4 results
Search Results
Now showing 1 - 4 of 4
- ItemHuman spermbots for patient-representative 3D ovarian cancer cell treatment(Cambridge : RSC Publ., 2020) Xu, Haifeng; Medina-Sánchez, Mariana; Zhang, Wunan; Seaton, Melanie P. H.; Brison, Daniel R.; Edmondson, Richard J.; Taylor, Stephen S.; Nelson, Louisa; Zeng, Kang; Bagley, Steven; Ribeiro, Carla; Restrepo, Lina P.; Lucena, Elkin; Schmidt, Christine K.; Schmidt, Oliver G.Cellular micromotors are attractive for locally delivering high concentrations of drug, and targeting hard-to-reach disease sites such as cervical cancer and early ovarian cancer lesions by non-invasive means. Spermatozoa are highly efficient micromotors perfectly adapted to traveling up the female reproductive system. Indeed, bovine sperm-based micromotors have shown potential to carry drugs toward gynecological cancers. However, due to major differences in the molecular make-up of bovine and human sperm, a key translational bottleneck for bringing this technology closer to the clinic is to transfer this concept to human material. Here, we successfully load human sperm with Doxorubicin (DOX) and perform treatment of 3D cervical cancer and patient-representative ovarian cancer cell cultures, resulting in strong anticancer cell effects. Additionally, we define the subcellular localization of the chemotherapeutic drug within human sperm, using high-resolution optical microscopy. We also assess drug effects on sperm motility and viability over time, employing sperm samples from healthy donors as well as assisted reproduction patients. Finally, we demonstrate guidance and release of human drug-loaded sperm onto cancer tissues using magnetic microcaps, and show the sperm microcap loaded with a second anticancer drug, camptothecin (CPT), which unlike DOX is not suitable for directly loading into sperm due to its hydrophobic nature. This co-drug delivery approach opens up novel targeted combinatorial drug therapies for future applications. © 2020 The Royal Society of Chemistry.
- ItemTumor cytotoxicity and immunogenicity of a novel V-jet neon plasma source compared to the kINPen(London : Nature Publishing Group, 2021) Miebach, Lea; Freund, Eric; Horn, Stefan; Niessner, Felix; Sagwal, Sanjeev Kumar; von Woedtke, Thomas; Emmert, Steffen; Weltmann, Klaus-Dieter; Clemen, Ramona; Schmidt, Anke; Gerling, Torsten; Bekeschus, SanderRecent research indicated the potential of cold physical plasma in cancer therapy. The plethora of plasma-derived reactive oxygen and nitrogen species (ROS/RNS) mediate diverse antitumor effects after eliciting oxidative stress in cancer cells. We aimed at exploiting this principle using a newly designed dual-jet neon plasma source (Vjet) to treat colorectal cancer cells. A treatment time-dependent ROS/RNS generation induced oxidation, growth retardation, and cell death within 3D tumor spheroids were found. In TUM-CAM, a semi in vivo model, the Vjet markedly reduced vascularized tumors' growth, but an increase of tumor cell immunogenicity or uptake by dendritic cells was not observed. By comparison, the argon-driven single jet kINPen, known to mediate anticancer effects in vitro, in vivo, and in patients, generated less ROS/RNS and terminal cell death in spheroids. In the TUM-CAM model, however, the kINPen was equivalently effective and induced a stronger expression of immunogenic cancer cell death (ICD) markers, leading to increased phagocytosis of kINPen but not Vjet plasma-treated tumor cells by dendritic cells. Moreover, the Vjet was characterized according to the requirements of the DIN-SPEC 91315. Our results highlight the plasma device-specific action on cancer cells for evaluating optimal discharges for plasma cancer treatment.
- ItemImmune mobilising T cell receptors redirect polyclonal CD8+ T cells in chronic HIV infection to form immunological synapses([London] : Macmillan Publishers Limited, part of Springer Nature, 2022) Wallace, Zoë; Kopycinski, Jakub; Yang, Hongbing; McCully, Michelle L.; Eggeling, Christian; Chojnacki, Jakub; Dorrell, LucyT cell exhaustion develops in human immunodeficiency virus (HIV) infection due to chronic viral antigenic stimulation. This adaptive response primarily affects virus-specific CD8+ T cells, which may remain dysfunctional despite viral load-reducing antiretroviral therapy; however, abnormalities may also be evident in non-HIV-specific populations. Both could limit the efficacy of cell therapies against viral reservoirs. Here, we show that bulk (polyclonal) CD8+ T cells from people living with HIV (PLWH) express proposed markers of dysfunctional HIV-specific T cells at high levels yet form lytic immunological synapses (IS) and eliminate primary resting infected (HIV Gaglo) CD4+ T cells, when redirected by potent bispecific T cell-retargeting molecules, Immune mobilising monoclonal T cell receptors (TCR) Against Virus (ImmTAV). While PLWH CD8+ T cells are functionally impaired when compared to CD8+ T cells from HIV-naïve donors, ImmTAV redirection enables them to eliminate Gaglo CD4+ T cells that are insensitive to autologous HIV-specific cytolytic T cells. ImmTAV molecules may therefore be able to target HIV reservoirs, which represent a major barrier to a cure.
- ItemDeep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors([London] : Macmillan Publishers Limited, part of Springer Nature, 2021) Ali, Nairveen; Bolenz, Christian; Todenhöfer, Tilman; Stenzel, Arnulf; Deetmar, Peer; Kriegmair, Martin; Knoll, Thomas; Porubsky, Stefan; Hartmann, Arndt; Popp, Jürgen; Kriegmair, Maximilian C.; Bocklitz, ThomasBladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder cancer, particularly for the detection of flat and small lesions. Here, we aim to demonstrate a BL image-based artificial intelligence (AI) diagnostic platform using 216 BL images, that were acquired in four different urological departments and pathologically identified with respect to cancer malignancy, invasiveness, and grading. Thereafter, four pre-trained convolution neural networks were utilized to predict image malignancy, invasiveness, and grading. The results indicated that the classification sensitivity and specificity of malignant lesions are 95.77% and 87.84%, while the mean sensitivity and mean specificity of tumor invasiveness are 88% and 96.56%, respectively. This small multicenter clinical study clearly shows the potential of AI based classification of BL images allowing for better treatment decisions and potentially higher detection rates.