Immunohistochemistry using an antibody to unphosphorylated connexin 43 to identify human myometrial interstitial cells

<p>Abstract</p> <p>Background</p> <p>Myometrial smooth myocytes contract as a result of electrical signalling via a process called excitation-contraction coupling. This process is understood in great detail at the cellular level but the generation and coordination of electrical signals throughout the myometrium are incompletely understood. Recent evidence concerning the vital role of interstitial cells of Cajal in tissue-level signalling in gastrointestinal tract, and the presence of similar cells in urinary tract smooth muscle may be relevant for future research into myometrial contractility but there remains a lack of evidence regarding these cells in the myometrium.</p> <p>Methods</p> <p>Single stain immunohistochemical and double stain immunofluorescence techniques visualised antibodies directed against total connexin 43, unphosphorylated connexin 43, KIT, alpha-SMA and prolyl 4-hydroxylase in myometrial biopsies from 26 women representing all stages of reproductive life.</p> <p>Results</p> <p>Myometrial smooth myocytes from term uterine biopsies expressed connexin 43 in a punctate pattern typical of gap junctions. However, on the boundaries of the smooth muscle bundles, cells were present with a more uniform staining pattern. These cells continued to possess the same staining characteristics in non-pregnant biopsies whereas the smooth myocytes no longer expressed connexin 43. Immunohistochemistry using an antibody directed against connexin 43 unphosphorylated at serine 368 showed that it is this isoform that is expressed continually by these cells. Double-stain immunofluorescence for unphosphorylated connexin 43 and KIT, an established marker for interstitial cells, revealed a complete match indicating these cells are myometrial interstitial cells (MICs). MICs had elongated cell processes and were located mainly on the surface of the smooth muscle bundles and within the fibromuscular septum. No particular arrangement of cells as plexuses was observed. Antibody to prolyl 4-hydroxylase identified fibroblasts as separate from MICs.</p> <p>Conclusion</p> <p>MICs are identified consistently on the boundaries of smooth muscle bundles in both the pregnant and non-pregnant uterus and are distinct from fibroblasts. The uniform distribution of connexin 43 on the cell membrane of MICs, rather than localisation in gap junction plaques, may represent the presence of connexin hemichannels. This antibody specificity may aid future study of this potentially important cell type.</p

Distributionally Robust Deep Learning using Hardness Weighted Sampling

Limiting failures of machine learning systems is vital for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM)aiming at addressing this need. However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM. We propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. Similar to a hard example mining strategy in essence and in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning, requiring minimal overhead computation. In contrast to typical ad hoc hard mining approaches, and exploiting recent theoretical results in deep learning optimization, we prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and finite number of layers and parameters. Our experiments on brain tumor segmentation in MRI demonstrate the feasibility and the usefulness of our approach. Using our hardness weighted sampling leads to a decrease of 2% of the interquartile range of the Dice scores for the enhanced tumor and the tumor core regions. The code for the proposed hard weighted sampler will be made publicly available

A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of a state-of-the-art backbone AI for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities

A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation

Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.</p

Evidence of time dependent degradation of polypropylene surgical mesh explanted from the abdomen and vagina of sheep.

The failure of polypropylene mesh is marked by significant side effects and debilitation, arising from a complex interplay of factors. One key contributor is the pronounced physico-mechanical mismatch between the polypropylene (PP) fibres and surrounding tissues, resulting in substantial physical damage, inflammation, and persistent pain. However, the primary cause of sustained inflammation due to polypropylene itself remains incompletely understood. This study comprises a comprehensive, multi-pronged investigation to unravel the effects of implantation on a presumed inert PP mesh in sheep. Employing both advanced and conventional techniques to discern the physical and chemical transformations of the implanted PP. Our analyses reveal a surface degradation and oxidation of polypropylene fibres after 60 days implantation, persisting and intensifying at the 180-day mark. The emergence and accumulation of PP debris in the tissue surrounding the implant also increased with implantation time. We demonstrate observable physical and mechanical alterations in the fibre surface and stiffness. Our study shows surface alterations which indicate that PP is evidently less chemically inert than was initially presumed. These findings underscore the need for a re-evaluation of the biocompatibility and long-term consequences of using PP mesh implants

Standardized postnatal management of infants with congenital diaphragmatic hernia in Europe: The CDH EURO Consortium Consensus - 2015 Update

In 2010, the congenital diaphragmatic hernia (CDH) EURO Consortium published a standardized neonatal treatment protocol. Five years later, the number of participating centers has been raised from 13 to 22. In this article the relevant literature is updated, and consensus has been reached between the members of the CDH EURO Consortium. Key updated recommendations are: (1) planned delivery after a gestational age of 39 weeks in a high-volume tertiary center; (2) neuromuscular blocking agents to be avoided during initial treatment in the delivery room; (3) adapt treatment to reach a preductal saturation of between 80 and 95% and postductal saturation >70%; (4) target PaCO2 to be between 50 and 70 mm Hg; (5) conventional mechanical ventilation to be the optimal initial ventilation strategy, and (6) intravenous sildenafil to be considered in CDH patients with severe pulmonary hypertension. This article represents the current opinion of all consortium members in Europe for the optimal neonatal treatment of CDH

Congenital Diaphragmatic hernia – a review

Congenital Diaphragmatic hernia (CDH) is a condition characterized by a defect in the diaphragm leading to protrusion of abdominal contents into the thoracic cavity interfering with normal development of the lungs. The defect may range from a small aperture in the posterior muscle rim to complete absence of diaphragm. The pathophysiology of CDH is a combination of lung hypoplasia and immaturity associated with persistent pulmonary hypertension of newborn (PPHN) and cardiac dysfunction. Prenatal assessment of lung to head ratio (LHR) and position of the liver by ultrasound are used to diagnose and predict outcomes. Delivery of infants with CDH is recommended close to term gestation. Immediate management at birth includes bowel decompression, avoidance of mask ventilation and endotracheal tube placement if required. The main focus of management includes gentle ventilation, hemodynamic monitoring and treatment of pulmonary hypertension followed by surgery. Although inhaled nitric oxide is not approved by FDA for the treatment of PPHN induced by CDH, it is commonly used. Extracorporeal membrane oxygenation (ECMO) is typically considered after failure of conventional medical management for infants ≥ 34 weeks’ gestation or with weight >2 kg with CDH and no associated major lethal anomalies. Multiple factors such as prematurity, associated abnormalities, severity of PPHN, type of repair and need for ECMO can affect the survival of an infant with CDH. With advances in the management of CDH, the overall survival has improved and has been reported to be 70-90% in non-ECMO infants and up to 50% in infants who undergo ECMO