Single-nucleus sequencing unveils heterogeneity in renal cell carcinomas microenvironment: insights into pathogenic origins and treatment-responsive cellular subgroups.

BACKGROUND: Different individuals with renal cell carcinoma (RCC) exhibit substantial heterogeneity in histomorphology, genetic alterations in the proteome, immune cell infiltration patterns, and clinical behavior. OBJECTIVES: This study aims to use single-nucleus sequencing on ten samples (four normal, three clear cell renal cell carcinoma (ccRCC), and three chromophobe renal cell carcinoma (chRCC)) to uncover pathogenic origins and prognostic characteristics in patients with RCC. METHODS: By using two algorithms, inferCNV and k-means, the study explores malignant cells and compares them with the normal group to reveal their origins. Furthermore, we explore the pathogenic factors at the gene level through Summary-data-based Mendelian Randomization and co-localization methods. Based on the relevant malignant markers, a total of 212 machine-learning combinations were compared to develop a prognostic signature with high precision and stability. Finally, the study correlates with clinical data to investigate which cell subtypes may impact patients' prognosis. RESULTS & CONCLUSION: Two main origin tumor cells were identified: Proximal tubule cell B and Intercalated cell type A, which were highly differentiated in epithelial cells, and three gene loci were determined as potential pathogenic genes. The best malignant signature among the 212 prognostic models demonstrated high predictive power in ccRCC: (AUC: 0.920 (1-year), 0.920 (3-year) and 0.930 (5-year) in the training dataset; 0.756 (1-year), 0.828 (3-year), and 0.832 (5-year) in the testing dataset. In addition, we confirmed that LYVE1+ tissue-resident macrophage and TOX+ CD8 significantly impact the prognosis of ccRCC patients, while monocytes play a crucial role in the prognosis of chRCC patients.

Integrating Bulk and Single-Cell RNA-Seq Data to Identify Prognostic Features Related to Activated Dendritic Cells in Clear-Cell Renal-Cell Carcinoma.

Dendritic cells (DCs) serve as key regulators in tumor immunity, with activated DCs potentiating antitumor responses through the secretion of pro-inflammatory cytokines and the expression of co-stimulatory molecules. Most current studies focus on the relationship between DC subgroups and clear-cell renal-cell carcinoma (ccRCC), but there is limited research on the connection between DCs and ccRCC from the perspective of immune activation. In this study, activated DC genes were identified in both bulk and single-cell RNA-seq data. A prognostic model related to activated DCs was constructed using univariate, multivariate Cox regression and LASSO regression. The prognostic model was validated in three external validation sets: GSE167573, ICGC, and E-MTAB-1980. The prognostic model consists of five genes, PLCB2, XCR1, IFNG, HLA-DQB2, and SMIM24. The expression of these genes was validated in tissue samples using qRT-PCR. Stratified analysis revealed that the prognostic model was able to better predict outcomes in advanced ccRCC patients. The risk scores were associated with tumor progression, tumor mutation burden, immune cell infiltration, and adverse outcomes of immunotherapy. Notably, there was a strong correlation between the expression of the five genes and the sensitivity to JQ1, a BET inhibitor. Molecular docking indicated high-affinity binding of the proteins encoded by these genes with JQ1. In conclusion, our study reveals the crucial role of activated DCs in ccRCC, offering new insights into predicting immune response, targeted therapy effectiveness, and prognosis for ccRCC patients.

Artificial intelligence in ovarian cancer drug resistance advanced 3PM approach: subtype classification and prognostic modeling.

