A Pyrophosphate Bifunctional Cathode with Inductive Effect for High-Voltage and Self-Charging Zinc Ion Battery.

With the growing demand for new energy storage devices, rechargeable aqueous zinc ion batteries (ZIBs) have attracted widespread attention due to their low cost and high safety. Among the cathode materials for ZIBs, polyanionic-based cathode materials with high voltage, high stability, and low cost have great potential. In this paper, tetragonal Na2VOP2O7 was prepared using a simple sol-gel method. The discharge platform voltage amounted to 1.8 V and had good rate and cycle performance due to the inductive effect of pyrophosphate. Then, a protective layer of Zn-hydroxyapatite (ZnHAP) modification was applied to the cathode surface, which can inhibit the hydrolysis of vanadium ions. The capacity was enhanced by 19 % after modification and the capacity retention after 100 cycles was also higher. Interestingly, the Na2VOP2O7 cathode also possesses a self-charging effect, recovering to 48 % of its initial capacity with an open-circuit voltage (OCV) of 1.1 V within a certain period, and light exposure can reduce the self-charging time by 83 %. These beneficial results indicate that the pyrophosphate bifunctional cathode with inductive effect has a great potential to construct high-voltage and multifunctional zinc ion battery.

Multi-energy blended CBCT spectral imaging and scatter-decoupled material decomposition using a spectral modulator with flying focal spot (SMFFS).

BACKGROUND: Cone-beam CT (CBCT) has been extensively employed in industrial and medical applications, such as image-guided radiotherapy and diagnostic imaging, with a growing demand for quantitative imaging using CBCT. However, conventional CBCT can be easily compromised by scatter and beam hardening artifacts, and the entanglement of scatter and spectral effects introduces additional complexity. PURPOSE: The intertwined scatter and spectral effects within CBCT pose significant challenges to the quantitative performance of spectral imaging. In this work, we present the first attempt to develop a stationary spectral modulator with flying focal spot (SMFFS) technology as a promising, low-cost approach to accurately solving the x-ray scattering problem and physically enabling spectral imaging in a unified framework, and with no significant misalignment in data sampling of spectral projections. METHODS: To deal with the intertwined scatter-spectral challenge, we propose a novel scatter-decoupled material decomposition (SDMD) method for SMFFS, which consists of four steps in total, including (1) spatial resolution-preserved and noise-suppressed multi-energy "residual" projection generation free from scatter, based on a hypothesis of scatter similarity; (2) first-pass material decomposition from the generated multi-energy residual projections in non-penumbra regions, with a structure similarity constraint to overcome the increased noise and penumbra effect; (3) scatter estimation for complete data; and (4) second-pass material decomposition for complete data by using a multi-material spectral correction method. Monte Carlo simulations of a pure-water cylinder phantom with different focal spot deflections are conducted to validate the scatter similarity hypothesis. Both numerical simulations using a clinical abdominal CT dataset, and physics experiments on a tabletop CBCT system using a Gammex multi-energy CT phantom and an anthropomorphic chest phantom, are carried out to demonstrate the feasibility of CBCT spectral imaging with SMFFS and our proposed SDMD method. RESULTS: Monte Carlo simulations show that focal spot deflections within a range of 2 mm share quite similar scatter distributions overall. Numerical simulations demonstrate that SMFFS with SDMD method can achieve better material decomposition and CT number accuracy with fewer artifacts. In physics experiments, for the Gammex phantom, the average error of the mean values ( E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ ) in selected regions of interest (ROIs) of virtual monochromatic image (VMI) at 70 keV is 8 HU in SMFFS cone-beam (CB) scan, and 19 and 210 HU in sequential 80/120 kVp (dual kVp, DKV) CB scan with and without scatter correction, respectively. For the chest phantom, the E RMSE ROI $E^{\text{ROI}}_{\text{RMSE}}$ in selected ROIs of VMIs is 12 HU for SMFFS CB scan, and 15 and 438 HU for sequential 80/140 kVp CB scan with and without scatter correction, respectively. Also, the non-uniformity among selected regions of the chest phantom is 14 HU for SMFFS CB scan, and 59 and 184 HU for the DKV CB scan with and without a traditional scatter correction method, respectively. CONCLUSIONS: We propose a SDMD method for CBCT with SMFFS. Our preliminary results show that SMFFS can enable spectral imaging with simultaneous scatter correction for CBCT and effectively improve its quantitative imaging performance.

