics in hippocampus of SE rats. Histological changes showed preservation of neuronal integrity. These findings highlight the protective effects of KD in the acute phase post-SE via ameliorating biochemical and histological changes involved. PRACTICAL APPLICATIONS: Epilepsy is the fourth most common neurological disorder that requires lifelong treatment. It stigmatizes patients and their families. The use of the ketogenic diet (KD) as a therapy for epilepsy developed from observations that fasting could reduce seizures. From 1920s, the KD was a common epilepsy treatment until it was gradually superseded by anticonvulsant drugs so that by the 1980s it was rarely used. However, there has been a resurgence of interest and usage of the KD for epilepsy since the turn of the century. Despite its long history, the mechanisms by which KD exhibits its anti-seizure action are not fully understood. Our study aims to identify the mechanism of KD which may help further studies to achieve the same benefits with a drug or supplement to overcome its unpalatability and gastrointestinal side effects.

A low-dimensional representation of arm movements and hand grip forces in post-stroke individuals

Characterizing post-stroke impairments in the sensorimotor control of arm and hand is essential to better understand altered mechanisms of movement generation. Herein, we used a decomposition algorithm to characterize impairments in end-effector velocity and hand grip force data collected from an instrumented functional task in 83 healthy control and 27 chronic post-stroke individuals with mild-to-moderate impairments. According to kinematic and kinetic raw data, post-stroke individuals showed reduced functional performance during all task phases. After applying the decomposition algorithm, we observed that the behavioural data from healthy controls relies on a low-dimensional representation and demonstrated that this representation is mostly preserved post-stroke. Further, it emerged that reduced functional performance post-stroke correlates to an abnormal variance distribution of the behavioural representation, except when reducing hand grip forces. This suggests that the behavioural repertoire in these post-stroke individuals is mostly preserved, thereby pointing towards therapeutic strategies that optimize movement quality and the reduction of grip forces to improve performance of daily life activities post-stroke.

Evolution of HLA-B Pharmacogenomics and the Importance of PGx Data Integration in Health Care System: A 10 Years Retrospective Study in Thailand

Background: The HLA-B is the most polymorphic gene, play a crucial role in drug-induced hypersensitivity reactions. There is a lot of evidence associating several risk alleles to life-threatening adverse drug reactions, and a few of them have been approved as valid biomarkers for predicting life-threatening hypersensitivity reactions.

Objectives: The objective of this present study is to present the progression of HLA-B pharmacogenomics (PGx) testing in the Thai population during a 10‐year period, from 2011 to 2020.

Methods: This was a retrospective observational cohort study conducted at the Faculty of Medicine Ramathibodi Hospital. Overall, 13,985 eligible patients who were tested for HLA-B risk alleles between periods of 2011–2020 at the study site were included in this study.

Results: The HLA PGx testing has been increasing year by year tremendously, 94 HLA-B testing was done in 2011; this has been raised to 2,880 in 2020. Carbamazepine (n = 4,069, 33%), allopurinol (n = 4,675, 38%), and abacavir (n = 3,246, 26%) were the most common drugs for which the HLA-B genotyping was performed. HLA-B*13:01, HLA-B*15:02 and HLA-B*58:01 are highly frequent, HLA-B*51:01 and HLA-B*57:01 are moderately frequent alleles that are being associated with drug induced hypersensitivity. HLA-B*59:01 and HLA-B*38:01 theses alleles are rare but has been reported with drug induced toxicity. Most of the samples were from state hospital (50%), 36% from private clinical laboratories and 14% from private hospitals.

Conclusion: According to this study, HLA-B PGx testing is increasing substantially in Thailand year after year. The advancement of research in this field, increased physician awareness of PGx, and government and insurance scheme reimbursement assistance could all be factors. Incorporating PGx data, along with other clinical and non-clinical data, into clinical decision support systems (CDS) and national formularies, on the other hand, would assist prescribers in prioritizing therapy for their patients. This will also aid in the prediction and prevention of serious adverse drug reactions.

Pre-Injury headache and post-traumatic headache in patients with mild traumatic brain injury: neuropsychological, psychiatric, and post-concussion symptom outcomes

PRIMARY OBJECTIVE

Headache, both before and after injury, has been associated with worse outcome following mild traumatic brain injury (MTBI). This study examined whether three MTBI patient groups - no headache (reported no pre-/post-injury headache), pre-injury headache (reported pre-injury headache, nearly all of whom also reported post-injury headache), and post-traumatic headache only (denied pre-injury headache and reported post-injury headache) - differed in acute-to-subacute outcomes.

RESEARCH DESIGN

Cross-sectional observational study.

METHODS AND PROCEDURES

Patients within 21 days of a MTBI (n = 291) completed neuropsychological tests and questionnaires evaluating depression, anxiety, and post-concussion symptoms.

MAIN OUTCOMES AND RESULTS

Neuropsychological test performances did not differ between headache groups. Participants with pre-injury headache and participants with post-traumatic headache only reported greater change in self-reported physical and cognitive symptoms than participants with no headache. Participants with pre-injury headache reported worse post-injury anxiety symptoms than participants with post-traumatic headache only.

