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Macrophage and tumor cell cross-talk is fundamental for lung tumor progression: we need to talk
(2020)
Regardless of the promising results of certain immune checkpoint blockers, current immunotherapeutics have met a bottleneck concerning response rate, toxicity, and resistance in lung cancer patients. Accumulating evidence forecasts that the crosstalk between tumor and immune cells takes center stage in cancer development by modulating tumor malignancy, immune cell infiltration, and immune evasion in the tumor microenvironment (TME). Cytokines and chemokines secreted by this crosstalk play a major role in cancer development, progression, and therapeutic management. An increased infiltration of Tumor-associated macrophages (TAMs) was observed in most of the human cancers, including lung cancer. In this review, we emphasize the role of cytokines and chemokines in TAM-tumor cell crosstalk in the lung TME. Given the role of cytokines and chemokines in immunomodulation, we propose that TAM-derived cytokines and chemokines govern the cancer-promoting immune responses in the TME and offer a new immunotherapeutic option for lung cancer treatment.
Tumor-associated macrophages (TAMs) influence lung tumor development by inducing immunosuppression. Transcriptome analysis of TAMs isolated from human lung tumor tissues revealed an up-regulation of the Wnt/β-catenin pathway. These findings were reproduced in a newly developed in vitro “trained” TAM model. Pharmacological and macrophage-specific genetic ablation of β-catenin reprogrammed M2-like TAMs to M1-like TAMs both in vitro and in various in vivo models, which was linked with the suppression of primary and metastatic lung tumor growth. An in-depth analysis of the underlying signaling events revealed that β-catenin–mediated transcriptional activation of FOS-like antigen 2 (FOSL2) and repression of the AT-rich interaction domain 5A (ARID5A) drive gene regulatory switch from M1-like TAMs to M2-like TAMs. Moreover, we found that high expressions of β-catenin and FOSL2 correlated with poor prognosis in patients with lung cancer. In conclusion, β-catenin drives a transcriptional switch in the lung tumor microenvironment, thereby promoting tumor progression and metastasis.
The production of light neutral mesons in AA collisions probes the physics of the Quark-Gluon Plasma (QGP), which is formed in heavy-ion collisions at the LHC. More specifically, the centrality dependent neutral meson spectra in AA collisions compared to its spectra in minimum-bias pp collisions, scaled with the number of hard collisions, provides information on the energy loss of partons traversing the QGP. The measurement allows to test with high precision the predictions of theoretical model calculations. In addition, the decay of the π0 and η mesons are the dominant back- grounds for all direct photon measurements. Therefore, pushing the limits of the precision of neutral meson production is key to learning about the temperature and space-time evolution of the QGP.
In the ALICE experiment neutral mesons can be detected via their decay into two photons. The latter can be reconstructed using the two calorimeters EMCal and PHOS or via conversions in the detector material. The excellent momentum resolution of the conversion photons down to very low pT and the high reconstruction efficiency and triggering capability of calorimeters at high pT, allow us to measure the pT dependent invariant yield of light neutral mesons over a wide kinematic range.
Combining state-of-the-art reconstruction techniques with the high statistics delivered by the LHC in Run 2 gives us the opportunity to enhance the precision of our measurements. In these proceedings, new ALICE run 2 preliminary results for neutral meson production in pp and Pb–Pb collisions at LHC energies are presented.
Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing fragmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data, in particular for university medicine.
Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.
Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, medical history, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.
Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
Background: The current COVID-19 pandemic has led to a surge of research activity. While this research provides important insights, the multitude of studies results in an increasing segmentation of information. To ensure comparability across projects and institutions, standard datasets are needed. Here, we introduce the “German Corona Consensus Dataset” (GECCO), a uniform dataset that uses international terminologies and health IT standards to improve interoperability of COVID-19 data.
Methods: Based on previous work (e.g., the ISARIC-WHO COVID-19 case report form) and in coordination with experts from university hospitals, professional associations and research initiatives, data elements relevant for COVID-19 research were collected, prioritized and consolidated into a compact core dataset. The dataset was mapped to international terminologies, and the Fast Healthcare Interoperability Resources (FHIR) standard was used to define interoperable, machine-readable data formats.
Results: A core dataset consisting of 81 data elements with 281 response options was defined, including information about, for example, demography, anamnesis, symptoms, therapy, medications or laboratory values of COVID-19 patients. Data elements and response options were mapped to SNOMED CT, LOINC, UCUM, ICD-10-GM and ATC, and FHIR profiles for interoperable data exchange were defined.
Conclusion: GECCO provides a compact, interoperable dataset that can help to make COVID-19 research data more comparable across studies and institutions. The dataset will be further refined in the future by adding domain-specific extension modules for more specialized use cases.
