Electrophysiological patterns of sciatic nerve in patients with arthrosis deformans of the hip

BACKGROUND: Neurological complications in sciatic nerve (SN) after a total hip replacement (THR) are observed in 0.93.2% of cases in patients with arthrosis deformans and age-related morphologic changes in SN. These cause the need for SN evaluation before THR. This research was aimed at the evaluation of the initial SN capacity with electrophysiological findings in patients with arthrosis deformans of the hip. MATERIALS AND METHODS: Electroneuromyography (ENMG) was used to evaluate fibular and tibial nerves M-responses as well as F-waves in 66 patients with dysplastic coxarthrosis and 12 patients with posttraumatic coxarthrosis. The findings were compared to those of the controls. RESULTS: Changes in ENMG findings for fibular nerve in 49 patients with dysplastic coxarthrosis were bilateral and showed significant difference only from the norm. In 19 of 66 cases (27.9%) low M-responses (р 0.02) were found in the side subject to THR. In 87.3% of cases, the signs of a decrease in the conductivity of proximal segments of the tibial nerve were revealed. In patients with posttraumatic coxarthrosis, the significant decrease in ENMG findings from both fibular and tibial nerves was observed in the affected side, they made up just 42-50% of those in the opposite side. Asymptomatic progress of denervation damage in hip and tibia muscles sometimes required needle EMG to fund the signs of motor innervation disorder. A-waves revealed in 65% of patients suggested local damage to one or both portions of SN. CONCLUSION: ENMG findings in patients with dysplastic arthrosis of the hip enabled revealing of the signs of neuropathy before surgeries and decreasing the risk of neurologic post-surgery complications.

Neural network models for spinal implementation of muscle synergies

Muscle synergies have been proposed as functional modules to simplify the complexity of body motor control; however, their neural implementation is still unclear. Converging evidence suggests that output projections of the spinal premotor interneurons (PreM-INs) underlie the formation of muscle synergies, but they exhibit a substantial variation across neurons and exclude standard models assuming a small number of unitary "modules" in the spinal cord. Here we compared neural network models for muscle synergies to seek a biologically plausible model that reconciles previous clinical and electrophysiological findings. We examined three neural network models: one with random connections (non-synergy model), one with a small number of spinal synergies (simple synergy model), and one with a large number of spinal neurons representing muscle synergies with a certain variation (population synergy model). We found that the simple and population synergy models emulate the robustness of muscle synergies against cortical stroke observed in human stroke patients. Furthermore, the size of the spinal variation of the population synergy matched well with the variation in spinal PreM-INs recorded in monkeys. These results suggest that a spinal population with moderate variation is a biologically plausible model for the neural implementation of muscle synergies.

Prediction of Diabetic Sensorimotor Polyneuropathy Using Machine Learning Techniques

Diabetic sensorimotor polyneuropathy (DSPN) is a major complication in patients with diabetes mellitus (DM), and early detection or prediction of DSPN is important for preventing or managing neuropathic pain and foot ulcer. Our aim is to delineate whether machine learning techniques are more useful than traditional statistical methods for predicting DSPN in DM patients. Four hundred seventy DM patients were classified into four groups (normal, possible, probable, and confirmed) based on clinical and electrophysiological findings of suspected DSPN. Three ML methods, XGBoost (XGB), support vector machine (SVM), and random forest (RF), and their combinations were used for analysis. RF showed the best area under the receiver operator characteristic curve (AUC, 0.8250) for differentiating between two categories—criteria by clinical findings (normal, possible, and probable groups) and those by electrophysiological findings (confirmed group)—and the result was superior to that of linear regression analysis (AUC = 0.6620). Average values of serum glucose, International Federation of Clinical Chemistry (IFCC), HbA1c, and albumin levels were identified as the four most important predictors of DSPN. In conclusion, machine learning techniques, especially RF, can predict DSPN in DM patients effectively, and electrophysiological analysis is important for identifying DSPN.

Questionnaires can reflect the severity of electrophysiological findings In diabetic patients

Objective: The aim of this study was to determine whether a relationship exists between pain questionnaires, diabetic peripheral neuropathy, and the severity of electrophysiological findings. Methods: Patients with diabetes mellitus (DM) whose clinical signs and symptoms were found to be consistent with diabetic neuropathy were included in the present study in Ankara City Hospital Neurology Department between June 2020-January 2021. It was designed as a prospective cross-sectional study. Demographic characteristics, type and duration of DM, the onset of neuropathic complaints, systemic diseases, and DM treatments were noted. Blood tests were performed on all patients. Participants were administered the Douleur Neuropathique 4 Questions (DN4), Leeds Assessment of Neuropathic Symptoms and Signs (LANSS), and Numerical Rating Scale (NRS) questionnaires and an electrophysiological examination. Results: The study included 108 patients. A statistically significant correlation was found between the severity of electrophysiological findings and the age of patients, duration of diabetes, and treatment modality. Additionally, a statistically significant correlation was found between the severity of electrophysiological findings and blood urea nitrogen (BUN) levels and monocyte and platelet counts. According to the NRS, a significant relation was found between BUN values and the severity of pain experienced. The LANSS predicted the severity of electrophysiological findings at a significantly higher accuracy than other questionnaires. Conclusion: Questionnaires can detect diabetic neuropathy, which is mostly asymptomatic, before the onset of DM complications. The LANSS questionnaire reflects the electrophysiological findings severity.

Multifocal Pattern Dystrophy Simulating Fundus Flavimaculatus: Multimodal Imaging for Early Diagnosis

Multifocal pattern dystrophy simulating fundus flavimaculatus (MPDSFF) is a clinical entity characterized by several clinicopathological, angiographic, tomographic, and electrophysiological findings. A 58-year-old caucasian female patient presented with bilateral floaters and metamorphopsia. Best-corrected visual acuity (VA) was 6/6 in both eyes and intraocular pressure was 14 and 15 mm Hg, respectively. Fundus examination, optical coherence tomography (OCT), autofluoresence (AF), fluorescein angiography (FA) and pattern Electroretinogram were employed for the diagnosis of this case. Clinical and imaging findings were consistent with MPDSFF. Noticeable progression was observed in OCT scans 6 months following the baseline visit, while no significant changes were observed over the following 12 months. Prognosis of VA in MPDSFF patients may remain relatively good even in the presence of considerable anatomic changes. Disease progression may be slow and significant reduction in VA may present only secondary to a choroidal neovascular membrane. Patient follow-up should include OCT scans, PERG, and AF in addition to VA and dilated fundus examination every 6–12 months. As relevant literature is limited and no effective treatment modality has been employed for this clinical entity, the identification of the cellular death pathway in pattern dystrophies may lead to an applicable management approach.