CONCLUSIONS

The pre-injury headache and post-traumatic headache only groups did not meaningfully differ in outcome within 21 days of MTBI, but they had worse physical and cognitive symptoms than participants with no headache. Future research should assess whether differences in outcome emerge further from injury and whether specific headache subtypes are differentially associated with outcome.

Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis

Machine learning approaches in diagnosis and prognosis of multiple sclerosis (MS) were analysed using retinal nerve fiber layer (RNFL) thickness, measured by optical coherence tomography (OCT). A cross-sectional study (72 MS patients and 30 healthy controls) was used for diagnosis. These 72 MS patients were involved in a 10-year longitudinal follow-up study for prognostic purposes. Structural measurements of RNFL thickness were performed using different Spectralis OCT protocols: fast macular thickness protocol to measure macular RNFL, and fast RNFL thickness protocol and fast RNFL-N thickness protocol to measure peripapillary RNFL. Binary classifiers such as multiple linear regression (MLR), support vector machines (SVM), decision tree (DT), k-nearest neighbours (k-NN), Naïve Bayes (NB), ensemble classifier (EC) and long short-term memory (LSTM) recurrent neural network were tested. For MS diagnosis, the best acquisition protocol was fast macular thickness protocol using k-NN (accuracy: 95.8%; sensitivity: 94.4%; specificity: 97.2%; precision: 97.1%; AUC: 0.958). For MS prognosis, our model with a 3-year follow up to predict disability progression 8 years later was the best predictive model. DT performed best for fast macular thickness protocol (accuracy: 91.3%; sensitivity: 90.0%; specificity: 92.5%; precision: 92.3%; AUC: 0.913) and SVM for fast RNFL-N thickness protocol (accuracy: 91.3%; sensitivity: 87.5%; specificity: 95.0%; precision: 94.6%; AUC: 0.913). This work concludes that measurements of RNFL thickness obtained with Spectralis OCT have a good ability to diagnose MS and to predict disability progression in MS patients. This machine learning approach would help clinicians to have valuable information.

Late-life depression accentuates cognitive weaknesses in older adults with small vessel disease

Neuroimaging features of small vessel disease (SVD) are highly prevalent in older adulthood and associated with significant variability in clinical symptoms, yet the factors predicting these symptom disparities are poorly understood. We employed a novel metric of SVD, peak width of skeletonized mean diffusivity (PSMD), to elucidate the relationship of late-life depression (LLD) to the cognitive presentation of vascular pathology. A total of 109 older adults without a diagnosis of a neurocognitive disorder were enrolled in the study; 44 with major depressive disorder and 65 age-matched controls. Subjects completed neuropsychological testing and magnetic resonance imaging including FLAIR and diffusion tensor imaging sequences, from which white matter hyperintensity volume and diffusion metrics (fractional anisotropy, mean diffusivity, PSMD) were quantified. In hierarchical models, the relationship between vascular burden and cognitive performance varied as a function of diagnostic status, such that the negative association between PSMD and processing speed was significantly stronger in participants with LLD compared to controls. Greater PSMD also predicted poorer performance on delayed memory and executive function tasks specifically among those with LLD, while there were no associations between PSMD and task performance among controls. PSMD outperformed conventional SVD and diffusion markers in predicting cognitive performance and dysexecutive behaviors in participants with LLD. These data suggest that LLD may confer a vulnerability to the cognitive manifestations of white matter abnormalities in older adulthood. PSMD, a novel biomarker of diffuse microstructural changes in SVD, may be a more sensitive marker of subtle cognitive deficits stemming from vascular pathology in LLD.

From corpora amylacea to wasteosomes: History and perspectives

Corpora amylacea (CA) have been described in several human organs and have been associated with ageing and several pathological conditions. Although they were first discovered two centuries ago, their function and significance have not yet been identified. Here, we provide a chronological summary of the findings on CA in various organs and identify their similarities. After collecting and integrating these findings, we propose to consider CA as waste containers created by specific cells, which sequester waste products and foreign products, and assemble them within a glycan structure. The containers are then secreted into the external medium or interstitial spaces, in this latter case subsequently being phagocytosed by macrophages. This proposal explains, among others, why CA are so varied in content, why only some of them contain fibrillary amyloid proteins, why all of them contain glycan structures, why some of them contain neo-epitopes and are phagocytosed, and why they can be intracellular or extracellular structures. Lastly, in order to avoid the ambiguity of the term amyloid (which can indicate starch-like structures but also insoluble fibrillary proteins), we propose renaming CA as "wasteosomes", emphasising the waste products they entrap rather than their misleading amyloid properties.

The clinical features, treatment and outcomes of 33 children from Northwestern China with Anti-N-methyl-D-aspartate receptor encephalitis

OBJECTIVES

We analyzed the clinical features and outcomes of children with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis in Northwestern China.

METHODS

We retrospectively recruited 33 pediatric patients with anti-NMDAR encephalitis in Northwestern China from December 2013 to April 2020. The demographics, clinical features, treatments, and outcomes were reviewed.

