Evaluation of the cytotoxic effects of sodium hypochlorite on human dental stem cells

Purpose: To investigate the influence of sodium hypochlorite (NaOCl) on human dental stem cell proliferation and differentiation.Method: Dental pulp stem cells (DPSCs), periodontal ligament stem cell (PDLSCs), and gingival mesenchymal stem cells (GMSCs) were treated with NaOCl. Cell viability was evaluated with cellular counting kit-8 (CCK8), and cellular adenosine triphosphate (ATP) levels were analyzed by bromodeoxyuridine (BrdU) incorporation and subsequent flow cytometry. Quantitative polymerase chain reaction (qPCR) and western blotting were performed to detect the expressions of differentiation markers.Results: The viability and ATP levels of all three stem cells types were impaired by NaOCl in a concentration- and time-dependent manners. However, the decrease ATP in GMSCs was less than the other two stem cell population (p < 0.05). NaOCl treatment significantly suppressed the proliferation of dental stem cells (p < 0.05). With regard to differentiation marker expression levels, the decrease in Stro-1 was greater in treatment groups when compared to control on Day 7, while increase in levels of dentin sialophosphoprotein (DSPP), bone sialoprotein (BSP), and osteocalcin (OC) was smaller (p < 0.05). The expressional changes of Stro-1, DSPP, BSP, and OC were more prominent in DPSMs and PDLSCs than in GMSCs.Conclusion: NaOCl dose-dependently impairs the viability, proliferation and differentiation of dental stem cells. Thus, its toxicity to dental stem cells needs to be considered in clinical application.Keywords: Dental stem cells, Sodium hypochlorite, Viability, Proliferation, Differentiatio

Transformer-QEC: Quantum Error Correction Code Decoding with Transferable Transformers

Quantum computing has the potential to solve problems that are intractable for classical systems, yet the high error rates in contemporary quantum devices often exceed tolerable limits for useful algorithm execution. Quantum Error Correction (QEC) mitigates this by employing redundancy, distributing quantum information across multiple data qubits and utilizing syndrome qubits to monitor their states for errors. The syndromes are subsequently interpreted by a decoding algorithm to identify and correct errors in the data qubits. This task is complex due to the multiplicity of error sources affecting both data and syndrome qubits as well as syndrome extraction operations. Additionally, identical syndromes can emanate from different error sources, necessitating a decoding algorithm that evaluates syndromes collectively. Although machine learning (ML) decoders such as multi-layer perceptrons (MLPs) and convolutional neural networks (CNNs) have been proposed, they often focus on local syndrome regions and require retraining when adjusting for different code distances. We introduce a transformer-based QEC decoder which employs self-attention to achieve a global receptive field across all input syndromes. It incorporates a mixed loss training approach, combining both local physical error and global parity label losses. Moreover, the transformer architecture's inherent adaptability to variable-length inputs allows for efficient transfer learning, enabling the decoder to adapt to varying code distances without retraining. Evaluation on six code distances and ten different error configurations demonstrates that our model consistently outperforms non-ML decoders, such as Union Find (UF) and Minimum Weight Perfect Matching (MWPM), and other ML decoders, thereby achieving best logical error rates. Moreover, the transfer learning can save over 10x of training cost.Comment: Accepted to ICCAD 2023, FAST ML for Science Workshop; 7 pages, 8 figure

PROTECTIVE EFFECTS OF CISTANCHES HERBA AQUEOUS EXTRACT ON CISPLATIN-INDUCED PREMATURE OVARIAN FAILURE IN MICE

Background: Chemotherapeutic treatment of premenopausal women has been linked to premature ovarian failure (POF). Cistanches Herba (CH) is a commonly used male impotence and female infertility treatment in China; however, whether CH protects ovaries from chemotherapeutic drug-induced POF remains unclear. In this study, we investigated the protective effects of CH in a mouse model of chemotherapeutic drug-induced POF. Materials and Methods: We administered low- and high-concentration CH to cisplatin-induced POF mice for 2 weeks and determined body and ovarian weights, as well as serum follicle-stimulating hormone (FSH) and estradiol concentrations, to evaluate ovarian function. In addition, we evaluated the protective mechanisms of CH by detecting the levels of apoptosis-related proteins and evaluating markers of mitochondrial function. Results: In POF mice, we observed reduced body and ovarian weights; elevated serum FSH and attenuated estradiol concentrations; apoptosis of ovarian granulosa with concomitant changes in apoptosis-related proteins (including caspase-3, poly adenosine diphosphate-ribose polymerase, Bcl-2, and Bax); and mitochondrial dysfunction, such as a reduction in mitochondrial numbers, destruction of ultrastructural morphology, decrease in ATPase activity, and decreases in mitochondrial membrane potential and mitofusin-2 (a mitochondria dynamin-like GTPase). Significantly, CH reversed, to an extent, functional and morphologic injuries and ovarian tissue apoptosis by up-regulating the level of Mfn2 and the ratio of Bcl-2/Bax. Furthermore, CH reduced cisplatin-induced mitochondrial dysfunction in ovarian tissues. Conclusion: The present findings showed that CH inhibited cisplatin-induced POF through interactions between Mfn2 and Bcl-2/Bax proteins and, possibly, by up-regulation of Mfn2 expression. Ultimately, CH protects ovarian tissues from cisplatin-induced apoptosis

