A Methylation-Gated DNAzyme Circuit for Spatially Controlled Imaging of MicroRNA in Cells and Animals.

Epigenetic modification plays an indispensable role in regulating routine molecular signaling pathways, yet it is rarely used to modulate molecular self-assembly networks. Herein, we constructed a bioorthogonal demethylase-stimulated DNA circuitry (DSC) system for high-fidelity imaging of microRNA (miRNA) in live cells and mice by eliminating undesired off-site signal leakage. The simple and robust DSC system is composed of a primary cell-specific circuitry regulation (CR) module and an ultimate signal-transducing amplifier (SA) module. After the modularly designed DSC system was delivered into target live cells, the DNAzyme of the CR module was site-specifically activated by endogenous demethylase to produce fuel strands for the subsequent miRNA-targeting SA module. Through the on-site and multiply guaranteed molecular recognitions, the lucid yet efficient DSC system realized the reliably amplified in vivo miRNA sensing and enabled the in-depth exploration of the demethylase-involved signal pathway with miRNA in live cells. Our bioorthogonally on-site-activated DSC system represents a universal and versatile biomolecular sensing platform via various demethylase regulations and shows more prospects for more different personalized theragnostics.

Screening of NSAIDs library identifies Tinoridine as a novel ferroptosis inhibitor for potential intervertebral disc degeneration therapy.

Low back pain (LBP) may profoundly impact the quality of life across the globe, and intervertebral disc degeneration (IVDD) is the major cause of LBP; however, targeted pharmaceutical interventions for IVDD are still lacking. Ferroptosis is a novel form of iron-dependent programmed cell death. Studies have showed that ferroptosis may closely associate with IVDD; thus, targeting ferroptosis may have great potential for IVDD therapy. Non-steroidal anti-inflammatory drugs (NSAIDs) are the first-line medications for LBP, while nuclear factor-erythroid 2-related factor-2 (Nrf2) is a key inhibitory protein for ferroptosis. In the current study, we conducted a molecular docking screening between NSAIDs library and Nrf2 protein. Tinoridine was shown to have a high binding affinity to Nrf2. The in vitro study in nucleus pulposus (NP) cells showed that Tinoridine may promote the expression and activity of Nrf2, it may also rescue RSL3-induced ferroptosis in NP cells. Knockdown of Nrf2 reverses the protective effect of Tinoridine on RSL3-induced ferroptosis in NP cells, suggesting that the inhibitory effect of Tinoridine on ferroptosis is through Nrf2. In vivo study demonstrated that Tinoridine may attenuate the progression of IVDD in rats. As NSAIDs are already clinically used for LBP therapy, the current study supports Tinoridine's application from the view of ferroptosis inhibition.

Prophylactic Effects of n-Acethylcysteine on Inflammation-induced Depression-like Behaviors in Mice.

Depression, affecting individuals worldwide, is a prevalent mental disease, with an increasing incidence. Numerous studies have been conducted on depression, yet its pathogenesis remains elusive. Recent advancements in research indicate that disturbances in synaptic transmission, synaptic plasticity, and reduced neurotrophic factor expression significantly contribute to depression's pathogenesis. In our study, we utilized adult male C57BL/6J mice. Lipopolysaccharide (LPS) can induce both chronic and acute depression-like symptoms in mice, a widely used model for studying depression associated with inflammation. N-acetylcysteine (NAC) exhibits anti-inflammatory and ameliorative effects on depressive symptoms. This study sought to determine whether NAC use could mitigate inflammatory depressive behavior through the enhancement of synaptic transmission, synaptic plasticity, and increasing levels of brain-derived neurotrophic factor (BDNF). In this study, we discovered that in mice modeled with depression-like symptoms, the expression levels of dendrites, BDNF, and miniature excitatory postsynaptic potential (mEPSC) in glutamatergic neurons, as well as the α-amino-3-hydroxy-5-methylisoxazole-4-propionic acid glutamate receptors (AMPARs) GluA1 and GluA2 subunits, were significantly decreased. These findings suggest an impairment in the synaptic transmission of glutamatergic neurons. Following treatment with NAC, the previously mentioned levels improved, indicating an enhancement in both synaptic transmission and synaptic plasticity. Our results suggest that NAC exerts a protective effect on mouse models of inflammatory depression, potentially through the enhancement of synaptic transmission and plasticity, as well as the restoration of neurotrophic factor expression. These findings offer vital animal experimental evidence supporting NAC's role in mitigating inflammatory depressive behaviors.

