Data Quality Estimation Via Model Performance: Machine Learning as a Validation Tool.

In our recent study, the attempt to classify neurosurgical operative reports into routinely used expert-derived classes exhibited an F-score not exceeding 0.74. This study aimed to test how improving the classifier (target variable) affected the short text classification with deep learning on real-world data.

Compact ring resonators of silicon nanorods for strong optomechanical interaction.

Optomechanical interaction in microstructures plays a more and more important role in the fields of quantum technology, information processing, and sensing, among others. It is still a challenge to obtain a strong optomechanical interaction in a compact device. Here, we propose and demonstrate that compact ring resonators consisting of silicon nanorods can realize strong optomechanical interaction even surpassing that of most optical microcavities.

5-ALA-guided tumor resection during awake speech mapping in gliomas located in eloquent speech areas: Single-center experience.

Background: Achieving maximal functionally safe resection of gliomas located within the eloquent speech areas is challenging, and there is a lack of literature on the combined use of 5-aminolevulinic acid (5-ALA) guidance and awake craniotomy.

Objective: The aim of this study was to describe our experience with the simultaneous use of 5-ALA fluorescence and awake speech mapping in patients with left frontal gliomas located within the vicinity of eloquent speech areas.

Materials And Methods: A prospectively collected database of patients was reviewed.

Reparative properties of human glioblastoma cells after single exposure to a wide range of X-ray doses.

Radiation therapy induces double-stranded DNA breaks in tumor cells, which leads to their death. A fraction of glioblastoma cells repair such breaks and reinitiate tumor growth. It was necessary to identify the relationship between high radiation doses and the proliferative activity of glioblastoma cells, and to evaluate the contribution of DNA repair pathways, homologous recombination (HR), and nonhomologous end joining (NHEJ) to tumor-cell recovery.

Predicting the Length of Stay in Neurosurgery with RuGPT-3 Language Model.

In this study, we update the evaluation of the Russian GPT3 model presented in our previous paper in predicting the length of stay (LOS) in neurosurgery. We aimed to assess the performance the Russian GPT-3 (ruGPT-3) language model in LOS prediction using narrative medical records in neurosurgery compared to doctors' and patients' expectations. Doctors appeared to have the most realistic LOS expectations (MAE = 2.

Multinomial Classification of Neurosurgical Operations Using Gradient Boosting and Deep Learning Algorithms.

This study aimed at testing the feasibility of neurosurgical procedures classification into 100+ classes using natural language processing and machine learning. A catboost algorithm and bidirectional recurrent neural network with a gated recurrent unit showed almost the same accuracy of ∼81%, with suggestions of correct class in top 2-3 scored classes up to 98.9%.

Noninvasive Glioma Grading with Deep Learning: A Pilot Study.

Gliomas are the most common neuroepithelial brain tumors, different by various biological tissue types and prognosis. They could be graded with four levels according to the 2007 WHO classification. The emergence of non-invasive histological and molecular diagnostics for nervous system neoplasms can revolutionize the efficacy and safety of medical care and radically reduce healthcare costs.

The Classification of Scientific Abstracts Using Text Statistical Features.

Automated abstracts classification could significantly facilitate scientific literature screening. The classification of short texts could be based on their statistical properties. This research aimed to evaluate the quality of short medical abstracts classification primarily based on text statistical features.

Length of Stay Prediction in Neurosurgery with Russian GPT-3 Language Model Compared to Human Expectations.

Patients, relatives, doctors, and healthcare providers anticipate the evidence-based length of stay (LOS) prediction in neurosurgery. This study aimed to assess the quality of LOS prediction with the GPT3 language model upon the narrative medical records in neurosurgery comparing to doctors' and patients' expectations. We found no significant difference (p = 0.

Semiautomatic Identification of Pulmonary Embolism in Electronic Health Records Through Sentence Labeling.

In this study, we tested the quality of the information extraction algorithm proposed by our group to detect pulmonary embolism (PE) in medical cases through sentence labeling. Having shown a comparable result (F1 = 0.921) to the best machine learning method (random forest, F1 = 0.

A Comparison of Word Embeddings to Study Complications in Neurosurgery.

Our study aimed to compare the capability of different word embeddings to capture the semantic similarity of clinical concepts related to complications in neurosurgery at the level of medical experts. Eighty-four sets of word embeddings (based on Word2vec, GloVe, FastText, PMI, and BERT algorithms) were benchmarked in a clustering task. FastText model showed the best close to the medical expertise capability to group medical terms by their meaning (adjusted Rand index = 0.

Best Research Practice Implementation: The Experience of the N.N. Burdenko National Medical Research Center of Neurosurgery.

Implementing the best research principles initiates an important shift in clinical research culture, improving efficiency and the level of evidence obtained. In this article, we share our own view on the best research practice and our experience introducing it into the scientific activities of the N.N.

Strong optomechanical coupling in chain-like waveguides of silicon nanoparticles with quasi-bound states in the continuum.

