A network-based method for brain disease gene prediction by integrating brain connectome and molecular network.

Brain disease gene identification is critical for revealing the biological mechanism and developing drugs for brain diseases. To enhance the identification of brain disease genes, similarity-based computational methods, especially network-based methods, have been adopted for narrowing down the searching space. However, these network-based methods only use molecular networks, ignoring brain connectome data, which have been widely used in many brain-related studies. In our study, we propose a novel framework, named brainMI, for integrating brain connectome data and molecular-based gene association networks to predict brain disease genes. For the consistent representation of molecular-based network data and brain connectome data, brainMI first constructs a novel gene network, called brain functional connectivity (BFC)-based gene network, based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data. Then, a multiple network integration method is proposed to learn low-dimensional features of genes by integrating the BFC-based gene network and existing protein-protein interaction networks. Finally, these features are utilized to predict brain disease genes based on a support vector machine-based model. We evaluate brainMI on four brain diseases, including Alzheimer's disease, Parkinson's disease, major depressive disorder and autism. brainMI achieves of 0.761, 0.729, 0.728 and 0.744 using the BFC-based gene network alone and enhances the molecular network-based performance by 6.3% on average. In addition, the results show that brainMI achieves higher performance in predicting brain disease genes compared to the existing three state-of-the-art methods.

A review and performance evaluation of clustering frameworks for single-cell Hi-C data.

The three-dimensional genome structure plays a key role in cellular function and gene regulation. Single-cell Hi-C (high-resolution chromosome conformation capture) technology can capture genome structure information at the cell level, which provides the opportunity to study how genome structure varies among different cell types. Recently, a few methods are well designed for single-cell Hi-C clustering. In this manuscript, we perform an in-depth benchmark study of available single-cell Hi-C data clustering methods to implement an evaluation system for multiple clustering frameworks based on both human and mouse datasets. We compare eight methods in terms of visualization and clustering performance. Performance is evaluated using four benchmark metrics including adjusted rand index, normalized mutual information, homogeneity and Fowlkes-Mallows index. Furthermore, we also evaluate the eight methods for the task of separating cells at different stages of the cell cycle based on single-cell Hi-C data.

DFinder: a novel end-to-end graph embedding-based method to identify drug-food interactions.

MOTIVATION: Drug-food interactions (DFIs) occur when some constituents of food affect the bioaccessibility or efficacy of the drug by involving in drug pharmacodynamic and/or pharmacokinetic processes. Many computational methods have achieved remarkable results in link prediction tasks between biological entities, which show the potential of computational methods in discovering novel DFIs. However, there are few computational approaches that pay attention to DFI identification. This is mainly due to the lack of DFI data. In addition, food is generally made up of a variety of chemical substances. The complexity of food makes it difficult to generate accurate feature representations for food. Therefore, it is urgent to develop effective computational approaches for learning the food feature representation and predicting DFIs. RESULTS: In this article, we first collect DFI data from DrugBank and PubMed, respectively, to construct two datasets, named DrugBank-DFI and PubMed-DFI. Based on these two datasets, two DFI networks are constructed. Then, we propose a novel end-to-end graph embedding-based method named DFinder to identify DFIs. DFinder combines node attribute features and topological structure features to learn the representations of drugs and food constituents. In topology space, we adopt a simplified graph convolution network-based method to learn the topological structure features. In feature space, we use a deep neural network to extract attribute features from the original node attributes. The evaluation results indicate that DFinder performs better than other baseline methods. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/23AIBox/23AIBox-DFinder. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Association between the atherogenic index of plasma trajectory and risk of heart failure among hypertensive patients: a prospective cohort study.