Physical Properties and Biochemical Composition of Extracellular Matrix-Derived Hydrogels Dictate Vascularization Potential in an Organ-Dependent Fashion.

The inherent extracellular matrix (ECM) originating from a specific tissue impacts the process of vascularization, specifically vascular network formation (VNF) orchestrated by endothelial cells (ECs). The specific contribution toward these processes of ECM from highly disparate organs such as the skin and lungs remains a relatively unexplored area. In this study, we compared VNF and ECM remodeling mediated by microvascular ECs within gel, lung, and combinations thereof (hybrid) ECM hydrogels. Irrespective of the EC source, the skin-derived ECM hydrogel exhibited a higher propensity to drive and support VNF compared to both lung and hybrid ECM hydrogels. There were distinct disparities in the physical properties of the three types of hydrogels, including viscoelastic properties and complex architectural configurations, including fiber diameter, pore area, and numbers among the fibers. The hybrid ECM hydrogel properties were unique and not the sum of the component ECM parts. Furthermore, cellular ECM remodeling responses varied with skin ECM hydrogels promoting matrix metalloproteinase 1 (MMP1) secretion, while hybrid ECM hydrogels exhibited increased MMP9, fibronectin, and collagen IV deposition. Principal component analysis (PCA) indicated that the influence of a gel's mechanical properties on VNF was stronger than the biochemical composition. These data indicate that the organ-specific properties of an ECM dictate its capacity to support VNF, while intriguingly showing that ECs respond to more than just the biochemical constituents of an ECM. The study suggests potential applications in regenerative medicine by strategically selecting ECM origin or combinations to manipulate vascularization, offering promising prospects for enhancing wound healing through pro-regenerative interventions.

Dynamic microphysiological system chip platform for high-throughput, customizable, and multi-dimensional drug screening.

Spheroids and organoids have attracted significant attention as innovative models for disease modeling and drug screening. By employing diverse types of spheroids or organoids, it is feasible to establish microphysiological systems that enhance the precision of disease modeling and offer more dependable and comprehensive drug screening. High-throughput microphysiological systems that support optional, parallel testing of multiple drugs have promising applications in personalized medical treatment and drug research. However, establishing such a system is highly challenging and requires a multidisciplinary approach. This study introduces a dynamic Microphysiological System Chip Platform (MSCP) with multiple functional microstructures that encompass the mentioned advantages. We developed a high-throughput lung cancer spheroids model and an intestine-liver-heart-lung cancer microphysiological system for conducting parallel testing on four anti-lung cancer drugs, demonstrating the feasibility of the MSCP. This microphysiological system combines microscale and macroscale biomimetics to enable a comprehensive assessment of drug efficacy and side effects. Moreover, the microphysiological system enables evaluation of the real pharmacological effect of drug molecules reaching the target lesion after absorption by normal organs through fluid-based physiological communication. The MSCP could serves as a valuable platform for microphysiological system research, making significant contributions to disease modeling, drug development, and personalized medical treatment.

Patterning Techniques Based on Metallized Electrospun Nanofibers for Advanced Stretchable Electronics.

Stretchable electronics have experienced remarkable progress, especially in sensors and wireless communication systems, attributed to their ability to conformably contact with rough or uneven surfaces. However, the development of complex, multifunctional, and high-precision stretchable electronics faces substantial challenges, including instability at rigid-soft interfaces and incompatibility with traditional high-precision patterning technologies. Metallized electrospun nanofibers emerge as a promising conductive filler, offering exceptional stretchability, electrical conductivity, transparency, and compatibility with existing patterning technologies. Here, this review focuses on the fundamental properties, preparation processes, patterning technologies, and application scenarios of conductive stretchable composites based on metallized nanofibers. Initially, it introduces the fabrication processes of metallized electrospun nanofibers and their advantages over alternative materials. It then highlights recent progress in patterning technologies, including collector collection, vapor deposition with masks, and lithography, emphasizing their role in enhancing precision and integration. Furthermore, the review shows the broad applicability and potential influence of metallized electrospun nanofibers in various fields through their use in sensors, wireless systems, semiconductor devices, and intelligent healthcare solutions. Ultimately, this review seeks to spark further innovation and address the prevailing challenges in stretchable electronics, paving the way for future breakthroughs in this dynamic field.

