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Bisphenol A (BPA) is a prevalent environmental contaminant found in plastics and known for its endocrine-disrupting properties, posing risks to both human health and the environment. Despite its widespread presence, the impact of BPA on papillary thyroid cancer (PTC) progression, especially under realistic environmental conditions, is not well understood. This study examined the effects of BPA on PTC using a 3D thyroid papillary tumor spheroid model, which better mimicked the complex interactions within human tissues compared to traditional 2D models. Our findings demonstrated that BPA, at environmentally relevant concentrations, could induce significant changes in PTC cells, including a decrease in E-cadherin expression, an increase in vimentin expression, and reduced thyroglobulin (TG) secretion. These changes suggest that BPA exposure may promote epithelial–mesenchymal transition (EMT), enhance invasiveness, and reduce cell differentiation, potentially complicating treatment, including by increasing resistance to radioiodine therapy. This research highlights BPA’s hazardous nature as an environmental contaminant and emphasizes the need for advanced in vitro models, like 3D tumor spheroids, to better assess the risks posed by such chemicals. It provides valuable insights into the environmental implications of BPA and its role in thyroid cancer progression, enhancing our understanding of endocrine-disrupting chemicals.

Cancer has been one of the leading causes of death globally, with metastases and recurrences contributing to this result. The detection of circulating tumor cells (CTCs), which have been implicated as a major population of cells that is responsible for seeding and migration of tumor sites, could contribute to early detection of metastasis and recurrences, consequently increasing the chances of cure. This review article focuses on the current progress in microfluidics technology in CTCs diagnostics, extending to the use of nanomaterials and surface modification techniques for diagnostic applications, with an emphasis on the importance of integrating microchannels, nanomaterials, and surface modification techniques in the isolating and detecting of CTCs.

Organ-on-a-chip (OOC) devices mimic human organs, which can be used for many different applications, including drug development, environmental toxicology, disease models, and physiological assessment. Image data acquisition and analysis from these chips are crucial for advancing research in the field. In this study, we propose a label-free morphology imaging platform compatible with the small airway-on-a-chip system. By integrating deep learning and image recognition techniques, we aim to analyze the differentiability of human small airway epithelial cells (HSAECs). Utilizing cell imaging on day 3 of culture, our approach accurately predicts the differentiability of HSAECs after 4 weeks of incubation. This breakthrough significantly enhances the efficiency and stability of establishing small airway-on-a-chip models. To further enhance our analysis capabilities, we have developed a customized MATLAB program capable of automatically processing ciliated cell beating images and calculating the beating frequency. This program enables continuous monitoring of ciliary beating activity. Additionally, we have introduced an automated fluorescent particle tracking system to evaluate the integrity of mucociliary clearance and validate the accuracy of our deep learning predictions. The integration of deep learning, label-free imaging, and advanced image analysis techniques represents a significant advancement in the fields of drug testing and physiological assessment. This innovative approach offers unprecedented insights into the functioning of the small airway epithelium, empowering researchers with a powerful tool to study respiratory physiology and develop targeted interventions.

The creation of bioactive substrates requires an appropriate interface molecular environment control and adequate biological species recognition with minimum nonspecific attachment. Herein, a straightforward approach utilizing chemical lift-off lithography to create a diluted self-assembled monolayer matrix for anchoring diverse biological probes is introduced. The strategy encompasses convenient operation, well-tunable pattern feature and size, large-area fabrication, high resolution and fidelity control, and the ability to functionalize versatile bioarrays. With the interface-contact-induced reaction, a preformed alkanethiol self-assembled monolayer on a Au surface is ruptured and a unique defect-rich diluted matrix is created. This post lift-off region is found to be suitable for insertion of a variety of biological probes, which allows for the creation of different types of bioactive substrates. Depending on the modifications to the experimental conditions, the processes of direct probe insertion, molecular structure change-required recognition, and bulky biological species binding are all accomplished with minimum nonspecific adhesion. Furthermore, multiplexed arrays via the integration of microfluidics are also achieved, which enables diverse applications of as-prepared substrates. By embracing the properties of well-tunable pattern feature dimension and geometry, great local molecular environment control, and wafer-scale fabrication characteristics, this chemical lift-off process has advanced conventional bioactive substrate fabrication into a more convenient route.

Liquid biopsies use blood or urine as test samples, which are able to be continuously collected in a non-invasive manner. The analysis of cancer-related biomarkers such as circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), microRNA, and exosomes provides important information in early cancer diagnosis, tumor metastasis detection, and postoperative recurrence monitoring assist with clinical diagnosis. However, low concentrations of some tumor markers, such as CTCs, ctDNA, and microRNA, in the blood limit its applications in clinical detection and analysis. Nanomaterials based on graphene oxide have good physicochemical properties and are now widely used in biomedical detection technologies. These materials have properties including good hydrophilicity, mechanical flexibility, electrical conductivity, biocompatibility, and optical performance. Moreover, utilizing graphene oxide as a biosensor interface has effectively improved the sensitivity and specificity of biosensors for cancer detection. In this review, we discuss various cancer detection technologies regarding graphene oxide and discuss the prospects and challenges of this technology.

Paper-based analytical devices are an emerging class of lightweight and simple-to-use analytical platform. However, challenges such as instrumental requirements and chemical reagents durability, represent a barrier for less-developed countries and markets. Herein, we report an advanced laminated device using red emitting copper nanocluster and RGB digital analysis for signal improvement. Upon RGB system assistance, the device signal-to-background ratio and the calibration sensitivity are highly enhanced under a filter-free setup. In addition, the calibration sensitivity, limit of detection, and coefficient of determination are on par with those determined by instrumental fluorescence analysis. Moreover, the limitation of using oxidation-susceptible fluorescent nanomaterials is overcome by the introduction of protecting tape barriers, antioxidative sheets, and lamination enclosing. The robustness of device is highly advanced, and the durability is prolonged to more than tenfold.

