Search Results (62,320)

Inositol hexakisphosphate (InsP₆) is the most abundant inositol polyphosphate both in plant and animal cells. Exogenous InsP₆ is known to inhibit cell proliferation and induce apoptosis in cancerous cells. However, cellular entry of exogenous InsP₆ is hindered due to the presence of highly negative charge on this molecule. Therefore, to enhance the cellular delivery of InsP₆ in cancerous cells, InsP₆ was encapsulated by chitosan (CS), a natural polysaccharide, via the ionic gelation method. Our hypothesis is that encapsulated InsP₆ will enter the cell more efficiently to trigger its apoptotic effects. The incorporation of InsP₆ into CS was optimized by varying the ratios of the two and confirmed by InsP₆ analysis via polyacrylamide gel electrophoresis (PAGE) and atomic absorption spectrophotometry (AAS). The complex was further characterized by Scanning Electron Microscopy (SEM) and Fourier Transform Infrared Spectroscopy (FTIR) for physicochemical changes. The data indicated morphological changes and changes in the spectral properties of the complex upon encapsulation. The encapsulated InsP₆ enters human breast cancer MCF-7 cells more efficiently than free InsP₆ and triggers apoptosis via a mechanism involving the production of reactive oxygen species (ROS). This work has potential for developing cancer therapeutic applications utilizing natural compounds that are likely to overcome the severe toxic effects associated with synthetic chemotherapeutic drugs. Full article

The concept of segregation analyses the unequal distribution of social groups between neighbourhoods. It rests on two assumptions: that of homogeneous neighbourhoods and of a market liberal housing system. Both assumptions are applicable the context of American cities, but they display severe limitations when applied to the European context. Vienna’s housing market is particularly highly segmented, not only throughout the city as a whole but also within neighbourhoods. In the densely built-up area, residential buildings of different segments with different underlying rent regulations and entry barriers can be found side by side. Therefore, buildings are expected to show varying tenant and owner structures, which undermines the idea of a homogeneous neighbourhood. Against this background, we analyse at the micro scale small neighbourhoods defined by 100 m grid cells in a case study of two inner-city Viennese districts (districts 6 and 7) characterised by a particularly vivid housing-transformation and commodification dynamic. Using a novel and fine-grained dataset combining building information with the socio-economic data of households, we investigate the patterns and dynamics of income inequality and income segregation, as well as the relationship between housing market segments and socio-economic patterns. As data comprise two cross-sections for the years 2011 and 2020/21, changes in the neighbourhoods during the house-price boom period are also considered. This leads us to ask the question: How do housing market segmentation and its related changes affect income inequality and segregation at the micro scale? Our analysis delivers two main results: Firstly, we show the existence of marked social variation and related dynamics at the micro scale, even within a small urban area. Secondly, we show that the spatial distribution of housing market segments has a strong impact on income inequality in the neighbourhood. Full article

The paper presents an analysis of the influence of climatic characteristics on the rating of land suitability for agricultural use. Soil fertility is one of the most important factors in land productivity and crop capacity; it is a complex value that depends not only on agrophysical and agrochemical soil properties but also on other natural factors, such as climate. There are different methodical approaches for a quantitative assessment of fertility level. The objectives of the research were to understand whether the distributions of active temperature sums and annual precipitation sums have a significant effect on the spatial and temporal heterogeneity of the rating assessment of land suitability for agricultural use in the example of the Leningrad region. The estimation and comparison between Semenov–Blagovidov’s method of quality land estimation and Karmanov’s method of appraisal of soils are given in this article. Karmanov’s method is highlighted in this paper for its ability to assess soil’s ecological indices more effectively than traditional methods. The research suggested that climate change may lead to increased variability in soil quality, with potential benefits for agriculture under certain climate scenarios, but at the same time, excessive temperatures in summer and precipitations might become a limiting factor, pushing down yields. The results of such assessment show that the performed calculation models can be used to forecast crop yields for future periods. Full article

