Introduction: Cell therapy is a rapidly growing area of research for the treatment of osteoarthritis (OA). This work is aimed to investigate the efficacy of intra-articular adipose-derived stromal cell (ASC) injection in the healing process on cartilage, synovial membrane and menisci in an experimental rabbit model. Methods: The induction of OA was performed surgically through bilateral anterior cruciate ligament transection (ACLT) to achieve eight weeks from ACLT a mild grade of OA. A total of 2 × 10⁶ and 6 × 10⁶ autologous ASCs isolated from inguinal fat, expanded in vitro and suspended in 4% rabbit serum albumin (RSA) were delivered in the hind limbs; 4% RSA was used as the control. Local bio-distribution of the cells was verified by injecting chloro-methyl-benzamido-1,1'-dioctadecyl-3,3,3'3'-tetra-methyl-indo-carbocyanine per-chlorate (CM-Dil) labeled ASCs in the hind limbs. Cartilage and synovial histological sections were scored by Laverty's scoring system to assess the severity of the pathology. Protein expression of some extracellular matrix molecules (collagen I and II), catabolic (metalloproteinase-1 and -3) and inflammatory (tumor necrosis factor- α) markers were detected by immunohistochemistry. Assessments were carried out at 16 and 24 weeks. Results: Labeled-ASCs were detected unexpectedly in the synovial membrane and medial meniscus but not in cartilage tissue at 3 and 20 days from ASC-treatment. Intra-articular ASC administration decreases OA progression and exerts a healing contribution in the treated animals in comparison to OA and 4% RSA groups. Conclusions: Our data reveal a healing capacity of ASCs in promoting cartilage and menisci repair and attenuating inflammatory events in synovial membrane inhibiting OA progression. On the basis of the local bio-distribution findings, the benefits obtained by ASC treatment could be due to a trophic mechanism of action by the release of growth factors and cytokines.

This study investigates the use of a low cost microphone combined with state-of-the-art machine learning (ML) algorithms as online process monitoring to differentiate various materials and process regimes of Laser-Powder Bed Fusion (LPBF). Three processing regimes (lack of fusion pores, conduction mode and keyhole pores) and three alloys (316L stainless steel, bronze (CuSn₈), and Inconel 718) were selected. Three conventional ML algorithms and a Convolutional Neural Network (CNN) were chosen to perform the classification tasks resulting in five main findings. First, we proved that the AE features are related to the laser-material interaction and not from undesired machine or environmental noise. Second, the process regimes are classified with high accuracy (> 87%) regardless of the algorithms and materials. Third, it is possible to build a single model from the three materials and still reach high classification accuracy (>86%) of the different regimes. Forth, the AE features used for the classifications are material and regime dependent. Finally, with LPBF processing of multi-materials on the rise, a strategy for classifying the material and the process regimes simultaneously using a CNN multi-label architecture reached a very high classification accuracy (≈ 93%). The results demonstrate the potential of our approaches for online LPBF process monitoring of different materials and regimes.