Low-molecular-weight (LMW) compounds, including both endogenous and exogenous molecules, are critical in characterizing diverse life processes, such as cancer. Specifically, the comprehensive characterization of LMW compounds aids in understanding the pharmacological effects of traditional Chinese medicine (TCM), enabling the identification of active ingredients and assessment of therapeutic efficacy against tumor cells. However, challenges arise in laser desorption/ionization mass spectrometry (LDI MS) due to the limited availability of existing matrices and the detection preference for specific molecules. Here, ZnS/Au (ZA) composites are developed as matrices for LDI MS, achieving a 100% detection coverage of 78 standard analytes in dual-ion modes. The composites are optimized with a loose ZnS substrate densely anchored with large Au nanoparticles, forming a Schottky heterostructure at the interface. This design results in the generation of stable hotspot layers and long-lifetime electron-hole pairs. Consequently, a wide coverage of phytochemicals (12 categories, 50 types) is identified from 11 TCMs. Further, the growth-inhibitory of TCMs on A549 lung cancer cells are validated based on metabolic profiles of endogenous molecules. This study provides an efficient tool to elucidate the active components and action mechanisms of the complex TCM system at the molecular level, thereby advancing the modernization and standardization of TCM.

Chemotherapy has been clinically used for cancer treatment. However, insufficient drug concentration at the target site limitsthe therapeutic effect. Here, we introduce an in vivo target drug enrichment method, which is based on spatiotemporal acousticnode modulation (STANM). In such a time-varying acoustic field, there are spatially-shifting acoustic radiation forces on the drug-loaded microparticles that move them toward the focus. Based on this mechanism, the enhanced enrichment by STANM showedan approximately 200-fold microparticle concentration increase compared with the control group of no acoustic enrichment. Thein vivo test in mice bladders verifies that the target drug enrichment based on STANM decreased tumor weight by 72.43% comparedto traditional chemotherapy. The proposed target drug enrichment method paves the way for enhanced cancer therapy.

INTRODUCTION Microglial activation can either support neuronal function or exacerbate damage, contributing to Alzheimer's disease (AD) progression. We investigated spatial relationships among microglial activation, neuronal health, and amyloid beta (A beta) in the AD spectrum. METHODS Forty healthy controls, 37 patients with mild cognitive impairment (MCI), and 62 patients with AD underwent whole-brain high-resolution 1H-magnetic resonance spectroscopic imaging (MRSI), [F-18]DPA-714, and [F-18]AV-45 positron emission tomography (PET). Regional and voxel-wise analyses assessed changes and associations of microglial activation with N-acetylaspartate (NAA) and A beta. RESULTS MCI and AD patients showed higher microglial activation and lower NAA, correlating with cognitive decline. In controls and MCI, microglial activation correlated positively with NAA and A beta in early amyloid-accumulating regions. Conversely, negative correlations with NAA emerged in the hippocampus in MCI and extended to temporal and occipital regions in AD. DISCUSSION For the first time, we identified two distinct spatial association patterns between [F-18]DPA-714 PET and NAA, shedding light on the complex interplay between neuroinflammation and neuronal health in AD.

Accurate detection of lymph node metastasis (LNM) is critical for colorectal cancer (CRC) staging and treatment planning, yet current histopathological assessment based on lymph nodes remains labor-intensive and operator-dependent. Here, we developed a tissue metabolic fingerprinting platform leveraging label-free ferric nanoparticle-enhanced laser desorption/ionization mass spectrometry (FELDI-MS) to directly acquire colorectal cancer tissue metabolic fingerprints (CRC-TMFs) from 276 primary CRC tissue samples (138 non-metastatic/LNM-, 138 metastatic/LNM+). Based on CRC-TMFs, we constructed a machine learning-based diagnostic model for LNM detection, achieving area under the curve (AUC) of 0.914. Furthermore, metabolic profiling revealed cysteine deficiency in LNM + tissues, concomitant with upregulation of glutamate-cysteine ligase catalytic subunit (GCLC), which catalyzes the rate-limiting step in glutathione biosynthesis from cysteine. Functional validation demonstrated that GCLC knockdown inhibited CRC cell proliferation and migration, underscoring its role in metastatic reprogramming. Our work not only introduces a rapid, operator-independent tool for precise LNM assessment but also highlights dysregulated cysteine-GCLC-glutathione metabolism as a key feature of metastatic reprogramming in CRC.

