Identification of Immune Infiltration-related Molecular Features in Ovarian Cancer Patients and Experimental Validation of Immune Response Molecular Mechanisms through Integrated WGCNA, Machine Learning, and Single-cell Sequencing Analysis.

BACKGROUND: Ovarian cancer is one of the most common gynecological malignancies globally, and immunotherapy has emerged as a promising treatment strategy in recent years. However, the effectiveness of immunotherapy is often limited by immune escape mechanisms. OBJECTIVE: To unravel the immune response mechanisms in ovarian cancer, this study aimed to employ integrated Weighted Gene Co-expression Network Analysis (WGCNA), machine learning, and single-- cell sequencing analysis to systematically investigate immune infiltration-related molecular features in ovarian cancer patients and experimentally validate the molecular mechanisms of the immune response. This research may provide a new theoretical foundation and treatment strategy for immune-based therapies in ovarian cancer. METHODS: Relevant ovarian cancer datasets were collected from public databases. The ConsensusCluster- Plus and ggplot2 R packages were used to perform dimensionality reduction and clustering analysis of immune infiltration-related genes. Various algorithms were employed to select the best ovarian cancer prognostic model with OC consistency. The prognostic value of angiogenesis and immune-related gene expression was evaluated through Kaplan-Meier survival analysis, and the impact of immune infiltration on immune function in ovarian cancer patients was assessed. Functional pathways were identified using the Gene Set Enrichment Analysis (GSEA) method, and the infiltration abundance of immune and stromal components was inferred using the single-sample Gene Set Enrichment Analysis (ssGSEA) method. The influence of angiogenesis on the cellular level of Ovarian Cancer (OC) was explored in single- cell sequencing data, followed by in vitro cell experiments for further validation. The effect of the angiogenesis model on OC was evaluated through the above-mentioned research and experiments, aiming to investigate the mechanism of targeted therapy strategies in ovarian cancer. RESULTS: Immune-related data were collected from ovarian cancer patients in this study. Through WGCNA analysis, the MEturquoise module was identified, and a total of 1018 hub genes were determined. A prediction model was constructed using machine learning, with CoxBoost+StepCox selected as the best model, leading to the identification of 10 genes associated with ovarian cancer. Patients with high AIDPS had shorter survival time, and GSEA analysis revealed enrichment in immune-related pathways. Single-sample gene set enrichment analysis demonstrated increased immune cell infiltration and malignant stromal changes in the high AIDPS group. Results from in vitro cell experiments showed that silencing RPL31 inhibited the proliferation and migration of ovarian cancer cells while enhancing immune response capability. CONCLUSION: AIDPS holds significant clinical significance in Ovarian Cancer (OC) with poor prognosis observed in patients with high AIDPS. These patients exhibit more significant genomic variations, denser immune cell infiltration, and greater tolerance toward immune therapy. Importantly, inhibiting the expression of RPL31, a key component of AIDPS, can significantly suppress the proliferation, migration, and invasive properties of ovarian cancer cells, while stimulating the cytotoxicity of effector T cells and promoting immune response, thus slowing down the progression of ovarian cancer.

An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medical Information Mart for Intensive Care (MIMIC-IV, v2.2, MIMIC-III, v1.4) were used in this study. The study cohort from MIMIC-IV was randomized to the training set (0.7) and the internal validation set (0.3), with MIMIC-III (2001 to 2008) as external validation. The features with more than 20% missing values were deleted and the remaining features were multiple interpolated. Lasso-CV that lasso linear model with iterative fitting along a regularization path in which the best model is selected by cross-validation was used to select important features for model development. Eight machine learning models including Random Forest (RF), Logistic Regression, Decision Tree, Extreme Gradient Boost (XGBoost), K Nearest Neighbor, Support Vector Machine, Generalized Linear Models in which the best model is selected by cross-validation (CV_glmnet), and Linear Discriminant Analysis (LDA) were developed. Shapley additive interpretation (SHAP) was used to improve the interpretability of the optimal model. At last, a total of 1043 patients were included, of whom 710 were from MIMIC-IV and 333 from MIMIC-III. Twenty-four clinically relevant parameters were selected for model construction. For the prediction of 28-day mortality of SALI in the internal validation set, the area under the curve (AUC (95% CI)) of RF was 0.79 (95% CI: 0.73-0.86), and which performed the best. Compared with the traditional disease severity scores including Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Systemic Inflammatory Response Syndrome (SIRS), and Acute Physiology Score III (APS III), RF also had the best performance. SHAP analysis found that Urine output, Charlson Comorbidity Index (CCI), minimal Glasgow Coma Scale (GCS_min), blood urea nitrogen (BUN) and admission_age were the five most important features affecting RF model. Therefore, RF has good predictive ability for 28-day mortality prediction in SALI. Urine output, CCI, GCS_min, BUN and age at admission(admission_age) within 24 h after intensive care unit(ICU) admission contribute significantly to model prediction.

