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1. A phase 1 study of entinostat in children and adolescents with recurrent or refractory solid tumors, including CNS tumors: Trial ADVL1513, Pediatric Early Phase‐Clinical Trial Network (PEP‐CTN)

   https://doi.org/10.1002/pbc.28892  

   Bukowinski, Andrew ; Chang, Bill ; Reid, Joel M. ; Liu, Xiaowei ; Minard, Charles G. ; Trepel, Jane B. ; Lee, Min‐Jung ; Fox, Elizabeth ; Weigel, Brenda J. ( January 2021 , Pediatric Blood & Cancer)

   Entinostat is an oral small molecule inhibitor of class I histone deacetylases (HDAC), which has not previously been evaluated in pediatrics. We conducted a phase I trial to determine the maximum tolerated dose/recommended phase 2 dose (MTD/RP2D), toxicity profile, pharmacokinetics (PK), and pharmacodynamics (PD) of entinostat in children with relapsed or refractory solid tumors including central nervous system (CNS) malignancies.

   A rolling six dose escalation design evaluated two dose levels. Entinostat oral tablet formulation was administered once per week, four doses per 28‐day cycle. PK and PD studies were performed.

   Twenty‐one eligible patients’ median (range) age was 14 years (6‐20). Six subjects were treated at 3 mg/m²dose level and 15 were treated in 4 mg/m²dose level. The study included patients with CNS tumors (n = 12), sarcomas (n = 6), or other solid tumors (n = 3). Eight patients were not fully evaluable for toxicity due to progression of disease prior to receiving the required percentage of protocol therapy. No cycle one dose‐limiting toxicity (DLT) was observed at either dose level. A three‐fold higher area under the curve (AUC) was achieved in our cohort compared to adults using a similar dosing schedule. The PD studies showed increase in acetylated lysine in peripheral blood leukocytes at both doses.

   Entinostat was well tolerated with no DLT observed. All patients experienced progression within the first two cycles, except one patient with ependymoma with stable disease. Based on PK and PD, the R2PD in pediatric patients with solid tumors is 4 mg/m²orally administered once weekly.

    

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2. Classification of Neurological Gait Disorders Using Multi-task Feature Learning

   https://doi.org/10.1109/CHASE.2017.78  

   Papavasileiou, Ioannis ; Zhang, Wenlong ; Wang, Xin ; Bi, Jinbo ; Zhang, Li ; Han, Song ( July 2017 , 2017 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE))

   As our population ages, neurological impairments and degeneration of the musculoskeletal system yield gait abnormalities, which can significantly reduce quality of life. Gait rehabilitative therapy has been widely adopted to help patients maximize community participation and living independence. To further improve the precision and efficiency of rehabilitative therapy, more objective methods need to be developed based on sensory data. In this paper, an algorithmic framework is proposed to provide classification of gait disorders caused by two common neurological diseases, stroke and Parkinson's Disease (PD), from ground contact force (GCF) data. An advanced machine learning method, multi-task feature learning (MTFL), is used to jointly train classification models of a subject's gait in three classes, post-stroke, PD and healthy gait. Gait parameters related to mobility, balance, strength and rhythm are used as features for the classification. Out of all the features used, the MTFL models capture the more important ones per disease, which will help provide better objective assessment and therapy progress tracking. To evaluate the proposed methodology we use data from a human participant study, which includes five PD patients, three post-stroke patients, and three healthy subjects. Despite the diversity of abnormalities, the evaluation shows that the proposed approach can successfully distinguish post-stroke and PD gait from healthy gait, as well as post-stroke from PD gait, with Area Under the Curve (AUC) score of at least 0.96. Moreover, the methodology helps select important gait features to better understand the key characteristics that distinguish abnormal gaits and design personalized treatment. 

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3. Generalizable deep learning model for early Alzheimer’s disease detection from structural MRIs

   https://doi.org/10.1038/s41598-022-20674-x  

   Liu, Sheng ; Masurkar, Arjun V. ; Rusinek, Henry ; Chen, Jingyun ; Zhang, Ben ; Zhu, Weicheng ; Fernandez-Granda, Carlos ; Razavian, Narges ( October 2022 , Scientific Reports)

