Singlet-triplet energy gaps in fluorine-substituted methylenes and silylenes

We report singlet and triplet state splittings (ΔE_(ST)) for fluorine‐substituted methylenes and silylenes using dissociation‐consistent configuration interaction (CI) (based on generalized valence bond wave functions). These relatively simple CI calculations emphasize correlation consistency between the singlet and triplet states. Values of ΔE_(ST) for CH_2, CF_2, SiH_2, and SiF_2 are in excellent agreement with available experimental results, and we expect the predictions for the other cases CHF (14.5) and SiHF (41.3) to be equally accurate. This result strongly suggests that the correct choice among the experimental values for ΔE_(ST) of CHF is 14.7±0.2 kcal/mol

Continuum of vasodilator stress from rest to contrast medium to adenosine hyperemia for fractional flow reserve assessment

Objectives: This study compared the diagnostic performance with adenosine-derived fractional flow reserve (FFR) ≤0.8 of contrast-based FFR (cFFR), resting distal pressure (Pd)/aortic pressure (Pa), and the instantaneous wave-free ratio (iFR). Background: FFR objectively identifies lesions that benefit from medical therapy versus revascularization. However, FFR requires maximal vasodilation, usually achieved with adenosine. Radiographic contrast injection causes submaximal coronary hyperemia. Therefore, intracoronary contrast could provide an easy and inexpensive tool for predicting FFR. Methods: We recruited patients undergoing routine FFR assessment and made paired, repeated measurements of all physiology metrics (Pd/Pa, iFR, cFFR, and FFR). Contrast medium and dose were per local practice, as was the dose of intracoronary adenosine. Operators were encouraged to perform both intracoronary and intravenous adenosine assessments and a final drift check to assess wire calibration. A central core lab analyzed blinded pressure tracings in a standardized fashion. Results: A total of 763 subjects were enrolled from 12 international centers. Contrast volume was 8 ± 2 ml per measurement, and 8 different contrast media were used. Repeated measurements of each metric showed a bias <0.005, but a lower SD (less variability) for cFFR than resting indexes. Although Pd/Pa and iFR demonstrated equivalent performance against FFR ≤0.8 (78.5% vs. 79.9% accuracy; p = 0.78; area under the receiver-operating characteristic curve: 0.875 vs. 0.881; p = 0.35), cFFR improved both metrics (85.8% accuracy and 0.930 area; p < 0.001 for each) with an optimal binary threshold of 0.83. A hybrid decision-making strategy using cFFR required adenosine less often than when based on either Pd/Pa or iFR. Conclusions: cFFR provides diagnostic performance superior to that of Pd/Pa or iFR for predicting FFR. For clinical scenarios or health care systems in which adenosine is contraindicated or prohibitively expensive, cFFR offers a universal technique to simplify invasive coronary physiological assessments. Yet FFR remains the reference standard for diagnostic certainty as even cFFR reached only ∼85% agreement

Assessment of Cardiovascular Fibrosis Using Novel Fluorescent Probes

Cardiovascular fibrosis resulted from pressure overload or ischemia could alter myocardial stiffness and lead to ventricular dysfunction. Fluorescently labeled collagen-binding protein CNA 35, derived from the surface component of Staphylococcus aureus, and a novel synthetic biphenylalanine containing peptide are applied to stain fibrosis associated collagen and myocytes, respectively. Detailed pathological characteristics of cardiovascular fibrosis could be identified clearly in 2 hours. This staining pair requires only simple staining and brief washing, generating less than 10 ml of waste. The image information collected by this novel fluorescent staining pair is compatible with it collected by the traditional Masson's Trichrome and Picrosirius Red staining which are widely used to stain cardiovascular fibrosis and isolated cells

Natural Products in the Prevention of Metabolic Diseases: Lessons Learned from the 20th KAST Frontier Scientists Workshop

The incidence of metabolic and chronic diseases including cancer, obesity, inflammation-related diseases sharply increased in the 21st century. Major underlying causes for these diseases are inflammation and oxidative stress. Accordingly, natural products and their bioactive components are obvious therapeutic agents for these diseases, given their antioxidant and anti-inflammatory properties. Research in this area has been significantly expanded to include chemical identification of these compounds using advanced analytical techniques, determining their mechanism of action, food fortification and supplement development, and enhancing their bioavailability and bioactivity using nanotechnology. These timely topics were discussed at the 20th Frontier Scientists Workshop sponsored by the Korean Academy of Science and Technology, held at the University of Hawaii at Manoa on 23 November 2019. Scientists from South Korea and the U.S. shared their recent research under the overarching theme of Bioactive Compounds, Nanoparticles, and Disease Prevention. This review summarizes presentations at the workshop to provide current knowledge of the role of natural products in the prevention and treatment of metabolic diseases

Layer-by-Layer Nanoparticles for Systemic Codelivery of an Anticancer Drug and siRNA for Potential Triple-Negative Breast Cancer Treatment

A single nanoparticle platform has been developed through the modular and controlled layer-by-layer process to codeliver siRNA that knocks down a drug-resistance pathway in tumor cells and a chemotherapy drug to challenge a highly aggressive form of triple-negative breast cancer. Layer-by-layer films were formed on nanoparticles by alternately depositing siRNA and poly-l-arginine; a single bilayer on the nanoparticle surface could effectively load up to 3500 siRNA molecules, and the resulting LbL nanoparticles exhibit an extended serum half-life of 28 h. In animal models, one dose via intravenous administration significantly reduced the target gene expression in the tumors by almost 80%. By generating the siRNA-loaded film atop a doxorubicin-loaded liposome, we identified an effective combination therapy with siRNA targeting multidrug resistance protein 1, which significantly enhanced doxorubicin efficacy by 4 fold in vitro and led to up to an 8-fold decrease in tumor volume compared to the control treatments with no observed toxicity. The results indicate that the use of layer-by-layer films to modify a simple liposomal doxorubicin delivery construct with a synergistic siRNA can lead to significant tumor reduction in the cancers that are otherwise nonresponsive to treatment with Doxil or other common chemotherapy drugs. This approach provides a potential strategy to treat aggressive and resistant cancers, and a modular platform for a broad range of controlled multidrug therapies customizable to the cancer type in a singular nanoparticle delivery system.Janssen Pharmaceutical Ltd. (TRANSCEND Grant)National Cancer Institute (U.S.) (Koch Institute Support (Core) Grant P30-CA14051)National Health and Medical Research Council (Australia) (CJ Martin Fellowship)National Science Foundation (U.S.). Graduate Research FellowshipNatural Sciences and Engineering Research Council of Canada (Postdoctoral Fellowship

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14