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Abstract Background Patients treated with anti-CD20 monoclonal antibodies could have a higher risk of adverse outcomes of coronavirus disease 2019 (COVID-19). The novel anti-CD20 monoclonal antibody obinutuzumab has shown greater B-cell depletion and superior in vitro efficacy than rituximab. We aimed to assess whether obinutuzumab would result in worse COVID-19 outcomes than rituximab. Methods We retrospectively reviewed 124 patients with B-cell lymphoma, 106 of whom received rituximab treatment and 18 of whom received obinutuzumab treatment. The adverse outcomes of COVID-19 were compared between patients in the two cohorts. Results The proportions of patients who were hospitalized (55.6% vs. 20.8%, p = 0.005), experienced prolonged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (38.9% vs. 2.9%, p

Abstract Background Previous studies indicated that excessive engagement in digital devices could lead to negative psychological impacts in general population. We aimed to determine the association of electronic screen exposure with depression among women in early pregnancy. Methods A cross-sectional study was conducted from June 2021 to June 2022. A total of 665 women in early pregnancy were recruited and the information included socio-demographic characteristics, screen exposure and Patient Health Questionnaire − 9 depression scale. Results Among the women in early pregnancy, the total daily smartphone viewing time was the longest (median [P25-P75], 5 [3–6] hours/day) in the three types of electronic screen exposure. The total daily smartphone viewing time (P = 0.015, OR[95%CI] = 1.09[1.11–1.18]), smartphone (P = 0.016, OR[95%CI] = 1.24[1.04–1.47]) and television viewing time (P = 0.006, OR[95%CI] = 1.35[1.09–1.67]) before nocturnal sleep were significantly associated with depression among women in early pregnancy. The thresholds calculated by receiver operator characteristic curves were 7.5 h/day, 1.5 h/day and 1.5 h/day, respectively. In addition, women with higher scores of smartphone addiction were more susceptible to depression (P

To investigate the effects of various types of farmland shelterbelts on soil quality and soil bacterial community diversity, this study focused on soil samples from four different shelterbelt types in the Alaer reclamation area, including Populus euphratica Oliv.- Populus tomentosa Carrière (PP), Elaeagnus angustifolia L.- Populus euphratica Oliv. (EP), Populus alba var. pyramidalis Bunge (P), and Salix babylonica L. (S). We analyzed their physical, chemical, biological properties as well as the differences in bacterial community structure, and explored the influencing factors on soil microbial community characteristics through microbial correlation network analysis. The results showed that: (1) There were significant differences in soil properties among the four types of farmland shelterbelts (p < 0.05), with P soils exhibiting the highest levels of organic matter, total nitrogen, and total phosphorus contents. (2) The Alpha diversity indices of soil bacteria showed significant differences among the four types of farmland shelterbelts (p < 0.05), with the P soils displayed the highest Chao1 and Shannon indices. (3) There were differences in the composition and abundance of dominant soil bacterial communities among different farmland shelterbelts, notably, the abundances of Verrucomicrobia, Acidobacteria, and Planctomycetes were significantly higher in P soils compared to the other three types. (4) The complexity of the correlation network between microbial species and environmental factors was highest in EP soils, soil microbial biomass nitrogen and available phosphorus were the main influencing factors. These findings indicated that different types of farmland shelterbelts had significant impacts on soil properties and soil bacterial communities. Soil bacterial communities were regulated by soil properties, their changes reflected a combined effect of soil characteristics and tree species.

Diffusional kurtosis imaging (DKI) is a methodology for measuring the extent of non-Gaussian diffusion in biological tissue, which has shown great promise in clinical diagnosis, treatment planning, and monitoring of many neurological diseases and disorders. However, robust, fast, and accurate estimation of kurtosis from clinically feasible data acquisitions remains a challenge. In this study, we first outline a new accurate approach of estimating mean kurtosis via the sub-diffusion mathematical framework. Crucially, this extension of the conventional DKI overcomes the limitation on the maximum b-value of the latter. Kurtosis and diffusivity can now be simply computed as functions of the sub-diffusion model parameters. Second, we propose a new fast and robust fitting procedure to estimate the sub-diffusion model parameters using two diffusion times without increasing acquisition time as for the conventional DKI. Third, our sub-diffusion-based kurtosis mapping method is evaluated using both simulations and the Connectome 1.0 human brain data. Exquisite tissue contrast is achieved even when the diffusion encoded data is collected in only minutes. In summary, our findings suggest robust, fast, and accurate estimation of mean kurtosis can be realised within a clinically feasible diffusion-weighted magnetic resonance imaging data acquisition time.

Abstract Background We previously conducted a case-control study and found that exposure to electronic screen before nocturnal sleep was associated with hypertensive disorders in pregnancy (HDP). Hence, we carried out this cohort study aiming to identify the effects of screen exposure time on the incidence rate and severity of HDP. Methods A retrospective cohort study was conducted from January 2022 and July 2022 from three hospitals in Wuxi and Changzhou cities. A total of 732 women were recruited and the information included socio-demographic characteristics, screen exposure and outcomes. Generalized estimating equations and binary non-conditional logistic models were applied to multivariate analysis, calculating the odds ratios (ORs) and 95% confidence intervals (CIs) of screen exposure time. Results The duration order of total screen time was smartphone > computer > television, while the duration order of screen time before nocturnal sleep was smartphone > television > computer. Multivariate analyses showed that the susceptibility of HDP among women who exposed to television before nocturnal sleep was 81.5% percent higher than those not exposed (P = 0.018, OR[95%CI] = 1.815[1.106–2.981]). In addition, total daily exposure time of television in the third trimester of pregnancy significantly increased the severity of HDP (P = 0.021, OR[95%CI] = 3.641[1.213–10.927]). Conclusions Based on this preliminary study, we would suggest that pregnant women do not watch television before nocturnal sleep. While in the third trimester of pregnancy, total exposure time of television should be limited. Investigations from other areas and experimental studies should be conducted to verify the conclusion.

Summary: Plant diseases caused by fungal pathogens pose a great threat to crop production. Conidiation of fungi is critical for disease epidemics and serves as a promising drug target. Here, we show that deacetylation of the FolTFIIS transcription elongation factor is indispensable for Fusarium oxysporum f. sp. lycopersici (Fol) conidiation. Upon microconidiation, Fol decreases K76 acetylation of FolTFIIS by altering the level of controlling enzymes, allowing for its nuclear translocation by FolIws1. Increased nuclear FolTFIIS enhances the transcription of sporulation-related genes and, consequently, enables microconidia production. Deacetylation of FolTFIIS is also critical for the production of macroconidia and chlamydospores, and its homolog has similar functions in Botrytis cinerea. We identify two FolIws1-targeting chemicals that block the conidiation of Fol and have effective activity against a wide range of pathogenic fungi without harm to the hosts. These findings reveal a conserved mechanism of conidiation regulation and provide candidate agrochemicals for disease management.