Nanoparticle alignment on the substrate attracts considerable attention due to its wide application in different fields, such as mechanical control, small size electronics, bio/chemical sensing, molecular manipulation, and energy harvesting. However, precise nanoparticle positioning and deposition control with high fidelity are still challenging. Herein, a straightforward strategy for high quality nanoparticle-alignment by chemical lift-off lithography (CLL) is demonstrated. This technique creates high resolution self-assembled monolayer (SAM) chemical patterns on gold substrates, enabling nanoparticle-selective deposition and precise alignment. The fabricated nanoparticle arrangement geometries and dimensions are well-controllable in a large area. With proper nanoparticle surface functionality control and adequate substrate molecular manipulation, well-defined nanoparticle arrays with single-particle-wide alignment resolution are achieved.

3D bioprinting enables the fabrication of printable tissues, including those for neural, cartilage, and skin repair. The mechanical properties, especially stiffness, of 3D-bioprinted scaffolds are crucial for modulating cell adhesion, growth, migration, and differentiation. The stiffness of a scaffold can be adjusted post-printing by modifying the hydrogel concentration, crosslinker concentration, light intensity during photocrosslinking, and duration of crosslinking. The optimization of these conditions to produce the desired scaffold stiffness for a particular cell type or application is a time-consuming and rigorous process. This study developed an innovative approach to predict the compression modulus of 3D-printed gelatin methacryloyl (GelMA) scaffolds using the Bayesian optimization (BO) algorithm. Through just 10 iterations (75 experimental data points), the model was able to predict > 13,000 possible compression modulus values in a search space comprising four independent variables (GelMA concentration, crosslinker concentration, ultraviolet light [UV] distance, and UV exposure time). This approach can be utilized in other photocrosslinkable bioinks for 3D printing that have a myriad of pre- or post-printing parameters that can affect scaffold stiffness.

Key message Two closely linked novel loci, qKRN2-1 and qKRN2-2 , associated with kernel row number were fine-mapped on chromosome 2, and a key candidate gene for qKRN2-1 was identified through expression analysis. Kernel row number (KRN) is a crucial factor influencing maize yield and serves as a significant target for maize breeding. The use of wild progenitor species can aid in identifying the essential traits for domestication and breeding. In this study, teosinte (MT1) served as the donor parent, the inbred maize line of Mo17 was used as the recurrent parent, we identified a major quantitative trait locus (QTL) for KRN, designated qKRN2 , into two closely linked loci, qKRN2-1 and qKRN2-2 . Here, fine mapping was performed to investigate two QTLs, qKRN2-1 and qKRN2-2 , within a genomic range of 272 kb and 775 kb, respectively. This was achieved using a progeny test strategy in an advanced backcross population, with the two QTLs explaining 33.49% and 35.30% of the phenotypic variance. Molecular marker-assisted selection resulted in the development of two nearly isogenic lines (NILs), qKRN2-1 and qKRN2-2 , which differed only in the segment containing the QTL. Notably, the maize (Mo17) alleles increased the KRN relative to teosinte by approximately 1.4 and 1.2 rows for qKRN2-1 and qKRN2-2 , respectively. Zm00001d002989 encodes a cytokinin oxidase/dehydrogenase and its expression in the immature ears exhibited significant differences among the qKRN2-1 NILs. In situ hybridization localized Zm00001d002989 to the primordia of the inflorescence meristem and spikelet pair meristems, is predicted to be the causal gene of qKRN2-1. The findings of this study deepen our understanding of the genetic basis of KRN and hold significant potential for improving maize grain yields.

The tassel competes with the ear for nutrients and shields the upper leaves, thereby reducing the yield of grain. The tassel branch number (TBN) is a pivotal determinant of tassel size, wherein the reduced TBN has the potential to enhance the transmission of light and reduce the consumption of nutrients, which should ultimately result in increased yield. Consequently, the TBN has emerged as a vital target trait in contemporary breeding programs that focus on compact maize varieties. In this study, QTL-seq technology and advanced population mapping were used to rapidly identify and dissect the major effects of the TBN on QTL. Advanced mapping populations (BC 4 F 2 and BC 4 F 3 ) were derived from the inbred lines 18–599 (8–11 TBN) and 3237 (0–1 TBN) through phenotypic recurrent selection. First, 13 genomic regions associated with the TBN were detected using quantitative trait locus (QTL)-seq and were located on chromosomes 2 and 5. Subsequently, validated loci within these regions were identified by QTL-seq. Three QTLs for TBN were identified in the BC 4 F 2 populations by traditional QTL mapping, with each QTL explaining the phenotypic variation of 6.13–18.17%. In addition, for the major QTL ( qTBN2-2 and qTBN5-1 ), residual heterozygous lines (RHLs) were developed from the BC 4 F 2 population. These two major QTLs were verified in the RHLs by QTL mapping, with the phenotypic variation explained (PVE) of 21.57% and 30.75%, respectively. Near-isogenic lines (NILs) of qTBN2-2 and qTBN5-1 were constructed. There were significant differences between the NILs in TBN. These results will enhance our understanding of the genetic basis of TBN and provide a solid foundation for the fine-mapping of TBN.