bluuebunny/crossref_metadata_2025 huggingface dataset:
Facilitating “Omics” for Phenotype Classification Using a User-Friendly AI-Driven Platform: Application in Cancer Prognostics
Authors: Filho Uraquitan Lima , Pais Tiago Alexandre , Pais Ricardo Jorge
Published: November 2023

Precision medicine approaches often rely on complex and integrative analyses of multiple biomarkers from “omics” data to generate insights that can help with either diagnostic, prognostic, or therapeutical decisions. Such insights are often made using machine learning (ML) models that perform sample classification for a particular phenotype (yes/no). Building such models is a challenge and time-consuming, requiring advanced coding skills and mathematical modelling expertise. Artificial intelligence (AI) is a methodological solution that has the potential to facilitate, optimize, and scale model development. In this work, we developed an AI-based, user-friendly, and code-free platform that fully automated the development of predictive models from quantitative “omics” data. Here, we show the application of this tool with the development of cancer survival prognostics models using real-life data from breast, lung, and renal cancer transcriptomes. In comparison to other models, our generated models rendered performances with competitive sensitivities (72–85%), specificities (76–85%), accuracies (75–85%), and Receiver Operating Characteristic curves with superior Areas Under the Curve (ROC-AUC of 77–86%). Further, we reported the associated sets of genes (biomarkers) and their expression patterns that were predictive of cancer survival. Moreover, we made our models available as online tools to generate prognostic predictions based on the gene expressions of the biomarkers. In conclusion, we demonstrated that our tool is a robust, user-friendly solution for developing bespoke predictive tools from “omics” data, which facilitate precision medicine applications to the point-of-care.
Tracking of Systemic Lupus Erythematosus (SLE) Longitudinally Using Biosensor and Patient-Reported Data: A Report on the Fully Decentralized Mobile Study to Measure and Predict Lupus Disease Activity Using Digital Signals—The OASIS Study
Authors: Jupe Eldon R. , Lushington Gerald H. , Purushothaman Mohan , Pautasso Fabricio , Armstrong Georg , Sorathia Arif , Crawley Jessica , Nadipelli Vijay R. , Rubin Bernard , Newhardt Ryan , Munroe Melissa E. , Adelman Brett
Published: November 2023

(1) Objective: Systemic lupus erythematosus (SLE) is a complex disease involving immune dysregulation, episodic flares, and poor quality of life (QOL). For a decentralized digital study of SLE patients, machine learning was used to assess patient-reported outcomes (PROs), QOL, and biometric data for predicting possible disease flares. (2) Methods: Participants were recruited from the LupusCorner online community. Adults self-reporting an SLE diagnosis were consented and given a mobile application to record patient profile (PP), PRO, and QOL metrics, and enlisted participants received smartwatches for digital biometric monitoring. The resulting data were profiled using feature selection and classification algorithms. (3) Results: 550 participants completed digital surveys, 144 (26%) agreed to wear smartwatches, and medical records (MRs) were obtained for 68. Mining of PP, PRO, QOL, and biometric data yielded a 26-feature model for classifying participants according to MR-identified disease flare risk. ROC curves significantly distinguished true from false positives (ten-fold cross-validation: p < 0.00023; five-fold: p < 0.00022). A 25-feature Bayesian model enabled time-variant prediction of participant-reported possible flares (P(true) > 0.85, p < 0.001; P(nonflare) > 0.83, p < 0.0001). (4) Conclusions: Regular profiling of patient well-being and biometric activity may support proactive screening for circumstances warranting clinical assessment.
Computational Fluid Dynamics Modeling of the Filtration of 2D Materials Using Hollow Fiber Membranes
Authors: Elahi Arash , Chaudhuri Santanu
Published: November 2023

The current study presents a computational fluid dynamics (CFDs) model designed to simulate the microfiltration of 2D materials using hollow fiber membranes from their dispersion. Microfiltration has recently been proposed as a cost-effective strategy for 2D material production, involving a dispersion containing a permeating solute (graphene), a fouling material (non-exfoliated graphite), and the solvent. The objective of the model is to investigate the effects of fouling of flat layered structure material (graphite) on the transmembrane pressure (TMP) of the system and the filtration of the permeating solute. COMSOL Multiphysics software was used to numerically solve the coupled Navier–Stokes and mass conservation equations to simulate the flow and mass transfer in the two-dimensional domain. For the TMP calculations, we used the resistance-in-series approach to link the fouling of the foulants to the TMP behavior. The foulant particles were assumed to form a polarization layer and cake on the membrane surface, leading to the increment of the TMP of the system. We also assumed the wettability of the polymeric membrane’s inner wall increases upon fouling due to the flat layered structure of the foulant, which results in the reduction in the TMP. This approach accurately reproduced the experimental TMP behavior with a Mean Absolute Error (MAE) of 0.007 psi. Furthermore, the permeation of the permeating solute was computed by incorporating a fouling-dependent membrane partition coefficient for these particles. The effects of the concentration polarization and cake formation fouling stages on the membrane partition coefficient were encapsulated into our defined model parameters, denoted as α and β, respectively. This formulation of the partition coefficient yielded permeate concentration profiles, which are in excellent agreement with the experiments. For three feed concentrations of 0.05, 0.1, and 0.3 g/L, our model reproduced the experimental permeate concentration profiles with MAEs of 0.0002, 0.0003, and 0.0022 g/L, respectively. The flexibility of this model enables the users to utilize the size and concentration-dependent α and β parameters and optimize their experimental microfiltration setups effectively.
Condensed DNA Nanosphere for DNA Origami Cryptography
Authors: Gao Rui , Cai Zhuang , Wang Jianbang , Liu Huajie
Published: November 2023

