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Published data were gathered for a meta-analysis to determine the difference in sperm parameters before and after administration of different types of coronavirus disease 2019 (COVID-19) vaccines, because the reproductive toxicity of COVID-19 vaccines has not yet been evaluated in clinical trials and COVID-19 has been associated with decreases in sperm quality. The preferred procedures for systematic reviews and meta-analyses were followed in the conduct and reporting of this study. The average sperm parameters of all sperm donors' multiple sperm donations were compared before and after receiving various COVID-19 vaccinations. Semen volume, total sperm motility, total sperm count, morphological change, and sperm concentration were the primary outcome measures. We compiled and analyzed the results of six studies on total sperm motility, six studies on semen volume, six studies on sperm concentration, two studies on morphological change, and two studies on total sperm count. Parameter comparisons with patients who had and had not been vaccinated were only reported in one of the included studies. When different types of COVID-19 vaccine injections were compared, no discernible differences in parameters were observed. According to the available data, the parameters of semen are unaffected by inactivated or messenger RNA (mRNA) COVID-19 vaccinations. To support these findings, additional prospectively designed research is required.

Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users to communicate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done to combine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered.

This paper presents a novel teleoperation system using Electroencephalogram (EEG) to control the motion of a wheeled mobile robot (WMR). Different from the other traditional motion controlling method, the WMR is braked with the EEG classification results. Furthermore, the EEG will be induced by using the online BMI (Brain Machine Interface) system, and adopting the non-intrusion induced mode SSVEP (steady state visually evoked potentials). Then, user's motion intention can be recognized by canonical correlation analysis (CCA) classifier, which will be converted into motion commands of the WMR. Finally, the teleoperation technique is utilized to manage the information of the movement scene and adjust the control instructions based on the real-time information. Bezier curve is used to parameterize the path planning of the robot, and the trajectory can be adjusted in real time by EEG recognition results. A motion controller based on error model is proposed to track the planned trajectory by using velocity feedback control, providing excellent track tracking performance. Finally, the feasibility and performance of the proposed teleoperation brain-controlled WMR system are verified using demonstration experiments.

Abstract Background This study aimed to use single-cell RNA-seq (scRNA-seq) to discover marker genes in endothelial cells (ECs) and construct a prognostic model for glioblastoma multiforme (GBM) patients in combination with traditional high-throughput RNA sequencing (bulk RNA-seq). Methods Bulk RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and The China Glioma Genome Atlas (CGGA) databases. 10x scRNA-seq data for GBM were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Key modules and differentially expressed genes (DEGs) were identified by weighted gene correlation network analysis (WGCNA). A non-negative matrix decomposition (NMF) algorithm was used to identify the different subtypes based on DEGs, and multivariate cox regression analysis to model the prognosis. Finally, differences in mutational landscape, immune cell abundance, immune checkpoint inhibitors (ICIs)-associated genes, immunotherapy effects, and enriched pathways were investigated between different risk groups. Results The analysis of scRNA-seq data from eight samples revealed 13 clusters and four cell types. After applying Fisher’s exact test, ECs were identified as the most important cell type. The NMF algorithm identified two clusters with different prognostic and immunological features based on DEGs. We finally built a prognostic model based on the expression levels of four key genes. Higher risk scores were significantly associated with poorer survival outcomes, low mutation rates in IDH genes, and upregulation of immune checkpoints such as PD-L1 and CD276. Conclusion We built and validated a 4-gene signature for GBM using 10 scRNA-seq and bulk RNA-seq data in this work.

BackgroundGlioblastoma (GBM) is a malignant primary brain tumor. This study focused on exploring the exosome-related features of glioblastoma to better understand its cellular composition and molecular characteristics.MethodsSingle-cell RNA sequencing (scRNA-seq) and spatial transcriptome RNA sequencing (stRNA-seq) were used to analyze the heterogeneity of glioblastomas. After data integration, cell clustering, and annotation, five algorithms were used to calculate scores for exosome-related genes(ERGs). Cell trajectory analysis and intercellular communication analysis were performed to explore exosome-related communication patterns. Spatial transcriptome sequencing data were analyzed to validate the findings. To further utilize exosome-related features to aid in clinical decision-making, a prognostic model was constructed using GBM’s bulk RNA-seq.ResultsDifferent cell subpopulations were observed in GBM, with Monocytes/macrophages and malignant cells in tumor samples showing higher exosome-related scores. After identifying differentially expressed ERGs in malignant cells, pseudotime analysis revealed the cellular status of malignant cells during development. Intercellular communication analysis highlighted signaling pathways and ligand-receptor interactions. Spatial transcriptome sequencing confirmed the high expression of exosome-related gene features in the tumor core region. A prognostic model based on six ERGs was shown to be predictive of overall survival and immunotherapy outcome in GBM patients. Finally, based on the results of scRNA-seq and prognostic modeling as well as a series of cell function experiments, BARD1 was identified as a novel target for the treatment of GBM.ConclusionThis study provides a comprehensive understanding of the exosome-related features of GBM in both scRNA-seq and stRNA-seq, with malignant cells with higher exosome-related scores exhibiting stronger communication with Monocytes/macrophages. In terms of spatial data, highly scored malignant cells were also concentrated in the tumor core region. In bulk RNA-seq, patients with a high exosome-related index exhibited an immunosuppressive microenvironment, which was accompanied by a worse prognosis as well as immunotherapy outcomes. Prognostic models constructed using ERGs are expected to be independent prognostic indicators for GBM patients, with potential implications for personalized treatment strategies for GBM. Knockdown of BARD1 in GBM cell lines reduces the invasive and value-added capacity of tumor cells, and thus BARD1-positively expressing malignant cells are a risk factor for GBM patients.

Crop straw contains huge amounts of exploitable energy, and efficient biomass degradation measures have attracted worldwide attention. Mining strains with high yields of cellulose-degrading enzymes is of great significance for developing clean energy and industrial production of related enzymes. In this study, we reported a high-quality genome sequence of Bacillus velezensis SSF6 strain using high-throughput sequencing technology (Illumina PE150 and PacBio) and assessed its lignocellulose degradation potential. The results demonstrated that the genome of B. velezensis SSF6 was 3.89 Mb and contained 4,015 genes, of which 2,972, 3,831 and 158 genes were annotated in the COGs (Clusters of Orthologous Groups), KEGG (Kyoto Encyclopedia of Genes and Genomes) and CAZyme (Carbohydrate-Active enZymes) databases, respectively, and contained a large number of genes related to carbohydrate metabolism. Furthermore, B. velezensis SSF6 has a high cellulose degradation capacity, with a filter paper assay (FPA) and an exoglucanase activity of 64.48 ± 0.28 and 78.59 ± 0.42 U/mL, respectively. Comparative genomic analysis depicted that B. velezensis SSF6 was richer in carbohydrate hydrolase gene. In conclusion, the cellulose-degrading ability of B. velezensis SSF6 was revealed by genome sequencing and the determination of cellulase activity, which laid a foundation for further cellulose degradation and bioconversion.

