HiLo microscopy with caustic illumination.

HiLo microscopy is an optical sectioning structured illumination microscopy technique based on computationally combining two images: one with uniform illumination and the other with structured illumination. The most widely used structured illumination in HiLo microscopy is random speckle patterns, due to their simplicity and resilience to tissue scattering. Here, we present a novel HiLo microscopy strategy based on random caustic patterns. Building on an off-the-shelf diffuser and a low-coherence LED source, we demonstrate that caustic HiLo can achieve 4.5 µm optical sectioning capability with a 20× 0.75 NA objective. In addition, with the distinct intensity statistical properties of caustic patterns, we show that our caustic HiLo outperforms speckle HiLo, achieving enhanced optical sectioning capability and preservation of fine features by imaging scattering fixed brain sections of 100 µm, 300 µm, and 500 µm thicknesses. We anticipate that this new structured illumination technique may find various biomedical imaging applications.

Bioavailability and ecological risk assessment of metal pollutants in ambient PM2.5 in Beijing.

Metal pollutants in fine particulate matter (PM2.5) are physiologically toxic, threatening ecosystems through atmospheric deposition. Biotoxicity and bioavailability are mainly determined by the active speciation of metal pollutants in PM2.5. As a megacity in China, Beijing has suffered severe particulate pollution over the past two decades, and the health effects of metal pollutants in PM2.5 have received significant attention. However, there is a limited understanding of the active forms of metals in PM2.5 and their ecological risks to plants, soil or water in Beijing. It is essential that the ecological risks of metal pollutants in PM2.5 are accurately evaluated based on their bioavailability, identifying the key pollutants and revealing historic trends to future risks control. A two-year project measured the chemical speciation of pollution elements (As, Cd, Cu, Cr, Ni, Mn, Pb, Sb, Sr, Ti, and Zn) in PM2.5 in Beijing, in particular their bioavailability, assessing ecological risks and identifying key pollutants. The mass concentrations of total and active species of pollution elements were 199.12 ng/m3 and 114.97 ng/m3, respectively. Active fractions accounted for 57.7 % of the total. Cd had the highest active proportion. Based on the risk assessment code (RAC), most pollution elements except Ti had moderate or high ecological risk, with RAC exceeding 30 %. Cd, with an RAC of 70 %, presented the strongest ecological risk. Comparing our data with previous research shows that concentrations of pollution elements in PM2.5 in Beijing have decreased over the past decade. However, although the total concentrations of Cd in PM2.5 have decreased by >50 % over the past decade, based on machine model simulation, its ecological risk has reduced by only 10 %. Our research shows that the ecological risks of pollution elements remain high despite their decreasing concentrations. Controlling the active species of metal pollutants in PM2.5 in Beijing in the future is vital.

1,4-Disubstituted Piperazin-2-Ones as Selective Late Sodium Current Inhibitors with QT Interval Shortening Properties in Isolated Rabbit Hearts.

Late sodium current (INa) inhibitors are a new subclass of antiarrhythmic agents. To overcome the drawbacks, e.g., low efficacy and inhibition effect on K+ current, of the FDA-approved late INa inhibitor ranolazine, chain amide 6a-6q, 1,4-disubstituted piperazin-2-ones 7a-7s, and their derivatives 8a-8n were successively designed, synthesized, and evaluated in vitro on the NaV1.5-transfected HEK293T cells by the whole-cell patch clamp recording assay at the concentration of 40 µM. Among the new skeleton compounds, 7d showed the highest efficacy (IC50 = 2.7 µM) and good selectivity (peak/late ratio >30 folds), as well as excellent pharmacokinetics properties in mice (T1/2 of 3.5 h, F = 90%, 3 mg/kg, po). It exhibited low hERG inhibition and was able to reverse the ATX-II-induced augmentation of late INa phenotype of LQT3 model in isolated rabbit hearts. These results suggest the application potentials of 7d in the treatments of arrhythmias related to the enhancement of late INa.

Enhanced Multiscale Human Brain Imaging by Semi-supervised Digital Staining and Serial Sectioning Optical Coherence Tomography.

