Refine
Has Fulltext
- yes (16)
Is part of the Bibliography
- yes (16)
Year of publication
Document Type
- Journal article (16)
Language
- English (16) (remove)
Keywords
- model (16) (remove)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (3)
- Julius-von-Sachs-Institut für Biowissenschaften (2)
- Lehrstuhl für Orthopädie (2)
- Physikalisches Institut (2)
- Institut für Anatomie und Zellbiologie (1)
- Institut für Geographie und Geologie (1)
- Institut für Humangenetik (1)
- Institut für Molekulare Infektionsbiologie (1)
- Institut für Psychologie (1)
- Institut für Theoretische Physik und Astrophysik (1)
- Klinik und Poliklinik für Mund-, Kiefer- und Plastische Gesichtschirurgie (1)
- Lehrstuhl für Biochemie (1)
- Medizinische Klinik und Poliklinik I (1)
- Rudolf-Virchow-Zentrum (1)
Associations between periodontitis and COPD: An artificial intelligence-based analysis of NHANES III
(2022)
A number of cross-sectional epidemiological studies suggest that poor oral health is associated with respiratory diseases. However, the number of cases within the studies was limited, and the studies had different measurement conditions. By analyzing data from the National Health and Nutrition Examination Survey III (NHANES III), this study aimed to investigate possible associations between chronic obstructive pulmonary disease (COPD) and periodontitis in the general population. COPD was diagnosed in cases where FEV (1)/FVC ratio was below 70% (non-COPD versus COPD; binary classification task). We used unsupervised learning utilizing k-means clustering to identify clusters in the data. COPD classes were predicted with logistic regression, a random forest classifier, a stochastic gradient descent (SGD) classifier, k-nearest neighbors, a decision tree classifier, Gaussian naive Bayes (GaussianNB), support vector machines (SVM), a custom-made convolutional neural network (CNN), a multilayer perceptron artificial neural network (MLP), and a radial basis function neural network (RBNN) in Python. We calculated the accuracy of the prediction and the area under the curve (AUC). The most important predictors were determined using feature importance analysis. Results: Overall, 15,868 participants and 19 feature variables were included. Based on k-means clustering, the data were separated into two clusters that identified two risk characteristic groups of patients. The algorithms reached AUCs between 0.608 (DTC) and 0.953% (CNN) for the classification of COPD classes. Feature importance analysis of deep learning algorithms indicated that age and mean attachment loss were the most important features in predicting COPD. Conclusions: Data analysis of a large population showed that machine learning and deep learning algorithms could predict COPD cases based on demographics and oral health feature variables. This study indicates that periodontitis might be an important predictor of COPD. Further prospective studies examining the association between periodontitis and COPD are warranted to validate the present results.
In the course of a screen designed to produce antibodies (ABs) with affinity to proteins in the honey bee brain we found an interesting AB that detects a highly specific epitope predominantly in the nuclei of Kenyon cells (KCs). The observed staining pattern is unique, and its unfamiliarity indicates a novel previously unseen nuclear structure that does not colocalize with the cytoskeletal protein f-actin. A single rod-like assembly, 3.7-4.1 mu m long, is present in each nucleus of KCs in adult brains of worker bees and drones with the strongest immuno-labelling found in foraging bees. In brains of young queens, the labelling is more sporadic, and the rod-like structure appears to be shorter (similar to 2.1 mu m). No immunostaining is detectable in worker larvae. In pupal stage 5 during a peak of brain development only some occasional staining was identified. Although the cellular function of this unexpected structure has not been determined, the unusual distinctiveness of the revealed pattern suggests an unknown and potentially important protein assembly. One possibility is that this nuclear assembly is part of the KCs plasticity underlying the brain maturation in adult honey bees. Because no labelling with this AB is detectable in brains of the fly Drosophila melanogaster and the ant Camponotus floridanus, we tentatively named this antibody AmBNSab (Apis mellifera Brain Neurons Specific antibody). Here we report our results to make them accessible to a broader community and invite further research to unravel the biological role of this curious nuclear structure in the honey bee central brain.
