Vascularized organs hold potential for various applications, such as organ transplantation, drug screening, and pathological model establishment. Nevertheless, the in vitro construction of such organs encounters many challenges, including the incorporation of intricate vascular networks, the regulation of blood vessel connectivity, and the degree of endothelialization within the inner cavities. Natural polymeric hydrogels, such as gelatin and alginate, have been widely used in three-dimensional (3D) bioprinting since 2005. However, a significant disparity exists between the mechanical properties of the hydrogel materials and those of human soft tissues, necessitating the enhancement of their mechanical properties through modifications or crosslinking. In this study, we aim to enhance the structural stability of gelatin–alginate hydrogels by crosslinking gelatin molecules with oxidized pullulan (i.e., a polysaccharide) and alginate molecules with calcium chloride (CaCl₂). A continuous small-diameter vascular network with an average outer diameter of 1 mm and an endothelialized inner surface is constructed by printing the cell-laden hydrogels as bioinks using a coaxial 3D bioprinter. The findings demonstrate that the single oxidized pullulan crosslinked gelatin and oxidized pullulan/CaCl₂ double-crosslinked gelatin–alginate hydrogels both exhibit a superior structural stability compared to their origins and CaCl₂ solely crosslinked gelatin–alginate hydrogels. Moreover, the innovative gelatin and gelatin–alginate hydrogels, which have excellent biocompatibilities and very low prices compared with other hydrogels, can be used directly for tissue/organ construction, tissue/organ repairment, and cell/drug transportation. Full article

We investigated the formation of graphite nanocrystals covered with graphite oxide for white light generation. The nanoparticles were formed using cost-efficient oxidation of a carbon-based dye pigment at different temperatures and verified using X-ray diffraction and Raman measurements. Formation of the graphite nanoparticles via thermal annealing was observed, while their light emission increased at higher oxidation temperatures. This was associated with a higher amount of oxygen defect groups. The time-resolved photoluminescence measurements showed linearly faster decays at shorter wavelengths and similar decays at different annealing temperatures. Broadband and linear vs. excitation emission spectra of the particles were found to be suitable for white-light-emitting devices and phosphor markers. The fast photoluminescence decay opens the possibility for the application of nanoparticles in optical wireless communication technology. Full article

In this paper, a nonlinear simulation block for a fish robot was designed using MATLAB Simulink. The simulation block incorporated added masses, hydrodynamic damping forces, restoring forces, and forces and moments due to dorsal fins, pectoral fins, and caudal fins into six-degree-of-freedom equations of motion. To obtain a linearized model, we used three different nominal surge velocities (i.e., 0.2 m/s, 0.4 m/s, and 0.6 m/s). After obtaining output responses by applying pseudo-random binary signal inputs to a nonlinear model, an identification tool was used to obtain approximated linear models between inputs and outputs. Utilizing the obtained linearized models, two-degree-of-freedom proportional, integral, and derivative controllers were designed, and their characteristics were analyzed. For the 0.4 m/s nominal surge velocity models, the gain margins and phase margins of the surge, pitch, and yaw controllers were infinity and 69 degrees, 26.3 dB and 85 degrees, and infinity and 69 degrees, respectively. The bandwidths of surge, pitch, and yaw control loops were determined to be 2.3 rad/s, 0.17 rad/s, and 2.0 rad/s, respectively. Similar characteristics were observed when controllers designed for linear models were applied to the nonlinear model. When step inputs were applied to the nonlinear model, the maximum overshoot and steady-state errors were very small. It was also found that the nonlinear plant with three different nominal surge velocities could be controlled by a single controller designed for a linear model with a nominal surge velocity of 0.4 m/s. Therefore, controllers designed using linear approximation models are expected to work well with an actual nonlinear model. Full article

With recent advancements in network technology and the increasing popularity of the internet, the use of social network services and Internet of Things devices has flourished, leading to a continuous generation of large volumes of graph stream data, where changes, such as additions or deletions of vertices and edges, occur over time. Additionally, owing to the need for the efficient use of storage space and security requirements, graph stream data compression has become essential in various applications. Even though various studies on graph compression methods have been conducted, most of them do not fully reflect the dynamic characteristics of graph streams and the complexity of large graphs. In this paper, we propose a compression scheme using provenance data to efficiently process and analyze large graph stream data. It obtains provenance data by analyzing graph stream data and builds a pattern dictionary based on this to perform dictionary-based compression. By improving the existing dictionary-based graph compression methods, it enables more efficient dictionary management through tracking pattern changes and evaluating their importance using provenance. Furthermore, it considers the relationships among sub-patterns using an FP-tree and performs pattern dictionary management that updates pattern scores based on time. Our experiments show that the proposed scheme outperforms existing graph compression methods in key performance metrics, such as compression rate and processing time. Full article