Decidualization of the uterine endometrium is a critical process for embryo implantation in mammals, primarily occurring on gestational day 8 in pregnant mice. However, the interplay between the maternal gut microbiome, metabolism, and the uterus at this specific time point remains poorly understood. This study employed a multi-omics approach to investigate the metabolic, gut microbiome, and transcriptomic changes associated with early pregnancy (gestational day 8 (E8)) in mice. Serum metabolomics revealed a distinct metabolic profile at E8 compared to controls, with the differential metabolites primarily enriched in amino acid metabolism pathways. The gut microbial composition showed that E8 mice exhibited higher alpha-diversity and a significant shift in beta-diversity. Specifically, the E8 group displayed a decrease in pathogenic Proteobacteria and an increase in beneficial Bacteroidetes and S24-7 taxa. Transcriptomics identified myriads of distinct genes between the E8 and control mice. The differentially expressed genes were enriched in pathways involved in alanine, aspartate, and glutamate metabolism, PI3K-Akt signaling, and the PPAR signaling pathway. Integrative analysis of the multi-omics data uncovered potential mechanistic relationships among the differential metabolites, gut microbiota, and uterine gene expression changes. Notably, the gene Asns showed strong correlations with specific gut S24-7 and metabolite L-Aspartatic acid, suggesting its potential role in mediating the crosstalk between the maternal environment and embryo development during early pregnancy. These findings provide valuable insights into the complex interplay between the maternal metabolome, the gut microbiome, and the uterine transcriptome in the context of early pregnancy, which may contribute to our understanding of the underlying mechanisms of embryo implantation and development. Full article

(1) Background: Nutrition is a critical aspect of health and well-being in the elderly population, as physiological changes associated with aging can impact nutrient utilization and dietary needs. The aim of this study was the assessment of nutritional screening and associated factors among community-dwelling elderly people. (2) Methods: This study is the first phase of an intervention trial of people aged 65 years and over who received primary health services and resided in the municipality of Archanes Asterousia in Crete, Greece. Nutritional risk was assessed using the Mini Nutritional Assessment. Diet-related factors were analyzed, including health status (oral hygiene, depression, cognitive decline, impaired functioning, quality of life), social factors (educational attainment, marital status, type of work before 60 years), and lifestyle factors (smoking, drinking, diet). (3) Results: A total of 730 elderly people were evaluated (males, 31.5%), with an average age (±SD) of 76.83 (±6.68) years. MNA was found to have a statistically significant connection with assessment of oral hygiene, mental state, Charlson comorbidity, functional independence (assessed with the Barthel scale), and quality of life. The exception was geriatric depression (GDS), with which no statistically significant association was found (p > 0.05). Nutritional risk analysis revealed 379 participants (51.9%) to be adequately nourished, 205 (28.1%) to be at risk of malnutrition, and 146 (20.0%) to be malnourished. (4) Conclusions: These results clearly demonstrated the key factors that contribute to the nutritional screening of elderly people and need to be addressed by health authorities and social services. Full article

