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Background: Despite advances in immunotherapy and targeted therapy, platinum-based chemotherapy remains crucial for many patients with advanced non-small cell lung cancer (NSCLC). Resistance to platinum chemotherapy is common, and predictive biomarkers are needed to tailor treatment to patients likely to respond. In vitro evidence implicates the transforming growth factor-β (TGF-β) superfamily ligands activin-A and growth differentiation factor 11 (GDF-11) in innate platinum resistance. We performed a validation study to assess their utility as predictive biomarkers of platinum chemotherapy response in advanced NSCLC., Patients and Methods: Our study included 123 adult patients with advanced NSCLC without a driver mutation treated with platinum chemotherapy. 98 patients were from a retrospective cohort and 25 from a prospective cohort. We performed immunohistochemistry staining for Activin-A, GDF-11 and TGF-β on tumour samples for each patient and analysed IHC expression with objective radiological response and overall survival., Results: The overall median survival was 14.8 months. We performed statistical analysis around a cytoplasmic score of 8/18 for Activin-A and GDF-11 based on previously published work, and 110/30 for TGF-β based on a calculated cutpoint for significance. No survival difference was detected between these groups for Activin-A (p=0.35), GDF-11 (p=0.57) or TGF-β (p=0.34). There was no association between rates of progressive disease and high Activin-A expression (p=0.43), high GDF-11 expression (p=1.0) or high TGF-β expression p=0.89)., Conclusion: Within the confines of our study, Activin-A, GDF-11 and TGF-β expression was not a predictor of objective radiological response to chemotherapy or overall survival., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Purpose: To investigate the performance of seizure detection methods and nursing staff response in our epilepsy monitoring unit (EMU)., Methods: We retrospectively reviewed 38 EMU patient admissions over a 1-year period capturing 133 epileptic and non-epileptic seizures with associated video-EEG data. We recorded detailed seizure event characteristics for further analysis., Results: Rates of seizure detection, alarm usage, and time to nursing response varied by seizure type. Patients self-activated the push button (PB) alarm for 31.1% of all seizures, but only 8.9% of focal impaired awareness (FIAS) and focal to bilateral tonic-clonic seizures (FBTCS). In comparison, the Persyst automated seizure alarm reliably detected both electrographic seizures (76.2% of electrographic seizures) and FIAS/FBTCS (87.2% of FIAS/FBTCS), with a false positive alarm rate (FAR) of 0.14/hour, or every 7.3 h. 11.4% of all seizures went unrecognized by nursing staff, of which the majority (80.0%) were FIAS. The PB alarm was of higher yield for alerting nurses to focal aware seizures (FAS) and psychogenic non-epileptic seizures (PNES) versus FIAS and FBTCS (p < 0.001). In contrast, nurses relied more on the automated Persyst software alarm to detect FIAS (p < 0.001). Time to nursing response was no different following audible alarm onset for the PB compared to the Persyst alarms (p = 0.14)., Conclusion: Automated seizure detection software plays an important role in our EMU in seizure recognition, particularly for alerting nurses to FIAS. More rigorous studies are needed to determine the best utilization of various monitoring techniques and to promote high quality standards and patient safety in the EMU., (Copyright © 2019 British Epilepsy Association. Published by Elsevier Ltd. All rights reserved.)

Objective: Microelectrode recording (MER) is used to identify the subthalamic nucleus (STN) during deep brain stimulation (DBS) surgery. Automated STN detection typically involves extracting quantitative features from MERs for classifier training. This study evaluates the ability of feature selection to identify optimal feature combinations for automated STN localization., Methods: We extracted 13 features from 65 MERs for classifier training. For logistic regression (LR) classification, we compared classifiers identified by feature selection to those containing all possible feature combinations. We used classification error as our metric with hold-one-patient-out cross-validation. We also compared patient-specific vs. independent normalization on classifier performance., Results: Feature selection and patient-specific normalization were superior to non-optimized, patient-independent classifiers. Feature selection, patient-specific normalization, and both produced relative error reductions of 4.95%, 31.36%, and 38.92%, respectively. Three of four feature-selected LR classifiers performed better than 99% of classifiers with all possible feature combinations. Optimal feature combinations were not predictable from individual feature performance., Conclusions: Feature selection reduces classification error in automated STN localization from MERs. Additional improvement from patient-specific normalization suggests these approaches are necessary for clinically reliable automation of MER interpretation., Significance: These findings represent an incremental advance in automated functional localization of STN from MER in DBS surgery., (Copyright © 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Recent research in cancer biology has suggested the hypothesis that tumors are initiated and driven by a small group of cancer stem cells (CSCs). Furthermore, cancer stem cell niches have been found to be essential in determining fates of CSCs, and several signaling pathways have been proven to play a crucial role in cellular behavior, which could be two important factors in cancer development. To better understand the progression, heterogeneity and treatment response of breast cancer, especially in the context of CSCs, we propose a mathematical model based on the cell compartment method. In this model, three compartments of cellular subpopulations are constructed: CSCs, progenitor cells (PCs), and terminal differentiated cells (TCs). Moreover, (1) the cancer stem cell niche is, considered by modeling its effect on division patterns (symmetric or asymmetric) of CSCs, and (2) the EGFR signaling pathway is integrated by modeling its role in cell proliferation, apoptosis. Our simulation results indicate that (1) a higher probability for symmetric division of CSC may result in a faster expansion of tumor population, and for a larger number of niches, the tumor grows at a slower rate, but the final tumor volume is larger; (2) higher EGFR expression correlates to tumors with larger volumes while a saturation function is observed, and (3) treatments that inhibit tyrosine kinase activity of EGFR may not only repress the tumor volume, but also decrease the CSCs percentages by shifting CSCs from symmetric divisions to asymmetric divisions. These findings suggest that therapies should be designed to effectively control or eliminate the symmetric division of CSCs and to reduce or destroy the CSC niches., (Copyright © 2010 Elsevier Ltd. All rights reserved.)

We report two patients with migraine, acute visual field defects and other neurological symptoms who were found to have high T(2) signal and FLAIR abnormalities on brain MRI in temporal and parieto-occipital regions. In these patients, the apparent diffusion coefficient (ADC) of their lesions was increased, distinguishing these lesions from those of ischemic stroke. Both were ultimately diagnosed with mitochondrial myopathy, encephalopathy, lactic acidosis and stroke-like episodes (MELAS). We conclude that conventional MRI when used with diffusion-weighted MR imaging may be invaluable in detecting mitochondrial-related CNS dysfunction.