bluuebunny/crossref_metadata_2025_split huggingface dataset:
Ion−π interactions for constructing organic luminescent materials
Authors: Jiang Guoyu , Yu Jia , Wang Jianguo , Tang Ben Zhong 
Published: October 2022

AbstractOrganic luminescent materials are one of the research hotspots worldwide. The luminescent efficiency of these materials can be tuned by introducing noncovalent interactions. Ion−π interactions, mainly including anion−π interactions and cation−π interactions, are relatively new but powerful noncovalent interactions with extensive applications in supramolecular chemistry. The use of ion−π interactions to fabricate highly luminescent materials is initiated since they were introduced to develop aggregation‐induced emission luminogens in 2017. Then, this research field is undergoing through challenging but very prospective developments. In this minireview, we start with a brief introduction of ion−π interactions and then make a summary of the reports on organic fluorescence based on ion−π interactions and room temperature phosphorescence induced by ion−π interactions. The applications of organic luminescent materials based on ion−π interactions in bioimaging, imaging‐guided anticancer and antibacterial treatment are also discussed. The challenges and prospects of using anion−π and cation−π interactions for constructing organic luminogens are presented in the final part. It is expected that this minireview will provide some guidelines for fabricating novel organic luminescent materials and further extend the application potential of ion−π interactions.
Advancing MXene Electrocatalysts for Energy Conversion Reactions: Surface, Stoichiometry, and Stability
Authors: Tsounis Constantine , Kumar Priyank V. , Masood Hassan , Kulkarni Rutvij Pankaj , Gautam Gopalakrishnan Sai , Müller Christoph R. , Amal Rose , Kuznetsov Denis A. 
Published: October 2022

AbstractMXenes, due to their tailorable chemistry and favourable physical properties, have great promise in electrocatalytic energy conversion reactions. To exploit fully their enormous potential, further advances specific to electrocatalysis revolving around their performance, stability, compositional discovery and synthesis are required. The most recent advances in these aspects are discussed in detail: surface functional and stoichiometric modifications which can improve performance, Pourbaix stability related to their electrocatalytic operating conditions, density functional theory and advances in machine learning for their discovery, and prospects in large scale synthesis and solution processing techniques to produce membrane electrode assemblies and integrated electrodes. This Review provides a perspective that is complemented by new density functional theory calculations which show how these recent advances in MXene material design are paving the way for effective electrocatalysts required for the transition to integrated renewable energy systems.
Relevance insensitivity: A framework of psychological biases in consumer behavior and beyond
Authors: Yang Yang , Li Xilin , Hsee Christopher K. 
Published: October 2022

AbstractIn judgment and choice, consumers show a variety of biases, from the sunk cost fallacy and projection bias to usage frequency neglect and erroneous price–quality inferences. This article explains these seemingly disparate biases and predicts new biases using an overarching framework based on the relevance insensitivity theory proposed by Hsee et al. (2019). According to the theory, many biases arise because people are insufficiently sensitive to the relevance (i.e., weight) of a cue variable to the target variable (the dependent variable). The direction of the bias depends on the normative relevance of the cue—people over‐rely on the cue when it is normatively irrelevant and under‐rely on the cue when it is normatively highly relevant. We show that ostensibly unique and universal biases are neither unique nor universal: All are manifestations of relevance insensitivity, and each bias attenuates or reverses as the cue variable's relevance changes.
American College of Rheumatology/EULAR Remission Criteria for Rheumatoid Arthritis: 2022 Revision
Authors: Studenic Paul , Aletaha Daniel , de Wit Maarten , Stamm Tanja A. , Alasti Farideh , Lacaille Diane , Smolen Josef S. , Felson David T. 
Published: October 2022