A series of M(x)CoCeMgAlO mixed oxides with different transition metals (M = Cu, Fe, Mn, and Ni) with an M content x = 3 at. %, and another series of Fe(x)CoCeMgAlO mixed oxides with Fe contents x ranging from 1 to 9 at. % with respect to cations, while keeping constant in both cases 40 at. % Co, 10 at. % Ce and Mg/Al atomic ratio of 3 were prepared via thermal decomposition at 750 °C in air of their corresponding layered double hydroxide (LDH) precursors obtained by coprecipitation. They were tested in a fixed bed reactor for complete methane oxidation with a gas feed of 1 vol.% methane in air to evaluate their catalytic performance. The physico-structural properties of the mixed oxide samples were investigated with several techniques, such as powder X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive X-ray spectroscopy (EDX), elemental mappings, inductively coupled plasma optical emission spectroscopy (ICP-OES), X-ray photoelectron spectroscopy (XPS), temperature-programmed reduction under hydrogen (H₂-TPR) and nitrogen adsorption–desorption at −196 °C. XRD analysis revealed in all the samples the presence of Co₃O₄ crystallites together with periclase-like and CeO₂ phases, with no separate M-based oxide phase. All the cations were distributed homogeneously, as suggested by EDX measurements and elemental mappings of the samples. The metal contents, determined by EDX and ICP-OES, were in accordance with the theoretical values set for the catalysts’ preparation. The redox properties studied by H₂-TPR, along with the surface composition determined by XPS, provided information to elucidate the catalytic combustion properties of the studied mixed oxide materials. The methane combustion tests showed that all the M-promoted CoCeMgAlO mixed oxides were more active than the M-free counterpart, the highest promoting effect being observed for Fe as the doping transition metal. The Fe(x)CoCeMgAlO mixed oxide sample, with x = 3 at. % Fe displayed the highest catalytic activity for methane combustion with a temperature corresponding to 50% methane conversion, T₅₀, of 489 °C, which is ca. 40 °C lower than that of the unpromoted catalyst. This was attributed to its superior redox properties and lowest activation energy among the studied catalysts, likely due to a Fe–Co–Ce synergistic interaction. In addition, long-term tests of Fe(3)CoCeMgAlO mixed oxide were performed, showing good stability over 60 h on-stream. On the other hand, the addition of water vapors in the feed led to textural and structural changes in the Fe(3)CoCeMgAlO system, affecting its catalytic performance in methane complete oxidation. At the same time, the catalyst showed relatively good recovery of its catalytic activity as soon as the water vapors were removed from the feed. Full article

Background/Aim: The stability of peri-implant tissues is crucial for the long-term success of dental implant treatments. A new cervical implant design has been developed to address the challenges associated with peri-implant tissue stability, featuring a concave cervical portion to increase tissue volume in this area. The present study aimed to clinically evaluate the effectiveness of the new cervical implant design in maintaining peri-implant tissue stability. Materials and Methods: Five clinical cases involving completely edentulous patients were selected, in which 25 implants were installed. The marginal bone level around each implant was assessed at three different time points—T0: immediately after the prosthesis installation, T1: 6 months post installation, and T2: at the last control visit, up to 38 months later. Measurements were taken to analyze changes in marginal bone levels (MBLs) and the keratinized mucosa (KM) over time. Furthermore, the keratinized mucosa (KM) around the implants was evaluated. Results: The mean and standard deviation values of the marginal bone levels at each time point were as follows—T0: 0.59 ± 0.55 mm; T1: 1.41 ± 0.59 mm; T2: 1.76 ± 0.69 mm. Statistical analysis showed significant differences across the time points (ANOVA p < 0.0001). The overall mean KM values were 3.85 mm for T1 and T2, showing the stability of the peri-implant soft tissues at ≥1-year controls. Conclusion: Within the limitations of the present study, the results showed that the Collo implants presented measured MBL values increasing within the time range analyzed in each case but within the normal values cited in the literature for these types of rehabilitation treatments. However, the measured KM values presented, in all cases, an average above the values referenced in the literature as a minimum for maintaining the health of the peri-implant tissues. Full article

The field of energy storage and conversion materials has witnessed transformative advancements owing to the integration of advanced in situ characterization techniques. Among them, numerous real-time characterization techniques, especially in situ transmission electron microscopy (TEM)/scanning TEM (STEM) have tremendously increased the atomic-level understanding of the minute transition states in energy materials during electrochemical processes. Advanced forms of in situ/operando TEM and STEM microscopic techniques also provide incredible insights into material phenomena at the finest scale and aid to monitor phase transformations and degradation mechanisms in lithium-ion batteries. Notably, the solid–electrolyte interface (SEI) is one the most significant factors that associated with the performance of rechargeable batteries. The SEI critically controls the electrochemical reactions occur at the electrode–electrolyte interface. Intricate chemical reactions in energy materials interfaces can be effectively monitored using temperature-sensitive in situ STEM techniques, deciphering the reaction mechanisms prevailing in the degradation pathways of energy materials with nano- to micrometer-scale spatial resolution. Further, the advent of cryogenic (Cryo)-TEM has enhanced these studies by preserving the native state of sensitive materials. Cryo-TEM also allows the observation of metastable phases and reaction intermediates that are otherwise challenging to capture. Along with these sophisticated techniques, Focused ion beam (FIB) induction has also been instrumental in preparing site-specific cross-sectional samples, facilitating the high-resolution analysis of interfaces and layers within energy devices. The holistic integration of these advanced characterization techniques provides a comprehensive understanding of the dynamic changes in energy materials. This review highlights the recent progress in employing state-of-the-art characterization techniques such as in situ TEM, STEM, Cryo-TEM, and FIB for detailed investigation into the structural and chemical dynamics of energy storage and conversion materials. Full article