Blocking Wnt Secretion Reduces Growth of Hepatocellular Carcinoma Cell Lines Mostly Independent of β-Catenin Signaling

AbstractAberrant activation of Wnt/β-catenin signaling plays a key role in the onset and development of hepatocellular carcinomas (HCC), with about half of them acquiring mutations in either CTNNB1 or AXIN1. However, it remains unclear whether these mutations impose sufficient β-catenin signaling or require upstream Wnt ligand activation for sustaining optimal growth, as previously suggested for colorectal cancers. Using a panel of nine HCC cell lines, we show that siRNA-mediated knockdown of β-catenin impairs growth of all these lines. Blocking Wnt secretion, by either treatment with the IWP12 porcupine inhibitor or knockdown of WLS, reduces growth of most of the lines. Unexpectedly, interfering with Wnt secretion does not clearly affect the level of β-catenin signaling in the majority of lines, suggesting that other mechanisms underlie the growth-suppressive effect. However, IWP12 treatment did not induce autophagy or endoplasmic reticulum (ER) stress, which may have resulted from the accumulation of Wnt ligands within the ER. Similar results were observed for colorectal cancer cell lines used for comparison in various assays. These results suggest that most colorectal and liver cancers with mutations in components of the β-catenin degradation complex do not strongly rely on extracellular Wnt ligand exposure to support optimal growth. In addition, our results also suggest that blocking Wnt secretion may aid in tumor suppression through alternative routes currently unappreciated

RobustState: Boosting Fidelity of Quantum State Preparation via Noise-Aware Variational Training

Quantum state preparation, a crucial subroutine in quantum computing, involves generating a target quantum state from initialized qubits. Arbitrary state preparation algorithms can be broadly categorized into arithmetic decomposition (AD) and variational quantum state preparation (VQSP). AD employs a predefined procedure to decompose the target state into a series of gates, whereas VQSP iteratively tunes ansatz parameters to approximate target state. VQSP is particularly apt for Noisy-Intermediate Scale Quantum (NISQ) machines due to its shorter circuits. However, achieving noise-robust parameter optimization still remains challenging. We present RobustState, a novel VQSP training methodology that combines high robustness with high training efficiency. The core idea involves utilizing measurement outcomes from real machines to perform back-propagation through classical simulators, thus incorporating real quantum noise into gradient calculations. RobustState serves as a versatile, plug-and-play technique applicable for training parameters from scratch or fine-tuning existing parameters to enhance fidelity on target machines. It is adaptable to various ansatzes at both gate and pulse levels and can even benefit other variational algorithms, such as variational unitary synthesis. Comprehensive evaluation of RobustState on state preparation tasks for 4 distinct quantum algorithms using 10 real quantum machines demonstrates a coherent error reduction of up to 7.1 $\times$ and state fidelity improvement of up to 96\% and 81\% for 4-Q and 5-Q states, respectively. On average, RobustState improves fidelity by 50\% and 72\% for 4-Q and 5-Q states compared to baseline approaches.Comment: Accepted to FASTML @ ICCAD 2023. 14 pages, 20 figure

Chitosan-salvianolic acid B coating on the surface of nickel-titanium alloy inhibits proliferation of smooth muscle cells and promote endothelialization

