Parental Warmth, Friendship Quality, Empathy and Bystander Defending Behavior in Cyberbullying Among Adolescents in China.

Adolescents' bystander defending behavior in cyberbullying contributes to reducing the harm done to cyberbullying victims. This study examined the relationships and underlying mechanisms of parental warmth, friendship quality, empathy and bystander defending behavior in cyberbullying among 848 Chinese adolescents (43.986% girls, mean age = 14.960 years old [SD = 1.398]). The results showed that parental warmth, friendship quality and empathy were all positively correlated with bystander defending behavior in cyberbullying among adolescents. Both friendship quality and empathy played a mediating role between parental warmth and bystander defending behavior in cyberbullying, indicating that parental warmth was associated with increased bystander defending behavior through higher levels of friendship quality and empathy, respectively. Friendship quality and empathy also played a chain mediating role between these two, indicating that parental warmth was linked to increased bystander defending behavior by first associating with higher level of friendship quality and subsequently with greater empathy. These results suggest that high degrees of parental warmth, friendship quality and empathy may all increase the likelihood of bystander defending behavior in cyberbullying among adolescents. This study provides practical implications for improving adolescent bystander defending behavior in cyberbullying.

Antibiotic resistance genes risks in relation to host pathogenicity and mobility in a typical hospital wastewater treatment process.

Hospital wastewaters (HWWs) serve as critical reservoirs for disseminating antibiotic resistance genes (ARGs) and antibiotic resistant bacteria (ARB). However, the dynamics and noteworthy shifts of ARGs and their associated pathogenicity, mobility, and resistome risks during HWWs treatment processes remain poorly understood. Utilizing metagenomic sequencing and assembly, we identified 817 ARG subtypes conferring resistance to 20 classes of antibiotics across 18 HWW samples from influent to effluent. Genes encoding resistance to multidrug, aminoglycoside and beta_lactam were the most prevalent ARG types, reflecting patterns observed in clinical settings. On-site treatment efforts decreased the relative abundance of ARGs by 77.4% from influent to secondary sedimentation, whereas chlorine disinfection significantly increased their abundance in the final effluent. Deterministic processes primarily drove the taxonomic assembly, with Proteobacteria being the most abundant phylum and serving as the primary host for 15 ARG types. Contig-based analysis further revealed 114 pathogenic ARB, with Escherichia coli, Pseudomonas alcaligenes, and Pseudomonas aeruginosa exhibiting multidrug-resistant. The contributions of host bacteria and pathogenic ARB varied throughout wastewater treatment. In addition, 7.10%-31.0 % ARGs were flanked by mobile genetic elements (MGEs), predominantly mediated by transposase (74.1%). Notably, tnpA exhibited the highest potential for ARG dissemination, frequently co-occurring with beta-lactam resistance genes (35.2%). Considering ARG profiles, pathogenic hosts, and transferability, raw influent exhibited the highest antibiotic resistome risk index (ARRI), followed by the final effluent. Chlorine disinfection exacerbated resistome risks by inducing potential pathogenic ARB and mobile ARGs, posing threats to the receiving environment. This study delineates ARG occurrence patterns, highlights mechanisms of ARG carriage and horizontal gene transfer, and provides insights for assessing resistance risks and prioritizing interventions in clinical settings.

R2-ISS staging combined with circulating plasma cells improves risk stratification for newly diagnosed multiple myeloma: a single-center real-world study.

We aimed to evaluate if circulating plasma cells (CPC) detected by flow cytometry could add prognostic value of R2-ISS staging. We collected the electronic medical records of 336 newly diagnosed MM patients (NDMM) in our hospital from January 2017 to June 2023. The median overall survival (OS) for patients and R2-ISS stage I-IV were not reached (NR), NR, 58 months and 53 months, respectively. There was no significant difference in OS between patients with stage I and patients with stage II (P = 0.309) or between patients with stage III and patients with stage IV (P = 0.391). All the cases were re-classified according to R2-ISS stage and CPC numbers ≥ 0.05% (CPC high) or<0.05% (CPC low) into four new risk groups: Group 1: R2-ISS stage I + R2-ISS stage II and CPC low, Group 2: R2-ISS stage II and CPC high + R2-ISS stage III and CPC low, Group 3: R2-ISS stage III and CPC high + R2-ISS stage IV and CPC low, Group 4: R2-ISS stage IV and CPC high. The median OS were NR, NR, 57 months and 32 months. OS of Group 1 was significantly longer than that of Group 2 (P = 0.033). OS in Group 2 was significantly longer than that of Group 3 (P = 0.007). OS in Group 3 was significantly longer than that of Group 4 (P = 0.041). R2-ISS staging combined with CPC can improve risk stratification for NDMM patients.

