Risk prediction model of uterine corpus endometrial carcinoma based on immune-related genes.

BACKGROUND: Given the significant role of immune-related genes in uterine corpus endometrial carcinoma (UCEC) and the long-term outcomes of patients, our objective was to develop a prognostic risk prediction model using immune-related genes to improve the accuracy of UCEC prognosis prediction. METHODS: The Limma, ESTIMATE, and CIBERSORT methods were used for cluster analysis, immune score calculation, and estimation of immune cell proportions. Univariate and multivariate analyses were utilized to develop a prognostic risk model for UCEC. Risk model scores and nomograms were used to evaluate the models. String constructs a protein-protein interaction (PPI) network of genes. The qRT-PCR, immunofluorescence, and immunohistochemistry (IHC) all confirmed the genes. RESULTS: Cluster analysis divided the immune-related genes into four subtypes. 33 immune-related genes were used to independently predict the prognosis of UCEC and construct the prognosis model and risk score. The analysis of the survival nomogram indicated that the model has excellent predictive ability and strong reliability for predicting the survival of patients with UCEC. The protein-protein interaction network analysis of key genes indicates that four genes play a pivotal role in interactions: GZMK, IL7, GIMAP, and UBD. The quantitative real-time polymerase chain reaction (qRT-PCR), immunofluorescence, and immunohistochemistry (IHC) all confirmed the expression of the aforementioned genes and their correlation with immune cell levels. This further revealed that GZMK, IL7, GIMAP, and UBD could potentially serve as biomarkers associated with immune levels in endometrial cancer. CONCLUSION: The study identified genes related to immune response in UCEC, including GZMK, IL7, GIMAP, and UBD, which may serve as new biomarkers and therapeutic targets for evaluating immune levels in the future.

Resection of inferior vena cava, abdominal aorta, bilateral common iliac arteries, and bilateral partial external iliac arteries with artificial vessel replacement during radical endometrial cancer surgery: a case report.

BACKGROUND: Endometrial carcinoma (EC) is a common malignant tumor of the female reproductive system, often accompanied by lymph node metastasis. Artificial vascular implantation is a common surgical treatment for mediastinal tumors and abdominal aortic aneurysms but is rarely used in gynecological surgery. CASE PRESENTATION: A 54-year-old female patient was first admitted to the hospital in January 2018 due to "irregular vaginal bleeding over 3 months". CT showed a mass in the uterine cavity, and several swollen lymph nodes in the retroperitoneum and pelvic cavity. The initial diagnosis was an endometrial malignant tumor. We performed radical endometrial cancer surgery with parallel resection of inferior vena cava, abdominal aorta, bilateral common iliac arteries, bilateral external iliac arteries, and artificial vessel replacement, which was successful, with good postoperative recovery and no lesion progression at 3 years postoperative follow-up. CONCLUSION: This is an early case of gynecological clinical use of prostheses. Through multidisciplinary cooperation, the surgical resection rate of patients with EC in radical surgery was improved without serious fatal complications and achieved a high long-term postoperative survival rate.

Recent advances in the development of DprE1 inhibitors using AI/CADD approaches.

Tuberculosis (TB) is a global lethal disease caused by Mycobacterium tuberculosis (Mtb). The flavoenzyme decaprenylphosphoryl-ß-d-ribose 2'-oxidase (DprE1) plays a crucial part in the biosynthesis of lipoarabinomannan and arabinogalactan for the cell wall of Mtb and represents a promising target for anti-TB drug development. Therefore, there is an urgent need to discover DprE1 inhibitors with novel scaffolds, improved bioactivity and high drug-likeness. Recent studies have shown that artificial intelligence/computer-aided drug design (AI/CADD) techniques are powerful tools in the discovery of novel DprE1 inhibitors. This review provides an overview of the discovery of DprE1 inhibitors and their underlying mechanism of action and highlights recent advances in the discovery and optimization of DprE1 inhibitors using AI/CADD approaches.

Discovery of N-(1-(6-Oxo-1,6-dihydropyrimidine)-pyrazole) Acetamide Derivatives as Novel Noncovalent DprE1 Inhibitors against Mycobacterium tuberculosis.

Decaprenylphosphoryl-ß-d-ribose oxidase (DprE1) is a promising target for treating tuberculosis (TB). Currently, most novel DprE1 inhibitors are discovered through high-throughput screening, while computer-aided drug design (CADD) strategies are expected to promote the discovery process. In this study, with the aid of structure-based virtual screening and computationally guided design, a series of novel scaffold N-(1-(6-oxo-1,6-dihydropyrimidine)-pyrazole) acetamide derivatives with significant antimycobacterial activities were identified. Among them, compounds LK-60 and LK-75 are capable of effectively suppressing the proliferation of Mtb with MICMtb values of 0.78-1.56 µM, comparable with isoniazid and much superior to the phase II candidate TBA-7371 (MICMtb = 12.5 µM). LK-60 is also the most active DprE1 inhibitor derived from CADD so far. Further studies confirmed their high affinity to DprE1, good safety profiles to gut microbiota and human cells, and synergy effects with either rifampicin or ethambutol, indicating their broad potential for clinical applications.