Background: Ovarian cancer (OC) is a common tumor of the female reproductive system, while Alzheimer's disease (AD) is a prevalent neurodegenerative disease that primarily affects cognitive function in the elderly. Monocytes are immune cells in the blood that can enter tissues and transform into macrophages, thus participating in immune and inflammatory responses. Overall, monocytes may play an important role in Alzheimer's disease and ovarian cancer. Methods: The CIBERSORT algorithm results indicate a potential crucial role of monocytes/macrophages in OC and AD. To identify monocyte marker genes, single-cell RNA-seq data of peripheral blood mononuclear cells (PBMCs) from OC and AD patients were analyzed. Enrichment analysis of various cell subpopulations was performed using the “irGSEA” R package. The estimation of cell cycle was conducted with the “tricycle” R package, and intercellular communication networks were analyzed using “CellChat”. For 134 monocyte-associated genes (MRGs), bulk RNA-seq data from two diseased tissues were obtained. Cox regression analysis was employed to develop risk models, categorizing patients into high-risk (HR) and low-risk (LR) groups. The model's accuracy was validated using an external GEO cohort. The different risk groups were evaluated in terms of immune cell infiltration, mutational status, signaling pathways, immune checkpoint expression, and immunotherapy. To identify characteristic MRGs in AD, two machine learning algorithms, namely random forest and support vector machine (SVM), were utilized. Results: Based on Cox regression analysis, a risk model consisting of seven genes was developed in OC, indicating a better prognosis for patients in the LR group. The LR group had a higher tumor mutation burden, immune cell infiltration abundance, and immune checkpoint expression. The results of the TIDE algorithm and the IMvigor210 cohort showed that the LR group was more likely to benefit from immunotherapy. Finally, ZFP36L1 and AP1S2 were identified as characteristic MRGs affecting OC and AD progression. Conclusion: The risk profile containing seven genes identified in this study may help further guide clinical management and targeted therapy for OC. ZFP36L1 and AP1S2 may serve as biomarkers and new therapeutic targets for patients with OC and AD.

Summary: Background: Only around 20–30% of patients with non-small-cell lung cancer (NCSLC) have durable benefit from immune-checkpoint inhibitors. Although tissue-based biomarkers (eg, PD-L1) are limited by suboptimal performance, tissue availability, and tumour heterogeneity, radiographic images might holistically capture the underlying cancer biology. We aimed to investigate the application of deep learning on chest CT scans to derive an imaging signature of response to immune checkpoint inhibitors and evaluate its added value in the clinical context. Methods: In this retrospective modelling study, 976 patients with metastatic, EGFR/ALK negative NSCLC treated with immune checkpoint inhibitors at MD Anderson and Stanford were enrolled from Jan 1, 2014, to Feb 29, 2020. We built and tested an ensemble deep learning model on pretreatment CTs (Deep-CT) to predict overall survival and progression-free survival after treatment with immune checkpoint inhibitors. We also evaluated the added predictive value of the Deep-CT model in the context of existing clinicopathological and radiological metrics. Findings: Our Deep-CT model demonstrated robust stratification of patient survival of the MD Anderson testing set, which was validated in the external Stanford set. The performance of the Deep-CT model remained significant on subgroup analyses stratified by PD-L1, histology, age, sex, and race. In univariate analysis, Deep-CT outperformed the conventional risk factors, including histology, smoking status, and PD-L1 expression, and remained an independent predictor after multivariate adjustment. Integrating the Deep-CT model with conventional risk factors demonstrated significantly improved prediction performance, with overall survival C-index increases from 0·70 (clinical model) to 0·75 (composite model) during testing. On the other hand, the deep learning risk scores correlated with some radiomics features, but radiomics alone could not reach the performance level of deep learning, indicating that the deep learning model effectively captured additional imaging patterns beyond known radiomics features. Interpretation: This proof-of-concept study shows that automated profiling of radiographic scans through deep learning can provide orthogonal information independent of existing clinicopathological biomarkers, bringing the goal of precision immunotherapy for patients with NSCLC closer. Funding: National Institutes of Health, Mark Foundation Damon Runyon Foundation Physician Scientist Award, MD Anderson Strategic Initiative Development Program, MD Anderson Lung Moon Shot Program, Andrea Mugnaini, and Edward L C Smith.

The essence of biomimetics in human–computer interaction (HCI) is the inspiration derived from natural systems to drive innovations in modern-day technologies. With this in mind, this paper introduces a biomimetic adaptive pure pursuit (A-PP) algorithm tailored for the four-wheel differential drive robot (FWDDR). Drawing inspiration from the intricate natural motions subjected to constraints, the FWDDR’s kinematic model mirrors non-holonomic constraints found in biological entities. Recognizing the limitations of traditional pure pursuit (PP) algorithms, which often mimic a static behavioral approach, our proposed A-PP algorithm infuses adaptive techniques observed in nature. Integrated with a quadratic polynomial, this algorithm introduces adaptability in both lateral and longitudinal dimensions. Experimental validations demonstrate that our biomimetically inspired A-PP approach achieves superior path-following accuracy, mirroring the efficiency and fluidity seen in natural organisms.

Abstract Objective Our study aimed to identify predictors as well as develop machine learning (ML) models to predict the risk of 30-day mortality in patients with sepsis-associated encephalopathy (SAE). Materials and methods ML models were developed and validated based on a public database named Medical Information Mart for Intensive Care (MIMIC)-IV. Models were compared by the area under the curve (AUC), accuracy, sensitivity, specificity, positive and negative predictive values, and Hosmer–Lemeshow good of fit test. Results Of 6994 patients in MIMIC-IV included in the final cohort, a total of 1232 (17.62%) patients died following SAE. Recursive feature elimination (RFE) selected 15 variables, including acute physiology score III (APSIII), Glasgow coma score (GCS), sepsis related organ failure assessment (SOFA), Charlson comorbidity index (CCI), red blood cell volume distribution width (RDW), blood urea nitrogen (BUN), age, respiratory rate, PaO2, temperature, lactate, creatinine (CRE), malignant cancer, metastatic solid tumor, and platelet (PLT). The validation cohort demonstrated all ML approaches had higher discriminative ability compared with the bagged trees (BT) model, although the difference was not statistically significant. Furthermore, in terms of the calibration performance, the artificial neural network (NNET), logistic regression (LR), and adapting boosting (Ada) models had a good calibration—namely, a high accuracy of prediction, with P-values of 0.831, 0.119, and 0.129, respectively. Conclusions The ML models, as demonstrated by our study, can be used to evaluate the prognosis of SAE patients in the intensive care unit (ICU). Online calculator could facilitate the sharing of predictive models.

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.

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.

