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Abstract Advances in cancer diagnosis and treatment have substantially improved patient outcomes and survival in recent years. However, up to 75% of cancer patients and survivors, including those with non-central nervous system (non-CNS) cancers, suffer from “brain fog” or impairments in cognitive functions such as attention, memory, learning, and decision-making. While we recognize the impact of cancer-related cognitive impairment (CRCI), we have not fully investigated and understood the causes, mechanisms and interplays of various involving factors. Consequently, there are unmet needs in clinical oncology in assessing the risk of CRCI and managing patients and survivors with this condition in order to make informed treatment decisions and ensure the quality of life for cancer survivors. The state-of-the-art neuroimaging technologies, particularly clinical imaging modalities like magnetic resonance imaging (MRI) and positron emission tomography (PET), have been widely used to study neuroscience questions, including CRCI. However, in-depth applications of these functional and molecular imaging methods in CRCI and their clinical implementation for CRCI management are largely limited. This scoping review provides the current understanding of contributing neurological factors to CRCI and applications of the state-of-the-art multi-modal neuroimaging methods in investigating the functional and structural alterations related to CRCI. Findings from these studies and potential imaging-biomarkers of CRCI that can be used to improve the assessment and characterization of CRCI as well as to predict the risk of CRCI are also highlighted. Emerging issues and perspectives on future development and applications of neuroimaging tools to better understand CRCI and incorporate neuroimaging-based approaches to treatment decisions and patient management are discussed.

Abstract Treating psoriasis presents a major clinical challenge because of the limitations associated with traditional topical glucocorticoid therapy. This study introduced a drug delivery system utilizing zinc-doped mesoporous silica nanoparticle (Zn-MSN) and microneedle (MN), designed to enhance drug utilization for prolonged anti-inflammatory and anti-itch effects. The MN system facilitated the transdermal delivery of betamethasone dipropionate (BD), allowing its slow release. The BD@Zn-MSN-MN system promoted the polarization of macrophages towards the anti-inflammatory M2 phenotype, achieving superior anti-inflammatory effects compared to the clinically used BD cream. Additionally, this study demonstrated that BD@Zn-MSN-MN could further alleviate itching in psoriasis-afflicted mice by decreasing the excitability of the transient receptor potential vanilloid V1 (TRPV1) ion channel positive neurons and reducing the release of calcitonin gene-related peptide (CGRP) in the dorsal root ganglion (DRG). These findings offer new insights and effective therapeutic options for the future design of transdermal drug delivery for psoriasis.

Abstract Background Sepsis, defined as a dysregulated inflammatory response to infection inducing organ dysfunction, is a common cause of mortality in both humans and animals. Early detection and treatment is essential for survival, but accurate diagnosis is challenging due to the lack of specific biomarkers for sepsis. This study explored the potential of the keratinocyte-derived chemokine (KC)-like protein in dogs as a biomarker of sepsis in dogs with bacterial uterine infection (pyometra). The aim was to compare KC-like concentrations in dogs with pyometra with or without sepsis and to assess associations between KC-like and clinical variables, including days of hospitalization as an outcome. Results A mouse KC ELISA was validated and used to determine the concentrations of KC-like in serum from 34 dogs with pyometra and 18 healthy controls. Dogs with pyometra were classified as having sepsis based on two different criteria for systemic inflammatory response syndrome (SIRS), resulting in 74% and 30% sepsis-positive, respectively. The concentration of KC-like protein was higher in pyometra dogs with sepsis than in pyometra dogs without sepsis (p

Abstract The analysis of genomic variations in offspring after implantation has been infrequently studied. In this study, we aim to investigate the extent of de novo mutations in humans from developing fetus to birth. Using high-depth whole-genome sequencing, 443 parent-offspring trios were studied to compare the results of de novo mutations (DNMs) between different groups. The focus was on fetuses and newborns, with DNA samples obtained from the families’ blood and the aspirated embryonic tissues subjected to deep sequencing. It was observed that the average number of total DNMs in the newborns group was 56.26 (54.17–58.35), which appeared to be lower than that the multifetal reduction group, which was 76.05 (69.70–82.40) (F = 2.42, P = 0.12). However, after adjusting for parental age and maternal pre-pregnancy body mass index (BMI), significant differences were found between the two groups. The analysis was further divided into single nucleotide variants (SNVs) and insertion/deletion of a small number of bases (indels), and it was discovered that the average number of de novo SNVs associated with the multifetal reduction group and the newborn group was 49.89 (45.59–54.20) and 51.09 (49.22–52.96), respectively. No significant differences were noted between the groups (F = 1.01, P = 0.32). However, a significant difference was observed for de novo indels, with a higher average number found in the multifetal reduction group compared to the newborn group (F = 194.17, P

Abstract While many organizations have successfully leveraged big data analytics capabilities to improve their performance, our understanding is limited on whether and how big data analytics capabilities affect social innovation in organizations. Based on the organizational information processing theory and the organizational learning theory, this study aims to investigate how big data analytics capabilities support social innovation, and how knowledge ambidexterity mediates this relationship. A total of 354 high-tech companies in China, this study shows that big data analytics management, big data analytics technology, and big data analytics personnel capabilities all have positive effects on social innovation. In addition, both knowledge exploration and knowledge exploitation play a mediating role in this process. Furthermore, a polynomial regression and response surface analysis shows that social innovation increases when knowledge exploration and knowledge exploitation are highly consistent but declines when knowledge exploration and knowledge exploitation are inconsistent. This study not only provides new perspectives for understanding how big data analytics capabilities contribute to social innovation, complementing the existing literature on big data analytics capabilities and social innovation, but also provides important practical guidance on how organizations can develop big data analytics capabilities to improve social innovation and solve social problems in the digital age.

Abstract Background Synonymous variants are non‐pathogenic due to non‐substitution of amino acids. However, synonymous exonic terminal nucleotide substitutions may affect splicing. Splicing variants are easily analyzed at RNA level for genes expressed in blood cells. Minigene analysis provides another method for splicing variant analysis of genes that are poorly or not expressed in peripheral blood. Methods Whole exome sequencing was performed to screen for potential pathogenic mutations in the proband, which were validated within the family by Sanger sequencing. The pathogenicity of the synonymous mutation was analyzed using the minigene technology. Results The proband harbored the compound heterogeneous variants c. [291G >A; 572‐50C >T] and c.681 + 1G >T in F7, of which the synonymous variant c.291G >A was located at the terminal position of exon 3. Minigene analysis revealed exon3 skipping due to this mutation, which may have subsequently affected protein sequence, structure, and function. Conclusion Our finding confirmed the pathogenicity of c.291G >A, thus extending the pathogenic mutation spectrum of F7, and providing insights for effective reproductive counseling.

Summary: Liver sinusoidal endothelial cells (LSECs) have a unique morphological structure known as “fenestra” that plays a crucial role in liver substance exchange and homeostasis maintenance. In this study, we demonstrate that ADAMTS18 protease is primarily secreted by fetal liver endothelial cells. ADAMTS18 deficiency leads to enlarged fenestrae and increased porosity of LSECs, microthrombus formation in liver vessels, and an imbalance of liver oxidative stress. These defects worsen carbon tetrachloride (CCl4)-induced liver fibrosis and diethylnitrosamine (DEN)/high-fat-induced hepatocellular carcinoma (HCC) in adult Adamts18-deficient mice. Mechanically, ADAMTS18 functions as a modifier of fibronectin (FN) to regulate the morphological acquisition of LSECs via the vascular endothelial growth factor A (VEGFA) signaling pathways. Collectively, a mechanism is proposed for LSEC morphogenesis and liver homeostasis maintenance via ADAMTS18-FN-VEGFA niches.

Emotion-Cause Pair Extraction (ECPE) aims to jointly extract emotion clauses and the corresponding cause clauses from a document, which is important for user evaluation or public opinion analysis. Existing research addresses the ECPE task through a two-step or an end-to-end approach. Although previous work shows promising performances, they suffer from two limitations: 1) they fail to take full advantage of emotion type information, which has advantages for modelling the dependencies between emotion and cause clauses from a semantic perspective; 2) they ignored the interaction between local and global information, which is important for ECPE. To address these issues, we propose an ECPE Pair Generator (ECPE-PG), with a Clause-Encoder layer, a Pre-Output layer and an Information Interaction-based Pair Generation (IIPG) Module embedded. This model first encodes clauses into vector representations through the Clause-Encoder layer and then preforms emotion clause extraction (EE), cause clause extraction (CE) and emotion type extraction (ETE), respectively, through the Pre-Output layer, on the basis of which the IIPG module analyzes the complex emotional logic of relationships between clauses and estimates the candidate pairs based on the interaction of global and local information. It should be noted that emotion type information is regarded as a crucial indication in the IIPG module to assist the identification of emotion-cause pairs. Experimental results show that our method outperforms the state-of-the-art methods on benchmark datasets.

This study focuses on the factors influencing consumers’ continuous participation willingness in online pre-sale activities. Based on perceived value and social exchange theory, it analyzes how perceived benefits (including practical benefits, hedonic benefits, and social benefits) and perceived costs (including search costs, waiting costs, and adjustment costs) affect consumer satisfaction and their continuous participation intention in online pre-sales. A total of 527 valid questionnaires were collected, and structural equation modeling was used for data analysis. The results indicate that perceived benefits are significantly positively correlated with consumer satisfaction and their willingness to continue participating, while perceived costs are significantly negatively correlated with both aspects. Additionally, the study found that product type moderates the effect of perceived benefits and perceived costs on consumer satisfaction. This research helps retailers understand consumers’ willingness to continuously participate in online pre-sales and provides valuable insights for developing pre-sale strategies.

Microbially induced nitrogen (N2) gas bubbles can desaturate subsurface areas and thus have been considered as an alternative ground improvement technique for mitigating soil liquefaction potential caused by earthquakes. However, the detailed mechanisms of subsurface N2 bubbles are not well understood and remain a subject of ongoing research. In this study, a transparent microfluidic device was utilized to mimic biological N2 gas bubble formation by nitrate-reducing bacteria and to visually characterize the entire process. During N2 gas formation, a limited number of bubble nucleation sites were identified, which gradually expanded upward through the preferential pore channels. N2 gas bubbles tended to create interconnected gas pockets rather than existing as evenly distributed small gas cavities. The degree of water saturation gradually reduced over a week as the bubbles were produced. The gas ganglia repeatedly grew until they reached the top boundary, which triggered a drastic expulsion of bubbles by ebullition. Despite fluctuations in saturation level, the residual saturation was maintained at around 73 %. Comparative experimental case studies of CO2 gas bubble formation were conducted to identify contrasting gas formation mechanisms. CO2 gas bubbles were generated via the abiotic decompression of a supersaturated CO2 solution under two distinct rates of pressure reduction. Rapid CO2 bubble formation led to uniform nucleation and 41 % residual saturation, while slower formation yielded 35 % due to stable liquid displacement by the gas front. This study highlights the potential of the microfluidic device as an experimental tool for visualizing subsurface gas formation mechanisms. The insights gained could further enhance and optimize geotechnical applications involving gas formation in highly saturated soils.

Background: Cervical intraepithelial neoplasia (CIN) encompasses a range of cervical lesions that are closely linked to cervical invasive carcinoma. Early detection and timely treatment of CIN are crucial for preventing the progression of the disease. However, no bibliometric analysis has been conducted in this area. This research aimed to employ bibliometric analysis to summarize the current research hotspots and estimate future research trends in the CIN field. Methods: Publications related to CIN (2013–2023) were retrieved from the Science-Citation-Index-Expanded-of-Web-of-Science-Core-Collection. CiteSpace, VOSviewer, and the bibliometric-Online-Analysis-Platform-of-Literature-Metrology were employed to analyze the yearly research output, collaborating institutions or countries, leading researchers, principal journals, co-referenced sources, and emerging keywords. Results: In total, 4677 articles on CIN that were published from 2013 to 2023 and met our criteria were extracted. Major publishing platforms were predominantly USA until 2017 when China emerged as the leading source of publications about CIN. The USA was the leading nation in international collaborations. The National-Cancer-Institute (NCI) was the institution with the most publications. Schiffman Mark produced the highest number of articles, with a total of 92. Ten major clusters were identified through co-cited keyword clustering, including prevalence, human papillomavirus, DNA methylation, p16, methylation, conization, HPV genotyping tests (VALGENT), deep learning, vaginal microbiome, and immunohistochemistry. Keyword burst analysis showed that photodynamic therapy and deep learning emerged as prominent research focal points with significant impact in resent three years. Conclusion: Global publications on CIN research showed a relatively stable trend over the past eleven years. Current research hotspots are deep learning and photodynamic therapy. This research offered organized data and insightful guidance for future studies, which may help better prevent, screen, and treat CIN.

Adult survivors of childhood brain tumors often present with cognitive deficits that affect their quality of life. Studying brain structure and function in brain tumor survivors can help understand the underlying mechanisms of their cognitive deficits to improve long-term prognosis of these patients. This study analyzed voxel-based morphometry (VBM) derived from T1-weighted MRI and the amplitude of low-frequency fluctuation (ALFF) from resting-state functional magnetic resonance imaging (rs-fMRI) to examine the structural and functional alterations in 35 brain tumor survivors using 35 matching healthy individuals as controls. Compared with healthy controls, brain tumor survivors had decreased gray matter volumes (GMV) in the thalamus and increased GMV in the superior frontal gyrus. Functionally, brain tumor survivors had lower ALFF values in the inferior temporal gyrus and medial prefrontal area and higher ALFF values in the thalamus. Importantly, we found concurrent but negatively correlated structural and functional alterations in the thalamus based on observed significant differences in GMV and ALFF values. These findings on concurrent brain structural and functional alterations provide new insights towards a better understanding of the cognitive deficits in brain tumor survivors.

