Multiplexed nanomaterial-assisted laser desorption/ionization for pan-cancer diagnosis and classification

AIScholar: An OpenFaaS-enhanced cloud platform for intelligent medical data analytics

This paper presents AIScholar, an intelligent research cloud platform developed based on artificial intelligence analysis methods and the OpenFaaS serverless framework, designed for intelligent analysis of clinical medical data with high scalability. AIScholar simplifies the complex analysis process by encapsulating a wide range of medical data analytics methods into a series of customizable cloud tools that emphasize ease of use and expandability, within OpenFaaS's serverless computing framework. As a multifaceted auxiliary tool in medical scientific exploration, AIScholar accelerates the deployment of computational resources, enabling clinicians and scientific personnel to derive new insights from clinical medical data with unprecedented efficiency. A case study focusing on breast cancer clinical data underscores the practicality that AIScholar offers to clinicians for diagnosis and decision-making. Insights generated by the platform have a direct impact on the physicians' ability to identify and address clinical issues, signifying its real-world application significance in clinical practice. Consequently, AIScholar makes a meaningful impact on medical research and clinical practice by providing powerful analytical tools to clinicians and scientific personnel, thereby promoting significant advancements in the analysis of clinical medical data.

Current advances and future prospects of blood-based techniques for identifying benign and malignant pulmonary nodules

Lung cancer is the leading cause of cancer-related mortality worldwide, highlighting the urgent need for more accurate and minimally invasive diagnostic tools to improve early detection and patient outcomes. While low-dose computed tomography (LDCT) is effective for screening in high-risk individuals, its high false-positive rate necessitates more precise diagnostic strategies. Liquid biopsy, particularly ctDNA methylation analysis, represents a promising alternative for non-invasive classification of indeterminate pulmonary nodules (IPNs). This review highlights the progress and clinical potential of liquid biopsy technologies, including traditional proteins markers, cfDNA, exosomes, metabolomics, circulating tumor cells (CTCs) and platelets, in lung cancer diagnosis. We discuss the integration of ctDNA methylation analysis with traditional imaging and clinical data to enhance the early detection of IPNs, as well as potential solutions to address the challenges of low biomarker concentration and background noise. By advancing precision diagnostics, liquid biopsy technologies could transform lung cancer management, improve survival rates, and reduce the disease burden.

Energy-Confinement 3D Flower-Shaped Cages for AI-Driven Decoding of Metabolic Fingerprints in Cardiovascular Disease Diagnosis

Rapid and accurate detection plays a critical role in improving the survival and prognosis of patients with cardiovascular disease, but traditional detection methods are far from ideal for those with suspected conditions. Metabolite analysis based on nanomatrix-assisted laser desorption/ionization mass spectrometry (NMALDI-MS) is considered to be a promising technique for disease diagnosis. However, the performance of core nanomatrixes has limited its clinical application. In this study, we constructed 3D flower-shaped cages based on controllable structured metal-organic frameworks and iron oxide nanoparticles with low thermal conductivity and significant photothermal effects. The elongation of the incident light path through multilayer reflection significantly enhances the effective light absorption area of the nanomatrixes. Concurrently, the alternating layered structure confines the thermal energy, reducing thermal losses. Moreover, the 3D structure increases affinity sites, expanding the detection coverage. This approach effectively enhances the laser ionization and thermal desorption efficiency during the LDI process. We applied this technology to analyze the serum metabolomes of patients with myocardial infarction, heart failure, and heart failure combined with myocardial infarction, achieving cost-effective, high-throughput, highly accurate, and user-friendly detection of cardiovascular diseases. Subsequently, deep analysis of detected serum fingerprints via artificial intelligence models screens potential metabolic biomarkers, providing a new paradigm for the accurate diagnosis of cardiovascular diseases.

Multi-cancer early detection tests for general population screening: a systematic literature review

Background

General population cancer screening in the United Kingdom is limited to selected cancers. Blood-based multi-cancer early detection tests aim to detect potential cancer signals from multiple cancers in the blood. The use of a multi-cancer early detection test for population screening requires a high specificity and a reasonable sensitivity to detect early-stage disease so that the benefits of earlier diagnosis and treatment can be realised.