Background: Ovarian cancer patients' resistance to first-line treatment posed a significant challenge, with approximately 70% experiencing recurrence and developing strong resistance to first-line chemotherapies like paclitaxel. Objectives: Within the framework of predictive, preventive, and personalized medicine (3PM), this study aimed to use artificial intelligence to find drug resistance characteristics at the single cell, and further construct the classification strategy and deep learning prognostic models based on these resistance traits, which can better facilitate and perform 3PM. Methods: This study employed "Beyondcell," an algorithm capable of predicting cellular drug responses, to calculate the similarity between the expression patterns of 21,937 cells from ovarian cancer samples and the signatures of 5201 drugs to identify drug-resistance cells. Drug resistance features were used to perform 10 multi-omics clustering on the TCGA training set to identify patient subgroups with differential drug responses. Concurrently, a deep learning prognostic model with KAN architecture which had a flexible activation function to better fit the model was constructed for this training set. The constructed patient subtype classifier and prognostic model were evaluated using three external validation sets from GEO: GSE17260, GSE26712, and GSE51088. Results: This study identified that endothelial cells are resistant to paclitaxel, doxorubicin, and docetaxel, suggesting their potential as targets for cellular therapy in ovarian cancer patients. Based on drug resistance features, 10 multi-omics clustering identified four patient subtypes with differential responses to four chemotherapy drugs, in which subtype CS2 showed the highest drug sensitivity to all four drugs. The other subtypes also showed enrichment in different biological pathways and immune infiltration, allowing for targeted treatment based on their characteristics. Besides, this study applied the latest KAN architecture in artificial intelligence to replace the MLP structure in the DeepSurv prognostic model, finally demonstrating robust performance on patients' prognosis prediction. Conclusions: This study, by classifying patients and constructing prognostic models based on resistance characteristics to first-line drugs, has effectively applied multi-omics data into the realm of 3PM. Supplementary Information: The online version contains supplementary material available at 10.1007/s13167-024-00374-4.

Association between timing of labor induction and neonatal and maternal outcomes: an observational study from China.

BACKGROUND: Growing evidence suggests that elective induction of labor at 39 weeks' gestation may lead to more favorable perinatal outcomes than expectant management, however, how to weigh the pros and cons of elective labor induction at 39 weeks, the expectation of spontaneous delivery at 40 or 41 weeks, or delayed labor induction at 40 or 41 weeks on neonatal and maternal outcomes remains a practical challenge in clinical decision-making. OBJECTIVE: We compared the neonatal and maternal outcomes between elective induction of labor at 39 weeks' gestation and expectant management in a real-world setting. We also divided the expectantly managed group and compared outcomes of the spontaneous delivery at 40 or 41 weeks' gestation group and the induced group at 40 or 41 weeks' gestation with those of the elective induction at 39 weeks' gestation group. STUDY DESIGN: This retrospective cohort study included 21,282 participants who delivered between January 1, 2019, and June 30, 2022. Participants were initially categorized into 3 groups at 39 weeks' gestation, namely elective induction of labor, spontaneous delivery, and expectant management, for the primary analysis in which elective induction was compared with expectant management. Subsequently, the expectant management group at 39 weeks' gestation was divided into 3 groups at 40 weeks, and participants who underwent expectant management at 40 weeks were then divided into 2 groups at 41 weeks' gestation, namely elective induction and spontaneous delivery. In total, 6 groups were compared in the secondary analysis with the elective induction at 39 weeks' gestation group serving as the reference group. RESULTS: At 39 weeks' gestational age, participants who underwent elective induction of labor had a significantly lower risk for the primary composite outcomes than participants who were managed expectantly (adjusted odds ratio, 0.72; 95% confidence interval, 0.55-0.95), and there was no significant difference in the risk for cesarean delivery between the 2 groups. After further dividing the expectantly managed group and comparing them with participants who underwent elective induction of labor at 39 weeks' gestation, those who underwent spontaneous delivery at 40 weeks' gestation had significantly lower risks for cesarean delivery (0.61; 0.52-0.71) and chorioamnionitis (0.78; 0.61-1.00) but a higher risk for fetal distress (1.39; 1.22-1.57); those with spontaneous delivery at 41 weeks' gestation had a significantly higher risk for fetal distress (1.44; 1.16-1.79), postpartum hemorrhage (1.83; 1.26-2.66), and prolonged or arrested labor (1.61; 1.02-2.54). Moreover, when compared with participants who underwent elective induction of labor at 39 weeks' gestation, participants who were induced later in gestation had significantly higher risks for adverse neonatal and maternal outcomes, especially at 40 weeks' gestation. CONCLUSION: Our findings indicate that elective induction of labor at 39 weeks' gestation was significantly associated with lower risks for adverse short-term neonatal and maternal outcomes when compared with expectant management. Moreover, our study highlights the nuanced trade-offs in risks and benefits between elective induction at 39 weeks' gestation and waiting for spontaneous labor or delayed induction at 40 or 41 weeks' gestation, thus providing valuable insights for clinical decision-making in practice.