BACKGROUND: The atherogenic index of plasma (AIP) has been shown to be positively correlated with cardiovascular events. However, it remains unclear whether hypertensive patients with long-term high AIP levels are at greater risk of developing heart failure (HF). Therefore, the aim of this study was to investigate the association between AIP trajectory and the incidence of HF in hypertensive patients. METHODS: This prospective study included 22,201 hypertensive patients from the Kailuan Study who underwent three waves of surveys between 2006 and 2010. Participants were free of HF or cancer before or during 2010. The AIP was calculated as the logarithmic conversion ratio of triglycerides to high-density lipoprotein cholesterol. Latent mixed modeling was employed to identify different trajectory patterns for AIP during the exposure period (2006-2010). Cox proportional hazard models were then used to estimate the hazard ratio (HR) and 95% confidence interval (CI) for incident HF among different trajectory groups. RESULTS: Four distinct trajectory patterns were identified through latent mixture modeling analysis: low-stable group (n = 3,373; range, -0.82 to -0.70), moderate-low stable group (n = 12,700; range, -0.12 to -0.09), moderate-high stable group (n = 5,313; range, 0.53 to 0.58), and elevated-increasing group (n = 815; range, 1.22 to 1.56). During a median follow-up period of 9.98 years, a total of 822 hypertensive participants experienced HF. After adjusting for potential confounding factors, compared with those in the low-stable group, the HR and corresponding CI for incident HF in the elevated-increasing group, moderate-high stable group, and moderate-low stable group were estimated to be 1.79 (1.21,2.66), 1.49 (1.17,1.91), and 1.27 (1.02,1.58), respectively. These findings remained consistent across subgroup analyses and sensitivity analyses. CONCLUSION: Prolonged elevation of AIP in hypertensive patients is significantly associated with an increased risk of HF. This finding suggests that regular monitoring of AIP could aid in identifying individuals at a heightened risk of HF within the hypertensive population.

Artificial intelligence automated surgical phases recognition in intraoperative videos of laparoscopic pancreatoduodenectomy.

BACKGROUND: Laparoscopic pancreatoduodenectomy (LPD) is one of the most challenging operations and has a long learning curve. Artificial intelligence (AI) automated surgical phase recognition in intraoperative videos has many potential applications in surgical education, helping shorten the learning curve, but no study has made this breakthrough in LPD. Herein, we aimed to build AI models to recognize the surgical phase in LPD and explore the performance characteristics of AI models. METHODS: Among 69 LPD videos from a single surgical team, we used 42 in the building group to establish the models and used the remaining 27 videos in the analysis group to assess the models' performance characteristics. We annotated 13 surgical phases of LPD, including 4 key phases and 9 necessary phases. Two minimal invasive pancreatic surgeons annotated all the videos. We built two AI models for the key phase and necessary phase recognition, based on convolutional neural networks. The overall performance of the AI models was determined mainly by mean average precision (mAP). RESULTS: Overall mAPs of the AI models in the test set of the building group were 89.7% and 84.7% for key phases and necessary phases, respectively. In the 27-video analysis group, overall mAPs were 86.8% and 71.2%, with maximum mAPs of 98.1% and 93.9%. We found commonalities between the error of model recognition and the differences of surgeon annotation, and the AI model exhibited bad performance in cases with anatomic variation or lesion involvement with adjacent organs. CONCLUSIONS: AI automated surgical phase recognition can be achieved in LPD, with outstanding performance in selective cases. This breakthrough may be the first step toward AI- and video-based surgical education in more complex surgeries.

Relationship of the trajectory of the triglyceride-glucose index with heart failure: the Kailuan study.