How is semantic information stored in the human mind and brain? Some philosophers and cognitive scientists argue for vectorial representations of concepts, where the meaning of a word is represented as its position in a high-dimensional neural state space. At the intersection of natural language processing and artificial intelligence, a class of very successful distributional word vector models has developed that can account for classic EEG findings of language, that is, the ease versus difficulty of integrating a word with its sentence context. However, models of semantics have to account not only for context-based word processing, but should also describe how word meaning is represented. Here, we investigate whether distributional vector representations of word meaning can model brain activity induced by words presented without context. Using EEG activity (event-related brain potentials) collected while participants in two experiments (English and German) read isolated words, we encoded and decoded word vectors taken from the family of prediction-based Word2vec algorithms. We found that, first, the position of a word in vector space allows the prediction of the pattern of corresponding neural activity over time, in particular during a time window of 300 to 500 ms after word onset. Second, distributional models perform better than a human-created taxonomic baseline model (WordNet), and this holds for several distinct vector-based models. Third, multiple latent semantic dimensions of word meaning can be decoded from brain activity. Combined, these results suggest that empiricist, prediction-based vectorial representations of meaning are a viable candidate for the representational architecture of human semantic knowledge.
Objective: To evaluate if 3 peptides derived from the cartilage oligomeric matrix protein (COMP), which wounded zones of cartilage secrete into synovial fluid, possess biological activity and might therefore be involved in the regulation of specific aspects of joint regeneration.
Methods: The 3 peptides were produced by chemical synthesis and then tested in vitro for known functions of the COMP C-terminal domain from which they derive, and which are involved in osteoarthritis: transforming growth factor-β (TGF-β) signaling, vascular homeostasis, and inflammation. Results. None of the peptides affected the gene expression of COMP in osteochondral progenitor cells (P > 0.05). We observed no effects on the vascularization potential of endothelial cells (P > 0.05). In cultured synovium explants, no differences on the expression of catabolic enzymes or proinflammatory cytokines were found when peptides were added (P > 0.05).
Discussion and conclusions: The 3 peptides tested do not regulate TGF-β signaling, angiogenesis and vascular tube formation, or synovial inflammation in vitro and therefore most likely do not play a major role in the disease process.
Background: Cognitive dysfunctions represent a core feature of schizophrenia and a predictor for clinical outcomes. One possible mechanism for cognitive impairments could involve an impairment in the experience-dependent modifications of cortical networks.
Methods: To address this issue, we employed magnetoencephalography (MEG) during a visual priming paradigm in a sample of chronic patients with schizophrenia (n = 14), and in a group of healthy controls (n = 14). We obtained MEG-recordings during the presentation of visual stimuli that were presented three times either consecutively or with intervening stimuli. MEG-data were analyzed for event-related fields as well as spectral power in the 1–200 Hz range to examine repetition suppression and repetition enhancement. We defined regions of interest in occipital and thalamic regions and obtained virtual-channel data.
Results: Behavioral priming did not differ between groups. However, patients with schizophrenia showed prominently reduced oscillatory response to novel stimuli in the gamma-frequency band as well as significantly reduced repetition suppression of gamma-band activity and reduced repetition enhancement of beta-band power in occipital cortex to both consecutive repetitions as well as repetitions with intervening stimuli. Moreover, schizophrenia patients were characterized by a significant deficit in suppression of the C1m component in occipital cortex and thalamus as well as of the late positive component (LPC) in occipital cortex.
Conclusions: These data provide novel evidence for impaired repetition suppression in cortical and subcortical circuits in schizophrenia. Although behavioral priming was preserved, patients with schizophrenia showed deficits in repetition suppression as well as repetition enhancement in thalamic and occipital regions, suggesting that experience-dependent modification of neural circuits is impaired in the disorder.
Das Leid der Frauen
(2020)
Die Erziehungswissenschaften sind eine Schlüsseldisziplin für die Zukunft unserer Gesellschaft; der Umgang der Menschen mit der Digitalisierung ist hier ein wichtiges Thema für Forschung und Lehre. An der Goethe-Universität kümmert sich eigens eine Arbeitsgruppe Medien darum, die unterschiedlichen Aktivitäten im Fachbereich und auch außerhalb zu begleiten und zu vernetzen.
Große Tasten, ein übersichtliches Display – die Hersteller von Mobiltelefonen haben sich geirrt, als sie spezielle Geräte für ältere Menschen entwickelten: Die Angebote sind gründlich gefloppt. Doch aus gerontologischer und erziehungswissenschaftlicher Sicht lohnt es sich durchaus, die besonderen Bedürfnisse von Seniorinnen und Senioren näher zu untersuchen. Nicht erst die Corona-Krise hat gezeigt, dass das Smartphone ein Schlüssel zu mehr Teilhabe und Lebensqualität sein kann. Dazu arbeitet Friedrich Wolf am Fachbereich Erziehungswissenschaften
Aus der Redaktion
(2020)