A major challenge in neuroscience is to visualize the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features, but suffers from staining variability, tissue damage and distortion that impedes accurate 3D reconstructions. Here, we present a new 3D imaging framework that combines serial sectioning optical coherence tomography (S-OCT) with a deep-learning digital staining (DS) model. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images. The DS model performs translation from S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples with consistent staining quality. Additionally, we show that DS enhances contrast across cortical layer boundaries. Furthermore, we showcase geometry-preserving 3D DS on cubic-centimeter tissue blocks and visualization of meso-scale vessel networks in the white matter. We believe that our technique offers the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.

Development of double-layer poly (amino acid) modified electrochemical sensor for sensitive and direct detection of betamethasone in cosmetics.

Screening for illegal use of glucocorticoids (GCs) in cosmetics by electrochemical methods is extremely challenging due to the poor electrochemical activity of GCs. In this study, poly-L-Serine/poly-Taurine modified electrode (P(Tau)/P(L-Ser)/GCE) was prepared for sensitive and direct determination of betamethasone in cosmetics by a simple two-step in situ electropolymerization reaction. The relevant parameters of preparation and electroanalytical conditions were respectively studied, including the concentration of polymerization solution, the number of scanning circles and the scanning rate. The SEM and EDS mapping demonstrated successful preparation of P(Tau)/P(L-Ser)/GCE. The electro-catalytic properties of the obtained electrodes were investigated using cyclic voltammetry and differential pulse voltammetry methods, showing a remarkable improvement of sensitivity for the detection of betamethasone due to the synergic effect of both P(L-Ser) and P(Tau). In addition, we investigated the electrochemical reduction of betamethasone on the surface of modified electrode. It was found that the process was controlled by diffusion effect and involved the transfer of two electrons and two protons. Then the electrochemical sensor method based on P(Tau)/P(L-Ser)/GCE was established and delivered a linear response to betamethasone concentration from 0.5 to 20 µg mL-1 with a limit of detection of 32.2 ng mL-1, with excellent recoveries (98.1%-106.8%) and relative standard deviations (＜4.8%). Furthermore, the established electrochemical sensor method was compared with conventional HPLC method. The results showed that both of them were comparable. Moreover, the established electrochemical sensor method was with the merits of short analysis time, environmentally friendly, low cost and easy to achieve in-site detection.

Temporal-spatial characteristics and sources of heavy metals in bulk deposition across China.

With the rapid development of industries, agriculture, and urbanization (including transportation and population growth), there has been a significant alteration in the emission and atmospheric deposition of heavy metal pollutants. This has consequently given rise to a range of ecological and environmental health issues. In this study, we conducted a comprehensive two-year investigation on the temporal and spatial distribution characteristics of heavy metals in atmospheric deposition across China based on the Nationwide Nitrogen Deposition Monitoring Network (NNDMN). The atmospheric bulk deposition of Lead (Pb), Arsenic (As), Nickel (Ni), Selenium (Se), Chromium (Cr) and Cadmium (Cd) were 6.32 ± 1.59, 4.49 ± 0.57, 1.31 ± 0.21, 1.05 ± 0.16, 0.60 ± 0.06 and 0.21 ± 0.03 mg m-2 yr-1, respectively, with a large variation among the different regions of China. The order for atmospheric deposition flux was Southwest China > Southeast China > North China > Northeast China > Qinghai-Tibet Plateau and rural area > urban area > background area. The concentrations of heavy metals in bulk deposition exhibit seasonal variation with higher levels observed during winter compared to summer and spring, which are closely associated with anthropogenic activities. The Positive Matrix Factorization (PMF) results indicated that combustion, industrial emissions and traffic are the primary contributors to atmospheric deposition of heavy metals. The single factor pollution index (Pi) of heavy metals is consistently below 1, and the composite pollution index (Ni) is 0.16 across China, indicating that atmospheric heavy metal deposition is at a pollution-free level. The comprehensive potential ecological risk index of heavy metals is 11.8, with Cd exhibiting the highest single factor potential ecological risk index at 7.09, suggesting that more attention should be paid to Cd deposition in China. The present study reveals the spatial-temporal distribution pattern of atmospheric heavy metals deposition in China, identifying regional source characteristics and providing a theoretical foundation and strategies for reducing emissions of atmospheric pollutants.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed computational miniature mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Xuetongsu attenuates bone destruction in collagen-induced arthritis mice by inhibiting osteoclast differentiation and promoting osteoclast apoptosis.