Abstract Introduction Although the combination of venetoclax (VEN) and hypomethylating agents (HMAs) results in impressive efficacy in acute myeloid leukemia (AML), there is still a subset of patients who are refractory. We investigated the outcomes of AML patients with monocytic differentiation who were treated with frontline VEN/HMA. Methods A total of 155 patients with newly diagnosed AML treated with frontline VEN/HMA were enrolled in the study. Monocyte‐like AML was identified by flow cytometry with typical expression of monocytic markers, and M5 was identified according to French, American, and British category. We compared the outcomes of patients with different characteristics. Results The rate of complete remission (CR) and CR with incomplete recovery of blood counts (CRi), progression‐free survival (PFS), and overall survival (OS) in monocyte‐like AML were inferior to those in nonmonocyte‐like AML (CR/CRi rates, 26.7% vs. 80.0%, p

Background: The course of organ dysfunction (OD) in Corona Virus Disease 2019 (COVID-19) patients is unknown. Herein, we analyze the temporal patterns of OD in intensive care unit-admitted COVID-19 patients. Methods: Sequential organ failure assessment scores were evaluated daily within 2 weeks of admission to determine the temporal trajectory of OD using group-based multitrajectory modeling (GBMTM). Results: A total of 392 patients were enrolled with a 28-day mortality rate of 53.6%. GBMTM identified four distinct trajectories. Group 1 (mild OD, n = 64), with a median APACHE II score of 13 (IQR 9-21), had an early resolution of OD and a low mortality rate. Group 2 (moderate OD, n = 140), with a median APACHE II score of 18 (IQR 13-22), had a 28-day mortality rate of 30.0%. Group 3 (severe OD, n = 117), with a median APACHR II score of 20 (IQR 13-27), had a deterioration trend of respiratory dysfunction and a 28-day mortality rate of 69.2%. Group 4 (extremely severe OD, n = 71), with a median APACHE II score of 20 (IQR 17-27), had a significant and sustained OD affecting all organ systems and a 28-day mortality rate of 97.2%. Conclusions: Four distinct trajectories of OD were identified, and respiratory dysfunction trajectory could predict nonpulmonary OD trajectories and patient prognosis.

Disseminated candidiasis is a severe complication in patients with hematological malignancies who have undergone chemotherapy or hematopoietic stem cell transplantation. It has a high mortality rate. When disseminated candidiasis caused by Candida tropicalis involves either the brain or heart, the prognosis is extremely poor. Traditional methods such as cultures are limited in diagnosing disseminated candidiasis. We describe a case report of a 55-year-old man with acute myeloid leukemia who developed candidemia caused by Candida tropicalis after chemotherapy, which disseminated extensively to the heart, brain, skin, liver, spleen and kidneys. In this instance, the patient was rapidly diagnosed with candida infection by metagenomic next generation sequencing, and successfully treated with combination therapy of isavuconazole and amphotericin B. The patient continued with treatment of leukemia while simultaneously receiving antifungal therapy, and both leukemia and disseminated candidiasis were effectively controlled. This case report provides real-world experience for treatment of patients with leukemia complicated by disseminated candidiasis.

Abstract Novel methods are required to enhance lung cancer detection, which has overtaken other cancer-related causes of death as the major cause of cancer-related mortality. Radiologists have long-standing methods for locating lung nodules in patients with lung cancer, such as computed tomography (CT) scans. Radiologists must manually review a significant amount of CT scan pictures, which makes the process time-consuming and prone to human error. Computer-aided diagnosis (CAD) systems have been created to help radiologists with their evaluations in order to overcome these difficulties. These systems make use of cutting-edge deep learning architectures. These CAD systems are designed to improve lung nodule diagnosis efficiency and accuracy. In this study, a bespoke convolutional neural network (CNN) with a dual attention mechanism was created, which was especially crafted to concentrate on the most important elements in images of lung nodules. The CNN model extracts informative features from the images, while the attention module incorporates both channel attention and spatial attention mechanisms to selectively highlight significant features. After the attention module, global average pooling is applied to summarize the spatial information. To evaluate the performance of the proposed model, extensive experiments were conducted using benchmark dataset of lung nodules. The results of these experiments demonstrated that our model surpasses recent models and achieves state-of-the-art accuracy in lung nodule detection and classification tasks.

An accurate ultra-short-term time series prediction of a power load is an important guarantee for power dispatching and the safe operation of power systems. Problems of the current ultra-short-term time series prediction algorithms include low prediction accuracy, difficulty capturing the local mutation features, poor stability, and others. From the perspective of series decomposition, a multi-scale sequence decomposition model (TFDNet) based on power spectral density and the Morlet wavelet transform is proposed that combines the multidimensional correlation feature fusion strategy in the time and frequency domains. By introducing the time-frequency energy selection module, the "prior knowledge" guidance module, and the sequence denoising decomposition module, the model not only effectively delineates the global trend and local seasonal features, completes the in-depth information mining of the smooth trend and fluctuating seasonal features, but more importantly, realizes the accurate capture of the local mutation seasonal features. Finally, on the premise of improving the forecasting accuracy, single-point load forecasting and quantile probabilistic load forecasting for ultra-short-term load forecasting are realized. Through the experiments conducted on three public datasets and one private dataset, the TFDNet model reduces the mean square error (MSE) and mean absolute error (MAE) by 19.80 and 11.20% on average, respectively, as compared with the benchmark method. These results indicate the potential applications of the TFDNet model.

Abstract Botrytis cinerea is the causative agent of gray mold, a fungal disease affecting a broad array of food crops and resulting in considerable agricultural and economic losses. Currently, chemical fungicides are the most effective control measure, but this practice is challenged by the growing resistance of pathogens to these fungicides. Scopoletin, a major phytoalexin of tobacco plants, has shown promise as an antifungal treatment. Here, we found that scopoletin was effective against B. cinerea infection of tomato leaves. It inhibited mycelial growth and conidial germination of B. cinerea and disrupted cell wall, cell membrane, and infection structure formation. Transcriptomic analysis identified 3495 differentially expressed genes in response to scopoletin treatment. Interestingly, scopoletin seems to deactivate the HOG pathway of B. cinerea. The growth defect caused by hyperactivation of the pathway, such as iprodione treatment and deletion of BcPtc1, the negative regulator of this pathway, was partly rescued by scopoletin. Moreover, it could downregulate phosphorylation levels of BcSak1 treated with iprodione and NaCl. In addition, the control efficiency of triadimefon was significantly increased by scopoletin. Thus, scopoletin is a promising, naturally derived, and sustainable treatment for gray mold, especially when combined with triadimefon.

