bluuebunny/crossref_metadata_2025_split huggingface dataset:
AGILE platform: a deep learning powered approach to accelerate LNP development for mRNA delivery
Authors: Xu Yue , Ma Shihao , Cui Haotian , Chen Jingan , Xu Shufen , Gong Fanglin , Golubovic Alex , Zhou Muye , Wang Kevin Chang , Varley Andrew , Lu Rick Xing Ze , Wang Bo , Li Bowen 
Published: July 2024

AbstractIonizable lipid nanoparticles (LNPs) are seeing widespread use in mRNA delivery, notably in SARS-CoV-2 mRNA vaccines. However, the expansion of mRNA therapies beyond COVID-19 is impeded by the absence of LNPs tailored for diverse cell types. In this study, we present the AI-Guided Ionizable Lipid Engineering (AGILE) platform, a synergistic combination of deep learning and combinatorial chemistry. AGILE streamlines ionizable lipid development with efficient library design, in silico lipid screening via deep neural networks, and adaptability to diverse cell lines. Using AGILE, we rapidly design, synthesize, and evaluate ionizable lipids for mRNA delivery, selecting from a vast library. Intriguingly, AGILE reveals cell-specific preferences for ionizable lipids, indicating tailoring for optimal delivery to varying cell types. These highlight AGILE’s potential in expediting the development of customized LNPs, addressing the complex needs of mRNA delivery in clinical practice, thereby broadening the scope and efficacy of mRNA therapies.
Electrocatalytic continuous flow chlorinations with iodine(I/III) mediators
Authors: Patra Tuhin , Arepally Sagar , Seitz Jakob , Wirth Thomas 
Published: July 2024

AbstractElectrochemistry offers tunable, cost effective and environmentally friendly alternatives to carry out redox reactions with electrons as traceless reagents. The use of organoiodine compounds as electrocatalysts is largely underdeveloped, despite their widespread application as powerful and versatile reagents. Mechanistic data reveal that the hexafluoroisopropanol assisted iodoarene oxidation is followed by a stepwise chloride ligand exchange for the catalytic generation of the dichloroiodoarene mediator. Here, we report an environmentally benign iodine(I/III) electrocatalytic platform for the in situ generation of dichloroiodoarenes for different reactions such as mono- and dichlorinations as well as chlorocyclisations within a continuous flow setup.
A solution for 4-propylguaiacol hydrodeoxygenation without ring saturation
Authors: Zhang Zihao , Li Qiang , Wu Xiangkun , Bourmaud Claire , Vlachos Dionisios G. , Luterbacher Jeremy , Bodi Andras , Hemberger Patrick 
Published: July 2024

AbstractWe investigate solvent effects in the hydrodeoxygenation of 4-propylguaiacol (4PG, 166 amu), a key lignin-derived monomer, over Ru/C catalyst by combined operando synchrotron photoelectron photoion coincidence (PEPICO) spectroscopy and molecular dynamics simulations. With and without isooctane co-feeding, ring-hydrogenated 2-methoxy-4-propylcyclohexanol (172 amu) is the first product, due to the favorable flat adsorption configuration of 4PG on the catalyst surface. In contrast, tetrahydrofuran (THF)—a polar aprotic solvent that is representative of those used for lignin solubilization and upgrading—strongly coordinates to the catalyst surface at the oxygen atom. This induces a local steric hindrance, blocking the flat adsorption of 4PG more effectively, as it needs more Ru sites than the tilted adsorption configuration revealed by molecular dynamics simulations. Therefore, THF suppresses benzene ring hydrogenation, favoring a demethoxylation route that yields 4-propylphenol (136 amu), followed by dehydroxylation to propylbenzene (120 amu). Solvent selection may provide new avenues for controlling catalytic selectivity.
Phonon-mediated unconventional superconductivity in rhombohedral stacked multilayer graphene
Authors: Viñas Boström Emil , Fischer Ammon , Profe Jonas B. , Zhang Jin , Kennes Dante M. , Rubio Angel 
Published: July 2024