ObjectiveThis study aimed to determine whether sarcopenia affects the all-cause mortality rate of patients with diabetic foot ulcers (DFUs).Research design and methodsThe clinic-based observational study included 217 patients treated at the Department of Endocrinology, the First Affiliated Hospital of Chongqing Medical University during a 4-year period. All subjects underwent dual-energy X-ray absorptiometry to determine their body composition during hospitalization. Diagnosis of sarcopenia was based on the Baumgartner diagnostic criteria. Patients were followed up regularly by phone calls until April 1, 2019, and their survival status was recorded.Univariate and multivariate Cox risk ratio regression models were used to analyze factors influencing the all-cause mortality rate of patients with DFUs.ResultsOf the 217 patients, 158 people survived (82.7%), 33 died (17.3%), and 26 were lost to follow-up. The median follow-up time was 23 (Range 11–34) months. The majority of patients were male (68.6%), with a mean age of 67.29 ± 11.14 years. The 5-year survival rate was 68.3% and 45.9% for all study patients (n = 217) and sarcopenia patients (n = 81), respectively. Multivariate Cox risk regression model showed that age (HR 1.042[95%CI:1.006, 1.078], P = 0.021), sarcopenia (HR 5.051[95%CI:1.968, 12.961], P = 0.001), and serum creatinine (HR 1.007[95%CI: 1.003, 1.010], P < 0.001) were independent risk factors for all-cause mortality rate of patients with DFUs. Kaplan-Meier survival curve indicated that the survival rate of patients with sarcopenia was significantly lower than non-sarcopenia patients (P < 0.001).ConclusionsSarcopenia is an independent risk factor for all-cause mortality of patients with DFUs and hence an important prognostic factor for patients with DFUs. Active prevention and improvement of sarcopenia can potentially improve the survival outcomes of this patient population.

IntroductionIn most instances, liver transplantation (LT) is the only available treatment for end‐stage liver diseases. However, LT could also induce serious liver diseases or injury, and the underlying mechanisms of LT-induced complications remain largely unknown, especially the mechanisms of the dysfunction of the immune system mediated by long noncoding RNAs (lncRNAs).MethodsIn this study, we globally analyzed the proportion of immune cells by using the transcriptome sequencing data (RNA-seq) of needle-core liver biopsies from pre- and post-transplantation recipients. Dysregulated lncRNAs were found to be correlated with the altered fractions of immune cells. We finally explored the potential targets of dysregulated lncRNAs and analyzed their functions in LT.ResultsWe found that in the samples, some immune cells changed significantly after LT, including CD4 T cells, NK cells and mast cells. The proportion of macrophages in different polarization states also changed significantly, with M0 macrophages increasing and M2 macrophages decreasing. Through weighted gene co-expression network analysis (WGCNA), 7 gene expression modules related to LT were identified. These modules were related to changes in the proportion of different immune cells. The functions of these modules represent the response modes of different functional genes after LT. Among these modules, MEtan and MEyellow modules were primarily enriched in apoptosis and inflammatory pathways. Twelve immunity-related lncRNAs were identified for the first time, and the regulatory network co-changing with immune cells was also identified. The co-expressed genes of these lncRNAs were highly enriched in apoptosis-related pathways. Many apoptosis-related genes were found to be up-regulated after LT.DiscussionIn summary, we speculated that the expression and regulation of these apoptotic genes may be related to the changes in the proportion of immune cells. Some of these lncRNAs and apoptosis-related genes have been reported to be related to cell proliferation and apoptosis. They are also potential biomarkers or therapeutic targets.

ObjectivesVideo-assisted thoracoscopic lobectomy has become the preferred surgical approach in experienced centers, and uniportal approaches are becoming increasingly used. But the uniportal approach is still not widely applied presumably due to the learning difficulties of this complex procedure. The use of surgical videos may be helpful to accelerate the learning of this new techniques as in other fields. In this study, we aimed to analyze the learning curve of uniportal video-assisted thoracoscopic lobectomy with the help of postoperative review of videos.Methods114 patients with early-stage lung cancer who underwent uniportal video-assisted thoracoscopic lobectomy performed from 2020 to 2021 were reviewed in this study. We recorded the operation video for each patient and reviewed all the videos after surgery. The learning curves were assessed using cumulative sum analysis and the collected data of perioperative outcomes were assessed.ResultsThe CUMSUM curve showed its inflection points were around case 38 and 53. It was less compared with previous studies, which about 57–140 cases are needed to attain the proficient phase. The perioperative outcomes were similar in each phase, which included intraoperative blood loss (79.00 ± 26.70 vs 70.67 ± 26.64 vs 70.56 ± 27.23, p=0.0119), the length of hospital stay (3.60 ± 1.52 days vs. 3.23 ± 0.90 days vs. 3.06 ± 0.88 days, p=0.053), the rate of prolonged air leak and conversion to open thoracotomy. There was also no significant difference in the numbers and station of lymph node dissection among the three phases.ConclusionsUniportal video-assisted thoracoscopic lobectomy is a safe and reliable approach. Recording and reviewing the operation video could help the surgeon to improve deficiencies and refine the procedure.

Summary: RNA-binding proteins (RBPs) have critical roles in N6-methyladenosine (m6A) modification process. We designed a Random Forest (RF) model to systematically analyze the interaction among RBPs and m6A modifications by integrating the binding signals from hundreds of RBPs. Accurate prediction of m6A sites demonstrated significant connections between RBP bindings and m6A modifications. The relative importance of different RBPs from the model provided a quantitative metric to evaluate their interactions with m6A modifications. Redundancy analysis showed that several RBPs may have similar binding patterns with m6A sites. The RF model exhibited fairly high prediction accuracy across cell lines, suggesting a conservative RBP interaction network regulates m6A occupancy. Specific RBPs can engage to the corresponding regional m6A sites and deploy distinct regulatory processes, such as cleavage site selection of the alternative polyadenylation (APA). We also integrated histone modifications into our RF model, which demonstrated H3K36me3 and H3K27me3 as determining features for m6A distribution.

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.

Abstract Background Estriol (E3) is a steroid hormone formed only during pregnancy in primates including humans. Although E3 is synthesized at large amounts through a complex pathway involving the fetus and placenta, it is not required for the maintenance of pregnancy and has classically been considered virtually inactive due to associated very weak canonical estrogen signaling. However, estrogen exposure during pregnancy may have an effect on organs both within and outside the reproductive system, and compounds with binding affinity for estrogen receptors weaker than E3 have been found to impact reproductive organs and the brain. Here, we explore potential effects of E3 on fetal development using mouse as a model system. Results We administered E3 to pregnant mice, exposing the fetus to E3. Adult females exposed to E3 in utero (E3-mice) had increased fertility and superior pregnancy outcomes. Female and male E3-mice showed decreased anxiety and increased exploratory behavior. The expression levels and DNA methylation patterns of multiple genes in the uteri and brains of E3-mice were distinct from controls. E3 promoted complexing of estrogen receptors with several DNA/histone modifiers and their binding to target genes. E3 functions by driving epigenetic change, mediated through epigenetic modifier interactions with estrogen receptors rather than through canonical nuclear transcriptional activation. Conclusions We identify an unexpected functional role for E3 in fetal reproductive system and brain. We further identify a novel mechanism of estrogen action, through recruitment of epigenetic modifiers to estrogen receptors and their target genes, which is not correlated with the traditional view of estrogen potency.