The progress of induced pluripotent stem cells derived from pigs: a mini review of recent advances.

Pigs (Sus scrofa) are widely acknowledged as an important large mammalian animal model due to their similarity to human physiology, genetics, and immunology. Leveraging the full potential of this model presents significant opportunities for major advancements in the fields of comparative biology, disease modeling, and regenerative medicine. Thus, the derivation of pluripotent stem cells from this species can offer new tools for disease modeling and serve as a stepping stone to test future autologous or allogeneic cell-based therapies. Over the past few decades, great progress has been made in establishing porcine pluripotent stem cells (pPSCs), including embryonic stem cells (pESCs) derived from pre- and peri-implantation embryos, and porcine induced pluripotent stem cells (piPSCs) using a variety of cellular reprogramming strategies. However, the stabilization of pPSCs was not as straightforward as directly applying the culture conditions developed and optimized for murine or primate PSCs. Therefore, it has historically been challenging to establish stable pPSC lines that could pass stringent pluripotency tests. Here, we review recent advances in the establishment of stable porcine PSCs. We focus on the evolving derivation methods that eventually led to the establishment of pESCs and transgene-free piPSCs, as well as current challenges and opportunities in this rapidly advancing field.

Spectroscopic and Theoretical Insights into H2 Activation on Uranium Monoxide: Homolytic H2 Cleavage Mediated by Intermediate OU(η2-H2).

Elucidating molecular-level interactions between dihydrogen (H2) and uranium oxides reveals fundamental insights into the intrinsic H2 activation mechanisms underlying processes such as heterogeneous catalysis over uranium oxides and corrosion of uranium induced by H2. Herein, the reactions of H2 with uranium monoxide (UO) molecules have been investigated via a combination of matrix-isolation infrared spectroscopy and quantum chemical calculations. A side-on bonded H2 complex, OU(η2-H2), is identified at 3733.7 and 800.3 cm-1. This species is regarded as a crucial intermediate along H2 activation pathways. Bonding analysis reveals cooperative U(π5f/6d) → H2(σ*) π// backdonation and U ← H2(σ) σ donation in OU(η2-H2) that facilitate the activation of the H2 moiety. Upon λ > 550 nm photoirradiation, OU(η2-H2) isomerizes into H2UO, indicating the homolytic H2 cleavage on UO. Mechanistic details of H2 adsorption and dissociation on UO molecules have been further elucidated.

Contribution to energy conservation and quality improvement of frozen pork via contact/contactless immersion freezing in NaCl solution.

High energy consumption and quality deterioration are major challenges in the meat freezing process. In this study, the energy consumption and qualities of frozen pork were investigated using three freezing methods: nonpackaged pork air freezing (NAF), contactless immersion freezing (PIF) and contact immersion freezing (NIF) with NaCl solution as a refrigerant. The results indicated that NIF could improve the energy conservation and freezing efficiency in >4 freezing treatment-times by increasing the unfrozen water content, decreasing the frozen heat load, shortening the freezing time and reducing evaporation loss. NIF could also increase the a* value of the pork and improve the water-holding capacity by facilitating the conversion of free water to immobilized-water. The two immersion freezing methods could reduce freezing-thawing loss and protein loss by alleviating muscle tissue freezing damage. These results provide a suitable application of immersion freezing with energy conservation, high efficiency and good quality of frozen-pork.

Pan-cancer analysis and the oncogenic role of Glypican 1 in hepatocellular carcinoma.

Recent studies indicate that Glypican 1 (GPC-1) is aberrantly expressed and plays a key role in certain cancers, but little is known in the hepatocellular carcinoma. Raw data from TCGA, GTEx and TIMER databases were utilized to comprehensively analyze GPC-1 expression landscape in pan-cancer, and the biological function of GPC-1 was investigated in liver cancer cells. The results revealed that GPC-1 is highly expressed in HCC, negatively correlated with survival, and also positively correlated with immune infiltration and clinical stage. Furthermore, GPC-1 promoted cell proliferation and inhibited apoptosis in the HCC cell lines. WGCNA analysis and HCCDB database revealed that Akt acted as a key molecule related to GPC-1, influencing biological functions and regulating cell malignant behaviors via the AKT signaling pathway. In conclusion, our findings provide a relatively comprehensive understanding of the oncogenic role of GPC-1 in HCC, implying that GPC-1 could serve as an innovative therapeutic target.