Abstract Ferroelectric tunnel junctions are promising towards high-reliability and low-power non-volatile memories and computing devices. Yet it is challenging to maintain a high tunnelling electroresistance when the ferroelectric layer is thinned down towards atomic scale because of the ferroelectric structural instability and large depolarization field. Here we report ferroelectric tunnel junctions based on samarium-substituted layered bismuth oxide, which can maintain tunnelling electroresistance of 7 × 105 with the samarium-substituted bismuth oxide film down to one nanometer, three orders of magnitude higher than previous reports with such thickness, owing to efficient barrier modulation by the large ferroelectric polarization. These ferroelectric tunnel junctions demonstrate up to 32 resistance states without any write-verify technique, high endurance (over 5 × 109), high linearity of conductance modulation, and long retention time (10 years). Furthermore, tunnelling electroresistance over 109 is achieved in ferroelectric tunnel junctions with 4.6-nanometer samarium-substituted bismuth oxide layer, which is higher than commercial flash memories. The results show high potential towards multi-level and reliable non-volatile memories.

Endoribonuclease DICER is an RNase III enzyme that mainly processes microRNAs in the cytoplasm but also participates in nuclear functions such as chromatin remodelling, epigenetic modification and DNA damage repair. The expression of nuclear DICER is low in most human cancers, suggesting a tight regulation mechanism that is not well understood. Here, we found that ubiquitin carboxyl‐terminal hydrolase 7 (USP7), a deubiquitinase, bounded to DICER and reduced its nuclear protein level by promoting its ubiquitination and degradation through MDM2, a newly identified E3 ubiquitin‐protein ligase for DICER. This USP7‐MDM2‐DICER axis impaired histone γ‐H2AX signalling and the recruitment of DNA damage response (DDR) factors, possibly by influencing the processing of small DDR noncoding RNAs. We also showed that this negative regulation of DICER by USP7 via MDM2 was relevant to human tumours using cellular and clinical data. Our findings revealed a new way to understand the role of DICER in malignant tumour development and may offer new insights into the diagnosis, treatment and prognosis of cancers.

Abstract Background Previous studies achieved low microbial detection rates in lymphoma patients with interstitial pneumonia (IP) after chemotherapy. However, the metagenomic next-generation sequencing (mNGS) is a comprehensive approach that is expected to improve the pathogen identification rate. Thus far, reports on the use of mNGS in lymphoma patients with chemotherapy-related IP remain scarce. In this study, we summarized the microbial detection outcomes of lymphoma patients with chemotherapy-related IP through mNGS testing of bronchoalveolar lavage fluid (BALF). Methods Fifteen lymphoma patients with chemotherapy-related IP were tested for traditional laboratory microbiology, along with the mNGS of BALF. Then, the results of mNGS and traditional laboratory microbiology were compared. Results Of the 15 enrolled patients, 11 received rituximab and 8 were administered doxorubicin hydrochloride liposome. The overall microbial yield was 93.3% (14/15) for mNGS versus 13.3% (2/15) for traditional culture methods (P ≤ 0.05). The most frequently detected pathogens were Pneumocystis jirovecii (12/15, 80%), Cytomegalovirus (4/15, 26.7%), and Epstein-Barr virus (3/15, 20%). Mixed infections were detected in 10 cases. Five patients recovered after the treatment with antibiotics alone without glucocorticoids. Conclusion Our findings obtained through mNGS testing of BALF suggested a high microbial detection rate in lymphoma patients with IP after chemotherapy. Notably, there was an especially high detection rate of Pneumocystis jirovecii. The application of mNGS in patients with chemotherapy-related IP was more sensitive.

Strontium aluminate, with suitable lattice parameters and environmentally friendly water solubility, has been strongly sought for use as a sacrificial layer in the preparation of freestanding perovskite oxide thin films in recent years. However, due to this material’s inherent water solubility, the methods used for the preparation of epitaxial films have mainly been limited to high-vacuum techniques, which greatly limits these films’ development. In this study, we prepared freestanding single-crystal perovskite oxide thin films on strontium aluminate using a simple, easy-to-develop, and low-cost chemical full-solution deposition technique. We demonstrate that a reasonable choice of solvent molecules can effectively reduce the damage to the strontium aluminate layer, allowing successful epitaxy of perovskite oxide thin films, such as 2-methoxyethanol and acetic acid. Molecular dynamics simulations further demonstrated that this is because of their stronger adsorption capacity on the strontium aluminate surface, which enables them to form an effective protective layer to inhibit the hydration reaction of strontium aluminate. Moreover, the freestanding film can still maintain stable ferroelectricity after release from the substrate, which provides an idea for the development of single-crystal perovskite oxide films and creates an opportunity for their development in the field of flexible electronic devices.

Abstract Steady‐state visual evoked potentials (SSVEP), significant in brain‐computer interfaces (BCI) and medical diagnostics, benefit from enhanced signal processing for improved analysis and interpretation. This study introduces a novel enhancement algorithm for SSVEP electroencephalogram (EEG) signals, employing fractional‐order differentiation operators combined with image processing techniques. Utilizing fractional‐order differentiation within a Laplace pyramid framework, the algorithm achieves hierarchical signal enhancement, facilitating detailed feature extraction and emphasizing SSVEP signal characteristics. This innovative approach merges the precision of fractional calculus with the structural benefits of the Laplace pyramid, leading to enhanced signal clarity and feature discrimination. The efficacy of this method was validated using canonical correlation analysis (CCA), filter bank CCA (FBCCA), and task‐related component analysis (TRCA) on a public dataset. Compared to conventional methods, our algorithm not only mitigates trend components in SSVEP signals but also significantly boosts the recognition accuracy of CCA, FBCCA, and TRCA algorithms. Experimental results indicate a marked improvement in recognition precision, underscoring the algorithm's potential to advance SSVEP‐based BCI research.

Aquaculture plays a vital role in global food production, with fish pond water quality directly impacting aquatic product quality. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) serves as a key producer of aquatic products in South China. Monitoring environmental changes in fish ponds serves as an indicator of their health. This study employed the extreme gradient boosting tree (BST) model of machine learning, utilizing Landsat imagery data, to assess Chlorophyll-a (Chl-a) concentration in GBA fish ponds from 2013 to 2022. The study also examined the corresponding spatiotemporal variations in Chl-a concentration. Key findings include: (1) clear seasonal fluctuations in Chl-a concentration, peaking in summer (56.7 μg·L−1) and reaching lows in winter (43.5 μg·L−1); (2) a slight overall increase in Chl-a concentration over the study period, notably in regions with rapid economic development, posing a heightened risk of eutrophication; (3) influence from both human activities and natural factors such as water cycle and climate, with water temperature notably impacting summer Chl-a levels; (4) elevated Chl-a levels in fish ponds compared to surrounding natural water bodies, primarily attributed to human activities, indicating an urgent need to revise breeding practices and address eutrophication. These findings offer a quantitative assessment of fish pond water quality and contribute to sustainable aquaculture management in the GBA.

