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This paper studies the dynamic effects of longevity on intergenerational policies and fertility, distinguishing between effects of expected and unexpected longevity gains. Old agents become poorer from unexpected longevity gains than from expected gains, as they cannot prepare (save) for the former in advance. In an overlapping-generations model with means-tested pay-as-you-go social security, we show that young agents reduce their fertility when longevity increases because they need to save more for their old age (“life-cycle effect”), and in the unexpected case, they also need to pay taxes to support the impoverished elderly (“policy effect”). Using cross-country panel data on mortality rates and social expenditure, we find that an unexpected increase in life expectancy at age 65 lowers total fertility rate growth and government family-related spending growth while raising government old-age spending growth.

This study aimed to develop and validate post- and preoperative models for predicting recurrence after curative-intent surgery using an FDG PET-CT metabolic parameter to improve the prognosis of patients with synchronous colorectal cancer liver metastasis (SCLM).In this retrospective multicenter study, consecutive patients with resectable SCLM underwent upfront surgery between 2006 and 2015 (development cohort) and between 2006 and 2017 (validation cohort). In the development cohort, we developed and internally validated the post- and preoperative models using multivariable Cox regression with an FDG metabolic parameter (metastasis-to-primary-tumor uptake ratio [M/P ratio]) and clinicopathological variables as predictors. In the validation cohort, the models were externally validated for discrimination, calibration, and clinical usefulness. Model performance was compared with that of Fong's clinical risk score (FCRS).A total of 374 patients (59.1 ± 10.5 years, 254 men) belonged in the development cohort and 151 (60.3 ± 12.0 years, 94 men) in the validation cohort. The M/P ratio and nine clinicopathological predictors were included in the models. Both postoperative and preoperative models showed significantly higher discrimination than FCRS (p.05) in the external validation (time-dependent AUC = 0.76 [95% CI 0.68-0.84] and 0.76 [0.68-0.84] vs. 0.65 [0.57-0.74], respectively). Calibration plots and decision curve analysis demonstrated that both models were well calibrated and clinically useful. The developed models are presented as a web-based calculator ( https://cpmodel.shinyapps.io/SCLM/ ) and nomograms.FDG metabolic parameter-based prognostic models are well-calibrated recurrence prediction models with good discriminative power. They can be used for accurate risk stratification in patients with SCLM.• In this multicenter study, we developed and validated prediction models for recurrence in patients with resectable synchronous colorectal cancer liver metastasis using a metabolic parameter from FDG PET-CT. • The developed models showed good predictive performance on external validation, significantly exceeding that of a pre-existing model. • The models may be utilized for accurate patient risk stratification, thereby aiding in therapeutic decision-making.

The limited localization and penetration of monoclonal antibodies (mAb) into solid tumors restricts their antitumor efficacy. Here, we describe a solid tumor–targeting antibody with enhanced tumor penetration activity. We designed a 22-residue peptide (A22p), which was extracted from the C-terminal basic region of semaphorin 3A (Sema3A) but modified to have higher affinity with neuropilin receptors (NRP), and genetically fused it to the C-terminus of Fc of human immunoglobulin G1 via a 15-residue (G4S)3 linker, generating Fc-A22p, for the bivalent binding to NRPs. In contrast to Fc or the monovalent A22p peptide alone, Fc-A22p homed to tumor vessels and induced vascular permeability through VE-cadherin downregulation and penetrated tumor tissues by interacting with NRPs in mice bearing human tumor xenografts. We extended the Fc-A22p platform by generating mAb-A22p antibodies of two clinically approved solid tumor–targeting mAbs, the anti-EGF receptor mAb cetuximab (erbitux), and the anti-Her2 mAb trastuzumab (herceptin). The mAb-A22p antibodies retained the intrinsic antigen binding, natural Fc-like biophysical properties, and productivity in mammalian cell cultures, comparable with those of the parent mAbs. In mouse xenograft tumor models, the mAb-A22p antibodies more efficiently homed to tumor vessels and spread into the extravascular tumor parenchyma, which significantly enhanced antitumor efficacy compared with the parent mAbs. Our results suggest that mAb-A22p is a superior format for solid tumor–targeting antibodies due to its enhanced tumor tissue penetration and greater antitumor efficacy compared with conventional mAbs. Mol Cancer Ther; 13(3); 651–61. ©2014 AACR.