Using structural equation modeling to investigate students' satisfaction with an undergraduate tutorial system.

BACKGROUND: The undergraduate tutorial system (UTS) is a crucial measure in China for adhering to the principle of prioritizing foundational education, innovating the undergraduate talent training mode, and building a powerful country of higher education. This study investigated undergraduate students' satisfaction with UTS and the influencing factors, aiming to promote the healthy and sustainable development of UTS and provide practical implications and suggestions for universities. METHODS: Based on relevant theories, we conducted a survey study and leveraged structural equation modeling to assess students' satisfaction with UTS and explore the influencing factors. RESULTS: Our Pearson correlation analysis showed that students' satisfaction with mentors was positively correlated with dimensions such as humanistic care (r = 0.844, P < 0.05), mentor assistance (r = 0.906, P < 0.05), and mentor-student communication (r = 0.908, P < 0.05). Path analysis showed that mentor-student communication (ß = 0.486, P < 0.01), mentor assistance (ß = 0.228, P < 0.05), humanistic care (ß = 0.105, P < 0.05) were positive factors affecting students' satisfaction with mentors, while satisfaction with mentors (ß = 0.923, P < 0.01) had a positive impact on students' satisfaction with UTS. Students' satisfaction with mentors explained 73.4% of the variation in students' satisfaction with UTS, indicating that satisfaction with mentors was an important intermediary variable of UTS students. CONCLUSION: The sustainable implementation of UTS requires the effort to improve student satisfaction, and the breakthrough of strengthening the targeted mentorship in "transmitting wisdom, imparting knowledge, and resolving doubts" for students. Efforts should also be devoted to fostering students' comprehensive skills and better serving the cultivation of talents in the new era.

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy.

BACKGROUND: Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized. METHODS: We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database. RESULTS: We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups. CONCLUSION: Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

Psychological status of pregnant women during the omicron pandemic outbreak in China.

BACKGROUND: Pregnant women faced great challenges and psychological and physiological changes of varying degrees during the omicron epidemic outbreak. It is important to recognize the potential impact of these challenges on the mental health of pregnant women and to provide appropriate resources and support to mitigate their effects. METHOD: By using the convenience sampling approach, a total of 401 pregnant women from two hospitals of different grades in two cities were included in the survey. The cross-sectional survey was conducted by basic characteristics, Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9), Insomnia Severity Index (ISI) and self-made questionnaire. RESULTS: Insomnia affected 207 participants (51.6%), depression affected 160 participants (39.9%) and anxiety affected 151 participants (37.7%). Moreover, pregnant women in provincial capital city were more likely to experience anxiety, depression and insomnia than those in county-level city (P < 0.01). Pregnant women's anxiety, depression and insomnia were positively correlated with the severity of COVID-19 infection (P < 0.05). However, COVID-19 infection had no appreciable impact on maternal demand for termination of pregnancy and cesarean section (P > 0.05). CONCLUSION: Pregnant women frequently suffer from anxiety disorder, depression and insomnia as a result of the omicron pandemic in China. During this period, the community and medical professionals should provide more psychological counseling, conduct health education and offer virtual prenatal care to pregnant women (particularly in the provincial capital city).

Identification and Validation of Tumor Microenvironment-Associated Signature in Clear-Cell Renal Cell Carcinoma through Integration of DNA Methylation and Gene Expression.

