Single-cell landscape of intratumoral heterogeneity and tumor microenvironment remolding in pre-nodal metastases of breast cancer.

Background: The metastasis of cancer cells is influenced by both their intrinsic characteristics and the tumor microenvironment (TME). However, the molecular mechanisms underlying pre-nodal metastases of breast cancer remain unclear.

Methods: We integrated a total of 216,963 cells from 54 samples across 6 single-cell datasets to profile the cellular landscape differences between primary tumors and pre-nodal metastases.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer.

Objective: To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features.

Methods: We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively.

Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer.

Rationale And Objectives: Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC.

Materials And Methods: In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.

Dual circuits originating from the ventral hippocampus independently facilitate affective empathy.

Affective empathy enables social mammals to learn and transfer emotion to conspecifics, but an understanding of the neural circuitry and genetics underlying affective empathy is still very limited. Here, using the naive observational fear between cagemates as a paradigm similar to human affective empathy and chemo/optogenetic neuroactivity manipulation in mouse brain, we investigate the roles of multiple brain regions in mouse affective empathy. Remarkably, two neural circuits originating from the ventral hippocampus, previously unknown to function in empathy, are revealed to regulate naive observational fear.

Harnessing genetic interactions for prediction of immune checkpoint inhibitors response signature in cancer cells.

Genetic interactions (GIs) refer to two altered genes having a combined effect that is not seen individually. They play a crucial role in influencing drug efficacy. We utilized CGIdb 2.

Unraveling Immune Heterogeneity across Pan-Cancer and Deep Insights in Lung Adenocarcinoma based on Alternative Splicing.

Alternative splicing (AS) participates in tumor development and tumor microenvironment formation. However, the landscape of immune infiltrating AS events (IIASE) in pan-cancer and mechanisms of AS in lung adenocarcinoma (LUAD) have not been comprehensively characterized. We systematically profiled the IIASE landscape of pan-cancer using data from The Cancer Genome Atlas (TCGA), analyzing both commonalities and specific characteristics among different cancer types.

Machine learning based on optimal VOI of multi-sequence MR images to predict lymphovascular invasion in invasive breast cancer.

Objectives: Lymphovascular invasion serves as a crucial prognostic indicator in invasive breast cancer, influencing treatment decisions. We aimed to develop a machine learning model utilizing optimal volumes of interest extracted from multisequence magnetic resonance images to predict lymphovascular invasion in patients with invasive breast cancer.

Materials And Methods: This study comprised 191 patients postoperatively diagnosed with invasive breast cancer through multi-sequence magnetic resonance imaging.

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer.

Objective: This study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC).

Methods: This study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018-July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60.

Frequency spillover effects between natural gas market, uncertainty, and stock market: new evidence from China.

This study investigates the correlation between the natural gas market, uncertainty, and the stock market in China. The research uses the BK model and considers both high- and low-frequency bands. According to the dynamic spillover indices and connectedness network, natural gas market, uncertainty, and stock market spillover influences are concentrated in the high-frequency band within 12 trading weeks and minor in the low-frequency band.

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI.

Background: Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method.

Purpose: To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI).

A new AAV tool for highly preferentially targeting hippocampal CA2.

Mice hippocampus contains three prominent subregions, CA1, CA3 and DG and is well regarded as an essential multiple task processor for learning, memory and cognition based on tremendous studies on these three subregions. The narrow region sandwiched between CA1 and CA3 called CA2 has been neglected for a long time. But it raises great attentions recently since this region manifests the indispensable role in social memory.

Multiparametric magnetic resonance imaging-derived deep learning network to determine ferroptosis-related gene signatures in gliomas.

Introduction: Ferroptosis-related gene (FRG) signature is important for assessing novel therapeutic approaches and prognosis in glioma. We trained a deep learning network for determining FRG signatures using multiparametric magnetic resonance imaging (MRI).

Methods: FRGs of patients with glioma were acquired from public databases.

Exploring time-varying impact of world pandemic uncertainty on China's commodity prices using TVP-SVAR-SV model.

Since the outbreak of the COVID-19 pandemic, a growing body of literature has focused on the impact of the uncertainty of the world pandemic (WPU) on commodity prices. Using the quarterly data from the first quarter of 2008 to the second quarter of 2020, we run the TVP-SVAR-SV model to study the time-varying impact of WPU on China's commodity prices. Specifically, we select minerals, non-ferrous metals, energy and steel commodities for a categorical comparison and measure the impact of WPU accordingly.