Detection of ovarian cancer via the spectral fingerprinting of quantum-defect-modified carbon nanotubes in serum by machine learning

Terahertz molecular vibrational sensing using 3D printed anapole meta-biosensor

Terahertz (THz) fingerprint sensing utilizes the absorption of fingerprints generated by the unique vibrational characteristics of molecules to achieve substance-specific identification. By taking full advance of the anapole mode induced-biosensor consisting of out-of-plane metal-insulator-metal (MIM) configuration, the D-glucose solutions down to physiological level are accurately detected by proposed metasurface biosensor through the electromagnetic induced absorption (EIA) effect induced by the interaction between the metasurface and molecular vibrational fingerprint. Besides, by utilizing the vibrational fingerprint sensing ability, the pure D-glutamic acid and D-lactose, as well as their mixture have been quantitatively characterized. In addition, with the aid of machine learning algorithms, the designed single resonance metasensor achieves 100% recognition of five molecules. This work brings a convincing strategy for trace label-free molecular recognition for various species, which might extend the promising potentials of THz sensing techniques toward biomedical testing and clinical diagnosis.

Metal-Ion Optical Fingerprinting Sensor Selection via an Analyte Classification and Feature Selection Algorithm

Accurate analyte classification remains a significant challenge in sensor technologies. We present the Analyte Classification and Feature Selection Algorithm (ACFSA), a computational tool designed to identify optimal sensor combinations from unique fingerprint patterns for analyte classification. We applied the ACFSA to a library of peptide-corona-functionalized single-walled carbon nanotubes (SWCNTs), developed as a near-infrared fluorescent, semiselective fingerprinting sensor set for detecting heavy metal ions. Inspired by natural metal-ion complexation sites, each SWCNT sensor in this library features a unique peptide sequence containing various amino acids for metal binding, revealing diverse optical response patterns to the various metal ions tested. The sensor library was further diversified using different SWCNT chiralities and photochemical modifications of the peptide coronae. The ACFSA was applied to the screening data of the fluorescence response of the 30 resulting SWCNT-peptide sensors to five metal-ion analytes. Through iterative dimensionality reduction and rational sensor selection, the algorithm identified the optimal fingerprinting sensors as a minimal two-sensor set with a 0.02% classification error. The final output of the ACFSA is thus an analyte classifier that serves as a unique analyte fingerprint pattern for the selected sensors. The developed peptide-SWCNT system serves as an effective proof-of-concept, illustrating the potential of our platform as a generally applicable tool for fingerprinting analytes and optimal sensor set selection in other sensor-analyte screening experiments.

Serum metabolic fingerprints encode functional biomarkers for ovarian cancer diagnosis: a large-scale cohort study

BACKGROUND

Ovarian cancer (OC) ranks as the most lethal gynaecological malignancy worldwide, with early diagnosis being crucial yet challenging. Current diagnostic methods like transvaginal ultrasound and blood biomarkers show limited sensitivity/specificity. This study aimed to identify and validate serum metabolic biomarkers for OC diagnosis using the largest cohort reported to date.

METHODS

We constructed a large-scale OC-associated cohort of 1432 subjects, including 662 OC, 563 benign ovarian disease, and 207 healthy control subjects, across retrospective (n = 1073) and set-aside validation (n = 359) cohorts. Serum metabolic fingerprints (SMFs) were recorded using nanoparticle-enhanced laser desorption/ionization mass spectrometry (NELDI-MS). A diagnostic panel was developed through machine learning of SMFs in the discovery cohort and validated in independent verification and set-aside validation cohorts. The identified metabolic biomarkers were further validated using liquid chromatography MS and their biological functions were assessed in OC cell lines.

FINDINGS

We identified a metabolic biomarker panel including glucose, histidine, pyrrole-2-carboxylic acid, and dihydrothymine. This panel achieved consistent areas under the curve (AUCs) of 0.87-0.89 for distinguishing between malignant and benign ovarian masses across all cohorts, and improved to AUCs of 0.95-0.99 when combined with risk of ovarian malignancy algorithm (ROMA). In vitro validation provided initial biological context for the metabolic alterations observed in our diagnostic panel.

INTERPRETATION

Our study established a reliable serum metabolic biomarker panel for OC diagnosis with potential clinical translations. The NELDI-MS based approach offers advantages of fast analytical speed (∼30 s/sample) and low cost (∼2-3 dollars/sample), making it suitable for large-scale clinical applications.

FUNDING

MOST (2021YFA0910100), NSFC (82421001, 823B2050, 824B2059, and 82173077), Medical-Engineering Joint Funds of Shanghai Jiao Tong University (YG2021GD02, YG2024ZD07, and YG2023ZD08), Shanghai Science and Technology Committee Project (23JC1403000), Shanghai Institutions of Higher Learning (2021-01-07-00-02-E00083), Shanghai Jiao Tong University Inner Mongolia Research Institute (2022XYJG0001-01-16), Sichuan Provincial Department of Science and Technology (2024YFHZ0176), Innovation Research Plan by the Shanghai Municipal Education Commission (ZXWF082101), Innovative Research Team of High-Level Local Universities in Shanghai (SHSMU-ZDCX20210700), Basic-Clinical Collaborative Innovation Project from Shanghai Immune Therapy Institute, Guangdong Basic and Applied Basic Research Foundation (2024A1515013255).

Understanding DNA-encoded carbon nanotube sorting and sensing via sub-nm-resolution structural determination

DNA has demonstrated the abilities to differentiate single-wall carbon nanotubes (SWCNTs) with various chiralities and manipulate their analyte sensing properties. However, the fundamental mechanisms underlying these remarkable abilities remain unclear due to the lack of high-resolution determination of DNA structures on SWCNTs. Here, we combine atomic force microscopy and single-particle cryo-electron microscopy to determine DNA structures on five different types of single-chirality SWCNTs, achieving unprecedented subnanometer resolution. This resolution enables the direct observation of left-handed helical DNA structures with pitches ranging from 1.59 to 2.20 nm, depending on the DNA sequence and nanotube chirality. These findings provide structural insights into the mechanisms by which DNA differentiates the chirality of SWCNTs, and governs the sensitivity, dynamic response range, and analyte differentiability of SWCNT sensors. We propose a non-Watson-Crick hydrogen-bonding network model, which not only accounts for the observed ordered DNA structures but also facilitates the design of DNA sequences for targeted SWCNT purification and desired SWCNT sensor performance.