Tujia ethnomedicine Xuetong (the stems of Kadsura heteroclita) have been widely used in folk for rheumatoid arthritis (RA), which can alleviate rheumatic pain through liquor soaking in folk. In this study, we aimed to evaluate the pharmacological effects and underlying mechanism of Xuetongsu (a key chemical component of Xuetong) on bone destruction. In our previous study, it was found that Xuetong extract can reduce adjuvant arthritic rats paw swelling and inhibit inflammatory factors in serum. Furthermore, Xuetongsu has been demonstrated to inhibit the proliferation of fibroblast-like synoviocytes, but its potential to inhibit bone destruction has not been explored. To address this, we employed the STRING database to predict protein interactions and utilized Autodock software to simulate the binding of Xuetongsu to target proteins. In this study, administration of Xuetongsu significantly alleviated paw swelling and bone destruction in C57BL/6 mice with collagen-induced arthritis (CIA). Mechanistic studies have indicated that Xuetongsu promotes apoptosis of mature osteoclasts in joint tissues by activating Caspase-3 and Bax, while inhibiting Bcl-2. Additionally, Xuetongsu inhibits osteoclast differentiation by suppressing RANKL, RANK, P-NF-κB, and NFATc1, and reduces bone resorption activity by inhibiting MMP-9, CTSK, and TRAP. Importantly, Xuetongsu exhibits good biocompatibility in major organs of mice. In summary, Xuetongsu has the potential to treat bone destruction by promoting apoptosis of mature osteoclasts, inhibiting osteoclast differentiation, and reducing bone resorption. This study reveals the pharmacological effects of Xuetongsu and its mechanism of action, which may contribute to the development of novel approaches for treating RA.

Recent advances of NFATc1 in rheumatoid arthritis-related bone destruction: mechanisms and potential therapeutic targets.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease characterized by inflammation of the synovial tissue and joint bone destruction, often leading to significant disability. The main pathological manifestation of joint deformity in RA patients is bone destruction, which occurs due to the differentiation and proliferation of osteoclasts. The transcription factor nuclear factor-activated T cell 1 (NFATc1) plays a crucial role in this process. The regulation of NFATc1 in osteoclast differentiation is influenced by three main factors. Firstly, NFATc1 is activated through the upstream nuclear factor kappa-B ligand (RANKL)/RANK signaling pathway. Secondly, the Ca2+-related co-stimulatory signaling pathway amplifies NFATc1 activity. Finally, negative regulation of NFATc1 occurs through the action of cytokines such as B-cell Lymphoma 6 (Bcl-6), interferon regulatory factor 8 (IRF8), MAF basic leucine zipper transcription factor B (MafB), and LIM homeobox 2 (Lhx2). These three phases collectively govern NFATc1 transcription and subsequently affect the expression of downstream target genes including TRAF6 and NF-κB. Ultimately, this intricate regulatory network mediates osteoclast differentiation, fusion, and the degradation of both organic and inorganic components of the bone matrix. This review provides a comprehensive summary of recent advances in understanding the mechanism of NFATc1 in the context of RA-related bone destruction and discusses potential therapeutic agents that target NFATc1, with the aim of offering valuable insights for future research in the field of RA. To assess their potential as therapeutic agents for RA, we conducted a drug-like analysis of potential drugs with precise structures.

Effects of vitamin C on the pharmacokinetics and pharmacodynamics of nimodipine in rats.

In recent years, researchers have shown a growing interest in the interactions between different pharmaceutical agents. An intriguing instance lies in the possible interaction between nimodipine and vitamin C. To investigate the pharmacokinetic and pharmacodynamic effects of vitamin C on nimodipine in rats, rats were randomly divided into a nimodipine only group and a combination group (nimodipine + vitamin C). The two groups were given intragastric administration and nimodipine blood concentrations were determined using high-performance liquid chromatography-tandem mass spectrum at different time points. Blood pressure and heart rate were measured via carotid artery cannulation. Pharmacokinetic differences were observed between the nimodipine only group and the combination group at the same dose. Compared with the nimodipine only group, the combination group's main pharmacokinetic parameters of peak concentration and area under the curve increased significantly, and the difference was statistically significant (p < 0.05); furthermore, the combination group exhibited a significant reduction in average blood pressure, while no significant effects on heart rate were observed. Vitamin C did not affect the activity of CYP450 in rat liver. The pharmacokinetic characteristics and pharmacodynamics of nimodipine were changed by vitamin C administration in rats.