Genome analysis of the mpox (formerly monkeypox) virus and characterization of core/variable regions.

Since smallpox was eradicated in 1980, the monkeypox virus (MPXV) has emerged as the most threatening orthopoxvirus in the world. In this study, we conducted a comprehensive analysis of the currently published complete genome sequences of the monkeypox virus. The core/variable regions were identified through core-pan analysis of MPXV. Besides single-nucleotide polymorphisms, our study also revealed that specific genes, multi-copy genes, repeat sequences, and recombination fragments are primarily distributed in the variable region. This result suggests that variable regions are not only more susceptible to single-base mutations, but also to events such as gene loss or gain, as well as recombination. Taken together, our results demonstrate the genomic characteristics of the core/variable regions of MPXV, and contribute to our understanding of the evolution of MPXV.

Gut microbiota composition is associated with disease severity and host immune responses in COVID-19.

Background: Human gut microbiota play a crucial role in the immune response of the host to respiratory viral infection. However, evidence regarding the association between the gut microbiome, host immune responses, and disease severity in coronavirus disease 2019 (COVID-19) remains insufficient. Methods: To better comprehend the interactions between the host and gut microbiota in COVID-19, we conducted 16S rRNA sequencing and characterized the gut microbiome compositions in stool samples from 40 COVID-19 patients and 33 non-pneumonia controls. We assessed several hematological parameters to determine the immune status. Results: We found that the gut microbial composition was significantly changed in COVID-19 patients, which was characterized by increased opportunistic pathogens and decreased commensal bacteria. The frequency of prevalent opportunistic pathogens Enterococcus and Lactobacillus increased, especially in severe patients; yet the abundance of butyrate-producing bacteria, Faecalibacterium, Roseburia, and Anaerostipes, decreased significantly, and Faecalibacterium prausnitzii might help discriminate severe patients from moderate patients and non-pneumonia people. Furthermore, we then obtained a correlation map between the clinical characteristics of COVID-19 and severity-related gut microbiota. We observed a notable correlation between the abundance of Enterococcus faecium and abnormal neutrophil or lymphocyte percentage in all COVID-19 patients. Faecalibacterium was positively correlated with lymphocyte counts, while negatively correlated with neutrophil percentage. Conclusion: These results suggested that the gut microbiome could have a potential function in regulating host immune responses and impacting the severity or consequences of diseases.

Pentagalloyl Glucose: A Review of Anticancer Properties, Molecular Targets, Mechanisms of Action, Pharmacokinetics, and Safety Profile.

Pentagalloyl glucose (PGG) is a natural hydrolyzable gallotannin abundant in various plants and herbs. It has a broad range of biological activities, specifically anticancer activities, and numerous molecular targets. Despite multiple studies available on the pharmacological action of PGG, the molecular mechanisms underlying the anticancer effects of PGG are unclear. Here, we have critically reviewed the natural sources of PGG, its anticancer properties, and underlying mechanisms of action. We found that multiple natural sources of PGG are available, and the existing production technology is sufficient to produce large quantities of the required product. Three plants (or their parts) with maximum PGG content were Rhus chinensis Mill, Bouea macrophylla seed, and Mangifera indica kernel. PGG acts on multiple molecular targets and signaling pathways associated with the hallmarks of cancer to inhibit growth, angiogenesis, and metastasis of several cancers. Moreover, PGG can enhance the efficacy of chemotherapy and radiotherapy by modulating various cancer-associated pathways. Therefore, PGG can be used for treating different human cancers; nevertheless, the data on the pharmacokinetics and safety profile of PGG are limited, and further studies are essential to define the clinical use of PGG in cancer therapies.

IV Vitamin C in Sepsis: A Latest Systematic Review and Meta-Analysis.

Sepsis is a high-incidence disease and demands intensive care. Finding effective treatment is the key to cure sepsis. Studies have shown a lower level of vitamin C in patients with sepsis. Therefore, vitamin C supplementation has become one of the measures to treat sepsis. However, the clinical studies of vitamin C in the treatment of sepsis have been controversial. We performed a meta-analysis to evaluate vitamin C's efficacy and safety in the treatment of sepsis. We searched four electronic databases: PubMed, Embase, Web of Science, and the Cochrane Library, and two researchers independently screened 24 eligible RCTs published in English. Our review demonstrates that intravenous (IV) vitamin C might improve short-term mortality (RR, 0.82; 95% CI, 0.65-1.02; P=0.07; and I 2 = 45%) and overall mortality (RR, 0.86; 95% CI, 0.74-1.01; P=0.06; and I 2 = 51%) of patients with sepsis. Moreover, the SOFA score of patients with sepsis improved significantly after treatment with vitamin C for over 72 hours (RR, 0.26; 95% CI, 0.09-0.42; P=0.002; and I 2 = 0%). The main results of our study were moderate-quality evidence. More high-quality, multicenter RCTs are needed to provide more substantial evidence on the efficacy and safety of IV vitamin C for sepsis.

