Stem Cell Research

A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors.

Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6is) have revolutionized breast cancer therapy. However, <50% of patients have an objective response, and nearly all patients develop resistance during therapy. To elucidate the underlying mechanisms, we constructed an interpretable deep learning model of the response to palbociclib, a CDK4/6i, based on a reference map of multiprotein assemblies in ...

Tracking mosquito-borne diseases via social media: a machine learning approach to topic modelling and sentiment analysis.

Mosquito-borne diseases (MBDs) are a major threat worldwide, and public consultation on these diseases is critical to disease control decision-making. However, traditional public surveys are time-consuming and labor-intensive and do not allow for timely decision-making. Recent studies have explored text analytic approaches to elicit public comments from social media for public health. Therefore, t...

Comparing robotic and manual injection methods in zebrafish embryos for high-throughput RNA silencing using CRISPR-RfxCas13d.

In this study, the authors compared the efficiency of automated robotic and manual injection methods for the CRISPR-RfxCas13d (CasRx) system for mRNA ...

Unravelling the skills of data scientists: A text mining analysis of Dutch university master programs in data science and artificial intelligence.

The growing demand for data scientists in both the global and Dutch labour markets has led to an increase in data science and artificial intelligence ...

Robot therapy aids mental health in patients with hematological malignancy during hematopoietic stem cell transplantation in a protective isolation unit.

Patients with hematological malignancy experience physical and psychological pain, such as a sense of isolation and confinement due to intensive chemo...

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures as...

Machine learning application identifies plasma markers for proteinuria in metastatic colorectal cancer patients treated with Bevacizumab.

BACKGROUND AND OBJECTIVES: Proteinuria is a common complication after the application of bevacizumab therapy in patients with metastatic colorectal ca...

[Artificial intelligence and acute kidney injury].

Digitalization is increasingly finding its way into intensive care units and with it artificial intelligence (AI) for critically ill patients. One pro...

Getting real about synthetic data ethics : Are AI ethics principles a good starting point for synthetic data ethics?

Synthetic data promises to be a viable alternative when data collection and data sharing may not be feasible or cost effective, but it raises distinct...

SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy.

Drug combination therapy shows promise in cancer treatment by addressing drug resistance, reducing toxicity, and enhancing therapeutic efficacy. Howev...

Neuroimaging data repositories and AI-driven healthcare-Global aspirations vs. ethical considerations in machine learning models of neurological disease.

Neuroimaging data repositories are data-rich resources comprising brain imaging with clinical and biomarker data. The potential for such repositories ...

Machine learning developed an intratumor heterogeneity signature for predicting prognosis and immunotherapy benefits in skin cutaneous melanoma.

Intratumor heterogeneity (ITH) is defined as differences in molecular and phenotypic profiles between different tumor cells and immune cells within a ...

The Combined Effect of Robot-assisted Therapy and Activities of Daily Living Training on Upper Limb Recovery in Persons With Subacute Stroke: A Randomized Controlled Trial.

OBJECTIVES: To evaluate the effectiveness of robot-assisted therapy (RAT) followed by activities of daily living (ADL) training in comparison with con...

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence.

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic o...

Personalized Drug Therapy: Innovative Concept Guided With Proteoformics.

Personalized medicine can reduce adverse effects, enhance drug efficacy, and optimize treatment outcomes, which represents the essence of personalized...

Ethics and artificial intelligence.

The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics i...

Deep hybrid modeling of a HEK293 process: Combining long short-term memory networks with first principles equations.

The combination of physical equations with deep learning is becoming a promising methodology for bioprocess digitalization. In this paper, we investig...

Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education.

The use of artificial intelligence (AI) in medicine, potentially leading to substantial advancements such as improved diagnostics, has been of increas...

Who is responsible? US Public perceptions of AI governance through the lenses of trust and ethics.

The governance of artificial intelligence (AI) is an urgent challenge that requires actions from three interdependent stakeholders: individual citizen...

CellT-Net: A Composite Transformer Method for 2-D Cell Instance Segmentation.

Cell instance segmentation (CIS) via light microscopy and artificial intelligence (AI) is essential to cell and gene therapy-based health care managem...