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In der vorliegenden experimentellen Arbeit wurde in in-vitro-Modellen der Zusammenhang zwischen Expression der Tumorantigene MAGE-A (melanoma-associated antigenes) und der Wirksamkeit von Chemotherapeutika untersucht. Die MAGE-Antigene MAGE-A1 bis MAGE-A12 (ohne A7) kommen in diversen malignen Tumoren vor; neben Melanomen auch in Tumoren der Lunge, Brust, Prostata, Ovarien, Harnblase, des Gastrointestinal-Trakts und des Kopf-Hals-Bereichs. Bereits vielfach wurden Zusammenhänge zwischen MAGE-A-Tumorantigen-Expression und einer erhöhten Tumorinvasivität, Zellproliferation, Metastasierungsrate und kürzerem Überleben hergestellt. In dieser Arbeit gelang nun der erstmalige Nachweis, dass MAGE-A-Tumorantigene die chemotherapeutische Wirksamkeit beeinflussen. Zunächst gelang der Nachweis, dass die Expression von MAGE-A11 mit geringer Cisplatin-Wirksamkeit korreliert. Eine im Anschluss generierte MAGE-A11 überexprimierende Zelllinie zeigte ein durchschnittlich um 9 % schlechteres Ansprechen auf Cisplatin als die Kontrollzelllinie.
In recent years, various forms of caloric restriction (CR) and amino acid or protein restriction (AAR or PR) have shown not only success in preventing age-associated diseases, such as type II diabetes and cardiovascular diseases, but also potential for cancer therapy. These strategies not only reprogram metabolism to low-energy metabolism (LEM), which is disadvantageous for neoplastic cells, but also significantly inhibit proliferation. Head and neck squamous cell carcinoma (HNSCC) is one of the most common tumour types, with over 600,000 new cases diagnosed annually worldwide. With a 5-year survival rate of approximately 55%, the poor prognosis has not improved despite extensive research and new adjuvant therapies. Therefore, for the first time, we analysed the potential of methionine restriction (MetR) in selected HNSCC cell lines. We investigated the influence of MetR on cell proliferation and vitality, the compensation for MetR by homocysteine, the gene regulation of different amino acid transporters, and the influence of cisplatin on cell proliferation in different HNSCC cell lines.
Medication-related osteonecrosis of the jaw (MRONJ) represents an adverse side effect of antiresorptive and antiangiogenic medications. It is associated with impaired quality of life, oral health, and oral function and can be classified into various stages. The purpose of this prospective clinical study is to evaluate the impact of stages I and II MRONJ on oral-health-related quality of life (OHRQoL) and related parameters. Patients’ OHRQoL, satisfaction with life, oral discomfort, and oral health were assessed using the German version of the Oral Health Impact Profile (OHIP-G49), visual analog scales (VAS), and Satisfaction with Life Scale (SWLS) at baseline (T0), 10 days (T1), and 3 months after treatment (T2) in 36 patients. Data were analyzed using Kolmogorov–Smirnov test, two-way mixed ANOVAs, and follow-up Mann–Whitney U tests. The impact of treatment effects on the original seven OHIP domain structures and the recently introduced four-dimensional OHIP structure were evaluated using linear regression analysis. Thirty-six patients received surgical MRONJ treatment. Before treatment, patients’ perceived OHRQoL, oral discomfort, oral health, and satisfaction with life were negatively affected by MRONJ. Surgical treatment significantly improved OHRQoL and related parameters (all p ≤ 0.012). This improvement was greater in patients with higher impairment at T0. OHRQoL and oral restrictions were still impaired after treatment in patients who needed prosthetic treatment. The four-dimensional structure revealed valuable information beyond the standard seven OHIP domains. Increased awareness of MRONJ risks and an interdisciplinary treatment approach for MRONJ patients are needed.
