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Background. Cervical cancer is the fourth common cancer among women. Its prognosis needs our more attention. Our purpose was to identity new prognostic gene sets to help other researchers develop more effective treatment for cervical cancer patients and improve the prognosis of patients. Methods. We used gene set variation analysis (GSVA) to calculate the enrichment scores of gene sets and identified three subtypes of cervical cancer through the Cox regression model, k-means clustering algorithm, and nonnegative matrix factorization method (NMF). Chi-square test was utilized to test whether a certain clinical characteristic is different among divided subtypes. We further screened the prognostic gene sets using differential analysis, univariate Cox regression analysis, and least absolute shrinkage and selection operator (LASSO) regression. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to analyze which pathways and function the genes from screened gene sets enriched. Search Tool for the Retrieval of Interacting Genes (STRING) was used to draw the protein-protein interaction network, and Cytoscape was used to visualize the hub genes of protein-protein interaction network. Results. We identified three novel subtypes of cervical cancer in The Cancer Genome Atlas (TCGA) samples and validated in Gene Expression Omnibus (GEO) samples. There were significant variations between the three subtypes in histological type, T stage, M stage, and N stage. T_GSE36888_UNTREATED_VS_IL2_TREATED_STAT5_AB_KNOCKIN_TCELL_2H_UP and N_HALLMARK_ANGIOGENESIS were screened prognostic gene sets. The prognostic model was as follows: riskScore = T _ GSE 36888 _ UNTREATED _ VS _ IL 2 _ TREATED _ STAT 5 _ AB _ KNOCKIN _ TCELL _ 2 H _ U P ∗ 2.617 + N _ HALLMARK _ ANGIOGENESI S ∗ 4.860 . Survival analysis presented that in these two gene sets, high enrichment scores were all significantly related to worse overall survival. The hub genes from T gene set included CXCL1, CXCL2, CXCL8, ALDOA, TALDO1, LDHA, CCL4, FCAR, FCER1G, SAMSN1, LILRB1, SH3PXD2B, PPM1N, PKM, and FKBP4. As for N gene sets, the hub genes included ITGAV, PTK2, SPP1, THBD, and APOH. Conclusions. Three novel subtypes and two prognostic gene sets were identified. 15 hub genes for T gene set and 5 hub genes for N gene set were discovered. Based on these findings, we can develop more and more effective treatments for cervical cancer patients. Based on the gene enriched pathways, we can development specific drugs targeting the pathways.

Background and Objective. Recent studies show that pyroptosis-related gene affects tumor cell proliferation, invasion, and migration. But the diagnostic and prognostic value of pyroptosis-related gene in clear cell renal cell carcinoma (ccRCC) is unknown. In the present research, a prognosis model according to pyroptosis-related gene was established to forecast prognosis among patients with ccRCC. Methods. The clinical information and RNA-seq data of ccRCC patients were collected from the TCGA dataset to first explore differential pyroptosis-related genes (PRGs). Univariate Cox regression and consensus clustering were applied to identify ccRCC subtypes. The prognostic PRGs were subjected to LASSO regression analysis to establish a prognostic model and to investigate its value and function. Finally, the relationship of the model immunity checkpoints and immunity infiltration was assessed. Results. The receiver operating characteristic (ROC) showed that the 1-year, 3-year, and 5-year prediction rates of the prognostic model were 0.715, 0.693, and 0.732, respectively. The high-risk group had lower overall survival and higher stage than the low-risk group. Functional enrichment analysis showed that PRGs were significantly enriched mainly in the PPAR pathway, inflammatory pathway, and immune activity. ccRCC patient prognosis correlates with immune components in the microenvironment, and immune checkpoint molecules are significantly expressed in the high-risk group. Immunotherapy may be effective in the high-risk group. Conclusion. Pyroptosis-related gene has an important impact on the progression of ccRCC and can be used as an independent predictor of patient prognosis. In addition, immune checkpoint molecules are significantly upregulated in high-risk populations, which may be a potential target for immunotherapy.

This study investigated the natural history of esophageal squamous cell carcinoma (ESCC) in rural Chinese. We sought to help provide more data to support ESCC screenings.This study was based on an existing Screening Program in Feicheng, China. Esophageal precancerous lesions were identified in 1753 cases, diagnosed from esophageal cancer screenings from 2006 to 2016. We followed up with them through endoscopic screening until October 1, 2017. Pathology results from various grades of precancerous lesions were recorded and the annual transition probabilities and incidence density of ESCC were calculated.As of October 1, 2017, a total of 4055.8 person-years has been observed. The ESCC incidence density of mild, moderate, and severe dysplasia (SD) was 0.17, 0.79, and 1.77 per 100 person-years, respectively. The median follow-up time of mild, moderate, and SD was 3.5, 2.3, and 2.2 years, respectively. The annual transition probability of mild, moderate, and SD to the next pathological level was 0.025, 0.038, and 0.016, respectively. The ESCC incidence density of males was 2.6 times higher than females (0.58 vs. 0.22), and the older age group (56-69 age group) had a ESCC incidence density 1.2 times higher than the younger group (40-55 age group) (0.45 vs. 0.39).The higher the grade of precancerous lesions, the higher the incidence density of ESCC. Screening of esophageal cancer in males and the elderly should be strengthened. It is recommended to reinforce follow-up management for untreated patients with SD/carcinoma in situ. For patients with mild and moderate dysplasia in high-risk rural Chinese populations, endoscopic follow-up intervals can be appropriately adjusted to once every 2 years.