ObjectiveIn 2011, the American College of Rheumatology (ACR) and EULAR endorsed provisional criteria for remission in rheumatoid arthritis (RA), both Boolean‐ and index‐based. Based on recent studies indicating that a higher threshold for the patient global assessment (PtGA) may improve agreement between the 2 sets of criteria, our goals were to externally validate a revision of the Boolean remission criteria using a higher PtGA threshold and to validate the provisionally endorsed index‐based criteria.MethodsWe used data from 4 randomized trials comparing biologic disease‐modifying antirheumatic drugs to methotrexate or placebo. We tested the higher proposed PtGA threshold of 2 cm (Boolean2.0) (range 0–10 cm) compared to the original threshold of 1 cm (Boolean1.0). We analyzed agreement between the Boolean‐ and index‐based criteria (Simplified Disease Activity Index [SDAI] and Clinical Disease Activity Index [CDAI]) for remission and examined how well each remission definition predicted later good physical function (Health Assessment Questionnaire [HAQ] score ≤0.5) and radiographic nonprogression.ResultsData from 2,048 trial participants, 1,101 with early RA and 947 with established RA, were included. The proportion of patients with disease in remission at 6 months after treatment initiation increased when using Boolean2.0 compared to Boolean1.0, from 14.8% to 20.6% in early RA and 4.2% to 6.0% in established RA. Agreement between Boolean2.0 and the SDAI or CDAI remission criteria was better than for Boolean1.0, particularly in early disease. Boolean2.0, SDAI, and CDAI remission criteria had similar positive likelihood ratios (LRs) to predict radiographic nonprogression and a HAQ score of ≤0.5 (positive LR 3.8–4.3). The omission of PtGA (BooleanX) worsened the prediction of good functional outcomes.ConclusionUsing the Boolean 2.0 criteria classifies more patients as achieving remission and increases the agreement with index‐based remission criteria without jeopardizing predictive value for radiographic or functional outcomes. This revised Boolean definition and the previously provisionally endorsed index‐based criteria were endorsed by ACR and EULAR.
Nitrogen/Phosphorus Dual‐Doped Hard Carbon Anode with High Initial Coulombic Efficiency for Superior Sodium Storage
Authors: Wu Sheng , Lu Xiaoyi , Zhang Kaili , Xu Junling , Sun Zhipeng 
Published: October 2022

AbstractAmong anode materials for sodium‐ion batteries (SIBs), hard carbon (HC) gains more attention due to its low cost, high electronic conductivity, and renewable resources. However, sluggish kinetics result in its low‐rate capability and unfavorable cycle stability. Moreover, HC often presents low initial Coulombic efficiency (ICE), which also limits the utilization of the battery capacity and energy density. Herein, nitrogen and phosphorus dual‐doped HC (denoted as NPHC) with network structure is synthesized by a facile interfacial polymerization method, which endows the NPHC with synergistic effects of the enlarged interlayer spacing and improved conductivity. Consequently, the obtained NPHC exhibits a high reversible capacity (286 mAh g−1 at 0.1 A g−1), excellent rate capability (144 mAh g−1 at 10 A g−1), high ICE (71 %), and remarkable cyclability over 2000 cycles at 1 A g−1. Moreover, the storage mechanism of sodium ions is examined by a series of ex‐situ characterizations. Additionally, when coupled with Na3V2(PO4)2F3 as a cathode, the full cell delivers superior cyclability (capacity retains 90 % over 300 cycles at 1 A g−1). This work may shed new insight into designing other carbon‐based electrode materials for high‐performance energy storage.
Research on nonstroke dementia screening and cognitive function prediction model for older people based on brain atrophy characteristics
Authors: Zhang Wei , Zheng Xiaoran , Li Renren , Liu Meng , Xiao Weixin , Huang Lihe , Xu Feiyang , Dong Ningxin , Li Yunxia 
Published: October 2022

AbstractBackgroundBrain atrophy is an important feature in dementia and is meaningful to explore a brain atrophy model to predict dementia. Using machine learning algorithm to establish a dementia model and cognitive function model based on brain atrophy characteristics is unstoppable.MethodWe acquired 157 dementia and 156 normal old people.s clinical information and MRI data, which contains 44 brain atrophy features, including visual scale assessment of brain atrophy and multiple linear measurement indexes and brain atrophy index. Five machine learning models were used to establish prediction models for dementia, general cognition, and subcognitive domains.ResultsThe extreme Gradient Boosting (XGBoost) model had the best effect in predicting dementia, with a sensitivity of 0.645, a specificity of 0.839, and the area under curve (AUC) of 0.784. In this model, the important brain atrophy features for predicting dementia were temporal horn ratio, cella media index, suprasellar cistern ratio, and the thickness of the corpus callosum genu.ConclusionFor nonstroke elderly people, the machine learning model based on clinical head MRI brain atrophy features had good predictive value for dementia, general cognitive impairment, immediate memory impairment, word fluency disorder, executive dysfunction, and visualspatial disorder.
D‐Peptide and D‐Protein Technology: Recent Advances, Challenges, and Opportunities**
Authors: Lander Alexander J. , Jin Yi , Luk Louis Y. P. 
Published: October 2022