Zusammenfassung In der vorliegenden retrospektiven Studie wurde untersucht, ob die präoperativ festgelegten Verlagerungsmaße mittels 3D-Rekonstruktionen aus DVT-Daten ermittelbar sind. Anschließend wurde anhand eines Patientenkollektivs die Umsetzung der Verlagerungsmaße evaluiert. Zur Auswertung wurden standardisierte Modelle und DVT-Scans von 35 Patienten herangezogen. Die Modelle sowie die DVT-Daten wurden im Zeitraum von November 2007 bis September 2009 erstellt. Alle Patienten wurden in der Klinik und Poliklinik für Mund-, Kiefer- und Plastische Gesichtschirurgie der Universität Würzburg aufgrund einer Dysgnathie behandelt. Für die Auswahl der Patienten spielte weder das Alter, das Geschlecht noch der Schweregrad der Dysgnathie eine Rolle. Die Auswertung erfolgte postoperativ durch zwei unabhängige Prüfer, wobei die Patienten zufällig verteilt wurden. Bevor die Umsetzung der Verlagerungsmaße evaluiert wurde, sind die Methodik und die Genauigkeit der Messungen überprüft worden. Die Vermessung der Modelle wurde manuell durchgeführt. Die Analyse der DVT-Daten erfolgte mit einer 3D-Software. Die Ergebnisse der Methodik sind statistisch deskriptiv ausgewertet und interpretiert worden. Für die Evaluation wurde eine kumulative Verteilung erstellt und bewertet. In dieser Studie konnte gezeigt werden, dass man anhand von prä- und postoperativ erstellten DVT-Daten die bei der präoperativen Modell-OP festgelegten Verlagerungsmaße mit den postoperativ erzeugten 3D-Rekonstruktionen vergleichend messen kann. Allerdings ist bei Diskrepanzen der Werte von weniger als 0,97mm von Messungenauigkeiten auszugehen. Desweiteren kann anhand dieser Nachuntersuchung festgehalten werden, dass die Ergebnisse bei 7 der 9 Parameter in 77%-95% der Fälle keine Diskrepanzen aufweisen, die über dem klinisch geforderten Maß liegen. Die einzigen Parameter, die aufgrund der Datenlage eine andere Interpretation nach sich ziehen, sind die Angaben, die hinsichtlich der sagittalen Verlagerung im Unterkiefer gemacht werden. Hierbei kommt es in etwa 40% der Fälle zu Differenzen zwischen den prä- und postoperativen Verlagerungsmaßen, die deutlich größer als 2mm sind. Dabei kann in ca. 60% der Fälle eine zu kleine und in ca. 40% eine zu große Verlagerung festgestellt werden. Eine Aussage über die Feststellung hinaus, dass diese Differenzen bestehen, ist mittels dieser Studie nicht zulässig. Dies liegt zum einen an dem kleinen Patientenkollektiv, das zusätzlich in sich inhomogen war und bei dem unterschiedliche Operationsverfahren zum Einsatz kamen. Die Gründe für diese Unterschiede bzw. deren klinische Relevanz sollte das Ziel einer künftigen Arbeit sein. Allerdings kann durch diese Arbeit gezeigt werden, dass die digitale Volumentomographie dazu verwendet werden kann, bei Dysgnathiepatienten das Operationsziel zu überprüfen und bei Komplikationen zu eruieren, ob der Fehler auf die skelettale Verlagerung zurückzuführen ist oder ob eine andere Ursache ausgemacht werden muss.
Menschliche Mundschleimheut wurde ex-vivo gegenüber Schwermetallen (Blei) oder polyzyklischen aromatischen Kohlenwasserstoffen (Benzopyren) für Zeiten von 5Min. bis 360Min exponiert. Immunhistochemisch wurdne im Anschluss Marker für Apoptose, oxitaven und nitrogenen Stress untersucht. Hierbei zeigten sich jeweils charakteristische Veränderungen für aktive Caspase-3, 3-Nitrotyrosine und 8-epi-PGF2alpha. Proben von Rauchern wurden mit Nichtraucherproben verglichen und zeigten verminderte Werte für oxidativen und nitrogenen Stress.
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.
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.
