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Microneurosurgical techniques remain crucial for managing neurosurgical diseases, especially in low- and middle-income countries (LMICs) where other advanced treatment modalities are not available. The global distribution of these techniques is uneven due to disparities in infrastructure, equipment, and training. Medical professionals from LMICs face barriers in reaching training centers in high-income countries, as well as in accessing microsurgical techniques. To address these disparities in microsurgery training, we offer free and accessible microsurgery training model by combining the donations of microsurgery kits with a comprehensive support system that includes live-streamed, offline, and in-person assistance within LMICs.

In the construction industry, which is Turkey's locomotive and the strategic sector, determining the kind of leadership that impacts employees' productivity is directly related to the success of the business. The identification of leadership types that will motivate and support employees has great importance in terms of construction businesses where the human element is at the forefront. From the point of view of the site chiefs, it is thought that it will benefit all the stakeholders in the construction sector to determine which leader type will motivate which employees. In this study, the productivity relations between the engineers working in construction companies constructing buildings in Adana Province and the employees who are the hierarchically lower-level employees of these persons were investigated using bi-directional surveys. The impact of leadership types on the employees’ productivity has been investigated using machine learning. The effects of ANFIS method and the use of genetic algorithm (GA) on the training of ANFIS for the classification are investigated. The data set, which was prepared within the scope of the study, was classified by ANFIS-genetic algorithm (ANFIS-GA), ANFIS-backpropagation algorithm (ANFIS-BP), and ANFIS-hybrid algorithm (ANFIS-HB) algorithms after the required preprocesses. The 10-fold cross-validation technique is used to test the performance of the classification methods. According to the obtained results, the highest accuracy rate of 82.18% is obtained when ANFIS-GA algorithm is used as a classifier. As a result of the study, it is concluded that for this data set, ANFIS, an artificial neural network-based algorithm, is more successful in determining the leadership perceptions of construction employees when it is trained by GA.

Vestibular schwannomas (VS) are slow-growing benign tumors commonly found in the cerebellopontine angle, potentially compressing brain structures and cranial nerves, including the abducens nerve (AN), responsible for lateral gaze. Although large VSs may compress the AN, postsurgical AN palsy is rare, and the clinical significance of AN variations, particularly in VS cases, remains unclear. We report a unique case of a 29-year-old female with a large left vestibular schwannoma. The patient underwent a left-sided microsurgical two-stage excision with an uneventful recovery (House-Brackmann facial grade I). During surgery, a rare variant of the AN was identified. This AN originated as two separate trunks and merged 3 mm distal to its origin in the same segment. To the best of our knowledge, arising and merging in the cisternal segment of AN variation has not been reported previously. Recognizing AN variations is vital for successful skull base surgeries, especially in preserving both branches during vestibular schwannoma surgery and ensuring ocular function.