AbstractUnderstanding the origin of superconductivity in correlated two-dimensional materials is a key step in leveraging material engineering techniques for next-generation nanoscale devices. While it is widely accepted that phonons fluctuations only mediate conventional (s-wave) superconductivity, the common phenomenology of superconductivity in Bernal bilayer and rhombohedral trilayer graphene, as well as in a large family of graphene-based moiré systems, suggests a common superconducting mechanism across these platforms. In particular, in all these platforms some superconducting regions violate the Pauli limit, indicating unconventional superconductivity, naively ruling out conventional phonon-mediated pairing as the underlying mechanism. Here we combine first principles simulations with effective low-energy theories to investigate the superconducting mechanism and pairing symmetry in rhombohedral stacked graphene multilayers. We find that phonon-mediated superconductivity explains the main experimental findings, namely the displacement field and doping level dependence of the critical temperature, and the presence of two superconducting regions with different pairing symmetries that depend on the parent normal state. In particular, we find that intra-valley phonon scattering favors a triplet f-wave pairing when combined with electronic correlations stabilizing a spin- and valley-polarized normal state. We also propose a so far unexplored superconducting region at higher hole doping densities nh ≈ 4 × 1012 cm−2, and demonstrate how this highly hole-doped regime can be reached in heterostructures consisting of monolayer α-RuCl3 and rhombohedral trilayer graphene. Our findings promote phonon-mediated pairing as a strong contender to explain superconductivity across a wide range of graphene platforms, and demonstrate that phonons can, in fact, stabilize unconventional superconducting orders.
Flexible adaptive sensing tonometry for medical-grade multi-parametric hemodynamic monitoring
Authors: Deng Mengkang , Du Chentao , Fang Jiayuan , Xu Changshun , Guo Chenhui , Huang Jiantao , Li Kang , Chen Lei , Zhang Yuan-Ting , Chang Yu , Pan Tingrui 
Published: July 2024

AbstractContinuous hemodynamic monitoring in a wearable means can play a crucial role in managing hypertension and preventing catastrophic cardiovascular events. In this study, we have described the fully wearable tonometric device, referred to as flexible adaptive sensing tonometry (FAST), which is capable of continuous and accurate monitoring of hemodynamic parameters within the medical-grade precision. In particular, the FAST system integrates a 1 × 8 unit array of highly sensitive and highly flexible iontronic sensing (FITS) with 1 mm spatial resolution and a closed-loop motion system. The flexible tonometric architecture has been used to determine the radial arterial position with high sensitivity and high conformability, which simplifies the biaxial searching process of the traditional applanation tonometry into a highly efficient uniaxial applanation while keeping the medical-precision assessments. Importantly, a self-calibration algorithm can be automatically implemented during the applanation process, from which the intra-arterial blood pressure wave can be continuously predicted within the medical-grade precision, and subsequently, multi-parametric hemodynamic analysis can be performed in real-time. Experimental validations on health volunteers have demonstrated that the FAST measurements are all within the required accuracy of the clinical standards for continuous pulse wave assessments, blood pressure monitoring as well as other key hemodynamic parameter evaluations. Therefore, the FAST system, by integrating the flexible iontronic sensing array, provides a real-time, medical-grade hemodynamic monitoring solution in a continuously wearable manner, from which remote patient-centered monitoring can be delivered with both medical precision and convenience.
Comprehensive real time remote monitoring for Parkinson’s disease using Quantitative DigitoGraphy
Authors: Hoffman Shannon L. , Schmiedmayer Paul , Gala Aryaman S. , Wilkins Kevin B. , Parisi Laura , Karjagi Shreesh , Negi Aarushi S. , Revlock Simon , Coriz Christopher , Revlock Jeremy , Ravi Vishnu , Bronte-Stewart Helen 
Published: July 2024

AbstractPeople with Parkinson’s disease (PWP) face critical challenges, including lack of access to neurological care, inadequate measurement and communication of motor symptoms, and suboptimal medication management and compliance. We have developed QDG-Care: a comprehensive connected care platform for Parkinson’s disease (PD) that delivers validated, quantitative metrics of all motor signs in PD in real time, monitors the effects of adjusting therapy and medication adherence and is accessible in the electronic health record. In this article, we describe the design and engineering of all components of QDG-Care, including the development and utility of the QDG Mobility and Tremor Severity Scores. We present the preliminary results and insights from an at-home trial using QDG-Care. QDG technology has enormous potential to improve access to, equity of, and quality of care for PWP, and improve compliance with complex time-critical medication regimens. It will enable rapid “Go-NoGo” decisions for new therapeutics by providing high-resolution data that require fewer participants at lower cost and allow more diverse recruitment.
Development of a multidecadal land reanalysis over High Mountain Asia
Authors: Maina Fadji Z. , Xue Yuan , Kumar Sujay V. , Getirana Augusto , McLarty Sasha , Appana Ravi , Forman Bart , Zaitchik Ben , Loomis Bryant , Maggioni Viviana , Zhou Yifan 
Published: July 2024

AbstractAnthropogenic and climatic changes affect the water and energy cycles in High Mountain Asia (HMA), home to over two billion people and the largest reservoirs of freshwater outside the polar zone. Despite their significant importance for water management, consistent and reliable estimates of water storage and fluxes over the region are lacking because of the high uncertainties associated with the estimates of atmospheric conditions and human management. Here, we relied on multivariate data assimilation (MVDA) to provide estimates of energy and water storage and fluxes that reflect the processes occurring in the region such as greening and irrigation-driven groundwater depletion. We developed and employed an ensemble precipitation estimate by blending different precipitation products thereby reducing the uncertainties and inconsistencies associated with precipitation in HMA. Then, we assimilated five variables that capture the changes in hydrology in response to climate change and anthropogenic activities. Overall, our results have shown that MVDA has allowed a better representation of the land surface processes including greening and irrigation-driven groundwater depletion in HMA.
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation
Authors: Jian Muwei , Chen Hongyu , Zhang Zaiyong , Yang Nan , Zhang Haorang , Ma Lifu , Xu Wenjing , Zhi Huixiang 
Published: July 2024

AbstractRecently, Computer-Aided Diagnosis (CAD) systems have emerged as indispensable tools in clinical diagnostic workflows, significantly alleviating the burden on radiologists. Nevertheless, despite their integration into clinical settings, CAD systems encounter limitations. Specifically, while CAD systems can achieve high performance in the detection of lung nodules, they face challenges in accurately predicting multiple cancer types. This limitation can be attributed to the scarcity of publicly available datasets annotated with expert-level cancer type information. This research aims to bridge this gap by providing publicly accessible datasets and reliable tools for medical diagnosis, facilitating a finer categorization of different types of lung diseases so as to offer precise treatment recommendations. To achieve this objective, we curated a diverse dataset of lung Computed Tomography (CT) images, comprising 330 annotated nodules (nodules are labeled as bounding boxes) from 95 distinct patients. The quality of the dataset was evaluated using a variety of classical classification and detection models, and these promising results demonstrate that the dataset has a feasible application and further facilitate intelligent auxiliary diagnosis.
Genome-scale community modelling elucidates the metabolic interaction in Indian type-2 diabetic gut microbiota
Authors: Beura Satyajit , Kundu Pritam , Das Amit Kumar , Ghosh Amit 
Published: July 2024

AbstractType-2 diabetes (T2D) is a rapidly growing multifactorial metabolic disorder that induces the onset of various diseases in the human body. The compositional and metabolic shift of the gut microbiota is a crucial factor behind T2D. Hence, gaining insight into the metabolic profile of the gut microbiota is essential for revealing their role in regulating the metabolism of T2D patients. Here, we have focused on the genome-scale community metabolic model reconstruction of crucial T2D-associated gut microbes. The model-based analysis of biochemical flux in T2D and healthy gut conditions showed distinct biochemical signatures and diverse metabolic interactions in the microbial community. The metabolic interactions encompass cross-feeding of short-chain fatty acids, amino acids, and vitamins among individual microbes within the community. In T2D conditions, a reduction in the metabolic flux of acetate, butyrate, vitamin B5, and bicarbonate was observed in the microbial community model, which can impact carbohydrate metabolism. The decline in butyrate levels is correlated with both insulin resistance and diminished glucose metabolism in T2D patients. Compared to the healthy gut, an overall reduction in glucose consumption and SCFA production flux was estimated in the T2D gut environment. Moreover, the decreased consumption profiles of branch chain amino acids (BCAAs) and aromatic amino acids (AAAs) in the T2D gut microbiota can be a distinct biomarker for T2D. Hence, the flux-level analysis of the microbial community model can provide insights into the metabolic reprogramming in diabetic gut microbiomes, which may be helpful in personalized therapeutics and diet design against T2D.
Predicting 28-day compressive strength of fibre-reinforced self-compacting concrete (FR-SCC) using MEP and GEP
Authors: Inqiad Waleed Bin , Siddique Muhammad Shahid , Ali Mujahid , Najeh Taoufik 
Published: July 2024

AbstractThe utilization of Self-compacting Concrete (SCC) has escalated worldwide due to its superior properties in comparison to normal concrete such as compaction without vibration, increased flowability and segregation resistance. Various other desirable properties like ductile behaviour, increased strain capacity and tensile strength etc. can be imparted to SCC by incorporation of fibres. Thus, this study presents a novel approach to predict 28-day compressive strength (C–S) of FR-SCC using Gene Expression Programming (GEP) and Multi Expression Programming (MEP) for fostering its widespread use in the industry. For this purpose, a dataset had been compiled from internationally published literature having six input parameters including water-to-cement ratio, silica fume, fine aggregate, coarse aggregate, fibre, and superplasticizer. The predictive abilities of developed algorithms were assessed using error metrices like mean absolute error (MAE), a20-index, and objective function (OF) etc. The comparison of MEP and GEP models indicated that GEP gave a simple equation having lesser errors than MEP. The OF value of GEP was 0.029 compared to 0.031 of MEP. Thus, sensitivity analysis was performed on GEP model. The models were also checked using some external validation checks which also verified that MEP and GEP equations can be used to forecast the strength of FR-SCC for practical uses.
OCT1 (SLC22A1) transporter kinetics and regulation in primary human hepatocyte 3D spheroids
Authors: Mickols Evgeniya , Meyer Alina , Handin Niklas , Stüwe Malin , Eriksson Jens , Rudfeldt Jakob , Blom Kristin , Fryknäs Mårten , Sellin Mikael E. , Lauschke Volker M. , Karlgren Maria , Artursson Per 
Published: July 2024

Abstract3D spheroids of primary human hepatocytes (3D PHH) retain a differentiated phenotype with largely conserved metabolic function and proteomic fingerprint over weeks in culture. As a result, 3D PHH are gaining importance as a model for mechanistic liver homeostasis studies and in vitro to in vivo extrapolation (IVIVE) in drug discovery. However, the kinetics and regulation of drug transporters have not yet been assessed in 3D PHH. Here, we used organic cation transporter 1 (OCT1/SLC22A1) as a model to study both transport kinetics and the long-term regulation of transporter activity via relevant signalling pathways. The kinetics of the OCT1 transporter was studied using the fluorescent model substrate 4-(4-(dimethylamino)styryl)-N-methylpyridinium (ASP+) and known OCT1 inhibitors in individual 3D PHH. For long-term studies, 3D PHH were treated with xenobiotics for seven days, after which protein expression and OCT1 function were assessed. Global proteomic analysis was used to track hepatic phenotypes as well as prototypical changes in other regulated proteins, such as P-glycoprotein and Cytochrome P450 3A4. ASP+ kinetics indicated a fully functional OCT1 transporter with a Km value of 14 ± 4.0µM as the mean from three donors. Co-incubation with known OCT1 inhibitors decreased the uptake of ASP+ in the 3D PHH spheroids by 35–52%. The long-term exposure studies showed that OCT1 is relatively stable upon activation of nuclear receptor signalling or exposure to compounds that could induce inflammation, steatosis or liver injury. Our results demonstrate that 3D PHH spheroids express physiologically relevant levels of fully active OCT1 and that its transporter kinetics can be accurately studied in the 3D PHH configuration. We also confirm that OCT1 remains stable and functional during the activation of key metabolic pathways that alter the expression and function of other drug transporters and drug-metabolizing enzymes. These results will expand the range of studies that can be performed using 3D PHH.
Associations between vicarious racism and psychoactive substance use depend on strength of ethnic identity
Authors: Cruz-Vespa Isabela , Dembling Sarah J. , Han Benjamin H. , Inagaki Tristen K. 
Published: July 2024