In recent years, there has been growing recognition that the stability of the top management team (TMT) significantly impacts the operation and management of companies. However, few studies have focused on the impact of TMT stability on innovation sustainability. Therefore, based on the upper echelon theory and the faultline theory, this paper takes China’s A-share listed companies from 2010 to 2022 as a sample to explore the impact of TMT stability on corporate innovation sustainability, as well as the moderating effect of executive faultlines on this impact. The results indicate that TMT stability is positively correlated with corporate innovation sustainability, whereas the executive faultlines significantly weaken this correlation. The mechanism test reveals that a stable senior management team can reduce an enterprise’s operational risk through the management functions of executives, alleviate the financing constraints serving as a stability signal sent by the company to investors and creditors, and thus promote the sustainability of innovation. Heterogeneity analysis demonstrates that the influence of TMT stability on corporate innovation sustainability is more pronounced in companies with a high percentage of executive shareholdings, non-state ownership, and CEOs possessing technical expertise. This paper combines the overall stability of the executive team with the differentiation of its internal subgroups, broadens the research perspective of the upper echelon theory, and serves as a valuable reference for the development of corporate executive teams. Full article

Regulatory cystathionine β-synthase (CBS) domains are widespread in proteins; however, difficulty in structure determination prevents a comprehensive understanding of the underlying regulation mechanism. Tetrameric microbial inorganic pyrophosphatase containing such domains (CBS-PPase) is allosterically inhibited by AMP and ADP and activated by ATP and cell alarmones diadenosine polyphosphates. Each CBS-PPase subunit contains a pair of CBS domains but binds cooperatively to only one molecule of the mono-adenosine derivatives. We used site-directed mutagenesis of Desulfitobacterium hafniense CBS-PPase to identify the key elements determining the direction of the effect (activation or inhibition) and the “half-of-the-sites” ligand binding stoichiometry. Seven amino acid residues were selected in the CBS1 domain, based on the available X-ray structure of the regulatory domains, and substituted by alanine and other residues. The interaction of 11 CBS-PPase variants with the regulating ligands was characterized by activity measurements and isothermal titration calorimetry. Lys100 replacement reversed the effect of ADP from inhibition to activation, whereas Lys95 and Gly118 replacements made ADP an activator at low concentrations but an inhibitor at high concentrations. Replacement of these residues for alanine increased the stoichiometry of mono-adenosine phosphate binding by twofold. These findings identified several key protein residues and suggested a “two non-interacting pairs of interacting regulatory sites” concept in CBS-PPase regulation. Full article

The utilization of biomass for multi-generation systems is garnering significant interest due to its potential in conserving primary energy and mitigating greenhouse gas emissions. However, enhancing its energy efficiency remains a critical challenge. This study introduces an innovative cogeneration system that combines biomass gasification with an externally fired gas turbine, organic Rankine cycle, and absorption refrigeration cycle. It undergoes thorough thermodynamic and exergoeconomic evaluations, with a dual-objective optimization conducted to identify the optimal operational conditions that achieve the highest exergy efficiency while minimizing product cost. The findings reveal that, in the base case, the thermal efficiency, exergy efficiency, and sum unit cost of the product (SUCP) of the system are 66.36%, 32.04%, and 8.71 USD/GJ, respectively. A parametric study illustrates that elevating the air compressor pressure ratio or the temperature difference at the cold end enhances thermal efficiency but reduces exergy efficiency. Additionally, the lowest unit cost of the product is attainable by optimizing the gas turbine inlet temperature. The performance of the system shows negligible sensitivity to the turbine inlet pressure of a bottoming organic Rankine cycle. Finally, optimization demonstrates a 9.7% increase in exergy efficiency and a 1.8% rise in the SUCP compared to the baseline scenario. The study suggests integrating with other energy sources for diversified product outputs and conducting environmental analyses in future research. Full article

