Search

Abstract Background Cardiac fibrosis plays a critical role in the progression of various forms of heart disease, significantly increasing the risk of sudden cardiac death. However, currently, there are no therapeutic strategies available to prevent the onset of cardiac fibrosis. Methods and results Here, biomimetic ATP-responsive nanozymes based on genetically engineered cell membranes are adapted to specifically recognize activated cardiac fibroblasts (CFs) for the treatment of cardiac fibrosis. By fusing the anti-FAP CAR genetically engineered cell membrane to zeolitic imidazole frameworks-90 (zif-90) cores loaded with antioxidant nanozymes CeO2 and siCTGF (siRNA targeting CTGF), these nanoparticles, called FM@zif-90/Ce/siR NPs, are demonstrated to effectively reduce the accumulation of myofibroblasts and the formation of fibrotic tissue, while restoring cardiac function. Conclusions These findings demonstrate that the combination of CeO2 and siCTGF has a beneficial curative effect on cardiac fibrosis, with significant translational potential.

Abstract Educational chatbots (EC) have shown their promise in providing instructional support. However, limited studies directly explored the impact of EC on learners’ emotional responses. This study investigated the induced emotions from interacting with micro-learning EC and how they impact learning motivation. In this context, the EC interactions encompassed greetings, biology learning content delivery, self-evaluation, and feedback. This study employed a between-subject experimental design involving 62 college students. Participants were randomly assigned to either the Metacognitive EC group, receiving metacognitive feedback, or the Neutral EC group, receiving neutral feedback. The results of T-tests demonstrated significant differences in specific induced emotions between the two groups while some similarities exist. Importantly, it unveiled that both Metacognitive EC and Neutral EC interactions evoked a spectrum of positive, negative, and ambivalent emotions, in which positive emotions surpassed the induced negative emotions. In general, metacognitive feedback induced fewer negative emotions than neutral feedback. PLS analysis supported the relationships between induced emotions and intrinsic motivation, with positive emotion, ambivalent emotions, and negative emotions influencing interest motivation, which, in turn, shaped other motivational components, including perceived competence, perceived value, and perceived pressure. However, the influence of positive emotion on interest was weaker in the Metacognitive than in the Neutral EC. In conclusion, the study revealed how induced emotions impact motivations and showed that the presence of metacognitive feedback reduced negative emotions and promoted motivation. These findings highlight the need for positive emotion element design and appropriate feedback that will impact learning motivations during educational chatbot interactions.

Knowledge tracing is a crucial task in personalized learning that models student mastery based on historical data to predict future performance. Currently, deep learning models in knowledge tracing predominantly use one-hot encodings of question, knowledge, and student IDs, showing promising results. However, they face a significant limitation: a vocabulary gap that impedes the processing of new IDs not seen during training. To address this, our paper introduces a novel method that incorporates aggregated features, termed ‘side information’, that captures essential attributes such as student ability, knowledge mastery, and question difficulty. Our approach utilizes side information to bridge the vocabulary gap caused by ID-based one-hot encoding in traditional models. This enables the model, once trained on one dataset, to generalize and make predictions on new datasets with unfamiliar students, knowledge, or questions without the need for retraining. This innovation effectively bridges the vocabulary gap, reduces the dependency on specific data representations, and improves the overall performance of the model. Experimental evaluations on five distinct datasets show that our proposed model consistently outperforms baseline models, using fewer parameters and demonstrating seamless adaptability to new contexts. Additionally, ablation studies highlight that including side information, especially regarding students and questions, significantly improves knowledge tracing effectiveness. In summary, our approach not only resolves the vocabulary gap challenge but also offers a more robust and superior solution across varied datasets.

Educational chatbots (ECs) can offer instructional feedback to enhance learning. However, the effect of metacognitive feedback on science education has not been fully explored. This study focuses on the effect of the EC with metacognitive feedback on students’ knowledge retention, transfer, and intrinsic motivation in the field of biology science. A between-group experimental design with 62 college students was conducted. Students in the experiment group received metacognitive feedback, whereas students in the control group received no feedback. The results of the ANCOVA test showed that students in the experiment group demonstrated better knowledge retention and transfer than those in the control group (F = 13.11, p = 0.001; F = 14.39, p < 0.001). Further, students in the experiment group reported more learning interest and higher perceived competence and value than those in the control group (F = 3.72, p = 0.001; F = 1.91, p = 0.009; F = 2.70, p = 0.004). In addition, correlation analysis revealed that perceived competence in the metacognitive feedback group was positively related to knowledge transfer (r = 0.39, p = 0.032). However, there was no significant difference in perceived pressure between both groups (p = 0.203). This study highlights the potential of the EC with metacognitive feedback for science learning.