Abstract Recently, lung cancer prediction based on imaging genomics has attracted great attention. However, such studies often have many challenges, such as small sample size, high-dimensional information redundancy, and the inefficiency of multimodal fusion. Therefore, in this paper, a deep convolution cascade attention fusion network (DCCAFN) based on imaging genomics is proposed for the prediction of lung cancer patients’ survival. The network consists of three modules: an image feature extraction module (IFEM), a gene feature extraction module (GFEM), and an attention fusion network (AFN). In the IFEM, a pretrained residual network based on transfer learning is used to extract deep image features to fully capture the computed tomography (CT) image information conducive to prognosis prediction. In the GFEM, the F-test is first used for gene screening to eliminate redundant information, and then, a cascade network with the convolution cascade module (CCM) that contains a convolution operation, a pooling operation, and an ensemble forest classifier is designed to better extract the gene features. In the AFN, a bimodal attention fusion mechanism is proposed to fuse deep image features and gene features to improve the performance of predicting lung cancer survival. The experimental results show that the DCCAFN model achieves good performance, and its accuracy and AUC are 0.831 and 0.816, respectively. It indicates that the model is an effective multimodal data fusion method for predicting the survival prognosis of lung cancer, which can greatly help physicians stratify patients' risks, and achieve personalized treatment for improving the quality of patients' lives.

Abstract Aim The aim of the study was to compare the associations between workplace violence and patient safety behaviour between male and female nursing interns. Design A cross‐sectional survey. Methods A cross‐sectional survey was carried out at three general hospitals in Shandong Province in China to collect data from 466 nursing interns. We evaluated the associations between workplace violence and patient safety behaviours in men and women using multiple linear regressions. Results Sex moderated the association between workplace violence and patient safety behaviour (B = 1.046, [SE = 0.477]; p = 0.029). Among male nursing interns, there was a significant association between workplace violence and patient safety (B = −1.353, 95% CI [−2.556, −0.151]; p = 0.028). In male nursing interns, verbal violence and sexual violence were significantly negatively associated with patient safety (B = −1.569, SE = 0.492, p = 0.002; B = −45.663, SE = 5.554, p

Abstract Machine learning is widely used to infer ground-level concentrations of air pollutants from satellite observations. However, a single pollutant is commonly targeted in previous explorations, which would lead to duplication of efforts and ignoration of interactions considering the interactive nature of air pollutants and their common influencing factors. We aim to build a unified model to offer a synchronized estimation of ground-level air pollution levels. We constructed a multi-output random forest (MORF) model and achieved simultaneous estimation of hourly concentrations of PM2.5, PM10, O3, NO2, CO, and SO2 in China, benefiting from the world’s first geostationary air-quality monitoring instrument Geostationary Environment Monitoring Spectrometer. MORF yielded a high accuracy with cross-validated R2 reaching 0.94. Meanwhile, model efficiency was significantly improved compared to single-output models. Based on retrieved results, the spatial distributions, seasonality, and diurnal variations of six air pollutants were analyzed and two typical pollution events were tracked.

● Summaries on sgRNAs design. ● Overview of the features of 43 web sgRNA designers. ● A platform to select optimal sgRNA design tool. CRISPR-mediated gene-editing technology has arguably driven an unprecedented revolution in biological sciences for its role in elucidating gene functions. A multitude of software has been developed for the design and analysis of CRISPR/Cas experiments, including predictive tools to design optimally guide RNA for various experimental operations. Different in silico sgRNA design tools have various application scenarios and identifying the optimal design tools can often be a challenge. This paper describes the sgRNA design workflow in experiments, the classification of sgRNA designers, previously published benchmarking work of in silico designers, and the criteria involved how to select an sgRNA web server. Through basic testing, this paper comprehensively overviews and compares the features of 43 web server designers to provide a reference for the readers. Ultimately, the project developed an integrated platform, called Aid-TG, which helps users find appropriate tools quickly.

Introduction: As the evaluation indices, cancer grading and subtyping have diverse clinical, pathological, and molecular characteristics with prognostic and therapeutic implications. Although researchers have begun to study cancer differentiation and subtype prediction, most of relevant methods are based on traditional machine learning and rely on single omics data. It is necessary to explore a deep learning algorithm that integrates multi-omics data to achieve classification prediction of cancer differentiation and subtypes.Methods: This paper proposes a multi-omics data fusion algorithm based on a multi-view graph neural network (MVGNN) for predicting cancer differentiation and subtype classification. The model framework consists of a graph convolutional network (GCN) module for learning features from different omics data and an attention module for integrating multi-omics data. Three different types of omics data are used. For each type of omics data, feature selection is performed using methods such as the chi-square test and minimum redundancy maximum relevance (mRMR). Weighted patient similarity networks are constructed based on the selected omics features, and GCN is trained using omics features and corresponding similarity networks. Finally, an attention module integrates different types of omics features and performs the final cancer classification prediction.Results: To validate the cancer classification predictive performance of the MVGNN model, we conducted experimental comparisons with traditional machine learning models and currently popular methods based on integrating multi-omics data using 5-fold cross-validation. Additionally, we performed comparative experiments on cancer differentiation and its subtypes based on single omics data, two omics data, and three omics data.Discussion: This paper proposed the MVGNN model and it performed well in cancer classification prediction based on multiple omics data.

Background: Esophageal squamous cell carcinoma is the most common malignant tumor of the upper gastrointestinal tract, which is prone to metastasis and has a poor prognosis. Lysine oxidase-like protein 2 (LOXL2) is an important intracellular protein that is highly expressed in a variety of tumors, leading to reduced patient survival. Studies have shown that LOXL2 is closely associated with tumor metastasis. However, the effect of LOXL2 on the biological function of esophageal squamous cell carcinoma (ESCC) and its related mechanisms are not fully understood. This study aimed to evaluate the potential prognostic and treatment value of LOXL2 in patients with ESCC. Results: The expression of LOXL2 was higher in ESCC tissues than in adjacent tissues. Positive LOXL2 expression was associated with poor tumor differentiation, lymph node metastases, and poor prognosis in ESCC patients. LOXL2 is an independent prognostic risk factor for ESCC. In vitro experiments showed that LOXL2 significantly promoted ESCC cell proliferation, migration, and invasion ability. LOXL2 altered the expression of the EMT marker, upregulating the mesenchymal marker (Snail) and downregulating the epithelial marker (E-cadherin). Mechanistically, LOXL2 induces EMT in esophageal squamous cell carcinoma by increasing FAK and SRC phosphorylation levels. Conclusions: Taken together, LOXL2 regulates the FAK/Src signaling pathway to promote malignant biological behavior and EMT process in ESCC cells. LOXL2 is a marker of poor prognosis, and LOXL2 could be a promising therapeutic target for the treatment of esophageal squamous carcinoma.How to cite: Liu Y, Liu X, Chen F, et al. LOXL2 promotes tumor proliferation and metastasis by FAK/Src signaling in esophageal squamous cell carcinoma. Electron J Biotechnol 2023;63. https://doi.org/10.1016/j.ejbt.2023.01.002.

