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Optical-based nanothermometry represents a transformative approach for precise temperature measurements at the nanoscale, which finds versatile applications across biology, medicine, and electronics. The assembly of ratiometric fluorescent 40 nm nanoparticles... 

The development of noninvasive methods for bladder cancer identification remains a critical clinical need. Recent studies have shown that atomic force microscopy (AFM), combined with pattern recognition machine learning, can detect bladder cancer by analyzing cells extracted from urine. However, these promising findings were limited by a relatively small patient cohort, resulting in modest statistical significance. In this study, we corroborated the AFM technique’s capability to identify bladder cancer cells with high accuracy using a controlled model system of genetically purified human bladder epithelial cell lines, comparing cancerous cells with nonmalignant controls. By processing AFM adhesion maps through machine learning algorithms, following previously established methods, we achieved an area under the ROC curve (AUC) of 0.97, with 91% accuracy in cancer cell identification. Furthermore, we enhanced cancer detection by incorporating multiple imaging channels recorded with AFM operating in Ringing mode, achieving an AUC of 0.99 and 93% accuracy. These results demonstrated strong statistical significance (p < 0.0001) in this well-defined model system. While this controlled study does not capture the biological variation present in clinical settings, it provides independent support for AFM-based detection methods and establishes a rigorous technical foundation for further clinical development of AFM imaging-based methods for bladder cancer detection. 

Nanoparticle-based imaging agents have gained massive attention for the targeted imaging of early-stage cancer. Among these, organic dye-entrapped/assembled nanoparticles have been recognized as potential imaging agents. However, they are limited by poor brightness, low stability, low reproducibil-ity and scalability, and selective surface engineering, which limits their translational potential. The mo-lecular assembly of amphiphilic precursor molecules and the chosen fluorophore can augment the brightness and stability of engineered nanoimaging agents. Herein, we describe an original engineering method for cancer cell membrane-covered ICG-cellulose acetate nanospheres (180 nm) as biomimetic ultra-bright nanoimaging agents for cancer cell imaging. The targeted cancer cell imaging is compared with folic acid-attached ICG-cellulose acetate nanospheres. Encapsulation of fluorescent organic mole-cules (660 dye molecules/ per nanoparticle) in the core of a polymeric network enhances the overall brightness and long-term photostability due to the entrapment of the loaded fluorescent cargo and poor permeation of oxygen to oxidize the dye. The amphiphilic nature of the selected polymeric network accommodates both hydrophilic and hydrophobic cargo molecules (e.g., imaging and therapeutics). The engineered fluorescent nanoparticles exhibited high brightness (780-980 MESF), uniform particle size distribution (180-240 nm), high stability (tested up to 90 days), good biocompatibility with normal cells (95 %), and high scalability (600 mL/batch). For targeted chemotherapeutics, DOX-loaded bio-mimetic nanoparticles demonstrate better chemotherapeutic response (more than 95 % cancer cell death) than folic acid-attached DOX-loaded nanoparticles (78 % cancer cell death) as identified with 24 h MTT assay. The engineered nanoparticles exhibited cancer cell imaging and therapeutics capabili-ties by delivering imaging and drug molecules in cancer mimicked environment in vitro. Our findings suggest that the engineered nanoparticles not only overcome the limitations of nano-imaging but also provide additional advantages for targeted cancer therapeutics. 

Here, we present a novel mechano-spectroscopic atomic force microscopy (AFM-MS) technique that overcomes the limitations of current spectroscopic methods by combining the high-resolution imaging capabilities of AFM with machine learning (ML) classification. AFM-MS employs AFM operating in sub-resonance tapping imaging mode, which enables the collection of multiple physical and mechanical property maps of a sample with sub-nanometer lateral resolution in a highly repeatable manner. By comparing these properties to a database of known materials, the technique identifies the location of constituent materials at each image pixel with the assistance of ML algorithms. We demonstrate AFM-MS on various material mixtures, achieving an unprecedented lateral spectroscopic resolution of 1.6 nm. This powerful approach opens new avenues for nanoscale material study, including the material identification and correlation of nanostructure with macroscopic material properties. The ability to map material composition with such high resolution will significantly advance the understanding and design of complex, nanostructured materials. 

Harnessing the power of mesoporous silica to encapsulate organic fluorescent dyes has led to the creation of an extraordinary class of nanocomposite photonic materials. These materials stand out for their ability to produce the brightest fluorescent particles known today, surpassing even the luminosity of quantum dots of similar spectrum and size. The synthesis of these materials offers precise control over the shape and size of the particles, ranging from the nano to the multi-micron scale. Just physical encapsulation of the dyes opens new possibilities for mixing different dyes within individual particles, paving the way for nearly limitless multiplexing capabilities. Moreover, this approach lays the groundwork for the development of highly sensitive sensors capable of detecting subtle changes in temperature and acidity at the nanoscale, among other parameters. This mini-review highlights the mechanism of synthesis, explains the nature of ultrabrightness, and describes the recent advancements and future prospects in the field of ultrabright fluorescent mesoporous silica particles, showcasing their potential for various applications. 

