The Gut–Brain Axis and Its Relation to Parkinson’s Disease: A Review

Parkinson’s disease is a chronic neurodegenerative disease characterized by the accumulation of misfolded alpha-synuclein protein (Lewy bodies) in dopaminergic neurons of the substantia nigra and other related circuitry, which contribute to the development of both motor (bradykinesia, tremors, stiffness, abnormal gait) and non-motor symptoms (gastrointestinal issues, urinogenital complications, olfaction dysfunction, cognitive impairment). Despite tremendous progress in the field, the exact pathways and mechanisms responsible for the initiation and progression of this disease remain unclear. However, recent research suggests a potential relationship between the commensal gut bacteria and the brain capable of influencing neurodevelopment, brain function and health. This bidirectional communication is often referred to as the microbiome–gut–brain axis. Accumulating evidence suggests that the onset of non-motor symptoms, such as gastrointestinal manifestations, often precede the onset of motor symptoms and disease diagnosis, lending support to the potential role that the microbiome–gut–brain axis might play in the underlying pathological mechanisms of Parkinson’s disease. This review will provide an overview of and critically discuss the current knowledge of the relationship between the gut microbiota and Parkinson’s disease. We will discuss the role of α-synuclein in non-motor disease pathology, proposed pathways constituting the connection between the gut microbiome and the brain, existing evidence related to pre- and probiotic interventions. Finally, we will highlight the potential opportunity for the development of novel preventative measures and therapeutic options that could target the microbiome–gut–brain axis in the context of Parkinson’s disease.

Design and Analysis of The Energy Storage and Return (ESAR) Foot Prosthesis Using Finite Element Method

ABSTRACT. Disability issue has increased in recent years due to the high number of accidents and vascular disease. Loss of limb function for people with amputations often results in an abnormal gait. Energy Storage And Return (ESAR) foot prostheses provide an alternative to help improve gait and minimize metabolic energy expenditure during the walking phase of amputees. This study used 3 designs with models from the Catia V5 Software. The finite element method analysis used Ansys Workbench 18.1 software to evaluate the three designs with a loading of 1.2 times the user's body weight with a maximum weight of 70 kg in normal walking activities. The simulated material is carbon fiber prepreg which has tensile strength, Young's modulus, Poisson ratio, and density of 513.72 MPa, 77.71 GPa, 0.14, and 1.37 g/cm3. The decision-making matrix method is used to determine the best foot prosthesis design according to predetermined criteria. The highest value in the decision-making matrix is 76 in Design 3. The chosen design (Design 3) after gait cycle analysis has a maximum von Mises stress value of 76.956 MPa and the safety factor value for each gait cycle heel strike loading model is 1.0762; foot flat 3.2509; toe-off 6.6263.

Cannabinoid Receptor Modulation in the 3-Acetylpyridine Cerebellar Ataxia Mouse Model

Cerebellar ataxia is a neurodegenerative disorder leading to severe motor incoordination. Recently it has been suggested that cannabinoids play a role in modulation of ataxic symptoms. In order to understand the possible therapeutic effect of cannabinoids for management of cerebellar ataxia, we used cannabinoid agonist/antagonists to target the cannabinoid type 1 receptor (CB1R) in the 3 acetyl pyridine (3AP) mouse model of ataxia. The role of the CB1R was examined by using three different doses of the CB1R agonist, WIN55,212-2 (WIN; 0.1, 0.5, 1 mg/kg) administrated 30 min prior to 3AP (55 mg/kg, i.p.) which leads to motor impairment through destruction of the inferior olive. In some recordings, the CB1R antagonist AM251(1 mg/kg) was given in combination with WIN. Locomotor activity and motor coordination were impaired by 3AP, and the application of WIN did not ameliorate this effect. However, the abnormal gait, rearing and grooming caused by 3AP were prevented by co-administration of AM251 with WIN. While the addition of the CB1R antagonist inhibition improved some ataxic symptoms, there was no effect of AM251 on balance or locomotor activity when co-administrated with WIN. Behavioral testing indicated that not only did WIN fail to exert any protective effect on ataxic symptoms, it exacerbated ataxic symptoms, suggesting that CB1R agonists may not be the ideal therapeutic drug in this disorder. When taken together, the findings from the present study indicate that cannabinoid modulation of ataxia symptoms may not act solely through CB1Rs and other cannabinoid receptors should be consider in future studies.

Interaction between the body condition score, gait, hindlimb conformation, and claw conformation in dairy cows in Aydin, Turkey

ABSTRACT: Breeding strategies aim to reduce lameness in dairies by using predictor traits to increase the selection of dairy cows. This study enhanced the present knowledge about association between the claw conformations (CC), hindlimb conformation (HiLC), gait, and body condition score (BCS) in dairy cows. A total of 166 lactating Holstein cows were enrolled in the study and scored for BCS, CC, HiLC, and gait. The abnormal HiLC group was statistically significant (P<0.001) to have more abnormal CC. Hock in (Hin) cows showed significance (P<0.001) with CC abnormalities. Cows with abnormal gait have less abnormal claw and limb conformation than normal cows (P=0.032). Lactation number (P=0.036) and abnormal HiLC (P<0.001) were significantly increased risk for CC. The present results might be useful for claw and hindlimb conformations, and breeding strategies of the herd.

