Alon Gorenshtein - research works

For each patient we streamed one 24-minute window near 24 hours after return of spontaneous circulation, computed the algorithmic burst-suppression ratio on a 5-second grid, and derived non-saturated occupancy, within-window stability, and short-horizon forecastability.

Main results The burst-suppression ratio occupied a non-saturated band (0.10 to 0.90) for a median of 0.250 of monitored time (95% CI 0.200 to 0.306) and sat toward suppression (median operating level 0.824).

We developed multiple lightweight deep learning models ResNet-18 (both pretrained on ImageNet and trained from scratch), ResNet-34, ResNet-50, and a custom CNN to classify glioma, meningioma, pituitary tumor, and no tumor MRI scans.

A dataset of 7023 images was employed, split into 5712 for training and 1311 for validation.

Learning rate optimization facilitated stable convergence, and loss metrics indicated effective generalization with minimal overfitting.

Our findings confirm that a moderately sized, transfer-learned network (ResNet-18) can deliver high diagnostic accuracy and robust performance for four-class brain tumor classification.

Confounding by indication was addressed with propensity overlap weighting; associations are reported as adjusted risk ratios (RRs) with bootstrap 95% CIs and E-values.

Results Among 13,285 treated adults (10,799 MIMIC-IV-ED; 2,486 MC-MED), opioid recipients were older and higher-acuity than dopamine-antagonist recipients (index admission 38.1% vs 16.4%).

In the MIMIC-IV-ED discharged primary-contrast population, overlap weighting reduced the maximum standardized mean difference from 0.35 to 0.002; pooled and site-specific balance diagnostics are provided in the Supplement.

In MIMIC-IV-ED, the cumulative all-cause return incidence by treatment class separated by day 3 and persisted through 30 days.

Among 19,501 visits (15,273 patients), 13,682 (70.2%) were treated.

A post-treatment pain score appeared at any time for 77.1% (95% CI, 76.4 to 77.8), but within 2 hours of the analgesic for only 47.9% and within 1 hour for 27.5%.

Completeness was similar across headache strata and comparator painful presentations.

In an independent ED (MC-MED, a different EHR), the score-selection effect replicated: relief rose from 71.1% (first) to 80.6% (last) and 83.4% (lowest).

Abstract Objective A calibration-derived score has been related to online P300-speller accuracy in the same session, always within the cohort measured.

Pooled estimation error was 0.098 (95% CI 0.091 to 0.107) against a benchmark of 0.146.

A protocol proxy for the stopping rule, median time per selection, reduced the between-cohort slope variance by 66%.

The four ALS cohorts alone gave an uncorrected between-cohort slope spread of 0.223, against 0.560 overall.

Out of 10,829 retrieved citations, 16 studies met the inclusion criteria.

Infectious etiologies included herpetic uveitis (pooled proportion 11.40%, 95% CI 7.45-17.04%), cytomegalovirus (3.09%, 95% CI 1.01-9.06%) and tuberculosis (5.10%, 95% CI 2.58-9.83%).

Non-infectious causes, such as sarcoidosis (5.21%, 95% CI 2.78-9.55%) and HLA-B27-associated uveitis (6.05%, 95% CI 3.35-10.71%), also contributed substantially.

High heterogeneity (I 2 > 75%) was noted across most etiologies, likely reflecting variable study populations, diagnostic criteria, and geographic factors.

Of 2,273 records, 10 studies met criteria (2005-2025; sample sizes 144-15373).

Infectious causes were less frequent: herpetic uveitis 2.22% (95% CI 1.47-3.34%), acute retinal necrosis (ARN) 0.94% (95% CI 0.39-2.25%), and syphilis 1.03% (95% CI 0.59-1.79%), although rates varied significantly across centers.

Data were pooled using a random-effects model, with heterogeneity assessed via I 2 statistics.

Out of a total of 8,974 studies initially considered, only 30 met the criteria for inclusion.

Juvenile idiopathic arthritis (JIA)-associated uveitis was most prevalent (15.59%, 95% CI: 11.92-20.14%) of all cases and was the leading defined non-infectious cause, Behçet's disease (4.8%) and Vogt-Koyanagi-Harada disease (3.63%) of all cases.

Of 2,465 records, 19 studies met inclusion criteria, conducted across Asia, Europe, South America, the Middle East, and Africa.

Several were more frequent among males: Behçet's disease (RR 0.52, 0.36-0.75), sympathetic ophthalmia (RR 0.49, 0.34-0.71), syphilitic uveitis (RR 0.41, 0.28-0.60), and ankylosing spondylitis-associated uveitis (RR 0.55, 0.47-0.65).

A total of 5,632 eligible articles were identified.

This article provides relevant information from 12 studies involving 6,849 patients, of which 3,414 were female.

The earliest publication date was September 16, 2021, while the latest was June 11, 2024.

High heterogenicity was found for fatigue, brain fog, and anxiety due to the diverse methodologies employed in the studies.

The results indicated significant differences in gut microbiota between migraine patients and controls.

Conversely, Veillonella exhibited elevated abundance compared to controls.

Out of a total of 1,738 studies initially considered, only 20 met the criteria for inclusion.

Most studies showed a greater number of female patients.

In Tokyo, sarcoidosis accounts for 8.9% of uveitis cases, making it the most systemic association cause.

To date, several potential interventions for brain fog have been considered.

We included studies on adult (aged > 18 years) individuals with proven long- COVID brain-fog symptoms from PubMed, MEDLINE, Central, Scopus, and Embase.

A search limit was set for articles published between 01/2020 and 31/12/2023.

We excluded studies lacking an objective assessment of brain fog symptoms and patients with preexisting neurological diseases that affected cognition before COVID-19 infection.

Included 1,621 images for glioma, 1,645 for meningioma, 1,757 for pituitary tumors, and 2,000 for non-tumor magnetic resonance imaging (MRI) images.

After fine-tuning, the model that demonstrated the highest performance on validation data was selected as the best model Results The deep learning model demonstrated exceptional performance across all diagnostic categories, with the training accuracy stabilizing at nearly 100% and validation accuracy closely mirroring this trend.