Data Quality-Aware Mixed-Precision Quantization via Hybrid Reinforcement Learning.

Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining suboptimal performance. Worse still, the conventional static quality-consistent training setting, i.e., all data is assumed to be of the same quality across training and inference, overlooks data quality changes in real-world applications which may lead to poor robustness of the quantized models. In this article, we propose a novel data quality-aware mixed-precision quantization framework, dubbed DQMQ, to dynamically adapt quantization bit-widths to different data qualities. The adaption is based on a bit-width decision policy that can be learned jointly with the quantization training. Concretely, DQMQ is modeled as a hybrid reinforcement learning (RL) task that combines model-based policy optimization with supervised quantization training. By relaxing the discrete bit-width sampling to a continuous probability distribution that is encoded with few learnable parameters, DQMQ is differentiable and can be directly optimized end-to-end with a hybrid optimization target considering both task performance and quantization benefits. Trained on mixed-quality image datasets, DQMQ can implicitly select the most proper bit-width for each layer when facing uneven input qualities. Extensive experiments on various benchmark datasets and networks demonstrate the superiority of DQMQ against existing fixed/mixed-precision quantization methods.

Disentangled Generation With Information Bottleneck for Enhanced Few-Shot Learning.

Few-shot learning (FSL) poses a significant challenge in classifying unseen classes with limited samples, primarily stemming from the scarcity of data. Although numerous generative approaches have been investigated for FSL, their generation process often results in entangled outputs, exacerbating the distribution shift inherent in FSL. Consequently, this considerably hampers the overall quality of the generated samples. Addressing this concern, we present a pioneering framework called DisGenIB, which leverages an Information Bottleneck (IB) approach for Disentangled Generation. Our framework ensures both discrimination and diversity in the generated samples, simultaneously. Specifically, we introduce a groundbreaking Information Theoretic objective that unifies disentangled representation learning and sample generation within a novel framework. In contrast to previous IB-based methods that struggle to leverage priors, our proposed DisGenIB effectively incorporates priors as invariant domain knowledge of sub-features, thereby enhancing disentanglement. This innovative approach enables us to exploit priors to their full potential and facilitates the overall disentanglement process. Moreover, we establish the theoretical foundation that reveals certain prior generative and disentanglement methods as special instances of our DisGenIB, underscoring the versatility of our proposed framework. To solidify our claims, we conduct comprehensive experiments on demanding FSL benchmarks, affirming the remarkable efficacy and superiority of DisGenIB. Furthermore, the validity of our theoretical analyses is substantiated by the experimental results. Our code is available at https://github.com/eric-hang/DisGenIB.

Acidity-targeting transition-aided universal chimeric antigen receptor T-cell (ATT-CAR-T) therapy for the treatment of solid tumors.

The use of CAR-T cells in treating solid tumors frequently faces significant challenges, mainly due to the heterogeneity of tumor antigens. This study assessed the efficacy of an acidity-targeting transition-aided universal chimeric antigen receptor T (ATT-CAR-T) cell strategy, which is facilitated by an acidity-targeted transition. Specifically, the EGFRvIII peptide was attached to the N-terminus of a pH-low insertion peptide. Triggered by the acidic conditions of the tumor microenvironment, this peptide alters its structure and selectively integrates into the membrane of solid tumor cells. The acidity-targeted transition component effectively relocated the EGFRvIII peptide across various tumor cell membranes; thus, allowing the direct destruction of these cells by EGFRvIII-specific CAR-T cells. This method was efficient even when endogenous antigens were absent. In vivo tests showed marked antigen modification within the acidic tumor microenvironment using this component. Integrating this component with CAR-T cell therapy showed high effectiveness in combating solid tumors. These results highlight the capability of ATT-CAR-T cell therapy to address the challenges presented by tumor heterogeneity and expand the utility of CAR-T cell therapy in the treatment of solid tumors.

Cross-modal credibility modelling for EEG-based multimodal emotion recognition.