Two-dimensional (2D) graphitic carbon nitride (g-C3N4) possesses a unique geometric configuration featuring a superimposed heterocyclic sp2 carbon and nitrogen framework. Its fluorescence may be at...

Effect of the second phase in the micro-galvanic corrosion of a commercial Mg alloy containing rare earth elements, cast WE43 alloy, was investigated in 0.6 M NaCl solution and 0.6 M Na2SO4 solution by scanning electron microscopy (SEM) observations, scanning Kelvin probe force microscopy (SKPFM) analysis, hydrogen evolution, weight loss measurement, and electrochemical techniques. It is confirmed that the second phase of cast WE43 alloy is more active than Mg matrix and exhibits an anodic role in the micro-galvanic corrosion with α-Mg matrix as cathode and dissolves preferentially in Na2SO4 solution, in contrast to the situation in NaCl solution. The corrosion rate of cast WE43 alloy in Na2SO4 solution is much higher than that in NaCl solution, which is different from the conventional wisdom and could be attributed to the different role of the second phase in the micro-galvanic corrosion in two solutions.

BackgroundCombining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment.MethodsWithin the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models.ResultsIn comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection.ConclusionThe XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

The phenomenon of T Cell exhaustion (TEX) entails a progressive deterioration in the functionality of T cells within the immune system during prolonged conflicts with chronic infections or tumors. In the context of ovarian cancer immunotherapy, the development, and outcome of treatment are closely linked to T-cell exhaustion. Hence, gaining an in-depth understanding of the features of TEX within the immune microenvironment of ovarian cancer is of paramount importance for the management of OC patients. To this end, we leveraged single-cell RNA data from OC to perform clustering and identify T-cell marker genes utilizing the Unified Modal Approximation and Projection (UMAP) approach. Through GSVA and WGCNA in bulk RNA-seq data, we identified 185 TEX-related genes (TEXRGs). Subsequently, we transformed ten machine learning algorithms into 80 combinations and selected the most optimal one to construct TEX-related prognostic features (TEXRPS) based on the mean C-index of the three OC cohorts. In addition, we explored the disparities in clinicopathological features, mutational status, immune cell infiltration, and immunotherapy efficacy between the high-risk (HR) and low-risk (LR) groups. Upon the integration of clinicopathological features, TEXRPS displayed robust predictive power. Notably, patients in the LR group exhibited a superior prognosis, higher tumor mutational load (TMB), greater immune cell infiltration abundance, and enhanced sensitivity to immunotherapy. Lastly, we verified the differential expression of the model gene CD44 using qRT-PCR. In conclusion, our study offers a valuable tool to guide clinical management and targeted therapy of OC.

Background: Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous cancer type that lacks early diagnostic markers and effective targets for immunotherapy. WD repeat-containing protein 72 (WDR72) mutations have been linked to renal dysfunction and renal tubular acidosis. However, the potential prognosis-predictive value of WDR72 in ccRCC remains unelucidated. Methods: The current research used The Cancer Genome Atlas (TCGA) data to explore the expression and prognosis-predictive value of WDR72 in various solid tumors, including ccRCC. The analysis was validated using the TCGA–ccRCC cohort and Human Protein Atlas. The biological processes and signaling pathways linked to the expression of WDR72 were identified by conducting Gene Ontology and gene set enrichment analysis. Cox regression analysis was utilized to measure the prognosis-predictive value of WDR72 in ccRCC. Moreover, the association of WDR72 expression levels with immune cell infiltration, methylation, m6A, tumor mutation burden, immune checkpoint molecules, and drug sensitivity was analyzed. Finally, the study was verified by conducting quantitative real-time polymerase chain reaction and cytological experiments in ccRCC. Results: A pan-cancer analysis noted that low WDR72 expression levels were strongly linked to poor overall survival and progression-free interval in individuals with ccRCC. The WDR72 levels were remarkably reduced in ccRCC tissues in comparison with the adjacent tissues, and WDR72 was identified as an independent predictor of patients with ccRCC. Pathway enrichment analysis showed a close relationship between genes related to WDR72 and fat metabolism. Low WDR72 expression in ccRCC was linked to higher immune cell infiltration, stronger tumor mutation burden, and higher immune checkpoint molecules. Furthermore, individuals with ccRCC and low WDR72 expression had lower IC50 values for most drugs. Conclusion: Low WDR72 expression is strongly linked to the progression, prognosis, and microenvironmental immune regulation of individuals with ccRCC. Moreover, it may hold potential as a valuable prognostic biological marker and therapeutic measure for ccRCC. The findings provide a basis for future research by exploring the role of WDR72 in ccRCC tumor development.