Background: Liraglutide, a long-acting glucagon-like peptide-1 receptor agonist (GLP1RA), is a well-established anti-diabetic drug, has also been approved for the treatment of obesity at a dose of 3 mg. There are a limited number of studies in the literature that have looked at changes in metabolite levels before and after liraglutide treatment in patients with obesity. To this end, in the present study we aimed to explore the changes in the plasma metabolomic profile, using liquid chromatography-high resolution mass spectrometry (LC-HRMS) in patients with obesity. Methods: A single-center prospective study was undertaken to evaluate the effectiveness of 3 mg liraglutide therapy in twenty-three patients (M/F: 8/15) with obesity, mean BMI 40.81 ± 5.04 kg/m², and mean age of 36 ± 10.9 years, in two groups: at baseline (pre-treatment) and after 12 weeks of treatment (post-treatment). An untargeted metabolomic profiling was conducted in plasma from the pre-treatment and post-treatment groups using LC-HRMS, along with bioinformatics analysis using ingenuity pathway analysis (IPA). Results: The metabolomics analysis revealed a significant (FDR p-value ≤ 0.05, FC 1.5) dysregulation of 161 endogenous metabolites (97 upregulated and 64 downregulated) with distinct separation between the two groups. Among the significantly dysregulated metabolites, the majority of them were identified as belonging to the class of oxidized lipids (oxylipins) that includes arachidonic acid and its derivatives, phosphorglycerophosphates, N-acylated amino acids, steroid hormones, and bile acids. The biomarker analysis conducted using MetaboAnalyst showed PGP (a21:0/PG/F1alpha), an oxidized lipid, as the first metabolite among the list of the top 15 biomarkers, followed by cysteine and estrone. The IPA analysis showed that the dysregulated metabolites impacted the pathway related to cell signaling, free radical scavenging, and molecular transport, and were focused around the dysregulation of NF-κB, ERK, MAPK, PKc, VEGF, insulin, and pro-inflammatory cytokine signaling pathways. Conclusions: The findings suggest that liraglutide treatment reduces inflammation and modulates lipid metabolism and oxidative stress. Our study contributes to a better understanding of the drug’s multifaceted impact on overall metabolism in patients with obesity. Full article

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area. Full article

Caused by nonlinear vibration, ocean white noise exhibits complex dynamic characteristics and nonlinear perception characteristics. To explore the potential application of ocean white noise in engineering and health fields, novel methods based on deep learning algorithms are proposed to generate ocean white noise, contributing to marine environment simulation in ocean engineering. A comparative study, including spectrum analysis and auditory testing, proved the superiority of the generation method using deep learning networks over general mathematical or physical methods. To further study the nonlinear perception characteristics of ocean white noise, novel experimental research based on multi-modal perception research methods was carried out within a constructed multi-modal perception system environment, including the following two experiments. The first audiovisual comparative experiment thoroughly explores the system’s user multi-modal perception experience and influence factors, explicitly focusing on the impact of ocean white noise on human perception. The second sound intensity testing experiment is conducted to further explore human multi-sensory interaction and change patterns under white noise stimulation. The experimental results indicate that user visual perception ability and state reach a relatively high level when the sound intensity is close to 50 dB. Further numerical analysis based on the experimental results reveals the internal influence relationship between user perception of multiple senses, showing a fluctuating influence law to user visual concentration and a curvilinear influence law to user visual psychology from the sound intensity of ocean white noise. This study underscores ocean white noise’s positive effect on human perception enhancement and concentration improvement, providing a research basis for multiple field applications such as spiritual healing, perceptual learning, and artistic creation for human beings. Importantly, it provides valuable references and practical insights for professionals in related fields, contributing to the development and utilization of the marine environment. Full article