Introduction: Intracranial stents are of paramount importance in managing cerebrovascular disorders. Nevertheless, the currently employed drug-eluting stents, although effective in decreasing in-stent restenosis, might impede the re-endothelialization process within blood vessels, potentially leading to prolonged thrombosis development and restenosis over time.Methods: This study aims to construct a multifunctional bioactive coating to enhance the biocompatibility of the stents. Salvianolic acid B (SALB), a bioactive compound extracted from Salvia miltiorrhiza, exhibits potential for improving cardiovascular health. We utilized dopamine as the base and adhered chitosan-coated SALB microspheres onto nickel-titanium alloy flat plates, resulting in a multifunctional drug coating.Results: By encapsulating SALB within chitosan, the release period of SALB was effectively prolonged, as evidenced by the in vitro drug release curve showing sustained release over 28 days. The interaction between the drug coating and blood was examined through experiments on water contact angle, clotting time, and protein adsorption. Cellular experiments showed that the drug coating stimulates the proliferation, adhesion, and migration of human umbilical vein endothelial cells.Discussion: These findings indicate its potential to promote re-endothelialization. In addition, the bioactive coating effectively suppressed smooth muscle cells proliferation, adhesion, and migration, potentially reducing the occurrence of neointimal hyperplasia and restenosis. These findings emphasize the exceptional biocompatibility of the newly developed bioactive coating and demonstrate its potential clinical application as an innovative strategy to improve stent therapy efficacy. Thus, this coating holds great promise for the treatment of cerebrovascular disease

Dichotomal functions of phosphorylated and unphosphorylated STAT1 in hepatocellular carcinoma

Abstract: Interferons (IFNs) with antiviral and immune-stimulatory functions have been widely used in prevention and treatment of hepatocellular carcinoma (HCC). Signal transducer and activator of transcription 1 (STAT1) is a key element of the IFN signaling, and the function of STAT1 is critically determined by its phosphorylation state. This study aims to understand the functions of phosphorylated (p-) and unphosphorylated (u-) STAT1 in HCC. We found that u-STAT1 is significantly elevated in patient HCC tumor tissues and predominantly expressed in cytoplasm; while p-STAT1 is absent. Loss of u-STAT1 potently arrested cell cycle and inhibited cell growth in HCC cells. Induction of p-STAT1 by IFN-α treatment effectively triggers the expression of interferon-stimulated genes (ISGs), but has moderate effect on HCC cell growth. Interestingly, both u-STAT1 and p-STAT1 are induced by IFN-α, through with distinct time-dependent process. Furthermore, the ISG induction patterns mediated by p-STAT1 and u-STAT1 are also distinct. Importantly, artificial blocking of the induction of u-STAT1, but not p-STAT1, sensitizes HCC cells to treatment of IFNs. Therefore, p-STAT1 and u-STAT1 exert dichotomal functions and coordinately regulate the responsiveness to IFN treatment in HCC. Key Messages: STAT1 is upregulated and predominantly presented as u-STAT1 in HCC, while p-STAT1 is absent.U-STAT1 sustains but p-STAT1 inhibits HCC growth.The dynamic change of phosphorylation state of STAT1 control the responsiveness to IFN treatment

Semisupervised Cross Domain Teacher–Student Mutual Training for Damaged Building Detection

Detection of damaged buildings is a form of object detection and is essential for disaster emergency response efforts. In recent years, deep learning has been widely used in object detection, with successful target detection models such as Faster-Rcnn and You Only Look Once (YOLO) being proposed. However, training deep learning models usually requires a large amount of labeled data. Due to the high threshold for aerial remote sensing data collection, labeled aerial data of collapsed buildings is very sparse. In addition, the limited area of damage in a single scene leads to insufficient feature diversity, which can easily lead to model overfitting. These issues restrict the development of deep learning in emergency response applications. To solve these problems, we propose a paradigm named cross-domain teacher–student mutual training. By using the Cycle-GAN-generated style transfer data through teacher network, pseudolabels are generated to train the student network. Then, the student network slowly updates the parameters of the teacher network to indirectly learn the generalization information of the satellite data domain. Networks trained in this way can achieve good results in detecting collapsed houses in aviation and satellite data. We tested the results on our self-built dataset, DB-ARSD, which includes bounding box labeling of the damaged buildings, and found that our method outperforms other object detection methods in both collapsed house prediction accuracy and domain transfer generalization performance

Electric Field Analysis of Press-Pack IGBTs

High voltage IGBT module is the ideal option for the VSC-HVDC power transmission application. At present, wire-bonded technology and press-pack technology are available packaging technologies for high voltage IGBT. The press-pack IGBTs have such advantages as low inductance, low thermal impedance and short circuit failure mode than the wire-bonded IGBT module, which especially suit for high voltage power transmission application by series connection. However, the electrical insulation failure modes of press-pack IGBTs are much less known with limited literature published. In this paper, we presented the electric field analysis of a 3D press-pack IGBT model under DC rating voltage test condition. The electric field distribution of the press-pack IGBT stack was solved as an electrostatic problem by employing the finite element method. The results revealed the potential electrical insulation failure modes of the press-pack IGBTs: corona discharge at the edge of silver plate, partial discharge at the micro gap between die and PEEK frame and creeping discharge at the surface of PEEK frame