The Evaluation of Gastric Cancer Lymphovascular Invasion Using CT Volume Perfusion.

Background: The best treatment option for patients with resectable gastric cancer is radical gastric cancer surgery. However, the postoperative overall survival rate is low. Lymphovascular invasion (LVI) is a risk factor for cancer recurrence and a stand-alone predictor of a poor post-operative prognosis for gastric cancer (GC) patients.

Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer.

Purpose: To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).

Materials And Methods: A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria.

A review of phytoremediation of environmental lead (pb) contamination.

An estimated one billion people globally are exposed to hazardous levels of lead (Pb), resulting in intellectual disabilities for over 600,000 children each year. This critical issue aligns with the expanding worldwide population and the demand for food security, emphasizing the urgency of effectively addressing heavy metal pollution especially from Pb for sustainable development. Phytoremediation, a highly favoured approach in conjunction with conventional physical, chemical, and microbial methods, is a promising approach to mitigating soil and environmental contamination.

Fe, N-Inducing Interfacial Electron Redistribution in NiCo Spinel on Biomass-Derived Carbon for Bi-functional Oxygen Conversion.

Herein, an interfacial electron redistribution is proposed to boost the activity of carbon-supported spinel NiCoO catalyst toward oxygen conversion via Fe, N-doping strategy. Fe-doping into octahedron induces a redistribution of electrons between Co and Ni atoms on NiCoFeO@N-carbon. The increased electron density of Co promotes the coordination of water to Co sites and further dissociation.

Computer-aided diagnosis of distal metastasis in non-small cell lung cancer by low-dose CT based radiomics and deep learning signatures.

Background: This study aimed to develop and validate radiomics and deep learning (DL) signatures for predicting distal metastasis (DM) of non-small cell lung cancer (NSCLC) in low-dose computed tomography (LDCT).

Methods: Images and clinical data were retrospectively collected for 381 NSCLC patients and prospectively collected for 114 patients at the Fifth Affiliated Hospital of Sun Yat-Sen University. Additionally, we enrolled 179 patients from the Jiangmen Central Hospital to externally validate the signatures.

Recent progress on phytoremediation of urban air pollution.

The rapid growth of population and economy has led to an increase in urban air pollutants, greenhouse gases, energy shortages, environmental degradation, and species extinction, all of which affect ecosystems, biodiversity, and human health. Atmospheric pollution sources are divided into direct and indirect pollutants. Through analysis of the sources of pollutants, the self-functioning of different plants can be utilized to purify the air quality more effectively.

Phytosphere purification of urban domestic wastewater.

Industrialization and overpopulation have polluted aquatic environments with significant impacts on human health and wildlife. The main pollutants in urban sewage are nitrogen, phosphorus, heavy metals and organic pollutants, which need to be treated with sewage, and the use of aquatic plants to purify wastewater has high efficiency and low cost. However, the effectiveness and efficiency of phytoremediation are also affected by temperature, pH, microorganisms and other factors.

Motor imagery electroencephalogram classification algorithm based on joint features in the spatial and frequency domains and instance transfer.

Introduction: Motor imagery electroencephalography (MI-EEG) has significant application value in the field of rehabilitation, and is a research hotspot in the brain-computer interface (BCI) field. Due to the small training sample size of MI-EEG of a single subject and the large individual differences among different subjects, existing classification models have low accuracy and poor generalization ability in MI classification tasks.

Methods: To solve this problem, this paper proposes a electroencephalography (EEG) joint feature classification algorithm based on instance transfer and ensemble learning.

Development and validation of a deep learning radiomics nomogram for preoperatively differentiating thymic epithelial tumor histologic subtypes.

Objectives: Using contrast-enhanced computed tomography (CECT) and deep learning technology to develop a deep learning radiomics nomogram (DLRN) to preoperative predict risk status of patients with thymic epithelial tumors (TETs).

Methods: Between October 2008 and May 2020, 257 consecutive patients with surgically and pathologically confirmed TETs were enrolled from three medical centers. We extracted deep learning features from all lesions using a transformer-based convolutional neural network and created a deep learning signature (DLS) using selector operator regression and least absolute shrinkage.

Enhanced Yield of Large-Sized TiCT MXene Polymers Nanosheets via Cyclic Ultrasonic-Centrifugal Separation.

Water pollution has spurred the development of membrane separation technology as a potential means of solving the issue. In contrast to the irregular and asymmetric holes that are easily made during the fabrication of organic polymer membranes, forming regular transport channels is essential. This necessitates the use of large-size, two-dimensional materials that can enhance membrane separation performance.

The carotid web: Current research status and imaging features.

The carotid web is commonly found in the carotid bulb or the beginning of the internal carotid artery. It presents as a thin layer of proliferative intimal tissue originating from the arterial wall and extending into the vessel lumen. A large body of research has proven that the carotid web is a risk factor for ischemic stroke.

Identifying Solitary Granulomatous Nodules from Solid Lung Adenocarcinoma: Exploring Robust Image Features with Cross-Domain Transfer Learning.

