Human spinal balance assessment relies considerably on sagittal radiographic parameter measurement. Deep learning could be applied for automatic landmark detection and alignment analysis, with mild to moderate standard errors and favourable correlations with manual measurement. In this study, based on 2210 annotated images of various spinal disease aetiologies, we developed deep learning models capable of automatically locating 45 anatomic landmarks and subsequently generating 18 radiographic parameters on a whole-spine lateral radiograph. In the assessment of model performance, the localisation accuracy and learning speed were the highest for landmarks in the cervical area, followed by those in the lumbosacral, thoracic, and femoral areas. All the predicted radiographic parameters were significantly correlated with ground truth values (all p < 0.001). The human and artificial intelligence comparison revealed that the deep learning model was capable of matching the reliability of doctors for 15/18 of the parameters. The proposed automatic alignment analysis system was able to localise spinal anatomic landmarks with high accuracy and to generate various radiographic parameters with favourable correlations with manual measurements.

A series of zinc(II) dipicolylamine (ZnDPA)-based drug conjugates have been synthesized to probe the potential of phosphatidylserine (PS) as a new antigen for small molecule drug conjugate (SMDC) development. Using in vitro cytotoxicity and plasma stability studies, PS-binding assay, in vivo pharmacokinetic studies, and maximum tolerated dose profiles, we provided a roadmap and the key parameters required for the development of the ZnDPA based drug conjugate. In particular, conjugate 24 induced tumor regression in the COLO 205 xenograft model and exhibited a more potent antitumor effect with a 70% reduction of cytotoxic payload compared to that of the marketed irinotecan when dosed at the same regimen. In addition to the validation of PS as an effective pharmacodelivery target for SMDC, our work also provided the foundation that, if applicable, a variety of therapeutic agents could be conjugated in the same manner to treat other PS-associated diseases.

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