Remimazolam: An Updated Review of a New Sedative and Anaesthetic.

Remimazolam (CNS7056) is a novel benzodiazepine for intravenous sedation; it has an ultra-short duration of action and was recently approved for use in procedural sedation and general anaesthesia. It acts on γ-aminobutyric acid type A receptors and is rapidly converted into an inactive metabolite by tissue esterase enzymes. Remimazolam has been successfully used in endoscopic inspection or surgery and general anaesthesia induction and maintenance with fast and predictable onset and recovery times, high procedure success rates, and minor respiratory and hemodynamic fluctuations and without serious drug-related adverse reactions. If needed, the effects of remimazolam can be reversed by flumazenil, which allows prompt termination of sedation. Although remimazolam has great potential for sedation in patients admitted to intensive care units, future studies are needed to evaluate its efficacy and safety in patients requiring sedation for a long period, and numerous studies are warranted to explore the optimal dose in different application scenarios. The review aimed to provide an introduction to the process of remimazolam synthesis and its current clinical uses and future clinical developments.

Artesunate relieves acute kidney injury through inhibiting macrophagic Mincle-mediated necroptosis and inflammation to tubular epithelial cell.

Artesunate is a widely used derivative of artemisinin for malaria. Recent researches have shown that artesunate has a significant anti-inflammatory effect on many diseases. However, its effect on acute kidney injury with a significant inflammatory response is not clear. In this study, we established a cisplatin-induced AKI mouse model and a co-culture system of BMDM and tubular epithelial cells (mTEC) to verify the renoprotective and anti-inflammatory effects of artesunate on AKI, and explored the underlying mechanism. We found that artesunate strongly down-regulated the serum creatinine and BUN levels in AKI mice, reduced the necroptosis of tubular cells and down-regulated the expression of the tubular injury molecule Tim-1. On the other hand, artesunate strongly inhibited the mRNA expression of inflammatory cytokines (IL-1ß, IL-6 and TNF-α), protein levels of inflammatory signals (iNOS and NF-κB) and necroptosis signals (RIPK1, RIPK3 and MLKL) in kidney of AKI mouse. Notably, the co-culture system proved that Mincle in macrophage can aggravate the inflammation and necroptosis of mTEC induced by LPS, and artesunate suppressed the expression of Mincle in macrophage of kidney in AKI mouse. Overexpression of Mincle in BMDM restored the damage and necroptosis inhibited by artesunate in mTEC, indicating Mincle in macrophage is the target of artesunate to protect tubule cells in AKI. Our findings demonstrated that artesunate can significantly improve renal function in AKI, which may be related to the inhibition of Mincle-mediated macrophage inflammation, thereby reducing the damage and necroptosis to tubular cells that provide new option for the treatment of AKI.

[Teratogenic effects of Xanthoceras sorbifolia Bunge seed on SD rats].

OBJECTIVE: To study the teratogenic effect caused by Xanthoceras sorbifolia Bunge seed on SD rats. METHODS: The experiments were performed in the groups of 2. 0, 4. 0 and 8. 0 g/kg, purified water negative control group and cyclophosphamide positive control group. On the 6 th to 15 th day of pregnancy, the SPF SD rats were exposed to Xanthoceras sorbifolia Bunge seed. All the rats were sacrificed on the day before delivery. Examination were performed on the bones stained by alizarin red and internal organs fixed with Bouins fluid. RESULTS: Maternal body weight, weight gain, uterine fetal weight, net weigh, bed number, corpus luteum number, absorbing births number, live births number, still birth number and percentage and the abnormal rate of appearance, bone, internal organs of each dose group of Xanthoceras sorbifolia, there was no statistical significant difference between Bunge seed groups and negative control group. CONCLUSION: Under the conditions of this experiment, the Xanthoceras sorbifolia Bunge seed had no maternal toxicity to pregnant SD rats, no teratogenic and developmental toxicity to fetal rats. No Observed Adverse Effect Level of maternal toxicity and the minimum teratogenic dose of fetal rats is >8. 0 g/kg.