Objective.The study of emotion recognition through electroencephalography (EEG) has garnered significant attention recently. Integrating EEG with other peripheral physiological signals may greatly enhance performance in emotion recognition. Nonetheless, existing approaches still suffer from two predominant challenges: modality heterogeneity, stemming from the diverse mechanisms across modalities, and fusion credibility, which arises when one or multiple modalities fail to provide highly credible signals.Approach.In this paper, we introduce a novel multimodal physiological signal fusion model that incorporates both intra-inter modality reconstruction and sequential pattern consistency, thereby ensuring a computable and credible EEG-based multimodal emotion recognition. For the modality heterogeneity issue, we first implement a local self-attention transformer to obtain intra-modal features for each respective modality. Subsequently, we devise a pairwise cross-attention transformer to reveal the inter-modal correlations among different modalities, thereby rendering different modalities compatible and diminishing the heterogeneity concern. For the fusion credibility issue, we introduce the concept of sequential pattern consistency to measure whether different modalities evolve in a consistent way. Specifically, we propose to measure the varying trends of different modalities, and compute the inter-modality consistency scores to ascertain fusion credibility.Main results.We conduct extensive experiments on two benchmarked datasets (DEAP and MAHNOB-HCI) with the subject-dependent paradigm. For the DEAP dataset, our method improves the accuracy by 4.58%, and the F1 score by 0.63%, compared to the state-of-the-art baseline. Similarly, for the MAHNOB-HCI dataset, our method improves the accuracy by 3.97%, and the F1 score by 4.21%. In addition, we gain much insight into the proposed framework through significance test, ablation experiments, confusion matrices and hyperparameter analysis. Consequently, we demonstrate the effectiveness of the proposed credibility modelling through statistical analysis and carefully designed experiments.Significance.All experimental results demonstrate the effectiveness of our proposed architecture and indicate that credibility modelling is essential for multimodal emotion recognition.

Does clinical experience affect the bracket bonding accuracy of guided bonding devices in vitro?

OBJECTIVES: To study whether and how the clinical experience of the operator affects the accuracy of bracket placement using guided bonding devices (GBDs) in vitro. MATERIALS AND METHODS: Five resin models were bonded virtually with brackets, and the corresponding GBDs were generated and three-dimensionally printed. Nine operators, which included three dental students, three orthodontic students, and three orthodontists, bonded the brackets on the resin models using GBDs on a dental mannequin. After being bonded with brackets, the models were scanned, and the actual and designed positions of the brackets were compared. RESULTS: There was no immediate debonding. The orthodontists spent a significantly shorter time (22.36 minutes) in bracket bonding than the dental students (24.62 minutes; P < .05). The brackets tended to deviate to the buccal side in the dental student group. Linear deviations tended to be smallest in the orthodontic student group, but no significant difference was found among operators with different clinical experience (P > .5). All linear and angular deviations in each group were under 0.5 mm and 2°, respectively. CONCLUSIONS: Clinical experience was positively related to the bonding accuracy using GBDs, especially in the buccolingual dimension. Inexperience also led to longer bonding duration. However, bonding accuracy was clinically acceptable in general.

3D-printed coloured tooth model for inlay preparation in pre-clinical dental education.