In this work, we present the innovative synthesis of salophen (acetaminosalol) derivatives in a solvent-free environment by high-speed ball milling, using a non-conventional activation method, which allowed obtaining compounds in a shorter time and with a better yield. Furthermore, for the first time, the salophen derivatives were deposited as composite films, using a matrix of poly 3,4-ethylene dioxythiophene:polystyrene sulfonate (PEDOT:PSS) polymer. Significant findings include the transformation from the benzoid to the quinoid form of PEDOT post-IPA treatment, as evidenced by Raman spectroscopy. SEM analysis revealed the formation of homogeneous films, and AFM provided insights into the changes in surface roughness and morphology post-IPA treatment, which may be crucial for understanding potential applications in electronics. The optical bandgap ranges between 2.86 and 3.2 eV for PEDOT:PSS-salophen films, placing them as organic semiconductors. The electrical behavior of the PEDOT:PSS-salophen films undergoes a transformation with the increase in voltage, from ohmic to space charge-limited conduction, and subsequently to constant current, with a maximum of 20 mA. These results suggest the possible use of composite films in organic electronics. Full article

Proteins form the fastest-growing therapeutic class. Due to their intrinsic instability, loss of native structure is common. Structure alteration must be carefully evaluated as structural changes may jeopardize the efficiency and safety of the protein-based drugs. Hydrogen deuterium exchange (HDX) has long been used to evaluate protein structure and dynamics. The rate of exchange constitutes a sensitive marker of the conformational state of the protein and of its stability. It is often monitored by mass spectrometry. Fourier transform infrared (FTIR) spectroscopy is another method with very promising capabilities. Combining protein microarrays with FTIR imaging resulted in high throughput HDX FTIR measurements. BaF₂ slides bearing the protein microarrays were covered by another slide separated by a spacer, allowing us to flush the cell continuously with a flow of N₂ gas saturated with ²H₂O. Exchange occurred simultaneously for all proteins and single images covering ca. 96 spots of proteins that could be recorded on-line at selected time points. Each protein spot contained ca. 5 ng protein, and the entire array covered 2.5 × 2.5 mm². Furthermore, HDX could be monitored in real time, and the experiment was therefore not subject to back-exchange problems. Analysis of HDX curves by inverse Laplace transform and by fitting exponential curves indicated that quantitative comparison of the samples is feasible. The paper also demonstrates how the whole process of analysis can be automatized to yield fast analyses. Full article

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively. Full article

The efficient management of urban water distribution networks is crucial for public health and urban development. One of the major challenges is the quick and accurate detection of leaks, which can lead to water loss, infrastructure damage, and environmental hazards. Many existing leak detection methods are ineffective, especially in complex and aging pipeline networks. If these limitations are not overcome, it can result in a chain of infrastructure failures, exacerbating damage, increasing repair costs, and causing water shortages and public health risks. The leak issue is further complicated by increasing urban water demand, climate change, and population growth. Therefore, there is an urgent need for intelligent systems that can overcome the limitations of traditional methodologies and leverage sophisticated data analysis and machine learning technologies. In this study, we propose a reliable and advanced method for detecting leaks in water pipes using a framework based on Long Short-Term Memory (LSTM) networks combined with autoencoders. The framework is designed to manage the temporal dimension of time-series data and is enhanced with ensemble learning techniques, making it sensitive to subtle signals indicating leaks while robustly dealing with noise signals. Through the integration of signal processing and pattern recognition, the machine learning-based model addresses the leak detection problem, providing an intelligent system that enhances environmental protection and resource management. The proposed approach greatly enhances the accuracy and precision of leak detection, making essential contributions in the field and offering promising prospects for the future of sustainable water management strategies. Full article