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OBJECTIVE: Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy. Approach: We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A Channel Module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the temporal module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting. Main results: OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band. Significance: The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs. .

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Antigen presentation by major histocompatibility complex class I (MHC-I) is crucial for T cell-mediated killing, and aberrant surface MHC-I expression is tightly associated with immune evasion. To address MHC-I downregulation, we conducted a high-throughput flow cytometry screen, identifying bleomycin (BLM) as a potent inducer of cell surface MHC-I expression. BLM-induced MHC-I augmentation rendered tumor cells more susceptible to T cells in coculture assays and enhanced antitumor responses in an adoptive cellular transfer mouse model. Mechanistically, BLM remodeled the tumor immune microenvironment, inducing MHC-I expression in a manner dependent on ataxia-telangiectasia mutated/ataxia telangiectasia and Rad3-related-NF-κB. Furthermore, BLM improved T cell-dependent immunotherapeutic approaches, including bispecific antibody therapy, immune checkpoint therapy, and autologous tumor-infiltrating lymphocyte therapy. Importantly, low-dose BLM treatment in mouse models amplified the antitumor effect of immunotherapy without detectable pulmonary toxicity. In summary, our findings repurpose BLM as a potential inducer of MHC-I, enhancing its expression to improve the efficacy of T cell-based immunotherapy.

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PURPOSE: Microstates represent the global and topographical distribution of electrical brain activity from scalp-recorded EEG. This study aims to explore EEG microstates of patients with focal epilepsy prior to medication, and employ extracted microstate metrics for predicting treatment outcomes with Oxcarbazepine monotherapy. METHODS: This study involved 25 newly-diagnosed focal epilepsy patients (13 females), aged 12 to 68, with various etiologies. Patients were categorized into Non-Seizure-Free (NSF) and Seizure-Free (SF) groups according to their first follow-up outcomes. From pre-medication EEGs, four representative microstates were identified by using clustering. The temporal parameters and transition probabilities of microstates were extracted and analyzed to discern group differences. With generating sample method, Support Vector Machine (SVM), Logistic Regression (LR), and Naïve Bayes (NB) classifiers were employed for predicting treatment outcomes. RESULTS: In the NSF group, Microstate 1 (MS1) exhibited a significantly higher duration (mean±std. = 0.092±0.008 vs. 0.085±0.008, p = 0.047), occurrence (mean±std. = 2.587±0.334 vs. 2.260±0.278, p = 0.014), and coverage (mean±std. = 0.240±0.046 vs. 0.194±0.040, p = 0.014) compared to the SF group. Additionally, the transition probabilities from Microstate 2 (MS2) and Microstate 3 (MS3) to MS1 were increased. In MS2, the NSF group displayed a stronger correlation (mean±std. = 0.618±0.025 vs. 0.571±0.034, p < 0.001) and a higher global explained variance (mean±std. = 0.083±0.035 vs. 0.055±0.023, p = 0.027) than the SF group. Conversely, Microstate 4 (MS4) in the SF group demonstrated significantly greater coverage (mean±std. = 0.388±0.074 vs. 0.334±0.052, p = 0.046) and more frequent transitions from MS2 to MS4, indicating a distinct pattern. Temporal parameters contribute major predictive role in predicting treatment outcomes of Oxcarbazepine, with area under curves (AUCs) of 0.95, 0.70, and 0.86, achieved by LR, NB and SVM, respectively. CONCLUSION: This study underscores the potential of EEG microstates as predictive biomarkers for Oxcarbazepine treatment responses in newly-diagnosed focal epilepsy patients.

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Long-term electroencephalogram (Long-Term EEG) has the capacity to monitor over a long period, making it a valuable tool in medical institutions. However, due to the large volume of patient data, selecting clean data segments from raw Long-Term EEG for further analysis is an extremely time-consuming and labor-intensive task. Furthermore, the various actions of patients during recording make it difficult to use algorithms to denoise part of the EEG data, and thus lead to the rejection of these data. Therefore, tools for the quick rejection of heavily corrupted epochs in Long-Term EEG records are highly beneficial. In this paper, a new reliable and fast automatic artifact rejection method for Long-Term EEG based on Isolation Forest (IF) is proposed. Specifically, the IF algorithm is repetitively applied to detect outliers in the EEG data, and the boundary of inliers is promptly adjusted by using a statistical indicator to make the algorithm proceed in an iterative manner. The iteration is terminated when the distance metric between clean epochs and artifact-corrupted epochs remains unchanged. Six statistical indicators (i.e., min, max, median, mean, kurtosis, and skewness) are evaluated by setting them as centroid to adjust the boundary during iteration, and the proposed method is compared with several state-of-the-art methods on a retrospectively collected dataset. The experimental results indicate that utilizing the min value of data as the centroid yields the most optimal performance, and the proposed method is highly efficacious and reliable in the automatic artifact rejection of Long-Term EEG, as it significantly improves the overall data quality. Furthermore, the proposed method surpasses compared methods on most data segments with poor data quality, demonstrating its superior capacity to enhance the data quality of the heavily corrupted data. Besides, owing to the linear time complexity of IF, the proposed method is much faster than other methods, thus providing an advantage when dealing with extensive datasets.

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