Mucin phenotype and microvessels in early gastic cancer: Magnifying endoscopy with narrow band imaging.

Backgrounds: In order to detect early gastric cancer (EGC), this research sought to assess the diagnostic utility of magnifying endoscopy (ME) as well as the significance of mucin phenotype and microvessel features. Methods: 402 individuals with an EGC diagnosis underwent endoscopic submucosal dissection (ESD) at the Department of ME between 2012 and 2020. After adjusting for image distortion, high-magnification endoscopic pictures were taken and examined to find microvessels in the area of interest. The microvessel density was measured as counts per square millimeter (counts/mm2) after segmentation, and the vascular bed's size was computed as a percentage of the area of interest. To identify certain properties of the microvessels, such as end-points, crossing points, branching sites, and connection points, further processing was done using skeletonized pixels. Results: According to the research, undifferentiated tumors often lacked the MS pattern and showed an oval and tubular microsurface (MS) pattern, but differentiated EGC tumors usually lacked the MS pattern and presented a corkscrew MV pattern. Submucosal invasion was shown to be more strongly associated with the destructive MS pattern in differentiated tumors as opposed to undifferentiated tumors. While lesions with a corkscrew MV pattern and an antrum or body MS pattern revealed greater MUC5AC expression, lesions with a loop MV pattern indicated higher MUC2 expression. Furthermore, CD10 expression was higher in lesions with a papillary pattern and an antrum or body MS pattern. Conclusion: These results imply that evaluating mucin phenotype and microvessel features in conjunction with magnifying endoscopy (ME) may be a useful diagnostic strategy for early gastric cancer (EGC) detection. Nevertheless, further investigation is required to confirm these findings and identify the best course of action for EGC diagnosis.

Self-Explainable Graph Neural Network for Alzheimer Disease and Related Dementias Risk Prediction: Algorithm Development and Validation Study.

BACKGROUND: Alzheimer disease and related dementias (ADRD) rank as the sixth leading cause of death in the United States, underlining the importance of accurate ADRD risk prediction. While recent advancements in ADRD risk prediction have primarily relied on imaging analysis, not all patients undergo medical imaging before an ADRD diagnosis. Merging machine learning with claims data can reveal additional risk factors and uncover interconnections among diverse medical codes. OBJECTIVE: The study aims to use graph neural networks (GNNs) with claim data for ADRD risk prediction. Addressing the lack of human-interpretable reasons behind these predictions, we introduce an innovative, self-explainable method to evaluate relationship importance and its influence on ADRD risk prediction. METHODS: We used a variationally regularized encoder-decoder GNN (variational GNN [VGNN]) integrated with our proposed relation importance method for estimating ADRD likelihood. This self-explainable method can provide a feature-important explanation in the context of ADRD risk prediction, leveraging relational information within a graph. Three scenarios with 1-year, 2-year, and 3-year prediction windows were created to assess the model's efficiency, respectively. Random forest (RF) and light gradient boost machine (LGBM) were used as baselines. By using this method, we further clarify the key relationships for ADRD risk prediction. RESULTS: In scenario 1, the VGNN model showed area under the receiver operating characteristic (AUROC) scores of 0.7272 and 0.7480 for the small subset and the matched cohort data set. It outperforms RF and LGBM by 10.6% and 9.1%, respectively, on average. In scenario 2, it achieved AUROC scores of 0.7125 and 0.7281, surpassing the other models by 10.5% and 8.9%, respectively. Similarly, in scenario 3, AUROC scores of 0.7001 and 0.7187 were obtained, exceeding 10.1% and 8.5% than the baseline models, respectively. These results clearly demonstrate the significant superiority of the graph-based approach over the tree-based models (RF and LGBM) in predicting ADRD. Furthermore, the integration of the VGNN model and our relation importance interpretation could provide valuable insight into paired factors that may contribute to or delay ADRD progression. CONCLUSIONS: Using our innovative self-explainable method with claims data enhances ADRD risk prediction and provides insights into the impact of interconnected medical code relationships. This methodology not only enables ADRD risk modeling but also shows potential for other image analysis predictions using claims data.

Value of endoscopic grading of gastroesophageal flap valve in gastroesophageal reflux disease.