Optical coherence tomography (OCT) A-scan backscattering signals provide depth-resolved textural information about internal structures. However, conventional OCT imaging is limited by refraction-induced distortion and speckle noise, hindering fine detail resolution. While multi-angle imaging systems alleviate these issues through incoherent compounding of backscattering signals, in vivo applications face challenges: limited angular coverage during surface scanning degrades backscatter intensity compounding quality, and the absence of angular information introduces artifacts in multi-view position-intensity alignment. Furthermore, excessive smoothing during speckle suppression obscures fine textures. Consequently, reconstructing ultra-fine structures from limited-angle, sparse-view measurements remains a critical challenge. To address this, we present Backscattering-Corrected Implicit Representation Tomography (BCIRT), a framework for reconstructing multi-angle low-coherence signals. We also develop a dedicated limited-angle imaging system for intraoperative BCIRT deployment. BCIRT formulates cross-view backscattering signals as a continuous function of spatial position, utilizing implicit neural representation (INR) for fitting. A physics-informed iterative mechanism inversely models ray propagation to determine corrected ray paths, enhancing the neural representation’s robustness against distortions. Leveraging these corrected paths, we introduce a dual dynamic line mixer and a contrastive-guided discriminative deblurring module to achieve high-resolution microstructure reconstruction with reduced speckle noise. Extensive experiments on biological samples and surgical resected samples demonstrate that our method achieves state-of-the-art performance, highlighting its potential for clinical applications and biomedical research.

Infective endocarditis (IE) continues to pose significant clinical challenges as a life-threatening condition associated with 30% mortality. The current diagnostic criteria, the 2023 Duke-International Society for Cardiovascular Infectious Diseases (ISCVID) criteria, present diagnostic challenges due to complex processes. Blood culture remains a cornerstone of IE diagnosis, enabling identification of the causative microorganism and guiding targeted antibiotic therapy. However, results typically take 2–5 days, significantly delaying critical treatment decisions. To overcome these limitations, we developed a nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI MS) platform capable of acquiring serum metabolic fingerprints (SMFs). When integrated with machine learning algorithms, this platform achieves accurate IE diagnosis (area under the curve (AUC) = 0.882) and rapid streptococcal classification within 10 min. Notably, our platform enables simultaneous IE diagnosis and classification via a single assay free of culture process. This integrated approach addresses the critical unmet need in IE management, offering transformative potential for timely therapeutic decision-making and improved patient outcomes.

Urinary stones (US) are common urological disease with a high incidence and a risk of comorbidities. An accurate and high-throughput strategy is greatly needed for US diagnosis. Herein, we designed metal-polyphenol networks mediated Au-Pt hybrid nanoparticles (AuMPN-Pt HNPs) and fabricated a dual-functional nanoplatform inte grating LDI-MS analysis and colorimetric assay for US diagnosis and subtype classification. The AuMPN-Pt HNPs exhibited superior LDI-MS performance with high sensitivity (limit of detection (LOD) < 2 µM) and good MS signal reproducibility (coefficients of variation (CVs) < 10 %) for accurate detection of metabolites. Meanwhile, AuMPN-Pt HNPs presented excellent peroxidase (POD)-like activity, allowing for direct and rapid detection of uric acid (UA) assisted by uricase with a LOD of 9.43 µM. Using the AuMPN-Pt HNPs assisted LDI-MS platform, we successfully achieved non-invasive US diagnosis by metabolic profiling of urine samples from US patients and the healthy controls (HCs), as well as subtype discrimination of calcium oxalate stones (CaOx stones) from UA stones by combining analysis of metabolic fingerprints from serum and urine samples. The subtype discrimi nation results were further verified by colorimetric platform for serum UA level analysis. The dual-functional nanoplatform exhibited outstanding sensitivity and specificity, providing innovative insights into disease diagnosis and subtype differentiation.

