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T he accuracy of microsurgical manipulations largely depends on the degree of the surgeon's hand tremor, which can be influenced by various factors (stress and anxiety, surgical instrument length, fatigue after a night shift, physical exercise, caffeine consumption, etc.). 1 Energy drinks, one of the most rapidly growing segments in the beverage market, are very popular among medical residents, especially during night shifts. The common motivation for energy drink consumption is the cognitive enhancement, boost of performance, and concentration. 2 Most of the energy drinks include high doses of caffeine and other legal stimulants such as taurine, inositol, panthenol, and B-complex vitamins. 3 Therefore, the use of energy drinks may affect the level of physiological hand tremor. 4 The purpose of this study was to assess the effect of energy drinks on hand tremor during microsurgical manipulations. The study enrolled 1 group of participants-11 neurosurgical residents (8 men and 3 women) from the Burdenko Neurosurgical Center. Average age was 23.9 years (range: 23-26 years). All residents had the same level of microsurgical practice (no more than 6 months of microsurgical training).Before the experiment, participants excluded caffeinecontaining drinks (tea, coffee, energy drinks) from their diet for at least 24 hours. During the experiment, the participants were asked to cut several threads of a surgical gauze using microsurgical scissors (Aesculap FD030R, Melsungen, Germany) and surgical microscope (Carl Zeis, Oberkocher, Germany) through a simulated bone window. The level of tremor was measured using an accelerometer (LIS331DLHTR; STMicroelectronics, Geneva, Switzerland) attached to the handle of the microscissors. The details of the experimental setting are shown in Supplemental Digital Contents 1-3. (See figure 1, Supplemental Digital Content 1, which displays the device developed for the tremor measurement. The system includes: 1-laptop, 2-original software, 3-USB

Intraoperative recording of cortico-cortical evoked potentials (CCEPs) enables studying effective connections between various functional areas of the cerebral cortex. The fundamental possibility of postoperative speech dysfunction prediction in neurosurgery based on CCEP signal variations could serve as a basis to develop the criteria for the physiological permissibility of intracerebral tumors removal for maximum preservation of the patients' quality of life.The aim of the study was to test the possibility of predicting postoperative speech disorders in patients with glial brain tumors by using the CCEP data recorded intraoperatively before the stage of tumor resection.Materials and Methods. CCEP data were reported for 26 patients. To predict the deterioration of speech functions in the postoperative period, we used four options for presenting CCEP data and several machine learning models: a random forest of decision trees, logistic regression, and support vector machine method with different types of kernels: linear, radial, and polynomial. Twenty variants of models were trained: each in 300 experiments with resampling. A total of 6000 tests were performed in the study.Results. The prediction quality metrics for each model trained in 300 tests with resampling were averaged to eliminate the influence of "successful" and "unsuccessful" data grouping. The best result with F1-score = 0.638 was obtained by the support vector machine with a polynomial kernel. In most tests, a high sensitivity score was observed, and in the best model, it reached a value of 0.993; the specificity of the best model was 0.370.Conclusion. This pilot study demonstrated the possibility of predicting speech dysfunctions based on CCEP data taken before the main stage of glial tumors resection; the data were processed using traditional machine learning methods. The best model with high sensitivity turned out to be insufficiently specific. Further studies will be aimed at assessing the changes in CCEP during the operation and their relationship with the development of postoperative speech deficit.