Maintaining the confidentiality and integrity of the messages during a transmission is one of the most important aims of encrypted communication systems. Many achievements were made using biomolecules to improve the quality of the messages in communication. At the same time, it is still a challenge to construct cooperative communications based on the interactions between biomolecules to achieve the confidentiality and integrity of the transmitted messages. DNA-based encrypted communications have been developed, and in particular, DNA-origami-based message encryption can combine steganography and pattern encryption and exhibits extremely high confidentiality. Nevertheless, limited by biological characteristics, encrypted messages based on DNA require a strict storage environment in the process of transmission. The integrity of the message encoded in the DNA may be damaged when the DNA is in an unfriendly and hard environment. Therefore, it is particularly significant to improve the stability of DNA when it is exposed to a harsh environment during transmission. Here, we encoded the information into the DNA strands that were condensed for encryption to form a nanosphere covered with a shell of SiO2, which brings high-density messages and exhibits higher stability than separated DNA. The solid shell of SiO2 could prevent DNA from contacting the harsh environment, thereby protecting the DNA structure and maintaining the integrity of the information. At the same time, DNA nanospheres can achieve high throughput input and higher storage density per unit volume, which contribute to confusing the message strand (M-strand) with the interference strand in the stored information. Condensing DNA into the nanosphere that is used for DNA origami cryptography has the potential to be used in harsh conditions with higher confidentiality and integrity for the transmitted messages.
The Role of Product Engineering Modelling in Aesthetic Design Education
Authors: Furferi Rocco , Buonamici Francesco
Published: November 2023

By progressively embracing the general principles of integrated, parametric, interdisciplinary design that considers the manufacturing elements of the imagined product, the modern aesthetic designer is called upon to broaden their knowledge and abilities. Especially when there is a need to produce complex shapes, when cost-effective, there are also numerous 3D printing technologies available today, to be used both in the conceptual phase (prototyping) and for actual production. The present paper aims to propose a discussion on the role of product engineering modelling in aesthetic design education. The progress of new 3D parametric modelling tools available to aesthetic designers is discussed, with a focus on the most cutting-edge features that have been released recently. The importance of parametric design education in general and the positive effects its application can have in the design process will also be discussed.
The Intersectionality of Gender, Sexual Identity, and Disability in Disaster Risk Reduction in OECD Countries: A Rapid Scoping Review
Authors: Crawford Tonia , Chang Kuo-yi Jade , Nila Farhana , Subramaniam Parvathi , Bethune Loriana , Parkinson Debra , Villeneuve Michelle
Published: November 2023

People with disability are disproportionally affected by disasters due to layers of marginalisation from an interaction of personal, social, economic, political, and environmental factors. These intersect with gender, gender identity, and sexual orientation, and result in additional discrimination and social exclusion that reinforce inequality and stigma. There has been little focus on the intersection of disability and gender in disability-inclusive disaster risk reduction (DIDRR) in high-income countries. This paper reports on a scoping review exploring the intersection of gender and sexual identity and disability in disaster in both peer-reviewed and grey literature. Building greater awareness of the specific needs of marginalised groups such as women, gender, and sexually diverse people into DIDRR will reduce the disproportionate impacts of disaster on these groups.
Image-to-Image Translation-Based Structural Damage Data Augmentation for Infrastructure Inspection Using Unmanned Aerial Vehicle
Authors: Gwon Gi-Hun , Lee Jin-Hwan , Kim In-Ho , Baek Seung-Chan , Jung Hyung-Jo
Published: November 2023

As technology advances, the use of unmanned aerial vehicles (UAVs) and image sensors for structural monitoring and diagnostics is becoming increasingly critical. This approach enables the efficient inspection and assessment of structural conditions. Furthermore, the integration of deep learning techniques has been proven to be highly effective in detecting damage from structural images, as demonstrated in our study. To enable effective learning by deep learning models, a substantial volume of data is crucial, but collecting appropriate instances of structural damage from real-world scenarios poses challenges and demands specialized knowledge, as well as significant time and resources for labeling. In this study, we propose a methodology that utilizes a generative adversarial network (GAN) for image-to-image translation, with the objective of generating synthetic structural damage data to augment the dataset. Initially, a GAN-based image generation model was trained using paired datasets. When provided with a mask image, this model generated an RGB image based on the annotations. The subsequent step generated domain-specific mask images, a critical task that improved the data augmentation process. These mask images were designed based on prior knowledge to suit the specific characteristics and requirements of the structural damage dataset. These generated masks were then used by the GAN model to produce new RGB image data incorporating various types of damage. In the experimental validation conducted across the three datasets to assess the image generation for data augmentation, our results demonstrated that the generated images closely resembled actual images while effectively conveying information about the newly introduced damage. Furthermore, the experimental validation of damage detection with augmented data entailed a comparative analysis between the performance achieved solely with the original dataset and that attained with the incorporation of additional augmented data. The results for damage detection consistently demonstrated that the utilization of augmented data enhanced performance when compared to relying solely on the original images.
U-Net Performance for Beach Wrack Segmentation: Effects of UAV Camera Bands, Height Measurements, and Spectral Indices
Authors: Tiškus Edvinas , Bučas Martynas , Gintauskas Jonas , Kataržytė Marija , Vaičiūtė Diana
Published: November 2023

