Lucian Eva

Background/Objectives: Distinct responses to optic-nerve injury motivate multidimensional, state-dependent control rather than a universal regeneration score. We tested whether public perturbational transcriptomes could define interpretable states and support computational policy compression. Methods: Five public mouse datasets were analyzed using six prespecified biological modules. Zero-preserving transformed treatment–control effects defined nine perturbation anchors. A 16-region engineering simulator was used to retrain a gradient-boosting student and select an explicit decision tree in disjoint simulated episode cohorts. Results: Longitudinal treatment-response correlations were 0.177–0.406, while same-time PTEN-derived interventions remained non-identical (pairwise Spearman rho 0.670–0.787). A newly trained student attained 85.67% action-label accuracy and macro-F1 0.598. A depth-3 tree (eight leaves, six features) retained 98.59% and 98.62% of the recalibrated MPC objective in 120 independent Standard and 120 OOD episodes, with failure-rate differences of +3.33 and +0.83 percentage points. The 95% bootstrap interval for the Standard failure increase exceeded the five-percentage-point selection limit. Greedy one-step control performed similarly to or slightly better than the approximate MPC. Additional post-review OOD simulations revealed sensitivity to episode sampling and alternative engineering dynamics, limiting the generalizability of the initial benchmark estimates. Conclusions: BioEdge-Rx demonstrates computational compression of a state-dependent policy within an assumed simulator. It does not establish causal treatment transitions, biological efficacy, sensor-level realizability, or superiority of multistep planning.

Background/Objectives: Optic nerve injury involves heterogeneous neuronal and microenvironmental responses that may require sequential, state-dependent intervention. BioEdge-RGC was developed as a transcriptomics-informed computational benchmark for transferring a globally planned model-predictive-control policy to compact regional neural models operating with incomplete observations and reduced communication. Methods: Public mouse optic-nerve-crush transcriptomic datasets were summarized into seven retinal ganglion cell modules and eight retinal environment modules and fused into a 15-dimensional temporal reference state for a 16-region, five-step simulator. A candidate-constrained MPC expert was approximated by a central neural teacher and local, neighbour-aware, and coordinated regional students trained using hybrid knowledge distillation or direct MPC supervision. Missing observations, severe simulated injury, INT8 quantization, local parameter perturbations, and an independent GSE229033 transcriptomic plausibility assessment were evaluated. Results: The central teacher retained 99.90% of the MPC objective, and the coordinated distilled student retained 99.69% of teacher performance while reducing modeled communication from 5520 to 1080 bytes per episode. The coordinated-versus-local mean-objective difference was +0.000053 and did not meet the predefined +0.008 threshold; direct MPC supervision performed at least as well as hybrid distillation. The principal objective-based conclusions were preserved across all nine local parameter conditions, whereas the coordinated failure-rate advantage was not parameter-robust. In GSE229033, six of seven modules changed in the predefined favorable direction, but the oriented composite bootstrap interval included zero. Conclusions: Compact regional neural policies reproduced MPC performance with minimal objective loss and substantially lower modeled communication. The external transcriptomic analysis provided limited plausibility support for the RGC state orientation, while neither analysis validated simulator dynamics, controller efficacy, biological treatment effects, or clinical applicability.

Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell single-session repair model—do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. Methods: We developed AI-BED-Fx, the first multi-fraction extension of the Jones–Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on–time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts—arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)—were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain–metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. Results: AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60–210 Gy_2.47; MEN 41–85 Gy_3.5; VS 46–68 Gy₃; BM 37–75 Gy₁₀) and biologically coherent dose–response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R² = 0.9991). Conclusions: AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose–response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned.

Background: Human polyomavirus JC (JCV) causes progressive multifocal leukoencephalopathy (PML), a deadly brain demyelinating illness stemming from oligodendrocyte lytic infection in immunocompromised patients, especially those with untreated HIV infection. Methods: We conducted a case series report on patients with HIV/AIDS who presented progressive multifocal leukoencephalopathy and were hospitalized at the “St. Parascheva” Clinical Hospital of Infectious Diseases in Iasi, northeastern Romania, to emphasize the comorbidities of HIV/AIDS cases. Hospital medical data from 10 January 2025 to 30 September 2025 served as the basis for this investigation. Results: We examined three cases that presented neurological symptoms (ataxia, aphasia, language comprehension, and expression disorders). The cases were evaluated imagistically via nuclear magnetic resonance, and we conducted a polymerase chain reaction test on the spinal fluid to confirm the presence of JCV. It was necessary to take a multidisciplinary approach with a neurologist or pneumologist. All cases were evaluated immunologically, revealing low Ly T CD4 levels and increased HIV viremia levels. Progressive multifocal leukoencephalopathy is an AIDS-defining disease, manifesting in immunocompromised patients, including late presenter cases, and patients who are non-adherent to their antiretroviral treatment. Therefore, it is important to test every patient who has mild to severe neurological symptoms for HIV. Furthermore, some cases require a multidisciplinary approach to ensure a better quality of life. Conclusions: Treating a patient with HIV requires a multidisciplinary strategy that includes a neurology specialist and access to antiretroviral treatment. To boost ART uptake, we must identify and remove barriers that impact patients and the healthcare system.