Purposes Collaborative Visual Simultaneous Localization and Mapping (CVSLAM) has attracted more and more researchers’ attention in the field of robotics owing to its low cost of required sensors, the ability to acquire rich environmental information, and its rapidity and flexibility. Realizing the efficient transmission of image information is one of the key problems that need to be solved to improve the efficiency of CVSLAM map building. In multi-machine cooperative operation, data transmission between robots is the most important part of their work, and data sharing is often affected by the communication bandwidth, so the study of efficient data processing methods has become a crucial part. Methods On the basis of the centralized collaborative ORB-SLAM2 framework, the problem of data transmission from individual robots to the central station is studied, and a general method of efficient data transmission based on feature compression is explored. In particular, a method based on arithmetic coding for differential coding of ORB features and compressed transmission is investigated. The method evaluates the amount of data to be transmitted after compression according to different coding modes of the features and selects the mode that minimizes the amount of data transmission to encode the features. Findings Without affecting the overall map building effect, the adopted method significantly reduces the amount of data during data transmission in the CVSLAM system, and at the same time shortens the data transmission time. Conclusions The experimental results based on the KITTI dataset show that the communication method with compression coding can effectively reduce the amount of data transmission and increase the transmission speed, which is an effective means to improve the efficiency of CVSLAM communication.

ABSTRACT: Melamine (MEL), enrofloxacin (ENR), sulfamethazine (SMZ), tetracycline (TC), and aflatoxin M1 (AFM1) are the main chemical contaminants in milk. It is necessary to detect these miscellaneous chemical contaminants in milk synchronously to ensure the safety of the milk. In this study, a multiple lateral flow immunoassay (LFIA) was developed for the detection of MEL, ENR, SMZ, TC, and AFM1 in milk. Under optimal experimental conditions, the cutoff values were 25 ng/mL for MEL, 1 ng/mL for ENR, 2.5 ng/mL for SMZ, 2.5 ng/mL for TC, and 0.25 ng/mL for AFM1 in milk samples. The limits of detection of LFIA were 0.173 ng/mL for MEL, 0.078 ng/mL for ENR, 0.059 ng/mL for SMZ, 0.082 ng/mL for TC, and 0.0064 ng/mL for AFM1. The recovery rates of LFIA in milk were 83.2–104.4% for MEL, 76.5–127.3% for ENR, 96.8–113.5% for SMZ, 107.1–166.6% for TC, and 93.5–130.3% for AFM1. The coefficients of variation were all less than 15%. As a whole, the developed multiple lateral flow immunoassay showed potential as a highly reliable and excellent tool for the rapid and sensitive screening of MEL, ENR, SMZ, TC, and AFM1 in milk.

Emerging evidence suggests that DNA methylation affects transcriptional regulation and expression perturbations of long non-coding RNAs (lncRNAs) in cancer. However, a comprehensive investigation into the transcriptional control of DNA methylation-mediated dysregulation of transcription factors (TFs) on lncRNAs has been lacking. Here, we integrated the transcriptome, methylome, and regulatome across 21 human cancers and systematically identified the transcriptional regulation of DNA methylation-mediated TF dysregulations (DMTDs) on lncRNAs. Our findings reveal that TF regulation of lncRNAs is significantly impacted by DNA methylation. Comparative analysis of DMTDs on mRNAs revealed a conserved pattern of TFs involvement. Pan-cancer Methylation TFs (MethTFs) and Methylation LncRNAs (MethLncRNAs) were identified, and were found to be closely associated with cancer hallmarks and clinical features. In-depth analysis of co-expressed mRNAs with pan-cancer MethLncRNAs unveiled frequent disruptions in cancer immunity, particularly in the context of inflammatory response. Furthermore, we identified five immune-related network modules that contribute to immune cell infiltration in cancer. Immune-related subtypes were subsequently classified, characterized by high levels of immune cell infiltration, expression of immunomodulatory genes, and relevant immune cytolytic activity score, major histocompatibility complex score, response to chemotherapy, and prognosis. Our findings provide valuable insights into cancer immunity from the epigenetic and transcriptional regulation perspective.