   Early diagnosis of Alzheimer’s disease plays a pivotal role in patient care and clinical trials. In this study, we have developed a new approach based on 3D deep convolutional neural networks to accurately differentiate mild Alzheimer’s disease dementia from mild cognitive impairment and cognitively normal individuals using structural MRIs. For comparison, we have built a reference model based on the volumes and thickness of previously reported brain regions that are known to be implicated in disease progression. We validate both models on an internal held-out cohort from The Alzheimer's Disease Neuroimaging Initiative (ADNI) and on an external independent cohort from The National Alzheimer's Coordinating Center (NACC). The deep-learning model is accurate, achieved an area-under-the-curve (AUC) of 85.12 when distinguishing between cognitive normal subjects and subjects with either MCI or mild Alzheimer’s dementia. In the more challenging task of detecting MCI, it achieves an AUC of 62.45. It is also significantly faster than the volume/thickness model in which the volumes and thickness need to be extracted beforehand. The model can also be used to forecast progression: subjects with mild cognitive impairment misclassified as having mild Alzheimer’s disease dementia by the model were faster to progress to dementia over time. An analysis of the features learned by the proposed model shows that it relies on a wide range of regions associated with Alzheimer's disease. These findings suggest that deep neural networks can automatically learn to identify imaging biomarkers that are predictive of Alzheimer's disease, and leverage them to achieve accurate early detection of the disease.

    

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4. Dementia detection from brain activity during sleep

   https://doi.org/10.1093/sleep/zsac286  

   Ye, Elissa M. ; Sun, Haoqi ; Krishnamurthy, Parimala V. ; Adra, Noor ; Ganglberger, Wolfgang ; Thomas, Robert J. ; Lam, Alice D. ; Westover, M. Brandon ( November 2022 , Sleep)

   Dementia is a growing cause of disability and loss of independence in the elderly, yet remains largely underdiagnosed. Early detection and classification of dementia can help close this diagnostic gap and improve management of disease progression. Altered oscillations in brain activity during sleep are an early feature of neurodegenerative diseases and be used to identify those on the verge of cognitive decline.

   Our observational cross-sectional study used a clinical dataset of 10 784 polysomnography from 8044 participants. Sleep macro- and micro-structural features were extracted from the electroencephalogram (EEG). Microstructural features were engineered from spectral band powers, EEG coherence, spindle, and slow oscillations. Participants were classified as dementia (DEM), mild cognitive impairment (MCI), or cognitively normal (CN) based on clinical diagnosis, Montreal Cognitive Assessment, Mini-Mental State Exam scores, clinical dementia rating, and prescribed medications. We trained logistic regression, support vector machine, and random forest models to classify patients into DEM, MCI, and CN groups.

   For discriminating DEM versus CN, the best model achieved an area under receiver operating characteristic curve (AUROC) of 0.78 and area under precision-recall curve (AUPRC) of 0.22. For discriminating MCI versus CN, the best model achieved an AUROC of 0.73 and AUPRC of 0.18. For discriminating DEM or MCI versus CN, the best model achieved an AUROC of 0.76 and AUPRC of 0.32.

   Our dementia classification algorithms show promise for incorporating dementia screening techniques using routine sleep EEG. The findings strengthen the concept of sleep as a window into neurodegenerative diseases.

    

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5. Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

   https://doi.org/10.1186/s12911-024-02453-y  

   Zhang, Yufeng ; Golbus, Jessica R. ; Wittrup, Emily ; Aaronson, Keith D. ; Najarian, Kayvan ( February 2024 , BMC Medical Informatics and Decision Making)

   Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time-consuming, requiring cardiologists with specialized expertise and training in heart failure and transplantation.

   In this study, we propose two logistic tensor regression-based models to predict patients with heart failure warranting evaluation for advanced heart failure therapies using irregularly spaced sequential electronic health records at the population and individual levels. The clinical features were collected at the previous visit and the predictions were made at the very beginning of the subsequent visit. Patient-wise ten-fold cross-validation experiments were performed. Standard LTR achieved an average F1 score of 0.708, AUC of 0.903, and AUPRC of 0.836. Personalized LTR obtained an F1 score of 0.670, an AUC of 0.869 and an AUPRC of 0.839. The two models not only outperformed all other machine learning models to which they were compared but also improved the performance and robustness of the other models via weight transfer. The AUPRC scores of support vector machine, random forest, and Naive Bayes are improved by 8.87%, 7.24%, and 11.38%, respectively.

   The two models can evaluate the importance of clinical features associated with advanced therapy referral. The five most important medical codes, including chronic kidney disease, hypotension, pulmonary heart disease, mitral regurgitation, and atherosclerotic heart disease, were reviewed and validated with literature and by heart failure cardiologists. Our proposed models effectively utilize EHRs for potential advanced therapies necessity in heart failure patients while explaining the importance of comorbidities and other clinical events. The information learned from trained model training could offer further insight into risk factors contributing to the progression of heart failure at both the population and individual levels.

    

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