PDF - 2613K, Figure S1: Structural analysis of NRP1, NRP2, and Sema3A, and their interactions to facilitate the understanding of the interactions between Sema3A and NRP1/2. Figure S2: SDS-PAGE analyses of the purified proteins showing that NRP1/2-b1b2 domains, Fc-A22, and Fc-A22p are well prepared. Figure S3: FACS and confocal fluorescence data showing that Fc-A22p, but not Fc, specifically interacts with cell-surface expressed NRP1/2 undergoing cellular internalization. Figure S4: Cell viability data showing that Fc-A22p does not cause significant cytotoxicity on HUVECs and tumor cells. Figure S5: Tumor tissue analyzing data showing that Fc-A22p homes to tumor, induces vascular permeability, and downregulates VE-cadherin in endothelial cells and E-cadherin in tumor cells. Figure S6: Confocal fluorescence data showing that Fc-A22p causes downregulation of VE-/E-cadherin and their dissociation from F-actin cytoskeleton resulting in disruption of cell-cell contacts. Figure S7: Data showing the expression and biochemical characterization of mAb-A22p antibodies, cetuximab-A22p and trastuzumab-A22p, compared with the parent mAbs. Figure S8: Cell proliferation and Western blotting data showing that cetuximab-A22p exerts in vitro biological activities comparable to those of cetuximab in A431 and FaDu tumor cells. Figure S9: Human tumor xenografted mouse data showing that mAb-A22p antibodies show increased tumor homing and penetration and thus superior in vivo anti-tumor efficacy, compared with the parent mAbs. Table S1: SPR data showing the kinetic binding parameters for the interactions of Fc-A22, Fc-A22p, VEGF165, and Sema3A with soluble NRP1-b1b2 and NRP2-b1b2 fragments. Table S2: Purification yields of Fc proteins and antibodies obtained from transient expression in HEK293F cells. Table S3: SPR data showing the kinetic binding parameters for the interactions of mAbs and mAb-A22p antibodies with the indicated proteins. Supplementary Materials and Methods: Detailed description of Reagents; Recombinant protein expression and purification; Construction, expression, and purification of antibodies; Binding analysis by ELISA; Surface plasmon resonance (SPR); Flow cytometric analysis; Cell proliferation assay; Western blotting and immunoprecipitation; Co-localization studies by confocal immunofluorescence microscopy; RNA Interference; Ex vivo tumor penetration assays.

Generation of water as a byproduct in chemical reactions is often detrimental because it lowers the yield of the target product. Although several water removal methods, using absorbents, inorganic membranes, and additional dehydration reactions, have been proposed, there is an increasing demand for a stable and simple system that can selectively remove water over a wide range of reaction temperatures. Herein we report a thermally rearranged polybenzoxazole hollow fiber membrane with good water permselectivity and stability at reaction temperatures of up to 400 °C. Common reaction engineering challenges, such as those due to equilibrium limits, catalyst deactivation, and water-based side reactions, have been addressed using this membrane in a reactor.

BACKGROUND: Autofluorescence imaging technology has been utilized for preserving or identifying parathyroid glands (PTGs) during thyroid surgery. We developed a wireless PTGs detection device linked with smart glasses that allows for real-time video recording and screen switching according to the light source. OBJECTIVE: This study aimed to confirm the feasibility of the device and whether it would help preserve the PTG during the surgery. METHODS: This prospective study was conducted in 30 patients with 66 PTGs. The device’s agreement with the physician’s judgment was evaluated, and we determined how many PTGs were preserved from thyroidectomy. RESULTS: The positive agreement rate for PTGs detection between the surgeon and device was 70.9%. Inadvertent parathyroidectomy was identified in surgical specimens of 6 patients (20%). No PTG was removed when it was confirmed by the device (0/39). Of the 27 glands not detected by the device, there was inadvertent removal of 6 PTGs. CONCLUSIONS: PTGs can be preserved successfully when the detection of them by the device is consistent with the surgeon’s discretion. A large-scale controlled study is necessary to demonstrate the practical effect of this device on hypoparathyroidism after thyroidectomy.

In this study, we investigated the effect of the sulfur content in the NiCl

We investigated localization and safe resection margins for breast cancer patients undergoing breast conserving surgery (BCS) using ultrasound-guided indocyanine green fluorescence (ICG-F) marking. From April 2016 to March 2019, we prospectively enrolled 114 patients who underwent BCS using US-guided ICG-F marking and we compared these results with 300 patients who underwent BCS using US-guided skin marking from January 2012 to December 2016. Clinical features, identification rates, status of resection margins, and re-operation rates were analyzed. The ICG-F identification rate was 100% (114/114). The mean approach time for resection of the lesion ICG-F using group was about 13 min. The positive rate of frozen resection margins was 10.5% using ICG-F and 25.0% using sono-guided skin marking (p p p p = 0.79). At follow-up after the operation using ICG-F, no complications occurred. Using ICG-F during BCS could be a safe, sophisticated method for localization.

