ABSTRACT
The combination of radiotherapy (RT) and immunotherapy shows promise in improving the clinical treatment of solid tumors; however, it faces challenges of low response rates and systemic toxicity. Herein, an implantable alginate/collagen hydrogel encapsulating C-C motif ligand 21 (CCL21)-expressing dendritic cells (CCL21-DCs@gel) was developed to potentiate the systemic antitumor effects of RT. The hydrogel functioned as a suitable reservoir for in vivo culture and proliferation of CCL21-DCs, thereby enabling sustained CCL21 release. The local CCL21 gradient induced by CCL21-DCs@gel significantly enhanced the efficacy of RT in suppressing primary tumor growth and inhibiting distant metastasis across several mouse models. Furthermore, the combination of RT with CCL21-DCs@gel provided complete prophylactic protection to mice. Mechanistic investigations revealed that CCL21-DCs@gel potentiated RT by promoting tumor lymphangiogenesis and attracting immune cell infiltration into the tumor. Collectively, these results suggest that CCL21-DCs@gel is a promising adjunct to RT for effectively eradicating tumors and preventing tumor recurrence.
Subject(s)
Chemokine CCL21 , Hydrogels , Animals , Humans , Mice , Alginates/chemistry , Cell Line, Tumor , Collagen/chemistry , Dendritic Cells/drug effects , Dendritic Cells/immunology , Hydrogels/chemistry , Immunotherapy/methods , Neoplasms/radiotherapy , Neoplasms/pathology , Neoplasms/immunologyABSTRACT
Drowning diagnosis is a complicated process in the autopsy, even with the assistance of autopsy imaging and the on-site information from where the body was found. Previous studies have developed well-performed deep learning (DL) models for drowning diagnosis. However, the validity of the DL models was not assessed, raising doubts about whether the learned features accurately represented the medical findings observed by human experts. In this paper, we assessed the medical validity of DL models that had achieved high classification performance for drowning diagnosis. This retrospective study included autopsy cases aged 8-91 years who underwent postmortem computed tomography between 2012 and 2021 (153 drowning and 160 non-drowning cases). We first trained three deep learning models from a previous work and generated saliency maps that highlight important features in the input. To assess the validity of models, pixel-level annotations were created by four radiological technologists and further quantitatively compared with the saliency maps. All the three models demonstrated high classification performance with areas under the receiver operating characteristic curves of 0.94, 0.97, and 0.98, respectively. On the other hand, the assessment results revealed unexpected inconsistency between annotations and models' saliency maps. In fact, each model had, respectively, around 30%, 40%, and 80% of irrelevant areas in the saliency maps, suggesting the predictions of the DL models might be unreliable. The result alerts us in the careful assessment of DL tools, even those with high classification performance.
Subject(s)
Autopsy , Deep Learning , Drowning , Tomography, X-Ray Computed , Humans , Drowning/diagnosis , Aged , Child , Aged, 80 and over , Tomography, X-Ray Computed/methods , Adolescent , Adult , Autopsy/methods , Middle Aged , Female , Retrospective Studies , Male , Young Adult , ROC Curve , Reproducibility of Results , Postmortem ImagingABSTRACT
Immune checkpoint blockade (ICB) has achieved groundbreaking results in clinical cancer therapy; however, only a subset of patients experience durable benefits. The aim of this study was to explore strategies for predicting tumor responses to optimize the intervention approach using ICB therapy. Methods: We used a bilateral mouse model for proteomics analysis to identify new imaging biomarkers for tumor responses to ICB therapy. A PET radiotracer was synthesized by radiolabeling the identified biomarker-targeting antibody with 124I. The radiotracer was then tested for PET prediction of tumor responses to ICB therapy. Results: We identified galectin-1 (Gal-1), a member of the carbohydrate-binding lectin family, as a potential negative biomarker for ICB efficacy. We established that Gal-1 inhibition promotes a sensitive immune phenotype within the tumor microenvironment (TME) for ICB therapy. To assess the pre-ICB treatment status of the TME, a Gal-1-targeted PET radiotracer, 124I-αGal-1, was developed. PET imaging with 124I-αGal-1 showed the pretreatment immunosuppressive status of the TME before the initiation of therapy, thus enabling the prediction of ICB resistance in advance. Moreover, the use of hydrogel scaffolds loaded with a Gal-1 inhibitor, thiodigalactoside, demonstrated that a single dose of thiodigalactoside-hydrogel significantly potentiated ICB and adoptive cell transfer immunotherapies by remodeling the immunosuppressive TME. Conclusion: Our study underscores the potential of Gal-1-targeted PET imaging as a valuable strategy for early-stage monitoring of tumor responses to ICB therapy. Additionally, Gal-1 inhibition effectively counteracts the immunosuppressive TME, resulting in enhanced immunotherapy efficacy.
Subject(s)
Galectin 1 , Immunotherapy , Positron-Emission Tomography , Tumor Microenvironment , Galectin 1/metabolism , Animals , Mice , Cell Line, Tumor , Immune Checkpoint Inhibitors/therapeutic use , Immune Checkpoint Inhibitors/pharmacology , Female , Treatment Outcome , Iodine Radioisotopes , HumansABSTRACT
The recent interest in microscopic autonomous systems, including microrobots, colloidal state machines, and smart dust, has created a need for microscale energy storage and harvesting. However, macroscopic materials for energy storage have noted incompatibilities with microfabrication techniques, creating substantial challenges to realizing microscale energy systems. Here, we photolithographically patterned a microscale zinc/platinum/SU-8 system to generate the highest energy density microbattery at the picoliter (10-12 liter) scale. The device scavenges ambient or solution-dissolved oxygen for a zinc oxidation reaction, achieving an energy density ranging from 760 to 1070 watt-hours per liter at scales below 100 micrometers lateral and 2 micrometers thickness in size. The parallel nature of photolithography processes allows 10,000 devices per wafer to be released into solution as colloids with energy stored on board. Within a volume of only 2 picoliters each, these primary microbatteries can deliver open circuit voltages of 1.05 ± 0.12 volts, with total energies ranging from 5.5 ± 0.3 to 7.7 ± 1.0 microjoules and a maximum power near 2.7 nanowatts. We demonstrated that such systems can reliably power a micrometer-sized memristor circuit, providing access to nonvolatile memory. We also cycled power to drive the reversible bending of microscale bimorph actuators at 0.05 hertz for mechanical functions of colloidal robots. Additional capabilities, such as powering two distinct nanosensor types and a clock circuit, were also demonstrated. The high energy density, low volume, and simple configuration promise the mass fabrication and adoption of such picoliter zinc-air batteries for micrometer-scale, colloidal robotics with autonomous functions.