TY - JOUR A1 - Koderer, Corinna A1 - Schmitz, Werner A1 - Wünsch, Anna Chiara A1 - Balint, Julia A1 - El-Mesery, Mohamed A1 - Volland, Julian Manuel A1 - Hartmann, Stefan A1 - Linz, Christian A1 - Kübler, Alexander Christian A1 - Seher, Axel T1 - Low energy status under methionine restriction is essentially independent of proliferation or cell contact inhibition JF - Cells N2 - Nonlimited proliferation is one of the most striking features of neoplastic cells. The basis of cell division is the sufficient presence of mass (amino acids) and energy (ATP and NADH). A sophisticated intracellular network permanently measures the mass and energy levels. Thus, in vivo restrictions in the form of amino acid, protein, or caloric restrictions strongly affect absolute lifespan and age-associated diseases such as cancer. The induction of permanent low energy metabolism (LEM) is essential in this process. The murine cell line L929 responds to methionine restriction (MetR) for a short time period with LEM at the metabolic level defined by a characteristic fingerprint consisting of the molecules acetoacetate, creatine, spermidine, GSSG, UDP-glucose, pantothenate, and ATP. Here, we used mass spectrometry (LC/MS) to investigate the influence of proliferation and contact inhibition on the energy status of cells. Interestingly, the energy status was essentially independent of proliferation or contact inhibition. LC/MS analyses showed that in full medium, the cells maintain active and energetic metabolism for optional proliferation. In contrast, MetR induced LEM independently of proliferation or contact inhibition. These results are important for cell behaviour under MetR and for the optional application of restrictions in cancer therapy. KW - methionine restriction KW - caloric restriction KW - mass spectrometry KW - LC/MS KW - liquid chromatography/mass spectrometry KW - metabolomics KW - L929 KW - amino acid KW - proliferation KW - contact inhibition Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-262329 SN - 2073-4409 VL - 11 IS - 3 ER - TY - JOUR A1 - Vollmer, Andreas A1 - Vollmer, Michael A1 - Lang, Gernot A1 - Straub, Anton A1 - Kübler, Alexander A1 - Gubik, Sebastian A1 - Brands, Roman C. A1 - Hartmann, Stefan A1 - Saravi, Babak T1 - Performance analysis of supervised machine learning algorithms for automatized radiographical classification of maxillary third molar impaction JF - Applied Sciences N2 - Background: Oro-antral communication (OAC) is a common complication following the extraction of upper molar teeth. The Archer and the Root Sinus (RS) systems can be used to classify impacted teeth in panoramic radiographs. The Archer classes B-D and the Root Sinus classes III, IV have been associated with an increased risk of OAC following tooth extraction in the upper molar region. In our previous study, we found that panoramic radiographs are not reliable for predicting OAC. This study aimed to (1) determine the feasibility of automating the classification (Archer/RS classes) of impacted teeth from panoramic radiographs, (2) determine the distribution of OAC stratified by classification system classes for the purposes of decision tree construction, and (3) determine the feasibility of automating the prediction of OAC utilizing the mentioned classification systems. Methods: We utilized multiple supervised pre-trained machine learning models (VGG16, ResNet50, Inceptionv3, EfficientNet, MobileNetV2), one custom-made convolutional neural network (CNN) model, and a Bag of Visual Words (BoVW) technique to evaluate the performance to predict the clinical classification systems RS and Archer from panoramic radiographs (Aim 1). We then used Chi-square Automatic Interaction Detectors (CHAID) to determine the distribution of OAC stratified by the Archer/RS classes to introduce a decision tree for simple use in clinics (Aim 2). Lastly, we tested the ability of a multilayer perceptron artificial neural network (MLP) and a radial basis function neural network (RBNN) to predict OAC based on the high-risk classes RS III, IV, and Archer B-D (Aim 3). Results: We achieved accuracies of up to 0.771 for EfficientNet and MobileNetV2 when examining the Archer classification. For the AUC, we obtained values of up to 0.902 for our custom-made CNN. In comparison, the detection of the RS classification achieved accuracies of up to 0.792 for the BoVW and an AUC of up to 0.716 for our custom-made CNN. Overall, the Archer classification was detected more reliably than the RS classification when considering all algorithms. CHAID predicted 77.4% correctness for the Archer classification and 81.4% for the RS classification. MLP (AUC: 0.590) and RBNN (AUC: 0.590) for the Archer classification as well as MLP 0.638) and RBNN (0.630) for the RS classification did not show sufficient predictive capability for OAC. Conclusions: The results reveal that impacted teeth can be classified using panoramic radiographs (best AUC: 0.902), and the classification systems can be stratified according to their relationship to OAC (81.4% correct for RS classification). However, the Archer and RS classes did not achieve satisfactory AUCs for predicting OAC (best AUC: 0.638). Additional research is needed to validate the results externally and to develop a reliable risk stratification tool based on the present findings. KW - oro-antral communication KW - oro-antral fistula KW - prediction KW - machine learning KW - teeth extraction KW - complications KW - classification KW - artificial intelligence Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281662 SN - 2076-3417 VL - 12 IS - 13 ER -