BACKGROUND: A high triglyceride-glucose index (TyG) is associated with a higher risk of incident heart failure. However, the effects of longitudinal patterns of TyG index on the risk of heart failure remain to be characterized. Therefore, in the present study, we aimed to characterize the relationship between the trajectory of TyG index and the risk of heart failure. METHODS: We performed a prospective study of 56,149 participants in the Kailuan study who attended three consecutive surveys in 2006-2007, 2008-2009, and 2010-2011 and had no history of heart failure or cancer before the third wave survey (2010-2011). The TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting plasma glucose (mg/dL)/2], and we used latent mixture modeling to characterize the trajectory of the TyG index over the period 2006-2010. Additionally, Cox proportional risk models were used to calculate the hazard ratio (HR) and 95% confidence interval (CI) for incident heart failure for the various TyG index trajectory groups. RESULTS: From 2006 to 2010, four different TyG trajectories were identified: low-stable (n = 13,554; range, 7.98-8.07), moderate low-stable (n = 29,435; range, 8.60-8.65), moderate high-stable (n = 11,262; range, 9.31-9.30), and elevated-stable (n = 1,898; range, 10.04-10.25). A total of 1,312 new heart failure events occurred during a median follow-up period of 10.04 years. After adjustment for potential confounders, the hazard ratios (HRs) and 95% confidence intervals (CIs) for incident heart failure for the elevated-stable, moderate high-stable, and moderate low-stable groups were 1.55 (1.15, 2.08), 1.32 (1.08, 1.60), and 1.17 (0.99, 1.37), respectively, compared to the low-stable group. CONCLUSIONS: Higher TyG index trajectories were associated with a higher risk of heart failure. This suggests that monitoring TyG index trajectory may help identify individuals at high risk for heart failure and highlights the importance of early control of blood glucose and lipids for the prevention of heart failure.

End-expiratory lung volumes as a potential indicator for COVID-19 associated acute respiratory distress syndrome: a retrospective study.

BACKGROUND: End-expiratory lung volume (EELV) has been observed to decrease in acute respiratory distress syndrome (ARDS). Yet, research investigating EELV in patients with COVID-19 associated ARDS (CARDS) remains limited. It is unclear whether EELV could serve as a potential metric for monitoring disease progression and identifying patients with ARDS at increased risk of adverse outcomes. STUDY DESIGN AND METHODS: This retrospective study included mechanically ventilated patients diagnosed with CARDS during the initial phase of epidemic control in Shanghai. EELV was measured using the nitrogen washout-washin technique within 48 h post-intubation, followed by regular assessments every 3-4 days. Chest CT scans, performed within a 24-hour window around each EELV measurement, were analyzed using AI software. Differences in patient demographics, clinical data, respiratory mechanics, EELV, and chest CT findings were assessed using linear mixed models (LMM). RESULTS: Out of the 38 patients enrolled, 26.3% survived until discharge from the ICU. In the survivor group, EELV, EELV/predicted body weight (EELV/PBW) and EELV/predicted functional residual capacity (EELV/preFRC) were significantly higher than those in the non-survivor group (survivor group vs. non-survivor group: EELV: 1455 vs. 1162 ml, P = 0.049; EELV/PBW: 24.1 vs. 18.5 ml/kg, P = 0.011; EELV/preFRC: 0.45 vs. 0.34, P = 0.005). Follow-up assessments showed a sustained elevation of EELV/PBW and EELV/preFRC among the survivors. Additionally, EELV exhibited a positive correlation with total lung volume and residual lung volume, while demonstrating a negative correlation with lesion volume determined through chest CT scans analyzed using AI software. CONCLUSION: EELV is a useful indicator for assessing disease severity and monitoring the prognosis of patients with CARDS.

Polystyrene nanoplastics at predicted environmental concentrations enhance the toxicity of copper on Caenorhabditis elegans.