Leveraging Smartphone Sensors for Detecting Abnormal Gait for Smart Wearable Mobile Technologies

Walking is one of the most common modes of terrestrial locomotion for humans. Walking is essential for humans to perform most kinds of daily activities. When a person walks, there is a pattern in it, and it is known as gait. Gait analysis is used in sports and healthcare. We can analyze this gait in different ways, like using video captured by the surveillance cameras or depth image cameras in the lab environment. It also can be recognized by wearable sensors. e.g., accelerometer, force sensors, gyroscope, flexible goniometer, magneto resistive sensors, electromagnetic tracking system, force sensors, and electromyography (EMG). Analysis through these sensors required a lab condition, or users must wear these sensors. For detecting abnormality in gait action of a human, we need to incorporate the sensors separately. We can know about one's health condition by abnormal human gait after detecting it. Understanding a regular gait vs. abnormal gait may give insights to the health condition of the subject using the smart wearable technologies. Therefore, in this paper, we proposed a way to analyze abnormal human gait through smartphone sensors. Though smart devices like smartphones and smartwatches are used by most of the person nowadays. So, we can track down their gait using sensors of these intelligent wearable devices. In this study, we used twenty-three (N=23) people to record their walking activities. Among them fourteen people have normal gait actions, and nine people were facing difficulties with their walking due to their illness. To do the stratification of the gait of the subjects, we have adopted five machine learning algorithms with addition a deep learning algorithm. The advantages of the traditional classification are analyzed and compared among themselves. After rigorous performance analysis we found support vector machine (SVM) showing 96% accuracy, highest among the tradition classifiers. 70%, 84%, and 95% accuracy is obtained by the logistic regression, Naïve Bayes, and k-Nearest Neighbor (kNN) classifiers, respectively. As per the state-of-the art, deep learning classifiers has been proven to outperform the traditional classifiers in similar binary classification problems. We have considered the scenario and applied the 2D convolutional neural network (2D-CNN) classification algorithm, which outperformed the other algorithms showing accuracy of 98%. The model can be optimized and can be integrated with the other sensors to be utilized in the mobile wearable devices.

COVID-19 Neuromuscular Involvement in Post-Acute Rehabilitation

Background: Coronavirus disease 2019 (COVID-19) is associated with muscle and nerve injuries as a consequence of prolonged critical illness or the infection itself. In this study, we evaluated neuromuscular involvement in patients who underwent post-acute intensive rehabilitation after COVID-19. Methods: Clinical and neurophysiological evaluations, including nerve conduction studies and electromyography, were performed on 21 consecutive patients admitted for rehabilitation after COVID-19. Results: Clinical signs suggesting muscle or nerve involvement (weakness, reduced deep tendon reflexes, impaired sensitivity, abnormal gait) were found in 19 patients. Neurophysiological examinations confirmed neuromuscular involvement in 17 patients: a likely association of critical illness myopathy (CIM) and critical illness polyneuropathy (CIP) was found in 5 patients; CIM alone was found in 4 patients; axonal sensory-motor polyneuropathy was found in 4 patients (CIP in 2 patients, metabolic polyneuropathy in 2 patients); Guillain-Barré syndrome was found in 2 patients (classical demyelinating sensory-motor polyneuropathy and acute motor axonal neuropathy, respectively); peroneal nerve injury was found in 1 patient; and pre-existing L4 radiculopathy was found in 1 patient. Conclusions: Neuromuscular involvement is a very common finding among patients admitted for rehabilitation after COVID-19, and proper investigation should be conducted when muscle or nerve injury is suspected for adequate rehabilitative strategy planning.

SURG-15. PITUITARY SPINDLE CELL ONCOCYTOMA: A CASE REPORT AND LITERATURE REVIEW

BACKGROUND Spindle cell oncocytoma (SCO) of the pituitary gland is an extremely rare non-functional WHO grade 1 tumor. SCO are often misdiagnosed as nonfunctional pituitary adenomas on pre-operative imaging. They are often hypervascular and locally adherent, which increases hemorrhage risk and limits surgical resection, leading to increased risk of recurrence. We report a case of SCO treated at our institution and provide a review of the current literature. METHODS A 75-year-old male with a history of hypertension, left thalamic stroke, Parkinson’s disease, and normal pressure hypertension presented to neurosurgery clinic with bitemporal hemianopsia, hyponatremia, and abnormal gait and mobility. Imaging showed an enhancing intra- and suprasellar, hyperdense tumor mass measuring 3.0 cm in diameter. We performed a systemic literature search in the PubMed database to identify previous reports of spindle cell oncocytoma. After exclusion of studies that did not meet criteria, 32 publications were selected for critical reading. RESULTS The patient underwent an endoscopic transsphenoidal resection of the tumor via a multi-disciplinary team. The tumor was fibrous and adherent to the intrasellar dura, with gross invasion of the diaphragm sella, necessitating partial resection of the diaphragm. The defect was repaired, and the patient made an uncomplicated recovery. Post-operatively, the patient experienced improved vision. Upon literature review, SCO present in older adults with an average age of 56.2 ± 14.7 with visual deficits (67.9%), headache (33.3%), hypopituitarism (24.7%), and nausea (11.1%). Full resection was achieved in 38.6% of cases leading to recurrence rate of 23.5% with an average time until recurrence of 32.5 months (range 1-120 months). CONCLUSION Careful surgical technique is needed due to SCO hypervascularity and strong adherence to minimize risk of injury to surrounding neurovascular structures. Long-term follow up is recommended due risk of recurrence.