A perception-based nanosensor platform to detect cancer biomarkers

Advancing personalized medicine: Integrating statistical algorithms with omics and nano-omics for enhanced diagnostic accuracy and treatment efficacy

Medical laboratory services enable precise measurement of thousands of biomolecules and have become an inseparable part of high-quality healthcare services, exerting a profound influence on global health outcomes. The integration of omics technologies into laboratory medicine has transformed healthcare, enabling personalized treatments and interventions based on individuals' distinct genetic and metabolic profiles. Interpreting laboratory data relies on reliable reference values. Presently, population-derived references are used for individuals, risking misinterpretation due to population heterogeneity, and leading to medical errors. Thus, personalized references are crucial for precise interpretation of individual laboratory results, and the interpretation of omics data should be based on individualized reference values. We reviewed recent advancements in personalized laboratory medicine, focusing on personalized omics, and discussed strategies for implementing personalized statistical approaches in omics technologies to improve global health and concluded that personalized statistical algorithms for interpretation of omics data have great potential to enhance global health. Finally, we demonstrated that the convergence of nanotechnology and omics sciences is transforming personalized laboratory medicine by providing unparalleled diagnostic precision and innovative therapeutic strategies.

Harnessing Cooperative Multivalency in Thioguanine for Surface-Enhanced Raman Scattering (SERS)-Based Differentiation of Polyfunctional Analytes Differing by a Single Functional Group

Small-molecule receptors are increasingly employed to probe various functional groups for (bio)chemical analysis. However, differentiation of polyfunctional analogs sharing multiple functional groups remains challenging for conventional mono- and bidentate receptors because their insufficient number of binding sites limits interactions with the least reactive yet property-determining functional group. Herein, we introduce 6-thioguanine (TG) as a supramolecular receptor for unique tridentate receptor-analyte complexation, achieving ≥97 % identification accuracy among 16 polyfunctional analogs across three classes: glycerol derivatives, disubstituted propane, and vicinal diols. Crucially, we demonstrate distinct spectral changes induced by the tridentate interaction between TG's three anchoring points and all the analyte's functional groups, even the least reactive ones. Notably, hydrogen bond (H-bond) networks formed in the TG-analyte complexes demonstrate additive effects in binding strength originating from good bond linearity, cooperativity, and resonance, thus strengthening complexation events and amplifying the differences in spectral changes induced among analytes. It also enhances spectral consistency by selectively forming a sole configuration that is stronger than the respective analyte-analyte interaction. Finally, we achieve 95.4 % accuracy for multiplex identification of a mixture consisting of multiple polyfunctional analogs. We envisage that extension to other multidentate non-covalent interactions enables the development of interference-free small molecule-based sensors for various (bio)chemical analysis applications.

Recent advances on applications of single-walled carbon nanotubes as cutting-edge optical nanosensors for biosensing technologies

Single-walled carbon nanotubes (SWCNTs) possess outstanding photophysical properties which has garnered interest towards utilizing these materials for biosensing and imaging applications. The near-infrared (NIR) fluorescence within the tissue transparent region along with their photostability and sizes in the nanoscale make SWCNTs valued candidates for the development of optical sensors. In this review, we discuss recent advances in the development and the applications of SWCNT-based nano-biosensors. An overview of SWCNT's structural and photophysical properties, sensor development, and sensing mechanisms are described. Examples of SWCNT-based optical nanosensors for detection of disease biomarkers, pathogens (bacteria and viruses), plant stressors, and environmental contaminants including heavy metals and disinfectants are provided. Molecular detection in biofluids, in vitro, and in vivo (small animal models and plants) are highlighted, and sensor integration into portable substrates for implantable and wearable sensing devices has been discussed. Recent advancements, which include high throughput assays and the use of machine learning models to predict more sensitive and robust sensing outcomes are discussed. Current limitations and future perspectives on translation of SWCNT optical probes into clinical practices have been provided.

An integrated platform for decoding hydrophilic peptide fingerprints of hepatocellular carcinoma using artificial intelligence and two-dimensional nanosheets

Hydrophilic peptides (HPs) play a critical role in the pathogenesis of hepatocellular carcinoma (HCC). However, the comprehensive and in-depth high-throughput analysis of specific changes in HPs associated with HCC remains unrealized, due to the complex nature of biological fluids and the challenges of mining complex patterns in large data sets. The clinical diagnosis of HCC still lacks a non-destructive and accurate classification method, given the limited specificity of widely used biomarkers. To address these challenges, we have established a multifunctional platform that integrates artificial intelligence computation, hydrophilic interaction extraction of HPs, and MALDI-MS testing. This platform aims to achieve highly sensitive HP fingerprinting for accurate diagnosis of HCC. The method not only facilitates efficient detection of HPs, but also achieves a remarkable 100.00% diagnostic accuracy for HCC in a test cohort, supported by machine learning algorithms. By constructing a panel of HPs with 10 characteristic features, we achieved 98% accuracy in the test cohort for rapid diagnosis and identified 62 HPs deeply involved in pathways related to liver diseases. This integrated strategy provides new research directions for future biomarker studies as well as early diagnosis and individualized treatment of HCC.

