Modeling methods of different tumor organoids and their application in tumor drug resistance research.

Tumor organoids were modeled to mimic culture conditions, allowing tumor-derived tissue cells or isolated and purified tumor stem cells to self-assemble into 3D preclinical models that are similar to tissues and organs . Compared with traditional models, tumor organoids not only resemble parental tumors in histology and genomics, capturing their heterogeneity and drug response, but also provide an efficient platform for long-term culture, maintaining genetic stability and enabling gene manipulation. Therefore, tumor organoids have unique advantages in cancer drug resistance research.

Magnetic Kirigami Metasurfaces with Reprogrammed Multifunctionalities.

Kirigami metasurfaces are two-dimensional engineered structures that leverage the principles of kirigami, the art of paper cutting, to create reconfigurable properties, such as selective transmission tunability, tunable refractive index, and shape-morphing. Although existing kirigami metasurfaces show applications in electromagnetic wave modulation, light and droplet manipulation, crawling locomotion, and actuators, their functionality remains limited, particularly at the nanoscale. Furthermore, reconfiguration of these systems is typically restricted to tethered methods such as mechanical stretching or wired electrical inputs.

Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification.

Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient. Previous results demonstrated that these methods can even improve performance on some classification tasks. This paper complements existing research by investigating how these techniques influence classification performance and computation costs compared to full fine-tuning.

High-throughput fabrication of soft magneto-origami machines.

Soft magneto-active machines capable of magnetically controllable shape-morphing and locomotion have diverse promising applications such as untethered biomedical robots. However, existing soft magneto-active machines often have simple structures with limited functionalities and do not grant high-throughput production due to the convoluted fabrication technology. Here, we propose a facile fabrication strategy that transforms 2D magnetic sheets into 3D soft magneto-active machines with customized geometries by incorporating origami folding.

Unique Catalytic Mechanism for Ru-Loaded Ternary Intermetallic Electrides for Ammonia Synthesis.

Intermetallic electrides have recently shown their priority as catalyst components in ammonia synthesis and CO activation. However, their function mechanism has been elusive since its inception, which hinders the further development of such catalysts. In this work, ternary intermetallic electrides La-TM-Si (TM = Co, Fe, and Mn) were synthesized as hosts of ruthenium (Ru) particles for ammonia synthesis catalysis.

Text mining occupations from the mental health electronic health record: a natural language processing approach using records from the Clinical Record Interactive Search (CRIS) platform in south London, UK.

Objectives: We set out to develop, evaluate and implement a novel application using natural language processing to text mine occupations from the free-text of psychiatric clinical notes.

Design: Development and validation of a natural language processing application using General Architecture for Text Engineering software to extract occupations from de-identified clinical records.

Setting And Participants: Electronic health records from a large secondary mental healthcare provider in south London, accessed through the Clinical Record Interactive Search platform.

Classification aware neural topic model for COVID-19 disinformation categorisation.

The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide. Not only is disinformation creating confusion about medical science amongst citizens, but it is also amplifying distrust in policy makers and governments. To help tackle this, we developed computational methods to categorise COVID-19 disinformation.

Engineering of a genome-reduced strain Bacillus amyloliquefaciens for enhancing surfactin production.

Background: Genome reduction and metabolic engineering have emerged as intensive research hotspots for constructing the promising functional chassis and various microbial cell factories. Surfactin, a lipopeptide-type biosurfactant with broad spectrum antibiotic activity, has wide application prospects in anticancer therapy, biocontrol and bioremediation. Bacillus amyloliquefaciens LL3, previously isolated by our lab, contains an intact srfA operon in the genome for surfactin biosynthesis.

CogStack - experiences of deploying integrated information retrieval and extraction services in a large National Health Service Foundation Trust hospital.

Background: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS).