4D label-free proteomics analysis of oxygen-induced retinopathy with or without anti-VEGF treatment.

Oxygen-induced retinopathy (OIR) animal model is widely used for retinopathy of prematurity (ROP) researches. The purpose of this study was to identify proteins and related pathways of OIR with or without anti-vascular endothelial growth factor (VEGF) treatment, for use as biomarkers in diagnosing and treating ROP. Nine samples were subjected to proteomic analysis. Retina specimens were collected from 3 OIR mice, 3 OIR mice with anti-VEGF treatment and 3 normal mice (control group). Liquid chromatography-tandem mass spectrometry analysis was performed using the 4D label-free technique. Statistically significant differentially expressed proteins, gene ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway representations, InterPro (IPR) and protein interactions were analyzed. In total, 4585 unique proteins were identified as differentially expressed proteins (DEPs). Enrichment analysis of the GO and KEGG indicated functional clusters related to peptide biosynthetic and metabolic process, cellular macromolecule biosynthetic process and nucleic acid binding in OIR group. For anti-VEGF treatment group, DEPs were clustered in DNA replication, PI3K/Akt signaling pathway and Jak/STAT signaling pathway. Proteomic profiling is useful for the exploration of molecular mechanisms of OIR and mechanisms of anti-VEGF treatment. These findings may be useful for identification of novel biomarkers for ROP pathogenesis and treatment.

Semi-supervised correction model for turbulence-distorted images.

Significant progress has been made in addressing turbulence distortion in recent years, but persistent challenges remain. Firstly, existing methods heavily rely on fully supervised optimization strategies and synthetic datasets, posing difficulties in effectively utilizing unlabeled real data for training. Secondly, most approaches construct networks in a straightforward manner, overlooking the representation model of phase distortion and point spread function (PSF) in spatial and channel dimensions. This oversight restricts the potential for distortion correction. To address these challenges, this paper proposes a semi-supervised atmospheric turbulence correction method based on the mean-teacher framework. Our approach imposes constraints on the unlabeled data of student networks using pseudo-labels generated by teacher networks, thereby enhancing the generalization ability by leveraging information from unlabeled data. Furthermore, we introduce to use no-reference image quality assessment criterion to select the most reliable pseudo-label for each unlabeled sample by predicting physical parameters that indicating the level of degradation. Additionally, we propose to combine sliding window-based self-attention with channel attention to facilitate local-global context interaction. This design is inspired by the representation of phase distortion and PSF, which can be characterized by coefficients and basis functions corresponding to the channel-wise representation of convolutional neural network features. Moreover, the base functions exhibit spatial correlation, akin to Zenike and Airy disks. Experimental results show that the proposed method surpasses state-of-the-art models.

Mechanistic involvement of noradrenergic neuronal neurotransmitter release in cutaneous vasoconstriction during autonomic dysreflexia in persons with spinal cord injury.

INTRODUCTION: Autonomic dysreflexia (AD) is a potentially life-threatening consequence in high (above T6) spinal cord injury that involves multiple incompletely understood mechanisms. While peripheral arteriolar vasoconstriction, which controls systemic vascular resistance, is documented to be pronounced during AD, the pathophysiological neurovascular junction mechanisms of this vasoconstriction are undefined. One hypothesized mechanism is increased neuronal release of norepinephrine and co-transmitters. We tested this by examining the effects of blockade of pre-synaptic neural release of norepinephrine and co-transmitters on cutaneous vasoconstriction during AD, using a novel non-invasive technique; bretylium (BT) iontophoresis followed by skin blood flow measurements via laser doppler flowmetry (LDF). METHODS: Bretylium, a sympathetic neuronal blocking agent (blocks release of norepinephrine and co-transmitters) was applied iontophoretically to the skin of a sensate (arm) and insensate (leg) area in 8 males with motor complete tetraplegia. An nearby untreated site served as control (CON). Cutaneous vascular conductance (CVC) was measured (CVC = LDF/mean arterial pressure) at normotension before AD was elicited by bladder stimulation. The percent drop in CVC values from pre-AD vs. AD was compared among BT and CON sites in sensate and insensate areas. RESULTS: There was a significant effect of treatment but no significant effect of limb/sensation or interaction of limb x treatment on CVC. The percent drop in CVC between BT and CON treated sites was 25.7±1.75 vs. 39.4±0.87, respectively (P = 0.004). CONCLUSION: Bretylium attenuates, but does not fully abolish vasoconstriction during AD. This suggests release of norepinephrine and cotransmitters from cutaneous sympathetic nerves is involved in cutaneous vasoconstriction during AD.

