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Background: Coronary heart disease (CHD) is an intricate and multifaceted cardiovascular disorder that contributes significantly to global morbidity and mortality. Early and accurate identification and diagnosis of CHD are paramount to ensuring patients receive optimal therapeutic interventions and satisfactory outcomes. Methods: Data on CHD gene expression were obtained from the Gene Expression Omnibus (GEO) repository and potential hub genes were screened through gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analyses. Functional validation of these hub genes was conducted by interfering with them in human umbilical vein endothelial cells (HUVECs). Cell proliferation and apoptosis were assessed through cell counting kit-8 (CCK-8) and flow cytometry assays, respectively, while enzyme-linked immunosorbent assay (ELISA), quantitative polymerase chain reaction (qPCR), Western blot, and immunofluorescence were used to measure the expression of key indicators. Results: We identified 700 upregulated differentially expressed genes (DEGs) and 638 downregulated DEGs in CHD, and utilized LASSO analyses to screen disease potential biomarkers, such as zinc finger protein 429 (ZNF429). Interference with ZNF429 in HUVECs mitigated the CHD-induced decrease in cell proliferation and increase in apoptosis. Moreover, the expression of interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), vascular cell adhesion molecule-1 (VCAM-1), intercellular adhesion molecule-1 (ICAM-1), cluster of differentiation 62E (CD62E), and cluster of differentiation 62P (CD62P) was reduced, leading to decreased cellular inflammation and adhesion. Conclusions: CHD-associated biomarker ZNF429 was identified through bioinformatics analysis to potentially regulate the expression of inflammatory factors IL-6, IL-1β, and TNF-α, along with adhesion molecules ICAM-1, VCAM-1, CD62E, and CD62P. This modulation influence was subsequently found to impact the progression of CHD. These findings offered valuable insights into potential targets for further investigation and therapeutic interventions for CHD management.

AbstractDuring urgent-start peritoneal dialysis (USPD) in end-stage renal disease (ESRD) patients, both adequate dialysis and skill training for fluid exchange are essential. However, automated peritoneal dialysis (APD) alone or manual fluid exchange peritoneal dialysis (MPD) alone could meet the above demands. Therefore, our study combined APD with MPD (A-MPD), and compared A-MPD with MPD, aiming to find the most appropriate treatment mode. This was a single-center, prospective, randomized controlled study. All eligible patients were randomized into the MPD and A-MPD groups. All patients underwent a five-day USPD treatment 48 h after catheter implantation, and they were followed up for six months after discharge. Overall, 74 patients were enrolled in this study. Among these, 14 and 60 patients quit due to complications during USPD and completed the study (A-MPD = 31, MPD = 29), respectively. Compared with MPD, the A-MPD treatment mode had a better effect on removing serum creatinine, blood urea nitrogen, and potassium and improving serum carbon dioxide combining power levels; it had less time expenditure on the fluid exchange by nurses (p

The development of wearable artificial livers was restricted to device miniaturization and bleeding risk with water-soluble anticoagulants. Herein, a double-deck column filled with solid anticoagulant microspheres and Kevlar porous microspheres (KPMs, bilirubin adsorbents) was connected with the principle machine of wearable artificial liver (approximately 9 kg) to treat hyperbilirubinemia beagles for the first time. With the initial normal dose of heparin, the double-deck column could afford 3 h hemoperfusion in whole blood without thrombus formation. The removal efficiency of the double-deck column for total bilirubin (TBIL) was 31.4%. Interestingly, the excessive amounts of hepatocyte metabolites were also decreased by approximately 25%. The “anticoagulant + column” realized safe and effective whole blood hemoperfusion without the plasma separation system and heparin pump; however, the proposed principle machine of wearable artificial liver and “anticoagulant + column” cannot completely replace the bio-liver now. The intelligence of the device and the versatility of the adsorbent need to be improved; moreover, advanced experimental techniques need to be developed to validate the survival rates in animals. Overall, this study is a meaningful trial for the development of wearable artificial livers in the future.

The treatment of Alzheimer's disease (AD) is one of the most difficult challenges in neurodegenerative diseases due to the insufficient blood‒brain barrier (BBB) permeability and unsatisfactory intra-brain distribution of drugs. Therefore, we established an ibuprofen and FK506 encapsulated drug co-delivery system (Ibu&FK@RNPs), which can target the receptor of advanced glycation endproducts (RAGE) and response to the high level of reactive oxygen species (ROS) in AD. RAGE is highly and specifically expressed on the lesion neurovascular unit of AD, this property helps to improve targeting specificity of the system and reduce unselective distribution in normal brain. Meanwhile, these two drugs can be specifically released in astrocytes of AD lesion in response to high levels of ROS. As a result, the cognition of AD mice was significantly improved and the quantity of Aβ plaques was decreased. Neurotoxicity was also alleviated with structural regeneration and functional recovery of neurons. Besides, the neuroinflammation dominated by NF-κB pathway was significantly inhibited with decreased NF-κB and IL-1β in the brain. Overall, Ibu&FK@RNPs can efficiently and successively target diseased BBB and astrocytes in AD lesion. Thus it significantly enhances intracephalic accumulation of drugs and efficiently treats AD by anti-neuroinflammation and neuroprotection.

