Gene regulatory network analysis identifies MYL1, MDH2, GLS, and TRIM28 as the principal proteins in the response of mesenchymal stem cells to Mg2+ ions.

Magnesium (Mg)-based implants have emerged as a promising alternative for orthopedic applications, owing to their bioactive properties and biodegradability. As the implants degrade, Mg2+ ions are released, influencing all surrounding cell types, especially mesenchymal stem cells (MSCs). MSCs are vital for bone tissue regeneration, therefore, it is essential to understand their molecular response to Mg2+ ions in order to maximize the potential of Mg-based biomaterials. In this study, we conducted a gene regulatory network (GRN) analysis to examine the molecular responses of MSCs to Mg2+ ions. We used time-series proteomics data collected at 11 time points across a 21-day period for the GRN construction. We studied the impact of Mg2+ ions on the resulting networks and identified the key proteins and protein interactions affected by the application of Mg2+ ions. Our analysis highlights MYL1, MDH2, GLS, and TRIM28 as the primary targets of Mg2+ ions in the response of MSCs during 1-21 days phase. Our results also identify MDH2-MYL1, MDH2-RPS26, TRIM28-AK1, TRIM28-SOD2, and GLS-AK1 as the critical protein relationships affected by Mg2+ ions. By offering a comprehensive understanding of the regulatory role of Mg2+ ions on MSCs, our study contributes valuable insights into the molecular response of MSCs to Mg-based materials, thereby facilitating the development of innovative therapeutic strategies for orthopedic applications.

Neural network surgery: Combining training with topology optimization.

With ever increasing computational capacities, neural networks become more and more proficient at solving complex tasks. However, picking a sufficiently good network topology usually relies on expert human knowledge. Neural architecture search aims to reduce the extent of expertise that is needed. Modern architecture search techniques often rely on immense computational power, or apply trained meta-controllers for decision making. We develop a framework for a genetic algorithm that is both computationally cheap and makes decisions based on mathematical criteria rather than trained parameters. It is a hybrid approach that fuses training and topology optimization together into one process. Structural modifications that are performed include adding or removing layers of neurons, with some re-training applied to make up for any incurred change in input-output behaviour. Our ansatz is tested on several benchmark datasets with limited computational overhead compared to training only the baseline. This algorithm can achieve a significant increase in accuracy (as compared to a fully trained baseline), rescue insufficient topologies that in their current state are only able to learn to a limited extent, and dynamically reduce network size without loss in achieved accuracy. On standard ML datasets, accuracy improvements compared to baseline performance can range from 20% for well performing starting topologies to more than 40% in case of insufficient baselines, or reduce network size by almost 15%.

What do cells regulate in soft tissues on short time scales?

Cells within living soft biological tissues seem to promote the maintenance of a mechanical state within a defined range near a so-called set-point. This mechanobiological process is often referred to as mechanical homeostasis. During this process, cells interact with the fibers of the surrounding extracellular matrix (ECM). It remains poorly understood, however, what individual cells actually regulate during these interactions, and how these micromechanical regulations are translated to the tissue-level to lead to what we observe as biomaterial properties. Herein, we examine this question by a combination of experiments, theoretical analysis, and computational modeling. We demonstrate that on short time scales (hours) - during which deposition and degradation of ECM fibers can largely be neglected - cells appear to not regulate the stress / strain in the ECM or their own shape, but rather only the contractile forces that they exert on the surrounding ECM. STATEMENT OF SIGNIFICANCE: Cells in soft biological tissues sense and regulate the mechanical state of the extracellular matrix to ensure structural integrity and functionality. This so-called mechanical homeostasis plays an important role in the natural history of various diseases such as aneurysms in the cardiovascular system or cancer. Yet, it remains poorly understood to date which target quantity cells regulate on the mircroscale and how it translates to the macroscale. In this paper, we combine experiments, computer simulations, and theoretical analysis to compare different hypotheses about this target quantity. This allows us to identify a likely candidate for it at least on short time scales and in the simplified environment of tissue equivalents.

A computational framework for modeling cell-matrix interactions in soft biological tissues.

Living soft tissues appear to promote the development and maintenance of a preferred mechanical state within a defined tolerance around a so-called set point. This phenomenon is often referred to as mechanical homeostasis. In contradiction to the prominent role of mechanical homeostasis in various (patho)physiological processes, its underlying micromechanical mechanisms acting on the level of individual cells and fibers remain poorly understood, especially how these mechanisms on the microscale lead to what we macroscopically call mechanical homeostasis. Here, we present a novel computational framework based on the finite element method that is constructed bottom up, that is, it models key mechanobiological mechanisms such as actin cytoskeleton contraction and molecular clutch behavior of individual cells interacting with a reconstructed three-dimensional extracellular fiber matrix. The framework reproduces many experimental observations regarding mechanical homeostasis on short time scales (hours), in which the deposition and degradation of extracellular matrix can largely be neglected. This model can serve as a systematic tool for future in silico studies of the origin of the numerous still unexplained experimental observations about mechanical homeostasis.

Mechanical homeostasis in tissue equivalents: a review.

There is substantial evidence that growth and remodeling of load bearing soft biological tissues is to a large extent controlled by mechanical factors. Mechanical homeostasis, which describes the natural tendency of such tissues to establish, maintain, or restore a preferred mechanical state, is thought to be one mechanism by which such control is achieved across multiple scales. Yet, many questions remain regarding what promotes or prevents homeostasis. Tissue equivalents, such as collagen gels seeded with living cells, have become an important tool to address these open questions under well-defined, though limited, conditions. This article briefly reviews the current state of research in this area. It summarizes, categorizes, and compares experimental observations from the literature that focus on the development of tension in tissue equivalents. It focuses primarily on uniaxial and biaxial experimental studies, which are well-suited for quantifying interactions between mechanics and biology. The article concludes with a brief discussion of key questions for future research in this field.