RESULTS

33 patients with anti-NMDAR encephalitis were enrolled in this study (a median age of 6.8 years, 20 females and 13 males). The initial symptoms included seizures (42.4%), psychiatric symptoms (39.4%), speech dysfunction (12.1%), and paralysis (6.1%). During the course of the disease, 31 patients (93.9%) presented with psychiatric symptoms, 29 patients (87.9%) presented with speech dysfunction, 25 patients (75.8%) presented with movement disorders and 24 patients (72.7%) presented with sleep disorders followed by seizures, consciousness disturbance, autonomic nervous dysfunction, paralysis, and hypoventilation. 12 patients (36.4%) had abnormal cerebrospinal fluid (CSF) findings, 10 patients (30.3%) exhibited abnormal brain magnetic resonance imaging (MRI) results, and 29 patients (87.9%) showed abnormal Electroencephalography (EEG) findings. None of the patients had tumors. All patients received first-line immunotherapy and 8 patients both received first and second-line immunotherapy. 30 of the 33 patients achieved good outcomes (score on the modified Rankin Scale [mRS] of 0-2), whilst the other 3 patients had poor outcomes (mRS score of 3-6).

CONCLUSIONS

Patients with higher CSF anti-NMDAR body titer were more likely to develop sleep disorders, consciousness disturbances and more severe disease states.

Patients' priorities and expectations on an EU registry for rare bone and mineral conditions

BACKGROUND

Understanding the natural history of rare bone and mineral conditions is essential for improving clinical practice and the development of new diagnostics and therapeutics. Recruitment and long-term participation in registries are key challenges for researchers.

METHODS

To understand the user needs, the European Reference Network on Rare Bone Diseases (ERN BOND) and European Patient Advocacy Groups developed and implemented a multinational survey about the patient's preferred database content and functionality through an iterative consensus process. The survey was disseminated by national and international patient groups and healthcare professionals. The findings were analysed using descriptive statistics and multivariate regression.

RESULTS

There were 493 eligible responses from 378 adults, 15 children and 100 parents, guardians or carers (PGC) across 22 rare bone and mineral conditions. Osteogenesis imperfecta constituted 53.4% of responses. Contents related to improving treatment and medical services scored the highest and contents about anxiety and socializing scored less highly. Additional content was recommended by 205 respondents. Respondents preferred data entry by their Healthcare Provider (HCP). However, less than 50% of adults received followup from their specialist HCP at least annually and 29% were followed up as needed.

CONCLUSIONS

This survey of individuals, their family, guardians and carers has prioritised the key components for an EU-based rare bone and mineral condition research database. The survey highlights issues around collecting psychosocial impacts as well as measures of HCP trust. The survey demonstrated that using only specialist centre visits for data collection, while preferred by patients, will miss a substantial number of individuals, limiting generalisability. Combined HCP and patient platforms will be required to collect representative and complete natural history data for this patient group.

Autoimmune diseases of the brain, imaging and clinical review

There is an extensive spectrum of autoimmune entities that can involve the central nervous system, which has expanded with the emergence of new imaging modalities and several clinicopathologic entities. Clinical presentation is usually non-specific, and imaging has a critical role in the workup of these diseases. Immune-mediated diseases of the brain are not common in daily practice for radiologists and, except for a few of them such as multiple sclerosis, there is a vague understanding about differentiating them from each other based on the radiological findings. In this review, we aim to provide a practical diagnostic approach based on the unique radiological findings for each disease. We hope our diagnostic approach will help radiologists expand their basic understanding of the discussed disease entities and narrow the differential diagnosis in specific clinical scenarios. An understanding of unique imaging features of these disorders, along with laboratory evaluation, may enable clinicians to decrease the need for tissue biopsy.

Longitudinal Correlation Analysis for Decoding Multi-modal Brain Development

Starting from childhood, the human brain restructures and rewires throughout life. Characterizing such complex brain development requires effective analysis of longitudinal and multi-modal neuroimaging data. Here, we propose such an analysis approach named Longitudinal Correlation Analysis (LCA). LCA couples the data of two modalities by first reducing the input from each modality to a latent representation based on autoencoders. A self-supervised strategy then relates the two latent spaces by jointly disentangling two directions, one in each space, such that the longitudinal changes in latent representations along those directions are maximally correlated between modalities. We applied LCA to analyze the longitudinal T1-weighted and diffusion-weighted MRIs of 679 youths from the National Consortium on Alcohol and Neurodevelopment in Adolescence. Unlike existing approaches that focus on either cross-sectional or single-modal modeling, LCA successfully unraveled coupled macrostructural and microstructural brain development from morphological and diffusivity features extracted from the data. A retesting of LCA on raw 3D image volumes of those subjects successfully replicated the findings from the feature-based analysis. Lastly, the developmental effects revealed by LCA were inline with the current understanding of maturational patterns of the adolescent brain.