We propose and demonstrate that strong optomechanical coupling can be achieved in a chain-like waveguide consisting of silicon nanorods. By employing quasi-bound states in the continuum and mechanical resonances at a frequency around 10 GHz, the optomechanical coupling rate can be above 2 MHz and surpass most microcavities. We have also studied cases with different optical wave numbers and size parameters of silicon, and a robust coupling rate has been verified, benefiting the experimental measurements and practical applications.

Prediction of Intraoperative Fluorescence of Brain Gliomas: Correlation between Tumor Blood Flow and the Fluorescence.

Introduction: The prediction of the fluorescent effect of 5-aminolevulinic acid (5-ALA) in patients with diffuse gliomas can improve the selection of patients. The degree of enhancement of gliomas has been reported to predict 5-ALA fluorescence, while, at the same time, rarer cases of fluorescence have been described in non-enhancing gliomas. Perfusion studies, in particular arterial spin labeling perfusion, have demonstrated high efficiency in determining the degree of malignancy of brain gliomas and may be better for predicting fluorescence than contrast enhancement.

A review of dielectric optical metasurfaces for spatial differentiation and edge detection.

Dielectric metasurfaces-based planar optical spatial differentiator and edge detection have recently been proposed to play an important role in the parallel and fast image processing technology. With the development of dielectric metasurfaces of different geometries and resonance mechanisms, diverse on-chip spatial differentiators have been proposed by tailoring the dispersion characteristics of subwavelength structures. This review focuses on the basic principles and characteristic parameters of dielectric metasurfaces as first- and second-order spatial differentiators realized via the Green's function approach.

Inter-Rater Reliability of Unstructured Text Labeling: Artificially vs. Naturally Intelligent Approaches.

Unstructured medical text labeling technologies are expected to be highly demanded since the interest in artificial intelligence and natural language processing arises in the medical domain. Our study aimed to assess the agreement between experts who judged on the fact of pulmonary embolism (PE) in neurosurgical cases retrospectively based on electronic health records and assess the utility of the machine learning approach to automate this process. We observed a moderate agreement between 3 independent raters on PE detection (Light's kappa = 0.

The Classification of Short Scientific Texts Using Pretrained BERT Model.

Automated text classification is a natural language processing (NLP) technology that could significantly facilitate scientific literature selection. A specific topical dataset of 630 article abstracts was obtained from the PubMed database. We proposed 27 parametrized options of PubMedBERT model and 4 ensemble models to solve a binary classification task on that dataset.

Effects of Hyperthermia on Intracranial Pressure and Cerebral Autoregulation in Patients with an Acute Brain Injury.

Hyperthermia is a common detrimental condition in patients with an acute brain injury (ABI), which can worsen their prognosis and outcome. The aim of this study was to evaluate the effects of hyperthermia on intracranial pressure (ICP) and cerebral autoregulation (CA).Eight patients with ABI were studied.

Rapid estimation of tumor cell percentage in brain tissue biopsy samples using inline cartridge extraction mass spectrometry.

Tumor cell percentage (TCP) is an essential characteristic of biopsy samples that directly affects the sensitivity of molecular testing in clinical practice. Apart from clarifying diagnoses, rapid evaluation of TCP combined with various neuronavigation systems can be used to support decision making in neurosurgery. It is known that ambient mass spectrometry makes it possible to rapidly distinguish healthy from malignant tissues.

Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues.

Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast identification of dissected tissues could assist the neurosurgery procedure.

Mambalgin-2 Induces Cell Cycle Arrest and Apoptosis in Glioma Cells via Interaction with ASIC1a.

Gliomas are fast growing and highly invasive brain tumors, characterized by tumor microenvironment acidification that drives glioma cell growth and migration. Channels containing Acid-sensing Ion Channel 1a subunit (ASIC1a) mediate amiloride-sensitive cation influx in late stage glioma cells, but not in normal astrocytes. Thus, selective targeting of ASIC1a can be a perspective strategy for glioma treatment.

Classification of Intracranial Hemorrhage Subtypes Using Deep Learning on CT Scans.

Intracranial hemorrhage is a pathological condition that requires fast diagnosis and decision making. Recently, a neural network model for classification of different intracranial hemorrhage types was proposed by a member of our research group Konstantin Kotik as part of the machine learning competition at Kaggle. Our current pilot study aimed to test this model on real-world CT scans from patients with intracranial hemorrhage treated at N.

The Classification of Scientific Literature for Its Topical Tracking on a Small Human-Prepared Dataset.

The number of scientific publications is constantly growing to make their processing extremely time-consuming. We hypothesized that a user-defined literature tracking may be augmented by machine learning on article summaries. A specific dataset of 671 article abstracts was obtained and nineteen binary classification options using machine learning (ML) techniques on various text representations were proposed in a pilot study.

Semiautomated Approach for Muscle Weakness Detection in Clinical Texts.

The automated detection of adverse events in medical records might be a cost-effective solution for patient safety management or pharmacovigilance. Our group proposed an information extraction algorithm (IEA) for detecting adverse events in neurosurgery using documents written in a natural rich-in-morphology language. In this paper, we challenge to optimize and evaluate its performance for the detection of any extremity muscle weakness in clinical texts.