The tumor microenvironment (TME) is crucial in tumor development, metastasis, and response to immunotherapy. DNA methylation can regulate the TME without altering the DNA sequence. However, research on the methylation-driven TME in clear-cell renal cell carcinoma (ccRCC) is still lacking. In this study, integrated DNA methylation and RNA-seq data were used to explore methylation-driven genes (MDGs). Immune scores were calculated using the ESTIMATE, which was employed to identify TME-related genes. A new signature connected with methylation-regulated TME using univariate, multivariate Cox regression and LASSO regression analyses was developed. This signature consists of four TME-MDGs, including AJAP1, HOXB9, MYH14, and SLC6A19, which exhibit high methylation and low expression in tumors. Validation was performed using qRT-PCR which confirmed their downregulation in ccRCC clinical samples. Additionally, the signature demonstrated stable predictive performance in different subtypes of ccRCC. Risk scores are positively correlated with TMN stages, immune cell infiltration, tumor mutation burden, and adverse outcomes of immunotherapy. Interestingly, the expression of four TME-MDGs are highly correlated with the sensitivity of first-line drugs in ccRCC treatment, especially pazopanib. Molecular docking indicates a high affinity binding between the proteins and pazopanib. In summary, our study elucidates the comprehensive role of methylation-driven TME in ccRCC, aiding in identifying patients sensitive to immunotherapy and targeted therapy, and providing new therapeutic targets for ccRCC treatment.

Effects of dietary supplementation with alkaline mineral complex on in vitro ruminal fermentation and bacterial composition.

Introduction: Dairy industry growth faces challenges in China due to inadequate forage, leading to high-concentrate diets and potential rumen issues. Buffering agents, like sodium bicarbonate, play a crucial role in stabilizing rumen pH. Alkaline Mineral Complex (AMC), a liquid additive with a pH of 14, shows promise in supporting dairy cow health and mitigating heat stress through ionization. Methods: This experiment was aimed to study the effect of adding AMC to total mixed ration (TMR) on in vitro ruminal fermentation and bacterial composition. AMCat 1, 2, 4, and 8 mL/kg was added to the substrate (0.5 g TMR). Nutrient digestibility was measured after 48 h fermentation, and fermentation parameters and microbial composition were measured after 48 h fermentation. Results and discussion: The results of the experiment indicated that: The different concentrations of AMC showed a significant impact on time taken for gas production to reach 1/2 of the total gas production (HT) parameters (p < 0.05). Linear pH increase occurs at 6 and 24 h with rising AMC concentration (p < 0.05), showing a quadratic trend at 12 h (p < 0.05). The optimal buffering effect on rumen acid-base balance was observed at a 2 mL/kg concentration of AMC. Microbial diversity analysis indicated that there was no significant change in α-diversity with different AMC concentrations (p > 0.05). The microbial level demonstrated no significant difference in species diversity of rumen fluid bacteria among the various AMC concentration treatment groups compared to the control group, further supporting that the advantage of adding AMC in stabilizing the rumen environment without altering the structure of the rumen microbiota. Besides, the addition of AMC significantly increased the concentrations of acetate, propionate, total fatty acids (TVFA), and NH3-N, suggesting that AMC contributed to enhancing the energy and nitrogen utilization efficiency in ruminants. Based on the above detection indicators, we recommend that the most favorable concentration is 2 mL/kg.

Causal association of monocytes with chronic kidney disease and the mediation role of frailty: A study integrating large-scale two-sample Mendelian randomization and single-cell analysis.

BACKGROUND: Recent research reported that frailty was prevalent among adults with chronic kidney disease (CKD) in clinical trials, and monocytes illustrated a similar difference in these two diseases compared to the normal. However, the scientific evidence for a causal relationship between these two diseases was lacking, with further exploration into whether monocytes co-regulate them. METHODS: We aimed to integrate large-scale Mendelian randomization (MR) and single-cell transcriptome analysis to determine whether there was a causal relationship between frailty and CKD (Bidirectional two-sample Mendelian determined the causal direction), whether monocytes impacted them, and whether the two diseases shared genetic variation sites. Based on 441 Genome-wide association study datasets, this study utilized five MR methods, multiple sensitivity analysis, and corresponding single-cell transcriptome datasets as proof. RESULTS: The association between frailty and CKD was significantly causal, and frailty increased the risk of CKD in patients (OR (95 %CI): 3.5597 (1.8369-6.8982), p = 0.000168909). The exposure monocyte can increase the risk of frailty and CKD in patients, especially with high expression of HLA genes in these cells. The existing two-sample MR results cannot reject the hypothesis that monocytes increase the risk of CKD by inducing frailty. rs9275271' 1mb genetic location above and below had been proven to be an effective genetic space for both frailty and CKD. CONCLUSION: We conducted the largest MR to date on frailty, monocyte, and CKD, and found a significant causal association between frailty and CKD, with the single-cell analysis confirmed. The exposure monocytes increased the risk of frailty and CKD, particularly with high expression of HLA genes in these cells. We identified a potential common genetic variant space, rs9275271, associated with frailty and CKD, providing insights into the genetic basis of these conditions.

