bluuebunny/crossref_metadata_2025_split huggingface dataset:
A New Platform Molecule from Gluconolactone? Access to Furanics, Rare Sugars, β‐Ketoamides and Amino Furanones
Authors: Jurys Arminas , Pedersen Christian Marcus 
Published: October 2024

AbstractGluconolactone serves as a readily available and inexpensive starting material for synthesizing the potential platform chemical (Z)‐3‐deoxy‐1,2 : 5,6‐di‐O‐isopropylidene‐D‐erythro‐hex‐3‐enolactone in two steps. In this work, the selective elimination of triacetone gluconolactone was optimized. The resulting product is a versatile molecule, capable of being transformed into various compound classes in one or a few steps, i. e. potentially a new biomass‐based platform chemical. This study demonstrates how it can be transformed into furanics, rare sugars, β‐ketoamides, and amino furanones.
Redox Cycling with Tellurium. Si−H Bond Activation by a Lewis Superacidic Tellurenyl Cation
Authors: Hejda Martin , Hupf Emanuel , Růžička Aleš , Dostál Libor , Beckmann Jens 
Published: October 2024

AbstractThe C,N‐chelated aryltellurenyl triflate [2‐(tBuNCH)C6H4Te][OTf] (1) activates the Si−H bonds in the tertiary silanes R3SiH via umpolung of H− to H+ to give rise to the iminium salts (tBuN(H)CH)C6H4TeSiR3][OTf] (2R, R=Et, Ph (elusive) and R=Si(CH3)3 isolated; OTf=O3SCF3) comprising Te−Si bonds, which are capable of generating silyl triflates, R3SiOTf, under attack of a second equivalent of 1. The unprecedented Si−H activation was utilized in main group redox catalysis using p‐quinones, which were converted into (silylated) hydroquinones.
Photo‐Triggered ROS‐Responsive Supramolecular Nanoprodrugs for Targeted and Synergistic Chemo/Photodynamic/Gas Therapy
Authors: Shi Jingtao , Ma Ke , Yang Yibo , Pei Yuxin , Pei Zhichao 
Published: October 2024

AbstractPhoto‐triggered ROS‐responsive supramolecular nanoprodrugs (BNN6@GBTC NPs) are constructed via supramolecular self‐assembly of amphiphilic prodrug molecule (GBTC) and NO donor (BNN6). BNN6@GBTC NPs possess good stability, ROS responsiveness, and selective HepG2 cells targetability via overexpressed galactose receptors on the cell membrane. When BNN6@GBTC NPs are taken up by HepG2 cells, they can generate ROS upon light irradiation, which can not only be used for photodynamic therapy, but also cleave the thioketal linkage to release camptothecin for chemotherapy. Meanwhile, BNN6 triggering release NO for gas therapy. BNN6@GBTC NPs enable targeted and synergistic chemo/photodynamic/gas therapy, which results in reduced damage to normal cells and enhanced anti‐cancer efficacy in vitro. This work provides a novel approach for the design of nanoprodrugs based on supramolecular self‐assembly to achieve the multimodal synergistic anti‐cancer therapy.
Na Ions Doped Fe2VO4 Cathode for High Performance Aqueous Zinc‐ion Batteries
Authors: Hu Yin‐Qiang , Lin Li , Hu Zhen‐Yu , Yu Yang , Zhang Yu , Liu Yu‐Hang , Wei Zhi‐Peng , Liu Wan‐Qiang , Tian Song‐Lin 
Published: October 2024

AbstractAqueous zinc ion batteries are thought to be a new generation of secondary batteries that will replace lithium‐ion batteries due to their great safety and inexpensive cost. In the cathode materials of aqueous zinc ion batteries with long life and high capacity, abundant active sites and crystal structure stability play an important role. In the present work, the strategy of Na+ intercalation of Fe2VO4 (FVO) is proposed, aiming at the insertion of Na+, which not only enriches the active sites, but also sodium and iron ions act as guest species with the negatively charged VOx lattice to provide strong electrostatic attraction to stabilize the lamellar structure. In terms of electrochemical performance, the discharge specific capacity is 370 mAh g−1 at a current density of 0.1 A g−1, and when the current density is arising 5 A g−1, the specific capacity also reaches 200 mAh g−1 after cycling 2000 with a capacity retention of 99 %, which is better than the electrochemical performance of Fe2VO4 (FVO) alone at 50 mAh g−1. The superior electrochemical performance proves that FVO−Na is an ideal cathode material for zinc ion batteries.
Benzofuroxans: A Possible +100 Years Old Misunderstanding?
Authors: Galkina Irina V. , Bakhtiyarov Dmitriy I. , Romanov Semen R. , Usupova Luiza M. , Bakhtiyarova Yulia V. , Fan Haiyan , Benassi Enrico 
Published: October 2024