Excessive nanoplastics not only pose a direct threat to the environment but also have the propensity to adsorb and interact with other pollutants, exacerbating their impact. The coexistence of nanoplastics and heavy metals in soils is a prevalent phenomenon. However, limited research existed about the joint effects of the two contaminants on soil organisms. In this paper, we ascertained the combined toxicity of polystyrene nanoplastics (PS-NPs) and copper (Cu2+) on soil organisms (Caenorhabditis elegans) at quantities that were present in the environment, further exploring whether the two toxicants were synergistic or antagonistic. The outcomes manifested that single exposure to low-dose PS-NPs (1 µg/L) would not cause significant damage to nematodes. After treatment with PS-NPs and Cu2+, the locomotion ability of nematode was impaired, accompanied by an elevation in reactive oxygen species (ROS) level and a biphasic response in antioxidant enzyme activity. Moreover, combined exposure to PS-NPs and Cu2+ induced the mRNA up-regulation of vit-6, cyp-35a2, hsp-16.2, age-1, and cep-1, both of which were stress-related genes. The comparative analysis between groups (with or without PS-NPs) revealed that the combined exposure group resulted in significantly greater toxic effects on nematodes compared with Cu2+ exposure alone. Furthermore, the addition of PS-NPs influenced the metabolic profiles of Caenorhabditis elegans under Cu2+ stress, with numerous differential metabolites associated with oxidative damage or defense mechanism. Overall, these findings manifested that PS-NPs at the expected environmental concentration elevated Cu2+ toxicity on nematodes.

Antibiotic Alternatives: Multifunctional Ultra-Small Metal Nanoclusters for Bacterial Infectious Therapy Application.

The prevalence of major bacterial infections has emerged as a significant menace to human health and life. Conventional treatment methods primarily rely on antibiotic therapy, but the overuse of these drugs has led to a decline in their efficacy. Moreover, bacteria have developed resistance towards antibiotics, giving rise to the emergence of superbugs. Consequently, there is an urgent need for novel antibacterial agents or alternative strategies to combat bacterial infections. Nanoantibiotics encompass a class of nano-antibacterial materials that possess inherent antimicrobial activity or can serve as carriers to enhance drug delivery efficiency and safety. In recent years, metal nanoclusters (M NCs) have gained prominence in the field of nanoantibiotics due to their ultra-small size (less than 3 nm) and distinctive electronic and optical properties, as well as their biosafety features. In this review, we discuss the recent progress of M NCs as a new generation of antibacterial agents. First, the main synthesis methods and characteristics of M NCs are presented. Then, we focus on reviewing various strategies for detecting and treating pathogenic bacterial infections using M NCs, summarizing the antibacterial effects of these nanoantibiotics on wound infections, biofilms, and oral infections. Finally, we propose a perspective on the remaining challenges and future developments of M NCs for bacterial infectious therapy.

An end-to-end heterogeneous graph representation learning-based framework for drug-target interaction prediction.

Accurately identifying potential drug-target interactions (DTIs) is a key step in drug discovery. Although many related experimental studies have been carried out for identifying DTIs in the past few decades, the biological experiment-based DTI identification is still timeconsuming and expensive. Therefore, it is of great significance to develop effective computational methods for identifying DTIs. In this paper, we develop a novel 'end-to-end' learning-based framework based on heterogeneous 'graph' convolutional networks for 'DTI' prediction called end-to-end graph (EEG)-DTI. Given a heterogeneous network containing multiple types of biological entities (i.e. drug, protein, disease, side-effect), EEG-DTI learns the low-dimensional feature representation of drugs and targets using a graph convolutional networks-based model and predicts DTIs based on the learned features. During the training process, EEG-DTI learns the feature representation of nodes in an end-to-end mode. The evaluation test shows that EEG-DTI performs better than existing state-of-art methods. The data and source code are available at: https://github.com/MedicineBiology-AI/EEG-DTI.

Diterpenoids with an unusual tricyclo[10.3.0.02,9]pentadecane skeleton from Pedilanthus tithymaloides as multidrug resistance modulators.

Three new diterpenoids with an unusual carbon skeleton, pedilanins A-C (1-3), and nine new jatrophane diterpenoids, pedilanins D-L (4-12), along with five known ones (13-17), were isolated from Pedilanthus tithymaloides. Compounds 1-3 characterize an unprecedented tricyclo[10.3.0.02,9]pentadecane skeleton. Compounds 4-8 are rare examples of the jatrophanes bearing a cyclic hemiketal substructure. Their structures were determined by an extensive analysis of HRESIMS, NMR, quantum-chemical calculation, DP4+ probability, and X-ray crystallographic data. In the bioassay, compounds 1-12 dramatically reversed multidrug resistance in cancer cells with the fold-reversals ranging from 17.9 to 396.8 at the noncytotoxic concentration of 10 µM. The mechanism results indicated that compounds 2 and 3 inhibited the P-glycoprotein (Pgp) transporter function, thus reversing the drug resistance.

Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels.

Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively.

Dual-Enzyme Cascade Composed of Chitosan Coated FeS2 Nanozyme and Glucose Oxidase for Sensitive Glucose Detection.

Immobilizing enzymes with nanozymes to catalyze cascade reactions overcomes many of the shortcomings of biological enzymes in industrial manufacturing. In the study, glucose oxidases were covalently bound to FeS2 nanozymes as immobilization carriers while chitosan encapsulation increased the activity and stability of the immobilized enzymes. The immobilized enzymes exhibited a 10% greater increase in catalytic efficiency than the free enzymes while also being more stable and catalytically active in environments with an alkaline pH of 9.0 and a high temperature of 100 °C. Additionally, the FeS2 nanozyme-driven double-enzyme cascade reaction showed high glucose selectivity, even in the presence of lactose, dopamine, and uric acid, with a limit of detection (LOD) (S/N = 3) as low as 1.9 × 10-6 M. This research demonstrates that nanozymes may be employed as ideal carriers for biological enzymes and that the nanozymes can catalyze cascade reactions together with natural enzymes, offering new insights into interactions between natural and synthetic biosystems.

Manipulating Selectivity of Hydroxyl Radical Generation by Single-Atom Catalysts in Catalytic Ozonation: Surface or Solution.

Hydroxyl radical-dominated oxidation in catalytic ozonation is, in particular, important in water treatment scenarios for removing organic contaminants, but the mechanism about ozone-based radical oxidation processes is still unclear. Here, we prepared a series of transitional metal (Co, Mn, Ni) single-atom catalysts (SACs) anchored on graphitic carbon nitride to accelerate ozone decomposition and produce highly reactive ·OH for oxidative destruction of a water pollutant, oxalic acid (OA). We experimentally observed that, depending on the metal type, OA oxidation occurred dominantly either in the bulk phase, which was the case for the Mn catalyst, or via a combination of the bulk phase and surface reaction, which was the case for the Co catalyst. We further performed density functional theory simulations and in situ X-ray absorption spectroscopy to propose that the ozone activation pathway differs depending on the oxygen binding energy of metal, primarily due to differential adsorption of O3 onto metal sites and differential coordination configuration of a key intermediate species, *OO, which is collectively responsible for the observed differences in oxidation mechanisms and kinetics.

Prone positioning improves ventilation-perfusion matching assessed by electrical impedance tomography in patients with ARDS: a prospective physiological study.

BACKGROUND: The physiological effects of prone ventilation in ARDS patients have been discussed for a long time but have not been fully elucidated. Electrical impedance tomography (EIT) has emerged as a tool for bedside monitoring of pulmonary ventilation and perfusion, allowing the opportunity to obtain data. This study aimed to investigate the effect of prone positioning (PP) on ventilation-perfusion matching by contrast-enhanced EIT in patients with ARDS. DESIGN: Monocenter prospective physiologic study. SETTING: University medical ICU. PATIENTS: Ten mechanically ventilated ARDS patients who underwent PP. INTERVENTIONS: We performed EIT evaluation at the initiation of PP, 3 h after PP initiation and the end of PP during the first PP session. MEASUREMENTS AND MAIN RESULTS: The regional distribution of ventilation and perfusion was analyzed based on EIT images and compared to the clinical variables regarding respiratory and hemodynamic status. Prolonged prone ventilation improved oxygenation in the ARDS patients. Based on EIT measurements, the distribution of ventilation was homogenized and dorsal lung ventilation was significantly improved by PP administration, while the effect of PP on lung perfusion was relatively mild, with increased dorsal lung perfusion observed. The ventilation-perfusion matched region was found to increase and correlate with the increased PaO2/FiO2 by PP, which was attributed mainly to reduced shunt in the lung. CONCLUSIONS: Prolonged prone ventilation increased dorsal ventilation and perfusion, which resulted in improved ventilation-perfusion matching and oxygenation. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04725227. Registered on 25 January 2021.