A bispecific monoclonal antibody (BsMAb) that can simultaneously recognize aflatoxin B1 (AFB1) and amantadine (AMD) was prepared. Quantum dot nanobead immunochromatographic assay (QB-ICA) based on the BsMAb was developed for the simultaneous detection of AFB1 and AMD in four feed samples (suckling pig feed, piglet feed, sow feed, and compound feed for laying ducks). Under optimal experimental conditions, the LOD values of QB-ICA for AFB1 in four feed samples were 0.188, 0.221, 0.233, and 0.306 μg/kg, respectively. The LOD values of QB-ICA for AMD in the same feed samples were 0.125, 0.187, 0.076, and 0.191 μg/kg, respectively. The recovery rates of QB-ICA for AFB1 and AMD ranged from 91.55% to 150.87% and from 91.62% to 138.42%, respectively. The variation coefficients were all less than 15%. The proposed QB-ICA had high reliability and specificity for the simultaneous detection of AFB1 and AMD, providing a platform for simultaneous detection of hazardous substances.

Chicken lung is generally utilized as a low-value feed in poultry slaughtering industry. In order to increase the added value of chicken lung, this study determined the proximate chemical composition of chicken lung and prepared antioxidant peptide by enzymatic hydrolysis. Firstly, using 1, 1-diphenyl-2-picrylhydrazyl (DPPH) scavenging rate and crude peptide content as indexes, the effects of the kinds of enzyme, pH, enzyme supplemental levels and hydrolysis time on antioxidant peptide activity were investigated by single factor experiment. Secondly, based on the results, Box-Behnken response surface designed was used to optimize the reaction conditions for enzymatic hydrolysis of chicken lung antioxidant peptide. The main influencing factors were pH, enzyme supplemental levels and hydrolysis time (P

A dual-target aptamer functionalized probes (DTAFP) was applied for the detection of aflatoxin B1 (AFB1) and zearalenone (ZEN) simultaneously, which has not been reported. Meanwhile, two functional materials for signal amplification of the DTAFP were synthesized: 1) a three-dimensional molybdenum disulfide-reduced graphene oxide (MoS2-rGO) as a favorable loading interface; 2) a double-probes gold nanoparticles (AuNPs) modified by Thionin (Thi) and 6-(Ferrocenyl) hexanethiol (FC6S) as distinguishable and non-interfering signals. Mycotoxins on the electrode surface release into solution under the function of the DTAFP, leading a reduction of the differential peak impulse in signal response. Under the optimum conditions, the aptasensor exhibited a detection range of 1.0 pg mL−1–100 ng mL−1 for AFB1 and ZEN, with no observable cross reactivity. In addition, the aptasensor performed excellent stability, reproducibility, specificity, and favorable recovery in the detection of edible oil. This work demonstrated a novel method for the construction of a simple, rapid, and sensitive aptasensor in the detection of multiple mycotoxins simultaneously.

Abstract Background Long noncoding RNAs (lncRNAs) are emerging as critical regulatory elements and play fundamental roles in the biology of various cancers. However, we are still lack of knowledge about their expression patterns and functions in human colorectal cancer (CRC). Methods Differentially expressed lncRNAs in CRC were identified by bioinformatics screen and the level of MIR22HG in CRC and control tissues were determined by qRT-PCR. Cell viability and migration capacities were examined by MTT and transwell assay. Mouse model was used to examine the function and rational immunotherapy of MIR22HG in vivo. Results We systematically investigated the expression pattern of lncRNAs and revealed MIR22HG acts as a tumor suppressor in CRC. The expression of MIR22HG was significantly decreased in CRC, which was mainly driven by copy number deletion. Reduced expression of MIR22HG was significantly associated with poor overall survival. Silencing of MIR22HG promoted cell survival, proliferation and tumor metastasis in vitro and in vivo. Mechanistically, MIR22HG exerts its tumor suppressive activity by competitively interacting with SMAD2 and modulating the activity of TGFβ pathway. Decreased MIR22HG promoted the epithelial-mesenchymal transition in CRC. Importantly, we found that MIR22HG expression is significantly correlated with CD8A and overexpression of MIR22HG triggers T cell infiltration, enhancing the clinical benefits of immunotherapy. Conclusion MIR22HG acts as a tumor suppressor in CRC. Our data provide mechanistic insights into the regulation of MIR22HG in TGFβ pathway and facilitates immunotherapy in cancer.

