No abstract

Field potential (FP) recording is an accessible means to capture the shifts in the activity of neuron populations. However, the spatial and composite nature of these signals has largely been ignored, at least until it became technically possible to separate activities from co-activated sources in different structures or those that overlap in a volume. The pathway-specificity of mesoscopic sources has provided an anatomical reference that facilitates transcending from theoretical analysis to the exploration of real brain structures. We review computational and experimental findings that indicate how prioritizing the spatial geometry and density of sources, as opposed to the distance to the recording site, better defines the amplitudes and spatial reach of FPs. The role of geometry is enhanced by considering that zones of the active populations that act as sources or sinks of current may arrange differently with respect to each other, and have different geometry and densities. Thus, observations that seem counterintuitive in the scheme of distance-based logic alone can now be explained. For example, geometric factors explain why some structures produce FPs and others do not, why different FP motifs generated in the same structure extend far while others remain local, why factors like the size of an active population or the strong synchronicity of its neurons may fail to affect FPs, or why the rate of FP decay varies in different directions. These considerations are exemplified in large structures like the cortex and hippocampus, in which the role of geometrical elements and regional activation in shaping well-known FP oscillations generally go unnoticed. Discovering the geometry of the sources in play will decrease the risk of population or pathway misassignments based solely on the FP amplitude or temporal pattern.

The majority of patients remain with an unidentified diagnosis, which is not detailed at the ultrastructural level, resulting in inadequate treatment and, as a consequence, low efficacy of pharmacotherapy and high mortality in this group of patients.

Relevance: The 5-year overall survival rate(s) in NSCLC p-stage IA is 73%, and the recurrence rate in radically treated patients is almost 10%. The study aimed to evaluate the prognostic significance of several clinical and morphological factors and apply machine learning algorithms to predict the results of overall survival of patients with lung cancer. Methods: The forms 030-6/y C34 – lung cancer (n=19,379) from the EROB database for 2014-2018 were analyzed, and the impact of risk factors on overall survival was assessed using the Kaplan-Meier method. Accordingly, the training data set for constructing forecasting models included 19,379 observations and 15 factors. The machine learning algorithms such as Random Forest Classifier, Gradient Boosting Classifier, Logistic Regression Model, Decision Tree Classifier, and K Nearest Neighbors (KNN) Classifier were implemented in the Python programming lan- guage. The results were evaluated by constructing an error matrix, calculating classification metrics: the proportion of correctly classified objects (accuracy) during training and validation (validation), accuracy (precision), completeness (recall), Kappa-Cohen. Results: In our study, 19,379 patients were analyzed, including 15,494 men (79.95%) and 3,885 women (20.04%). At the time of the study, 6,171 men (39.8%) and 1,962 women (49.5%) were alive. Median survival was 8.3 months (SE – 0.154 months, 95% CI – 7.96-8.56) in men and 15.43 months (SE – 1.0 months, 95% CI – 13.497-17.363) in women. At diagnosis, 1,037 patients (5.35%) had stage I disease, other 4,145 (21.38%) had stage II. Most patients (61.4%) had advanced stage NSCLC: 9,189 people (47.4%) were diagnosed with stage III, and 4,655 (24%) – with stage IV. The reliability of differences in median survival (χ2=3991.6, p=0.00) indicated the prognostic significance of the tumor process stage and its influence on the patient’s survival. Also, the revealed significant difference in the median survival of patients with various morphological forms of lung cancer sug- gests the prognostic significance of the morphological factor (the difference between those indicators was statistically significant, χ2=623.4 p=0.000). Conclusion: Machine learning models can predict the risk of fatal outcomes for patients after surgical treatment and registration in the EROB database. The creation of patient-oriented systems to support medical decision-making makes it possible to choose the optimal strategies for adju- vant therapy, dispensary observation, and frequency of diagnostic studies

Objective The purpose of the study is to analyze the immediate outcomes and results of video-assisted thoracoscopic lobectomy and lung resection performed in the surgical department of the AOC between 2014 and 2018. Methods For the period from 2014 to 2018, 118 patients with peripheral lung cancer were operated on in the surgical department of the AOC. The following operations were performed: lobectomy in 92 cases (78%), of which: upper lobectomy, 44 (47.8%); average lobectomy, 13 (14.1%); lower lobectomy, 32 (35%); bilobectomy, 3 (3.3%). All patients underwent extensive lymphadenectomy on the side of the operation. In 22 patients, for various reasons, preservation of thoracotomy was performed. Results The absence of N0 lymph node damage was observed in 82 patients (70%), the first-order lymph node damage N1 in 13 (11%), N2 in 13 (11%), N3 in 5 (4%), and NX in 5 (4%). Histological examination revealed: squamous cell carcinoma − 35.1%, adenocarcinoma − 28.5%, undifferentiated carcinoma − 8.3%, NSCLC − 5.6%, NEO − 4.6%, sarcoma − 1.8%. At the same time, in 12.7% of patients, mts was detected − lung damage, and in 3.4%, malignant cells were not detected. Most patients were activated on the first day after surgery. Conclusion An analysis of the direct results of the study allows us to conclude that video-assisted thoracoscopic surgery is a highly effective, minimally invasive, safe method for treating peripheral lung cancer, which allows us to recommend it for wider use in oncological practice.