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Reconstruction of the donor site after radial forearm flap harvesting is a common procedure in maxillofacial plastic surgery. It is normally carried out with split-thickness or full-thickness free skin grafts. Unfortunately, free skin graft transplantation faces wound healing impairments such as necrosis, (partial) graft loss, or tendon exposure. Several studies have investigated methods to reduce these impairments and demonstrated improvements if the wound bed is optimised, for example, through negative-pressure wound therapy or vacuum-assisted closure. However, these methods are device-dependent, expansive, and time-consuming. Therefore, the application of platelet-rich fibrin (PRF) to the wound bed could be a simple, cost-effective, and device-independent method to optimise wound-bed conditions instead. In this study, PRF membranes were applied between the wound bed and skin graft. Results of this study indicate improvements in the PRF versus non-PRF group (93.44% versus 86.96% graft survival, p = 0.0292). PRF applied to the wound bed increases graft survival and reduces impairments. A possible explanation for this is the release of growth factors, which stimulate angiogenesis and fibroblast migration. Furthermore, the solid PRF membranes act as a mechanical barrier (“lubrication” layer) to protect the skin graft from tendon motion. The results of this study support the application of PRF in donor-site reconstruction with free skin grafts.
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.
Background
Chronic kidney disease (CKD) is a common comorbid condition in coronary heart disease (CHD). CKD predisposes the patient to acute kidney injury (AKI) during hospitalization. Data on awareness of kidney dysfunction among CHD patients and their treating physicians are lacking. In the current cross-sectional analysis of the German EUROASPIRE IV sample we aimed to investigate the physician’s awareness of kidney disease of patients hospitalized for CHD and also the patient’s awareness of CKD in a study visit following hospital discharge.
Methods
All serum creatinine (SCr) values measured during the hospital stay were used to describe impaired kidney function (eGFR\(_{CKD-EPI}\) < 60 ml/min/1.73m2) at admission, discharge and episodes of AKI (KDIGO definition). Information extracted from hospital discharge letters and correct ICD coding for kidney disease was studied as a surrogate of physician’s awareness of kidney disease. All patients were interrogated 0.5 to 3 years after hospital discharge, whether they had ever been told about kidney disease by a physician.
Results
Of the 536 patients, 32% had evidence for acute or chronic kidney disease during the index hospital stay. Either condition was mentioned in the discharge letter in 22%, and 72% were correctly coded according to ICD-10. At the study visit in the outpatient setting 35% had impaired kidney function. Of 158 patients with kidney disease, 54 (34%) were aware of CKD. Determinants of patient’s awareness were severity of CKD (OR\(_{eGFR}\) 0.94; 95%CI 0.92–0.96), obesity (OR 1.97; 1.07–3.64), history of heart failure (OR 1.99; 1.00–3.97), and mentioning of kidney disease in the index event’s hospital discharge letter (OR 5.51; 2.35–12.9).
Conclusions
Although CKD is frequent in CHD, only one third of patients is aware of this condition. Patient’s awareness was associated with kidney disease being mentioned in the hospital discharge letter. Future studies should examine how raising physician’s awareness for kidney dysfunction may improve patient’s awareness of CKD.
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.
Inflammation is a central aspect of tumour biology and can contribute significantly to both the origination and progression of tumours. The NFκB pathway is one of the most important signal transduction pathways in inflammation and is, therefore, an excellent target for cancer therapy. In this work, we examined the influence of four NFκB inhibitors — Cortisol, MLN4924, QNZ and TPCA1 — on proliferation, inflammation and sensitisation to apoptosis mediated by the death ligand FasL in the HNSCC cell lines PCI1, PCI9, PCI13, PCI52 and SCC25 and in the human dermal keratinocyte cell line HaCaT. We found that the selection of the inhibitor is critical to ensure that cells do not respond by inducing counteracting activities in the context of cancer therapy, e.g., the extreme IL-8 induction mediated by MLN4924 or FasL resistance mediated by Cortisol. However, TPCA1 was qualified by this in vitro study as an excellent therapeutic mediator in HNSCC by four positive qualities: (1) proliferation was inhibited at low μM-range concentrations; (2) TNFα-induced IL-8 secretion was blocked; (3) HNSCC cells were sensitized to TNFα-induced cell death; and (4) FasL-mediated apoptosis was not disrupted.