Advancements in nanomaterials for nanosensors: a comprehensive review

Nanomaterials (NMs) exhibit unique properties that render them highly suitable for developing sensitive and selective nanosensors across various domains. This review aims to provide a comprehensive overview of nanomaterial-based nanosensors, highlighting their applications and the classification of frequently employed NMs to enhance sensitivity and selectivity. The review introduces various classifications of NMs commonly used in nanosensors, such as carbon-based NMs, metal-based NMs, and others, elucidating their exceptional properties, including high thermal and electrical conductivity, large surface area-to-volume ratio and good biocompatibility. A thorough examination of literature sources was conducted to gather information on NMs-based nanosensors' characteristics, properties, and fabrication methods and their application in diverse sectors such as healthcare, environmental monitoring, industrial processes, and security. Additionally, advanced applications incorporating machine learning techniques were analyzed to enhance the sensor's performance. This review advances the understanding and development of nanosensor technologies by providing insights into fabrication techniques, characterization methods, applications, and future outlook. Key challenges such as robustness, biocompatibility, and scalable manufacturing are also discussed, offering avenues for future research and development in this field.

Systematic Selection of High-Affinity ssDNA Sequences to Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) have gained significant interest for their potential in biomedicine and nanoelectronics. The functionalization of SWCNTs with single-stranded DNA (ssDNA) enables the precise control of SWCNT alignment and the development of optical and electronic biosensors. This study addresses the current gaps in the field by employing high-throughput systematic selection, enriching high-affinity ssDNA sequences from a vast random library. Specific base compositions and patterns are identified that govern the binding affinity between ssDNA and SWCNTs. Molecular dynamics simulations validate the stability of ssDNA conformations on SWCNTs and reveal the pivotal role of hydrogen bonds in this interaction. Additionally, it is demonstrated that machine learning could accurately distinguish high-affinity ssDNA sequences, providing an accessible model on a dedicated webpage (http://service.k-medai.com/ssdna4cnt). These findings open new avenues for high-affinity ssDNA-SWCNT constructs for stable and sensitive molecular detection across diverse scientific disciplines.

Hyperspectral Detection of the Fluorescence Shift between Chirality-Sorted Empty and Water-Filled Single-Wall Carbon Nanotube Enantiomers

Single-wall carbon nanotubes (SWCNTs) have extraordinary electronic and optical properties that depend strongly on their exact chiral structure and their interaction with their inner and outer environment. The fluorescence (PL) of semiconducting SWCNTs, for instance, will shift depending on the molecules with which the SWCNT's hollow core is filled. These interaction-induced shifts are challenging to resolve on the ensemble level in samples containing a mixture of different filling contents due to the relatively large inhomogeneous line width of the ensemble SWCNT PL compared to the size of these shifts. To circumvent this inhomogeneous broadening, single-tube spectroscopy and hyperspectral imaging are often applied, which until now required time-consuming statistical studies. Here, we present hyperspectral PL microscopy combined with automated SWCNT segmenting based on either principal component analysis or a convolutional neural network, capable of both spatially and spectrally resolving the PL along the length of many individual SWCNTs at the same time and automatically fitting peak positions and line widths of individual SWCNTs. The methodology is demonstrated by accurately determining the emission shifts and line widths of thousands of left- and right-handed empty and water-filled SWCNTs coated with a chiral surfactant, resulting in four statistical distributions which cannot be resolved in ensemble spectroscopy of unsorted samples. The results demonstrate a robust method to quickly probe ensemble properties with single-enantiomer spectral resolution. Moreover, it promises to be an absolute quantitative method to characterize the relative abundances of SWCNTs with different handedness or filling content in macroscopic samples, simply by counting individual species.

Predicting Serotonin Detection with DNA-Carbon Nanotube Sensors across Multiple Spectral Wavelengths

Owing to the value of DNA-wrapped single-walled carbon nanotube (SWNT)-based sensors for chemically specific imaging in biology, we explore machine learning (ML) predictions DNA-SWNT serotonin sensor responsivity as a function of DNA sequence based on the whole SWNT fluorescence spectra. Our analysis reveals the crucial role of DNA sequence in the binding modes of DNA-SWNTs to serotonin, with a smaller influence of SWNT chirality. Regression ML models trained on existing data sets predict the change in the fluorescence emission in response to serotonin, ΔF/F, at over a hundred wavelengths for new DNA-SWNT conjugates, successfully identifying some high- and low-response DNA sequences. Despite successful predictions, we also show that the finite size of the training data set leads to limitations on prediction accuracy. Nevertheless, incorporating entire spectra into ML models enhances prediction robustness and facilitates the discovery of novel DNA-SWNT sensors. Our approaches show promise for identifying new chemical systems with specific sensing response characteristics, marking a valuable advancement in DNA-based system discovery.