High-Speed Hyperspectral Imaging for Near Infrared Fluorescence and Environmental Monitoring

Hyperspectral imaging captures both spectral and spatial information from a sample but is intrinsically slow. The near infrared (NIR, > 800 nm) is advantageous for imaging applications because it falls into the tissue transparency window and also contains vibrational overtone and combination modes useful for molecular fingerprinting. Here, fast hyperspectral NIR imaging is demonstrated using a spectral phasor transformation (HyperNIR). A liquid crystal variable retarder (LCVR) is used for tunable, wavelength-dependent sine- and cosine-filtering that transforms optical signals into a 2D spectral (phasor) space. Spectral information is thus obtained with just three images. The LCVR can be adjusted to cover a spectral range from 900 to 1600 nm in windows tunable from 50 to 700 nm, which enables distinguishing NIR fluorophores with emission peaks less than 5 nm apart. Furthermore, label-free hyperspectral NIR reflectance imaging is demonstrated to identify plastic polymers and monitor in vivo plant health. The approach uses the full camera resolution and reaches hyperspectral frame rates of 0.2 s-1, limited only by the switching rate of the LCVR. HyperNIR facilitates straightforward hyperspectral imaging for applications in biomedicine and environmental monitoring.

AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis

BACKGROUND

Emerging evidence underscores the potential application of artificial intelligence (AI) in discovering noninvasive blood biomarkers. However, the diagnostic value of AI-derived blood biomarkers for ovarian cancer (OC) remains inconsistent.

OBJECTIVE

We aimed to evaluate the research quality and the validity of AI-based blood biomarkers in OC diagnosis.

METHODS

A systematic search was performed in the MEDLINE, Embase, IEEE Xplore, PubMed, Web of Science, and the Cochrane Library databases. Studies examining the diagnostic accuracy of AI in discovering OC blood biomarkers were identified. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies-AI tool. Pooled sensitivity, specificity, and area under the curve (AUC) were estimated using a bivariate model for the diagnostic meta-analysis.

RESULTS

A total of 40 studies were ultimately included. Most (n=31, 78%) included studies were evaluated as low risk of bias. Overall, the pooled sensitivity, specificity, and AUC were 85% (95% CI 83%-87%), 91% (95% CI 90%-92%), and 0.95 (95% CI 0.92-0.96), respectively. For contingency tables with the highest accuracy, the pooled sensitivity, specificity, and AUC were 95% (95% CI 90%-97%), 97% (95% CI 95%-98%), and 0.99 (95% CI 0.98-1.00), respectively. Stratification by AI algorithms revealed higher sensitivity and specificity in studies using machine learning (sensitivity=85% and specificity=92%) compared to those using deep learning (sensitivity=77% and specificity=85%). In addition, studies using serum reported substantially higher sensitivity (94%) and specificity (96%) than those using plasma (sensitivity=83% and specificity=91%). Stratification by external validation demonstrated significantly higher specificity in studies with external validation (specificity=94%) compared to those without external validation (specificity=89%), while the reverse was observed for sensitivity (74% vs 90%). No publication bias was detected in this meta-analysis.

CONCLUSIONS

AI algorithms demonstrate satisfactory performance in the diagnosis of OC using blood biomarkers and are anticipated to become an effective diagnostic modality in the future, potentially avoiding unnecessary surgeries. Future research is warranted to incorporate external validation into AI diagnostic models, as well as to prioritize the adoption of deep learning methodologies.

TRIAL REGISTRATION

PROSPERO CRD42023481232; https://www.crd.york.ac.uk/PROSPERO/view/CRD42023481232.

Clinical Applications of Targeted Nanomaterials

Targeted nanomaterials are at the forefront of advancements in nanomedicine due to their unique and versatile properties. These include nanoscale size, shape, surface chemistry, mechanical flexibility, fluorescence, optical behavior, magnetic and electronic characteristics, as well as biocompatibility and biodegradability. These attributes enable their application across diverse fields, including drug delivery. This review explores the fundamental characteristics of nanomaterials and emphasizes their importance in clinical applications. It further delves into methodologies for nanoparticle programming alongside discussions on clinical trials and case studies. We discussed some of the promising nanomaterials, such as polymeric nanoparticles, carbon-based nanoparticles, and metallic nanoparticles, and their role in biomedical applications. This review underscores significant advancements in translating nanomaterials into clinical applications and highlights the potential of these innovative approaches in revolutionizing the medical field.

Controlled Introduction of sp3 Quantum Defects in Fluorescent Carbon Nanotubes by Mechanochemistry

Precise control over low-dimensional materials holds an immense potential for their applications in sensing, imaging and information processing. The controlled introduction of sp3 quantum defects (color centers) can be used to tailor the optoelectronic properties of single-walled carbon nanotubes (SWCNTs) in the tissue transparency (>800 nm) and the telecommunication window. However, an uncontrolled functionalization of SWCNTs with defects leads to a loss of the NIR fluorescence. Here, we use mechanochemistry with aryldiazonium salts to create quantum defects in SWCNTs. The reaction proceeds within 5 min without solvents or illumination and can be controlled by a varied mechanochemical energy impact. By working in the solid-state, this method presents an alternative synthetic route and marks a crucial step for tailoring photophysics of SWCNTs.

Photophysical Properties of Tandem Quantum Defects in Carbon Nanotubes

Single-walled carbon nanotubes (SWCNTs) are versatile near-infrared (NIR) fluorophores that can be chemically functionalized to create biosensors. Numerous noncovalent approaches were developed to detect analytes, but these design concepts can be susceptible to nonspecific binding and reduced stability. In contrast, covalent modification of SWCNTs with quantum defects can be utilized to tune their fluorescence properties and enable new molecular recognition concepts. Here, we present and assess four different synthetic pathways/sequences to modify SWCNTs covalently with both sp3 quantum defects and DNA-based guanine defects. We find that it is possible to create two defect types without disrupting the optical properties or chemical stability. Interestingly, the emission peak associated with sp3 defects (E11*) shifts around 3 nm when combined with guanine defects, indicating a coupling between the two defect types. However, it is far lower than the red-shift in bandgap-related emission (E11) by guanine quantum defects (40 nm). We furthermore demonstrate that combinations of defects can be used for (bio)sensing. In summary, the combination of multiple quantum defect types in SWCNTs provides a platform for multifunctional biosensors and a new design space that can be explored.

Controlled Quantum Well Formation on DNA-Wrapped Carbon Nanotubes via Peroxide-Mediated Aryl Diazonium Reduction

Quantum well defect-modified single-walled carbon nanotubes are nanomaterials with wide-ranging applications in biosensing, imaging, quantum computing, and catalysis. The most common method for covalent functionalization of nanotubes for biosensing applications involves reactions with aryl diazonium salts to generate sp3 aryl defect sites, commonly followed by wrapping with single-stranded DNA for aqueous dispersion. We describe herein a rapid aryl diazonium functionalization reaction directly compatible with DNA-wrapped nanotubes, mediated by hydrogen peroxide. The reaction uses mild aqueous conditions at physiological pH and can be easily monitored in real-time via fluorescence analysis to control the degree of functionalization. Overall, this reaction greatly simplifies the production of covalently functionalized DNA-wrapped carbon nanotubes, expanding their potential for industrial and biomedical applications.

