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Maniaki, E.

Publications and source records attributed to Maniaki, E..

2 recordsLinked to original sources

MiniCARbids: Minimalistic human binding domains specifically tailored to CAR T applications

Traditionally, chimeric antigen receptor (CAR) T cells employ single-chain variable fragments (scFvs) as binding entities. While scFvs represent a convenient option due to their broad availability, they also come with drawbacks, in particular their tendency to cluster and their relatively large size. Moreover, most scFvs used in the CAR field are of non-human origin, potentially causing immunogenicity. Therefore, we established an engineering platform for minimalistic CAR binding domains (miniCARbids), which combine several critical advantages: (i) human origin, (ii) small size, (iii) efficient expression in T cells and (iv) single-domain architecture, among others. We demonstrate that miniCARbids can be engineered to recognize various antigens with antibody-like affinities, while being stable and aggregation-resistant. When miniCARbids are incorporated into CARs, they induce high anti-tumor potency in both adapter and conventional CAR formats. Remarkably, CD22-directed miniCARbid-based CARs showed similar or even more efficient tumor clearance in leukemia-bearing mice when compared with a CAR comprising the clinically tested m971-1xG4S scFv. Together, we introduce the miniCARbid engineering platform, enabling the generation of small, human antigen-binding domains with high potency in CAR T cells against virtually any target antigen.

synthetic biology↗

Accelerometer-derived classifiers for early detection of degenerative joint disease in cats

Decreased mobility is a clinical sign of degenerative joint disease (DJD) in cats, which is highly prevalent, with 61% of cats aged six years or older showing radiographic evidence of DJD. Radiographs can reveal morphological changes and assess joint degeneration, but they cannot determine the extent of pain experienced by cats. Additionally, there is no universal objective assessment method for DJD-associated pain in cats. Developing an accurate evaluation model could enable earlier treatment, slow disease progression, and improve cats well-being. This study aimed to predict early signs of DJD in cats using accelerometers and machine learning techniques. Cats were restricted to indoors or limited outdoor access, including being walked on a lead or allowed into enclosed areas for short periods. Fifty-six cats were fitted with collar-mounted sensors that collected accelerometry data over 14 days, with data from 51 cats included in the analysis. Cat owners assessed their cats mobility and assigned condition scores, validated through clinical orthopaedic examinations. The study group comprised 24 healthy cats (no owner-reported mobility changes) and 27 unhealthy cats (owner-reported mobility changes, suggestive of early DJD). Data were segmented into 60-second windows centred around peaks of high activity. Using a Support Vector Machine (SVM) algorithm, the model achieved 78% (confidence interval: 0.65, 0.88) area under the curve (AUC), with 68% sensitivity (0.64, 0.77) at 75% specificity (0.68, 0.79). These results demonstrate the potential of accelerometry and machine learning to aid early DJD diagnosis and improve management, offering significant advances in non-invasive diagnostic techniques for cats.

animal behavior and cognition↗