Search

Abstract Hepatocellular carcinoma (HCC) is a highly aggressive malignancy with increasing global prevalence and is one of the leading causes of cancer-related mortality in the human population. Developing robust clinical prediction models and prognostic stratification strategies is crucial for developing individualized treatment plans. A range of novel forms of programmed cell death (PCD) plays a role in the pathological progression and advancement of HCC, and in-depth study of PCD is expected to further improve the prognosis of HCC patients. Sixteen patterns (apoptosis, autophagy, anoikis, lysosome-dependent cell death, immunogenic cell death, necroptosis, ferroptosis, netosis, pyroptosis, disulfidptosis, entotic cell death, cuproptosis, parthanatos, netotic cell death, alkaliptosis, and oxeiptosis) related to PCD were collected from the literatures and used for subsequent analysis. Supervised (Elastic net, Random Forest, XgBoost, and Boruta) and unsupervised (Nonnegative Matrix Factorization, NMF) clustering algorithms were applied to develop and validate a novel classifier for the individualized management of HCC patients at the transcriptomic, proteomic and single-cell levels. Multiple machine learning algorithms developed a programmed cell death index (PCDI) comprising five robust signatures (FTL, G6PD, SLC2A1, HTRA2, and DLAT) in four independent HCC cohorts, and a higher PCDI was predictive of higher pathological grades and worse prognoses. Furthermore, a higher PCDI was found to be correlated with the presence of a repressive tumor immune microenvironment (TME), as determined through an integrated examination of bulk and single-cell transcriptome data. In addition, patients with TP53 mutation had higher PCDI in comparison with TP53 WT patients. Three HCC subtypes were identified through unsupervised clustering (NMF), exhibiting distinct prognoses and significant biological processes, among the three subtypes, PCDcluster 3 was of particular interest as it contained a large proportion of patients with high risk and low metabolic activity. Construction and evaluation of the Nomogram model was drawn based on the multivariate logistic regression analysis, and highlighted the robustness of the Nomogram model in other independent HCC cohorts. Finally, to explore the prognostic value, we also validated the frequent upregulation of DLAT in a real-world cohort of human HCC specimens by qPCR, western blot, and immunohistochemical staining (IHC). Together, our work herein comprehensively emphasized PCD-related patterns and key regulators, such as DLAT, contributed to the evolution and prognosis of tumor foci in HCC patients, and strengthened our understanding of PCD characteristics and promoted more effective risk stratification strategies.

Abstract Background Pulmonary arterial hypertension (PAH) is a progressive and devastating disease characterized by pulmonary vascular remodeling which is associated with the malignant phenotypes of pulmonary vascular cells. Recently, the effects of heat shock protein 110 (Hsp110) in human arterial smooth muscle cells were reported. However, the underlying roles and mechanisms of Hsp110 in human pulmonary arterial endothelial cells (HPAECs) that was disordered firstly at the early stage of PAH remain unknown. Methods In this research, the expression of Hsp110 in PAH human patients and rat models was investigated, and the Hsp110 localization was determined both in vivo and in vitro. The roles and mechanism of elevated Hsp110 in excessive cell proliferation and migration of HPAECs were assessed respectively exposed to hypoxia. Small molecule inhibitors targeting Hsp110-STAT3 interaction were screened via fluorescence polarization, anti-aggregation and western blot assays. Moreover, the effects of compound 6 on HPAECs abnormal phenotypes in vitro and pulmonary vascular remodeling of hypoxia-indued PAH rats in vivo by interrupting Hsp110-STAT3 interaction were evaluated. Results Our studies demonstrated that Hsp110 expression was increased in the serum of patients with PAH, as well as in the lungs and pulmonary arteries of PAH rats, when compared to their respective healthy subjects. Moreover, Hsp110 levels were significantly elevated in HPAECs under hypoxia and mediated its aberrant phenotypes. Furthermore, boosted Hsp110-STAT3 interaction resulted in abnormal proliferation and migration via elevating p-STAT3 and c-Myc in HPAECs. Notably, we successfully identified compound 6 as potent Hsp110-STAT3 interaction inhibitor, which effectively inhibited HPAECs proliferation and migration, and significantly ameliorated right heart hypertrophy and vascular remodeling of rats with PAH. Conclusions Our studies suggest that elevated Hsp110 plays a vital role in HPAECs and inhibition of the Hsp110-STAT3 interaction is a novel strategy for improving vascular remodeling. In addition, compound 6 could serve as a promising lead compound for developing first-in-class drugs against PAH.

