Evaluating dynamic contrast-enhanced MRI for differentiating HER2-zero, HER2-low, and HER2-positive breast cancers in patients undergoing neoadjuvant chemotherapy.

Objectives: To quantitatively assess the differences in parameters of dynamic contrast-enhanced MRI (DCE-MRI) in HER2-zero, HER2-low, or HER2-positive tumors, and to build optimal model for early prediction of HER2-low breast cancer (BC).

Materials And Methods: Clinical and DCE-MRI data from 220 BC patients receiving neoadjuvant chemotherapy (NACT) were retrospectively analyzed. Quantitative and semi-quantitative DCE-MRI parameters were compared in the HER2-zero, HER2-low, or HER2-positive groups before and after early NACT.

A case report of Asymptomatic hyperCKemia and literature review.

The case report of a female with Asymptomatic hyperCKemia along with a literature review is presented. The objective of this report is to highlight an effective diagnosis and treatment option for Asymptomatic hyperCKemia patients, as well as to bring to attention a rare and benign cause of CK elevation, which can lead to diagnostic and therapeutic errors.

Role of high-resolution magnetic resonance imaging in preoperative tumor-node-metastasis staging evaluation of esophageal cancer: a narrative review.

Background And Objective: Esophageal cancer (EC) is an aggressive disease characterized by high mortality rates and a propensity for locoregional or distant recurrence. The treatment strategies and prognostic estimation for EC depend on accurate pre-treatment tumor-node-metastasis (TNM) staging. The objective of this review was to illustrate the role of various imaging modalities in achieving accurate preoperative TNM staging of EC, with a particular focus on the utilization of advanced high-resolution magnetic resonance imaging (MRI) sequences for T classification, which have shown promise in enhancing the delineation of tumor depth and extent.

Tissue-matched analysis of MRI evaluating the tumor infiltrating lymphocytes in hepatocellular carcinoma.

Tumor-infiltrating lymphocytes (TILs) play critical roles in the tumor microenvironment and immunotherapy response. This study aims to explore the feasibility of multi-parametric magnetic resonance imaging (MRI) in evaluating TILs and to develop an evaluation model that considers spatial heterogeneity. Multi-parametric MRI was performed on hepatocellular carcinoma (HCC) mice (N = 28).

Contrasting Multi-Source Temporal Knowledge Graphs for Biomedical Hypothesis Generation.

Hypothesis Generation (HG) aims to expedite biomedical researches by generating novel hypotheses from existing scientific literature. Most existing studies focused on modeling static snapshots of the corpus, neglecting the temporal evolution of scientific terms. Despite recent efforts to learn term evolution from Knowledge Bases (KBs) for HG, the temporal information from multi-source KBs is still overlooked, which contains important, up-to-date knowledge.

Multiparametric MR characterization for human epithelial growth factor receptor 2 expression in bladder cancer: an exploratory study.

Purpose: To investigate the application value of multiparametric MRI in evaluating the expression status of human epithelial growth factor receptor 2 (HER2) in bladder cancer (BCa).

Methods: From April 2021 to July 2023, preoperative imaging manifestations of 90 patients with pathologically confirmed BCa were retrospectively collected and analyzed. All patients underwent multiparametric MRI including synthetic MRI, DWI, from which the T1, T2, proton density (PD) and apparent diffusion coefficient (ADC) values were obtained.

Molecular evidence provides new insights into the evolutionary origin of an ancient traditional Chinese medicine, the domesticated "Baizhi".

Introduction: "Baizhi" is a famous herbal medicine in China, and it includes four landraces named as 'Hangbaizhi', 'Chuanbaizhi', 'Qibaizhi', and 'Yubaizhi'. Long-term artificial selection had caused serious degradation of these germplasms. Determining the wild progenitor of the landraces would be benefit for their breed improvements.

Prediction of breast cancer and axillary positive-node response to neoadjuvant chemotherapy based on multi-parametric magnetic resonance imaging radiomics models.

Purpose: Accurate identification of primary breast cancer and axillary positive-node response to neoadjuvant chemotherapy (NAC) is important for determining appropriate surgery strategies. We aimed to develop combining models based on breast multi-parametric magnetic resonance imaging and clinicopathologic characteristics for predicting therapeutic response of primary tumor and axillary positive-node prior to treatment.

Materials And Methods: A total of 268 breast cancer patients who completed NAC and underwent surgery were enrolled.

Predicting One-year Recurrence of HCC based on Gadoxetic Acid-enhanced MRI by Machine Learning Models.

Objective: Accurate prediction of recurrence risk after resction in patients with Hepatocellular Carcinoma (HCC) may help to individualize therapy strategies. This study aimed to develop machine learning models based on preoperative clinical factors and multiparameter Magnetic Resonance Imaging (MRI) characteristics to predict the 1-year recurrence after HCC resection.

Methods: Eighty-two patients with single HCC who underwent surgery were retrospectively analyzed.

Multi-modality radiomics model predicts axillary lymph node metastasis of breast cancer using MRI and mammography.

Objectives: We aimed to develop a multi-modality model to predict axillary lymph node (ALN) metastasis by combining clinical predictors with radiomic features from magnetic resonance imaging (MRI) and mammography (MMG) in breast cancer. This model might potentially eliminate unnecessary axillary surgery in cases without ALN metastasis, thereby minimizing surgery-related complications.

Methods: We retrospectively enrolled 485 breast cancer patients from two hospitals and extracted radiomics features from tumor and lymph node regions on MRI and MMG images.

Radiomics nomogram for predicting disease-free survival after partial resection or radical cystectomy in patients with bladder cancer.

