Orientation-Dependent Oxygen Evolution Catalytic Performance and Mechanistic Insights of Epitaxial CoS Thin Films.

Cobalt sulfide (CoS) nanomaterials exhibit an efficient electrochemical catalytic performance due to their unique properties and electronic structure. The preparation of epitaxial CoS thin films with varying crystal orientations and the study of their catalytic kinetics and mechanisms remain significant gaps. This study addresses the preparation of epitaxial CoS thin films with orientations of (100), (110), and (111) on yttrium-doped zirconia (YSZ) substrates using pulsed laser deposition.

Regulating Asymmetric Charge Distribution in CuMoS Nanosheets for Enhanced Photocatalytic CO Reduction.

Photocatalytic reduction of CO to high-value-added chemicals represents a promising strategy for effective CO utilization, and rationally regulating the electronic structure of the catalyst is the key to enhancing photocatalytic performance. Herein, it is demonstrated that in situ doping of atomic indium into the lattice of the CuMoS catalyst results in remarkable enhancements in photocatalytic CO reduction performance. A record gas product yield of 104.

Strong Replaces Weak: Hydrogen Bond-Anchored Electrolyte Enabling Ultra-Stable and Wide-Temperature Aqueous Zinc-Ion Capacitors.

Despite aqueous electrolytes offer a great opportunity for large-scale energy storage owing to their safety and cost-effectiveness, their practical application suffers from the parasitic side reactions and poor temperature adaptability stemming from weak hydrogen-bond (HB) network in free water. Here, we propose the guiding thought "strong replaces weak" to design hydrogen bond-anchored electrolyte by introducing sulfolane (SL) for disrupting the regular weak HB network and contributing to superior temperature tolerance. Judiciously combined experimental characterization and theoretical calculation confirm that SL can remodel the primary solvation shell of metal ions, customize stable electrode interface chemistry and restrain the side reactions.

High catalytic activity and abundant active sites in MC monolayer for nitrogen reduction reaction.

Developing highly efficient single-atom catalysts (SACs) for the nitrogen reduction reaction (NRR) to ammonia production has garnered significant attention in the scientific community. However, achieving high activity and selectivity remains challenging due to the lack of innate activity in most existing catalysts or insufficient active site density. This study delves into the potential of MC materials (M = Cr, Ir, Mn, Mo, Os, Re, Rh, Ru, W, Fe, Cu, and Ti) with high transition metal coverage as SACs for NRR using first-principles calculations.

F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

Purpose: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC).

Methods: A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology.

Histone demethylase KDM4A mediating macrophage polarization: A potential mechanism of trichloroethylene induced liver injury.

Trichloroethylene (TCE) is a commonly used organic solvent in industry. Our previous studies have found that TCE can cause liver injury accompanied by macrophage polarization, but the specific mechanism is unclear. The epigenetic regulation of macrophage polarization is mainly focused on histone modification.

Multiscale and multiperception feature learning for pancreatic lesion detection based on noncontrast CT.

. Pancreatic cancer is one of the most malignant tumours, demonstrating a poor prognosis and nearly identically high mortality and morbidity, mainly because of the difficulty of early diagnosis and timely treatment for localized stages.o develop a noncontrast CT (NCCT)-based pancreatic lesion detection model that could serve as an intelligent tool for diagnosing pancreatic cancer early, overcoming the challenges associated with low contrast intensities and complex anatomical structures present in NCCT images.

Ultra-thin nanosheets decorated in-situ S-doped 3D interconnected carbon network as interlayer modified Li-S batteries separator for accelerating adsorption-catalytic synergistic process of LiPSs.

The shuttle effect of soluble lithium polysulfides (LiPSs) is primarily responsible for the unstable performance of lithium-sulfur (Li-S) batteries, which has severely impeded their continued development. In order to solve this problem, a special strategy is proposed. Specifically, ultra-thin NiCo based layered double hydroxides (named LDH or NiCo-LDH) nanosheets are implanted into a pre-designed 3D interconnected carbon networks (SPC) to obtain porous composite materials (named SPC-LDH).

Computed tomography and guidelines-based human-machine fusion model for predicting resectability of the pancreatic cancer.

Background And Aim: The study aims to develop a hybrid machine learning model for predicting resectability of the pancreatic cancer, which is based on computed tomography (CT) and National Comprehensive Cancer Network (NCCN) guidelines.

Method: We retrospectively studied 349 patients. One hundred seventy-one cases from Center 1 and 92 cases from Center 2 were used as the primary training cohort, and 66 cases from Center 3 and 20 cases from Center 4 were used as the independent test dataset.

Enhanced Spatial Charge Separation in a Niobium and Tantalum Nitride Core-Shell Photoanode: In Situ Interface Bonding for Efficient Solar Water Splitting.

Tantalum nitride (Ta N ) has emerged as a promising photoanode material for photoelectrochemical (PEC) water splitting. However, the inefficient electron-hole separation remains a bottleneck that impedes its solar-to-hydrogen conversion efficiency. Herein, we demonstrate that a core-shell nanoarray photoanode of NbN -nanorod@Ta N ultrathin layer enhances light harvesting and forms a spatial charge-transfer channel, which leads to the efficient generation and extraction of charge carriers.

Glutamine metabolic microenvironment drives M2 macrophage polarization to mediate trastuzumab resistance in HER2-positive gastric cancer.

Background: Trastuzumab is a first-line targeted therapy for human epidermal growth factor receptor-2 (HER2)-positive gastric cancer. However, the inevitable occurrence of acquired trastuzumab resistance limits the drug benefit, and there is currently no effective reversal measure. Existing researches on the mechanism of trastuzumab resistance mainly focused on tumor cells themselves, while the understanding of the mechanisms of environment-mediated drug resistance is relatively lacking.

