Various methods for evaluating the visual quality of landscapes have been continuously studied. In the era of the rapid development of big data, methods to obtain evaluation data efficiently and accurately have received attention. However, few studies have been conducted to optimize the evaluation methods for landscape visual quality. Here, we aim to develop an evaluation model that is model fine-tuned using Scenic Beauty Evaluation (SBE) results. In elucidating the methodology, it is imperative to delve into the intricacies of refining the evaluation process. First, fine-tuning the model can be initiated with a scoring test on a small population, serving as an efficient starting point. Second, determining the optimal hyperparameter settings necessitates establishing intervals within a threshold range tailored to the characteristics of the dataset. Third, from the pool of fine-tuned models, selecting the one exhibiting optimal performance is crucial for accurately predicting the visual quality of the landscape within the study population. Lastly, through the interpolation process, discernible differences in landscape aesthetics within the core monitoring area can be visually distinguished, thereby reinforcing the reliability and practicality of the new method. In order to demonstrate the efficiency and practicality of the new method, we chose the core section of the famous Beijing–Hangzhou Grand Canal in Wujiang District, China, as a case study. The results show the following: (1) Fine-tuning the model can start with a scoring test on a small population. (2) The optimal hyperparameter setting intervals of the model need to be set in a threshold range according to different dataset characteristics. (3) The model with optimal performance is selected among the four fine-tuning models for predicting the visual quality of the landscape in the study population. (4) After the interpolation process, the differences in landscape aesthetics within the core monitoring area can be visually distinguished. We believe that the new method is efficient, accurate, and practically applicable for improving landscape visual quality evaluation. Full article

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance. Full article

Purpose: Given the emergence of PSMA-targeted diagnostic agents and therapeutics, we sought to investigate patterns of FOLH1 expression in RCC and their impacts on RCC outcomes. Methods: We conducted a pooled multi-institutional analysis of patients with RCC having undergone DNA and RNA next-generation sequencing. FOLH1-high/low expression was defined as the ≥75th/<25th percentile of RNA transcripts per million (TPM). Angiogenic, T-effector, and myeloid expression signatures were calculated using previously defined gene sets. Kaplan–Meier estimates were calculated from the time of tissue collection or therapy start. Results: We included 1,724 patients in the analysis. FOLH1 expression was significantly higher in clear cell (71%) compared to non-clear cell RCC tumors (19.0 versus 3.3 TPM, p < 0.001) and varied by specimen site (45% primary kidney/55% metastasis, 13.6 versus 9.9 TPM, p < 0.001). FOLH1 expression was correlated with angiogenic gene expression (Spearman = 0.76, p < 0.001) and endothelial cell abundance (Spearman = 0.76, p < 0.001). While OS was similar in patients with FOLH1-high versus -low ccRCC, patients with FOLH1-high clear cell tumors experienced a longer time on cabozantinib treatment (9.7 versus 4.6 months, respectively, HR 0.57, 95% CI 0.35–0.93, p < 0.05). Conclusions: We observed differential patterns of FOLH1 expression based on histology and tumor site in RCC. FOLH1 was correlated with angiogenic gene expression, increased OS, and a longer duration of cabozantinib treatment. Full article