An analytical form of ring artifact correction for computed tomography based on directional gradient domain optimization.

BACKGROUND: Ring artifact is a common problem in Computed Tomography (CT), which can lead to inaccurate diagnoses and treatment plans. It can be caused by various factors such as detector imperfections, anti-scatter grids, or other nonuniform filters placed in the x-ray beam. Physics-based corrections for these x-ray source and detector non-uniformity, in general cannot completely get rid of the ring artifacts. Therefore, there is a need for a robust method that can effectively remove ring artifacts in the image domain while preserving details. PURPOSE: This study aims to develop an effective method for removing ring artifacts from reconstructed CT images. METHODS: The proposed method starts by converting the reconstructed CT image containing ring artifacts into polar coordinates, thereby transforming these artifacts into stripes. Relative Total Variation is used to extract the image's overall structural information. For the efficient restoration of intricate details, we introduce Directional Gradient Domain Optimization (DGDO) and design objective functions that make use of both the image's gradient and its overall structure. Subsequently, we present an efficient analytical algorithm to minimize these objective functions. The image obtained through DGDO is then transformed back into Cartesian coordinates, finalizing the ring artifact correction process. RESULTS: Through a series of synthetic and real-world experiments, we have effectively demonstrated the prowess of our proposed method in the correction of ring artifacts while preserving intricate details in reconstructed CT images. In a direct comparison, our method has exhibited superior visual quality compared to several previous approaches. These results underscore the remarkable potential of our approach for enhancing the overall quality and clinical utility of CT imaging. CONCLUSIONS: The proposed method offers an analytical solution for removing ring artifacts from CT images while preserving details. As ring artifacts are a common problem in CT imaging, this method has high practical value in the medical field. The proposed method can improve image quality and reduce the difficulty of disease diagnosis, thereby contributing to better patient care.

PractiCPP: a deep learning approach tailored for extremely imbalanced datasets in cell-penetrating peptide prediction.

MOTIVATION: Effective drug delivery systems are paramount in enhancing pharmaceutical outcomes, particularly through the use of cell-penetrating peptides (CPPs). These peptides are gaining prominence due to their ability to penetrate eukaryotic cells efficiently without inflicting significant damage to the cellular membrane, thereby ensuring optimal drug delivery. However, the identification and characterization of CPPs remain a challenge due to the laborious and time-consuming nature of conventional methods, despite advances in proteomics. Current computational models, however, are predominantly tailored for balanced datasets, an approach that falls short in real-world applications characterized by a scarcity of known positive CPP instances. RESULTS: To navigate this shortfall, we introduce PractiCPP, a novel deep-learning framework tailored for CPP prediction in highly imbalanced data scenarios. Uniquely designed with the integration of hard negative sampling and a sophisticated feature extraction and prediction module, PractiCPP facilitates an intricate understanding and learning from imbalanced data. Our extensive computational validations highlight PractiCPP's exceptional ability to outperform existing state-of-the-art methods, demonstrating remarkable accuracy, even in datasets with an extreme positive-to-negative ratio of 1:1000. Furthermore, through methodical embedding visualizations, we have established that models trained on balanced datasets are not conducive to practical, large-scale CPP identification, as they do not accurately reflect real-world complexities. In summary, PractiCPP potentially offers new perspectives in CPP prediction methodologies. Its design and validation, informed by real-world dataset constraints, suggest its utility as a valuable tool in supporting the acceleration of drug delivery advancements. AVAILABILITY AND IMPLEMENTATION: The source code of PractiCPP is available on Figshare at https://doi.org/10.6084/m9.figshare.25053878.v1.

