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Recent studies show the importance of hydrogel geometry for various applications, such as encoding, micromachines, or tissue engineering. However, fabricating hydrogel structures with micrometer-sized features, advanced geometry, and precise control of porosity remains challenging. This work presents hierarchically structured hydrogels, so-called hydrogel patches, with internally deviating regions on a micron-scale. These regions are defined in a one-step, high-throughput fabrication process via stop-flow lithography. Between the specified projection pattern during fabrication, an interconnecting lower crosslinked and more porous hydrogel network forms, resulting in at least two degrees of crosslinking within the patches. A detailed investigation of patch formation is performed for two material systems and pattern variations, revealing basic principles for reliable patch formation. In addition to the two defined crosslinked regions, further regions are implemented in the patches by adapting the pattern accordingly. The variations in pattern geometry impact the mechanical characteristics of the hydrogel patches, which display pattern-dependent compression behavior due to predefined compression points. Cell culture on patches, as one possible application, reveals that the patch pattern determines the cell area of L929 mouse fibroblasts. These results introduce hierarchically structured hydrogel patches as a promising and versatile platform system with high customizability., (© 2024 The Author(s). Small published by Wiley‐VCH GmbH.)

Background: High-resolution manometry (HRM) is vital in evaluating patients for surgery at the gastroesophageal (GE) junction. Previously, we reported manometry alters surgery choices at the GE junction over 50% of the time, and its components, i.e., abnormal motility and distal contractile integral (DCI), are vital in decision-making. This single-institution retrospective study examines how HRM characteristics, reported with the Chicago classification, can alter the intended surgical plans for foregut surgery., Methods: We collected data on pre-operative symptoms for patients undergoing HRM studies from 2012 to 2016, i.e., Upper GI X-rays, 48-h pH studies, DeMeester scores, upper endoscopy, and biopsy reports. HRM results were further categorized via Chicago classification (i.e., normal or abnormal motility). The DCI was determined; Patients not seen by a surgeon were excluded. Then a single surgeon, blinded to patient identity and HRM results, determined the planned procedure. The reviewer was then exposed to the HRM results; procedural plans were revised if needed. HRM results were then evaluated to determine which factors most influenced the surgical decisions., Results: 298 HRM studies were initially identified; 114 met search criteria. Overall, HRM altered the planned procedure in 50.9% of cases (n = 58), with abnormal motility in 54.4% (62/114) cases. Abnormal motility findings corresponded to 70.6% (41/58) of the patients in which HRM changed the surgery decision. A DCI of < 1000 was identified in only 31.6% (36/114) of all patients, but 39.7% (23/58) of cases where the surgical decision was altered. A DCI of > 5000 was identified in only 10.5% (12/114) of all patients but 10.3% (6/58) of cases with altered surgical decisions. A DCI < 1000 and abnormal motility were generally associated with a partial fundoplication., Conclusions: This study demonstrates the impact of identifying abnormal motility via the Chicago classification and factors like DCI on surgical choice at the GE junction., (© 2023. The Author(s).)

Magnetic resonance imaging (MRI) is widely used for ischemic stroke lesion detection in mice. A challenge is that lesion segmentation often relies on manual tracing by trained experts, which is labor-intensive, time-consuming, and prone to inter- and intra-rater variability. Here, we present a fully automated ischemic stroke lesion segmentation method for mouse T2-weighted MRI data. As an end-to-end deep learning approach, the automated lesion segmentation requires very little preprocessing and works directly on the raw MRI scans. We randomly split a large dataset of 382 MRI scans into a subset (n = 293) to train the automated lesion segmentation and a subset (n = 89) to evaluate its performance. We compared Dice coefficients and accuracy of lesion volume against manual segmentation, as well as its performance on an independent dataset from an open repository with different imaging characteristics. The automated lesion segmentation produced segmentation masks with a smooth, compact, and realistic appearance that are in high agreement with manual segmentation. We report dice scores higher than the agreement between two human raters reported in previous studies, highlighting the ability to remove individual human bias and standardize the process across research studies and centers., (© 2023. Springer Nature Limited.)