Abstract Background The survival benefits of recurrent laryngeal nerve lymph node dissection (RLNLD) in esophageal squamous cell carcinoma (ESCC) are still under debate, and the prognostic value of unilateral RLNLD has been rarely studied. Therefore, the aim of the present study was to investigate the clinical significance and outcomes of RLNLD in ESCC in a large‐scale cohort study, to shed light on the outcomes of unilateral RLNLD, and to identify the factors that affect the prognostic outcome of RLNLD. Methods We retrospectively reviewed 1153 patients with thoracic ESCC who underwent right thoracotomy with lymphadenectomy. The impact of RLNLD on disease‐free survival (DFS) and overall survival (OS) was estimated using the Kaplan–Meier method and Cox proportional hazard models. Inverse probability of treatment weighting (IPTW) was performed to adjust for differences in baseline variables in pairwise comparisons. Subgroup analysis of survival and postoperative complications was conducted for selective RLNLD. Results RLN lymph node (LN) metastasis was independently associated with tumor location and most other LN station metastases. RLNLD was an independent prognostic factor for DFS and OS. Both patients who underwent unilateral and bilateral RLNLD had significantly better DFS and OS than the non‐RLNLD patients. Furthermore, pairwise comparisons with IPTW confirmed these results, and we found that patients who underwent bilateral RLNLD had better survival than those who underwent unilateral RLNLD. However, subgroup analysis showed that there was no survival benefit and higher morbidity after bilateral RLNLD for patients with cancer in the lower thoracic esophagus, and elderly and female patients. Conclusion RLN LN metastasis is very frequent in ESCC, and both unilateral and bilateral RLNLD have considerable survival benefits. Selective RLNLD with better survival and lower morbidity was recommend for some defined subgroups.

Abstract To evaluate and compare the efficacy of two techniques for the treatment of acute acromioclavicular joint dislocation, the charts of 60 patients diagnosed with acute Rockwood type IV and V acromioclavicular joint dislocation that undergone arthroscopic fixation procedure with single tunnel technique (N = 30, 30.7 ± 5.7 years old) or coracoid sling technique (N = 30, 30.1 ± 5.4 years old) fixation were retrospectively reviewed. The Visual Analog Scale pain score, Constant shoulder functionality score, Karlsson acromioclavicular joint score, the time of return to sports and activity, and plain radiographs of the affected shoulder at different time points of follow-up were recorded for a minimum of 2 years post-op. The majority of the patients recovered to their preoperative activity levels with few complications. The average postoperative acromioclavicular and coracoclavicular distances were significantly narrower than preoperative measurements in both groups without significant difference between the two groups at 2 years post-op (P

Abstract Background Expression quantitative trait loci (eQTLs) analyses have been widely used to identify genetic variants associated with gene expression levels to understand what molecular mechanisms underlie genetic traits. The resultant eQTLs might affect the expression of associated genes through transcriptional or post-transcriptional regulation. In this study, we attempt to distinguish these two types of regulation by identifying genetic variants associated with mRNA stability of genes (stQTLs). Results Here, we presented a computational framework that takes advantage of recently developed methods to infer the mRNA stability of genes based on RNA-seq data and performed association analysis to identify stQTLs. Using the Genotype-Tissue Expression (GTEx) lung RNA-Seq data, we identified a total of 142,801 stQTLs for 3942 genes and 186,132 eQTLs for 4751 genes from 15,122,700 genetic variants for 13,476 genes on the autosomes, respectively. Interestingly, our results indicated that stQTLs were enriched in the CDS and 3’UTR regions, while eQTLs are enriched in the CDS, 3’UTR, 5’UTR, and upstream regions. We also found that stQTLs are more likely than eQTLs to overlap with RNA binding protein (RBP) and microRNA (miRNA) binding sites. Our analyses demonstrate that simultaneous identification of stQTLs and eQTLs can provide more mechanistic insight on the association between genetic variants and gene expression levels.

Abstract To improve cancer precision medicine, prognostic and predictive biomarkers are critically needed to aid physicians in deciding treatment strategies in a personalized fashion. Due to the heterogeneous nature of cancer, most biomarkers are expected to be valid only in a subset of patients. Furthermore, there is no current approach to determine the applicability of biomarkers. In this study, we propose a framework to improve the clinical application of biomarkers. As part of this framework, we develop a clinical outcome prediction model (CPM) and a predictability prediction model (PPM) for each biomarker and use these models to calculate a prognostic score (P-score) and a confidence score (C-score) for each patient. Each biomarker’s P-score indicates its association with patient clinical outcomes, while each C-score reflects the biomarker applicability of the biomarker’s CPM to a patient and therefore the confidence of the clinical prediction. We assessed the effectiveness of this framework by applying it to three biomarkers, Oncotype DX, MammaPrint, and an E2F4 signature, which have been used for predicting patient response, pathologic complete response versus residual disease to neoadjuvant chemotherapy (a classification problem), and recurrence-free survival (a Cox regression problem) in breast cancer, respectively. In both applications, our analyses indicated patients with higher C scores were more likely to be correctly predicted by the biomarkers, indicating the effectiveness of our framework. This framework provides a useful approach to develop and apply biomarkers in the context of cancer precision medicine.

Abstract Background B‐Raf V600E mutations account for about half of all skin cutaneous melanoma cases, and patients with this mutation are sensitive to BRAF inhibitors. However, aberrations in other genes in the MAPK/ERK pathway may cascade a similar effect as B‐Raf V600E mutations, rendering those patients sensitive to BRAF inhibitors. We rationalized that defining a signature based on B‐Raf pathway activity may be more informative for prognosis and drug sensitivity prediction than a binary indicator such as mutation status. Methods In this study, we defined a B‐Raf signature score using RNA‐seq data from TCGA. A higher score is shown to not only predict B‐Raf mutation status, but also predict other aberrations that could similarly activate the MAPK/ERK pathway, such as B‐Raf amplification, RAS mutation, and EGFR amplification. Results We showed that patients dichotomized by the median B‐Raf score is more significantly stratified than by other metrics of measuring B‐Raf aberration, such as mutation status, gene expression, and protein expression. We also demonstrated that high B‐Raf score predicts higher sensitivity to B‐Raf inhibitors SB590885 and PLX4720, as expected, but also correlated with sensitivity to drugs targeting other relevant oncogenic pathways. Conclusion The BRAF signature may better help guide targeted therapy for melanoma, and such a framework can be applied to other cancers and mutations to provide more information than mutation status alone.

The coronavirus disease-2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has seriously affected public health around the world. In-depth studies on the pathogenic mechanisms of SARS-CoV-2 is urgently necessary for pandemic prevention. However, most laboratory studies on SARS-CoV-2 have to be carried out in bio-safety level 3 (BSL-3) laboratories, greatly restricting the progress of relevant experiments. In this study, we used a bacterial artificial chromosome (BAC) method to assemble a SARS-CoV-2 replication and transcription system in Vero E6 cells without virion envelope formation, thus avoiding the risk of coronavirus exposure. Furthermore, an improved real-time quantitative reverse transcription PCR (RT-qPCR) approach was used to distinguish the replication of full-length replicon RNAs and transcription of subgenomic RNAs (sgRNAs). Using the SARS-CoV-2 replicon, we demonstrated that the nucleocapsid (N) protein of SARS-CoV-2 facilitates the transcription of sgRNAs in the discontinuous synthesis process. Moreover, two high-frequency mutants of N protein, R203K and S194L, can obviously enhance the transcription level of the replicon, hinting that these mutations likely allow SARS-CoV-2 to spread and reproduce more quickly. In addition, remdesivir and chloroquine, two well-known drugs demonstrated to be effective against coronavirus in previous studies, also inhibited the transcription of our replicon, indicating the potential applications of this system in antiviral drug discovery. Overall, we developed a bio-safe and valuable replicon system of SARS-CoV-2 that is useful to study the mechanisms of viral RNA synthesis and has potential in novel antiviral drug screening.

