Machine Learning to Generate Adjustable Dose Distributions in Head-and-Neck Cancer Radiation Therapy

In this work, we propose a Machine Learning model that generates an adjustable 3D dose distribution for external beam radiation therapy for head-and-neck cancer treatments. In contrast to existing Machine Learning methods that provide a single model, we create pairs of models for each organ-at-risk, namely lower-extreme and upper-extreme models. These model pairs for an organ-at-risk propose doses that give lower and higher doses to that organ-at-risk, while also encapsulating the dose trade-off to other organs-at-risk. By weighting and combining the model pairs for all organs-at-risk, we are able to dynamically create adjustable dose distributions that can be used, in real-time, to move doses between organs-at-risk, thereby customizing the dose distribution to the needs of a particular patient. We leverage a key observation that the training data set inherently contains the clinical trade-offs. We show that the adjustable distributions are able to provide reasonable clinical dose latitude in the trade-off of doses between organs-at-risk

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

Protective Effect of Opuntia dillenii Haw Fruit against Lead Acetate-Induced Hepatotoxicity: In Vitro and In Vivo Studies

Lead is one of the most common environmental contaminants in the Earth’s crust, which induces a wide range of humans biochemical changes. Previous studies showed that Opuntia dillenii (OD) fruit possesses several antioxidant and anti-inflammatory properties. The present study evaluates OD fruit hydroalcoholic extract (OHAE) hepatoprotective effects against lead acetate- (Pb-) induced toxicity in both animal and cellular models. Male rats were grouped as follows: control, Pb (25 mg/kg/d i.p.), and groups 3 and 4 received OHAE at 100 and 200 mg/kg/d + Pb (25 mg/kg/d i.p.), for ten days of the experiment. Thereafter, we evaluated the levels of alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), catalase (CAT) activity and malondialdehyde (MDA) in serum, and liver histopathology. Additionally, the cell study was also done using the HepG2 cell line for measuring the direct effects of the extract on cell viability, oxidative stress MDA, and glutathione (GSH) and inflammation tumor necrosis factor-α (TNF-α) following the Pb-induced cytotoxicity. Pb significantly increased the serum levels of ALT, AST, ALP, and MDA and liver histopathological scores but notably decreased CAT activity compared to the control group (p<0.001 for all cases). OHAE (100 and 200 mg/kg) significantly reduced the levels of serum liver enzyme activities and MDA as well as histopathological scores while it significantly increased CAT activity compared to the Pb group (p<0.001–0.05 for all cases). OHAE (20, 40, and 80 μg/ml) concentration dependently and significantly reduced the levels of MDA and TNF-α, while it increased the levels of GSH and cell viability in comparison to the Pb group (p<0.001–0.05 for all cases). These data suggest that OHAE may have hepatoprotective effects against Pb-induced liver toxicity both in vitro and in vivo by its antioxidant and anti-inflammatory activities

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.</p

Decoding clinical biomarker space of COVID-19:exploring matrix factorization-based feature selection methods

Abstract One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases