The need for Alpha2-Macroglobulin (α2-M) detection has increased because it plays an important role in the diagnosis of diabetic nephropathy (DN). However, few sensors can realize the high-sensitive detection for α2-M with characteristics of being fast, flexible, wearable and portable. Herein, a biosensor based on a MnFe₂O₄@chitosan/MWCNTs/PDMS composite film was developed for α2-M detection. Due to the excellent magnetoelastic effect of MnFe₂O₄ nanoparticles, the stress signal of the biosensor surface induced by the specific antibody–antigen binding was transformed into the electrical and magnetic signal. Chitosan-coated MnFe₂O₄ particles were used to provide biological modification sites for the α2-M antibody, which simplified the conventional biological functionalization modification process. The MnFe₂O₄@chitosan particles were successfully prepared by a chemical coprecipitation method and the property was studied by TEM, FT-IR and XRD. MWCNTs were employed to enhance electrical conductivity and the sensitivity of the biosensor. The detection limit (LOD) was reduced to 0.1299 ng·mL⁻¹ in the linear range from 10 ng∙mL⁻¹ to 100 µg·mL⁻¹, which was significantly lower than the limit of health diagnostics. The biosensor is fabricated by a simple method, with advantages of being rapid and highly-sensitive, and having selective detection of α2-M, which provides a novel method for the early diagnosis of DN, and it has potential in the point of care (PoC) field.

Automatic modulation classification (AMC) under low signal-to-noise ratio (SNR) and complex channel conditions remains a significant challenge due to the trade-off between robustness and efficiency. This study proposes a lightweight temporal convolutional network (TCN) and Mamba fusion architecture designed to enhance modulation recognition performance. In the modulation signal denoising stage, a non-local adaptive thresholding denoising module (NATM) is introduced to explicitly improve the effective signal-to-noise ratio. In the parallel feature extraction stage, TCN captures local symbol-level dependencies, while Mamba models long-range temporal relationships. In the output stage, their outputs are integrated through additive layer-wise fusion, which prevents parameter explosion. Experiments were conducted on the RadioML 2016.10A, 2016.10B, and 2018.01A datasets with leakage-controlled partitioning strategies including GroupKFold and Leave-One-SNR-Out cross-validation. The proposed method achieves up to a 3.8 dB gain in the required signal-to-noise ratio at 90 percent accuracy compared with state-of-the-art baselines, while maintaining a substantially lower parameter count and reduced inference latency. The denoising module provides clear robustness improvements under low signal-to-noise ratio conditions, particularly below −8 dB. The results show that the proposed network strikes a balance between accuracy and efficiency, highlighting its application potential in real-time wireless receivers under resource constraints.

The application of competitive immunoassay brings many advantages to the detection of trace biomolecules and has the potential to be applied to urine-based clinical practice. However, this type of detection method has strict requirements for secondary antibody incubation processes and analysis equipment, leading to strong demands for convenient, rapid, and inexpensive detection platforms. In this study, a mechanical competitive immunosensor (MCI) was proposed for the detection of human serum albumin (HSA) based on goat anti-rabbit@Fe ₂ O ₃ magnetic beads with magnetic sensitization. With the doping of Fe ₂ O ₃ , the conversion layer of MCI responds more accurately and rapidly to stress. In addition, goat anti-rabbit conjugated with aminated Fe ₂ O ₃ nanoparticles were introduced as secondary antibodies for signal amplification. Under the synergistic effect of the magnetic force of magnetic beads and the stress caused by the specific binding of antigen and antibody, the deformation of the film was amplified, which can effectively change the conductive pathway formed by doped carbon nanotubes, resulting in a larger output electric signal. Through competitive immunoassay for HSA, a limit of detection (LOD) of 68 ng/mL was achieved, which was an order of magnitude lower than direct detection methods. With high reproducibility and stability, MCI demonstrated effectiveness in the detection of HSA at a clinically significant concentration range (0.1-50 μg/mL). Moreover, MCI showed excellent specificity and selectivity, which held promise to offer an alternative tool for clinical diagnosis of urine HSA levels in nephrotic patients.

The African swine fever virus (ASFV), which is highly infectious and lethal, lacks a specific drug for treatment, resulting in the death of infected pigs. Therefore, accurate detection of the ASFV is of utmost importance. Currently, most detection methods for the ASFV primarily focus on polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA). In recent years, novel detection techniques such as double-antigen sandwich side-flow detection and fluorescent immunochromatography bands (FICTS) have emerged successively. However, these methods are intricate to operate and require expensive equipment, posing significant limitations in clinical experiments. Herein, we propose a low-cost, fast and portable magnetoelastic-chip integrated microfluidic biosensor for the detection of the ASFV. The biosensor is mainly composed of the magnetoelastic (ME) chip, an air-core coil, a reaction chamber and a microfluidic channel. It is proposed for the first time to integrate air-core coils into PDMS-based microfluidic biosensor, realizing the integration and flexibility compared to traditional ME sensors. In this experiment, 3D printing technology and microfluidic technology are used to make the prepared biosensor compact, which can realize the integration of sampling and detection, and has the advantages of less reagent consumption, low pollution level, and fast detection speed. We detect the ASFV antigen content by analyzing the resonance frequency of the ME chip that is extremely sensitive to changes in mass. The results show that the biosensor has a linear response with the logarithm of ASFV concentrations in the range of 10 pg/ml-1 mg/ml, and the detection limit (0.83pg/ml) is lower than other detection methods. In addition, the biosensor prepared in this experiment will play a great role in the detection of the ASFV, and has great potential in preventing the outbreak of the African swine fever (ASF) epidemic.