This study delves into the application of the U-Net convolutional neural network (CNN) model for beach wrack (BW) segmentation and monitoring in coastal environments using multispectral imagery. Through the utilization of different input configurations, namely, “RGB”, “RGB and height”, “5 bands”, “5 bands and height”, and “Band ratio indices”, this research provides insights into the optimal dataset combination for the U-Net model. The results indicate promising performance with the “RGB” combination, achieving a moderate Intersection over Union (IoU) of 0.42 for BW and an overall accuracy of IoU = 0.59. However, challenges arise in the segmentation of potential BW, primarily attributed to the dynamics of light in aquatic environments. Factors such as sun glint, wave patterns, and turbidity also influenced model accuracy. Contrary to the hypothesis, integrating all spectral bands did not enhance the model’s efficacy, and adding height data acquired from UAVs decreased model precision in both RGB and multispectral scenarios. This study reaffirms the potential of U-Net CNNs for BW detection, emphasizing the suitability of the suggested method for deployment in diverse beach geomorphology, requiring no high-end computing resources, and thereby facilitating more accessible applications in coastal monitoring and management.
The Evolution of Psychological Distress Levels in University Students in Spain during Different Stages of the COVID-19 Pandemic: Risk and Protective Factors
Authors: Matud María Pilar , Zueco Jesús , Del Pino-Espejo Maria José , Fortes Demelsa , Beleña María Ángeles , Santos Cristina , Díaz Amelia
Published: November 2023

The present study assesses the evolution of stressful events and psychological distress in male and female students over three different time periods of the COVID-19 pandemic in Spain: the initial “lockdown”, with no face-to-face teaching; the “new normality” period, when classes were resumed; and two years after the first wave of the pandemic. The participants were 1200 Spanish university students who were assessed for psychological distress, COVID-19-associated stressful events, social support, and self-esteem. Female students reported more stressful events and higher levels of psychological distress than male students during the “lockdown” and “new normality” time periods of the first wave of the pandemic. However, these differences disappeared in the third period tested, two years after the first wave of the pandemic, with female and male students showing no differences in psychological distress or in the number of stressful events. The main risk predictors of psychological distress during the first wave of the pandemic were lower self-esteem and having suffered a high number of stressful events. The last variable, number of stressful events associated with COVID-19, lost most its effect two years later, when only self-esteem presented a strong and highly significant predictive role.
Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder Perspectives
Authors: Hasanein Ahmed M. , Sobaih Abu Elnasr E.
Published: November 2023

The incorporation of artificial intelligence (AI) into education has heralded a transformative era in the way students learn and faculties teach. Among the burgeoning array of AI tools, ChatGPT stands out as a versatile and powerful resource. Developed by OpenAI, ChatGPT is an AI-driven conversational model that generates human-like responses. This research draws on the Constructivism Learning Theory to uncover the key drivers pushing higher education students to use ChatGPT for academic purposes, and the multifaceted consequences it brings to the academic environment, by integrating the perspectives of key stakeholders: students, faculty, and education experts/leaders. The key findings of in-depth, face-to-face, interviews with key stakeholders revealed 12 main drivers that motivate students and their faculty to use ChatGPT mainly for learning purposes. However, the findings identified the multifaceted (six positive and another six negative) consequences of using ChatGPT for academic purposes. Recommendations for mitigating the negative consequences of ChatGPT were discussed with key stakeholders, particularly education experts/leaders, who were more concerned about using ChatGPT for academic reasons. The research reveals that higher education institutions should establish clear guidelines as a part of higher education policy, supplemented with training sessions for students and their faculty, about the responsible use of ChatGPT for academic purposes to mitigate any ethical concerns.
Flame Structure at Elevated Pressure Values and Reduced Reaction Mechanisms for the Combustion of CH4/H2 Mixtures
Authors: Gerasimov Ilya E. , Bolshova Tatyana A. , Osipova Ksenia N. , Dmitriev Artëm M. , Knyazkov Denis A. , Shmakov Andrey G.
Published: November 2023