Rapid analysis of the chemical constituents in Qiangli Dingxuan tablets using ultra-performance liquid chromatography coupled with quadrupole time-of-flight tandem mass spectrometry.

Qiangli Dingxuan (QLDX) tablet is a widely recognized traditional Chinese medicine formula that has been extensively used in China for decades to treat vertigo, tinnitus, and dizziness owing to its outstanding therapeutic outcomes. However, the complexity of the chemical components in this tablet makes it challenging to separate and identify these components. This study presented an effective and sensitive strategy for the rapid separation and simultaneous structural identification of QLDX tablet components using ultra-performance liquid chromatography with quadrupole time-of-flight tandem mass spectrometry and the UNIFI platform. Based on retention times, accurate masses, fragment ions, related literature, and authentic standards, 119 compounds were identified or tentatively characterized; these included 9 iridoids, 12 lignans, 21 phenylpropanoids, 27 flavonoids, 7 phthalides, and 43 others. Among them, 36 were confirmed using reference standards. The representative compounds with various chemical structures were studied by analyzing their fragmentation patterns and characteristic ions. In conclusion, this study established a rapid approach for characterizing the chemical constituents in QLDX tablet. The proposed approach provides a basis for qualitative analysis and quality control in the manufacturing process and is beneficial for advancing investigations into the efficacy and mechanism of action of this tablet.

Silicon nutrition improves the quality and yield of rice under dry cultivation.

BACKGROUND: Dry cultivation of rice is a water-saving, emission reduction and labor-saving rice farming method. However, the development of rice under dry cultivation is hampered by the limitations of dry cultivation on rice yield and rice quality. We hypothesized that additional silicon (Si) would be a measure to address these limitations or challenges. RESULTS: In the present study, we set up field trials with three treatments: flooded cultivation (W), dry cultivation (D) and dry cultivation plus Si. Yield and quality were reduced under D treatment compared to W treatment. The addition of Si promoted root development, increased plant height and leaf area, increased photosynthetic enzyme activity, net photosynthetic rate and SPAD values, and increased biomass under dry crop conditions. Under the drought conditions, silica up-regulated the expression of AGPSI, SBEI, SBEIIb, SSI and SSII-1 genes and the activities of ADP-glucose pyrophosphorylase (AGPase), soluble starch synthetase (SSS) and starch branching enzyme (SBE) enzymes, which reduced protein, amylose, chalkiness percentage and chalkiness degree, increased brown rice rate, milled rice rate and head milled rice rate, and also improved rice quality. In addition, the increase of AGPase, SSS and SBE enzyme activities promoted the filling rate and the number of spikes was guaranteed, whereas the yield was improved by promoting the seed setting rate and 1000-grain weight. CONCLUSION: The results of the present study indicate that adding appropriate amounts of Si fertilizer can improve the yield and quality of rice under dry cultivation by regulating source supply capacity and grain starch synthesis. © 2023 Society of Chemical Industry.

Joint duration-cost-quality optimization model for complex product supply chains under contingency conditions.

The Graphical Evaluation and Review Technique (GERT) and complex networks are used to simulate and analyse complex product supply chain networks based on the characteristics of complex product supply chain networks. And the traditional GERT is improved by constructing a grey parametric GERT network with restricted output results, taking into account the fact that the duration, product quality and product cost of each supplier in a complex product supply chain are interval values rather than definite values, and that customers have restrictions on the duration, product quality and product cost of the final product. The functional relationship between product quality, product cost and duration is analysed, and two satisfaction functions for duration and cost are constructed in order to quantify the multi-objective requirements of shortening duration, saving product cost and guaranteeing product quality for complex products under emergency situations. Then, a duration-cost-quality model for complex product supply chains in contingency situations is constructed to obtain the better duration, product cost and product quality of each supplier by optimising the indicator parameters in the network. Finally, the scientific validity and effectiveness of the model and method are verified by means of arithmetic example. The results show that the method is able to analyse the optimal duration, product quality and product cost of each supplier, and the main manufacturer can obtain an optimised combination of duration, cost and quality for a complex product supply chain in different contingency situation. To further promote the sustainable and secure development of complex product supply chains, this paper also suggests the integration of data sharing and blockchain technology with complex product supply chains to develop dynamic supply chain feedback management systems.

