LncRNA NRON promotes tumorigenesis by enhancing MDM2 activity toward tumor suppressor substrates.

The E3 ligase MDM2 promotes tumor growth and progression by inducing ubiquitin-mediated degradation of P53 and other tumor-suppressing proteins. Here, we identified an MDM2-interacting lncRNA NRON, which promotes tumor formation by suppressing both P53-dependent and independent pathways. NRON binds to MDM2 and MDMX (MDM4) via two different stem-loops, respectively, and induces their heterogenous dimerization, thereby enhancing the E3 ligase activity of MDM2 toward its tumor-suppressing substrates, including P53, RB1, and NFAT1. NRON knockdown dramatically inhibits tumor cell growth in vitro and in vivo. More importantly, NRON overexpression promotes oncogenic transformation by inducing anchorage-independent growth in vitro and facilitating tumor formation in immunocompromised mice. Clinically, NRON expression is significantly associated with poor clinical outcome in breast cancer patients. Together, our data uncover a pivotal role of lncRNA that induces malignant transformation of epithelial cells by inhibiting multiple tumor suppressor proteins.

Gsα Regulates Macrophage Foam Cell Formation During Atherosclerosis.

BACKGROUND: Many cardiovascular pathologies are induced by signaling through G-protein-coupled receptors via Gsα (G protein stimulatory α subunit) proteins. However, the specific cellular mechanisms that are driven by Gsα and contribute to the development of atherosclerosis remain unclear. METHODS: High-throughput screening involving data from single-cell and bulk sequencing were used to explore the expression of Gsα in atherosclerosis. The differentially expression and activity of Gsα were analyzed by immunofluorescence and cAMP measurements. Macrophage-specific Gsα knockout (Mac-GsαKO) mice were generated to study the effect on atherosclerosis. The role of Gsα was determined by transplanting bone marrow and performing assays for foam cell formation, Dil-ox-LDL (oxidized low-density lipoprotein) uptake, chromatin immunoprecipitation, and luciferase reporter assays. RESULTS: ScRNA-seq showed elevated Gnas in atherosclerotic mouse aorta's cholesterol metabolism macrophage cluster, while bulk sequencing confirmed increased GNAS expression in human plaque macrophage content. A significant upregulation of Gsα and active Gsα occurred in macrophages from human and mouse plaques. Ox-LDL could translocate Gsα from macrophage lipid rafts in short-term and promote Gnas transcription through ERK1/2 activation and C/EBPß phosphorylation via oxidative stress in long-term. Atherosclerotic lesions from Mac-GsαKO mice displayed decreased lipid deposition compared with those from control mice. Additionally, Gsα deficiency alleviated lipid uptake and foam cell formation. Mechanistically, Gsα increased the levels of cAMP and transcriptional activity of the cAMP response element binding protein, which resulted in increased expression of CD36 and SR-A1. In the translational experiments, inhibiting Gsα activation with suramin or cpGN13 reduced lipid uptake, foam cell formation, and the progression of atherosclerotic plaques in mice in vivo. CONCLUSIONS: Gsα activation is enhanced during atherosclerotic progression and increases lipid uptake and foam cell formation. The genetic or chemical inactivation of Gsα inhibit the development of atherosclerosis in mice, suggesting that drugs targeting Gsα may be useful in the treatment of atherosclerosis.

Genomic Evidence for the Nonpathogenic State in HIV-1-Infected Northern Pig-Tailed Macaques.

