Gangyin Luo

High-throughput experiments, unidirectional fluid replacement, real-time process monitoring, and simultaneous drug sensitivity and toxicity tests are hard to achieve on most existing tumor organoid chips. Here, we developed a gravity-driven organoid perfusion (GDOP) platform facilitating scalable throughput and supporting drug sensitivity and toxicity assessment on organoids. The unidirectional perfusion capability and optimized operational parameters of the GDOP chip were validated through fluid dynamics simulations. Using this platform, we successfully established uniform on-chip triple-negative breast cancer (TNBC) organoids, with endpoint detection results aligning closely with clinical diagnosis. Throughout the drug treatment process, we monitored and then analyzed the morphological and grayscale changes of the organoids. The sensitivity and toxicity tests revealed the optimal concentration range for the 3 chemotherapeutic drugs. In addition, on-chip brain organoids were established, which lays a feasible foundation for future drug toxicity tests of complex organoids. The GDOP platform, combined with its integrated evaluation method, provides a powerful and reliable approach for advancing organoid-based researches.

Photonic jets (PJs) generated from mesoscale dielectric particles can achieve sub-diffraction-scale light field constraints and significant near-field intensity enhancement, which have important application value in the fields of nanoimaging, optical sensing, and laser processing. Recent studies show that the axial-extension and transverse-focus characteristics of PJs can be effectively regulated through interface engineering methods, such as using double-layer structures and truncated geometries. Such structures can be referred to as Janus microstructures separated by surface refracted interfaces. However, systematic research on the effect of incident light polarization on the formation and regulation of PJs on the surface interfaces of Janus systems is lacking. In this study, the PJ characteristics under polarization regulation in curved-interface Janus microcylinders are systematically investigated by performing full-wave numerical simulations. The results show that polarization modulation introduces a new degree of freedom for regulating the energy flow distribution and morphology of PJs. An appropriate polarization state can be selected to effectively regulate key characteristic parameters, such as the length, peak intensity, and full width at half maximum of the nanojet, without changing the particle geometry or material composition. This study reveals the synergy between the surface-interface Janus structures and polarization engineering, providing a new physical method for the flexible regulation of PJs in near-field optics.

Lung organoids play a crucial role in modeling drug responses in pulmonary diseases. However, their morphological analysis remains hindered by manual detection inefficiencies and the high computational cost of existing algorithms. To overcome these challenges, this study proposes Orga-Dete—a lightweight, high-precision detection model based on YOLOv11n—which first employs data augmentation to mitigate the small-scale dataset and class imbalance issues, then optimizes via a triple co-optimization strategy: a bi-directional feature pyramid network for enhanced multi-scale feature fusion, MPCA for stronger micro-organoid feature response, and EMASlideLoss to address class imbalance. Validated on a lung organoid microscopy dataset, Orga-Dete achieves 81.4% mAP@0.5 with only 2.25 M parameters and 6.3 GFLOPs, surpassing the baseline model YOLOv11n by 3.5%. Ablation experiments confirm the synergistic effects of these modules in enhancing morphological feature extraction. With its balance of precision and efficiency, Orga-Dete offers a scalable solution for high-throughput organoid analysis, underscoring its potential for personalized medicine and drug screening.

Organoids are miniature simplified in vitro model systems that simulate organ structure and function. Despite their utility, challenges remain in addressing organoid assembly and related data analysis. In the context of integrating organoid technology with deep learning, this study proposes a lightweight deep algorithm to efficiently handle high-throughput, multimodal, and fine-grained organoid images, with a primary focus on intestinal organoid images.The study employs the lightweight YOLOv10n as the baseline model for organoid image analysis, introduces a novel organoid image information fusion architecture, and completes the theoretical and engineering design of a specialized algorithm framework for organoid images. Through rigorous experiments—including model comparison analyses, organoid receptive field visualization, and organoid feature attention distribution studies—and performance comparisons with classical models, this work demonstrates how deep learning overcomes the fine-grained analysis challenge in organoid images. Notably, the approach reduces model complexity while enhancing computational efficiency and inference speed for organoid images. This study achieves state-of-the-art organoid recognition performance with minimized computational overhead, offering a new pattern recognition methodology for organoid morphological evaluation. In conclusion, this research presents an innovative technical tool that integrates superior computational performance with real-time multi-dimensional scientific prediction of organoid morphology.

