Histological processing of thermosensitive electrospun poly(ε-caprolactone)/poly(l-lactide) (PCL/PLA) scaffolds fails, as poly(ε-caprolactone) (PCL) is characterized by its low-melting temperature (Tm = 60 °C). Here, we present an optimized low-temperature preparation method for the histological processing of un-/cellularized thermosensitive PCL/PLA scaffolds. Our study is aimed at the establishment of an optimized dehydration and low-melting-point paraffin-embedding method of electrospun PCL/PLA scaffolds (un-/cellularized). Furthermore, we compared this method with (a) automatized dehydration and standard paraffin embedding, (b) gelatin embedding followed by automatized dehydration and standard paraffin embedding, (c) cryofixation, and (d) acrylic resin embedding methods. We investigated pepsin and proteinase K antigen retrieval for their efficiency in epitope demasking at low temperatures and evaluated protocols for immunohistochemistry and immunofluorescence for cytokeratin 7 (CK7) and in situ padlock probe technology for beta actin (ACTB). Optimized dehydration and low-melting-point paraffin embedding preserved the PCL/PLA scaffold, as the diameter and structure of its fibers were unchanged. Cells attached to the PCL/PLA scaffolds showed limited alterations in size and morphology compared to control. Epitope demasking by enzymatic pepsin digestion and immunostaining of CK7 displayed an invasion of attached cells into the scaffold. Expression of ACTB and CK7 was shown by a combination of mRNA-based in situ padlock probe technology and immunofluorescence. In contrast, gelatin stabilization followed by standard paraffin embedding led to an overall shrinkage and melting of fibers, and therefore, no further analysis was possible. Acrylic resin embedding and cyrofixation caused fiber structures that were nearly unchanged in size and diameter. However, acrylic resin-embedded scaffolds are limited to 3 µm sections, whereas cyrofixation led to a reduction of the cell size by 14% compared to low-melting paraffin embedding. The combination of low-melting-point paraffin embedding and pepsin digestion as an antigen retrieval method offers a successful opportunity for histological investigations in thermosensitive specimens.

Innate immune cells react to electromagnetic fields (EMF) by generating reactive oxygen species (ROS), crucial intracellular messengers. Discrepancies in applied parameters of EMF studies, e.g., flux densities, complicate direct comparison of downstream anti-oxidative responses and immune regulatory signaling. We therefore compared the impact of different EMF flux densities in human leukemic THP1 cells and peripheral blood mononuclear cells (PBMC) of healthy donors to additionally consider a potential disparate receptivity based on medical origin. ROS levels increased in THP1 cells stimulated with lipopolysaccharide (LPS) after one hour of EMF exposure. Moreover, weak EMF mitigated the depletion of the reducing agent NAD(P)H in THP1. Neither of these effects occurred in PBMC. Landscaping transcriptional responses to varied EMF revealed elevation of the anti-oxidative enzymes PRDX6 (2-fold) and DHCR24 (6-fold) in THP1, implying involvement in lipid metabolism. Furthermore, our study confirmed anti-inflammatory effects of EMF by 6-fold increased expression of IL10. Strikingly, THP1 responded to weak EMF, while PBMC were primarily affected by strong EMF, yet with severe cellular stress and enhanced rates of apoptosis, indicated by HSP70 and caspase 3 (CASP3). Taken together, our results emphasize an altered susceptibility of immune cells of different origin and associate EMF-related effects with anti-inflammatory signaling and lipid metabolism.

The editorial, “Clinical and translational mode of single‐cell measurements: An artificial intelligent single‐cell,” introduces the innovative clinical artificial intelligence single‐cell (caiSC) system, which merges AI with single‐cell informatics to advance real‐time diagnostics, disease monitoring, and treatment prediction. By combining clinical data and multimodal molecular inputs, caiSC facilitates personalized medicine, promising enhanced diagnostic precision and tailored therapeutic approaches. Despite its potential, caiSC lacks comprehensive data coverage across cell types and diseases, presenting challenges in data quality and model robustness. The article explores development strategies such as data expansion, machine learning advancements, and interpretability improvements. Future applications of caiSC could include digital cell twins, offering in‐depth simulations of cellular behavior to support drug discovery and personalized treatments. Regulatory considerations are discussed, underscoring the need for SaMD/AIaMD certifications for clinical use. Ultimately, with further refinement, caiSC could transform clinical decision‐making, driving personalized, precision medicine, and improved patient outcomes.Key points: Integration of AI with Single‐Cell Informatics for Precision Medicine: The caiSC system combines artificial intelligence and single‐cell data to improve diagnostics, treatment predictions, and personalized medical decision‐making. Challenges in Data Coverage and Model Robustness: caiSC currently faces limitations due to incomplete data across cell types, diseases, and organs, as well as challenges in data quality and high computational demands, which affect model accuracy and clinical applicability. Future Potential and Regulatory Needs: The caiSC framework's development could lead to innovations such as digital cell twins, enabling personalized simulations of cellular responses for better treatment planning, though regulatory certification is essential for safe clinical use.

Microscopic analysis of mucus quantity and composition is crucial in research and diagnostics on muco‐obstructive diseases. Currently used image‐based methods are unable to extract concrete numeric values of mucosal proteins, especially on the expression of the key mucosal proteins MUC5AC and MUC5B. Since their levels increase under pathologic conditions such as extensive exposure to cigarette smoke, it is imperative to quantify them to improve treatment strategies of pulmonary diseases. This study presents a simple, image‐based, and high‐processing computational method that allows determining the ratio of MUC5AC and MUC5B within the overall airway mucus while providing information on their spatial distribution. The presented pipeline was optimized for automated downstream analysis using a combination of bright field and immunofluorescence imaging suitable for tracheal and bronchial tissue samples, and air–liquid interface (ALI) cell cultures. To validate our approach, we compared tracheal tissue and ALI cell cultures of isolated primary normal human bronchial epithelial cells derived from smokers and nonsmokers. Our data indicated 18‐fold higher levels of MUC5AC in submucosal glands of smokers covering about 8% of mucosal areas compared to <1% in nonsmoking individuals, confirming results of previous studies. We further identified a subpopulation of nonsmokers with slightly elevated glandular MUC5AC levels suggesting moderate exposure to second‐hand smoke or fine particulate air pollution. Overall, this study demonstrates a novel, user‐friendly and freely available tool for digital pathology and the analysis of therapeutic interventions tested in ALI cell cultures.

Immunostaining in clinical routine and research highly depends on standardized staining methods and quantitative image analyses. We qualitatively and quantitatively compared antigen retrieval methods (no pretreatment, pretreatment with pepsin, and heat-induced pretreatment with pH 6 or pH 9) for 17 antibodies relevant for placenta and implantation diagnostics and research. Using our newly established, comprehensive automated quantitative image analysis approach, fluorescent signal intensities were evaluated. Automated quantitative image analysis found that 9 out of 17 antibodies needed antigen retrieval to show positive staining. Heat induction proved to be the most efficient form of antigen retrieval. Eight markers stained positive after pepsin digestion, with β-hCG and vWF showing enhanced staining intensities. To avoid the misinterpretation of quantitative image data, the qualitative aspect should always be considered. Results from native placental tissue were compared with sections of a placental invasion model based on thermo-sensitive scaffolds. Immunostaining on placentas in vitro leads to new insights into fetal development and maternal pathophysiological pathways, as pregnant women are justifiably excluded from clinical studies. Thus, there is a clear need for the assessment of reliable immunofluorescent staining and pretreatment methods. Our evaluation offers a powerful tool for antibody and pretreatment selection in placental research providing objective and precise results.