Causal effects of gut microbiome on HIV infection: a two-sample mendelian randomization analysis.

BACKGROUND: The causal association between gut microbiome and HIV infection remains to be elucidated. We conducted a two-sample mendelian randomization analysis to estimate the causality between gut microbiome and HIV infection. METHODS: Publicly released genome-wide association studies summary data were collected to perform the mendelian analysis. The GWAS summary data of gut microbiome was retrieved from the MiBioGen consortium, which contains 18 340 samples from 24 cohorts. GWAS summary data of HIV infection was collected from the R5 release of FinnGen consortium, including 357 HIV infected cases and 218 435 controls. The SNPs were selected as instrumental variables according to our selection rules. And SNPs with a F-statistics less than ten were regarded as weak instrumental variables and excluded. Mendelian randomization analysis was conducted by five methods, including inverse variance weighted (IVW), MR-Egger, weighted median, weighted mode, and simple mode. The Cochran's Q test and MR-Egger intercept test were performed to identify heterogeneity and pleiotropy. Leave-one-out analysis were used to test the sensitivity of the results. RESULTS: Fifteen gut microbiota taxa showed causal effects on HIV infection according to the MR methods. Four taxa were observed to increase the risk of HIV infection, including Ruminococcaceae (OR: 2.468[1.043, 5.842], P: 0.039), Ruminococcaceae UCG005 (OR: 2.051[1.048, 4.011], P: 0.036), Subdoligranulum (OR: 3.957[1.762, 8.887], P < 0.001) and Victivallis (OR: 1.605[1.012, 2.547], P=0.044). Erysipelotrichaceae was protective factor of HIV infection (OR: 0.278[0.106, 0.731], P < 0.001) and Methanobrevibacter was also found to be associated with reduced risk of HIV infection (OR: 0.509[0.265, 0.980], P=0.043). Horizontal pleiotropy was found for Fusicatenibacter (P<0.05) according to the MR-Egger regression intercept analysis. No heterogeneity was detected. CONCLUSION: Our results demonstrate significant causal effects of gut microbiome on HIV infection. These findings facilitate future studies to develop better strategies for HIV prophylaxis through gut microbiome regulation. Further explorations are also warranted to dissect the mechanism of how gut microbiome affects HIV susceptibility.

Landscape of infiltrated immune cell characterization in COVID-19.

Purpose: Although the role of SARS-CoV-2-specfic immune cells has been revealed, a comprehensive understanding of immune patterns remains unknown. Methods: In this work, unsupervised consensus clustering analysis was used to classify 240 coronavirus disease 2019 (COVID-19) patients into different immune subtypes. Next, we performed differentially expressed analysis between different immune subtypes. Functional enrichment and pathway analyses were employed to reveal the biological significance of these differentially expressed genes (DEGs). Besides, we compared feature score of some DEGs between whole blood and lung tissues. Then, we utilized the "GSVA" algorithm to construct an immune cell infiltrating (ICI) tool based on the categories of these DEGs. Finally, we developed a nomogram associated with severity of COVID-19. Results: As a result, we identified two immune subtypes, and 238 DEGs which mainly participated in some immune-related functions and the COVID-19 pathway. Most importantly, the 238 DEGs could reflect the characterization of immune patterns in lung tissues. ICI scores were markedly negative associated with immune scores. It was worth noting that ICI score was a strong indicator for severity of COVID-19 and could accurately predict the severity of COVID-19. Conclusion: Our findings could provide more valuable strategies for the management of COVID-19.

Psychological Status of Medical Staff in Obstetrics and Gynecology Hospitals during the Omicron Pandemic Outbreak in China.