Accurate DNA Sequence Prediction for Sorting Target-Chirality Carbon Nanotubes and Manipulating Their Functionalities

Synthetic single-wall carbon nanotubes (SWCNTs) contain various chiralities, which can be sorted by DNA. However, finding DNA sequences for this purpose mainly relies on trial-and-error methods. Predicting the right DNA sequences to sort SWCNTs remains a substantial challenge. Moreover, it is even more daunting to predict sequences for sorting SWCNTs with target chirality. Here, we present a deep-learning (DL) enhanced strategy for the accurate prediction of DNA sequences capable of sorting target-chirality nanotubes. We first experimentally screened 216 DNA sequences using aqueous two-phase (ATP) separation, resulting in 116 resolving sequences that can purify 17 distinct single-chirality SWCNTs. These experimental results created a comprehensive training data set. We utilized the recently released 3D molecular representation learning framework, Uni-Mol, to construct a DL workflow that maps atomistic-level structural information on DNA sequences into the feature space. This information captures the structural features of DNA molecules that are crucial for their interactions with SWCNTs. This may account for the superior performance of our DL models. The models successfully predicted resolving sequences for (6,5), (6,6), and (7,4) SWCNTs with accuracy rates of 87.5, 90, and 70%, respectively. Importantly, the discovery of numerous resolving sequences for (6,5) SWCNTs allows us to systematically manipulate the sequence-dependent absorption spectral shift, photoluminescence intensity, and surfactant sensitivity of DNA-(6,5) hybrids and elucidate the underlying mechanisms.

Complexity in the Photofunctionalization of Single-Wall Carbon Nanotubes with Hypochlorite

The reaction of aqueous suspensions of single-wall carbon nanotubes (SWCNTs) with UV-excited sodium hypochlorite has previously been reported to be an efficient route for doping nanotubes with oxygen atoms. We have investigated how this reaction system is affected by pH level, dissolved O2 content, and radical scavengers and traps. Products were characterized with near-IR fluorescence, Raman, and XPS spectroscopy. The reaction is greatly accelerated by removal of dissolved O2 and strongly suppressed by TEMPO, a radical trap. Alcohols added as radical scavengers alter the reaction efficiency and the product peak emission wavelengths. Photofunctionalization with 300 nm irradiation is substantially less efficient at pH levels low enough to protonate the OCl- ion to HOCl. We deduce that in mildly treated high pH samples, the main product is sp2 hybridized O-doped adducts formed by reaction of SWCNTs with atomic oxygen in its 3P (ground) level. By contrast, treatment under low pH conditions leads to sp3 hybridized SWCNT adducts formed by the addition of secondary radicals from reactions of •OH and •Cl. There is also evidence for additional photoreactions of product species under stronger irradiation. Researchers using photoexcited hypochlorite for SWCNT functionalization should be alert to the range of products and the sensitivity to reaction conditions in this system.

Translocation mechanism of anticancer drugs through membrane with the assistance of graphene quantum dot

In recent years, as a new type of quasi-zero-dimensional nanomaterials, graphene quantum dots (GQDs) have shown excellent performance in advanced drug targeted delivery and controlled release. In this work, the delivery process of model drugs translocating into POPC lipid membrane with the assistance of GQDs was investigated via molecular dynamics (MD) simulation. Our simulation results demonstrated that a single doxorubicin (DOX) or deoxyadenine (DA) molecule is difficult to penetrate into the cell membrane. GQD7 could form sandwich-like structure with DOX and assist DOX to enter into the POPC membrane. However, due to the weak interaction with DA, both GQD7 and GQD19 can not assist DA translocating the POPC membrane in the limited MD simulation time. The drug delivery process for DOX could be divided into two steps: 1. GQDs and DOX aggregated into a cluster; 2. the aggregates enter into the POPC membrane. In all our simulation systems, if GQDs loaded with model drugs and entered the cell membrane, it had little effect on the cell membrane structure, and the cell membrane could maintain high integrity and stability. These results may promote the molecular design and application of GQD-based drug delivery systems.

A mirror-image experiment: Sorting carbon nanotubes by L-DNA

DNA has found increasing applications in molecular engineering, yet its chiral property has rarely been utilized. Here, we report a mirror-image experiment using naturally occurring D-DNA and its enantiomer L-DNA to sort a chiral mixture of single-wall carbon nanotubes (SWCNTs). We find that parity conservation leads to a robust experimental outcome: changing DNA chirality results in handedness inversion of the purified nanotube. This finding provides a straightforward solution to the challenging problem of nanotube enantiomer sorting and a materials foundation for applications in fields such as spintronics and chiral sensing. To illustrate the latter, we show that enantiomeric pairs of DNA-SWCNTs can serve as bilateral chiral gauges for quantifying the degree of molecular chirality.

Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor

Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases, has become a standard prostate cancer (PCa) screening method due to exceptional diagnosis performance. However, PI-RADS 3 lesions are an unmet medical need because PI-RADS provides diagnosis accuracy of only 30-40% at most, accompanied by a high false-positive rate. Here, we propose an explainable artificial intelligence (XAI) based PCa screening system integrating a highly sensitive dual-gate field-effect transistor (DGFET) based multi-marker biosensor for ambiguous lesions identification. This system produces interpretable results by analyzing sensing patterns of three urinary exosomal biomarkers, providing a possibility of an evidence-based prediction from clinicians. In our results, XAI-based PCa screening system showed a high accuracy with an AUC of 0.93 using 102 blinded samples with the non-invasive method. Remarkably, the PCa diagnosis accuracy of patients with PI-RADS 3 was more than twice that of conventional PI-RADS scoring. Our system also provided a reasonable explanation of its decision that TMEM256 biomarker is the leading factor for screening those with PI-RADS 3. Our study implies that XAI can facilitate informed decisions, guided by insights into the significance of visualized multi-biomarkers and clinical factors. The XAI-based sensor system can assist healthcare professionals in providing practical and evidence-based PCa diagnoses.

High-Throughput Approaches to Engineer Fluorescent Nanosensors

Optical sensors are powerful tools to identify and image (biological) molecules. Because of their optoelectronic properties, nanomaterials are often used as building blocks. To transduce the chemical interaction with the analyte into an optical signal, the interplay between surface chemistry and nanomaterial photophysics has to be optimized. Understanding these aspects promises major opportunities for tailored sensors with optimal performance. However, this requires methods to create and explore the many chemical permutations. Indeed, many current approaches are limited in throughput. This affects the chemical design space that can be studied, the application of machine learning approaches as well as fundamental mechanistic understanding. Here, an overview of selection-limited and synthesis-limited approaches is provided to create and identify molecular nanosensors. Bottlenecks are discussed and opportunities of non-classical recognition strategies are highlighted such as corona phase molecular recognition as well as the requirements for high throughput and scalability. Fluorescent carbon nanotubes are powerful building blocks for sensors and their huge chemical design space makes them an ideal platform for high throughput approaches. Therefore, they are the focus of this article, but the insights are transferable to any nanosensor system. Overall, this perspective aims to provide a fresh perspective to overcome current challenges in the nanosensor field.