AbstractRacism is a pervasive threat to health with differential impact based on race and ethnicity. Considering the continued perpetration and visibility of racism online and in the news, vicarious racism, or “secondhand” racism when hearing about or witnessing racism being committed against members of one’s ethnic or racial group, is a particularly urgent threat in the context of such disparities and their subsequent health consequences. The current study examines if frequency of exposure to vicarious racism and the emotional impact of those experiences are linked to psychoactive substance use, and explores the role of ethnic identity in moderating these relationships. In a cross-sectional survey, 504 adult participants aged 18–78 (M age = 30.15, SD = 11.52, 52.6% female) identifying as Black/African American or Latine reported on their experiences with vicarious racism and alcohol, marijuana, and tobacco use over the past 30 days. Logistic regression was utilized to test hypotheses. Primary findings indicate that greater emotional impact of vicarious racism was associated with a 50% increase in odds of alcohol consumption and that ethnic identity moderated the association between vicarious racism and marijuana use. Greater emotional impact of vicarious racism was related to more marijuana use for those lower on ethnic identity, whereas there was no association for those higher on ethnic identity. Vicarious racism was not related to tobacco use. Results suggest that ethnic identity might be protective in the association of vicarious racism on substance use. Further research on this topic is needed as vicarious racism becomes an increasingly common experience among marginalized populations.
Enhancement of lemongrass essential oil physicochemical properties and antibacterial activity by encapsulation in zein-caseinate nanocomposite
Authors: Alsakhawy Sara A. , Baghdadi Hoda H. , El-Shenawy Moustafa A. , El-Hosseiny Lobna S. 
Published: July 2024

AbstractEssential oils (EOs) represent a pivotal source for developing potent antimicrobial drugs. However, EOs have seldom found their way to the pharmaceutical market due to their instability and low bioavailability. Nanoencapsulation is an auspicious strategy that may circumvent these limitations. In the current study, lemongrass essential oil (LGO) was encapsulated in zein-sodium caseinate nanoparticles (Z-NaCAS NPs). The fabricated nanocomposite was characterized using dynamic light scattering, Fourier-transform infrared spectroscopy, differential scanning calorimetry, and transmission electron microscopy. The antimicrobial activity of LGO loaded NPs was assessed in comparison to free LGO against Staphylococcus epidermidis, Enterococcus faecalis, Escherichia coli, and Klebsiella pneumoniae. Furthermore, their antibacterial mechanism was examined by alkaline phosphatase, lactate dehydrogenase, bacterial DNA and protein assays, and scanning electron microscopy. Results confirmed the successful encapsulation of LGO with particle size of 243 nm, zeta potential of – 32 mV, and encapsulation efficiency of 84.7%. Additionally, the encapsulated LGO showed an enhanced thermal stability and a sustained release pattern. Furthermore, LGO loaded NPs exhibited substantial antibacterial activity, with a significant 2 to 4 fold increase in cell wall permeability and intracellular enzymes leakage versus free LGO. Accordingly, nanoencapsulation in Z-NaCAS NPs improved LGO physicochemical and antimicrobial properties, expanding their scope of pharmaceutical applications.
Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making
Authors: Saleh Neven , Hassan Mohammed A. , Salaheldin Ahmed M. 
Published: July 2024

AbstractSkin cancer is a type of cancer disease in which abnormal alterations in skin characteristics can be detected. It can be treated if it is detected early. Many artificial intelligence-based models have been developed for skin cancer detection and classification. Considering the development of numerous models according to various scenarios and selecting the optimum model was rarely considered in previous works. This study aimed to develop various models for skin cancer classification and select the optimum model. Convolutional neural networks (CNNs) in the form of AlexNet, Inception V3, MobileNet V2, and ResNet 50 were used for feature extraction. Feature reduction was carried out using two algorithms of the grey wolf optimizer (GWO) in addition to using the original features. Skin cancer images were classified into four classes based on six machine learning (ML) classifiers. As a result, 51 models were developed with different combinations of CNN algorithms, without GWO algorithms, with two GWO algorithms, and with six ML classifiers. To select the optimum model with the best results, the multicriteria decision-making approach was utilized to rank the alternatives by perimeter similarity (RAPS). Model training and testing were conducted using the International Skin Imaging Collaboration (ISIC) 2017 dataset. Based on nine evaluation metrics and according to the RAPS method, the AlexNet algorithm with a classical GWO yielded the optimum model, achieving a classification accuracy of 94.5%. This work presents the first study on benchmarking skin cancer classification with many models. Feature reduction not only reduces the time spent on training but also improves classification accuracy. The RAPS method has proven its robustness in the problem of selecting the best model for skin cancer classification.