Temporal dynamics of woolly mammoth genome erosion prior to extinction.

A number of species have recently recovered from near-extinction. Although these species have avoided the immediate extinction threat, their long-term viability remains precarious due to the potential genetic consequences of population declines, which are poorly understood on a timescale beyond a few generations. Woolly mammoths (Mammuthus primigenius) became isolated on Wrangel Island around 10,000 years ago and persisted for over 200 generations before becoming extinct around 4,000 years ago. To study the evolutionary processes leading up to the mammoths' extinction, we analyzed 21 Siberian woolly mammoth genomes. Our results show that the population recovered quickly from a severe bottleneck and remained demographically stable during the ensuing six millennia. We find that mildly deleterious mutations gradually accumulated, whereas highly deleterious mutations were purged, suggesting ongoing inbreeding depression that lasted for hundreds of generations. The time-lag between demographic and genetic recovery has wide-ranging implications for conservation management of recently bottlenecked populations.

Thromboelastometry-Based Prophylaxis for Venous Thromboembolism in the Acute Period Following Isolated Severe Traumatic Brain Injury.

Objective: Traumatic brain injury (TBI) is an independent risk factor for venous thromboembolism (VTE). This study aimed to determine the optimal timing for initiating pharmacological thromboprophylaxis for VTE in patients with isolated severe TBI using rotational thromboelastometry (ROTEM). Methods: This single-center observational study enrolled 115 patients aged 18-59 years with isolated severe TBI within the first 48 hours after injury. Results: Using ROTEM data, we identified hypercoagulation due to an increase in clot density (MCF EXTEM >72), which was attributed to fibrinogen (MCF FIBTEM >25). From day 4, hypercoagulation occurred in 14.8% of the patients. By day 7, these changes were observed in 85.2% of patients. According to brain computed tomography findings, patients who received early VTE chemoprophylaxis on days 3-4 after severe TBI did not experience progression of hemorrhagic foci. Conclusion: Our results emphasize the clinical significance of thromboelastometry in patients with isolated severe traumatic TBI. Anticoagulant prophylaxis started on 3-4 days after severe TBI was relatively safe, and most patients did not experience hemorrhagic foci progression. The data acquired in this study may enable the optimization of VTE chemoprophylactic approaches, thereby reducing the associated risks to patients.

Reconstruction of orbital walls after resection of cranioorbital meningiomas: a systematic review and meta-analysis of individual patient data.

Following meningioma removal, there are numerous methods available for reconstructing the orbital wall. This systematic review seeks to summarize the published data on the surgical treatment of cranioorbital meningiomas, and to analyze the effectiveness and safety of various techniques and materials used for the reconstruction of bony orbital walls. We conducted a search of the two databases and included original articles with a series of 10 or more cases. Descriptive statistics and meta-analysis of individual patient date were performed. The analysis included a total of 858 patients from 29 sources. No reconstruction of the orbital walls was performed in 525 patients (61.2%), while 333 observations (38.8%) involved resection followed by reconstruction. A relative improvement in eye position was achieved in 94.4% of cases with a 95% CI of (88.92%; 97.25%). However, normalization of eye position, regardless of reconstruction technique, was only present in 6.22% of cases with a 95% CI of (1.24%; 25.9%). The best results were observed with the use of autologous bone implants (64%, 95% CI [33.35%; 86.33%]) and titanium implants (55.78%, 95% CI [2.86%; 98.18%]). In cases of endoscopic resection and microsurgical resection without reconstruction, symmetrical eye position accounted for only 1.94% (95% CI [0%; 96.71%]) and 2.35% (95% CI [0.13%; 31.23%]), respectively. The frequency of normalization of eye position differed significantly (p < 0.01) among the subgroups. A total of 49 postoperative complications were registered, with wound infection (1.52%, 95% CI [0.86%; 2.65%]) and wound cerebrospinal fluid leak (1.32%, 95% CI [0.6%; 2.91%]) being the most frequent. No significant differences were found in the rates of complications among the different subgroups. One of the primary objectives of cranioorbital meningioma surgery is to correct the position of the eye. Simultaneous reconstruction of the bony orbital leads to better cosmetic outcomes. Postoperative complications did not depend on the reconstructive technique or the materials.