BackgroundColorectal cancer (CRC) is a highly prevalent cancer, and the global healthcare system bears a significant burden due to its incidence. Modulating the gut microbiota is a promising approach to enhance the efficacy of CRC treatment and reduce its adverse effects. The causal relationship between specific microorganisms’ presence and CRC development has been widely validated. However, few studies have investigated this relationship using bibliometric methods. Therefore, this study analyzed the research hotspots and trends in human gut microbiology and CRC over the last two decades from a bibliometric perspective. The study aims to provide novel insights into basic and clinical research in this field.MethodsThe articles and reviews on gut microbiota in CRC were obtained from the Web of Science Core Collection (WOSCC) on November 2, 2022. CiteSpace and VOSviewer were used to conduct the bibliometric and knowledge-map analysis.ResultsA total of 2,707 publications were obtained, with a rapid increase in the number of publications since 2015. The United States and China are the main contributors in this field and have established a network of partnerships in several countries. 414 academic journals have published articles on this topic. The author with the highest number of publications is Jun Yu from the Chinese University of Hong Kong. In addition to “intestinal flora” and “colorectal cancer,” high frequency terms in the keyword co-occurrence network analysis included inflammatory bowel disease, Fusobacterium nucleatum, inflammation, long-chain fatty acids, ulcerative colitis, bile acids, and resistant starch. Analysis of keyword trends using burst testing revealed that biomarkers, abnormal crypt foci, bifidobacteria, β-glucuronidase, short-chain fatty acids, bile acids, and DNA methylation are at the forefront of research in this area.ConclusionThe findings of this study provide a bibliometric analysis and visualization of the key research areas in gut microbiota and CRC over the past 20 years. The results suggest that the role of gut microbiota in CRC and its underlying mechanisms should be closely monitored, particularly in the areas of biomarkers, metabolic pathways, and DNA methylation, which may emerge as hot topics in this field.

A total of 137 farmland soil samples were collected around a lead/zinc smelter within 64 km2. The concentration, spatial distribution, and potential source of nine heavy metal(oid)s (As, Cd, Co, Cr, Cu, Ni, Pb, V, and Zn) in soils and their potential ecological risk were investigated in detail. The results showed that the average concentrations of Cd, Pb, Cr and Zn in these soils were higher than their background value in Henan Province, and the average content of Cd was 2.83 times of the risk screening values in the national standard of China (GB 15618-2018). According to the distribution of different heavy metal(oid)s in soils, Cd and Pb in soil decrease gradually with the increase of distance from the smelter to the surrounding area. This indicates that the Pb and Cd originate from smelters via airborne practices according to the typical air pollution diffusion model. The distribution of Zn, Cu, and As were similar to Cd and Pb. However, Ni, V, Cr, and Co were mainly affected by soil parent materials. The potential ecological risk of Cd was higher than those of other elements, and the risk grade of the other eight elements was mainly low. The polluted soils with significantly high and high potential ecological risk covered 93.84% of all the studied regions. This should be of serious concern to government. The results of a principal component analysis (PCA) and cluster analysis (CA) show that Pb, Cd, Zn, Cu, and As were the elements mainly stemmed from smelter and other types of plants, with a contribution rate of 60.08%, while Co, Cr, Ni, and V are mainly caused by nature, with a contribution rate of 26.26%.

Due to the rapid development of China’s economy, the demand for wood is steadily increasing. Eucalyptus species have been introduced in large quantities because of their fast growth, strong adaptability, and wide utility. To understand the phenological changes in introduced Eucalyptus in its new range, we carried out a field investigation to examine leaf functional and chemical defense traits of three introduced species (E. saligna, E. grandis and E. robusta) over latitudinal and altitudinal gradients in southern China. We sampled multiple stands of each species, and measured the leaf physical characteristics (e.g., leaf width, leaf thickness, and specific leaf area [SLA]), leaf nitrogen (N) and phosphorus (P) content, and phenolic compounds. We found that many functional traits (e.g., leaf size and thickness) decreased at lower latitudes, especially in E. grandis, possibly to reduce heat and water loss under higher temperatures. In E. grandis, we found that leaf P was lower at higher latitudes and altitude, and phenolics increased with elevation, while in E. robusta, both leaf N and P decreased with altitude. These findings suggested that both species were more conservative in resource allocation, with E. grandis possessing enhanced chemical defenses in response to the conditions experienced at higher elevations. In addition, we found the tree populations at the northern range limit of E. robusta had lower SLA, suggesting a more conservative growth strategy, In contrast, small populations in the northern part of the ranges of E. grandis had higher SLA, indicating range expansion at the edge of the species’ geographic distribution. Overall, it is particularly important to consider intraspecific trait differences across wide geographic areas when studying the spread of invasive species in the new range.