INTRODUCTION: Accurate inlay preparation is extremely important in pre-clinical training. However, there is a lack of tools to guide students to efficiently practise inlay preparation. Therefore, a 3D-printed coloured tooth model for inlay preparation was designed to guide beginners to practise inlay preparation by themselves according to different colour prompts. This study aimed to evaluate the benefits of using a 3D-printed coloured tooth model in the pre-clinical training on inlay preparation. MATERIALS AND METHODS: Twenty-eight students in their fourth-year undergraduate dental program participated in this study. The participants were randomly assigned to two groups for the inlay preparation. Group 1 prepared a plain tooth model for the first and fourth attempts and a 3D-printed coloured tooth model for the second and third attempts (n = 14). Group 2 prepared four plain tooth models (n = 14). The first and fourth tooth models prepared by both groups were scored using an evaluation system (Fair Grade 2000, NISSIN). Next, questionnaires answered by students were used to evaluate the benefits of using a 3D-printed coloured tooth model and self-evaluate hands-on ability using a grading system (1 = strongly agree, 2 = agree, 3 = neutral, 4 = disagree, and 5 = strongly disagree). The scores were evaluated statistically using the Mann-Whitney U test, and the given grades are displayed as percentages and mean values. RESULTS: There was an overall increase in the clinical confidence of all students after repeated attempts to prepare an inlay; however, students from group 1, who had used the 3D-printed coloured tooth model, had more positive experiences and comments. The 3D-printed coloured tooth model for inlay preparation has been widely praised by participants. Comparing the average score of the first and fourth preparations, the average score of group 1 increased by 12% (Ø 54.46 ± 8.33, Ø 61.11 ± 7.13, p = .090), while that of group 2 increased by 0.72% (Ø 56.39 ± 9.59, Ø 56.80 ± 8.46, p = .925). CONCLUSION: Students favoured the use of the 3D-printed coloured tooth model, and this improved the average score for inlay preparation. The 3D-printed coloured tooth model for inlay preparation is expected to play an important role in dental education in the future.

Comparative transcriptomics provides novel insights into the mechanisms of selenium accumulation and transportation in tea cultivars (Camellia sinensis (L.) O. Kuntze).

Tea plants (Camellia sinensis) show discrepancies in selenium accumulation and transportation, the molecular mechanisms of which are not well understood. Hence, we aimed to conduct a systematic investigation of selenium accumulation and transportation mechanisms in different tea cultivars via transcriptome analysis. The Na2SeO3 and Na2SeO4 treatments improved selenium contents in the roots and leaves of three tea cultivars. The high selenium-enrichment ability (HSe) tea cultivars accumulated higher selenium contents in the leaves than did the low selenium-enrichment ability (LSe) tea cultivars. Transcriptome analysis revealed that differentially expressed genes (DEGs) under the Na2SeO3 and Na2SeO4 treatments were enriched in flavonoid biosynthesis in leaves. DEGs under the Na2SeO3 treatment were enriched in glutathione metabolism in the HSe tea cultivar roots compared to those of the LSe tea cultivar. More transporters and transcription factors involved in improving selenium accumulation and transportation were identified in the HSe tea cultivars under the Na2SeO3 treatment than in the Na2SeO4 treatment. In the HSe tea cultivar roots, the expression of sulfate transporter 1;2 (SULTR1;2) and SULTR3;4 increased in response to Na2SeO4 exposure. In contrast, ATP-binding cassette transporter genes (ABCs), glutathione S-transferase genes (GSTs), phosphate transporter 1;3 (PHT1;3), nitrate transporter 1 (NRT1), and 34 transcription factors were upregulated in the presence of Na2SeO3. In the HSe tea cultivar leaves, ATP-binding cassette subfamily B member 11 (ABCB11) and 14 transcription factors were upregulated under the Na2SeO3 treatment. Among them, WRKY75 was explored as a potential transcription factor that regulated the accumulation of Na2SeO3 in the roots of HSe tea cultivars. This study preliminary clarified the mechanism of selenium accumulation and transportation in tea cultivars, and the findings have important theoretical significance for the breeding and cultivation of selenium-enriched tea cultivars.

Collaborative Contrastive Refining for Weakly Supervised Person Search.

