Efficient Transfection of Blood−Brain Barrier Endothelial Cells by Lipoplexes and Polyplexes in the Presence of Nuclear Targeting NLS-PEG-Acridine Conjugates

Brain capillary endothelial cells of the blood−brain barrier (BBB) are difficult targets for nonviral transfection even for the most potent transfection agents. Efficient protection and nuclear delivery of plasmid DNA are the key requirements for enhancing the transfection. We designed novel DNA intercalating conjugates of PEG−tris(acridine) with a short nuclear localization signal (NLS) peptide and investigated the effect of their complexes with luciferase-encoded plasmid DNA on lipoplex- and polyplex-mediated transfection of murine brain capillary endothelial bEnd.3 cells. These intercalation complexes protected DNA from nucleolytic degradation forming a protective PEG layer around plasmid DNA and could be efficiently condensed by Lipofectamine2000 or Exgen500 into nanosized particles. Complexation of plasmid DNA with a PEG-acridine/NLS-PEG-acridine mixture (9:1 w/w), taken in an amount equal to 5−6 NLS peptides per DNA molecule, significantly enhanced both lipo- and polyplex transfection efficacies and increased the number of transfected bEnd.3 endothelial cells in the presence of serum. Comparative transgene expression efficiency was significantly higher at longer PEG linker and optimal conjugate-to-DNA weight ratio, especially, at lower N/P ratio for both transfection agents, reaching 15−16-fold for lipoplexes and 10−11-fold for polyplexes. In addition, the NLS-PEG-acridine conjugates did not increase cytotoxicity of lipoplexes and polyplexes to bEnd.3 cells. These conjugates can serve as promising components for development of systemic nonviral transfecting approach to the transfection of the BBB and temporary modulation of its drug permeability

Image_1_Application of the Machine-Learning Model to Improve Prediction of Non-Sentinel Lymph Node Metastasis Status Among Breast Cancer Patients.tif

BackgroundPerforming axillary lymph node dissection (ALND) is the current standard option after a positive sentinel lymph node (SLN). However, whether 1–2 metastatic SLNs require ALND is debatable. The probability of metastasis in non-sentinel lymph nodes (NSLNs) can be calculated using nomograms. In this study, we developed an individualized model using machine-learning (ML) methods to select potential variables, which influence NSLN metastasis.Materials and MethodsCohorts of patients with early breast cancer who underwent SLN biopsy and ALND between 2012 and 2021 were created (training cohort, N 157 and validation cohort, N 58) for the development of the nomogram. Three ML methods were trained in the training set to create a strong predictive model. Finally, the multiple iterations of the least absolute shrinkage and selection operator regression method were used to determine the variables associated with NSLN status.ResultsFour independent variables (positive SLN number, absence of lymph node hilum, lymphovascular invasion (LVI), and total number of SLNs harvested) were combined to generate the nomogram. The area under the receiver operating characteristic curve (AUC) value of 0.759 was obtained in the entire set. The AUC values for the training set and the test set were 0.782 and 0.705, respectively. The Hosmer-Lemeshow test of the model fit accuracy was identified with p = 0.759.ConclusionThis study developed a nomogram that incorporates ultrasound (US)-related variables using the ML method and serves to clinically predict the non-metastatic status of NSLN and help in the selection of the appropriate treatment option.</p

sj-pdf-1-teu-10.1177_13548166211058497 – Supplemental Material for Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains

Supplemental Material, sj-pdf-1-teu-10.1177_13548166211058497 for Spillover effects from news to travel and leisure stocks during the COVID-19 pandemic: Evidence from the time and frequency domains by Ying Wang, Hongwei Zhang, Wang Gao and Cai Yang in Tourism Economics</p

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Graphene Oxide and Its Derivatives as Adsorbents for PFOA Molecules

Effective, low-cost adsorbents are needed to remove perfluoroalkyl and polyfluoroalkyl substances (PFAS) from water sources. Carbon-based materials are promising PFAS adsorbents. Here, we explore the potential of graphite oxide (GO) and its derivatives as PFAS adsorbents by studying the adsorption of perfluorooctanoic acid (PFOA), a model PFAS molecule, on GO surfaces with O/C ratios up to 16.7% using molecular dynamics simulations. An adsorption free energy of approximately −30 kJ/mol (or −310 meV) is obtained for pristine graphene in pure water, and adsorbed PFOA molecules diffuse rapidly. As the O/C ratio increases, hydrophobic interactions’ contribution to PFOA adsorption diminishes, but that by electrostatic interactions becomes important. Overall, adsorption is weakened, but favorable adsorption still occurs at an O/C ratio of 16.7%. The in-plane diffusion coefficient of adsorbed PFOA molecules decreases by more than 45 times as the O/C ratio increases to 8.3% but increases significantly when the O/C ratio increases further to 16.7%. Adding salt improves the adsorption owing to the salting-out and screening effects but slows the diffusion of adsorbed PFOA molecules, and these effects are more pronounced at low O/C ratios. These results show that GOs are effective PFOA adsorbents. Such effectiveness, along with GO’s potentially low cost and the possibility of regenerating spent GO by removing adsorbed PFOA molecules through a mild electrical potential, makes GO a promising adsorbent for PFOA and similar molecules. The insights from the present study can help the rational design of GOs to realize their full potential

