bluuebunny/crossref_metadata_2025_split huggingface dataset:
Histidinal‐Based Potent Antimalarial Agents
Authors: Meena Chhuttan L. , Hingamire Tejashri , Gupta Tanya , Deshmukh Bhagyashree , Karmodiya Krishanpal , Joshi Rakesh , Shanmugam Dhanasekaran , Sanjayan Gangadhar J. 
Published: February 2023

AbstractHerein we report the synthesis and evaluation of peptide‐histidinal conjugated drug scaffolds, which have the potential to target the hemoglobin‐degrading proteases falcipain‐2/3 from the human malaria parasite. Scaffolds with various substitutions were tested for antimalarial activity, and compounds8 g,8 h, and15exhibited EC50 values of ∼0.018 μM, ∼0.069 μM, and ∼0.02 μM, respectively. Structure‐based docking studies on falcipain‐2/3 proteases (PDB:2GHU and PDB:3BWK) revealed that compounds8 g,8 h, and15interact strongly with binding sites of falcipain‐2/3 in a substrate‐like manner. In silico ADME studies revealed that the molecules of interest showed no or minimal violations of drug‐likeness parameters. Further, phenotypic assays revealed that compound8 gand its biotinylated version inhibit hemoglobin degradation in the parasite food vacuole. The identification of falcipain‐2/3 targeting potent inhibitors of the malaria parasite can serve as a starting point for the development of lead compounds as future antimalarial drug candidates.
Ru(II) Complexes with Enaminone Structures for Rapid Sterilization of Staphylococcus aureus and MRSA with Little Accumulation of Drug Resistance
Authors: Jian Yao , Peng Yatong , Zhou Wanpeng , Xu Yunli , Li Chao , Wang Xuesong , Zhou Qianxiong 
Published: February 2023

AbstractDrug‐resistant bacteria, such as methicillin‐resistant Staphylococcus aureus (MRSA), pose a serious threat to human life. Therefore, there is urgent need to develop antibiotics with new chemical structures and antibacterial mechanisms, especially those that elicit little drug resistance after long‐term use. Herein we synthesized three novel ruthenium complexes (Ru1−Ru3) containing the enaminone structures for the first time. At a concentration of 5 μM, Ru1−Ru3 can lead to a CFU reduction of about 5 log units towards S. aureus and MRSA. Interestingly, Ru3 displayed rapid bactericidal effects and could decrease the CFU numbers of both pathogens by 5 log units within 40 min. The control compounds (Ru4 and Ru5) without the enaminone structures displayed very poor antibacterial activity under the same conditions. Moreover, S. aureus did not show apparent drug resistance towards Ru3 after 20 passages incubation with a sublethal concentration. These results highlight the critical role of enaminone structures for antibacterial applications.
Machine learning and deep learning in medicine and neuroimaging
Authors: Sánchez Fernández Iván , Peters Jurriaan M. 
Published: February 2023

AbstractArtificial intelligence is the science and engineering of machines that can mimic human intelligence. Machine learning is the subfield of artificial intelligence in which computers have the ability to learn and iteratively improve their performance without being explicitly programmed. Deep learning algorithms learn by processing the data with increasing levels of abstraction in each layer. We present a narrative review of the relevant literature with a particular focus on deep learning for image classification and image segmentation in neuroimaging. For the first time in history, computers can automatically perform some clinically relevant tasks at the level, or even above the level, of the relevant medical specialists. A turning point in machine learning occurred in the 2010s as a result of (1) the multiple technical improvements that machine learning has been accumulating over several decades, (2) the exponential increase in computing power, and (3) the wide availability of very large databases with millions of observations and thousands of variables. Machine learning is starting to be successfully applied to several areas of medicine, including predictive analytics, decision support, natural language processing of free‐text notes, and automatic interpretation of electrophysiological recordings. Among all the applications of machine learning in medicine, deep learning for computer vision is the one that has enjoyed the greatest success. The emphasis of this review is the application of convolutional neural networks for image classification and for image segmentation in neuroimaging. Machine learning and deep learning are increasingly integrated into the clinical workflow and applied in neuroimaging interpretation. Natural language processing is likely to gain increasing importance in medicine in the near future. Complex decision‐making that mimics human thinking with reinforcement learning is still far away on the horizon.
A Systematic Study on the Redox Potentials of Phenazine‐Derivatives in Aqueous Media: A Combined Computational and Experimental Work
Authors: de la Cruz Carlos , Sanz Roberto , Suárez Anisley , Ventosa Edgar , Marcilla Rebeca , Mavrandonakis Andreas 
Published: February 2023