Glaucoma refers to a spectrum of progressive optic neuropathies and remains the leading cause of irreversible blindness worldwide. Its insidious onset poses serious challenges to conventional diagnostic methods and clinicians striving to detect early-stage disease for timely and effective intervention. Artificial intelligence (AI) has demonstrated its ability to process and analyze large datasets which can help identify subtle changes in early glaucomatous clinical presentation. This study reviews the current state of AI utilization in glaucoma and elucidates the strengths and limitations of existing approaches. We dissect the role of AI in various domains: enhancing early detection and diagnosis, monitoring disease progression, and refining treatment strategies to optimize patient outcomes. Furthermore, we address the ethical, legal, and social implications, alongside the inherent limitations of AI in the clinical setting. Despite these challenges, AI holds transformative potential for glaucoma management. Future directions emphasize the need for interdisciplinary collaboration, advanced and explainable algorithm development, and equitable healthcare access to fully realize the promise of AI in combating this vision-threatening condition. Full article

Coastal urbanization and the consequent proliferation of artificial structures greatly impact rocky reef communities, productive and diverse marine environments that play a crucial role in the functioning of broader coastal ecosystems. This study, conducted along a 7 km stretch of coastline at increasing distance from the port of Genoa (Ligurian Sea), investigated whether the alternating presence of artificial and natural reefs leads to discernible differences in the biota inhabiting these two reef types. The study area is one of the most anthropized areas of the Mediterranean Sea, exhibiting nearly 60% coastal artificialization, which severely impacts coastal ecosystems, favouring the replacement of sensitive species with more tolerant species. Ten reefs (5 natural and 5 artificial) were surveyed by scuba diving at about a 6-m depth, employing quadrats of 50 cm × 50 cm to estimate visually the percent cover of conspicuous sessile organisms. The artificial reefs hosted a similar number of species (18) to their natural counterparts (19) but exhibited a distinct community composition: the former were especially characterized by Jania rubens and filamentous algae, with the latter characterized by Peyssonnelia squamaria and Mesophyllum lichenoides. This difference, however, became negligible where coastal habitat fragmentation (here measured with a purposely devised Fragmentation Index) was minimal. Reducing fragmentation may therefore represent a management strategy to minimize the potential impact of artificial structures on marine biodiversity. Full article

Mutual exclusion is a fundamental problem in concurrent/parallel/distributed systems. The first pure-software solution to this problem for two processes, which is not based on hardware instructions like test-and-set, was proposed in 1965 by Th.J. Dekker and communicated by E.W. Dijkstra. The correctness of this algorithm has generally been studied under the strong memory model, where the read and write operations on a memory cell are atomic or indivisible. In recent years, some variants of the algorithm have been proposed to make it RW-safe when using the weak memory model, which makes it possible, e.g., for multiple read operations to occur simultaneously to a write operation on the same variable, with the read operations returning (flickering) a non-deterministic value. This paper proposes a novel approach to formal modeling and reasoning on a mutual exclusion algorithm using Timed Automata and the Uppaal tool, and it applies this approach through exhaustive model checking to conduct a thorough analysis of the Dekker’s algorithm and some of its variants proposed in the literature. This paper aims to demonstrate that model checking, although necessarily limited in the scalability of the number $\mathrm{N}$ of the processes due to the state explosions problem, is effective yet powerful for reasoning on concurrency and process action interleaving, and it can provide significant results about the correctness and robustness of the basic version and variants of the Dekker’s algorithm under both the strong and weak memory models. In addition, the properties of these algorithms are also carefully studied in the context of a tournament-based binary tree for $\mathrm{N}\ge 2$ processes. Full article

There is great concern in equine sport over the potential use of pharmaceutical agents capable of editing the genome or modifying the expression of gene products. Synthetic oligonucleotides are short, single-stranded polynucleotides that represent a class of agents capable of modifying gene expression products with a high potential for abuse in horseracing. As these substances are not covered by most routine anti-doping analytical approaches, they represent an entire class of compounds that are not readily detectable. The nucleotide sequence for each oligonucleotide is highly specific, which makes targeted analysis for these agents problematic. Accordingly, we have developed a non-targeted approach to detect the presence of specific product ions that are not naturally present in ribonucleic acids. Briefly, serum samples were extracted using solid-phase extraction with a mixed-mode cartridge following the disruption of protein interactions to isolate the oligonucleotides. Following the elution and concentration steps, chromatographic separation was achieved utilizing reversed-phase liquid chromatography. Following an introduction to a Thermo Q Exactive HF mass spectrometer using electrospray ionization, analytes were detected utilizing a combination of full-scan, parallel reaction monitoring and all ion fragmentation scan modes. The limits of detection were determined along with the accuracy, precision, stability, recovery, and matrix effects using a representative 13mer oligonucleotide. Following method optimization using the 13mer oligonucleotide, the method was applied to successfully detect the presence of specific product ions in three unique oligonucleotide sequences targeting equine-specific transcripts. Full article