Objective

To undertake a systematic literature review of the clinical effectiveness evidence on blood-based multi-cancer early detection tests for screening.

Methods

Comprehensive searches of electronic databases (including MEDLINE and EMBASE) and trial registers were undertaken in September 2023 to identify published and unpublished studies of multi-cancer early detection tests. Test manufacturer websites and reference lists of included studies and pertinent reviews were checked for additional studies. The target population was individuals aged 50-79 years without clinical suspicion of cancer. Outcomes of interest included test accuracy, number and proportion of cancers detected (by site and stage), time to diagnostic resolution, mortality, potential harms, health-related quality of life, acceptability and satisfaction. The risk of bias was assessed using the quality assessment of diagnostic accuracy studies-2 checklist. Results were summarised using narrative synthesis. Stakeholders contributed to protocol development, report drafting and interpretation of review findings.

Results

Over 8000 records were identified. Thirty-six studies met the inclusion criteria: 1 ongoing randomised controlled trial, 13 completed cohort studies, 17 completed case-control studies and 5 ongoing cohort or case-control studies. Individual tests claimed to detect from 3 to over 50 different types of cancer. Diagnostic accuracy of currently available multi-cancer early detection tests varied substantially: Galleri® (GRAIL, Menlo Park, CA, USA) sensitivity 20.8-66.3%, specificity 98.4-99.5% (three studies); CancerSEEK (Exact Sciences, Madison, WI, USA) sensitivity 27.1-62.3%, specificity 98.9- 99.1% (two studies); SPOT-MAS™ (Gene Solutions, Ho Chi Minh City, Vietnam) sensitivity 72.4-100%, specificity 97.0-99.9% (two studies); Trucheck™ (Datar Cancer Genetics, Bayreuth, Germany) sensitivity 90.0%, specificity 96.4% (one study); Cancer Differentiation Analysis (AnPac Bio, Shanghai, China) sensitivity 40.0%, specificity 97.6% (one study). AICS® (AminoIndex Cancer Screening; Ajinomoto, Tokyo, Japan) screens for individual cancers separately, so no overall test performance statistics are available. Where reported, sensitivity was lower for detecting earlier-stage cancers (stages I-II) compared with later-stage cancers (stages III-IV). Studies of seven other multi-cancer early detection tests at an unclear stage of development were also summarised.

Limitations

Study selection was complex; it was often difficult to determine the stage of development of multi-cancer early detection tests. The evidence was limited; there were no completed randomised controlled trials and most included studies had a high overall risk of bias, primarily owing to limited follow-up of participants with negative test results. Only one study of Galleri recruited asymptomatic individuals aged over 50 in the United States of America; however, study results may not be representative of the United Kingdom's general screening population. No meaningful results were reported relating to patient-relevant outcomes, such as mortality, potential harms, health-related quality of life, acceptability or satisfaction.

Conclusions

All currently available multi-cancer early-detection tests reported high specificity (> 96%). Sensitivity was highly variable and influenced by study design, population, reference standard test used and length of follow-up.

Future work

Further research should report patient-relevant outcomes and consider patient and service impacts.

Study registration

This study is registered as PROSPERO CRD42023467901.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: NIHR161758) and is published in full in Health Technology Assessment; Vol. 29, No. 2. See the NIHR Funding and Awards website for further award information.

Development of a urine-based metabolomics approach for multi-cancer screening and tumor origin prediction

Background

Cancer remains a leading cause of mortality worldwide. A non-invasive screening solution was required for early diagnosis of cancer. Multi-cancer early detection (MCED) tests have been considered to address the challenge by simultaneously identifying multiple types of cancer within a single test using minimally invasive blood samples. However, a multi-cancer screening strategy utilizing urine-based metabolomics has not yet been developed.

Methods

We enrolled 911 cancer patients with 548 lung cancer (LC), 177 with gastric cancer (GC), and 186 with colorectal cancer (CRC), alongside 563 individuals with non-cancerous benign diseases and 229 healthy controls (HC) and investigated the metabolic profiles of urine samples. Participants were randomly allocated to discovery and validation cohorts. The discovery cohort was used for identifying multi-cancer and tissue-specific signatures to build the cancer screening and tumor origin prediction models, while the validation cohort was employed for assessing the performance of these models.

