Explore the vast range of titles available.

CA19-9 values are regularly measured in patients with pancreatic cancer. Certainly, its potential as a biomarker has been compromised by false negative results in CA19-9 negative patients and false positive results in benign pancreatico-biliary diseases. For detection of PDAC recurrence, however, CA19-9 might play an important role. The aim of this study is to analyze the accuracy of CA19-9 for detecting recurrence of pancreatic cancer. All included patients were treated either at the University Medical Center Goettingen, or at the Department of Interdisciplinary Oncology and Pneumonology, DRK-Kliniken Nordhessen, Kassel. We analyzed data of 93 patients with pancreatic cancer in the training set and 41 in the validation set, both retrospectively. Pre- and postoperative CA19-9 values and results of imaging techniques were compared. We performed ROC-analysis. The association between longitudinally measured CA19-9 values and relapse was studied with a joint model between a random effects model for the longitudinal CA19-9 measurements and a Cox proportional hazards models for the survival data. In the test set (n = 93 patients) the median follow-up time was 644 days (22 months). Overall, 71 patients (76.3%) developed recurrence during follow-up. Patients with CA19-9 values of <10kU/l were considered as CA19-9 negative patients (n = 11) and excluded from further analysis. Among the rest, approximately 60% of the patients showed significantly elevated CA19-9 prior to detection of recurrence by imaging techniques. Recurrence was shown by 2.45 times elevated CA19-9 values with 90% positive predictive value. In the validation set, 2.45 times elevated CA19-9 values showed recurrence with 90% sensitivity and 83,33% specificity, with an area under the curve of 95%. Based on measured CA19-9 values during follow-up care, the joint model estimates in recurrence-free patients the probability of recurrence-free survival. CA19-9 elevation is an early and reliable sign for PDAC recurrence. On the strength of a very high accuracy in CA19-9 positive patients, it should be considered to use CA19-9 for therapy decision even without a correlate of imaging technics. Using the joint model, follow-up care of PDAC patients after curative therapy can be stratified.

Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we investigated how ML algorithms may assist in echocardiographic pulmonary hypertension (PH) prediction, where current guidelines recommend integrating several echocardiographic parameters. In our database of 90 patients with invasively determined pulmonary artery pressure (PAP) with corresponding echocardiographic estimations of PAP obtained within 24 hours, we trained and applied five ML algorithms (random forest of classification trees, random forest of regression trees, lasso penalized logistic regression, boosted classification trees, support vector machines) using a 10 times 3-fold cross-validation (CV) scheme. ML algorithms achieved high prediction accuracies: support vector machines (AUC 0.83; 95% CI 0.73-0.93), boosted classification trees (AUC 0.80; 95% CI 0.68-0.92), lasso penalized logistic regression (AUC 0.78; 95% CI 0.67-0.89), random forest of classification trees (AUC 0.85; 95% CI 0.75-0.95), random forest of regression trees (AUC 0.87; 95% CI 0.78-0.96). In contrast to the best of several conventional formulae (by Aduen et al.), this ML algorithm is based on several echocardiographic signs and feature selection, with estimated right atrial pressure (RAP) being of minor importance. Using ML, we were able to predict pulmonary hypertension based on a broader set of echocardiographic data with little reliance on estimated RAP compared to an existing formula with non-inferior performance. With the conceptual advantages of a broader and unbiased selection and weighting of data our ML approach is suited for high level assistance in PH prediction.

Background Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the machine learning community made recent elaborations on interpretability methods explaining data point-specific decisions of deep learning techniques. We believe that such explanations can assist the need in personalized precision medicine decisions via explaining patient-specific predictions. Methods Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g., distant metastasis in cancer, for each individual patient. Results We extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset and then apply the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. Conclusions The developed method could be potentially highly useful on interpreting classification results in the context of different omics data and prior knowledge molecular networks on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.

The primary objective was to create and establish a new formula that predicts the individual probability of macular hole closure for eyes with full thickness macular holes (FTMH) accompanied by vitreomacular traction (VMT) which received enzymatic vitreolysis using intravitreally administered ocriplasmin. The secondary objective was to evaluate the forecast reliability of a previously published formula for VMT resolution in VMT-only eyes (Odds IVO-Success  = e Intercept  × OR years  × OR ln(µm) ; Probability IVO-Success  = Odds IVO-Success /(Odds IVO-Success  + 1)) on VMT resolution using the current dataset of eyes with FTMH accompanied by VMT. Retrospective analysis of the OASIS, ORBIT, and INJECT-studies. Patients with FTMH and VMT with complete information (n = 213) were included. The effect of gender, age, FTMH diameter, lens status and the presence of epiretinal membranes (ERM) on FTMH closure was assessed using separate univariate logistic regression analyses. With regard to VMT release separate univariate regression analyses were carried out and results were compared with formerly published data of VMT resolution in eyes with VMT only. Overall, 126 eyes (63%) experienced VMT resolution within 28 days. Younger age (p < 0.0001) and VMT diameter (p = 0.041) had a significant impact on VMT release. Overall, 81 eyes (38%) treated with ocriplasmin showed FTMH closure within 28 days. Univariate analysis of the different predictors analyzed revealed that FTMH diameter < 250 µm had a significant impact on treatment success (p = 0.0495). It was not possible to calculate and establish a new multivariate formula that can predict the individual FTMH closure probability for eyes with FTMHs and VMT. However, the results of VMT release prediction in eyes with FTMHs accompanied by VMT matched the prediction of VMT release in eyes with VMT only when using the previously published formula. All in all, predictors for calculating the individual probability of VMT resolution on the one hand and FTMH closure on the other hand are different suggesting diverse pathophysiological mechanisms.

The objective of the study was to estimate if altered levels of alpha-synuclein can be detected in tear fluid of patients with Parkinson’s disease (PD). Therefore, tear fluid samples of 75 PD patients, 75 control subjects and 31 atypical Parkinsonian patients were collected and analyzed in triplicates using an ultra-sensitive single molecule array (SIMOA) system and applying a human alpha-synuclein immunoassay. In PD, levels of total soluble alpha-synuclein were significantly increased compared to control subjects (p = 0.03; AUC PD vs. controls 0.60). There was no difference comparing PD patients stratified by Hoehn & Yahr stages and atypical Parkinsonian syndromes stratified by tauopathies and non-PD-synucleinopathies against each other (p > 0.05). In conclusion, alpha-synuclein can be detected and quantified in tear fluid, revealing small but significant differences in total alpha-synuclein levels between PD and control subjects. Tear fluid can be collected non-invasively and risk-free, therefore presenting a promising source for further biomarker research.

ObjectivesA university-based randomized clinical study evaluated the 5-year performance of chairside-fabricated zirconia-reinforced lithium silicate (ZLS)-ceramic partial crowns.Material and methodsForty-five patients were restored with 61 chairside-fabricated ZLS-restorations (Cerec SW 4.2, Dentsply Sirona, Germany; Vita Suprinity, Vita Zahnfabrik, Germany). Deviating from the manufacturers’ recommendations, restorations with reduced minimum material thicknesses (MMT) were fabricated: group 1, MMT = 0.5–0.74 mm (n = 31); group 2, MMT = 0.75–1.0 mm (n = 30). For luting, a self-adhesive cement (SAC) or a total-etch technique with a composite cement (TEC) was applied. Statistical evaluation was performed by time-to-event analysis (Kaplan–Meier). Possible covariates of the survival (SVR) and success rates (SCR), evaluated in a Cox regression model, were MMT, restoration position (premolar/molar), and cementation technique (SAC vs. TEC).ResultsForty patients (54 restorations, premolars, n = 23; molars, n = 31) participated in the 5-year follow-up. Five losses due to ceramic fractures occurred in group 1 (n = 28) (SVR: 83.0% [95% confidence interval (CI): 0.71–0.96]). Group 2 (n = 26) showed no losses (SVR: 100%). The success rate for partial crowns placed on premolars was 100% and 69% (95% CI: 0.54–0.84) for molar restorations. Recementation was required in 4 restorations with SAC (SCR: 86% [95% CI: 0.73–0.99]; SCR-DC: 100%). Restorations in group 2 showed a significantly reduced risk of material fracture hazard ratio (HR) = 0.09, p = 0.0292) compared with the restorations in group 1. Molar partial crowns showed an increased risk for a clinical intervention (HR = 5.26, p = 0.0222) compared to premolar restorations.ConclusionsMaterial thickness and position of the restoration are risk factors influencing the survival and success rate of ZLS-ceramic partial crowns.Clinical relevanceObservation of an MMT of at least 0.75–1.0 mm for ZLS-ceramics is essential to avoid material-related fractures.Clinical trial registration: German Clinical Trails Register (trial number: DRKS00005611)