Regulating the thickness of the carbon coating layer in iron/carbon heterostructures to enhance the catalytic performance for oxygen evolution reaction.

The exploration of high-performance electrocatalysts for the oxygen evolution reaction (OER) is crucial and urgent for the fast development of green and renewable hydrogen energy. Herein, an ultra-fast and energy-efficient preparation strategy (microwave-assisted rapid in-situ pyrolysis of organometallic compounds induced by carbon nanotube (CNT)) is developed to obtain iron/carbon (Fe/C) heterogeneous materials (Fe/FeC particles wrapped by carbon coating layer). The thickness of the carbon coating layer can be adjusted by changing the content and form of carbon in the metal sources during the fast preparation process.

A multimodal model fusing multiphase contrast-enhanced CT and clinical characteristics for predicting lymph node metastases of pancreatic cancer.

To develop a multimodal model that combines multiphase contrast-enhanced computed tomography (CECT) imaging and clinical characteristics, including experts' experience, to preoperatively predict lymph node metastasis (LNM) in pancreatic cancer patients.We proposed a new classifier fusion strategy (CFS) based on a new evidential reasoning (ER) rule (CFS-nER) by combining nomogram weights into a previous ER rule-based CFS. Three kernelled support tensor machine-based classifiers with plain, arterial, and venous phases of CECT as the inputs, respectively, were constructed.

Prenatal low-dose antibiotic exposure and children allergic diseases at 4 years of age: A prospective birth cohort study.

Background: Based on a medical record or questionnaire survey approach, previous epidemiological studies have investigated associations between maternal antibiotic exposure during pregnancy and childhood allergic diseases. However, biomonitoring studies on the prenatal low-dose antibiotic exposure, mainly from the environment and contaminated food, and in relation to children allergic diseases, are missing.

Objectives: This research aimed to examine the associations between prenatal low-dose antibiotic exposure measured at multiple time points and children current allergic diseases at 4 years of age.

On the robustness of deep learning-based lung-nodule classification for CT images with respect to image noise.

Robustness is an important aspect when evaluating a method of medical image analysis. In this study, we investigated the robustness of a deep learning (DL)-based lung-nodule classification model for CT images with respect to noise perturbations. A deep neural network (DNN) was established to classify 3D CT images of lung nodules into malignant or benign groups.

Efficacy of Qishen Yiqi Drop Pill for Chronic Heart Failure: An Updated Meta-Analysis of 85 Studies.

Background: Despite evidence for beneficial effects of Qishen Yiqi Drop Pill (QSYQ) on congestive heart failure, the majority of studies are based on insufficient sample sizes. The aim of this study was to evaluate the therapeutic effects of QSYQ using a meta-analysis approach. .

A manifold learning regularization approach to enhance 3D CT image-based lung nodule classification.

Purpose: Diagnosis of lung cancer requires radiologists to review every lung nodule in CT images. Such a process can be very time-consuming, and the accuracy is affected by many factors, such as experience of radiologists and available diagnosis time. To address this problem, we proposed to develop a deep learning-based system to automatically classify benign and malignant lung nodules.

A collection input based support tensor machine for lesion malignancy classification in digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) with improved lesion conspicuity and characterization has been adopted in screening practice. DBT-based diagnosis strongly depends on physicians' experience, so an automatic lesion malignancy classification model using DBT could improve the consistency of diagnosis among different physicians. Tensor-based approaches that use the original imaging data as input have shown promising results for many classification tasks.

Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features.

To predict lung nodule malignancy with a high sensitivity and specificity for low dose CT (LDCT) lung cancer screening, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep convolutional neural network (CNN). First, we extracted twenty-nine HF, including nine intensity features, eight geometric features, and twelve texture features based on grey-level co-occurrence matrix (GLCM). We then trained 3D CNNs modified from three 2D CNN architectures (AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at the output layer.

[A radiomic approach to differential diagnosis of renal cell carcinoma in patients with hydronephrosis and renal calculi].

To explore the application of radiomic analysis in differential diagnosis of renal cell carcinoma in patients with hydronephrosis and renal calculi using supervised machine learning methods.The abdominal CT scan data were retrospectively analyzed for 66 patients with pathologically confirmed hydronephrosis and renal calculi, among whom 35 patients had renal cell carcinoma. In each case 18 non-texture features and 344 texture features were extracted from the region of interest (ROI).

Multi-Objective-Based Radiomic Feature Selection for Lesion Malignancy Classification.

Objective: accurately classifying the malignancy of lesions detected in a screening scan is critical for reducing false positives. Radiomics holds great potential to differentiate malignant from benign tumors by extracting and analyzing a large number of quantitative image features. Since not all radiomic features contribute to an effective classifying model, selecting an optimal feature subset is critical.

A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT.

We developed a kernelled support tensor machine (KSTM)-based model with tumor tensors derived from pre-treatment PET and CT imaging as input to predict distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). The patient cohort included 110 early stage NSCLC patients treated with SBRT, 25 of whom experienced failure at distant sites. Three-dimensional tumor tensors were constructed and used as input for the KSTM-based classifier.

Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer.

Distant failure is the main cause of human cancer-related mortalities. To develop a model for predicting distant failure in non-small cell lung cancer (NSCLC) and cervix cancer (CC) patients, a shell feature, consisting of outer voxels around the tumor boundary, was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images.

Trichloroethylene Alters Th1/Th2/Th17/Treg Paradigm in Mice: A Novel Mechanism for Chemically Induced Autoimmunity.

The role of environmental factors in autoimmune diseases has been increasingly recognized. While major advance has been made in understanding biological pathogen-induced autoimmune diseases, chemically triggered autoimmunity is poorly understood. Trichloroethylene (TCE), a common environmental pollutant, has recently been shown to induce autoimmunity.