Spatiotemporal Regulation of Cell Fate in Living Systems Using Photoactivatable Artificial DNA Membraneless Organelles.

Coacervates formed by liquid-liquid phase separation emerge as important biomimetic models for studying the dynamic behaviors of membraneless organelles and synchronously motivating the creation of smart architectures with the regulation of cell fate. Despite continuous progress, it remains challenging to balance the trade-offs among structural stability, versatility, and molecular communication for regulation of cell fate and systemic investigation in a complex physiological system. Herein, we present a self-stabilizing and fastener-bound gain-of-function methodology to create a new type of synthetic DNA membraneless organelle (MO) with high stability and controlled bioactivity on the basis of DNA coacervates. Specifically, long single-strand DNA generated by rolling circle amplification (RCA) is selected as the scaffold that assembles into membraneless coacervates via phase separation. Intriguingly, the as-formed DNA MO can recruit RCA byproducts and other components to achieve self-stabilization, nanoscale condensation, and function encoding. As a proof of concept, photoactivatable DNA MO is constructed and successfully employed for time-dependent accumulation and spatiotemporal management of cancer in a mouse model. This study offers new, important insights into synthetic membraneless organelles for the basic understanding and manipulation of important life processes.

Rapid chemical characterization and pharmacological mechanism of Fining Granules in the treatment of chronic bronchitis based on UHPLC-Q-exactive orbitrap mass spectrometer and network pharmacology.

Background: Senecio cannabifolius Less. is a perennial herb belonging to the Compositae family that has been used in traditional medicine as an antitussive and expectorant for treating chronic bronchitis and acute respiratory infections. Traditionally, Feining Granules are prepared from water extracts of the raw plant material. However, the chemical composition and pharmacological mechanisms of Feining Granules have not been thoroughly investigated. Methods: A systematic strategy for the rapid detection and identification of the constituents of Feining Granules was developed using ultrahigh-performance liquid chromatography-quadrupole-exactive orbitrap mass spectrometry (MS) with parallel reaction monitoring. Results: Overall, 162 compounds, including flavonoids, alkaloids, organic acids, and others, were identified unambiguously and tentatively by comparing the retention times and MS fragmentation with reference standards and literature data. Ninety-nine of these were reported for the first time to the best of our knowledge. Network pharmacology suggests that Feining Granules can be used to treat chronic bronchitis as they contain active components associated with the ALB, VEGFA, and SRC target genes influenced by HIF-1, VEGF, and other signaling pathways. Conclusion: These results provide information that can help understand the effective substances of S. cannabifolius Less. and improve quality control.

Using deep learning-based artificial intelligence electronic images in improving middle school teachers' literacy.

With the rapid development of societal information, electronic educational resources have become an indispensable component of modern education. In response to the increasingly formidable challenges faced by secondary school teachers, this study endeavors to analyze and explore the application of artificial intelligence (AI) methods to enhance their cognitive literacy. Initially, this discourse delves into the application of AI-generated electronic images in the training and instruction of middle school educators, subjecting it to thorough analysis. Emphasis is placed on elucidating the pivotal role played by AI electronic images in elevating the proficiency of middle school teachers. Subsequently, an integrated intelligent device serves as the foundation for establishing a model that applies intelligent classification and algorithms based on the Structure of the Observed Learning Outcome (SOLO). This model is designed to assess the cognitive literacy and teaching efficacy of middle school educators, and its performance is juxtaposed with classification algorithms such as support vector machine (SVM) and decision trees. The findings reveal that, following 600 iterations of the model, the SVM algorithm achieves a 77% accuracy rate in recognizing teacher literacy, whereas the SOLO algorithm attains 80%. Concurrently, the spatial complexities of the SVM-based and SOLO-based intelligent literacy improvement models are determined to be 45 and 22, respectively. Notably, it is discerned that, with escalating iterations, the SOLO algorithm exhibits higher accuracy and reduced spatial complexity in evaluating teachers' pedagogical literacy. Consequently, the utilization of AI methodologies proves highly efficacious in advancing electronic imaging technology and enhancing the efficacy of image recognition in educational instruction.

Aptamer-Based Nongenetic Reprogramming of CARs Enables Flexible Modulation of T Cell-Mediated Tumor Immunotherapy.