Hypophosphatasia (HPP) is a rare genetic disease with diverse symptoms and a heterogeneous severity of onset with underlying mutations in the ALPL gene encoding the ectoenzyme Tissue-nonspecific alkaline phosphatase (TNAP). Considering the establishment of zebrafish (Danio rerio) as a new model organism for HPP, the aim of the study was the spatial and temporal analysis of alpl expression in embryos and adult brains. Additionally, we determined functional consequences of Tnap inhibition on neural and skeletal development in zebrafish. We show that expression of alpl is present during embryonic stages and in adult neuronal tissues. Analyses of enzyme function reveal zones of pronounced Tnap-activity within the telencephalon and the mesencephalon. Treatment of zebrafish embryos with chemical Tnap inhibitors followed by axonal and cartilage/mineralized tissue staining imply functional consequences of Tnap deficiency on neuronal and skeletal development. Based on the results from neuronal and skeletal tissue analyses, which demonstrate an evolutionary conserved role of this enzyme, we consider zebrafish as a promising species for modeling HPP in order to discover new potential therapy strategies in the long-term.
A search for the associated production of the Higgs boson with a top quark pair (tt (b) over barH) is reported. The search is performed in multilepton final states using a data set corresponding to an integrated luminosity of 36.1 fb(-1) of proton-proton collision data recorded by the ATLAS experiment at a center-of-mass energy root s = 13 TeV at the Large Hadron Collider. Higgs boson decays to WW*, tau tau, and ZZ* are targeted. Seven final states, categorized by the number and flavor of charged-lepton candidates, are examined for the presence of the Standard Model Higgs boson with a mass of 125 GeVand a pair of top quarks. An excess of events over the expected background from Standard Model processes is found with an observed significance of 4.1 standard deviations, compared to an expectation of 2.8 standard deviations. The best fit for the (tt (b) over barH) production cross section is sot (tt (b) over barH) = 790(-210)(+230) fb, in agreement with the Standard Model prediction of 507(-50)(+35) fb. The combination of this result with other tt (b) over barH searches from the ATLAS experiment using the Higgs boson decay modes to b (b) over bar, gamma gamma and ZZ* -> 4l, has an observed significance of 4.2 standard deviations, compared to an expectation of 3.8 standard deviations. This provides evidence for the tt (b) over barH production mode.
Electrophilic oxylipins trigger a heat-shock-like response in the absence of heat through the canonical heat-shock transcription factor A1, thereby helping to cope with stresses associated with protein damage.Abiotic and biotic stresses are often characterized by an induction of reactive electrophile species (RES) such as the jasmonate 12-oxo-phytodienoic acid (OPDA) or the structurally related phytoprostanes. Previously, RES oxylipins have been shown massively to induce heat-shock-response (HSR) genes including HSP101 chaperones. Moreover, jasmonates have been reported to play a role in basal thermotolerance. We show that representative HSR marker genes are strongly induced by RES oxylipins through the four master regulator transcription factors HSFA1a, b, d, and e essential for short-term adaptation to heat stress in Arabidopsis. When compared with Arabidopsis seedlings treated at the optimal acclimation temperature of 37 A degrees C, the exogenous application of RES oxylipins at 20 A degrees C induced a much weaker induction of HSP101 at both the gene and protein expression levels which, however, was not sufficient to confer short-term acquired thermotolerance. Moreover, jasmonate-deficient mutant lines displayed a wild-type-like HSR and were not compromised in acquiring thermotolerance. Hence, the OPDA- and RES oxylipin-induced HSR is not sufficient to protect seedlings from severe heat stress but may help plants to cope better with stresses associated with protein unfolding by inducing a battery of chaperones in the absence of heat.
Controversy surrounds neutrophil function in cancer because neutrophils were shown to provide both pro-and antitumor functions. We identified a heterogeneous subset of low-density neutrophils (LDNs) that appear transiently in self-resolving inflammation but accumulate continuously with cancer progression. LDNs display impaired neutrophil function and immunosuppressive properties, characteristics that are in stark contrast to those of mature, high-density neutrophils (HDNs). LDNs consist of both immature myeloid-derived suppressor cells (MDSCs) and mature cells that are derived from HDNs in a TGF-beta-dependent mechanism. Our findings identify three distinct populations of circulating neutrophils and challenge the concept that mature neutrophils have limited plasticity. Furthermore, our findings provide a mechanistic explanation to mitigate the controversy surrounding neutrophil function in cancer.
