Search Results (163)

Triple-negative breast cancer (TNBC) and inflammatory breast cancer (IBC) are the most aggressive molecular subtypes of breast cancer. Poor clinical outcomes highlight the pressing need to discover novel targets for the effective treatment of these diseases. LMP7 (β5i/PSMB8), a proteolytic subunit of the immunoproteasome, is implicated in the pathogenesis of multiple myeloma, autoimmune and inflammatory diseases, and inflammation-related cancers. However, the role of LMP7 in TNBC and IBC remains poorly characterized. Here, we evaluated the function of LMP7 in TNBC and IBC using the selective LMP7 inhibitor M3258. In human TNBC patient samples, LMP7 expression correlated strongly with CD8⁺ T cell infiltration and activation markers. M3258 inhibited LMP7 activity, reduced viability, and induced apoptosis in TNBC/IBC cell lines in vitro. In a novel immunocompetent in vivo model of TNBC/IBC, M3258 reduced tumor growth and the tumor abundance of M2 macrophages. Additionally, M3258 activated tumor-infiltrating CD8⁺ T cells and suppressed the expression of specific inflammatory pathway gene signatures in immune cells. Co-culture with M2 macrophages enhanced the invasiveness of TNBC/IBC cells, which was effectively suppressed by M3258 treatment. Our results demonstrate for the first time that LMP7 shapes the pro-tumorigenic microenvironment of TNBC/IBC, in part by modulating the pathogenic role of M2 macrophages. These findings suggest that LMP7 may represent a novel target for therapeutic intervention in TNBC/IBC. Full article

The spine is the most affected region, which compromises functionality and generates absenteeism, increased health care costs, and disability retirement rates. Based on the biopsychosocial model, it is believed that chronic back problems are the result of a complex network of factors, both individual and contextual. A cross-sectional study was developed with data from the 2013 National Health Survey, the United Nations Development Programme, and the National Register of Health Establishments (state level) for the second and third levels of aggregation, respectively. Multilevel Poisson regression was performed at three levels. The prevalence of chronic back problems was 18.5% (95% CI 17.8; 19.1), with a higher prevalence in females (RP = 1.23; 95% CI 1.15; 1.30), those aged above 49 years (RP = 1.75; 95% CI 1.61; 1.90), those performing heavy activities at work (RP = 1.37; 95% CI 1.28; 1.46), those with depressive days (RP = 1.70; 95% CI 1.50; 1.94), those who were smokers (RP = 1.37; 95% CI 1.27; 1.48), and those in states with a higher coefficient of Family Health Support Team per 100,000 inhabitants (PR = 1.28; 95% CI 1.07; 1.54). Chronic spine problems were associated with biological and behavioral factors and were more strongly associated with the coefficient of Family Health Support Team in Brazilian municipalities. Full article

Occupancy detection for large buildings enables optimised control of indoor systems based on occupant presence, reducing the energy costs of heating and cooling. Through machine learning models, occupancy detection is achieved with an accuracy of over 95%. However, to achieve this, large amounts of data are collected with little consideration of which of the collected data are most useful to the task. This paper demonstrates methods to identify if data may be removed from the imbalanced time-series training datasets to optimise the training process and model performance. It also describes how the calculation of the class density of a dataset may be used to identify if a dataset is applicable for data reduction, and how dataset fusion may be used to combine occupancy datasets. The results show that over 50% of a training dataset may be removed from imbalanced datasets while maintaining performance, reducing training time and energy cost by over 40%. This indicates that a data-centric approach to developing artificial intelligence applications is as important as selecting the best model. Full article

Flooding is one of the most devastating natural disasters worldwide, with increasing frequency due to climate change. Traditional hydrological models require extensive data and computational resources, while machine learning (ML) models struggle to capture spatial dependencies. To address this, we propose a modified U-Net architecture that integrates prior hydrological knowledge of permanent water bodies to improve flood susceptibility mapping in Northumberland County, UK. By embedding domain-specific insights, our model achieves a higher area under the curve (AUC) (0.97) compared to the standard U-Net (0.93), while also reducing training time by converging three times faster. Additionally, we integrate a Grad-CAM module to provide visualisations explaining the areas of attention from the model, enabling interpretation of its decision-making, thus reducing barriers to its practical implementation. Full article