Graphene nanoplatelets (GNPs) have significant potential as reinforcement agents in metallic materials. Understanding the interfacial structure between GNPs and an aluminum matrix (AlM) is crucial for this composite (GNPs/AlMC) design and application. Studies on the effects of heat treatment on the interfacial character and its influence on mechanical properties at the atomic scale are, therefore, of high importance. This study presents molecular dynamics simulations investigating the effects of heat treatment on the interfacial structural evolution between GNPs and AlM and how such behavior tunes mechanical performance. The results show that the number of interfacial bonds increases significantly when the heat treatment temperature reaches 900 K, below which it stays at low level. Compared with the heat treatment temperature, the pressure has less effect on the interfacial bonds of GNPs/AlMCs. Moreover, defective GNPs with pores at certain heat treatment temperatures promote the formation of Al-C bonds at the interface and improve the mechanical properties of GNPs/AlMCs. These findings underscore the potential for utilizing heat treatment and defect engineering to improve interfacial bonding and achieve improved mechanical performance in GNPs/AlMCs.

Abstract Aims and Objectives The aim of this study was to assess the sleep quality in dialysis patients during the COVID‐19 epidemic and explore the association between negative psychology (including depression, anxiety, and stress) and sleep quality in this population. Design A cross‐sectional study including three centres. Methods (Patients or Public Contribution) This cross‐sectional study included 378 dialysis patients from April to May 2022 in three dialysis centres in Shanghai. Methods. Depression, anxiety, stress, and sleep quality were measured by the Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale‐14 (PSS‐14), and Pittsburgh sleep quality index (PSQI), respectively. With a threshold of 5 to classify participants into good and poor sleep quality, with HADS/PSS‐14 scores as independent variables (per standard deviation (SD) increment), respectively and binary Logistic regression model was constructed to explore the association between the three negative psychological aspects of depression, anxiety, and stress and sleep quality. Results The median PSQI score was 11.0 (mean ± SD: 11.8 ± 4.8). Among them, poor sleep quality (i.e., PSQI >5) was reported by 90.2% of participants. After adjusting for sociodemographic and disease‐related information, HADS‐depression was associated with a significant 49% (odds ratio (OR): 1.49; 95% CI 1.02–2.18) increase in the risk of poor sleep quality for each additional SD (2.4). Correspondingly, for each SD (7.1) increase in PSS‐14, the risk of poor sleep quality was significantly increased by 95% (OR: 1.95; 95% CI 1.35–2.82). Conclusion During the COVID‐19 pandemic, there was a significant negative association between negative psychology, such as depression and stress, and sleep quality in dialysis patients, and this relationship was independent of the dialysis modality. Relevance to Clinical Practice In the context of the rampant COVID‐19, the vast majority of dialysis‐dependent chronic kidney disease presents with severe sleep quality problems, and negative psychology is a potential influencing factor.

CyVerse, the largest publicly-funded open-source research cyberinfrastructure for life sciences, has played a crucial role in advancing data-driven research since the 2010s. As the technology landscape evolved with the emergence of cloud computing platforms, machine learning and artificial intelligence (AI) applications, CyVerse has enabled access by providing interfaces, Software as a Service (SaaS), and cloud-native Infrastructure as Code (IaC) to leverage new technologies. CyVerse services enable researchers to integrate institutional and private computational resources, custom software, perform analyses, and publish data in accordance with open science principles. Over the past 13 years, CyVerse has registered more than 124,000 verified accounts from 160 countries and was used for over 1,600 peer-reviewed publications. Since 2011, 45,000 students and researchers have been trained to use CyVerse. The platform has been replicated and deployed in three countries outside the US, with additional private deployments on commercial clouds for US government agencies and multinational corporations. In this manuscript, we present a strategic blueprint for creating and managing SaaS cyberinfrastructure and IaC as free and open-source software.

IntroductionThe programmed death-1 (PD-1) immune checkpoint inhibitor pembrolizumab is currently approved in the US for the first-line (1L) treatment of recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC), either alone or in combination with platinum and 5-fluorouracil (5-FU). However, the toxicity of 5-FU has motivated the study of alternate combinations that replace 5-FU with a taxane. The objective of the current study was to describe the baseline characteristics, treatment patterns and sequences, and real-world outcomes of individuals receiving pembrolizumab + platinum + taxane as 1L treatment for R/M HNSCC in the US.MethodsThis was a retrospective study of US adults ≥18 years of age receiving pembrolizumab + platinum + taxane as 1L treatment for R/M HNSCC, using electronic health record data from a nationwide de-identified database. Real-world overall survival (rwOS), time on treatment (rwToT), and time to next treatment (rwTTNT) outcomes were assessed using Kaplan–Meier analysis.ResultsThe study population comprised 83 individuals (80.7% male) with a median age of 64 years. The most common tumor site was the oropharynx (48.2%); 70.0% of these tumors were HPV-positive. A total of 71.1% of the study population had an Eastern Cooperative Oncology Group performance status of 0–1 at index date, 71.8% had a combined positive score for programmed death ligand-1 (PD-L1) expression of ≥1, and 30.8% had a score of ≥20. The median (95% CI) rwOS was 14.9 (8.8–23.3) months, rwToT was 5.3 (4.0–8.2) months, and rwTTNT was 8.7 (6.8–12.3) months. Among the 24 individuals who received a subsequent therapy, the most common second-line therapies were cetuximab-based (n = 9) or pembrolizumab-containing (n = 8) regimens.ConclusionsThe rwOS and other real-world outcomes observed for this study population further support pembrolizumab + platinum + taxane combination therapy as a potential 1L treatment option for R/M HNSCC.

[This corrects the article DOI: 10.1371/journal.pone.0224086.].

Abstract Background Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. Methods Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. Results Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F1 score by 33.7%. Conclusions Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.

Credit risk assessment is an important process in bank financial risk management. Traditional machine-learning methods cannot solve the problem of data islands and the high error rate of two-way decisions, which is not conducive to banks’ accurate credit risk assessment of users. To this end, this paper establishes a federated three-way decision incremental naive Bayes bank user credit risk assessment model (FTwNB) that supports asymmetric encryption, uses federated learning to break down data barriers between banks, and uses asymmetric encryption to protect data security for federated processes. At the same time, the model combines the three-way decision methods to realize the three-way classification of user credit (good, bad and delayed judgment), so as to avoid the loss of bank interests caused by the forced division of uncertain users. In addition, the model also incorporates incremental learning steps to eliminate training samples with poor data quality to further improve the model performance. This paper takes German Credit data and Default of Credit Card Clients data as examples to conduct simulation experiments. The result shows that the performance of the FTwNB model has been greatly improved, which verifies that it has good credit risk assessment capabilities.

Orbitally shaken bioreactors are extensively applied to the mass production of mammalian cells in suspension culture. Commonly used vessels are cylindrical tanks and their main disadvantage is the accumulation of cells at the bottom center. To eliminate this accumulation issue, an annular cylindrical vessel is proposed for orbitally shaken bioreactors using numerical simulation. This approach eliminates the center of the vessel physically and thus removes the possibility of such cell accumulation. The absence of a central section in the vessel significantly improves the dissolved oxygen concentration and cell suspension ratio in the culture medium without increasing the shear stress. It also efficiently removes the possibility of cell aggregation at the bottom. An advanced machine learning technology - Bayesian optimization algorithm is also employed in the hollow orbitally shaken bioreactor to optimize the three main control parameters of the reactor, namely, diameter ratio (di/do), shaking velocity (ω), and shaking radius (R). The obtained data related to mass transfer ability, suspension ratio, and shear stress are analyzed. The numerical simulation results show that the “optimal” bioreactor is preferable for cell cultivation compared with the “initial state”, owing to its high capability for suspending cells. It can be concluded that the methodology described in this work is a feasible and reliable tool for performance prediction and process optimization in biotechnology.

