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Pretargeted Mitochondrial Delivery of Organoarsenicals for Cancer Immunotherapy
Three-dimensional spatiotemporal analysis for the assessment of retinal capillary perfusion using a clinical OCT system
Abstract Growing evidence suggests that subtle changes in retinal microcirculation may precede structural damage in vision-threatening diseases. Among these, perfusion heterogeneity within the retinal capillary network has emerged as a promising biomarker for early detection and disease monitoring. Recent advances in optical coherence tomography (OCT) and OCT-based angiography (OCTA) have enabled high-resolution, three-dimensional imaging of retinal morphology and vasculature. However, commercial systems remain limited in their ability to accurately analyze retinal perfusion dynamics due to reliance on proprietary and undisclosed post-processing algorithms. This paper introduces an effective protocol for spatial and temporal analysis of capillary perfusion heterogeneity using unprocessed OCTA volume data acquired by a commercial retinal imaging system. The proposed method employs a novel analysis utilizing the depth-resolved pixel-wise coefficient of variation (CoV) to quantitatively estimate retinal capillary perfusion heterogeneity. Comparison between the proposed method and conventional CoV analysis emphasizes the reliability of the new approach, incorporating depth-dependent signals. By using unprocessed OCTA data, the proposed method can provide more accurate measurements of retinal perfusion heterogeneity. Furthermore, the image processing techniques developed in this study could serve as a foundation for future research in other retinal vascular disorders.
Editorial Note: Urinary Exosomal microRNA-451-5p Is a Potential Early Biomarker of Diabetic Nephropathy in Rats
Ultradurable Regenerative Propane Dehydrogenation Catalyst by Fluorination-Induced Confining and Positioning
Catalytic epoxidation of oleic acid through in situ hydrolysis for biopolyol formation
The structure of mass political belief systems: A network approach to understanding the left-right spectrum
Many socially consequential beliefs, notably political and religious ideologies, consist not of single propositions in isolation from others but as systems of many propositions. Philip Converse, one of the most influential political scientists of the twentieth century, proposed that such systems can be understood as networks of propositions and predicted that they would be highly intercorrelated in those with strong ideological commitments but less so in people who are less ideological. We used recent advances in network psychometrics to test this account in relation to the political beliefs of a representative sample of 2,058 UK adults, who rated themselves on the left-right dimension and then reported their attitudes toward 18 policy issues. We divided participants into equally-sized groups of left-wing, centrist and right-wing participants and found that, as Converse had predicted, the networks of those at either end of the left-right continuum were similar in structure, being significantly more interconnected than the networks of those who identified themselves as centrists, even though the actual beliefs were (for the most part) polar opposites. This finding, which was robust to sensitivity checks, aligns with previous research which has shown that people at the political extremes, compared to those in the centre, are more certain about their beliefs and less likely to change them over time. In each ideological group we also identified the same three communities of beliefs which mapped onto classic accounts of authoritarian attitudes, altruism and cooperativeness, and personal liberty. Attitudes towards gay rights had the highest predictability index in all three networks and was the most central node in the right and centre networks, suggesting that these attitudes play a largely unrecognised but important role in ideological positioning. Our analytical approach has implications for not only political beliefs but all organized belief systems.
Rational Design and Synthesis of a Metal–Organic Framework Featuring Cu(I)–Carbon Bonds for Enhanced Artificial Photosynthesis
In flight fragmentation reduces bomb size range and hazard during explosive volcanic eruptions
Abstract Coarse, molten fragments of low-viscosity magma (volcanic bombs) that are ejected during explosive volcanic eruptions represent a source of hazard and a record of past eruptions. After ejection, bombs tend to break up during flight, but how much this affects their dispersal is unclear. Here, we use high-speed and high-definition imaging of three recent explosive eruptions to parameterise the in-flight fragmentation of bombs. We estimate that in-flight fragmentation involves 73% of bombs coarser than $$\sim$$ 0.2 m, with bomb-to-bomb collisions and aerodynamic frictional (drag) forces being the main drivers of in-flight fragmentation, depending on eruption style. Drag force increases with increasing bomb velocity and size, selectively fragmenting the coarsest and fastest bombs, acting as a self-limiting factor for the range and energy of falling bombs. These findings pose a quantitative basis for incorporating the in-flight fragmentation processes into the interpretation of volcanic deposits and for modelling hazards from falling bombs.
