bluuebunny/crossref_metadata_2025_split huggingface dataset:
Psychosocial Needs of People Living With Pleural Mesothelioma and Family Carers: A Mixed Methods Study
Authors: Breen Lauren J. , Same Anne , Peddle‐McIntyre Carolyn J. , Sidhu Calvin , Fitzgerald Deirdre , Tan Ai Ling , Carey Renee N. , Wilson Chanelle , Lee Y. C. Gary 
Published: November 2024

ABSTRACTBackgroundMesothelioma is a cancer of growing global incidence, especially in developing countries, with unique complex psychosocial impacts on patients and their carers.AimsTo provide a comprehensive understanding of the psychosocial needs of people living with pleural mesothelioma and family carers.MethodsA mixed methods design with 61 semi‐structured interviews and psychometrically validated questionnaires to assess pleural mesothelioma patients' (n = 36) quality of life and frailty and carers' (n = 25) caregiving experiences, quality of life, and pre‐loss prolonged grief symptoms.ResultsPeople with mesothelioma (29 men, 7 women, aged 46–89 years) indicated moderate quality of life; 18 (50%) met criteria for frailty. Current carers (21 women, 4 men; aged 41–79 years) generally reported positive caregiving experiences and high quality of life; 5 (20%) scored in the range indicative of risk for prolonged grief disorder. Four themes were generated: a desire for tailored information with bespoke detail, assistance to coordinate tasks of treatment, improved social and peer support, and effective psychological services. Needs varied, with main concerns being about breaking the news to spouses/children, the impact of the disease and death on family, loss of personal future, managing psychological symptoms, and avoiding burden.ConclusionsThese specific and unmet psychosocial needs provide a strong basis for individualised care pathways to address these needs via the integration of psychology into the multidisciplinary care team and the development and evaluation of mental health and wellbeing interventions for mesothelioma patients and carers. Doing so will reduce psychosocial distress and improve residual vitality.
An Overview on Disease Burden and Management of Respiratory Syncytial Virus Infections in Older Adults in the Asia‐Pacific Region
Authors: Lai Chih‐Cheng , Hsueh Po‐Ren 
Published: November 2024

ABSTRACTRespiratory syncytial virus (RSV) represents a significant burden on older adults (aged ≥ 50 years) globally and can lead to acute respiratory tract infections with substantial morbidity and mortality. However, there is a significant gap in knowledge regarding RSV infection in older adults, particularly in the Asia‐Pacific region. This knowledge gap underscores the need for targeted and comprehensive studies to fully understand the nuanced epidemiology of RSV in ageing populations. This review synthesises data from various countries, emphasising the impact of RSV on older populations in the Asia‐Pacific region. The overall proportions of RSV‐related ARIs among older patients ranged from 0.2% to 5.6%. Among older adult patients with CAP, RSV accounted for 1.1%–10.3% of cases. However, it is crucial to note that the diversity in reported percentages highlights the influence of factors such as geographic location, health care settings and diagnostic practices. The most common symptoms observed in older adults with RSV infection were cough, sputum production and fever, followed by dyspnoea, sore throat and rhinorrhoea. Most of the old adults with RSV infection had underlying diseases, and RSV can cause significant morbidity and mortality in old adults. Treatment of RSV infections predominantly involve supportive care, with aerosolised ribavirin reserved for severe cases, especially immunocompromised patients. Emerging antiviral agents, including fusion and nucleoprotein inhibitors, offer promising avenues for future therapeutics. The recent approval of the bivalent RSV prefusion F protein‐based vaccine for individuals aged 60 and older represents a milestone in preventive strategies. In conclusion, RSV infection remains a significant threat to older adults in the Asia‐Pacific region, necessitating ongoing research and surveillance efforts. The recent vaccine approval marks a positive milestone, but further studies are crucial for refining prevention and treatment approaches.
ℓ1$$ {\ell}_1 $$‐Penalized Multinomial Regression: Estimation, Inference, and Prediction, With an Application to Risk Factor Identification for Different Dementia Subtypes
Authors: Tian Ye , Rusinek Henry , Masurkar Arjun V. , Feng Yang 
Published: November 2024

