Electrocatalysts for the Formation of Hydrogen Peroxide by Oxygen Reduction Reaction.

Hydrogen peroxide (HO) is a widely used strong oxidant, and its traditional preparation methods, anthraquinone method, and direct synthesis method, have many drawbacks. The method of producing HO by two-electron oxygen reduction reaction (2e ORR) is considered an alternative strategy for the traditional anthraquinone method due to its high efficiency, energy saving, and environmental friendliness, but it remains a big challenge. In this review, we have described the mechanism of ORR and the principle of electrocatalytic performance testing, and have summarized the standard performance evaluation techniques for electrocatalysts to produce HO.

Combination of Deep Learning Grad-CAM and Radiomics for Automatic Localization and Diagnosis of Architectural Distortion on DBT.

Rationale And Objectives: Detection and diagnosis of architectural distortion (AD) on digital breast tomosynthesis (DBT) is challenging. This study applied artificial intelligence (AI) using deep learning (DL) algorithms to detect AD, followed by radiomics for classification.

Materials And Methods: 500 cases with AD on DBT reports were identified; the earlier 292 cases for training, and the later 208 cases for testing.

Different radiomics annotation methods comparison in rectal cancer characterisation and prognosis prediction: a two-centre study.

Objectives: To explore the performance differences of multiple annotations in radiomics analysis and provide a reference for tumour annotation in large-scale medical image analysis.

Methods: A total of 342 patients from two centres who underwent radical resection for rectal cancer were retrospectively studied and divided into training, internal validation, and external validation cohorts. Three predictive tasks of tumour T-stage (pT), lymph node metastasis (pLNM), and disease-free survival (pDFS) were performed.

Preoperative Differentiation of HER2-Zero and HER2-Low from HER2-Positive Invasive Ductal Breast Cancers Using BI-RADS MRI Features and Machine Learning Modeling.

Background: Accurate determination of human epidermal growth factor receptor 2 (HER2) is important for choosing optimal HER2 targeting treatment strategies. HER2-low is currently considered HER2-negative, but patients may be eligible to receive new anti-HER2 drug conjugates.

Purpose: To use breast MRI BI-RADS features for classifying three HER2 levels, first to distinguish HER2-zero from HER2-low/positive (Task-1), and then to distinguish HER2-low from HER2-positive (Task-2).

Incidence of dreaming during ciprofol anesthesia for painless gastroscopy.

Background: Dreaming is often reported by patients who undergo propofol-based sedation, but there have not been any studies to date focused on the incidence of dreaming and factors associated therewith following the administration of ciprofol anesthesia in patients undergoing painless gastroscopy. The present study was thus developed with the goal of assessing the incidence of dreaming.

Methods: In total, this study enrolled 200 patients undergoing painless gastroscopy.

Magnetic Resonance Imaging Features Associated with a High and Low Expression of Tumor-Infiltrating Lymphocytes: A Stratified Analysis According to Molecular Subtypes.

A total of 457 patients, including 241 HR+/HER2- patients, 134 HER2+ patients, and 82 TN patients, were studied. The percentage of TILs in the stroma adjacent to the tumor cells was assessed using a 10% cutoff. The low TIL percentages were 82% in the HR+ patients, 63% in the HER2+ patients, and 56% in the TN patients ( < 0.

Evaluation of Association Between Menstrual Cycle Timing and Quantitative Background Parenchymal Enhancement on Breast MRI in Premenopausal Women.

Objectives: To evaluate the influence of menstrual cycle timing on quantitative background parenchymal enhancement and to assess an optimal timing of breast MRI in premenopausal women.

Methods: A total of 197 premenopausal women were enrolled, 120 of which were in the malignant group and 77 in the benign group. Two radiologists depicted the regions of interest (ROI) of the three consecutive biggest slices of glandular tissue in the unaffected side and calculated the ratio (=[SI - SI]/SI) in ROI from the precontrast and early phase to assess BPE quantitatively.

Deep Learning-Based Multiparametric MRI Model for Preoperative T-Stage in Rectal Cancer.

Background: Conventional MRI staging can be challenging in the preoperative assessment of rectal cancer. Deep learning methods based on MRI have shown promise in cancer diagnosis and prognostication. However, the value of deep learning in rectal cancer T-staging is unclear.

