High-throughput omics data present challenges for binary classification due to platform variability, batch effects, missing values, and high dimensionality. This study presents a novel Rank-Based Learning (RBL) method that leverages relative feature rankings to improve robustness and generalizability. We evaluated RBL against established methods like Logistic Regression (LR) and Random Forest (RF) using simulated data and two real-world plasma proteomics datasets: early-stage small cell lung cancer (SCLC) and duodenopancreatic neuroendocrine tumors (dpNET) in patients with Multiple Endocrine Neoplasia type 1 (MEN1). In simulation experiments, RBL outperformed LR under conditions involving batch effects, missing data, and varying numbers of true differential features. In SCLC, RBL yielded a test AUC of 0.76 (95% CI: 0.42-1.00), surpassing LR with Lasso (0.65 [95% CI: 0.47-0.84]) and RF with feature importance (0.59 [95% CI: 0.33-0.87]). In dpNET, RBL achieved an AUC of 0.83 (95% CI: 0.67-0.97) on the development set and 0.80 (95% CI: 0.54-0.98) on the test set, outperforming LR with Lasso (0.57 [95% CI: 0.40-0.77]) and RF with feature importance (0.53 [95% CI: 0.29-0.77]). By emphasizing feature ranking rather than absolute expression levels, RBL effectively mitigates the impact of non-biological variation. Overall, RBL improves the predictive accuracy of diagnostic models for complex diseases and provides a promising framework for developing more reliable and generalizable diagnostic tools from omics data, moving them closer to clinical application.

Intercellular communication is crucial for organ function, with extracellular vesicles (EVs) acting as common messengers for almost all cells. This study proposes a novel EV-mediated intercellular communication system that uses a modulation technique regulated via altered intracellular-cytosolic calcium dynamics regulation. As a case study, intercellular communication within a cardiac muscle is considered with cardiomyocyte cells serving as transceivers. Through molecular communication theory, we propose a comprehensive model addressing EV release kinetics, propagation, degradation, and uptake. A linear time-invariant Poisson channel model is developed and closed-form expressions, verified through particle-based simulations, are derived for EV detection probabilities at the receiver. A closed-form bit error probability is derived and validated through Monte Carlo simulations. By selecting an optimal receiver threshold: 1) bit error rate (BER) in EV-mediated intercellular communication is robust against adverse effects from cardiac disorders, such as myocardial infarction; 2) transmitter design can be optimized by minimizing actuation signal amplitude and pulse width; 3) BER stays stable across heart rates for different distances, demonstrating the robustness of EV-mediated communication. This study enhances our ability to engineer precise and reliable intra-body cardiac communications, which may offer valuable applications for cardiovascular disease treatment, for example, the realization of biological lead-less multi-nodal pacemakers.

Novel non-invasive procedures, such as targeted drug delivery may enhance the efficacy of treatments of cardiac disorders. A possible solution is to utilize a molecular communication paradigm based on cell-derived nano-sized vesicles --- extracellular vesicles (EVs) --- that can be used as vehicles for therapeutic biological cargo. EVs can be engineered with specific cell-targeting transmembrane proteins to improve their pharmacokinetic properties. Here, we study the transport of EVs in a non-cellular component of cardiac tissue referred to as the cardiac extracellular matrix (ECM). We inspect EV diffusion and advection in cardiac ECM by considering 1) tortuosity, which describes the convoluted pathway of EV transportation, 2) volume fraction, which characterizes the porosity of the cardiac ECM; and 3) EV degradation according to their half-life in the body. We analytically describe the EV transportation dynamics via a partial differential equation and solve it numerically using finite element methods. The presented findings indicate that EV propagation is dependent on the cardiac ECM hindrance sources. Bulk flow in the cardiac ECM, however, can mediate the EVs to reach their distant target cells.