Solid tumors always exhibit local hypoxia, resulting in the high metastasis and inertness to chemotherapy. Reconstruction of hypoxic tumor microenvironment (TME) is considered a potential therapy compared to directly killing tumor cells. However, the insufficient oxygen delivery to deep tumor and the confronting “Warburg effect” compromise the efficacy of hypoxia alleviation. Herein, we construct a cascade enzyme-powered nanomotor (NM-si), which can simultaneously provide sufficient oxygen in deep tumor and inhibit the aerobic glycolysis to potentiate anti-metastasis in chemotherapy. Catalase (Cat) and glucose oxidase (GOx) are co-adsorbed on our previously reported CAuNCs@HA to form self-propelled nanomotor (NM), with hexokinase-2 (HK-2) siRNA further condensed (NM-si). The persistent production of oxygen bubbles from the cascade enzymatic reaction propels NM-si to move forward autonomously and in a controllable direction along H2O2 gradient towards deep tumor, with hypoxia successfully alleviated in the meantime. The autonomous movement also facilitates NM-si with lysosome escaping for efficient HK-2 knockdown to inhibit glycolysis. In vivo results demonstrated a promising anti-metastasis effect of commercially available albumin-bound paclitaxel (PTX@HSA) after pre-treated with NM-si for TME reconstruction. This cascade enzyme-powered nanomotor provides a potential prospect in reversing the hypoxic TME and metabolic pathway for reinforced anti-metastasis of chemotherapy.

Abstract The treatment of autism spectrum disorder (ASD) is one of the most difficult challenges in neurodevelopmental diseases, because of the unclear pathogenesis research and low brain‐lesion targeting efficiency. Besides, maternal immune activation has been reported as the most mature and widely used model of ASD and aspirin‐triggered lipoxin A4 is a potent anti‐inflammatory mediator being involved in the resolution of neuroinflammation in ASD. Therefore, an aspirin encapsulated cascade drug delivery system (Asp@TMNPs) is established, which can successively target the blood–brain barrier (BBB) and microglial cells and response to the acid microenvironment in lysosome. As a result, the mitochondrial oxidative stress, DNA damage, and inflammation of microglial cells are prominently alleviated. After the treatment of Asp@TMNPs, the social interaction, stereotype behavior, and anxious condition of ASD mice are notably improved and the activation of microglial cells is inhibited. Overall, this system successively penetrates the BBB and targets microglial cells, therefore, it significantly enhances the intracephalic drug accumulation and improves anti‐neuroinflammatory efficacy of aspirin, providing a promising strategy for ASD treatment.

Due to the non-invasiveness and high precision of electroencephalography (EEG), the combination of EEG and artificial intelligence (AI) is often used for emotion recognition. However, the internal differences in EEG data have become an obstacle to classification accuracy. To solve this problem, considering labeled data from similar nature but different domains, domain adaptation usually provides an attractive option. Most of the existing researches aggregate the EEG data from different subjects and sessions as a source domain, which ignores the assumption that the source has a certain marginal distribution. Moreover, existing methods often only align the representation distributions extracted from a single structure, and may only contain partial information. Therefore, we propose the multi-source and multi-representation adaptation (MSMRA) for cross-domain EEG emotion recognition, which divides the EEG data from different subjects and sessions into multiple domains and aligns the distribution of multiple representations extracted from a hybrid structure. Two datasets, i.e., SEED and SEED IV, are used to validate the proposed method in cross-session and cross-subject transfer scenarios, experimental results demonstrate the superior performance of our model to state-of-the-art models in most settings.

As a non-invasive, low-cost medical imaging technology, magnetic resonance imaging (MRI) has become an important tool for brain tumor diagnosis. Many scholars have carried out some related researches on MRI brain tumor segmentation based on deep convolutional neural networks, and have achieved good performance. However, due to the large spatial and structural variability of brain tumors and low image contrast, the segmentation of MRI brain tumors is challenging. Deep convolutional neural networks often lead to the loss of low-level details as the network structure deepens, and they cannot effectively utilize the multi-scale feature information. Therefore, a deep convolutional neural network with a multi-scale attention feature fusion module (MAFF-ResUNet) is proposed to address them. The MAFF-ResUNet consists of a U-Net with residual connections and a MAFF module. The combination of residual connections and skip connections fully retain low-level detailed information and improve the global feature extraction capability of the encoding block. Besides, the MAFF module selectively extracts useful information from the multi-scale hybrid feature map based on the attention mechanism to optimize the features of each layer and makes full use of the complementary feature information of different scales. The experimental results on the BraTs 2019 MRI dataset show that the MAFF-ResUNet can learn the edge structure of brain tumors better and achieve high accuracy.