Innovating the design of chimeric antigen receptors (CARs) beyond conventional structures would be necessary to address the challenges of efficacy, safety, and applicability in T cell-based cancer therapy, whereas excessive genetic modification might complicate CAR design and manufacturing, and increase gene editing risks. In this work, we used aptamers as the antigen-recognition unit to develop a nongenetic CAR engineering strategy for programming the antitumor activity and specificity of CAR T cells. Our results demonstrated that aptamer-functionalized CAR (Apt-CAR) T cells could be directly activated by recognizing target antigens on cancer cells, and then impart a cytotoxic effect for cancer elimination in vitro and in vivo. The designable antigen recognition capability of Apt-CAR T cells allows for easy modulation of their efficacy and specificity. Additionally, multiple features, e.g., tunable antigen-binding avidity and the tumor microenvironment responsiveness, could be readily integrated into Apt-CAR design without T cell re-engineering, offering a new paradigm for developing adaptable immunotherapeutics.

Correlation between fat-to-muscle mass ratio and cognitive impairment in elderly patients with type 2 diabetes mellitus: a cross-sectional study.

BACKGROUND: Fat to muscle mass ratio (FMR), a novel index integrating fat and muscle composition, has garnered attention in age-related conditions such as type 2 diabetes mellitus (T2DM) and neurodegenerative diseases. Despite this research on the relationship between FMR and cognitive impairment (CI) in T2DM remains scarce. This study aimed to investigate the sex-specific association between FMR and CI in elderly T2DM patients. METHODS: A total of 768 elderly (> 60 years) T2DM in-patients (356 men and 412 women) were recruited from the Department of Endocrinology at Tianjin Nankai University affiliated hospital. Bioelectrical Impedance Analysis (BIA) was used to assess body composition, and Montreal Cognitive Assessment (MoCA) was used to evaluate cognitive performance. T2DM patients were categorized into normal cognitive function (NC) and cognitive impairment (CI) groups based on MoCA scores and stratified by sex. Binary logistic regression was employed to examine the association between FMR and CI. RESULTS: Among the participants, 42.7% of men and 56.3% of women experienced cognitive deterioration. Women with CI exhibited lower body mass index (BMI) and skeletal muscle mass index (SMI), while men with cognitive disorders showed lower SMI, FMR, and higher fat mass index (FMI). FMR was consistently unrelated to cognition in females, irrespective of adjustment made. However, in males, FMR was significantly associated with an increasing risk of cognitive dysfunction after adjusting for demographic and clinical variables (OR: 1.175, 95% CI: 1.045-1.320, p = 0.007). Furthermore, for each 0.1 increase in FMR, the incidence of CI rose by 31.1% after additional adjustment for BMI. In males, the prevalence of CI increased sequentially across FMR quartiles (p < 0.05). CONCLUSION: Elderly T2DM men with high FMR had unfavorable cognitive function. FMR is independently associated with an increased risk of CI in male T2DM patients regardless of BMI.

[Research progress of magnesium and magnesium alloy implants in sports medicine].

Objective: To review the research progress of magnesium and magnesium alloy implants in the repair and reconstruction of sports injury. Methods: Relevant literature of magnesium and magnesium alloys for sports injury repair and reconstruction was extensively reviewed. The characteristics of magnesium and its alloys and their applications in the repair and reconstruction of sports injuries across various anatomical sites were thoroughly discussed and summarized. Results: Magnesium and magnesium alloys have advantages in mechanical properties, biosafety, and promoting tendon-bone interface healing. Many preclinical studies on magnesium and magnesium alloy implants for repairing and reconstructing sports injuries have yielded promising results. However, successful clinical translation still requires addressing issues related to mechanical strength and degradation behavior, where alloying and surface treatments offer feasible solutions. Conclusion: The clinical translation of magnesium and magnesium alloy implants for repairing and reconstructing sports injuries holds promise. Subsequent efforts should focus on optimizing the mechanical strength and degradation behavior of magnesium and magnesium alloy implants. Conducting larger-scale biocompatibility testing and developing novel magnesium-containing implants represent new directions for future research.

Engineering a Dual-Receptor Targeted Multivalent Probe for Enhanced Magnetic Resonance Imaging of Metastatic Cancer.