In this study, spherical or hexagonal NaYF₄:Yb,Er nanoparticles (UCNPs) with sizes of 25 nm (S-UCNPs) and 120 nm (L-UCNPs) were synthesized by high-temperature coprecipitation and subsequently modified with three kinds of polymers. These included poly(ethylene glycol) (PEG) and poly(N,N-dimethylacrylamide-co-2-aminoethylacrylamide) [P(DMA-AEA)] terminated with an alendronate anchoring group, and poly(methyl vinyl ether-co-maleic acid) (PMVEMA). The internalization of nanoparticles by rat mesenchymal stem cells (rMSCs) and C6 cancer cells (rat glial tumor cell line) was visualized by electron microscopy and the cytotoxicity of the UCNPs and their leaches was measured by the real-time proliferation assay. The comet assay was used to determine the oxidative damage of the UCNPs. An in vivo study on mice determined the elimination route and potential accumulation of UCNPs in the body. The results showed that the L- and S-UCNPs were internalized into cells in the lumen of endosomes. The proliferation assay revealed that the L-UCNPs were less toxic than S-UCNPs. The viability of rMSCs incubated with particles decreased in the order S-UCNP@Ale-(PDMA-AEA) > S-UCNP@Ale-PEG > S-UCNPs > S-UCNP@PMVEMA. Similar results were obtained in C6 cells. The oxidative damage measured by the comet assay showed that neat L-UCNPs caused more oxidative damage to rMSCs than all coated UCNPs while no difference was observed in C6 cells. An in vivo study indicated that L-UCNPs were eliminated from the body via the hepatobiliary route; L-UCNP@Ale-PEG particles were almost eliminated from the liver 96 h after intravenous application. Pilot fluorescence imaging confirmed the limited in vivo detection capabilities of the nanoparticles. Full article

In this study, Fe₃O₄/Ag magnetite-silver (MSx) nanocomposites were investigated as catalysts for advanced oxidation processes by coupling the plasmonic effect of silver nanoparticles and the ferromagnetism of iron oxide species. A surfactant-free co-precipitation synthesis method yielded pure Fe₃O₄ magnetite and four types of MSx nanocomposites. Their characterisation included structural, compositional, morphological and optical analyses, revealing Fe₃O₄ magnetite and Ag silver phases with particle sizes ranging from 15 to 40 nm, increasing with the silver content. The heterostructures with silver reduced magnetite particle aggregation, as confirmed by dynamic light scattering. The UV–Vis spectra showed that the Fe:Ag ratio strongly influenced the absorbance, with a strong absorption band around 400 nm due to the silver phase. The oxidation kinetics of organic pollutants, monitored by in situ luminescence measurements using rhodamine B as a model system, demonstrated the higher performance of the developed catalysts with increasing Ag content. The specific surface area measurements highlighted the importance of active sites in the synergistic catalytic activity of Fe₃O₄/Ag nanocomposites in the photo-Fenton reaction. Finally, the straightforward fabrication of diverse Fe₃O₄/Ag heterostructures combining magnetism and plasmonic effects opens up promising possibilities for heterogeneous catalysis and environmental remediation. Full article

Background: Parents play a crucial role in the care of infants during their stay in the neonatal intensive care unit (NICU). Recent studies have reported a decrease in parental participation due to the coronavirus disease (COVID-19) pandemic, which has led to restricted access policies in hospitals. The aim of this study was to describe the barriers to good parental participation during their stay in the neonatal intensive care unit in the COVID-19 era. Methods: This was a quantitative, observational study. Results: A total of 270 parents participated in this study. Mothers’ participation in care was higher than that of fathers (p = 0.017). Parents who lived at the birth of their first child reported a better level of participation in care compared to those who lived at the birth of their second-born (p = 0.005). Parents of extremely preterm neonates reported a lower interaction with their infants than parents of term newborns (p < 0.001). Conclusions: Some disadvantaged categories reported lower scores for cultural and linguistic minorities, parents of multiple children, and fathers. The COVID-19 pandemic has made several family-centred care activities impossible, with a higher impact on those who benefited most of these facilities. This study was prospectively approved by the IRB-CRRM of the University “G. d’Annunzio” Chieti-Pescara on 23 January 2024 (approval number CRRM: 2023_12_07_01). Full article

The solutions to the radial Schrödinger equation for a pseudoharmonic potential and Kratzer potential have been studied separately in the past. Despite different reports on the Kratzer potential, the fundamental theoretical quantities such as Fisher information have not been reported. In this study, we obtain the solution to the radial Schrödinger equation for the combination of the pseudoharmonic and Kratzer potentials in the presence of a constant-dependent potential, utilizing the concepts and formalism of the supersymmetric and shape invariance approach. The position expectation value and momentum expectation value are calculated employing the Hellmann–Feynman Theory. These expectation values are then used to calculate the Fisher information for both position and momentum spaces in both the absence and presence of the constant-dependent potential. The results obtained revealed that the presence of the constant-dependent potential leads to an increase in the energy eigenvalue, as well as in the position and momentum expectation values. Additionally, the constant-dependent potential increases the Fisher information for both position and momentum spaces. Furthermore, the product of the position expectation value and the momentum expectation value, along with the product of the Fisher information, satisfies both Fisher’s inequality and Cramer–Rao’s inequality. Full article