Understanding and controlling the combustion of clean and efficient fuel blends, like methane + hydrogen, is essential for optimizing energy production processes and minimizing environmental impacts. To extend the available experimental database on CH4 + H2 flame speciation, this paper reports novel measurement data on the chemical structure of laminar premixed burner-stabilized CH4/H2/O2/Ar flames. The experiments cover various equivalence ratios (φ = 0.8 and φ = 1.2), hydrogen content amounts in the CH4/H2 blend (XH2 = 25%, 50% and 75%), and different pressures (1, 3 and 5 atm). The flame-sampling molecular-beam mass spectrometry (MBMS) technique was used to detect reactants, major products, and several combustion intermediates, including major flame radicals. Starting with the detailed model AramcoMech 2.0, two reduced kinetic mechanisms with different levels of detail for the combustion of CH4/H2 blends are reported: RMech1 (30 species and 70 reactions) and RMech2 (21 species and 31 reactions). Validated against the literature data for laminar burning velocity and ignition delays, these mechanisms were demonstrated to reasonably predict the effect of pressure and hydrogen content in the mixture on the peak mole fractions of intermediates and adequately describe the new data for the structure of fuel-lean flames, which are relevant to gas turbine conditions.
Numerical Prediction on In-Cylinder Mixture Formation and Combustion Characteristics for SIDI Engine Fueled with Hydrogen: Effect of Injection Angle and Equivalence Ratio
Authors: Oh Sehyun , Park Jungsoo
Published: November 2023

Although their ease of transport, storage, and use makes hydrocarbon fuels dominant in commercial energy systems, the emission of harmful gases, including greenhouse gases, is a fatal disadvantage. Despite ongoing research to improve thermal efficiency and reduce the emissions of internal combustion engines using conventional hydrocarbon fuels, achieving net-zero carbon requires decarbonizing fuels rather than reducing the use of internal combustion engines. Hence, transitioning away from hydrocarbon fuels and evolving internal combustion engines into clean engines using carbon-free fuels, such as hydrogen, is necessary. This study designs a 2.0 L research engine and numerically analyzes its combustion characteristics and spray behavior by varying the spray angle and equivalence ratio. When comparing the turbulence kinetic energy at a 45-degree spray angle with that at 30 degrees and 60 degrees, on average, there was a difference of approximately 37.54 m2/s2 and 26.21 m2/s2, respectively. However, misfires occur in the lean condition. Although hydrogen has a wide flammability range, poor mixture formation under lean conditions can result in misfires. The 60-degree spray angle resulted in the highest combustion temperatures and pressures for all equivalence ratio conditions, consequently leading to the highest emissions of nitrogen oxides. Specifically, at a lambda value of 2.5, the 60-degree spray angle emitted approximately 29 ppm, 0 ppm, and 161 ppm of nitrogen oxides for each respective spray angle.
Barley Vinegar Relieves Loperamide-Induced Constipation in Mice via the Modulation of the Gut Microbiota and Serum Metabolism
Authors: Li Yan , Wang Aixia , Nie Mengzi , Chen Zhiying , Wang Lili , Liu Liya , Wang Fengzhong , Tong Litao
Published: November 2023

This study investigated the effect of barley vinegar on constipation by regulating the structure of intestinal microbiota and the level of short-chain fatty acids (SCFAs). BALB/c mice with loperamide-induced constipation were treated with barley vinegar in the intervention. After treatment, constipation-related factors were identified. The effect of barley vinegar on the composition of the intestinal microbiota was evaluated by means of 16S rDNA gene sequencing, and the content of SCFAs in enteral feces was determined via the GC-MS method. Treating constipated mice with barley vinegar accelerated gastrointestinal peristalsis, inhibited the inflammatory response, protected the intestinal barrier, upregulated the production of beneficial intestinal bacteria, and downregulated the production of harmful intestinal bacteria. These therapeutic effects are attributed to reversed gut microbiota dysfunction, which favors the production of intestinal metabolites such as SCFAs. The purgative function of highland barley vinegar may improve the intestinal environment by regulating the balance of intestinal flora and the concentration of SCFAs. In addition, LC-MS metabolomics was used to analyze the effect of barley vinegar on intestinal metabolites in mice with constipation. The results show that the treatment of barley vinegar inhibited the decrease in aspartate, L-threonine, L-serine, L-proline, 3,4-dihydroxymandelic acid, epinephrine, glyceric acid, and 3,4-dihydroxymandelic acid content in intestinal metabolites caused by constipation. 4-2 hydroxy benzene acetic acid and fumaric acid content increased. KEGG pathway analysis showed that digestive system, amino acid and lipid metabolism pathways were important pathways for highland barley vinegar relieving constipation. This study proves that highland barley vinegar mainly regulates lipid metabolism, the digestive system and amino acid metabolism to maintain a steady state, prevent intestinal injury, and improve constipation. In short, this study demonstrates that highland barley vinegar can alleviate constipation in mice and repair colitis damage.
Computational Analysis of Fractional-Order KdV Systems in the Sense of the Caputo Operator via a Novel Transform
Authors: AlBaidani Mashael M. , Ganie Abdul Hamid , Khan Adnan
Published: November 2023

The main features of scientific efforts in physics and engineering are the development of models for various physical issues and the development of solutions. In order to solve the time-fractional coupled Korteweg–De Vries (KdV) equation, we combine the novel Yang transform, the homotopy perturbation approach, and the Adomian decomposition method in the present investigation. KdV models are crucial because they can accurately represent a variety of physical problems, including thin-film flows and waves on shallow water surfaces. The fractional derivative is regarded in the Caputo meaning. These approaches apply straightforward steps through symbolic computation to provide a convergent series solution. Different nonlinear time-fractional KdV systems are used to test the effectiveness of the suggested techniques. The symmetry pattern is a fundamental feature of the KdV equations and the symmetrical aspect of the solution can be seen from the graphical representations. The numerical outcomes demonstrate that only a small number of terms are required to arrive at an approximation that is exact, efficient, and trustworthy. Additionally, the system’s approximative solution is illustrated graphically. The results show that these techniques are extremely effective, practically applicable for usage in such issues, and adaptable to other nonlinear issues.
Analysis of Connected Vehicle Data to Quantify National Mobility Impacts of Winter Storms for Decision Makers and Media Reports
Authors: Desai Jairaj , Mathew Jijo K. , Li Howell , Sakhare Rahul Suryakant , Horton Deborah , Bullock Darcy M.
Published: November 2023

Traditional techniques of monitoring roadway mobility during winter weather have relied on embedded road sensors, roadside cameras, radio reports from public safety staff, or public incident reports. However, widely available connected vehicle (CV) data provides government agencies and media with a unique opportunity to monitor the mobility impact of inclement weather events in near real-time. This study presents such a use case that analyzed over 500 billion CV records characterizing the spatial and temporal impact of a winter storm that moved across the country from 21 to 26 December 2022. The analysis covered 97,000 directional miles of interstate roadway and processed over 503 billion CV records. At the storm’s peak on 22 December at 5:26 PM Eastern Time, nearly 4800 directional miles of interstate roadway were operating under 45 mph, a widely accepted indicator of degraded interstate conditions. The study presents a methodological approach to systematically assess the mobility impact of this winter event on interstate roadways at a national and regional level. The paper then looks at a case study on Interstate 70, a 4350 directional mile route passing through ten states. Statewide comparison showed Ohio was most impacted, with 9% of mile-hours operating below 45 mph on 23 December. High-Resolution Rapid Refresh weather data provided by the National Oceanic and Atmospheric Administration was integrated into the analysis to provide a visualization of the storm’s temporal path and severity. We believe the proposed metrics and visualizations are effective tools for communicating the severity and geographic impact of extreme weather events to broad non-technical audiences.
Simulated Radio and Neutrino Imaging of a Microquasar
Authors: Smponias Theodoros
Published: November 2023

Microquasar stellar systems emit electromagnetic radiation and high-energy particles. Thanks to their location within our own galaxy, they can be observed in high detail. Still, many of their inner workings remain elusive; hence, simulations, as the link between observations and theory, are highly useful. In this paper, both high-energy particle and synchrotron radio emissions from simulated microquasar jets are calculated using special relativistic imaging. A finite ray speed imaging algorithm is employed on hydrodynamic simulation data, producing synthetic images seen from a stationary observer. A hydrodynamical model is integrated in the above emission models. Synthetic spectra and maps are then produced that can be compared to observations from detector arrays. As an application, the model synthetically observes microquasars during an episodic ejection at two different spatio-temporal scales: one on the neutrino emission region scale and the other on the synchrotron radio emission scale. The results are compared to the sensitivity of existing detectors.
Advanced Hydrogel-Based Strategies for Enhanced Bone and Cartilage Regeneration: A Comprehensive Review
Authors: De Leon-Oliva Diego , Boaru Diego Liviu , Perez-Exposito Roque Emilio , Fraile-Martinez Oscar , García-Montero Cielo , Diaz Raul , Bujan Julia , García-Honduvilla Natalio , Lopez-Gonzalez Laura , Álvarez-Mon Melchor , Saz Jose V. , de la Torre Basilio , Ortega Miguel A.
Published: November 2023

Bone and cartilage tissue play multiple roles in the organism, including kinematic support, protection of organs, and hematopoiesis. Bone and, above all, cartilaginous tissues present an inherently limited capacity for self-regeneration. The increasing prevalence of disorders affecting these crucial tissues, such as bone fractures, bone metastases, osteoporosis, or osteoarthritis, underscores the urgent imperative to investigate therapeutic strategies capable of effectively addressing the challenges associated with their degeneration and damage. In this context, the emerging field of tissue engineering and regenerative medicine (TERM) has made important contributions through the development of advanced hydrogels. These crosslinked three-dimensional networks can retain substantial amounts of water, thus mimicking the natural extracellular matrix (ECM). Hydrogels exhibit exceptional biocompatibility, customizable mechanical properties, and the ability to encapsulate bioactive molecules and cells. In addition, they can be meticulously tailored to the specific needs of each patient, providing a promising alternative to conventional surgical procedures and reducing the risk of subsequent adverse reactions. However, some issues need to be addressed, such as lack of mechanical strength, inconsistent properties, and low-cell viability. This review describes the structure and regeneration of bone and cartilage tissue. Then, we present an overview of hydrogels, including their classification, synthesis, and biomedical applications. Following this, we review the most relevant and recent advanced hydrogels in TERM for bone and cartilage tissue regeneration.
Liposomal and Ethosomal Gels: From Design to Application
Authors: Sguizzato Maddalena , Cortesi Rita
Published: November 2023