Noninvasive monitoring of cancer metastasis is essential to improving clinical outcomes. Molecular MRI (mMRI) is a special implementation of noninvasive molecular imaging that promises to offer a powerful means for early detection and analysis of pathological states of cancer by tracking molecular markers. However, this is often hindered by the challenging issue of obtaining transformable mMRI contrast agents with high sensitivity, specificity, and broad applicability, given the high tumor heterogeneity and complex metastatic features. Herein, we present a dual-receptor targeted, multivalent recognition strategy and report a new class of mMRI probes for enhanced imaging of metastatic cancer. This probe is designed by covalently conjugating Gd-chelate with phenylboronic acid and an aptamer via an affordable polymerization chemistry to concurrently target two different cell-membrane receptors that are commonly overexpressed and highly implicated in both tumorigenesis and metastasis. Moreover, the polymerization chemistry allows the probe to contain a bunch of targeting ligands and signal reporters in a single chain, which not only leads to more than 2-fold enhancement in T1 relaxivity at 1.5 T compared to the commercial contrast agent but also enables it to actively target tumor cells in a multivalent recognition manner, contributing to a much higher imaging contrast than single-receptor targeted probes and the commercial agent in mouse models with lung metastases, yet without inducing systemic side effects. We expect this study to offer a useful molecular tool to promote transformable applications of mMRI and a better understanding of molecular mechanisms involved in cancer development.

The use of deep learning integrating image recognition in language analysis technology in secondary school education.

This work aims to investigate the application of advanced deep learning algorithms and image recognition technologies to enhance language analysis tools in secondary education, with the goal of providing educators with more effective resources and support. Based on artificial intelligence, this work integrates data mining techniques related to deep learning to analyze and study language behavior in secondary school education. Initially, a framework for analyzing language behavior in secondary school education is constructed. This involves evaluating the current state of language behavior, establishing a framework based on evaluation comments, and defining indicators for analyzing language behavior in online secondary school education. Subsequently, data mining technology and image and character recognition technology are employed to conduct data mining for online courses in secondary schools, encompassing the processing of teaching video images and character recognition. Finally, an experiment is designed to validate the proposed framework for analyzing language behavior in secondary school education. The results indicate specific differences among the grouped evaluation scores for each analysis indicator. The significance p values for the online classroom discourse's speaking rate, speech intelligibility, average sentence length, and content similarity are -0.56, -0.71, -0.71, and -0.74, respectively. The aim is to identify the most effective teaching behaviors for learners and enhance the support for online course instruction.

Recent advances in nanomedicine for metabolism-targeted cancer therapy.

Metabolism denotes the sum of biochemical reactions that maintain cellular function. Different from most normal differentiated cells, cancer cells adopt altered metabolic pathways to support malignant properties. Typically, almost all cancer cells need a large number of proteins, lipids, nucleotides, and energy in the form of ATP to support rapid division. Therefore, targeting tumour metabolism has been suggested as a generic and effective therapy strategy. With the rapid development of nanotechnology, nanomedicine promises to have a revolutionary impact on clinical cancer therapy due to many merits such as targeting, improved bioavailability, controllable drug release, and potentially personalized treatment compared to conventional drugs. This review comprehensively elucidates recent advances of nanomedicine in targeting important metabolites such as glucose, glutamine, lactate, cholesterol, and nucleotide for effective cancer therapy. Furthermore, the challenges and future development in this area are also discussed.

Artificial DNA Framework Channel Modulates Antiapoptotic Behavior in Ischemia-Stressed Cells via Destabilizing Promoter G-Quadruplex.

Regulating folding/unfolding of gene promoter G-quadruplexes (G4s) is important for understanding the topological changes in genomic DNAs and the biological effects of such changes on important cellular events. Although many G4-stabilizing ligands have been screened out, effective G4-destabilizing ligands are extremely rare, posing a great challenge for illustrating how G4 destabilization affects gene function in living cells under stress, a long-standing question in neuroscience. Herein, we report a distinct methodology able to destabilize gene promoter G4s in ischemia-stressed neural cells by mitigating the ischemia-induced accumulation of intracellular K+ with an artificial membrane-spanning DNA framework channel (DFC). We also show that ischemia-triggered K+ influx is positively correlated to anomalous stabilization of promoter G4s and downregulation of Bcl-2, an antiapoptotic gene with neuroprotective effects against ischemic injury. Intriguingly, the DFC enables rapid transmembrane transport of excessive K+ mediated by the internal G4 filter, leading to the destabilization of endogenous promoter G4 in Bcl-2 and subsequent turnover of gene expression at both transcription and translation levels under ischemia. Consequently, this work enriches our understanding of the biological roles of endogenous G4s and may offer important clues to study the cellular behaviors in response to stress.

Metabolic-dysfunction associated steatotic liver disease-related diseases, cognition and dementia: A two-sample mendelian randomization study.

