The use of microRNAs as clinical cancer biomarkers is hindered by the absence of accurate, sensitive and rapid assays for their detection in biofluids. Here we report a biosensing approach, SpLig-HEMT, that combines an RNA splint-ligation reaction with an AlGaN/GaN high-electron-mobility transistor biosensor for ultrasensitive miRNA detection. In this system, HEMT functions as a highly effective voltage amplifier to enhance detection sensitivity, while the splint-ligation reaction ensures precise discrimination of single-nucleotide mutations. By detecting miRNA-21, the SpLig-HEMT biosensor achieves an exceptional limit of detection of 10⁻¹⁸ M within 30 min, with a dynamic range from 10⁻¹⁸ M to 10⁻¹³ M. No detectable response is observed for one-mismatch miR-21. Furthermore, the SpLig-HEMT biosensor enables direct analysis of blood serum samples, effectively distinguishing between healthy individuals and patients with ovarian cancer. This study addresses critical challenges in miRNA detection and presents a promising tool for cancer diagnosis and prognosis.

With growing demand straining urban transit systems’ resilience in managing outburst passenger flows, existing approaches focused on offline and single-modal evacuations remain limited. This study proposes an online multi-modal evacuation framework that coordinates on-duty taxis, buses, and metros while minimizing impact on their regular services. We develop a data-driven agent-based environment to update multi-modal transit data and stranded passenger information in real time. Two coordination strategies are introduced: (1) an independent strategy using a decentralized training and distributed execution algorithm, and (2) a collaborative strategy using a hybrid centralized training and distributed execution algorithm. To dynamically assess evacuation effectiveness, we design a resilience framework with three metrics: robustness, rapidity, and resourcefulness. These metrics are transformed into demand-responsive feedback at each time step, enabling agents to proactively generate resilient evacuation plans. In a real-world case study triggered by a railway disruption, our approach outperforms genetic algorithms and multi-agent deep deterministic policy gradient algorithms in computation time and solution quality under offline conditions. Simulated new environments further validate its online applicability, demonstrating its potential for real-world deployment.

The COVID-19 pandemic has highlighted the need for rapid and sensitive detection of SARS-CoV-2. Here, we report an ultrasensitive SARS-CoV-2 immunosensor by integration of an AlGaN/GaN high-electron-mobility transistor (HEMT) and anti-SARS-CoV-2 spike protein antibody. The AlGaN/GaN HEMT immunosensor has demonstrated the capability to detect SARS-CoV-2 spike proteins at an impressively low concentration of 10−22 M. The sensor was also applied to pseudoviruses and SARS-CoV-2 ΔN virions that display the Spike proteins with a single virion particle sensitivity. These features validate the potential of AlGaN/GaN HEMT biosensors for point of care tests targeting SARS-CoV-2. This research not only provides the first HEMT biosensing platform for ultrasensitive and label-free detection of SARS-CoV-2.