Radiomics Enhances Diagnostic and Prognostic Value of Diffusion Kurtosis Imaging in Diffuse Axonal Injury.

The aim of our study was to investigate the potential of advanced radiomics in analyzing diffusion kurtosis MRI (DKI) to increase the informativeness of DKI in diffuse axonal injury (DAI). We hypothesized that DKI radiomic features could be used to detect microstructural brain injury and predict outcomes in DAI. The study enrolled 31 patients with DAI (mean age 31.48 ± 11.10 years, 8 (25.8%) female) and 12 healthy volunteers (mean age 33.67 ± 11.06 years, 4 (33.3%) female). A total of 342,300 radiomic features were calculated (2282 features per each combination of 10 parametric DKI maps with 15 ROIs). Our results showed that several radiomic features were capable of distinguishing between healthy and injured brain tissue and accurately predicting outcomes with an accuracy of over 0.9. Advanced DKI radiomic features show high diagnostic and prognostic potential in DAI and may outperform average ROI values in DKI maps.

Intraoperative Neurophysiologic Monitoring in Predicting Dysphagia After Brainstem and Fourth Ventricle Surgery.

OBJECTIVE: Dysphagia represents the main complication of posterior fossa neurosurgery. Adequate diagnosis of this complication is warranted to prevent untimely extubation with subsequent aspiration. Intraoperative neurophysiologic monitoring (IONM) modalities may be used for this purpose. However, it is not known which IONM modality may be significant for diagnosis. This study aimed to define the most significant IONM modality for dysphagia prognostication after posterior fossa neurosurgery. METHODS: The analysis included 46 patients (34 with tumors of the fourth ventricle and 12 with brainstem localization) who underwent surgical excision of the tumor. Neurologic symptoms before and after neurosurgery were noted and magnetic resonance imaging with the subsequent volume estimation of the removed mass was performed, followed by an IONM findings analysis (mapping of the nucleus of the caudal cranial nerves [CN] and corticobulbar motor-evoked potentials [CoMEP]). RESULTS: Aggravation of dysphagia was noted in 24% of the patients, more often in patients with tumor localization in the fourth ventricle (26%) than in those with brainstem mass lesions (16%). Mapping of the caudal cranial nerve nuclei did not correlate with the dysfunction of these structures. CoMEP was significantly associated with the neurologic state of the CN. The decrease in CoMEP is a significant prognostic factor for postoperative bulbar symptoms appearance or aggravation. CONCLUSIONS: Mapping the CN is an important identification tool. The CoMEP modality should be used intraoperatively to determine the functional state of the CN and predict postoperative dysphagia.

Supraorbital eyebrow approach and pterional approach in surgical treatment of ruptured and unruptured aneurysms: a propensity score-matched analysis.

The aim of this study is to reveal efficacy and efficiency of the supraorbital eyebrow approach (SEA) in clipping ruptured and unruptured aneurysms where both SEA and pterional approach (PA) are considered feasible by comparing SEA and PA using propensity score matching. A total of 229 patients who underwent surgical clipping of an aneurysm from 2013 to 2022 and met inclusion criteria were recruited in the study. A study group of 67 patients treated via the SEA and a comparison group of 162 patients treated via the PA were formed. Then, the subgroups of 66 patients each, with balanced incoming factors, were analyzed using the propensity score technique. The safety and efficacy endpoints were compared. Successful clipping was achieved in all cases of both groups. There were no patients in the SEA group who required conversion to the standard craniotomy. There were no procedure-related deaths in this series. No patient experienced early or late rebleeding in either group. Mean blood loss volume in the SEA group was lower than in the PA group by approximately 77.5 ml (p < 0.001). There were favorable differences in the SEA group regarding postoperative neurological deficit (p = 0.016), postoperative epileptic seizure rate (p = 0.013), ischemic and hemorrhagic complications (p = 0.028 and 0.0009, respectively), and outcomes (p < 0.001). Patients' satisfaction with cosmetic results measured by the visual analog scale was rated highly in both groups (p = 0.081). For patients where SEA provides adequate exposure, it results in safety and cosmetic outcomes not inferior to the PA.