BACKGROUND: The results of current studies on metabolic-dysfunction associated steatotic liver disease (MASLD)-related diseases, cognition and dementia are inconsistent. This study aimed to elucidate the effects of MASLD-related diseases on cognition and dementia. METHODS: By using single-nucleotide polymorphisms (SNPs) associated with different traits of NAFLD (chronically elevated serum alanine aminotransferase levels [cALT], imaging-accessed and biopsy-proven NAFLD), metabolic dysfunction-associated steatohepatitis, and liver fibrosis and cirrhosis, we employed three methods of mendelian randomization (MR) analysis (inverse-variance weighted [IVW], weighted median, and MR-Egger) to determine the causal relationships between MASLD-related diseases and cognition and dementia. We used Cochran's Q test to examine the heterogeneity, and MR-PRESSO was used to identify outliers (NbDistribution = 10000). The horizontal pleiotropy was evaluated using the MR-Egger intercept test. A leave-one-out analysis was used to assess the impact of individual SNP on the overall MR results. We also repeated the MR analysis after excluding SNPs associated with confounding factors. RESULTS: The results of MR analysis suggested positive causal associations between MASLD confirmed by liver biopsy (p of IVW = 0.020, OR = 1.660, 95%CI = 1.082-2.546) and liver fibrosis and cirrhosis (p of IVW = 0.009, OR = 1.849, 95%CI = 1.169-2.922) with vascular dementia (VD). However, there was no evidence of a causal link between MASLD-related diseases and cognitive performance and other types of dementia (any dementia, Alzheimer's disease, dementia with lewy bodies, and frontotemporal dementia). Sensitivity tests supported the robustness of the results. CONCLUSIONS: This two-sample MR analysis suggests that genetically predicted MASLD and liver fibrosis and cirrhosis may increase the VD risk. Nonetheless, the causal effects of NAFLD-related diseases on VD need more in-depth research.

Risk factors for cognitive impairment in middle-aged type 2 diabetic patients: a cross-sectional study.

OBJECTIVE: The aim of this study was to investigate risk factors for cognitive impairment (CI) and explore the relationship between obesity and cognition in hospitalised middle-aged patients with type 2 diabetes (T2DM). METHODS: Subjects were divided into normal cognitive function (NCF) (n=320) and CI (n=204) groups based on the results of the Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE). The risk factors for CI were determined by logistic regression analysis and generalised linear modelling. The associations between obesity parameters (body mass index (BMI) and waist circumference (WC)) and cognitive ability were studied with the use of linear regression analysis, piecewise regression modelling and interaction analysis. The receiver operating characteristic curve analysis was used to examine the diagnostic value of influencing factors for cc RESULTS: The prevalence of CI was 38.9% in hospitalised middle-aged T2DM patients (median age, 58 years). Age, WC, hypoglycaemic episode within past 3 months and cerebrovascular disease (CVD) were identified as independent risk factors for CI, while the independent protective factors were education, diabetic dietary pattern, overweight and obesity. BMI was a protective factor for the MoCA score within a certain range, whereas WC was a risk factor for the MMSE and MoCA scores. The area under the curve for the combination of BMI and WC was 0.754 (p<0.001). CONCLUSION: Age, education, diabetic dietary pattern, WC, overweight, obesity, hypoglycaemic episode in 3 months and CVD may be potential influencing factors for the occurrence of CI in hospitalised middle-aged population with T2DM. The combination of BMI and WC may represent a good predictor for early screening of CI in this population. Nevertheless, more relevant prospective studies are still needed.

Topological Single-stranded DNA Encoding and Programmable Assembly of Molecular Nanostructures for NIR-II Cancer Theranostics.

Molecular nanotechnology promises to offer privileged access to developing NIR-II materials with precise structural and functional manipulation for transformable theranostic applications. However, the lack of an affordable, yet general, method makes this goal currently inaccessible. By virtue of the intriguing nucleic acid chemistry, here we present an artificial base-directed topological single-strand DNA encoding design that enables one-step synthesis of valence-controlled NIR-II molecular nanostructures and spatial assembly of these nanostructures to modulate their behaviors in living systems. As proof-of-concept studies, we construct ultrasmall Ag2 S quantum dots and pH-responsive, size-tunable CuS assemblies for in vivo NIR-II fluorescence imaging and deep tumor photothermal therapy. This work paves a new way for creating functionally diversified architectures and broadens the scope of DNA-encoded material engineering.

Aptamer Conjugate-Based Ratiometric Fluorescent Probe for Precise Imaging of Therapy-Induced Cancer Senescence.