AbstractPhenazines are an emerging class of organic compounds that have been recently utilized in aqueous redox flow batteries, a promising technology for large‐scale energy storage. A virtual screening based on density functional theory calculations is used to investigate the redox potentials of around 100 phenazine derivatives in aqueous media containing various electron‐donating or electron‐withdrawing groups at different positions. The calculations identify the crucial positions that should be functionalized with multiple hydroxy groups to design new anolytes. The combined experimental–computational methodology reported herein guides the development of a new molecule with a record low reversible redox potential as a potential anolyte for aqueous redox flow batteries.
Ru‐Catalyzed Direct Asymmetric Reductive Amination of Bio‐Based Levulinic Acid and Ester for the Synthesis of Chiral Pyrrolidinone
Authors: Chakrabortty Soumyadeep , Zheng Shasha , Kallmeier Fabian , Baráth Eszter , Tin Sergey , de Vries Johannes G. 
Published: February 2023

AbstractDirect asymmetric reductive amination of bio‐based levulinic acid (LA) to the enantioenriched 5‐methylpyrrolidinone is achieved by using a readily available chiral Ru/bisphosphine catalyst with excellent enantioselectivity (up to 96 % ee) and high isolated yield (up to 89 %). Methyl levulinate (ML), a byproduct from the industrial production of 2,5‐furandicarboxylic acid (FDCA), can be used instead of LA with similar reactivity and selectivity. Mass spectrometry and isotope labelling studies indicate that the chiral lactam is formed via imine‐enamine tautomerization/cyclization followed by asymmetric hydrogenation of the cyclic enamide.
Spreading the Landscape of Dual Ion Batteries: from Electrode to Electrolyte
Authors: Liu Meiqi , Zhang Wei , Zheng Weitao 
Published: February 2023

AbstractInvited for this month′s cover is the group of Weitao Zheng at the Jilin University. The image shows the working mechanism of dual‐ion batteries (DIBs). The Review itself is available at 10.1002/cssc.202201375.
Synthesis and anti‐HIV activity of non‐nucleoside reverse‐transcriptase inhibitor DB02 phosphate derivatives based on water‐soluble optimization
Authors: Wang Jing‐Bo , Ma Meng‐Di , Lu Nan , Yang Yu‐Zhuo , Yang Jin‐Xuan , Li Yi‐Ming , Xie Cong‐Qiang , Ma Ning‐Yu , Luo Rong‐Hua , Wang Yue‐Ping , Yang Liu‐Meng , Zhang Hong‐Bin , Zheng Yong‐Tang , He Yan‐Ping 
Published: February 2023

AbstractTo improve the water solubility of anti‐human immunodeficiency virus (HIV) agent DB02, an excellent non‐nucleoside reverse‐transcriptase inhibitor (NNRTI) obtained in our previous efforts, we designed and synthesized four phosphate derivatives of DB02 based on the molecular model of DB02 with RT. Here, the antiviral activity of these four derivatives was detected, leading to the discovery of compound P‐2, which possessed a superior potency to the lead compound DB02 against wild‐type HIV‐1 and a variety of HIV‐resistant mutant viruses significantly. Furthermore, the water solubility of P‐2 was nearly 17 times higher than that of DB02, and the pharmacokinetic test in rats showed that P‐2 demonstrate significantly improved oral bioavailablity of 14.6%. Our study showed that the introduction of a phosphate ester group at the end of the C‐2 side chain of DB02 was beneficial to the improvement of its antiviral activity and pharmacokinetic properties, which provided a promising lead for the further development of S‐DACOs type of NNRTIs.
Does the skin heat up before it breaks down in diabetic foot ulceration?
Authors: aan de Stegge Wouter B. , Van Netten Jaap J. , Bus Sicco A. 
Published: February 2023