AbstractSince 1912 benzofuroxans have been believed to exhibit a molecular structure characterised by the presence of two fused rings, i. e. a benzene ring and a pentatomic heterocycle containing a N−O−N fragment wherein one of the two N atoms is involved in a further N
O bond, namely benzo[1,2‐c]1,2,5‐oxadiazole N‐oxide. In this work, quantum chemical calculation results along with vibrational spectroscopic data raise fundamental questions about the electronic structure and the molecular topology of some 4,6‐dinitro‐derivatives of this class of compounds. An alternative interpretation is offered corroborated by the experimental vibrational and electronic spectra.
Membrane Separations in Biomass Processing
Authors: Mandalika Anurag S. , Runge Troy M. , Ragauskas Arthur J. 
Published: October 2024

AbstractThe development of integrated biorefineries and the greater utilization of biomass resources to reduce dependence on fossil fuel‐derived products require research emphasis not just on conversion strategies but also on improving separations associated with biorefining. A significant roadblock towards developing biorefineries is the lack of effective separation techniques evidenced by the relative deficiency of literature in this area. Additionally, high conversion yields may only be realized if effective separations generate feedstock of sufficient purity – this makes research into biomass conversion strategies all the more critical. In this review, the challenges associated with biomass separations are discussed, followed by an overview of the most appropriate separation strategies for processing biomass. One of the unit operations most appealing for biorefining, membrane separations (MS), is then considered along with a review of the recent literature utilizing this technique in biomass processing.
Comprehensive review and updated analysis of DNA methylation in hepatocellular carcinoma: From basic research to clinical application
Authors: Su Lin , Bu Jiawen , Yu Jiahui , Jin Mila , Meng Guanliang , Zhu Xudong 
Published: October 2024

AbstractHepatocellular carcinoma (HCC) is a primary malignant tumour, ranking second in global mortality rates and posing significant health threats. Epigenetic alterations, particularly DNA methylation, have emerged as pivotal factors associated with HCC diagnosis, therapy, prognosis and malignant progression. However, a comprehensive analysis of the DNA methylation mechanism driving HCC progression and its potential as a therapeutic biomarker remains lacking. This review attempts to comprehensively summarise various aspects of DNA methylation, such as its mechanism, detection methods and biomarkers aiding in HCC diagnosis, treatment and prognostic assessment of HCC. It also explores the role of DNA methylation in regulating HCC's malignant progression and sorafenib resistance, alongside elaborating the therapeutic effects of DNA methyltransferase inhibitors on HCC. A detailed examination of these aspects underscores the significant research on DNA methylation in tumour cells to elucidate malignant progression mechanisms, identify diagnostic markers and develop new tumour‐specific inhibitors for HCC.Key points
A comprehensive summary of various aspects of DNA methylation, such as its mechanism, detection methods and biomarkers aiding in diagnosis and treatment.
The role of DNA methylation in regulating hepatocellular carcinoma's (HCC) malignant progression and sorafenib resistance, alongside elaborating therapeutic effects of DNA methyltransferase inhibitors.
Deep research on DNA methylation is critical for discovering novel tumour‐specific inhibitors for HCC.
Application of mass cytometry in multiparametric characterization of precancerous cervical lesions
Authors: Pešut Ena , Šimić Ivana , Kužilkova Daniela , Kalina Tomáš , Fureš Rajko , Erceg Ivkošić Ivana , Milutin Gašperov Nina , Sabol Ivan 
Published: October 2024