Background Necroptosis is a novel type of programmed cell death distinct from apoptosis. However, the role of necroptosis in ovarian cancer (OC) remains unclear. The present study investigated the prognostic value of necroptosis-related genes (NRGs) and the immune landscape in OC. Methods The gene expression profiling and clinical information were downloaded from the TCGA and GTEx databases. Differentially expressed NRGs (DE-NRGs) between OC and normal tissueswere identified. The regression analyses were conducted to screen the prognostic NRGs and construct the predictive risk model. Patients were then divided into high- and low-risk groups, and the GO and KEGG analyses were performed to explore bioinformatics functions between the two groups. Subsequently, the risk level and immune status correlations were assessed through the ESTIMATE and CIBERSORT algorithms. The tumor mutation burden (TMB) and the drug sensitivity were also analyzed based on the two-NRG signature in OC. Results Totally 42 DE-NRGs were identified in OC. The regression analyses screened out two NRGs (MAPK10 and STAT4) with prognostic values for overall survival. The ROC curve showed a better predictive ability in five-year OS using the risk score. Immune-related functions were significantly enriched in the high- and low-risk group. Macrophages M1, T cells CD4 memory activated, T cells CD8, and T cells regulatory infiltration immune cells were associated with the low-risk score. The lower tumor microenvironment score was demonstrated in the high-risk group. Patients with lower TMB in the low-risk group showed a better prognosis, and a lower TIDE score suggested a better immune checkpoint inhibitor response in the high-risk group. Besides, cisplatin and paclitaxel were found to be more sensitive in the low-risk group. Conclusions MAPK10 and STAT4 can be important prognosis factors in OC, and the two-gene signature performs well in predicting survival outcomes. Our study provided novel ways of OC prognosis estimation and potential treatment strategy.

Background Immunosuppression is a key feature of cancer, promoting tumor growth and progression. Regulatory T cells (Tregs) are a specialized subset of T lymphocytes that mediate immunosuppression in cancer. However, the role of Treg-related genes in ovarian cancer (OC) remains largely unexplored.Methods To identify marker genes related to Tregs in OC, we utilized scRNA-seq analysis. Treg scores were then constructed using single-sample gene set enrichment analysis (ssGSEA) based on these markers. To identify gene modules with the strongest correlation with Treg scores in bulk RNA-seq data, we employed the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm. Multiple machine learning algorithms were then used to construct risk models with superior predictive performance, which were validated using external independent datasets. A risk score was developed for each OC sample based on the optimal model to evaluate differences in prognosis, immune infiltration, pathway activity, and immunotherapy between high and low-risk groups.Results We identified 365 genes regulating Treg activity using the WGCNA algorithm, and found that 70 of these genes were associated with the prognosis of OC based on univariate cox analysis. Using a combination of the Random Survival Forest (RSF) and Lasso algorithms, we constructed a risk model with the highest c-index based on the assigned risk scores. The model's performance was validated using four external datasets. Our analysis revealed that the low-risk group had a better prognosis, more immune cell infiltration, higher immune checkpoint expression, and significant differences in pathway enrichment and immunotherapy efficacy between the different risk groups.Conclusion Our findings provide new insights into the role of Treg cells in the development and progression of OC and highlight the potential for developing novel Treg-targeted therapies for the treatment of this disease.

BackgroundBladder cancer (BLCA) is the most common malignancy of the urinary tract. On the other hand, disulfidptosis, a mechanism of disulfide stress-induced cell death, is closely associated with tumorigenesis and progression. Here, we investigated the impact of disulfidptosis-related genes (DRGs) on the prognosis of BLCA, identified various DRG clusters, and developed a risk model to assess patient prognosis, immunological profile, and treatment response.MethodsThe expression and mutational characteristics of four DRGs were first analyzed in bulk RNA-Seq and single-cell RNA sequencing data, IHC staining identified the role of DRGs in BLCA progression, and two DRG clusters were identified by consensus clustering. Using the differentially expressed genes (DEGs) from these two clusters, we transformed ten machine learning algorithms into more than 80 combinations and finally selected the best algorithm to construct a disulfidptosis-related prognostic signature (DRPS). We based this selection on the mean C-index of three BLCA cohorts. Furthermore, we explored the differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between high and low-risk groups. To visually depict the clinical value of DRPS, we employed nomograms. Additionally, we verified whether DRPS predicts response to immunotherapy in BLCA patients by utilizing the Tumour Immune Dysfunction and Rejection (TIDE) and IMvigor 210 cohorts.ResultsIn the integrated cohort, we identified several DRG clusters and DRG gene clusters that differed significantly in overall survival (OS) and tumor microenvironment. After the integration of clinicopathological features, DRPS showed robust predictive power. Based on the median risk score associated with disulfidptosis, BLCA patients were divided into low-risk (LR) and high-risk (HR) groups, with patients in the LR group having a better prognosis, a higher tumor mutational load and being more sensitive to immunotherapy and chemotherapy.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of BLCA patients, offering new insights into individualized treatment.

