Structural changes in DNA by binding mitochondrion-targeted monofunctional platinum(II) complexes using molecular dynamics simulation study.

Triphenylphosphonium (PhP, TPP) is a highly effective mitochondrial targeting group, an example of using which on mitochondrion-targeted monofunctional platinum(II) agent as anticancer drug was OPT, with the -CHPhP group at ortho position of the pyriplatin pyridine ring. To study how carrier ligands might affect the efficacy of OPT, we constructed two platinum(II) agents with bulky bidentate ligands based on OPT. DNA structural changes caused by these three platinum(II) agents using molecular dynamics simulations were analysed.

Detection of glaucoma progression on longitudinal series of en-face macular optical coherence tomography angiography images with a deep learning model.

Background/aims: To design a deep learning (DL) model for the detection of glaucoma progression with a longitudinal series of macular optical coherence tomography angiography (OCTA) images.

Methods: 202 eyes of 134 patients with open-angle glaucoma with ≥4 OCTA visits were followed for an average of 3.5 years.

Improving long COVID-related text classification: a novel end-to-end domain-adaptive paraphrasing framework.

The emergence of long COVID during the ongoing COVID-19 pandemic has presented considerable challenges for healthcare professionals and researchers. The task of identifying relevant literature is particularly daunting due to the rapidly evolving scientific landscape, inconsistent definitions, and a lack of standardized nomenclature. This paper proposes a novel solution to this challenge by employing machine learning techniques to classify long COVID literature.

Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry.

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clustering (DCAEC) model, to cluster label-free IFC images without any prior knowledge of input labels. The DCAEC model first encodes the input images into the latent representations and then clusters based on the latent representations.

Improve the performance of CT-based pneumonia classification via source data reweighting.

Pneumonia is a life-threatening disease. Computer tomography (CT) imaging is broadly used for diagnosing pneumonia. To assist radiologists in accurately and efficiently detecting pneumonia from CT scans, many deep learning methods have been developed.

Brain tumor classification based on neural architecture search.

Brain tumor is a life-threatening disease and causes about 0.25 million deaths worldwide in 2020. Magnetic Resonance Imaging (MRI) is frequently used for diagnosing brain tumors.

Neural architecture search for pneumonia diagnosis from chest X-rays.

Pneumonia is one of the diseases that causes the most fatalities worldwide, especially in children. Recently, pneumonia-caused deaths have increased dramatically due to the novel Coronavirus global pandemic. Chest X-ray (CXR) images are one of the most readily available and common imaging modality for the detection and identification of pneumonia.

Assessment of Motor Dysfunction with Virtual Reality in Patients Undergoing [I]FP-CIT SPECT/CT Brain Imaging.

[I]FP-CIT SPECT has been valuable for distinguishing Parkinson disease (PD) from essential tremor. However, its performance for quantitative assessment of motor dysfunction has not been established. A virtual reality (VR) application was developed and compared with [I]FP-CIT SPECT/CT for detection of severity of motor dysfunction.

Structural Studies of the 3',3'-cGAMP Riboswitch Induced by Cognate and Noncognate Ligands Using Molecular Dynamics Simulation.

Riboswtich RNAs can control gene expression through the structural change induced by the corresponding small-molecule ligands. Molecular dynamics simulations and free energy calculations on the aptamer domain of the 3',3'-cGAMP riboswitch in the ligand-free, cognate-bound and noncognate-bound states were performed to investigate the structural features of the 3',3'-cGAMP riboswitch induced by the 3',3'-cGAMP ligand and the specificity of ligand recognition. The results revealed that the aptamer of the 3',3'-cGAMP riboswitch in the ligand-free state has a smaller binding pocket and a relatively compact structure versus that in the 3',3'-cGAMP-bound state.

Molecular dynamics simulation on the allosteric analysis of the c-di-GMP class I riboswitch induced by ligand binding.

Riboswitches are RNA molecules that regulate gene expression using conformation change, affected by binding of small molecule ligands. Although a number of ligand-bound aptamer complex structures have been solved, it is important to know ligand-free conformations of the aptamers in order to understand the mechanism of specific binding by ligands. In this paper, we use dynamics simulations on a series of models to characterize the ligand-free and ligand-bound aptamer domain of the c-di-GMP class I (GEMM-I) riboswitch.