Periodontitis is one of the most prevalent diseases worldwide. The degree of radiographic bone loss can be used to assess the course of therapy or the severity of the disease. Since automated bone loss detection has many benefits, our goal was to develop a multi-object detection algorithm based on artificial intelligence that would be able to detect and quantify radiographic bone loss using standard two-dimensional radiographic images in the maxillary posterior region. This study was conducted by combining three recent online databases and validating the results using an external validation dataset from our organization. There were 1414 images for training and testing and 341 for external validation in the final dataset. We applied a Keypoint RCNN with a ResNet-50-FPN backbone network for both boundary box and keypoint detection. The intersection over union (IoU) and the object keypoint similarity (OKS) were used for model evaluation. The evaluation of the boundary box metrics showed a moderate overlapping with the ground truth, revealing an average precision of up to 0.758. The average precision and recall over all five folds were 0.694 and 0.611, respectively. Mean average precision and recall for the keypoint detection were 0.632 and 0.579, respectively. Despite only using a small and heterogeneous set of images for training, our results indicate that the algorithm is able to learn the objects of interest, although without sufficient accuracy due to the limited number of images and a large amount of information available in panoramic radiographs. Considering the widespread availability of panoramic radiographs as well as the increasing use of online databases, the presented model can be further improved in the future to facilitate its implementation in clinics.
Oroantral communication (OAC) is a common complication after tooth extraction of upper molars. Profound preoperative panoramic radiography analysis might potentially help predict OAC following tooth extraction. In this exploratory study, we evaluated n = 300 consecutive cases (100 OAC and 200 controls) and trained five machine learning algorithms (VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50) to predict OAC versus non-OAC (binary classification task) from the input images. Further, four oral and maxillofacial experts evaluated the respective panoramic radiography and determined performance metrics (accuracy, area under the curve (AUC), precision, recall, F1-score, and receiver operating characteristics curve) of all diagnostic approaches. Cohen's kappa was used to evaluate the agreement between expert evaluations. The deep learning algorithms reached high specificity (highest specificity 100% for InceptionV3) but low sensitivity (highest sensitivity 42.86% for MobileNetV2). The AUCs from VGG16, InceptionV3, MobileNetV2, EfficientNet, and ResNet50 were 0.53, 0.60, 0.67, 0.51, and 0.56, respectively. Expert 1–4 reached an AUC of 0.550, 0.629, 0.500, and 0.579, respectively. The specificity of the expert evaluations ranged from 51.74% to 95.02%, whereas sensitivity ranged from 14.14% to 59.60%. Cohen's kappa revealed a poor agreement for the oral and maxillofacial expert evaluations (Cohen's kappa: 0.1285). Overall, present data indicate that OAC cannot be sufficiently predicted from preoperative panoramic radiography. The false-negative rate, i.e., the rate of positive cases (OAC) missed by the deep learning algorithms, ranged from 57.14% to 95.24%. Surgeons should not solely rely on panoramic radiography when evaluating the probability of OAC occurrence. Clinical testing of OAC is warranted after each upper-molar tooth extraction.
Objective: This study aims to critically evaluate the effectiveness and accuracy of a time safing and cost-efficient open-source algorithm for in-house planning of mandibular reconstructions using the free osteocutaneous fibula graft. The evaluation focuses on quantifying anatomical accuracy and assessing the impact on ischemia time.
Methods: A pilot study was conducted, including patients who underwent in-house planned computer-aided design and manufacturing (CAD/CAM) of free fibula flaps between 2021 and 2023. Out of all patient cases, we included all with postoperative 3D imaging in the study. The study utilized open-source software tools for the planning step, and three-dimensional (3D) printing techniques. The Hausdorff distance and Dice coefficient metrics were used to evaluate the accuracy of the planning procedure.
Results: The study assessed eight patients (five males and three females, mean age 61.75 ± 3.69 years) with different diagnoses such as osteoradionecrosis and oral squamous cell carcinoma. The average ischemia time was 68.38 ± 27.95 min. For the evaluation of preoperative planning vs. the postoperative outcome, the mean Hausdorff Distance was 1.22 ± 0.40. The Dice Coefficients yielded a mean of 0.77 ± 0.07, suggesting a satisfactory concordance between the planned and postoperative states. Dice Coefficient and Hausdorff Distance revealed significant correlations with ischemia time (Spearman's rho = −0.810, p = 0.015 and Spearman's rho = 0.762, p = 0.028, respectively). Linear regression models adjusting for disease type further substantiated these findings.
Conclusions: The in-house planning algorithm not only achieved high anatomical accuracy, as reflected by the Dice Coefficients and Hausdorff Distance metrics, but this accuracy also exhibited a significant correlation with reduced ischemia time. This underlines the critical role of meticulous planning in surgical outcomes. Additionally, the algorithm's open-source nature renders it cost-efficient, easy to learn, and broadly applicable, offering promising avenues for enhancing both healthcare affordability and accessibility.