We report a stable and efficient Fe–Co catalyst derived from N-coordinated Co single-atom carbon (FeK/Co–NC) for CO2 conversion to long-chain hydrocarbons with a C5+ selectivity of up to 42.4% at a...

Controlling the surface composition of alloy catalysts can provide an opportunity to improve their activity and selectivity for a target reaction. However, since the various effects of alloys are often mixed, it is difficult to maximize the expected benefits of the alloy formation. In the present study, we prepared an IrRu bimetallic catalyst by using a wetness impregnation method and studied the reaction performance of NO reduction by CO in the presence and absence of O2. A series of spectroscopic analyses confirmed the formation of the IrRu bimetallic alloy. Further, we investigate the active-site ensemble effect in the IrRu bimetallic catalyst that selectively reduces NO to N2 using CO as a reducing agent in the presence of excess oxygen. Mechanistic insight gained through the density functional theory calculations revealed that the IrRu alloy catalyst has moderate binding energies for all the adsorbates and intermediates, resulting in the acceleration of critical surface reactions, such as CO oxidation and N–NO disproportionation. In addition, a series of theoretical surface models suggest that the selectivity promotion on the IrRu alloy surface originates from the ensemble effect, while ligand and strain effects are detrimental to the catalyst performance. This study lays the foundation for the design of bimetallic catalysts that can provide maximal promotion of NO reduction with minimal use of precious metals.

We report the synthesis of sulfur-doped molybdenum carbide (Mo2C) catalysts with high activity towards electrochemical hydrogen evolution and excellent stability under acidic conditions. We show that sulfur – a well-known catalyst poison – acts as a promoter by weakening the hydrogen binding energy of Mo2C, which is generally considered to bind hydrogen too strongly. Physical and chemical characterizations of sulfur-doped Mo2C catalysts suggest that sulfur is successfully doped into the Mo2C crystal lattice. We found the optimal sulfur loading to be around 7 wt%, which required −92 mV overpotential for −10 mA cm−2 with Tafel slope value of 51 mV dec−1. In our analysis, the sulfur atoms in the lattice occupied the original hydrogen adsorption sites in the Mo2C lattice. This weakens the hydrogen binding energy, to which we attribute the higher activity towards hydrogen evolution.

Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C2 compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH4 conversion, C2 yield, and selectivities for C2 and coke) with an error of < 5%. Trained ML models are applied to maximize the C2 yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C2 yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.

The purpose of this study covered the diagnostic accuracy and usefulness of positron emission tomography/computed tomography (PET/CT) imaging in muscle invasive bladder cancer patients through previously published literature. Through 30 September, 2019, the PubMed database was searched for eligible articles that evaluated PET/CT imaging in bladder cancer patients. In general, FDG PET/CT, the most commonly used PET/CT imaging, does not show good performance for the detection of primary lesions; however, according to the literature it could accurately assess pelvic lymph node (LN) status better than other imaging technologies and it was especially helpful in determining extra-pelvic recurrences. More recently, non-FDG PET/CT imaging, such as C-11 acetate and C-11 choline, has been introduced. Although further research is required, preliminary results show the potential of these techniques to overcome the drawbacks of FDG. This concise study will overview the role of PET/CT when treating muscle-invasive bladder cancer (MIBC).

Molecular dynamics (MD) simulations are useful in understanding the interaction between solid materials and molecules. However, performing MD simulations is possible only when interatomic potentials are available and constructing such interatomic potentials usually requires additional computational work. Recently, generating interatomic potentials was shown to be much easier when machine learning (ML) algorithms were used. In addition, ML algorithms require new descriptors for improved performance. Here, we present an ML approach with several categories of atomic descriptors to predict the parameters necessary for MD simulations, such as the potential energies and the atomic forces. We propose several atomic descriptors based on structural information and find that better descriptors can be generated from eXtreme gradient boosting (XGBoost). Moreover, we observe fewer descriptors that perform better in predicting the potential energies and the forces during methane activation processes on a catalytic Pt(111) surface. These results were consistently observed in two different ML algorithms: fully-connected neural network (FNN) and XGBoost. Taking into account the advantages of FNN and XGBoost, we propose an efficient ML model for estimating potential energies. Our findings will be helpful in developing new ML potentials for long-time MD simulations.