Weakly supervised person search involves training a model with only bounding box annotations, without human-annotated identities. Clustering algorithms are commonly used to assign pseudo-labels to facilitate this task. However, inaccurate pseudo-labels and imbalanced identity distributions can result in severe label and sample noise. In this work, we propose a novel Collaborative Contrastive Refining (CCR) weakly-supervised framework for person search that jointly refines pseudo-labels and the sample-learning process with different contrastive strategies. Specifically, we adopt a hybrid contrastive strategy that leverages both visual and context clues to refine pseudo-labels, and leverage the sample-mining and noise-contrastive strategy to reduce the negative impact of imbalanced distributions by distinguishing positive samples and noise samples. Our method brings two main advantages: 1) it facilitates better clustering results for refining pseudo-labels by exploring the hybrid similarity; 2) it is better at distinguishing query samples and noise samples for refining the sample-learning process. Extensive experiments demonstrate the superiority of our approach over the state-of-the-art weakly supervised methods by a large margin (more than 3% mAP on CUHK-SYSU). Moreover, by leveraging more diverse unlabeled data, our method achieves comparable or even better performance than the state-of-the-art supervised methods.

AutoEER: automatic EEG-based emotion recognition with neural architecture search.

Objective.Emotion recognition based on electroencephalography (EEG) is garnering increasing attention among researchers due to its wide-ranging applications and the rise of portable devices. Deep learning-based models have demonstrated impressive progress in EEG-based emotion recognition, thanks to their exceptional feature extraction capabilities. However, the manual design of deep networks is time-consuming and labour-intensive. Moreover, the inherent variability of EEG signals necessitates extensive customization of models, exacerbating these challenges. Neural architecture search (NAS) methods can alleviate the need for excessive manual involvement by automatically discovering the optimal network structure for EEG-based emotion recognition.Approach.In this regard, we propose AutoEER (AutomaticEEG-basedEmotionRecognition), a framework that leverages tailored NAS to automatically discover the optimal network structure for EEG-based emotion recognition. We carefully design a customized search space specifically for EEG signals, incorporating operators that effectively capture both temporal and spatial properties of EEG. Additionally, we employ a novel parameterization strategy to derive the optimal network structure from the proposed search space.Main results.Extensive experimentation on emotion classification tasks using two benchmark datasets, DEAP and SEED, has demonstrated that AutoEER outperforms state-of-the-art manual deep and NAS models. Specifically, compared to the optimal model WangNAS on the accuracy (ACC) metric, AutoEER improves its average accuracy on all datasets by 0.93%. Similarly, compared to the optimal model LiNAS on the F1 Ssore (F1) metric, AutoEER improves its average F1 score on all datasets by 4.51%. Furthermore, the architectures generated by AutoEER exhibit superior transferability compared to alternative methods.Significance.AutoEER represents a novel approach to EEG analysis, utilizing a specialized search space to design models tailored to individual subjects. This approach significantly reduces the labour and time costs associated with manual model construction in EEG research, holding great promise for advancing the field and streamlining research practices.

XTQA: Span-Level Explanations for Textbook Question Answering.

Textbook question answering (TQA) is the task of correctly answering diagram or nondiagram (ND) questions given large multimodal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans' understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture toward span-level eXplanations for TQA (XTQA). In this article, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence and then use our proposed coarse-to-fine grained explanation extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result mean intersection over union (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (ND: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits. We have released our code in https://github.com/dr-majie/opentqa.

Experience of pediatric nurses in nursing dying children - a qualitative study.

PURPOSE: To explore pediatric nurses' challenges and effective coping strategies in caring for dying children. METHODS: A descriptive qualitative study was adopted. Data were collected using a semi-structured interview with ten nurses from the pediatric, pediatric emergency, and neonatology departments. RESULTS: Three themes were generated: stressors, consequences, and coping strategies. Ten sub-themes were generalized: negative emotions; helplessness; questioning rescue behavior; fear of communication; lack of workforce for night rescue; compassion fatigue; burnout; changes in life attitudes; self-regulation; leadership approval and no accountability. CONCLUSIONS: Through qualitative research, nurses' challenges and effective coping strategies in caring for dying children were found, which provides information for nurses' career development and related policy formulation in China. CLINICAL RELEVANCE: While there are many articles in China on hospice care, there is little research on the nurses' experience of caring for dying children. Many studies have mentioned the adverse consequences of caring for dying children in foreign countries, leading to post-traumatic stress disorder (PTSD). However, domestic discussion of such problems is rare, and no corresponding coping strategies exist. This study explores pediatric nurses' challenges and effective coping strategies in caring for dying children.