Multidimensional Interactive Cascading Nanochips for Detection of Multiple Liver Diseases via Precise Metabolite Profiling

It is challenging to detect and differentiate multiple diseases with high complexity/similarity from the same organ. Metabolic analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is a promising platform for disease diagnosis, while the enhanced property of its core nanomatrix materials has plenty of room for improvement. Herein, a multidimensional interactive cascade nanochip composed of iron oxide nanoparticles (FeNPs)/MXene/gold nanoparticles (AuNPs), IMG, is reported for serum metabolic profiling to achieve high-throughput detection of multiple liver diseases. MXene serves as a multi-binding site and an electron-hole source for ionization during NMALDI-MS analysis. Introduction of AuNPs with surface plasmon resonance (SPR) properties facilitates surface charge accumulation and rapid energy conversion. FeNPs are integrated into the MXene/Au nanocomposite to sharply reduce the thermal conductivity of the nanochip with negligible heat loss for strong thermally-driven desorption, and construct a multi-interaction proton transport pathway with MXene and AuNPs for strong ionization. Analysis of these enhanced serum fingerprint signals detected from the IMG nanochip through a neural network model results in differentiation of multiple liver diseases via a single pass and revelation of potential metabolic biomarkers. The promising method can rapidly and accurately screen various liver diseases, thus allowing timely treatment of liver diseases.

Carbon Nanomaterial Fluorescent Probes and Their Biological Applications

Fluorescent carbon nanomaterials have broadly useful chemical and photophysical attributes that are conducive to applications in biology. In this review, we focus on materials whose photophysics allow for the use of these materials in biomedical and environmental applications, with emphasis on imaging, biosensing, and cargo delivery. The review focuses primarily on graphitic carbon nanomaterials including graphene and its derivatives, carbon nanotubes, as well as carbon dots and carbon nanohoops. Recent advances in and future prospects of these fields are discussed at depth, and where appropriate, references to reviews pertaining to older literature are provided.

Progress of NIR-II fluorescence imaging technology applied to disease diagnosis and treatment

Near-infrared two-region (NIR-II, 1000-1700 nm) fluorescence imaging has received widespread attention because of its high in vivo penetration depth, high imaging resolution, fast imaging speed and high efficiency, dynamic imaging, and high clinical translatability. This paper reviews the application of NIR-II imaging technology in disease diagnosis and treatment. The paper highlights the latest research progress of commonly used NIR-II imaging materials and the latest progress of multifunctional diagnostic platforms based on NIR-II imaging technology, and discusses the challenges and directions for the development and utilization of novel NIR-II imaging probes.

Ratiometric Imaging of Catecholamine Neurotransmitters with Nanosensors

Neurotransmitters are important signaling molecules in the brain and are relevant in many diseases. Measuring them with high spatial and temporal resolutions in biological systems is challenging. Here, we develop a ratiometric fluorescent sensor/probe for catecholamine neurotransmitters on the basis of near-infrared (NIR) semiconducting single wall carbon nanotubes (SWCNTs). Phenylboronic acid (PBA)-based quantum defects are incorporated into them to interact selectively with catechol moieties. These PBA-SWCNTs are further modified with poly(ethylene glycol) phospholipids (PEG-PL) for biocompatibility. Catecholamines, including dopamine, do not affect the intrinsic E11 fluorescence (990 nm) of these (PEG-PL-PBA-SWCNT) sensors. In contrast, the defect-related E11* emission (1130 nm) decreases by up to 35%. Furthermore, this dual functionalization allows tuning selectivity by changing the charge of the PEG polymer. These sensors are not taken up by cells, which is beneficial for extracellular imaging, and they are functional in brain slices. In summary, we use dual functionalization of SWCNTs to create a ratiometric biosensor for dopamine.

Addressing Signal Drift and Screening for Detection of Biomarkers with Carbon Nanotube Transistors

Electrical biosensors, including transistor-based devices (i.e., BioFETs), have the potential to offer versatile biomarker detection in a simple, low-cost, scalable, and point-of-care manner. Semiconducting carbon nanotubes (CNTs) are among the most explored nanomaterial candidates for BioFETs due to their high electrical sensitivity and compatibility with diverse fabrication approaches. However, when operating in solutions at biologically relevant ionic strengths, CNT-based BioFETs suffer from debilitating levels of signal drift and charge screening, which are often unaccounted for or sidestepped (but not addressed) by testing in diluted solutions. In this work, we present an ultrasensitive CNT-based BioFET called the D4-TFT, an immunoassay with an electrical readout, which overcomes charge screening and drift-related limitations of BioFETs. In high ionic strength solution (1X PBS), the D4-TFT repeatedly and stably detects subfemtomolar biomarker concentrations in a point-of-care form factor by increasing the sensing distance in solution (Debye length) and mitigating signal drift effects. Debye length screening and biofouling effects are overcome using a poly(ethylene glycol)-like polymer brush interface (POEGMA) above the device into which antibodies are printed. Simultaneous testing of a control device having no antibodies printed over the CNT channel confirms successful detection of the target biomarker via an on-current shift caused by antibody sandwich formation. Drift in the target signal is mitigated by a combination of: (1) maximizing sensitivity by appropriate passivation alongside the polymer brush coating; (2) using a stable electrical testing configuration; and (3) enforcing a rigorous testing methodology that relies on infrequent DC sweeps rather than static or AC measurements. These improvements are realized in a relatively simple device using printed CNTs and antibodies for a low-cost, versatile platform for the ongoing pursuit of point-of-care BioFETs.