ABSTRACTHigh‐dimensional multinomial regression models are very useful in practice but have received less research attention than logistic regression models, especially from the perspective of statistical inference. In this work, we analyze the estimation and prediction error of the contrast‐based ‐penalized multinomial regression model and extend the debiasing method to the multinomial case, providing a valid confidence interval for each coefficient and value of the individual hypothesis test. We also examine cases of model misspecification and non‐identically distributed data to demonstrate the robustness of our method when some assumptions are violated. We apply the debiasing method to identify important predictors in the progression into dementia of different subtypes. Results from extensive simulations show the superiority of the debiasing method compared to other inference methods.
Shape Mediation Analysis in Alzheimer's Disease Studies
Authors: Zhou Xingcai , Yeon Miyeon , Wang Jiangyan , Ding Shengxian , Lei Kaizhou , Zhao Yanyong , Liu Rongjie , Huang Chao ,   The Alzheimer's Disease Neuroimaging Initiative
Published: November 2024

ABSTRACTAs a crucial tool in neuroscience, mediation analysis has been developed and widely adopted to elucidate the role of intermediary variables derived from neuroimaging data. Typically, structural equation models (SEMs) are employed to investigate the influences of exposures on outcomes, with model coefficients being interpreted as causal effects. While existing SEMs have proven to be effective tools for mediation analysis involving various neuroimaging‐related mediators, limited research has explored scenarios where these mediators are derived from the shape space. In addition, the linear relationship assumption adopted in existing SEMs may lead to substantial efficiency losses and decreased predictive accuracy in real‐world applications. To address these challenges, we introduce a novel framework for shape mediation analysis, designed to explore the causal relationships between genetic exposures and clinical outcomes, whether mediated or unmediated by shape‐related factors while accounting for potential confounding variables. Within our framework, we apply the square‐root velocity function to extract elastic shape representations, which reside within the linear Hilbert space of square‐integrable functions. Subsequently, we introduce a two‐layer shape regression model to characterize the relationships among neurocognitive outcomes, elastic shape mediators, genetic exposures, and clinical confounders. Both estimation and inference procedures are established for unknown parameters along with the corresponding causal estimands. The asymptotic properties of estimated quantities are investigated as well. Both simulated studies and real‐data analyses demonstrate the superior performance of our proposed method in terms of estimation accuracy and robustness when compared to existing approaches for estimating causal estimands.
Quantum Computation of Hydride Ion using Variational Quantum Algorithm
Authors: Kalam Abdul , Kumar Shubham , Kumar Ashok , Panigrahi Prasanta K. 
Published: November 2024

AbstractBecause of remarkable reactivity and strong electron‐electron correlation effects, the precise prediction of ground state energy and chemical reactivity of hydride ion is an essential objective in quantum chemistry. Leveraging variational quantum algorithms offers a promising avenue for studying molecular properties using current noisy intermediate‐scale quantum devices. This work utilises the variational approach to anticipate the ground state, reactivity, and single‐electron detachment energy of the three‐body hydride ion. We investigated both Hardware‐Efficient Ansatz (HEA) and Chemistry‐inspired ansatz based on a Unitary Coupled Cluster (UCC) on both noiseless and noisy IBM simulators. Modern error‐mitigating techniques, such as Zero‐Noise Extrapolation (ZNE) with unitary folding and measurement error mitigation, have been implemented to significantly reduce errors in noisy environments. This study contributes to our understanding of the quantum computational nuances of the hydride ion and addresses the question of whether quantum computers can retain the correlation energies for these correlated ions.
Identification of a Novel HIV‐1 Integrase Strand Transfer Inhibitor: A Synergistic Approach Combining Pharmacophore Modelling and In Vitro Assays
Authors: Sayyed Sharif Karim , Quraishi Marzuqa , Jobby Renitta , Rameshkumar Neelamegam , Sonawane Tareeka , Ravichandran Vinothkannan 
Published: November 2024