Previously, the analysis of atomic force microscopy (AFM) images allowed us to distinguish normal from cancerous/precancerous human epithelial cervical cells using only the fractal dimension parameter. High-resolution maps of adhesion between the AFM probe and the cell surface were used in that study. However, the separation of cancerous and precancerous cells was rather poor (the area under the curve (AUC) was only 0.79, whereas the accuracy, sensitivity, and specificity were 74%, 58%, and 84%, respectively). At the same time, the separation between premalignant and malignant cells is the most significant from a clinical point of view. Here, we show that the introduction of machine learning methods for the analysis of adhesion maps allows us to distinguish precancerous and cancerous cervical cells with rather good precision (AUC, accuracy, sensitivity, and specificity are 0.93, 83%, 92%, and 78%, respectively). Substantial improvement in sensitivity is significant because of the unmet need in clinical practice to improve the screening of cervical cancer (a relatively low specificity can be compensated by combining this approach with other currently existing screening methods). The random forest decision tree algorithm was utilized in this study. The analysis was carried out using the data of six precancerous primary cell lines and six cancerous primary cell lines, each derived from different humans. The robustness of the classification was verified using K-fold cross-validation (K = 500). The results are statistically significant at p < 0.0001. Statistical significance was determined using the random shuffle method as a control. 

Abstract We provide exact analytical solutions for the magnetic field produced by prescribed current distributions located inside a toroidal filament of finite thickness. The solutions are expressed in terms of toroidal functions, which are modifications of the Legendre functions. In application to the MHD equilibrium of a twisted toroidal current loop in the solar corona, the Grad–Shafranov equation is decomposed into an analytic solution describing an equilibrium configuration against the pinch-effect from its own current and an approximate solution for an external strapping field to balance the hoop force. Our solutions can be employed in numerical simulations of coronal mass ejections (CMEs). When superimposed on the background solar coronal magnetic field, the excess magnetic energy of the twisted current loop configuration can be made unstable by applying flux cancellation to reduce the strapping field. Such loss of stability accompanied by the formation of an expanding flux rope is typical for the Titov & Démoulin eruptive event generator. The main new features of the proposed model are as follows: the filament is filled with finiteβplasma with finite mass and energy, the model describes an equilibrium solution that will spontaneously erupt due to magnetic reconnection of the strapping magnetic field arcade, and there are analytic expressions connecting the model parameters to the asymptotic velocity and total mass of the resulting CME, providing a way to connect the simulated CME properties to multipoint coronograph observations. 

Here we address an important roadblock that prevents the use of bright fluorescent nanoparticles as individual ratiometric sensors: the possible variation of fluorescence spectra between individual nanoparticles. Ratiometric measurements using florescent dyes have shown their utility in measuring the spatial distribution of temperature, acidity, and concentration of various ions. However, the dyes have a serious limitation in their use as sensors; namely, their fluorescent spectra can change due to interactions with the surrounding dye. Encapsulation of the d, e in a porous material can solve this issue. Recently, we demonstrated the use of ultrabright nanoporous silica nanoparticles (UNSNP) to measure temperature and acidity. The particles have at least two kinds of encapsulated dyes. Ultrahigh brightness of the particles allows measuring of the signal of interest at the single particle level. However, it raises the problem of spectral variation between particles, which is impossible to control at the nanoscale. Here, we study spectral variations between the UNSNP which have two different encapsulated dyes: rhodamine R6G and RB. The dyes can be used to measure temperature. We synthesized these particles using three different ratios of the dyes. We measured the spectra of individual nanoparticles and compared them with simulations. We observed a rather small variation of fluorescence spectra between individual UNSNP, and the spectra were in very good agreement with the results of our simulations. Thus, one can conclude that individual UNSNP can be used as effective ratiometric sensors. 

It has been recently demonstrated that atomic force microscopy (AFM) allows for the rather precise identification of malignancy in bladder and cervical cells. Furthermore, an example of human colorectal epithelial cells imaged in AFM Ringing mode has demonstrated the ability to distinguish cells with varying cancer aggressiveness with the help of machine learning (ML). The previously used ML methods analyzed the entire cell image. The problem with such an approach is the lack of information about which features of the cell surface are associated with a high degree of aggressiveness of the cells. Here we suggest a machine-learning approach to overcome this problem. Our approach identifies specific geometrical regions on the cell surface that are critical for classifying cells as highly or lowly aggressive. Such localization gives a path to colocalize the newly identified features with possible clustering of specific molecules identified via standard bio-fluorescence imaging. The biological interpretation of the obtained information is discussed.