AbstractCervical cancer (CC) is the fourth most common malignant tumor in women worldwide. Detecting different biomarkers together on single cells by novel method mass cytometry could contribute to more precise screening. Liquid‐based cytology (LBC) cervical samples were collected (N = 53) from women categorized as normal and precancerous lesions. Human papillomavirus was genotyped by polymerase chain reaction, while simultaneous examination of the expression of 29 proteins was done by mass cytometry (CyTOF). Differences in cluster abundances were assessed with Spearman's rank correlation as well as high dimensional data analysis (t‐SNE, FlowSOM). Cytokeratin (ITGA6, Ck5, Ck10/13, Ck14, Ck7) expression patterns allowed determining the presence of different cells in the cervical epithelium. FlowSOM analysis enabled to phenotype cervical cells in five different metaclusters and find new markers that could be important in CC screening. The markers Ck18, Ck18, and CD63 (Metacluster 3) showed significantly increasing associated with severity of the precancerous lesions (Spearman rank correlation rho 0.304, p = 0.0271), while CD71, KLF4, LRIG1, E‐cadherin, Nanog and p53 (Metacluster 1) decreased with severity of the precancerous lesions (Spearman rank correlation rho −0.401, p = 0.0029). Other metaclusters did not show significant correlation, but metacluster 2 (Ck17, MCM, MMP7, CD29, E‐cadherin, Nanog, p53) showed higher abundance in low‐ and high‐grade intraepithelial lesion cases. CyTOF appears feasible and should be considered when examining novel biomarkers on cervical LBC samples. This study enabled us to characterize different cells in the cervical epithelium and find markers and populations that could distinguish precancerous lesions.
Systematic Reviews on Eating Disorders and Autism Spectrum Disorder Emphasize a Call to Further Expand High‐Quality Research
Authors: Sharp William G. 
Published: October 2024

ABSTRACTTwo recent systematic reviews provide important insights into the current state of the eating disorder literature regarding autistic traits, ASD prevalence, and experience with accessing treatment, with a specific focus on anorexia nervosa (AN). The extant literature provides converging evidence for elevated autistic traits in individuals with AN, while evidence for increased prevalence of ASD in AN is less robust. Presence of autistic traits or a formal ASD diagnosis both appear to elevate the potential for a more complicated experience during eating disorder treatment. Together, this work provides compelling justification to expand the breadth of inquiry regarding eating disorders and ASD, with a specific call for increased methodological rigor when designing and conducting future research. My commentary calls for specific steps to advance our understanding of ASD and eating disorders.
Long‐term tracking captures the timing of ontogenetic niche shifts in northeast Pacific white sharks
Authors: Logan Ryan K. , Anderson James M. , Burns Echelle S. , Samara Chacon Yamilla , Freedman Ryan , García‐Rodríguez Emiliano , Jahn Elizabeth , Lyons Kady , Meese Emily N. , Merson Zachariah S. , O'Sullivan John , Rex Patrick T. , Spurgeon Emily , Stirling Brian S. , White Connor F. , Winkler Chuck , Jorgensen Salvador , Sosa‐Nishizaki Oscar , Lowe Christopher G. 
Published: October 2024

AbstractOntogenetic changes in area use, habitat use, and trophic interactions play an important role in the ecology, demography, and ultimately population dynamics of many species. Assumed to be driven by shifting life‐history requirements, trophic niche shifts in white sharks (Carcharodon carcharias) are well documented, but the timing of the spatial niche shift that is hypothesized to occur with the trophic niche shift remains poorly understood. To document how fine‐scale area use varies as sharks age and the timing of the ontogenetic spatial shift of this top predator, we tracked individual white sharks tagged as young‐of‐the‐year or young juveniles over multiple years. Using data from juvenile white sharks detected over multiple years in a nursery habitat with a high‐density receiver array, we found no difference in area use with age. However, using a coast‐wide receiver array including nursery and adult habitat, we found the probability of detecting a juvenile white shark in nursery habitat decreased with age, with a concurrent increasing probability of detection in adult habitat. As the conservation and management of this species relies on understanding nursery habitat use and age‐related movements, data presented here address an important knowledge gap for the understudied juvenile to subadult life stages and the ontogenetic habitat shift of this species.
A flexible data‐driven approach to co‐producing drought vulnerability assessments
Authors: Crausbay Shelley D. , Hall Kimberly R. , Cross Molly S. , Halabisky Meghan , Rangwala Imtiaz , Anderson Jesse , Schwend Ann 
Published: October 2024