Industrial process data are usually multivariate, nonlinear, and highly dimensional. Deep learning based on variable correlation are widely used in nonlinear dynamical systems monitoring of industrial process, and most of the existing studies have focused on extracting data with single features. In this paper, from the perspective of improving detection accuracy and reducing detection time, a novel fault diagnosis approach based on correlation analysis and improved two-stream neural networks is proposed to address the problems that traditional neural networks cannot extract the data temporal and spatial features simultaneously. The novel algorithm mainly adopts long short-term memory and convolutional neural networks to extract the data features respectively. And the canonical correlation analysis algorithm is used as the activation function to train the two-stream neural networks in depth. Relevant experiments are designed for comparative analysis, and the results show that the proposed algorithm fully improves the detection accuracy and data smoothing. The method was applied to the fault detection process of nonlinear dynamical traction systems and the fault detection rate was improved by 5%.

Abstract Chromatin accessibility is essential for transcriptional activation of genomic regions. It is well established that transcription factors (TFs) and histone modifications (HMs) play critical roles in chromatin accessibility regulation. However, there is a lack of studies that quantify these relationships. Here we constructed a two-layer model to predict chromatin accessibility by integrating DNA sequence, TF binding, and HM signals. By applying the model to two human cell lines (GM12878 and HepG2), we found that DNA sequences had limited power for accessibility prediction, while both TF binding and HM signals predicted chromatin accessibility with high accuracy. According to the HM model, HM features determined chromatin accessibility in a cell line shared manner, with the prediction power attributing to five core HM types. Results from the TF model indicated that chromatin accessibility was determined by a subset of informative TFs including both cell line-specific and generic TFs. The combined model of both TF and HM signals did not further improve the prediction accuracy, indicating that they provide redundant information in terms of chromatin accessibility prediction. The TFs and HM models can also distinguish the chromatin accessibility of proximal versus distal transcription start sites with high accuracy.

Abstract Background Lung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care. Methods We inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets. Results We found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy. Conclusions Our findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.

Abstract The tumor suppressor p53, encoded by the TP53 gene, is mutated or nullified in nearly 50% of human cancers. It has long been debated whether TP53 mutations can be utilized as a biomarker to predict clinical outcomes of cancer patients. In this study, we applied computational methods to calculate p53 deficiency scores (PDSs) that reflect the inactivation of the p53 pathway, instead of TP53 mutation status. Compared to TP53 mutation status, the p53 deficiency gene signature is a powerful predictor of overall survival and drug sensitivity in a variety of cancer types and treatments. Interestingly, the PDSs predicted clinical outcomes more accurately than drug sensitivity in cell lines, suggesting that tumor heterogeneity and/or tumor microenvironment may play an important role in predicting clinical outcomes using p53 deficiency gene signatures.

Deep learning-based Computer-Aided Diagnosis has gained immense attention in recent years due to its capability to enhance diagnostic performance and elucidate complex clinical tasks. However, conventional supervised deep learning models are incapable of recognizing novel diseases that do not exist in the training dataset. Automated early-stage detection of novel infectious diseases can be vital in controlling their rapid spread. Moreover, the development of a conventional CAD model is only possible after disease outbreaks and datasets become available for training (viz. COVID-19 outbreak). Since novel diseases are unknown and cannot be included in training data, it is challenging to recognize them through existing supervised deep learning models. Even after data becomes available, recognizing new classes with conventional models requires a complete extensive re-training. The present study is the first to report this problem and propose a novel solution to it. In this study, we propose a new class of CAD models, i.e., Deep-Precognitive Diagnosis, wherein artificial agents are enabled to identify unknown diseases that have the potential to cause a pandemic in the future. A de novo biologically-inspired Conv-Fuzzy network is developed. Experimental results show that the model trained to classify Chest X-Ray (CXR) scans into normal and bacterial pneumonia detected a novel disease during testing, unseen by it in the training sample and confirmed to be COVID-19 later. The model is also tested on SARS-CoV-1 and MERS-CoV samples as unseen diseases and achieved state-of-the-art accuracy. The proposed model eliminates the need for model re-training by creating a new class in real-time for the detected novel disease, thus classifying it on all subsequent occurrences. Second, the model addresses the challenge of limited labeled data availability, which renders most supervised learning techniques ineffective and establishes that modified fuzzy classifiers can achieve high accuracy on image classification tasks.

Programmed death-ligand 1 (PD-L1), a critical immune checkpoint ligand, is a transmembrane protein synthesized in the endoplasmic reticulum of tumor cells and transported to the plasma membrane to interact with programmed death 1 (PD-1) expressed on T cell surface. This interaction delivers coinhibitory signals to T cells, thereby suppressing their function and allowing evasion of antitumor immunity. Most companion or complementary diagnostic devices for assessing PD-L1 expression levels in tumor cells used in the clinic or in clinical trials require membranous staining. However, the mechanism driving PD-L1 translocation to the plasma membrane after de novo synthesis is poorly understood. Herein, we showed that mind bomb homolog 2 (MIB2) is required for PD-L1 transportation from the trans-Golgi network (TGN) to the plasma membrane of cancer cells. MIB2 deficiency led to fewer PD-L1 proteins on the tumor cell surface and promoted antitumor immunity in mice. Mechanistically, MIB2 catalyzed nonproteolytic K63-linked ubiquitination of PD-L1, facilitating PD-L1 trafficking through Ras-associated binding 8–mediated (RAB8-mediated) exocytosis from the TGN to the plasma membrane, where it bound PD-1 extrinsically to prevent tumor cell killing by T cells. Our findings demonstrate that nonproteolytic ubiquitination of PD-L1 by MIB2 is required for its transportation to the plasma membrane and tumor cell immune evasion.