Cochlear Implant Artifacts Removal in EEG-Based Objective Auditory Rehabilitation Assessment.

Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. Electroencephalography (EEG), favored for its CI compatibility and non-invasiveness, has become a staple in clinical objective assessments of cerebral plasticity post-implantation. However, the electrical activity of CI distorts neural responses, and EEG susceptibility to these artifacts presents significant challenges in obtaining reliable neural responses. Despite the use of various artifact removal techniques in previous studies, the automatic identification and reduction of CI artifacts while minimizing information loss or damage remains a pressing issue in objectively assessing advanced auditory functions in CI recipients. To address this problem, we propose an approach that combines machine learning algorithms-specifically, Support Vector Machines (SVM)-along with Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) to automatically detect and minimize electrical artifacts in EEG data. The innovation of this research is the automatic detection of CI artifacts using the temporal properties of EEG signals. By applying EEMD and ICA, we can process and remove the identified CI artifacts from the affected EEG channels, yielding a refined signal. Comparative analysis in the temporal, frequency, and spatial domains suggests that the corrected EEG recordings of CI recipients closely align with those of peers with normal hearing, signifying the restoration of reliable neural responses across the entire scalp while eliminating CI artifacts.

SLC25A19 is a novel prognostic biomarker related to immune invasion and ferroptosis in HCC.

SLC25A19 is a mitochondrial thiamine pyrophosphate (TPP) carrier that mediates TPP entry into the mitochondria. SLC25A19 has been recognized to play a crucial role in many metabolic diseases, but its role in cancer has not been clearly reported. Based on clinical data from The Cancer Genome Atlas (TCGA), the following parameters were analyzed among HCC patients: SLC25A19 expression, enrichment analyses, immune infiltration, ferroptosis and prognosis analyses. In vitro, the SLC25A19 high expression was validated by qRT-PCR and Immunohistochemistry. Subsequently, a series of cell function experiments, including CCK8, EdU, clone formation, trans-well and scratch assays, were conducted to illustrate the effect of SLC25A19 on the growth and metastasis of cancer cells. Meanwhile, indicators related to ferroptosis were also detected. SCL25A19 is highly expressed in HCC and predicts a poor prognosis. Elevated SLC25A19 expression in HCC patients was markedly associated with T stage, pathological status (PS), tumor status (TS), histologic grade (HG), and AFP. Our results indicate that SLC25A19 has a generally good prognosis predictive and diagnostic ability. The results of gene enrichment analyses showed that SLC25A19 is significantly correlated with immune infiltration, fatty acid metabolism, and ferroptosis marker genes. In vitro experiments have confirmed that silencing SLC25A19 can significantly inhibit the proliferation and migration ability of cancer cells and induce ferroptosis in HCC. In conclusion, these findings indicate that SLC25A19 is novel prognostic biomarker related to immune invasion and ferroptosis in HCC, and it is an excellent candidate for therapeutic target against HCC.

Dynamic Reconfiguration and Local Polarization of NiFe-Layered Double Hydroxide-Bi2MoO6- x Heterojunction for Enhancing Piezo-Photocatalytic Nitrogen Oxidation to Nitric Acid.

Constructing heterojunctions with vacancies has garnered substantial attention in the field of piezo-photocatalysis. However, the presence of interfacial vacancies can serve as charge-trapping sites, leading to the localization of electrons and hindering interfacial charge transfer. Herein, dual oxygen vacancies in the NiFe-layered double hydroxide and Bi2MoO6- x induced interfacial bonds have been designed for the piezo-photocatalytic N2 oxidation to NO3 -. Fortunately, it achieves sensational nitric acid production rates (7.23 mg g-1 h-1) in the absence of cocatalysts and sacrificial agents, which is 6.03 times of pure Bi2MoO6 that under ultrasound and light illumination. Theoretical and experimental results indicate that interfacial bonds act as "charge bridge" and "strain center" to break the carrier local effect and negative effects with piezocatalysis and photocatalysis for promoting exciton dissociation and charge transfer. Moreover, the strong electronic interaction of the interfacial bond induces internal reconstruction under ultrasound for promoting the local polarization and adsorption of N2, which accelerates the fracture of the N≡N bonds and reduces the activation energy of the reaction. The research not only establishes a novel approach for optimizing the combined effects of piezo-catalysis and photocatalysis, but also achieves equilibrium between the synergistic impacts of vacancies and heterojunctions.