Background: cardiovascular diseases, including acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC), are significant causes of morbidity and mortality worldwide. Timely differentiation of these conditions is essential for effective patient management and improved outcomes. Methods: We conducted a review focusing on studies that applied artificial intelligence (AI) techniques to differentiate between acute myocardial infarction (AMI) and takotsubo cardiomyopathy (TTC). Inclusion criteria comprised studies utilizing various AI modalities, such as deep learning, ensemble methods, or other machine learning techniques, for discrimination between AMI and TTC. Additionally, studies employing imaging techniques, including echocardiography, cardiac magnetic resonance imaging, and coronary angiography, for cardiac disease diagnosis were considered. Publications included were limited to those available in peer-reviewed journals. Exclusion criteria were applied to studies not relevant to the discrimination between AMI and TTC, lacking detailed methodology or results pertinent to the AI application in cardiac disease diagnosis, not utilizing AI modalities or relying solely on invasive techniques for differentiation between AMI and TTC, and non-English publications. Results: The strengths and limitations of AI-based approaches are critically evaluated, including factors affecting performance, such as reliability and generalizability. The review delves into challenges associated with model interpretability, ethical implications, patient perspectives, and inconsistent image quality due to manual dependency, highlighting the need for further research. Conclusions: This review article highlights the promising advantages of AI technologies in distinguishing AMI from TTC, enabling early diagnosis and personalized treatments. However, extensive validation and real-world implementation are necessary before integrating AI tools into routine clinical practice. It is vital to emphasize that while AI can efficiently assist, it cannot entirely replace physicians. Collaborative efforts among clinicians, researchers, and AI experts are essential to unlock the potential of these transformative technologies fully. Full article

This paper pioneers a novel approach in electromagnetic (EM) system analysis by synergistically combining Bayesian Neural Networks (BNNs) informed by Latin Hypercube Sampling (LHS) with advanced thermal–mechanical surrogate modeling within COMSOL simulations for high-frequency low-pass filter modeling. Our methodology transcends traditional EM characterization by integrating physical dimension variability, thermal effects, mechanical deformation, and real-world operational conditions, thereby achieving a significant leap in predictive modeling fidelity. Through rigorous evaluation using Mean Squared Error (MSE), Maximum Learning Error (MLE), and Maximum Test Error (MTE) metrics, as well as comprehensive validation on unseen data, the model’s robustness and generalization capability is demonstrated. This research challenges conventional methods, offering a nuanced understanding of multiphysical phenomena to enhance reliability and resilience in electronic component design and optimization. The integration of thermal variables alongside dimensional parameters marks a novel paradigm in filter performance analysis, significantly improving simulation accuracy. Our findings not only contribute to the body of knowledge in EM diagnostics and complex-environment analysis but also pave the way for future investigations into the fusion of machine learning with computational physics, promising transformative impacts across various applications, from telecommunications to medical devices. Full article

The aim of this study was to evaluate the effect of local administration of melatonin (MLT) on molecular biomarkers and calvaria bone critical defects in female rats with or without osteoporosis, associated or not with a xenogeneic biomaterial. Forty-eight female rats were randomly divided into two groups: (O) ovariectomized and (S) placebo groups. After 45 days of osteoporosis induction, two critical-size defects (5 mm diameter) were created on the calvaria. The groups were subdivided according to the following treatment: (C) Clot, MLT, MLT associated with Bio-Oss^® (MLTBO), and Bio-Oss^® (BO). After 45 days, the defect samples were collected and processed for microtomography, histomorphometry, and biomolecular analysis (Col-I, BMP-2, and OPN). All animals had one femur harvested to confirm the osteoporosis. Microtomography analysis demonstrated a bone mineral density reduction in the O group. Regarding bone healing, the S group presented greater filling of the defects than the O group; however, in the O group, the defects treated with MLT showed higher mineral filling than the other treatments. There was no difference between the treatments performed in the S group (p = 0.05). Otherwise, O-MLT had neoformed bone higher than in the other groups (p = 0.05). The groups that did not receive biomaterial demonstrated lower levels of Col-I secretion; S-MLT and S-MLTBO presented higher levels of OPN, while O-C presented statistically lower results (p < 0.05); O-BO showed greater BMP-2 secretion (p < 0.05). In the presence of ovariectomy-induced osteoporosis, MLT treatment increased the newly formed bone area, regulated the inflammatory response, and increased OPN expression. Full article