Background: Medical staff in China faced great challenges and psychological and physiological changes of varying degrees during the omicron epidemic outbreak. It is important to recognize the potential impact of these challenges on the mental health of medical staff and to provide appropriate resources and support to mitigate their effects. Methods: A total of 354 medical staff in two obstetrics and gynecology hospitals of different grades were included in this survey using convenience sampling. The cross-sectional self-report questionnaires survey was conducted using the Basic Characteristics Questionnaire, Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9), and Insomnia Severity Index (ISI). Results: There were 169 (47.7%) participants suffering from anxiety disorder. Working with fever, working in obstetrics, and working with protective clothing were the risk factors for anxiety in medical staff (p < 0.05). One hundred and ninety-six (55.4%) participants were depressed. Working with fever and working in obstetrics were the risk factors for depression in medical staff (p < 0.05). There were 117 (33.1%) participants suffering from insomnia. Working with fever, high educational level, and severe COVID-19 infection status were the risk factors for insomnia in medical staff (p < 0.05). Moreover, medical staff in a provincial hospital were more anxious and depressed than those in a county hospital. At last, there were more participants working with fever in obstetrics (p < 0.05). Conclusion: Anxiety disorder, depression, and insomnia were common among obstetrics and gynecology medical staff during the outbreak of omicron pandemic. During this period, more resources for psychological counselling should be provided to the hospital as well as more reasonable staffing arrangements, and working while having a fever is prohibited, especially in provincial hospital.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy.

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.

Mononuclear phagocyte system-related multi-omics features yield head and neck squamous cell carcinoma subtypes with distinct overall survival, drug, and immunotherapy responses.

BACKGROUND: Recent research reported that mononuclear phagocyte system (MPS) can contribute to immune defense but the classification of head and neck squamous cell carcinoma (HNSCC) patients based on MPS-related multi-omics features using machine learning lacked. METHODS: In this study, we obtain marker genes for MPS through differential analysis at the single-cell level and utilize "similarity network fusion" and "MoCluster" algorithms to cluster patients' multi-omics features. Subsequently, based on the corresponding clinical information, we investigate the prognosis, drugs, immunotherapy, and biological differences between the subtypes. A total of 848 patients have been included in this study, and the results obtained from the training set can be verified by two independent validation sets using "the nearest template prediction". RESULTS: We identified two subtypes of HNSCC based on MPS-related multi-omics features, with CS2 exhibiting better predictive prognosis and drug response. CS2 represented better xenobiotic metabolism and higher levels of T and B cell infiltration, while the biological functions of CS1 were mainly enriched in coagulation function, extracellular matrix, and the JAK-STAT signaling pathway. Furthermore, we established a novel and stable classifier called "getMPsub" to classify HNSCC patients, demonstrating good consistency in the same training set. External validation sets classified by "getMPsub" also illustrated similar differences between the two subtypes. CONCLUSIONS: Our study identified two HNSCC subtypes by machine learning and explored their biological difference. Notably, we constructed a robust classifier that presented an excellent classifying prediction, providing new insight into the precision medicine of HNSCC.

Ultrashort vertical-channel MoS2 transistor using a self-aligned contact.

Two-dimensional (2D) semiconductors hold great promises for ultra-scaled transistors. In particular, the gate length of MoS2 transistor has been scaled to 1 nm and 0.3 nm using single wall carbon nanotube and graphene, respectively. However, simultaneously scaling the channel length of these short-gate transistor is still challenging, and could be largely attributed to the processing difficulties to precisely align source-drain contact with gate electrode. Here, we report a self-alignment process for realizing ultra-scaled 2D transistors. By mechanically folding a graphene/BN/MoS2 heterostructure, source-drain metals could be precisely aligned around the folded edge, and the channel length is only dictated by heterostructure thickness. Together, we could realize sub-1 nm gate length and sub-50 nm channel length for vertical MoS2 transistor simultaneously. The self-aligned device exhibits on-off ratio over 105 and on-state current of 250 µA/µm at 4 V bias, which is over 40 times higher compared to control sample without self-alignment process.

Integrating Machine Learning and Mendelian Randomization Determined a Functional Neurotrophin-Related Gene Signature in Patients with Lower-Grade Glioma.

