Neurobrucellosis complicated by primary pyogenic ventriculitis: a case report.

Neurobrucellosis is a serious focal brucella infection. Ventriculitis is a special form of central nervous infection where pyogenic infection of the ependymal linings hinders antibiotics' accessibility to the cerebrospinal fluids and leads to protracted infection. We present a case of a 37-year-old Shepherd who had low-grade fever for 5 months followed by a brief history of vomiting, abdominal pain, and gait imbalance. Investigations showed neutrophilic leukocytosis, high titers of serum anti-brucella antibodies, and lymphocytic pleocytosis. Mycobacterial tuberculosis workup was negative. Magnetic resonance imaging of the brain revealed cervical and spinal meningeal enhancement in addition to mild hydrocephalus. The patient was presumptively diagnosed with neurobrucellosis. He received treatment with ceftriaxone-based combination antibiotics therapy for 6 months with complete resolution of his symptoms. Central nervous infection by brucella is a challenging diagnosis. The possibility of primary ventriculitis due to Brucella infection mandates early recognition and prolonged antimicrobial therapy to achieve full recovery.

New-onset severe eosinophilic granulomatosis with polyangiitis following the third dose of mRNA COVID-19 vaccine: A case report.

Eosinophilic granulomatosis with polyangiitis (EGPA) is a complex multifactorial disease that results in multisystemic inflammation of the small- and medium-sized arteries. The exact pathogenesis of this syndrome is poorly understood, but it is postulated to result from a combination of eosinophilic dysfunction, genetic predisposition, and the development of autoantibodies after exposure to an unknown stimulus. We describe a case of new-onset EGPA following the third dose of the Pfizer-BioNTech mRNA vaccine in an infection-naive middle-aged man with a background history of allergic respiratory symptoms. The patient developed acute onset of mononeuritis multiplex, pauci-immune glomerulonephritis, and leucocytoclastic vasculitis 10 days after receiving the booster dose. His laboratory markers including eosinophil count, antineutrophil cytoplasmic antibodies, and renal function tests improved markedly after the initiation of pulse steroid therapy and rituximab infusion. However, his peripheral muscle weakness and neuropathic pain did not respond to the initial therapy but improved later with intravenous cyclophosphamide and intravenous immunoglobulin. To the best of our knowledge, this is the fourth case report of post-coronavirus disease 2019 vaccination precipitation of EGPA. All reported cases including our report were in patients with previous allergic manifestations who received mRNA-based coronavirus disease 2019 vaccines, and all the patients developed mononeuritis multiplex at presentation. Despite the few reported cases of post-vaccination autoimmune phenomena, the temporal association between vaccination administration and disease onset does not indicate causality, given the mass vaccination programmes employed. However, the novel use of the mRNA platform in vaccine delivery necessitates vigilant monitoring by the scientific committee.

A Machine Learning-Based Severity Prediction Tool for the Michigan Neuropathy Screening Instrument.

Diabetic sensorimotor polyneuropathy (DSPN) is a serious long-term complication of diabetes, which may lead to foot ulceration and amputation. Among the screening tools for DSPN, the Michigan neuropathy screening instrument (MNSI) is frequently deployed, but it lacks a straightforward rating of severity. A DSPN severity grading system has been built and simulated for the MNSI, utilizing longitudinal data captured over 19 years from the Epidemiology of Diabetes Interventions and Complications (EDIC) trial. Machine learning algorithms were used to establish the MNSI factors and patient outcomes to characterise the features with the best ability to detect DSPN severity. A nomogram based on multivariable logistic regression was designed, developed and validated. The extra tree model was applied to identify the top seven ranked MNSI features that identified DSPN, namely vibration perception (R), 10-gm filament, previous diabetic neuropathy, vibration perception (L), presence of callus, deformities and fissure. The nomogram's area under the curve (AUC) was 0.9421 and 0.946 for the internal and external datasets, respectively. The probability of DSPN was predicted from the nomogram and a DSPN severity grading system for MNSI was created using the probability score. An independent dataset was used to validate the model's performance. The patients were divided into four different severity levels, i.e., absent, mild, moderate, and severe, with cut-off values of 10.50, 12.70 and 15.00 for a DSPN probability of less than 50, 75 and 100%, respectively. We provide an easy-to-use, straightforward and reproducible approach to determine prognosis in patients with DSPN.

Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature.

An intelligent insole system may monitor the individual's foot pressure and temperature in real-time from the comfort of their home, which can help capture foot problems in their earliest stages. Constant monitoring for foot complications is essential to avoid potentially devastating outcomes from common diseases such as diabetes mellitus. Inspired by those goals, the authors of this work propose a full design for a wearable insole that can detect both plantar pressure and temperature using off-the-shelf sensors. The design provides details of specific temperature and pressure sensors, circuit configuration for characterizing the sensors, and design considerations for creating a small system with suitable electronics. The procedure also details how, using a low-power communication protocol, data about the individuals' foot pressure and temperatures may be sent wirelessly to a centralized device for storage. This research may aid in the creation of an affordable, practical, and portable foot monitoring system for patients. The solution can be used for continuous, at-home monitoring of foot problems through pressure patterns and temperature differences between the two feet. The generated maps can be used for early detection of diabetic foot complication with the help of artificial intelligence.

Atypical Guillain-Barre syndrome with T6 sensory level.

Guillain-Barré syndrome is an acute immune-mediated demyelinating disease. Typical features include progressive ascending lower extremity weakness and areflexia. Several variants have been described that can make the diagnosis challenging. Here, we report a case of GBS presenting with progressive lower limb weakness and T6 sensory level.

Lesion Localization and Prognosis Using Electrodiagnostic Studies in Facial Diplegia: A Rare Variant of Guillain-Barre Syndrome.

Background The etiology of facial nerve palsy is diverse and includes herpes zoster virus, Guillain-Barre syndrome (GBS), otitis media, Lyme disease, sarcoidosis, human immunodeficiency virus, etc. The lower motor neuron type facial nerve palsy is usually caused by an ipsilateral facial nerve lesion; however, it may be caused by a central lesion of the facial nerve nucleus and tract in the pons. Facial diplegia is an extremely rare condition that occurs in approximately 0.3% to 2.0% of all facial palsies. Electrodiagnostic studies including direct facial nerve conduction, facial electromyography (EMG), and blink reflex studies are useful for the prognosis and lesion localization in facial nerve palsy. Methodology This retrospective, observational study was conducted at the Neurophysiology Unit, Hamad General Hospital, Doha, Qatar. This study included 11 patients with bilateral facial weakness who visited for electrodiagnostic studies in the neurophysiology laboratory. Results In total, eight (72.7%) patients had facial diplegia, eight (72.7%) had hypo/areflexia, seven (63.6%) had facial numbness, and five (45.5%) had cerebrospinal fluid albuminocytological dissociation. The most frequent cause of facial diplegia in this study was GBS (81.9%). Direct facial nerve conduction stimulation showed that nine (81.8%) patients had bilateral facial nerve low compound muscle action potential amplitudes. The bilateral blink reflex study showed that eight (88.8%) patients had absent bilateral evoked responses. Finally, the EMG study showed that five (55.5%) patients had active denervation in bilateral sample facial muscles. Conclusions Bilateral facial nerve palsy is an extremely rare condition with a varied etiology. Electrodiagnostic studies are useful in detecting the underlying pathophysiologic processes, prognosis, and central or peripheral lesion localization in patients with facial diplegia.

Performance Analysis of Conventional Machine Learning Algorithms for Diabetic Sensorimotor Polyneuropathy Severity Classification Using Nerve Conduction Studies.