Pharmacology of a Plant Virus Immunotherapy Candidate for Peritoneal Metastatic Ovarian Cancer

Due to the increasing incidence of cancer, there is a need to develop new platforms that can combat this disease. Cancer immunotherapy is a platform that takes advantage of the immune system to recognize and eradicate tumors and metastases. Our lab has identified a plant virus nanoparticle, cowpea mosaic virus (CPMV) as a promising approach for cancer immunotherapy. When administered intratumorally, CPMV relieves the immune system of tumor-induced immunosuppression and reprograms the tumor microenvironment into an activated state to launch systemic antitumor immunity. The efficacy of CPMV has been tested in many tumor models and in canine cancer patients with promising results: tumor shrinkage, systemic efficacy (abscopal effect), and immune memory to prevent recurrence. To translate this drug candidate from the bench to the clinic, studies that investigate the safety, pharmacology, and toxicity are needed. In this work, we describe the efficacy of CPMV against a metastatic ovarian tumor model and investigate the biodistribution of CPMV after single or repeated intraperitoneal administration in tumor-bearing and healthy mice. CPMV shows good retention in the tumor nodules and broad bioavailability with no apparent organ toxicity based on histopathology. Data indicate persistence of the viral RNA, which remains detectable 2 weeks post final administration, a phenomenon also observed with some mammalian viral infections. Lastly, while protein was not detected in stool or urine, RNA was shed through excretion from mice; however, there was no evidence that RNA was infectious to plants. Taken together, the data indicate that systemic administration results in broad bioavailability with no apparent toxicity. While RNA is shed from the subjects, data suggest agronomical safety. This data is consistent with prior reports and provides support for translational efforts.

Machine learning-assisted fluorescence sensor array for qualitative and quantitative analysis of pyrethroid pesticides

The simultaneous detection of multiple residues of pyrethroid pesticides (PPs) on vegetables and fruits is still challenging using traditional nanosensing methods due to the high structural similarity of PPs. In this work, sensor arrays composed of three nanocomposite complexes (rhodamine B-CD@Au, rhodamine 6G-CD@Au, and coumarin 6-CD@Au) were constructed to discriminate between structurally similar PPs. Four PPs, deltamethrin, fenvalerate, cyfluthrin, and fenpropathrin, were successfully discriminated. The ability of these sensor units was derived from the different affinity between receptor/analyte and receptor/dye, as well as the non-linear relationship between fluorescence signal and analyte concentration. Upon multivariate pattern recognition analysis, the array performed high-throughput identification of four PPs in unknown samples with 100% classification accuracy. In addition, good accuracy of predicting concentration using the "stepwise prediction" strategy combined with the machine learning method was achieved.

Uterine washings as a novel method for early detection of ovarian cancer: Trials and tribulations

Given the tubal origin of high-grade serous ovarian cancer (HGSC), we sought to investigate intrauterine lavage (IUL) as a novel method of biomarker detection. IUL and serum samples were collected from patients with HGSC or benign pathology. Although CA-125 and HE4 concentrations were significantly higher in IUL samples compared to serum, they were similar between IUL samples from patients with HGSC vs benign conditions. In contrast, CA-125 and HE4 serum concentrations differed between HGSC and benign pathology (P =.002 for both). IUL and tumor samples from patients with HGSC were subjected to targeted panel sequencing and droplet digital PCR (ddPCR). Tumor mutations were found in 75 % of matched IUL samples. Serum CA-125 and HE4 biomarker levels allowed for better differentiation of HGSC and benign pathology compared to IUL samples. We believe using IUL for early detection of HGSC requires optimization, and current strategies should focus on prevention until early detection strategies improve.

Revolutionizing Cancer Care: Advances in Carbon-Based Materials for Diagnosis and Treatment

Cancer involves intricate pathological mechanisms marked by complexities such as cytotoxicity, drug resistance, stem cell proliferation, and inadequate specificity in current chemotherapy approaches. Cancer therapy has embraced diverse nanomaterials renowned for their unique magnetic, electrical, and optical properties to address these challenges. Despite the expanding corpus of knowledge in this area, there has been less advancement in approving nano drugs for use in clinical settings. Nanotechnology, and more especially the development of intelligent nanomaterials, has had a profound impact on cancer research and treatment in recent years. Due to their large surface area, nanoparticles can adeptly encapsulate diverse compounds. Furthermore, the modification of nanoparticles is achievable through a broad spectrum of bio-based substrates, including DNA, aptamers, RNA, and antibodies. This functionalization substantially enhances their theranostic capabilities. Nanomaterials originating from biological sources outperform their conventionally created counterparts, offering advantages such as reduced toxicity, lower manufacturing costs, and enhanced efficiency. This review uses carbon nanomaterials, including graphene-based materials, carbon nanotubes (CNTs) based nanomaterials, and carbon quantum dots (CQDs), to give a complete overview of various methods used in cancer theranostics. We also discussed their advantages and limitations in cancer diagnosis and treatment settings. Carbon nanomaterials might significantly improve cancer theranostics and pave the way for fresh tumor diagnosis and treatment approaches. More study is needed to determine whether using nano-carriers for targeted medicine delivery may increase material utilization. More insight is required to explore the correlation between heightened cytotoxicity and retention resulting from increased permeability.