On the role of asymmetric molecular geometry in high-performance organic solar cells.

Although asymmetric molecular design has been widely demonstrated effective for organic photovoltaics (OPVs), the correlation between asymmetric molecular geometry and their optoelectronic properties is still unclear. To access this issue, we have designed and synthesized several symmetric-asymmetric non-fullerene acceptors (NFAs) pairs with identical physical and optoelectronic properties. Interestingly, we found that the asymmetric NFAs universally exhibited increased open-circuit voltage compared to their symmetric counterparts, due to the reduced non-radiative charge recombination. From our molecular-dynamic simulations, the asymmetric NFA naturally exhibits more diverse molecular interaction patterns at the donor (D):acceptor (A) interface as compared to the symmetric ones, as well as higher D:A interfacial charge-transfer state energy. Moreover, it is observed that the asymmetric structure can effectively suppress triplet state formation. These advantages enable a best efficiency of 18.80%, which is one of the champion results among binary OPVs. Therefore, this work unambiguously demonstrates the unique advantage of asymmetric molecular geometry, unveils the underlying mechanism, and highlights the manipulation of D:A interface as an important consideration for future molecular design.

Insights into vaccines for elderly individuals: from the impacts of immunosenescence to delivery strategies.

Immunosenescence increases the risk and severity of diseases in elderly individuals and leads to impaired vaccine-induced immunity. With aging of the global population and the emerging risk of epidemics, developing adjuvants and vaccines for elderly individuals to improve their immune protection is pivotal for healthy aging worldwide. Deepening our understanding of the role of immunosenescence in vaccine efficacy could accelerate research focused on optimizing vaccine delivery for elderly individuals. In this review, we analyzed the characteristics of immunosenescence at the cellular and molecular levels. Strategies to improve vaccination potency in elderly individuals are summarized, including increasing the antigen dose, preparing multivalent antigen vaccines, adding appropriate adjuvants, inhibiting chronic inflammation, and inhibiting immunosenescence. We hope that this review can provide a review of new findings with regards to the impacts of immunosenescence on vaccine-mediated protection and inspire the development of individualized vaccines for elderly individuals.

Multiply Guaranteed Catalytic DNA Circuit for Cancer-Cell-Selective Imaging of miRNA and Robust Evaluation of Drug Resistance.

Catalytic DNA circuits are desirable for sensitive bioimaging in living cells; yet, it remains a challenge to monitor these intricate signal communications because of the uncontrolled circuitry leakage and insufficient cell selectivity. Herein, a simple yet powerful DNA-repairing enzyme (APE1) activation strategy is introduced to achieve the site-specific exposure of a catalytic DNA circuit for realizing the selectively amplified imaging of intracellular microRNA and robust evaluation of the APE1-involved drug resistance. Specifically, the circuitry reactants are firmly blocked by the enzyme recognition/cleavage site to prevent undesirable off-site circuitry leakage. The caged DNA circuit has no target-sensing activity until its circuitry components are activated via the enzyme-mediated structural reconstitution and finally transduces the amplified fluorescence signal within the miRNA stimulation. The designed DNA circuit demonstrates an enhanced signal-to-background ratio of miRNA assay as compared with the conventional DNA circuit and enables the cancer-cell-selective imaging of miRNA. In addition, it shows robust sensing performance in visualizing the APE1-mediated chemoresistance in living cells, which is anticipated to achieve in-depth clinical diagnosis and chemotherapy research.

Adaptive Point-Line Fusion: A Targetless LiDAR-Camera Calibration Method with Scheme Selection for Autonomous Driving.

Accurate calibration between LiDAR and camera sensors is crucial for autonomous driving systems to perceive and understand the environment effectively. Typically, LiDAR-camera extrinsic calibration requires feature alignment and overlapping fields of view. Aligning features from different modalities can be challenging due to noise influence. Therefore, this paper proposes a targetless extrinsic calibration method for monocular cameras and LiDAR sensors that have a non-overlapping field of view. The proposed solution uses pose transformation to establish data association across different modalities. This conversion turns the calibration problem into an optimization problem within a visual SLAM system without requiring overlapping views. To improve performance, line features serve as constraints in visual SLAM. Accurate positions of line segments are obtained by utilizing an extended photometric error optimization method. Moreover, a strategy is proposed for selecting appropriate calibration methods from among several alternative optimization schemes. This adaptive calibration method selection strategy ensures robust calibration performance in urban autonomous driving scenarios with varying lighting and environmental textures while avoiding failures and excessive bias that may result from relying on a single approach.