Elastomers are made of chain-like molecules to form networks that can sustain large deformation. Rubbers are thermosetting elastomers that are obtained from irreversible curing reactions. Curing reactions create permanent bonds between the molecular chains. On the other hand, thermoplastic elastomers do not need curing reactions. Incorporation of appropriated filler particles, as has been practiced for decades, can significantly enhance mechanical properties of elastomers. However, there are fundamental questions about polymer matrix composites (PMCs) that still elude complete understanding. This is because the macroscopic properties of PMCs depend not only on the overall volume fraction ($\varphi$) of the filler particles, but also on their spatial distribution (i.e., primary, secondary, and tertiary structure). This work aims at reviewing how the mechanical properties of PMCs are related to the microstructure of filler particles and to the interaction between filler particles and polymer matrices. Overall, soft rubbery matrices dictate the elasticity/hyperelasticity of the PMCs while the reinforcement involves polymer–particle interactions that can significantly influence the mechanical properties of the polymer matrix interface. For $\varphi$ values higher than a threshold, percolation of the filler particles can lead to significant reinforcement. While viscoelastic behavior may be attributed to the soft rubbery component, inelastic behaviors like the Mullins and Payne effects are highly correlated to the microstructures of the polymer matrix and the filler particles, as well as that of the polymer–particle interface. Additionally, the incorporation of specific filler particles within intelligently designed polymer systems has been shown to yield a variety of functional and responsive materials, commonly termed smart materials. We review three types of smart PMCs, i.e., magnetoelastic (M-), shape-memory (SM-), and self-healing (SH-) PMCs, and discuss the constitutive models for these smart materials. Full article

The smart grid, as a crucial part of modern energy systems, handles extensive and diverse data, including inputs from various sensors, metering devices, and user interactions. Outsourcing data storage to remote cloud servers presents an economical solution for enhancing data management within the smart grid ecosystem. However, ensuring data privacy before transmitting it to the cloud is a critical consideration. Therefore, it is common practice to encrypt the data before uploading them to the cloud. While encryption provides data confidentiality, it may also introduce potential issues such as limiting data owners’ ability to query their data. The searchable attribute-based encryption (SABE) not only enables fine-grained access control in a dynamic large-scale environment but also allows for data searches on the ciphertext domain, making it an effective tool for cloud data sharing. Although SABE has become a research hotspot, existing schemes often have limitations in terms of computing efficiency on the client side, weak security of the ciphertext and the trapdoor. To address these issues, we propose an efficient server-aided ciphertext-policy searchable attribute-based encryption scheme (SA-CP-SABE). In SA-CP-SABE, the user’s data access authority is consistent with the search authority. During the search process, calculations are performed not only to determine whether the ciphertext matches the keyword in the trapdoor, but also to assist subsequent user ciphertext decryption by reducing computational complexity. Our scheme has been proven under the random oracle model to achieve the indistinguishability of the ciphertext and the trapdoor and to resist keyword-guessing attacks. Finally, the performance analysis and simulation of the proposed scheme are provided, and the results show that it performs with high efficiency. Full article