Therapy-induced cellular senescence has been increasingly recognized as a key mechanism to promote various aspects of carcinogenesis in a nonautonomous manner. Thus, real-time imaging monitoring of cellular senescence during cancer therapy is imperative not only to further elucidate its roles in cancer progression but also to provide guidance for medical management of cancer. However, it has long been a challenging task due to the lack of effective imaging molecule tools with high specificity and accuracy toward cancer senescence. Herein, we report the rational design, synthesis, and evaluation of an aptamer conjugate-based ratiometric fluorescent probe for precise imaging of therapy-induced cancer senescence. Unlike traditional senescence imaging systems, our probe targets two senescence-associated markers at both cellular and subcellular dimensions, namely, aptamer-mediated membrane marker recognition for active cell targeting and lysosomal marker-triggered ratiometric fluorescence changes of two cyanine dyes for site-specific, high-contrast imaging. Moreover, such a two-channel fluorescence response is activated after a one-step reaction and at the same location, avoiding the diffusion-caused signal decay previously encountered in dual-marker activated probes, contributing to spatiotemporally specific imaging of therapy-induced cancer senescence in living cells and three-dimensional multicellular tumor spheroids. This work may offer a valuable tool for a basic understanding of cellular senescence in cancer biology and interventions.

Chemical Amplification-Enabled Topological Modification of Nucleic Acid Aptamers for Enhanced Cancer-Targeted Theranostics.

Site-specific chemical conjugation has long been a challenging endeavor in the field of ligand-directed modification to produce homogeneous conjugates for precision medicine. Here, we develop a chemical amplification-enabled topological modification (Chem-ATM) methodology to establish a versatile platform for the programmable modification of nucleic acid aptamers with designated functionalities. Differing from conventional conjugation strategies, a three-dimensional artificial base is designed in Chem-ATM as a chemical amplifier, giving access to structurally and functionally diversified conjugation of aptamers, with precise control over loading capacity but in a sequence-independent manner. Meanwhile, the sp3 hybridized atom-containing amplifier enables planar-to-stereo conformational transformation of the entire conjugate, eliciting high steric hindrance against enzymatic degradation in complex biological environments. The versatility of Chem-ATM is successfully demonstrated by its delivery of anticancer drugs and imaging agents for enhanced therapy and high-contrast noninvasive tumor imaging in xenograft and orthotopic tumor models. This study offers a different perspective for ligand-directed chemical conjugation to enrich the molecular toolbox for bioimaging and drug development.

Relationships Between Body Composition and Cognitive Impairment in Hospitalised Middle-Aged Type 2 Diabetic Patients.

Objective: The aim of this study was to elucidate the relationship between specific body composition and the risk of Cognitive Impairment (CI) in middle-aged Type 2 Diabetes Mellitus (T2DM) patients. Methods: This cross-sectional study included 504 hospitalized patients with T2DM from the Department of Endocrinology and Metabolism of the Tianjin Union Medical Center. Subjects were grouped by sex, and cognitive status was assessed using the Montreal Cognitive Assessment (MoCA). The relationship between body composition and cognitive ability was investigated with the use of linear regression analysis. The association between body composition and CI risk was determined by logistic regression analysis. Results: The prevalence of CI was 39.3% in middle-aged T2DM patients. After adjusting for age, education, marriage status, carotid atherosclerosis, cerebrovascular disease and hemoglobin, multiple linear regression analysis showed that lean mass index (LMI), body mass index (BMI) and appendicular skeletal muscle index (SMI) were significant predictors for the MoCA scores in men (p < 0.05). In addition, BMI (OR 0.913, 95% CI 0.840-0.992) and LMI (OR 0.820, 95% CI 0.682-0.916) were independent protective factors for CI in males. After adjusted for age, education, marriage status, dietary control of diabetes and cerebrovascular disease, visceral obesity (VO, OR 1.950, 95% CI 1.033-3.684) and abdominal obesity (AO, OR 2.537, 95% CI 1.191-5.403) were risk factors for CI in female patients. Conclusion: The results suggest that there may be different mechanisms underlying the relationship of body compositions and cognitive performance between middle-aged male and female patients with T2DM. In addition, our finding of potential determinants of cognitive impairment may facilitate the development of intervention programs for middle-aged type 2 diabetic patients. Nevertheless, more large prospective studies looking at cognition and changes in body composition over time are needed in the future to further support their association.

Mechanisms of autophagy and endoplasmic reticulum stress in the reversal of platinum resistance of epithelial ovarian cancer cells by naringin.