Results

We identified and annotated a total of 360 metabolites from the urine samples. Using the LASSO regression algorithm, 18 metabolites were characterized as urinary metabolic biomarkers and exhibited excellent discriminative performance between cancer patients and HC with AUC of 0.96 in the validation cohort. In comparison with the performance of traditional tumor markers CEA, the screening model performed higher sensitivity across the cancer stages, with a particularly increase in sensitivity among early-stage cancer patients. Moreover, the screening model also exhibited in high classification of cancers from non-cancerous group, comprising with HC and benign disease participants. Furthermore, two non-overlapping metabolic panels were selected to differentiate LC from Non-LC and GC from CRC with the AUC values of 0.87 and 0.83 in validation cohorts, respectively. Additionally, the model accurately predicted the origin of three lethal cancers: lung, gastric, and colorectal, with an overall accuracy of 0.75. The AUC values for LC, GC, and CRC were 0.88, 0.88, and 0.80, respectively.

Discussion

Our study demonstrates the potential of urine-based metabolomics for multi-cancer early detection. The approach offers non-invasive cancer screening, promising widespread implementation in population-based programs for early detection and improved outcomes. Further validation and expansion are needed for broader clinical applicability.

Rapid detection of perfluorooctanoic acid by surface enhanced Raman spectroscopy and deep learning

Perfluorooctanoic acid (PFOA) has received increasing concerns in recent years due to its wide distribution and potential toxicity. Existing detection techniques of PFOA require complex pre-treatment, therefore often taking several hours. Here, we developed a rapid PFOA detection mode to detect approximate concentrations of PFOA (ranging from 10-15 to 10-3 mol/L) in deionized water, and detecting one sample takes only 20 min. The detection mode was achieved using a deep learning model trained by a large surface enhanced Raman spectra dataset, based on the agglomeration of PFOA with crystal violet. In addition, transfer learning approach was used to fine tune the model, the fine-tuned model was generalizable across water samples with different impurities and environments to determine whether meet the safety standards of PFOA, the accuracy was 96.25 % and 94.67 % for tap water and lake water samples, respectively. The mechanism and specificity of the detection mode were further confirmed by molecular dynamics simulation. Our work provides a promising solution for PFOA detection, especially in the context of the increasingly widespread application of PFOA.

Diagnosis and Biomarker Screening of Endometrial Cancer Enabled by a Versatile Exosome Metabolic Fingerprint Platform

Exosomes have emerged as a revolutionary tool for liquid biopsy (LB), as they carry specific cargo from cells. Profiling the metabolites of exosomes is crucial for cancer diagnosis and biomarker discovery. Herein, we propose a versatile platform for exosomal metabolite assay of endometrial cancer (EC). The platform is based on a nanostructured composite material comprising gold nanoparticle-coated magnetic COF with aptamer modification (Fe3O4@COF@Au-Apt). The unique design and novel synthesis strategy of Fe3O4@COF@Au-Apt provide the material with a large specific surface area, enabling the efficient and specific isolation of exosomes. The exosomes captured Fe3O4@COF@Au-Apt can be directly used as the laser desorption/ionization mass spectrometry (LDI-MS) matrix for rapid exosomal metabolic patterns. By integrating these functionalities into a single platform, the analytical process is simplified, eliminating the need for additional elution steps and minimizing potential sample loss, resulting in large-scale exosomal metabolic fingerprints. Combining with machine learning algorithms on the metabolic patterns, accurate discrimination between endometrial patients (EGs) and benign controls (CGs) was achieved, and the area under the receiver operating characteristic curve of the blind test cohort was 0.924. Confusion matrix analysis of important metabolic fingerprint features further demonstrates the high accuracy of the proposed approach toward EC diagnosis, with an overall accuracy of 94.1%. Moreover, four metabolites, namely, hydroxychalcone, l-acetylcarnitine, elaidic acid, and glutathione, have been identified as potential biomarkers of EC. These results highlight the great value of the integrated exosome metabolic fingerprint platform in facilitating low-cost and high-throughput characterization of exosomal metabolites for cancer diagnosis and biomarker discovery.