Effective and optimal decision-making can enhance system performance, potentially leading to a positive reputation and financial gains. Multi-criteria decision-making (MCDM) is an important research topic widely applied to practical decision-making problems. Using the basic idea of symmetry to balance the arrangement where elements or features have an equality or similarity in distribution, MCDM provides robust decisions in such multi-dimensional complex issues. This study proposes MultiFuzzTOPS, a decision-making model to deal with complexity of multi-criteria decision-making. The proposed MultiFuzzTOPS leverages the fuzzy logic and soft sets such as type-2 soft sets (T2SS) and technique for order preference by similarity to ideal solution (TOPSIS) for decision-making. We validate the proposed model by implementing it to solve the pesticide selection problem in food science by considering various criteria for the selection of pesticides. Our proposed MultiFuzzTOPS recommends the best pesticide compared with its counterparts because it covers the maximum information for the selection of the best alternative. Results are ranked on the basis of the Hamming distance and similarity coefficient. We also validate the effectiveness by performing the sensitivity analysis, and the validation shows the reliability and effectiveness of our proposed model. Full article

Background: Malaria results in more than 550,000 deaths each year due to drug resistance in the most lethal Plasmodium (P.) species P. falciparum. A full P. falciparum genome was published in 2002, yet 44.6% of its genes have unknown functions. Improving the functional annotation of genes is important for identifying drug targets and understanding the evolution of drug resistance. Results: Genes function by interacting with one another. So, analyzing gene co-expression networks can enhance functional annotations and prioritize genes for wet lab validation. Earlier efforts to build gene co-expression networks in P. falciparum have been limited to a single network inference method or gaining biological understanding for only a single gene and its interacting partners. Here, we explore multiple inference methods and aim to systematically predict functional annotations for all P. falciparum genes. We evaluate each inferred network based on how well it predicts existing gene–Gene Ontology (GO) term annotations using network clustering and leave-one-out crossvalidation. We assess overlaps of the different networks’ edges (gene co-expression relationships), as well as predicted functional knowledge. The networks’ edges are overall complementary: 47–85% of all edges are unique to each network. In terms of the accuracy of predicting gene functional annotations, all networks yielded relatively high precision (as high as 87% for the network inferred using mutual information), but the highest recall reached was below 15%. All networks having low recall means that none of them capture a large amount of all existing gene–GO term annotations. In fact, their annotation predictions are highly complementary, with the largest pairwise overlap of only 27%. We provide ranked lists of inferred gene–gene interactions and predicted gene–GO term annotations for future use and wet lab validation by the malaria community. Conclusions: The different networks seem to capture different aspects of the P. falciparum biology in terms of both inferred interactions and predicted gene functional annotations. Thus, relying on a single network inference method should be avoided when possible. Supplementary data: Attached. Full article

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes. Full article

Large language models (LLMs) have the ability to generate text by stringing together words from their extensive training data. The leading AI text generation tool built on LLMs, ChatGPT, has quickly grown a vast user base since its release, but the domains in which it is being heavily leveraged are not yet known to the public. To understand how generative AI is reshaping print media and the extent to which it is being implemented already, methods to distinguish human-generated text from that generated by AI are required. Since college students have been early adopters of ChatGPT, we sought to study the presence of generative AI in newspaper articles written by collegiate journalists. To achieve this objective, an accurate AI detection model is needed. Herein, we analyzed university newspaper articles from different universities to determine whether ChatGPT was used to write or edit the news articles. We developed a detection model using classical machine learning and used the model to detect AI usage in the news articles. The detection model showcased a 93% accuracy in the training data and had a similar performance in the test set, demonstrating effectiveness in AI detection above existing state-of-the-art detection tools. Finally, the model was applied to the task of searching for generative AI usage in 2023, and we found that ChatGPT was not used to revise articles to any appreciable measure to write university news articles at the schools we studied. Full article