OBJECTIVE: Our previous studies showed that naringin (Nar) can effectively reverse the cisplatin resistance of ovarian cancer cells. This study aims to explore the potential mechanism by which Nar reverses cisplatin resistance in ovarian cancer. METHODS: The proliferative activity of cells was evaluated using CCK8 and cell clone formation assays. Autophagic flux in cells was evaluated via LC3B immunofluorescence and monodansylcadaverine (MDC) staining. The expression levels of autophagy, endoplasmic reticulum (ER) stress, and apoptosis-related proteins were detected via Western blotting. Autophagy and ER stress were regulated using siATG5, siLC3B, rapamycin (Rap), chloroquine (CQ), 4-phenylbutyric acid (4-PBA), and thapsigargin (TG). siATG5 and siLC3B are short interfering RNAs (siRNAs) used to knock down the expression of ATG5 and LC3B genes, respectively. RESULTS: Nar inhibited autophagy in SKOV3/DDP cells by activating the PI3K/AKT/mTOR pathway. And Nar increased the levels of ER stress-related proteins, namely, P-PERK, GRP78, and CHOP, and promoted apoptosis in SKOV3/DDP cells. Moreover, treatment with the inhibitor of ER stress alleviated apoptosis induced by Nar in SKOV3/DDP cells. In addition, compared to cisplatin or naringin alone, the combination of Nar and cisplatin significantly reduced the proliferative activity of SKOV3/DDP cells. And siATG5, siLC3B, CQ or TG pretreatment further inhibited the proliferative activity of SKOV3/DDP cells. Conversely, Rap or 4-PBA pretreatment alleviated the cell proliferation inhibition caused by Nar combined with cisplatin. CONCLUSION: Nar not only inhibited the autophagy in SKOV3/DDP cells by regulating the PI3K/AKT/mTOR signalling pathway, but also promoted apoptosis in SKOV3/DDP cells by targeting ER stress. Nar can reverse the cisplatin resistance in SKOV3/DDP cells through these two mechanisms.

Bioinspired molecular engineering of bivalent aptamers by ligand-induced self-dimerization.

A bioinspired "point-and-shoot" molecular strategy is described for engineering noncovalent aptamers via K+-induced reassembly of a split G-quadruplex sequence at the end of monomeric aptamers. This design compensates for insufficient cell recognition of monovalent aptamers in biological environments and minimizes structure-induced nonspecific cell binding, expending the aptamer toolbox available for complex systems.

Dual-Parameter Recognition-Directed Design of the Activatable Fluorescence Probe for Precise Imaging of Cellular Senescence.

Specific imaging of cellular senescence emerges as a promising strategy for early diagnosis and treatment of various age-related diseases. The currently available imaging probes are routinely designed by targeting a single senescence-related marker. However, the inherently high heterogeneity of senescence makes them inaccessible to achieve specific and accurate detection of broad-spectrum cellular senescence. Here, we report the design of a dual-parameter recognition fluorescent probe for precise imaging of cellular senescence. This probe remains silent in non-senescent cells, yet produces bright fluorescence after sequential responses to two senescence-associated markers, namely, SA-ß-gal and MAO-A. In-depth studies reveal that this probe allows for high-contrast imaging of senescence, independent of the cell source or stress type. More impressively, such dual-parameter recognition design further allows it to distinguish senescence-associated SA-ß-gal/MAO-A from cancer-related ß-gal/MAO-A, compared to commercial or previous single-marker detection probes. This study offers a valuable molecular tool for imaging cellular senescence, which is expected to significantly expand the basic studies on senescence and facilitate advances of senescence-related disease theranostics.

Engineering Hierarchical Recognition-Mediated Senolytics for Reliable Regulation of Cellular Senescence and Anti-Atherosclerosis Therapy.

Precise regulation of vascular senescence represents a far-reaching strategy to combat age-related diseases. However, the high heterogeneity of senescence, alongside the lack of targeting and potent senolytics, makes it very challenging. Here we report a molecular design to tackle this challenge through multidimensional, hierarchical recognition of three hallmarks commonly shared among senescence, namely, aptamer-mediated recognition of a membrane marker for active cell targeting, a self-immolative linker responsive to lysosomal enzymes for switchable drug release, and a compound against antiapoptotic signaling for clearance. Such senolytic can target and trigger severe cell apoptosis in broad-spectrum senescent endothelial cells, and importantly, distinguish them from the quiescent state. Its potential for in vivo treatment of vascular diseases is successfully illustrated in a model of atherosclerosis, with effective suppression of the plaque progression yet negligible side effects.