Association Between Neutrophil Percentage to-Albumin Ratio, Neutrophil to-Lymphocyte Ratio, Platelet-to Lymphocyte Ratio and in-Hospital Mortality in Elder Patients with Bloodstream Infections.

Background: Bloodstream infections (BSI) is one of the major complications in elder inpatients, which is closely related to inflammation. Neutrophil percentage-to-albumin ratio (NPAR), Neutrophil-to-lymphocyte ratio (NLR), and Platelet-to-lymphocyte (PLR) are convenient predictors of inflammation and poor prognosis for a wide range of diseases. However, the association of NPAR, NLR and PLR with in-hospital mortality in elder inpatients with BSI are unclear.

Functional lipid nanoparticles for safe delivery of macromolecular antibiotics to gram-negative bacteria.

Lipid nanoparticles (LNPs) hold great potential for delivery of macromolecular antimicrobials. Herein, we designed a series of anionic LNPs capable of delivering cationic polymyxin B (PMB) for effective and safe treatment of Gram-negative bacterial infection. The use of anionic lipid induced self-assembly of PMB, encapsulating cationic PMB molecules into LNPs via electrostatic interactions (PMB-LNPs).

PIAA: Pre-imaging all-round assistant for digital radiography.

Background: In radiography procedures, radiographers' suboptimal positioning and exposure parameter settings may necessitate image retakes, subjecting patients to unnecessary ionizing radiation exposure. Reducing retakes is crucial to minimize patient X-ray exposure and conserve medical resources.

Objective: We propose a Digital Radiography (DR) Pre-imaging All-round Assistant (PIAA) that leverages Artificial Intelligence (AI) technology to enhance traditional DR.

Characteristics of the Perianthic Endophytic Fungal Communities of the Rare Horticultural Plant and Their Changes under Artificial Cultivation.

Flower endophytic fungi play a major role in plant reproduction, stress resistance, and growth and development. However, little is known about how artificial cultivation affects the endophytic fungal community found in the tepals of rare horticultural plants. In this research, we used high-throughput sequencing technology combined with bioinformatics analysis to reveal the endophytic fungal community of tepals in and the effects of artificial cultivation on the community composition and function of these plants, using tepals of from wild habitat, cultivated campus habitat, and cultivated field habitat as research objects.

Reproductive toxicity investigation of silica nanoparticles in male pubertal mice.

Silica nanoparticles (SiNPs), one of the most produced nanoparticles (NPs) in the world, are used in all aspects of life. The increased application of SiNPs, especially in medicine, has raised considerable concern regarding their toxicological impact. Previous studies have shown that SiNPs can pass through the reproductive barrier and cause reproductive organ dysfunction by destroying Sertoli cells, Leydig cells, and germ cells.

Silica nanoparticles cause spermatogenesis dysfunction in mice via inducing cell cycle arrest and apoptosis.

The widespread use of silica nanoparticles (SiNPs) has increased the risk of human exposure, which raised concerns about their adverse effects on human health, especially the reproductive system. Previous studies have shown that SiNPs could cause damage to reproductive organs, but the specific mechanism is still unclear. In this study, to investigate the underlying mechanism of male reproductive toxicity induced by SiNPs, 40 male mice at the age of 8 weeks were divided into two groups and then intraperitoneally injected with vehicle control or 10 mg/kg SiNPs per day for one week.

ELNet:Automatic classification and segmentation for esophageal lesions using convolutional neural network.

Automatic and accurate esophageal lesion classification and segmentation is of great significance to clinically estimate the lesion statuses of the esophageal diseases and make suitable diagnostic schemes. Due to individual variations and visual similarities of lesions in shapes, colors, and textures, current clinical methods remain subject to potential high-risk and time-consumption issues. In this paper, we propose an Esophageal Lesion Network (ELNet) for automatic esophageal lesion classification and segmentation using deep convolutional neural networks (DCNNs).

Automatic brain tissue segmentation based on graph filter.

Background: Accurate segmentation of brain tissues from magnetic resonance imaging (MRI) is of significant importance in clinical applications and neuroscience research. Accurate segmentation is challenging due to the tissue heterogeneity, which is caused by noise, bias filed and partial volume effects.

Methods: To overcome this limitation, this paper presents a novel algorithm for brain tissue segmentation based on supervoxel and graph filter.