AbstractIntensifying weather events are key characteristics of climate change that are fundamentally changing ecological disturbance regimes. Intensifying drought is a particular threat to species, ecosystems, and ecosystem services worldwide. Proactive drought adaptation measures are acutely needed, but without a better understanding of drought vulnerability at the appropriate scale and geography, such measures may not be effective, or even anticipated as potential options. A recent conceptual framework for ecological drought aligns a holistic suite of potential drivers with the key components of climate change vulnerability (exposure, sensitivity, and adaptive capacity). We leverage the ecological drought framework and components of vulnerability to introduce a six‐step process for developing a drought vulnerability assessment (DVA) that (1) is place‐based and avoids mismatches between assessment geography and management action, (2) uses existing empirical datasets and leverages machine learning techniques and remotely sensed data from a recent drought, (3) emphasizes the inclusion of stakeholders and the importance of data visualization and science communication, and (4) is flexible and adaptable to a wide range of planning contexts. We illustrate the DVA process with a case study for forested watersheds in the Missouri Headwaters (MH), Montana, USA, that is focused on the impact of an early 2000s drought event on forest health. We show how the DVA provides insights on drought vulnerability that are helpful starting points for co‐developing region‐specific management actions to prepare for the next drought, including strategies to enhance ecologically available water, reduce competition for water, promote ecosystem persistence under drought conditions, and prioritize sites for forest restoration, transition, or protection. The work described here provides a model for developing a DVA in other places that, when used in a participatory adaptation planning process, supports the implementation of effective adaptation strategies.
Electrocardiograph analysis for risk assessment of heart failure with preserved ejection fraction: A deep learning model
Authors: Gao Zheng , Yang Yuqing , Yang Zhiqiang , Zhang Xinyue , Liu Chao 
Published: October 2024

AbstractAimsHeart failure with preserved ejection fraction (HFpEF) requires an efficient screening method. We developed a deep learning model (DLM) to screen HFpEF risk using electrocardiograms (ECGs).Methods and resultsA cohort study was conducted utilising data from Cohorts A and B. A convolutional neural network‐long short‐term memory (CNN‐LSTM) DLM was employed. HFpEF risk was determined by left ventricular end‐diastolic pressure (LVEDP) and clinical symptoms. The DLM was trained by ECGs. LVEDP for each patient was collected through invasive left ventricular catheterisation. Cohort A and B comprised data from individuals at high risk for HFpEF (LVEDP > 12 mmHg) and low risk for HFpEF (LVEDP ≤ 12 mmHg). The model was trained on Cohort A and prospectively validated on Cohort B.ResultsA total of 238 patients underwent ECG and left ventricular catheterisation for model training in Cohort A, and 117 patients for validation in Cohort B. The DLM achieved 78% accuracy in assessing HFpEF risk in Cohort A, while in Cohort B, it demonstrated 78% accuracy, 71.9% specificity, and 71.7% sensitivity. In the validation Cohort B, the DLM‐identified high‐risk HFpEF group exhibited significantly higher prevalence of diabetes (22.03%–11.86%, P < 0.01), higher BMI indices (25.92–24.22 kg/cm2, P < 0.01), and lower usage history of calcium channel blockers (CCB) (11.76%–28.81%, P < 0.01) compared with the DLM‐identified low‐risk HFpEF group. Traditional HFpEF indicators, including B‐type natriuretic peptide (BNP) (22–20 pg/mL, P = 0.71) and E/E′ (8.25–8.5, P = 0.66), did not exhibit significant differences between the two groups.ConclusionsThe DLM offers an accurate, cost‐effective tool for HFpEF risk assessment, potentially facilitating early detection and improved clinical management.
Prescription time trends in patients with high‐impact chronic pain: A National Patient Registry Study
Authors: Bruun Karin D. , Thorarinsson Caroline T. , Vaegter Henrik B. , Zegers Floor D. , Nørgård Bente M. , Wod Mette 
Published: October 2024

