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Institute
- Medizin (390) (remove)
Nodular lymphocyte-predominant Hodgkin lymphoma (NLPHL) can show variable histological growth patterns and present remarkable overlap with T-cell/histiocyte-rich large B-cell lymphoma (THRLBCL). Previous studies suggest that NLPHL histological variants represent progression forms of NLPHL and THRLBCL transformation in aggressive disease. Since molecular studies of both lymphomas are limited due to the low number of tumor cells, the present study aimed to learn if a better understanding of these lymphomas is possible via detailed measurements of nuclear and cell size features in 2D and 3D sections. Whereas no significant differences were visible in 2D analyses, a slightly increased nuclear volume and a significantly enlarged cell size were noted in 3D measurements of the tumor cells of THRLBCL in comparison to typical NLPHL cases. Interestingly, not only was the size of the tumor cells increased in THRLBCL but also the nuclear volume of concomitant T cells in the reactive infiltrate when compared with typical NLPHL. Particularly CD8+ T cells had frequent contacts to tumor cells of THRLBCL. However, the nuclear volume of B cells was comparable in all cases. These results clearly demonstrate that 3D tissue analyses are superior to conventional 2D analyses of histological sections. Furthermore, the results point to a strong activation of T cells in THRLBCL, representing a cytotoxic response against the tumor cells with unclear effectiveness, resulting in enhanced swelling of the tumor cell bodies and limiting proliferative potential. Further molecular studies combining 3D tissue analyses and molecular data will help to gain profound insight into these ill-defined cellular processes.
Murine acetaminophen-induced acute liver injury (ALI) serves as paradigmatic model for drug-induced hepatic injury and regeneration. As major cause of ALI, acetaminophen overdosing is a persistent therapeutic challenge with N-acetylcysteine clinically used to ameliorate parenchymal necrosis. To identify further treatment strategies that serve patients with poor N-acetylcysteine responses, hepatic 3′mRNA sequencing was performed in the initial resolution phase at 24 h/48 h after sublethal overdosing. This approach disclosed 45 genes upregulated (≥5-fold) within this time frame. Focusing on C5aR1, we observed in C5aR1-deficient mice disease aggravation during resolution of intoxication as evidenced by increased liver necrosis and serum alanine aminotransferase. Moreover, decreased hepatocyte compensatory proliferation and increased caspase-3 activation at the surroundings of necrotic cores were detectable in C5aR1-deficient mice. Using a non-hypothesis-driven approach, herein pro-regenerative/-resolving effects of C5aR1 were identified during late acetaminophen-induced ALI. Data concur with protection by the C5a/C5aR1-axis during hepatectomy and emphasize the complex role of inflammation during hepatic regeneration and repair.
Knowledge discovery in biomedical data using supervised methods assumes that the data contain structure relevant to the class structure if a classifier can be trained to assign a case to the correct class better than by guessing. In this setting, acceptance or rejection of a scientific hypothesis may depend critically on the ability to classify cases better than randomly, without high classification performance being the primary goal. Random forests are often chosen for knowledge-discovery tasks because they are considered a powerful classifier that does not require sophisticated data transformation or hyperparameter tuning and can be regarded as a reference classifier for tabular numerical data. Here, we report a case where the failure of random forests using the default hyperparameter settings in the standard implementations of R and Python would have led to the rejection of the hypothesis that the data contained structure relevant to the class structure. After tuning the hyperparameters, classification performance increased from 56% to 65% balanced accuracy in R, and from 55% to 67% balanced accuracy in Python. More importantly, the 95% confidence intervals in the tuned versions were to the right of the value of 50% that characterizes guessing-level classification. Thus, tuning provided the desired evidence that the data structure supported the class structure of the data set. In this case, the tuning made more than a quantitative difference in the form of slightly better classification accuracy, but significantly changed the interpretation of the data set. This is especially true when classification performance is low and a small improvement increases the balanced accuracy to over 50% when guessing.
Vaccination represents one of the fundamentals in the fight against SARS-CoV-2. Myocarditis has been reported as a rare but possible adverse consequence of different vaccines, and its clinical presentation can range from mild symptoms to acute heart failure. We report a case of a 29-year-old man who presented with fever and retrosternal pain after receiving SARS-CoV-2 vaccine. Cardiac magnetic resonance imaging and laboratory data revealed typical findings of acute myocarditis.
Ferroptosis is an iron-dependent form of cell death, which is triggered by disturbed membrane integrity due to an overproduction of lipid peroxides. Induction of ferroptosis comprises several alterations, i.e. altered iron metabolism, response to oxidative stress, or lipid peroxide production. At the physiological level transcription, translation, and microRNAs add to the appearance and/or activity of building blocks that negatively or positively balance ferroptosis. Ferroptosis contributes to tissue damage in the case of, e.g., brain and heart injury but may be desirable to overcome chemotherapy resistance. For a more complete picture, it is crucial to also consider the cellular microenvironment, which during inflammation and in the tumor context is dominated by hypoxia. This graphical review visualizes basic mechanisms of ferroptosis, categorizes general inducers and inhibitors of ferroptosis, and puts a focus on microRNAs, iron homeostasis, and hypoxia as regulatory components.