In line with Sustainable Development Goal 9 (sustainable industrialisation and innovation), environmentally responsible engineering designs in modern robotics should consider factors such as renewability, sustainability, and biodegradability. The robotics sector is growing at an exponential rate and, as a consequence, its contribution to e-waste is a growing concern. Our work contributes to the technological development of caninoid necro-robots, robots that are built from the skeletons of deceased dogs. The already formed skeletal structures of deceased dogs (and other animals) are ideal natural material replacements for synthetic robotic architectures such as plastics, metals, and composites. Since dog skeletons are disarticulated, simple but effective methods need to be developed to rearticulate their bodies. The canine head is essentially a large end effector, but its mandible is held together by a fibrocartilaginous joint (symphysis) that degrades at a higher rate than the bone itself. The degradation of the symphysis would ordinarily negate the utility of a canine head as a necro-robotic end effector; however, in this research, we consider simple methods of mandible reinforcement to circumvent this problem. Our research uses 3D scans of a real canine head, which is modelled using the finite element method to ascertain optimal geometrical reinforcements for the mandible. The full head structures and their reinforcements are printed and adhesively connected to determine the most effective reinforcing strategy of the mandible. Here, we elucidate geometrically selected reinforcement designs that are evidenced through mechanical testing, to successfully increase the stiffness of a disarticulated mandible. Full article

Bacterial viruses (phages) are abundant and ecologically impactful, but laboratory-based experimental model systems vastly under-represent known phage diversity, particularly for ssDNA phages. Here, we characterize the genomes and infection properties of two unrelated marine flavophages—ssDNA generalist phage phi18:4 (6.5 Kbp) and dsDNA specialist phage phi18:1 (39.2 Kbp)—when infecting the same Cellulophaga baltica strain #18 (Cba18), of the class Flavobacteriia. Phage phi18:4 belongs to a new family of ssDNA phages, has an internal lipid membrane, and its genome encodes primarily structural proteins, as well as a DNA replication protein common to ssDNA phages and a unique lysis protein. Phage phi18:1 is a siphovirus that encodes several virulence genes, despite not having a known temperate lifestyle, a CAZy enzyme likely for regulatory purposes, and four DNA methyltransferases dispersed throughout the genome that suggest both host modulation and phage DNA protection against host restriction. Physiologically, ssDNA phage phi18:4 has a shorter latent period and smaller burst size than dsDNA phage phi18:1, and both phages efficiently infect this host. These results help augment the diversity of characterized environmental phage–host model systems by studying infections of genomically diverse phages (ssDNA vs. dsDNA) on the same host. Full article

The description of gas permeation across heterogeneous materials has been studied with many methods, mainly focusing on composites with high aspect ratios and low filler volume fractions. In the present work, the extension of these approaches to semicrystalline polymers is studied, considering a wide range of crystalline volume fractions to tackle applications ranging from membranes to barrier materials. A numerical approach focused on tortuosity effects related to the presence of impermeable crystalline domains was considered. Algorithms based on random sequential adsorption and Voronoi tessellation were used to reproduce the morphology of semicrystalline polymers. The flux reduction across the microstructures generated due to the presence of impermeable crystals was calculated by solving local mass balance through a finite volume method. Using this strategy, it was possible to investigate the effect of crystallites’ arrangement, size distribution, orientation and shape on the relative permeability and the tortuosity of semicrystalline membranes. The results were analyzed considering existing macroscopic models and new analytical equations were proposed in order to account on such morphological effects for the prediction of the tortuosity in semicrystalline polymers. Full article

Background: Antimicrobial resistance (AMR) is a global health challenge with significant global variation. Little is known about the prevalence on a smaller geographical scale. Objectives: This study aimed to explore the geo-temporal variation in antibiotic resistance in Escherichia coli (E. coli) urinary isolates in the Illawarra Shoalhaven region, a region south of Sydney. Methods: Data from urine E. coli isolates from people living in the community were geospatially analysed from 2008 to 2018. The proportion of resistant isolates was mapped by antibiotic type (amoxicillin with clavulanic acid, cefalexin, norfloxacin, and trimethoprim), postcode, and year. Results: Resistance varied by antibiotic, postcode, and over time, with some postcodes showing increased resistance one year and a decrease the following year. Areas with consistently higher resistance included metropolitan, port, and lake regions. We found low resistance in E. coli to amoxicillin with clavulanate, cefalexin, and norfloxacin (<5% to 10–19%) and the highest resistance for trimethoprim (10–19% to 30–39%). Overall, from 2008 to 2018, E. coli resistance to all four antibiotics increased in this region. Conclusions: This study shows temporal and geospatial changes in E. coli AMR over small geospatial areas, indicating the opportunity for geospatial analysis to assist in area-specific empirical treatment guidance. Full article