Millet shell polyphenols prevent atherosclerosis by protecting the gut barrier and remodeling the gut microbiota in ApoE-/- mice

Atherosclerosis, the major cause of cardiovascular disease, is a chronic inflammatory disease. The anti-inflammatory effect of certain polyphenols has been recognized. Active polyphenols were extracted from millet shells (MSPs), and their main components including 3-hydroxybenzylhydrazine, luteolin-3',7-diglucoside, N-acetyltyramine, p-coumaric acid, vanillin, sinapic acid, ferulic acid and isophorone exhibited the anti-atherosclerotic potential in vitro. To explore the anti-atherosclerotic activity of MSPs in vivo, a classic atherosclerosis model was constructed in ApoE^-/- mice fed with a high-fat diet. The results showed that MSPs effectively inhibited the development of atherosclerotic plaques in the aorta and reduced the levels of lipopolysaccharide (LPS) and inflammatory cytokines such as tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β). A further study found that the expression of tight junction proteins (occludin, zona occludens-1 and claudin1) was obviously up-regulated in the MSPs-treated group at the mRNA and protein levels. Interestingly, MSPs significantly changed the structure of gut microbiota in ApoE^-/- mice with a high-fat diet, which is characterized by the enriched Oscillospira and Ruminococcus, and the abridged Allobaculum at the genus level. Collectively, these results suggest that MSPs regulate the integrity of the gut barrier and the structure of the gut microbiota, ultimately inhibiting the development of atherosclerotic plaques. This study provides new insights into the potential cardiovascular protective effects induced by millet shell polyphenols.

Machine learning and big data provide crucial insight for future biomaterials discovery and research

Machine learning have been widely adopted in a variety of fields including engineering, science, and medicine revolutionizing how data is collected, used, and stored. Their implementation has led to a drastic increase in the number of computational models for the prediction of various numerical, categorical, or association events given input variables. We aim to examine recent advances in the use of machine learning when applied to the biomaterial field. Specifically, quantitative structure properties relationships offer the unique ability to correlate microscale molecular descriptors to larger macroscale material properties. These new models can be broken down further into four categories: regression, classification, association, and clustering. We examine recent approaches and new uses of machine learning in the three major categories of biomaterials: metals, polymers, and ceramics for rapid property prediction and trend identification. While current research is promising, limitations in the form of lack of standardized reporting and available databases complicates the implementation of described models. Herein, we hope to provide a snapshot of the current state of the field and a beginner's guide to navigating the intersection of biomaterials research and machine learning. STATEMENT OF SIGNIFICANCE: Machine learning and its methods have found a variety of uses beyond the field of computer science but have largely been neglected by those in realm of biomaterials. Through the use of more computational methods, biomaterials development can be expediated while reducing the need for standard trial and error methods. Within, we introduce four basic models that readers can potentially apply to their current research as well as current applications within the field. Furthermore, we hope that this article may act as a "call to action" for readers to realize and address the current lack of implementation within the biomaterials field.

Spectrum of anti-myelin oligodendrocyte glycoprotein antibody (MOG-Ab)-associated diseases: an Indian perspective

Myelin oligodendrocyte glycoprotein antibody (MOG-Ab) is involved in the pathogenesis of central nervous system (CNS) demyelination disorders. We aimed to explore the spectrum of MOG-Ab-associated diseases in eastern India. A single-center, prospective observational study was done over a period of 2 years in a tertiary care hospital of eastern India. Patients with CNS demyelination disorders who tested positive for MOG-Ab using live cell-based assay were included in the study; while, those with age less than 1 year, documented preexisting CNS structural lesions, developmental delays or diagnosed multiple sclerosis were excluded. Demographic profile, clinical spectrum, disease course, radiological features as well as response to treatment were analyzed among included patients. Twenty MOG-Ab-positive patients were included (M:F 1:1.85). The median age of symptom onset was 10.5 years. The median follow-up of patients was 13 months. Acute disseminated encephalomyelitis (ADEM) was the commonest presentation at first attack (55%), followed by optic neuritis (ON) (45%). Patients with ADEM had a significantly lower age at first attack (p = 0.025). Monophasic and relapsing disease courses were seen in 45% and 55% patients, respectively. While all patients with only ADEM had a monophasic course, 77.8% with ON had a relapsing course. Among patients who presented with isolated transverse myelitis, 75% had a monophasic course and all had disease confined to the spinal cord. Good response to corticosteroids was seen in majority of participants. Second-line drugs were needed in 55% patients, rituximab being the commonest second-line agent used. 35% patients had significant disability (EDSS > 4) at last follow-up. MOG-Ab-associated diseases have diverse clinical phenotypes characterized by age-dependent pattern-specific courses.

