Uterine arteries pulsatility index by Doppler ultrasound in the prediction of preeclampsia: an updated systematic review and meta-analysis.

BACKGROUND: Preeclampsia is a common pregnancy complication with serious potential risks for maternal and neonatal health. Early prediction of preeclampsia is crucial for timely prevention, surveillance, and treatment to improve maternal and neonatal outcomes. This systematic review aimed to summarize the available evidence on the prediction of preeclampsia based on Doppler ultrasound of uterine arteries at different gestational ages. METHODS: A systematic literature search and meta-analysis were conducted to evaluate the sensitivity and specificity of the pulsatility index of Doppler ultrasound of uterine arteries for predicting preeclampsia. The timing of ultrasound scans within and beyond 20 weeks of gestational age was compared to assess its effect on the sensitivity and specificity of the pulsatility index. RESULTS: This meta-analysis included 27 studies and 81,673 subjects (3309 preeclampsia patients and 78,364 controls). The pulsatility index had moderate sensitivity (0.586) and high specificity for predicting preeclampsia (0.879) (summary point: sensitivity 0.59; 1-specificity 0.12). Subgroup analysis revealed that ultrasound scans performed within 20 weeks of gestational age did not significantly affect the sensitivity and specificity for predicting preeclampsia. The summary receiver operator characteristic curve showed the pulsatility index's optimal range of sensitivity and specificity. CONCLUSIONS: The uterine arteries pulsatility index measured by Doppler ultrasound is useful and effective for predicting preeclampsia and should be implemented in the clinical practice. The timing of ultrasound scans at different gestational age ranges does not significantly affect the sensitivity and specificity.

Effects of Cement Dosage, Curing Time, and Water Dosage on the Strength of Cement-Stabilized Aeolian Sand Based on Macroscopic and Microscopic Tests.

Aeolian sand is distributed worldwide, exhibiting poor grading, low cohesion, and loose structure. Infrastructure construction in desert areas sometimes requires stabilization of the sand, with cement as the primary curing agent. This study first employed orthogonal experiments to evaluate critical factors, e.g., curing time, cement dosage, and water dosage, affecting the unconfined compressive strength (UCS) of the aeolian sand stabilized with cement (ASC). Each of the aforementioned factors were set at five levels, namely curing time (7, 14, 28, 60, and 90 days), cement dosage (3%, 5%, 7%, 9%, and 11%), and water dosage (3%, 6%, 9%, 12%, and 15%), respectively. The water and cement dosages were percentages of the mass of the natural aeolian sand. The results indicated that the sensitivity of the influencing factors on the UCS of ASC was cement dosage, curing time, and water dosage in descending order. The UCS of ASC positively correlated with curing time and cement dosage, while it first increased and then decreased with the water dosage increase. The optimal conditions were 90 days' curing time, 11% cement dosage, and 9% water dosage. The microscopic analyses of ASC using optical microscopy, scanning electron microscopy (SEM), and X-ray diffraction (XRD) revealed that hydration products enhanced strength by bonding loose particles and filling pores, thereby improving compaction. The quantity and compactness of hydration products in the aeolian-cement reaction system increased with the increases in cement dosage and curing time, and low water dosage inhibited the hydration reaction. This study can provide insights into the stabilization mechanism of aeolian sand, aiding infrastructure development in desert regions.

lncRNA signature mediates mitochondrial permeability transition-driven necrosis in regulating the tumor immune microenvironment of cervical cancer.

Mitochondrial permeability transition (MPT)-driven necrosis (MPTDN) was a regulated variant of cell death triggered by specific stimuli. It played a crucial role in the development of organisms and the pathogenesis of diseases, and may provide new strategies for treating various diseases. However, there was limited research on the mechanisms of MPTDN in cervical cancer (CESC) at present. In this study, Weighted Gene Co-expression Network Analysis (WGCNA) was performed on differentially expressed genes in CESC. The module MEyellow, which showed the highest correlation with the phenotype, was selected for in-depth analysis. It was found that the genes in the MEyellow module may be associated with the tumor immune microenvironment (TIME). Through COX univariate regression and LASSO regression analysis, 6 key genes were identified. These genes were further investigated from multiple perspectives, including their independent diagnostic value, prognostic value, specific regulatory mechanisms in the tumor immune microenvironment, drug sensitivity analysis, and somatic mutation analysis. This study provided a comprehensive exploration of the mechanisms of action of these 6 key genes in CESC patients. And qRT-PCR validation was also conducted. Through COX univariate regression and LASSO coefficient screening of the MEyellow module, 6 key genes were identified: CHRM3-AS2, AC096734.1, BISPR, LINC02446, LINC00944, and DGUOK-AS1. Evaluation of the independent diagnostic value of these 6 key genes revealed that they can serve as independent diagnostic biomarkers. Through correlation analysis among these 6 genes, a potential regulatory mechanism among them was identified. Therefore, a risk prognostic model was established based on the collective action of these 6 genes, and the model showed good performance in predicting the survival period of CESC patients. By studying the relationship between these 6 key genes and the tumor microenvironment of CESC patients from multiple angles, it was found that these 6 genes are key regulatory factors in the tumor immune microenvironment of CESC patients. Additionally, 16 drugs that are associated with these 6 key genes were identified, and 8 small molecule drugs were predicted based on the lncRNA-mRNA network. The 6 key genes can serve as independent biomarkers for diagnosis, and the Risk score of these genes when acting together can be used as an indicator for predicting the clinical survival period of CESC patients. Additionally, these 6 key genes were closely related to the tumor immune microenvironment of CESC patients and were the important regulatory factors in the tumor immune microenvironment of CESC patients.