Background: Depression ranks among the most severe mental health conditions, and poses a burden on global health. Agarwood, an aromatic medicinal plant, has shown potential for improving mental symptoms. As a common folk medicine, agarwood has been applied as an alternative method for mental disorders such as depression through aromatherapy. Previous studies have found that the therapeutic effects of agarwood aromatherapy are primarily related to its volatile components. This study aimed to examine the antidepressant properties and underlying mechanisms of agarwood essential oil (AEO), a collection of the volatile components of agarwood utilized through aromatherapy inhalation and injection administration in mice. Methods: A lipopolysaccharide (LPS)-induced inflammatory depression model was used to evaluate the effects of AEO inhalation and injection on depression-like symptoms. Behavioral assessments included the open-field, tail suspension, and forced swimming tests. Western blot (WB) and ELISA techniques were used to further verify the mechanistic insights. Results: In the LPS-induced depression-like model, AEO inhalation and injection significantly improved depression-like symptoms, decreased immobility duration in both the tail suspension and forced swimming tests in model mice, and reduced the levels of inflammatory cytokines IL-1β, IL-6, and TNF-α. WB experiments demonstrated that AEO inhibited the NF-κB/IκB-α inflammatory pathway and activated the BDNF/TrkB/CREB pathway in the hippocampus of the LPS-depression model mice. Notably, AEO extracted by hydrodistillation was more effective in alleviating LPS-induced depressive-like behaviors than using supercritical CO₂ fluid extraction. Conclusions: Both the inhalation and the injection administration of AEO exerted notable antidepressant effects, potentially associated with reducing inflammation levels in the brain, downregulating inflammatory NF-κB/IκB-α, and upregulating the neuroprotective BDNF/TrkB/CREB signaling pathway. In the future, it is necessary to further determine the pharmacodynamic components, key targets and specific molecular mechanisms of AEO’s antidepressant effects so as to provide more support for the neuroprotective research of medicinal plants. Full article

Background: Immunization coverage varies across Papua New Guinea. In East New Britain (ENB) Province in 2022, only 65.5% and 50.2% of children under one year received their first dose of pentavalent (DTP1) and measles–rubella (MR1) vaccine, respectively. This study aimed to examine barriers and enablers to routine immunization in areas of un(der)-vaccination in ENB. Methods: A face-to-face survey was conducted with caregivers of children aged 12–23 months in ENB. We used Poisson regression to calculate incidence rate ratios (IRR) and 95% confidence intervals (95% CI) for factors associated with timely receipt of DTP1 or MR1 vaccines, defined as a child who was vaccinated between –2 and +30 days of the vaccine schedule. Delayed receipt is defined as a child who was vaccinated >30 days from the recommended due date. Results: Among 237 caregivers surveyed, 59.9% of children were vaccinated within the “timely” window for DTP1 and 34.1% for MR1. Timely DTP1 receipt was associated with a facility-based birth (IRR:1.93; 95% CI: 1.10–3.38) and trusting healthcare workers “very much”, compared to “a little or moderately” (IRR:1.53; 95% CI: 1.17–1.99). For MR1, the caregiver having completed tertiary/vocational education (IRR:1.79; 95% CI: 1.15–2.78), reporting taking a child to be vaccinated is affordable (IRR:1.52; 95% CI: 1.04–2.22), and healthcare workers explaining immunization services and answering associated questions (IRR:1.68; 95% CI: 1.18–2.41) were associated with timely vaccination. Conclusions: Activities to improve timely vaccination in ENB could include strengthening healthcare worker interpersonal communication skills to optimize trust and incentivizing women to give birth in a health facility. Full article

The phase separation of high-density polyethylene (HDPE)–polypropylene (PP) blends was studied using atomic force microscopy in tapping mode to obtain height and phase images. The results are compared with those from scanning electron microscopy imaging and are connected to the thermomechanical properties of the blends, characterised through differential scanning calorimetry, dynamic mechanical analysis (DMA), and tensile testing. Pure PP, as well as 10:90 and 20:80 weight ratio HDPE–PP blends, showed a homogeneous morphology, but the 25:75 HDPE–PP blends exhibited a sub-micrometre droplet-matrix structure, and the 50:50 HDPE–PP blends displayed a more complex co-continuous nano/microphase-separated structure. These complex phase separation morphologies correlate with the increased loss modulus (viscous properties) of the corresponding blends as measured by DMA, demonstrating the potential for the creation of strong and simultaneously tough, energy-absorbing materials for numerous applications. Full article