BackgroundHepatocellular carcinoma (HCC) is a complex disease with a poor outlook for patients in advanced stages. Immune cells play an important role in the progression of HCC. The metabolism of sphingolipids functions in both tumor growth and immune infiltration. However, little research has focused on using sphingolipid factors to predict HCC prognosis. This study aimed to identify the key sphingolipids genes (SPGs) in HCC and develop a reliable prognostic model based on these genes.MethodsThe TCGA, GEO, and ICGC datasets were grouped using SPGs obtained from the InnateDB portal. A prognostic gene signature was created by applying LASSO-Cox analysis and evaluating it with Cox regression. The validity of the signature was verified using ICGC and GEO datasets. The tumor microenvironment (TME) was examined using ESTIMATE and CIBERSORT, and potential therapeutic targets were identified through machine learning. Single-cell sequencing was used to examine the distribution of signature genes in cells within the TME. Cell viability and migration were tested to confirm the role of the key SPGs.ResultsWe identified 28 SPGs that have an impact on survival. Using clinicopathological features and 6 genes, we developed a nomogram for HCC. The high- and low-risk groups were found to have distinct immune characteristics and response to drugs. Unlike CD8 T cells, M0 and M2 macrophages were found to be highly infiltrated in the TME of the high-risk subgroup. High levels of SPGs were found to be a good indicator of response to immunotherapy. In cell function experiments, SMPD2 and CSTA were found to enhance survival and migration of Huh7 cells, while silencing these genes increased the sensitivity of Huh7 cells to lapatinib.ConclusionThe study presents a six-gene signature and a nomogram that can aid clinicians in choosing personalized treatments for HCC patients. Furthermore, it uncovers the connection between sphingolipid-related genes and the immune microenvironment, offering a novel approach for immunotherapy. By focusing on crucial sphingolipid genes like SMPD2 and CSTA, the efficacy of anti-tumor therapy can be increased in HCC cells.

The corrosion behavior and corrosion resistance of WE43 and AZ80 immersed in NaCl and Na2SO4 solutions were investigated, respectively. Two alloys were immersed in 0.6 M NaCl and Na2SO4 solution to observe the corrosion morphologies. Hydrogen evolution and weight loss experiments were conducted to obtain the corrosion rates. Electrochemical tests were used to characterized detailed corrosion situation. The results show that, when immersed in Na2SO4 solution, WE43 alloy shows a unique micro-galvanic corrosion behavior. On the other hand, the corrosion rate of WE43 in Na2SO4 solution is much faster than that in NaCl solution, which is the direct opposite of AZ80 and most magnesium alloys. The protection of the surface film maybe the key factor to the unexpected phenomena.

Objective: To describe the clinical characteristics of Covid-19 in infants during the Omicron variant outbreak in Henan, 2022. Methods: We retrospectively analyzed the clinical data, epidemiological exposure, and maternal vaccination status of infants infected with Omicron variant admitted to Zhengzhou First People’s Hospital from October 20, 2022 to December 15, 2022. Results: We admitted 21 infants (12 males and 9 females). All had epidemiological exposure in household or nursing institution settings. The most common respiratory manifestations were fever (19 cases), cough (11 cases), and nasal congestion (8 cases). The most frequent gastrointestinal manifestations were anorexia and diarrhea (8 cases each) and vomiting (4 cases). Of the 12 infants who underwent complete blood count, 8 had normal white blood cell counts and 3 had leukopenia. C-reactive protein was normal in 11 cases and mildly elevated in 1 case. Chest CT showed increased lung markings in 7 cases, focal consolidation in 6 cases each, and ground-glass opacities in 5 cases. The mean time to negative conversion was 6.7 days. All 21 infants recovered and were discharged after symptomatic treatment, with a mean length of stay of 11.5 days. Two weeks after discharge, none had recurrence or retested positive for SARS-CoV-2 RNA. We found no correlations between maternal vaccination status and clinical symptoms or SARS-CoV-2 RNA negative conversion time (P>0.05). Conclusion: All Covid-19 infants had epidemiological exposure in household or nursing institution settings. They mostly had mild infection with respiratory and/or gastrointestinal symptoms and a good short-term prognosis.

BackgroundHead and neck squamous cell carcinoma (HNSCC) is the most common head and neck cancer and is highly aggressive and heterogeneous, leading to variable prognosis and immunotherapy outcomes. Circadian rhythm alterations in tumourigenesis are of equal importance to genetic factors and several biologic clock genes are considered to be prognostic biomarkers for various cancers. The aim of this study was to establish reliable markers based on biologic clock genes, thus providing a new perspective for assessing immunotherapy response and prognosis in patients with HNSCC.MethodsWe used 502 HNSCC samples and 44 normal samples from the TCGA-HNSCC dataset as the training set. 97 samples from GSE41613 were used as an external validation set. Prognostic characteristics of circadian rhythm-related genes (CRRGs) were established by Lasso, random forest and stepwise multifactorial Cox. Multivariate analysis revealed that CRRGs characteristics were independent predictors of HNSCC, with patients in the high-risk group having a worse prognosis than those in the low-risk group. The relevance of CRRGs to the immune microenvironment and immunotherapy was assessed by an integrated algorithm.Results6-CRRGs were considered to be strongly associated with HNSCC prognosis and a good predictor of HNSCC. The riskscore established by the 6-CRRG was found to be an independent prognostic factor for HNSCC in multifactorial analysis, with patients in the low-risk group having a higher overall survival (OS) than the high-risk group. Nomogram prediction maps constructed from clinical characteristics and riskscore had good prognostic power. Patients in the low-risk group had higher levels of immune infiltration and immune checkpoint expression and were more likely to benefit from immunotherapy.Conclusion6-CRRGs play a key predictive role for the prognosis of HNSCC patients and can guide physicians in selecting potential responders to prioritise immunotherapy, which could facilitate further research in precision immuno-oncology.