BackgroundMachine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. ObjectiveThis study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. MethodsThe data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. ResultsA total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. ConclusionsThis study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Allium L. is one of the largest genera of angiosperms, including garlic, onion, green Chinese onion, Chinese chive and other important vegetable crops with unique spicy flavor. S-alk(en)ylcysteine sulfoxides, the unique secondary metabolites of Allium, which giving Allium spicy flavor and medicinal value, are the precursors of various volatile sulfur compounds. Therefore, it is of great significance to study the metabolic pathway of S-alk(en)ylcysteine sulfoxides in Allium. Seven S-alk(en)ylcysteine sulfoxides have been found in Allium plants. These S-alk(en)ylcysteine sulfoxides are mainly synthesized in leaves through glutathione pathway, and then transported to the cytoplasm of storage organs such as bulbs for accumulation. At present, there are many studies on the catabolism of S-alk(en)ylcysteine sulfoxides in Allium, while few studies on the biosynthesis of S-alk(en)ylcysteine sulfoxides. Only two biosynthetic enzymes, γ-glutamyl transpeptidase (GGT) and flavin-containing monooxygenase (FMO), have been confirmed at the molecular level. In addition, S-alk(en)ylcysteine sulfoxides are the downstream products of plant sulfur metabolism. The upstream of S-alk(en)ylcysteine sulfoxide metabolism involves the absorption and transport of sulfur-containing compounds, the metabolism of cysteine and glutathione, and the changes of these metabolic processes may also affect the biosynthesis of S-alk(en)ylcysteine sulfoxides. With the rapid development of omics technology, genomics, transcriptomics and metabolomics have been applied in the study of Allium plants, especially the completion of garlic genome sequence assembly, which provides great convenience for the study of S-alk(en)ylcysteine sulfoxides metabolism pathway in Allium plants. Two aspects of research should be strengthened in the future: one is to continue to clone and identify the key enzyme genes in the biosynthesis pathway of S-alk(en)ylcysteine sulfoxides and study their functions; another is to strengthen the study of sulfur metabolism in Allium plants, so as to lay a foundation for the study of the regulation of S-alk(en)ylcysteine sulfoxides biosynthesis. These studies will provide a reference for further analyzing the metabolic pathway of S-alk(en)ylcysteine sulfoxides in Allium and regulating the flavor of Allium by molecular breeding technology.

There are many problems in Social Internet of Things(IoTs), such as complex topology information, different degree of association between nodes and overlapping communities. The idea of set pair information grain computing and clustering is introduced to solve the above problems so as to accurately describe the similarity between nodes and fully explore the multi-community structure. A Set Pair Three-Way Overlapping Community Discovery Algorithm for Weighted Social Internet of Things (WSIoT-SPTOCD) is proposed. In the local network structure, which fully considers the topological information between nodes, the set pair connection degree is used to analyze the identity, difference and reverse of neighbor nodes. The similarity degree of different neighbor nodes is defined from network edge weight and node degree, and the similarity measurement method of set pair between nodes based on the local information structure is proposed. According to the number of nodes' neighbors and the connection degree of adjacent edges, the clustering intensity of nodes is defined, and an improved algorithm for initial value selection of k-means is proposed. The nodes are allocated according to the set pair similarity between nodes and different communities. Three-way community structures composed of a positive domain, boundary domain and negative domain are generated iteratively. Next, the overlapping node set is generated according to the calculation results of community node membership. Finally, experiments are carried out on artificial networks and real networks. The results show that WSIoT-SPTOCD performs well in terms of standardized mutual information, overlapping community modularity and F1.

In this paper, the scattering characteristics of polarized light are studied based on the microsurface element theory, and the Polarization Bi-directional Reflection Distribution Function (pBRDF) model applicable to rough surfaces is established according to the three-component model, and the expression of polarization of reflected light from the target surface is derived based on the model, and the accuracy of the model is verified by comparing the model with the conventional model. The modeling is also carried out from the scattering perspective, and the relationship between different artificial target particle diameters and polarization characteristics is analyzed. The results show that the Degree Of Polarization (DOP) is strongly related to the detection zenith angle. The DOP of the target is largest in the direction of the mirror reflection of the source. The polarization characteristics of different materials are different, and the relative error of the established model is significantly smaller than that of the conventional model. And the scattering model can make preliminary classification of different man-made targets according to different particle diameters, which can provide theoretical support for the analysis of the polarization characteristics of the target surface.

IntroductionDogs play an important role in society, which increased during the covid epidemics. This has led to a much higher workload for the veterinarians. Therefore, there is a need for efficient diagnostic tools to identify risk of malignant diseases. Here the development of a new test that can solve some of these problems is presented. It is based on serum Thymidine Kinase 1 (TK1), which is a biomarker for cell proliferation and cell lysis.MethodsAnti-TK1 monoclonal antibodies were produced against two different epitopes, the active site of the TK1 protein and the C-terminal region of canine TK1. The antibodies were developed with hybridoma technology and validated using dot blot, Quartz Crystal Microbalance (QCM) technology, western blots, immunoprecipitation (IP), and enzyme-linked immunosorbent assay (ELISA). Clinical evaluation of Canine TK1 ELISA was done by using sera from 131 healthy dogs and 93 dogs with lymphoma. The two selected Anti-TK1 monoclonal antibodies have Kd values in the range of 10−9 M and further analysis with dot and western blots confirmed the high affinity binding of these antibodies. A sandwich Canine TK1 ELISA was developed using the anti-TK1 antibodies, and TK1 concentrations in serum samples were determined using dog recombinant TK1 as a standard.ResultsSerum TK1 protein levels were significantly higher in dogs with lymphoma compared to those in healthy dogs (p

BackgroundPatients with colorectal cancer (CRC) often present with multiple comorbidities, and many of these can affect treatment and survival. However, previous comorbidity studies primarily focused on diseases in commonly used comorbidity indices. The comorbid status of CRC patients with respect to the entire spectrum of chronic diseases has not yet been investigated. ObjectiveThis study aimed to systematically analyze all chronic diagnoses and diseases co-occurring, using a network-based approach and large-scale administrative health data, and provide a complete picture of the comorbidity pattern in patients newly diagnosed with CRC from southwest China. MethodsIn this retrospective observational study, the hospital discharge records of 678 hospitals from 2015 to 2020 in Sichuan Province, China were used to identify new CRC cases in 2020 and their history of diseases. We examined all chronic diagnoses using ICD-10 (International Classification of Diseases, 10th Revision) codes at 3 digits and focused on chronic diseases with >1% prevalence in at least one subgroup (1-sided test, P