The benefit of the diffusion kurtosis imaging in presurgical evaluation in patients with focal MR-negative epilepsy

The effectivity of diffusion-weighted MRI methods in detecting the epileptogenic zone (EZ) was tested. Patients with refractory epilepsy (N=25) who subsequently underwent resective surgery were recruited. First, the extent of white matter (WM) asymmetry from mean kurtosis (MK) was calculated in order to detect the lobe with the strongest impairment. Second, a newly developed metric was used, reflecting a selection of brain areas with concurrently increased mean Diffusivity, reduced fractional Anisotropy, and reduced mean Kurtosis (iDrArK). A two-step EZ detection was performed as (1) lobe-specific detection, (2) iDrArK voxel-wise detection (with a possible lobe-specific restriction if the result of the first step was significant in a given subject). The method results were compared with the surgery resection zones. From the whole cohort (N=25), the numbers of patients with significant results were: 10 patients in lobe detection and 9 patients in EZ detection. From these subsets of patients with significant results, the impaired lobe was successfully detected with 100% accuracy; the EZ was successfully detected with 89% accuracy. The detection of the EZ using iDrArK was substantially more successful when compared with solo diffusional parameters (or their pairwise combinations). For a subgroup with significant results from step one (N=10), iDrArK without lobe restriction achieved 37.5% accuracy; lobe-restricted iDrArK achieved 100% accuracy. The study shows the plausibility of MK for detecting widespread WM changes and the benefit of combining different diffusional voxel-wise parameters.

Pathogenic Factors Identification of Brain Imaging and Gene in Late Mild Cognitive Impairment

Mild cognitive impairment (MCI) is a dangerous signal of severe cognitive decline. It can be separated into two steps: early MCI (EMCI) and late MCI (LMCI). As the post-state of MCI and pre-state of Alzheimer's disease (AD), LMCI receives insufficient attention in the field of brain science, causing the internal mechanism of LMCI has not been well understood. To better explore the focus and pathological mechanism of LMCI, a method called genetic evolved random forest (GERF) is applied. Resting functional magnetic resonance imaging (rfMRI) and gene data are obtained from 62 subjects (36 LMCI and 26 normal controls), and Pearson correlation analysis is adopted to perform the multimodal fusion of two types of data to construct fusion features. We identified pathogenic brain regions and genes that are highly related to LMCI using GERF and achieves a good effect. Compared with the normal control (NC) group, the abnormal brain regions of LMCI are PUT.L, PreCG.L, IFGtriang.R, REC.R, DCG.R, PoCG.L, and HES.L, and the pathogenic genes are FHIT, RF00019, FRMD4A, PTPRD, and RBFOX1. More importantly, most of these risk genes and abnormal brain regions have been confirmed to be related to AD and MCI in previous studies. In this study, we mapped them to LMCI with higher accuracies, so as to provide a more robust understanding of the physiological mechanism of MCI.

Stroke-induced immunosuppression: implications for the prevention and prediction of post-stroke infections

Stroke produces a powerful inflammatory cascade in the brain, but also a suppression of the peripheral immune system, which is also called stroke-induced immunosuppression (SIIS). The main processes that lead to SIIS are a shift from a lymphocyte phenotype T-helper (Th) 1 to a Th2 phenotype, a decrease of the lymphocyte counts and NK cells in the blood and spleen, and an impairment of the defense mechanisms of neutrophils and monocytes. The direct clinical consequence of SIIS in stroke patients is an increased susceptibility to stroke-associated infections, which is enhanced by clinical factors like dysphagia. Among these infections, stroke-associated pneumonia (SAP) is the one that accounts for the highest impact on stroke outcome, so research is focused on its early diagnosis and prevention. Biomarkers indicating modifications in SIIS pathways could have an important role in the early prediction of SAP, but currently, there are no individual biomarkers or panels of biomarkers that are accurate enough to be translated to clinical practice. Similarly, there is still no efficient therapy to prevent the onset of SAP, and clinical trials testing prophylactic antibiotic treatment and β-blockers have failed. However, local immunomodulation could open up a new research opportunity to find a preventive therapy for SAP. Recent studies have focused on the pulmonary immune changes that could be caused by stroke similarly to other acquired brain injuries. Some of the traits observed in animal models of stroke include lung edema and inflammation, as well as inflammation of the bronchoalveolar lavage fluid.

Going Beyond Saliency Maps: Training Deep Models to Interpret Deep Models

Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically rely on saliency maps to quantify the voxel-wise or feature-level importance for classification through partial derivatives. Despite providing some level of localization, these maps are not human-understandable from the neuroscience perspective as they often do not inform the specific type of morphological changes linked to the brain disorder. Inspired by the image-to-image translation scheme, we propose to train simulator networks to inject (or remove) patterns of the disease into a given MRI based on a warping operation, such that the classifier increases (or decreases) its confidence in labeling the simulated MRI as diseased. To increase the robustness of training, we propose to couple the two simulators into a unified model based on conditional convolution. We applied our approach to interpreting classifiers trained on a synthetic dataset and two neuroimaging datasets to visualize the effect of Alzheimer's disease and alcohol dependence. Compared to the saliency maps generated by baseline approaches, our simulations and visualizations based on the Jacobian determinants of the warping field reveal meaningful and understandable patterns related to the diseases.

Effects of combat sports on functional network connectivity in adolescents

PURPOSE

To explore the effects of combat sports on functional network connectivity (FNC) in healthy adolescents.

METHODS

Resting-state fMRI data were acquired from the combat sports (CS) group (n = 32) and non-athlete healthy control (HC) group (n = 29). Resting-state networks (RSNs) were obtained based on independent component analysis (ICA), and FNC analysis was performed. Then, the intra-network and inter-network connections were compared between the two groups.