Abstract Background Most nursing interns have suffered some form of workplace violence in clinical settings, which has been linked to the jeopardizing of patient safety. Although previous research studies have examined the effect of workplace violence on patient safety, few studies have examined whether workplace violence is associated with patient safety through professional identity and professional burnout among nursing interns. Aims To test whether professional identity and professional burnout play mediating roles in the relationship of workplace violence and patient safety among nursing interns. Design Cross‐sectional study. Methods The study included 466 nursing interns from three tertiary grade A hospitals. The Workplace Violence Scale, the Professional Identity Scale, the Maslach Burnout Inventory‐General Survey, and the Patient Safety Behaviour Scale were used to gather data. Associations among workplace violence, professional identity, professional burnout, and patient safety were assessed by correlation and the serial‐multiple mediation analysis. Results Workplace violence, professional identity, professional burnout and patient safety were significantly correlated. Workplace violence can have a direct positive impact on patient safety of nursing interns, but also an indirect impact on patient safety through three paths: the independent mediating role of professional identity, the independent mediating role of professional burnout, and the chain mediating role of professional identity and professional burnout. Conclusions Our findings suggest that workplace violence can affect patient safety through decreasing professional identity and increasing professional burnout among nursing interns. Interventions aimed at decreasing workplace violence among nursing interns would be beneficial for professional attitude and patient safety.

Cysticercus pisiformis is a kind of tapeworm larvae of Taenia pisiformis, which parasitizes the liver envelope, omentum, mesentery, and rectum of rodents such as rabbits. Cysteine protease inhibitors derived from helminth were immunoregulatory molecules of intermediate hosts and had an immunomodulatory function that regulates the production of inflammatory factors. Thus, in the present research, the recombinant Stefin of C. pisiformis was confirmed to have the potential to fight inflammation in LPS-Mediated RAW264.7 murine macrophages. CCK8 test showed that rCpStefin below 50 μg/mL concentration did not affect cellular viability. Moreover, the NO production level determined by the Griess test was decreased. In addition, the secretion levels of IL-1β, IL-6, and TNF-α as measured by ELISA were decreased. Furthermore, it exerted anti-inflammatory activity by decreasing the production of proinflammatory cytokines and proinflammatory mediators, including IL-1β, IL-6, TNF-α, iNOS, and COX-2 at the gene transcription level, as measured by qRT-PCR. Therefore, Type I cystatin derived from C. pisiformis suppresses the LPS-Mediated inflammatory response of the intermediate host and is a potential candidate for the treatment of inflammatory diseases.

Iris laevigata Fisch. is an excellent ornamental plant in cold regions due to its unique ornamental ability and strong cold resistance. However, the flowering period of the population is only about 20 days, greatly limiting its potential uses in landscaping and the cutting flower industry. In addition, I. laevigata is often challenged with various abiotic stresses including high salinity and drought in its native habitats. Thus, breeding novel cultivars with delayed flowering time and higher resistance to abiotic stress is of high importance. In this study, we utilized sequencing data from the I. laevigata transcriptome to identify WRKYs and characterized IlWRKY22, a key transcription factor that modulates flowering time and abiotic stress responses. IlWRKY22 is induced by salt and drought stress. We cloned IlWRKY22 and found that it is a Group IIe WRKY localized in the nucleus. Overexpressing IlWRKY22 in Arabidopsis thaliana (L.) Heynh. and Nicotiana tabacum L. resulted in a delayed flowering time in the transgenic plants. We created transgenic N. tabacum overexpressing IlWRKY22, which showed significantly improved resistance to both salt and drought compared to the control plants. Thus, our study revealed a unique dual function of IlWRKY22, an excellent candidate gene for breeding novel Iris cultivars of desirable traits.

Abstract With the advancement of sequencing technology, cell separation, and whole‐genome amplification techniques, single cell technology for genome sequencing is emerging gradually. In comparison to traditional genome sequencing at the multi‐cellular level, single‐cell sequencing can not only measure the gene expression level more accurately but also can detect a small amount of gene expression or rare noncoding RNA. This technology has garnered increasing interest among researchers engaged in single‐cell studies in recent years. Here, we developed a reproducible computational workflow for scRNA‐seq data analysis which including tasks like quality control, normalization, data correction, pseudotime analysis, copy number analysis, etc. We illustrate the application of these steps using publicly available datasets and provide practical recommendations for their implementation. This study serves as a comprehensive tutorial for researchers keen on single‐cell data analysis, aiding users in constructing and refining their own analysis pipelines.

The oral cavity is the second most microbially rich region of the human body, and many studies have shown that there is a strong association between microorganisms and oral health. Some pathogenic bacteria produce biofilms and harmful metabolites in the mouth that may cause oral problems such as oral malodor, periodontitis, and dental caries. Altering the oral microbiota by using probiotics may alleviate oral health problems. Thus, using multi-function screening, we aimed to identify probiotics that can significantly improve oral health. The main parameters were the inhibition of pathogenic bacteria growth, inhibition of biofilm formation, reduction in the production of indole, H2S, and NH3 metabolites that cause halitosis, increase in the production of H2O2 to combat harmful bacteria, and co-aggregation with pathogens to prevent their adhesion and colonization in the oral cavity. Tolerance to cholic acid and choline was also assessed. Bifidobacterium animalis ZK-77, Lactobacillus salivarius ZK-88, and Streptococcus salivarius ZK-102 had antibacterial activity and inhibited biofilm production to prevent caries. They also improved the oral malodor parameter, H2S, NH3, and indole production. The selected probiotics (especially L. salivarius ZK-88) alleviated the inflammation in the oral cavity of rats with periodontitis. The analysis of the gingival crevicular fluid microbiome after probiotic intervention showed that B. animalis ZK-77 likely helped to restore the oral microbiota and maintain the oral microecology. Next, we determined the best prebiotics for each candidate probiotic in order to obtain a formulation with improved effects. We then verified that a probiotics/prebiotic combination (B. animalis ZK-77, L. salivarius ZK-88, and fructooligosaccharides) significantly improved halitosis and teeth color in cats. Using whole-genome sequencing and acute toxicity mouse experiments involving the two probiotics, we found that neither probiotic had virulence genes and they had no significant effects on the growth or development of mice, indicating their safety. Taking the results together, B. animalis ZK-77 and L. salivarius ZK-88 can improve oral health, as verified by in vivo and in vitro experiments. This study provides a reference for clinical research and also provides new evidence for the oral health benefits of probiotics.