Data_Sheet_1_Drivers of university–business cooperation of university faculty from the social cognitive theory perspective.CSV

As an independent research field, there is growing attention to university–business cooperation (UBC). However, few studies focus on the driving factors of UBC, which remains an open problem in this area. This study analyzes a broad mix of drivers underlying seven UBC activities, namely, curriculum development and design (CDD), student mobility (SD), lifelong learning (LLL), professional mobility (PM), research and development (R&D), commercialization (COM), and entrepreneurship (ENT), and discusses the internal mechanism and external environment of higher education institutions (HEIs) as the moderator variable affecting UBC activities and individual motivations. Specifically, based on the social cognition theory, the independent variables include motivations (money, career, research, education, and social), the internal mechanism (support mechanism, strategic mechanism, and management mechanism), and the external environment (policy environment, economic environment, and cultural environment) are designed. The aforementioned seven UBC activities are taken as dependent variables. This work takes university faculty as the research object. Through empirical analysis, it demonstrates that the combination of driving factors of different UBC activities has its particularity. Furthermore, the results showed that the internal mechanism and external environment of HEIs could positively moderate the relationship between individual motivations and UBC activities. In terms of theoretical contribution, this study reveals the combination of factors that drive university faculty to engage in UBC. On the other hand, it can provide a reference for policymakers and managers to better development of UBC.</p

Enhanced Recovery of Oil Mixtures from Calcite Nanopores Facilitated by CO₂ Injection

Slow production, preferential recovery of light hydrocarbons, and low recovery factors are common challenges in oil production from unconventional reservoirs dominated by nanopores. Gas injection-based techniques such as CO2 Huff-n-Puff have shown promise in addressing these challenges. However, a limited understanding of the recovery of oil mixtures on the nanopore scale hinders their effective optimization. Here, we use molecular dynamics simulations to study the recovery of an oil mixture (C10 + C19) from a single 4 nm-wide calcite dead-end pore, both with and without CO2 injection. Without CO2 injection, oil recovery is much faster than expected from oil vaporization and features an undesired selectivity, i.e., the preferential recovery of lighter C10. With CO2 injection, oil recovery is accelerated and its selectivity toward C10 is greatly mitigated. These recovery behaviors are understood by analyzing the spatiotemporal evolution of C10, C19, and CO2 distributions in the calcite pore. In particular, we show that interfacial phenomena (e.g., the strong adsorption of oil and CO2 on pore walls, their competition, and their modulation of transport behavior) and bulk phenomena (e.g., solubilization of oil by CO2 in the middle portion of the pore) play crucial roles in determining the oil recovery rate and selectivity

Copper-Catalyzed Amide Radical-Directed Cyanation of Unactivated C_sp³–H Bonds

A method for site-selective intermolecular δ/ε-Csp3–H cyanation of aliphatic sulfonamides is developed using TsCN as the cyanating reagent, catalyzed by a Cu­(I)/phenanthroline complex. The mild, expeditious, and modular protocol allows efficient remote Csp3–H cyanation with good functional group tolerance and high regioselectivity. Mechanistic studies indicate that the reaction might proceed through a Cu­(I)-mediated N–F bond cleavage to generate an amidyl radical, 1,5-HAT, and cyano group transfer of the resulting carbon radical with TsCN

Data_Sheet_1_Application of the Machine-Learning Model to Improve Prediction of Non-Sentinel Lymph Node Metastasis Status Among Breast Cancer Patients.docx

BackgroundPerforming axillary lymph node dissection (ALND) is the current standard option after a positive sentinel lymph node (SLN). However, whether 1–2 metastatic SLNs require ALND is debatable. The probability of metastasis in non-sentinel lymph nodes (NSLNs) can be calculated using nomograms. In this study, we developed an individualized model using machine-learning (ML) methods to select potential variables, which influence NSLN metastasis.Materials and MethodsCohorts of patients with early breast cancer who underwent SLN biopsy and ALND between 2012 and 2021 were created (training cohort, N 157 and validation cohort, N 58) for the development of the nomogram. Three ML methods were trained in the training set to create a strong predictive model. Finally, the multiple iterations of the least absolute shrinkage and selection operator regression method were used to determine the variables associated with NSLN status.ResultsFour independent variables (positive SLN number, absence of lymph node hilum, lymphovascular invasion (LVI), and total number of SLNs harvested) were combined to generate the nomogram. The area under the receiver operating characteristic curve (AUC) value of 0.759 was obtained in the entire set. The AUC values for the training set and the test set were 0.782 and 0.705, respectively. The Hosmer-Lemeshow test of the model fit accuracy was identified with p = 0.759.ConclusionThis study developed a nomogram that incorporates ultrasound (US)-related variables using the ML method and serves to clinically predict the non-metastatic status of NSLN and help in the selection of the appropriate treatment option.</p