IntroductionThere is a need to understand the current treatment landscape for LA HNSCC in the real-world setting.MethodsThis retrospective study assessed real-world outcomes and treatment patterns of 1,158 adult patients diagnosed with locally advanced (stage III-IVB) HNSCC initiating chemoradiotherapy (CRT) within the period January 2015 to December 2017 in a large network of US community oncology practices. Structured data were abstracted from electronic health records. Demographic, clinical and treatment characteristics were analyzed descriptively overall and stratified by index treatment (cisplatin + radiotherapy [RT], cisplatin + other chemotherapy + RT, or cetuximab + RT). Time to next treatment (TTNT) and overall survival (OS) were measured using the Kaplan-Meier method, and median duration of treatment was assessed. OS was compared across treatment cohorts using multinomial logistic regression with inverse probability treatment weighting. To identify covariates associated with OS, a multivariable adjusted Cox proportional hazard model was used.ResultsThis study examined 22,782 records, of which 2124 had stage III to stage IVB and no other cancers, and 1158 met all eligibility criteria. Among the treatment cohorts analyzed (cisplatin + RT, cisplatin + other chemotherapy + RT, or cetuximab + RT), cisplatin + RT was the most common concurrent chemotherapy (65.8%). Among 1158 patients, 838 (72.4%) did not initiate subsequent treatment and 139 (12.0%) died. The median TTNT and median OS were only reached by the cetuximab + RT cohort. Among patients with oropharynx primary tumor location, patients with human papilloma virus (HPV) positive status had the longest time on treatment and highest survival at 60 months. Covariates associated with improved survival were never/former tobacco use, HPV positive status, and overweight or obese body mass index. Covariates associated with poorer survival were age of 60+ years, primary tumor location of hypopharynx or oral cavity and Eastern Cooperative Oncology Group performance status score of 2+.ConclusionThese data describe real-world treatment patterns in locally advanced head and neck squamous cell cancer and sets the baseline to assess outcomes for future studies on the community oncology population.

Recent achievement of BN-graphene alloy material has enabled the potential of bandgap tuning through both sub-10 nm width control and BN concentration variation. However, its mechanics, which is necessary for prediction of stability in functional applications, is not well studied. Here, molecular dynamics simulation is performed to conduct uniaxial tensile test for BN-doped graphene nanoribbons (BN-GNR) with varying widths and BN atom fractions. Efforts are made to study the constitutive relations for the edges and the whole BN-GNR and explore the fracture mechanisms of the hybrid nanoribbons. The substantial softening effect of the edges induced by wrinkling alters the impact of BN concentration on the stiffness in the sub-20 nm regime deviating from the linear behaviour observed in the bulk case. Fracture properties are unexpectedly independent of BN concentrations unlike in the bulk and the failure behaviour is rather decided by the graphene ribbon edge structure. Here the armchair edges serve as the source of crack nucleation at an early stage leading to weakened strength and reduced stretchability, whereas zigzag edges do not promote early crack nucleation and leads to the size dependence of fracture properties.

BackgroundPembrolizumab, a PD-1 immune checkpoint inhibitor, is approved as first-line (1L) treatment for recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) as monotherapy or in combination with platinum and 5-fluorouracil chemotherapy. Limited data exist on the use of these regimens in real-world settings.ObjectiveOur primary objectives were to describe baseline characteristics and real-world overall survival (rwOS), time on treatment (rwToT), and time to next treatment (rwTTNT) among individuals with R/M HNSCC receiving approved 1L pembrolizumab therapies. We also aimed to identify baseline factors associated with choice of 1L pembrolizumab therapy and with rwOS.MethodsThis was a retrospective cohort study of adults with R/M HNSCC receiving 1L pembrolizumab monotherapy or pembrolizumab plus chemotherapy. We used Kaplan-Meier analyses to assess real-world outcomes, logistic regression modeling to identify factors associated with choice of 1L pembrolizumab therapy, and Cox proportional hazards models to identify factors associated with rwOS.ResultsThe study population included 431 individuals receiving 1L pembrolizumab monotherapy and 215 receiving 1L pembrolizumab plus chemotherapy. The use of 1L pembrolizumab monotherapy was associated with higher baseline combined positive score for PD-L1 expression, older age, higher Eastern Cooperative Oncology Group performance status (ECOG PS), laryngeal tumor site, and human papillomavirus (HPV)-positive tumor status. The pembrolizumab monotherapy group had a median (95% CI) rwOS of 12.1 (9.2–15.1) months, rwToT of 4.2 (3.5–4.6) months, and rwTTNT of 6.5 (5.4–7.4) months. Among this group, HPV-positive tumor status and lower ECOG PS were associated with longer rwOS, and oral cavity tumor site with shorter rwOS. The pembrolizumab plus chemotherapy cohort had a median (95% CI) rwOS of 11.9 (9.0–16.0) months, rwToT of 4.9 (3.8–5.6) months, and rwTTNT of 6.6 (5.8–8.3) months. In this group, HPV-positive tumor status was associated with longer rwOS.ConclusionsThis study adds to clinical trial data by summarizing real-world treatment outcomes with 1L pembrolizumab-containing therapies in a more heterogeneous population. Overall survival outcomes in both treatment groups were similar to those observed in the registration clinical trial. These findings support the use of pembrolizumab as standard of care for R/M HNSCC.

BackgroundTin and its compounds can cause serious harm to human respiratory system and nervous system, but there is no corresponding national standard method for the determination of tin in PM2.5. ObjectiveTo establish a method for the determination of tin and its compounds in PM2.5 by atomic fluorescence spectrometry (AFS) after ultrasonic extraction with concentrated hydrochloric acid. MethodsWe extracted a fixed volume of air at a constant speed through a sampler with preset cutting characteristics to trap PM2.5 in the ambient air on quartz filter membranes. By selecting extraction solvent, comparing extraction temperature and time, and adjusting the acidity of solution to be measured, the sample pretreatment process was optimized, and a method for the determination of tin and its compounds in PM2.5 by AFS was proposed, and its performance indexes such as linearity, detection limit, and lower limit of quantification were obtained. The accuracy and precision of the method were evaluated by the standard addition recovery test with blank quartz filter membranes, and the interference test was carried out by adding standard urban particulate samples. The proposed method and the method recommended by the “Handbook on Monitoring and Protection of Air Pollution (Haze) Effects on Population Health (2020)” (the Handbook) were applied to actual samples, and the results were compared. ResultsThis experiment used concentrated hydrochloric acid as the extraction solvent. The higher the reaction temperature and the longer the reaction time, the higher the recovery rate. Therefore, 70 ℃ water bath ultrasonic extraction for 3 h was selected. In terms of the proposed method, the linear range of detection was from 5.00 μg·L−1 to 50.00 μg·L−1, with a correlation coefficient ≥0.999 and a detection limit of 0.27 μg·L−1. When the quantitative detection of the lower limit was 0.90 μg·L−1，and the sampling volume was 144 m3, the limit of quantification was 1.25 ng·m−3. The recovery rate of standard addition of blank quartz filter membranes was 94.1%-97.5%, with a relative standard deviation ≤3.2%; the recovery rate of standard addition of standard urban particulate matter samples was 93.5%-103.0%, and the relative standard deviation was ≤2.1%, indicating that coexisting components in PM2.5 samples would not affect the determination of tin. For the 10 quartz filter membrane samples of PM2.5 monitoring, the results of tin by the established method (extraction with concentrated hydrochloric acid) were higher than those of the Handbook recommended method (extraction with nitric acid), and the difference is (3.61±0.54) ng·m−3(t=21.303, P

Abstract Background An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately. Methods In this study, we proposed a novel approach combining network analytics and machine learning to predict the LOS in elderly patients with chronic diseases at the PoA. Two networks, including multimorbidity network (MN) and patient similarity network (PSN), were constructed and novel network features were created. Five machine learning models (eXtreme Gradient Boosting, Gradient Boosting Decision Tree, Random Forest, Linear Support Vector Machine, and Deep Neural Network) with different input feature sets were developed to compare their performance. Results The experimental results indicated that the network features can bring significant improvements to the performances of the prediction models, suggesting that the MN and PSN are useful for LOS predictions. Conclusion Our predictive framework which integrates network science with data mining can forecast the LOS effectively at the PoA and provide decision support for hospital managers, which highlights the potential value of network-based machine learning in healthcare field.