Background: Diabetic sensorimotor polyneuropathy (DSPN) is a major form of complication that arises in long-term diabetic patients. Even though the application of machine learning (ML) in disease diagnosis is very common and well-established in the field of research, its application in DSPN diagnosis using nerve conduction studies (NCS), is very limited in the existing literature. Method: In this study, the NCS data were collected from the Diabetes Control and Complications Trial (DCCT) and its follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) clinical trials. The NCS variables are median motor velocity (m/sec), median motor amplitude (mV), median motor F-wave (msec), median sensory velocity (m/sec), median sensory amplitude (µV), Peroneal Motor Velocity (m/sec), peroneal motor amplitude (mv), peroneal motor F-wave (msec), sural sensory velocity (m/sec), and sural sensory amplitude (µV). Three different feature ranking techniques were used to analyze the performance of eight different conventional classifiers. Results: The ensemble classifier outperformed other classifiers for the NCS data ranked when all the NCS features were used and provided an accuracy of 93.40%, sensitivity of 91.77%, and specificity of 98.44%. The random forest model exhibited the second-best performance using all the ten features with an accuracy of 93.26%, sensitivity of 91.95%, and specificity of 98.95%. Both ensemble and random forest showed the kappa value 0.82, which indicates that the models are in good agreement with the data and the variables used and are accurate to identify DSPN using these ML models. Conclusion: This study suggests that the ensemble classifier using all the ten NCS variables can predict the DSPN severity which can enhance the management of DSPN patients.

Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals Using Novel Wavelet Packet Decomposition in Combination with Canonical Correlation Analysis.

The electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals, highly non-stationary in nature, greatly suffers from motion artifacts while recorded using wearable sensors. Since successful detection of various neurological and neuromuscular disorders is greatly dependent upon clean EEG and fNIRS signals, it is a matter of utmost importance to remove/reduce motion artifacts from EEG and fNIRS signals using reliable and robust methods. In this regard, this paper proposes two robust methods: (i) Wavelet packet decomposition (WPD) and (ii) WPD in combination with canonical correlation analysis (WPD-CCA), for motion artifact correction from single-channel EEG and fNIRS signals. The efficacy of these proposed techniques is tested using a benchmark dataset and the performance of the proposed methods is measured using two well-established performance matrices: (i) difference in the signal to noise ratio ( ) and (ii) percentage reduction in motion artifacts ( ). The proposed WPD-based single-stage motion artifacts correction technique produces the highest average (29.44 dB) when db2 wavelet packet is incorporated whereas the greatest average (53.48%) is obtained using db1 wavelet packet for all the available 23 EEG recordings. Our proposed two-stage motion artifacts correction technique, i.e., the WPD-CCA method utilizing db1 wavelet packet has shown the best denoising performance producing an average and values of 30.76 dB and 59.51%, respectively, for all the EEG recordings. On the other hand, for the available 16 fNIRS recordings, the two-stage motion artifacts removal technique, i.e., WPD-CCA has produced the best average (16.55 dB, utilizing db1 wavelet packet) and largest average (41.40%, using fk8 wavelet packet). The highest average and using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and 26.40%, respectively, for all the fNIRS signals using fk4 wavelet packet. In both EEG and fNIRS modalities, the percentage reduction in motion artifacts increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques are employed in comparison with the single-stage WPD method. In addition, the average also increases when WPD-CCA techniques are used instead of single-stage WPD for both EEG and fNIRS signals. The increment in both and values is a clear indication that two-stage WPD-CCA performs relatively better compared to single-stage WPD. The results reported using the proposed methods outperform most of the existing state-of-the-art techniques.

Machine Learning-Based Diabetic Neuropathy and Previous Foot Ulceration Patients Detection Using Electromyography and Ground Reaction Forces during Gait.