Monodisperse nanoparticles (NPs) of CoFe2O4 were synthesized as efficient catalyst precursors for CO2 hydrogenation to produce high value-added C2–C4 olefin products, which are important building b...

Identifying highly efficient catalysts for direct CO2 hydrogenation to liquid hydrocarbons is crucial for CO2 utilization via chemical conversion using renewable resources, the so-called Power-to-Liquids. In this study, well-defined mesoporous carbon (MPC) of 6.9 nm pore size was synthesized as a support material for Fe oxycarbide nanoparticles, which serve as active sites for the CO2 hydrogenation reaction, combining reverse water-gas shift and Fischer-Tropsch reactions. The unique physicochemical properties of MPC favored the formation of an active carbidic Fe phase and the rapid diffusion of produced hydrocarbons which resulted in an enhanced CO2 conversion and long-chain hydrocarbon selectivity. As a result, the MPC supported Fe catalyst showed C5+ hydrocarbon selectivity of 44.5% with a CO2 conversion rate of 50.6% (300 °C, 2.5 MPa, H2/CO2 = 3).

Active phase and reaction mechanism identification is imperative when designing catalysts for target reactions. Although computational analysis often suggests effective methods, the complexity and computational cost can be problematic, especially for Fe-based catalysts with various oxidation numbers. Herein, we elucidated the Fe phase involved in CO2 hydrogenation through a density-functional-theory-based energetics study and suggested promoters by performing microkinetic analysis. Our theoretical model suggested that (1) Fe3O4 is a major active phase for the reverse water–gas shift (RWGS) reaction; (2) direct dissociation of CO2 is preferred over hydrogenation; and (3) surface oxygen removal determines the overall reaction rate over most Fe surfaces. Using CO2 and OH binding energies as activity descriptors to screen promoters, Cu and Zn were found to accelerate the RWGS rate by facilitating OH removal. The proposed microkinetic modelling approach will aid in predicting complex heterogeneous catalysis processes involving diverse intermediates and surface phase transitions.

Understanding the origin of the high activity of Fe-Cu-K catalysts during CO2 hydrogenation for liquid hydrocarbon production is imperative. However, designing suitable catalysts for efficient power-to-liquid processes remains challenging. In this work, we determined how Cu and K modified the Fe structure and revealed the origin of their synergistic effect in promoting its catalytic performance during CO2 hydrogenation to long-chain hydrocarbons. Detailed analyses confirmed that K addition facilitated incorporation of Cu into the Fe bulk lattice and helped maintain either a metallic or carbide active phase during the reaction. Electronic structure calculations suggested that formation of Fe-Cu alloy facilitated CO2 hydrogenation, regardless of the reaction pathway. A series of scaling relations between C binding energies and other adsorbates suggested that the presence of K facilitated CO formation and C–C coupling reactions over the Cu and Fe driven catalysis, respectively. Hence, the coexistence of Cu and K in the Fe catalyst is essential for producing liquid hydrocarbons from CO2.

PURPOSE: This study aimed to compare lung perfusion scan with single photon emission computed tomography/computed tomography (SPECT/CT) for the evaluation of lung function and to elucidate the most appropriate modality for the prediction of postoperative lung function in patients with lung cancer. METHODS: A total of 181 patients underwent Tc-99m macroaggregated albumin lung perfusion scan and SPECT/CT to examine the ratio of diseased lung and diseased lobe. Forty-one patients with lung cancer underwent both preoperative and postoperative pulmonary function tests within 1 month to predict postoperative pulmonary function. Predicted postoperative forced expiratory volume in 1 s (ppoFEV(1)) was calculated by the % radioactivity of lung perfusion scan and SPECT, and the % volume of the residual lung, assessed on CT. RESULTS: The ratios of diseased lung as seen on lung perfusion scan and SPECT showed significant correlation, but neither modality correlated with CT. The ratios of the diseased lung and diseased lobe based on CT were higher than the ratios based on either perfusion scan or SPECT, because CT overestimated the function of the diseased area. The lobar ratio of both upper lobes was lower based on the perfusion scan than on SPECT but was higher for both lower lobes. Actual postoperative FEV(1) showed significant correlation with ppoFEV(1) based on lung perfusion SPECT and perfusion scan. CONCLUSIONS: We suggest SPECT/CT as the primary modality of choice for the assessment of the ratio of diseased lung area. Both perfusion scan and SPECT/CT can be used for the prediction of postoperative lung function.

For a stable methane to olefins, aromatics, and hydrogen (MTOAH) reaction, 0.27–0.43 wt % Fe-containing silica catalysts were synthesized through various preparation methods and tested. The presenc...