BackgroundGlioblastoma multiforme (GBM) is the most common cancer of the central nervous system, while Parkinson’s disease (PD) is a degenerative neurological condition frequently affecting the elderly. Neurotrophic factors are key factors associated with the progression of degenerative neuropathies and gliomas.MethodsThe 2601 neurotrophic factor-related genes (NFRGs) available in the Genecards portal were analyzed and 12 NFRGs with potential roles in the pathogenesis of Parkinson’s disease and the prognosis of GBM were identified. LASSO regression and random forest algorithms were then used to screen the key NFRGs. The correlation of the key NFRGs with immune pathways was verified using GSEA (Gene Set Enrichment Analysis). A prognostic risk scoring system was constructed using LASSO (Least absolute shrinkage and selection operator) and multivariate Cox risk regression based on the expression of the 12 NFRGs in the GBM cohort from The Cancer Genome Atlas (TCGA) database. We also investigated differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between risk groups. Finally, the accuracy of the model genes was validated using multi-omics mutation analysis, single-cell sequencing, QT-PCR, and HPA.ResultsWe found that 4 NFRGs were more reliable for the diagnosis of Parkinson’s disease through the use of machine learning techniques. These results were validated using two external cohorts. We also identified 7 NFRGs that were highly associated with the prognosis and diagnosis of GBM. Patients in the low-risk group had a greater overall survival (OS) than those in the high-risk group. The nomogram generated based on clinical characteristics and risk scores showed strong prognostic prediction ability. The NFRG signature was an independent prognostic predictor for GBM. The low-risk group was more likely to benefit from immunotherapy based on the degree of immune cell infiltration, expression of immune checkpoints (ICs), and predicted response to immunotherapy. In the end, 2 NFRGs (EN1 and LOXL1) were identified as crucial for the development of Parkinson’s disease and the outcome of GBM.ConclusionsOur study revealed that 4 NFRGs are involved in the progression of PD. The 7-NFRGs risk score model can predict the prognosis of GBM patients and help clinicians to classify the GBM patients into high and low risk groups. EN1, and LOXL1 can be used as therapeutic targets for personalized immunotherapy for patients with PD and GBM.

This research is aim at a deeper understanding of factors that leading Chinese rural teachers' (CRTs) turnover in their profession. The study regarded in-service CRTs (n = 408) as the participants, adopted the semi-structured interview and an online questionnaire to collect the data, and used grounded theory and FsQCA to analysis the data based. We have found that (A) welfare allowance, emotional support, and working environment can be substituted equivalently to increase CRTs' retention intention while professional identity regarded as the core condition; (B) career development is neglected for two reasons: one is CRTs compensated for inadequate social support by self-improvement; another is CRTs adapted themselves to accept professional stagnation; (C) the CRTs will have a strong intention to retain but lack enthusiasm for teaching because lack professional identity when the external environment is favorable. This study clarified the complicated causal relationships between CRTs' retention intention and its factors, contributed to the practical development of CRTs workforce.

BackgroundImmune checkpoint inhibitor-related myocarditis is the deadliest complication of immunotherapy. However, the underlying pathophysiological mechanisms of its occurrence and development remain unclear. Due to the long-term lack of effective early diagnosis and treatment options, it is of great significance to understand the pathophysiological mechanism of immune checkpoint inhibitor-related myocarditis.MethodsTissue samples from three patients with immune checkpoint inhibitor-related myocarditis and three control tissue samples were collected for protein analysis. Differentially expressed proteins were screened out using quantitative proteomics technology based on TMT markers. Protein–protein interaction (PPI) and Gene Ontology (GO) functional enrichment analyses of cross-factors were subsequently performed. Combined with the PD-L1 subcellular organelle- level protein interaction network, we searched for hub proteins involved in immune checkpoint inhibitor-related myocarditis and explored potential drug sensitivity and disease correlation.ResultsA total of 306 differentially expressed proteins were identified in immune checkpoint inhibitor-related myocarditis. Enrichment analysis showed that the differentially expressed proteins were closely related to mitochondrial metabolism. By analyzing mitochondria-related proteins and PD-L1-related proteins, we found four hub proteins, mammalian target of rapamycin (mTOR), Glycogen synthase kinase 3β (GSK3β), Protein tyrosine phosphatase non-receptor type 11 (PTPN11), and Mitofusin 2 (MFN2), indicating that they are closely related to immune checkpoint inhibitor-related myocarditis. Finally, we explored potential drugs for the treatment of immune checkpoint inhibitor-related myocarditis.ConclusionMitochondrial metabolism is involved in the process of immune checkpoint inhibitor-related myocarditis, and we identified four hub proteins, which may become new biomarkers for the early diagnosis and treatment of immune checkpoint inhibitor-related myocarditis.

Abdominal aortic aneurysm (AAA) is a life-threatening disease and there is currently a lack of effective treatment to prevent it rupturing. ScRNA-seq studies of AAA are still lacking. In the study, we analyzed the published AAA scRNA-seq datasets from the mouse elastase-induced model, CaCl2 treatment model, Ang II-induced model and human by using bioinformatic approaches and in silico analysis. A total of 26 cell clusters were obtained and 11 cell types were identified from multiple mouse AAA models. Also, the proportion of Mφ/Mo increased in the AAA group and Mφ/Mo was divided into seven subtypes. There were significant differences in transcriptional regulation patterns of Mφ/Mo in different AAA models. The enrichment pathways of upregulated or downregulated genes from Mφ/Mo in the three mouse datasets were different. The actived regulons of Mφ/Mo had strong specificity and the repressed regulons showed high consistency. The co-upregulated genes as well as actived regulons and co-downregulated genes as well as repressed regulons were closely correlated and formed regulatory networks. Mφ/Mo from human AAA dataset was divided into five subtypes. The proportion of three macrophage subpopulations increased but the proportion of two monocyte subpopulations decreased. In the AAA group, the upregulated or downregulated genes of Mφ/Mo were enriched in different pathways. After further analyzing the genes in Mφ/Mo of both mouse and human scRNA-seq datasets, two genes were upregulated in the four datasets, IL-1B and THBS1. In conclusion, in silico analysis of scRNA-seq revealed that Mφ/Mo and their regulatory related genes as well as interaction networks played an important role in the pathogenesis of AAA.

The molecular determinants of lung cancer histologic subtypes are not well understood. Here the authors analyze lung cancers of mixed histology and find that histologic subtypes are associated with transcriptomic features rather than genomic profiles in most tumors.

High-grain diet is commonly used in intensive production to boost yield in short term, which may cause adverse effects such as rumen and colonic acidosis in ruminants. Maize is one of the key components of high-grain diet, and different processing methods of maize affect the digestive absorption and gastrointestinal development of ruminants. To investigate the effects of maize form in high-grain diets on colonic fermentation and bacterial community of weaned lambs, twenty-two 2.5-month-old healthy Hu lambs were fed separately a maize meal low-grain diet (19.2% grain; CON), a maize meal high-grain diet (50.4% grain; CM), and a whole maize high-grain diet (50.4% grain; CG). After 7 weeks of feeding, the total volatile fatty acid concentration (P = 0.035) were significantly higher in lambs from CM than that from CON. The sequencing results of colonic content microbial composition revealed that the relative abundance of genera Parasutterella (P = 0.028), Comamonas (P = 0.031), Butyricicoccus (P = 0.049), and Olsenella (P = 0.010) were higher in CM than those in CON; compared with CM, the CG diet had the higher relative abundance of genera Bacteroides (P = 0.024) and Angelakisella (P = 0.020), while the lower relative abundance of genera Olsenella (P = 0.031) and Paraprevotella (P = 0.006). For colonic mucosal microbiota, the relative abundance of genera Duncaniella (P = 0.024), Succiniclasticum (P = 0.044), and Comamonas (P = 0.012) were significantly higher in CM than those in CON. In comparison, the relative abundance of genera Alistipes (P = 0.020) and Campylobacter (P = 0.017) were significantly lower. And the relative abundance of genera Colidextribacter (P = 0.005), Duncaniella (P = 0.032), Christensenella (P = 0.042), and Lawsonibacter (P = 0.018) were increased in the CG than those in the CM. Furthermore, the CG downregulated the relative abundance of genes encoding infectious-disease-parasitic (P = 0.049), cancer-specific-types (P = 0.049), and neurodegenerative-disease (P = 0.037) in colonic microbiota than those in the CM. Overall, these results indicated that maize with different grain sizes might influence the colonic health of weaned lambs by altering the composition of the colonic bacterial community.

