AIMC Topic: Copper

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Copper price prediction using LSTM recurrent neural network integrated simulated annealing algorithm.

PloS one
Copper is an important mineral and fluctuations in copper prices can affect the stable functioning of some countries' economies. Policy makers, futures traders and individual investors are very concerned about copper prices. In a recent paper, we use...

Bacterial metabolism-triggered-chemiluminescence-based point-of-care testing platform for sensitive detection and photothermal inactivation of Staphylococcus aureus.

Analytica chimica acta
Post-operative pathogenic infections in liver transplantation seriously threaten human health. It is essential to develop novel methods for the highly sensitive and rapid detection of Staphylococcus aureus (S. aureus). Interestingly, the combination ...

Aggregation-induced electrochemiluminescence enhancement of Ag-MOG for amyloid β 42 sensing.

Analytica chimica acta
This study aimed to introduce an immunosensor for measuring amyloid β 42 (Aβ) levels by aggregation-induced enhanced electrochemiluminescence (ECL). Metal-organic gels (MOGs) are novel soft materials with advantages such as high gel stability, good l...

Deep learning enables the discovery of a novel cuproptosis-inducing molecule for the inhibition of hepatocellular carcinoma.

Acta pharmacologica Sinica
Hepatocellular carcinoma (HCC) is one of the most common and deadly cancers in the world. The therapeutic outlook for HCC patients has significantly improved with the advent and development of systematic and targeted therapies such as sorafenib and l...

Hybrid chain reaction and selective recognition-based homogeneous dual-fluorescence analysis of circulating tumor cells in clinical ovarian cancer samples.

Analytica chimica acta
BACKGROUND: Oncological analysis is important in tumor diagnosis. We constructed a dual-fluorescence and binary visual analysis system for circulating tumor cells (CTCs) using the folate receptor as a biomarker, combined with hybridization chain reac...

Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning.

Hereditas
BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in ...

Cuproptosis facilitates immune activation but promotes immune escape, and a machine learning-based cuproptosis-related signature is identified for predicting prognosis and immunotherapy response of gliomas.

CNS neuroscience & therapeutics
AIMS: Cell death, except for cuproptosis, in gliomas has been extensively studied, providing novel targets for immunotherapy by reshaping the tumor immune microenvironment through multiple mechanisms. This study aimed to explore the effect of cupropt...

Usefulness of copper filters in digital chest radiography based on the relationship between effective detective quantum efficiency and deep learning-based segmentation accuracy of the tumor area.

Radiological physics and technology
This study aimed to determine the optimal radiographic conditions for detecting lesions on digital chest radiographs using an indirect conversion flat-panel detector with a copper (Cu) filter. First, we calculated the effective detective quantum effi...

Deep learning reveals cuproptosis features assist in predict prognosis and guide immunotherapy in lung adenocarcinoma.

Frontiers in endocrinology
BACKGROUND: Cuproptosis is a recently found non-apoptotic cell death type that holds promise as an emerging therapeutic modality in lung adenocarcinoma (LUAD) patients who develop resistance to radiotherapy and chemotherapy. However, the Cuproptosis'...

Selecting Machine Learning Models to Support the Design of Al/CuO Nanothermites.

The journal of physical chemistry. A
Novel properties associated with nanothermites have attracted great interest for several applications, including lead-free primers and igniters. However, the prediction of quantitative structure-energetic performance relationships is still challengin...