Objectives: To create a MRI-derived radiomics nomogram that combined clinicopathological factors and radiomics signature (Rad-score) for predicting disease-free survival (DFS) in patients with bladder cancer (BCa) following partial resection (PR) or radical cystectomy (RC), including lymphadenectomy (LAE).

Methods: Finally, 80 patients with BCa after PR or RC with LAE were enrolled. Patients were randomly split into training (n = 56) and internal validation (n = 24) cohorts.

Amide Proton Transfer-Weighted MRI and Diffusion-Weighted Imaging in Bladder Cancer: A Complementary Tool to the VI-RADS.

Rationale And Objectives: To investigate the feasibility of amide proton transfer-weighted (APTw) and diffusion-weighted Magnetic Resonance Imaging (MRI) as a means by which to add value to the Vesical Imaging Reporting and Data System (VI-RADS) for discriminating muscle invasive bladder cancer (MIBC) from nonmuscle invasive bladder cancer (NMIBC).

Materials And Methods: This prospective study enrolled participants with pathologically confirmed bladder cancer (BCa) who underwent preoperative multiparametric MRI, including APTw and diffusion-weighted MRI, from July 2020 to January 2023. The exclusion criteria were lesions smaller than 10 mm, missing smooth muscle layer in the operation specimen, neoadjuvant therapy before MRI, inadequate image quality, and malignancy other than urothelial neoplasm.

High power density output and durability of microbial fuel cells enabled by dispersed cobalt nanoparticles on nitrogen-doped carbon as the cathode electrocatalyst.

To endow microbial fuel cells (MFCs) with low cost, long-term stability and high-power output, a novel cobalt-based cathode electrocatalyst (Nano-Co@NC) is synthesized from a polygonal metal-organic framework ZIF-67. After calcining the resultant ZIF-67, the as-synthesized Nano-Co@NC is characteristic of cobalt nanoparticles (Nano-Co) embedded in nitrogen-doped carbon (NC) that inherits the morphology of ZIF-67 with a large surface area. The Nano-Co particles that are highly dispersed and firmly fixed on NC not only ensure electrocatalytic activity of Nano-Co@NC toward the oxygen reduction reaction on the cathode, but also inhibit the growth of non-electrogenic bacteria on the anode.

A facile synthesis of FeS/FeC nanoparticles highly dispersed on grown N-doped CNTs as cathode electrocatalysts for microbial fuel cells.

A novel composite of iron sulfide, iron carbide and nitrogen carbides (Nano-FeS/FeC@NCNTs) as a cathode electrocatalyst for microbial fuel cells (MFCs) is synthesized by a one-pot solid state reaction, which yields a unique configuration of FeS/FeC nanoparticles highly dispersed on grown nitrogen-doped carbon nanotubes (NCNTs). The highly dispersed FeS/FeC nanoparticles possess large active sites, while the NCNTs provide an electronically conductive network. Consequently, the resultant Nano-FeS/FeC@NCNTs exhibit excellent electrocatalytic activity towards the oxygen reduction reaction (ORR), with a half-wave potential close to that of Pt/C (about 0.

A narrative review on machine learning in diagnosis and prognosis prediction for tongue squamous cell carcinoma.

Background: Tongue squamous cell carcinoma (TSCC) is the most common subtype of oral cavity squamous cell carcinoma (OCSCC), and it also has the worst prognosis. It is crucial to find an effective way to solve the challenges in diagnosis and prognosis prediction for TSCC. Machine learning (ML) has been widely used in medical research and has shown good performance.

Amide Proton Transfer-weighted MRI in Predicting Histologic Grade of Bladder Cancer.

Background Bladder cancer is classified into high and low grades with different clinical treatments and prognoses. Thus, accurate preoperative evaluation of the histologic grade through imaging techniques is essential. Purpose To investigate the potential of amide proton transfer-weighted (APTw) MRI in evaluating the grade of bladder cancer and to evaluate whether APTw MRI can add value to diffusion-weighted imaging (DWI) at MRI.

Multiparametric MRI Evaluation of VI-RADS for Bladder Tumors Located at the Ureteral Orifice.

Background The Vesical Imaging Reporting and Data System (VI-RADS) based on multiparametric MRI scans standardizes preoperative bladder cancer staging. However, limitations have been reported for VI-RADS, particularly for ureteral orifice tumors. Purpose To investigate the diagnostic performance and interobserver agreement of VI-RADS in evaluating muscle invasion for bladder tumors located at the ureteral orifice.

Hypoxia-induced blood-brain barrier dysfunction is prevented by pericyte-conditioned media via attenuated actomyosin contractility and claudin-5 stabilization.

The blood-brain barrier (BBB) regulates molecular and cellular entry from the cerebrovasculature into the surrounding brain parenchyma. Many diseases of the brain are associated with dysfunction of the BBB, where hypoxia is a common stressor. However, the contribution of hypoxia to BBB dysfunction is challenging to study due to the complexity of the brain microenvironment.

Prediction of Early Treatment Response to Initial Conventional Transarterial Chemoembolization Therapy for Hepatocellular Carcinoma by Machine-Learning Model Based on Computed Tomography.

Purpose: The treatment response to initial conventional transarterial chemoembolization (cTACE) is essential for the prognosis of patients with hepatocellular carcinoma (HCC). This study explored and verified the feasibility of machine-learning models based on clinical data and contrast-enhanced computed tomography (CT) image findings to predict early responses of HCC patients after initial cTACE treatment.

Patients And Methods: Overall, 110 consecutive unresectable HCC patients who were treated with cTACE for the first time were retrospectively enrolled.

Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.

Objective: To investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way.

Materials And Methods: Patients diagnosed with clinical T2-4 stage breast cancer from March 2016 to July 2020 were retrospectively enrolled. The molecular subtypes and AR expression in pre-treatment biopsy specimens were assessed.