We show that the topological Kitaev spin liquid on the honeycomb lattice is extremely fragile against the second-neighbor Kitaev coupling K\(_2\), which has recently been shown to be the dominant perturbation away from the nearest-neighbor model in iridate Na\(_2\)IrO\(_3\), and may also play a role in \(\alpha\)-RuCl\(_3\) and Li\(_2\)IrO\(_3\). This coupling naturally explains the zigzag ordering (without introducing unrealistically large longer-range Heisenberg exchange terms) and the special entanglement between real and spin space observed recently in Na\(_2\)IrO\(_3\). Moreover, the minimal K\(_1\) - K\(_2\) model that we present here holds the unique property that the classical and quantum phase diagrams and their respective order-by-disorder mechanisms are qualitatively different due to the fundamentally different symmetries of the classical and quantum counterparts.
The role of serum amyloid A (SAA) proteins, which are ligands for toll-like receptors, was analyzed in human bone marrow-derived mesenchymal stem cells (hMSCs) and their osteogenic offspring with a focus on senescence, differentiation andmineralization. In vitro aged hMSC developed a senescence-associated secretory phenotype (SASP), resulting in enhanced SAA1/2, TLR2/4 and proinflammatory cytokine (IL6, IL8, IL1\(\beta\), CXCL1, CXCL2) expression before entering replicative senescence. Recombinant human SAA1 (rhSAA1) induced SASP-related genes and proteins in MSC, which could be abolished by cotreatment with the TLR4-inhibitor CLI-095. The same pattern of SASP-resembling genes was stimulated upon induction of osteogenic differentiation, which is accompanied by autocrine SAA1/2 expression. In this context additional rhSAA1 enhanced the SASP-like phenotype, accelerated the proinflammatory phase of osteogenic differentiation and enhanced mineralization. Autocrine/paracrine and rhSAA1 via TLR4 stimulate a proinflammatory phenotype that is both part of the early phase of osteogenic differentiation and the development of senescence. This signaling cascade is tightly involved in bone formation and mineralization, but may also propagate pathological extraosseous calcification conditions such as calcifying inflammation and atherosclerosis.
In classical conditioning, an initially neutral stimulus (conditioned stimulus, CS) becomes associated with a biologically salient event (unconditioned stimulus, US), which might be pain (aversive conditioning) or food (appetitive conditioning). After a few associations, the CS is able to initiate either defensive or consummatory responses, respectively. Contrary to aversive conditioning, appetitive conditioning is rarely investigated in humans, although its importance for normal and pathological behaviors (e.g., obesity, addiction) is undeniable. The present study intents to translate animal findings on appetitive conditioning to humans using food as an US. Thirty-three participants were investigated between 8 and 10 am without breakfast in order to assure that they felt hungry. During two acquisition phases, one geometrical shape (avCS+) predicted an aversive US (painful electric shock), another shape (appCS+) predicted an appetitive US (chocolate or salty pretzel according to the participants' preference), and a third shape (CS) predicted neither US. In a extinction phase, these three shapes plus a novel shape (NEW) were presented again without US delivery. Valence and arousal ratings as well as startle and skin conductance (SCR) responses were collected as learning indices. We found successful aversive and appetitive conditioning. On the one hand, the avCS+ was rated as more negative and more arousing than the CS and induced startle potentiation and enhanced SCR. On the other hand, the appCS+ was rated more positive than the CS and induced startle attenuation and larger SCR. In summary, we successfully confirmed animal findings in (hungry) humans by demonstrating appetitive learning and normal aversive learning.
Background: RNA-seq and its variant differential RNA-seq (dRNA-seq) are today routine methods for transcriptome analysis in bacteria. While expression profiling and transcriptional start site prediction are standard tasks today, the problem of identifying transcriptional units in a genome-wide fashion is still not solved for prokaryotic systems.
Results: We present RNASEG, an algorithm for the prediction of transcriptional units based on dRNA-seq data. A key feature of the algorithm is that, based on the data, it distinguishes between transcribed and un-transcribed genomic segments. Furthermore, the program provides many different predictions in a single run, which can be used to infer the significance of transcriptional units in a consensus procedure. We show the performance of our method based on a well-studied dRNA-seq data set for Helicobacter pylori.
Conclusions: With our algorithm it is possible to identify operons and 5'- and 3'-UTRs in an automated fashion. This alleviates the need for labour intensive manual inspection and enables large-scale studies in the area of comparative transcriptomics.