This article reconsiders E. Schrödinger’s cat paradox experiment from a new perspective, grounded in the interpretation of quantum mechanics that belongs to the class of interpretations designated as “reality without realism” (RWR) interpretations. These interpretations assume that the reality ultimately responsible for quantum phenomena is beyond conception, an assumption designated as the Heisenberg postulate. Accordingly, in these interpretations, quantum physics is understood in terms of the relationships between what is thinkable and what is unthinkable, with, physical, classical, and quantum, corresponding to thinkable and unthinkable, respectively. The role of classical physics becomes unavoidable in quantum physics, the circumstance designated as the Bohr postulate, which restores to classical physics its position as part of fundamental physics, a position commonly reserved for quantum physics and relativity. This view of quantum physics and relativity is maintained by this article as well but is argued to be sufficient for understanding fundamental physics. Establishing this role of classical physics is a distinctive contribution of the article, which allows it to reconsider Schrödinger’s cat experiment, but has a broader significance for understanding fundamental physics. RWR interpretations have not been previously applied to the cat experiment, including by N. Bohr, whose interpretation, in its ultimate form (he changed it a few times), was an RWR interpretation. The interpretation adopted in this article follows Bohr’s interpretation, based on the Heisenberg and Bohr postulates, but it adds the Dirac postulate, stating that the concept of a quantum object only applies at the time of observation and not independently. Full article

Escape room games are educational gamification technologies that consist of introducing a team of players into a physical or digital space in search of clues to answer puzzles, riddles or enigmas and solve a mystery or problem. This study aims to determine the effectiveness of escape room games on the training of nursing students in an international context. A systematic review was carried out in MEDLINE, WOS, SCOPUS, CINAHL and LILACS databases using the MeSH terms “Education, Nursing” and “Educational Technology”, and the free term “Escape room”, combined with Boolean operators AND/OR. Intervention studies in Spanish, English and Portuguese were included, without limitation for the year of publication. Selection and critical appraisal were conducted by two independent reviewers. A total of n = 13 interventional studies were included (n = 2 Randomized Clinical Trials and n = 11 quasi-experimental design). Escape rooms are a recent and growing educational methodology, increasingly used in academia and in the training of nurses and nursing students. However, it is necessary to expand their use and the quality of the studies in a greater number of contexts. Furthermore, it is necessary to homogenize and standardize validated instruments to evaluate the effectiveness of escape rooms in the nursing education area. Full article

Filamentous fungi exhibit unparalleled potential as cell factories for protein production, owing to their adeptness in protein secretion and remarkable proficiency in post-translational modifications. This review delineates the role of filamentous fungi in bio-input technology across different generations and explores their capacity to generate secondary metabolites. Our investigation highlights filamentous fungi as frontrunners in the production of bioactive compounds, emphasizing the imperative nature of elucidating their metabolic repertoire. Furthermore, we delve into common strategies for genetic transformation in filamentous fungi, elucidating the underlying principles, advantages, and drawbacks of each technique. Taking a forward-looking approach, we explore the prospects of genome engineering, particularly the CRISPR-Cas9 technique, as a means to propel protein secretion in filamentous fungi. Detailed examination of the protein secretion pathways in these fungi provides insights into their industrial applications. Notably, extensive research within the scientific community has focused on Aspergillus and Trichoderma species for the industrial production of proteins and enzymes. This review also presents practical examples of genetic engineering strategies aimed at augmenting enzyme secretion in filamentous fungi for various industrial applications. These findings underscore the potential of filamentous fungi as versatile platforms for protein production and highlight avenues for future research and technological advancement in this field. Full article

Background: The pathogenic fungus Candida albicans is a leading agent of death in immunocompromised individuals with a growing trend of antifungal resistance. Methods: The purpose is to induce resistance to drugs in a sensitive C. albicans strain followed by whole-genome sequencing to determine mechanisms of resistance. Strains will be assayed for pathogenicity attributes such as ergosterol and chitin content, growth rate, virulence, and biofilm formation. Results: We observed sequential increases in ergosterol and chitin content in fluconazole-resistant isolates by 78% and 44%. Surface thickening prevents the entry of the drug, resulting in resistance. Resistance imposed a fitness trade-off that led to reduced growth rates, biofilm formation, and virulence in our isolates. Sequencing revealed mutations in genes involved in resistance and pathogenicity such as ERG11, CHS3, GSC2, CDR2, CRZ2, and MSH2. We observed an increase in the number of mutations in key genes with a sequential increase in drug-selective pressures as the organism increased its odds of adapting to inhospitable environments. In ALS4, we observed two mutations in the susceptible strain and five mutations in the resistant strain. Conclusion: This is the first study to induce resistance followed by genotypic and phenotypic analysis of isolates to determine mechanisms of drug resistance. Full article