HIV-1 is a highly host-specific retrovirus that infects humans but not most nonhuman primates. Thus, the lack of a suitable primate model that can be directly infected with HIV-1 hinders HIV-1/AIDS research. In the previous study, we have found that the northern pig-tailed macaques (NPMs) are susceptible to HIV-1 infection but show a nonpathogenic state. In this study, to understand this macaque-HIV-1 interaction, we assembled a de novo genome and longitudinal transcriptome for this species during the course of HIV-1 infection. Using comparative genomic analysis, a positively selected gene, Toll-like receptor 8, was identified with a weak ability to induce an inflammatory response in this macaque. In addition, an interferon-stimulated gene, interferon alpha inducible protein 27, was upregulated in acute HIV-1 infection and acquired an enhanced ability to inhibit HIV-1 replication compared with its human ortholog. These findings coincide with the observation of persistently downregulated immune activation and low viral replication and can partially explain the AIDS-free state in this macaque following HIV-1 infection. This study identified a number of unexplored host genes that may hamper HIV-1 replication and pathogenicity in NPMs and provided new insights into the host defense mechanisms in cross-species infection of HIV-1. This work will facilitate the adoption of NPM as a feasible animal model for HIV-1/AIDS research.

Population pharmacokinetics of daptomycin in critically ill patients receiving extracorporeal membrane oxygenation.

BACKGROUND: Daptomycin is widely used in critically ill patients for Gram-positive bacterial infections. Extracorporeal membrane oxygenation (ECMO) is increasingly used in this population and can potentially alter the pharmacokinetic (PK) behaviour of antibiotics. However, the effect of ECMO has not been evaluated in daptomycin. Our study aims to explore the effect of ECMO on daptomycin in critically ill patients through population pharmacokinetic (PopPK) analysis and to determine optimal dosage regimens based on both efficacy and safety considerations. METHODS: A prospective, open-label PK study was carried out in critically ill patients with or without ECMO. The total concentration of daptomycin was determined by UPLC-MS/MS. NONMEM was used for PopPK analysis and Monte Carlo simulations. RESULTS: Two hundred and ninety-three plasma samples were collected from 36 critically ill patients, 24 of whom received ECMO support. A two-compartment model with first-order elimination can best describe the PK of daptomycin. Creatinine clearance (CLCR) significantly affects the clearance of daptomycin while ECMO has no significant effect on the PK parameters. Monte Carlo simulations showed that, when the MICs for bacteria are  ≥1 mg/L, the currently recommended dosage regimen is insufficient for critically ill patients with CLCR > 30 mL/min. Our simulations suggest 10 mg/kg for patients with CLCR between 30 and 90 mL/min, and 12 mg/kg for patients with CLCR higher than 90 mL/min. CONCLUSIONS: This is the first PopPK model of daptomycin in ECMO patients. Optimal dosage regimens considering efficacy, safety, and pathogens were provided for critical patients based on pharmacokinetic-pharmacodynamic analysis.

Interpretable deep learning architectures for improving drug response prediction performance: myth or reality?

MOTIVATION: Interpretable deep learning (DL) models that can provide biological insights, in addition to accurate predictions, are of great interest to the biomedical community. Recently, interpretable DL models that incorporate signaling pathways have been proposed for drug response prediction (DRP). While these models improve interpretability, it is unclear whether this comes at the cost of less accurate DRPs, or a prediction improvement can also be obtained. RESULTS: We comprehensively and systematically assessed four state-of-the-art interpretable DL models using three pathway collections to assess their ability in making accurate predictions on unseen samples from the same dataset, as well as their generalizability to an independent dataset. Our results showed that models that explicitly incorporate pathway information in the form of a latent layer perform worse compared to models that incorporate this information implicitly. However, in most evaluation setups, the best performance was achieved using a black-box multilayer perceptron, and the performance of a random forests baseline was comparable to those of the interpretable models. Replacing the signaling pathways with randomly generated pathways showed a comparable performance for the majority of the models. Finally, the performance of all models deteriorated when applied to an independent dataset. These results highlight the importance of systematic evaluation of newly proposed models using carefully selected baselines. We provide different evaluation setups and baseline models that can be used to achieve this goal. AVAILABILITY AND IMPLEMENTATION: Implemented models and datasets are provided at https://doi.org/10.5281/zenodo.7787178 and https://doi.org/10.5281/zenodo.7101665, respectively.

Classification of cognitive ability of healthy older individuals using resting-state functional connectivity magnetic resonance imaging and an extreme learning machine.