Sensory-Guided Establishment of Sensory Lexicon and Investigation of Key Flavor Components for Goji Berry Pulp.

Many customers prefer goji berry pulp, well-known for its high nutritional content, over fresh goji berries. However, there is limited research on its sensory lexicon and distinctive flavor compounds. This study focused on developing a sensory lexicon for goji berry pulp and characterizing its aroma by sensory and instrumental analysis. Sensory characteristics of goji berry pulp were evaluated by our established lexicon. A total of 83 aromatic compounds in goji berry pulp were quantified using HS-SPME-GC-Orbitrap-MS. By employing OAV in combination, we identified 17 aroma-active compounds as the key ingredients in goji berry pulp. Then, we identified the potentially significant contributors to the aroma of goji berry pulp by combining principal component analysis and partial least squares regression (PLSR) models of aroma compounds and sensory attributes, which included 3-ethylphenol, methyl caprylate, 2-hydroxy-4-methyl ethyl valerate, benzeneacetic acid, ethyl ester, hexanal, (E,Z)-2,6-nonadienal, acetylpyrazine, butyric acid, 2-ethylhexanoic acid, 2-methyl-1-propanol, 1-pentanol, phenylethyl alcohol, and 2-nonanone. This study provides a theoretical basis for improving the quality control and processing technology of goji berry pulp.

Triple Cascade Quantum-Strip for Heart Failure Point-of-Care Testing.

Heart failure (HF) is a life-threatening syndrome. Timely and accurate bedside monitoring of the occurrence and progression of HF via measurements of multiple HF-related biomarkers remains a challenge. Here, we report a triple cascade quantum-strip (TCQS) sensing strategy for the rapid and selective multiplex-tracing of three clinically validated HF biomarkers (BNP/NT-proBNP/ST2) in serum. High selectivity to the three biomarkers is achieved by controlling the individual recognition ability of three target-specific quantum immunoprobes and tuning their simultaneous use to BNP/NT-proBNP/ST2 recognition without mutual interference, which allows the three biomarkers to be directly enriched from serum samples. Benefiting from the fast release-binding kinetics of target-bound immunoprobes on TCQS, recognizable fluorescent signals can be rapidly read out through combining with a self-designed smartphone-based portable reader. This rapid and simple profiling strategy results in good specificity and sensitivity with LODs of 0.097, 0.072, and 0.948 ng/mL for BNP, NT-proBNP, and ST2, respectively, which match the need of clinical applications. Real serum samples are tested with an accuracy of 92.86% for HF diagnosis, validating the capability of the smartphone-read TCQS for practical applications. In particular, the simultaneous detection of the TCQS sensing strategy for BNP/NT-proBNP/ST2 will facilitate the accurate monitoring of HF occurrence, risk stratification, progression, and prognosis as a powerful POCT tool.

Construction of metal-organic framework/cellulose nanofibers-based hybrid membranes and their ion transport property for efficient osmotic energy conversion.

The development of advanced nanofluidic membranes with better ion selectivity, efficient energy conversion and high output power density remains challenging. Herein, we prepared nanofluidic hybrid membranes based on TEMPO oxidized cellulose nanofibers (T-CNF) and manganese-based metal organic framework (MOF) using a simple in situ synthesis method. Incorporated T-CNF endows the MOF/T-CNF hybrid membrane with a high cation selectivity up to 0.93. Nanoporous MOF in three-dimensional interconnected nanochannels provides massive ion transport pathways. High transmembrane ion flux and low ion permeation energy barrier are correlated with a superior energy conversion efficiency (36 %) in MOF/T-CNF hybrid membrane. When operating under 50-fold salinity gradient by mixing simulated seawater and river water, the MOF/T-CNF hybrid membrane achieves a maximum power density value of 1.87 W m-2. About 5-fold increase in output power density was achieved compared to pure T-CNF membrane. The integration of natural nanofibers with high charge density and nanoporous MOF materials is demonstrated an effective and novel strategy for the enhancement of output power density of nanofluidic membranes, showing the great potential of MOF/T-CNF hybrid membranes as efficient nanofluidic osmotic energy generators.

