Considering the lack of official vaccines and medicines for Ebola virus infection, reliable diagnostic methods are necessary for the control of the outbreak and the spread of the disease. We developed a microfluidic-chip-based portable system for fast and parallel detection of four Ebola virus species. The system is based on reverse transcription loop-mediated isothermal amplification (RT-LAMP) and consists of four specific LAMP primers, a disc microfluidic chip, and a portable real-time fluorescence detector. It could specifically and parallelly distinguish four species of the Ebola virus after only one sampling, including the Zaire Ebola virus, the Sudan Ebola virus, the Bundibugyo Ebola virus, and the Tai Forest Ebola virus, without cross-contamination. The limit of detection was as small as 10 copies per reaction, while the total consumption of sample and reagent was 0.94 μL per reaction. The final results could be obtained in 50 min after one addition of sample and reagent mixture. This approach provides simplicity, high sensitivity, and multi-target parallel detection at a low cost, which could enable convenient and effective on-site detections of the Ebola virus in the outdoors, remote areas, and modern hospitals.

Background: Current histopathology- and molecular-based gold standards for diagnosing adult diffuse gliomas (ADGs) have inherent limitations in reproducibility and interobserver concordance, while being time-intensive and resource-demanding. Although hyperspectral imaging (HSI)-based computer-aided pathology shows potential for automated diagnosis, it often yields suboptimal accuracy due to the lack of complementary spatial and structural tumor information. This study introduces a multimodal fusion framework integrating HSI with routinely acquired preoperative magnetic resonance imaging (MRI) to enable automated, high-precision ADG diagnosis. Methods: We developed the Hyperspectral Attention Fusion Network (HAFNet), incorporating residual learning and channel attention to jointly capture HSI patterns and MRI-derived radiomic features. The dataset comprised 1931 HSI cubes (400–1000 nm, 300 spectral bands) from histopathological patches of six major World Health Organization (WHO)-defined glioma subtypes in 30 patients, together with their routinely acquired preoperative MRI sequences. Informative wavelengths were selected using mutual information. Radiomic features were extracted with the PyRadiomics package. Model performance was assessed via stratified 5-fold cross-validation, with accuracy and area under the curve (AUC) as primary endpoints. Results: The multimodal HAFNet achieved a macro-averaged AUC of 0.9886 and a classification accuracy of 98.66%, markedly outperforming the HSI-only baseline (AUC 0.9267, accuracy 87.25%; p < 0.001), highlighting the complementary value of MRI-derived radiomic features in enhancing discrimination beyond spectral information. Conclusions: Integrating HSI biochemical and microstructural insights with MRI radiomics of morphology and context, HAFNet provides a robust, reproducible, and efficient framework for accurately predicting 2021 WHO types and grades of ADGs, demonstrating the significant added value of multimodal integration for precise glioma diagnosis.