Exploiting modulation of the Blood-Brain and Blood-Tumor Barrier permeability by Translational Focused Ultrasound for therapeutic delivery to CNS metastases

Transcranial low-intensity focused ultrasound is a unique technology to modulate the integrity of tight endothelial junctions and transiently increase BBB/BTB permeability to enhance therapeutic delivery. Despite promising early studies, present literature lacks agreement on key experimental conditions, which restricts our knowledge and the technique\u27s widespread translation. This dissertation first provides a critical review of the current gaps in knowledge regarding the universal use of LiFUS in preclinical and clinical use. We then identify key parameters for translational and predictable opening of the BBB using a 3T MRI coupled with a clinical device. Our investigation highlights that passive permeability of the BBB following LiFUS is correlated to microbubble and cavitation dose. We also observe a biphasic and size-dependent increase in BBB permeability. Next, we studied the application of the clinical LiFUS parameters to evaluate efficacy and survival of combinatorial chemotherapy in a preclinical model of breast cancer brain metastases. When LiFUS was combined with paclitaxel and Doxil, we saw an additive increase in therapeutic efficacy and slower tumor progression. Lastly we explored the time-dependent effect of LiFUS on brain physiology and identified inflammation-associated understudied areas for improving LiFUS assisted CNS therapy

Künstliche Intelligenz in der Zahnheilkunde: Scoping-Review und Schließung beobachteter Wissenslücken durch eine methodische und eine klinische Studie

Objectives: The aims of this dissertation were to (1) conduct a scoping review of stud-ies on machine learning (ML) in dentistry and appraise their robustness, (2) perform a benchmarking study to systematically compare various ML algorithms for a specific dental task, and (3) evaluate the influence of a ML-based caries detection software on diagnostic accuracy and decision-making in a randomized controlled trial. Methods: The scoping review included studies using ML in dentistry published between 1st January 2015 and 31st May 2021 on MEDLINE, IEEE Xplore, and arXiv. The risk of bias and reporting quality were assessed with the QUADAS‐2 and TRIPOD checklists, respectively. In the benchmarking study, 216 ML models were built using permutations of six ML model architectures (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network, and Mask Attention Network), 12 model backbones of varying complexities (ResNet18, ResNet34, ResNet50, ResNet101, ResNet152, VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169, and Dense-Net201), and three initialization strategies (random, ImageNet, and CheXpert weights). 1,625 dental bitewing radiographs were used for training and testing. Five-fold cross-validation was carried out and model performance assessed using F1-score. In the clin-ical trial, each one of 22 dentists examined 20 randomly selected bitewing images for proximal caries; 10 images were evaluated with ML and 10 images without ML. Accura-cy in lesion detection and the suggested treatment were evaluated. Results: The scoping review included 168 studies, describing different ML tasks, mod-els, input data, methods to generate reference tests, and performance metrics, imped-ing comparison across studies. The studies showed considerable risk of bias and mod-erate adherence to reporting standards. In the benchmarking study, more complex models only minimally outperformed their simpler counterparts, if at all. Models initial-ized by ImageNet or CheXpert weights outperformed those using random weights (p<0.05). The clinical trial demonstrated that dentists using ML showed increased accu-racy (area under the receiver operating characteristic [mean (95% confidence interval): 0.89 (0.87–0.90)]) compared with those not using ML [0.85 (0.83–0.86); p<0.05], pri-marily due to their higher sensitivity [0.81 (0.74–0.87) compared to 0.72 (0.64–0.79); p<0.05]. Notably, dentists using ML also showed a higher frequency of invasive treat-ment decisions than those not using it (p<0.05). Conclusion: To facilitate comparisons across ML studies in dentistry, a minimum (core) set of outcomes and metrics should be developed, and researchers should strive to improve robustness and reporting quality of their studies. ML model choice should be performed on an informed basis, and simpler models may often be similarly capable as more complex ones. ML can increase dentists’ diagnostic accuracy but also lead to more invasive treatment.Ziele: Die Ziele dieser Dissertation waren, (1) ein Scoping-Review von Studien über maschinelles Lernen (ML) in der Zahnmedizin, (2) eine Benchmarking-Studie zum systematischen Vergleich verschiedener ML-Algorithmen für eine bestimmte zahnmedizinische Aufgabe, und (3) eine randomisierte kontrollierte Studie zur Bewertung einer ML-basierten Karies-Erkennungssoftware bezüglich diagnostischer Genauigkeit und Einfluss auf den Entscheidungsprozess durchzuführen. Methoden: Das Scoping-Review umfasste Studien über ML in der Zahnmedizin, veröffentlicht vom 1. Januar 2015 bis 31. Mai 2021 auf MEDLINE, IEEE Xplore und arXiv. Bias-Risiko und Berichtsqualität wurden mit den Checklisten QUADAS-2 beziehungsweise TRIPOD bewertet. In der Benchmarking-Studie wurden 216 ML-Modelle durch Permutationen von sechs Architekturen (U-Net, U-Net++, Feature Pyramid Networks, LinkNet, Pyramid Scene Parsing Network und Mask Attention Network), 12 Backbones (Res-Net18, ResNet34, ResNet50, ResNet101, ResNet152, VGG13, VGG16, VGG19, DenseNet121, DenseNet161, DenseNet169 und DenseNet201) und drei Initialisierungsstrategien (zufällige-, ImageNet- und CheXpert-Gewichtungen) erstellt. Zum Training und Testen wurden 1.625 Bissflügel-Röntgenaufnahmen genutzt. Es wurde eine fünffache Kreuzvalidierung durchgeführt und die Modellleistung anhand des F1-Scores bewertet. In der klinischen Studie untersuchten 22 Zahnärzte jeweils 20 zufällig ausgewählte Bissflügelbilder auf Approximalkaries; 10 Bilder wurden mit und 10 Bilder ohne ML ausgewertet. Die Genauigkeit in der Erkennung von Läsionen sowie die abgeleitete Therapieempfehlung wurden bewertet. Ergebnisse: Das Scoping-Review schloss 168 Studien ein, in denen verschiedene ML-Aufgaben, Modelle, Eingabedaten, Methoden zur Generierung von Referenztests und Leistungsmetriken beschrieben wurden. Die Studien zeigten ein erhebliches Bias-Risiko und eine mäßige Einhaltung der Berichtsstandards. In der Benchmarking-Studie hatten komplexere Modelle gegenüber einfachen Modellen allenfalls geringe Vorteile. Mit ImageNet- oder CheXpert-Gewichtungen initialisierte Modelle übertrafen solche mit Zufallsgewichtungen (p<0,05). In der klinischen Studie erreichten Zahnärzte mit ML eine höhere Genauigkeit bei der Kariesdetektion (Receiver-Operating-Charakteristik [Mittelwert (95 % Konfidenzintervall) 0,89 (0,87–0,90)]) als ohne ML [0,85 (0,83–0,86); p<0,05], hauptsächlich aufgrund höherer Sensitivität [0,81 (0,74–0,87) verglichen mit 0,72 (0,64–0,79); p<0,05]. Zahnärzte mit ML wählten auffallend häufiger invasive Behandlungen als ohne ML (p<0,05). Schlussfolgerung: Zur besseren Vergleichbarkeit von ML-Studien in der Zahnmedizin, sollten Core Outcomes und Metriken definiert sowie Robustheit und Berichtsqualität verbessert werden. Die Entwicklung von ML-Modellen sollte auf informierter Basis erfolgen, bei oft ähnlicher Leistung von einfacheren und komplexeren Modellen. ML kann die diagnostische Genauigkeit erhöhen, aber auch zu mehr invasiven Behandlungen führen

