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Exploring Glypican-3 targeted CAR-NK treatment and potential therapy resistance in hepatocellular carcinoma
Hepatocellular carcinoma (HCC) is the most prevalent form of primary liver cancer and the second leading cause of cancer-related mortality globally. Despite advancements in current HCC treatment, it remains a malignancy with poor prognosis. Therefore, developing novel treatment options for patients with HCC is urgently needed. Chimeric antigen receptor (CAR)-modified natural killer (NK) cells have demonstrated potent anti-tumor effects, making them as a promising immunotherapy strategy for cancer treatment. Glypican-3 (GPC3), a cell surface oncofetal glycoprotein, is highly expressed in most HCC tissues, but not in normal tissues, and functions as a key driver of carcinogenesis. Given its high expression level on the cell surface, GPC3 is considered as an attractive immunotherapy target for HCC. In this study, two GPC3-specific CAR-NK cells, NK92MI/HN3 and NK92MI/HS20, were established using NK92MI cells, a modified IL-2-independent NK cell line. These cell lines were engineered with third generation GPC3-specific CARs, and their activities were subsequently evaluated in the treatment of HCC. We found that NK92MI/HN3 cells, rather than NK92MI/HS20 cells, exhibited a significant cytotoxicity effect against GPC3+ HepG2 cells in vitro and efficiently suppressed tumor growth in a xenograft model using NSG mice. In addition, irradiated NK92MI/HN3 cells displayed similar anti-tumor efficacy to unirradiated NK92MI/HN3 cells. Furthermore, we observed that NK92MI/HN3 cells showed higher killing activity against the GPC3 isoform 2 overexpression cell line (Sk-Hep1-v2) than those with GPC3 isoform 1 overexpression cell line (Sk-Hep1-v1). This suggest that the presence of different GPC3 isoforms in HCC may impact the cytotoxicity activity of NK92MI/HN3 cells and potentially influence therapeutic outcomes. These findings highlight the effective anti-HCC effects of NK92MI/HN3 cells and reveal the role of GPC3 isoforms in influencing therapy outcomes, suggesting that isoform analysis should be considered to optimize CAR-NK therapies to improve patient outcomes.
Practitioner, patient and public views on the acceptability of mobile stroke units in England and Wales: A mixed methods study
Background Evidence for Mobile Stroke Units (MSUs) demonstrates that onset to treatment times for intravenous thrombolysis can be reduced and access to mechanical thrombectomy might be improved. Despite growing use of MSUs internationally, to date there have been no studies in NHS England and NHS Wales exploring the acceptability of MSUs to clinicians, patient and public representatives and other key stakeholders, which are important when considering potential feasibility and implementation. Methods This study used a mixed methods design with a cross-sectional survey and qualitative workshops and interviews between October 2023 to May 2024. Survey data were collected from clinicians involved in emergency stroke care. Qualitative data involved clinical and non-clinical professionals involved in stroke care alongside patient and public representatives with experience of stroke. Survey data were descriptively analysed while content analysis was used on open-ended questions. Qualitative data were thematically analysed, prior to triangulation using a convergent coding matrix. Results The study results, drawn from 25 respondents to the survey and 21 participants in qualitative workshops, found that almost all participants had positive affective attitudes to the concept of MSUs. However, several key areas of concern were identified that need to be addressed prior to implementing MSUs. These concerns included how MSUs would be staffed; whether and how telemedicine could contribute; the types of economic impacts; extent to which triage systems could accurately identify stroke patients for MSUs to attend; where the base location and geographic coverage of MSUs should be, the impact of MSUs on equitable access to stroke care, and how to improve public awareness of MSUs. Conclusion Whilst MSUs are mostly acceptable to key stakeholders, numerous areas of concern need to be addressed prior to MSU implementation. We recommend further research to address these issues prior to implementation in the NHS.
Covalent Plant Natural Product that Potentiates Antitumor Immunity
In vivo mapping of sodium homeostasis disturbances in individual ALS patients: A brain 23Na MRI study
Objective Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by significant heterogeneity among patients. 23Na MRI maps abnormal sodium homeostasis that reflects metabolic alterations and energetic failure contributing to the neurodegenerative process. In this study, we investigated disease severity at the individual level in ALS patients using brain 23Na MRI. Methods 1H and 23Na brain MRI were collected prospectively from 28 ALS patients. Individual map of abnormal total sodium concentration (TSC) was computed using voxel-based statistical mapping for each patient compared to a local database of 62 healthy controls. Clinical data included the revised ALS functional rating scale (ALSFRS-R), ALSFRS-R slope, ALSFRS-R at 6-month and survival time. Results Individual maps quantifying voxels with TSC increase evidenced a high heterogeneity between patients consistent with clinical presentation. The main areas involved were the corticospinal tracts. Half of patients showed abnormal TSC increase within more than 1% of whole brain voxels. Patients with TSC increase had worse clinical severity: higher ALSFRS-R slope (p = 0.02), lower ALSFRS-R at 6-month (p = 0.04), and shorter survival (p = 0.04). ALS patients with limited TSC increase had slower progression of disability or predominant lower motor neuron phenotype or shorter disease duration. Discussion This study mapping sodium homeostasis disturbances at the individual level in ALS patients through 23Na MRI evidenced heterogeneity of TSC increase among patients associated with clinical presentation and disease severity. These findings suggest that TSC increase detected at the individual level by 23Na MRI may be a useful marker of the clinical heterogeneity of ALS patients, a factor that is likely to greatly influence the results of therapeutic trials.