BackgroundOvarian cancer (OC) is one of the most common and most malignant gynecological malignancies in gynecology. On the other hand, dysregulation of copper metabolism (CM) is closely associated with tumourigenesis and progression. Here, we investigated the impact of genes associated with copper metabolism (CMRGs) on the prognosis of OC, discovered various CM clusters, and built a risk model to evaluate patient prognosis, immunological features, and therapy response.Methods15 CMRGs affecting the prognosis of OC patients were identified in The Cancer Genome Atlas (TCGA). Consensus Clustering was used to identify two CM clusters. lasso-cox methods were used to establish the copper metabolism-related gene prognostic signature (CMRGPS) based on differentially expressed genes in the two clusters. The GSE63885 cohort was used as an external validation cohort. Expression of CM risk score-associated genes was verified by single-cell sequencing and quantitative real-time PCR (qRT-PCR). Nomograms were used to visually depict the clinical value of CMRGPS. Differences in clinical traits, immune cell infiltration, and tumor mutational load (TMB) between risk groups were also extensively examined. Tumour Immune Dysfunction and Rejection (TIDE) and Immune Phenotype Score (IPS) were used to validate whether CMRGPS could predict response to immunotherapy in OC patients.ResultsIn the TCGA and GSE63885 cohorts, we identified two CM clusters that differed significantly in terms of overall survival (OS) and tumor microenvironment. We then created a CMRGPS containing 11 genes to predict overall survival and confirmed its reliable predictive power for OC patients. The expression of CM risk score-related genes was validated by qRT-PCR. Patients with OC were divided into low-risk (LR) and high-risk (HR) groups based on the median CM risk score, with better survival in the LR group. The 5-year AUC value reached 0.74. Enrichment analysis showed that the LR group was associated with tumor immune-related pathways. The results of TIDE and IPS showed a better response to immunotherapy in the LR group.ConclusionOur study, therefore, provides a valuable tool to further guide clinical management and tailor the treatment of patients with OC, offering new insights into individualized treatment.

Background: ACER2 is a critical gene regulating cancer cell growth and migration, whereas the immunological role of ACER2 in the tumor microenvironment (TME) is scarcely reported. Thus, we lucubrate the potential performance of ACER2 in bladder cancer (BLCA).Methods: We initially compared ACER2 expressions in BLCA with normal urothelium tissues based on data gathered from the Cancer Genome Atlas (TCGA) and our Xiangya cohort. Subsequently, we systematically explored correlations between ACER2 with immunomodulators, anti-cancer immune cycles, tumor-infiltrating immune cells, immune checkpoints and the T-cell inflamed score (TIS) to further confirm its immunological role in BLCA TME. In addition, we performed ROC analysis to illustrate the accuracy of ACER2 in predicting BLCA molecular subtypes and explored the response to several cancer-related treatments. Finally, we validated results in an immunotherapy cohort and Xiangya cohort to ensure the stability of our study.Results: Compared with normal urinary epithelium, ACER2 was significantly overexpressed in several cell lines and the tumor tissue of BLCA. ACER2 can contribute to the formation of non-inflamed BLCA TME supported by its negative correlations with immunomodulators, anti-cancer immune cycles, tumor-infiltrating immune cells, immune checkpoints and the TIS. Moreover, BLCA patients with high ACER2 expression were inclined to the luminal subtype, which were characterized by insensitivity to neoadjuvant chemotherapy, chemotherapy and radiotherapy but not to immunotherapy. Results in the IMvigor210 and Xiangya cohort were consistent.Conclusion: ACER2 could accurately predict the TME and clinical outcomes for BLCA. It would be served as a promising target for precision treatment in the future.

The Ordos Basin is a sedimentary basin located in Inner Mongolia, China, where coal and uranium coexist. Water inrush disasters have always been one of the main disasters that threaten the safety of coal mine production, and thus, the study and division of groundwater potential regions are of great significance for the prevention of water inrush disasters and in situ leaching of sandstone-type uranium ore. A new method combining truncated Gaussian simulation and sedimentary facies control was established to predict the groundwater potential area. Taking a typical aquifer, the Zhiluo Formation, as an example, based on high-resolution sequence stratigraphy, geophysics, sedimentary geology, and geostatistical theory, the plane distribution of sand bodies was predicted. Furthermore, the relationship between rock porosity and electricity porosity was established to calculate the regional porosity. Combined with truncated Gaussian simulation and facies-controlled modeling methods, a facies-controlled heterogeneous property model was established to analyze the heterogeneous effective porosity of the aquifer in the study area. Groundwater potential areas were quantitatively evaluated by 3D modeling analysis. The results of the evaluated model were verified by actual data and provide a geological guarantee for the accurate mining of deep coal and uranium ore. A 3D distributed model of chemical elements, which is meaningful for in situ leaching uranium mining, is expected in future research.