Attention guided neural ODE network for breast tumor segmentation in medical images.

Breast cancer is the most common cancer in women. Ultrasound is a widely used screening tool for its portability and easy operation, and DCE-MRI can highlight the lesions more clearly and reveal the characteristics of tumors. They are both noninvasive and nonradiative for assessment of breast cancer.

THE PROTECTIVE EFFECT OF C23 IN A RAT MODEL OF CARDIAC ARREST AND RESUSCITATION.

Background : Systemic inflammation acts as a contributor to neurologic deficits after cardiac arrest (CA) and cardiopulmonary resuscitation (CPR). Extracellular cold-inducible RNA-binding, protein (CIRP) has been demonstrated to be responsible in part for the inflammation through binding to toll-like receptor 4 (TLR4) after cerebral ischemia. The short peptide C23 derived from CIRP has a high affinity for TLR4, we hypothesize that C23 reduces systemic inflammation after CA/CPR by blocking the binding of CIRP to TLR4.

Multi-modality radiomics nomogram based on DCE-MRI and ultrasound images for benign and malignant breast lesion classification.

Objective: To develop a multi-modality radiomics nomogram based on DCE-MRI, B-mode ultrasound (BMUS) and strain elastography (SE) images for classifying benign and malignant breast lesions.

Material And Methods: In this retrospective study, 345 breast lesions from 305 patients who underwent DCE-MRI, BMUS and SE examinations were randomly divided into training (n = 241) and testing (n = 104) datasets. Radiomics features were extracted from manually contoured images.

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.

Background: A wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions.

Different effects of the DRD4 genotype on intrinsic brain network connectivity strength in drug-naïve children with ADHD and healthy controls.

The dopamine D4 receptor gene (DRD4) has been consistently reported to be associated with attention-deficit/hyperactivity disorder (ADHD). Recent studies have linked DRD4 to functional connectivity among specific brain regions. The current study aimed to compare the effects of the DRD4 genotype on functional integrity in drug-naïve ADHD children and healthy children.

Larger thalamus correlated with inattentive severity in the inattentive subtype of ADHD without comorbidity.

Previous studies of brain structural abnormalities in attention-deficit/hyperactivity disorder (ADHD) samples scarcely excluded comorbidity or analyzed them in subtypes. This study aimed to identify neuroanatomical alterations related to diagnosis and subtype of ADHD participants without comorbidity. In our cross-sectional analysis, we used T-weighted structural MRI images of individuals from the ADHD-200 database.

A chemometric strategy for accurately identifying illegal additive compounds in health foods by using ultra-high-performance liquid chromatography coupled to high resolution mass spectrometry.

The accurate identification of unknown illegal additive compounds in complex health foods continues to be a challenging task in routine analysis, because massive false positive results can be screened with ultra-high-performance liquid chromatography coupled to high-resolution mass spectrometry-based untargeted techniques and must be manually filtered out. To address this problem, we developed a chemometric-based strategy, in which data analysis was first performed by using XCMS, MS-DIAL, Mzmine2, and AntDAS2, to select those that provided acceptable results to extract common features (CFs), which can be detected by all of the selected methods. Then, CFs whose contents were significantly higher in the suspected illegal additive group were screened.

Radiomics Model for Evaluating the Level of Tumor-Infiltrating Lymphocytes in Breast Cancer Based on Dynamic Contrast-Enhanced MRI.

Background: To help identify potential breast cancer (BC) candidates for immunotherapies, we aimed to develop and validate a radiology-based biomarker (radiomic score) to predict the level of tumor-infiltrating lymphocytes (TILs) in patients with BC.

Patients And Methods: This retrospective study enrolled 172 patients with histopathology-confirmed BC assigned to the training (n = 121) or testing (n = 51) cohorts. Radiomic features were extracted and selected using Analysis-Kit software.

Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue.

Background: Computer-aided methods have been widely applied to diagnose lesions detected on breast MRI, but fully-automatic diagnosis using deep learning is rarely reported.

Purpose: To evaluate the diagnostic accuracy of mass lesions using region of interest (ROI)-based, radiomics and deep-learning methods, by taking peritumor tissues into consideration.

Study Type: Retrospective.