NFFA-Europe Pilot - D16.2 - Report on the first data services

This document describes the initial set of data services available in NFFA Europe Pilot

Optochiasmatic tuberculoma as the sole manifestation of late recurrent tuberculosis

Brain tuberculomas account for 10-20% of space occupying brain lesions in developing countries. Most lesions are observed at time of tuberculosis diagnosis or soon after starting treatment. We herein describe a 32 year-old patient with a 14-month history of headache and progressive visual loss. Her past medical history revealed pulmonary tuberculosis treated eight years before. A brain MRI showed a T1- and T2-weighted isointense contrast-enhancing lesion in the optic chiasm. A presumptive diagnosis of optochiasmatic tuberculoma was made and isoniazid, rifampin, pyrazinamide, and ethambutol were started. Despite treatment, the patient evolved to blindness. The prompt recognition of this condition is extremely important since the presence of optochiasmal enhancement is associated with blindness in patients with tuberculosis.Tuberculomas cerebrais são responsáveis por 10-20% das lesões parenquimatosas em países em desenvolvimento. A maioria destas lesões é observada ao diagnóstico de tuberculose ou logo após o início do tratamento. Descrevemos um caso de uma paciente de 32 anos com história de 14 meses de evolução de perda visual progressiva e cefaleia. A história patológica revelou tuberculose pulmonar 8 anos antes. A ressonância magnética do crânio mostrou uma lesão isointensa nas sequências T1 e T2 captantes de contraste no quiasma óptico. Fizemos o diagnóstico presuntivo de tuberculoma ótico-quiasmático e inciamos isoniazida, rifampicina, pirazinamida e etambutol. Apesar do tratamento, a paciente evoluiu para amaurose bilateral. O rápido diagnóstico desta condição é extremamente importante já que a presença de captação de contraste está associada à amaurose em pacientes com tuberculose

Combined microcomputed tomography, biomechanical and histomorphometric analysis of the peri-implant bone: A pilot study in minipig model

Objectives To present a practical approach that combines biomechanical tests, microcomputed tomography (μCT) and histomorphometry, providing quantitative results on bone structure and mechanical properties in a minipig model, in order to investigate the specific response to an innovative dental biomaterial. Methods Titanium implants with innovative three-dimensional scaffolds were inserted in the tibias of 4 minipigs. Primary stability and osseointegration were investigated by means of insertion torque (IT) values, resonance frequency analysis (RFA), bone-to-implant contact (BIC), bone mineral density (BMD) and stereological measures of trabecular bone. Results A significant positive correlation was found between IT and RFA (r = 0.980, p = 0.0001). BMD at the implant sites was 18% less than the reference values (p = 0.0156). Peri-implant Tb.Th was 50% higher, while Tb.N was 50% lower than the reference zone (p < 0.003) and they were negatively correlated (r = -0.897, p = 0.006). Significance μCT increases evaluation throughput and offers the possibility for qualitative three-dimensional recording of the bone-implant system as well as for non-destructive evaluation of bone architecture and mineral density, in combination with conventional analysis methods. The proposed multimodal approach allows to improve accuracy and reproducibility for peri-implant bone measurements and could support future investigations

feasibility and outcome of haploidentical hematopoietic stem cell transplantation with post transplant high dose cyclophosphamide for children and adolescents with hematologic malignancies an aieop gitmo retrospective multicenter study

Post-transplant high-dose cyclophosphamide (PTCy) is a novel approach to prevent graft-versus-host disease (GVHD) and rejection in patients given haploidentical hematopoietic stem cell transplantation (HSCT). Thirty-three patients with high-risk hematologic malignancies and lacking a match-related or -unrelated donor were treated with PTCy haploidentical HSCT in 5 Italian AIEOP centers. Nineteen patients had a nonmyeloablative preparative regimen (57%), and 14 patients received a full myeloablative conditioning regimen (43%). No patients received serotherapy; GVHD prophylaxis was based on PTCy (50 mg/kg on days +3 and +4) combined with mycophenolate plus tacrolimus or cyclosporine A. Neutrophil and platelet engraftment was achieved on days +17 (range, 14 to 37) and +27 (range, 16 to 71). One patient had autologous reconstitution for anti-HLA antibodies. Acute GVHD grades II to IV and III to IV and chronic GVHD developed in 22% (95% CI, 11 to 42), 3% (95% CI, 0 to 21), and 4% (95% CI, 0 to 27) of cases, respectively. The 1-year overall survival rate was 72% (95% CI, 56 to 88), progression-free survival rate was 61% (95% CI, 43 to 80), cumulative incidence of relapse was 24% (95% CI, 13 to 44), and transplant-related mortality was 9% (95% CI, 3 to 26). The univariate analysis for risk of relapse incidence showed how 3 significant variables, mother as donor (P = .02), donor gender as female (P = .04), and patient gender as female (P = .02), were significantly associated with a lower risk of relapse. Disease progression was the main cause of death. PTCy is a safe procedure also for children and adolescents who have already received several lines of chemotherapy. Among the different diseases, a trend for better 1-year rates of overall survival was obtained for nonacute leukemia patients