This study investigates the determinants of non-hydro renewable energy (NHRE) consumption across 31 provincial regions in China, spanning from 2015 to 2022. Utilizing fixed effects and moderating effects, the research analyzes the impacts of research and development intensity (RDI), urbanization (UR), and human capital (HC) on NHRE consumption (NHREC). Additionally, the moderating roles of industrial structure (IS) and tax (TA) are examined, along with control variables such as informationization and economic development. The findings reveal that increased RDI significantly boosts NHREC by enhancing technological advancements. UR also positively affects NHREC, particularly in rapidly urbanizing regions, while HC provides a skilled workforce that drives renewable energy projects. However, the study identifies that concentrated industrial structures and high taxes can negatively moderate these positive impacts, highlighting the complex interplay of these factors. Policy recommendations include creating “Renewable Energy Innovation Hubs” in underdeveloped regions to attract startups and researchers, developing “Solar Cities” with mandated solar panels on all buildings, and introducing a “Carbon Offset Lottery” to incentivize investments in renewable energy.

Abstract Abnormal expression and remodeling of cytoskeletal regulatory proteins are important mechanisms for tumor development and chemotherapy resistance. This study systematically analyzed the relationship between differential expression of cytoskeleton genes and prognosis in gastric cancer (GC). We found the Arf GTP-activating protein ASAP1 plays a key role in cytoskeletal remodeling and prognosis in GC patients. Here we analyzed the expression level of ASAP1 in tissue microarrays carrying 564 GC tissues by immunohistochemistry. The results showed that ASAP1 expression was upregulated in GC cells and can be served as a predictor of poor prognosis. Moreover, ASAP1 promoted the proliferation, migration, and invasion of GC cells both in vitro and in vivo. We also demonstrated that ASAP1 inhibited the ubiquitin-mediated degradation of IQGAP1 and thus enhanced the activity of CDC42. The activated CDC42 upregulated the EGFR-MAPK pathway, thereby promoting the resistance to chemotherapy in GC. Taken together, our results revealed a novel mechanism by which ASAP1 acts in the progression and chemotherapy resistance in GC. This may provide an additional treatment option for patients with GC.

Background Dual/double stimulation (DS) is an ovarian stimulation strategy that has emerged in recent years; it is characterized by two rounds of ovarian stimulation and oocyte retrieval in the same menstrual cycle. DS can greatly shorten the time required to obtain valid embryos in assisted reproduction. For fertility preservation, DS can speed up oocyte storage process. However, factors influencing luteal phase ovarian stimulation (LPS) outcomes in DS have not been elucidated. Methods A total of 156 cycles from 78 cases were studied. Patients were grouped and analyzed according to their follicular phase ovarian stimulation (FPS) types. Female ages, ovarian stimulation protocols, number of oocytes retrieved, embryo quality were recorded. Comparisons of outcomes were conducted between different groups. Results Our study found that LPS obtained similar outcomes to follicular phase stimulation (FPS), and that the choice of FPS protocol affected the efficiency of LPS, the antagonist protocol and progestin-primed ovarian stimulation (PPOS) protocol resulted in better embryo outcomes in LPS. In LPS of DS, sufficient stimulation duration was the guarantee of embryo quality (number of available embryos: β = 0.145, 95% CI [0.078–0.211], P = 0.000; number of high-quality embryos: β = 0.114, 95% CI [0.057–0.171], P = 0.000). Discussion This study provided ideas for the precise use of DS. We suggest to further expand the sample size of DS in the future, conduct prospective controlled studies, unify the sample size of each subgroup, include the ovarian reserve of patients in the grouping basis, and exclude the influence of male factors. We hope that this study will help further refinement of DS so as to maximize patient benefits from it. Conclusion When the DS strategy is considered in the follicular phase, the antagonist protocol and PPOS protocol are more recommended for better embryo outcomes in LPS. During LPS, adequate ovarian stimulation duration is the most important guarantee for LPS efficiency.