RESULTS

Compared with the HC group, the CS group demonstrated increased intra-network FC within the sensorimotor network (SMN), visual network (VIN), and cerebellum network (P < 0.01, FDR correction). Besides, decreased inter-network FC was found in the SMN-VIN, SMN-auditory network, SMN-default mode network, attention network (AN)-VIN, and AN-executive control network connections (P < 0.01, FDR correction).

CONCLUSION

This study showed a complex relationship between combat sports and FNC in adolescents. The observed FNC patterns in the CS group may reflect training-related brain network optimization, early signs of subclinical brain damage, or preexisting differences. The extensive effects of combat sports on FNC in adolescents could expand our understanding of neuromodulatory mechanisms.

Dystonia and leveraging oral pharmacotherapy

Dystonia is a clinically diverse disorder, characterized by sustained or intermittent muscle contractions causing abnormal and often repetitive movements and/or postures. Accurate clinical diagnosis is tantamount to effective dystonia management. Current guidelines in the treatments of dystonia, including oral therapy, are prescribed to improve symptoms and to restore functional capacity. Identifying treatable causes from co-existing phenomenologies is relevant to provide the most optimal and disease-specific medications. In other forms of dystonia, genetic factors may affect outcome. Moreover, proper selection of patients, early initiation of medications and customized drug titration are keys to increasing the chances of success when using oral therapies for dystonia. Treatment of dystonia primarily involves agents that target dopamine and acetylcholine receptors. Other drugs used include benzodiazepines, baclofen, antiepileptics, some antipsychotics drugs and antihistamine, with different levels of evidence of effectiveness. Unfortunately, most of the widely used drugs have low levels of evidence and are primarily based on anecdotal experiences. Finally, other adjunctive therapeutic strategies are often necessary to complement oral therapy.

Seizures and Epilepsy After Stroke: Epidemiology, Biomarkers and Management

Stroke is the leading cause of seizures and epilepsy in older adults. Patients who have larger and more severe strokes involving the cortex, are younger, and have acute symptomatic seizures and intracerebral haemorrhage are at highest risk of developing post-stroke epilepsy. Prognostic models, including the SeLECT and CAVE scores, help gauge the risk of epileptogenesis. Early electroencephalogram and blood-based biomarkers can provide information additional to the clinical risk factors of post-stroke epilepsy. The management of acute versus remote symptomatic seizures after stroke is markedly different. The choice of an ideal antiseizure medication should not only rely on efficacy but also consider adverse effects, altered pharmacodynamics in older adults, and the influence on the underlying vascular co-morbidity. Drug-drug interactions, particularly those between antiseizure medications and anticoagulants or antiplatelets, also influence treatment decisions. In this review, we describe the epidemiology, risk factors, biomarkers, and management of seizures after an ischaemic or haemorrhagic stroke. We discuss the special considerations required for the treatment of post-stroke epilepsy due to the age, co-morbidities, co-medication, and vulnerability of stroke survivors.

Brain activity during real-time walking and with walking interventions after stroke: a systematic review

Investigations of real-time brain activations during walking have become increasingly important to aid in recovery of walking after a stroke. Individual brain activation patterns can be a valuable biomarker of neuroplasticity during the rehabilitation process and can result in improved personalized medicine for rehabilitation. The purpose of this systematic review is to explore the brain activation characteristics during walking post-stroke by determining: (1) if different components of gait (i.e., initiation/acceleration, steady-state, complex) result in different brain activations, (2) whether brain activations differ from healthy individuals. Six databases were searched resulting in 22 studies. Initiation/acceleration showed bilateral activation in frontal areas; steady-state and complex walking showed broad activations with the majority exploring and finding increases in frontal regions and some studies also showing increases in parietal activation. Asymmetrical activations were often related to performance asymmetry and were more common in studies with slower gait speed. Hyperactivations and asymmetrical activations commonly decreased with walking interventions and as walking performance improved. Hyperactivations often persisted in individuals who had experienced severe strokes. Only a third of the studies included comparisons to a healthy group: individuals post-stroke employed greater brain activation compared to young adults, while comparisons to older adults were less clear and limited. Current literature suggests some indicators of walking recovery however future studies investigating more brain regions and comparisons with healthy age-matched adults are needed to further understand the effect of stroke on walking-related brain activation.

Deep learning-based pupil model predicts time and spectral dependent light responses

Although research has made significant findings in the neurophysiological process behind the pupillary light reflex, the temporal prediction of the pupil diameter triggered by polychromatic or chromatic stimulus spectra is still not possible. State of the art pupil models rested in estimating a static diameter at the equilibrium-state for spectra along the Planckian locus. Neither the temporal receptor-weighting nor the spectral-dependent adaptation behaviour of the afferent pupil control path is mapped in such functions. Here we propose a deep learning-driven concept of a pupil model, which reconstructs the pupil's time course either from photometric and colourimetric or receptor-based stimulus quantities. By merging feed-forward neural networks with a biomechanical differential equation, we predict the temporal pupil light response with a mean absolute error below 0.1 mm from polychromatic (2007 [Formula: see text] 1 K, 4983 [Formula: see text] 3 K, 10,138 [Formula: see text] 22 K) and chromatic spectra (450 nm, 530 nm, 610 nm, 660 nm) at 100.01 ± 0.25 cd/m2. This non-parametric and self-learning concept could open the door to a generalized description of the pupil behaviour.