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.

Abstract Background Deoxythymidine triphosphate (dTTP) is an essential building block of DNA, and defects in enzymes involved in dTTP synthesis cause neurodegenerative disorders. For instance, mutations in DTYMK, the gene coding for thymidylate kinase (TMPK), cause severe microcephaly in human. However, the mechanism behind this is not well-understood. Here we used the zebrafish model and studied (i) TMPK, an enzyme required for both the de novo and the salvage pathways of dTTP synthesis, and (ii) thymidine kinases (TK) of the salvage pathway in order to understand their role in neuropathology. Results Our findings reveal that maternal-stored dNTPs are only sufficient for 6 cell division cycles, and the levels of dNTPs are inversely correlated to cell cycle length during early embryogenesis. TMPK and TK activities are prominent in the cytosol of embryos, larvae and adult fish and brain contains the highest TMPK activity. During early development, TMPK activity increased gradually from 6 hpf and a profound increase was observed at 72 hpf, and TMPK activity reached its maximal level at 96 hpf, and remained at high level until 144 hpf. The expression of dtymk encoded Dtymk protein correlated to its mRNA expression and neuronal development but not to the TMPK activity detected. However, despite the high TMPK activity detected at later stages of development, the Dtymk protein was undetectable. Furthermore, the TMPK enzyme detected at later stages showed similar biochemical properties as the Dtymk enzyme but was not recognized by the Dtymk specific antibody. Conclusions Our results suggest that active dNTP synthesis in early embryogenesis is vital and that Dtymk is essential for neurodevelopment, which is supported by a recent study of dtymk knockout zebrafish with neurological disorder and lethal outcomes. Furthermore, there is a novel TMPK-like enzyme expressed at later stages of development.

In order to study the controlled factors and variation of gas content in deep low-rank coal reservoirs, taking No.4 coal of Jurassic Yan’an Formation in Huanglong coalfield as an example, collects the production data and prepares the coal samples. Carrying out the coal rock and coal quality test and multi-temperature methane isothermal adsorption experiment to explore the influence of different temperature and pressure conditions on coal adsorption capacity, and analyzes the variation characteristics of gas content with burial depth combined with geological conditions. The results show that the sedimentary, structural and hydrogeological conditions have a certain influence on the formation and preservation of coalbed methane. The gas content increases with the increase of coalification degree and vitrinite content, and decreases with the increase of inertinite content, ash yield and volatile yield. With the increase of moisture, the gas content increased first and then decreased. When the pressure is less than 4 MPa, the adsorption capacity increases about 4-4.7m 3 /t with the increase of pressure, and the increasing trend slows down when the pressure is greater than 4 MPa. The adsorption capacity decreased more obviously with the increase of temperature when the temperature increased from 25°C to 35°C. Based on the prediction model of saturated adsorption capacity of deep coal reservoirs, it is found that there is a gas content critical conversion depth 800-900 m. The gas content shows a process of rapid increase (900 m) with the increase of burial depth. Below 800 m, the positive effect of reservoir pressure is dominant, and the gas content increases with burial depth. Above 900 m, the negative effect of reservoir temperature is dominant, and the gas content decreases with burial depth. The study results provide a theoretical basis for the development of deep low-rank coalbed methane resources.

COVID-19 has been affecting the world unprecedentedly and will remain widely prevalent due to its elusive pathophysiological mechanism and the continuous emergence of new variants. Critically ill patients with COVID-19 are commonly associated with cytokine storm, multiple organ dysfunction, and high mortality. To date, growing evidence has shown that extracorporeal hemoadsorption can exert its adjuvant effect to standard of care by regulating immune homeostasis, reducing viremia, and decreasing endotoxin activity in critically ill COVID-19 cases. However, the selection of various hemofilters, timing of initiation and termination of hemoadsorption therapy, anticoagulation management of extracorporeal circuits, identification of target subgroups, and ultimate survival benefit remain controversial. The purpose of this narrative review is to comprehensively summarize the rationale for the use of hemoadsorption in critically ill patients with COVID-19 and to gather the latest clinical evidence in this field.

A 75-year-old woman on hemodialysis for end-stage renal failure due to polycystic kidney disease developed dark spots on her limbs. She had been treated for extended spectrum beta-lactamase–producing Escherichia coli bacteremia by a rectovaginal fistula and was on long-term oral minocycline (cumulative dose 45 g). Physical examination revealed dark patches on her forearms and lower legs but no trunk hyperpigmentation or visual impairment. Blood tests were normal. Skin biopsy confirmed minocycline-induced hyperpigmentation. Minocycline-induced pigmentation is categorized into types I–IV, each with unique clinical and histopathological features. Types I and II are reversible upon discontinuing minocycline, whereas types III and IV are permanent. The patient was diagnosed with type II pigmentation, generally occurring with a cumulative dose exceeding 70–100 g; however, her lower dose (45 g) led to pigmentation, possibly influenced by her vitamin D deficiency. Clinicians should evaluate the antimicrobial indication and treatment period, considering not only the benefits but also the side effects and antimicrobial resistance. If minocycline is used, attention should be paid to minocycline-induced hyperpigmentation, and this possibility should be communicated to patients to enable early detection.

RNA-protein interactions (RPIs) are promising targets for developing new molecules of therapeutic interest. Nevertheless, challenges arise from the lack of methods and feedback between computational and experimental techniques during the drug discovery process. Here, we tackle these challenges by developing a drug screening approach that integrates chemical, structural and cellular data from both advanced computational techniques and a method to score RPIs in cells for the development of small RPI inhibitors; and we demonstrate its robustness by targeting Y-box binding protein 1 (YB-1), a messenger RNA-binding protein involved in cancer progression and resistance to chemotherapy. This approach led to the identification of 22 hits validated by molecular dynamics (MD) simulations and nuclear magnetic resonance (NMR) spectroscopy of which 11 were found to significantly interfere with the binding of messenger RNA (mRNA) to YB-1 in cells. One of our leads is an FDA-approved poly(ADP-ribose) polymerase 1 (PARP-1) inhibitor. This work shows the potential of our integrative approach and paves the way for the rational development of RPI inhibitors.

