Description Generation Using Variational Auto-Encoders for Precursor microRNA.

Micro RNAs (miRNA) are a type of non-coding RNA involved in gene regulation and can be associated with diseases such as cancer, cardiovascular, and neurological diseases. As such, identifying the entire genome of miRNA can be of great relevance. Since experimental methods for novel precursor miRNA (pre-miRNA) detection are complex and expensive, computational detection using Machine Learning (ML) could be useful.

Integrating histopathology and transcriptomics for spatial tumor microenvironment profiling in a melanoma case study.

Local structures formed by cells in the tumor microenvironment (TME) play an important role in tumor development and treatment response. This study introduces SPoTLIghT, a computational framework providing a quantitative description of the tumor architecture from hematoxylin and eosin (H&E) slides. We trained a weakly supervised machine learning model on melanoma patients linking tile-level imaging features extracted from H&E slides to sample-level cell type quantifications derived from RNA-sequencing data.

Quantifying the effect of nutritional interventions on metabolic resilience using personalized computational models.

The manifestation of metabolic deteriorations that accompany overweight and obesity can differ greatly between individuals, giving rise to a highly heterogeneous population. This inter-individual variation can impede both the provision and assessment of nutritional interventions as multiple aspects of metabolic health should be considered at once. Here, we apply the Mixed Meal Model, a physiology-based computational model, to characterize an individual's metabolic health A population of 342 personalized models were generated using data for individuals with overweight and obesity from three independent intervention studies, demonstrating a strong relationship between the model-derived metric of insulin resistance (ρ = 0.

Deep-learning-based joint rigid and deformable contour propagation for magnetic resonance imaging-guided prostate radiotherapy.

Background: Deep learning-based unsupervised image registration has recently been proposed, promising fast registration. However, it has yet to be adopted in the online adaptive magnetic resonance imaging-guided radiotherapy (MRgRT) workflow.

Purpose: In this paper, we design an unsupervised, joint rigid, and deformable registration framework for contour propagation in MRgRT of prostate cancer.

Strengths and challenges in current lung cancer care: Timeliness and diagnostic procedures in six Dutch hospitals.

Objectives: Timely diagnosis of lung cancer (LC) is crucial to achieve optimal patient care and outcome. Moreover, the number of procedures required to obtain a definitive diagnosis can have a large influence on the life expectancy of a patient. Here, adherence with existing Dutch guidelines for timeliness and type and number of invasive and imaging procedures was assessed.

How neural networks learn to classify chaotic time series.

We tackle the outstanding issue of analyzing the inner workings of neural networks trained to classify regular-vs-chaotic time series. This setting, well-studied in dynamical systems, enables thorough formal analyses. We focus specifically on a family of networks dubbed large Kernel convolutional neural networks (LKCNNs), recently introduced by Boullé et al.

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data.

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug.

Microenvironments Matter: Advances in Brain-on-Chip.

To highlight the particular needs with respect to modeling the unique and complex organization of the human brain structure, we reviewed the state-of-the-art in devising brain models with engineered instructive microenvironments. To acquire a better perspective on the brain's working mechanisms, we first summarize the importance of regional stiffness gradients in brain tissue, varying per layer and the cellular diversities of the layers. Through this, one can acquire an understanding of the essential parameters in emulating the brain in vitro.

Co-Culture of Glomerular Endothelial Cells and Podocytes in a Custom-Designed Glomerulus-on-a-Chip Model Improves the Filtration Barrier Integrity and Affects the Glomerular Cell Phenotype.

Crosstalk between glomerular endothelial cells and glomerular epithelial cells (podocytes) is increasingly becoming apparent as a crucial mechanism to maintain the integrity of the glomerular filtration barrier. However, studies directly investigating the effect of this crosstalk on the glomerular filtration barrier are scarce because of the lack of suitable experimental models. Therefore, we developed a custom-made glomerulus-on-a-chip model recapitulating the glomerular filtration barrier, in which we investigated the effects of co-culture of glomerular endothelial cells and podocytes on filtration barrier function and the phenotype of these respective cell types.

Liquid biopsy-based decision support algorithms for diagnosis and subtyping of lung cancer.

Objectives: Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors.

Leveraging molecular structure and bioactivity with chemical language models for de novo drug design.

Generative chemical language models (CLMs) can be used for de novo molecular structure generation by learning from a textual representation of molecules. Here, we show that hybrid CLMs can additionally leverage the bioactivity information available for the training compounds. To computationally design ligands of phosphoinositide 3-kinase gamma (PI3Kγ), a collection of virtual molecules was created with a generative CLM.

Up-front mutation detection in circulating tumor DNA by droplet digital PCR has added diagnostic value in lung cancer.

Identification of actionable mutations in advanced stage non-squamous non-small-cell lung cancer (NSCLC) patients is recommended by guidelines as it enables treatment with targeted therapies. In current practice, mutations are identified by next-generation sequencing of tumor DNA (tDNA-NGS), which requires tissue biopsies of sufficient quality. Alternatively, circulating tumor DNA (ctDNA) could be used for mutation analysis.

Engineering Organ-on-a-Chip to Accelerate Translational Research.

We guide the use of organ-on-chip technology in tissue engineering applications. Organ-on-chip technology is a form of microengineered cell culture platform that elaborates the in-vivo like organ or tissue microenvironments. The organ-on-chip platform consists of microfluidic channels, cell culture chambers, and stimulus sources that emulate the in-vivo microenvironment.

Ambulance dispatching during a pandemic: Tradeoffs of categorizing patients and allocating ambulances.

Amidst a pandemic, operators of emergency medical service (EMS) systems aim at upholding service at sufficiently low response times while reducing the infection probability of their personnel. Designating ambulances to serve only infected patients and suspected cases may reduce the outage probabilities of ambulances and consequently the response times of the EMS. We investigate the benefits that EMS personnel and patients can gain from such a split.

Editorial for the Special Issue on Microfluidic Brain-on-a-Chip.

A little longer than a decade of Organ-on-Chip (OoC) developments has passed [...