Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction.

Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50-85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50-85 years) downloaded from multiple open-source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia-associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aß40, Aß42, T-tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70-85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.

An electrochemical aptasensor based on AuPt alloy nanoparticles for ultrasensitive detection of amyloid-ß oligomers.

Amyloid-ß oligomer is an important biomarker and a potential therapeutic target of Alzheimer's disease in its early stage. Here, we combined superhydrophobic carbon fiber paper (CFP) with AuPt alloy nanoparticles to prepare a CFP/AuPt nanocomposite with larger specific surface area and hydrophobic surface. On this basis, we constructed an electrochemical aptasensor based on CFP/AuPt for the ultrasensitive detection of amyloid-ß oligomers. The surface-coated AuPt nanoparticles greatly enhanced the electroactive area, and the hydrophobic surface increased the resisting nonspecific adsorption performance of sensor. A combination of these two features significantly improved the sensitivity and specificity of the sensor. This electrochemical aptasensor based on CFP/AuPt displayed a low detection limit of 0.16 pg/mL. This work shows a promising future in clinical diagnosis of Alzheimer's disease and provides a possible solution to electrochemical biosensors that are susceptible to interference in biological fluids.

Effects of a high energy and low protein diet on hepatic and plasma characteristics and Cidea and Cidec mRNA expression in liver and adipose tissue of laying hens with fatty liver hemorrhagic syndrome.

Cidea and Cidec are two members of Cell death-inducing DNA fragmentation factor-alpha-like effector family proteins, which could be involved in lipid or fat metabolism. To better understand the roles of Cidea and Cidec in fatty liver hemorrhagic syndrome (FLHS), 150 healthy 155-day-old Hyline Brown laying hens were randomly divided into control group (fed with basic diet) and experimental group (fed with high-energy low-protein [HELP] diet). Analysis of the liver by tissue sectioning and hematoxylin and eosin staining showed that the HELP diet induced micro-vesicular steatosis in laying hens. Subsequently, based on the liver color scores and the range of lipid accumulation observed in histological examination, we classified livers with <50% vacuolization as mild FLHS and >50% as severe FLHS. The results showed that the levels of Cidea and Cidec mRNA expression were markedly elevated in the liver and adipose tissues with FLHS and the levels of Cidea and Cidec mRNA expression in the liver with severe FLHS were significantly higher than that in the liver with mild FLHS. Thus, the present study revealed that the Cidea and Cidec genes may be involved in pathways of FLHS formation.

Impacts of biochar and oyster shells waste on the immobilization of arsenic in highly contaminated soils.

Soil contamination is a serious problem with deleterious impacts on global sustainability. Readily available, economic, and highly effective technologies are therefore urgently needed for the rehabilitation of contaminated sites. In this study, two readily available materials prepared from bio-wastes, namely biochar and oyster shell waste, were evaluated as soil amendments to immobilize arsenic in a highly As-contaminated soil (up to 15,000 mgAs/kg). Both biochar and oyster shell waste can effectively reduce arsenic leachability in acid soils. After application of the amendments (2-4% addition, w/w), the exchangeable arsenic fraction decreased from 105.8 to 54.0 mg/kg. The application of 2%biochar +2% oyster shell waste most effectively reduced As levels in the column leaching test by reducing the arsenic concentration in the porewater by 62.3% compared with the treatment without amendments. Biochar and oyster shell waste also reduced soluble As(III) from 374.9 ± 18.8 µg/L to 185.9 ± 16.8 µg/L and As(V) from 119.8 ± 13.0 µg/L to 56.4 ± 2.6 µg/L at a pH value of 4-5. The treatment using 4% (w/w) amendments did not result in sufficient As immobilization in highly contaminated soils; high soluble arsenic concentrations (upto193.0 µg/L)were found in the soil leachate, particularly in the form of As(III), indicating a significant potential to pollute shallow groundwater aquifers. This study provides valuable insights into the use of cost-effective and readily available materials for soil remediation and investigates the mechanisms underlying arsenic immobilization in acidic soils.