Abstract Pancreatic ductal adenocarcinoma (PDAC) is the most frequent and aggressive pancreatic tumor characterized by high metastatic risk and special tumor microenvironment. To comprehensively delineate the complex intra-tumoral heterogeneity and the underlying mechanism during metastatic lesions malignant progression, single-cell RNA sequencing (scRNA-seq) was employed. PCA and TSNE were used for dimensionality reduction analysis and cell clustering. Find All Markers function was used to calculate differential genes in each cluster, and Do Heatmap function was used to plot the distribution of differential genes in each cluster. GSVA was employed to assign pathway activity estimates to individual cells. Lineage trajectory progression was inferred by monocle. CNV status was inferred to compare the heterogeneity among patients and subtypes by infercnv. Ligand-receptor interactions were identified by CellPhoneDB, and regulons network of cells was analyzed by SCENIC. Through RNA-sequencing of 6236 individual cells from 5 liver metastatic PDAC lesions, 10 major cell clusters are identified by using unbiased clustering analysis of expression profiling and well-known cell markers. Cells with high CNV level were considered as malignant cells and pathway analyses were carried out to highlight intratumor heterogeneity in PDAC. Pseudotime trajectory analysis revealed that components of multiple tumor-related pathways and transcription factors (TFs) were differentially expressed along PDAC progression. The complex cellular communication suggested potential immunotherapeutic targets in PDAC. Regulon network identified multiple candidates for promising cell-specific transcriptional factors. Finally, metastatic-related genes expression levels and signaling pathways were validated in bulk RNA Sequencing data. This study contributed a comprehensive single-cell transcriptome atlas and contributed into novel insight of intratumor heterogeneity and molecular mechanism in metastatic PDAC.

Abstract Background Increasing evidence supports that infiltration M2 Macrophages act as pivotal player in tumor progression of pancreatic ductal adenocarcinoma (PDAC). Nonetheless, comprehensive analysis of M2 Macrophage infiltration and biological roles of hub genes (FAM53B) in clinical outcome and immunotherapy was lack. Method The multiomic data of PDAC samples were downloaded from distinct datasets. CIBERSORT algorithm was performed to uncover the landscape of TIME. Weighted gene co-expression network analysis (WGCNA) was performed to identify candidate module and significant genes associated with M2 Macrophages. Kaplan-Meier curve and receiver operating characteristic (ROC) curves were applied for prognosis value validation. Mutation data was analyzed by using “maftools” R package. Gene set variation analysis (GSVA) was employed to assign pathway activity estimates to individual sample. Immunophenoscore (IPS) was implemented to estimate immunotherapeutic significance of risk score. The half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs was predicted by using the pRRophetic algorithm. Finally, quantitative real-time polymerase chain reaction was used to determine FAM53B mRNA expression and TIMER database was utilized to uncover its possible role in immune infiltration of PDAC. Results Herein, 17,932 genes in 234 samples (214 tumor and 20 normal) were extracted from three platforms. Taking advantage of WGCNA, significant module (royalblue) and 135 candidate genes were considered as M2 Macrophages-related genes. Subsequently, risk signature including 5 hub genes was developed by multiple analysis, which exhibited excellent prognostic performance. Besides, comprehensive prognostic nomogram was constructed to quantitatively estimate risk. Then, intrinsic link between risk score with tumor mutation burden (TMB) was explored. Additionally, risk score significantly correlated with diversity of tumor immune microenvironment (TIME). PDAC samples within different risk presented diverse signaling pathways activity and experienced significantly distinct sensitivity to administering chemotherapeutic or immunotherapeutic agents. Finally, the biological roles of FAM53B were revealed in PDAC. Conclusions Taken together, comprehensive analyses of M2 Macrophages profiling will facilitate prognostic prediction, delineating complexity of TIME, and contribute insight into precision therapy for PDAC.

