Thiele, Frederic

The spinal cord has a poor ability to regenerate after an injury, which may be due to cell loss, cyst formation, inflammation, and scarring. A promising approach to treating a spinal cord injury (SCI) is the use of biomaterials. We have developed a novel hydrogel scaffold fabricated from oligo(poly(ethylene glycol) fumarate) (OPF) as a 0.08 mm thick sheet containing polymer ridges and a cell-attractive surface on the other side. When the cells are cultured on OPF via chemical patterning, the cells attach, align, and deposit ECM along the direction of the pattern. Animals implanted with the rolled scaffold sheets had greater hindlimb recovery compared to that of the multichannel scaffold control, which is likely due to the greater number of axons growing across it. The immune cell number (microglia or hemopoietic cells: 50–120 cells/mm2 in all conditions), scarring (5–10% in all conditions), and ECM deposits (Laminin or Fibronectin: approximately 10–20% in all conditions) were equal in all conditions. Overall, the results suggest that the scaffold sheets promote axon outgrowth that can be guided across the scaffold, thereby promoting hindlimb recovery. This study provides a hydrogel scaffold construct that can be used in vitro for cell characterization or in vivo for future neuroprosthetics, devices, or cell and ECM delivery.

Abstract Machine learning is a powerful tool that is increasingly being used in many research areas, including neuroscience. The recent development of new algorithms and network architectures, especially in the field of deep learning, has made machine learning models more reliable and accurate and useful for the biomedical research sector. By minimizing the effort necessary to extract valuable features from datasets, they can be used to find trends in data automatically and make predictions about future data, thereby improving the reproducibility and efficiency of research. One application is the automatic evaluation of micrograph images, which is of great value in neuroscience research. While the development of novel models has enabled numerous new research applications, the barrier to use these new algorithms has also decreased by the integration of deep learning models into known applications such as microscopy image viewers. For researchers unfamiliar with machine learning algorithms, the steep learning curve can hinder the successful implementation of these methods into their workflows. This review explores the use of machine learning in neuroscience, including its potential applications and limitations, and provides some guidance on how to select a fitting framework to use in real-life research projects.

Intra-arterial drug delivery circumvents the first-pass effect and is believed to increase both efficacy and tolerability of primary and metastatic brain tumor therapy. The aim of this update is to report on pertinent articles and clinical trials to better understand the research landscape to date and future directions. Elsevier’s Scopus and ClinicalTrials.gov databases were reviewed in August 2021 for all possible articles and clinical trials of intra-arterial drug injection as a treatment strategy for brain tumors. Entries were screened against predefined selection criteria and various parameters were summarized. Twenty clinical trials and 271 articles satisfied all inclusion criteria. In terms of articles, 201 (74%) were primarily clinical and 70 (26%) were basic science, published in a total of 120 different journals. Median values were: publication year, 1986 (range, 1962–2021); citation count, 15 (range, 0–607); number of authors, 5 (range, 1–18). Pertaining to clinical trials, 9 (45%) were phase 1 trials, with median expected start and completion years in 2011 (range, 1998–2019) and 2022 (range, 2008–2025), respectively. Only one (5%) trial has reported results to date. Glioma was the most common tumor indication reported in both articles (68%) and trials (75%). There were 215 (79%) articles investigating chemotherapy, while 13 (65%) trials evaluated targeted therapy. Transient blood–brain barrier disruption was the commonest strategy for articles (27%) and trials (60%) to optimize intra-arterial therapy. Articles and trials predominately originated in the United States (50% and 90%, respectively). In this bibliometric and clinical trials analysis, we discuss the current state and trends of intra-arterial therapy for brain tumors. Most articles were clinical, and traditional anti-cancer agents and drug delivery strategies were commonly studied. This was reflected in clinical trials, of which only a single study had reported outcomes. We anticipate future efforts to involve novel therapeutic and procedural strategies based on recent advances in the field.