HLA Risk Alleles in Aromatic Antiepileptic Drug-Induced Maculopapular Exanthema

To characterize human leukocyte antigen (HLA) loci as risk factors in aromatic antiepileptic drug-induced maculopapular exanthema (AED-MPE). A case-control study was performed to investigate HLA loci involved in AED-MPE in a southern Han Chinese population. Between January 2007 and June 2019, 267 patients with carbamazepine (CBZ), oxcarbazepine (OXC), or lamotrigine (LTG) associated MPE and 387 matched drug-tolerant controls from six centers were enrolled. HLA-A/B/C/DRB1 genotypes were determined using sequence-based typing. Potential risk alleles were validated by meta-analysis using data from different populations and in silico analysis of protein-drug interactions. HLA-DRB1*04:06 was significantly associated with OXC-MPE (p = 0.002, p _c = 0.04). HLA-B*38:02 was associated with CBZ-MPE (p = 0.03). When pooled, HLA-A*24:02, HLA-A*30:01, and HLA-B*35:01 additionally revealed significant association with AED-MPE. Logistic regression analysis showed a multiplicative interaction between HLA-A*24:02 and HLA-B*38:02 in CBZ-MPE. Meta-analysis of data from different populations revealed that HLA-24*:02 and HLA-A*30:01 were associated with AED-MPE (p = 0.02 and p = 0.04, respectively). In silico analysis of protein-drug interaction demonstrated that HLA-A*24:02 and HLA-A*30:01 had higher affinities with the three aromatic AEDs than the risk-free HLA-A allele. HLA-DRB1*04:06 showed relatively specific high affinity with S-monohydroxy derivative of OXC. HLA-DRB1*04:06 is a specific risk allele for OXC-induced MPE in the Southern Han Chinese. HLA-A*24:02, possibly HLA-A*30:01, are common risk factors for AED-MPE. The multiplicative risk potential between HLA-A*24:02 and HLA-B*38:02 suggests that patients with two risk alleles are at greater risk than those with one risk allele. Inclusion of these HLA alleles in pre-treatment screening would help estimating the risk of AED-MPE.

How does the brain change in chronic migraine? Developing disease biomarkers

BACKGROUND

Validated chronic migraine biomarkers could improve diagnostic, prognostic, and predictive abilities for clinicians and researchers, as well as increase knowledge on migraine pathophysiology.

OBJECTIVE

The objective of this narrative review is to summarise and interpret the published literature regarding the current state of development of chronic migraine biomarkers.

FINDINGS

Data from functional and structural imaging, neurophysiological, and biochemical studies have been utilised towards the development of chronic migraine biomarkers. These biomarkers could contribute to chronic migraine classification/diagnosis, prognosticating patient outcomes, predicting response to treatment, and measuring treatment responses early after initiation. Results show promise for using measures of brain structure and function, evoked potentials, and sensory neuropeptide concentrations for the development of chronic migraine biomarkers, yet further optimisation and validation are still required.

CONCLUSIONS

Imaging, neurophysiological, and biochemical changes that occur with the progression from episodic to chronic migraine could be utilised for developing chronic migraine biomarkers that might assist with diagnosis, prognosticating individual patient outcomes, and predicting responses to migraine therapies. Ultimately, validated biomarkers could move us closer to being able to practice precision medicine in the field and thus improve patient care.

Disruptions of Anaerobic Gut Bacteria Are Associated with Stroke and Post-stroke Infection: a Prospective Case-Control Study

In recent years, preclinical studies have illustrated the potential role of intestinal bacterial composition in the risk of stroke and post-stroke infections. The results of these studies suggest that bacteria capable of producing volatile metabolites, including trimethylamine-N-oxide (TMAO) and butyrate, play opposing, yet important roles in the cascade of events leading to stroke. However, no large-scale studies have been undertaken to determine the abundance of these bacterial communities in stroke patients and to assess the impact of disrupted compositions of the intestinal microbiota on patient outcomes. In this prospective case-control study, rectal swabs from 349 ischemic and hemorrhagic stroke patients (median age, 71 years; IQR: 67-75) were collected within 24 h of hospital admission. Samples were subjected to 16S rRNA amplicon sequencing and subsequently compared with samples obtained from 51 outpatient age- and sex-matched controls (median age, 72 years; IQR, 62-80) with similar cardiovascular risk profiles but without active signs of stroke. Plasma protein biomarkers were analyzed using a combination of nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Alpha and beta diversity analyses revealed higher disruption of intestinal communities during ischemic and hemorrhagic stroke compared with non-stroke matched control subjects. Additionally, we observed an enrichment of bacteria implicated in TMAO production and a loss of butyrate-producing bacteria. Stroke patients displayed two-fold lower plasma levels of TMAO than controls (median 1.97 vs 4.03 μM, Wilcoxon p < 0.0001). Finally, lower abundance of butyrate-producing bacteria within 24 h of hospital admission was an independent predictor of enhanced risk of post-stroke infection (odds ratio 0.77, p = 0.005), but not of mortality or functional patient outcome. In conclusion, aberrations in trimethylamine- and butyrate-producing gut bacteria are associated with stroke and stroke-associated infections.