BackgroundAIDS is a highly prevalent and life‐threatening global epidemic that severely compromises the host's immune system, increasing vulnerability to opportunistic diseases. The absence of definitive curative drugs emphasizes the importance and necessity of discovering novel anti‐HIV agents.ObjectiveThis study aims to discover a natural molecular entity that acts as an Integrase strand transfer inhibitor (INSTI) with enhanced potency against HIV.MethodsA ligand‐based pharmacophore model was developed for 4 FDA‐approved INSTIs, with the potential for treating HIV‐1. AutoDock facilitated molecular docking and free energy calculation to discern IN activity. Subsequently, MD simulations assessed interaction stability. ADMET analysis preceded an in vitro anti‐HIV strand transfer assay.ResultsThe generated model revealed a specific interaction involving Mg2+ ion chelation. Crucial residues of HIV‐1 IN and their respective free‐binding energies were identified. The lead compound exhibited superior in silico characteristics which were substantiated by 100 ns MD simulations and MM‐PBSA analysis. Additionally, the in vitro assay demonstrated potent inhibition with the lowest IC50, forming strong molecular interactions with IN.ConclusionThese findings showed valuable insights for the strategic development of new antiretroviral treatments (ART), paving the path for the development of natural therapeutic agents for HIV treatment.
C18‐DABCO‐AA: A Novel Brønsted Acidic Gemini Surfactant for the Environmental Benign Synthesis of 2,3‑Dihydroquinazolin‑4(1H)‑ones in Water at Ambient Temperature
Authors: Rajmane Vijaya , Shirke Sunita , Sutar Omkar , Kumbhar Arjun , Rajmane Archana 
Published: November 2024

AbstractWe have successfully synthesized a new acidic surfactant named C18‐DABCO‐AA, which contains octadecyl and acetic acid substituent on DABCO (1,4‐diazabicyclo [2.2.2] octane) unit, with an excellent yield. The synthesized surfactant was analyzed using FT‐IR, 1H NMR, and 13C NMR spectroscopic techniques. Its thermal behavior was analyzed by using the TGA–DTA technique. Our analysis indicates that the synthesized surfactant has excellent surfactant properties. Its critical micelle concentration (CMC) is 0.80 mM as determined by the conductivity method and UV–vis absorption measurement at 298.15 K. Surfactant also showed strong acidic properties with a pKa of 3.3. It is the best catalyst for the synthesis of 2,3‑dihydroquinazolin‑4(1H)‐ones (DHQs) in water at 40 °C. The synthesis is achieved via two‐component reactions of various aromatic aldehydes and anthranilamide in water at a CMC concentration of C18‐DABCO‐AA. The method was also extended for synthesizing DHQs via two‐component reactions of different ketones and anthranilamide under similar reaction conditions. The products were achieved in with yields ranging from good to excellent within 60–90 min. Additionally, the aqueous solution with C18‐DABCO‐AA was reused up to five times with a minor decrease in product yield. The present protocol is fast, energy‐efficient, and can be used on a large scale for synthetic purposes. Additionally, it boasts impressive environmentally friendly credentials.
Theoretical Investigation of Substitution Effect on the First Position of Coumarin Derivatives
Authors: Gupta Puja O. , Sharma Suryapratap J. , Sekar Nagaiyan 
Published: November 2024

AbstractDensity functional theory (DFT) and time‐dependent DFT (TD‐DFT) were used to investigate the linear and nonlinear optical properties of 1‐substituted coumarins. The molecular electrostatic potential plot, frontier molecular orbitals, and natural bond order analysis were used to study charge transfer properties. The bandgap exhibited an inverse relationship with the chemical potential (µ) and electrophilicity index (ω), while the chemical hardness (η) and hyperhardness (Γ) showed a direct relationship. First‐order hyperpolarizability (β0) and second‐order hyperpolarizability (γ) were directly correlated with the global reactivity descriptors μ and ω and inversely correlated with η and Γ.
Substrate Directed Highly Diastereoselective Synthesis of Eliglustat: A Drug for Gaucher Disease
Authors: Adarsh Doddegowdana Ramegowda , Teja Thadakamalla Ravi , Vinaykumar Allam , Sridhar B. , Reddy B. V. Subba 
Published: November 2024