BackgroundMetabolic dysfunction-associated fatty liver disease (MAFLD) has become the most common chronic liver disease. MAFLD is a major risk factor for end-stage liver disease including cirrhosis and primary liver cancer. The pathogenesis of MAFLD is complex and has not yet been clarified. To the best of our knowledge, few studies have conducted quantitative bibliometric analysis to evaluate published MAFLD research. In this study, we conducted a comprehensive analysis of MAFLD publications over the past decade to summarize the current research hotspots and predict future research directions in this field.MethodsArticles into MAFLD published from 2012 to 2021 were identified from the Science Citation Index-Expanded of Web of Science Core Collection. CiteSpace software, VOSviewer, the “bibliometrix” R package, and the Online Analysis Platform of Literature Metrology were used to analyze the current publication trends and hotspots.ResultsWe retrieved 13959 English articles about MAFLD published from 2012 to 2021. Primary sites of publication were dominated by the United States until 2014, when China became the source of most published MAFLD-related research papers. The United States was found to be the most engaged country in international cooperative efforts. Shanghai Jiao Tong University was the most productive institution. Loomba R was the most productive author with 123 articles. The co-cited keyword cluster tag showed ten main clusters: #0 liver fibrosis, #1 hemoglobin, #2 metabolic associated fatty liver disease, #3 egcg, #4 myocardial infarction, #5 heart disease, #6 pnpla3, #7 hepatocellular carcinoma, #8 noninvasive marker, and #9 children. Keyword burst analysis showed that gut microbiota was the highest-intensity research hotspot.ConclusionIn the past decade, the number of publications on MAFLD increased dramatically, especially in the last three years. Gut microbiota became an important research direction for etiological and therapeutic investigations into MAFLD. Insulin resistance was also a key factor in studying the development of MAFLD in recent years. Liver fibrosis was an important focus of disease development. This study provides systematic information, helps guide future research, and helps to identify mechanisms and new treatment methods for MAFLD.

Objectives: This study involved a multi-omics analysis of glioblastoma (GBM) samples to elaborate the potential mechanism of drug treatment.Methods: The GBM cells treated with or without orexin A were acquired from sequencing analysis. Differentially expressed genes/proteins/metabolites (DEGs/ DEPs/ DEMs) were screened. Next, combination analyses were conducted to investigate the common pathways and correlations between the two groups. Lastly, transcriptome-proteome-metabolome association analysis was carried out to determine the common pathways, and the genes in these pathways were analyzed through Kaplan-Meier (K-M) survival analysis in public databases. Cell and animal experiments were performed to investigate the anti-glioma activity of orexin A.Results: A total of 1,527 DEGs, 52 DEPs, and 153 DEMs were found. Moreover, the combination analyses revealed that 6, 4, and 1 common pathways were present in the transcriptome-proteome, proteome-metabolome, and transcriptome-metabolome, respectively. Certain correlations were observed between the two data sets. Finally, 11 common pathways were discovered in association analysis, and 138 common genes were screened out in these common pathways. Six genes showed significant differences in terms of survival in both TCGA and CGGA. In addition, orexin A inhibited the proliferation, migration, and invasion of glioma in vitro and in vivo.Conclusion: Eleven common KEGG pathways with six common genes were found among different omics participations, revealing the underlying mechanisms in different omics and providing theoretical basis and reference for multi-omics research on drug treatment.

With the spread of COVID-19 around the world, the education industry faces enormous challenges. Some colleges and universities have launched online teaching. Comprehensive online teaching and student health checkups help students complete the set teaching content and return to school as soon as possible. With the development of big data, combined with the epidemic risk we are facing, the rational use of big data and the internet for innovative online education has become a mainstream teaching method. Colleges and universities are not yet familiar with the development prospects and future of online education. Through the research of this paper, we can understand the combination of online education and the development of big data and promote its application in colleges and universities. Not only have innovative online education platforms such as MOOC and DingTalk been widely used, but innovative online education methods such as virtual classrooms also have been created. Based on the current epidemic background, this paper analyzes the development of online education, introduces the impact of the combination of online education and big data, and introduces innovative online education technologies and their effects. It helps online education under the influence of the new coronavirus epidemic, operating big data technology to analyze the current prospects and development of online education, showing the combination of big data technology and online education through the analysis of big data technology, and ending with more expectations on other aspects of the use of big data, which affects the online education industry as well as other industries. Finally, we summarize the combination of big data and innovative online education since the emergence of COVID-19 and introduce the concepts and methods of combining online education and big data technology in detail. The online education platform also makes a reasonable introduction. The thesis can be used to understand the problems and challenges faced by innovative online education in the context of the new coronavirus epidemic and look forward to the future on this basis.

Dysregulated alternative splicing (AS) plays critical roles in driving cancer progression, and the underlying mechanisms remain largely unknown. Here, we demonstrated that PHF5A, a component of U2 small nuclear ribonucleoproteins, was frequently upregulated in colorectal cancer (CRC) samples and associated with poor prognosis. PHF5A promoted proliferation and metastasis of CRC cells in vitro and in vivo. Transcriptomic analysis identified PHF5A-regulated AS targets and pathways. Particularly, PHF5A induced TEAD2 exon 2 inclusion to activate YAP signaling, and interference of TEAD2-L partially reversed the PHF5A-mediated tumor progression. Pharmacological inhibition of PHF5A using pladienolide B had potent antitumor activity. Collectively, these data revealed the oncogenic role of PHF5A in CRC through regulating AS and established PHF5A as potential therapeutic target.

Abstract Background Thymidine kinase 1 (TK1) plays a key role in the synthesis of deoxythymidine triphosphate (dTTP) and is thus important for DNA replication and cell proliferation. The expression of TK1 is highest during S-phase, and it is rapidly degraded after mitosis. In cancer cells, TK1 is upregulated, resulting in leakage of excess TK1 into the blood. Consequently, serum TK1 has been used as a diagnostic and prognostic cancer biomarker, mainly in human medicine. The aims of this work were to characterize equine TK1 and to evaluate its suitability as a serum biomarker for equine lymphoma. Results Equine TK1 was cloned, expressed in E. coli and affinity purified. The purified recombinant horse TK1 showed broad substrate specificity, phosphorylating pyrimidine deoxyribo- and ribonucleosides and, to some extent, purine deoxynucleosides, including anticancer and antiviral nucleoside analogues. ATP was the preferred phosphate donor. Serum TK1 activity was measured in samples collected from horses with confirmed or suspected lymphoma and control horses with and without concurrent diseases. Serum TK1 activity levels were significantly higher in horses with lymphoma (p

Abstract Clear-cell renal cell carcinoma is one of the most common tumors disagnosed, with nearly one third of patients diagnosed with metastatic ccRCC. Although an increasing number of studies has revealed that piwi-interacting RNAs are aberrantly expressed in diverse types of cancers, few of them explored the detailed molecular mechanism of piRNAs in carcinogenesis, particularly in ccRCC. In this study, differentially expressed piRNAs associated with ccRCC were selected by using piRNA-sequencing combined with TCGA data analysis, and piR-57125 was identified. PiR-57125 was found remarkably downregulated in ccRCC samples. Functionally, knockdown of piR-57125 promoted migration and invasion of ccRCC, while overexpression of piR-57125 suppressed ccRCC metastasis. In vivo lung metastasis model also confirmed the same results. CCL3 was identified as the direct target of piR-57125 which could potentially reverse the inhibition effect of piR-57125 in ccRCC metastasis. Further study revealed that piR-57125 modulated ccRCC metastasis through the AKT/ERK pathway. These data indicate that piR-57125 restrains ccRCC metastasis by directly targeting CCL3 and inhibiting the AKT/ERK pathway, and could be a potential therapeutic target for ccRCC.

Background: Acute kidney injury is a highly common and multifactorial renal disease resulting in significant morbidity and mortality, especially sepsis-induced acute kidney injury. There is no effective therapy available to treat or prevent sepsis-induced acute kidney injury. One of the specialized pro-resolving mediators, Resolvin D1 exhibits special anti-inflammatory effects in several inflammatory disease models, but there is little evidence about the effect and mechanism of Resolvin D1 in sepsis-induced acute kidney injury. Methods: We conducted experiments to explore the effect and mechanism of Resolvin D1 in sepsis-induced acute kidney injury. In vitro, human proximal tubular epithelial cells were used to test the apoptosis ratio, cell viability and reactive oxygen species level. In vivo, C57BL/6 mice were injected with lipopolysaccharide to establish a sepsis-induced acute kidney injury model. Renal function and structure, apoptosis ratio of kidney cells, mitochondrial structure and function and related protein and gene levels were assessed. Results: In vitro, the resolvin D1-treated group showed higher cell viability and lower reactive oxygen species levels and apoptosis ratios than the LPS group. In vivo, Resolvin D1 can not only improve renal function and mitochondrial function but also reduce the apoptosis ratio, while mediating mitochondrial dynamics and inhibiting NF-κB pathway. Conclusions: Resolvin D1 has a good renoprotective effect by maintaining mitochondrial dynamics and inhibiting the NF-κB pathway.

Skewed XCI plays an important role in the phenotypic heterogeneities of many X-linked disorders, even involving in diseases caused by XCI-escaping genes. DDX3X-related intellectual disability is more common in females and less common in males, who usually inherit from unaffected heterozygous mothers. As an X inactivation (XCI) escaping gene, the role of skewed XCI in the phenotype of DDX3X mutant female is unknown. Here we reported a DDX3X: c.694_711dup18 de novo heterozygous mutation in a female with intellectual disability on the maternal X chromosome on the basis of SNPs detected by PCR-sanger sequencing. AR assay revealed that the maternal mutant X chromosome was extremely inactivated in the proband. Using RNA sequencing and whole-exome sequencing, we quantified allelic read counts and allele-specific expression, and confirmed that the mutant X chromosome was inactive. Further, we verified that the mutant DDX3X allele had a lower expression level by RNA sequencing and RT-PCR, and the normal and mutated DDX3X expression accounted for respectively 70% and 30% of total. In conclusion, we found a symptomatic female with extreme skewing XCI in the DDX3X mutant allele. It was discovered that XCI in the mutant allele was insufficient to reverse the phenotype of DDX3X-related neurodevelopmental disorder. It contributed to a better understanding of the role of skewed XCI in phenotypic differences, which can aid in the genetic counseling and prenatal diagnosis of disorders in females with DDX3X defects.

BackgroundThe prevalence of metabolic associated fatty liver disease (MAFLD) presented a booming growth over recent years in the whole world. MAFLD was associated with a higher risk of end-stage liver disease, hepatocellular carcinoma and liver transplantation. Accumulating evidence indicated that gut microbiota and MAFLD were interrelated and interacted with each other. However, to the knowledge of the authors, no bibliometric quantitative analysis has been carried out to evaluate the links between the gut microbiota and MAFLD. This study aimed to use bibliometric analysis to evaluate current publication trends and hotspots in the links between the gut microbiota and MAFLD, in order to advance research in this field.MethodsThe articles regarding the links between gut microbiota and MAFLD from 2002 to 2021 were identified from the Science Citation Index-Expanded of Web of Science Core Collection. CiteSpace software, Vosviewer, the R package “bibliometrix” and the Online Analysis Platform of Literature Metrology were used to analyze current publication trends and hotspots in this field.ResultsA total of 707 articles were retrieved regarding the links between gut microbiota and MAFLD from 2002 to 2021. The USA occupied the leading role until 2015 and the dominance of China started in 2016. The USA was the most frequently involved country in international cooperation. Shanghai Jiao Tong University was the most productive institution. Ina Bergheim was the most productive author, publishing 14 articles. The co-citation keywords cluster label displayed ten main clusters: probiotics, bile acid, immune function, adolescents, nutritional genomics, high fat diet, systems biology, lipopolysaccharides, phosphatidylcholine, and oxidative stress. Keyword bursts analysis indicated that diet induced obesity, metabolic syndrome, ppar alpha, and lactobacillus were the research hotspots with high strength.ConclusionThe number of publications covering the links of gut microbiota and MAFLD increased dramatically in the past decade and especially became exponential growth in the last 3 years. Probiotics and bile acid will be the research direction of great importance in the etiology and novel treatment for MAFLD. This study provided systematic information and instructive assistance for future research work, that helped to discover the mechanisms and new treatments of MAFLD.

Land subsidence is a typical geo-hazard in Cangzhou, North China Plain, having caused severe damages to transportation networks, public utilities, and other civil infrastructures. The mechanism of land subsidence and its affecting factors were revealed by theoretical model analysis, and doing geotechnical tests and monitoring of soil deformation as well as groundwater level. Theoretical analysis shows that groundwater withdrawal from both unconfined and confined aquifers can lead to land subsidence. Land subsidence of the Cangzhou Plain is mainly caused by the exploitation of deep confined groundwater. Geotechnical tests show a general trend that the compressive modulus increases with the increase of soil depth. The strata of the Upper Pleistocene and Holocene are mostly normally consolidated and partially under-consolidated, which are prone to produce large compression after pumping. Land subsidence in the Cangzhou Plain has a strong hysteresis because of seepage consolidation and creep. The deformation characteristics of strata change at different depth. The shallow aquifers are mainly elastic deformation. The area of severe land subsidence in the Cangzhou Plain is closely related to rainfall, mainly because the large amount of water are used for agricultural irrigation. InSAR results show a sudden change in regional subsidence across the Cangdong fault, which is caused by the difference in the thickness of Quaternary sediments and the difference in the deep confined groundwater level.

In this paper, an improved Internet of Things (IoT) network security situation assessment model is designed to solve the problems arising from the existing IoT network security situation assessment approach regarding feature extraction, validity, and accuracy. Firstly, raw data are dimensionally reduced using independent component analysis (ICA), and the weights of all features are calculated and fused using the maximum relevance minimum redundancy (mRMR) algorithm, Spearman’s rank correlation coefficient, and extreme gradient boosting (XGBoost) feature importance method to filter out the optimal subset of features. Piecewise chaotic mapping and firefly perturbation strategies are then used to optimize the sparrow search algorithm (SSA) to achieve fast convergence and prevent getting trapped in local optima, and then the optimized algorithm is used to improve the light gradient boosting machine (LightGBM) algorithm. Finally, the improved LightGBM method is used for training to calculate situation values based on a threat impact to assess the IoT network security situation. The research findings reveal that the model attained an evaluation accuracy of 99.34%, sustained a mean square error at the 0.00001 level, and reached its optimum convergence value by the 45th iteration with the fastest convergence speed. This enables the model to more effectively evaluate the IoT network security status.