Physiologically Based Pharmacokinetic Models for Prediction of Complex CYP2C8 and OATP1B1 (SLCO1B1) Drug-Drug-Gene Interactions : A Modeling Network of Gemfibrozil, Repaglinide, Pioglitazone, Rifampicin, Clarithromycin and Itraconazole

Background Drug–drug interactions (DDIs) and drug–gene interactions (DGIs) pose a serious health risk that can be avoided by dose adaptation. These interactions are investigated in strictly controlled setups, quantifying the efect of one perpetrator drug or polymorphism at a time, but in real life patients frequently take more than two medications and are very heterogenous regarding their genetic background. Objectives The frst objective of this study was to provide whole-body physiologically based pharmacokinetic (PBPK) models of important cytochrome P450 (CYP) 2C8 perpetrator and victim drugs, built and evaluated for DDI and DGI studies. The second objective was to apply these models to describe complex interactions with more than two interacting partners. Methods PBPK models of the CYP2C8 and organic-anion-transporting polypeptide (OATP) 1B1 perpetrator drug gemfbrozil (parent–metabolite model) and the CYP2C8 victim drugs repaglinide (also an OATP1B1 substrate) and pioglitazone were developed using a total of 103 clinical studies. For evaluation, these models were applied to predict 34 diferent DDI studies, establishing a CYP2C8 and OATP1B1 PBPK DDI modeling network. Results The newly developed models show a good performance, accurately describing plasma concentration–time profles, area under the plasma concentration–time curve (AUC) and maximum plasma concentration (Cmax) values, DDI studies as well as DGI studies. All 34 of the modeled DDI AUC ratios (AUC during DDI/AUC control) and DDI Cmax ratios (Cmax during DDI/Cmax control) are within twofold of the observed values. Conclusions Whole-body PBPK models of gemfbrozil, repaglinide, and pioglitazone have been built and qualifed for DDI and DGI prediction. PBPK modeling is applicable to investigate complex interactions between multiple drugs and genetic polymorphisms

Feeding Kinematics, Suction, and Hydraulic Jetting Performance of Harbor Seals (Phoca vitulina)

The feeding kinematics, suction and hydraulic jetting capabilities of captive harbor seals (Phoca vitulina) were characterized during controlled feeding trials. Feeding trials were conducted using a feeding apparatus that allowed a choice between biting and suction, but also presented food that could be ingested only by suction. Subambient pressure exerted during suction feeding behaviors was directly measured using pressure transducers. The mean feeding cycle duration for suction-feeding events was significantly shorter (0.15±0.09 s; P<0.01) than biting feeding events (0.18±0.08 s). Subjects feeding in-water used both a suction and a biting feeding mode. Suction was the favored feeding mode (84% of all feeding events) compared to biting, but biting comprised 16% of feeding events. In addition, seals occasionally alternated suction with hydraulic jetting, or used hydraulic jetting independently, to remove fish from the apparatus. Suction and biting feeding modes were kinematically distinct regardless of feeding location (in-water vs. on-land). Suction was characterized by a significantly smaller gape (1.3±0.23 cm; P<0.001) and gape angle (12.9±2.02°), pursing of the rostral lips to form a circular aperture, and pursing of the lateral lips to occlude lateral gape. Biting was characterized by a large gape (3.63±0.21 cm) and gape angle (28.8±1.80°; P<0.001) and lip curling to expose teeth. The maximum subambient pressure recorded was 48.8 kPa. In addition, harbor seals were able to jet water at food items using suprambient pressure, also known as hydraulic jetting. The maximum hydraulic jetting force recorded was 53.9 kPa. Suction and hydraulic jetting where employed 90.5% and 9.5%, respectively, during underwater feeding events. Harbor seals displayed a wide repertoire of behaviorally flexible feeding strategies to ingest fish from the feeding apparatus. Such flexibility of feeding strategies and biomechanics likely forms the basis of their opportunistic, generalized feeding ecology and concomitant breadth of diet

Microscopic origin of Cooper pairing in the iron-based superconductor Ba₁₋ₓKₓFe₂As₂

Resolving the microscopic pairing mechanism and its experimental identification in unconventional superconductors is among the most vexing problems of contemporary condensed matter physics. We show that Raman spectroscopy provides an avenue towards this aim by probing the structure of the pairing interaction at play in an unconventional superconductor. As we study the spectra of the prototypical Fe-based superconductor Ba1−xKxFe2As2 for 0.22 ≤ x ≤ 0.70 in all symmetry channels, Raman spectroscopy allows us to distill the leading s-wave state. In addition, the spectra collected in the B1g symmetry channel reveal the existence of two collective modes which are indicative of the presence of two competing, yet sub-dominant, pairing tendencies of dx2−y2 symmetry type. A comprehensive functional Renormalization Group and random-phase approximation study on this compound confirms the presence of the two sub-leading channels, and consistently matches the experimental doping dependence of the related modes. The consistency between the experimental observations and the theoretical modeling suggests that spin fluctuations play a significant role in superconducting pairing

Automated detection of atrial fibrillation using long short-term memory network with RR interval signals

Atrial Fibrillation (AF), either permanent or intermittent (paroxysnal AF), increases the risk of cardioembolic stroke. Accurate diagnosis of AF is obligatory for initiation of effective treatment to prevent stroke. Long term cardiac monitoring improves the likelihood of diagnosing paroxysmal AF. We used a deep learning system to detect AF beats in Heart Rate (HR) signals. The data was partitioned with a sliding window of 100 beats. The resulting signal blocks were directly fed into a deep Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The system was validated and tested with data from the MIT-BIH Atrial Fibrillation Database. It achieved 98.51% accuracy with 10-fold cross-validation (20 subjects) and 99.77% with blindfold validation (3 subjects). The proposed system structure is straight forward, because there is no need for information reduction through feature extraction. All the complexity resides in the deep learning system, which gets the entire information from a signal block. This setup leads to the robust performance for unknown data, as measured with the blind fold validation. The proposed Computer-Aided Diagnosis (CAD) system can be used for long-term monitoring of the human heart. To the best of our knowledge, the proposed system is the first to incorporate deep learning for AF beat detection