Jasmin Kevric

Introduction: The COVID-19 pandemic significantly influenced mental health worldwide. This study assessed the use of antidepressants, anxiolytics, antipsychotics, and mood stabilizers in Sarajevo Canton across three periods: before, during, and after the pandemic. Methods: The data used in this study were taken from the database of the production system of the Public Institution “Apoteke Sarajevo,” Bosnia and Herzegovina. The database contains a total of 1,764,582 records on the sale of individual drugs, i.e., 2,361,059 original packages of drugs that are the subject of this research. Prescription data for psychotropic medications in the adult population were analyzed and compared between the pre-pandemic, pandemic, and post-pandemic phases. Results: Antidepressant use increased by 2.76% during the pandemic and by an additional 3.82% afterward. Anxiolytics showed a reduction of 5.29% during the pandemic and a further 18.14% decline post-pandemic. Antipsychotic prescriptions increased by 5.26% during the pandemic but decreased by 8.65% in the following period. Mood stabilizers increased by 6.8% during the pandemic and fell by 5.57% afterward. The most commonly used drugs were escitalopram among antidepressants, lamotrigine among mood stabilizers, bromazepam and diazepam among anxiolytics, and sulpiride, haloperidol, and promazine among antipsychotics. Conclusion: The pandemic was associated with a rise in antidepressant and mood stabilizer use, likely reflecting heightened psychological stress and social isolation, whereas anxiolytics declined and antipsychotics fluctuated. These findings underline the lasting impact of the pandemic on community mental health and the need for improved preventive and therapeutic strategies.

Introduction/Objective: Reliable identification of individuals from physiological signals is an active research direction in biometrics. Electroencephalography (EEG) is one such modality because brain signals carry individual-specific patterns. This study investigates whether a deep learning–based EEG identification framework can achieve reliable closed-set individual identification on a 109-subject benchmark under a standard subject-dependent evaluation protocol. Verification scenarios and the associated metrics (FAR, FRR, EER) are out of scope and are not reported. This work proposes and validates an attention-augmented Bi-GRU model for EEG-based individual identification, aiming to achieve high identification accuracy on a large multi-subject public dataset. Methods: Time-domain features were extracted from 64-channel EEG recordings collected during left-hand motor movement/imagery tasks from 109 subjects. A three-layer Bi-GRU model with a self-attention mechanism was trained for a 109-class identification task using a 70/15/15% subject-dependent train–validation–test split. Training robustness was supported through training-only SMOTE-based class balancing and Gaussian-noise augmentation. Results: Under the adopted subject-dependent split, the proposed model achieved a mean test accuracy of 96.68% ± 0.25% across three independent training runs (95% Student-t CI 96.04–97.31%), a top-5 accuracy of 99.37% ± 0.15%, and a small validation–test gap of +0.37 ± 0.90 percentage points. Performance was higher than several traditional machine learning and simpler deep learning baselines and was comparable to reported transformer and graph neural network results. Discussion: The attention mechanism highlighted discriminative temporal patterns from EEG signals; performance under the subject-dependent split indicated that our model was able to maintain stable identification performance across the evaluated recordings. Conclusion: The attention-enhanced Bi-GRU is able to capture discriminative temporal EEG patterns and shows strong identification performance under the adopted protocol, supporting further investigation of EEG-based biometric identification systems.

Introduction/Objective: Glioblastoma (GBM) is the most lethal primary brain malignancy, with a 12-15-month median survival. This study externally validates quantum-inspired machine learning for GBM risk stratification using the BraTS 2020 cohort, assessing cross-domain and cross-tumor-type generalization. The aim was to validate the quantum radiomics methodology on an independent GBM dataset and evaluate cross-domain generalizability from genomics to radiomics applications. Methods: A total of 75 radiomics features were extracted from multi-modal MRI (T1, T1ce, T2, FLAIR) of 494 GBM patients. Quantum transformation expanded features to 715 dimensions. A calibrated ensemble model was trained for 12-month survival risk stratification with a 60/15/25% train/validation/test split. Results: Test performance achieved 96.80% accuracy [95% CI: 94.92%-98.68%], 99.97% AUC- ROC, 100% precision, 93.55% sensitivity, and 100% specificity. Perfect specificity (zero false positives) enables confident low-risk prediction. Performance outperformed publicized benchmarks: traditional radiomics (73-76%), simple deep learning (82-88%), and complex deep learning models (89-92%). The small validation-test gap (98.65% and 96.80%) also indicates adequate generalization. Discussion: The significant advantage over current methods suggests that quantum-inspired feature transformation is capable of capturing nonlinear patterns in radiomics data, which conventional methodologies fail to do. Perfect specificity has clinical significance, indicating that confident identification of low-risk patients can be achieved for decisions about treatment planning. The out-of-domain success of cross-domain from genomics to radiomics indicates domain independence for the quantum transform. Conclusion: Quantum radiomics generalizes to external validation with clinical-grade performance. Cross-domain validation indicates the domain-agnostic nature of quantum transformation as an improvement to biomedical data and its potential for clinical treatment planning.