Generating health technology assessment evidence for rare diseases

Objectives: Rare diseases are often heterogeneous in their progression and response to treatment, with only a small population for study. This provides challenges for evidence generation to support HTA, so novel research methods are required. Methods: Discussion with an expert panel was augmented with references and case studies to explore robust approaches for HTA evidence generation for rare disease treatments. Results: Traditional RCTs can be modified using sequential, three-stage or adaptive designs to gain more power from a small patient population or to focus trial design. However, such designs need to maintain important design aspects such as randomization and blinding and be analyzed to take account of the multiple analyses performed. N-of-1 trials use within-patient randomization to test repeat periods of treatment and control until a response is clear. Such trials could be particularly valuable for rare diseases and when prospectively planned across several patients and analyzed using Bayesian techniques, a population effect can be estimated that might be of value to HTA. When the optimal outcome is unclear in a rare disease, disease specific patient reported outcomes can elucidate impacts on patients’ functioning and wellbeing. Likewise, qualitative research can be used to elicit patients’ perspectives, with just a small number of patients. Conclusions: International consensus is needed on ways to improve evidence collection and assessment of technologies for rare diseases, which recognize the value of novel study designs and analyses in a setting where the outcomes and effects of importance are yet to be agreed.</p

Targets of tumor infiltrating lymphocyte reactivity in pancreatic cancer

With little to no progress in the treatment of pancreatic cancer over the past decades, novel approaches to cover the high unmet medical need are long overdue. However, the long lasting notion of PDAC being a ‘non-immunogenic’ neoplasm lacking uniform infiltration of effector lym- phocytes and holding an immunosuppressive microenvironment has so far precluded the use of adoptive T cell therapy in this malignancy. Furthermore, the overall low mutational burden of pancreatic cancer led to the assumption of decreased frequencies in immunogenic neoepi- topes targeted by T cells. Nevertheless, the recent identification of frequently occurring effector T cells recognizing their autologous tumor in pancreatic cancer indicates a potential use of PDAC derived TILs against the malignancy. In addition, exome analyses of larger patient co- horts indicated that formation of immunogenic, mutation derived epitopes in PDAC might be more frequent than previously expected. Nevertheless, to date functional analyses identifying and characterizing the epitopes recognized by pancreatic cancer TIL are yet to be carried out. Thus, we set out to develop screening approaches to reliably identify mutation derived epitopes within pancreatic cancer patients. This development was approached with two complementary strategies, first by looking into the antigens recognized by PDAC TILs and second by directly identifying the epitopes presented on the surface of the PDAC derived tumor cells. We addressed the antigen recognition by developing a novel expression based screening sys- tem. Since PDAC is frequently infiltrated by CD4+ and CD8+ T cells (both of which populations potentially mount anti tumor immune responses), combined with a low number of tumor muta- tions, we needed an approach able to screen for both, MHC-I and MHC-II restricted antigens. Therefore, we designed a system based on shuttling antigens into both, the major histocom- patibility complex (MHC)-I and -II antigen presentation pathways, in order to define the reac- tivity of PDAC TILs in a completely unbiased fashion (i.e. not depending on MHC restriction and/or epitope prediction algorithms). Furthermore, we validated the use of a targeted mass spectrometry based approach to directly identify epitopes presented on the surface of PDAC derived cells lines. In addition, the application of both approaches in a small patient cohort was used to draw initial conclusions on the feasibility of both approaches for an extension to larger patient cohorts. Taken together, the presented study describes the development and validation of screening methodologies capable of identifying antigens in the context of low mutational burden malig- nancies such as pancreatic cancer. These methodologies lay the foundation for the functional proof of concept studies of antigen reactive T cells infiltrating pancreatic cancer, opening new ways for the application of adoptive T cell transfer in this fatal disease

Computer-Aided Designed/Computer-Aided Manufactured and Conventional Techniques in Maxillofacial Reconstruction with Free Fibula Flaps

We treated 26 patients via vascularized osteocutaneous fibula flaps for maxillofacial osseous reconstruction between September 2012 and October 2015. The CAD/CAM technique was attempted for all patients needing bony maxillofacial reconstructions. The time interval from deciding to use the CAD/CAM technique and receiving the hardware depended on the capacity of the CAD/CAM providing companies. It usually takes between 3 and 4 weeks. Hence, the CAD/CAM technique was not used for patients with rapid tumor growth or pathologic fractures of the mandible. In these urgent cases, surgery could not be delayed and the conventional technique was used. In the abovementioned time period, 11 patients underwent osseous reconstruction using CAD/CAM and 15 patients using the conventional technique. Data were collected and evaluated according to demographics, medical history, number of bone segments, and complications. Time measurements of virtual planning sessions, flap harvesting, flap ischemia, tourniquet inflation, total reconstruction, and overall operating times were additionally recorded