Electronic configuration inversion in CdIn2S4 for efficient photocatalytic hydrogen peroxide generation coupled with selective benzylamine oxidation.

Vacancies engineering has sparked a huge interest in enhancing photocatalytic activity, but monovacancy simultaneously conducts as either electron or hole acceptor and redox reaction, worsening charge transfer and catalytic performance. Here, the concept of electronic inversion has been proposed through the simultaneous introduction of surface oxygen and S vacancies in CdIn2S4 (OSv-CIS). Consequently, under mild conditions, the well-designed OSv-CIS-200 demonstrated a strong rate of N-benzylidenebenzylamine production (2972.07 µmol g-1 h-1) coupled with Hydrogen peroxide (H2O2) synthesis (2362.33 µmol g-1 h-1) (PIH), which is 12.4 times higher than that of CdIn2S4. Density functional theory (DFT) simulation and characterization studies demonstrate that oxygen is introduced into the lattice on the surface of the material, reversing the charge distribution of the S vacancy and enhancing the polarity of the total charge distribution. It not only provides a huge built-in electric field (BEF) for guiding the orientation of the charge transfer, but also acts as a long-distance active site to accelerate reaction and prevent H2O2 decomposition. Our work offers a straightforward connection between the atomic defect and intrinsic properties for designing high-efficiency materials.

Machine learning-based prediction of off-pump coronary artery bypass grafting-associated acute kidney injury.

Background: The cardiac surgery-associated acute kidney injury (CSA-AKI) occurs in up to 1 out of 3 patients. Off-pump coronary artery bypass grafting (OPCABG) is one of the major cardiac surgeries leading to CSA-AKI. Early identification and timely intervention are of clinical significance for CSA-AKI. In this study, we aimed to establish a prediction model of off-pump coronary artery bypass grafting-associated acute kidney injury (OPCABG-AKI) after surgery based on machine learning methods. Methods: The preoperative and intraoperative data of 1,041 patients who underwent OPCABG in Chest Hospital, Tianjin University from June 1, 2021 to April 30, 2023 were retrospectively collected. The definition of OPCABG-AKI was based on the 2012 Kidney Disease Improving Global Outcomes (KDIGO) criteria. The baseline data and intraoperative time series data were included in the dataset, which were preprocessed separately. A total of eight machine learning models were constructed based on the baseline data: logistic regression (LR), gradient-boosting decision tree (GBDT), eXtreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and decision tree (DT). The intraoperative time series data were extracted using a long short-term memory (LSTM) deep learning model. The baseline data and intraoperative features were then integrated through transfer learning and fused into each of the eight machine learning models for training. Based on the calculation of accuracy and area under the curve (AUC) of the prediction model, the best model was selected to establish the final OPCABG-AKI risk prediction model. The importance of features was calculated and ranked by DT model, to identify the main risk factors. Results: Among 701 patients included in the study, 73 patients (10.4%) developed OPCABG-AKI. The GBDT model was shown to have the best predictions, both based on baseline data only (AUC =0.739, accuracy: 0.943) as well as based on baseline and intraoperative datasets (AUC =0.861, accuracy: 0.936). The ranking of importance of features of the GBDT model showed that use of insulin aspart was the most important predictor of OPCABG-AKI, followed by use of acarbose, spironolactone, alfentanil, dezocine, levosimendan, clindamycin, history of myocardial infarction, and gender. Conclusions: A GBDT-based model showed excellent performance for the prediction of OPCABG-AKI. The fusion of preoperative and intraoperative data can improve the accuracy of predicting OPCABG-AKI.