In cancer, long noncoding RNAs (lncRNAs) can regulate immune-related pathways. Here, the authors present ImmLnc, an algorithm that can help prioritise immune-related lncRNAs in cancer immunotherapy research

To ensure food safety and prevent the toxic effects of paclobutrazol (PBZ) and carbofuran (CAR) on humans, a sensitive and rapid method for the detection of PBZ and CAR in fruits and vegetables is required. Herein, a highly sensitive PBZ monoclonal antibody (PBZ mAb) and CAR monoclonal antibody (CAR mAb) with half-inhibitory concentrations (IC50) at 0.77 and 0.82 ng mL−1 were prepared, respectively. We proposed a novel dual-color immunochromatographic assay (ICA) with two test lines (T1 and T2) and an independent control line (C) based on chrysanthemum-like Au@Polydopamine (AuNC@PDA) and colloidal gold (AuNPs) for the simultaneous and sensitive detection of PBZ and CAR with naked-eye detection limits of 10 and 5 μg kg−1, respectively. The limits of detection (LOD) for PBZ and CAR were 0.117 and 0.087 μg kg−1 in orange, 0.109 and 0.056 μg kg−1 in grape, and 0.131 and 0.094 μg kg−1 in cabbage mustard, respectively. The average recoveries of PBZ and CAR in orange, grape, and cabbage mustard were 97.86−102.83%, with coefficients of variation from 8.94 to 11.05. The detection results of this method for 30 samples (orange, grapes, and cabbage mustard) agreed well with those of liquid chromatography–tandem mass spectrometry. The novel dual-color ICA was sensitive, rapid, and accurate for the simultaneous detection of PBZ and CAR in real samples.

LincRNAs enriched with high H3K4me1 and low H3K4me3 signals often have the enhancer-like features which are named as enhancer-associated lincRNAs (elincRNAs). ElincRNAs are considered to be indispensable for target gene transcription, which play important roles in development, signaling events, and even diseases. In this study, we developed a regularized regression model to identify elincRNAs by integrating the genomic, epigenomic, and regulatory data. Application of the proposed method to mouse ESCs reveals that besides the basic well-known epigenetic features H3K4me1 and H3K4me3, more specific epigenetic features, such as high DNA methylation, high H3K122ac, and H3K36me3 were contributed to mark elincRNAs with the best accuracy and precision. Finally, 3729 elincRNAs were identified in mouse ESCs. Furthermore, the elincRNAs and canonical lincRNAs exhibit distinct genomic features, and elincRNAs have the higher CGI enrichment and lower sequence conservation. Through the analysis of transcription regulation, we found that elincRNAs were significantly regulated by NANOG, POU5F1, SOX2 and ESRRB, and were involved in the core transcriptional regulatory circuitry controlling ES cell state Function enrichment analysis further discovered that elincRNAs tended to regulate specific embryonic development biological processes. These results indicated that these two types of lincRNAs had both specific epigenetic and transcriptional regulation mechanism and display distinct functional characters. In conclusion, we presented a credible computational model to prioritize novel elincRNAs, and depicted the atlas of elincRNAs in mouse ESCs, which would help dissect the function roles of lncRNAs during the mammalian development and diseases.

This study aimed to examine sustainable effects of chatbot-based formative feedback on intrinsic motivation, cognitive load, and learning performance. A longitudinal quasi-experimental design with 173 undergraduate students was conducted. The experiment is a between-subject design. Students either received formative feedback from a chatbot or a teacher. Utilizing linear mixed model and t-test for data analysis, results showed the following. First, chatbot-based feedback resulted in increased learning interest, perceived choice, and value while decreasing perceived pressure over time. Second, chatbot-based feedback was effective in reducing cognitive load, particularly when learning contents involved conceptual or difficult knowledge. Finally, chatbot-based feedback was found to be more efficient and effective in supporting the mastery of application-based knowledge compared with teacher-based feedback. This study has practical implications for the design of chatbots, and it also enriches the methods of providing ongoing formative feedback in large-scale classrooms.

Gold nanoparticles (AuNPs) have been widely used in catalysis, photothermal therapy, and targeted drug delivery. Carrageenan oligosaccharide (CAO) derived from marine red algae was used as a reducing and capping agent to obtain AuNPs by an eco-friendly, efficient, and simple synthetic route for the first time. The synthetic conditions of AuNPs were optimized by response surface methodology (RSM), and the CAO-AuNPs obtained were demonstrated to be ellipsoidal, stable and crystalline by means of transmission electron microscopy (TEM), scanning electron microscopy (SEM) and X-ray diffraction (XRD). The CAO-AuNPs showed localized surface plasmon resonance (LSPR) oscillation at about 530 nm with a mean diameter of 35 ± 8 nm. The zeta potential of CAO-AuNPs was around −20 mV, which was related to the negatively charged CAO around AuNPs. The CAO-AuNPs exhibited significant cytotoxic activities to HCT-116 and MDA-MB-231 cells, which could be a promising nanomaterial for drug delivery.