Learning Representations by Graphical Mutual Information Estimation and Maximization.

The rich content in various real-world networks such as social networks, biological networks, and communication networks provides unprecedented opportunities for unsupervised machine learning on graphs. This paper investigates the fundamental problem of preserving and extracting abundant information from graph-structured data into embedding space without external supervision. To this end, we generalize conventional mutual information computation from vector space to graph domain and present a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graph and hidden representation. Except for standard GMI which considers graph structures from a local perspective, our further proposed GMI++ additionally captures global topological properties by analyzing the co-occurrence relationship of nodes. GMI and its extension exhibit several benefits: First, they are invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing methods; Second, they can be efficiently estimated and maximized by current mutual information estimation methods; Lastly, our theoretical analysis confirms their correctness and rationality. With the aid of GMI, we develop an unsupervised embedding model and adapt it to the specific anomaly detection task. Extensive experiments indicate that our GMI methods achieve promising performance in various downstream tasks, such as node classification, link prediction, and anomaly detection.

Exosomal miR-628-5p from M1 polarized macrophages hinders m6A modification of circFUT8 to suppress hepatocellular carcinoma progression.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common type of liver cancer. CircFUT8 has been shown to be upregulated in cancers, but its function in HCC remains unclear. Tumor-associated macrophages (TAMs) are one of the main components of the tumor microenvironment (TME), and M1 macrophages function as tumor suppressors in cancers. Exosomes exert an important role in the TME, and circRNAs can be modified by m6A. We investigated the function of circFUT8 in HCC and its interaction with exosomes, M1 macrophages, and m6A. METHODS: CircFUT8 expression was detected in HCC cells, and its effects on HCC cell growth were verified through functional assays. Mechanism assays including RNA pull down, RNA-binding protein immunoprecipitation (RIP), and luciferase reporter assays were undertaken to verify how circFUT8 may interact with miR-628-5p, and how these molecules may modulate HCC cell malignancy via interacting with exosomes and macrophages. RESULTS: CircFUT8 was upregulated in HCC cells and it accelerated HCC cell growth. Exosomes derived from M1 macrophages transferred miR-628-5p to HCC cells to inhibit human methyltransferase-like 14 (METTL14) expression. METTL14 promoted circFUT8 m6A modification and facilitated its nuclear export to the cytoplasm, where M1 macrophages regulated the circFUT8/miR-552-3p/CHMP4B pathway, thereby suppressing HCC progression. CONCLUSION: M1 macrophages-derived exosomal miR-628-5p inhibited the m6A modification of circFUT8, inhibiting HCC development.

Collagenous gastritis in a young Chinese woman: A case report.

BACKGROUND: Collagenous gastritis (CG) is a rare condition whose pathogenesis may be related to immune abnormalities. We report a case of CG from China. CASE SUMMARY: A 24-year-old woman presented with recurrent abdominal distension and discomfort for 3 mo. Upper gastrointestinal endoscopy found diffuse nodular elevation-depression changes in the mucosa of the entire gastric corpus. Endoscopic ultrasound showed predominant involvement of the lamina propria and submucosa, and computed tomography imaging showed mild enhancement of the gastric wall. Pathological histology revealed that the thickness of the subepithelial collagen band was about 40 µm, and the Masson trichrome staining result was positive and the Congo red staining result was negative. This case is consistent with the child-adolescent type of CG. CONCLUSION: Serum pepsinogen I, pepsinogen II, pepsinogen I/II ratio, and gastrin-17 may be potential non-invasive monitoring markers. Currently, treatments for CG vary, and the likely prognosis is unknown. Individual cases of gastric cancer in patients with CG have been reported.

Biased Complementary-Label Learning Without True Labels.

