The analysis of albumin has clinical significance in diagnostic tests and obvious value to research studies on the albumin-mediated drug delivery and therapeutics. The present immunoassay, instrumental techniques, and colorimetric methods for albumin detection are either expensive, troublesome, or insensitive. Herein, a class of water-soluble tetrazolate-functionalized derivatives with aggregation-induced emission (AIE) characteristics is introduced as novel fluorescent probes for albumin detection. They can be selectively lighted up by site-specific binding with albumin. The resulting albumin fluorescent assay exhibits a low detection limit (0.21 nM), high robustness in aqueous buffer (pH = 6–9), and a broad tunable linear dynamic range (0.02–3000 mg/L) for quantification. The tetrazolate functionality endows the probes with a superior water solubility (>0.01 M) and a high binding affinity to albumin (K D = 0.25 μM). To explore the detection mechanism, three unique polar binding sites on albumin are computationally identified, where the multivalent tetrazolate–lysine interactions contribute to the tight binding and restriction of the molecular motion of the AIE probes. The key role of lysine residues is verified by the detection of poly-l-lysine. Moreover, we applied the fluorogenic method to quantify urinary albumin in clinical samples and found it a feasible and practical strategy for albumin analysis in complex biological fluids.

Photodynamic therapy (PDT), a treatment involving lightactivated drugs (photosensitizers, PSs) and reactive oxygen species (ROS) generation, has attracted much interest of biomedical researchers owing to its non-invasion. [1][2][3] In PDT, Two-photon photodynamic therapy (TP-PDT) is emerging as a powerful strategy for stereotactic targeting of diseased areas, but ideal photosensitizers (PSs) are currently lacking. This work reports a smart PS with aggregationinduced emission (AIE) feature, namely DPASP, for TP-PDT with excellent performances. DPASP exhibits high affinity to mitochondria, superior photostability, large two-photon absorption cross section as well as efficient reactive oxygen species generation, enabling it to achieve photosensitization both in vitro and in vivo under two-photon excitation. Moreover, its capability of stereotactic ablation of targeted cells with high-precision is also successfully demonstrated. All these merits make DPASP a promising TP-PDT candidate for accurate ablation of abnormal tissues with minimal damages to surrounding areas in the treatment of various diseases.

The crossing of blood–brain barrier (BBB) is essential for glioblastoma (GBM) therapy, and homotypic targeting is an effective strategy to achieve BBB crossing. In this work, GBM patient-derived tumor cell membrane (GBM-PDTCM) is prepared to cloak gold nanorods (AuNRs). Relying on the high homology of the GBM-PDTCM to the brain cell membrane, GBM-PDTCM@AuNRs realize efficient BBB crossing and selective GBM targeting. Meanwhile, owing to the functionalization of Raman reporter and lipophilic fluorophore, GBM-PDTCM@AuNRs are able to generate fluorescence and Raman signals at GBM lesion, and almost all tumor can be precisely resected in 15 min by the guidance of dual signals, ameliorating the surgical treatment for advanced GBM. In addition, photothermal therapy for orthotopic xenograft mice is accomplished by intravenous injection of GBM-PDTCM@AuNRs, doubling the median survival time of the mice, which improves the nonsurgical treatment for early GBM. Therefore, benefiting from homotypic membrane-enhanced BBB crossing and GBM targeting, all-stage GBM can be treated with GBM-PDTCM@AuNRs in distinct ways, providing an alternative idea for the therapy of tumor in the brain.

No abstract

Subarachnoid hemorrhage (SAH) is a severe subtype of stroke caused by the rupturing of blood vessels in the brain. The ability to accurately assess the degree of bleeding in an SAH model is crucial for understanding the brain-damage mechanisms and developing therapeutic strategies. However, current methods are unable to monitor microbleeding owing to their limited sensitivities. Herein, a new bleeding assessment system using a bioprobe TTVP with aggregation-induced emission (AIE) characteristics is demonstrated. TTVP is a water-soluble, small-molecule probe that specifically interacts with blood. Taking advantage of its AIE characteristics, cell membranes affinity, and albumin-targeting ability, TTVP fluoresces in bleeding areas and detects the presence of blood with a high signal-to-noise (S/N) ratio. The degree of SAH bleeding in an endovascular perforation model is clearly evaluated based on the intensity of the fluorescence observed in the brain, which enables the ultrasensitive detection of mirco-bleeding in the SAH model in a manner that outperforms the current imaging strategies. This method serves as a promising tool for the sensitive analysis of the degree of bleeding in SAHs and other hemorrhagic diseases.

Development of a practical point-of-care test for urinalysis is crucial for early diagnosis and treatment of chronic kidney disease (CKD). However, the classical gold standard detection method depends on sophisticated instruments and complicated procedures, impeding them from being utilized in resource-limited settings and daily screening. Herein, we report a rapid point-of-care device for the simultaneous quantification of microalbuminuria and leukocyte using one drop of urine. A luminogen (TTVP) with an aggregation-induced emission property can selectively activate its near-infrared fluorescence in the presence of albumin and leukocyte via hydrophobic or electrostatic interactions. The fluorescence signals from urine albumin and leukocyte could be well-separated combined with the coffee-ring effect. Using a smartphone-based detection device, simultaneous quantification of urine albumin and leukocyte was successfully achieved, which only took 20 min and required one drop of urine. The performance of this system is also verified with 120 clinical samples, which might serve as a simple, low-cost, and rapid tool for CKD screening and disease monitoring at the point of care.

No abstract

Cytopathological examination plays a crucial role in cancer diagnosis as it reflects the cellular pathology of cancer. However, this process traditionally relies on the visual examination by cytopathologists. Recent advancements in computer and digital imaging technologies have enabled the application of artificial intelligence (AI)‐based models to identify tumor cells in images, thereby assisting cytopathologists in achieving enhanced performance. AI‐based models can improve the accuracy and reproducibility of image evaluation and streamline clinical workflows. Moreover, AI‐based models can analyze a diverse range of sample types, including peripheral blood, urine, ascites, and bone marrow. AI‐based cytopathological recognition can help clinicians screen and diagnose cancer, predict prognosis and recurrence of cancers, such as leukemia, cervical cancer, urothelial carcinoma, and gastric cancer. Additionally, AI‐based models can predict the types of mutations in leukemia. A growing number of studies emphasize the potential of computational image analysis and deep learning‐based AI to build novel diagnostic tools that are conducive to the biomedical field. This review describes the recent developments in AI‐based cytopathological recognition and offers a perspective on how AI tools of cytopathology can help improve cancer diagnosis and prognosis prediction. Future developments in AI model applications can further contribute to the improvement of human health.