BACKGROUND: Quantitative determination of the correlation between cognitive ability and functional biomarkers in the older brain is essential. To identify biomarkers associated with cognitive performance in the older, this study combined an index model specific for resting-state functional connectivity (FC) with a supervised machine learning method. METHODS: Performance scores on conventional cognitive test scores and resting-state functional MRI data were obtained for 98 healthy older individuals and 90 healthy youth from two public databases. Based on the test scores, the older cohort was categorized into two groups: excellent and poor. A resting-state FC scores model (rs-FCSM) was constructed for each older individual to determine the relative differences in FC among brain regions compared with that in the youth cohort. Brain areas sensitive to test scores could then be identified using this model. To suggest the effectiveness of constructed model, the scores of these brain areas were used as feature matrix inputs for training an extreme learning machine. classification accuracy (CA) was then tested in separate groups and validated by N-fold cross-validation. RESULTS: This learning study could effectively classify the cognitive status of healthy older individuals according to the model scores of frontal lobe, temporal lobe, and parietal lobe with a mean accuracy of 86.67%, which is higher than that achieved using conventional correlation analysis. CONCLUSION: This classification study of the rs-FCSM may facilitate early detection of age-related cognitive decline as well as help reveal the underlying pathological mechanisms.

Adaptive Segmentation Algorithm for Subtle Defect Images on the Surface of Magnetic Ring Using 2D-Gabor Filter Bank.

In order to realize the unsupervised segmentation of subtle defect images on the surface of small magnetic rings and improve the segmentation accuracy and computational efficiency, here, an adaptive threshold segmentation method is proposed based on the improved multi-scale and multi-directional 2D-Gabor filter bank. Firstly, the improved multi-scale and multi-directional 2D-Gabor filter bank was used to filter and reduce the noise on the defect image, suppress the noise pollution inside the target area and the background area, and enhance the difference between the magnetic ring defect and the background. Secondly, this study analyzed the grayscale statistical characteristics of the processed image; the segmentation threshold was constructed according to the gray statistical law of the image; and the adaptive segmentation of subtle defect images on the surface of small magnetic rings was realized. Finally, a classifier based on a BP neural network is designed to classify the scar images and crack images determined by different threshold segmentation methods. The classification accuracies of the iterative method, the OTSU method, the maximum entropy method, and the adaptive threshold segmentation method are, respectively, 85%, 87.5%, 95%, and 97.5%. The adaptive threshold segmentation method proposed in this paper has the highest classification accuracy. Through verification and comparison, the proposed algorithm can segment defects quickly and accurately and suppress noise interference effectively. It is better than other traditional image threshold segmentation methods, validated by both segmentation accuracy and computational efficiency. At the same time, the real-time performance of our algorithm was performed on the advanced SEED-DVS8168 platform.

Understanding regulating effects of protein-anionic octenyl succinic anhydride-modified starch interactions on the structural, rheological, digestibility and release properties of starch.

BACKGROUND: Proteins and anionic octenyl succinic anhydride (OSA)-modified starch (OSA-starch) are common ingredients in food systems. The interactions between OSA-starch and protein are found to alter the structural and functional properties of the protein-OSA-starch complexes. In this regard, the close understanding of the relationship among the molecular interactions between whey protein isolate (WPI) and OSA-high amylose corn starch (HAS), structure changes and rheological, digestibility and release properties of WPI-OSA-HAS was investigated. RESULTS: The molecular interactions of WPI-OSA-HAS were significant for increasing the surface rough, solubility, storage modulus and loss modulus, but decreasing the R1047/1022 values. For the nutritional evaluation, the anti-digestibility of WPI-OSA-HAS was enhanced with increased resistant starch + slowly digestible starch contents and decreased equilibrium hydrolysis percentage and kinetic constant. During the digestion, part of the starch granule, OSA groups and WPI were lost, but the loss was lower than for OSA-HAS. Furthermore, the results of curcumin-loaded WPI-OSA-HAS in simulated gastrointestinal fluids demonstrated that curcumin could be gradually released to simulate colonic fluid. Notably, the interaction between WPI and OSA-HAS depended on the WPI concentration with the stronger molecular interactions obtained at 35% concentration. CONCLUSION: These results provided important information concerning how to adjust the rheological, anti-digestibility and release properties of WPI-OSA-HAS through altering the electrostatic interactions and hydrophobic interactions of WPI-OSA-HAS. © 2024 Society of Chemical Industry.