A machine learning approach for predicting 72-hour mortality of hypothermic patients only using non-invasive parameters: A multi-center retrospective cohort study
Objectives Accurately predicting the mortality risk of hypothermia patients is crucial for clinical decision-making, offering ample time for physicians to intervene. However, existing methods are invasive and difficult to implement in pre-hospital settings. Methods In this study, records of 2,700 hypothermia patients from 125 hospitals were extracted from the eICU Collaborative Research database, comprising 360 non-survivors and 2,340 survivors. Four machine learning methods were utilized to develop a mortality prediction model for hypothermia patients based on non-invasive physiological parameters. Data from 122 hospitals were used for model training, while the remainder were utilized for performance validation. Results The proposed machine learning prediction model achieved an area under the receiver operating characteristic curve (AUC) of 0.869 (95%CI: 0.840–0.895), demonstrating good mortality predictive performance for hypothermia patients only using non-invasive physiological parameters. Optimal and minimal feature subsets were selected for each machine learning method. The optimal feature subsets contained only 70.6% of the overall features for XGBoost models, while the AUC values increased by 0.039 compared to that of the entire feature subset. The interpretability analysis results highlight the vital importance of the temperature feature in predicting mortality during episodes of hypothermia in patients. Conclusions This study developed a mortality prediction method for hypothermia patients only using non-invasive parameters, demonstrating robustness and reliability during multi-center validation. It can offer decision support for remote areas and disaster sites where it is difficult to access invasive parameters.
Construction of Kondo Chains by Engineering Porphyrin π-Radicals on Au(111)
Correction: Garcinone D mitigates amyloid β42-Induced neurotoxicity: unravelling mechanisms of neuroprotection
Plant-recycling of waste tyre rubber into asphalt binder for sustainability: Insight into physicochemical behavior during terminal production
Crumb Rubber Modified Asphalt (CRMA) offers a vital pathway for global waste tire recycling, with swelling duration critically governing its performance during terminal production. This study examines physicochemical interactions between rubber particles and base asphalt under varied swelling durations. Systematic analyses employing extraction test, FTIR spectroscopy, rotational viscosity measurements, rheological test and Separation test assessed impacts on molecular structure, viscoelastic characteristics, rheological behavior and Storage stability. Results demonstrated that prolonged swelling time minimally influences residual rubber content (Δ ≤ 0.5%) but intensifies thermo-oxidative degradation of rubber particles in oxidation and desulfurization, thereby activating interfacial interactions of rubber particles and bitumen in CRMA binder. Extended swelling time will be able to initially elevate the viscosity through rubber-oil absorption and subsequently reduce the viscosity through dominant degradation, while progressively diminishing temperature susceptibility. Optimized swelling time at 4h can enhance complex modulus and viscoelastic balance through synergistic effects of rubber degradation and interfacial interactions, significantly improving high-temperature rutting resistance of CRMA binder. The softening point difference (SPD) of CRMA4 can be enhanced by 41% to satisfy better storage requirement. The study on the plant-recycling of waste tire rubber in modification of asphalt binder can provide more understandings in the rubberized asphalt binder production through combined analysis of rubber-asphalt compatibility and interfacial strength.
Regiodivergent Access to α- and β-Amino Acids via Solvent-Controlled Rh-Catalyzed Carboamidation of β,γ-Unsaturated Carboxylic Acids
Pharmacokinetic, docking, and DFT analyses reveal Moringa oleifera phytochemicals as inhibitors of HIF-1α/VEGF/GLUT1 signaling pathway in breast cancer
Editorial Note: The Pharmacological Chaperone AT2220 Increases Recombinant Human Acid α-Glucosidase Uptake and Glycogen Reduction in a Mouse Model of Pompe Disease
Spatial Conformation of Ionizable Lipids Regulates Endosomal Membrane Disruption
Epigenetic regulation of electromechanical continuity might determine phenotypic heterogeneity in SCN5A mutation carriers in Brugada syndrome
Prescription opioid misuse in people with chronic noncancer pain: A multi-variable analysis of sociodemographic, clinical, and psychological factors
Previous research has identified associations between sociodemographic, clinical, and psychological factors and prescription opioid misuse in individuals with chronic noncancer pain (CNCP). A two-study design was used to identify the factors with the most robust association with prescription opioid misuse (Study 1) and to cross-validate these associations in a second sample of people with CNCP to select a reduced number of variables (Study 2). Study 1 included 187 people with CNCP. Point biserial and bivariate correlations, and chi-square analysis showed that the variables significantly associated with opioid misuse were impulsiveness, anxiety sensitivity (AS), pain acceptance, pain catastrophizing, anxiety, depression, posttraumatic stress symptoms (PTSD), and social desirability (medium effect sizes). A family history of alcohol/drug abuse and being between 16 and 45 years of age also reached statistical significance. Study 2 included 179 people with CNCP. The results corroborated the associations found between opioid misuse and impulsiveness, AS, pain acceptance, pain catastrophizing, anxiety, depression, and PTSD. Logistic regression showed that AS, PTSD, and pain acceptance contributed significantly to the unique variance in prescription opioid misuse. Therefore, when prescribing opioids, clinicians should increase the supervision of those people with high AS, PTSD, and low pain acceptance. Evaluating these three variables in people with CNCP who are eligible for opioid therapy could aid in their therapeutic management and help prevent possible iatrogenic effects of opioids. Likewise, this could significantly enhance individual well-being and help mitigate the social problem associated with the misuse of prescription opioids.