AbstractA highly diastereoselective total synthesis of Eliglustat has been accomplished starting from a readily available D‐serine. This novel route involves a four‐step telescoped process to afford the keto intermediate (4) in 74% overall yield. The diastereoselective reduction of 4 using sodium borohydride provides the alcohol, a key intermediate 5, with excellent selectivity (>99 dr) and yield (95%). The total synthesis of Eliglustat from D‐serine has been accomplished in ten steps with an overall yield of 21%. This process not only gives a desirable yield but also avoids the use of hazardous conditions.
Cell Adhesion and Local Cytokine Control on Protein‐Functionalized PNIPAM‐co‐AAc Hydrogel Microcarriers
Authors: Rauer Sebastian Bernhard , Stüwe Lucas , Steinbeck Lea , de Toledo Marcelo Augusto Szymanski , Fischer Gereon , Wennemaring Simon , Marschick Jonas , Koschmieder Steffen , Wessling Matthias , Linkhorst John 
Published: November 2024

AbstractAchieving adequate cell densities remains a major challenge in establishing economic biotechnological and biomedical processes. A possible remedy is microcarrier‐based cultivation in stirred‐tank bioreactors (STBR), which offers a high surface‐to‐volume ratio, appropriate process control, and scalability. However, despite their potential, commercial microcarriers are currently limited to material systems featuring unnatural mechanical properties and low adaptability. Because matrix stiffness and ligand presentation impact phenotypical attributes, differentiation potential, and genetic stability, biotechnological processes can significantly benefit from microcarrier systems tailorable toward cell‐type specific requirements. This study introduces hydrogel particles co‐polymerized from poly(N‐isopropylacrylamide) (PNIPAM) and acrylic acid (AAc) as a platform technology for cell expansion. The resulting microcarriers exhibit an adjustable extracellular matrix‐like softness, an adaptable gel charge, and functional carboxyl groups, allowing electrostatic and covalent coupling of cell adhesive and cell fate‐modulating proteins. These features enable the attachment and growth of L929 mouse fibroblast cells in static microtiter plates and dynamic STBR cultivations while also providing vital growth factors, such as interleukin‐3, to myeloblast‐like 32D cells over 20 days of cultivation. The study explores the effects of different educt compositions on cell‐particle interactions and reveals that PNIPAM‐co‐AAc microcarriers can provide both covalently coupled and diffusively released cytokine to adjacent cells.
Capturing Dynamic Assembly of Nanoscale Proteins During Network Formation
Authors: Hughes Matt D G , Cook Kalila R , Cussons Sophie , Boroumand Ahmad , Tyler Arwen I I , Head David , Brockwell David J , Dougan Lorna 
Published: November 2024

AbstractThe structural evolution of hierarchical structures of nanoscale biomolecules is crucial for the construction of functional networks in vivo and in vitro. Despite the ubiquity of these networks, the physical mechanisms behind their formation and self‐assembly remains poorly understood. Here, this study uses photochemically cross‐linked folded protein hydrogels as a model biopolymer network system, with a combined time‐resolved rheology and small‐angle x‐ray scattering (SAXS) approach to probe both the load‐bearing structures and network architectures respectively thereby providing a cross‐length scale understanding of the network formation. Combining SAXS, rheology, and kinetic modeling, a dual formation mechanism consisting of a primary formation phase is proposed, where monomeric folded proteins create the preliminary protein network scaffold; and a subsequent secondary formation phase, where both additional intra‐network cross‐links form and larger oligomers diffuse to join the preliminary network, leading to a denser more mechanically robust structure. Identifying this as the origin of the structural and mechanical properties of protein networks creates future opportunities to understand hierarchical biomechanics in vivo and develop functional, designed‐for‐purpose, biomaterials.
Sainsc: A Computational Tool for Segmentation‐Free Analysis of In Situ Capture Data
Authors: Müller‐Bötticher Niklas , Tiesmeyer Sebastian , Eils Roland , Ishaque Naveed 
Published: November 2024