The role of nanoparticles and nanomaterials in cancer diagnosis and treatment: a comprehensive review

Cancer's pathological processes are complex and present several challenges for current chemotherapy methods. These challenges include cytotoxicity, multidrug resistance, the proliferation of cancer stem cells, and a lack of specificity. To address these issues, researchers have turned to nanomaterials, which possess distinct optical, magnetic, and electrical properties due to their size range of 1-100 nm. Nanomaterials have been engineered to improve cancer treatment by mitigating cytotoxicity, enhancing specificity, increasing drug payload capacity, and improving drug bioavailability. Despite a growing corpus of research on this subject, there has been limited progress in permitting nanodrugs for medical use. The advent of nanotechnology, particularly advances in intelligent nanomaterials, has transformed the field of cancer diagnosis and therapy. Nanoparticles' large surface area allows them to successfully encapsulate a large number of molecules. Nanoparticles can be functionalized with various bio-based substrates like RNA, DNA, aptamers, and antibodies, enhancing their theranostic capabilities. Biologically derived nanomaterials offer economical, easily producible, and less toxic alternatives to conventionally manufactured ones. This review offers a comprehensive overview of cancer theranostics methodologies, focusing on intelligent nanomaterials such as metal, polymeric, and carbon-based nanoparticles. I have also critically discussed their benefits and challenges in cancer therapy and diagnostics. Utilizing intelligent nanomaterials holds promise for advancing cancer theranostics, and improving tumor detection and treatment. Further research should optimize nanocarriers for targeted drug delivery and explore enhanced permeability, cytotoxicity, and retention effects.

Nanoreporter Identifies Lysosomal Storage Disease Lipid Accumulation Intracranially

Dysregulated lipid metabolism contributes to neurodegenerative pathologies and neurological decline in lysosomal storage disorders as well as more common neurodegenerative diseases. Niemann-Pick type A (NPA) is a fatal neurodegenerative lysosomal storage disease characterized by abnormal sphingomyelin accumulation in the endolysosomal lumen. The ability to monitor abnormalities in lipid homeostasis intracranially could improve basic investigations and the development of effective treatment strategies. We investigated the carbon nanotube-based detection of intracranial lipid content. We found that the near-infrared emission of a carbon nanotube-based lipid sensor responds to lipid accumulation in neuronal and in vivo models of NPA. The nanosensor detected lipid accumulation intracranially in an acid sphingomyelinase knockout mouse via noninvasive near-infrared spectroscopy. This work indicates a tool to improve drug development processes in NPA, other lysosomal storage diseases, and neurodegenerative diseases.

Mixed-Solvent Engineering as a Way around the Trade-Off between Yield and Purity of (7,3) Single-Walled Carbon Nanotubes Obtained Using Conjugated Polymer Extraction

The inability to purify nanomaterials such as single-walled carbon nanotubes (SWCNTs) to the desired extent hampers the progress in nanoscience. Various SWCNT types can be purified by extraction, but it is challenging to establish conditions giving rise to the isolation of high-purity fractions. The problem stems from the fact that common organic solvents or water cannot provide an optimal environment for purification. Consequently, one must often decide between the separation yield and purity of the product. This article reports how through the self-synthesis of poly(9,9-dioctylfluorene-alt-benzothiadiazole) with tailored characteristics, in-depth elucidation of the extraction process, and mixed-solvent engineering, a high-yield isolation of monochiral (7,3) SWCNTs is developed. The combination of toluene and tetralin affords a separation medium of unique properties, wherein both high yield and exceptional purity can be attained simultaneously. The reported results pave the way for further research on this rare chirality, which, as illustrated herein, is much more reactive than any of the previously separated SWCNTs.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.

Advances in miniaturized nanosensing platforms for analysis of pathogenic bacteria and viruses

Pathogenic bacteria and viruses are the main causes of infectious diseases all over the world. Early diagnosis of such infectious diseases is a critical step in management of their spread and treatment of the infection in its early stages. Therefore, the innovation of smart sensing platforms for point-of-care diagnosis of life-threatening infectious diseases such as COVID-19 is a prerequisite to isolate the patients and provide them with suitable treatment strategies. The developed diagnostic sensors should be highly sensitive, specific, ultrafast, portable, cheap, label-free, and selective. In recent years, different nanosensors have been developed for the detection of bacterial and viral pathogens. We focus here on label-free miniaturized nanosensing platforms that were efficiently applied for pathogenic detection in biological matrices. Such devices include nanopore sensors and nanostructure-integrated lab-on-a-chip sensors that are characterized by portability, simplicity, cost-effectiveness, and ultrafast analysis because they avoid the time-consuming sample preparation steps. Furthermore, nanopore-based sensors could afford single-molecule counting of viruses in biological specimens, yielding high-sensitivity and high-accuracy detection. Moreover, non-invasive nanosensors that are capable of detecting volatile organic compounds emitted from the diseased organ to the skin, urine, or exhaled breath were also reviewed. The merits and applications of all these nanosensors for analysis of pathogenic bacteria and viruses in biological matrices will be discussed in detail, emphasizing the importance of artificial intelligence in advancing specific nanosensors.