AbstractTotal chemical protein synthesis provides access to entire D‐protein enantiomers enabling unique applications in molecular biology, structural biology, and bioactive compound discovery. Key enzymes involved in the central dogma of molecular biology have been prepared in their D‐enantiomeric forms facilitating the development of mirror‐image life. Crystallization of a racemic mixture of L‐ and D‐protein enantiomers provides access to high‐resolution X‐ray structures of polypeptides. Additionally, D‐enantiomers of protein drug targets can be used in mirror‐image phage display allowing discovery of non‐proteolytic D‐peptide ligands as lead candidates. This review discusses the unique applications of D‐proteins including the synthetic challenges and opportunities.
Nanostructuring Pt‐Pd Bimetallic Electrocatalysts for CO2 Reduction Using Atmospheric Pressure Atomic Layer Deposition
Authors: Li Ming , Fu Shilong , Saedy Saeed , Rajendrakumar Aparna , Tichelaar Frans D. , Kortlever Ruud , van Ommen J. Ruud 
Published: October 2022

AbstractPreparing supported nanoparticles with a well‐defined structure, uniform particle size, and composition using conventional catalyst synthesis methods, such as impregnation, precipitation, and deposition‐precipitation is challenging. Furthermore, these liquid phase methods require significant solvent consumption, which has sustainable issues and requires complex purification processes, usually leaving impurities on the catalyst, affecting its selectivity and activity. In this work, we employed atomic layer deposition (ALD, a vapor phase synthesis method) to synthesize electrocatalysts with well‐controlled core‐shell and alloy structures for CO2 reduction to formic acid. With this approach, the structural control of the catalysts is down to the atomic scale, and the effect of core‐shell and alloy structure on Pt−Pd bimetallic catalysts has been investigated. It is shown that the Pt−Pd alloy catalyst displays a 46 % faradaic efficiency toward formic acid, outperforming Pt@Pd and Pd@Pt core‐shell structures that show faradaic efficiencies of 22 % and 11 %, respectively. Moreover, both core‐shell bimetallic catalysts (Pd@Pt and Pt@Pd) are not stable under electroreduction conditions. These catalysts restructure to more thermodynamically stable structures, such as segregated clusters or alloy particles, during the electrochemical reduction reaction, altering the catalytic selectivity.
A synthetic review of some recent extensions of ComDim
Authors: Jouan‐Rimbaud Bouveresse Delphine , Rutledge Douglas N. 
Published: October 2022

AbstractThe simultaneous analysis of several data matrices related to the same set of samples can be done with the use of multiblock methods. Common components and specific weights analysis (CCSWA), also called ComDim, is one method enabling the analysis of such data. CCSWA, which is a multiblock version of principal components analysis (PCA), is particularly interesting as it can easily be modified and transformed, either by replacing its PCA‐based data decomposition by another multivariate method, such as independent components analysis or partial least‐squares regression, for example, or by segmenting the data matrices before multiblock analysis, or both. These modifications aim at improving the models and their interpretation. This article will give a concise description of ComDim and then a presentation of several recent extensions of the method, showing its usefulness as both a supervised and a non‐supervised method.
A phase 2 study of first‐line nivolumab in patients with locally advanced or metastatic cutaneous squamous‐cell carcinoma
Authors: Munhoz Rodrigo R. , Nader‐Marta Guilherme , de Camargo Veridiana P. , Queiroz Marcello M. , Cury‐Martins Jade , Ricci Hermínia , de Mattos Marcela R. , de Menezes Thiago A. F. , Machado Guilherme U. C. , Bertolli Eduardo , Barros Milton , de Souza Carina E. , Franke Fábio , Ferreira Fabio O. , Feher Olavo , de Castro Gilberto 
Published: October 2022