Purpose: This study aimed to find suitable source domain data in cross-domain transfer learning to extract robust image features. Then, a model was built to preoperatively distinguish lung granulomatous nodules (LGNs) from lung adenocarcinoma (LAC) in solitary pulmonary solid nodules (SPSNs).

Methods: Data from 841 patients with SPSNs from five centres were collected retrospectively.

Phytoremediation of cadmium from soil, air and water.

Potentially toxic elements (PTEs) pose a great threat to ecosystems and long-term exposure causes adverse effects to wildlife and humans. Cadmium induces a variety of diseases including cancer, kidney dysfunction, bone lesions, anemia and hypertension. Here we review the ability of plants to accumulate cadmium from soil, air and water under different environmental conditions, focusing on absorption mechanisms and factors affecting these.

Nano Ag/CoO Catalyzed Rapid Decomposition of Bark for Production Biofuels and Biochemicals.

Biomass energy has attracted widespread attention due to its renewable, storage, huge production and clean and pollution-free advantages. Using bark (RPB) as raw material, biogas and bio-oil produced by pyrolysis of RPB were detected and analyzed by TG-DTG, TG-FTIR and PY-GC-MS under the action of nanocatalysis. TG results showed that CH and CO flammable gases were produced by pyrolysis.

A strategy for bioremediation of nuclear contaminants in the environment.

Radionuclides released from nuclear contamination harm the environment and human health. Nuclear pollution spread over large areas and the costs associated with decontamination is high. Traditional remediation methods include both chemical and physical, however, these are expensive and unsuitable for large-scale restoration.

A discussion of microplastics in soil and risks for ecosystems and food chains.

Microplastics are among the major contaminations in terrestrial and marine environments worldwide. These persistent organic contaminants composed of tiny particles are of concern due to their potential hazards to ecosystem and human health. Microplastics accumulates in the ocean and in terrestrial ecosystems, exerting effects on living organisms including microbiomes, fish and plants.

Pyrolysis of Bunge Leaves as for Extracted Bio-Oil Material.

Biomass rapid pyrolysis technology is easy to implement in continuous production and industrial application, and has become one of the leading technologies in the field of world renewable energy development. Agricultural and forestry waste is an important resource of renewable energy in China. In general, abandoned leaves in forest areas cause serious waste of resources.

Nano Catalysis of Biofuels and Biochemicals from Scop. Wood for Bio-Oil Raw Material.

Scop. as a precious landscape shrub and a good afforestation species that is used in the pharmaceutical industry. In this paper, TG-FTIR, TG-DTG, and Py-GC/MS were used to study the biomaterials of used as biofuels and biochemicals under the catalysis of nano-Mo/FeO.

A brain-like classification method for computed tomography images based on adaptive feature matching dual-source domain heterogeneous transfer learning.

Transfer learning can improve the robustness of deep learning in the case of small samples. However, when the semantic difference between the source domain data and the target domain data is large, transfer learning easily introduces redundant features and leads to negative transfer. According the mechanism of the human brain focusing on effective features while ignoring redundant features in recognition tasks, a brain-like classification method based on adaptive feature matching dual-source domain heterogeneous transfer learning is proposed for the preoperative aided diagnosis of lung granuloma and lung adenocarcinoma for patients with solitary pulmonary solid nodule in the case of small samples.

Incorporation of Nanocatalysts for the Production of Bio-Oil from Wood.

Biomass has been recognized as the most common source of renewable energy. In recent years, researchers have paved the way for a search for suitable biomass resources to replace traditional fossil fuel energy and provide high energy output. Although there are plenty of studies of biomass as good biomaterials, there is little detailed information about wood () as a potential bio-oil material.

Predicting lymphovascular invasion in clinically node-negative breast cancer detected by abbreviated magnetic resonance imaging: Transfer learning vs. radiomics.

Objective: To compare the performance of abbreviated breast magnetic resonance imaging (AB-MRI)-based transfer learning (TL) algorithm and radionics analysis for lymphovascular invasion (LVI) prediction in patients with clinically node-negative invasive breast cancer (IBC).

Methods: Between November 2017 and October 2020, 233 clinically node-negative IBCs detected by AB-MRI were retrospectively enrolled. One hundred thirty IBCs from center 1 (37 LVI-positive and 93 LVI-negative) were assigned as the training cohort and 103 from center 2 (25 LVI-positive and 78 LVI-negative) as the validation cohort.

A CT-based deep learning model for subsolid pulmonary nodules to distinguish minimally invasive adenocarcinoma and invasive adenocarcinoma.

Objective: To develop and validate a deep learning nomogram (DLN) model constructed from non-contrast computed tomography (CT) images for discriminating minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) in patients with subsolid pulmonary nodules (SSPNs).

Materials And Methods: In total, 365 consecutive patients who presented with SSPNs and were pathologically diagnosed with MIA or IAC after surgery, were recruited from two medical institutions from 2016 to 2019. Deep learning features were selected from preoperative CT images using convolutional neural network.