SRFormer: Empowering Regression-Based Text Detection Transformer with Segmentation

Existing techniques for text detection can be broadly classified into two primary groups: segmentation-based methods and regression-based methods. Segmentation models offer enhanced robustness to font variations but require intricate post-processing, leading to high computational overhead. Regression-based methods undertake instance-aware prediction but face limitations in robustness and data efficiency due to their reliance on high-level representations. In our academic pursuit, we propose SRFormer, a unified DETR-based model with amalgamated Segmentation and Regression, aiming at the synergistic harnessing of the inherent robustness in segmentation representations, along with the straightforward post-processing of instance-level regression. Our empirical analysis indicates that favorable segmentation predictions can be obtained at the initial decoder layers. In light of this, we constrain the incorporation of segmentation branches to the first few decoder layers and employ progressive regression refinement in subsequent layers, achieving performance gains while minimizing additional computational load from the mask. Furthermore, we propose a Mask-informed Query Enhancement module. We take the segmentation result as a natural soft-ROI to pool and extract robust pixel representations, which are then employed to enhance and diversify instance queries. Extensive experimentation across multiple benchmarks has yielded compelling findings, highlighting our method's exceptional robustness, superior training and data efficiency, as well as its state-of-the-art performance

Intrathecal Delivery of Nanoparticles for Treatment of Metastatic Medulloblastoma

The morbidity associated with pediatric medulloblastoma, particularly in patients who develop leptomeningeal metastases, remains high in the absence of effective therapies. Administration of substances directly into the cerebrospinal fluid (CSF) is one approach to circumvent the blood brain barrier (BBB) and focus delivery of drugs to the site of tumor, but high rates of CSF turnover prevent adequate drug accumulation and lead to rapid systemic clearance and toxicity. Here, we show that the nanoparticle encapsulation of talazoparib (BMN-673) significantly improves the therapeutic index when delivered intrathecally and leads to sustained drug retention at tumor site. We demonstrate that administration of these particles into the CSF, alone or in combination with temozolomide, is a highly effective therapy leading to tumor regression and prevention of leptomeningeal spread in mouse models of medulloblastoma. These results provide a rationale for harnessing nanoparticles for the delivery of drugs limited by brain penetration and therapeutic index, and demonstrate important advantages in tolerability and efficacy for encapsulated drugs delivered locoregionally

An ingestible self-orienting system for oral delivery of macromolecules

Biomacromolecules have transformed our capacity to effectively treat diseases; however, their rapid degradation and poor absorption in the gastrointestinal (GI) tract generally limit their administration to parenteral routes. An oral biologic delivery system must aid in both localization and permeation to achieve systemic drug uptake. Inspired by the leopard tortoise’s ability to passively reorient, we developed an ingestible self-orienting millimeter-scale applicator (SOMA) that autonomously positions itself to engage with GI tissue. It then deploys milliposts fabricated from active pharmaceutical ingredients directly through the gastric mucosa while avoiding perforation. We conducted in vivo studies in rats and swine that support the applicator’s safety and, using insulin as a model drug, demonstrated that the SOMA delivers active pharmaceutical ingredient plasma levels comparable to those achieved with subcutaneous millipost administration.National Institutes of Health (U.S.) (grant EB-000244