Background and Objectives: Gemcitabine has been used to treat various solid cancers, including, since 1997, metastatic pancreatic cancer. Here, we developed an HPLC-UV method to determine serum gemcitabine levels and use it in pharmacokinetic studies. Materials and Methods: The analysis was performed after a single protein precipitation step on a reversed-phase column, isocratically eluted with sodium phosphate buffer and methanol. For the pharmacokinetic study, NOD/SCID mice received a single dose of gemcitabine at 100 mg/kg by either subcutaneous (SC) or intraperitoneal (IP) administration. Blood samples were collected at 5, 15, and 30 min and 1, 2, 4, and 6 h after the administration of gemcitabine for further analysis. Results: The duration of the analysis was ~12.5 min. The calibration curve was linear (r² = 0.999) over the range of 1–400 μM. The mean recovery of GEM was 96.53% and the limit of detection was 0.166 μΜ. T_1/2, Tmax, Cmax, AUC_0–t, and clearance were 64.49 min, 5.00 min, 264.88 μmol/L, 9351.95 μmol/L*min, and 0.0103(mg)/(μmol/L)/min, respectively, for the SC administration. The corresponding values for the IP administration were 59.34 min, 5.00 min, 300.73 μmol/L, 8981.35 μmol/L*min and 0.0108(mg)/(μmol/L)/min (not statistically different from the SC administration). Conclusions: A simple, valid, sensitive, and inexpensive method for the measurement of gemcitabine in serum has been developed. This method may be useful for monitoring gemcitabine levels in cancer patients as part of therapeutic drug monitoring. Full article

In this study, the most effective application method of guayule resin and its effects on termite and fungal decay biological performances of wood strand-based (WSB) panels were explored. Southern yellow pine (Pinus spp. L.) wood strands were mixed with phenol formaldehyde (PF) resin to a target resin content of 5.00% and hot-pressed to manufacture the control WSB panels. For the in-situ process, a guayule resin solution was prepared and sprayed on the wood strands immediately after spraying the PF resin to a target content of 5.00%. For brushing and spraying methods, a sub-set of the control panel specimens were further brushed or sprayed with guayule resin solution on all surfaces. To understand the effects of guayule on durability, specimens cut from control and treated panels were subjected to termite resistance and fungal degradation soil block tests. The in-situ specimens with 5.00% guayule were subjected to tensile, internal bond, water absorption, and thickness swelling tests to find out whether guayule affects the mechanical performance of WSB panels. The results showed that in-situ treatment resulted in a significant reduction in the mechanical properties of wood stand-based panels. The sprayed technique resulted in more durable panels, as the mass loss was 2.21% for termites and 3.24% for fungi specimens, which decreased by 76.66% and 80.86%, respectively, when compared to the WSB controls. Full article

Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% S_α, 98.36% E_ξ, and 89.37% F_β on the EORSSD dataset. Full article

The Ordovician thick dolostone in Shuntogol area of the Tarim Basin has the potential to form a large-scale reservoir, but its genesis and reservoir development model are still unclear. Starting from a sedimentary sequence, this study takes a batch of dolostone samples obtained from new drilling cores in recent years as the research object. On the basis of core observation and thin section identification, trace elements, cathodoluminescence, carbon and oxygen isotopes, rare earth elements, and X-ray diffraction order degree tests were carried out to discuss the origin of the dolomite and summarize the development model of the dolostone reservoir. The analysis results show that the Ordovician dolomite in the study area had a good crystalline shape, large thickness, high Fe and Mn values, and mostly showed bright red light or bright orange–red light under cathode rays. The ratio of δ¹⁸O values to seawater values at the same time showed a negative bias; the δCe values were negative anomalies, the δEu values were positive anomalies, and the order degree was high. This indicates that the dolomitization process occurred in a relatively closed diagenetic environment. The Ordovician carbonate rocks in the study area were low-lying during the sedimentary period, and with the rise of sea level, the open platform facies continued to develop. When the Middle and Lower Ordovician series entered the burial stage, the main hydrocarbon source rocks of the lower Cambrian Series entered the oil generation peak, and the resulting formation overpressure provided the dynamic source for the upward migration of the lower magnesium-rich fluid, and the dolomitization fluid entered the karst pore system in the target layer to produce all the dolomitization. This set of dolostone reservoirs is large in scale and can be used as a favorable substitute area for deep carbonate exploration for continuous study. Full article