Abstract The 5-methylcytosine (m5C) RNA methyltransferase NSUN2 is involved in the regulation of cell proliferation and metastasis formation and is upregulated in multiple cancers. However, the biological significance of NSUN2 in gastric cancer (GC) and the modification of NSUN2 itself have not been fully investigated. Here, we analyzed the expression level of NSUN2 in tissue microarrays containing 403 GC tissues by immunohistochemistry. NSUN2 was upregulated in GC, and that it was a predictor of poor prognosis. NSUN2 promotes the proliferation, migration, and invasion of GC cells in vitro. We also demonstrated that small ubiquitin-like modifier (SUMO)-2/3 interacts directly with NSUN2 by stabilizing it and mediating its nuclear transport. This facilitates the carcinogenic activity of NSUN2. Furthermore, m5C bisulfite sequencing (Bis-seq) in NSUN2-deficient GC cells showed that m5C-methylated genes are involved in multiple cancer-related signaling pathways. PIK3R1 and PCYT1A may be the target genes that participate in GC progression. Our findings revealed a novel mechanism by which NSUN2 functions in GC progression. This may provide new treatment options for GC patients.

Purpose Tumor RNA vaccines can activate dendritic cells to generate systemic anti-tumor immune response. However, due to easily degraded of RNA, direct RNA vaccine is less effective. In this study, we optimized the method for preparing PEGylated liposom-polycationic DNA complex (LPD) nanoliposomes, increased encapsulate amount of total RNA derived from CT-26 colorectal cancer cells. Tumor RNA LPD nanoliposomes vaccines improved anti-tumor immune response ability of tumor RNA and can effectively promote anti-tumor therapeutic effect of oxaliplatin. Methods Total tumor-derived RNA was extracted from colorectal cancer cells (CT-26 cells), and loaded to our optimized the LPD complex, resulting in the LPD nanoliposomes. We evaluated the characteristics (size, zeta potential, and stability), cytotoxicity, transfection ability, and tumor-growth inhibitory efficacy of LPD nanoliposomes. Results The improved LPD nanoliposomes exhibited a spherical shape, RNA loading efficiency of 9.07%, the average size of 120.37 ± 2.949 nm and zeta potential was 3.34 ± 0.056 mV. Also, the improved LPD nanoliposomes showed high stability at 4 °C, with a low toxicity and high cell transfection efficacy toward CT-26 colorectal cancer cells. Notably, the improved LPD nanoliposomes showed tumor growth inhibition by activating anti-tumor immune response in CT-26 colorectal cancer bearing mice, with mini side effects toward the normal organs of mice. Furthermore, the effect of the improved LPD nanoliposomes in combination with oxaliplatin can be better than that of oxaliplatin alone. Conclusion The improved LPD nanoliposomes may serve as an effective vaccine to induce antitumor immunity, presenting a new treatment option for colorectal cancer.