OBJECTIVE: To investigate the significance of endoscopic grading (Hill's classification) of gastroesophageal flap valve (GEFV) in the examination of patients with gastroesophageal reflux disease (GERD). METHODS: One hundred and sixty-two patients undergoing gastroscopy in the Department of Gastroenterology, Xingyi People's Hospital between Apr. 2022 and Sept. 2022 were selected by convenient sampling, and data such as GEFV grade, and findings of esophageal high-resolution manometry (HRM) and esophageal 24-h pH/impedance reflux monitoring, and Los Angeles (LA) classification of reflux esophagitis (RE) were collected and compared. RESULTS: Statistically significant differences in age (F = 9.711, P < 0.001) and hiatal hernia (χ = 35.729, P < 0.001) were observed in patients with different GEFV grades. The resting LES pressures were 12.12 ± 2.79, 10.73 ± 2.68, 9.70 ± 2.29, and 8.20 ± 2.77 mmHg (F = 4.571, P < 0.001) and LES lengths were 3.30 ± 0.70, 3.16 ± 0.68, 2.35 ± 0.83, and 2.45 ± 0.62 (F = 3.789, P = 0.011), respectively, in patients with GEFV grades I-IV. DeMeester score (Z = 5.452, P < 0.001), AET4 (Z = 5.614, P < 0.001), acid reflux score (upright) (Z = 7.452, P < 0.001), weak acid reflux score (upright) (Z = 3.121, P = 0.038), liquid reflux score (upright) (Z = 3.321, P = 0.031), acid reflux score (supine) (Z = 6.462, P < 0.001), mixed reflux score (supine) (Z = 3.324, P = 0.031), gas reflux score (supine) (Z = 3.521, P = 0.024) were different in patients with different GEFV grades, with statistically significant differences. Pearson correlation analysis revealed a positive correlation between RE grade and LA classification of GERD (r = 0.662, P < 0.001), and the severity of RE increased gradually with the increase of the Hill grades of GEFV. CONCLUSION: The Hill grade of GEFV is related to age, hiatal hernia, LES pressure, and the consequent development and severity of acid reflux and RE. Evaluation of esophageal motility and reflux based on the Hill grade of GEFV is of significance for the diagnosis and treatment of GERD.

Interactions of traditional and biodegradable microplastics with neonicotinoid pesticides.

Neonicotinoid pesticides (NNPs) and microplastics (MPs) are two emerging contaminants in agricultural environment. However, the interaction between MPs (especially biodegradable plastics) and NNPs is currently unclear. Therefore, taking thiacloprid (THI) as an example of NNPs, this study explores the adsorption-desorption process and mechanism of NNPs on MPs (traditional and biodegradable plastics), and analyzed the main factors affecting the adsorption (pH, salinity and dissolved organic matter). In addition, by using diffusive gradients in thin-films device, this study assessed the impact of MPs on the bioavailability of NNPs in soil. The results showed that the maximum adsorption capacity of polyamide 6 (96.49 µg g-1) for THI was greater than that of poly (butylene adipate co-terephthalate) (88.78 µg g-1). Aging increased the adsorption amount of THI (5.53 %-15.8 %) due to the higher specific surface area and reduced contact angle of MPs, but the adsorption mechanism remained unchanged. The desorption amount of THI from MPs in simulated intestinal fluid is 1.30-1.36 times. The MPs in soil alter the distribution of THI in the soil, increasing the bioavailability of THI while inhibiting its degradation. The results highlighted the significance of examining the combined pollution caused by MPs and NNPs.

Hinesol attenuates DSS-induced ulcerative colitis through the suppression of Src-mediated NF-κB and chemokine signaling pathway.

