Tumor-induced angiogenesis leads to the development of leaky tumor vessels devoid of structural and morphological integrity. Due to angiogenesis, elevated interstitial fluid pressure (IFP) and low blood perfusion emerge as common properties of the tumor microenvironment that act as barriers for drug delivery. In order to overcome these barriers, normalization of vasculature is considered to be a viable option. However, insight is needed into the phenomenon of normalization and in which conditions it can realize its promise. In order to explore the effect of microenvironmental conditions and drug scheduling on normalization benefit, we build a mathematical model that incorporates tumor growth, angiogenesis and IFP. We administer various theoretical combinations of antiangiogenic agents and cytotoxic nanoparticles through heterogeneous vasculature that displays a similar morphology to tumor vasculature. We observe differences in drug extravasation that depend on the scheduling of combined therapy; for concurrent therapy, total drug extravasation is increased but in adjuvant therapy, drugs can penetrate into deeper regions of tumor.

BACKGROUND Atrial fibrillation (AF), a common cause of stroke, often is asymptomatic. Smartphones and smartwatches can detect AF using heart rate patterns inferred using photoplethysmography (PPG); however, enhanced accuracy is required to reduce false positives in screening populations. OBJECTIVE The purpose of this study was to test the hypothesis that a deep learning algorithm given raw, smartwatch-derived PPG waveforms would discriminate AF from normal sinus rhythm better than algorithms using heart rate alone. METHODS Patients presenting for cardioversion of AF (n 5 51) were given wrist-worn fitness trackers containing PPG sensors (Jawbone Health). Standard 12-lead electrocardiograms over-read by board-certified cardiac electrophysiologists were used as the reference standard. The accuracy of PPG signals to discriminate AF from sinus rhythm was evaluated by conventional measures of heart rate variability, a long short-term memory (LSTM) neural network given heart rate data only, and a deep convolutional-recurrent neural net (DNN) given the raw PPG data. RESULTS From among 51 patients with persistent AF (age 63.6 6 11.3 years; 78% male; 88% white), we randomly assigned 40 to train and 11 to test the algorithms. Whereas logistic regression analysis of heart rate variability yielded an area under the receiver operating characteristic curve (AUC) of 0.717 (sensitivity 0.741; specificity 0.584), the LSTM model given heart rate data exhibited AUC of 0.954 (sensitivity 0.810; specificity 0.921), and the DNN model given raw PPG data yielded the highest AUC of 0.983 (sensitivity 0.985; specificity 0.880). CONCLUSION A deep learning model given the raw PPG-based signal resulted in AF detection with high accuracy, performing better than conventional analyses relying on heart rate series data alone.

Elevated interstitial fluid pressure is one of the barriers of drug delivery in solid tumors. Recent studies have shown that normalization of tumor vasculature by anti-angiogenic factors may improve the delivery of conventional cytotoxic drugs, possibly by increasing blood flow, decreasing interstitial fluid pressure, and enhancing the convective transvascular transport of drug molecules. Delivery of large therapeutic agents such as nanoparticles and liposomes might also benefit from normalization therapy since their transport depends primarily on convection. In this study, a mathematical model is presented to provide supporting evidence that normalization therapy may improve the delivery of 100 nm liposomes into solid tumors, by both increasing the total drug extravasation and providing a more homogeneous drug distribution within the tumor. However these beneficial effects largely depend on tumor size and are stronger for tumors within a certain size range. It is shown that this size effect may persist under different microenvironmental conditions and for tumors with irregular margins or heterogeneous blood supply.

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