Nasopharyngeal carcinoma (NPC) is one of the most common malignancies in the head and neck with a complex etiology, such as environmental factors, genetic factors, and Epstein–Barr virus infection. The NOP2/Sun domain family, member 2 (NSUN2) is a methyltransferase of m5C methylation modification that has been reported to be involved in the occurrence and progression of various tumors, but its role in NPC remains unclear. In this study, we found that NSUN2 was upregulated in NPC and predicted a poor prognosis for NPC patients in both GEO datasets and our tissue microarrays containing 125 NPC tissues. Next, we demonstrated that NSUN2 promoted the proliferation, migration, and invasion of NPC cells in vitro. Additionally, the differential expression genes between NSUN2-high and low expression patients were mainly enriched in multi-immune cell activation and proliferation. Furthermore, NSUN2 negatively regulates immune cell infiltration in the tumor microenvironment (TME) of NPC, which indicates that the NSUN2 level may be negatively correlated with the sensitivity of immunotherapy and chemotherapy. In conclusion, our findings highlight that NSUN2 might act as an important oncogene involved in NPC progression and serve as a potential biomarker to predict poor prognosis and drug sensitivity of NPC patients.

Background: Increasing evidence supports that competing endogenous RNAs (ceRNAs) and tumor immune infiltration act as pivotal players in tumor progression of hepatocellular carcinoma (HCC). Nonetheless, comprehensive analysis focusing on ceRNAs and immune infiltration in HCC is lacking.Methods: RNA and miRNA sequencing information, corresponding clinical annotation, and mutation data of HCC downloaded from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) project were employed to identify significant differentially expressed mRNAs (DEMs), miRNAs (DEMis), and lncRNAs (DELs) to establish a ceRNA regulatory network. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene ontology (GO) enrichment pathways were analyzed to functionally annotate these DEMs. A multigene-based risk signature was developed utilizing least absolute shrinkage and selection operator method (LASSO) algorithm. Moreover, survival analysis and receiver operating characteristic (ROC) analysis were applied for prognostic value validation. Seven algorithms (TIMER, XCELL, MCPcounter, QUANTISEQ, CIBERSORT, EPIC, and CIBERSORT-ABS) were utilized to characterize tumor immune microenvironment (TIME). Finally, the mutation data were analyzed by employing “maftools” package.Results: In total, 136 DELs, 128 DEMis, and 2,028 DEMs were recognized in HCC. A specific lncRNA–miRNA–mRNA network consisting of 3 lncRNAs, 12 miRNAs, and 21 mRNAs was established. A ceRNA-based prognostic signature was established to classify samples into two risk subgroups, which presented excellent prognostic performance. In additional, prognostic risk-clinical nomogram was delineated to assess risk of individual sample quantitatively. Besides, risk score was significantly associated with contexture of TIME and immunotherapeutic targets. Finally, potential interaction between risk score with tumor mutation burden (TMB) was revealed.Conclusion: In this work, comprehensive analyses of ceRNAs coexpression network will facilitate prognostic prediction, delineate complexity of TIME, and contribute insight into precision therapy for HCC.

BackgroundIncreasing evdence supports the suggestion that the immune cell infiltration (ICI) patterns play a pivotal role in tumor progression in breast cancer (BRCA). Nonetheless, there has been no comprehensive analysis of the ICI patterns effects on the clinical outcomes and immunotherapy.MethodsMultiomic data for BRCA samples were downloaded from TCGA. ESTIMATE algorithm, ssGSEA method, and CIBERSORT analysis were used to uncover the landscape of the tumor immune microenvironment (TIME). BRCA subtypes based on the ICI pattern were identified by consensus clustering and principal-component analysis was performed to obtain the ICI scores to quantify the ICI patterns in individual tumors. Their prognostic value was validated by the Kaplan-Meier survival curves. Gene set enrichment analysis (GSEA) was applied for functional annotation. Immunophenoscore (IPS) was employed to explore the immunotherapeutic role of the ICI scores. Finally, the mutation data was analyzed by using the “maftools” R package.ResultsThree different immune infiltration patterns with a distinct prognosis and biological signature were recognized among 1,198 BRCA samples. The characteristics of TIME under these three patterns were highly consistent with three known immune profiles: immune- excluded, immune-desert, and immune-inflamed phenotypes, respectively. The identification of the ICI patterns within individual tumors based on the ICI score, developed under the ICI-related signature genes, contributed into dissecting biological processes, clinical outcome, immune cells infiltration, immunotherapeutic effect, and genetic variation. High ICI score subtype, characterized with a suppression of immunity, suggested an immune-exhausted phenotype. Abundant effective immune cells were discovered in the low ICI score patients, which corresponded to an immune-activated phenotype and might present an immunotherapeutic advantage. Immunophenoscore was implemented as a surrogate of immunotherapeutic outcome, low-ICI scores samples obtained a significantly higher immunophenoscore. Enrichment of the JAK/STAT and VEGF signal pathways were activated in the ICI low-score subgroup. Finally, the synergistic effect between the ICI score and the tumor mutation burden (TMB) was confirmed.ConclusionThis work comprehensively elucidated that the ICI patterns served as an indispensable player in complexity and diversity of TIME. Quantitative identification of the ICI patterns in individual tumor will contribute into mapping the landscape of TIME further optimizing precision immunotherapy.

Background: Hepatocellular carcinoma (HCC) is a tumor with high morbidity and high mortality worldwide. DNA methylation, one of the most common epigenetic changes, might serve a vital regulatory role in cancer.Methods: To identify categories based on DNA methylation data, consensus clustering was employed. The risk signature was yielded by systematic bioinformatics analyses based on the remarkably methylated CpG sites of cluster 1. Kaplan–Meier analysis, variable regression analysis, and ROC curve analysis were further conducted to validate the prognosis predictive ability of risk signature. Gene set enrichment analysis (GSEA) was performed for functional annotation. To uncover the context of tumor immune microenvironment (TIME) of HCC, we employed the ssGSEA algorithm and CIBERSORT method and performed TIMER database exploration and single-cell RNA sequencing analysis. Additionally, quantitative real-time polymerase chain reaction was employed to determine the LRRC41 expression and preliminarily explore the latent role of LRRC41 in prognostic prediction. Finally, mutation data were analyzed by employing the “maftools” package to delineate the tumor mutation burden (TMB).Results: HCC samples were assigned into seven subtypes with different overall survival and methylation levels based on 5′-cytosine-phosphate-guanine-3′ (CpG) sites. The risk prognostic signature including two candidate genes (LRRC41 and KIAA1429) exhibited robust prognostic predictive accuracy, which was validated in the external testing cohort. Then, the risk score was significantly correlated with the TIME and immune checkpoint blockade (ICB)–related genes. Besides, a prognostic nomogram based on the risk score and clinical stage presented powerful prognostic ability. Additionally, LRRC41 with prognostic value was corroborated to be closely associated with TIME characterization in both expression and methylation levels. Subsequently, the correlation regulatory network uncovered the potential targets of LRRC41 and KIAA1429. Finally, the methylation level of KIAA1429 was correlated with gene mutation status.Conclusion: In summary, this is the first to identify HCC samples into distinct clusters according to DNA methylation and yield the CpG-based prognostic signature and quantitative nomogram to precisely predict prognosis. And the pivotal player of DNA methylation of genes in the TIME and TMB status was explored, contributing to clinical decision-making and personalized prognosis monitoring of HCC.

Human cytomegalovirus (HCMV) is an oncogenic virus associated with tumorigenesis. Our previous study revealed that the HCMV US31 gene interacted with NF-κB2 and mediated inflammation through macrophages. However, there are few reports on the role of US31 in gastric cancer (GC). The aim of this study was to investigate the expression of the US31 gene in GC tissue and assess its role in the occurrence and development of GC. US31 expression in 573 cancer tissues was analyzed using immunohistochemistry. Results showed that US31 was significantly associated with tumor size (P = 0.005) and distant metastasis (P < 0.001). Higher US31 expression indicated better overall survival in GC patients. Overexpression of US31 significantly inhibited the proliferation, migration, and invasion of GC cells in vitro (P < 0.05). Furthermore, expression levels of CD4, CD66b, and CD166 were positively correlated with US31, suggesting that it was involved in regulating the tumor immune microenvironment of GC. RNA sequencing, along with quantitative real-time polymerase chain reaction, confirmed that the expression of US31 promoted immune activation and secretion of inflammatory cytokines. Overall, US31 inhibited the malignant phenotype and regulated tumor immune cell infiltration in GC; these results suggest that US31 could be a potential prognostic factor for GC and may open the door for a new immunotherapy strategy.

