Aragonite-based scaffold for the treatment of knee osteochondral defects: results of a prospective multi-center trial at 2 years' follow-up

PURPOSE: To evaluate the clinical and MRI outcomes after the implantation of a nanostructured cell free aragonite-based scaffold in patients affected by knee chondral and osteochondral lesions. METHODS: 126 patients (94 men, 32 women; age 32.7±8.8 years) were included according to the following criteria: grade III or IV chondra/osteochondral lesions in the femoral condyles or throclea; 2) no limb axial deviation (i.e. varus or valgus knee > 5°); 3) no signs of knee instability; 4) no concurrent tibial or patellar chondral/osteochondral defects. All patients were treated by arthrotomic implantation of an aragonite based-scaffold by a press-fit technique. Patients were prospectively evaluated by IKDC, Tegner, Lysholm and KOOS scores preoperatively and then at 6, 12, 18 and 24-months follow-up. MRI was also performed to evaluate the amount of defect filling by regenerated cartilage. Failures were defined as the need for re-intervention in the index knee within the follow-up period. RESULTS: Average defect size was 2±1.3 cm2 and in most cases a single scaffold was used. A significant improvement in each clinical score was recorded from basal level to 24 months’ follow-up. In particular, the IKDC subjective score increased from 42.14±16 to 70.94±24.69 and the Tegner score improved from 2.95±1.90 to 4.82±1.85 (p<0.0005). Lysholm score and all the subscales of KOOS showed a similar trend over time. Age of the patient at implantation, size of the defect and BMI were correlated with lower clinical outcome. The presence of OA didn’t influence the clinical results. MRI evaluation showed a significant increase in defect filling over time, with the highest value reached at 24 months. Failures occurred in eleven patients (8.7%). CONCLUSION: The aragonite-based biomimetic osteochondral scaffold proved to be safe, and encouraging clinical and radiographic outcomes were documented up to 2 years’ follow-up.

Real-time anomaly detection of gamma-ray bursts for the Cherenkov Telescope Array using deep learning

The Cherenkov Telescope Array (CTA) will be the next-generation ground-based observatory to study the universe in the very-high-energy domain. The observatory will rely on a Science Alert Generation (SAG) system to analyze the real-time data from the telescopes and generate science alerts. The SAG system will play a crucial role in the search and follow-up of transients from external alerts, enabling multi-wavelength and multi-messenger collaborations. It will maximize the potential for the detection of the rarest phenomena, such as gamma-ray bursts (GRBs), which are the science case for this study. This study presents an anomaly detection method based on deep learning for detecting gamma-ray burst events in real-time. The performance of the proposed method is evaluated and compared against the Li&Ma standard technique in two use cases of serendipitous discoveries and follow-up observations, using short exposure times. The method shows promising results in detecting GRBs and is flexible enough to allow real-time search for transient events on multiple time scales. The method does not assume background nor source models and doe not require a minimum number of photon counts to perform analysis, making it well-suited for real-time analysis. Future improvements involve further tests, relaxing some of the assumptions made in this study as well as post-trials correction of the detection significance. Moreover, the ability to detect other transient classes in different scenarios must be investigated for completeness. The system can be integrated within the SAG system of CTA and deployed on the onsite computing clusters. This would provide valuable insights into the method's performance in a real-world setting and be another valuable tool for discovering new transient events in real-time. Overall, this study makes a significant contribution to the field of astrophysics by demonstrating the effectiveness of deep learning-based anomaly detection techniques for real-time source detection in gamma-ray astronomy.

Interfaces between national and EU law: time limits in cross-border civil proceedings and their impact on the free circulation of judgments

The thesis aims at exploring possible legal solutions to remove the obstacles to the free circulation of judgments in the civil justice area that arise from the remarkably diverging national rules on procedural time limits. As shown by the case-law of the CJEU, time limits have recently come under closer scrutiny. The interplay between national and EU law illustrates that time limits raise significant deficiencies connected with the right to a fair trial under Art. 6 ECHR and Art. 47 CFR – e.g. the effective recovery of claims, effective judicial protection, effective cross-border enforcement of judgments – which negatively impact EU cross-border civil litigation. In order to overcome some of the weaknesses of the current legal framework governing the cross-border enforcement of judgments and strengthen the parties’ fundamental procedural rights the PhD thesis intends to determine whether and, to what extent time limits can be harmonised at EU level. EU action on time limits would indeed favour the speed, efficiency and proportionality of cross-border proceedings without sacrificing the fairness of the judicial process and the equality of the parties