The use of lipid-based nanosystems for topical administration represents an innovative “green” approach, being composed of materials, defined as GRAS (generally recognized as safe), characterized by low toxicity, biocompatibility, and biodegradability [...]
Explorative Image Analysis of Methylene Blue Interactions with Gelatin in Polypropylene Nonwoven Fabric Membranes: A Potential Future Tool for the Characterization of the Diffusion Process
Authors: Zidek Jan , Sudakova Anna , Smilek Jiri , Nguyen Duc Anh , Ngoc Hung Le , Ha Le Minh
Published: November 2023

This manuscript explores the interaction between methylene blue dye and gelatin within a membrane using spectroscopy and image analysis. Emphasis is placed on methylene blue’s unique properties, specifically its ability to oscillate between two distinct resonance states, each with unique light absorption characteristics. Image analysis serves as a tool for examining dye diffusion and absorption. The results indicate a correlation between dye concentrations and membrane thickness. Thinner layers exhibit a consistent dye concentration, implying an even distribution of the dye during the diffusion process. However, thicker layers display varying concentrations at different edges, suggesting the establishment of a diffusion gradient. Moreover, the authors observe an increased concentration of gelatin at the peripheries rather than at the center, possibly due to the swelling of the dried sample and a potential water concentration gradient. The manuscript concludes by suggesting image analysis as a practical alternative to spectral analysis, particularly for detecting whether methylene blue has been adsorbed onto the macromolecular network. These findings significantly enhance the understanding of the complex interactions between methylene blue and gelatin in a membrane and lay a solid foundation for future research in this field.
The Effects of Potassium Fertilizer on the Active Constituents and Metabolites of Bulbs from Lilium davidii var. unicolor
Authors: Jin Lei , Yuan Qing , Bi Jiao , Zhang Gang , Zhang Ping
Published: November 2023

Lilium davidii var. unicolor (Lanzhou lily) is rich in nutrients, making it an important economic plant widely used in the fields of food and medicine. In this study, potted lily bulbs were treated with nutrient solutions containing K+ and nutrient solutions without K+ (CK and KT). The contents of nutrients in lily bulbs at different stages after treatment were compared. It was found that the application of potassium fertilizers increased the content of total phenols, flavonoids, and flavanols in lily bulbs and the antioxidant activity in the bulbs. Simultaneously, the study observed that potassium fertilizers could impact the accumulation of polysaccharides and saponins. Furthermore, employing non-targeted metabolomics, the secondary metabolites of mature Lanzhou lily bulbs were scrutinized both with and without potassium fertilization (KT and CK). A total of 607 metabolites were identified, including 573 in positive ion mode and 34 in negative ion mode. These metabolites were classified into 13 categories at the superclass level, with lipids and lipid molecules (37.93%), organic acids and their derivatives (16.52%), organic oxygen compounds (14.88%), and phenylpropanoids and polyketides (13.61%) being the most prominent. Differential metabolite enrichment analysis between the experimental and control groups showed that the differential metabolites were mainly concentrated in metabolic pathways related to amino acid biosynthesis, such as arginine and proline metabolism, beta-alanine metabolism, alanine, aspartate, and glutamate metabolism. Additionally, it was found that the application of potassium fertilizer increased the accumulation of amino acids in Lanzhou lily bulbs. Overall, this study provides a theoretical reference for the development of nutrients and efficient cultivation techniques for L. davidii var. unicolor bulbs.
Shoot Yield and Mineral Nutrient Concentrations of Six Microgreens in the Brassicaceae Family Affected by Fertigation Rate
Authors: Li Tongyin , Arthur Jacob D. , Bi Guihong
Published: November 2023

Microgreens have become an important specialty crop valued by their varying texture, vibrant colors, and nutrient-dense features. As the number of species and cultivars rapidly increases for microgreen production, fertigation requirements in relation to shoot production and nutrient compositions remain unclear. This study aimed to investigate the shoot yield, visual quality, and mineral nutrient concentrations of six microgreens in the Brassicaceae family including the ‘Waltham’ broccoli, ‘Red Acre’ cabbage, Daikon radish, ‘Red Russian’ kale, pea, and Rambo radish in two experiments in December 2020 and January 2021. Each microgreen was fertigated with 120 mL of fertilizer solution daily for five consecutive days with a rate of 0, 70, 140, 210, or 280 mg·L−1 N from a general-purpose fertilizer. Broccoli, Daikon radish, and kale similarly produced the highest fresh shoot weights of 916.5 to 984 g·m−2 in December 2020, while pea produced the highest fresh shoot weight of 2471 g·m−2 in January 2021 among cultivars. The fertigation rates of 140, 210, and 280 mg·L−1 N resulted in similar fresh and dry shoot weights of selected microgreens, suggesting 140 mg·L−1 N should be sufficient for microgreen fertilization. Mineral nutrients in microgreens varied among cultivars: pea microgreens had the highest nitrogen (N) concentrations of 70.6 to 75.2 mg·g−1 in December 2020 and 72.1 to 75.4 mg·g−1 in January 2021; and cabbage microgreens were rich in calcium (Ca) in both experiments. The kale, pea, and Rambo radish microgreens contained the highest concentrations of iron (Fe) and manganese (Mn) in December 2020. The fertigation rate affected macronutrient concentrations but did not affect micronutrient concentrations including Fe, Mn, or zinc (Zn).
Effect of Microplastic Particles on the Rheological Properties of Human Saliva and Mucus
Authors: Przekop Rafał , Michalczuk Urszula , Penconek Agata , Moskal Arkadiusz
Published: November 2023

