A novel GARP humanized mouse model for efficacy assessment of GARP-targeting therapies.

Although breakthroughs have been achieved with immune checkpoint inhibitors (ICI) therapy, some tumors do not respond to those therapies due to primary or acquired resistance. GARP, a type I transmembrane cell surface docking receptor mediating latent transforming growth factor-ß (TGF-ß) and abundantly expressed on regulatory T lymphocytes and platelets, is a potential target to render these tumors responsive to ICI therapy, and enhancing anti-tumor response especially combined with ICI. To facilitate these research efforts, we developed humanized mouse models expressing humanized GARP (hGARP) instead of their mouse counterparts, enabling in vivo assessment of GARP-targeting agents. We created GARP-humanized mice by replacing the mouse Garp gene with its human homolog. Then, comprehensive experiments, including expression analysis, immunophenotyping, functional assessments, and pharmacologic assays, were performed to characterize the mouse model accurately. The Tregs and platelets in the B-hGARP mice (The letter B is the first letter of the company's English name, Biocytogen.) expressed human GARP, without expression of mouse GARP. Similar T, B, NK, DCs, monocytes and macrophages frequencies were identified in the spleen and blood of B-hGARP and WT mice, indicating that the humanization of GARP did not change the distribution of immune cell in these compartments. When combined with anti-PD-1, monoclonal antibodies (mAbs) against GARP/TGF-ß1 complexes demonstrated enhanced in vivo anti-tumor activity compared to monotherapy with either agent. The novel hGARP model serves as a valuable tool for evaluating human GARP-targeting antibodies in immuno-oncology, which may enable preclinical studies to assess and validate new therapeutics targeting GARP. Furthermore, intercrosses of this model with ICI humanized models could facilitate the evaluation of combination therapies.

Lower-Limb Motor Assessment With Corticomuscular Coherence of Multiple Muscles During Ankle Dorsiflexion After Stroke.

Motor impairment after stroke is generally caused by damage to the neural networks that control movement. Corticomuscular coherence (CMC) is a valid method to analyze the functional connectivity of the corticospinal pathway between the cerebral cortex and muscles. However, current studies on CMC in stroke patients only focused on the upper limbs. The functional connectivity between the brain and lower limbs in stroke patients has not been well studied. Therefore, twelve stroke patients and fifteen healthy controls were recruited and their electroencephalogram (EEG) and electromyogram (EMG) of Tibialis Anterior (TA), Lateral Gastrocnemius (LG) and Medial Gastrocnemius (MG) during unilateral static ankle dorsiflexion were recorded. We found the mean beta and gamma CMC values of Cz electrode of stroke patients were significantly lower than those of healthy controls (p < 0.05). The brain topography showed significant coherence in the center of the cerebral cortex in healthy controls, while there was no significant coherence in stroke patients. For clinical assessment, there was a significant positive correlation between CMC and lower limb Fugl-Meyer Assessment (FMA) for Cz-TA in beta band (r = 0.6296, p = 0.0282), Cz-LG in beta band (r = 0.6816, p = 0.0147), and Cz-MG in gamma band (r = 0.6194, p = 0.0317). A multiple linear regression model was established between CMC and lower limb FMA ( R2 = 0.6600 , p = 0.0280). Therefore, CMC between the cerebral cortex and lower limb muscles may be used as a new rehabilitation assessment biomarker in stroke.

Generative Bayesian image super resolution with natural image prior.

We propose a new single image super resolution (SR) algorithm via Bayesian modeling with a natural image prior modeled by a high-order Markov random field (MRF). SR is one of the long-standing and active topics in image processing community. It is of great use in many practical applications, such as astronomical observation, medical imaging, and the adaptation of low-resolution contents onto high-resolution displays. One category of the conventional approaches for image SR is formulating the problem with Bayesian modeling techniques and then obtaining its maximum-a-posteriori solution, which actually boils down to a regularized regression task. Although straightforward, this approach cannot exploit the full potential offered by the probabilistic modeling, as only the posterior mode is sought. On the other hand, current Bayesian SR approaches using the posterior mean estimation typically use very simple prior models for natural images to ensure the computational tractability. In this paper, we present a Bayesian image SR approach with a flexible high-order MRF model as the prior for natural images. The minimum mean square error (MMSE) criteria are used for estimating the HR image. A Markov chain Monte Carlo-based sampling algorithm is presented for obtaining the MMSE solution. The proposed method cannot only enjoy the benefits offered by the flexible prior, but also has the advantage of making use of the probabilistic modeling to perform a posterior mean estimation, thus is less sensitive to the local minima problem as the MAP solution. Experimental results indicate that the proposed method can generate competitive or better results than state-of-the-art SR algorithms.