Gastric cancer (GC) is one of the most common malignancies with high mortality and substantial morbidity. Although the traditional treatment strategies for GC revolve around surgery, radiotherapy, and chemotherapy, none have been able to optimally treat most affected patients. To improve clinical outcomes and overcome potential GC resistance, we established a three-dimensional (3D) culturing platform that accurately predicts drug responses in a time- and cost-effective manner. We collected tumor tissues from patients following surgeries and cultured them for 3 days using our protocol. We first evaluated cell proliferation, viability, and apoptosis using the following markers: Ki67 and cleaved caspase 3 (Cas3). We demonstrated that cell viability was maintained for 72 h in culture and that the tumor microenvironments and vascular integrities of the tissues were intact throughout the culture period. We then administered chemotherapeutics to assess drug responses and found differential sensitivity across different patient-derived tissues, enabling us to determine individualized medication plans. Overall, our study validated this rapid, cost-effective, scalable, and reproducible protocol for GC tissue culture that can be employed for drug response assessments. Our 3D culture platform paves a new way for personalized medication in GC and other tumors and can greatly impact future oncological research.

Background. Postsurgical gastroparesis syndrome (PGS) after subtotal gastrectomy imposes significant social and economic burdens. We aimed to investigate the relationship between preoperative blood glucose level and PGS and develop a nomogram for individualized prediction. Patients and Methods. We retrospectively analyzed 633 patients with gastric cancer who underwent subtotal gastrectomy. Preoperative blood glucose levels were evaluated via receiver operating characteristic (ROC) curve analysis. Chi-squared tests and multivariable logistic regression analyses were used to develop a predictive model for PGS, presented as a nomogram, which was assessed for its clinical usefulness. Results. Thirty-eight of 633 patients were diagnosed with PGS. Based on the ROC curve analysis, the preoperative blood glucose cutoff value for PGS was 6.25 mmol/L. The predictors of PGS included preoperative hyperglycemia (odds ratio (OR) 2.3, P=0.03), body mass index (BMI; OR 0.21, P=0.14 for BMI24), and the anastomotic method (OR 7.3, P=0.001 for Billroth II and OR 5.9, P=0.15 for Roux-en-Y). The predictive model showed good discrimination ability, with a C-index of 0.710, and was clinically useful. Conclusions. Preoperative hyperglycemia effectively predicts PGS. We present a nomogram incorporating the preoperative blood glucose level, BMI, anastomotic method, and tumor size, for individualized prediction of PGS.

Under complex working conditions, vehicle adaptive cruise control (ACC) systems with fixed weight coefficients cannot guarantee good car following performance under all conditions. In order to improve the tracking and comfort of vehicles in different modes, a fuzzy model predictive control (Fuzzy-MPC) algorithm is proposed. Based on the comprehensive consideration of safety, comfort, fuel economy and vehicle limitations, the objective function and constraints are designed. A relaxation factor vector is introduced to soften the hard constraint boundary in order to solve this problem, for which there was previously no feasible solution. In order to maintain driving stability under complex conditions, a multi-objective optimization method, which can update the weight coefficient online, is proposed. In the numerical simulation, the values of velocity, relative distance, acceleration and acceleration change rate under different conditions are compared, and the results show that the proposed algorithm has better tracking and stability than the traditional algorithm. The effectiveness and reliability of the Fuzzy-MPC algorithm are verified by co-simulation with the designed PID lower layer control algorithm with front feedforward and feedback.