In complementary-label learning (CLL), the complementary transition matrix, denoting the probabilities that true labels flip into complementary labels (CLs) which specify classes observations do not belong to, is crucial to building statistically consistent classifiers. Most existing works implicitly assume that the transition probabilities are identical, which is not true in practice and may lead to undesirable bias in solutions. Few recent works have extended the problem to a biased setting but limit their explorations to modeling the transition matrix by exploiting the complementary class posteriors of anchor points (i.e., instances that almost certainly belong to a specific class). However, due to the severe corruption and unevenness of biased CLs, both anchor points and complementary class posteriors are difficult to predict accurately in the absence of true labels. In this article, rather than directly predicting these two error-prone items, we instead propose a divided-T estimator as an alternative to effectively learn transition matrices from only biased CLs. Specifically, we exploit semantic clustering to mitigate the adverse effects arising from CLs. By introducing the learned semantic clusters as an intermediate class, we factorize the original transition matrix into the product of two easy-to-estimate matrices that are not reliant on the two error-prone items. Both theoretical analyses and empirical results justify the effectiveness of the divided- T estimator for estimating transition matrices under a mild assumption. Experimental results on benchmark datasets further demonstrate that the divided- T estimator outperforms state-of-the-art (SOTA) methods by a substantial margin.

PHR1 positively regulates phosphate starvation-induced anthocyanin accumulation through direct upregulation of genes F3'H and LDOX in Arabidopsis.

MAIN CONCLUSION: Phosphate deficiency promotes anthocyanin accumulation in Arabidopsis through direct binding of PHR1 to the P1BS motifs on the promoters of F3'H and LDOX and thereby upregulating their expression. Phosphorus is one of the essential elements for plants, and plants mainly absorb inorganic phosphate (Pi) from soil. But Pi deficiency is a common factor limiting plant growth and development. Anthocyanin accumulation in green tissues (such as leaves) is one of the characteristics of many plants in response to Pi starvation. However, little is known about the mechanism by which Pi starvation induces anthocyanin accumulation. Here, we found that the mutation of the gene PHOSPHATE STARVATION RESPONSE1 (PHR1), which encodes a key factor involved in Pi starvation signaling in Arabidopsis, significantly attenuates anthocyanin accumulation under Pi-limiting conditions. Moreover, the expression of several Pi deficiency-upregulated genes that are involved in anthocyanin biosyntheses, such as flavanone 3'-hydroxylase (F3'H), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (LDOX), and production of anthocyanin pigment 1 (PAP1), was significantly lower in the phr1-1 mutant than in the wild type (WT). Both yeast one-hybrid (Y1H) analysis and chromatin immunoprecipitation quantitative PCR (ChIP-qPCR) showed that PHR1 can interact with the promoters of F3'H and LDOX, but not DFR and PAP1. By electrophoretic mobility shift assay (EMSA), it was further confirmed that the PHR1-binding sequence (P1BS) motifs located on the F3'H and LDOX promoters are required for the PHR1 bindings. Also, in Arabidopsis protoplasts, PHR1 enhanced the transcriptional activity of the F3'H and LDOX promoters, but these effects were markedly impaired when the P1BS motifs were mutated. Taken together, these results indicate that PHR1 positively regulates Pi starvation-induced anthocyanin accumulation in Arabidopsis, at least in part, by directly binding the P1BS motifs located on the promoters to upregulate the transcription of anthocyanin biosynthetic genes F3'H and LDOX.

Weakly Supervised Learning for Textbook Question Answering.

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multi-modal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://github.com/dr-majie/WSTQ.

Hypoxia-induced LncRNA DACT3-AS1 upregulates PKM2 to promote metastasis in hepatocellular carcinoma through the HDAC2/FOXA3 pathway.