Objective: Peritonitis is a severe complication in peritoneal dialysis (PD). This study was performed to identify predictors and establish a risk score for treatment failure in peritonitis patients.Methods: A single-center, retrospective observational study was conducted. The basic demographic characteristics, clinical and laboratory data of all patients with peritonitis during the study period were documented and analyzed. Multivariate logistic regression was applied to examine independent predictors of treatment failure, and a risk prediction score was established.Results: Three hundred fourteen episodes experienced by 241 patients were included in the final analysis. Logistic regression analysis indicated that PD duration (OR 1.017; P 0.005), fibrinogen (OR 1.327; P 0.021), high-density lipoprotein (OR 0.443; P 0.032), fungal infection (OR 63.413; P < 0.001), intestinal obstruction (OR 5.186, P 0.007), and diabetes mellitus (OR 2.451; P 0.018), hemodialysis history (OR 2.804, P 0.006) were independent predictors of treatment failure. The risk prediction score system showed a good calibration (P > 0.05) and discrimination (AUROC 0.80, P < 0.001).Conclusions: Fibrinogen, PD duration, fungal infection, hemodialysis history, concurrent intestinal obstruction, or diabetes mellitus were independent risk factors for a poor peritonitis outcome, while the high-density lipoprotein was a protective factor. This novel risk prediction score system may be used to predict a high risk of treatment failure effectively.

Polymer binders are critical auxiliary additives to Li-ion batteries that provide adhesion and cohesion for electrodes to maintain conductive networks upon charge/discharge processes. Therefore, polymer binders become interconnected electrode structures affecting electrochemical performances, especially in LiFePO4 cathodes with one-dimensional Li+ channels. In this paper, recent improvements in the polymer binders used in the LiFePO4 cathodes of Li-ion batteries are reviewed in terms of structural design, synthetic methods, and working mechanisms. The polymer binders were classified into three types depending on their effects on the performances of LiFePO4 cathodes. The first consisted of PVDF and related composites, and the second relied on waterborne and conductive binders. Profound insights into the ability of binder structures to enhance cathode performance were discovered. Overcoming the bottleneck shortage originating from olivine structure LiFePO4 using efficient polymer structures is discussed. We forecast design principles for the polymer binders used in the high-performance LiFePO4 cathodes of Li-ion batteries. Finally, perspectives on the application of future binder designs for electrodes with poor conductivity are presented to provide possible design directions for chemical structures.

To realize the synergistic treatment of dairy cow manure solids and dairy cattle breeding sewage, this study produced phosphorus-modified hydrochar by dairy cow manure solids impregnated with potassium phosphate (K3PO4). And then, the adsorption characteristics of Cu (II) and ammonium (NH4+) in dairy cattle breeding sewage under different dosages and adsorption time conditions of modified hydrochar were explored. The results show that the specific surface area and total pore volume of the hydrochar were increased by phosphorus-modified. The adsorption amount of hydrochar per unit-mass decreased with the increase in the dosage. With the increase in the dosage, the adsorption capacity of Cu (II) decreased from the initial 26.16 mg/g to 3.38 mg/g. The adsorption of Cu (II) and NH4+ in sewage by hydrochar was mainly chemical adsorption, which was mainly affected by chelation and ion exchange. This adsorption was more inclined to single-molecular layer adsorption. Both the pH values and the ionic strength influenced the competitive adsorption between Cu (II) and NH4+. The higher the pH value was, the greater the adsorption amount, and the stronger the adsorption capacity by hydrochar. Moreover, the increase in the ionic strength decreases the adsorption capacity of hydrochar. [ABSTRACT FROM AUTHOR]

The cost of dairy manure treatment and bedding material purchase increases the operating cost of the dairy farm. Membrane-covered aerobic fermentation system has been widely used for dealing with dairy manure and recycling the final product as bedding material. However, the microbial safety in each processing step is still uncertain. To better understand the bacterial community dynamics during the whole bedding conversion process, a full-chain and large-scale experiment including 16-day membrane-covered aerobic fermentation and 11-day bedding material application was conducted. The results showed that the pile temperatures in the fermentation stage rapidly increased to 80°C and maintained >50°C for more than 11 days and the use of fermentation product as bedding material provided cows with a stable and comfortable bedding environment. The Chao1 and Shannon index decreased at the end of the fermentation stage and remained stable in the application stage, indicating that membrane-covered aerobic fermentation effectively killed some pathogenic bacteria and guaranteed both the maturity and stability of the final product. The dominant bacteria in the fermentation stage were Acinetobacter, Thermus, and Rhodothermus at genus level. Seven common potential pathogens of mastitis (Staphylococcus, Enterococcus, Serratia, Pseudomonas, Corynebacterium, Mycobacterium, and Bacillus) were found at the end of fermentation stage but the relative abundance was low (0.0025%-0.2727%). The dominant bacteria in the application stage mainly included Acinetobacter, Pseudomonas, and Flavobacterium at the genus level. The relative abundance of Pseudomonas increased in the application stage, which was a reminder to the dairy farm to pay attention to the disinfection and timely replacement of bedding material to prevent the occurrence of dairy mastitis. The results of this study contributed deep understanding of the microorganism-driven bedding conversion process and provide practical guidance and cautions for the bedding materials application. [ABSTRACT FROM AUTHOR]