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

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

Soil contamination of polycyclic aromatic hydrocarbons (PAHs), especially caused by the mixture of two or more PAHs, raised great environmental concerns. However, research on the migration and transformation processes of PAHs in soils and their interactions with native communities is limited. In this work, soil samples from uncontaminated sites around the industrial parks in Handan, Hengshui, and Shanghai were artificially supplemented with three concentrations of anthracene (Ant), 9-chloroanthracene (9-ClAnt), benzopyrene (BaP), and chrysene (Chr). Ryegrass was planted to investigate the degradation of PAHs and its interaction with native soil organisms in the constructed ryegrass–microbe–soil microcosmic system. The bacterial and fungal communities in soil were affected by PAHs; their species diversity and relative abundance changed after exposure to different concentrations of PAHs, among which Lysobacter, Bacillus, Pseudomonas, and Massilia bacteria were correlated to the degradation of PAHs. On the 56th day, the contents of BaP, Chr, and Ant decreased with the degradation process, while the degradation of 9-ClAnt was limited. Nineteen intermediates, including hydroxylation and carboxylated compounds, were identified. The present research would help clarify the potential interactions between PAHs and native organisms in contaminated sites, providing fundamental information for evaluating the transformation risks of PAHs in the natural environment. Full article

In recent decades, there has been growing interest in the thermal conversion of various alternative fuels, such as biomass and waste-derived fuels. One of the technological solutions for the so-called direct co-combustion of fuels is to create mixtures of various fuels, called mixed secondary fuel. However, mixed secondary fuel has different properties compared to primary fuels. Due to this, by properly selecting the types and proportions of mixtures, it is possible to eliminate their potentially negative impact on both combustion technology and the natural environment. In this paper, we decided to prepare mixed secondary fuel by mixing sunflower husk pellets with RDF (refuse-derived fuel) in a ratio of 1:1 and then analyze the combustion process of the produced fuel in fluidized bed conditions. The results obtained on the basis of the presented research indicate that the mixed secondary fuel eliminated the impact of alkali metal compounds on reducing the melting point of ash and, consequently, on the combustion process of the mixed secondary fuel. An additional benefit is the reduction of emissions of harmful compounds into the atmosphere occurring during the combustion of municipal waste and compliance with the concept of the circular economy. Full article

Background: In genomics, highly sensitive point mutation detection is particularly relevant for cancer diagnosis and early relapse detection. Next-generation sequencing combined with unique molecular identifiers (UMIs) is known to improve the mutation detection sensitivity. Methods: We present an open-source bioinformatics framework named Interface for Point Mutation Identification (IMPI) with a graphical user interface (GUI) for processing especially small-scale NGS data to identify variants. IMPI ensures detailed UMI analysis and clustering, as well as initial raw read processing, and consensus sequence building. Furthermore, the effects of custom algorithm and parameter settings for NGS data pre-processing and UMI collapsing (e.g., UMI clustered versus unclustered (raw) reads) can be investigated. Additionally, IMPI implements optimization and quality control methods; an evolution strategy is used for parameter optimization. Results: IMPI was designed, implemented, and tested using BCR::ABL1 fusion gene kinase domain sequencing data. In summary, IMPI enables a detailed analysis of the impact of UMI clustering and parameter setting changes on the measured allele frequencies. Conclusions: Regarding the BCR::ABL1 data, IMPI’s results underlined the need for caution while designing specialized single amplicon NGS approaches due to methodical limitations (e.g., high PCR-mediated recombination rate). This cannot be corrected using UMIs. Full article

Bone mechanotransduction is a critical process during skeletal development in embryogenesis and organogenesis. At the same time, the type and level of mechanical loading regulates bone remodeling throughout the adult life. The aberrant mechanosensing of bone cells has been implicated in the development and progression of bone loss disorders, but also in the bone-specific aspect of other clinical entities, such as the tumorigenesis of solid organs. Novel treatment options have come into sight that exploit the mechanosensitivity of osteoblasts, osteocytes, and chondrocytes to achieve efficient bone regeneration. In this regard, runt-related transcription factor 2 (Runx2) has emerged as a chief skeletal-specific molecule of differentiation, which is prominent to induction by mechanical stimuli. Polycystins represent a family of mechanosensitive proteins that interact with Runx2 in mechano-induced signaling cascades and foster the regulation of alternative effectors of mechanotransuction. In the present narrative review, we employed a PubMed search to extract the literature concerning Runx2, polycystins, and their association from 2000 to March 2024. The keywords stated below were used for the article search. We discuss recent advances regarding the implication of Runx2 and polycystins in bone remodeling and regeneration and elaborate on the targeting strategies that may potentially be applied for the treatment of patients with bone loss diseases. Full article

The displacement micropump with passive check valves is an attractive solution for precise insulin infusion in patients with type I diabetes. Unlike most insulin pumps that push insulin from a cartridge using a piston, a displacement micropump will first pull insulin from the reservoir before infusing it into the patient. This dual sequence introduces new challenges in terms of insulin stability, notably if the reservoir is not pressurized. After an introduction to displacement micropumps and a brief review of the insulin degradation mechanism, micropump design rules are discussed in light of microfluidic theory. Full article