Growing evidence has revealed that hypoxia is involved in multiple stages of cancer development. However, there are limited reports on the effects of long noncoding RNAs (lncRNAs) on hepatocellular carcinoma (HCC) progression under hypoxia. The main purposes of this study were to analyze the effect of the novel lncRNA DACT3-AS1 on metastasis in HCC and to elucidate the related molecular mechanism. Bioinformatics tools were employed. RT-qPCR or western blot assays were conducted to detect RNA or protein expression. Clinical samples and in vivo assays were utilized to reveal the role of DACT3-AS1 in HCC. Other mechanism and functional analyses were specifically designed and performed as well. Based on the collected data, this study revealed that HIF-1α transcriptionally activates DACT3-AS1 expression under hypoxia. DACT3-AS1 was verified to promote metastasis in HCC. Mechanistically, DACT3-AS1 promotes the interaction between HDAC2 and FOXA3 to stimulate FOXA3 deacetylation, which consequently downregulates the FOXA3 protein. Furthermore, FOXA3 serves as a transcription factor that can bind to the PKM2 promoter region, thus hindering PKM2 expression. To summarize, this study uncovered that HIF-1α-induced DACT3-AS1 promotes metastasis in HCC and can upregulate PKM2 via the HDAC2/FOXA3 pathway in HCC cells.

Circ_0036412 affects the proliferation and cell cycle of hepatocellular carcinoma via hedgehog signaling pathway.

BACKGROUND: Hepatocellular carcinoma (HCC), as the most common type of liver cancer, is characterized by high recurrence and metastasis. Circular RNA (circRNA) circ_0036412 was selected for studying the underlying mechanisms of HCC. METHODS: Quantitative real time-polymerase chain reaction (qRT-PCR) and western blot analyzed gene and protein expression. Functional experiments evaluated HCC cell proliferation, apoptosis and cell cycle in vitro. In vivo experiments detected HCC carcinogenesis in vivo. Fluorescence in situ hybridization (FISH) assays evaluated the subcellular distribution. Luciferase reporter, Chromatin immunoprecipitation (ChIP), DNA pulldown, RNA-binding protein immunoprecipitation (RIP), and RNA pulldown assays detected the underlying mechanisms. RESULTS: Circ_0036412 is overexpressed in HCC cells and features circular structure. PRDM1 activates circ_0036412 transcription to regulate the proliferation and cell cycle of HCC cells in vitro. Circ_0036412 modulates Hedgehog pathway. GLI2 propels HCC growth in vivo. Circ_0036412 up-regulates GLI2 expression by competitively binding to miR-579-3p, thus promoting the proliferation and inhibiting cell cycle arrest of HCC cells. Circ_0036412 stabilizes GLI2 expression by recruiting ELAVL1. Circ_0036412 propels the proliferation and inhibits cell cycle arrest of HCC cells in vitro through Hedgehog pathway. CONCLUSIONS: Circ_0036412 affects the proliferation and cell cycle of HCC via Hedgehog signaling pathway. It offers an insight into the targeted therapies of HCC.

A Dual Functional Drug Delivery System that Combines Photothermal Therapy and Immunotherapy to Treat Tumors.

Cancer is one of the main diseases threatening human health. Immunotherapy, in which cancer is treated by activating immune cells and inducing the body's immune response, has rapidly developed. Photothermal therapy (PTT), a new treatment method that ablates tumors by light irradiation, has attracted great attention for its good therapeutic effect and low toxic side effects. In the present study, we combined photothermal and immunotherapy to design a novel nanoparticle delivery system by loading indoleamine 2,3-dioxygenase (IDO) inhibitors and toll-like receptor (TLR) agonists into polydopamine (PDA) nanoparticles coated with polyethylene imine (PEI). This delivery system has the advantages of high homogeneity, good stability, excellent biocompatibility, and low toxicity. In vitro antitumor studies showed that the system effectively inhibited the proliferation of mouse breast carcinoma cells and induced cell apoptosis. From the in vivo studies, we found that the system inhibited the growth of mouse breast carcinoma, facilitated the maturation of antigen-presenting cells, promoted T lymphocyte differentiation, and induced the body's immune response. The present study developed a dual functional drug delivery system combining photothermal therapy and immunotherapy to efficiently improve antitumor therapy with potential clinical application.