In this paper, we address the challenges of frequent backup and recovery operations associated with an unstable energy supply in energy-harvesting nonvolatile processors (NVPs). To address this problem, this paper adopts a retention state, which can allow the system to retain data in a volatile cache to wait for power recovery instead of immediately backing up the data. In addition, in an energy-harvesting NVP, important issues include how to back up and recover data, how to perform cache replacement, and how to transmit data to different parts of the cache in such a way that the performance and energy consumption can be improved and a successful backup can be guaranteed. Therefore, we propose a performance-oriented cache management scheme based on a retention state to generate near-optimal data migration, replacement and backup solutions for applications in polynomial time. This scheme uses two energy thresholds to pre-back up and back up data to reduce the backup and recovery operations according to the retention state. We evaluate the performance and energy consumption of our proposed algorithms in comparison with those of the dual-threshold SLC STT-RAM scheme and morphable cache scheme. The evaluation shows that the proposed scheme can achieve 18.58% and 4.54% lower average energy consumption than the SLC STT-RAM and morphable cache schemes, respectively, with comparable performance. The experimental results also show that compared with the SLC STT-RAM and morphable cache schemes, the proposed algorithm can achieve 17.90% and 8.85% performance improvement, respectively, on average.

Accurate single-cell capture is a crucial step for single cell biological and chemical analysis. Conventional single-cell capturing often confront operational complexity, limited efficiency, cell damage, large scale but low accuracy, incompetence in the acquirement of nano-upgraded single-cell liquid. Flow cytometry has been widely used in large-scale single-cell detection, while precise single-cell isolation relies on both a precision operating platform and a microscope, which is not only extremely inefficient, but also not conducive to couple with modern analytical instruments. Herein, we develop a modular single-cell pipette (mSCP) microfluidic chip with high efficiency and strong applicability for accurate direct capture of single viable cell from cell suspensions into nanoliter droplets (30−1000 nL). The mSCP is used as a sampling platform for the detection of CdTe quantum dots in single cells with electrothermal atomic absorption spectrometry (ETAAS) for the first time. It also ensures precise single-cell sampling and detection by inductively coupled plasma mass spectrometry (ICP-MS).

Uterine corpus endometrial carcinoma (UCEC) is a malignant disease globally, and there is no unified prognostic signature at present. In our study, two clusters were identified. Cluster 1 showed better prognosis and higher infiltration level, such as tumor microenvironment (TME), tumor mutation burden (TMB), and immune checkpoint genes expression. Gene set enrichment analysis (GSEA) indicated that some tumor-related pathways and immune-associated pathways were exposed. What is more, six pyroptosis-related long noncoding RNAs (lncRNAs) (PRLs) were applied to establish a prognostic signature through multiple Cox regression analysis. In both training and testing sets, patients with higher risk score had poorer survival than patients with low risk. The area under the curve (AUC) of receiver operating characteristic (ROC) curves performed that the survival probability was better in people with lower risk score. Mechanism analysis revealed that high risk score was correlated with reduced immune infiltration and T cells exhaustion, matching the definition of an "immune-desert" phenotype. Patients with lower risk score were characterized by higher immune checkpoint gene expression and TMB and have a sensitive response to immunotherapy and chemotherapy compared with patients with high risk score. The signature has accurate prediction ability of UCEC and is a promising therapeutic target to improve the effect of immunotherapy.

Many evidences have showed that some intelligence agencies (often called big brother) attempt to monitor citizens’ communication by providing coerced citizens a lot of subverted cryptographic algorithms and coercing them to adopt these algorithms. Since legalized services on large number of various applications and system architectures depend on digital signature techniques, in the context some coerced users who use double authentication preventing signatures to design some novel digital signature techniques, have some convincing dissertations to defuse requests from authorities and big brothers creating some corresponding subverted signatures. As rapid progress in quantum computers, National Security Agency advisory memorandum and announcement of National Institute of Standards and Technology procedures from standardization focus on some cryptographic algorithms which are post quantum secure. Motivated by these issues, we design an algorithm substitution attack against Fiat-Shamir family based on lattices (e.g., BLISS, BG, Ring-TESLA, PASSSign and GLP) that are proven post-quantum computational secure. We also show an efficient deterable way to eliminate big brother’s threat by leaking signing keys from signatures on two messages to be public. Security proof shows that our schemes satisfy key extraction, undetectability and deterability. Through parameters analysis and performance evaluation, we demonstrate that our deterring subverted Fiat-Shamir signature is practical, which means that it can be applied to privacy and protection in some system architectures.

Healthcare cyber-physical system significantly facilitates healthcare services and patient treatment effectiveness by analyzing patients’ health information data conveniently. Nevertheless, it also develops the threats to the confidentiality of health information, patients’ privacy, and decidability of medical disputes. And, with the advances of quantum computing technology, most existing anonymous authentication schemes are becoming a growing threat to traditional cryptosystems. To address these problems, for healthcare cyber-physical systems, we propose a new lattice-based self-enhancement authorized accessible privacy authentication scheme by using a strong designated verifier double-authentication-preventing signature technique, called SEAPA. The SEAPA achieves three security and privacy requirements including unforgeability, anonymity for patients’ information, and self-enhancement for patients themselves. A detailed security proof shows our proposal achieves those required security goals. Finally, our construction is demonstrated by parameter analysis and performance evaluation to have reasonable efficiency.