AbstractAimsMost diabetic foot ulcers are caused by tissue stress from being ambulatory in people without protective sensation. These ulcers are suggested to be preceded by local skin temperature increase due to inflammation of the underlying tissue, a so‐called hotspot. Evidence to support this mechanism of ulcer development is meagre at best. We investigated if foot ulcers are preceded by increased skin temperature in people with diabetes and foot ulcer history.Material and MethodsParticipants measured temperature at 6–8 plantar foot locations each day for 18 months and identified a hotspot with a temperature difference >2.2°C between corresponding foot locations for two consecutive days.ResultsTwenty‐nine of 151 participants developed a non‐traumatic ulcer while adhering to temperature measurements. In the 2 months prior to ulceration, 8 (28%) had a true hotspot (i.e. at/adjacent to the ulcer location) and the hotspot was on average no longer present 9 days before ulceration. Seven (24%) participants had a false hotspot (i.e. at another location) and 14 (48%) had no hotspot.ConclusionsThe skin of the majority of the ulcers does not heat up before it breaks down or, when it does, not directly before breakdown, questioning the foot temperature increase—uslcer association.
Distribution patterns and drivers of nonendemic and endemic glires species in China
Authors: Meng Lei , Zhou Lizhi 
Published: February 2023

AbstractSpatial patterns and determinants of species richness in complex geographical regions are important subjects of current biogeography and biodiversity conservation research. Glires are small herbivorous mammal species with limited migratory ability that may serve as an indicator of biodiversity and ecosystems. Herein, we aimed to evaluate how multiple ecological hypotheses could explain the species richness patterns of glires in China. Initially, we constructed a mapping grid cell operating units of 80 × 80 km2 which covered China's land mass and mapped the distribution ranges of the 237 glires species that had recorded. The glires taxa were separated into three response variables based on their distribution: (a) all species, (b) nonendemic species, and (c) endemic species. The species richness patterns of the response variables were evaluated using four predictor sets: (a) hydrothermal characteristics, (b) climatic seasonality, (c) habitat heterogeneity, and (d) human factors. We performed regression tree analysis, multiple linear regression analysis, and variation partitioning analyses to determine the effects of predictors on spatial species patterns. The results showed that the distribution pattern of species richness was the highest in the Hengduan Mountains and surrounding areas in southwest China. However, only a few endemic species adapted to high‐latitude environments. It was found that there are differences about the determinants between nonendemic and endemic species. Habitat heterogeneity was the most influential determinant for the distribution patterns of nonendemic species richness. Climatic seasonality was the best predictor to determine the richness distribution pattern of endemic species, whereas this was least affected by human factors. Furthermore, it should be noted that hydrothermal characteristics were not strong predictors of richness patterns for all or nonendemic species, which may be due to the fact that there are also more species in some areas with less precipitation or energy. Therefore, glires are likely to persist in areas with characteristics of high habitat heterogeneity and stable climate.
Temperature is positively associated with tree mortality in California subalpine forests containing whitebark pine
Authors: Young Derek J. N. , Slaton Michèle R. , Koltunov Alexander 
Published: February 2023

AbstractWhitebark pine (Pinus albicaulis Engelm.) is a keystone high‐elevation tree species occurring across much of western North America, yet it has been listed as threatened under the US Endangered Species Act due to rapid population declines and extensive ongoing pressures from white pine blister rust (Cronartium ribicola), mountain pine beetle (Dendroctonus ponderosae), and increasing temperature and aridity associated with climate change. Past research has shown that whitebark pine mortality is more likely in hotter and drier sites, but no broad‐extent analyses spanning multiple ecoregions have directly quantified the relationship between mortality probability and its environmental drivers at a fine spatial scale. To address this gap, we used spatially continuous high‐resolution (30 m) maps of mortality in California produced by a remote sensing‐based change detection algorithm, and we developed generalized additive models to explain this mortality variability using relevant biophysical variables. We found that maximum daily temperature was strongly positively associated with mortality intensity and that mortality was also generally more intense in sites with greater solar exposure (e.g., south‐facing slopes). Our results support the interpretation that as climate warms and aridity increases, populations in “trailing edge” (i.e., hot, dry) sites are most vulnerable. The environmental relationships we quantify will aid in identifying the locations and environments with the greatest risk of future mortality as well as those likely to serve as refugia.
Toward data‐ and mechanistic‐driven volcano plots in electrocatalysis
Authors: Exner Kai S. 
Published: February 2023