Single-Walled Carbon Nanotubes as Optical Transducers for Nanobiosensors In Vivo

Semiconducting single-walled carbon nanotubes (SWCNTs) may serve as signal transducers for nanobiosensors. Recent studies have developed innovative methods of engineering molecularly specific sensors, while others have devised methods of deploying such sensors within live animals and plants. These advances may potentiate the use of implantable, noninvasive biosensors for continuous drug, disease, and contaminant monitoring based on the optical properties of single-walled carbon nanotubes (SWCNTs). Such tools have substantial potential to improve disease diagnostics, prognosis, drug safety, therapeutic response, and patient compliance. Outside of clinical applications, such sensors also have substantial potential in environmental monitoring or as research tools in the lab. However, substantial work remains to be done to realize these goals through further advances in materials science and engineering. Here, we review the current landscape of quantitative SWCNT-based optical biosensors that have been deployed in living plants and animals. Specifically, we focused this review on methods that have been developed to deploy SWCNT-based sensors in vivo as well as analytes that have been detected by SWCNTs in vivo. Finally, we evaluated potential future directions to take advantage of the promise outlined here toward field-deployable or implantable use in patients.

Scalable Photonic Nose Development through Corona Phase Molecular Recognition

Breath sensors promise early disease diagnosis through noninvasive, rapid analysis, but have struggled to reach clinical use due to challenges in scalability and multivariate data extraction. The current breath sensor design necessitates various channel materials and surface functionalization methods, which delays the process. Additionally, the limited options for channel materials that provide optimum sensitivity and selectivity further restrict the array size to a maximum of only 10 to 20 channels. To address these limitations, we propose a breath sensing array design process based on Corona Phase Molecular Recognition (CoPhMoRe), which enables the creation of an expansive library of nanoparticle interfaces and broad fingerprints for multiple analytes in the breath. Although CoPhMoRe has predominantly been utilized for liquid-phase sensing, its recent application to gas-phase sensing has shown significant potential for breath sensing. We introduce the recent demonstrations in the field and present the concept of a CoPhMoRe-based photonic-nose sensor array, leveraging fluorescent nanomaterials such as near-infrared single-walled carbon nanotubes. Additionally, we identified four critical milestones for translating CoPhMoRe into breath sensors for practical clinical applications.

Prognostic prediction models for postoperative patients with stage I to III colorectal cancer based on machine learning

BACKGROUND

Colorectal cancer (CRC) is characterized by high heterogeneity, aggressiveness, and high morbidity and mortality rates. With machine learning (ML) algorithms, patient, tumor, and treatment features can be used to develop and validate models for predicting survival. In addition, important variables can be screened and different applications can be provided that could serve as vital references when making clinical decisions and potentially improving patient outcomes in clinical settings.

AIM

To construct prognostic prediction models and screen important variables for patients with stage I to III CRC.

METHODS

More than 1000 postoperative CRC patients were grouped according to survival time (with cutoff values of 3 years and 5 years) and assigned to training and testing cohorts (7:3). For each 3-category survival time, predictions were made by 4 ML algorithms (all-variable and important variable-only datasets), each of which was validated via 5-fold cross-validation and bootstrap validation. Important variables were screened with multivariable regression methods. Model performance was evaluated and compared before and after variable screening with the area under the curve (AUC). SHapley Additive exPlanations (SHAP) further demonstrated the impact of important variables on model decision-making. Nomograms were constructed for practical model application.

RESULTS

Our ML models performed well; the model performance before and after important parameter identification was consistent, and variable screening was effective. The highest pre- and postscreening model AUCs 95% confidence intervals in the testing set were 0.87 (0.81-0.92) and 0.89 (0.84-0.93) for overall survival, 0.75 (0.69-0.82) and 0.73 (0.64-0.81) for disease-free survival, 0.95 (0.88-1.00) and 0.88 (0.75-0.97) for recurrence-free survival, and 0.76 (0.47-0.95) and 0.80 (0.53-0.94) for distant metastasis-free survival. Repeated cross-validation and bootstrap validation were performed in both the training and testing datasets. The SHAP values of the important variables were consistent with the clinicopathological characteristics of patients with tumors. The nomograms were created.

CONCLUSION

We constructed a comprehensive, high-accuracy, important variable-based ML architecture for predicting the 3-category survival times. This architecture could serve as a vital reference for managing CRC patients.

Designing nanotheranostics with machine learning

The inherent limits of traditional diagnoses and therapies have driven the development and application of emerging nanotechnologies for more effective and safer management of diseases, herein referred to as 'nanotheranostics'. Although many important technological successes have been achieved in this field, widespread adoption of nanotheranostics as a new paradigm is hindered by specific obstacles, including time-consuming synthesis of nanoparticles, incomplete understanding of nano-bio interactions, and challenges regarding chemistry, manufacturing and the controls required for clinical translation and commercialization. As a key branch of artificial intelligence, machine learning (ML) provides a set of tools capable of performing time-consuming and result-perception tasks, thus offering unique opportunities for nanotheranostics. This Review summarizes the progress and challenges in this emerging field of ML-aided nanotheranostics, and discusses the opportunities in developing next-generation nanotheranostics with reliable datasets and advanced ML models to offer better clinical benefits to patients.

Enhancing physician support in pancreatic cancer diagnosis: New M-F-RCNN artificial intelligence model using endoscopic ultrasound

Background and study aims Endoscopic ultrasound (EUS) is vital for early pancreatic cancer diagnosis. Advances in artificial intelligence (AI), especially deep learning, have improved medical image analysis. We developed and validated the Modified Faster R-CNN (M-F-RCNN), an AI algorithm using EUS images to assist in diagnosing pancreatic cancer. Methods We collected EUS images from 155 patients across three endoscopy centers from July 2022 to July 2023. M-F-RCNN development involved enhancing feature information through data preprocessing and utilizing an improved Faster R-CNN model to identify cancerous regions. Its diagnostic capabilities were validated against an external set of 1,000 EUS images. In addition, five EUS doctors participated in a study comparing the M-F-RCNN model's performance with that of human experts, assessing diagnostic skill improvements with AI assistance. Results Internally, the M-F-RCNN model surpassed traditional algorithms with an average precision of 97.35%, accuracy of 96.49%, and recall rate of 5.44%. In external validation, its sensitivity, specificity, and accuracy were 91.7%, 91.5%, and 91.6%, respectively, outperforming non-expert physicians. The model also significantly enhanced the diagnostic skills of doctors. Conclusions: The M-F-RCNN model shows exceptional performance in diagnosing pancreatic cancer via EUS images, greatly improving diagnostic accuracy and efficiency, thus enhancing physician proficiency and reducing diagnostic errors.

