Photoinduced Lignin C-C Bond Cleavage and Chemodivergent Functionalization via Iron Catalysis.

The photocatalytic conversion of lignin into value-added chemicals especially those functionalized molecules represent one of the most important strategies for sustainable and environmental-friendly development. Cleavage of C-C bonds in lignin under mild photocatalytic conditions for refining lignin into useful molecules is meaningful but challenging. Meanwhile, the assembly of diverse functional groups into active lignin fragments during the depolymerization is of great challenging.

Multi-Dimensional Medical Image Fusion With Complex Sparse Representation.

In the field of medical imaging, the fusion of data from diverse modalities plays a pivotal role in advancing our understanding of pathological conditions. Sparse representation (SR), a robust signal modeling technique, has demonstrated noteworthy success in multi-dimensional (MD) medical image fusion. However, a fundamental limitation appearing in existing SR models is their lack of directionality, restricting their efficacy in extracting anatomical details from different imaging modalities.

A Combined Model Integrating Radiomics and Deep Learning Based on Contrast-Enhanced CT for Preoperative Staging of Laryngeal Carcinoma.

Rationale And Objectives: Accurate staging of laryngeal carcinoma can inform appropriate treatment decision-making. We developed a radiomics model, a deep learning (DL) model, and a combined model (incorporating radiomics features and DL features) based on the venous-phase CT images and explored the performance of these models in stratifying patients with laryngeal carcinoma into stage I-II and stage III-IV, and also compared these models with radiologists.

Materials And Methods: Three hundreds and nineteen patients with pathologically confirmed laryngeal carcinoma were randomly divided into a training set (n = 223) and a test set (n = 96).

Using deeply time-series semantics to assess depressive symptoms based on clinical interview speech.

Introduction: Depression is an affective disorder that contributes to a significant global burden of disease. Measurement-Based Care (MBC) is advocated during the full course management, with symptom assessment being an important component. Rating scales are widely used as convenient and powerful assessment tool, but they are influenced by the subjectivity and consistency of the raters.

Acute ischemic Stroke combined with Stanford type A aortic dissection: A case report and literature review.

Background: Acute aortic dissection (AAD) is a high mortality disease that can lead to acute ischemic strokes (AIS). Some of the patients with AAD combined with AIS initially present with neurological symptoms, which can easily lead to missed or delayed AAD diagnosis. This is attributed to the lack of physician awareness or the urgency of patient thrombolysis.

Predicting the effective thermal conductivity of unfrozen soils with various water contents based on artificial neural network.

The effective thermal conductivity of soils is a crucial parameter for many applications such as geothermal engineering, environmental science, and agriculture and engineering. However, it is pretty challenging to accurately determine it due to soils' complex structure and components. In the present study, the influences of different parameters, including silt content (), sand content (), clay content (), quartz content (), porosity, and water content on the effective thermal conductivity of soils, were firstly analyzed by the Pearson correlation coefficient.

MicroRNAs as potential biomarkers for the diagnosis of Traumatic Brain Injury: A systematic review and meta-analysis.

Traumatic brain injury (TBI) is a sudden trauma on the head, in which severe TBI (sTBI) is usually associated with death and long-term disability. MicroRNAs (miRNAs) are potential biomarkers of diverse diseases, including TBI. However, few systematic reviews and meta-analyses have been conducted to determine the clinical value of miRNAs expression in TBI patients.

Master clinical medical knowledge at certificated-doctor-level with deep learning model.

Mastering of medical knowledge to human is a lengthy process that typically involves several years of school study and residency training. Recently, deep learning algorithms have shown potential in solving medical problems. Here we demonstrate mastering clinical medical knowledge at certificated-doctor-level via a deep learning framework Med3R, which utilizes a human-like learning and reasoning process.

Emulsified isoflurane treatment inhibits the cell cycle and respiration of human bronchial epithelial 16HBE cells in a p53-independent manner.

Emulsified isoflurane (EIso), as a result of its rapid anesthetic induction, recovery and convenience, is widely used as a novel intravenous general anesthetic. Treatment with EIso can reduce injuries caused by ischemia/reperfusion (I/R) to organs, including the heart, lung and liver, without knowing understanding the molecular mechanism. The present study hypothesized that treatment with EIso can affect the physiological processes of human lung bronchial epithelial cells (16HBE) prior to I/R.

Tailored Electrospinning of WO₃ Nanobelts as Efficient Ultraviolet Photodetectors with Photo-Dark Current Ratios up to 1000.

In this work, polycrystalline WO3 nanobelts were fabricated via an electrospinning process combined with subsequent air calcination. The resultant products were characterized by X-ray diffraction, field-emission scanning electron microscopy, and high-resolution transmission electron microscopy in regard to the structures. It has been found that the applied voltage during the electrospinning process played the determined role in the formation of the WO3 nanobelts, allowing the controlled growth of the nanobelts.