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. We redesigned the target variable based on three strict principles when applicable: pathology, localization, and manipulation type. The deep learning significantly improved with the best result of operative report classification into 13 classes (accuracy = 0.995, F1 = 0.990). Reasonable text classification with machine learning should be a two-way process: the model performance must be ensured by the unambiguous textual representation reflected in corresponding target variables. At the same time, the validity of human-generated codification can be inspected via machine learning.

Radiogenomics in NF2-Associated Schwannomatosis (Neurofibromatosis Type II): Exploratory Data Analysis.

Our pilot study aimed at exploratory radiogenomic data analysis in patients with NF2-associated schwannomatosis (formerly neurofibromatosis type II) to assume the potential of image biomarkers in this pathology. Fifty-three unrelated patients (37 (69.8%) women, avg. age 30.2 ± 11.2 y.o.) were enrolled in the study. First-order, gray-level co-occurrence matrix (GLCM), gray-level run length matrix (GLRLM), and geometry-based statistics were calculated (3718 features per region of interest). We demonstrated imaging patterns and statistically significant differences in radiomic features potentially related to the genotype and clinical phenotype of the disease. However, the clinical utility of these patterns should be further evaluated. The study was supported by the Russian Science Foundation grant 21-15-00262.

The Assessment of Glioblastoma Metabolic Activity via 11C-Methionine PET and Radiomics.

Nowadays, the quantitative analysis of PET/CT data in patients with glioblastoma is not strictly standardized in the clinic and does not exclude the human factor. This study aimed to evaluate the relationship between the radiomic features of glioblastoma 11C-methionine PET images and the tumor-to-normal brain (T/N) ratio determined by radiologists in clinical routine. PET/CT data were obtained for 40 patients (mean age 55 ± 12 years; 77.5% men) with a histologically confirmed diagnosis of glioblastoma. Radiomic features were calculated for the whole brain and tumor-containing regions of interest using the RIA package for R. We redesigned the original RIA functions for GLCM and GLRLM calculation to reduce computation time significantly. Machine learning over radiomic features was applied to predict T/N with the best median correlation between the true and predicted values of 0.73 (p = 0.01). The present study showed a reproducible linear relationship between 11C-methionine PET radiomic features and a T/N indicator routinely assessed in brain tumors. Radiomics enabled utilizing texture properties of PET/CT neuroimaging that may reflect the biological activity of glioblastoma and can potentially augment the radiological assessment.

Genomics of adaptive evolution in the woolly mammoth.

Ancient genomes provide a tool to investigate the genetic basis of adaptations in extinct organisms. However, the identification of species-specific fixed genetic variants requires the analysis of genomes from multiple individuals. Moreover, the long-term scale of adaptive evolution coupled with the short-term nature of traditional time series data has made it difficult to assess when different adaptations evolved. Here, we analyze 23 woolly mammoth genomes, including one of the oldest known specimens at 700,000 years old, to identify fixed derived non-synonymous mutations unique to the species and to obtain estimates of when these mutations evolved. We find that at the time of its origin, the woolly mammoth had already acquired a broad spectrum of positively selected genes, including ones associated with hair and skin development, fat storage and metabolism, and immune system function. Our results also suggest that these phenotypes continued to evolve during the last 700,000 years, but through positive selection on different sets of genes. Finally, we also identify additional genes that underwent comparatively recent positive selection, including multiple genes related to skeletal morphology and body size, as well as one gene that may have contributed to the small ear size in Late Quaternary woolly mammoths.

Burr hole microsurgical subtemporal selective amygdalohippocampectomy.

INTRODUCTION: At present, selective amygdalohippocampectomy (SAH) has become popular in the treatment of drug-resistant mesial temporal lobe epilepsy (TLE). However, there is still an ongoing discussion about the advantages and disadvantages of this approach. METHODS: The study included a consecutive series of 43 adult patients with drug-resistant TLE, involving 24 women and 19 men (1.8/1). Surgeries were performed at the Burdenko Neurosurgery Center from 2016 to 2019. To perform subtemporal SAH through the burr hole with the diameter of 14 mm, we used two types of approaches: preauricular, 25 cases, and supra-auricular, 18 cases. The follow-up ranged from 36 to 78 months (median 59 months). One patient died 16 months after surgery (accident). RESULTS: By the third year after surgery, Engel I outcome was achieved in 80.9% (34 cases) of cases and Engel II in 4 (9.5%) and Engel III and Engel IV in 4 (9.6%) cases. Among the patients with Engel I outcomes, anticonvulsant therapy was completed in 15 (44.1%), and doses were reduced in 17 (50%) cases. Verbal and delayed verbal memory decreased after surgery in 38.5% and 46.1%, respectively. Verbal memory was mainly affected by preauricular approach in comparison with supra-auricular (p = 0.041). In 15 (51.7%) cases, minimal visual field defects were detected in the upper quadrant. At the same time, visual field defects did not extend into the lower quadrant and inside the 20° of the upper affected quadrant in any case. CONCLUSIONS: Burr hole microsurgical subtemporal SAH is an effective surgical procedure for drug-resistant TLE. It involves minimal risks of loss of visual field within the 20° of the upper quadrant. Supra-auricular approach, compared to preauricular, results in a reduction in the incidence of upper quadrant hemianopia and is associated with a lower risk of verbal memory impairment.

Anatomical and Technical Preparations of the Human Brain for White Matter Fibers Dissection with Electromagnetic Neuronavigation Assistance Technical Nuances for Application.

BACKGROUND: Klinger's fiber dissection technique is widely used for studying the anatomy of white matter. Herein, we present a technical description of Klinger's proposed fiber dissection algorithm with neuronavigation assistance which allows for a more accurate determination of the projection of association fibers. METHODS: An anatomical study was conducted on 8 hemispheres of the human brain, prepared according to the Klingler fiber dissection technique. In all the cases, a frameless electromagnetic navigation system was used. For each anatomical specimen, an individualized support device was three-dimensional -printed and placed it into the magnetic resonance imaging (MRI) gantry. MRI study of each anatomical specimen was performed using a specific protocol that enabled a subsequent three-dimensional visualization of the anatomical structures as follows: FSPGR (Fast SPoiled Gradient Recalled echo) BRAVO (BRAin VOlume Imaging), T2 CUBE, FLAIR (FLuid Attenuated Inversion Recovery) CUBE, CUBE DIR (double inversion recovery) WHITE MATTER, and CUBE DIR GRAY MATTER. RESULTS: The average time required to register an anatomical specimen in the navigation system was 7 minutes 28 seconds. In all of the 8 cases, the anatomical structures were correctly identified using neuronavigation. Moreover, the choice of MRI mode depends on the purpose of the study and the region of interest in the brain. CONCLUSIONS: Electromagnetic navigation is an accurate and useful technique. It allows the researcher the ability to virtually project the association fibers and their cortico-cortical terminations to the surface of the brain, even at the final stages of dissection when the superficial structures are removed. To obtain accurate targeting, it is important to use the appropriate neuronavigation protocol.

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%. The classification of neurosurgical procedures via machine learning appears to be a technically solvable task which can be additionally improved considering data enhancement and classes verification.