Drawing inspiration from the doubly reentrant microstructures on springtail skins, surfaces exhibiting repellency to liquids with very low surface tension, are well developed. However, utilizing doubly reentrant microstructures for controllable wettability switching, especially for liquids with low surface tensions, still remains a challenge. Herein, a strategy is presented involving two-photon lithography-assisted molding to fabricate doubly reentrant soft microstructures (DRSMs) with real-time magnetic responsiveness. Owing to the high spatial resolution enabled by two-photon absorption and the dissolvable molding template, the 3D reentrant features are well preserved in the DRSMs based on the magnetic elastomer. Accordingly, the fabricated magnetic DRSMs exhibit switchable repellency for nearly all commonly used solvents within 1 s. Furthermore, the additional doubly reentrant micro-fence design surrounding the doubly reentrant micropillars allows the DRSMs to switch wettability even when fully immersed in liquids. These DRSMs demonstrate sufficient abilities for on-demand analyte enrichment and drug release. This work may not only serve as a design strategy to enhance the responsiveness of DRSMs but also significantly simplify the wetting switching system, which is highly attractive across various fields in biomedical and microelectronic applications.

Prostate cancer (PCa) remains a major global health burden, yet current screening tools often lead to overdiagnosis due to low specificity, highlighting the urgent need for more precise diagnostic approaches. Prostatic fluid (PSF) represents a promising but underexplored biofluid with exceptional diagnostic potential due to its direct contact with the PCa microenvironment. Here, we employed molecule-level interpretable surface-enhanced Raman spectroscopy (SERS) to comprehensively investigate PCa-associated alterations in two PSF components including metabolites and small extracellular vesicles (sEVs) and explored their potential interrelations via correlation analysis. Through molecule-resolvable SERS spectral set (MORE SERSome) technique, we identified ergothioneine and deoxyguanosine as differential metabolites between PCa and benign prostatic hyperplasia patients. We further constructed a fusion diagnostic model by integrating metabolites and sEVs information. The fusion model significantly outperformed the diagnostic accuracy by applying any single component, suggesting diagnostic complementarity between PSF metabolites and sEVs. Integration with clinical variables such as age and plasma prostate-specific antigen concentration further enhanced performance with the area under the curve as high as 0.93 for PCa diagnosis, substantially surpassing existing screening methods. These findings strengthen the importance of in-depth analysis of specific PSF components and further promise the potential of SERS-based PSF profiling as a noninvasive strategy for PCa diagnosis and biopsy guidance.

Accurate boundary segmentation of volumetric images is a critical task for image-guided diagnosis and computer-assisted intervention. It is challenging to address the boundary confusion with explicit constraints. Existing methods of refining boundaries overemphasize the slender structure while overlooking the dynamic interactions between boundaries and neighboring regions. In this paper, we reconceptualize the mechanism of boundary generation via introducing Pushing and Pulling interactions, then propose a unified network termed PP-Net to model shape characteristics of the confused boundary region. Specifically, we first propose the semantic difference module (SDM) from the pushing branch to drive the boundary towards the ground truth under diffusion guidance. Additionally, the class clustering module (CCM) from the pulling branch is introduced to stretch the intersected boundary along the opposite direction. Thus, pushing and pulling branches will furnish two adversarial forces to enhance representation capabilities for the faint boundary. Experiments are conducted on four public datasets and one in-house dataset plagued by boundary confusion. The results demonstrate the superiority of PP-Net over other segmentation networks, especially on the evaluation metrics of Hausdorff Distance and Average Symmetric Surface Distance. Besides, SDM and CCM can serve as plug-and-play modules to enhance classic U-shape baseline models, including recent SAM-based foundation models. Source codes are available at https://github.com/EndoluminalSurgicalVision-IMR/PnPNet.