Dual Infrared 2-Photon Microscopy Achieves Minimal Background Deep Tissue Imaging in Brain and Plant Tissues

Traditional deep fluorescence imaging has primarily focused on red-shifting imaging wavelengths into the near-infrared (NIR) windows or implementation of multi-photon excitation approaches. Here, we combine the advantages of NIR and multiphoton imaging by developing a dual-infrared two-photon microscope to enable high-resolution deep imaging in biological tissues. We first computationally identify that photon absorption, as opposed to scattering, is the primary contributor to signal attenuation. We next construct a NIR two-photon microscope with a 1640 nm femtosecond pulsed laser and a NIR PMT detector to image biological tissues labeled with fluorescent single-walled carbon nanotubes (SWNTs). We achieve spatial imaging resolutions close to the Abbe resolution limit and eliminate blur and background autofluorescence of biomolecules, 300 μm deep into brain slices and through the full 120 μm thickness of a Nicotiana benthamiana leaf. We also demonstrate that NIR-II two-photon microscopy can measure tissue heterogeneity by quantifying how much the fluorescence power law function varies across tissues, a feature we exploit to distinguish Huntington's Disease afflicted mouse brain tissues from wildtype. Our results suggest dual-infrared two-photon microscopy could accomplish in-tissue structural imaging and biochemical sensing with a minimal background, and with high spatial resolution, in optically opaque or highly autofluorescent biological tissues.

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.

Investigation of tumour environments through advancements in microtechnology and nanotechnology

Cancer has a significant negative social and economic impact on both developed and developing countries. As a result, understanding the onset and progression of cancer is critical for developing therapies that can improve the well-being and health of individuals with cancer. With time, study has revealed, the tumor microenvironment has great influence on this process. Micro and nanoscale engineering techniques can be used to study the tumor microenvironment. Nanoscale and Microscale engineering use Novel technologies and designs with small dimensions to recreate the TME. Knowing how cancer cells interact with one another can help researchers develop therapeutic approaches that anticipate and counteract cancer cells' techniques for evading detection and fighting anti-cancer treatments, such as microfabrication techniques, microfluidic devices, nanosensors, and nanodevices used to study or recreate the tumor microenvironment. Nevertheless, a complicated action just like the growth and in cancer advancement, and their intensive association along the environment around it that has to be studied in more detail.

Clinical translation of nanomedicine with integrated digital medicine and machine learning interventions

Nanomaterials based therapeutics transform the ways of disease prevention, diagnosis and treatment with increasing sophistications in nanotechnology at a breakneck pace, but very few could reach to the clinic due to inconsistencies in preclinical studies followed by regulatory hinderances. To tackle this, integrating the nanomedicine discovery with digital medicine provide technologies as tools of specific biological activity measurement. Hence, overcome the redundancies in nanomedicine discovery by the on-site data acquisition and analytics through integrating intelligent sensors and artificial intelligence (AI) or machine learning (ML). Integrated AI/ML wearable sensors directly gather clinically relevant biochemical information from the subject's body and process data for physicians to make right clinical decision(s) in a time and cost-effective way. This review summarizes insights and recommend the infusion of actionable big data computation enabled sensors in burgeoning field of nanomedicine at academia, research institutes, and pharmaceutical industries, with a potential of clinical translation. Furthermore, many blind spots are present in modern clinically relevant computation, one of which could prevent ML-guided low-cost new nanomedicine development from being successfully translated into the clinic was also discussed.

[2 + 2] Cycloaddition Produces Divalent Organic Color-Centers with Reduced Heterogeneity in Single-Walled Carbon Nanotubes

Organic color centers (OCCs), generated by the covalent functionalization of single-walled carbon nanotubes, have been exploited for chemical sensing, bioimaging, and quantum technologies. However, monovalent OCCs can assume at least 6 different bonding configurations on the sp2 carbon lattice of a chiral nanotube, resulting in heterogeneous OCC photoluminescence emissions. Herein, we show that a heat-activated [2 + 2] cycloaddition reaction enables the synthesis of divalent OCCs with a reduced number of atomic bonding configurations. The chemistry occurs by simply mixing enophile molecules (e.g., methylmaleimide, maleic anhydride, and 4-cyclopentene-1,3-dione) with an ethylene glycol suspension of SWCNTs at elevated temperature (70-140 °C). Unlike monovalent OCC chemistries, we observe just three OCC emission peaks that can be assigned to the three possible bonding configurations of the divalent OCCs based on density functional theory calculations. Notably, these OCC photoluminescence peaks can be controlled by temperature to decrease the emission heterogeneity even further. This divalent chemistry provides a scalable way to synthesize OCCs with tightly controlled emissions for emerging applications.

Current advance of nanotechnology in diagnosis and treatment for malignant tumors

Cancer remains a significant risk to human health. Nanomedicine is a new multidisciplinary field that is garnering a lot of interest and investigation. Nanomedicine shows great potential for cancer diagnosis and treatment. Specifically engineered nanoparticles can be employed as contrast agents in cancer diagnostics to enable high sensitivity and high-resolution tumor detection by imaging examinations. Novel approaches for tumor labeling and detection are also made possible by the use of nanoprobes and nanobiosensors. The achievement of targeted medication delivery in cancer therapy can be accomplished through the rational design and manufacture of nanodrug carriers. Nanoparticles have the capability to effectively transport medications or gene fragments to tumor tissues via passive or active targeting processes, thus enhancing treatment outcomes while minimizing harm to healthy tissues. Simultaneously, nanoparticles can be employed in the context of radiation sensitization and photothermal therapy to enhance the therapeutic efficacy of malignant tumors. This review presents a literature overview and summary of how nanotechnology is used in the diagnosis and treatment of malignant tumors. According to oncological diseases originating from different systems of the body and combining the pathophysiological features of cancers at different sites, we review the most recent developments in nanotechnology applications. Finally, we briefly discuss the prospects and challenges of nanotechnology in cancer.