Small-cell lung cancer (SCLC) is an aggressive disease with limited therapeutic options. Here the authors perform an immunogenomic analysis of limited-stage SCLC, revealing a homogeneous mutational landscape, but limited T-cell infiltration and a cold and heterogeneous T cell repertoire.

Abstract Background The natural indirect effect (NIE) and mediation proportion (MP) are two measures of primary interest in mediation analysis. The standard approach for mediation analysis is through the product method, which involves a model for the outcome conditional on the mediator and exposure and another model describing the exposure–mediator relationship. The purpose of this article is to comprehensively develop and investigate the finite-sample performance of NIE and MP estimators via the product method. Methods With four common data types with a continuous/binary outcome and a continuous/binary mediator, we propose closed-form interval estimators for NIE and MP via the theory of multivariate delta method, and evaluate its empirical performance relative to the bootstrap approach. In addition, we have observed that the rare outcome assumption is frequently invoked to approximate the NIE and MP with a binary outcome, although this approximation may lead to non-negligible bias when the outcome is common. We therefore introduce the exact expressions for NIE and MP with a binary outcome without the rare outcome assumption and compare its performance with the approximate estimators. Results Simulation studies suggest that the proposed interval estimator provides satisfactory coverage when the sample size ≥500 for the scenarios with a continuous outcome and sample size ≥20,000 and number of cases ≥500 for the scenarios with a binary outcome. In the binary outcome scenarios, the approximate estimators based on the rare outcome assumption worked well when outcome prevalence less than 5% but could lead to substantial bias when the outcome is common; in contrast, the exact estimators always perform well under all outcome prevalences considered. Conclusions Under samples sizes commonly encountered in epidemiology and public health research, the proposed interval estimator is valid for constructing confidence interval. For a binary outcome, the exact estimator without the rare outcome assumption is more robust and stable to estimate NIE and MP. An R package mediateP is developed to implement the methods for point and variance estimation discussed in this paper.

AIM: To investigate the effect of Staphylococcus aureus (S. aures) lysates (SALs) on herpes simplex virus type-I (HSV1) infection in human corneal epithelial (HCE) cells and in a mouse model of HSV1 keratitis. METHODS: HCE, Vero, HeLa, and BV2 cells were infected with HSV1 [HSV1 f strain, HSV1f; HSV-1-H129 with green fluorescent protein (GFP) knock-in, HSV1g]. Pre- or post-infection, SAL at various concentrations was added to the culture medium for 24h. GFP fluorescence in HSV1g or plaque formation by HSV1f were examined. The effects of heat-treated SAL, precooled acetone-precipitated SAL, and SAL subjected to ultrafiltration (100 kDa) were evaluated. The effects of other bacterial components and lysates on HSV1 infection were also tested, including lipoteichoic acid (LTA), peptidoglycan (PGN), staphylococcal protein A (SPA), and α-hemolysin from S. aureus (α-toxin) as well as lysates from a wild-type S. aureus strain, S. epidermidis, and Escherichia coli (W-SAL, SEL, and ECL, respectively). In addition, SAL eye drops were applied topically to BALB/c mice with HSV1 keratitis, followed by in vivo observations. RESULTS: The cytopathic effect, plaque formation (HSV1f), and GFP expression (HSV1g) in infected cells were inhibited by SAL in a dose-dependent manner. The active component of SAL (≥100 kDa) was heat-sensitive and retained activity after acetone precipitation. In HSV1g-infected cells, treatment with LTA-sa, α-toxin, PGN-sa, or SPA did not inhibit GFP expression. SAL, W-SAL, and SEL (but not ECL) decreased GFP expression. In mice with HSV1 keratitis, SAL reduced corneal lesions by 71%. CONCLUSION: The results of this study demonstrate that SAL can be used to inhibit HSV1 infection, particularly keratitis. Further studies are needed to determine the active components and mechanism underlying the effects of SAL.

The molecular mechanisms of resistance to immune checkpoint therapy remain elusive. Here, the authors perform immunogenomic analysis of TCGA data and data from clinical trials for antiPD-1/PD-L1 therapy and highlight the association of 9p21 loss with a cold tumor microenvironment and resistance to therapy.

In recent year, many deep learning have been playing an important role in the detection of cancers. This study aimed to real-timely differentiate a pancreatic cancer (PC) or a non-pancreatic cancer (NPC) lesion via endoscopic ultrasonography (EUS) image. A total of 1213 EUS images from 157 patients (99 male, 58 female) with pancreatic disease were used for training, validation and test groups. Before model training, regions of interest (ROIs) were manually drawn to mark the PC and NPC lesions using Labelimage software. Yolov5m was used as the algorithm model to automatically distinguish the presence of pancreatic lesion. After training the model based on EUS images using YOLOv5, the parameters achieved convergence within 300 rounds (GIoU Loss: 0.01532, Objectness Loss: 0.01247, precision: 0.713 and recall: 0.825). For the validation group, the mAP0.5 was 0.831, and mAP@.5:.95 was 0.512. In addition, the receiver operating characteristic (ROC) curve analysis showed this model seemed to have a trend of more AUC of 0.85 (0.665 to 0.956) than the area under the curve (AUC) of 0.838 (0.65 to 0.949) generated by physicians using EUS detection without puncture, although pairwise comparison of ROC curves showed that the AUC between the two groups was not significant (z= 0.15, p = 0.8804). This study suggested that the YOLOv5m would generate attractive results and allow for the real-time decision support for distinction of a PC or a NPC lesion.

Background: Parkinson’s disease (PD) is a neurodegenerative disease commonly seen in the elderly. On the other hand, cuprotosis is a new copper-dependent type of cell death that can be observed in various diseases.Methods: This study aimed to identify potential novel biomarkers of Parkinson’s disease by biomarker analysis and to explore immune cell infiltration during the onset of cuprotosis. Gene expression profiles were retrieved from the GEO database for the GSE8397, GSE7621, GSE20163, and GSE20186 datasets. Three machine learning algorithms: the least absolute shrinkage and selection operator (LASSO), random forest, and support vector machine-recursive feature elimination (SVM-RFE) were used to screen for signature genes for Parkinson’s disease onset and cuprotosis-related genes (CRG). Immune cell infiltration was estimated by ssGSEA, and cuprotosis-related genes associated with immune cells and immune function were examined using spearman correlation analysis. Nomogram was created to validate the accuracy of these cuprotosis-related genes in predicting PD disease progression. Classification of Parkinson’s specimens using consensus clustering methods.Result: Three PD datasets from the Gene Expression Omnibus (GEO) database were combined after eliminating batch effects. By ssGSEA, we identified three cuprotosis-related genes ATP7A, SLC31A1, and DBT associated with immune cells or immune function in PD and more accurate for the diagnosis of Parkinson’s disease course. Patients could benefit clinically from a characteristic line graph based on these genes. Consistent clustering analysis identified two subtypes, with the C2 subtype exhibiting higher immune cell infiltration and immune function.Conclusion: In conclusion, our study reveals that several newly identified cuprotosis-related genes intervene in the progression of Parkinson’s disease through immune cell infiltration.