Pyroptosis, a form of inflammatory programmed cell death, is accompanied by inflammation and participate in the body's immune response. The expression of pyroptosis-related genes (PRGs) is associated with tumor prognosis in ovarian cancer (OC), but it is still unknown whether pyroptosis can affect tumor immune microenvironment (TME) of OC. Based on 30 PRGs, we comprehensively assessed the pyroptosis patterns by using PRGscore and correlated them with TME features in 474 OC patients. Finally, we identified three pyroptosis modification patterns and TME immune characteristics of these patterns were in response to three immune phenotypes (immune-desert, immune-inflamed, and immune-excluded phenotypes). PRGscore can predict patient survival, staging, grading, and immunotherapy efficacy. Low PRGscore was associated with better survival advantage and increased mutation burden. Low PRGscore patients showed significantly better therapeutic effects and clinical results in chemotherapy and immunotherapy. Besides, the capability of PRGscore in predicting prognosis and immunotherapy sensitivity was further verified in other three tumor cohorts. In conclusion, the comprehensive assessment of OC pyroptosis modifications can help enhancing our understanding of TME immune infiltration and provide better personalized treatment tactics for OC patients.

The issue caused by increasing energy consumption of internet data centers (IDCs) has received critical attention. Hence, it is important to optimize the energy system of IDCs for energy saving and carbon emissions reduction. In order to explore the utilization of hydrogen in IDCs and the advantages of water cooling system, IDCs decarbonized by hydrogen-water-based energy systems are developed in this paper, which makes full use of both electricity and heating energy generated by fuel cells, and renewable energy. The integrated planning-and-scheduling problem is formulated as a two-stage stochastic mixed-integer programming problem to determine the optimal capacity of energy facilities and operation strategies. The developed IDCs are investigated with energy demand and solar radiation in typical days. It is found the developed IDCs are environmental friendly and cost-effective. The power usage efficiency can be as low as 1.02 and carbon emission reduction can be up to 59.1% when hydrogen price is U.S. department of energy target price 2$/kg.

Cuproptosis is a new form of cell death, the second form of metal ion-induced cell death defined after ferroptosis. Recently, cuproptosis has been suggested to be associated with tumorigenesis. However, the relationship between cuproptosis and patient prognosis in clear cell renal cell carcinoma (ccRCC) in the context of immunotherapy remains unknown. The aim of this study was to investigate the correlation between cuproptosis-related long non-coding RNA (lncRNA) and ccRCC in terms of immunity as well as prognosis. Clinical information on lncRNAs associated with differences in cuproptosis genes in ccRCC and normal tissues was collected from The Cancer Genome Atlas (TCGA) dataset. Univariate Cox regression was used to screen lncRNAs. A total of 11 lncRNAs closely associated with cuproptosis were further screened and established using the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression, and the samples were randomly divided into training and test groups. A risk prognostic model was constructed using the training group, and the model was validated using the test group. We investigated the predictive ability of the prognostic risk model in terms of clinical prognosis, tumor mutation, immune escape, immunotherapy, tumor microenvironment, immune infiltration levels, and tumor drug treatment of ccRCC. Using the median risk score, patients were divided into low and high-risk groups. Kaplan-Meier curves showed that the overall survival (OS) of patients in the high-risk group was significantly worse than low-risk group (p < 0.001). Receiver operating characteristic (ROC) curves further validated the reliability of our model. The model consistently and accurately predicted prognosis at 1, 3, and 5 years, with an AUC above 0.7. Tumor cell genes generally precede morphological abnormalities; therefore, the model we constructed can effectively compensate for the traditional method of evaluating the prognosis of patients with renal cancer, and our model was also clinically meaningful in predicting ccRCC staging. In addition, lower model risk scores determined by mutational load indicated a good chance of survival. The high-risk group had greater recruitment of immune cells, while the anti-immune checkpoint immunotherapy was less efficacious overall than that of the low-risk group. Tumor and immune-related pathways were enriched, and anti-tumor agents were selected to improve the survival of ccRCC. This prognostic risk model is based on the levels of cuproptosis-associated lncRNAs and provides a new perspective in the clinical assessment and precise treatment of ccRCC.

Endometrial cancer has the highest incidence of uterine corpus cancer, the sixth most typical cancer in women until 2020. High recurrence rate and frequent adverse events were reported in either standard chemotherapy or combined therapy. Hence, developing precise diagnostic and prognostic approaches for endometrial cancer was on demand. Four hypoxia-related genes were screened for the EC prognostic model by the univariate, LASSO, and multivariate Cox regression analysis from the TCGA dataset. QT-PCR and functional annotation analysis were performed. Associations between predicted risk and immunotherapy and chemotherapy responses were investigated by evaluating expressions of immune checkpoint inhibitors, infiltrated immune cells, m6a regulators, and drug sensitivity. The ROC curve and calibration plot indicated a fair predictability of our prognostic nomogram model. NR3C1 amplification, along with IL-6 and SRPX suppressions, were detected in tumor. High stromal score and enriched infiltrated aDCs and B cells in the high-risk group supported the hypothesis of immune-deserted tumor. Hypoxia-related molecular subtypes of EC were then identified via the gene signature. Cluster 2 patients showed a significant sensitivity to Vinblastine. In summary, our hypoxia signature model accurately predicted the survival outcome of EC patients and assessed translational and transcriptional dysregulations to explore targets for precise medical treatment.