Bio-Inspired Trace Hydroxyl-Rich Electrolyte Additives for High-Rate and Stable Zn-Ion Batteries at Low Temperatures.

High-rate and stable Zn-ion batteries working at low temperatures are highly desirable for practical applications, but are challenged by sluggish kinetics and severe corrosion. Herein, inspired by frost-resistant plants, we report trace hydroxyl-rich electrolyte additives that implement a dual remodeling effect for high-performance low-temperature Zn-ion batteries. The additive with high Zn absorbability not only remodels Zn2+ primary solvent shell by alternating H2 O molecules, but also forms a shielding layer thus remodeling the Zn surface, which effectively enhances fast Zn2+ de-solvation reaction kinetics and prohibits Zn anode corrosion. Taking trace α-D-glucose (αDG) as a demonstration, the electrolyte obtains a low freezing point of -55.3 °C, and the Zn//Zn cell can stably cycle for 2000 h at 5 mA cm-2 under -25 °C, with a high cumulative capacity of 5000 mAh cm-2 . A full battery that stably operates for 10000 cycles at -50 °C is also demonstrated.

FBLIM1 mRNA is a novel prognostic biomarker and is associated with immune infiltrates in glioma.

Glioma is the most common primary brain tumor. Filamin-binding LIM protein 1 (FBLIM1) has been identified in multiple cancers and is suspected of playing a part in the development of tumors. However, the potential function of FBLIM1 mRNA in glioma has not been investigated. In this study, the clinical information and transcriptome data of glioma patients were, respectively, retrieved from the TCGA and CGGA databases. The expression level of FBLIM1 mRNA was shown to be aberrant in a wide variety of malignancies. Significantly, when glioma samples were compared to normal brain samples, FBLIM1 expression was shown to be significantly elevated in the former. A poor prognosis was related to high FBLIM1 expression, which was linked to more advanced clinical stages. Notably, multivariate analyses demonstrated that FBLIM1 expression was an independent predictor for the overall survival of glioma patients. Immune infiltration analysis disclosed that FBLIM1 expression had relevance with many immune cells. The results of RT-PCR suggested that FBLIM1 expression was markedly elevated in glioma specimens. Functional experiments unveiled that the knockdown of FBLIM1 mRNA suppressed glioma cell proliferation. In general, we initially discovered that FBLIM1 mRNA might be a possible prognostic marker in glioma.

Research progress of additional pathogenic mutations in chronic neutrophilic leukemia.

Chronic neutrophilic leukemia (CNL) is a rare type of myeloproliferative neoplasm (MPN). Due to its nonspecific clinical symptoms and lack of specific molecular markers, it was previously difficult to distinguish it from other diseases with increased neutrophils. However, the discovery of the CSF3R mutation in CNL 10 years ago and the update of the diagnostic criteria by the World Health Organization (WHO) in 2016 brought CNL into a new era of molecular diagnosis. Next-generation sequencing (NGS) technology has led to the identification of numerous mutant genes in CNL. While CSF3R is commonly recognized as the driver mutation of CNL, other mutations have also been detected in CNL using NGS, including mutations in other signaling pathway genes (CBL, JAK2, NARS, PTPN11) and chromatin modification genes (ASXL1, SETBP1, EZH2), DNA methylation genes (DNMT3A, TET2), myeloid-related transcription factor genes (RUNX1, GATA2), and splicing and RNA metabolism genes (SRSF2, U2AF1). The coexistence of these mutated genes and CSF3R mutations, as well as the different evolutionary sequences of clones, deepens the complexity of CNL molecular biology. The purpose of this review is to summarize the genetic research findings of CNL in the last decade, focusing on the common mutated genes in CNL and their clinical significance, as well as the clonal evolution pattern and sequence of mutation acquisition in CNL, to provide a basis for the appropriate management of CNL patients.