Fluorescence changes in carbon nanotube sensors correlate with THz absorption of hydration

Single wall carbon nanotubes (SWCNTs) functionalized with (bio-)polymers such as DNA are soluble in water and sense analytes by analyte-specific changes of their intrinsic fluorescence. Such SWCNT-based (bio-)sensors translate the binding of a molecule (molecular recognition) into a measurable optical signal. This signal transduction is crucial for all types of molecular sensors to achieve high sensitivities. Although there is an increasing number of SWCNT-based sensors, there is yet no molecular understanding of the observed changes in the SWCNT's fluorescence. Here, we report THz experiments that map changes in the local hydration of the solvated SWCNT upon binding of analytes such as the neurotransmitter dopamine or the vitamin riboflavin. The THz amplitude signal serves as a measure of the coupling of charge fluctuations in the SWCNTs to the charge density fluctuations in the hydration layer. We find a linear (inverse) correlation between changes in THz amplitude and the intensity of the change in fluorescence induced by the analytes. Simulations show that the organic corona shapes the local water, which determines the exciton dynamics. Thus, THz signals are a quantitative predictor for signal transduction strength and can be used as a guiding chemical design principle for optimizing fluorescent biosensors.

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.

Optical Aptamer-Based Cytokine Nanosensor Detects Macrophage Activation by Bacterial Toxins

Overactive or dysregulated cytokine expression is a hallmark of many acute and chronic inflammatory diseases. This is true for acute or chronic infections, neurodegenerative diseases, autoimmune diseases, cardiovascular diseases, cancer, and others. Cytokines such as interleukin-6 (IL-6) are known therapeutic targets and biomarkers for such inflammatory diseases. Platforms for cytokine detection are, therefore, desirable tools for both research and clinical applications. Single-walled carbon nanotubes (SWCNT) are versatile nanomaterials with near-infrared fluorescence that can serve as transducers for optical sensors. When functionalized with an analyte-specific recognition element, SWCNT emission may become sensitive and selective toward the desired target. SWCNT-aptamer sensors are easily assembled, inexpensive, and biocompatible. In this work, we introduced a nanosensor design based on SWCNT and a DNA aptamer specific to IL-6. We first evaluated several SWCNT-aptamer constructs based on this simple direct complexation method, wherein the aptamer both solubilizes the SWCNT and confers sensitivity to IL-6. The sensor limit of detection, 105 ng/mL, lies in the relevant range for pathological IL-6 levels. Upon investigation of sensor kinetics, we found rapid response within seconds of antigen addition which continued over the course of 3 h. We found that this sensor construct is stable and the aptamer is not displaced from the nanotube surface during IL-6 detection. Finally, we investigated the ability of this sensor construct to detect macrophage activation caused by bacterial lipopolysaccharides (LPS) in an in vitro model of disease, finding rapid and sensitive detection of macrophage-expressed IL-6. We are confident that further development of this sensor will have novel implications for diagnosis of acute and chronic inflammatory diseases, in addition to contributing to the understanding of the role of cytokines in these diseases.

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.

Optical Nanosensor Passivation Enables Highly Sensitive Detection of the Inflammatory Cytokine Interleukin-6

Interleukin-6 (IL-6) is known to play a critical role in the progression of inflammatory diseases such as cardiovascular disease, cancer, sepsis, viral infection, neurological disease, and autoimmune diseases. Emerging diagnostic and prognostic tools, such as optical nanosensors, experience challenges in translation to the clinic in part due to protein corona formation, dampening their selectivity and sensitivity. To address this problem, we explored the rational screening of several classes of biomolecules to be employed as agents in noncovalent surface passivation as a strategy to screen interference from nonspecific proteins. Findings from this screening were applied to the detection of IL-6 by a fluorescent-antibody-conjugated single-walled carbon nanotube (SWCNT)-based nanosensor. The IL-6 nanosensor exhibited highly sensitive and specific detection after passivation with a polymer, poly-l-lysine, as demonstrated by IL-6 detection in human serum within a clinically relevant range of 25 to 25,000 pg/mL, exhibiting a limit of detection over 3 orders of magnitude lower than prior antibody-conjugated SWCNT sensors. This work holds potential for the rapid and highly sensitive detection of IL-6 in clinical settings with future application to other cytokines or disease-specific biomarkers.

Exhaled volatolomics profiling facilitates personalized screening for gastric cancer

Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87-0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89-0.94, 95%CI) in discovery cohort and 0.88 (0.83-0.91, 95%CI) accuracy with an AUC of 0.91 (0.87-0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.

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.

Advancing Intelligent Organ-on-a-Chip Systems with Comprehensive In Situ Bioanalysis

In vitro models are essential to a broad range of biomedical research, such as pathological studies, drug development, and personalized medicine. As a potentially transformative paradigm for 3D in vitro models, organ-on-a-chip (OOC) technology has been extensively developed to recapitulate sophisticated architectures and dynamic microenvironments of human organs by applying the principles of life sciences and leveraging micro- and nanoscale engineering capabilities. A pivotal function of OOC devices is to support multifaceted and timely characterization of cultured cells and their microenvironments. However, in-depth analysis of OOC models typically requires biomedical assay procedures that are labor-intensive and interruptive. Herein, the latest advances toward intelligent OOC (iOOC) systems, where sensors integrated with OOC devices continuously report cellular and microenvironmental information for comprehensive in situ bioanalysis, are examined. It is proposed that the multimodal data in iOOC systems can support closed-loop control of the in vitro models and offer holistic biomedical insights for diverse applications. Essential techniques for establishing iOOC systems are surveyed, encompassing in situ sensing, data processing, and dynamic modulation. Eventually, the future development of iOOC systems featuring cross-disciplinary strategies is discussed.

A novel defined programmed cell death related gene signature for predicting the prognosis of serous ovarian cancer

PURPOSE

This study aims to explore the contribution of differentially expressed programmed cell death genes (DEPCDGs) to the heterogeneity of serous ovarian cancer (SOC) through single-cell RNA sequencing (scRNA-seq) and assess their potential as predictors for clinical prognosis.

METHODS

SOC scRNA-seq data were extracted from the Gene Expression Omnibus database, and the principal component analysis was used for cell clustering. Bulk RNA-seq data were employed to analyze SOC-associated immune cell subsets key genes. CIBERSORT and single-sample gene set enrichment analysis (ssGSEA) were utilized to calculate immune cell scores. Prognostic models and nomograms were developed through univariate and multivariate Cox analyses.

RESULTS

Our analysis revealed that 48 DEPCDGs are significantly correlated with apoptotic signaling and oxidative stress pathways and identified seven key DEPCDGs (CASP3, GADD45B, GNA15, GZMB, IL1B, ISG20, and RHOB) through survival analysis. Furthermore, eight distinct cell subtypes were characterized using scRNA-seq. It was found that G protein subunit alpha 15 (GNA15) exhibited low expression across these subtypes and a strong association with immune cells. Based on the DEGs identified by the GNA15 high- and low-expression groups, a prognostic model comprising eight genes with significant prognostic value was constructed, effectively predicting patient overall survival. Additionally, a nomogram incorporating the RS signature, age, grade, and stage was developed and validated using two large SOC datasets.