Justification: In Mexico, the number of unidentified bodies has been steadily rising for years. By now, more than 50,000 bodies are considered unidentified. Forensic laboratories that could perform comparative molecular genetic investigation are often overburdened and examinations can take months. Therefore, pragmatic approaches that can help to identify more unknown bodies must be sought. The increased use of distinctive physical features might be one, and the high rate of tattooed people in Mexico points towards a great potential of tattoos as a tool for identification. The prerequisite for a comparison of antemortem (missing persons) and postmortem (unknown bodies) data is an objective description of the particularities, e.g., of the tattoos. The aim of this study was to establish an objective classification for tattoo motives, taking into consideration local preferences.
Methods: In the database of the medicolegal services of the Instituto Jaliscience de Ciencias Forenses (IJCF) in Guadalajara, postmortem data of 1000 tattooed bodies from 2019 were evaluated. According to sex and age, the tattooed body localization and the tattoo motives were categorized.
Results: The 1000 tattooed deceased showed tattoos on 2342 body localizations. The motives were grouped and linked to the following 11 keywords (with decreasing frequency): letters/numbers, human, symbol (other), plant, symbol (religious), animal, object, fantasy/demon/comic, tribal/ornament/geometry, other, unrecognizable.
Conclusion: Using the proposed classification, tattoo motives can be described objectively and classified in a practical way. If used for antemortem (missing persons) and postmortem (unknown bodies) documentation, motives can be searched and compared efficiently—helping to identify unknown bodies.
Background & Aims: Elimination of chronic HBV/HDV infection remains a major global health challenge. Targeting excessive hepatitis B surface antigen (HBsAg) release may provide an interesting window of opportunity to break immune tolerance and to achieve a functional cure using additional antivirals.
Methods: We evaluated a HBsAg-specific human monoclonal antibody, as part of either a prophylactic or therapeutic strategy, against HBV/HDV infection in cell culture models and in human-liver chimeric mice. To assess prophylactic efficacy, mice were passively immunized prior to infection with HBV or HBV/HDV (coinfection and superinfection setting). Therapeutic efficacy was assessed in HBV and HBV/HDV-coinfected mice receiving 4 weeks of treatment. Viral parameters (HBV DNA, HDV RNA and HBsAg) were assessed in mouse plasma.
Results: The antibody could effectively prevent HBV/HDV infection in a dose-dependent manner with IC50 values of ∼3.5 ng/ml. Passive immunization showed complete protection of mice from both HBV and HBV/HDV coinfection. Moreover, HDV superinfection was either completely prevented or at least attenuated in HBV-infected mice. Finally, antibody treatment in mice with established HBV/HDV infection resulted in a significant decline in viremia and a concomitant drop in on-treatment HBsAg, with a moderate viral rebound following treatment cessation.
Conclusion: We present data on a valuable antibody candidate that could complement other antivirals in strategies aimed at achieving functional cure of chronic HBV and HDV infection.
Impact and implications: Patients chronically infected with HBV may eventually develop liver cancer and are at great risk of being superinfected with HDV, which worsens and accelerates disease progression. Unfortunately, current treatments can rarely eliminate both viruses from chronically infected patients. In this study, we present data on a novel antibody that is able to prevent chronic HBV/HDV infection in a mouse model with a humanized liver. Moreover, antibody treatment of HBV/HDV-infected mice strongly diminishes viral loads during therapy. This antibody is a valuable candidate for further clinical development.
Bacteria that are capable of organizing themselves as biofilms are an important public health issue. Knowledge discovery focusing on the ability to swarm and conquer the surroundings to form persistent colonies is therefore very important for microbiological research communities that focus on a clinical perspective. Here, we demonstrate how a machine learning workflow can be used to create useful models that are capable of discriminating distinct associated growth behaviors along distinct phenotypes. Based on basic gray-scale images, we provide a processing pipeline for binary image generation, making the workflow accessible for imaging data from a wide range of devices and conditions. The workflow includes a locally estimated regression model that easily applies to growth-related data and a shape analysis using identified principal components. Finally, we apply a density-based clustering application with noise (DBSCAN) to extract and analyze characteristic, general features explained by colony shapes and areas to discriminate distinct Bacillus subtilis phenotypes. Our results suggest that the differences regarding their ability to swarm and subsequently conquer the medium that surrounds them result in characteristic features. The differences along the time scales of the distinct latency for the colony formation give insights into the ability to invade the surroundings and therefore could serve as a useful monitoring tool.
The development of epilepsy (epileptogenesis) involves a complex interplay of neuronal and immune processes. Here, we present a first-of-its-kind mathematical model to better understand the relationships among these processes. Our model describes the interaction between neuroinflammation, blood-brain barrier disruption, neuronal loss, circuit remodeling, and seizures. Formulated as a system of nonlinear differential equations, the model reproduces the available data from three animal models. The model successfully describes characteristic features of epileptogenesis such as its paradoxically long timescales (up to decades) despite short and transient injuries or the existence of qualitatively different outcomes for varying injury intensity. In line with the concept of degeneracy, our simulations reveal multiple routes toward epilepsy with neuronal loss as a sufficient but non-necessary component. Finally, we show that our model allows for in silico predictions of therapeutic strategies, revealing injury-specific therapeutic targets and optimal time windows for intervention.