SLC1A5 is a key regulator of glutamine metabolism and a prognostic marker for aggressive luminal breast cancer

Cancer cells exhibit altered metabolism, often relying on glutamine (Gln) for growth. Breast cancer (BC) is a heterogeneous disease with varying clinical outcomes. We investigated the role of the amino acid transporter SLC1A5 (ASCT2) and its association with BC subtypes and patient outcomes. In large BC cohorts, SLC1A5 mRNA (n = 9488) and SLC1A5 protein (n = 1274) levels were assessed and correlated their expression with clinicopathological features, molecular subtypes, and patient outcomes. In vitro SLC1A5 knockdown and inhibition studies in luminal BC cell lines (ZR-75-1 and HCC1500) were used to further explore the role of SLC1A5 in Gln metabolism. Statistical analysis was performed using chi-squared tests, ANOVA, Spearman's correlation, Kaplan-Meier analysis, and Cox regression. SLC1A5 mRNA and SLC1A5 protein expression were strongly correlated in luminal B, HER2 + and triple-negative BC (TNBC). Both high SLC1A5 mRNA and SLC1A5 protein expression were associated with larger tumour size, higher grade, and positive axillary lymph node metastases (P < 0.01). Importantly, high SLC1A5 expression correlated with poor BC-specific survival specifically in the highly proliferative luminal subtype (P < 0.001). Furthermore, SLC1A5 knockdown by siRNA or GPNA inhibition significantly reduced cell proliferation and glutamine uptake in ZR-75-1 cells. Our findings suggest SLC1A5 plays a key role in the aggressive luminal BC subtype and represents a potential therapeutic target. Further research is needed to explore SLC1A5 function in luminal BC and its association with Gln metabolism pathways

Role of hepatocellular senescence in the development of hepatocellular carcinoma and the potential for therapeutic manipulation

Accumulation of senescent hepatocytes is universal in chronic liver disease (CLD). This study investigates an association between hepatocyte senescence and hepatocellular carcinoma (HCC) and explores the therapeutic role of sirolimus. Background liver biopsies from 15 patients with cirrhosis and HCC and 45 patients with cirrhosis were stained for p16, a marker of cell senescence. STAM™ mice were randomized into 3 groups of 5 at 4 weeks of age and administered vehicle ± sirolimus intraperitoneally, thrice weekly, from 4 to 18 weeks of age. Placebo group was an administered vehicle, early sirolimus group was an administered vehicle with sirolimus, late sirolimus group was an administered vehicle from 4 to 12 weeks then vehicle with sirolimus from 12 to 18 weeks. The primary outcome was HCC nodule development. Senescent hepatocyte burden and senescence-associated secretory phenotype (SASP) factors were assessed in mice livers. In the human study, age (OR 1.282, 95% CI 1.086–1.513, p = 0.003) and p16 (OR 1.429, 95% CI 1.112–1.838, p = 0.005) were independently associated with HCC. In the animal study, all three groups exhibited similar MASLD activity scores (p = 0.39) and fibrosis area (p = 0.92). The number and the maximum diameter of HCC nodules were significantly lower in the early sirolimus group compared to placebo and late sirolimus group. The gene expression of SASP factors was similar in all groups. Protein levels of some SASP factors (TNFα, IL1β, IL-2, CXCL15) were significantly lower in sirolimus administered groups compared to placebo group. The study demonstrates an independent association between senescent hepatocyte burden and HCC. It indicates a potential chemoprophylactic role for sirolimus through SASP factor inhibition. These early results could inform a future human clinical trial

How do CaO/CuO materials evolve in integrated calcium and chemical looping cycles?

