Tumor promoter TPA activates Wnt/β-catenin signaling in a casein kinase 1-dependent manner.

The tumor promoter 12-O-tetra-decanoylphorbol-13-acetate (TPA) has been defined by its ability to promote tumorigenesis on carcinogen-initiated mouse skin. Activation of Wnt/β-catenin signaling has a decisive role in mouse skin carcinogenesis, but it remains unclear how TPA activates Wnt/β-catenin signaling in mouse skin carcinogenesis. Here, we found that TPA could enhance Wnt/β-catenin signaling in a casein kinase 1 (CK1) ε/δ-dependent manner. TPA stabilized CK1ε and enhanced its kinase activity. TPA further induced the phosphorylation of LRP6 at Thr1479 and Ser1490 and the formation of a CK1ε-LRP6-axin1 complex, leading to an increase in cytosolic β-catenin. Moreover, TPA increased the association of β-catenin with TCF4E in a CK1ε/δ-dependent way, resulting in the activation of Wnt target genes. Consistently, treatment with a selective CK1ε/δ inhibitor SR3029 suppressed TPA-induced skin tumor formation in vivo, probably through blocking Wnt/β-catenin signaling. Taken together, our study has identified a pathway by which TPA activates Wnt/β-catenin signaling

Inhibition of EGFR nuclear shuttling decreases irradiation resistance in HeLa cells

Introduction. Cervical cancer is a leading cause of mortality in women worldwide. The resistance to irradiation at the advanced stage is the main reason for the poor prognosis and high mortality. This work aims to elucidate the molecular mechanism underlying the radio-resistance. Material and methods. In this study, we determined the pEGFR-T654 and pDNA-PK-T2609 expression level changes in irradiated HeLa cells treated with T654 peptide, a nuclear localization signal (NLS) inhibitor, to inhibit EGFR nuclear transport. Cell viability, cell cycle and migratory capacity were analyzed. Xenograft animal model was used to evaluate the effect of EGFR nuclear transport inhibition on the tumor growth in vivo. Results. The enhanced translocation of nuclear EGFR in the irradiated HeLa cells correlated with the increasing level of pEGFR-T654 and pDNA-PK-T2609. Inhibition of EGFR nuclear translocation by NLS peptide inhibitor attenuated DNA damage repair in the irradiated HeLa cells, decreased cell viability and promoted cell death through arrest at G0 phase. NLS peptide inhibitor impaired the migratory capacity of irradiated HeLa cells, and negatively affected tumorigenesis in xenograft mice. Conclusions. This work puts forward a potential molecular mechanism of the irradiation resistance in cervical cancer cells, providing a promising direction towards an efficient therapy of cervical cancer

Inhibition of EGFR nuclear shuttling decreases irradiation resistance in HeLa cells

Introduction. Cervical cancer is a leading cause of mortality in women worldwide. The resistance to irradiation at the advanced stage is the main reason for the poor prognosis and high mortality. This work aims to elucidate the molecular mechanism underlying the radio-resistance. Material and methods. In this study, we determined the pEGFR-T654 and pDNA-PK-T2609 expression level changes in irradiated HeLa cells treated with T654 peptide, a nuclear localization signal (NLS) inhibitor, to inhibit EGFR nuclear transport. Cell viability, cell cycle and migratory capacity were analyzed. Xenograft animal model was used to evaluate the effect of EGFR nuclear transport inhibition on the tumor growth in vivo. Results. The enhanced translocation of nuclear EGFR in the irradiated HeLa cells correlated with the increasing level of pEGFR-T654 and pDNA-PK-T2609. Inhibition of EGFR nuclear translocation by NLS peptide inhibitor attenuated DNA damage repair in the irradiated HeLa cells, decreased cell viability and promoted cell death through arrest at G0 phase. NLS peptide inhibitor impaired the migratory capacity of irradiated HeLa cells, and negatively affected tumorigenesis in xenograft mice. Conclusions. This work puts forward a potential molecular mechanism of the irradiation resistance in cervical cancer cells, providing a promising direction towards an efficient therapy of cervical cancer. (Folia Histochemica et Cytobiologica 2017, Vol. 55, No. 2, 43–51

On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs

Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two types of artificial node features,i.e., positional and structural node features, and provide insights on why each of them is more appropriate for certain tasks,i.e., positional node classification, structural node classification, and graph classification. Extensive experimental results on 10 benchmark datasets validate our insights, thus leading to a practical guideline on the choices between different artificial node features for GNNs on non-attributed graphs. The code is available at https://github.com/zjzijielu/gnn-exp/.Comment: This paper has been accepted to the Sixth International Workshop on Deep Learning on Graphs (DLG-KDD'21) (co-located with KDD'21

Enhancing High-Resolution 3D Generation through Pixel-wise Gradient Clipping

High-resolution 3D object generation remains a challenging task primarily due to the limited availability of comprehensive annotated training data. Recent advancements have aimed to overcome this constraint by harnessing image generative models, pretrained on extensive curated web datasets, using knowledge transfer techniques like Score Distillation Sampling (SDS). Efficiently addressing the requirements of high-resolution rendering often necessitates the adoption of latent representation-based models, such as the Latent Diffusion Model (LDM). In this framework, a significant challenge arises: To compute gradients for individual image pixels, it is necessary to backpropagate gradients from the designated latent space through the frozen components of the image model, such as the VAE encoder used within LDM. However, this gradient propagation pathway has never been optimized, remaining uncontrolled during training. We find that the unregulated gradients adversely affect the 3D model's capacity in acquiring texture-related information from the image generative model, leading to poor quality appearance synthesis. To address this overarching challenge, we propose an innovative operation termed Pixel-wise Gradient Clipping (PGC) designed for seamless integration into existing 3D generative models, thereby enhancing their synthesis quality. Specifically, we control the magnitude of stochastic gradients by clipping the pixel-wise gradients efficiently, while preserving crucial texture-related gradient directions. Despite this simplicity and minimal extra cost, extensive experiments demonstrate the efficacy of our PGC in enhancing the performance of existing 3D generative models for high-resolution object rendering.Comment: Accepted at ICLR 2024. Project page: https://fudan-zvg.github.io/PGC-3