Stochastic Formation of Quantum Defects in Carbon Nanotubes

Small perturbations in the structure of materials significantly affect their properties. One example is single wall carbon nanotubes (SWCNTs), which exhibit chirality-dependent near-infrared (NIR) fluorescence. They can be modified with quantum defects through the reaction with diazonium salts, and the number or distribution of these defects determines their photophysics. However, the presence of multiple chiralities in typical SWCNT samples complicates the identification of defect-related emission features. Here, we show that quantum defects do not affect aqueous two-phase extraction (ATPE) of different SWCNT chiralities into different phases, which suggests low numbers of defects. For bulk samples, the bandgap emission (E11) of monochiral (6,5)-SWCNTs decreases, and the defect-related emission feature (E11*) increases with diazonium salt concentration and represents a proxy for the defect number. The high purity of monochiral samples from ATPE allows us to image NIR fluorescence contributions (E11 = 986 nm and E11* = 1140 nm) on the single SWCNT level. Interestingly, we observe a stochastic (Poisson) distribution of quantum defects. SWCNTs have most likely one to three defects (for low to high (bulk) quantum defect densities). Additionally, we verify this number by following single reaction events that appear as discrete steps in the temporal fluorescence traces. We thereby count single reactions via NIR imaging and demonstrate that stochasticity plays a crucial role in the optical properties of SWCNTs. These results show that there can be a large discrepancy between ensemble and single particle experiments/properties of nanomaterials.

Molecular Recognition-Modulated Hetero-Assembly of Nanostructures for Visualizable and Portable Detection of Circulating miRNAs

Biomolecular markers, particularly circulating microRNAs (miRNAs) play an important role in diagnosis, monitoring, and therapeutic intervention of cancers. However, existing detection strategies remain intricate, laborious, and far from being developed for point-of-care testing. Here, we report a portable colorimetric sensor that utilizes the hetero-assembly of nanostructures driven by base pairing and recognition for direct detection of miRNAs. Following hybridization, two sizes of nanoparticles modified with single-strand DNA can be robustly assembled into heterostructures with strong optical resonance, exhibiting distinct structure colors. Particularly, the large nanoparticles are first arranged into nanochains to enhance scattering signals of small nanoparticles, which allows for sensitive detection and quantification of miRNAs without the requirement of target extraction, amplification, and fluorescent labels. Furthermore, we demonstrate the high specificity and single-base selectivity of testing different miRNA samples, which shows great potential in the diagnosis, staging, and monitoring of cancers. These heterogeneous assembled nanostructures provide an opportunity to develop simple, fast, and convenient tools for miRNAs detection, which is suitable for many scenarios, especially in low-resource setting.

Monitoring the Formation of Fibrin Clots as Part of the Coagulation Cascade Using Fluorescent Single-Walled Carbon Nanotubes

Blood coagulation is a critical defense mechanism against bleeding that results in the conversion of liquid blood into a solid clot through a complicated cascade, which involves multiple clotting factors. One of the final steps in the coagulation pathway is the conversion of fibrinogen to insoluble fibrin mediated by thrombin. Because coagulation disorders can be life-threatening, the development of novel methods for monitoring the coagulation cascade dynamics is of high importance. Here, we use near-infrared (NIR)-fluorescent single-walled carbon nanotubes (SWCNTs) to image and monitor fibrin clotting in real time. Following the binding of fibrinogen to a tailored SWCNT platform, thrombin transforms the fibrinogen into fibrin monomers, which start to polymerize. The SWCNTs are incorporated within the clot and can be clearly visualized in the NIR-fluorescent channel, where the signal-to-noise ratio is improved compared to bright-field imaging in the visible range. Moreover, the diffusion of individual SWCNTs within the fibrin clot gradually slows down after the addition of thrombin, manifesting a coagulation rate that depends on both fibrinogen and thrombin concentrations. Our platform can open new opportunities for coagulation disorder diagnostics and allow for real-time monitoring of the coagulation cascade with a NIR optical signal output in the biological transparency window.

Monitoring the Activity and Inhibition of Cholinesterase Enzymes using Single-Walled Carbon Nanotube Fluorescent Sensors

Cholinesterase enzymes are involved in a wide range of bodily functions, and their disruption is linked to pathologies such as neurodegenerative diseases and cancer. While cholinesterase inhibitors are used as drug treatments for diseases such as Alzheimer and dementia at therapeutic doses, acute exposure to high doses, found in pesticides and nerve agents, can be lethal. Therefore, measuring cholinesterase activity is important for numerous applications ranging from the search for novel treatments for neurodegenerative disorders to the on-site detection of potential health hazards. Here, we present the development of a near-infrared (near-IR) fluorescent single-walled carbon nanotube (SWCNT) optical sensor for cholinesterase activity and demonstrate the detection of both acetylcholinesterase and butyrylcholinesterase, as well as their inhibition. We show sub U L^-1 sensitivity, demonstrate the optical response at the level of individual nanosensors, and showcase an optical signal output in the 900-1400 nm range, which overlaps with the biological transparency window. To the best of our knowledge, this is the longest wavelength cholinesterase activity sensor reported to date. Our near-IR fluorescence-based approach opens new avenues for spatiotemporal-resolved detection of cholinesterase activity, with numerous applications such as advancing the research of the cholinergic system, detecting on-site potential health hazards, and measuring biomarkers in real-time.