Gut microbiota changes in animal models of spinal cord injury: a preclinical systematic review and meta-analysis.

BACKGROUND: An increasing number of studies show that the intestinal flora is closely related to spinal cord injury. Many researchers are exploring the changes in the richness, diversity, and evenness of intestinal flora in spinal cord injury animal models to identify the characteristic bacteria. METHODS: A comprehensive literature search was conducted using three databases: PubMed, Embase, and Web of Science. A meta-analysis was performed using R 4.3.1 to evaluate the comparison of microbiota diversity, richness, and evenness and the relative abundance of intestinal microbiota in animals with spinal cord injury and blank controls. RESULTS: Fifteen studies were included in the meta-analysis, of which 12 involved gut microbiota distribution indicators and 11 included intestinal microflora relative abundance indicators. Meta-analysis of high-dimensional indicators describing the distribution of the gut microbiota identified a substantial decline in the evenness and richness of the intestinal flora. In addition, the Actinobacteria phylum and Erysipelotrichales and Clostridiales orders were significantly different between the spinal cord injury and sham groups; therefore, they may be the characteristic bacteria in spinal cord injury models. CONCLUSION: Our meta-analysis suggested that the gut microbiota in the spinal cord injury animal model group was altered compared with that in the control group, with varying degrees of changes in richness and evenness and potentially pathogenic characteristic flora. More rigorous methodological studies are needed because of the high heterogeneity and limited sample size. Further research is needed to clinically apply intestinal microbiota and potentially guide fecal microbiota transplantation therapy.

Our meta-analysis showed that spinal cord injury significantly decreased the richness and evenness of intestinal flora in experimental animals.No statistically significant changes in the phyla flora during spinal cord injury have been found.Erysipelotrichales and Clostridiales may be the characteristic flora of gut microbiota changes during spinal cord injury.

Dual-energy head cone-beam CT using a dual-layer flat-panel detector: Hybrid material decomposition and a feasibility study.

BACKGROUND: Flat panel detector (FPD) based cone-beam computed tomography (CT) has made tremendous progress in the last two decades, with many new and advanced medical and industrial applications keeping emerging from diagnostic imaging and image guidance for radiotherapy and interventional surgery. The current cone-beam CT (CBCT), however, is still suboptimal for head CT scan which requires a high standard of image quality. While the dual-layer FPD technology is under extensive development and is promising to further advance CBCT from qualitative anatomic imaging to quantitative dual-energy CT, its potential of enabling head CBCT applications has not yet been fully investigated. PURPOSE: The relatively moderate energy separation from the dual-layer FPD and the overall low signal level especially at the bottom-layer detector, could raise significant challenges in performing high-quality dual-energy material decomposition (MD). In this work, we propose a hybrid, physics and model guided, MD algorithm that attempts to fully use the detected x-ray signals and prior-knowledge behind head CBCT using dual-layer FPD. METHODS: Firstly, a regular projection-domain MD is performed as initial results of our approach and for comparison as conventional method. Secondly, based on the combined projection, a dual-layer multi-material spectral correction (dMMSC) is applied to generate beam hardening free images. Thirdly, the dMMSC corrected projections are adopted as a physics-model based guidance to generate a hybrid MD. A set of physics experiments including fan-beam scan and cone-beam scan using a head phantom and a Gammex Multi-Energy CT phantom are conducted to validate our proposed approach. RESULTS: The combined reconstruction could reduce noise by about 10% with no visible resolution degradation. The fan-beam studies on the Gammex phantom demonstrated an improved MD performance, with the averaged iodine quantification error for the 5-15 mg/ml iodine inserts reduced from about 5.6% to 3.0% by the hybrid method. On fan-beam scan of the head phantom, our proposed hybrid MD could significantly reduce the streak artifacts, with CT number nonuniformity (NU) in the selected regions of interest (ROIs) reduced from 23 Hounsfield Units (HU) to 4.2 HU, and the corresponding noise suppressed from 31 to 6.5 HU. For cone-beam scan, after scatter correction (SC) and cone-beam artifact reduction (CBAR), our approach can also significantly improve image quality, with CT number NU in the selected ROI reduced from 24.2 to 6.6 HU and the noise level suppressed from 22.1 to 8.2 HU. CONCLUSIONS: Our proposed physics and model guided hybrid MD for dual-layer FPD based head CBCT can significantly improve the robustness of MD and suppress the low-signal artifact. This preliminary feasibility study also demonstrated that the dual-layer FPD is promising to enable head CBCT spectral imaging.