AbstractBackgroundHigh‐impact chronic pain (HICP), defined as chronic pain with a significant impact on daily function, affects approximately 8% of the Western population. In Denmark, HICP still remains to be described at the population level. Some patients with HICP are referred to the Danish pain centres, where they are registered with a procedural code. We conducted a nationwide registry‐based study of all Danish patients registered with a visit to a pain centre from January 2005 to March 2022, to explore time trends in the prescription of analgesics and sedatives in this HICP subpopulation. Furthermore, data on socioeconomics and hospital diagnoses are reported.MethodsWe used data from the Central Person Registry, the Danish National Patient Registry, the Danish National Prescription Registry, combined with data on socioeconomic information from Statistics Denmark. Data was collected on prescriptions 3 months before the first visit to a pain centre. Prescriptions were stratified into five calendar periods: before 2011, 2011–2013, 2014–2016, 2017–2019, and 2020–2022.ResultsWe identified 66,577 patients referred to a pain centre. Over 15 years, prescriptions before referral declined for opioids (from 53.2% to 31.7%), NSAIDs (from 28.3% to 23.5%), antidepressants (from 20.5% to 16.8%), anxiolytics (from 12.3% to 3.2%), and sleep medication (from 15.8% to 7.6%). In contrast, prescriptions increased for paracetamol (from 31.1% to 48.9%) and gabapentinoids (from 19.2% to 27.7%).ConclusionsIn patients with HICP visiting Danish pain centres, prescriptions before referral decreased for opioids over 15 years, with a simultaneously increased prescription of gabapentinoids (gabapentin and pregabalin).Significance StatementThis nationwide study of 66,577 Danish patients with high‐impact chronic pain reveals a significant decrease in filled opioid prescriptions over the past 15 years, with a simultaneous rise in gabapentinoid use before referral to pain centres. These findings suggest a shift in clinical practice towards alternative pain management strategies. The study underscores the need for continued research into the long‐term effects of these changes and their impact on patient outcomes.
Missing Risk Factor Prediction in Cardiovascular Disease Using a Blended Dataset and Optimizing Classification With a Stacking Algorithm
Authors: Mauya Jannatul , Sahriar Saad , Akther Sanjida , Amin Ruhul , Ruhi Sabba , Reza Md. Shamim 
Published: October 2024

ABSTRACTMachine learning is important in the treatment of heart disease because it is capable of analyzing large amounts of patient data, such as medical records, imaging tests, and genetic information, in order to identify patterns and predict the risk of developing heart disease. However, most ML algorithms require more accurate data in order to build an accurate prediction model and do not tolerate missing values. Handling missing risk factors is critical during dataset preprocessing and becomes more difficult when the risk factors are completely missing. Removing this completely missing feature may result in the loss of critical information, but there are no readily available imputation methods, which presents a significant challenge. To overcome this difficulty, in this study, we take an attempt to impute using statistical multiple linear regression and Huber regression (HR) methods using four blended datasets (Statlog, Cleveland, Hungarian, and Switzerland) sourced from the UCI ML repository. The entire dataset comprises 14 attributes, including one target variable; however, in the Switzerland dataset, one feature value (“serum cholesterol”) is entirely missing. Missing “serum cholesterol” is recognized as a predisposing factor including “chest pain,” “supreme heartbeat rate,” “type of defect,” “exercise induced ST stress related to rest,” and “exercise generated angina” in the proposed imputation methods. We also proposed applying the majority voting ensemble technique in an individual's and integrated dataset using ML algorithms as part of the risk factor identification strategy. The results show that our proposed stacked algorithm for the combined dataset with the ensemble features significantly improved accuracy by 93.47%, and an AUC score of 94.50% demonstrated more accurate and early prediction than the previous research and also provided the model's diversity, resilience, generalization, and adaptability to varied datasets.
Isotherm, kinetic, and thermodynamic studies of adsorption of copper (II) and nickel (II) ions using low‐cost treated orange peel from aqueous solutions
Authors: Canpolat Mutlu , Altunkaynak Yalçın 
Published: October 2024

AbstractThe aim of this study was to utilize processed orange peel waste (TOP) as an adsorbent to remove Cu(II) and Ni(II) ions from aqueous solutions. As a result of systematic experiments to determine the optimal conditions, it was determined that the most suitable conditions for the effective removal of Cu(II) ions were 400 mg/L initial concentration, 100 min contact time, 0.2 g adsorbent dosage, and a solution pH of 5.92. Similarly, the optimal conditions for the removal of Ni(II) ions were determined by systematic experiments to be 300 mg/L initial concentration, 0.2 g adsorbent dosage, 100 min contact time, and a solution pH of 6.19. The systematic experiments also included further investigation of the surface properties of TOP, and promising results were obtained by tests at three different temperatures (298, 308, and 318 K). The adsorption capacities for Cu(II), Ni(II), and Ni(II) were determined as 72.99, 75.18, and 76.33 mg/g, 42.55, 44.44, and 46.29 mg/g, respectively. Further analysis of the adsorption kinetics revealed that the pseudo‐second‐order model accurately represented the experimental data for both ions. Thermodynamic investigations provided strong evidence that the adsorption process of these noble metal ions on TOP is endothermic and spontaneous. The results of this study emphasize that TOP, with its low cost, easy‐to‐use nature, and high adsorption capacity, can be considered a long‐term solution for environmental remediation and water treatment in sustainable engineering applications.