BackgroundCutaneous squamous‐cell carcinoma (CSCC) is among the most frequent malignancies worldwide. For those not amenable to treatment with curative intent, immune checkpoint inhibition (ICI) with anti‐programmed death receptor 1 (PD‐1) antibodies has emerged as a novel therapeutic option. In this study, the authors sought to investigate the activity of the anti‐PD‐1 agent nivolumab in patients with advanced CSCC (aCSCC).MethodsCA209‐9JC was an open‐label, single‐arm, phase 2 study to evaluate the safety and/or efficacy of nivolumab in systemic treatment‐naive patients with aCSCC. Nivolumab (3 mg/kg) was administered every 2 weeks until disease progression, unacceptable toxicity, or 12 months of treatment. The primary end point was the best objective response rate (BORR) as per RECIST 1.1 criteria. Secondary end points included safety, progression‐free survival (PFS), and overall survival (OS).ResultsTwenty‐four patients with aCSCC were enrolled with a median age of 74 years (range, 48–93). Among the 24 patients evaluable for response, the BORR was 58.3% (14/24); there were no complete responses. With a median follow‐up of 17.6 months, median duration of response has not been reached, and the estimated median PFS and OS were 12.7 and 20.7 months, respectively. Prior exposure to radiotherapy was associated with worse outcomes (p = .035, univariate analysis). Treatment‐related adverse events of any grade and grade ≥ 3 occurred in 21 (87.5%) and six (25%) patients, respectively, and one patient discontinued nivolumab due to toxicities.ConclusionsNivolumab resulted in robust antitumor activity, sustained responses, and good tolerability in systemic treatment‐naive patients with aCSCC. These data provide further evidence to support the use of ICI as the standard treatment of aCSCC.
Analysis of path loss and channel capacity in quad element MIMO antenna for terahertz communication systems
Authors: Singh Ajit Kumar , Mahto Santosh Kumar , Kumar Praveen , Sinha Rashmi 
Published: October 2022

AbstractA quad element multiple‐input‐multiple‐output (MIMO) antenna with improved impedance bandwidth of 0.126 THz (0.198–0.324 THz) by using defected ground structure (DGS) is proposed for terahertz applications. The four elements are placed orthogonally to one another to reduce mutual coupling and envelope correlation coefficient (ECC). The maximum isolation of the proposed MIMO antenna is 34 dB at 0.290 THz frequency. The diversity performance characteristics of the proposed antenna are studied in terms of ECC, diversity gain (DG), mean effective gain (MEG), total active reflection coefficient (TARC), and isolation between the ports, and the obtained values are 0.05, 9.9, 3, −5, −10, respectively. Each antenna has a peak gain of 4.4 dBi with an average radiation efficiency of 92.5%. The proposed MIMO antenna is designed on a Rogers RO4003 substrate having a dielectric constant of 3.55, and a loss tangent of 0.0027 with a dimension of m3. Further analyses of path loss in free space, atmospheric absorption, and roughness factor in indoor surfaces for THz communication are also studied. The designed MIMO antenna is suitable for high‐speed short distance communication, video‐rate imaging, biomedical imaging, sensing, and security scanning in the THz frequency band.
Statistical property parameterization of simple rocking block response
Authors: Lachanas Christos G. , Vamvatsikos Dimitrios , Dimitrakopoulos Elias G. 
Published: October 2022

AbstractThe parametric representation of rocking fragilities is statistically investigated. Initially, the potential normalization of the rocking parameters to reduce the problem's dimensionality is tackled by undertaking comparisons both on a single‐record and a sample‐of‐records basis. It is found that the slenderness angle can be normalized out when probabilistically considering the rocking response of simple rocking blocks with the same semi‐diagonal length. Then, the robustness of the lognormal distribution for characterizing the rocking motion is investigated. Sets of pulse‐like and ordinary ground motions are employed to test the lognormal fit for the full range of rocking response when the peak ground acceleration or the peak ground velocity are employed as intensity measures. In both cases, the lognormal distribution offers an adequate, but often imperfect, baseline model of the rocking fragility curves. Instead, a shifted lognormal that accounts for the absence of response below the rocking initiation intensity is an enhanced solution that can form the basis for offering simplified response model surrogates.
Circulating tumor‐associated autoantibodies as novel diagnostic biomarkers in pancreatic adenocarcinoma
Authors: Zhuang Liping , Huang Changjing , Ning Zhouyu , Yang Lina , Zou Wenbin , Wang Peng , Cheng Chien‐Shan , Meng Zhiqiang 
Published: October 2022