Prognostic significance of fibrinogen and neutrophil/lymphocyte ratio score and D-dimer/Albumin ratio for prognosis in patients with aneurysmal subarachnoid hemorrhage.

BACKGROUND: Recent research indicates that systemic inflammation significantly affects the overall prognosis of individuals with aneurysmal subarachnoid hemorrhage. To delve deeper into this issue, a retrospective study was undertaken. The study aimed to investigate the relationship between fibrinogen and neutrophil/lymphocyte ratio scores, D-dimer/Albumin ratios, and the Glasgow Outcome Scale at 6 months post-discharge for patients with aSAH. METHODS: A retrospective analysis was conducted on 321 patients who experienced aneurysmal subarachnoid hemorrhage. These patients were monitored using the Glasgow Outcome Scale six months after being discharged from Huizhou Central People's Hospital. Patients with GOS scores between 1 and 3 were classified as having a poor prognosis, while those with scores ranging from 4 to 5 were considered to have a good prognosis. To create distinct sets, patients were randomly divided into both training and validation groups. The best cut-off value for the D-dimer/Albumin ratio was established through ROC curves, and the scores for fibrinogen and the neutrophil/lymphocyte ratio were calculated. Utilizing multivariate logistic regression analysis, independent risk factors linked to an unfavorable prognosis in aSAH patients were identified. A nomogram model was developed and validated based on these findings, providing an improved approach for evaluating the prognostic influence of risk factors. To gauge the model's predictive performance, several analytical tools such as ROC curves, calibration curves, and decision curve analysis were employed. This comprehensive approach ensured a thorough assessment of the prognostic prediction capabilities of the model. RESULTS: Multivariate regression analysis revealed that Age (OR=3.87, 95%CI=1.54-9.73, p=0.004), Pneumonia (OR=3.54, 95%CI=1.41-8.86, p=0.007), WFNS (OR=3.24, 95%CI=1.23-8.54, p=0.017), DAR (OR=2.88, 95%CI=1.13-7.34, p=0.027), and F-NLR (OR=3.12, 95%CI=1.22-7.97, p=0.017) were identified as independent risk factors influencing the prognosis of patients with aSAH. Additionally, the area under the ROC curve was 0.866 (95%CI=0.805-0.927) for the training set and 0.924 (95%CI=0.849-0.999) for the validation set. The calibration curve analysis demonstrated a minor error of 0.02 for the training set and 0.051 for the validation set. Furthermore, both the training set and validation set displayed significant clinical benefits according to the DCA curves, underscoring the meaningful utility of the developed nomogram. CONCLUSIONS: Fibrinogen and neutrophil/lymphocyte ratio scores, and the D-dimer/Albumin ratio emerged as significant independent risk factors for prognosticating the outcomes of patients with aSAH. Leveraging these factors, a robust nomogram model was meticulously developed, showcasing its impressive precision in prognostic predictions. These results underscore the promising clinical applicability of these biomarkers as effective prognostic indicators for individuals afflicted by aSAH.

Astrocyte senescence-like response related to peripheral nerve injury-induced neuropathic pain.