Stratification and prognostic evaluation of breast cancer subtypes defined by obesity-associated genes.

OBJECTIVE: Breast cancer was the most common type of cancer among women worldwide, significantly impacting their quality of life and survival rates. And obesity has been widely accepted as an important risk factor for breast cancer. However, the specific mechanisms by which obesity affects breast cancer were still unclear. Therefore, studying the impact mechanisms of obesity as a risk factor for breast cancer was of utmost importance. METHODS: This study was based on TCGA breast cancer RNA transcriptomic data and the GeneCard obesity gene set. Through single and multiple factor Cox analysis and LASSO coefficient screening, seven hub genes were identified. The independent mechanisms of these seven hub genes were evaluated from various aspects, including survival data, genetic mutation data, single-cell sequencing data, and immune cell data. Additionally, the risk prognosis model and the neural network diagnostic model were established to further investigate these seven hub genes. In order to achieve precision treatment for breast cancer (BRCA), based on the RNA transcriptomic data of the seven genes, 1226 BRCA patients were divided into two subtypes: BRCA subtype 1 and BRCA subtype 2. By studying and comparing the immune microenvironment, investigating the mechanisms of differential gene expression, and exploring the mechanisms of subnetworks, we aim to explore the clinical differences in the presentation of BRCA subtypes and achieve precision treatment for BRCA. Finally, qRT-PCR experiments were conducted to validate the conclusions of the bioinformatics analysis. RESULTS: The 7 hub genes showed good diagnostic independence and can serve as excellent biomarkers for molecular diagnosis. However, they do not perform well as independent prognostic molecular markers for BRCA patients. When predicting the survival of BRCA patients, their AUC values at 1 year, 3 years, and 5 years are mostly below 0.5. Nevertheless, through the establishment of the risk prognosis model considering the combined effect of the seven hub genes, it was found that the survival prediction of BRCA patients can be significantly improved. The risk prognosis model, compared to the independent use of the seven hub genes as prognostic markers, achieved higher timeROC AUC values at 1 year, 3 years, and 5 years, with values of 0.651, 0.669, and 0.641 respectively. Additionally, the neural network diagnostic model constructed from the 7 genes performs well in diagnosing BRCA, with an AUC value of 0.94, accurately identifying BRCA patients. The two subtypes identified by the seven hub genes exhibited significant differences in survival period, with subtype 1 having a poor prognosis. The differential mechanisms between the two subtypes mainly originate from regulatory differences in the immune microenvironment. Finally, the results of this study's bioinformatics analysis were validated through qRT-PCR experiments. CONCLUSION: 7 hub genes serve as excellent independent biomarkers for molecular diagnosis, and the neural network diagnostic model can accurately distinguish BRCA patients. In addition, based on the expression levels of these seven genes in BRCA patients, two subtypes can be reliably identified: BRCA subtype 1 and BRCA subtype 2, and these two subtypes showed significant differences in BRCA patient survival prognosis, proportion of immune cells, and expression levels of immune cells. Among them, patients with subtype 1 of BRCA had a poor prognosis.

A nine-long non-coding RNA signature for prognosis prediction of patients with lung squamous cell carcinoma.

BACKGROUND: Lung squamous cell carcinoma (LUSC) is malignant disease with poor therapeutic response and unfavourable prognosis. OBJECTIVE: This study aims to develop a long non-coding RNA (lncRNA) signature for survival prediction in patients with LUSC. METHODS: We obtained lncRNA expression profiles of 493 LUSC cases from The Cancer Genome Atlas, and randomly divided the samples into a training set (n= 296) and a testing set (n= 197). Univariate Cox regression and random survival forest algorithm were performed to select optimum survival-related lncRNAs. RESULTS: A lncRNA-focused risk score model was then constructed for prognosis prediction in the training set and further validated in the testing set and the entire set. Finally, bioinformatics analysis was carried out to explore the potential signaling pathways associated with the prognostic lncRNAs. A set of 9 lncRNAs were found to be strongly correlated with overall survival of LUSC patients. These 9 lncRNAs were integrated into a prognostic signature, which could separate patients into high- and low-risk groups with significantly different survival times in the training set (median: 30.5 vs. 80.5 months, log-rank P< 0.001). This signature was also confirmed in the testing set and the entire set. Besides, the prognostic value of the 9-lncRNA signature was independent of clinical features and maintained stable in stratified analyses. Functional enrichment study suggested that the 9 lncRNAs may be mainly involved in metabolism-related pathways, phosphatidylinositol signaling system, p53 signaling pathway, and notch signaling pathway. CONCLUSIONS: Our study demonstrated the potential clinical implication of the 9-lncRNA signature for survival prediction of LUSC patients.