Effect of low voltage AC fields on cardiovascular implants

Coronary Artery Stents have been the preferred form of treatment for vascular occlusive disease, due to the minimally invasive surgical procedure, post-operative recovery time and cost, when compared to open coronary bypass surgery. The cellular response upon applying an AC electric field to type 316LM Stainless Steel stent mimics was investigated in this paper. The highest RBC adhesion was observed at voltages higher than 88 mV and lower than 74mV. Their unique alignment along the lines of fracture on the stent surface at 88 mV was a phenomenon caused by an increase in electrical conductivity in these regions. Being able to control RBC adhesion may have various clinical implications such as inhibition of thrombus formation, and provide a basis to analyse whether electric fields may be applied to cancer therapy as well

Design and testing of hydrophobic core/hydrophilic shell nano/micro particles for drug-eluting stent coating

In this study, we designed a novel drug-eluting coating for vascular implants consisting of a core coating of the anti-proliferative drug docetaxel (DTX) and a shell coating of the platelet glycoprotein IIb/IIIa receptor monoclonal antibody SZ-21. The core/shell structure was sprayed onto the surface of 316L stainless steel stents using a coaxial electrospray process with the aim of creating a coating that exhibited a differential release of the two drugs. The prepared stents displayed a uniform coating consisting of nano/micro particles. In vitro drug release experiments were performed, and we demonstrated that a biphasic mathematical model was capable of capturing the data, indicating that the release of the two drugs conformed to a diffusion-controlled release system. We demonstrated that our coating was capable of inhibiting the adhesion and activation of platelets, as well as the proliferation and migration of smooth muscle cells (SMCs), indicating its good biocompatibility and anti-proliferation qualities. In an in vivo porcine coronary artery model, the SZ-21/DTX drug-loaded hydrophobic core/hydrophilic shell particle coating stents were observed to promote re-endothelialization and inhibit neointimal hyperplasia. This core/shell particle-coated stent may serve as part of a new strategy for the differential release of different functional drugs to sequentially target thrombosis and in-stent restenosis during the vascular repair process and ensure rapid re-endothelialization in the field of cardiovascular disease

The Sirt1 activator SRT3025 provides atheroprotection in Apoe−/− mice by reducing hepatic Pcsk9 secretion and enhancing Ldlr expression

