Traditional diagnosis of infectious diseases relies on advanced analyzers in central hospitals, which is not sufficient for rapid control of epidemics, especially in resource-limited areas, raising the importance of the development of diagnostic systems for point-of-care testing (POCT). Here, we developed a simple and cost-effective digital microfluidic (DMF) platform in combination with colorimetric loop-mediated isothermal amplification (LAMP) for simple on-site diagnosis of diseases by the naked eye. The DMF chip contained four parallel units for the simultaneous detection of multiple genes and samples at a time. After amplification, the results were visualized by endpoint detection with concentrated dry neutral red on the chip. The whole process could be completed within 45 min and the on-chip LAMP reaction was shortened to 20 min. The analytical performance of this platform was evaluated by the detection of Enterocytozoon hepatopenaei, infectious hypodermal and hematopoietic necrosis virus, and white spot syndrome virus genes of shrimp. The DMF-LAMP assay showed a detection limit of 10¹ copies per μl for each target, exhibiting comparable sensitivity to the conventional LAMP assay but was more efficient. The sensitivity was also competitive with that of microfluidic-based LAMP assays using other POCT devices such as centrifugal discs regarding the detection of the same targets. Moreover, the proposed device possessed a simple chip structure and high flexibility to integrate multiplex analysis, which was beneficial for further widespread use in POCT. The practicability of the DMF-LAMP assay was verified by the testing of field shrimp. The result of the DMF-LAMP assay indicated a good agreement with the qPCR method, with Cohen's kappa values ranging from 0.91 to 1.00 depending on different targets. For the first time, an image processing method was established based on RGB analysis under different lighting conditions, and a universally adaptable positive threshold was determined regardless of lighting conditions. Paired with a smartphone, the objective analytical method was facile to implement in the field. Moreover, the DMF-LAMP system is easy to extend for a broad range of bioassays, with the advantages of low-cost, rapid detection, ease of use, good sensitivity, and easy readout.

The coronavirus disease 2019 pandemic has spread worldwide and caused more than six million deaths globally. Therefore, a timely and accurate diagnosis method is of pivotal importance for controlling the dissemination and expansions. Nucleic acid detection by the reverse transcription-polymerase chain reaction (RT-PCR) method generally requires centralized diagnosis laboratories and skilled operators, significantly restricting its use in rural areas and field settings. The digital microfluidic (DMF) technique provides a better option for simultaneous detections of multiple pathogens with fewer specimens and easy operation. In this study, we developed a novel digital microfluidic RT-qPCR platform for multiple detections of respiratory pathogens. This method can simultaneously detect eleven respiratory pathogens, namely, mycoplasma pneumoniae (MP), chlamydophila pneumoniae (CP), streptococcus pneumoniae (SP), human respiratory syncytial virus A (RSVA), human adenovirus (ADV), human coronavirus (HKU1), human coronavirus 229E (HCoV-229E), human metapneumovirus (HMPV), severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), influenza A virus (FLUA) and influenza B virus (FLUB). The diagnostic performance was evaluated using positive plasmids samples and clinical specimens compared with off-chip individual RT-PCR testing. The results showed that the limit of detections was around 12 to 150 copies per test. The true positive rate, true negative rate, positive predictive value, negative predictive value, and accuracy of DMF on-chip method were 93.33%, 100%, 100%, 99.56%, and 99.85%, respectively, as validated by the off-chip RT-qPCR counterpart. Collectively, this study reported a cost-effective, high sensitivity and specificity on-chip DMF RT-qPCR system for detecting multiple respiratory pathogens, which will greatly contribute to timely and effective clinical management of respiratory infections in medical resource-limited settings.

Recently, significant progress has been made in the development of microdevices for point-of-care infectious disease detection. However, most microdevices only allow limited steps, such as DNA amplification on the chip, while sample preparation, such as lysis and DNA extraction, is conducted off the chip using the traditional method. In this study, an all-in-one platform was developed, which incorporated all necessary procedures for nucleic acid detection. Our on-chip DNA extraction method utilized the magnetic bead-based technology on a hybrid channel-digital microfluidics (C-DMF) microdevice. It yielded high recovery rates, varying from 88.43% to 95.83%, with pathogen concentrations of 10³–10⁶ CFU/mL. In particular, the on-chip method exhibited significantly higher efficacy compared to the traditional off-chip manual method, for the DNA extraction of E. coli and S. aureus, representing Gram-negative and Gram-positive bacteria, respectively, at a sample concentration of 10³ CFU/mL. To address the need for rapid and accessible diagnostics, colorimetric LAMP amplification was integrated into the proposed microdevice. The results were visually detectable with the naked eye, making it user-friendly for non-specialists. In addition, this platform demonstrated impressive sensitivity in simultaneously detecting common foodborne pathogens in spiked meat samples, achieving the LOD of 10²–10³ CFU/mL. The entire process, from sampling to result, was fully automated and only required approximately 60 min, offering promising applicability in resource-limited and on-site testing scenarios.

Multimodal image fusion aims to integrate complementary cues, such as functional contrast and fine anatomical texture, into a single, clinically informative image. To address structural detail loss and inadequate semantic retention, we propose a GAN-based fusion network that combines prior-guided structural preservation with dynamic, input-adaptive perception. The architecture comprises one generator and two modality-specific discriminators. The generator is conditioned on segmentation-derived structural masks that act as explicit priors to preserve critical regions. In addition, a Dynamic Attention Module (DAM) adaptively extracts and aggregates information from different modalities, improving feature awareness. The two discriminators impose complementary spatial and frequency domain constraints, thus improving realism and anatomical consistency in the fused outputs. Experiments on multiple public medical fusion benchmarks show that our method outperforms state-of-the-art approaches in structural retention, detail rendition, and cross-modal complementarity. We further validate its effectiveness on infrared-visible datasets.