Head and neck squamous cell carcinoma (HNSCC) is a widespread disease with a low survival rate and a high risk of recurrence. Nowadays, immune checkpoint inhibitor (ICI) treatment is approved for HNSCC as a first-line treatment in recurrent and metastatic disease. ICI treatment yields a clear survival benefit, but overall response rates are still unsatisfactory. As shown in different cancer models, hepatocyte growth factor/mesenchymal–epithelial transition (HGF/Met) signaling contributes to an immunosuppressive microenvironment. Therefore, we investigated the relationship between HGF and programmed cell death protein 1 (PD-L1) expression in HNSCC cell lines. The preclinical data show a robust PD-L1 induction upon HGF stimulation. Further analysis revealed that the HGF-mediated upregulation of PD-L1 is MAP kinase-dependent. We then hypothesized that serum levels of HGF and soluble programmed cell death protein 1 (sPD-L1) could be potential markers of ICI treatment failure. Thus, we determined serum levels of these proteins in 20 HNSCC patients before ICI treatment and correlated them with treatment outcomes. Importantly, the clinical data showed a positive correlation of both serum proteins (HGF and sPD-L1) in HNSCC patient’s sera. Moreover, the serum concentration of sPD-L1 was significantly higher in ICI non-responsive patients. Our findings indicate a potential role for sPD-L1 as a prognostic marker for ICI treatment in HNSCC.
The enzyme pyruvate kinase M2 (PKM2) plays a major role in the switch of tumor cells from oxidative phosphorylation to aerobic glycolysis, one of the hallmarks of cancer. Different allosteric inhibitors or activators and several posttranslational modifications regulate its activity. Head and neck squamous cell carcinoma (HNSCC) is a common disease with a high rate of recurrence. To find out more about PKM2 and its modulation in HNSCC, we examined a panel of HNSCC cells using real-time cell metabolic analysis and Western blotting with an emphasis on phosphorylation variant Tyr105 and two reagents known to impair PKM2 activity. Our results show that in HNSCC, PKM2 is commonly phosphorylated at Tyrosine 105. Its levels depended on tyrosine kinase activity, emphasizing the importance of growth factors such as EGF (epidermal growth factor) on HNSCC metabolism. Furthermore, its correlation with the expression of CD44 indicates a role in cancer stemness. Cells generally reacted with higher glycolysis to PKM2 activator DASA-58 and lower glycolysis to PKM2 inhibitor Compound 3k, but some were more susceptible to activation and others to inhibition. Our findings emphasize the need to further investigate the role of PKM2 in HNSCC, as it could aid understanding and treatment of the disease.
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.
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.
All forms of restriction, from caloric to amino acid to glucose restriction, have been established in recent years as therapeutic options for various diseases, including cancer. However, usually there is no direct comparison between the different restriction forms. Additionally, many cell culture experiments take place under static conditions. In this work, we used a closed perfusion culture in murine L929 cells over a period of 7 days to compare methionine restriction (MetR) and glucose restriction (LowCarb) in the same system and analysed the metabolome by liquid chromatography mass spectrometry (LC-MS). In addition, we analysed the inhibition of glycolysis by 2-deoxy-D-glucose (2-DG) over a period of 72 h. 2-DG induced very fast a low-energy situation by a reduced glycolysis metabolite flow rate resulting in pyruvate, lactate, and ATP depletion. Under perfusion culture, both MetR and LowCarb were established on the metabolic level. Interestingly, over the period of 7 days, the metabolome of MetR and LowCarb showed more similarities than differences. This leads to the conclusion that the conditioned medium, in addition to the different restriction forms, substantially reprogramm the cells on the metabolic level.