Pollution by plastic microparticles is rising rapidly. One avenue of human exposure to nanoparticles is through inhalation. The main source of microplastics in indoor environments, leading to unintended inhalation, is synthetic fabric used in clothing. Other sources include curtains, carpets, furniture, wall paints, and floor finishes. Occupational exposure is particularly significant in waste management and recycling operations, during exposure to high heat, during high-energy treatment of polymer composites, and during 3D printing. In outdoor environments, exposure can happen through breathing in contaminated aerosols from ocean waves or airborne particles from dried wastewater treatments. Airborne particles affect human health in various ways, including via direct interactions with the epithelium and its mucus layer after deposition in the mouth and respiratory system. Exposure due to the ingestion of microplastics present in various environmental compartments may occur either directly or indirectly via the food chain or drinking water. This study aimed to determine the effects of plastic microparticles on the rheology of mucus and saliva, and, thus, their functioning. The experiments used artificial mucus, saliva, and plastic nanoparticles (namely, PS—polystyrene and PE—polyethylene). The rheological properties of saliva and mucus were determined via the use of an oscillatory rheometer at various temperatures (namely, 36.6 °C and 40 °C, which correspond to healthy and ill humans). The results were compared with those obtained for pure saliva and mucus. An increase in apparent viscosity was observed for saliva, which is behavior typical of for solid particle suspensions in liquids. In contrast, for mucus, the effect was the opposite. The influence of the presence of the particles on the parameters of the constitutive viscosity equations was studied. Plastic micro- and nanoparticles in the saliva and mucus may interfere with their physiological functions.
Differential Influence of Environmental Factors on Malaria Due to Vector Control Interventions in Uganda
Authors: Sadoine Margaux L. , Smargiassi Audrey , Liu Ying , Gachon Philippe , Fournier Michel , Dueymes Guillaume , Namuganga Jane Frances , Dorsey Grant , Nasri Bouchra , Zinszer Kate
Published: November 2023

Background: Few studies have explored how vector control interventions may modify associations between environmental factors and malaria. Methods: We used weekly malaria cases reported from six public health facilities in Uganda. Environmental variables (temperature, rainfall, humidity, and vegetation) were extracted from remote sensing sources. The non-linearity of environmental variables was investigated, and negative binomial regression models were used to explore the influence of indoor residual spraying (IRS) and long-lasting insecticidal nets (LLINs) on associations between environmental factors and malaria incident cases for each site as well as pooled across the facilities, with or without considering the interaction between environmental variables and vector control interventions. Results: An average of 73.3 weekly malaria cases per site (range: 0–597) occurred between 2010 and 2018. From the pooled model, malaria risk related to environmental variables was reduced by about 35% with LLINs and 63% with IRS. Significant interactions were observed between some environmental variables and vector control interventions. There was site-specific variability in the shape of the environment–malaria risk relationship and in the influence of interventions (6 to 72% reduction in cases with LLINs and 43 to 74% with IRS). Conclusion: The influence of vector control interventions on the malaria–environment relationship need to be considered at a local scale in order to efficiently guide control programs.
Inflammation and Psoriasis: A Comprehensive Review
Authors: Man Alessandra-Mădălina , Orăsan Meda Sandra , Hoteiuc Oana-Alina , Olănescu-Vaida-Voevod Maria-Cristina , Mocan Teodora
Published: November 2023

Psoriasis is an immune-mediated disease with a strong genetic component that brings many challenges to sick individuals, such as chronic illness, and which has multiple associated comorbidities like cardiovascular disease, metabolic syndrome, inflammatory bowel disease, and psychological disorders. Understanding the interplay between the innate and adaptative immune system has led to the discovery of specific cytokine circuits (Tumor Necrosis Factor-alpha (TNF-α), IL-23, IL-17), which has allowed scientists to discover new biomarkers that can be used as predictors of treatment response and pave the way for personalized treatments. In this review, we describe the footprint psoriasis leaves on the skin and beyond, key pathophysiological mechanisms, current available therapeutic options, and drawbacks faced by existing therapies, and we anticipate potential future perspectives that may improve the quality of life of affected individuals.
The Applicability of Chromatographic Retention Modeling on Chiral Stationary Phases in Reverse-Phase Mode: A Case Study for Ezetimibe and Its Impurities
Authors: Ferencz Elek , Kelemen Éva-Katalin , Obreja Mona , Tóth Gergő , Urkon Melinda , Zöldhegyi Arnold , Sipos Emese , Szabó Zoltán-István
Published: November 2023

