AIM: To investigate microelectrode recording (MER)-induced microlesion effect (MLE) on the motor symptoms of 30 patients with Parkinson's disease (PD) who underwent deep brain stimulation of the subthalamic nucleus. MATERIAL and METHODS: MER-induced MLE was evaluated based on the difference between tremor, rigidity, and bradykinesia scores in the preoperative off-state and intraoperative state following MER and before test stimulation. RESULTS: MLE scores improved by 21.7% [left (L) side] and by 13.6% [right (R) side] from baseline (p<0.05). Tremor scores improved by 31.5% (L) and by 14.2% (R) (p<0.05), rigidity scores improved by 17.3% (L) and by 14.2% (R) (p<0.05) and bradykinesia scores improved by 20.6% (L) and by 11.5% (R) (p 0.05). CONCLUSION: MER-induced MLE improved motor symptoms and was not correlated with the number of microelectrodes used during the procedure.

Neurologic complications after solid-organ transplant reveal a great spectrum of pathologies. Intracranial hemorrhages, cerebral ischemic lesions, infarctions, lymphoproliferative disorders, and infections, including aspergillosis, have been observed after liver transplant. Fungi constitute nearly 5% of all central nervous system infections, mainly occurring in immunocompromised patients. The most common causative agent is Aspergillus species. It presents either as maxillary sinusitis or pulmonary infection. Brain involvement of Aspergillus carries a high rate of mortality. Aspergillosis presents in the forms of meningitis, mycotic aneurysms, infarctions, and mass lesions. Aspergillosis does not have a specific radiologic appearance. Parenchymal aspergillosis has heterogenous signal intensity (hypointense on T1-weighted and hyperintense on T2-weighted images). Here, we present 3 patients who underwent solid-organ transplant and developed central nervous system aspergillosis. Different modalities of neurosurgical intervention were performed in combination with chemotherapy as part of their fungal therapy.

Purpose: Analyzing and interpreting large amounts of complex health care data are becoming more insufficient by traditional statistical approaches. However, analyzing Big Data (BD) by machine learning (ML) supports the storage, classification of patient information. Therefore, improves disease identification, treatment evaluation, surgical planning, and outcome prediction. The current study aims to create a competing risk model to identify prognostic factors in glioblastoma (GB). Materials and methods: The study included 31663 patients diagnosed with GB between 2007 and 2018. The data in the study were taken from the Surveillance, Epidemiology, and End Results (SEER) database. Overall survivals (OS), age, race, gender, primary site, laterality, surgery and tumor size at the time of diagnosis, vital status, and follow-up time (months) were selected for the analyzes. Results: The median OS of the patients was found to be 9.00±0.09 months. In addition, all variables in the table were statistically significant risk factors for survival except gender. Therefore, surgery, age, laterality, primary site, tumor size, race, gender variables were used as independent risk factors, and vital status was used as a dependent variable for ML analysis. Looking at the ML results, hybrid model gave the best results according to Accuracy, F-measure, and MCC performance criteria. According to hybrid model, which has the best performance, the diagnosis of alive/dead in 84 and 74 out of 100 patients can be interpreted as correct for 1- and 2-year, respectively. Conclusions: The model created by ML was 84.9% and 74.1% successful in predicting 1- and 2-year survival in GB patients, respectively. Recognition of the fundamental ideas will allow neurosurgeons to understand BD and help evaluate the extraordinary amount of data within the associated healthcare field.

Neurol India is an peer-reviewed biomedical periodical of Neurological Society of India.

Objective: The term big data refers to high volumes of data that are difficult to store, process, and analyze through traditional database technologies. This paper aims to discover the big data knowledge and awareness of Turkish neurosurgeons. Methods: An online survey link was sent to all members of the Turkish Neurosurgical Society (n=1707 neurosurgeons) by e-mail. The survey, which was available during September and October 2021, was filled by 211 specialists (12.4%) neurosurgeons. Participants were scored based on the questions asked in “Big Data” and the scores were compared with baseline characteristics.There was no statistically significant correlation between baseline characteristics (age, gender, the residency/current institution, academic title, the presence of a Ph.D. degree, and professional specialist period) and survey score. The survey scores of the participants who used a big data platform, wrote code, thought that the use of big data was beneficial, and wanted to learn more about this subject were statistically significantly higher. Conclusions: Obtaining information about big data seems to be related only to personal interests. Necessary arrangements should be made in the training programs of neurosurgeons on this topic, which is gaining in popularity and provides useful information for daily clinical practice.