[177Lu]Lu-labeled anti-claudin-18.2 antibody demonstrated radioimmunotherapy potential in gastric cancer mouse xenograft models

PURPOSE

Gastric cancer (GC), one of the most prevalent and deadliest tumors worldwide, is often diagnosed at an advanced stage with limited treatment options and poor prognosis. The development of a CLDN18.2-targeted radioimmunotherapy probe is a potential treatment option for GC.

METHODS

The CLDN18.2 antibody TST001 (provided by Transcenta) was conjugated with DOTA and radiolabeled with the radioactive nuclide 177Lu. The specificity and targeting ability were evaluated by cell uptake, imaging and biodistribution experiments. In BGC823CLDN18.2/AGSCLDN18.2 mouse models, the efficacy of [177Lu]Lu-TST001 against CLDN18.2-expressing tumors was demonstrated, and toxicity was evaluated by H&E staining and blood sample testing.

RESULTS

[177Lu]Lu-TST001 was labeled with an 99.17%±0.32 radiochemical purity, an 18.50 ± 1.27 MBq/nmol specific activity and a stability of ≥ 94% after 7 days. It exhibited specific and high tumor uptake in CLDN18.2-positive xenografts of GC mouse models. Survival studies in BGC823CLDN18.2 and AGSCLDN18.2 tumor-bearing mouse models indicated that a low dose of 5.55 MBq and a high dose of 11.10 MBq [177Lu]Lu-TST001 significantly inhibited tumor growth compared to the saline control group, with the 11.1 MBq group showing better therapeutic efficacy. Histological staining with hematoxylin and eosin (H&E) and Ki67 immunohistochemistry of residual tissues confirmed tumor tissue destruction and reduced tumor cell proliferation following treatment. H&E showed that there was no significant short-term toxicity observed in the heart, spleen, stomach or other important organs when treated with a high dose of [177Lu]Lu-TST001, and no apparent hematotoxicity or liver toxicity was observed.

CONCLUSION

In preclinical studies, [177Lu]Lu-TST001 demonstrated significant antitumor efficacy with acceptable toxicity. It exhibits strong potential for clinical translation, providing a new promising treatment option for CLDN18.2-overexpressing tumors, including GC.

Dynamic Gesture Recognition Based on FMCW Millimeter Wave Radar: Review of Methodologies and Results

As a convenient and natural way of human-computer interaction, gesture recognition technology has broad research and application prospects in many fields, such as intelligent perception and virtual reality. This paper summarized the relevant literature on gesture recognition using Frequency Modulated Continuous Wave (FMCW) millimeter-wave radar from January 2015 to June 2023. In the manuscript, the widely used methods involved in data acquisition, data processing, and classification in gesture recognition were systematically investigated. This paper counts the information related to FMCW millimeter wave radar, gestures, data sets, and the methods and results in feature extraction and classification. Based on the statistical data, we provided analysis and recommendations for other researchers. Key issues in the studies of current gesture recognition, including feature fusion, classification algorithms, and generalization, were summarized and discussed. Finally, this paper discussed the incapability of the current gesture recognition technologies in complex practical scenes and their real-time performance for future development.

Correction of image distortion in large-field ssEM stitching by an unsupervised intermediate-space solving network

MOTIVATION

Serial-section electron microscopy (ssEM) is a powerful technique for cellular visualization, especially for large-scale specimens. Limited by the field of view, a megapixel image of whole-specimen is regularly captured by stitching several overlapping images. However, suffering from distortion by manual operations, lens distortion or electron impact, simple rigid transformations are not adequate for perfect mosaic generation. Non-linear deformation usually causes 'ghosting' phenomenon, especially with high magnification. To date, existing microscope image processing tools provide mature rigid stitching methods but have no idea with local distortion correction.

RESULTS

In this article, following the development of unsupervised deep learning, we present a multi-scale network to predict the dense deformation fields of image pairs in ssEM and blend these images into a clear and seamless montage. The model is composed of two pyramidal backbones, sharing parameters and interacting with a set of registration modules, in which the pyramidal architecture could effectively capture large deformation according to multi-scale decomposition. A novel 'intermediate-space solving' paradigm is adopted in our model to treat inputted images equally and ensure nearly perfect stitching of the overlapping regions. Combining with the existing rigid transformation method, our model further improves the accuracy of sequential image stitching. Extensive experimental results well demonstrate the superiority of our method over the other traditional methods.

AVAILABILITY AND IMPLEMENTATION

The code is available at https://github.com/HeracleBT/ssEM_stitching.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.