Untargeted Metabolomics of Feces Reveals Diagnostic and Prognostic Biomarkers for Active Tuberculosis and Latent Tuberculosis Infection: Potential Application for Precise and Non-Invasive Identification.

Purpose: Distinguishing latent tuberculosis infection (LTBI) from active tuberculosis (ATB) is important to control the prevalence of tuberculosis; however, there is currently no effective method. The aim of this study was to discover specific metabolites through fecal untargeted metabolomics to discriminate ATB, individuals with LTBI, and healthy controls (HC) and to probe the metabolic perturbation associated with the progression of tuberculosis. Patients and Methods: Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was performed to comprehensively detect compounds in fecal samples from HC, LTBI, and ATB patients. Differential metabolites between the two groups were screened, and their underlying biological functions were explored. Candidate metabolites were selected and enrolled in LASSO regression analysis to construct diagnostic signatures for discriminating between HC, LTBI, and ATB. A receiver operating characteristic (ROC) curve was applied to evaluate diagnostic value. A nomogram was constructed to predict the risk of progression of LTBI. Results: A total of 35 metabolites were found to exist differentially in HC, LTBI, and ATB, and eight biomarkers were selected. Three diagnostic signatures based on the eight biomarkers were constructed to distinguish between HC, LTBI, and ATB, demonstrating excellent discrimination performance in ROC analysis. A nomogram was successfully constructed to evaluate the risk of progression of LTBI to ATB. Moreover, 3,4-dimethylbenzoic acid has been shown to distinguish ATB patients with different responses to etiological tests. Conclusion: This study constructed diagnostic signatures based on fecal metabolic biomarkers that effectively discriminated HC, LTBI, and ATB, and established a predictive model to evaluate the risk of progression of LTBI to ATB. The results provide scientific evidence for establishing an accurate, sensitive, and noninvasive differential diagnosis scheme for tuberculosis.

The potential mechanism of the progression from latent to active tuberculosis based on the intestinal microbiota alterations.

INTRODUCTION: Tuberculosis (TB) poses a serious challenge to global health systems. The altered intestinal microbiota is associated with the pathogenesis of TB, but the exact links remain unclear. METHODS: 16 S rDNA sequencing was performed to comprehensively detect the changes in the intestinal microbiota of feces from active TB (ATB), latent TB infection (LTBI) and healthy controls (HC). RESULTS: The rarefaction curves demonstrated the sequencing results' validity. The alpha diversity was lowest in ATB, while highest in HC. Boxplot of beta diversity showed significant differences in every two groups. LDA Effect Size (LEfSe) Analysis revealed differences in probiotic bacteria like Romboutsia, Bifidobacterium and Lactobacillus in LTBI, and pro-inflammatory bacteria like R. gnavus, Streptococcus and Erysipelatoclostridium in ATB, corresponding to the cluster analysis. PICRUST2 analysis revealed the pentose phosphate pathway was active in ATB and LTBI (more active in ATB). The differences between the groups are statistically significant at the P<0.05 level. CONCLUSION: Our study indicated that from LTBI to ATB, some intestinal microbiota inhibit the synthesis of interferon (INF)-γ and interleukin (IL)-17, promoting the survival and spread of Mycobacterium tuberculosis (M. tb). In addition, the metabolites secreted by intestinal microbiota and dysbiosis in intestine also have an effect on the development of LTBI to ATB.

Fast hydrothermal co-liquefaction of corn stover and cow manure for biocrude and hydrochar production.

Fast Hydrothermal liquefaction (HTL) has emerged as a versatile means of converting wet biomass into bio-crude oil. This study was aimed to explore a fast hydrothermal co-liquefaction (co-HTL) platform to valorize corn stover and cow manure by evaluating several reaction parameters (i.e., residence time, reaction temperature, and feedstocks mass ratio). The highest yield (over 24 wt%) of bio-crude oil was achieved under the moderate condition (400 °C, 16 min, and the mass ratio of 1:1). The Higher heating value (HHV) of bio-crude oil was around 34 MJ/kg. Up to 43% of selectivity toward phenols in bio-crude oil was gained from fast co-HTL maintained for 30 min. The properties of hydrochar were comprehensively characterized by CHNS elemental analysis, SEM, EDX, and FTIR. The highest HHV of hydrochar was 27.31 MJ/kg, suggesting the high potential as a solid fuel. CO2 as the dominant gaseous fraction were identified and quantified by GC.