Change-point detection is a challenging problem that has a number of applications across various real-world domains. The primary objective of CPD is to identify specific time points where the underlying system undergoes transitions between different states, each characterized by its distinct data distribution. Precise identification of change points in time series omics data can provide insights into the dynamic and temporal characteristics inherent to complex biological systems. Many change-point detection methods have traditionally focused on the direct estimation of data distributions. However, these approaches become unrealistic in high-dimensional data analysis. Density ratio methods have emerged as promising approaches for change-point detection since estimating density ratios is easier than directly estimating individual densities. Nevertheless, the divergence measures used in these methods may suffer from numerical instability during computation. Additionally, the most popular $\alpha$-relative Pearson divergence cannot measure the dissimilarity between two distributions of data but a mixture of distributions. To overcome the limitations of existing density ratio-based methods, we propose a novel approach called the Pearson-like scaled-Bregman divergence-based (PLsBD) density ratio estimation method for change-point detection. Our theoretical studies derive an analytical expression for the Pearson-like scaled Bregman divergence using a mixture measure. We integrate the PLsBD with a kernel regression model and apply a random sampling strategy to identify change points in both synthetic data and real-world high-dimensional genomics data of Drosophila. Our PLsBD method demonstrates superior performance compared to many other change-point detection methods. Full article

Dopamine is a key neurotransmitter involved in physiological processes such as motor control, motivation, reward, cognitive function, and maternal and reproductive behaviors. Therefore, dysfunctions of the dopaminergic system are related to a plethora of human diseases. Dopamine, via different circuitries implicated in compulsive behavior, reward, and habit formation, also represents a key player in substance use disorder and the formation and perpetuation of mechanisms leading to addiction. Here, we propose dopamine as a model not only of neurotransmission but also of neuromodulation capable of modifying neuronal architecture. Abuse of substances like methamphetamine, cocaine, and alcohol and their consumption over time can induce changes in neuronal activities. These modifications lead to synaptic plasticity and finally to morphological and functional changes, starting from maladaptive neuro-modulation and ending in neurodegeneration. Full article

Ubiquitin-specific protease 18 (USP18) is a protein recognized for its dual enzymatic and non-enzymatic nature. It is involved in many physiological processes like the cell cycle and cell signaling. It also suppresses heart muscle remodeling upon an increase in the afterload. The role of USP18 in kidney pathology remains unknown. The objective of the study was to assess the relationship between serum and urine USP18 levels, the factors contributing to cardiovascular risk, and the markers of kidney disease activity at different stages of chronic kidney disease (CKD). One hundred participants, aged between 24 and 85 years (mean 53.1 ± 17.1 years), were included. Five groups (n = 20 each) were recruited according to their renal status (healthy individuals, patients with proteinuric glomerulonephritis, patients with non-proteinuric CKD, patients who were treated with hemodialysis, and kidney transplant recipients). The measurements of serum and urine USP18 levels were performed using ELISA. The median serum USP18 level was the highest in healthy participants (1143.0 pg/mL) and kidney transplant recipients (856.6 pg/mL), whereas, in individuals with different forms of CKD, it fitted within the range of 402.1–471.9 pg/mL. Urinary USP18 reached the highest level in the group of CKD patients not yet on dialysis (303.3 pg/mL). Only in this group did it correlate with serum creatinine and urea concentrations. Our results suggest the inhibition of cardioprotective USP18 signaling when kidney function is impaired. Moreover, an increased level of urinary USP18 may indicate chronic tubular damage. Full article