Maintaining high CO2 uptake is critical for combined Ca-Cu looping applications, however, the long-term behaviour of combined Ca and Cu materials under repeated cycling conditions remains less understood. This study examined three materials with a fixed Cu/Ca mole ratio of 1.6 to analyse the material phase evolution and identify factors influencing CO2 uptake. The materials underwent 50 TGA cycles in two distinct looping applications: blast furnace gas (BFG) cycling (reduction-carbonation-oxidation) and flue gas cycling (carbonation-reduction-oxidation).Different preparation methods significantly affected the initial phase distribution. The multi-grain precipitate material (MGP), prepared to minimise the chemical contact between Ca and Cu, primarily contained separate CaO and CuO phases; while the multi-stage mechanically mixed materials (MM1 and MM2), in which there was extensive contact between the Ca and Cu, exhibited mixed Ca-Cu-O phases along with separate CuO. However, the initial phase distribution had little influence on the longer-term CO2 uptake with the accessibility of CaO and cycling conditions having a more significant impact. BFG cycling consistently resulted 70–100; % greater CO2 uptake than flue gas cycling, highlighting the strong influence of cycling conditions

Income disaster model with optimal consumption

We propose a continuous-time income disaster model with optimal consumption. We endogenously determine the stochastic discount factor (SDF) in an incomplete market caused by income disaster. We then derive optimal consumption decisions for two types of agents, one who is exposed to income disaster and another who is not. We find a large incomplete-markets precautionary savings term between the two agents, which pushes the interest rate down and helps to resolve the risk-free rate puzzle. Interestingly, with income disaster the equilibrium interest rate is a decreasing function of risk aversion while the equity premium is an increasing function. Finally, our model can better match empirical marginal propensities to consume numbers and explain the low-consumption-high-savings puzzle

Ethical practice in participant-centred linguistic research

This article outlines ethical principles for 'participant-centred linguistic research' (PCLR), a term we coin to incorporate a range of linguistic research approaches that place importance on the involvement of participants. Linguistics, as a field, has strengthened its focus on participant-centred and socially situated research, recognising the value of better understanding our participants' practices and linguistic knowledge. However, this also brings ethical challenges for our research practice. Drawing on three differing UK-based case studies from the authors' own work, the article explores complex issues that can arise during PCLR and establishes four key principles that cut across our varied experiences. Firstly, we address participant consent and confidentiality, establishing the principles: 1. Informed consent and ethics protocols are dialogic processes and 2. Expectations around confidentiality and anonymity can shift during a project. Secondly, we address our research relationships with participants, our key principles being: 3. The researcher-participant relationship is complex and variable and 4. Close attention must be paid to power dynamics within the research setting. Ultimately, we argue that the human interactions and relationships involved in PCLR mean research may inevitably be somewhat unpredictable; researchers therefore need an understanding of the ethical parameters of their practice to navigate these complexities

Prevalence and distribution of Plasmodium falciparum multidrug resistant 1 D1246Y allele among children in Ibadan Southwest, Nigeria

The emergence and spread of the Plasmodium falciparum multidrug-resistant 1 (Pfmdr1) allele pose a significant setback to global efforts to control and eradicate malaria infection by diminishing the efficacy of commonly prescribed antimalarial drugs, particularly in Sub-Saharan Africa, where malaria remains endemic. The Pfmdr1 D1246Y mutation is of specific importance due to its potential role in modulating parasite susceptibility to antimalarial medicines and treatment outcomes. This study aimed to determine the presence and prevalence of the wild-type and mutant D1246Y alleles of Pfmdr1 among children in Ibadan, Southwest Nigeria. A total of 133 archived DNA samples collected between March 2016 and June 2021 from children aged 6 to 132 months with varying malaria phenotypes (asymptomatic infection, uncomplicated, and severe malaria) were analyzed. The Pfmdr1 D1246Y allele was amplified via nested PCR, and the mutation was detected using the restriction enzyme EcoRV. The digested nested PCR products were resolved on a 2% agarose gel and visualized under ultraviolet light. All statistical analyses were performed using SPSS version 25, and statistical significance was set at p ≤ 0.05. Among the 133 samples, 97 (72.9%) were successfully genotyped. Of these, 50 (51.55%) carried the wild-type allele, while 47 (48.45%) had the mutant allele. Notably, the Pfmdr1-1246Y mutation was detected in all severe malaria cases (41/41, 100%), whereas its prevalence was significantly lower in asymptomatic (3/36, 8.3%) and uncomplicated malaria cases (3/20, 15%). The difference in mutation prevalence across the malaria phenotypes was statistically significant (p < 0.05). The study provided valuable insight into the coexistence of wild-type and mutant Pfmdr1 D1246Y alleles within the population. It revealed a significantly higher mutation rate in all severe malaria cases, while the wild-type allele remained more prevalent overall. These findings contribute to a deeper understanding of the possible role of the wild-type and mutant D1246Y alleles in the various clinical manifestations of malaria