Artificial Intelligence: On the Way to Doctor's Trust.

The submission is devoted to reflections on the role of trust to modern IT systems, especially based on the AI technologies. Its purpose is to draw the attention of the medical informatics community to the need to achieve trust at all stages of the life cycle of MDSS and other information systems.

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 = 2.54), while the model's predictions (MAE = 3.53) were closest to the patients' (MAE = 3.47) but inferior to them (p = 0.011). A detailed analysis showed a solid quality of ruGPT-3 performance based on narrative clinical texts. Considering our previous findings obtained with recurrent neural networks and FastText vector representation, we estimate the new result as important but probably improveable.

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. Twelve experiments with machine learning models over the sets of text features were performed on a dataset of 671 article abstracts. Each experiment was repeated 300 times to estimate the classification quality, ending up with 3600 tests total. We achieved the best result (F1 = 0.775) using a random forest machine learning model with keywords and three-dimensional Word2Vec embeddings. The classification of scientific abstracts might be implemented using straightforward and computationally inexpensive methods presented in this paper. The approach we described is expected to facilitate literature selection by researchers.

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. Our pilot study aimed to evaluate the diagnostic accuracy of deep learning (DL) in subtyping gliomas by WHO grades (I-IV) based on preoperative magnetic resonance imaging (MRI) from Burdenko Neurosurgery Center's database. A total of 707 MRI studies was included. A "3D classification" approach predicting tumor type for the entire patient's MRI data showed the best result (accuracy = 83%, ROC AUC = 0.95), consistent with that of other authors who used different methodologies. Our preliminary results proved the separability of MR T1 axial images with contrast enhancement by WHO grade using DL.

The Prediction of Functional Outcome After Microsurgical Treatment of Unruptured Intracranial Aneurysm Based on Machine Learning.

Our study aimed to create a machine learning model to predict patients' functional outcomes after microsurgical treatment of unruptured intracranial aneurysms (UIA). Data on 615 microsurgically treated patients with UIA were collected retrospectively from the Electronic Health Records at N.N. Burdenko Neurosurgery Center (Moscow, Russia). The dichotomized modified Rankin Scale (mRS) at the discharge was used as a target variable. Several machine learning models were utilized: a random forest upon decision trees (RF), logistic regression (LR), support vector machine (SVM). The best result with F1-score metric = 0.904 was produced by the SVM model with a label-encode method. The predictive modeling based on machine learning might be promising as a decision support tool in intracranial aneurysm surgery.

Development and Optimization of a Silica Column-Based Extraction Protocol for Ancient DNA.

Rapid and cost-effective retrieval of endogenous DNA from ancient specimens remains a limiting factor in palaeogenomic research. Many methods have been developed to increase ancient DNA yield, but modifications to existing protocols are often based on personal experience rather than systematic testing. Here, we present a new silica column-based extraction protocol, where optimizations were tested in controlled experiments. Using relatively well-preserved permafrost samples, we tested the efficiency of pretreatment of bone and tooth powder with a bleach wash and a predigestion step. We also tested the recovery efficiency of MinElute and QIAquick columns, as well as Vivaspin columns with two molecular weight cut-off values. Finally, we tested the effect of uracil-treatment with two different USER enzyme concentrations. We find that neither bleach wash combined with a predigestion step, nor predigestion by itself, significantly increased sequencing efficiency. Initial results, however, suggest that MinElute columns are more efficient for ancient DNA extractions than QIAquick columns, whereas different molecular weight cut-off values in centrifugal concentrator columns did not have an effect. Uracil treatments are effective at removing DNA damage even at concentrations of 0.15 U/µL (as compared to 0.3 U/µL) of ancient DNA extracts.

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 = 0.682). Word embedding models can accurately reflect clinical concepts' semantic and linguistic similarities, promising their robust usage in medical domain-specific NLP tasks.