Machine learning in prenatal MRI predicts postnatal ventricular abnormalities in fetuses with isolated ventriculomegaly.

OBJECTIVES: To evaluate the intracranial structures and brain parenchyma radiomics surrounding the occipital horn of the lateral ventricle in normal fetuses (NFs) and fetuses with ventriculomegaly (FVs), as well as to predict postnatally enlarged lateral ventricle alterations in FVs. METHODS: Between January 2014 and August 2023, 141 NFs and 101 FVs underwent 1.5 T balanced steady-state free precession (BSSFP), including 68 FVs with resolved lateral ventricles (FVM-resolved) and 33 FVs with stable lateral ventricles (FVM-stable). Demographic data and intracranial structures were analyzed. To predict the enlarged ventricle alterations of FVs postnatally, logistic regression models with 5-fold cross-validation were developed based on lateral ventricle morphology, blended-cortical or/and subcortical radiomics characteristics. Validation of the models' performance was conducted using the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA). RESULTS: Significant alterations in cerebral structures were observed between NFs and FVs (p < 0.05), excluding the maximum frontal horn diameter (FD). However, there was no notable distinction between the FVM-resolved and FVM-stable groups (all p > 0.05). Based on subcortical-radiomics on the aberrant sides of FVs, this approach exhibited high efficacy in distinguishing NFs from FVs in the training/validation set, yielding an impressive AUC of 1/0.992. With an AUC value of 0.822/0.743 in the training/validation set, the Subcortical-radiomics model demonstrated its ability to predict lateral ventricle alterations in FVs, which had the greatest predictive advantages indicated by DCA. CONCLUSIONS: Microstructural alterations in subcortical parenchyma associated with ventriculomegaly can serve as predictive indicators for postnatal lateral ventricle variations in FVs. CLINICAL RELEVANCE STATEMENT: It is critical to gain pertinent information from a solitary fetal MRI to anticipate postnatal lateral ventricle alterations in fetuses with ventriculomegaly. This approach holds the potential to diminish the necessity for recurrent prenatal ultrasound or MRI examinations. KEY POINTS: Fetal ventriculomegaly is a dynamic condition that affects postnatal neurodevelopment. Machine learning and subcortical-radiomics can predict postnatal alterations in the lateral ventricle. Machine learning, applied to single-fetal MRI, might reduce required antenatal testing.

Global research trends in radiotherapy for breast cancer: a systematic bibliometric analysis.

PURPOSE: Breast cancer is the most common malignant tumor in women. Radiotherapy (RT) is an important adjunctive therapy for breast cancer, but the current international research trend of RT in breast cancer treatment and management is unclear. This bibliometric analysis was conducted to investigate the current trends and hot topics in this area. MATERIALS AND METHODS: The Web of Science Core Collection (WoSCC; Clarivate) database was searched, VOSviewer 1.6.18 and CiteSpace 6.1.R2 software were employed for the quantitative and qualitative analysis. RESULTS: 12,268 publications were included in this bibliometric analysis. There was an increasing trend of publications and international collaborations in the topic. The United States and The University of Texas MD Anderson Cancer Center were the most productive countries and institutions, respectively. The analysis of journals showed researches focused on both basic and clinical medicine on breast cancer RT. Park Won published the most papers and Fisher B had the most co-citations. The most co-cited paper was published in the Lancet. Survival, risk, chemotherapy, mastectomy, and surgery were regarded as current research hotspots through the analysis of keywords. CONCLUSION: Through quantitative and qualitative bibliometric analyses, this study provides insights into the research trends and potential research hotspots on breast cancer RT.

Valproic acid counteracts polycyclic aromatic hydrocarbons (PAHs)-induced tumorigenic effects by regulating the polarization of macrophages.

Polycyclic aromatic hydrocarbons (PAHs) are common persistent organic pollutants that are carcinogenic, teratogenic and mutagenic, causing a variety of harm to human health. In this study, we investigated the mechanism of how valproic acid (VPA) interferes with the carcinogenesis of PAHs protect normal tissues via the regulation of macrophages' function. Using the established model of transformed malignant breast cancer by 7,12-dimethylbenz[a]anthracene (DMBA), a representative PAH carcinogen, we discovered VPA induces the polarization of macrophages toward the M1 phenotype in the tumor tissues, facilitates the expression of pro-inflammatory cytokines such as IFN-γ, IL-12 and TNF-α, activates CD8+ T cells to secret Granzyme B thus to promote the apoptosis of tumor cells and suppresses the viability of vascular endothelial cells in tissue stroma of tumor. Surprisingly, VPA selectively induces macrophages to polarize towards the M2 phenotype in normal tissues and promotes the expression of anti-inflammatory cytokines such as IL-10 to enhance cell proliferation. Additionally, at the cellular level, VPA can directly regulate the polarization of macrophages to affect the growth of vascular endothelial cells by simulating the living conditions of tumor and normal cells. Collectively, VPA exerts an interventional effect on tumor growth and a protective effect on normal tissues by regulation of selective macrophages' polarization in their microenvironment.

Global Research Trends in Radiotherapy for Gliomas: A Systematic Bibliometric Analysis.

BACKGROUND: Gliomas are the most common primary malignant tumor of the central nervous system. At the present time, radiotherapy (RT) is the cornerstone of therapeutic approaches, but the worldwide research trends in RT for gliomas in the world are unclear. This bibliometric analysis aimed to investigate trends in glioma RT research since 2011. METHODS: The Web of Science database was searched, and SigmaPlot 14.0 and VOSviewer software were used to analyze and visualize the trends. RESULTS: Bibliometric analysis of 5106 articles was conducted. While the number of publications per year was relatively steady, the number of citations grew exponentially from 356 in 2011 to 21,051 in 2020. Articles originated from 89 countries, with the majority from the United States. Journal of Neuro-Oncology and Texas A&M University were the journal and institution, respectively, with the highest number of publications. The articles mainly involved oncology, neurology, radiology, and surgery. CONCLUSIONS: This bibliometric analysis found an increased focus on glioblastoma, survival, and chemotherapy in glioma RT research.

[Analysis of volatile oil in herb of pimpinella candolleana by SPME-GC-MS].

OBJECTIVE: To analyze components of volatile oil from the herb of Pimpinella candolleana. METHOD: The components of volatile oil were investigated by SPME-GC-MS. RESULT: Sixty-five compounds were identified which accounted for 92. 17% of total volatile oil. CONCLUSION: The main constituents in the essential oil were alpha-zingiberene (24.82%), pregeijerene (16.27%), beta-bisabolene (4. 82%), 2-isopropyl-5-methyl-9-methylene-bicyclo [ 4. 4. 0] dec-l-ene (4.03%), beta-sesquiphellandrene (3.98%), trans-beta-farnesene (3.68%), ar-curcumene (3.54%).