Evolution of Preclinical Models for Glioblastoma Modelling and Drug Screening

Purpose of ReviewIsocitrate dehydrogenase wild-type glioblastoma is an extremely aggressive and fatal primary brain tumour, characterised by extensive heterogeneity and diffuse infiltration of brain parenchyma. Despite multimodal treatment and diverse research efforts to develop novel therapies, there has been limited success in improving patient outcomes. Constructing physiologically relevant preclinical models is essential to optimising drug screening processes and identifying more effective treatments.Recent FindingsTraditional in-vitro models have provided critical insights into glioblastoma pathophysiology; however, they are limited in their ability to recapitulate the complex tumour microenvironment and its interactions with surrounding cells. In-vivo models offer a more physiologically relevant context, but often do not fully represent human pathology, are expensive, and time-consuming. These limitations have contributed to the low translational success of therapies from trials to clinic. Organoid and glioblastoma-on-a-chip technology represent significant advances in glioblastoma modelling and enable the replication of key features of the human tumour microenvironment, including its structural, mechanical, and biochemical properties. Organoids provide a 3D system that captures cellular heterogeneity and tumour architecture, while microfluidic chips offer dynamic systems capable of mimicking vascularisation and nutrient exchange. Together, these technologies hold tremendous potential for high throughput drug screening and personalised, precision medicine.SummaryThis review explores the evolution of preclinical models in glioblastoma modelling and drug screening, emphasising the transition from traditional systems to more advanced organoid and microfluidic platforms. Furthermore, it aims to evaluate the advantages and limitations of both traditional and next-generation models, investigating their combined potential to address current challenges by integrating complementary aspects of specific models and techniques

Integrating phenotyping and modelling approaches StomaGAN: improving image-based analysis of stomata through generative adversarial networks

Stomata regulate gas exchange between plants and the atmosphere, but analysing their morphology is challenging due to anatomical variability and artifacts during image acquisition. Deep learning (DL) can address these challenges but often requires large and diverse datasets, which are costly and error prone to produce. Generative adversarial networks (GANs) offer a solution by generating artificial data via unsupervised learning. However, GANs often suffer from problems including mode collapse, vanishing gradients, and network failure, particularly with small datasets. Here, we present StomaGAN, a deep convolutional GAN (DCGAN) with tailored modifications to address common GAN issues. We collected 559 stomatal impressions of field, or faba bean (Vicia faba) consisting of ~3,000 stoma, 80% of which were used to train StomaGAN. Evaluation metrics, including generator and discriminator loss progression and a mean Fréchet Inception Distance (FID) score of 61.4 across eight experimental runs confirms successful training. To validate StomaGAN, we generated artificial images to train a deep convolutional neural network (DCNN) based on the DeepLabV3 framework for stomata detection from real, unseen images. The DCNN achieved a mean Interception over Union (IoU) of 0.95 on artificial training images and a 0.91 on real, unseen, images across varying magnifications. Our results demonstrate that StomaGAN effectively generates high-quality synthetic datasets, enabling reliable stomatal detection and enhancing phenotypic analysis. This approach reduces the need for extensive manual data collection and simplifies complex morphological assessments

Waterborne debris impact forces on wall structures: Elastic analytical model integrating the effects of the structural mass

To evaluate the structural safety against waterborne debris impacts, the impact loads are usually computed with analytical models such as those proposed by ASCE/SEI 7–22. These models often assume a massless structure to simplify the analytical formulations, which can be an oversimplifying and inaccurate assumption in cases where the structure is heavier and more flexible than the debris. To address this problem, we aim to define the domain in which the existing models are inaccurate and to propose a new analytical model to accurately compute the debris impact forces through comprehensive finite element simulations and analytical modelling. We defined such a domain in the design space of structure-to-debris mass and stiffness ratios and assessed which are the most accurate analytical models to compute debris impact forces across this space. Our proposed model significantly improves upon the overestimating results of the ASCE/SEI 7–22 model when both stiffness and mass are important in determining the impact forces