Generative Fractional Diffusion Models

We generalize the continuous time framework for score-based generative models from an underlying Brownian motion (BM) to an approximation of fractional Brownian motion (FBM). We derive a continuous reparameterization trick and the reverse time model by representing FBM as a stochastic integral over a family of Ornstein-Uhlenbeck processes to define generative fractional diffusion models (GFDM) with driving noise converging to a non-Markovian process of infinite quadratic variation. The Hurst index $H\in(0,1)$ of FBM enables control of the roughness of the distribution transforming path. To the best of our knowledge, this is the first attempt to build a generative model upon a stochastic process with infinite quadratic variation

Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres

We develop a new type of model for solving the task of inverting the transmission effects of multi-mode optical fibres through the construction of an SO+(2, 1) equivariant neural network. This model takes advantage of the of the azimuthal correlations known to exist in fibre speckle patterns and naturally accounts for the difference in spatial arrangement between input and speckle patterns. In addition, we use a second post-processing network to remove circular artifacts, fill gaps, and sharpen the images, which is required due to the nature of optical fibre transmission. This two stage approach allows for the inspection of the predicted images produced by the more robust physically motivated equivariant model, which could be useful in a safety-critical application, or by the output of both models, which produces high quality images. Further, this model can scale to previously unachievable resolutions of imaging with multi-mode optical fibres and is demonstrated on 256 × 256 pixel images. This is a result of improving the trainable parameter requirement from O(N 4) to O(m), where N is pixel size and m is number of fibre modes. Finally, this model generalises to new images, outside of the set of training data classes, better than previous models

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology

We present DiffInfinite, a hierarchical diffusion model that generates arbitrarily large histological images while preserving long-range correlation structural information. Our approach first generates synthetic segmentation masks, subsequently used as conditions for the high-fidelity generative diffusion process. The proposed sampling method can be scaled up to any desired image size while only requiring small patches for fast training. Moreover, it can be parallelized more efficiently than previous large-content generation methods while avoiding tiling artefacts. The training leverages classifier-free guidance to augment a small, sparsely annotated dataset with unlabelled data. Our method alleviates unique challenges in histopathological imaging practice: large-scale information, costly manual annotation, and protective data handling. The biological plausibility of DiffInfinite data is validated in a survey by ten experienced pathologists as well as a downstream segmentation task. Furthermore, the model scores strongly on anti-copying metrics which is beneficial for the protection of patient data

Data Models for Dataset Drift Controls in Machine Learning With Images

Camera images are ubiquitous in machine learning research. They also play a central role in the delivery of important services spanning medicine and environmental surveying. However, the application of machine learning models in these domains has been limited because of robustness concerns. A primary failure mode are performance drops due to differences between the training and deployment data. While there are methods to prospectively validate the robustness of machine learning models to such dataset drifts, existing approaches do not account for explicit models of the primary object of interest: the data. This makes it difficult to create physically faithful drift test cases or to provide specifications of data models that should be avoided when deploying a machine learning model. In this study, we demonstrate how these shortcomings can be overcome by pairing machine learning robustness validation with physical optics. We examine the role raw sensor data and differentiable data models can play in controlling performance risks related to image dataset drift. The findings are distilled into three applications. First, drift synthesis enables the controlled generation of physically faithful drift test cases. The experiments presented here show that the average decrease in model performance is ten to four times less severe than under post-hoc augmentation testing. Second, the gradient connection between task and data models allows for drift forensics that can be used to specify performance-sensitive data models which should be avoided during deployment of a machine learning model. Third, drift adjustment opens up the possibility for processing adjustments in the face of drift. This can lead to speed up and stabilization of classifier training at a margin of up to 20% in validation accuracy. A guide to access the open code and datasets is available at https://github.com/aiaudit-org/raw2logit.Comment: LO and MA contributed equall