Networks of as-dispersed, polymer-wrapped (6,5) single-walled carbon nanotubes for selective Cu2+ and glyphosate sensing

Networks of semiconducting single-walled carbon nanotubes (SWNTs) can be used as the transducing layer for sensors based on water-gated transistors. To add specific sensing capabilities, SWNTs are often functionalized with additional moieties or selective membranes are applied, thus increasing the complexity of the fabrication process. Here we demonstrate that drop-cast networks of monochiral (6,5) SWNTs, which are commonly dispersed in organic solvents with the polyfluorene-bipyridine copolymer PFO-BPy, can be employed directly and without additional functionalization or ion-selective membranes to detect Cu²⁺ ions over a wide range of concentrations in aqueous solutions. The observed voltage shifts of water-gated transistors with these (6,5) SWNT networks directly correlate with the cupric ion concentration. They result from induced n-doping due to the complexation of positive copper ions to the bipyridine units of the wrapping polymer. Furthermore, the competitive binding of Cu²⁺ to the herbicide glyphosate as well as to biologically relevant pyrophosphates can be used for the direct detection and quantification of these molecules at nano- to micromolar concentrations.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Application of carbon nanotubes in sensing/monitoring of pancreas and liver cancer

Liver and pancreatic tumors are among the third leading causes of cancer-associated death worldwide. In addition to poor prognosis, both cancer types are diagnosed at advanced and metastatic stages without typical prior symptoms. Unfortunately, the existing theranostic approaches are inefficient in cancer diagnosis and treatment. Carbon nanotubes (CNTs) have attracted increasing attention in this context due to their distinct properties, including variable functionalization capability, biocompatibility, and excellent thermodynamic and optical features. As a consequence, they are now regarded as one of the most promising materials for this application. The current review aims to summarize and discuss the role of CNT in pancreatic and liver cancer theranostics. Accordingly, the breakthroughs achieved so far are classified based on the cancer type and analyzed in detail. The most feasible tactics utilizing CNT-based solutions for both cancer diagnosis and treatment are presented from the biomedical point of view. Finally, a future outlook is provided, which anticipates how the R&D community can build on the already developed methodologies and the subsequent biological responses of the pancreatic and liver cancer cells to the directed procedures.

Nano-omics: nanotechnology-based multidimensional harvesting of the blood-circulating cancerome

Over the past decade, the development of 'simple' blood tests that enable cancer screening, diagnosis or monitoring and facilitate the design of personalized therapies without the need for invasive tumour biopsy sampling has been a core ambition in cancer research. Data emerging from ongoing biomarker development efforts indicate that multiple markers, used individually or as part of a multimodal panel, are required to enhance the sensitivity and specificity of assays for early stage cancer detection. The discovery of cancer-associated molecular alterations that are reflected in blood at multiple dimensions (genome, epigenome, transcriptome, proteome and metabolome) and integration of the resultant multi-omics data have the potential to uncover novel biomarkers as well as to further elucidate the underlying molecular pathways. Herein, we review key advances in multi-omics liquid biopsy approaches and introduce the 'nano-omics' paradigm: the development and utilization of nanotechnology tools for the enrichment and subsequent omics analysis of the blood-circulating cancerome.

Multiple-Aptamer-Integrated DNA-Origami-Based Chemical Nose Sensors for Accurate Identification of Cancer Cells

Developing simple, rapid, and accurate methods for cancer cell identification could facilitate early cancer diagnosis and tumor metastasis research. Herein, we develop a novel chemical nose sensor that employs the collective recognition abilities of a set of multiple-aptamer-integrated DNA origami (MADO) probes for discriminative identification of cancer cells. By controlling the types and/or copies of aptamers assembled on the DNA origami nanostructure, we constructed five MADO probes with differential binding affinities (ranging from 3.08 to 78.92 nM) to five types of cells (HeLa, MDA-MB-468, MCF-7, HepG2, and MCF-10A). We demonstrate the utility of the MADO-based chemical nose sensor in the identification of blinded unknown cell samples with a 95% accuracy. This sensing platform holds great potential for applications in medical diagnostics.

Biosensing with Fluorescent Carbon Nanotubes

Biosensors are powerful tools for modern basic research and biomedical diagnostics. Their development requires substantial input from the chemical sciences. Sensors or probes with an optical readout, such as fluorescence, offer rapid, minimally invasive sensing of analytes with high spatial and temporal resolution. The near-infrared (NIR) region is beneficial because of the reduced background and scattering of biological samples (tissue transparency window) in this range. In this context, single-walled carbon nanotubes (SWCNTs) have emerged as versatile NIR fluorescent building blocks for biosensors. Here, we provide an overview of advances in SWCNT-based NIR fluorescent molecular sensors. We focus on chemical design strategies for diverse analytes and summarize insights into the photophysics and molecular recognition. Furthermore, different application areas are discussed-from chemical imaging of cellular systems and diagnostics to in vivo applications and perspectives for the future.