Antibiofilm activity of ultra-small gold nanoclusters against Fusobacterium nucleatum in dental plaque biofilms.

Pathogenic dental plaque biofilms are universal and harmful, which can result in oral infections and systemic diseases. Many conventional therapeutic methods have proven insufficient or ineffective against plaque biofilms. Therefore, new strategies are urgently needed. Fusobacterium nucleatum (F. nucleatum), a periodontal pathogen associated with a variety of oral and systemic diseases, is thought to be central to the development and structure of dental plaques. Here, ultra-small gold nanoclusters (AuNCs) were prepared. They exhibited potent antibacterial activity against F. nucleatum through enhanced destruction of bacterial membranes and generation of reactive oxygen species. Furthermore, due to their excellent penetration, the AuNCs could inhibit biofilm formation and destroy mature biofilms in vitro. Their antibiofilm efficacy was further confirmed in a mouse model, where they reduced biofilm accumulation and ameliorated inflammation. Meanwhile, the disruption of oral and gut microbiota caused by colonization of oral F. nucleatum could be partially restored through AuNCs treatment. Therefore, AuNCs could be considered as promising antibiofilm agents and have great potential in the clinical treatment of dental plaque.

A cross-sectional survey on the lung ultrasound training and practice of respiratory therapists in mainland China.

PURPOSE: This national study aimed to investigate the lung ultrasound (LUS) training and practice of respiratory therapists (RTs) in mainland China. METHODS: A cross-sectional multicenter survey was conducted from May 22, 2021 to August 12, 2021, through online platforms. This survey included RTs in mainland China. The survey was divided into four sections: (1) demographic characteristics and basic information; (2) basic information about LUS training and practice; (3) LUS practice details; and (4) Other ultrasound training and practice. RESULTS: A total of 514 responses were received, and 494 valid responses were included in the analysis. 81.2% (401/494) participants' highest degree of education was a bachelor's degree, and 43.1% (213/494) participants were at level II in terms of job ranking. 99.2%(490/494) participants agreed that the RTs needed to learn lung ultrasound, but only 12.3% (61/494) participants had received a LUS training course. Further, 66.2% (327/494) experienced participants responded to Sect. 3. Most of RTs used LUS when the patient had hypoxia (265/327, 81%) or dyspnea (260/317, 79.5%); they also used it during spontaneous breathing trial(SBT) (191/327, 58.4%) or in prone position (177/327, 54.1%). The A-line (302/327, 92.4%), B-line (299/327, 91.4%), lung slide (263/327, 80.4%), and bat sign (259/327, 79.2%) were well known as LUS signs. Also, 30.6% (100/327) participants did not use the LUS protocol in their clinical practice, and only 25.4%(83/327) participants said they had used LUS scores. Moreover, 55.7% (182/327) participants frequently changed the respiratory therapy strategy according to LUS results. CONCLUSIONS: We should improve the number and workplace of RTs in mainland China in the future. We should also standardize the application of LUS practice and training for RTs in mainland China and establish corresponding certification pathways.

Two New Eremophilane-Type Sesquiterpenoids from Ligularia sagitta.

