Browse Articles

Preclinical and clinical evaluation of [64Cu]Cu-PSMA-Q PET/CT for prostate cancer detection and its comparison with [18F]FDG imaging

Development of anti-fouling endoscope tip hood for gastrointestinal endoscopy

Using pet insurance claims to predict occurrence of vector-borne and zoonotic disease in humans in the United States

Synchronizing controlled logistics terminals between simulated and visualized production lines using an ASTAK method

Cannabidiol exerts teratogenic effects on developing zebrafish through the sonic hedgehog signaling pathway

Clinical experience of the expanded carrier screening for recessive genetic diseases in a large cohort study in Southern central China

Accessing the role of tourism, renewable energy, and green finance in shaping the sustainable future

Machine-learning-aided analysis of relationship between crystal defects and macroscopic mechanical properties of TWIP steel

Abstract Establishing efficient methods to obtain quantitative data on crystal defect evolution is vital for understanding material properties. Dynamic Transmission Electron Microscopy (TEM) captures crystal defects in materials undergoing plastic deformation, generating vast datasets with high temporal and spatial resolution. However, manual analysis of these images is labor-intensive, and automated, unbiased analysis remains a challenge. In this study, we developed a U-net-based machine learning approach to analyze TEM videos of crystal defect evolution in Twinning-Induced Plasticity (TWIP) steels with different grain sizes. The method overcame challenges like field-of-view translation and nonuniform defect motion. This approach quantitatively measured defect evolution as a function of time and strain with the same temporal resolution as the original videos, detecting even minor changes with high accuracy. We use this technique to quantitatively reveal the switch of the dominant plastic deformation mechanism with grain size and the relaxation of elastic strain due to the rapid increase in stacking faults. Our results validate the use of U-net models for efficient semantic segmentation of TEM videos, enabling accurate quantitative analysis. This work advances TEM video analysis and provides new insights into the deformation mechanisms of materials.

Frequent failure of nutrients to increase plant biomass supports the need for precision fertilization in agriculture

Genome-wide association study of plasma amino acids and Mendelian randomization for cardiometabolic traits

Deep learning based dual stage model for accurate nasogastric tube positioning in chest radiographs

Abstract Accurate placement of nasogastric tubes (NGTs) is crucial for ensuring patient safety and effective treatment. Traditional methods relying on manual inspection are susceptible to human error, highlighting the need for innovative solutions. This study introduces a deep-learning model that enhances the detection and analysis of NGT positioning in chest radiographs. By integrating advanced segmentation and classification techniques, the model leverages the nnU-Net framework for segmenting critical regions and the ResNet50 architecture, pre-trained with MedCLIP, for classifying NGT placement. Trained on 1799 chest radiographs, the model demonstrates remarkable performance, achieving a Dice Similarity Coefficient of 65.35% for segmentation and an Area Under the Curve of 99.72% for classification. These results underscore its ability to accurately distinguish between correct and incorrect placements, outperforming traditional approaches. This method not only enhances diagnostic precision but also has the potential to streamline clinical workflows and improve patient care. A functional prototype of the model is accessible at https://ngtube.ziovision.ai.

Weight-adjusted waist index as a new predictor of osteoporosis in postmenopausal patients with T2DM

Prognostic molecular subtype reveals the heterogeneity of tumor immune microenvironment in gastric cancer

A suppression-modification gene drive for malaria control targeting the ultra-conserved RNA gene mir-184

Abstract Gene drive technology presents a promising approach to controlling malaria vector populations. Suppression drives are intended to disrupt essential mosquito genes whereas modification drives aim to reduce the individual vectorial capacity of mosquitoes. Here we present a highly efficient homing gene drive in the African malaria vector Anopheles gambiae that targets the microRNA gene mir-184 and combines suppression with modification. Homozygous gene drive (miR-184D) individuals incur significant fitness costs, including high mortality following a blood meal, that curtail their propensity for malaria transmission. We attribute this to a role of miR-184 in regulating solute transport in the mosquito gut. However, females remain fully fertile, and pure-breeding miR-184D populations suitable for large-scale releases can be reared under laboratory conditions. Cage invasion experiments show that miR-184D can spread to fixation thereby reducing population fitness, while being able to propagate a separate antimalarial effector gene at the same time. Modelling indicates that the miR-184D drive integrates aspects of population suppression and population replacement strategies into a candidate strain that should be evaluated further as a tool for malaria eradication.

Machine learning approach for optimizing usability of healthcare websites

Chromosomal instability in human trophoblast stem cells and placentas

Mixed matrix membrane of poly(4-methyl-1-pentyne) and ZIF-8 for enhanced CO2 separation over H2 and CH4

An individual-based modelling study estimating the impact of maternity service delivery on health in Malawi

Abstract Maternal and perinatal morbidity and mortality remain high in Malawi, partially due to gaps in the coverage and quality of health services. We developed an individual-based model of maternal and perinatal health and healthcare in Malawi, situated in a ‘whole-health system, all-disease’ framework ( Thanzi La Onse ). We modelled sixteen scenarios estimating the impact of current and improved coverage and quality of antenatal, intrapartum, and postnatal services from 2023 to 2030. Whilst current service delivery is inferred to avert morbidity and mortality, the largest reductions in the stillbirth, maternal and neonatal mortality rates were observed when the use and quality of all services was maximised concurrently (a 10%, 52% and 57% reduction respectively). When services were considered in isolation, generally, increased coverage without quality improvement did not impact mortality or DALYs. In only three scenarios was a sufficient reduction in neonatal mortality observed to achieve target 3.2 of the United Nation’s Sustainable Development Goals (SDG), and in no scenarios was a reduction in maternal mortality sufficient to achieve SDG target 3.1 observed, reaffirming that system wide investments are essential to achieve these goals.

Surface modification of electrospun polycaprolactone and zein using cold atmospheric plasma for tissue engineering applications

Single-cell RNA sequencing dissects the immunosuppressive signatures in Helicobacter pylori-infected human gastric ecosystem