Carbon Nanotubes-Based Assays for Cancer Detection and Screening

Carbon nanotubes (CNTs) were considered a potential cargo for cancer therapy and diagnosis following researchers’ shared goal of finding a new delivery system to enhance the pharmacological performance of the administered drugs. To date, several excellent reviews have focused on the role of CNTs as drug delivery systems, although there is currently no existing study that gathers all the advances in research-connected carbon nanotubes-based assay development for the early detection of cancer. In this review article, we will focus on the emerging role of CNTs as anticancer detection agents.

Machine Learning-Guided Systematic Search of DNA Sequences for Sorting Carbon Nanotubes

The prerequisite of utilizing DNA in sequence-dependent applications is to search specific sequences. Developing a strategy for efficient DNA sequence screening represents a grand challenge due to the countless possibilities of sequence combination. Herein, relying on sequence-dependent recognition between DNA and single-wall carbon nanotubes (SWCNTs), we demonstrate a method for systematic search of DNA sequences for sorting single-chirality SWCNTs. Different from previously documented empirical search, which has a low efficiency and accuracy, our approach combines machine learning and experimental investigation. The number of resolving sequences and the success rate of finding them are improved from ∼10² to ∼10³ and from ∼10% to >90%, respectively. Moreover, the resolving sequence patterns determined from 5-mer and 6-mer short sequences can be extended to sequence search in longer DNA subspaces.

Glycopolymer-Wrapped Carbon Nanotubes Show Distinct Interaction of Carbohydrates With Lectins

Glyconanomaterials with unique nanoscale property and carbohydrate functionality show vast potential in biological and biomedical applications. We investigated the interactions of noncovalent complexes of single-wall carbon nanotubes that are wrapped by disaccharide lactose-containing glycopolymers with the specific carbohydrate-binding proteins. The terminal galactose (Gal) of glycopolymers binds to the specific lectin as expected. Interestingly, an increased aggregation of nanotubes was also observed when interacting with a glucose (Glc) specific lectin, likely due to the removal of Glc groups from the surface of nanotubes resulting from the potential binding of the lectin to the Glc in the glycopolymers. This result indicates that the wrapping conformation of glycopolymers on the surface of nanotubes potentially allows improved accessibility of the Glc for specific lectins. Furthermore, it shows that the interaction between Glc groups in the glycopolymers and nanotubes play a key role in stabilizing the nanocomplexes. Overall, our results demonstrate that nanostructures can enable conformation-dependent interactions of glycopolymers and proteins and can potentially lead to the creation of versatile optical sensors for detecting carbohydrate-protein interactions with enhanced specificity and sensitivity.

Dendron-Polymer Hybrids as Tailorable Responsive Coronae of Single-Walled Carbon Nanotubes

Functional composite materials that can change their spectral properties in response to external stimuli have a plethora of applications in fields ranging from sensors to biomedical imaging. One of the most promising types of materials used to design spectrally active composites are fluorescent single-walled carbon nanotubes (SWCNTs), noncovalently functionalized by synthetic amphiphilic polymers. These coated SWCNTs can exhibit modulations in their fluorescence spectra in response to interactions with target analytes. Hence, identifying new amphiphiles with interchangeable building blocks that can form individual coronae around the SWCNTs and can be tailored for a specific application is of great interest. This study presents highly modular amphiphilic polymer-dendron hybrids, composed of hydrophobic dendrons and hydrophilic polyethylene glycol (PEG) that can be synthesized with a high degree of structural freedom, for suspending SWCNTs in aqueous solution. Taking advantage of the high molecular precision of these PEG-dendrons, we show that precise differences in the chemical structure of the hydrophobic end groups of the dendrons can be used to control the interactions of the amphiphiles with the SWCNT surface. These interactions can be directly related to differences in the intrinsic near-infrared fluorescence emission of the various chiralities in a SWCNT sample. Utilizing the susceptibility of the PEG-dendrons toward enzymatic degradation, we demonstrate the ability to monitor enzymatic activity through changes in the SWCNT fluorescent signal. These findings pave the way for a rational design of functional SWCNTs, which can be used for optical sensing of enzymatic activity in the near-infrared spectral range.

Optical Detection of Stereoselective Interactions with DNA-Wrapped Single-Wall Carbon Nanotubes

DNA-wrapped carbon nanotubes have been explored increasingly as sensitive near-infrared fluorescence probes for biomolecules. However, notably missing in previous studies is an inquiry on stereoselective interactions between DNA-wrapped carbon nanotubes and biomolecules. Here, enantiopure (+) and (-)(6,5), and (-)(8,3) as well as achiral (11,0) carbon nanotubes wrapped with specific resolving DNA sequences are used to demonstrate their stereoselective detection of amino acid enantiomers. Furthermore, stereoselective sensing abilities are found to be retained by dispersions containing a multitude of chiral nanotube structures. The fluorescence response profiles of six different DNA-wrapped carbon nanotube dispersions to nine standard amino acids, and their enantiomers, demonstrate that DNA-wrapped carbon nanotubes are exquisitely sensitive to the stereoconfiguration and side-chain functionality of amino acids in a manner that is dependent on both DNA sequence and nanotube chirality. Implications of our findings are discussed in the context of developing a machine learning-aided multiplexed biosensing scheme called a molecular perceptron.