Sentinel lymph node (SN) tumor burden is becoming increasingly important and is likely to be included in future N classifications in melanoma. Our aim was to investigate the prognostic significance of melanoma infiltration of various anatomically defined lymph node substructures. This retrospective cohort study included 1250 consecutive patients with SN biopsy. The pathology protocol required description of metastatic infiltration of each of the following lymph node substructures: intracapsular lymph vessels, subcapsular and transverse sinuses, cortex, paracortex, medulla, and capsule. Within the SN with the highest tumor burden, the SN invasion level (SNIL) was defined as follows: SNIL 1 = melanoma cells confined to intracapsular lymph vessels, subcapsular or transverse sinuses; SNIL 2 = melanoma infiltrating the cortex or paracortex; SNIL 3 = melanoma infiltrating the medulla or capsule. We classified 338 SN-positive patients according to the non-metric SNIL. Using Kaplan–Meier estimates and Cox models, recurrence-free survival (RFS), melanoma-specific survival (MSS) and nodal basin recurrence rates were analyzed. The median follow-up time was 75 months. The SNIL divided the SN-positive population into three groups with significantly different RFS, MSS, and nodal basin recurrence probabilities. The MSS of patients with SNIL 1 was virtually identical to that of SN-negative patients, whereas outgrowth of the metastasis from the parenchyma into the fibrous capsule or the medulla of the lymph node indicated a very poor prognosis. Thus, the SNIL may help to better assess the benefit-risk ratio of adjuvant therapies in patients with different SN metastasis patterns.

Whether fluent-sounding utterances of adults who stutter are normally articulated is unclear. We asked 15 patients and 17 matched controls to utter a pseudoword while recording real-time MRI at 55 frames per second in a midsagittal plane, and we automatically clustered participants for distances between sites of articulation. Clustering was successful in 80% of the cases, indicating major differences in the movement patterns of fluent sounding utterances in both groups.

Background The main natural reservoir for Campylobacter jejuni is the avian intestinal tract. There, C. jejuni multiplies optimally at 42 °C – the avian body temperature. After infecting humans through oral intake, the bacterium encounters the lower temperature of 37 °C in the human intestinal tract. Proteome profiling by label-free mass spectrometry (DIA-MS) was performed to examine the processes which enable C. jejuni 81–176 to thrive at 37 °C in comparison to 42 °C. In total, four states were compared with each other: incubation for 12 h at 37 °C, for 24 h at 37 °C, for 12 h at 42 °C and 24 h at 42 °C. Results It was shown that the proteomic changes not only according to the different incubation temperature but also to the length of the incubation period were evident when comparing 37 °C and 42 °C as well as 12 h and 24 h of incubation. Altogether, the expression of 957 proteins was quantifiable. 37.1 − 47.3% of the proteins analyzed showed significant differential regulation, with at least a 1.5-fold change in either direction (i.e. log 2 FC ≥ 0.585 or log 2 FC ≤ -0.585) and an FDR-adjusted p -value of less than 0.05. The significantly differentially expressed proteins could be arranged in 4 different clusters and 16 functional categories. Conclusions The C. jejuni proteome at 42 °C is better adapted to high replication rates than that at 37 °C, which was in particular indicated by the up-regulation of proteins belonging to the functional categories “replication” (e.g. Obg, ParABS, and NapL), “DNA synthesis and repair factors” (e.g. DNA-polymerase III, DnaB, and DnaE), “lipid and carbohydrate biosynthesis” (e.g. capsular biosynthesis sugar kinase, PrsA, AccA, and AccP) and “vitamin synthesis, metabolism, cofactor biosynthesis” (e.g. MobB, BioA, and ThiE). The relative up-regulation of proteins with chaperone function (GroL, DnaK, ClpB, HslU, GroS, DnaJ, DnaJ-1, and NapD) at 37 °C in comparison to 42 °C after 12 h incubation indicates a temporary lower-temperature proteomic response. Additionally the up-regulation of factors for DNA uptake (ComEA and RecA) at 37 °C compared to 42 °C indicate a higher competence for the acquisition of extraneous DNA at human body temperature.

Bulk and single-cell DNA sequencing has enabled reconstructing clonal substructures of somatic tissues from frequency and cooccurrence patterns of somatic variants. However, approaches to characterize phenotypic variations between clones are not established. Here we present cardelino ( https://github.com/single-cell-genetics/cardelino ), a computational method for inferring the clonal tree configuration and the clone of origin of individual cells assayed using single-cell RNA-seq (scRNA-seq). Cardelino flexibly integrates information from imperfect clonal trees inferred based on bulk exome-seq data, and sparse variant alleles expressed in scRNA-seq data. We apply cardelino to a published cancer dataset and to newly generated matched scRNA-seq and exome-seq data from 32 human dermal fibroblast lines, identifying hundreds of differentially expressed genes between cells from different somatic clones. These genes are frequently enriched for cell cycle and proliferation pathways, indicating a role for cell division genes in somatic evolution in healthy skin. Cardelino leverages variant information from single-cell RNA-seq data for inferring clonal tree configuration and mapping cells to their clone of origin.