AbstractThe present application note summarizes an advanced methodology that allows for deriving potential‐dependent volcano curves for energy storage and conversion processes. The conventional approach relies on the combination of density functional theory calculations and scaling relations for a single mechanistic pathway as well as a discussion of electrocatalytic activity by means of the potential‐determining step, determined at the equilibrium potential of the reaction. Herein, it is illustrated how several reaction mechanisms can be factored into the volcano curve and how the rate‐determining step based on the descriptor Gmax(U) can be derived by a rigorous thermodynamic analysis of adsorption free energies fed by a data‐inspired methodology.
Effect of ground motion processing and filtering on the response of rocking structures
Authors: Elmorsy Medhat , Vassiliou Michalis F. 
Published: February 2023

AbstractWith growing interest in rocking structures, it is important to quantify the effects of ground motion processing schemes on rocking response. To this end, this paper studied the influence of different ground motion correction schemes and parameter values on the rocking displacement spectrum. When the problem is treated on an individual ground motion basis, then it seems that ground motion correction does have an influence on the rocking response, especially for more slender blocks. This influence is larger for causal filtering. However, no specific trend related to the cut off period of the filter can be observed (at least for cut off periods longer than or equal to 10 s) or on the type of the recording device (analog or digital). On the other hand, when treating the problem statistically (by comparing the statistics of the response due to sets of ground motion, not a single ground motion), the effect is significantly reduced: The processing schemes do not induce any significant bias to the rocking response and the motion‐to‐motion variability seems more important than the ground motion correction method. This conclusion applies to both analog and digital records, both causal and acausal filters, and to all near field pulse like, near field no pulse like, and far field records.
A deep learning model for online doctor rating prediction
Authors: Kulshrestha Anurag , Krishnaswamy Venkataraghavan , Sharma Mayank 
Published: February 2023

AbstractPredicting doctor ratings is a critical task in the healthcare industry. A patient usually provides ratings to a few doctors only, leading to the data sparsity issue, which complicates the rating prediction task. The study attempts to improve the prediction methodologies used in the doctor rating prediction systems. The study proposes a novel deep learning (DL) model for online doctor rating prediction based on a hierarchical attention bidirectional long short‐term memory (ODRP‐HABiLSTM) network. A hierarchical self‐attention bidirectional long short‐term memory (HA‐BiLSTM) network incorporates a textual review's word and sentence level information. A highway network is used to refine the representations learned by BiLSTM. The resulting latent patient and doctor representations are utilized to predict the online doctor ratings. Experimental findings based on real‐world doctor reviews from Yelp.com across two medical specialties demonstrate the proposed model's superior performance over state‐of‐the‐art benchmark models. In addition, robustness analysis is used to strengthen the findings.
Cross‐sectional return dispersion and stock market volatility: Evidence from high‐frequency data
Authors: Niu Zibo , Demirer Riza , Suleman Muhammad Tahir , Zhang Hongwei 
Published: February 2023

AbstractThis paper investigates whether the cross‐sectional variance (CSV) of stock returns and its asymmetric components contain incremental information to predict stock market volatility under a high‐frequency, heterogeneous autoregressive (HAR) model framework. We present novel evidence that CSV is a powerful predictor of future realized volatility, both in‐ and out‐of‐sample, even after controlling for the well‐established predictors obtained from intraday data. Further analysis suggests that distinguishing between positive and negative CSV components in the forecasting model enhances the predictive capability of volatility models at all out‐of‐sample forecasting horizons, with the asymmetric HAR‐type‐ACSV model consistently outperforming all alternative HAR‐type variations. We argue that the asymmetries in the predictive relation between CSV and volatility are largely driven by the disagreement among market participants that spikes during bad times. Finally, economic analysis shows that incorporating CSV in the forecasting model can generate sizeable economic gains for a mean–variance investor, suggesting that out‐of‐sample predictive ability of CSV can be exploited in forward looking investment strategies to enhance investment returns.
Bioactivity and health effects of garlic essential oil: A review
Authors: Huang Lei , Liu Zhenxin , Wang Jing , Fu Jiaolong , Jia Yonglu , Ji Lilian , Wang Taoyun 
Published: February 2023