Profilin isoforms in Dictyostelium discoideum

AbstractEukaryotic cells contain a large number of actin binding proteins of different functions, locations and concentrations. They bind either to monomeric actin (G-actin) or to actin filaments (F-actin) and thus regulate the dynamic rearrangement of the actin cytoskeleton. The Dictyostelium discoideum genome harbors representatives of all G-actin binding proteins including actobindin, twinfilin, and profilin. A phylogenetic analysis of all profilins suggests that two distinguishable groups emerged very early in evolution and comprise either vertebrate and viral profilins or profilins from all other organisms. The newly discovered profilin III isoform in D. discoideum shows all functions that are typical for a profilin. However, the concentration of the third isoform in wild type cells reaches only about 0.5% of total profilin. In a yeast-2-hybrid assay profilin III was found to bind specifically to the proline-rich region of the cytoskeleton-associated vasodilator-stimulated phosphoprotein (VASP). Immunolocalization studies showed similar to VASP the profilin III isoform in filopodia and an enrichment at their tips. Cells lacking the profilin III isoform show defects in cell motility during chemotaxis. The low abundance and the specific interaction with VASP argue against a significant actin sequestering function of the profilin III isoform

Interacting Arrays of Steps and Lines in Random Media

The phase diagram of two interacting planar arrays of directed lines in random media is obtained by a renormalization group analysis. The results are discussed in the contexts of the roughening of reconstructed crystal surfaces, and the pinning of flux line arrays in layered superconductors. Among the findings are a glassy flat phase with disordered domain structures, a novel second-order phase transition with continuously varying critical exponents, and the generic disappearance of the glassy ``super-rough'' phases found previously for a single array.Comment: 4 pages, REVTEX 3.0, uses epsf,multicol, 3 .eps-figures, submitted to PR

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Patients With Cerebrovascular Disease

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method-the U-net -is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of similar to 0.88, a 95HD of similar to 47 voxels and an AVD of similar to 0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice similar to 0.76, 95HD similar to 59, AVD similar to 1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologies

A U-Net Deep Learning Framework for High Performance Vessel Segmentation in Paitents with Cerebrovascular Disease

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method—the U-net—is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We performed both quantitative and qualitative analyses. The U-net models yielded high performance for both the full and the reduced architecture: A Dice value of ~0.88, a 95HD of ~47 voxels and an AVD of ~0.4 voxels. The visual analysis revealed excellent performance in large vessels and sufficient performance in small vessels. Pathologies like cortical laminar necrosis and a rete mirabile led to limited segmentation performance in few patients. The U-net outperfomed the traditional graph-cuts method (Dice ~0.76, 95HD ~59, AVD ~1.97). Our work highly encourages the development of clinically applicable segmentation tools based on deep learning. Future works should focus on improved segmentation of small vessels and methodologies to deal with specific pathologie

A field study of data analysis exercises in a bachelor physics course using the internet platform VISPA

Bachelor physics lectures on particle physics and astrophysics were complemented by exercises related to data analysis and data interpretation at the RWTH Aachen University recently. The students performed these exercises using the internet platform VISPA, which provides a development environment for physics data analyses. We describe the platform and its application within the physics course, and present the results of a student survey. The students acceptance of the learning project was positive. The level of acceptance was related to their individual preference for learning with a computer. Furthermore, students with good programming skills favor working individually, while students who attribute themselves having low programming abilities favor working in teams. The students appreciated approaching actual research through the data analysis tasks.Comment: 21 pages, 8 figures, 1 table, for the internet platform VISPA see http://vispa.physik.rwth-aachen.d

Complication rates of peripherally inserted central catheters vs implanted ports in patients receiving systemic anticancer therapy: A retrospective cohort study

While implanted port catheters ("PORTs") have historically been the standard device for intravenous systemic anticancer therapy, the use of peripherally inserted central catheters (PICCs) has increased continuously and reliable catheter selection guidelines are lacking. We compare complication rates of PORTs and PICCs in cancer treatment in a retrospective study of 3365 patients with both solid organ (n = 2612) and hematologic (n = 753) malignancies, between 2001 and 2021. 26.4% (n = 890) of all patients were treated via PICCs and 73.6% (2475) via PORTs. 20.7% (578) experienced a major catheter-related complication with a higher rate in PICCs than in PORTs (23.5% vs 14.9%, P < .001). Among major complications, infections and mechanical complications were more common in PICCs than in PORTs (11.9% vs 6.4%, P = .001, 7.3% vs 4.2%, P = .002), whereas the rate of thrombosis was similar (3.4% vs 3.0%, P = .9). While PORTs had a higher rate of periprocedural complications (2.7% vs 1.1%, P < .05), PICCs overall complication rate exceeded PORTs within 3 days from implantation. Median follow-up was 49 (PICC) and 60 weeks (PORT). PORTs are safer and therefore should be preferred in this setting regardless of catheter dwell time