Red fruits contain bioactive substances including phenolic acids and flavonoids, which provide many health advantages for the human body. Industries find them intriguing because of their color and their ability to prevent chronic ailments such as metabolic, degenerative, and cardiovascular disorders. Nevertheless, the resilience of these organic molecules is influenced by several environmental, physical, and chemical phenomena. Therefore, the beneficial health properties of red fruits may diminish during postharvest processing. In this scenario, many postharvest methods have been implemented to enhance the shelf life and preserve the bioactive components of red fruits. The objectives of this review were to provide a comprehensive assessment of the health benefits of red fruits, and to explore the possibilities of edible coatings in retaining their freshness and protecting their bioactive contents. Co-occurrence networks were built using VOSviewer software to produce a two-dimensional map based on term frequency, and the examination of the 1364 keywords obtained from the scientific papers revealed the presence of at least 71 co-occurrences that provide insight into many natural components used in edible coatings for red fruits, such as proteins, polysaccharides, lipids, phospholipids, and minerals. The review examined their composition, functioning, application techniques, limits, safety considerations, legal regulations, and potential future developments. This review has shown that an edible coating may act as a protective layer on the surface of the fruit, alter the interior gas composition, reduce water loss, and postpone fruit ripening, thereby enhancing the health-promoting properties. Full article

While research has identified several inhibitors of the main protease (Mpro) of SARS-CoV-2, a significant portion of these compounds exhibit reduced activity in the presence of reducing agents, raising concerns about their effectiveness in vivo. Furthermore, the conventional biosafety level 3 (BSL-3) for cellular assays using viral particles poses a limitation for the widespread evaluation of Mpro inhibitor efficacy in a cell-based assay. Here, we established a BSL-1 compatible cellular assay to evaluate the in vivo potential of Mpro inhibitors. This assay utilizes mammalian cells expressing a tagged Mpro construct containing N-terminal glutathione S-transferase (GST) and C-terminal hemagglutinin (HA) tags and monitors Mpro autodigestion. Using this method, GC376 and boceprevir effectively inhibited Mpro autodigestion, suggesting their potential in vivo activity. Conversely, carmofur and ebselen did not exhibit significant inhibitory effects in this assay. We further investigated the inhibitory potential of selenoneine on Mpro using this approach. Computational analyses of binding energies suggest that noncovalent interactions play a critical role in facilitating the covalent modification of the C145 residue, leading to Mpro inhibition. Our method is straightforward, cost-effective, and readily applicable in standard laboratories, making it accessible to researchers with varying levels of expertise in infectious diseases. Full article

Abstract: Objective: Immune–metabolic interactions may have causal and therapeutic impacts on abdominal aortic aneurysms (AAAs). However, due to the lack of research on the relationship between immune–metabolic interactions and AAAs, further exploration of the mechanism faces challenges. Methods: A two-sample, two-step mediation analysis with Mendelian randomization (MR) based on genome-wide association studies (GWASs) was performed to determine the causal associations among blood immune cell signatures, metabolites, and AAAs. The stability, heterogeneity, and pleiotropy of the results were verified using a multivariate sensitivity analysis. Results: After multiple two-sample MRs using the AAA data from two large-scale GWAS databases, we determined that the human leukocyte antigen-DR (HLA-DR) levels on HLA-DR + natural killer (NK) cells (HLA-DR/NK) were associated with the causal effect of an AAA, with consistent results in the two databases (FinnGen: odds ratio (OR) = 1.054, 95% confidence interval (CI): 1.003–1.067, p-value = 0.036; UK Biobank: OR = 1.149, 95% CI: 1.046–1.261, p-value = 0.004). The metabolites associated with the risk of developing an AAA were enriched to find a specific metabolic model. We also found that the ratio of adenosine 5′-monophosphate (AMP) to threonine could act as a potential mediator between the HLA/NK and an AAA, with a direct effect (beta effect = 0.0496) and an indirect effect (beta effect = 0.0029). The mediation proportion was 5.56%. Conclusions: Our study found that an up-regulation of HLA-DR on HLA-DR/NK cells can increase the risk of an AAA via improvements in the AMP-to-threonine ratio, thus providing a potential new biomarker for the prediction and treatment of AAAs. Full article