Looking at the BiG picture: incorporating bipartite graphs in drug response prediction.

MOTIVATION: The increasing number of publicly available databases containing drugs' chemical structures, their response in cell lines, and molecular profiles of the cell lines has garnered attention to the problem of drug response prediction. However, many existing methods do not fully leverage the information that is shared among cell lines and drugs with similar structure. As such, drug similarities in terms of cell line responses and chemical structures could prove to be useful in forming drug representations to improve drug response prediction accuracy. RESULTS: We present two deep learning approaches, BiG-DRP and BiG-DRP+, for drug response prediction. Our models take advantage of the drugs' chemical structure and the underlying relationships of drugs and cell lines through a bipartite graph and a heterogeneous graph convolutional network that incorporate sensitive and resistant cell line information in forming drug representations. Evaluation of our methods and other state-of-the-art models in different scenarios shows that incorporating this bipartite graph significantly improves the prediction performance. In addition, genes that contribute significantly to the performance of our models also point to important biological processes and signaling pathways. Analysis of predicted drug response of patients' tumors using our model revealed important associations between mutations and drug sensitivity, illustrating the utility of our model in pharmacogenomics studies. AVAILABILITY AND IMPLEMENTATION: An implementation of the algorithms in Python is provided in https://github.com/ddhostallero/BiG-DRP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Comparative study on the treatment of refractory organics in landfill leachate by homogeneous and heterogeneous Fenton advanced oxidation processes.

With the advancement of the landfill stabilization process of municipal solid waste, landfill leachate containing a large amount of refractory organic matter is formed. In this study, homogeneous Fenton and heterogeneous Fenton-like (activation by zero valent iron (Fe0), pyrite (FeS2) and magnetite (Fe3O4) as solid iron materials) processes have been compared for the removal of refractory organics from landfill leachate. The removal efficiency of organics in the Fenton process was slightly higher than those in the Fenton-like processes. The removal efficiencies based on total organic carbon, UV absorbance at 254 nm (UV254) and colour number in the Fenton process were as high as 57.42%, 71.63% and 81.03%, respectively. In the Fenton-like processes, the Fe0/H2O2 process achieved 35.74%, 66.24% and 86.29% removal efficiencies, respectively. Moreover, the degradation effect on refractory organic substances proved to be better. In the Fenton-like processes, the activation mechanisms with Fe0 and FeS2 involve the homogeneous activation of Fe2+ in solution and heterogeneous activation of iron oxides produced during the reaction, respectively. With Fe3O4, the activation mechanism is mainly a heterogeneous process involving its intrinsic iron oxide constituents. This study may provide a theoretical basis for the treatment of refractory organics in landfill leachate.

Removal of refractory organics from landfill leachate by in situ electrogenerated H2O2 combined with an Fe0 Fenton-like process.

Landfill leachate contains a large amount of refractory organic matter, which will cause harm to the environment if not appropriately treated. In this study, the refractory organic matter in landfill leachate has been treated by in situ electrogenerated H2O2 combined with an Fe0 Fenton-like process, aiming to explore a cleaner and more efficient process for leachate treatment. The results showed that the current, initial pH and oxygen flow rate have significant influences on H2O2 production. The current and oxygen flow rate are positively correlated with H2O2 production, and neutral conditions are more favourable. Under the conditions of a current of 200 mA, an initial pH of 7.0 and an oxygen flow rate of 0.3 L/min, H2O2 production reached 2.81 mM, the current efficiency was close to 80% and the highest removal efficiency of organic matter reached 40.70%. The absorbance at 280 nm (E280) decreased from 0.1669 to 0.1180, and the ratios E240/E420, E250/E365 and E300/E400 in the UV and visible regions changed from 0.7825, 5.4492 and 0.2422 to 1.3135, 7.3745 and 0.2966, respectively. The maximum fluorescence intensities due to humic-like acid and fulvic-like acid substances decreased from 1275 and 1246 to 595.9 and 711.0, respectively. Spectral analysis further showed that the complex structure of refractory organic matter in the landfill leachate was obviously destroyed, and the relative content of humus decreased significantly. This study may provide a theoretical basis for the effective treatment of refractory organic matter in landfill leachate by in situ electrogenerated H2O2 combined with a Fenton-like process.

Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.

OBJECTIVES: Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients. METHODS: An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19. RESULTS: A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001). CONCLUSIONS: Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT: • AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.

Novel green synthesis and characterization of a chemotherapeutic supplement by silver nanoparticles containing Berberis thunbergii leaf for the treatment of human pancreatic cancer.

In recent years, silver nanoparticles have been used as modern chemotherapeutic drugs to treat several cancers such as pancreatic, breast, prostate, and blood cancers. No previous reports demonstrated the in vitro anti-human pancreatic cancer effects of the novel chemotherapeutic drug formulated by silver nanoparticles containing Berberis thunbergii leaf (AgNPs). The synthesized AgNPs were characterized using different techniques including UV-vis. and FT-IR spectroscopy, X-ray diffraction, scanning electron microscopy (SEM), and TEM. All techniques approved the synthesized silver nanoparticles. The SEM and TEM exhibited a uniform spherical morphology and an average size of about 15 nm for the biosynthesized nanoparticles, respectively. The 4-(dimethylamino)benzaldehyde,2,2-diphenyl-1- pikrilhydrazil (DPPH) test revealed similar antioxidant potentials for B. thunbergii leaf aqueous extract, AgNPs, and butylated hydroxytoluene. AgNPs inhibited half of the DPPH molecules in the concentration of 108 µg/mL. To survey the anti-human pancreatic cancer activities of AgNO3 , B. thunbergii leaf aqueous extract, and AgNPs, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assay was used on common human pancreatic cancer cell lines. AgNPs had very low cell viability and anti-human pancreatic cancer effects dose-dependently against PANC-1, AsPC-1, and MIA PaCa-2. The IC50 values of the AgNPs were 259, 268, and 141 µg/mL against PANC-1, AsPC-1, and MIA PaCa-2 cell lines, respectively. It is thought that the AgNPs obtained can be used as an anticancer drug for the diagnosis of pancreatic cancer in humans after acceptance of the above findings in clinical study trials.

Accuracy and stability evaluation of different blood sampling methods in blood gas analysis in emergency patients: A retrospective study.

BACKGROUND: To evaluate the accuracy and stability of arterial blood gas (ABG) results by comparison with venous measurements from routine blood tests, and to compare the accuracy and performance of two sampling syringes, pre-heparinized syringe (PHS) and disposable arterial blood syringe (DABS), in ABG analysis. METHODS: We retrospectively analyzed the practical use of PHS and DABS in collecting ABG samples, involving 500 and 400 patients, respectively. For each patient, in addition to the ABG sample, a venous blood sample was also collected using a venous blood collection tube (VBCT) and used for routine blood tests. Accordingly, patients were referred to as the PHS + VBCT group and DABS + VBCT group. The correlation between arterial and venous values of each blood parameter in each group was evaluated using the interclass correlation coefficient (ICC). Bland-Altman was performed to evaluate the agreement between arterial and venous values and compare the performance of PHS and DABS in ABG sample collection. RESULTS: In the PHS + VBCT group, arterial K+ , Na+ , hemoglobin (Hb), and hematocrit (HCT) were 0.32 mmol/L, 2.90 mmol/L, 2.21 g/L, and 1.27% significantly lower their corresponding venous values while arterial Cl- was 7.60 mmol/L significantly higher than venous Cl- . In the DABS + VBCT group, arterial K+ and Na+ were 0.20 mmol/L and 1.19 mmol/L significantly lower while Cl- and HCT in arterial blood were 5.34 mmol/L and 0.66% significantly higher than their corresponding venous values. In both groups, arterial K+ , Na+ , Hb, and HCT values were highly consistent with their corresponding venous values, with all ICCs greater than 0.70, especially Hb and HCT. Bland-Altman analysis demonstrated that arterial K+ and Na+ were more consistent with venous counterparts in the DABS + VBCT group, with a narrower 95% limits of agreement than the PHS + VBCT group (K+ , -0.7-0.3 mmol/L vs. -1.1 to 0.5 mmol/L; Na+ , -5.8 to 3.4 mmol/L vs. -8.2 to 2.4 mmol/L). CONCLUSION: Arterial blood gas analysis of K+ , Na+ , Hb, and HCT using PHS or DABS for blood sampling is accurate and stable, especially DABS, which can provide clinicians with fast and reliable blood gas results.