The application of machine learning to air pollution research: A bibliometric analysis.

Machine learning (ML) is an advanced computer algorithm that simulates the human learning process to solve problems. With an explosion of monitoring data and the increasing demand for fast and accurate prediction, ML models have been rapidly developed and applied in air pollution research. In order to explore the status of ML applications in air pollution research, a bibliometric analysis was made based on 2962 articles published from 1990 to 2021. The number of publications increased sharply after 2017, comprising approximately 75% of the total. Institutions in China and United States contributed half of all publications with most research being conducted by individual groups rather than global collaborations. Cluster analysis revealed four main research topics for the application of ML: chemical characterization of pollutants, short-term forecasting, detection improvement and optimizing emission control. The rapid development of ML algorithms has increased the capability to explore the chemical characteristics of multiple pollutants, analyze chemical reactions and their driving factors, and simulate scenarios. Combined with multi-field data, ML models are a powerful tool for analyzing atmospheric chemical processes and evaluating the management of air quality and deserve greater attention in future.

Fourier ptychographic topography.

Topography measurement is essential for surface characterization, semiconductor metrology, and inspection applications. To date, performing high-throughput and accurate topography remains challenging due to the trade-off between field-of-view (FOV) and spatial resolution. Here we demonstrate a novel topography technique based on the reflection-mode Fourier ptychographic microscopy, termed Fourier ptychograhpic topography (FPT). We show that FPT provides both a wide FOV and high resolution, and achieves nanoscale height reconstruction accuracy. Our FPT prototype is based on a custom-built computational microscope consisting of programmable brightfield and darkfield LED arrays. The topography reconstruction is performed by a sequential Gauss-Newton-based Fourier ptychographic phase retrieval algorithm augmented with total variation regularization. We achieve a synthetic numerical aperture (NA) of 0.84 and a diffraction-limited resolution of 750 nm, increasing the native objective NA (0.28) by 3×, across a 1.2 × 1.2 mm2 FOV. We experimentally demonstrate the FPT on a variety of reflective samples with different patterned structures. The reconstructed resolution is validated on both amplitude and phase resolution test features. The accuracy of the reconstructed surface profile is benchmarked against high-resolution optical profilometry measurements. In addition, we show that the FPT provides robust surface profile reconstructions even on complex patterns with fine features that cannot be reliably measured by the standard optical profilometer. The spatial and temporal noise of our FPT system is characterized to be 0.529 nm and 0.027 nm, respectively.

EEG assessment of brain dysfunction for patients with chronic primary pain and depression under auditory oddball task.

In 2019, the International Classification of Diseases 11th Revision International Classification of Diseases (ICD-11) put forward a new concept of "chronic primary pain" (CPP), a kind of chronic pain characterized by severe functional disability and emotional distress, which is a medical problem that deserves great attention. Although CPP is closely related to depressive disorder, its potential neural characteristics are still unclear. This paper collected EEG data from 67 subjects (23 healthy subjects, 22 patients with depression, and 22 patients with CPP) under the auditory oddball paradigm, systematically analyzed the brain network connection matrix and graph theory characteristic indicators, and classified the EEG and PLI matrices of three groups of people by frequency band based on deep learning. The results showed significant differences in brain network connectivity between CPP patients and depressive patients. Specifically, the connectivity within the frontoparietal network of the Theta band in CPP patients is significantly enhanced. The CNN classification model of EEG is better than that of PLI, with the highest accuracy of 85.01% in Gamma band in former and 79.64% in Theta band in later. We propose hyperexcitability in attentional control in CPP patients and provide a novel method for objective assessment of chronic primary pain.

The myo-inositol biosynthesis rate-limiting enzyme ISYNA1 suppresses the stemness of ovarian cancer via Notch1 pathway.