Hematopoietic Stem Cell Transplantation as Treatment for Patients with DOCK8 Deficiency.

BACKGROUND: Biallelic variations in the dedicator of cytokinesis 8 (DOCK8) gene cause a combined immunodeficiency with eczema, recurrent bacterial and viral infections, and malignancy. Natural disease outcome is dismal, but allogeneic hematopoietic stem cell transplantation (HSCT) can cure the disease. OBJECTIVE: To determine outcome of HSCT for DOCK8 deficiency and define possible outcome variables. METHODS: We performed a retrospective study of the results of HSCT in a large international cohort of DOCK8-deficient patients. RESULTS: We identified 81 patients from 22 centers transplanted at a median age of 9.7 years (range, 0.7-27.2 years) between 1995 and 2015. After median follow-up of 26 months (range, 3-135 months), 68 (84%) patients are alive. Severe acute (III-IV) or chronic graft versus host disease occurred in 11% and 10%, respectively. Causes of death were infections (n = 5), graft versus host disease (5), multiorgan failure (2), and preexistent lymphoma (1). Survival after matched related (n = 40) or unrelated (35) HSCT was 89% and 81%, respectively. Reduced-toxicity conditioning based on either treosulfan or reduced-dose busulfan resulted in superior survival compared with fully myeloablative busulfan-based regimens (97% vs 78%; P = .049). Ninety-six percent of patients younger than 8 years at HSCT survived, compared with 78% of those 8 years and older (P = .06). Of the 73 patients with chimerism data available, 65 (89%) had more than 90% donor T-cell chimerism at last follow-up. Not all disease manifestations responded equally well to HSCT: eczema, infections, and mollusca resolved quicker than food allergies or failure to thrive. CONCLUSIONS: HSCT is curative in most DOCK8-deficient patients, confirming this approach as the treatment of choice. HSCT using a reduced-toxicity regimen may offer the best chance for survival.

Higher patient satisfaction with antidepressants correlates with earlier drug release dates across online user-generated medical databases.

Studies establishing the use of new antidepressants often rely simply on proving efficacy of a new compound, comparing against placebo and single compound. The advent of large online databases in which patients themselves rate drugs allows for a new Big Data-driven approach to compare the efficacy and patient satisfaction with sample sizes exceeding previous studies. Exemplifying this approach with antidepressants, we show that patient satisfaction with a drug anticorrelates with its release date with high significance, across different online user-driven databases. This finding suggests that a systematic reevaluation of current, often patent-protected drugs compared to their older predecessors may be helpful, especially given that the efficacy of newer agents relative to older classes of antidepressants such as monoamine oxidase inhibitors (MAOIs) and tricyclic antidepressants (TCAs) is as yet quantitatively unexplored.

The evolution of radiation dose over time: Measurement of a patient cohort undergoing whole-body examinations on three computer tomography generations.

OBJECTIVES: To evaluate and compare the radiation dose and image quality of whole-body-CT (WBCT) performed on the 3rd-generation dual-source-CT (DSCT) with 2nd-generation DSCT and 64-slices-Single-Source-CT (SSCT) in a large patient cohort. MATERIAL AND METHODS: Using a monitoring and tracking software 1451, 747 and 1861 patients scanned with a one-spiral-thorax-abdomen-pelvis-CT-examination on a 3rd-, 2nd-generation DSCT and SSCT, respectively, were extracted from the PACS server. For the intra-individual analysis, 203 patients on the 3rd-generation DSCT were identified. Out of those 203 patients, 155 had the same examination on the 2nd-generation DSCT, 91 patients had the same examination on the SSCT and 43 patients had an examination on all three CT-generations. Automatic tube current modulation was active on all three CT-generations, whereas automatic tube voltage selection was only available on both DSCT-generations. Dose was recorded by the size-specific-dose-estimate-method (SSDE); signal-to-noise-ratio (SNR) and contrast-to-noise-ratio (CNR) were calculated placing a ROI on the ascending aorta/liver and the subcutaneous adipose tissue at comparable level. Image quality of axillary and mediastinal lymph nodes and adrenal glands was assessed by two experienced radiologists. RESULTS: Subjective image quality was excellent throughout all three CT-generations (p=0.38-0.98). Quantitative image quality in both DSCT generations was superior to SSCT (p<0.001). SNR and CNR in the liver parenchyma were superior in the 3rd-generation DSCT compared to the 2nd generation DSCT (p<0.001), whereas there was no difference in the aorta. In the inter-individual analysis, CTDIvol was lower by 26.9% and 44.3% in the 3rd-generation DSCT, when compared to the 2nd-generation DSCT and SSCT, respectively; SSDE was lower by 31.5% and 51% in the 3rd-generation DSCT, when compared to the 2nd-generation DSCT and SSCT, respectively. In the intra-individual comparison CTDIVol in the 3rd-generation DSCT was lower by 33% and 45%, when compared to the 2nd-gneration DSCT and the SSCT, respectively. Consequently, SSDE in the 3rd-generation DSCT was lower by 29% and by 43% when compared to the 2nd-generation DSCT and SSCT, respectively. CONCLUSION: State-of-the-art CT-equipment substantially reduce radiation dose without affecting image quality.