Summary: Hydrogen is one of the most promising future energy sources due to its highly efficient energy storage and carbon-free features. However, the energy input required for a hydrogen production protocol is an essential factor affecting its widespread adoption. Water electrolysis for hydrogen production currently serves a vital role in the industrial field, but the high overpotential of the oxygen evolution reaction (OER) dramatically impedes its practical application. The formaldehyde oxidation reaction (FOR) has emerged as a more thermodynamically favorable alternative, and the innovation of compatible electrodes may steer the direction of technological evolution. We have designed Au-Vo-NiO/CC as a catalyst that triggers the electrocatalytic oxidation of formaldehyde, efficiently producing H2 at the ultra-low potential of 0.47 V (vs. RHE) and maintaining long-term stability. Integrated with the cathodic hydrogen evolution reaction (HER), this bipolar H2 production protocol achieves a nearly 100% Faraday efficiency (FE).

We define a symmetric universal character by using the affine Yangian of gl(1), which is related with a pair of 2D Young diagram which is considered as 3D Young diagrams with only one layer in z-axis direction. We also define the symmetric universal character hierarchy and prove that all symmetric universal characters are tau functions of this integrable hierarchy. Then, we construct the Boson-Fermion correspondence in the symmetric universal character hierarchy and symmetric universal character can be realized through them. Finally, the (m,n)-soliton solution of the symmetric universal character hierarchy is given by vertex operators.

The aim of our study was to evaluate the relationship between body mass index (BMI) and all-cause mortality among elderly Chinese. The subjects of our study were a cohort of 13 319 elderly Chinese enrolled between 2008 and 2018. Participants were classified in three groups: underweight (

Abstract Background Studies indicate lower, comparable, and higher cardiovascular risks in women vs men in normal glucose regulation (NGR), prediabetes, and diabetes, respectively. However, this sex difference is uncertain and aging might play a part. We aimed to estimate sex differences in arterial stiffness in NGR, prediabetes, or diabetes and the potential modifications by age. Methods We used baseline data of 9618 participants aged ≥40 years in a large community‐based cohort study in Shanghai. Glycemic status was determined by history of diabetes, fasting and 2‐h post‐load glucose levels, and hemoglobin A1c levels. Arterial stiffness was examined by brachial‐ankle pulse wave velocity (ba‐PWV). Multivariable linear regression analysis was conducted to examine the associations between sex and ba‐PWV levels in glycemic and age categories. Results Before adjustment for age, women had lower, comparable, and higher ba‐PWV vs men in the NGR, prediabetes, and diabetes groups, respectively. In participants aged 40–59 years, women were associated with lower ba‐PWV levels in generally all glycemic strata after adjustment for age and other confounders. In participants aged ≥60 years, women were associated with significantly higher ba‐PWV levels (β coefficient = 71.5; 95% confidence interval = 23.4, 119.7) and the sex difference was attenuated in the groups of prediabetes and diabetes with a borderline significant interaction between sex and glycemic status (p for interaction = .068). Conclusions The sex difference in cardiovascular risks in adults with NGR, prediabetes, or diabetes was dependent on age. Our findings provide new evidence for prioritizing preventive treatment against atherosclerosis in men vs women with different glycemic status.

CRISPR-mediated epigenome editing enables gene expression regulation without changing the underlying DNA sequence, and thus has vast potential for basic research and gene therapy. Effective selection of a single guide RNA (sgRNA) with high on-target efficiency and specificity would facilitate the application of epigenome editing tools. Here we performed an extensive analysis of CRISPR-mediated epigenome editing tools on thousands of experimentally examined on-target sites and established EpiCas-DL, a deep learning framework to optimize sgRNA design for gene silencing or activation. EpiCas-DL achieves high accuracy in sgRNA activity prediction for targeted gene silencing or activation and outperforms other available in silico methods. In addition, EpiCas-DL also identifies both epigenetic and sequence features that affect sgRNA efficacy in gene silencing and activation, facilitating the application of epigenome editing for research and therapy. EpiCas-DL is available at http://www.sunlab.fun:3838/EpiCas-DL.

The experiment was conducted for developing a sugar-free, low-fat and low-calorie angel cake with no-eggy flavour and palatability, so as to broaden the application of mulberry and satisfy the public's demand for novel baked foods. Using single-factors and response surface experiments for optimization of the processing technology, the experiment focused on the effect of main ingredients on the sensory quality and hardness of angel cake, i.e. the amount of mulberry juice, xylitol, egg white and low gluten flour, as well as the baking temperature and time. The texture properties of mulberry sugar-free angel cake and ordinary angel cake were measured under the same storage conditions. The results showed that the optimal processing parameters were: Low gluten flour 45 g, mulberry juice 33 g, xylitol 45 g, egg white 135 g, cream of tarta 3 g, lemon juice 2 g and milk 30 g. The best baking conditions was that the surface fire was 165 ℃ and the primer fire was 150 ℃ with 35 min. Under these conditions, the sensory score of mulberry angel cake was 91.8. The experiments of the texture characteristics of the cakes showed that the addition of mulberry juice could reduce the hardness and chewiness of the cakes with better water holding capacity and softer texture, simultaneously, it could improve the cohesiveness and adhesion, so that the cakes had concentrated stoma and compact structure.

The power industry involves people’s livelihood. While ensuring the normal operation of the power system, power companies also need to consider strengthening line loss management to promote grid efficiency. Through grounded theory, 19 factors affecting regional distribution network line loss are extracted from the management and technical perspectives. The Apriori algorithm is used to analyze the data of Chuzhou distribution network line loss rate, and the connection between different influencing factors and line loss rate is analyzed. Association rules are used to filter out the key influencing factors. Finally, the calculation results of the Apriori algorithm and the interpretation structure model (ISM) are combined to make up for the shortcomings of ISM’s strong subjectivity and draw the influence path diagram of the regional distribution network line loss rate based on Apriori-ISM. The results show that measurement errors, the number of strain-resistant sections, the total number of special transformers, the cross-sectional area of conductors, the number of aging transformers, load switching, power theft, and operating time are the direct factors affecting the line loss rate of the regional distribution network, whose confidence levels are 100%, 81.06%, 91.76%, 89.36%, 89.38%, 83.23%, 84.39% and 90.53% respectively, and the correlation is strong.

This study takes the central, provincial and municipal Environmental Protection Interviews (multi-hierarchical EPI) as a mechanism innovation for precise environmental regulation in Yangtze River Delta (YRD) and constructs a multi-cutoff regression discontinuity design to accurately measure the policy effects of multi-hierarchical EPI on air quality improvement in YRD. Overall, the findings demonstrate that EPI at all levels is correlated with improving the air quality for interviewed cities in the short term, which tends to weaken after 3–6 months and may even rebound to the initial level after a longer period. The improvement effect of EPI on air quality is primarily reflected in controlling the pollutant concentration of PM2.5, PM10 and NO2. Moreover, there were great differences in the policy effects of the multi-hierarchical EPI for air quality improvement amongst cities in YRD. The higher administrative level of EPI, the stronger policy effects of the corresponding EPI were. The provincial and municipal EPI are likely to involve initiative regulation and exert greater effects. Some cities under the provincial and municipal EPI could break through the hierarchical constraints and shift to stronger air quality improvement effects, providing new reference for China's precise environmental governance mechanism.

Ramshorn snails from the family Planorbidae are important freshwater snails due to their low trophic level, and some of them act as intermediate hosts for zoonotic trematodes. There are about 250 species from 40 genera of Planorbidae, but only 14 species from 5 genera (Anisus, Biomphalaria, Bulinus, Gyraulus, and Planorbella) have sequenced complete mitochondrial genomes (mitogenomes). In this study, we sequenced and assembled a high-quality mitogenome of a ramshorn snail, Polypylis sp. TS-2018, which represented the first mitogenome of the genus. The mitogenome of Polypylis sp. TS-2018 is 13,749 bp in length, which is shorter than that of most gastropods. It contains 13 protein-coding genes (PCGs), 22 transfer RNA (tRNA) genes, and 2 ribosomal RNA (rRNA). We compared mitogenome characteristics, selection pressure, and gene rearrangement among all of the available mitogenomes of ramshorn snails. We found that the nonsynonymous and synonymous substitution rates (Ka/Ks) of most PCGs indicated purifying and negative selection, except for atp8 of Anisus, Biomphalaria, and Gyraulus, which indicated positive selection. We observed that transpositions and reverse transpositions occurred on 10 tRNAs and rrnS, which resulted in six gene arrangement types. We reconstructed the phylogenetic trees using the sequences of PCGs and rRNAs and strongly supported the monophyly of each genus, as well as three tribes in Planorbidae. Both the gene rearrangement and phylogenetic results suggested that Polypylis had a close relationship with Anisus and Gyraulus, while Bulinus was the sister group to all of the other genera. Our results provide useful data for further investigation of species identification, population genetics, and phylogenetics among ramshorn snails.

With the increasing popularization and application of the smart grid, the harm of the data silo issue in the smart grid is more and more prominent. Therefore, it is especially critical to promote data interoperability and sharing in the smart grid. Existing data-sharing schemes generally lack effective incentive mechanisms, and data holders are reluctant to share data due to privacy and security issues. Because of the above issues, a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory is proposed. Firstly, several basic assumptions about the evolutionary game model are given, and the evolutionary game payoff matrix is established. Then, we analyze the stabilization strategy of the evolutionary game based on the payoff matrix, and propose a dynamic incentive mechanism for smart grid data sharing based on evolutionary game theory according to the analysis results, aiming to encourage user participation in data sharing. We further write the above evolutionary game model into a smart contract that can be invoked by the two parties involved in data sharing. Finally, several factors affecting the sharing of data between two users are simulated, and the impact of different factors on the evolutionary stabilization strategy is discussed. The simulation results verify the positive or negative incentives of these parameters in the data-sharing game process, and several factors influencing the users’ data sharing are specifically analyzed. This dynamic incentive mechanism scheme for smart grid data sharing based on evolutionary game theory provides new insights into effective incentives for current smart grid data sharing.

Imaging genomics based on artificial intelligence technology has shown great potential in personalized treatment and precision medicine, providing important theoretical basis and technical support for the intelligent diagnosis and prognosis prediction of lung cancer. In order to facilitate the more effective exploration and wider application of diagnosis and prognosis of lung cancer, the relevant research progress is described in detail, expecting that such review will be beneficial to the researchers in this field. First, it introduces the role and application of artificial intelligence in image genomics, and the main research direction of artificial intelligence technology in lung cancer imaging genomics, which are reviewed from lung cancer gene phenotype identification, image gene bidirectional correlation analysis, and prognosis prediction. In addition, the advantages and disadvantages of radiomics and deep learning algorithm are evaluated in each direction, as well as the facing problems and challenges. Second, it summarizes the main challenges facing intelligent imaging genomics at this stage and presents future research directions. Although imaging genomics based on artificial intelligence has made some achievements in lung cancer diagnosis, survival recurrence prediction, efficacy response evaluation, and understanding tumor biological mechanism, it also needs to combining the actual clinical needs, establish a unified, standard, perfect computer-assisted clinical program further explore interpretability, reproducibility, and universal verification for the application of artificial intelligence in lung cancer imaging genomics, so as to provide guarantee for the intelligent diagnosis treatment and evaluation of lung cancer.

Enhancer of zeste homolog 2 (EZH2) is the lysine methyltransferase of polycomb repressive complex 2 (PRC2) that catalyzes H3K27 tri-methylation. Aberrant expression and loss-of-function mutations of EZH2 have been demonstrated to be tightly associated with the pathogenesis of various myeloid malignancies characterized by ineffective erythropoiesis, such as myelodysplastic syndrome (MDS). However, the function and mechanism of EZH2 in human erythropoiesis still remains largely unknown. Here, we demonstrated that EZH2 regulates human erythropoiesis in a stage-specific, dual-function manner by catalyzing histone and non-histone methylation. During the early erythropoiesis, EZH2 deficiency caused cell cycle arrest in the G1 phase, which impaired cell growth and differentiation. Chromatin immunoprecipitation sequencing and RNA sequencing discovered that EZH2 knockdown caused a reduction of H3K27me3 and upregulation of cell cycle proteindependent kinase inhibitors. In contrast, EZH2 deficiency led to the generation of abnormal nuclear cells and impaired enucleation during the terminal erythropoiesis. Interestingly, EZH2 deficiency downregulated the methylation of HSP70 by directly interacting with HSP70. RNA-sequencing analysis revealed that the expression of AURKB was significantly downregulated in response to EZH2 deficiency. Furthermore, treatment with an AURKB inhibitor and small hairpin RNAmediated AURKB knockdown also led to nuclear malformation and decreased enucleation efficiency. These findings strongly suggest that EZH2 regulates terminal erythropoiesis through a HSP70 methylation-AURKB axis. Our findings have implications for improved understanding of ineffective erythropoiesis with EZH2 dysfunction.