[Factors Affecting the Surgical Level of Major Amputations in Patients with Severe Diabetic Foot].

Objective: To investigate the main factors affecting the surgical level of major amputations in patients with severe diabetic foot. Methods: A case-control study was conducted to analyze the clinical data of severe diabetic foot patients who had major amputations and were admitted to the Intensive Care Unit (ICU), Air Force Hospital of PLA Eastern Theater Command between July 2020 and July 2022. According to their surgical level of amputation, patients were divided into transtibial amputation (TT) group and transfemoral amputation (TF) group. Correlation analysis was performed with the clinical data of the patients, and multivariate logistic regression was performed to screen for relevant factors affecting the surgical level of major amputation. Results: The data of 48 patients with major amputations were collected, including 15 patients in the TT group and 33 patients in the TF group. The proportion of patients who had cardiovascular and cerebrovascular complications in the TT group was lower than that in the TF group (26.67% [4/15] vs. 57.58% [19/33], P<0.05), the proportion of patients who had lower extremity arterial intervention history was higher in the TT group than that in the TF group (40% [6/15] vs. 9.09% [3/33], P<0.05), and the proportion of patients who had elevated creatinine level was lower in the TT group than that in the TF group (70.31±22.98 vs. 127.98±108.38, P<0.05). Moreover, the history of lower extremity arterial intervention may be an independent protective factor for determining the surgical level of major amputations (odds ratio [ OR]=0.15, 95% confidence interval [ CI]: 0.03-0.72, P=0.018). Conclusion: History of cardiovascular and cerebrovascular diseases, serum creatinine level and history of lower extremity arterial intervention are the main factors affecting the surgical level of major amputations in patients with severe diabetic foot, and the history of lower extremity arterial intervention may be an independent protective factor.

Tubular epithelial cells derived-exosomes containing CD26 protects mice against renal ischemia/reperfusion injury by maintaining proliferation and dissipating inflammation.

Ischemia-reperfusion injury (IR) is the leading cause of acute kidney injury (AKI). No effective drugs to treat IR-related AKI are currently available. Recent pre-clinical trials have evaluated the therapeutic potential of extracellular vesicles-exosomes to chronic kidney disease. Here, we found exosomes derived from the tubular epithelial cell in IR condition (ExoIR) enriched CD26, compared with control (ExoNormal). Tracking exosomes in vivo certified tubular epithelial cell uptake exosomes. We have isolated exosomes with overexpression of CD26 (ExoCD26+) from culture media from tubular epithelial cell line transferred by adenovirus vectors. After administration of exosomes (100 mg) or bovine serum albumin (BSA, equivalent protein control) in IR or sham operation mice after 72 h via tail vein injection, the renal function impairment and histology injury were relived in mice receiving ExoCD26+. Immunofluorescence staining with proliferating cell nuclear antigen revealed ExoCD26+ recovered proliferation of cells partly after IR injury. Cell cycle modulator, p53 and p21 were upregulated in IR mice receiving BSA control, ExoNormal, and ExoIR. ExoCD26+ significantly blunt this protein upregulation. Inflammatory cell infiltration and chemokine receptor (CXCR4) were dissipated in IR mice receiving ExoCD26+. Downstream chemokine of CXCR4, stromal derived factor-1 (SDF1) also decreased after administration of ExoCD26+ in IR mice. Finally, ExoCD26+ suppressed inundant collagenⅠ expression in IR kidney. In conclusion, Tubular epithelial cells derived-exosomes containing CD26 might be one of the therapy modes for IR-AKI by maintaining proliferation and dissipating inflammation.