With the escalating demand for Astragalus polysaccharides products developed from Radix Astragali (RA), the necessity for quality control of polysaccharides in RA has become increasingly urgent. In this study, a specific method for the simultaneous determination of seven monosaccharides in polysaccharides extracted from Radix Astragali (RA) has been developed and validated using ultra-performance liquid chromatography equipped with an ultraviolet detector (UHPLC-UV) for the first time. The 1-phenyl-3-methyl-5-pyrazolone (PMP) derivatizations were separated on a C18 column (Waters ACQUITY^TM, Milfor, MA, USA, 1.8 µm, 2.1 × 100 mm) using gradient elution with a binary system of 5 mm ammonium formate (0.1% formic acid)-acetonitrile for 24 min. Additionally, seven monosaccharides showed good linear relationships (R², 0.9971–0.9995), adequate precision (RSD < 4.21%), and high recoveries (RSD < 4.70%). The established method was used to analyze 109 batches of RA. Results showed that the Astragalus polysaccharides (APSs) mainly consist of mannose (Man), rhamnose (Rha), glucose (Glu), galactose (Gal), arabinose (Ara), xylose (Xyl); and fucose (Fuc); however, their composition was different among RA samples from different growth patterns, species, growth years, and origins, and the growth mode of RA and the age of wild-simulated RA can be accurately distinguished by principal component analysis (PCA). In addition, the immunological activity of APSs were also evaluated jointly by measurement of the NO release with RAW264.7, with the results showing that APSs have a promoting effect on the release of NO and exhibit a significant correlation with Man, Glu, Xyl, and Fuc contents. Accordingly, the new established monosaccharides analytical method and APS-immune activity determination in this study can provide a reference for quality evaluation and the establishment of quality standards for RA. Full article

The genus Acinetobacter comprises both environmental and clinically relevant species associated with hospital-acquired infections. Among them, Acinetobacter baumannii is a critical priority bacterial pathogen, for which the research and development of new strategies for antimicrobial treatment are urgently needed. Acinetobacter spp. produce a variety of structurally diverse capsular polysaccharides (CPSs), which surround the bacterial cells with a thick protective layer. These surface structures are primary receptors for capsule-specific bacteriophages, that is, phages carrying tailspikes with CPS-depolymerizing/modifying activities. Phage tailspike proteins (TSPs) exhibit hydrolase, lyase, or esterase activities toward the corresponding CPSs of a certain structure. In this study, the data on all lytic capsule-specific phages infecting Acinetobacter spp. with genomes deposited in the NCBI GenBank database by January 2024 were summarized. Among the 149 identified TSPs encoded in the genomes of 143 phages, the capsular specificity (K specificity) of 46 proteins has been experimentally determined or predicted previously. The specificity of 63 TSPs toward CPSs, produced by various Acinetobacter K types, was predicted in this study using a bioinformatic analysis. A comprehensive phylogenetic analysis confirmed the prediction and revealed the possibility of the genetic exchange of gene regions corresponding to the CPS-recognizing/degrading parts of different TSPs between morphologically and taxonomically distant groups of capsule-specific Acinetobacter phages. Full article

A self-sufficient bifunctional enzyme integrating reductive amination and coenzyme regeneration activities was developed and successfully employed to synthesize (S)-cyclopropylglycine with an improved reaction rate 2.1-fold over the native enzymes and a short bioconversion period of 6 h at a high substrate concentration of 120 g·L⁻¹ and space–time yield of (S)-cyclopropylglycine up to 377.3 g·L⁻¹·d⁻¹, higher than that of any previously reported data. Additionally, (S)-cyclopropylglycine could be continuously synthesized for 90 h with the enzymes packed in a dialysis tube, providing 634.6 g of (S)-cyclopropylglycine with >99.5% ee and over 95% conversion yield up to 12 changes. These results confirmed that the newly developed NADH-driven biocatalytic system could be utilized as a self-sufficient biocatalyst for industrial application in the synthesis of (S)-cyclopropylglycine, which provides a chiral center and cyclopropyl fragment for the frequent synthesis of preclinical/clinical drug molecules. Full article