Cancer stem cells (CSCs) play a central role in ovarian cancer (OC), understanding regulatory mechanisms governing their stemness is critical. Here, we report ISYNA1, the rate-limiting enzyme in myo-inositol biosynthesis, as a suppressor of OC regulating cancer stemness. We identified ISYNA1 as a differentially expressed gene in normal ovary and ovarian cancer tissues, as well as OC cells and OCSCs. Low ISYNA1 expression correlated with poor prognosis in OC patients. In addition, ISYNA1 was negatively correlated with cancer stem cell (CSC) markers, and ISYNA1-related pathways were enriched in Wnt, Notch, and other critical cancer pathways. ISYNA1 deficiency promoted OC cell growth, migration, and invasion ability in vitro and in vivo. Knockdown of ISYNA1 increased stemness of OC cells, including self-renewal, CSC markers expression, ALDH activity, and proportion of CD44+/CD117+ CSCs. Conversely, ectopic overexpression of ISYNA1 suppresses cell proliferation, migration, invasion and stemness of OC cells. Mechanistically, ISYNA1 inhibits OC stemness by regulating myo-inositol to suppress Notch1 signaling. In summary, these data provide evidence that ISYNA1 act as a tumor suppressor in OC and a regulator of stemness, providing insight into potentially targetable pathways for ovarian cancer therapy.

Robust single-shot 3D fluorescence imaging in scattering media with a simulator-trained neural network.

Imaging through scattering is a pervasive and difficult problem in many biological applications. The high background and the exponentially attenuated target signals due to scattering fundamentally limits the imaging depth of fluorescence microscopy. Light-field systems are favorable for high-speed volumetric imaging, but the 2D-to-3D reconstruction is fundamentally ill-posed, and scattering exacerbates the condition of the inverse problem. Here, we develop a scattering simulator that models low-contrast target signals buried in heterogeneous strong background. We then train a deep neural network solely on synthetic data to descatter and reconstruct a 3D volume from a single-shot light-field measurement with low signal-to-background ratio (SBR). We apply this network to our previously developed Computational Miniature Mesoscope and demonstrate the robustness of our deep learning algorithm on scattering phantoms with different scattering conditions. The network can robustly reconstruct emitters in 3D with a 2D measurement of SBR as low as 1.05 and as deep as a scattering length. We analyze fundamental tradeoffs based on network design factors and out-of-distribution data that affect the deep learning model's generalizability to real experimental data. Broadly, we believe that our simulator-based deep learning approach can be applied to a wide range of imaging through scattering techniques where experimental paired training data is lacking.

Zinc finger and SCAN domain containing 1, ZSCAN1, is a novel stemness-related tumor suppressor and transcriptional repressor in breast cancer targeting TAZ.

Introduction: Cancer stem cells (CSCs) targeted therapy holds the potential for improving cancer management; identification of stemness-related genes in CSCs is necessary for its development. Methods: The Cancer Genome Atlas (TCGA) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets were used for survival analysis. ZSCAN1 correlated genes was identified by Spearman correlation analysis. Breast cancer stem-like cells (BCSLCs) were isolated by sorting CD44+CD24- cells from suspension cultured breast cancer (BC) spheroids. The sphere-forming capacity and sphere- and tumor-initiating capacities were determined by sphere formation and limiting dilution assays. The relative gene expression was determined by qRT-PCR, western blot. Lentivirus system was used for gene manipulation. Nuclear run-on assay was employed to examine the levels of nascent mRNAs. DNA pull-down and Chromatin immunoprecipitation (ChIP) assays were used for determining the interaction between protein and target DNA fragments. Luciferase reporter assay was used for evaluating the activity of the promoter. Results and discussion: ZSCAN1 is aberrantly suppressed in BC, and this suppression indicates a bad prognosis. Ectopic expression of ZSCAN1 inhibited the proliferation, clonogenicity, and tumorigenicity of BC cells. ZSCAN1-overexpressing BCSLCs exhibited weakened stemness properties. Normal human mammary epithelial (HMLE) cells with ZSCAN1 depletion exhibited enhanced stemness properties. Mechanistic studies showed that ZSCAN1 directly binds to -951 ~ -925bp region of WWTR1 (encodes TAZ) promoter, inhibits WWTR1 transcription, thereby inhibiting the stemness of BCSCs. Our work thus revealed ZSCAN1 as a novel stemness-related tumor suppressor and transcriptional repressor in BC.