Raman spectroscopy in biological applications faces challenges due to complex spectra, characterized by peaks of varying widths and significant biological background noise. Convolutional neural networks (CNNs) are widely used for spectrum classification due to their ability to capture local peak features. In this study, we introduce a multiscale CNN designed to detect weak biomolecule signals and differentiate spectra with features that cannot be statistically distinguished. The approach is further enhanced by a new visualization technique tailored for multiscale spectral analysis, providing clear insights into classification results. Using the classification of cholera toxin B subunit (CTB)-treated versus untreated cell membrane samples, whose spectra cannot be statistically differentiated, the optimized multiscale CNN achieved superior performance compared to traditional machine learning methods and existing multiscale CNNs, with accuracy (99.22%), sensitivity (99.27%), specificity (99.16%), and precision (99.20%). Our new visualization method, based on gradients of activation maps with respect to class scores, generates saliency scores that capture sample variations, with decision-making relying on consistently identified peak features. By visualizing the effects of different kernel sizes, Grad-AM highlights features at varying scales, aligning closely with spectral features and enhancing CNN interpretability in complex biomolecular analysis. These advancements demonstrate the potential of our method to improve spectral analysis and reveal previously hidden peaks in complex biological environments.

Our findings demonstrate that calcium oxalate monohydrate crystals can size-selectively influence the organization of fluid phospholipid membranes and facilitate the size-selective adhesion, potentially via water-mediated hydrogen bonding.

The formation of supported lipid bilayers (SLBs) on hydrogels can act as a biocompatible anti-fouling interface. However, generating continuous and mobile SLBs on materials other than conventional glass or mica remains a significant challenge. The interaction between lipid membrane vesicles and a typical hydrogel is usually insufficient to induce membrane vesicle rupture and form a planar lipid membrane. In this study, we demonstrate that the water absorption ability of a dried polyacrylamide (PAAm) hydrogel could serve as a driving force to facilitate the formation of the hydrogel–SLBs. The absorption driving force vanishes after the hydrogels are fully hydrated, leaving no extra interaction hindering lipid lateral mobility in the formed SLBs. Our fluorescence recovery after photobleaching (FRAP) results show that SLBs only form on hydrogels with adequate absorption abilities. Moreover, we discovered that exposure to oxygen during drying could lead to the formation of an oxidized crust on the PAAm hydrogel surface, impeding SLB formation. Therefore, minimizing oxygen exposure during drying is crucial to achieving high-quality hydrogel surfaces for SLB formation. This water absorption method enables the straightforward fabrication of hydrogel–SLBs without the need for additional substrates or charges, thereby expanding their potential applications.