Targeted therapy is essential for the 15–30% invasive breast cancer patients with human epidermal growth factor receptor 2 (HER2) over-expression. However, current HER2 diagnosing methods rely on complex manual works and highly subjective interpretations. To more accurately and objectively assess the HER2 amplification status of formalin fixed paraffin embedded (FFPE) samples, a droplet digital PCR (ddPCR) assay based on multi-reference genes was developed. We established a four-fluorescence ddPCR assay using breast cancer cell lines (T-47D and SK-BR-3) and validated it on 101 clinical breast cancer FFPE samples. Compared to clinicopathological results, the ddPCR assay based on two out of three reference genes demonstrated superior sensitivity (82.6%), specificity (98.7%), and consistency (95.0%) in determining HER2 status over assays using single or three reference genes. Whole genome sequencing of the abnormal cases further confirmed that the ddPCR assay outperformed clinical immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and quantitative PCR (qPCR) in accuracy. Our findings demonstrate that the multi-reference gene ddPCR assay significantly improves the accuracy of HER2 status detection and reduces errors associated with chromosome 17 abnormalities. This method holds promise as a complementary or alternative approach to conventional IHC and FISH testing in tissue biopsies and is also feasible for liquid biopsies.

In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that this paper proposes a plasma quality detection model based on improved "You Only Look Once" 5th version (YOLOv5). Then the model presented in this paper and the evaluation system ‌were introduced‌ into the plasma datasets, and ‌the average accuracy of the final classification reached 98.7%‌. The results of this paper's experiment were obtained through the combination of several key algorithm modules including‌ omni-dimensional dynamic convolution, pooling with separable kernel attention, residual bi-fusion feature pyramid network, ‌and‌ re-parameterization convolution. The method of this paper‌ obtains the feature information of spatial mapping efficiently, and enhances the average recognition accuracy of plasma quality detection. This paper presents a high-efficiency detection method for plasma images, aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.

Intestinal organoids are indispensable tools for exploring intestinal disorders. Deep learning methodologies are often employed in morphological analysis to evaluate the condition of these organoids. Nonetheless, prevailing analytical techniques face obstacles such as many organisational overlaps and tiny targets lead to a high incidence of errors and limited applicability. This paper presents Deliod, a streamlined intestinal organoid detection model founded on YOLOv8 and designed to automate the identification of organoid morphology. Deliod performed excellently compared to leading detection models when applied to an intestinal organoid dataset, attaining an mAP50 of 87.5%. Ablation experiments verified the module’s efficacy in improving detection performance. Furthermore, Deliod features a modest parameter count of 5.41 M and a computational load of 16.6 GFLOPs, facilitating the broader application of the detection model in the realm of intestinal organoid image recognition. This streamlined model not only enables efficient and accurate recognition of organoid morphology but also minimizes hardware deployment requirements, broadening its range of potential applications.

Background: Membrane oxygenators facilitate extracorporeal gas exchange, necessitating the monitoring of blood gas. Recent advances in normothermic machine perfusion (NMP) for ex vivo liver offer solutions to the shortage of donor liver. However, maintaining physiological blood gas levels during prolonged NMP is complex and costly. Methods: We introduce a noninvasive and economical approach for regulating the blood gas during NMP of ex vivo porcine livers. By monitoring gas fractions at the outlet of oxygenator, real‐time adjustments of blood gas can be made without the online blood gas analyzer. The method involves constructing multivariate linear regression (MLR) models, aligning target setpoints of gas, and employing active disturbance rejection control (ADRC) to achieve closed‐loop regulation. Results: Ex vivo porcine liver perfusion experiments demonstrated the effectiveness of the method, maintaining blood gas within physiological levels over 24 h (oxygen partial pressure: 150.36 ± 3.33 mmHg, carbon dioxide partial pressure: 41.34 ± 0.91 mmHg). Conclusion: ADRC‐based continuous regulation of gas fraction at the outlet of oxygenator is a feasible and effective approach for managing blood gas during ex vivo porcine liver perfusion.

In regular biosample cryopreservation operations, dropwise pipetting and continuous swirling are ordinarily needed to prevent cell damage (e.g. sudden osmotic change, toxicity and dissolution heat) caused by the high-concentration cryoprotectant (CPA) addition process. The following CPA removal process after freezing and rewarming also requires multiple sample transfer processes and manual work. In order to optimize the cryopreservation process, especially for trace sample preservation, here we present a microfluidic approach integrating CPA addition, sample storage, CPA removal and sample resuspension processes on a 30 × 30 × 4 mm³ three-layer chip. The sample solution could be added into CPA solution with pre-generated increasing concentration to decrease possible osmotic damage. Utilizing specially designed microfluidic structure and fluid field analysis, on-chip sample enrichment and CPA removal were achieved. A novel dead-end micro valve strategy with a simplified control module was applied and evaluated to assist on-chip mixing and sample pellet resuspension. The entire biosample cryopreservation process was also performed that verified the functions of the integrated microfluidic platform. Altogether, this developed platform could be an effective approach to realize automatic, all-in-one, low-damage cryopreservation operation.