CONCLUSION

GNA15 emerged as an independent and excellent prognostic marker for SOC patients. This study provides valuable insights into the prognostic potential of DEPCDGs in SOC, presenting new avenues for personalized treatment strategies.

Potential Application Prospects of Biomolecule-Modified Two-Dimensional Chiral Nanomaterials in Biomedicine

Chirality, one of the most fundamental properties of natural molecules, plays a significant role in biochemical reactions. Nanomaterials with chiral characteristics have superior properties, such as catalytic properties, optoelectronic properties, and photothermal properties, which have significant potential for specific applications in nanomedicine. Biomolecular modifications such as nucleic acids, peptides, proteins, and polysaccharides are sources of chirality for nanomaterials with great potential for application in addition to intrinsic chirality, artificial macromolecules, and metals. Two-dimensional (2D) nanomaterials, as opposed to other dimensions, due to proper surface area, extensive modification sites, drug loading potential, and simplicity of preparation, are prepared and utilized in diagnostic applications, drug delivery research, and tumor therapy. Current advanced studies on 2D chiral nanomaterials for biomedicine are focused on novel chiral development, structural control, and materials sustainability applications. However, despite the advances in biomedical research, chiral 2D nanomaterials still confront challenges such as the difficulty of synthesis, quality control, batch preparation, chiral stability, and chiral recognition and selectivity. This review aims to provide a comprehensive overview of the origins, synthesis, applications, and challenges of 2D chiral nanomaterials with biomolecules as cargo and chiral modifications and highlight their potential roles in biomedicine.

An optical aptamer-based cytokine nanosensor detects macrophage activation by bacterial toxins

Overactive or dysregulated cytokine expression is hallmark of many acute and chronic inflammatory diseases. This is true for acute or chronic infection, neurodegenerative diseases, autoimmune diseases, cardiovascular disease, cancer, and others. Cytokines such as interleukin-6 (IL-6) are known therapeutic targets and biomarkers for such inflammatory diseases. Platforms for cytokine detection are therefore desirable tools for both research and clinical applications. Single-walled carbon nanotubes (SWCNT) are versatile nanomaterials with near-infrared fluorescence that can serve as transducers for optical sensors. When functionalized with an analyte-specific recognition element, SWCNT emission may become sensitive and selective towards the desired target. SWCNT-aptamer sensors are easily assembled, inexpensive, and biocompatible. In this work, we introduced a nanosensor design based on SWCNT and a DNA aptamer specific to IL-6. We first evaluated several SWCNT-aptamer constructs based on this simple direct complexation method, wherein the aptamer both solubilizes the SWCNT and confers sensitivity to IL-6. The sensor limit of detection, 105 ng/mL, lies in the relevant range for pathological IL-6 levels. Upon investigation of sensor kinetics, we found rapid response within seconds of antigen addition which continued over the course of three hours. We found that this sensor construct is stable, and the aptamer is not displaced from the nanotube surface during IL-6 detection. Finally, we investigated the ability of this sensor construct to detect macrophage activation caused by bacterial lipopolysaccharides (LPS) in an in vitro model of disease, finding rapid and sensitive detection of macrophage-expressed IL-6. We are confident further development of this sensor will have novel implications for diagnosis of acute and chronic inflammatory diseases, in addition to contributing to the understanding of the role of cytokines in these diseases.

Nonmetallic graphite for tumor magnetic hyperthermia therapy

Magnetic hyperthermia therapy (MHT) has garnered immense interest due to its exceptional spatiotemporal specificity, minimal invasiveness and remarkable tissue penetration depth. Nevertheless, the limited magnetothermal heating capability and the potential toxicity of metal ions in magnetic materials based on metallic elements significantly impede the advancement of MHT. Herein, we introduce the concept of nonmetallic materials, with graphite (Gra) as a proof of concept, as a highly efficient and biocompatible option for MHT of tumors in vivo for the first time. The Gra exhibits outstanding magnetothermal heating efficacy owing to the robust eddy thermal effect driven by its excellent electrical conductivity. Furthermore, being composed of carbon, Gra offers superior biocompatibility as carbon is an essential element for all living organisms. Additionally, the Gra boasts customizable shapes and sizes, low cost, and large-scale production capability, facilitating reproducible and straightforward manufacturing of various Gra implants. In a mouse tumor model, Gra-based MHT successfully eliminates the tumors at an extremely low magnetic field intensity, which is less than one-third of the established biosafety threshold. This study paves the way for the development of high-performance magnetocaloric materials by utilizing nonmetallic materials in place of metallic ones burdened with inherent limitations.

Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection

On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%-93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5 min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.

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.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

Nanotechnological advances in cancer: therapy a comprehensive review of carbon nanotube applications

Nanotechnology is revolutionising different areas from manufacturing to therapeutics in the health field. Carbon nanotubes (CNTs), a promising drug candidate in nanomedicine, have attracted attention due to their excellent and unique mechanical, electronic, and physicochemical properties. This emerging nanomaterial has attracted a wide range of scientific interest in the last decade. Carbon nanotubes have many potential applications in cancer therapy, such as imaging, drug delivery, and combination therapy. Carbon nanotubes can be used as carriers for drug delivery systems by carrying anticancer drugs and enabling targeted release to improve therapeutic efficacy and reduce adverse effects on healthy tissues. In addition, carbon nanotubes can be combined with other therapeutic approaches, such as photothermal and photodynamic therapies, to work synergistically to destroy cancer cells. Carbon nanotubes have great potential as promising nanomaterials in the field of nanomedicine, offering new opportunities and properties for future cancer treatments. In this paper, the main focus is on the application of carbon nanotubes in cancer diagnostics, targeted therapies, and toxicity evaluation of carbon nanotubes at the biological level to ensure the safety and real-life and clinical applications of carbon nanotubes.

High-throughput detection allied with machine learning for precise monitoring of significant serum metabolic changes in Helicobacter pylori infection

High-throughput detection of large-scale samples is the foundation for rapidly accessing massive metabolic data in precision medicine. Machine learning is a powerful tool for uncovering valuable information hidden within massive data. In this work, we achieved the extraction of a single fingerprinting of 1 μL serum within 5 s through a high-throughput detection platform based on functionalized nanoparticles. We quickly obtained over a thousand serum metabolic fingerprintings (SMFs) including those of individuals with Helicobacter pylori (HP) infection. Combining four classical machine learning models and enrichment analysis, we attempted to extract and confirm useful information behind these SMFs. Based on all fingerprint signals, all four models achieved area under the curve (AUC) values of 0.983-1. In particular, orthogonal partial least squares discriminant analysis (OPLS-DA) model obtained value of 1 in both the discovery and validation sets. Fortunately, we identified six significant metabolic features, all of which can greatly contribute to the monitoring of HP infection, with AUC values ranging from 0.906 to 0.985. The combination of these six significant metabolic features can enable the precise monitoring of HP infection in serum, with over 95 % of accuracy, specificity and sensitivity. The OPLS-DA model displayed optimal performance and the corresponding scatter plot visualized the clear distinction between HP and HC. Interestingly, they exhibit a consistent reduction trend compared to healthy controls, prompting us to explore the possible metabolic pathways and potential mechanism. This work demonstrates the potential alliance between high-throughput detection and machine learning, advancing their application in precision medicine.