Background: Lumbar disc herniation (LDH) has a substantial impact on health-related quality of life (HRQoL), mainly through pain, reduced mobility, and functional limitations. To better reflect these outcomes, we utilized the SF-36 survey, a well-established tool commonly used in both clinical and population research. The purpose was twofold: to evaluate HRQoL and to explore the main factors linked with activity limitation in a community-based group of Romanian patients. Methods: This cross-sectional study included 120 participants with LDH who completed the SF-36 questionnaire. Internal consistency was high (Cronbach’s α = 0.922). Descriptive statistics, Chi-square tests, t-tests, and multivariate regressions were performed using SPSS 18.0. Results: Of the respondents, 53.3% were female and 58.4% were over 60 years old. While 55% reported good or very good health, 26.7% had severe Daily Activity Limitation (DAL) based on the SF-36 physical function score, and 62.5% scored in the poorer range (33–45) of the Physical and Emotional Status (PES) composite derived from the SF-36. Female sex and age over 60 were each associated with nearly a twofold-higher risk of moderate-to-severe DAL (OR = 2.20, 95% CI: 1.04–4.65), while reporting severe or very severe pain increased this risk more than fourfold (OR = 4.64, 95% CI: 1.89–24.21). Conclusions: In patients with LDH, poor self-rated health, older age, female sex, and high pain intensity were significantly associated with reduced functional capacity. The SF-36 proved to be a valuable tool for identifying vulnerable subgroups that require targeted rehabilitation and psychosocial support. From a public health perspective, these findings also support the use of SF-36 in community screening and in guiding resource allocation for multidisciplinary rehabilitation programs.

Background: Lumbar disc herniation (LDH) recurrence remains a pressing clinical challenge, with limited predictive tools available to support early identification and personalized intervention. Predicting recurrence after lumbar disc herniation (LDH) remains clinically important but algorithmically difficult due to extreme class imbalance and low signal-to-noise ratio. Objective: This study proposes a hybrid machine learning framework that integrates supervised classifiers, unsupervised anomaly detection, and decision threshold tuning to predict LDH recurrence using routine clinical data. Methods: A dataset of 977 patients from a Romanian neurosurgical center was used. We trained a deep neural network, random forest, and an autoencoder (trained only on non-recurrence cases) to model baseline and anomalous patterns. Their outputs were stacked into a meta-classifier and optimized via sensitivity-focused threshold tuning. Evaluation was performed via stratified cross-validation and external holdout testing. Results: Baseline models achieved high accuracy but failed to recall recurrence cases (0% sensitivity). The proposed ensemble reached 100% recall internally with a threshold of 0.05. Key predictors included hospital stay duration, L4–L5 herniation, obesity, and hypertension. However, external holdout performance dropped to 0% recall, revealing poor generalization. Conclusions: The ensemble approach enhances detection of rare recurrence cases under internal validation but exhibits poor external performance, emphasizing the challenge of rare-event modeling in clinical datasets. Future work should prioritize external validation, longitudinal modeling, and interpretability to ensure clinical adoption.

Background and Objectives: Healthcare-associated infections (HAIs) are on the rise worldwide because the range of etiologic agents involved is very diverse and their antimicrobial resistance poses a threat to population health in the third millennium. Materials and Methods: We conducted a retrospective, longitudinal, descriptive clinical–epidemiological study in a hospital with a neurosurgical profile in northeastern Romania (“Prof. Dr. N. Oblu” Clinical Emergency Hospital in Iasi), during 2020–2024. The study is centered on the involvement of Acinetobacter spp. in the occurrence and evolution of HAIs. Results: The highest incidence of Acinetobacter spp. HAIs was recorded in the intensive care unit (ICU)—82.78% compared to neurosurgical wards (15.38%), with predominance in males (69.23%) and rural residence patients (55.67%). Most HAIs were represented by ventilator-associated pneumonia (42.13%) and lower respiratory tract infections (23.08%). Strains with high virulence and pathogenicity (CR-MDR, ESBL-MDR) were found, with the highest proportion of CR-MDR strains (88.27%). Conclusions: Our study provides useful data for surveillance of the antimicrobial resistance of pathogens involved in HAIs at the hospital level and for guiding antibiotic therapy in hospital-acquired infections.