AbstractTo develop a superior diagnostic approach for pancreatic adenocarcinoma (PAAC), the present study prospectively included 338 PAAC patients, 294 normal healthy volunteers (NHV), 122 chronic pancreatitis (CP) patients and 100 patients with non‐PAAC malignancies. In the identification phase, HuProt Human Proteome Microarray, comprising 21 065 proteins, was used to identify serum tumor‐associated autoantibodies (TAAbs) candidates differentiating PAAC (n = 30) from NHV (n = 30). A PAAC‐focused array containing 165 differentially expressed TAAbs identified was subsequently adopted in the validation phase (n = 712) for specificity and sensitivities. The multivariate TAAbs signature for differentiation PAAC from controls (NHV + CP) identified five candidates, namely the IgG‐type TAAbs against CLDN17, KCNN3, SLAMF7, SLC22A11 and OR51F2. Multivariate logistic performance model of y = (22.893 × CA19‐9 + 0.68 × CLDN17 − 4.012) showed a significant better diagnostic accuracy than that of CA19‐9 and CLDN17 in differentiating PAAC from controls (NHV + CP) (AUC = 0.97, 0.92 and 0.82, respectively, P‐value < .0001). We further tested the autoantigen level of CLDN17 by ELISA in 82 sera samples from PAAC (n = 42), CP (n = 24) and NHV (n = 16). Similarly, the model showed superior diagnostic performance than that of CA19‐9 and CLDN17 (AUC = 0.93, 0.83 and 0.81, respectively, P‐value < .0001) in differentiating PAAC from controls. In conclusion, our study is the first to characterize the circulating TAAbs signatures in PAAC. The results showed that CLDN17 combined with CA19‐9 provided potentially clinical value and may serve as noninvasive novel biomarkers for PAAC diagnosis.
Viral and non‐viral gene therapy using 3D (bio)printing
Authors: Jahangiri Sepideh , Rahimnejad Maedeh , Nasrollahi Boroujeni Narges , Ahmadi Zarrin , Motamed Fath Puria , Ahmadi Sepideh , Safarkhani Moein , Rabiee Navid 
Published: October 2022

AbstractThe overall success in launching discovered drugs is tightly restricted to the high rate of late‐stage failures, which ultimately inhibits the distribution of medicines in markets. As a result, it is imperative that methods reliably predict the effectiveness and, more critically, the toxicity of medicine early in the drug development process before clinical trials be continuously innovated. We must stay up to date with the fast appearance of new infections and diseases by rapidly developing the requisite vaccinations and medicines. Modern in vitro models of disease may be used as an alternative to traditional disease models, and advanced technology can be used for the creation of pharmaceuticals as well as cells, drugs, and gene delivery systems to expedite the drug discovery procedure. Furthermore, in vitro models that mimic the spatial and chemical characteristics of native tissues, such as a 3D bioprinting system or other technologies, have proven to be more effective for drug screening than traditional 2D models. Viral and non‐viral gene delivery vectors are a hopeful tool for combinatorial gene therapy, suggesting a quick way of simultaneously deliver multiple genes. A 3D bioprinting system embraces an excellent potential for gene delivery into the different cells or tissues for different diseases, in tissue engineering and regeneration medicine, in which the precise nucleic acid is located in the 3D printed tissues and scaffolds. Non‐viral nanocarriers, in combination with 3D printed scaffolds, are applied to their delivery of genes and controlled release properties. There remains, however, a big obstacle in reaching the full potential of 3D models because of a lack of in vitro manufacturing of live tissues. Bioprinting advancements have made it possible to create biomimetic constructions that may be used in various drug discovery research applications. 3D bioprinting also benefits vaccinations, medicines, and relevant delivery methods because of its flexibility and adaptability. This review discusses the potential of 3D bioprinting technologies for pharmaceutical studies.