Ultrasensitive and specific detection of low-abundance tumor biomarkers remains a major challenge for early and minimally invasive cancer diagnosis. Here, we present a high-performance biosensing platform that integrates a genetically engineered bacterial S-layer with an AlGaN/GaN high-electron-mobility transistor (HEMT) sensor for label-free detection of tumor antigens. As a proof-of-concept, the ovarian cancer antigen human epididymis protein 4 (HE4) was selected . Specifically, the S-layer protein rSbpA was fused with HE4-specific nanobody 1G8 to construct a bifunctional membrane capable of self-assembling into an ordered biorecognition layer on the sensor surface. Compared to conventional chemical crosslinking, S-layer-driven assembly increased antibody loading by 50 % and minimized nonspecific adsorption in plasma environments. The resulting HEMT sensor detected HE4 across a dynamic linear range (10⁻²¹ to 10⁻¹⁴ M), identifying patients with ovarian cancer with 100 % diagnostic accuracy (AUC = 1.0). This study establishes a versatile and modular biosensing strategy for ultra-low-abundance biomarker detection with broad potential applications in the precision diagnostics of cancer and other diseases.

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.