Our study considered porous Bouligand structured polymer, comprising polymer fibres with porous spaces between them. These are more complicated structures than the non-porous Bouligand, since the addition of porosity into the material creates a secondary variable besides fibre pitch. There is currently no analytical model available to predict the modulus of such materials. Our paper explores the correlation between porosity, polymer fibre pitch angle, and flexural modulus in porous Bouligand structured polymers. Our structures were digitally manufactured using stereolithography (SLA) additive manufacturing methods, after which they were subjected to three-point bending tests. Our aim was to simply and parametrically develop an analytical model that would capture the influences of both porosity and polymer fibre pitch angle on the flexural modulus of the material. Our model is expressed as $E_f=E_{poro}(a{\theta }_f^3+b{\theta }_f^2+c{\theta }_f+d)$, and we derive this by applying non-linear regression to our experimental data. This model predicts the flexural modulus, $E_f$, of porous Bouligand structured polymer as a function of both porosity and pitch angle. Here, $E_{poro}$ is defined as the solid material modulus, $E_{solid}$, multiplied by porosity, $\varphi$ and is a linear reduction in the modulus as a function of increasing porosity, while ${\theta }_f$ signifies the polymer fibre pitch angle. This relationship is relatively accurate within the range of 10° ≤ θ_f ≤ 50°, and for porosity values ranging from $0.277\le 0.356$, as supported by our evidence to date. Full article

Ocean energy, e.g., waves, tidal current, and thermal and salinity gradient, can be used to produce electricity. These marine-based renewable energy technologies are at relatively early stages of development and potentially deployed at various sea conditions. In the past, numerous studies were undertaken to explore the feasibility of harvesting of the marine energy in Malaysia; however, those studies were limited to a specific location (i.e., the east coast of Peninsular Malaysia and East Malaysia) and the consideration of sea level rise effect was not studied. This study assessed the potential of tidal and wave energy resources in Malaysia’s waters with the effect of projected sea level rise and was undertaken through numerical modeling using MIKE 21 software. The research outcomes were tidal and wave energy contours for Malaysia’s waters with an inclusion of the sea level rise projection for 2060 and 2100, as well as a potential site determined for tidal and wave energy harvesting. The simulation results highlight the significant potential of tidal and wave energy in specific locations around Malaysia and its coastal regions, as well as in the South China Sea’s offshore regions. By incorporating sea level rise projections into tidal and wave simulations, we revealed a notable increase in tidal and wave power. Full article

Conventional strategies aimed at mitigating beam-hardening artifacts in computed tomography (CT) can be categorized into two main approaches: (1) postprocessing following conventional reconstruction and (2) iterative reconstruction incorporating a beam-hardening model. While the former fails in low-dose and/or limited-data cases, the latter substantially increases computational cost. Although deep learning-based methods have been proposed for several cases of limited-data CT, few works in the literature have dealt with beam-hardening artifacts, and none have addressed the problems caused by randomly selected projections and a highly limited span. We propose the deep learning-based prior image constrained (PICDL) framework, a hybrid method used to yield CT images free from beam-hardening artifacts in different limited-data scenarios based on the combination of a modified version of the Prior Image Constrained Compressed Sensing (PICCS) algorithm that incorporates the L2 norm (L2-PICCS) with a prior image generated from a preliminary FDK reconstruction with a deep learning (DL) algorithm. The model is based on a modification of the U-Net architecture, incorporating ResNet-34 as a replacement of the original encoder. Evaluation with rodent head studies in a small-animal CT scanner showed that the proposed method was able to correct beam-hardening artifacts, recover patient contours, and compensate streak and deformation artifacts in scenarios with a limited span and a limited number of projections randomly selected. Hallucinations present in the prior image caused by the deep learning model were eliminated, while the target information was effectively recovered by the L2-PICCS algorithm. Full article

The majority of materials used for membrane-based separation of gas mixtures are non-renewable and non-biodegradable, and the assessment of alternative bio-based polymers requires expensive and time-consuming experimental campaigns. This effort can be reduced by adopting suitable modelling approaches. In this series of works, we propose various modelling approaches to assess the CO₂/CH₄ separation performance of eight different copolymers of 3-hydroxybutyrate and 3-hydroxyvalerate (PHBV) using a limited amount of experimental data for model calibration. In part 1, we adopted a fully atomistic approach based on Molecular Dynamics (MD), while, in this work, we propose a multiscale methodology where a molecular description of the polymers is bridged to a macroscopic prediction of its gas sorption behaviour. PHBV structures were simulated using MD to obtain pressure–volume–temperature data, which were used to parametrise the Sanchez–Lacombe Equation of State. This, in turn, allows for the evaluation of the CO₂ and CH₄ solubility in the copolymers at various pressures and compositions with little computational effort, enabling the estimate of the sorption-based selectivity. The gas separation performance obtained with this multiscale technique was compared to results obtained with a fully atomistic model and experimental data. The solubility–selectivity for the CO₂/CH₄ mixture is in reasonable agreement between the two models and the experimental data. The multiscale method presented is a time-efficient alternative to fully atomistic methods and detailed experimental campaigns and can accelerate the introduction of renewable materials in different applications. Full article