Design and synthesis of hierarchical MnO-Fe3O4@C/expanded graphite composite for sensitive electrochemical detection of bisphenol A.

Hierarchically nanostructured binary transition metal oxide-based materials with high conductivity and catalytic activity are quite attractive for the electrochemical quantitative detection of environmental pollutants due to their natural abundance, variable oxidation state, and excellent synergies between metal sites. Herein, a new hierarchical MnO-Fe3O4@C/expanded graphite (EG) composite is designed and synthesized through a simple and in situ annealing method with the utilization of bimetallic organic framework (FeMn-MOF)/EG precursor. The synthesized MnO-Fe3O4@C/EG composite possesses a unique hierarchical nanoarchitecture that small-sized bimetallic oxide nanoparticles of 10-40 nm completely encapsulated by amorphous carbon layers of 2-4 nm are uniformly distributed on the EG platform. This distinctive structure combines the advantages of high conductivity, excellent catalytic activity, and strong stability. Resultantly, when it is applied to monitor environmental endocrine disruptors, the sensor exhibits a significant catalytic effect on the electrochemical oxidation of bisphenol A (BPA), inducing an amplified response current. In addition, the sensor shows a wide linear range of 1-50 µM and 50-400 µM for the BPA monitor, giving a sensitivity of 5208.8 and 1641.9 µA mM-1 cm-2, respectively. This study offers a new approach to design hierarchical binary metal oxide-based sensing materials as well as to explore their electrochemical properties and applications for the determination of emerging contaminants.

Delayed Crystallization Kinetics Allowing High-Efficiency All-Polymer Photovoltaics with Superior Upscaled Manufacturing.

Though encouraging performance is achieved in small-area organic photovoltaics (OPVs), reducing efficiency loss when evoluted to large-area modules is an important but unsolved issue. Considering that polymer materials show benefits in film-forming processability and mechanical robustness, a high-efficiency all-polymer OPV module is demonstrated in this work. First, a ternary blend consisting of two polymer donors, PM6 and PBQx-TCl, and one polymer acceptor, PY-IT, is developed, with which triplet state recombination is suppressed for a reduced energy loss, thus allowing a higher voltage; and donor-acceptor miscibility is compromised for enhanced charge transport, thus resulting in improved photocurrent and fill factor; all these contribute to a champion efficiency of 19% for all-polymer OPVs. Second, the delayed crystallization kinetics from solution to film solidification is achieved that gives a longer operation time window for optimized blend morphology in large-area module, thus relieving the loss of fill factor and allowing a record efficiency of 16.26% on an upscaled module with an area of 19.3 cm2 . Besides, this all-polymer system also shows excellent mechanical stability. This work demonstrates that all-polymer ternary systems are capable of solving the upscaled manufacturing issue, thereby enabling high-efficiency OPV modules.

Over 18% Efficiency Ternary Organic Solar Cells with 300 nm Thick Active Layer Enabled by an Oligomeric Acceptor.

The development of high-efficiency thickness-insensitive organic solar cells (OSCs) is crucially important for the mass production of solar panels. However, increasing the active layer thickness usually induces a substantial loss in efficiency. Herein, a ternary strategy in which an oligomer DY-TF is incorporated into PM6:L8-BO system as a guest component is adopted to break this dilemma. The S···F intramolecular noncovalent interactions in the backbone endow DY-TF with a high planarity. Upon the addition of DY-TF, the crystallinity of the blend is effectively improved, leading to increased charge carrier mobility, which is highly desirable in the fabrication of thick-film devices. As a result, thin-film PM6:L8-BO:DY-TF-based device (110 nm) shows a power conversion efficiency (PCE) of 19.13%. Impressively, when the active layer thickness increases to 300 nm, an efficiency of 18.23% (certified as 17.8%) is achieved, representing the highest efficiency reported for 300 nm thick OSCs thus far. Additionally, blade-coated thick device (300 nm) delivers a promising PCE of 17.38%. This work brings new insights into the construction of efficient OSCs with high thickness tolerance, showing great potential for roll-to-roll printing of large-area solar cells.