Prediction accuracy of L- and M-cone based human pupil light models

Multi-channel LED luminaires offer a powerful tool to vary retinal receptor signals while keeping visual parameters such as color or brightness perception constant. This technology could provide new fields of application in indoor lighting since the spectrum can be enhanced individually to the users' favor or task. One possible application would be to optimize a light spectrum by using the pupil diameter as a parameter to increase the visual acuity. A spectral- and time-dependent pupil model is the key requirement for this aim. We benchmarked in our work selected L- and M-cone based pupil models to find the estimation error in predicting the pupil diameter for chromatic and polychromatic spectra at 100 cd/m2. We report an increased estimation error up to 1.21 mm for 450 nm at 60-300 s exposure time. At short exposure times, the pupil diameter was approximately independent of the used spectrum, allowing to use the luminance for a pupil model. Polychromatic spectra along the Planckian locus showed at 60-300 s exposure time, a prediction error within a tolerance range of ± 0.5 mm. The time dependency seems to be more essential than the spectral dependency when using polychromatic spectra.

Association between HLA genotype and antiseizure medications (ASMs)-induced maculopapular eruption among epilepsy patients in Xinjiang, China

PURPOSE

To determine genetic associations between antiseizure medications (ASMs)-induced maculopapular eruption (MPE) and human leukocyte antigen (HLA) variants in Xinjiang, China.

METHODS

A total of 409 patients were enrolled in this study, including 36 subjects with ASMs-induced MPE (case group) and 373 ASMs-tolerant patients (control group). We detected the whole sequence of the HLA alleles in the 409 Uighur patients using next-generation sequencing (NGS). The carried rate of HLA alleles were compared between the case group, the control group, and the general Chinese population.

RESULTS

The carried rate of HLA-A*03:01, HLA-B*07:02, HLA-C*01:02, and HLA-DRB1 *06:09 allele was significantly higher in the ASMs-MPE group when compared with both the ASMs-tolerant group (p = 0.000, odds ratio (OR) = 23.92, 95 % confidence interval (CI) = 3.96-74.84; p = 0.000, OR = 21.40, 95 % CI = 3.55-67.52; p = 0.030, OR = 4.69, 95 % CI = 0.90-61.73 and p = 0.000, OR = 18.94, 95 % CI = 3.15-60.58; respectively), and the general Chinese population (p = 0.000, OR = 21.34, 95 % CI = 3.31-38.98; p = 0.000, OR = 23.92, 95 % CI = 3.96-74.84; p = 0.019, OR = 5.53, 95 % CI = 0.14-19.86 and p = 0.000, OR = 21.67, 95 % CI = 3.68-11.73; respectively). Moreover, the carried rate of the HLA-B*4001 allele was significantly higher in the ASMs-tolerant group when compared with the ASMs-MPE group (p = 0.024, OR = 5.13, 95 % CI = 1.01-9.99).

CONCLUSIONS

In this study, we identified for the first time that the carried rate of HLA-A*03:01, HLA-B*07:02, HLA-C*01:02, and HLA-DRB1 *06:09 allele was significantly higher in the ASMs-MPE group when compared with the ASMs-tolerant group.

Psychological distress and lack of PINK1 promote bioenergetics alterations in peripheral blood mononuclear cells

Psychological distress induces oxidative stress and alters mitochondrial metabolism in the nervous and immune systems. Psychological distress promotes alterations in brain metabolism and neurochemistry in wild-type (WT) rats in a similar manner as in Parkinsonian rats lacking endogenous PTEN-induced kinase 1 (PINK1), a serine/threonine kinase mutated in a recessive forms of Parkinson’s disease. PINK1 has been extensively studied in the brain, but its physiological role in peripheral tissues and the extent to which it intersects with the neuroimmune axis is not clear. We surmised that PINK1 modulates the bioenergetics of peripheral blood mononuclear cells (PBMCs) under basal conditions or in situations that promote oxidative stress as psychological distress. By using an XF metabolic bioanalyzer, PINK1-KO-PBMCs showed significantly increased oxidative phosphorylation and basal glycolysis compared to WT cells and correlated with motor dysfunction. In addition, psychological distress enhanced the glycolytic capacity in PINK1-KO-PBMCs but not in WT-PBMCs. The level of antioxidant markers and brain-derived neurotrophic factor were altered in PINK1-KO-PBMCs and by psychological distress. In summary, our data suggest that PINK1 is critical for modulating the bioenergetics and antioxidant responses in PBMCs whereas lack of PINK1 upregulates compensatory glycolysis in response to oxidative stress induced by psychological distress.