AbstractSpatially resolved transcriptomics (SRT) has become the method of choice for characterising the complexity of biomedical tissue samples. Until recently, scientists were restricted to SRT methods that can profile a limited set of target genes at high spatial resolution or transcriptome‐wide but at a low spatial resolution. Through recent developments, there are now methods that offer both subcellular spatial resolution and full transcriptome coverage. However, utilising these new methods' high spatial resolution and gene resolution remains elusive due to several factors, including low detection efficiency and high computational costs. Here, we present Sainsc (Segmentation‐free analysis of in situ capture data), which combines a cell‐segmentation‐free approach with efficient data processing of transcriptome‐wide nanometre‐resolution spatial data. Sainsc can generate cell‐type maps with accurate cell‐type assignment at the nanometre scale, together with corresponding maps of the assignment scores that facilitate interpretation of the local confidence of cell‐type assignment. We demonstrate its utility and accuracy for different tissues and technologies. Compared to other methods, Sainsc requires lower computational resources and has scalable performance, enabling interactive data exploration. Sainsc is compatible with common data analysis frameworks and is available as open‐source software in multiple programming languages.
MUPPAAL: Efficient Elimination and Reduction of Useless Mutants in Real‐Time Model‐Based Systems
Authors: Cuartas Jaime , Cortés David , Betancourt Joan , Aranda Jesús , Cordy Maxime , Ortiz James , Perrouin Gilles , Schobbens Pierre‐Yves 
Published: November 2024

ABSTRACTTo assess test quality, mutation testing (MT) creates mutants by injecting artificial faults into the system and evaluates the ability of tests to distinguish these mutants. Tests distinguishing more mutants have also been proven empirically to detect more real faults. MT has been applied to many domains. We focus on MT for timed safety‐critical systems modelled as Timed Automata (TA). While powerful, MT usually yields equivalent and duplicate mutants, the former having the same behaviour as the original system and the latter other mutants. Such useless mutants bring no value, waste execution time and can be difficult to detect. We integrate useless mutant detection and removal strategies in our mutation framework MUPPAAL. MUPPAAL leverages existing equivalence‐avoiding mutation operators and focuses on detecting mutant duplicates using a scalable bisimulation algorithm and a fast approximate one based on biased simulation. We also demonstrate how to design an operator that reduces the occurrence of mutant duplicates. We evaluate MUPPAAL on six systems, demonstrating that (1) mutant duplicates account for up to 32% of all generated mutants, (2) our bisimulation approach scales effectively with these systems and (3) biased simulations further enhance performance. Our heuristic is 10 times faster than bisimulation and limits the exploration to two times the number of exact duplicates compared to up to 10 times for the baseline.
Modeling Land Use and Land Cover Changes and Its Atmospheric Pollutant Concentration in the Coal Mining Area of Ramgarh District of Jharkhand, India, Using Multi‐Layer Perceptron Neural Networks (MLPNN)
Authors: Ahmad Shazada , Kaur Navneet , Badar Mahammad Shahbaz , Shakeel Adnan , Ahmed Farid 
Published: November 2024

ABSTRACTLand use refers to anthropogenic phenomena in the natural environment; humans utilize land resources for their developmental activities. On the other hand, the ecosystems of land use and land cover alter the natural world—the artificial infrastructure leads toward a busted concrete jungle instead of a green footprint. The global green footprint is continually shrinking owing to overutilization of natural resources. The present research examines the land use pattern that changes from 1990 to 2021 and projected projections for 2041 and 2061 in the Ramgarh District. The study also focuses on how artificial modifications alter the concentration level of pollutants in the atmosphere. The Landsat data utilized for 1990, 2000, 2011, and 2021 were incorporated into the LULC map using supervised classification and for analysis of future predictions for 2041 and 2061 using an ANN‐based on MLPNNs (multi‐layer perceptron neural networks) for Ramgarh District. It also focuses on the trend and patterns of atmospheric pollutants from data using NASA‐GIOVANNI MERRA‐2. The current study reveals that in 1990, water bodies, coal mining, vegetation, built‐up, agriculture, and barren land were 3.01%, 2.24%, 54.07%, 3.64%, 36.85%, and 0.18 %. However, in 2021, water bodies decreased to 1.61%, vegetation to 45.47%, barren land to 0.65%, and an increasing tendency was observed in built‐up areas to 6.65%, coal mining to 2.43%, and farmland to 43.19%. A significant trend in atmospheric pollutants, such as CO2, SO2, SO4, NO2, and dust, is observed in the Ramgarh district. The importance of this study is to attain the maximum level of environmental sustainability; it would also encourage the local level planning fitted during the extraction of natural resources.