Vertical impedance electrode array for spatiotemporal dynamics monitoring of 3D cells under drug diffusion effect.

Although three-dimensional (3D) tumor models feature more accurate responses to drugs, the Matrigel scaffold affects the drug diffusion effect. Obtaining accurate drug spatiotemporal response characteristics is of great significance in the drug screening domain. However, the conventional cell-based sensors are difficult to perform spatiotemporal dynamics impedance monitoring of 3D cells and evaluate the anti-cancer pharmacological effect. Here, we proposed a biosensing platform involving a vertical impedance electrode array (VIEA) chip and a multichannel detection system. The platform can dynamically record 3D cell impedance in the vertical direction, which is consistent with time- and location-dependent drug penetration, closely related to spatiotemporal cell viability under drug effects. The subtle changes of impedance signals in different locations induced by drug diffusion can be detected, which demonstrates its high performance in drug systematic evaluation. The universal and high-content 3D cell biosensing platform is believed to have promising potential in pharmacodynamics investigation and preclinical drug screening.

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations. Using the DCAEC model, we achieve a balanced accuracy of 91.9% for human white blood cell (WBC) clustering and 97.9% for WBC/leukemia clustering using the 3D IFC images and 3D DCAEC model. Above all, although no human recognizable features can separate the clusters of cells with protein localization, we demonstrate the fused DCAEC model can achieve a cluster balanced accuracy of 85.3% from the label-free 2D transmission and 3D side scattering images. To reveal how the neural network recognizes features beyond human ability, we use the gradient-weighted class activation mapping method to discover the cluster-specific visual patterns automatically. Evaluation results show that the automatically identified salient image regions have strong cluster-specific visual patterns for different clusters, which we believe is a stride for the interpretable neural network for cell analysis with high-throughput IFCs.

IDAF: Iterative Dual-Scale Attentional Fusion Network for Automatic Modulation Recognition.

Recently, deep learning models have been widely applied to modulation recognition, and they have become a hot topic due to their excellent end-to-end learning capabilities. However, current methods are mostly based on uni-modal inputs, which suffer from incomplete information and local optimization. To complement the advantages of different modalities, we focus on the multi-modal fusion method. Therefore, we introduce an iterative dual-scale attentional fusion (iDAF) method to integrate multimodal data. Firstly, two feature maps with different receptive field sizes are constructed using local and global embedding layers. Secondly, the feature inputs are iterated into the iterative dual-channel attention module (iDCAM), where the two branches capture the details of high-level features and the global weights of each modal channel, respectively. The iDAF not only extracts the recognition characteristics of each of the specific domains, but also complements the strengths of different modalities to obtain a fruitful view. Our iDAF achieves a recognition accuracy of 93.5% at 10 dB and 0.6232 at full signal-to-noise ratio (SNR). The comparative experiments and ablation studies effectively demonstrate the effectiveness and superiority of the iDAF.

A Novel Dual PI3K/mTOR Inhibitor, XIN-10, for the Treatment of Cancer.

An imbalance in PI3K/AKT/mTOR pathway signaling in humans often leads to cancer. Therefore, the investigation of anti-cancer medications that inhibit PI3K and mTOR has emerged as a significant area of research. The aim of this study was to explore the effect of XIN-10, a dual PI3K/mTOR inhibitor, on the growth as well as antiproliferation of tumor cells and to investigate the anti-tumor mechanism of XIN-10 by further exploration. We screened three cell lines for more in-depth exploration by MTT experiments. From the AO staining, cell cycle and apoptosis, we found that XIN-10 had a more obvious inhibitory effect on the MCF-7 breast cancer cell line and used this as a selection for more in-depth experiments. A series of in vitro and in vivo experiments showed that XIN-10 has superior antiproliferative activity compared with the positive drug GDC-0941. Meanwhile, through the results of protein blotting and PCR experiments, we concluded that XIN-10 can block the activation of the downstream pathway of mTOR by inhibiting the phosphorylation of AKT(S473) as well as having significant inhibitory effects on the gene exons of PI3K and mTOR. These results indicate that XIN-10 is a highly potent inhibitor with low toxicity and has a strong potential to be developed as a novel PI3Kα/mTOR dual inhibitor candidate for the treatment of positive breast cancer.