BACKGROUND: Peripheral nerve damage causes neuroinflammation, which plays a critical role in establishing and maintaining neuropathic pain (NeP). The mechanisms contributing to neuroinflammation remain poorly elucidated, and pharmacological strategies for NeP are limited. Thus, in this study, we planned to explore the possible link between astrocyte senescence and NeP disorders following chronic sciatic nerve injury. METHODS: An NeP animal model was established by inducing chronic constrictive injury (CCI) to the sciatic nerve in adult rats. A senolytic drug combination of dasatinib and quercetin was gavaged daily from the first postoperative day until the end of the study. Paw mechanical withdrawal threshold (PMWT) and paw thermal withdrawal latency (PTWL) were evaluated to assess behaviors in response to pain in the experimental rats. Senescence-associated ß-galactosidase staining, western blot analysis, and immunofluorescence were applied to examine the levels of proinflammatory factors and severity of the senescence-like response in the spinal cord. Lipopolysaccharide (LPS) was administered to induce senescence of spinal astrocytes in primary cultures in vitro, to explore the potential impacts of senescence on the secretion of proinflammatory factors. Furthermore, single-cell RNA sequencing (scRNA-seq) was conducted to identify senescence-related molecular responses in spinal astrocytes under neuropathic pain. RESULTS: Following sciatic nerve CCI, rats exhibited reduced PMWT and PTWL, increased levels of spinal proinflammatory factors, and an enhanced degree of senescence in spinal astrocytes. Treatment with dasatinib and quercetin effectively attenuated spinal neuroinflammation and mitigated the hypersensitivities of the rats subjected to sciatic nerve CCI. Mechanistically, the dasatinib-quercetin combination reversed senescence in LPS-stimulated primary cultured astrocytes and decreased the levels of proinflammatory factors. The scRNA-seq data revealed four potential senescence-related genes in the spinal astrocyte population, and the expression of clusterin (CLU) protein was validated via in vitro experiments. CONCLUSION: The findings indicate the potential role of astrocyte senescence in neuroinflammation following peripheral nerve injury, and suggest that targeting CLU activation in astrocytes might provide a novel therapeutic strategy to treat NeP.

Effect of High Fat Diet on Disease Development of Polycystic Ovary Syndrome and Lifestyle Intervention Strategies.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder that affects premenopausal women. The etiology of PCOS is multifaceted, involving various genetic and epigenetic factors, hypothalamic-pituitary-ovarian dysfunction, androgen excess, insulin resistance, and adipose-related mechanisms. High-fat diets (HFDs) has been linked to the development of metabolic disorders and weight gain, exacerbating obesity and impairing the function of the hypothalamic-pituitary-ovarian axis. This results in increased insulin resistance, hyperinsulinemia, and the release of inflammatory adipokines, leading to heightened fat synthesis and reduced fat breakdown, thereby worsening the metabolic and reproductive consequences of PCOS. Effective management of PCOS requires lifestyle interventions such as dietary modifications, weight loss, physical activity, and psychological well-being, as well as medical or surgical interventions in some cases. This article systematically examines the pathological basis of PCOS and the influence of HFDs on its development, with the aim of raising awareness of the connection between diet and reproductive health, providing a robust approach to lifestyle interventions, and serving as a reference for the development of targeted drug treatments.

Electrochemical preparation of Al- Li alloy from Urea-LiCl molten salt at 353-393 K.

Al-Li alloy was prepared by electrodeposition on solid aluminum electrode in a urea-LiCl (77.5:22.5, mol%) molten salt system at 353-393 K. The results of cyclic voltammetry and chronopotentiometry show that underpotential deposition of Li occurs on the Al electrode and forms phases of Al(α) and AlLi(δ) which are confirmed by XRD measurements. Scanning electron microscopy observation of deposits indicates that Al-Li alloy with the thickness of 143 µm was obtained by potentiostatic electrolysis at -0.5 V vs. Li+/Li. The Li content in the alloy obtained at 373 K can reach 6.9 wt%. Galvanostatic electrolysis at 20 A demonstrated that Al-Li alloy coating on aluminum plate can be obtained from urea-LiCl electrolyte at 373 K in the atmosphere.