Mechanistic modeling is useful for predicting and modulating selectivity even in early chromatographic method development. This approach is also in accordance with current analytical quality using design principles and is highly welcomed by the authorities. The aim of this study was to investigate the separation behavior of two different types of chiral stationary phases (CSPs) for the separation of ezetimibe and its related substances using the mechanistic retention modeling approach offered by the Drylab software (version 4.5) package. Based on the obtained results, both CSPs presented with chemoselectivity towards the impurities of ezetimibe. The cyclodextrin-based CSP displayed a higher separation capacity and was able to separate seven related substances from the active pharmaceutical ingredient, while the cellulose-based column enabled the baseline resolution of six impurities from ezetimibe. Generally, the accuracy of predicted retention times was lower for the polysaccharide CSP, which could indicate the presence of additional secondary interactions between the analytes and the CSP. It was also demonstrated that the combination of mechanistic modeling and an experimental design approach can be applied to method development on CSPs in reverse-phase mode. The applicability of the methods was tested on spiked artificial placebo samples, while intraday and long-term (2 years) method repeatability was also challenged through comparing the obtained retention times and resolution values. The results indicated the excellent robustness of the selected setpoints. Overall, our findings indicate that the chiral columns could offer orthogonal selectivity to traditional reverse-phase columns for the separation of structurally similar compounds.
Dipeptidyl Peptidase 4 Stimulation Induces Adipogenesis-Related Gene Expression of Adipose Stromal Cells
Authors: Lai Hsiao-Chi , Chen Pei-Hsuan , Tang Chia-Hua , Chen Lee-Wei
Published: November 2023

Adipogenesis has emerged as a new therapeutic target for regulating metabolism and achieving anti-inflammatory and anti-atherosclerotic effects via the release of adiponectin. However, at present, the effects and mechanism of action of dipeptidyl peptidase 4 (DPP4) stimulation on adiponectin production and adipogenesis have not been clarified. Here, we investigated the effects of DPP4 stimulation with monocyte chemoattractant protein-1 (MCP-1) on platelet-derived growth factor receptor alpha (PDGFRα) expression in adipose tissue and blood adiponectin levels. Stromal vascular fractions (SVFs) purified from human subcutaneous adipose tissue and inguinal adipose tissue of obese and diabetic (Leprdb/db) mice were treated with 50 ng of MCP-1 and plasma from control (Lepr+/+) mice supplemented with 10 ng or 50 ng of MCP-1. Treatment of SVFs from human subcutaneous adipose tissues with 50 ng of MCP-1 significantly increased AdipoQ, DPP4, peroxisome proliferator-activated receptor gamma (PPARγ), fatty-acid-binding protein (FABP4), and SERBF1 mRNA expression. MCP-1-supplemented plasma increased adiponectin, CCAAT-Enhancer-binding protein alpha (C/EBPα), DPP4, IL-33, and PDGFRα mRNA expression and adiponectin and DPP4 protein expression, while decreasing the expression of IL-10 mRNA in SVFs compared with the levels in the plasma treatment group. MCP-1-supplemented plasma was shown to increase PPARγ, PPARγ2, adiponectin, DPP4, and FABP4 and decrease IL-10 mRNA expression in PDGFRα cells from adipose tissue. Meanwhile, MCP-1-supplemented plasma increased MCP-1, PDGFRα, TNFα, adiponectin, and IL-1β and decreased IL-10 and FOXP3 mRNA expression in DPP4 cells. Moreover, the injection of MCP-1-supplemented plasma into adipose tissue increased the proportion of DPP4+ cells among PDGFRα+ cells from adipose tissue and plasma adiponectin levels of Leprdb/db mice compared with the levels in the plasma injection group. Our results demonstrate that DPP4+ cells are important adipose progenitor cells. Stimulation of DPP4 with MCP-1 increases adipogenesis-related gene expression and the population of DPP4+ cells among PDGFRα+ cells in SVFs and blood adiponectin levels. DPP4 stimulation could be a novel therapy to increase local adipogenesis and systemic adiponectin levels.
Human Breast Milk miRNAs: Their Diversity and Potential for Preventive Strategies in Nutritional Therapy
Authors: Kaeffer Bertrand
Published: November 2023

The endogenous miRNAs of breast milk are the products of more than 1000 nonprotein-coding genes, giving rise to mature small regulatory molecules of 19–25 nucleotides. They are incorporated in macromolecular complexes, loaded on Argonaute proteins, sequestrated in exosomes and lipid complexes, or present in exfoliated cells of epithelial, endothelial, or immune origins. Their expression is dependent on the stage of lactation; however, their detection depends on progress in RNA sequencing and the reappraisal of the definition of small RNAs. Some miRNAs from plants are detected in breast milk, opening the possibility of the stimulation of immune cells from the allergy repertoire. Each miRNA harbors a seeding sequence, which targets mRNAs, gene promoters, or long noncoding RNAs. Their activities depend on their bioavailability. Efficient doses of miRNAs are estimated to be roughly 100 molecules in the cytoplasm of target cells from in vitro and in vivo experiments. Each miRNA is included in networks of stimulation/inhibition/sequestration, driving the expression of cellular phenotypes. Three types of stress applied during lactation to manipulate miRNA supply were explored using rodent offspring: a foster mother, a cafeteria diet, and early weaning. This review presents the main mature miRNAs described from current mothers’ cohorts and their bioavailability in experimental models as well as studies assessing the potential of miR-26 or miR-320 miRNA families to alter offspring phenotypes.