Metallic Transition Metal Dichalcogenides of Group VIB: Preparation, Stabilization, and Energy Applications.

Layered transition metal dichalcogenides (TMDs) of group VIB have been widely used in the realms of energy storage and conversions. Along with the existence of semiconducting states, their metallic phases have recently attracted numerous attentions owing to their fascinating physical and chemical properties. Many efforts have been devoted to obtain metallic TMDs with high purity and yield. Nevertheless, such metallic phase is thermodynamically metastable and tends to convert into semiconducting phase, which necessitates the exploration over effective strategies to ensure the stability. In this review, typical fabrication routes are introduced and those critical factors during preparation are elaborately discussed. Moreover, the stabilized strategies are summarized with concrete examples highlighting the key mechanisms toward efficient stabilization. Finally, emerging energy applications are overviewed. This review presents comprehensive research status of metallic group VIB TMDs, aiming to facilitate further scientific investigations and promote future practical applications in the fields of energy storage and conversion.

Urinary exosomal CD26 is associated with recovery from acute kidney injury in intensive care units: a prospective cohort study.

OBJECTIVES: Currently there is no validated method to predict renal reversal and recovery after acute kidney injury (AKI). As exosomes have the potential for AKI prognosis and CD26 is involved in the mechanisms in AKI, this study aims to investigate whether urinary exosomal CD26 is associated with renal-related outcomes and explore its prospect as a novel prognosis biomarker. METHODS: This was a single-center, prospective cohort study. A total of 133 AKI patients and 68 non-AKI patients admitted to ICU in Qilu Hospital Shandong University from January 2017 to January 2018. Urine samples were collected at enrollment and the relative expression of CD26 (CD26 percentage) in urinary exosomes was examined, that was then categorized into a low-CD26 level and a high-CD26 level. RESULTS: CD26 percentage was significantly lower in the AKI cohort than in the control cohort. Within the AKI cohort, a high-CD26 level was associated with lower incidence of major adverse kidney events within 90 days, but higher incidence of reversal within 28 days. In AKI survivors, a high-CD26 level had a 4.67-, 3.50- and 4.66-fold higher odds than a low-CD26 level for early reversal, recovery and reversal, respectively, after adjustment for clinical factors. Prediction performance was moderate for AKI survivors but improved for non-septic AKI survivors. CONCLUSIONS: Urinary exosomal CD26 is associated with renal reversal and recovery from AKI and is thus a promising prognosis biomarker.

The microvesicle/CD36 complex triggers a prothrombotic phenotype in patients with non-valvular atrial fibrillation.

The mechanisms responsible for platelet activation, the prothrombotic state, in non-valvular atrial fibrillation (NVAF) are still obscure. Microvesicles (MVs) can transfer various messages to target cells and may be helpful for exploring the detailed mechanisms. We aimed to investigate the possible mechanisms by which proatherogenic factors of NVAF contribute to platelet activation. Two hundred and ten patients with NVAF were stratified as being at 'low to moderate risk' or 'high risk' for stroke according to the CHADS2 score. Levels of platelet-derived MVs (PMVs) and platelet activation were examined. CD36-positive or CD36-deficient human platelets were stimulated by MVs isolated from NVAF patients with or without various inhibitors in vitro. Levels of PMVs and platelet activation markers enhanced significantly in high-risk patients. The MVs isolated from plasma of NVAF patients bound to platelet CD36 and activated platelets by phosphorylating the mitogen-activated protein kinase 4/Jun N-terminal kinase 2 (MKK4/JNK2) pathways. However, CD36 deficiency protected against MV-induced activation of platelets. We reveal a possible mechanism of platelet activation in NVAF and suggest that the platelet CD36 might be an effective target in preventing the prothrombotic state in NVAF.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.