Background/Objectives: Pontine cavernomas are rare and challenging vascular malformations, representing a critical subset of brainstem lesions due to their deep location and proximity to essential neural structures. When hemorrhagic, these lesions can cause rapid neurological deterioration, posing life-threatening risks. Management requires a delicate balance between aggressive intervention and preserving vital functions. This case report presents the successful surgical treatment of a giant hemorrhagic pontine cavernoma, highlighting the integration of advanced imaging, precision surgical techniques, and multidisciplinary care to achieve an exceptional patient outcome. Methods: A 47-year-old female presented with acute neurological deterioration, including severe right-sided hemiparesis, dysphagia, and obnubilation. High-resolution MRI, including susceptibility-weighted imaging, confirmed a giant hemorrhagic pontine cavernoma causing brainstem compression. An urgent left-sided pterional craniotomy with a transsylvian approach was performed to access the lesion. Subtotal resection and hematoma evacuation were carried out to relieve brainstem compression while preserving critical structures. Postoperative recovery and lesion stability were evaluated through clinical assessments and imaging after three months. Results: Postoperatively, the patient exhibited marked neurological recovery, with near-complete resolution of hemiparesis, restored swallowing function, and significant functional improvement. Follow-up imaging confirmed a stable residual lesion, no recurrence of hemorrhage, and a well-preserved ventricular system. The combination of early intervention and tailored surgical strategies resulted in a highly favorable outcome. Conclusions: This case underscores the complexity of managing giant hemorrhagic pontine cavernomas and demonstrates that carefully planned surgical intervention, combined with advanced imaging and patient-focused care, can yield remarkable outcomes. It highlights the critical importance of early diagnosis, meticulous surgical planning, and future innovations in neurovascular surgery to improve outcomes in these rare but high-stakes cases.

Background/Objective: This study explores the use of AI-powered radiomics to classify and monitor brain metastasis progression and regression following Gamma Knife radiosurgery (GKRS) based on MRI imaging. A clinical decision support application was developed using Streamlit to provide real-time, AI-driven predictions for treatment monitoring. Methods: MRI scans from 60 patients (3194 images) were analyzed using a transfer learning-enhanced AlexNet deep learning model. Class imbalance was mitigated through dynamic class weighting and data augmentation to ensure equitable performance across all classes. Optimized preprocessing pipelines ensured dataset standardization. Model performance was evaluated using accuracy, precision, recall, F1-scores, and AUC, with 95% confidence intervals. Additionally, a comparative analysis of Gamma Knife radiosurgery (GKRS) outcomes and predictive modeling demonstrated strong correlations between tumor volume evolution and treatment response. The AI predictions and visualizations were integrated into a Streamlit-based application to ensure clinical usability and ease of access. The AI-driven approach effectively classified progression and regression patterns, reinforcing its potential for clinical integration. Results: The transfer learning model achieved flawless classification accuracy (100%; 95% CI: 100–100%) along with perfect precision, recall, and F1-scores. The AUC score of 1.0000 (95% CI: 1.0000–1.0000) indicated excellent discrimination between progression and regression cases. Compared to the baseline AlexNet model (99.53% accuracy; 95% CI: 98.90–100.00%), the TL-enhanced model resolved all misclassifications. Tumor volume analysis identified the baseline size as a key predictor of progression (Pearson r = 0.795, r = 0.795, r = 0.795, p < 0.0001, p < 0.0001, and p < 0.0001). The training time (420.12 s) was faster than ResNet-50 (443.38 s) and EfficientNet-B0 (439.87 s), while achieving equivalent metrics. Despite 100% accuracy, the model requires multi-center validation for generalizability. Conclusions: This study demonstrates that transfer learning with dynamic class weighting provides a highly accurate and reliable framework for monitoring brain metastases post-GKRS. The Streamlit-based AI application enhances clinical decision-making by improving diagnostic precision and reducing variability. Explainable AI techniques, such as Grad-CAM visualizations, improve interpretability and support clinical adoption. These findings emphasize the transformative potential of AI in personalized treatment strategies, extending applications to genomic profiling, survival modeling, and longitudinal follow-ups for brain metastasis management.