Signal Amplification and Near-Infrared Translation of Enzymatic Reactions by Nanosensors

Enzymatic reactions are used to detect analytes in a range of biochemical methods. To measure the presence of an analyte, the enzyme is conjugated to a recognition unit and converts a substrate into a (colored) product that is detectable by visible (VIS) light. Thus, the lowest enzymatic turnover that can be detected sets a limit on sensitivity. Here, we report that substrates and products of horseradish peroxidase (HRP) and β-galactosidase change the near-infrared (NIR) fluorescence of (bio)polymer modified single-walled carbon nanotubes (SWCNTs). They translate a VIS signal into a beneficial NIR signal. Moreover, the affinity of the nanosensors leads to a higher effective local concentration of the reactants. This causes a non-linear sensor-based signal amplification and translation (SENSAT). We find signal enhancement up to ≈120x for the HRP substrate p-phenylenediamine (PPD), which means that reactions below the limit of detection in the VIS can be followed in the NIR (≈1000 nm). The approach is also applicable to other substrates such as 3,3'-5,5'-tetramethylbenzidine (TMB). An adsorption-based theoretical model fits the observed signals and corroborates the sensor-based enhancement mechanism. This approach can be used to amplify signals, translate them into the NIR and increase sensitivity of biochemical assays.

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.

An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators

OBJECTIVE

To construct a machine learning diagnostic model integrating feature dimensionality reduction techniques and artificial neural network classifiers to develop the value of clinical routine blood indexes for the auxiliary diagnosis of ovarian cancer.

METHODS

Patients with ovarian cancer clearly diagnosed in our hospital were collected as a case group (n = 185), and three groups of patients with other malignant otolaryngology tumors (n = 138), patients with benign otolaryngology diseases (n = 339) and those with normal physical examination (n = 92) were used as an overall control group. In this paper, a fully automated segmentation network for magnetic resonance images of ovarian cancer is proposed to improve the reproducibility of tumor segmentation results while effectively reducing the burden on radiologists. A pre-trained Res Net50 is used to the three edge output modules are fused to obtain the final segmentation results. The segmentation results of the proposed network architecture are compared with the segmentation results of the U-net based network architecture and the effect of different loss functions and region of interest sizes on the segmentation performance of the proposed network is analyzed.

RESULTS

The average Dice similarity coefficient, average sensitivity, average specificity (specificity) and average hausdorff distance of the proposed network segmentation results reached 83.62%, 89.11%, 96.37% and 8.50, respectively, which were better than the U-net based segmentation method. For ROIs containing tumor tissue, the smaller the size, the better the segmentation effect. Several loss functions do not differ much. The area under the ROC curve of the machine learning diagnostic model reached 0.948, with a sensitivity of 91.9% and a specificity of 86.9%, and its diagnostic efficacy was significantly better than that of the traditional way of detecting CA125 alone. The model was able to accurately diagnose ovarian cancer of different disease stages and showed certain discriminative ability for ovarian cancer in all three control subgroups.

CONCLUSION

Using machine learning to integrate multiple conventional test indicators can effectively improve the diagnostic efficacy of ovarian cancer, which provides a new idea for the intelligent auxiliary diagnosis of ovarian cancer.

Intelligent quantitative recognition of sulfide using machine learning-based ratiometric fluorescence probe of metal-organic framework UiO-66-NH2/Ppix

Sulfides possess either high toxicity or play crucial physiological role such as gas transmitter dependent upon dosage, hence the significant for their rapid sensitive and selective concentration determination. Herein, a machine learning enhanced ratiometric fluorescence sensor was engineered for sulfide determination by incorporating the nanometal-organic framework (UiO-66-NH2) along with protoporphyrin IX (Ppix). The blue fluorescence at 431 nm originated from the moiety of UiO-66-NH2 by 365 nm excitation serves as an internal calibration reference signal, while the red fluorescence at 629 nm from the moiety of Ppix serves as the analytical signal, and the intensity is correlated to the amount of sulfides. The fluorescence color of the sensor gradually varies from blue to red upon sequential addition of copper and sulfide ions, resulting in RGB (Red, Green, Blue) feature values for corresponding sulfide concentrations, which facilities the advanced data processing techniques using machine learning algorithms. On the basis of fluorescence image fingerprint extraction and machine learning algorithms, an online data analysis model was developed to improve the precision and accuracy of sulfide determination. The established model employed Linear Discriminant Analysis (LDA) and was subjected to rigorous cross-validation to ensure its robustness. By analyzing the correlation between RGB feature values and sulfide concentrations, the study highlighted a significant positive relationship between the red feature values and sulfide concentrations. The application of machine learning techniques on the ratiometric fluorescence signal of the UiO-66-NH2/Ppix probe demonstrated its potential for intelligent quantitative determination of sulfides, offering a valuable and efficient tool for pollution detection and real-time rapid environmental monitoring.

Photo-Activated, Solid-State Introduction of Luminescent Oxygen Defects into Semiconducting Single-Walled Carbon Nanotubes

Oxygen defects in semiconducting single-walled carbon nanotubes (SWCNTs) are localized disruptions in the carbon lattice caused by the formation of epoxy or ether groups, commonly through wet-chemical reactions. The associated modifications of the electronic structure can result in luminescent states with emission energies below those of pristine SWCNTs in the near-infrared range, which makes them promising candidates for applications in biosensing and as single-photon emitters. Here, we demonstrate the controlled introduction of luminescent oxygen defects into networks of monochiral (6,5) SWCNTs using a solid-state photocatalytic approach. UV irradiation of SWCNTs on the photoreactive surfaces of the transition metal oxides TiOx and ZnOx in the presence of trace amounts of water and oxygen results in the creation of reactive oxygen species that initiate radical reactions with the carbon lattice and the formation of oxygen defects. The created ether-d and epoxide-l defect configurations give rise to two distinct red-shifted emissive features. The chemical and dielectric properties of the photoactive oxides influence the final defect emission properties, with oxygen-functionalized SWCNTs on TiOx substrates being brighter than those on ZnOx or pristine SWCNTs on glass. The photoinduced functionalization of nanotubes is further employed to create lateral patterns of oxygen defects in (6,5) SWCNT networks with micrometer resolution and thus spatially controlled defect emission.

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.