Bone marrow-derived mesenchymal stem cells attenuate complete Freund's adjuvant-induced inflammatory pain by inhibiting the expression of P2X3.

Bone marrow-derived mesenchymal stem cells (BMSCs) show a good property for pain treatment by modulating inflammatory response. However, the underlying therapeutic effect and related mechanism of BMSCs on inflammatory pain remain unclear. Therefore, we explored the function and potential mechanism of BMSCs performing in a complete Freund's adjuvant (CFA)-induced inflammatory pain model in this study. Here, BMSCs were injected into the CFA-treated rats, and we used behavioural tests to evaluate the changes in hypersensitivity. High-throughput sequencing was used to screen out the hub genes. Molecular biology experiments were performed to detect the level of P2X3 or inflammatory mediators in rats and observed the distribution of P2X3 in neural cells. Furthermore, the function of the P2X3 was explored via inhibitor and activator experiments. Finally, we found that BMSCs alleviated hyperalgesia and spinal levels of pro-inflammatory factors in CFA-treated rats. High-throughput sequencing showed that P2X3 and P2X7 were identified as hub genes, and only the expression level of P2X3 was significantly down-regulated after BMSCs treatment. Immunohistochemistry showed that P2X3 mainly colocalized with microglia and astrocytes. The levels of P2X3 and pro-inflammatory factors were all significantly reduced after BMSC injection. Moreover, similar attenuation was found in the CFA-treated rats after injecting the P2X3 inhibitor, and a P2X3 antagonist reversed the attenuation induced by the BMSCs. These findings suggest that BMSCs exerted a therapeutic effect on inflammatory pain by inhibiting the expression of P2X3 and the excessive production of inflammatory mediators was associated with an increased P2X3 level and BMSC therapy reverse these effects.

The Biological Implication of Semicarbazide-Sensitive Amine Oxidase (SSAO) Upregulation in Rat Systemic Inflammatory Response under Simulated Aerospace Environment.

The progress of space science and technology has ushered in a new era for humanity's exploration of outer space. Recent studies have indicated that the aerospace special environment including microgravity and space radiation poses a significant risk to the health of astronauts, which involves multiple pathophysiological effects on the human body as well on tissues and organs. It has been an important research topic to study the molecular mechanism of body damage and further explore countermeasures against the physiological and pathological changes caused by the space environment. In this study, we used the rat model to study the biological effects of the tissue damage and related molecular pathway under either simulated microgravity or heavy ion radiation or combined stimulation. Our study disclosed that ureaplasma-sensitive amino oxidase (SSAO) upregulation is closely related to the systematic inflammatory response (IL-6, TNF-α) in rats under a simulated aerospace environment. In particular, the space environment leads to significant changes in the level of inflammatory genes in heart tissues, thus altering the expression and activity of SSAO and causing inflammatory responses. The detailed molecular mechanisms have been further validated in the genetic engineering cell line model. Overall, this work clearly shows the biological implication of SSAO upregulation in microgravity and radiation-mediated inflammatory response, providing a scientific basis or potential target for further in-depth investigation of the pathological damage and protection strategy under a space environment.

Intrathecal liproxstatin-1 delivery inhibits ferroptosis and attenuates mechanical and thermal hypersensitivities in rats with complete Freund's adjuvant-induced inflammatory pain.

Previous studies have confirmed the relationship between iron-dependent ferroptosis and a peripheral nerve injury-induced neuropathic pain model. However, the role of ferroptosis in inflammatory pain remains inconclusive. Therefore, we aimed to explore whether ferroptosis in the spinal cord and dorsal root ganglion contributes to complete Freund's adjuvant (CFA)-induced painful behaviors in rats. Our results revealed that various biochemical and morphological changes were associated with ferroptosis in the spinal cord and dorsal root ganglion tissues of CFA rats. These changes included iron overload, enhanced lipid peroxidation, disorders of anti-acyl-coenzyme A synthetase long-chain family member 4 and glutathione peroxidase 4 levels, and abnormal morphological changes in mitochondria. Intrathecal treatment of liproxstatin-1 (a ferroptosis inhibitor) reversed these ferroptosis-related changes and alleviated mechanical and thermal hypersensitivities in CFA rats. Our study demonstrated the occurrence of ferroptosis in the spinal cord and dorsal root ganglion tissues in a rodent model of inflammatory pain and indicated that intrathecal administration of ferroptosis inhibitors, such as liproxstatin-1, is a potential therapeutic strategy for treating inflammatory pain.