Long non-coding RNAs (lncRNAs) have been extensively studied as important regulators of tumor development in various cancers. Tumor protein 53 target gene 1 (TP53TG1) is a newly identified lncRNA in recent years, and several studies have shown that TP53TG1 may play oncogenic or anti-oncogenic roles in different cancers. Nevertheless, the role of TP53TG1 in the development of cervical cancer is unclear. In our study, pan-cancer analysis showed that high expression of TP53TG1 was significantly associated with a better prognosis. We then constructed a TP53TG1 overexpression model in HeLa cell line to explore its functions and molecular targets. We found that TP53TG1 overexpression significantly inhibited cell proliferation and induced apoptosis, demonstrating that TP53TG1 may be a novel anti-oncogenic factor in cervical cancer. Furthermore, overexpression of TP53TG1 could activate type I interferon signaling pathways and inhibit the expression of genes involved in DNA damage responses. Meanwhile, TP53TG1 could affect alternative splicing of genes involved in cell proliferation or apoptosis by regulating the expression of many RNA-binding protein genes. Competing endogenous RNA (ceRNA) network analysis demonstrated that TP53TG1 could act as the sponge of several miRNAs to regulate the expression level of target genes. In conclusion, our study highlights the essential role of lncRNA TP53TG1 in the development of cervical cancer and suggests the potential regulatory mechanisms.

This study is to investigate the changes of lymphocyte subsets and the gut microbiota in Chinese Han patients with spinal cord injury (SCI). We enrolled 23 patients with SCI and 21 healthy controls. Blood and fecal samples were collected. The proportion of lymphocyte subsets was detected by flow cytometry. 16S rDNA sequencing of the V4 region was used to analyze the gut microbiota. The changes of the gut microbiota were analyzed by bioinformatics. Correlation analysis between gut microbiota and lymphocyte subsets was performed. CD4 + cells, CD4 + /CD8 + ratio and CD4 + CD8 + cells in peripheral blood of SCI patients were significantly lower than those of the control group (P < 0.05). There was no significant difference in B cells and CIK cells between the SCI group and the control group. The gut microbiota community diversity index of SCI patients was significantly higher than that of healthy controls. In SCI patients, the relative abundance of Lachnospiraceae (related to lymphocyte subset regulation), Ruminococcaceae (closely related to central nervous system diseases), and Escherichia-Shigella (closely related to intestinal infections) increased significantly, while the butyrate producing bacteria (Fusobacterium) that were beneficial to the gut were dramatically decreased. Correlation analysis showed that the five bacterial genera of SCI patients, including Lachnospiraceae UCG-008, Lachnoclostridium 12, Tyzzerella 3, Eubacterium eligens group, and Rumencocciucg-002, were correlated with T lymphocyte subsets and NK cells. In the SCI group, the flora Prevotella 9, Lachnospiraceae NC2004 group, Veillonella, and Sutterella were positively correlated with B cells. However, Fusobacterium and Akkermansia were negatively correlated with B cells. Moreover, Roseburia and Ruminococcaceae UCG-003 were positively correlated with CIK cells. Our results suggest that the gut microbiota of patients with SCI is associated with lymphocyte subsets. Therefore, it is possible to improve immune dysregulation in SCI patients by modulating gut microbiota, which may serve as a new therapeutic method for SCI.

As the predominant treatment option of the immunotherapy for advanced esophageal cancer (EC), the application of programmed death 1 (PD-1) and programmed death-ligand 1 (PD-L1) inhibitors brings new hope to clinical practice. However, a considerable portion of patients do not response to this therapy, meanwhile most patients sensitive to PD-1 or PD-L1 antibody initially will develop resistance to the treatment eventually. To break through the limits of clinical effect, it is of critical importance to make a profound understanding of the mechanisms of so called primary resistance and acquired resistance. Subsequently, exploring potent predictors to identify suitable patients for anti-PD-1/PD-L1 treatment and investigating efficient strategies to overcome drug resistance will be helpful to expend the benefit of immunotherapy. In the present view, we summarized the potential predictive factors for anti-PD-1/PD-L1 immunotherapy in EC, and demonstrated the plausible mechanisms of resistance to PD-1/PD-L1 blockade as well as its feasible solutions.

Background and purposeWith the clinical application of minimally invasive surgery and concept of enhanced recovery after surgery, the incidence of postoperative complications in lung cancer patients has been significantly reduced. However, postoperative subcutaneous emphysema (SE) becomes the main factor affecting the early discharge of patients. The aim of this study was to analyze the clinical outcome and risk factors for postoperative SE in lung cancer patients.MethodsThe clinical data of 414 lung cancer patients who were admitted to the Department of Thoracic Surgery, West China Hospital, Sichuan University from September 2021 to December 2021 were prospectively collected. The incidence, severity and treatment of patients who had SE, surgery approach, application of drainage tube and clinical information were analyzed.ResultsThe incidence rate of postoperative SE in patients with lung cancer was 33.09% (137/414) and mild cases accounted for the vast majority (30.19%, 125/414). Multivariate analysis indicated that male [odds ratio (OR) = 2.247, P = .014] and advanced age (OR = 1.021, P = .043) were main risk factors for postoperative SE in patients with lung cancer. Conservative treatment was the main treatment option for SE (98.5%, 135/137). The average hospital stay in the subcutaneous emphysema group (5.49 ± 4.41 days) was significantly longer than that in the non-subcutaneous emphysema group (4.44 ± 3.32 days) (P = .014) and no significant statistical difference in the average total hospital cost between the two groups (7,798.31 ± 1,414.85$ vs. 7,501.14 ± 1,605.18$, P = .072).ConclusionPostoperative SE in patients with minimally invasive lung cancer is mainly mild, and conservative treatment is appropriate for most cases.

With the steady improvement of advanced manufacturing processes and big data technologies, modern industrial systems have become large-scale. To enhance the sensitivity of fault detection (FD) and overcome the drawbacks of the centralized FD framework in dynamic systems, a new data-driven FD method based on Hellinger distance and subspace techniques is proposed for dynamic systems. Specifically, the proposed approach uses only system input/output data collected via sensor networks, and the distributed residual signals can be generated directly through the stable kernel representation of the process. Based on this, each sensor node can obtain the identical residual signal and test statistic through the average consensus algorithms. In addition, this paper integrates the Hellinger distance into the residual signal analysis for improving the FD performance. Finally, the effectiveness and accuracy of the proposed method have been verified in a real multiphase flow facility.