Preventive Care

Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.

Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25% of patients within the first year post-chemotherapy. Recognizing at-risk patients before DOXO initiation offers pathways for alternative treatments or early protective actions. We analyzed data from 78 Brazilian breast cancer patients, with 34.6% developing cardiotoxicity within a year of their fin...

Cell clone selection-impact of operation modes and medium exchange strategies on clone ranking.

Bioprocessing has been transitioning from batch to continuous processes. As a result, a considerable amount of resource was dedicated to optimising strategies for continuous production. However, the focus has been on developing a suitable and scalable perfusion strategy with little attention given to the selection of optimal cell clones. Cell line development and lead clone selection are critical ...

Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.

Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targe...

Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation.

With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is c...

Applications of Machine Learning Approaches for the Discovery of SARS-CoV-2 PLpro Inhibitors.

The global impact of SARS-CoV-2 highlights the need for treatments beyond vaccination, given the limited availability of effective medications. While ...

Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning.

Nailfold Capillaroscopy (NFC) is a simple, non-invasive diagnostic tool used to detect microvascular changes in nailfold. Chronic pathological changes...

Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.

Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effecti...

Dynamic spectrum-driven hierarchical learning network for polyp segmentation.

Accurate automatic polyp segmentation in colonoscopy is crucial for the prompt prevention of colorectal cancer. However, the heterogeneous nature of p...

Image-based fuzzy logic control for pressure-driven droplet microfluidics as autosampler for multimodal imaging microscopy.

Here we present a highly customisable image-based fuzzy logic control (FLC) method for pressure-driven droplet microfluidics. The system is designed t...

Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.

In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment de...

Machine Learning-Assisted High-Throughput Screening of Nanozymes for Ulcerative Colitis.

Ulcerative colitis (UC) is a chronic gastrointestinal inflammatory disorder with rising prevalence. Due to the recurrent and difficult-to-treat nature...

AI image analysis as the basis for risk-stratified screening.

Artificial intelligence (AI) has emerged as a transformative tool in breast cancer screening, with two distinct applications: computer-aided cancer de...

FocusUNet: Pioneering dual attention with gated U-Net for colonoscopic polyp segmentation.

The detection and excision of colorectal polyps, precursors to colorectal cancer (CRC), can improve survival rates by up to 90%. Automated polyp segme...

The influence of factors related to public health campaigns on vaccination behavior among population of Wuxi region, China.

BACKGROUND: Public health campaigns are essential for promoting vaccination behavior, but factors such as socioeconomic status, geographical location,...

Leveraging AI to improve disease screening among American Indians: insights from the Strong Heart Study.

Screening tests for disease have their performance measured through sensitivity and specificity, which inform how well the test can discriminate betwe...

Nationwide real-world implementation of AI for cancer detection in population-based mammography screening.

Artificial intelligence (AI) in mammography screening has shown promise in retrospective evaluations, but few prospective studies exist. PRAIM is an o...

Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images.

Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images rela...

Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning.

Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen s...

DIFLF: A domain-invariant features learning framework for single-source domain generalization in mammogram classification.

BACKGROUND AND OBJECTIVE: Single-source domain generalization (SSDG) aims to generalize a deep learning (DL) model trained on one source dataset to mu...

Accelerated Screening of Highly Sensitive Gas Sensor Materials for Greenhouse Gases Based on DFT and Machine Learning Methods.

Greenhouse gases (GHGs) have caused great harm to the ecological environment, so it is necessary to screen gas sensor materials for detecting GHGs. In...