Diabetic neuropathy (DN) is one of the prevalent forms of neuropathy that involves alterations in biomechanical changes in the human gait. Diabetic foot ulceration (DFU) is one of the pervasive types of complications that arise due to DN. In the literature, for the last 50 years, researchers have been trying to observe the biomechanical changes due to DN and DFU by studying muscle electromyography (EMG) and ground reaction forces (GRF). However, the literature is contradictory. In such a scenario, we propose using Machine learning techniques to identify DN and DFU patients by using EMG and GRF data. We collected a dataset from the literature which involves three patient groups: Control (n = 6), DN (n = 6), and previous history of DFU (n = 9) and collected three lower limb muscles EMG (tibialis anterior (TA), vastus lateralis (VL), gastrocnemius lateralis (GL)), and three GRF components (GRFx, GRFy, and GRFz). Raw EMG and GRF signals were preprocessed, and different feature extraction techniques were applied to extract the best features from the signals. The extracted feature list was ranked using four different feature ranking techniques, and highly correlated features were removed. In this study, we considered different combinations of muscles and GRF components to find the best performing feature list for the identification of DN and DFU. We trained eight different conventional ML models: Discriminant analysis classifier (DAC), Ensemble classification model (ECM), Kernel classification model (KCM), k-nearest neighbor model (KNN), Linear classification model (LCM), Naive Bayes classifier (NBC), Support vector machine classifier (SVM), and Binary decision classification tree (BDC), to find the best-performing algorithm and optimized that model. We trained the optimized the ML algorithm for different combinations of muscles and GRF component features, and the performance matrix was evaluated. Our study found the KNN algorithm performed well in identifying DN and DFU, and we optimized it before training. We found the best accuracy of 96.18% for EMG analysis using the top 22 features from the chi-square feature ranking technique for features from GL and VL muscles combined. In the GRF analysis, the model showed 98.68% accuracy using the top 7 features from the Feature selection using neighborhood component analysis for the feature combinations from the GRFx-GRFz signal. In conclusion, our study has shown a potential solution for ML application in DN and DFU patient identification using EMG and GRF parameters. With careful signal preprocessing with strategic feature extraction from the biomechanical parameters, optimization of the ML model can provide a potential solution in the diagnosis and stratification of DN and DFU patients from the EMG and GRF signals.

An investigation to study the effects of Tai Chi on human gait dynamics using classical machine learning.

Tai Chi has been proven effective in preventing falls in older adults, improving the joint function of knee osteoarthritis patients, and improving the balance of stroke survivors. However, the effect of Tai Chi on human gait dynamics is still less understood. Studies conducted in this domain only relied on statistical and clinical measurements on the time-series gait data. In recent years machine learning has proven its ability in recognizing complex patterns from time-series data. In this research work, we have evaluated the performance of several machine learning algorithms in classifying the walking gait of Tai Chi masters (people expert on Tai Chi) from the normal subjects. The study is designed in a longitudinal manner where the Tai Chi naive subjects received 6 months of Tai Chi training and the data was recorded during the initial and follow-up sessions. A total of 57 subjects participated in the experiment among which 27 were Tai Chi masters. We have introduced a gender, BMI-based scaling of the features to mitigate their effects from the gait parameters. A hybrid feature ranking technique has also been proposed for selecting the best features for classification. The research reports 88.17% accuracy and 93.10% ROC AUC values from subject-wise 5-fold cross-validation for the Tai Chi masters' vs normal subjects' walking gait classification for the "Single-task" walking scenarios. We have also got fairly good accuracy for the "Dual-task" walking scenarios (82.62% accuracy and 84.11% ROC AUC values). The results indicate that Tai Chi clearly has an effect on the walking gait dynamics. The findings and methodology of this study could provide preliminary guidance for applying machine learning-based approaches to similar gait kinematics analyses.

COVID-19 vaccine causing Guillain-Barre syndrome, a rare potential side effect.

Patients with neurological symptoms should be enquired about recent vaccination history. It is important after the COVID-19 mRNA vaccine, which is newly introduced as it might link to the development of a wider variety of neurological diseases.

Expert review and recommendations for the management of acute, chronic, and neuropathic pain in Qatar.

BACKGROUND: Pain management is an evolving area of expertise in Qatar. Gaps in knowledge, inadequate training for physicians and nurses, and the absence of policies/guidelines are the main barriers to effective pain management in Qatar. In addition, the use of certain pain medication, especially opioids, is highly regulated, limiting their availability in outpatient pain management. These factors are responsible for the undertreatment of pain in Qatar. This study aimed to standardize evidence-based local recommendations for pharmacological treatment of pain in Qatar. METHODS: An expert panel of physicians from different disciplines, with experience in diagnosis and treatment of the three pain types (i.e., acute, chronic, and neuropathic), was convened for two face-to-face meetings in Doha, Qatar, on November 29, 2019, and on February 22, 2020, with subsequent virtual meetings. A literature search was performed on Medline and Google Scholar databases from inception till December 2019, and all relevant articles were selected. Based on these articles and repeated feedback from the authors, the final pain treatment protocols were developed. RESULTS: Recommendations for the treatment of acute pain, based on pain severity, followed three approaches: acetaminophen/paracetamol or non-steroidal anti-inflammatory drugs (NSAIDs) for mild pain and moderate pain and referral to a pain specialist for severe pain. Acetaminophen/paracetamol or NSAIDs is recommended for chronic pain, and the use of opioids was strongly discouraged because of its long-term side effects. For neuropathic pain, tricyclic antidepressants or gabapentin or pregabalin or serotonin-norepinephrine reuptake inhibitors were recommended first-line agents. Non-responders must be referred to neurologists or a pain specialist. CONCLUSION: The expert panel provides recommendations for the management of acute, chronic, and neuropathic pain based on international guidelines adapted to local practice and treatment availability in Qatar. More importantly, the panel has recommended taking extreme caution in the use of opioids for long-term management of chronic pain and to refer the patient to a pain specialist clinician as required.

Ischaemic stroke as the presenting feature of COVID-19: a series of three cases from Qatar.

Most cases of stroke associated with coronavirus disease 2019 (COVID-19) occur during the course of a characteristic COVID-19 respiratory illness. We report three patients where the presenting feature of COVID-19 was stroke. Two patients had no respiratory symptoms throughout their clinical course. In each case, COVID-19 was confirmed by a reverse transcription polymerase chain reaction (RT-PCR) test and the diagnosis of ischaemic stroke by brain imaging. The patients were relatively young (40, 45 and 50 years). None had a prior history of cerebrovascular events. Stroke risk factors were absent in one, limited to overweight and smoking in another but more prominent in the third patient. Two patients had large vessel occlusion and elevated D-dimer levels. Multiple infarcts were seen in two patients. Clinicians should consider the possibility of COVID-19 in patients presenting with stroke and conversely consider investigating for stroke if a patient with COVID-19, even if mildly ill, develops acute neurological symptoms.

Mitochondrial abnormalities in dermatomyositis: characteristic pattern of neuropathology.

The objective of the work described in this paper was to evaluate mitochondrial abnormalities in perifascicular atrophic fibers in muscle biopsies from patients with dermatomyositis (DM). We localized cytochrome c oxidase (COX) and succinate dehydrogenase (SDH) histochemically in muscle biopsies of 12 patients with DM, and 12 control patients with neurogenic atrophy. These two histochemical techniques were also combined on single tissue sections in order to accentuate any COX-negative fibers. Eleven out of 12 patients (91.6%) with DM showed histochemical evidence of mitochondrial dysfunction in perifascicular distribution. Similar abnormalities in histochemical staining were not seen in comparably sized myofibers that were atrophic due to denervation. It is concluded that abnormal SDH and COX histochemical activities in atrophic perifascicular fibers are characteristic of dermatomyositis. These abnormal staining characteristics could not be accounted for solely by myofiber atrophy, or by generalized abnormalities in histochemical staining.