Two new eremophilane-type sesquiterpenoids, sagittacins F and G (1 and 2), together with one known isomer of sagittacin F (3) were isolated from the leaves and stems of Ligularia sagitta. Their structures were elucidated by interpretation of spectroscopic data and the absolute configurations of 1 and 3 were determined by X-ray spectroscopy. Compound 1 belongs to a rare class of eremophilane-type sesquiterpenoid featuring an α-oriented hydroxy group at C-1. A nitric oxide (NO) production inhibitory assay was applied to evaluate their anti-inflammatory activities by using LPS-induced RAW 264.7 cells. Compounds 2 and 3 exhibited modest NO production inhibitions with IC50 values of 45.15±2.72 and 49.83±2.34 µM, respectively.

A Cruciform Petal-like (ZIF-8) with Bactericidal Activity against Foodborne Gram-Positive Bacteria for Antibacterial Food Packaging.

Most antibacterial nanomaterials used in food packaging act by releasing reactive oxygen species (ROS), which cannot efficiently have an inhibitory effect by penetrating the cell wall of Gram-positive Staphylococcus aureus. In this work, we used the cruciform petal-like zeolite imidazole framework-8 (ZIF-8) synthesized in the water phase which can release active Zn compounds in aqueous solution and exert a stronger inhibitory effect on S. aureus. The experimental results demonstrated that the aqueous cruciform petal-like ZIF-8 has the same photocatalytic activity as traditional ZIF-8 and can be applied in photocatalytic bacterial inactivation. The cruciform petal-like ZIF-8 was also shown to release active Zn compounds in aqueous solution with a better antibacterial effect against S. aureus, reaching 95% inactivation efficiency. The antibacterial effect was therefore 70% higher than that of traditional ZIF-8. Based on its excellent antibacterial properties, we loaded petal-like ZIF-8, PDA and PVA onto ordinary fibers to prepare ZIF-8-Film. The results further showed that ZIF-8-Film has a high filtration capacity, which can be used in antibacterial packaging material with the required air permeability. Moreover, ZIF-8-Flim can clean the surface on its own and can maintain a sterile environment. It is different from other disposable materials on the market in that it can be reused and has a self-disinfection function.

FOXO3a accumulation and activation accelerate oxidative stress-induced podocyte injury.

Podocyte injury is the primary cause of glomerular injury in diabetic nephropathy (DN). Advanced oxidation protein products (AOPPs), the triggers and markers of oxidative stress in DN, have been linked to podocyte damage. However, the underlying mechanism is not yet clear. Here, we investigated the potential role of FOXO3a, a key transcription factor in the response to stress, in mediating AOPPs-induced podocyte injury. We found that FOXO3a expression was increased in the glomeruli of kidney biopsies from patients with DN and it was positively correlated with proteinuria. The serum from patients with DN significantly increased FOXO3a and its downstream genes FasL and Bim, thereby inducing the high level of cleaved caspase3 and the loss of nephrin and podocin expressions in podocytes. Blockade of AOPPs signaling by a neutralizing antibody against the receptor of advanced glycation end products (αRAGE) abolished the effect of DN serum on podocytes, confirming the pathogenic role of AOPPs in DN serum. Downregulation of FOXO3a decreased AOPPs-induced podocyte apoptosis and restored the levels of podocyte markers nephrin and podocin, and upregulation of FOXO3a exacerbated these changes in podocytes after AOPPs treatment. Furthermore, FOXO3a specifically activated proapoptotic genes in podocytes only in the presence of AOPPs. Mechanistically, AOPPs increased the FOXO3a protein levels by inhibiting their autophagic degradation in a ROS/mTOR-dependent manner. Moreover AOPPs activated the accumulated FOXO3a by maintaining FOXO3a in the nucleus, and this process was dependent on ROS-mediated AKT signaling deactivation. These studies suggest that FOXO3a plays a critical role in mediating AOPPs-induced podocyte injury and reveal a new mechanistic linkage of oxidative stress, FOXO3a activation and podocyte injury in DN.