As a common inflammatory bowel disease, ulcerative colitis (UC) is featured with inflammation, oxidative damage, and the impairment of intestinal mucosal barrier, which bring threat to patients' quality of live. Hinesol, derived from Atractylodes lancea, is a unique sesquiterpenoid. Our study proposed to survey the effects and mechanism of hinesol in UC. UC mouse model was constructed using dextran sulfate sodium (DSS). Lipopolysaccharide (LPS) was applied for RAW264.7 cells stimulation to construct cell inflammatory model. The changes of disease activity index (DAI), body weight, colon length, and intestinal pathology in mice were analyzed to estimate the severity of colitis. Enzyme-linked immunosorbent assay was applied to check the changes of interleukin (IL)-1ß, IL-18, IL-6, and tumor necrosis factor (TNF)-α. The levels of myeloperoxidase (MPO), superoxide dismutase (SOD), glutathione peroxidase (GSH-px), catalase (CAT), and malondialdehyde (MDA) were estimated by corresponding reagent kit. The changes of phosphorylated (p)-NF-κB P65, and p-IκBα, ZO-1, Occludin, Claudin-1, Src, XCL1, CCL2, and CXCL16 protein were examined using western blot. Flow cytometry and cell counting kit-8 assay were utilized for assessment of cell apoptosis and viability. We found that DSS reduced mice body weight, increased DAI, shorten colon length, and led to severe enteric mucosal injury, while hinesol improved the above symptoms induced by DSS. In DSS mice, hinesol raised the levels of ZO-1, Occludin, Claudin-1, SOD, GSH-px, and CAT and decreased the levels of TNF-α, IL-18, IL-1ß, IL-6, MPO, and MDA. Additionally, in DSS mice and LPS-stimulated RAW264.7 cells, hinesol inhibited the high expression of Src, XCL1, CCL2, CXCL16, p-NF-κB P65, and p-IκBα. The molecular docking showed that there was a good interaction between hinesol and Src. Moreover, in LPS-stimulated RAW 264.7 cells, Src overexpression partially reversed the inhibition of hinesol on cell apoptosis, pro-inflammatory factors, and oxidative stress. In conclusion, hinesol alleviated DSS-induced colitis, which might have a bearing on the inhibition of Src-mediated NF-κB and chemokine signaling pathway.

A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.

The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non-targeted screening method for P&VDs for their comprehensive risk assessment. In this study, a modified support vector machine (SVM)-assisted metabolomics approach by screening eligible variables to represent marker compounds of 124 multi-class P&VDs in maize was developed based on the results of high-performance liquid chromatography-tandem mass spectrometry. Principal component analysis and orthogonal partial least squares discriminant analysis indicate the existence of variables with obvious inter-group differences, which were further investigated by S-plot plots, permutation tests, and variable importance in projection to obtain eligible variables. Meanwhile, SVM recursive feature elimination under the radial basis function was employed to obtain the weight-squared values of all the variables ranging from large to small for the screening of eligible variables as well. Pairwise t-tests and fold changes of concentration were further employed to confirm these eligible variables to represent marker compounds. The results indicate that 120 out of 124 P&VDs can be identified by the SVM-assisted metabolomics method, while only 109 P&VDs can be found by the metabolomics method alone, implying that SVM can promote the screening accuracy of the metabolomics method. In addition, the method's practicability was validated by the real contaminated maize samples, which provide a bright application prospect in non-targeted screening of contaminants. The limits of detection for 120 P&VDs in maize samples were calculated to be 0.3~1.5 µg/kg.

Deep Learning-Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation.

Objective: To validate deep learning models' ability to predict post-transplantation major adverse cardiovascular events (MACE) in patients undergoing liver transplantation (LT). Patients and Methods: We used data from Optum's de-identified Clinformatics Data Mart Database to identify liver transplant recipients between January 2007 and March 2020. To predict post-transplantation MACE risk, we considered patients' demographics characteristics, diagnoses, medications, and procedural data recorded back to 3 years before the LT procedure date (index date). MACE is predicted using the bidirectional gated recurrent units (BiGRU) deep learning model in different prediction interval lengths up to 5 years after the index date. In total, 18,304 liver transplant recipients (mean age, 57.4 years [SD, 12.76]; 7158 [39.1%] women) were used to develop and test the deep learning model's performance against other baseline machine learning models. Models were optimized using 5-fold cross-validation on 80% of the cohort, and model performance was evaluated on the remaining 20% using the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR). Results: Using different prediction intervals after the index date, the top-performing model was the deep learning model, BiGRU, and achieved an AUC-ROC of 0.841 (95% CI, 0.822-0.862) and AUC-PR of 0.578 (95% CI, 0.537-0.621) for a 30-day prediction interval after LT. Conclusion: Using longitudinal claims data, deep learning models can efficiently predict MACE after LT, assisting clinicians in identifying high-risk candidates for further risk stratification or other management strategies to improve transplant outcomes based on important features identified by the model.

Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study.

BACKGROUND: Research on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival. AIM: To investigate the prognosis of GMA and develop predictive model. METHODS: From the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA's prognosis. RESULTS: This model had an AUC of 0.7433 [95% confidence intervals (95%CI): 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA: 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI: 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors' use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA: Regional nodes examined [hazard ratio (HR): 0.98, 95%CI: 0.97-0.98, P < 0.001)] and chemotherapy (HR: 0.62, 95%CI: 0.58-0.66, P < 0.001). CONCLUSION: The deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.

