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Diffuse axonal injury (DAI) following sudden acceleration and deceleration can lead to cognitive function decline. Various treatments have been proposed. Repetitive transcranial magnetic stimulation (rTMS), a non-invasive stimulation technique, is a potential treatment for enhancing neuroplasticity in cases of brain injury. The therapeutic efficacy of rTMS on cognitive function remains unconfirmed. This study investigated the effects of rTMS and the underlying molecular biomechanisms using a rat model of DAI. Sprague-Dawley rats (n = 18) were randomly divided into two groups: one receiving rTMS after DAI and the other without brain stimulation. All rats were subjected to sudden acceleration and deceleration using a DAI modeling machine to induce damage. MRI was performed to confirm the DAI lesion. The experimental group received rTMS at a frequency of 1 Hz over the frontal cortex for 10 min daily for five days. To assess spatial memory, we conducted the Morris water maze (MWM) test one day post-brain damage and one day after the five-day intervention. A video tracking system recorded the escape latency. After post-MWM tests, all rats were euthanized, and their brain tissues, particularly from the hippocampus, were collected for immunohistochemistry and western blot analyses. The escape latency showed no difference on the MWM test after DAI, but a significant difference was observed after rTMS between the two groups. Immunohistochemistry and western blot analyses indicated increased expression of BDNF, VEGF, and MAP2 in the hippocampal brain tissue of the DAI-T group. In conclusion, rTMS improved cognitive function in the DAI rat model. The increased expression of BDNF, VEGF, and MAP2 in the DAI-T group supports the potential use of rTMS in treating cognitive impairments associated with DAI.
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BACKGROUND: The evaluation of gait function and severity classification of stroke patients are important to determine the rehabilitation goal and the level of exercise. Physicians often qualitatively evaluate patients' walking ability through visual gait analysis using naked eye, video images, or standardized assessment tools. Gait evaluation through observation relies on the doctor's empirical judgment, potentially introducing subjective opinions. Therefore, conducting research to establish a basis for more objective judgment is crucial. OBJECTIVE: To verify a deep learning model that classifies gait image data of stroke patients according to Functional Ambulation Category (FAC) scale. METHODS: Gait vision data from 203 stroke patients and 182 healthy individuals recruited from six medical institutions were collected to train a deep learning model for classifying gait severity in stroke patients. The recorded videos were processed using OpenPose. The dataset was randomly split into 80% for training and 20% for testing. RESULTS: The deep learning model attained a training accuracy of 0.981 and test accuracy of 0.903. Area Under the Curve(AUC) values of 0.93, 0.95, and 0.96 for discriminating among the mild, moderate, and severe stroke groups, respectively. CONCLUSION: This confirms the potential of utilizing human posture estimation based on vision data not only to develop gait parameter models but also to develop models to classify severity according to the FAC criteria used by physicians. To develop an AI-based severity classification model, a large amount and variety of data is necessary and data collected in non-standardized real environments, not in laboratories, can also be used meaningfully.
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RATIONALE: Joubert syndrome (JS) is a rare genetic disorder that presents with various neurological symptoms, primarily involving central nervous system dysfunction. Considering the etiology of JS, peripheral nervous system abnormalities cannot be excluded; however, cases of JS accompanied by peripheral nervous system abnormalities have not yet been reported. Distinct radiological findings on brain magnetic resonance imaging were considered essential for the diagnosis of JS. However, recently, cases of JS with normal or nearly normal brain morphology have been reported. To date, there is no consensus on the most appropriate diagnostic method for JS when imaging-based diagnostic approach is challenging. This report describes the case of an adult patient who exhibited bilateral peroneal neuropathies and was finally diagnosed with JS through genetic testing. PATIENT CONCERNS AND DIAGNOSIS: A 27-year-old man visited our outpatient clinic due to a gait disturbance that started at a very young age. The patient exhibited difficulty maintaining balance, especially when walking slowly. Oculomotor apraxia was observed on ophthalmic evaluation. During diagnostic workups, including brain imaging and direct DNA sequencing, no conclusive findings were detected. Only nerve conduction studies revealed profound bilateral peroneal neuropathies. We performed whole genome sequencing to obtain a proper diagnosis and identify the gene mutation responsible for JS. LESSONS: This case represents the first instance of peripheral nerve dysfunction in JS. Further research is needed to explore the association between JS and peripheral nervous system abnormalities. Detailed genetic testing may serve as a valuable tool for diagnosing JS when no prominent abnormalities are detected in brain imaging studies.