Biology subjects

Nauber, R.

Publications and source records attributed to Nauber, R..

4 recordsLinked to original sources

bioRxiv2026

In Vitro Fertilization using Magnetotactic Sperm Cells

In vitro fertilization (IVF) is essential for many couples facing infertility, e.g. in cases of low sperm count (oligospermia), where natural fertilization is unlikely. Medical microrobotics, making use of microscopic devices designed to perform targeted tasks inside the body under imaging guidance and controlled actuation, represents a promising strategy to guide sperm cells toward the oocyte. This approach may significantly reduce the time, invasiveness, and patient burden of conventional IVF, with long-term potential for in vivo assisted reproduction. Here, we report the first successful in vitro fertilization (IVF) using magnetically functionalized spermatozoa, termed magnetotactic sperm cells (MSCs), as a step toward in vivo microrobotic guidance of sperm cells for targeted artificial insemination. We present a protocol for the preparation of MSCs for their use in IVF, resulting in samples largely free of non-functionalized sperm cells (99.69% purity). We systematically evaluate the effect of particle functionalization on sperm health, including acrosome integrity, DNA fragmentation, mitochondrial membrane potential, oxidative stress, and epithelial interactions, and observe no adverse effects. Notably, MSCs showed improved mitochondrial membrane integrity compared to the control samples after two hours of incubation. Using MSCs, we successfully performed complete IVF cycles that resulted in embryos developing to the blastocyst stage at a comparable rate as non-functionalized sperm cells of the same concentration. Lower concentrations of non-functionalized sperm cells (comparable to those remaining in the MSC sample after purification) did not result in any development of embryos to blastocysts. To facilitate manipulation and translation, we implemented automated image-based recognition, magnetic manipulation, and pre-clustering routines that increased guidance efficiency and are compatible with standard IVF workflows. Together, these results demonstrate that magnetic functionalization can be applied without compromising key sperm quality metrics and can enable directed sperm guidance for assisted oocyte fertilization. This work provides a practical framework for integrating microrobotic sperm manipulation into assisted-reproduction workflows and supports further development toward automated in vitro and eventual in vivo applications.

bioengineering↗

bioRxiv2025

Magnetically Controlled Microrobots for In Vivo Non-Invasive Embryo Transfer

Infertility affects millions worldwide and is often linked to factors such as poor sperm quality and female reproductive organ disorders. Despite significant advancements in in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), implantation rates remain low, ranging from 17 to 21% after three days of incubation, mainly due to stress, lifestyle factors, and uterine conditions. Extended embryo culture techniques have shown promise in improving pregnancy rates. However, the availability of high-quality blastocysts remains a major challenge. Intrafallopian transfer techniques, such as gamete/zygote intrafallopian transfer (GIFT/ZIFT), were introduced to improve fertilization and early embryo development, particularly for patients with repeated embryo implantation failure (10-30% of assisted reproduction technology (ART) cases, particularly in women >35). However, these methods have declined due to advancements in IVF and the variability in laparoscopy procedures used for GIFT/ZIFT. To address these challenges, we propose a non-invasive microrobotic embryo transfer ({micro}ET) technique using remotely controlled microcarriers comparable in size to embryos. We demonstrate the capabilities of magnetically actuated spiral microrobots, fabricated using laser direct writing for capturing, transporting, and releasing embryos into murine uteri. Additionally, we characterize their motion performance and implement image-guided closed-loop control and dual ultrasound (US)/photoacoustic (PA) tracking for deep-tissue interventions. Recognizing the importance of clinical translation, we present preliminary studies on gelatin-based microrobots, a biodegradable alternative, and explore endometrial remodeling after the in vivo transfer of microrobots carrying embryo-like structures. Our findings show that these microrobots can effectively transport embryos, support their development, and enable minimally invasive delivery, providing a more natural, targeted, and non-invasive strategy for in vivo assisted reproduction. One-Sentence SummaryWe propose a non-invasive {micro}ET technique using magnetically actuated microcarriers to improve embryo cargo-delivery in assisted reproduction, addressing challenges in infertility treatments by enabling precise, image-guided embryo delivery with minimal invasiveness.

bioengineering↗

bioRxiv2025

Photoacoustics-guided Real-Time Closed-loop Control of Magnetic Microrobots through Deep Learning

Medical microrobots promise to increase the efficacy and reduce the invasiveness of certain medical procedures in the future. Real-time tracking of the microrobot, actuation, and closed-loop control of its position under in vivo conditions is crucial to fulfill the task at hand. We present a system for closed-loop control of magnetic microrobots using dual-mode ultrasound and photoacoustic imaging. It employs GPU-accelerated beamforming and tracking to achieve real-time operation with a closed-loop cycle time of 100 ms. Artifacts from simultaneous imaging and magnetic actuation are suppressed through time-multiplexing. To address the challenge of detecting microrobots in low-contrast, strong-background images, we implemented real-time Deep Learning-based tracking. A custom dataset of various types of microrobots is curated from long-duration closed-loop control measurements and employed to fine-tune a pre-trained detection model. We introduce a platform for real-time closed-loop control of microrobots and demonstrate its performance with a 300 m spiral-shaped microrobot following a figure-of-8 shape under photoacoustic imaging guidance. The localization error is evaluated against an optical reference measurement. Our results show that photoacoustic-based tracking significantly outperforms ultrasound tracking, with the deep learning approach further reducing missed detections. This demonstrates the algorithms ability to generalize to a previously unseen type of microrobot. We envision this platform to advance medical microrobotics research by providing real-time closed-loop control of untethered microrobots under deep tissue.

bioengineering↗

bioRxiv2025

Influence of hyperparameters on the performance of deep learning-based microrobotic localization under phantom tissue

For the effective operation of medical microrobots within living organisms and precise targeting, it is imperative to employ imaging techniques closely integrated with real-time deep-tissue tracking methods. However, due to a typically low Signal-to-Noise ratio images with strong background, it is hard for traditional tracking methods to achieve sufficient accuracy. This challenge can be addressed by deep learning-based tracking with a real-time detection model. However, a multitude of design choices and Hyperparameters influence the performance. In this study we compared the influence of the hyperparameters and model architecture versions of the "you only look once" (YOLO) network. We use experimental data from a magnetic microrobot imaged with Photoacoustics through 5 mm phantom tissue to evaluate the tracking in comparison with an optical reference. The deep-learning based methods consistently achieved lower missing-detection ratios. Regarding the Root Mean Square localization error, we observed that increasing the weight of the box loss function and utilizing the distribution focal loss can enhance the performance by 10%. Furthermore, it can be seen that YOLOv9 consistently outperformed its predecessor YOLOv8. This study quantifies the robustness of deep-learning based tracking of medical microrobots under tissues.

bioengineering↗