Development and validation of a multi-parameter nomogram for venous thromboembolism in gastric cancer patients: a retrospective analysis.

Objective: Gastric cancer (GC), one of the highest venous thromboembolism (VTE) incidence rates in cancer, contributes to considerable morbidity, mortality, and, prominently, extra cost. However, up to now, there is not a high-quality VTE model to steadily predict the risk for VTE in China. Consequently, setting up a prediction model to predict the VTE risk is imperative. Methods: Data from 3,092 patients from December 15, 2017, to December 31, 2022, were retrospectively analyzed. Multiple logistic regression analysis was performed to assess risk factors for GC, and a nomogram was constructed based on screened risk factors. A receiver operating curve (ROC) and calibration plot was created to evaluate the accuracy of the nomogram. Results: The risk factors of suffering from VTE were older age (OR = 1.02, 95% CI [1.00-1.04]), Karnofsky Performance Status (KPS) ≥ 70 (OR = 0.45, 95% CI [0.25-0.83]), Blood transfusion (OR = 2.37, 95% CI [1.47-3.84]), advanced clinical stage (OR = 3.98, 95% CI [1.59-9.99]), central venous catheterization (CVC) (OR = 4.27, 95% CI [2.03-8.99]), operation (OR = 2.72, 95% CI [1.55-4.77]), fibrinogen degradation product (FDP) >5 µg/mL (OR = 1.92, 95% CI [1.13-3.25]), and D-dimer > 0.5 mg/L (OR = 2.50, 95% CI [1.19-5.28]). The area under the ROC curve (AUC) was 0.82 in the training set and 0.85 in the validation set. Conclusion: Our prediction model can accurately predict the risk of the appearance of VTE in gastric cancer patients and can be used as a robust and efficient tool for evaluating the possibility of VTE.

N6-methyladenosine modified circPAK2 promotes lymph node metastasis via targeting IGF2BPs/VEGFA signaling in gastric cancer.

Circular RNAs (circRNAs) have emerged as key regulators of cancer occurrence and progression, as well as promising biomarkers for cancer diagnosis and prognosis. However, the potential mechanisms of circRNAs implicated in lymph node (LN) metastasis of gastric cancer remain unclear. Herein, we identify a novel N6-methyladenosine (m6A) modified circRNA, circPAK2, which is significantly upregulated in gastric cancer tissues and metastatic LN tissues. Functionally, circPAK2 enhances the migration, invasion, lymphangiogenesis, angiogenesis, epithelial-mesenchymal transition (EMT), and metastasis of gastric cancer in vitro and in vivo. Mechanistically, circPAK2 is exported by YTH domain-containing protein 1 (YTHDC1) from the nucleus to the cytoplasm in an m6A methylation-dependent manner. Moreover, increased cytoplasmic circPAK2 interacts with Insulin-Like Growth Factor 2 mRNA-Binding Proteins (IGF2BPs) and forms a circPAK2/IGF2BPs/VEGFA complex to stabilize VEGFA mRNA, which contributes to gastric cancer vasculature formation and aggressiveness. Clinically, high circPAK2 expression is positively associated with LN metastasis and poor prognosis in gastric cancer. This study highlights m6A-modified circPAK2 as a key regulator of LN metastasis of gastric cancer, thus supporting circPAK2 as a promising therapeutic target and prognostic biomarker for gastric cancer.

Large-scale 2D heterostructures from hydrogen-bonded organic frameworks and graphene with distinct Dirac and flat bands.

The current strategies for building 2D organic-inorganic heterojunctions involve mostly wet-chemistry processes or exfoliation and transfer, leading to interface contaminations, poor crystallizing, or limited size. Here we show a bottom-up procedure to fabricate 2D large-scale heterostructure with clean interface and highly-crystalline sheets. As a prototypical example, a well-ordered hydrogen-bonded organic framework is self-assembled on the highly-oriented-pyrolytic-graphite substrate. The organic framework adopts a honeycomb lattice with faulted/unfaulted halves in a unit cell, resemble to molecular "graphene". Interestingly, the topmost layer of substrate is self-lifted by organic framework via strong interlayer coupling, to form effectively a floating organic framework/graphene heterostructure. The individual layer of heterostructure inherits its intrinsic property, exhibiting distinct Dirac bands of graphene and narrow bands of organic framework. Our results demonstrate a promising approach to fabricate 2D organic-inorganic heterostructure with large-scale uniformity and highly-crystalline via the self-lifting effect, which is generally applicable to most of van der Waals materials.