AbstractGarlic (Allium sativum L.), the underground bulb of the Allium plant in the family Liliaceae, is a common and popular spice that has historically been used to prevent and treat many different diseases such as pain, deafness, diarrhea, tumors, and other healthy problems. Garlic essential oil contains a variety of organosulfur compounds, such as the most representative diallyl disulfides (DADS) and diallyl trisulfides (DATS), which have attracted great interest in medicine, food, and agriculture because of their rich biological activities. This paper reviews the research progress on the composition and bioactivities of garlic essential oil mixtures and the bioactivity of some typical monomeric sulfides in garlic essential oil. The active mechanisms of representative sulfides in garlic essential oil were analyzed, and the applications of garlic essential oil in functional food, food additives, and clinical treatment were discussed. Combined with the current research status, the limitations and development direction of garlic essential oil in the study of molecular mechanism were discussed, which is of great significance to the development of garlic essential oil as a natural and safe alternative medicine for treatment.
Vanillin‐Derived Thermally Reprocessable and Chemically Recyclable Schiff‐Base Epoxy Thermosets
Authors: Subramaniyan Sathiyaraj , Bergoglio Matteo , Sangermano Marco , Hakkarainen Minna 
Published: February 2023

AbstractThe paradigm shift from traditional petroleum‐based non‐recyclable thermosets to biobased repeatedly recyclable materials is required to move toward circular bioeconomy. Here, two mechanically and chemically recyclable extended vanillin‐derived epoxy thermosets are successfully fabricated by introduction of Schiff‐base/imine covalent dynamic bonds. Thermoset 1 (T1) is based on linear monomer 1 (M1) with two alcohol end groups and one imine bond, while thermoset 2 (T2) is based on branched monomer 2 (M2) with three alcohol end‐groups and three imine‐groups. Thermosets are obtained by reaction of monomer 1 (M1) and monomer 2 (M2) with trimethylolpropane triglycidyl ether. The structure of the monomers and thermosets is confirmed by nuclear magnetic resonance and Fourier transform infrared spectroscopic techniques. Both thermosets exhibit good thermal and mechanical properties and they are stable in common organic solvents. Furthermore, they can be thermally reprocessed through compression molding with good recovery of the mechanical properties. Last but not least, the fabricated thermosets can be rapidly and completely chemically recycled to water‐soluble aldehydes and amines by imine hydrolysis at room temperature in 0.1 m HCl solution. This is promising for development of future materials with multiple circularity by different routes.
Real‐world clinical outcome and safety of adjuvant therapy in stage III melanoma patients: Data from two Academic Italian Institutions
Authors: De Falco Vincenzo , Suarato Gabriella , Napolitano Rossella , Argenziano Giuseppe , Famiglietti Vincenzo , Amato Annarita , Servetto Alberto , Bianco Roberto , Formisano Luigi , Terrano Vincenzo , Esposito Alfonso , Giugliano Maria Cristina , Ciardiello Davide , Ciardiello Fortunato , Napolitano Stefania , Troiani Teresa 
Published: February 2023

AbstractAdjuvant immunotherapy (IO) and targeted therapy (TT) have improved relapse‐free survival (RFS) in patients with stage III melanoma, although about 25% of them relapse within a year. However, real‐world data on treatment efficacy and safety as well as management of treatment recurrences are still limited. We retrospectively analyzed 113 patients with stage III melanoma who received at least one cycle of anti‐PD‐1 (nivolumab or pembrolizumab) or dabrafenib + trametinib as adjuvant therapy. Most of patients included into the analyses harbor BRAV600E mutation (66.4%) and had a stage IIIC melanoma (63.7%). Immunotherapy was administered in 48.7% of patients, whereas targeted therapy in 51.3% At data cut‐off, median RFS was not reached with 12‐ and 24‐months RFS of 81% and 64%, respectively. No new adverse events were registered. Thirty patients (26.5%) relapsed, mainly at distant sites. Patient treated with IO recurred mostly during adjuvant treatment (ON‐treatment) while patients treated with TT relapsed at the end of treatment (OFF‐treatment). At relapse, surgery, radiotherapy and systemic therapy were used alone or in combination. Among patients who started a first‐line therapy, an excellent response switching to a different treatment was observed. Real‐world outcomes and safety of adjuvant treatment for resected stage III melanoma appear comparable to clinical trials data. Moreover, management of recurrences depends on type of relapse (loco‐regional vs distant) and timing (during vs OFF treatment). Furthermore, patients who relapse after adjuvant TT respond well to subsequent anti‐PD1 based therapy.