Sepsis is an abnormally dysregulated immune response against infection in which the human immune system ranges from a hyper-inflammatory phase to an immune-suppressive phase. Current assessment methods are limiting owing to time-consuming and laborious sample preparation protocols. We propose a rapid label-free imaging-based technique to assess the immune status of individual human monocytes. High-resolution intracellular compositions of individual monocytes are quantitatively measured in terms of the three-dimensional distribution of refractive index values using holotomography, which are then analyzed using machine-learning algorithms to train for the classification into three distinct immune states: normal, hyper-inflammation, and immune suppression. The immune status prediction accuracy of the machine-learning holotomography classifier was 83.7% and 99.9% for one and six cell measurements, respectively. Our results suggested that this technique can provide a rapid deterministic method for the real-time evaluation of the immune status of an individual.

Mitochondrial morphology and structural changes are closely associated with metabolic dysfunction and disease progression. However, the structural complexity of mitochondria presents a major challenge for accurate segmentation and analysis. Most existing methods focus on delineating entire mitochondria but lack the capability to resolve fine internal features, particularly cristae. In this study, we introduce MitoStructSeg, a deep learning-based framework for mitochondrial structure segmentation and quantitative analysis. The core of MitoStructSeg is AMM-Seg, a novel model that integrates domain adaptation to improve cross-sample generalization, dual-channel feature fusion to enhance structural detail extraction, and continuity learning to preserve spatial coherence. This architecture enables accurate segmentation of both mitochondrial membranes and intricately folded cristae. MitoStructSeg further incorporates a quantitative analysis module that extracts key morphological metrics, including surface area, volume, and cristae density, allowing comprehensive and scalable assessment of mitochondrial morphology. The effectiveness of our approach has been validated on both human myocardial tissue and mouse kidney tissue, demonstrating its robustness in accurately segmenting mitochondria with diverse morphologies. In addition, we provide an open source, user-friendly tool to ensure practical usability.