Light-Enabled Reversible Hydrogen Storage of Borohydrides Activated by Photogenerated Vacancies
AI enabled, mobile soil pH classification with colorimetric paper sensors for sustainable agriculture
For optimizing production yield while limiting negative environmental impact, sustainable agriculture benefits from real-time, on-the-spot chemical analysis of soil at low cost. Colorimetric paper sensors are ideal candidates, however, their automated readout and analysis in the field is needed. Using mobile technology for paper sensor readout could, in principle, enable the application of machine-learning models for transforming colorimetric data into threshold-based classes that represent chemical concentration. Such a classification method could provide a basis for soil management decisions where high-resolution lab analysis is not required or available. In tropical regions, where reliable soil data is difficult to acquire, this approach would be particularly useful. Here, we report a mobile chemical analysis system based on colorimetric paper sensors that operates under tropical field conditions. A standard smartphone equipped with a dedicated software application automatically classifies the paper sensor results into three classes—low, medium, or high soil pH—which provides a basis for soil correction. The classification task is performed by a machine-learning model which was trained on the colorimetric pH indicators deployed on the paper sensor. By mapping topsoil pH on a test site with an area of 9 hectares, the mobile system was benchmarked in the field against standard soil lab analysis. The mobile system has correctly classified soil pH in 97% of test cases, while reducing the analysis turnaround time from days (soil lab) to minutes (mobile). By performing on-the-spot analyses using the mobile system in the field, a 9-fold increase of spatial resolution reveals pH-variations not detectable in the standard compound mapping mode of lab analysis. We discuss how the mobile analysis can support smallholder farmers and enable sustainable agriculture practices by avoiding excessive soil correction. The system can be extended to perform multi-parameter chemical tests of soil nutrients for applications in environmental monitoring at marginal manufacturing cost.
Exploring the protective role of caffeine against Taraxacum-Induced ribotoxic stress mediated through autophagy and mitochondrial depolarization
Design of polymorphic heterogeneous shell in relaxor antiferroelectrics for ultrahigh capacitive energy storage
Immobilized Saccharomyces cerevisiae viable cells for electrochemical biosensing of Cu(II)
Correction for Zhang et al., Facet-switching of rate-determining step on copper in CO <sub>2</sub> -to-ethylene electroreduction
A dexterous and compliant aerial continuum manipulator for cluttered and constrained environments
Comparative analysis of FACT devices for optimal improvement of power quality in unbalanced distribution systems
Memristor-based feature learning for pattern classification
Abstract Inspired by biological processes, feature learning techniques, such as deep learning, have achieved great success in various fields. However, since biological organs may operate differently from semiconductor devices, deep models usually require dedicated hardware and are computation-complex. High energy consumption has made deep model growth unsustainable. We present an approach that directly implements feature learning using semiconductor physics to minimize disparity between model and hardware. Following this approach, a feature learning technique based on memristor drift-diffusion kinetics is proposed by leveraging the dynamic response of a single memristor to learn features. The model parameters and computational operations of the kinetics-based network are reduced by up to 2 and 4 orders of magnitude, respectively, compared with deep models. We experimentally implement the proposed network on 180 nm memristor chips for various dimensional pattern classification tasks. Compared with memristor-based deep learning hardware, the memristor kinetics-based hardware can further reduce energy and area consumption significantly. We propose that innovations in hardware physics could create an intriguing solution for intelligent models by balancing model complexity and performance.
Magnetic polymeric ionic liquid for both catalysis application and magnetic solid phase extraction approach
Bioinspired hydrophobic pseudo-hydrogel for programmable shape-morphing
Exposure of insects to current use pesticide residues in soil and vegetation along spatial and temporal distribution in agricultural sites
Abstract Current use pesticides (CUPs) are recognised as the largest deliberate input of bioactive substances into terrestrial ecosystems and one of the main factors responsible for the current decline in insects in agricultural areas. To quantify seasonal insect exposure in the landscape at a regional scale (Rhineland-Palatine in Germany), we analysed the presence of multiple (93) active ingredients in CUPs across three different agricultural cultivation types (with each three fields: arable, vegetable, viticulture) and neighbouring meadows. We collected monthly soil and vegetation samples over a year. A total of 71 CUP residues in different mixtures was detected, with up to 28 CUPs in soil and 25 in vegetation in single samples. The concentrations and numbers of CUPs in vegetation fluctuated over the sampling period, peaking in the summer months in the vegetation but remaining almost constant in topsoil. We calculated in-field additive risks for earthworms, collembola, and soil-living wild bees using the measured soil concentrations of CUPs. Our results call for the need to assess CUP mixture risks at low concentrations, as multiple residues are chronically present in agricultural areas. Since this risk is not addressed in regulation, we emphasise the urgent need to implement global pesticide reduction targets.