Aims The deacetylase sirtuin 1 (Sirt1) exerts beneficial effects on lipid metabolism, but its roles in plasma LDL-cholesterol regulation and atherosclerosis are controversial. Thus, we applied the pharmacological Sirt1 activator SRT3025 in a mouse model of atherosclerosis and in hepatocyte culture. Methods and results Apolipoprotein E-deficient (Apoe−/−) mice were fed a high-cholesterol diet (1.25% w/w) supplemented with SRT3025 (3.18 g kg−1 diet) for 12 weeks. In vitro, the drug activated wild-type Sirt1 protein, but not the activation-resistant Sirt1 mutant; in vivo, it increased deacetylation of hepatic p65 and skeletal muscle Foxo1. SRT3025 treatment decreased plasma levels of LDL-cholesterol and total cholesterol and reduced atherosclerosis. Drug treatment did not change mRNA expression of hepatic LDL receptor (Ldlr) and proprotein convertase subtilisin/kexin type 9 (Pcsk9), but increased their protein expression indicating post-translational effects. Consistent with hepatocyte Ldlr and Pcsk9 accumulation, we found reduced plasma levels of Pcsk9 after pharmacological Sirt1 activation. In vitro administration of SRT3025 to cultured AML12 hepatocytes attenuated Pcsk9 secretion and its binding to Ldlr, thereby reducing Pcsk9-mediated Ldlr degradation and increasing Ldlr expression and LDL uptake. Co-administration of exogenous Pcsk9 with SRT3025 blunted these effects. Sirt1 activation with SRT3025 in Ldlr−/− mice reduced neither plasma Pcsk9, nor LDL-cholesterol levels, nor atherosclerosis. Conclusion We identify reduction in Pcsk9 secretion as a novel effect of Sirt1 activity and uncover Ldlr as a prerequisite for Sirt1-mediated atheroprotection in mice. Pharmacological activation of Sirt1 appears promising to be tested in patients for its effects on plasma Pcsk9, LDL-cholesterol, and atherosclerosi

Augmented Vision for Dental Students’ Education in Detecting Proximal Carious Lesions on Bitewing Radiographs: A Randomized Controlled Trial

This two-arm, parallel, randomized controlled trial aimed to assess the effect of augmented vision (AV, using interactive color overlays) on the education of dental students in detecting proximal carious lesions on bitewing radiographs compared to black-and-white textbook-like illustrations. Forty-eight preclinical third-year dental students were randomized using a random number generator into two learning groups: test (AV, allowing interaction with color-highlighted carious lesions, n = 24) and control (showing the native radiograph and a black-and-white illustration displaying the carious lesion, n = 24). First, students had 2 weeks to assess 50 bitewings (lesion prevalence on the tooth level: 54.5%) in the test or control. Due to the nature of the intervention, participants could not be blinded toward the intervention. After that, they were asked to detect lesions on 10 independent bitewings and to assess lesion extent (outer/inner enamel; outer/middle/inner dentin). The reference test was constituted by two experienced dentists. No significant differences in accuracy (test 0.84 [95% CI: 0.79, 0.88]; control 0.83 [0.78, 0.87]), AUC (test 0.82 [0.81, 0.84]; control 0.81 [0.80, 0.83]) and F1 score (test 0.79 [0.75, 0.82]; control 0.77 [0.72, 0.81]) were observed between groups. Students of both groups showed difficulties in differentiating enamel from dentin carious lesions. While AV was reported to be motivating by students, it did not increase their accuracy

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies

Kupffer Cells and Blood Monocytes Orchestrate the Clearance of Iron-Carbohydrate Nanoparticles from Serum.

Intravenous (IV) iron nanoparticle preparations are widely used to treat iron deficiency. The mechanism of mononuclear phagocyte system-mediated clearance of IV iron nanoparticles is unknown. The early uptake and homeostasis of iron after injection of ferric carboxymaltose (FCM) in mice was studied. An increase in serum iron was observed at 2.5 h followed by a return to baseline by 24 h. An increase in circulating monocytes was observed, particularly Ly6Chi and Ly6Clow. FCM was also associated with a time-dependent decrease in liver Kupffer cells (KCs) and increase in liver monocytes. The increase in liver monocytes suggests an influx of iron-rich blood monocytes, while some KCs underwent apoptosis. Adoptive transfer experiments demonstrated that following liver infiltration, blood monocytes differentiated to KCs. KCs were also critical for IV iron uptake and biodegradation. Indeed, anti-Colony Stimulating Factor 1 Receptor (CSF1R)-mediated depletion of KCs resulted in elevated serum iron levels and impaired iron uptake by the liver. Gene expression profiling indicated that C-C chemokine receptor type 5 (CCR5) might be involved in monocyte recruitment to the liver, confirmed by pharmaceutical inhibition of CCR5. Liver KCs play a pivotal role in the clearance and storage of IV iron and KCs appear to be supported by the expanded blood monocyte population