Diagnosis and treatment of hepatocellular carcinoma with pelvic metastasis expressing AFP: a case report

This report presents the case of a 68-year-old female patient with hepatocellular carcinoma (HCC) who experienced persistently elevated alpha-fetoprotein (AFP) levels following resection of the primary liver tumor. The patient had previously undergone transcatheter arterial chemoembolization (TACE) and liver tumor resection, but postoperative AFP levels continued to rise, suggesting the possibility of extrahepatic metastasis. PET-CT scans revealed an irregular soft tissue mass in the recto-uterine pouch, which was later confirmed as a HCC metastasis through needle biopsy. The patient subsequently received radioactive seed implantation therapy, leading to a significant decrease in AFP levels. This case highlights the rarity of isolated pelvic metastasis in HCC patients and underscores the importance of AFP in postoperative monitoring. The combination of PET-CT imaging and pathological biopsy is instrumental in improving the detection rate of HCC metastases, enabling more accurate treatment planning for patients

Histocompatibility and Long-Term Results of the Follicular Unit-Like Wigs after Xenogeneic Hair Transplantation: An Experimental Study in Rabbits

Objective. This study was designed to observe the histocompatibility and long-term results of wigs after xenogeneic hair transplantation and to explore the possibility of industrial products in clinical application. Methods. The human hair and melted medical polypropylene were preceded into the follicular unit-like wigs according to the natural follicular unit by extrusion molding. 12 New Zealand rabbits were used as experimental animals for wigs transplantation. The histocompatibility of polypropylene and human hair was observed by H&E staining and scanning electron microscope. The loss rate of wigs was calculated to evaluate the long-term result after transplantation. Results. Mild infiltration by inflammatory cells around the polypropylene and human hair were seen during the early period after transplantation, accompanied with local epithelial cell proliferation. The inflammatory cells were decreased after 30 days with increased collagen fibers around the polypropylene and human hair. The follicular unit-like wigs maintained a good histocompatibility in one year. The degradation of hair was not significant. The loss rate of wigs was 4.1 ± 4.0% in one year. The appearance of hair was satisfactory. Conclusions. We successfully developed a follicular unit-like wigs, which were made of xenogeneic human hair with medical polypropylene, showing a good histocompatibility, a low loss rate, and satisfactory appearance in a year after transplantation. The follicular unit-like wigs may have prospective industrial products in clinical application

Privately Compute the Item with Maximal Weight Sum in Set Intersection

Private Set Intersection (PSI) is a cryptographic primitive that allows two parties to obtain the intersection of their private input sets while revealing nothing more than the intersection. PSI and its numerous variants, which compute on the intersection of items and their associated weights, have been widely studied. In this paper, we revisit the problem of finding the best item in the intersection according to weight sum introduced by Beauregard et al. (SCN \u2722), which is a special variant of PSI. We present two new protocols that achieve the functionality. The first protocol is based on Oblivious Pseudorandom Function (OPRF), additively homomorphic encryption and symmetric-key encryption, while the second one is based on Decisional Diffie-Hellman (DDH) assumption, additively homomorphic encryption and symmetric-key encryption. Both protocols are proven to be secure against semi-honest adversaries. Compared with the original protocol proposed by Beauregard et al. (abbreviated as the FOCI protocol), which requires all weights in the input sets to be polynomial in magnitude, our protocols remove this restriction. We compare the performance of our protocols with the FOCI protocol both theoretically and empirically. We find out that the performance of FOCI protocol is primarily affected by the size of the intersection and the values of elements’ weights in intersection when fixing set size, while the performance of ours is independent of these two factors. In particular, in the LAN setting, when the set sizes are $n=10000$, intersection size of $\frac{n}{2}$, the weights of the elements are uniformly distributed as integers from $\left[0, n-1\right]$, our DDH-based protocol has a similar run-time to the FOCI protocol. However, when the weights of the elements belonging to $\left[0, 10n-1\right]$ and $\left[0, 100n-1\right]$, our DDH-based protocol is between a factor $2\times$ and $5\times$ faster than the FOCI protocol

Efficient and Practical Multi-party Private Set Intersection Cardinality Protocol

We present an efficient and simple multi-party private set intersection cardinality (PSI-CA) protocol that allows several parties to learn the intersection size of their private sets without revealing any other information. Our protocol is highly efficient because it only utilizes the Oblivious Key-Value Store and zero-sharing techniques, without incorporating components such as OPPRF (Oblivious Programmable Pseudorandom Function) which is the main building block of multi-party PSI-CA protocol by Gao et al. (PoPETs 2024). Our protocol exhibits better communication and computational overhead than the state-of-the-art. To compute the intersection between 16 parties with a set size of $2^{20}$ each, our PSI-CA protocol only takes 5.84 seconds and 326.6 MiB of total communication, which yields a reduction in communication by a factor of up to 2.4× compared to the state-of-the-art multi-party PSI-CA protocol of Gao et al. (PoPETs 2024). We prove that our protocol is secure in the presence of a semi-honest adversary who may passively corrupt any $(t-2)$-out-of-$t$ parties once two specific participants are non-colluding