Molecular residual disease (MRD), detected by circulating tumor DNA (ctDNA) can be involved in the entire process of solid tumor management, including recurrence prediction, efficacy evaluation, and risk stratification. Currently, the detection technologies are divided into two main categories, as follows: tumor-agnostic and tumor informed. Tumor-informed assay obtains mutation information by sequencing tumor tissue samples before blood MRD monitoring, followed by formulation of a personalized MRD panel. Tumor-agnostic assays are carried out using a fixed panel without the mutation information from primary tumor tissue. The choice of testing strategy may depend on the level of evidence from ongoing randomized clinical trials, investigator preference, cost-effectiveness, patient economics, and availability of tumor tissue. The review describes the difference between tumor informed and tumor agnostic detection. In addition, the clinical application of ctDNA MRD in solid tumors was introduced, with emphasis on lung cancer, colorectal cancer, Urinary system cancer, and breast cancer. [ABSTRACT FROM AUTHOR]

IntroductionMathematical word problems refer to word problems where the information that is presented needs to be integrated, typically into a mathematical formula, to arrive at a solution to the problem. When solving mathematics word problems, elementary school students often have difficulties improving their performance due to a lack of self-regulated learning (SRL). However, SRL can be developed by adopting an appropriate teaching approach which offers quantitative feedback or learning prompts. With the sophistication of interactive and data-driven feedback technology, it is possible to provide timely and personalized strategies for promoting students’ SRL.MethodsIn this study, an interactive e-book editing platform was used to design self-regulation-level-based feedback(SRLF) and task-level-based feedback(TLF) teaching models, which were respectively conducted in two similar fifth-grade classes for the mathematics word problem solving lessons.ResultsUsing ANCOVA and repeated ANOVA, this study found that (1) the SRLF had a remarkably greater impact on elementary school students’ mathematics word problem-solving performance than the TLF, with a partial η2-value of .107; (2) In the short period of time, there was no significant difference between the two kinds of feedback on the learners’ SRL. The TLF was slightly superior to the SRLF, especially in terms of total self-regulated learning scores and cognitive strategies; (3) The TLF had a significant interaction effect on self-regulated learning and cognitive strategies, respectively with a partial η2-value of .059 and .056.

Abstract Background Accumulating evidence shows that mesenchymal stem cell-derived extracellular vesicles (EVs) hold great promise to promote hair growth. However, large-scale production of EVs is still a challenge. Recently, exosome-mimetic nanovesicles (NV) prepared by extruding cells have emerged as an alternative strategy for clinical-scale production. Here, ReNcell VM (ReN) cells, a neural progenitor cell line was serially extruded to produce NV. Results ReN-NV were found to promote dermal papilla cell (DPC) proliferation. In addition, in a mouse model of depilation-induced hair regeneration, ReN-NV were injected subcutaneously, resulting in an acceleration of hair follicle (HF) cycling transition at the site. The underlying mechanism was indicated to be the activation of Wnt/β-catenin signaling pathway. Furthermore, miR-100 was revealed to be abundant in ReN-NV and significantly up-regulated in DPCs receiving ReN-NV treatment. miR-100 inhibition verified its important role in ReN-NV-induced β-catenin signaling activation. Conclusion These results provide an alternative agent to EVs and suggest a strategy for hair growth therapy.

The influence of epoxidized soybean oil (ESO) plasticizer on the mould and algal resistances of wood plastic composites (WPCs) was studied using artificial accelerated tests. The macro- and micro-morphology of the colonization of algae and mould on the surface and fracture morphology of the WPCs samples were observed by digital camera and scanning electron microscopy (SEM). The water absorption and thickness swelling rates of different WPCs specimens with various addition amounts of ESO were also tested. The results indicated that as more of the ESO was added, the mould or algal resistance became weaker, especially on the surfaces of the specimens, which became harshly colonized when the addition amount of ESO was beyond 15 phr. The colonization speed and intensity of the mould were higher than the algae at the same addition level of ESO and the same testing time. The hyphae and spores of mould, but not algae, were found on the inner fracture layers.