Objective: CKAP4 (Cytoskeleton Associated Protein 4) has been reported as an important regulator of carcinogenesis. A great deal of uncertainty still surrounds the possible molecular mechanism of CKAP4 involvement in GBM. We aimed to specifically elucidate the putative role of CKAP4 in the development of GBM. Methods: We identified divergent proteomics landscapes of GBM and adjacent normal tissues using mass spectrometry-based label-free quantification. Bioinformatics analysis of differentially expressed proteins (DEPs) led to the identification of CKAP4 as a hub gene. Based on the Chinese Glioma Genome Atlas data, we characterized the elevated expression of CKAP4 in GBM and developed a prognostic model. The influence of CKAP4 on malignant behavior of GBM was detected in vitro and vivo, as well as its downstream target and signaling pathways. Results: The prognosis model displayed accuracy and reliability for the probability of survival of patients with gliomas. CKAP4 knockdown remarkably reduced the malignant potential of GBM cells, whereas its overexpression reversed these effects in GBM cells and xenograft mice. Moreover, we demonstrated that overexpression of CKAP4 leads to increased FOXM1 (Forkhead Box M1) expression in conjunction with an increased level of AKT and ERK phosphorylation. Inhibition of both pathways had synergistic effects, resulting in greater effectiveness of inhibition. CKAP4 could reverse the deregulation of FOXM1 triggered by inhibition of AKT and ERK signaling. Conclusions: This is the first study to reveal a CKAP4-FOXM1 signaling cascade that contributes to the malignant phenotype of GBMs. The CKAP4-based prognostic model would facilitate individualized treatment decisions for glioma patients.

Objective: China has undergone tremendous social changes in the last few decades. This study aimed to research the trends of the suicide rates from 2002 to 2019, and to differentiate effects attributable to age, period, and cohort by gender and residence in China. Methods: Suicide mortality data were obtained from China's Ministry of Health Vital Registration System. Joinpoint regression model was used to estimate the average annual percentage change (AAPC) of the suicide rates and ratios by gender and residence. The age-period-cohort framework was performed to analyze the underlying mechanisms for suicide mortality trends. Results: Over the observation period, the significant decrease in suicide mortality rates in China for the economic development and urbanization was observed but to different degrees across gender and regional subgroups. The male-to-female ratio of suicide rates increased year by year (AAPC: 1.9%, 95% CI: 0.2% to 3.7%) while the urban-rural ratio changed little (AAPC: 0.9%, 95% CI: −1.8% to 3.7%). The age-period-cohort analysis revealed a marked increased effect of age and overall decreased effect of both period and cohort on suicide mortality rates. However, the recent cohort has presented an inversely increasing effect. Conclusion: The suicide rate has fallen sharply in China which has undergone tremendous socioeconomic changes. The varied changes in the suicide rate of different residence-, gender-, and age-groups as well as the age, period, and cohort effect on suicide risk further indicate the relationship of development and the suicide rates may be neither static nor identical on different subgroups in a rapidly changing society.

Objective: Investigation of the regulatory mechanisms of cell stemness in cholangiocarcinoma (CCA) is essential for developing effective therapies to improve patient outcomes. The purpose of this study was to investigate the function and regulatory mechanism of m6A modifications in CCA cell stemness. Methods: Interleukin 6 (IL-6) treatment was used to induce an inflammatory response, and loss-of-function studies were conducted using mammosphere culture assays. Chromatin immunoprecipitation, polysome profiling, and methylated RNA immunoprecipitation analyses were used to identify signaling pathways. The in vitro findings were verified in a mice model. Results: We first identified that m6A writers were highly expressed in CCAs and further showed that STAT3 directly bound to the gene loci of m6A writers, showing that IL-6/STAT3 signaling regulated expressions of m6A writers. Downregulating m6A writers prevented cell proliferation and migration in vitro and suppressed CCA tumorigenesis in vivo. Notably, the knockdown of m6A writers inhibited CCA cell stemness that was triggered by IL-6 treatment. Mechanistically, IGF2BP2 was bound to CTNNB1 transcripts, significantly enhancing their stability and translation, and conferring stem-like properties. Finally, we confirmed that the combination of m6A writers, IGF2BP2, and CTNNB1 distinguished CCA tissues from normal tissues. Conclusions: Overall, this study showed that the IL-6-triggered inflammatory response facilitated the expressions of m6A writers and cell stemness in an m6A-IGF2BP2-dependent manner. Furthermore, the study showed that m6A modification was a targetable mediator of the response to inflammation factor exposure, was a potential diagnostic biomarker for CCA, and was critical to the progression of CCA.

Abstract Background The RNA profiles of tumor-educated platelets (TEPs) possess pathological features that could be used for early cancer detection. However, the utility of TEP RNA profiling in detecting early colorectal cancer (CRC) versus noncancerous colorectal diseases has not yet been investigated. This study assesses the diagnostic capacity of TEP RNA profiles in a cohort of patients with CRC and noncancerous diseases. Methods Transcriptome sequencing for platelets isolated from 132 patients with CRC at early and late stages and 190 controls consisting of healthy donors and patients with ulcerative disease, Crohn’s disease, polyps, and adenomas was performed and analyzed using binary particle swarm optimization coupled with support vector machine to identify genes that contributed to the classification of CRC patients versus controls. The area under the receiver operating curves (AUROCs) and the accuracy of TEP RNA profiles in CRC diagnosis were assessed. Results TEP RNA profiling achieved high performance in distinguishing and staging CRC patients from the controls. Using the swarm intelligence algorithm, the 921 most contributive genes that classified CRC patients from the controls were identified. AUROCs of 0.928 for the training set via leave-one-out cross-validation and 0.92 for the validation set were achieved, both of which were significantly higher than the clinically utilized serum biomarkers: carcinoembryonic antigen and cancer antigen 19-9. Notably, an AUROC of 0.915 in an external validation set was achieved. For predicting different CRC stages, an AUROC of 0.984 was achieved in the training set and 1.000 in the internal validation set. Conclusions RNA profiles of TEPs are of potential diagnostic value for identifying early CRC from noncancerous diseases. Prospective studies are needed to validate its clinical relevance.

Defects are an inevitable occurrence during the manufacturing and use of ferromagnetic materials, making it crucial to study the microscopic mechanism of magnetostrictive properties of ferromagnetic materials with defects. This paper conducts molecular dynamics simulations on low-dimensional iron thin films containing hole or crack defects, analyzes and compares the impact of defect size on magnetostrictive properties, and investigates the microscopic mechanism of their effects. The results indicate that the saturation magnetostrictive strains of the defect models do not increase monotonically as the defect size increases. Additionally, it is discovered that the arrangement of atomic magnetic moments in the initial magnetic moment configuration also affects the magnetostrictive properties. When controlling the size of the hole or crack within a certain defect area, it is found that the hole size has less influence on the initial magnetic moment configuration, resulting in a smaller corresponding change in the saturation strain and thus having a lesser impact on the magnetostrictive properties. Conversely, when the crack size changes, the arrangement of the atomic magnetic moments in the initial magnetic moment configuration changes more significantly, resulting in a greater corresponding change in saturation strain, and thus having a greater impact on the magnetostriction performance.