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

Charge-elimination strategy for constructing RNA-selective fluorescent probe undisturbed by mitochondria.

Visualizing Ribonucleic acid (RNA) dynamics inside live cells is crucially important for the research of life science. However, almost all of the reported RNA probes target RNA with cationic groups, and mitochondria with high negative transmembrane potential may bring significant interferences. As a result, precise visualization of RNA in living cells is still a greatly challenging task. To overcome this problem, in this work, we proposed a novel charge-elimination strategy to construct a fluorescent probe (H-SMBT) specific for RNA undisturbed by mitochondria in live cells. Probe H-SMBT was designed to target the negative groove of RNA with a cationic group, and an additional hydroxyl group was modified to overcome the interference from mitochondria. H-SMBT will change from cationic structure to a charge-eliminated state in mitochondria with weak alkalic environment and detach from mitochondria, and therefore, it can exclusively stain RNA in live cells. Using M-SMBT with a methoxy group as a comparative molecule, we confirmed that the phenol group in H-SMBT played a decisive role to achieve the RNA specificity. Furthermore, H-SMBT can fast stain live cells in 5 min with excellent RNA selectivity. The probe can also monitor cellular damage processes, and successfully be applied to live zebrafish imaging due to the good tissue permeability. This work provides a new design strategy for constructing RNA-selective fluorescent probes avoiding the interference from mitochondria, and the designed RNA probe can be widely used for RNA-related life science research.

Regulation of FOXO3 on neuronal ferroptosis after intracerebral hemorrhage via modulating NOX4 transcription.

INTRODUCTION: Intracerebral hemorrhage (ICH) is known to trigger neuronal ferroptosis while forkhead box O3 (FOXO3) is implicated in ICH. This study aimed to determine the specific effect of FOXO3 on neuronal ferroptosis after ICH. METHODS: The ICH mouse model was established through the injection of bacterial collagenase type IV and the cell model was established in Hemin-induced HT-22 cells. Subsequently, neurological functions, brain water content, and histopathological changes in mice were assessed. HT-22 cell activity was examined via cell counting kit-8 (CCK-8) method, and the levels of FOXO3, NADPH oxidase 4 (NOX4), and glutathione peroxidase 4 (GPX4) in brain tissues and HT-22 cells were measured. Fe2+ concentration and the levels of reactive oxygen species (ROS), malondialdehyde (MDA), and glutathione (GSH) in the tissues and cells were examined. The binding relationship between FOXO3 and the NOX4 promoter region was determined via chromatin-immunoprecipitation (Ch-IP). Rescue experiments were designed to probe the role of NOX4 in the regulation of FOXO3 on neuronal ferroptosis. RESULTS: FOXO3 was highly-expressed in ICH models while silencing FOXO3 alleviated brain damage, edema, and inflammatory infiltration in ICH mice. Meanwhile, silencing FOXO3 enhanced cell activity, diminished ROS and MDA activities and Fe2+ concentration, and elevated GSH and GPX4 levels in the tissues or cells. FOXO3 could bind to the NOX4 promoter and upregulate NOX4 transcription. NOX4 overexpression partially neutralized the repressive role of silencing FOXO3 in neuronal ferroptosis. CONCLUSION: Silencing FOXO3 attenuated ICH-induced neuronal ferroptosis via down-regulating NOX4 transcription levels, thus ameliorating post-ICH brain damage.