Recent researches reported that neurotrophins can promote glioma growth/invasion but the relevant model for predicting patients' survival in Lower-Grade Gliomas (LGGs) lacked. In this study, we adopted univariate Cox analysis, LASSO regression, and multivariate Cox analysis to determine a signature including five neurotrophin-related genes (NTGs), CLIC1, SULF2, TGIF1, TTF2, and WEE1. Two-sample Mendelian Randomization (MR) further explored whether these prognostic-related genes were genetic variants that increase the risk of glioma. A total of 1306 patients have been included in this study, and the results obtained from the training set can be verified by four independent validation sets. The low-risk subgroup had longer overall survival in five datasets, and its AUC values all reached above 0.7. The risk groups divided by the NTGs signature exhibited a distinct difference in targeted therapies from the copy-number variation, somatic mutation, LGG's surrounding microenvironment, and drug response. MR corroborated that TGIF1 was a potential causal target for increasing the risk of glioma. Our study identified a five-NTGs signature that presented an excellent survival prediction and potential biological function, providing new insight for the selection of LGGs therapy.

Gut microbiome dysbiosis in men who have sex with men increases HIV infection risk through immunity homeostasis alteration.

Objectives: Recent studies pointed out that gut microbiome dysbiosis in HIV infection was possibly confounded in men who have sex with men (MSM), but there is a lack of evidence. It also remained unclear how MSM-associated gut microbiome dysbiosis affected human health. This study aimed to compare the differences in gut microbiome changes between HIV and MSM and reveal the potential impacts of MSM-associated gut microbiome dysbiosis on the immune system. Methods: We searched available studies based on the PubMed database, and all gut microbiome changes associated with HIV infection and MSM were extracted from the enrolled studies. The gutMgene database was used to identify the target genes and metabolites of the gut microbiome. Bioinformatic technology and single-cell RNA sequencing data analysis were utilized to explore the impacts of these gut microbiome changes on human immunity. Results: The results showed significant overlaps between the gut microbiome associated with HIV and that of MSM. Moreover, bioinformatic analysis revealed that gut microbiome dysbiosis in MSM had an impact on several pathways related to immunity, including the IL-17 signaling pathway and Th17 cell differentiation. Additionally, target genes of MSM-associated gut microbiome were found to be highly expressed in monocytes and lymphocytes, suggesting their potential regulatory role in immune cells. Furthermore, we found that MSM-associated gut microbiome could produce acetate and butyrate which were reported to increase the level of inflammatory factors. Conclusion: In conclusion, this study highlighted that MSM-associated gut microbiome dysbiosis might increase the risk of HIV acquisition by activating the immune system. Further studies are expected to elucidate the mechanism by which gut microbiome dysbiosis in MSM modulates HIV susceptibility.

Identification of Molecular Subtypes and Prognostic Characteristics of Adrenocortical Carcinoma Based on Unsupervised Clustering.

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with a poor prognosis. Increasing evidence highlights the significant role of immune-related genes (IRGs) in ACC progression and immunotherapy, but the research is still limited. Based on the Cancer Genome Atlas (TCGA) database, immune-related molecular subtypes were identified by unsupervised consensus clustering. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regression were employed to further establish immune-related gene signatures (IRGS). An evaluation of immune cell infiltration, biological function, tumor mutation burden (TMB), predicted immunotherapy response, and drug sensitivity in ACC patients was conducted to elucidate the applicative efficacy of IRGS in precision therapy. ACC patients were divided into two molecular subtypes through consistent clustering. Furthermore, the 3-gene signature (including PRKCA, LTBP1, and BIRC5) based on two molecular subtypes demonstrated consistent prognostic efficacy across the TCGA and GEO datasets and emerged as an independent prognostic factor. The low-risk group exhibited heightened immune cell infiltration, TMB, and immune checkpoint inhibitors (ICIs), associated with a favorable prognosis. Pathways associated with drug metabolism, hormone regulation, and metabolism were activated in the low-risk group. In conclusion, our findings suggest IRGS can be used as an independent prognostic biomarker, providing a foundation for shaping future ACC immunotherapy strategies.