Advances and Prospects in Liquid Biopsy Techniques for Malignant Tumor Diagnosis and Surveillance

Liquid biopsy technology provides invaluable support for the early diagnosis of tumors and surveillance of disease course by detecting tumor-related biomarkers in bodily fluids. Currently, liquid biopsy techniques are mainly divided into two categories: biomarker and label-free. Biomarker liquid biopsy techniques utilize specific antibodies or probes to identify and isolate target cells, exosomes, or molecules, and these techniques are widely used in clinical practice. However, they have certain limitations including dependence on tumor markers, alterations in cell biological properties, and high cost. In contrast, label-free liquid biopsy techniques directly utilize physical or chemical properties of cells, exosomes, or molecules for detection and isolation. These techniques have the advantage of not needing labeling, not impacting downstream analysis, and low detection cost. However, most are still in the research stage and not yet mature. This review first discusses recent advances in liquid biopsy techniques for early tumor diagnosis and disease surveillance. Several current techniques are described in detail. These techniques exploit differences in biomarkers, size, density, deformability, electrical properties, and chemical composition in tumor components to achieve highly sensitive tumor component identification and separation. Finally, the current research progress is summarized and the future research directions of the field are discussed.

Aggregation-Induced Emission Luminogen: Role in Biopsy for Precision Medicine

Biopsy, including tissue and liquid biopsy, offers comprehensive and real-time physiological and pathological information for disease detection, diagnosis, and monitoring. Fluorescent probes are frequently selected to obtain adequate information on pathological processes in a rapid and minimally invasive manner based on their advantages for biopsy. However, conventional fluorescent probes have been found to show aggregation-caused quenching (ACQ) properties, impeding greater progresses in this area. Since the discovery of aggregation-induced emission luminogen (AIEgen) have promoted rapid advancements in molecular bionanomaterials owing to their unique properties, including high quantum yield (QY) and signal-to-noise ratio (SNR), etc. This review seeks to present the latest advances in AIEgen-based biofluorescent probes for biopsy in real or artificial samples, and also the key properties of these AIE probes. This review is divided into: (i) tissue biopsy based on smart AIEgens, (ii) blood sample biopsy based on smart AIEgens, (iii) urine sample biopsy based on smart AIEgens, (iv) saliva sample biopsy based on smart AIEgens, (v) biopsy of other liquid samples based on smart AIEgens, and (vi) perspectives and conclusion. This review could provide additional guidance to motivate interest and bolster more innovative ideas for further exploring the applications of various smart AIEgens in precision medicine.

Multiscale Structured Trimetal Oxide Heterojunctions for Urinary Metabolic Phenotype-Dependent Screening of Early and Small Hepatocellular Carcinoma

Developing a standardized screening tool for the detection of early and small hepatocellular carcinoma (HCC) through urinary metabolic analysis poses a challenging yet intriguing research endeavor. In this study, a range of intricately interlaced 2D rough nanosheets featuring well-defined sharp edges is fabricated, with the aim of constructing diverse trimetal oxide heterojunctions exhibiting multiscale structures. By carefully engineering synergistic effects in composition and structure, including improved adsorption, diffusion, and other surface-driven processes, the optimized heterojunctions demonstrate a substantial enhancement in signal intensity compared to monometallic or bimetallic oxides, as well as fragmented trimetallic oxides. Additionally, optimal heterojunctions enable the extraction of high-quality urinary metabolic fingerprints using high-throughput mass spectrometry. Leveraging machine learning, discrimination of HCC patients from high-risk and healthy populations achieves impressive performance, with area under the curve values of 0.940 and 0.916 for receiver operating characteristic and precision-recall curves, respectively. Six crucial metabolites are identified, enabling accurate detection of early, small-tumor, alpha-fetoprotein-negative HCC (93.3%-97.3%). A comprehensive screening strategy tailored to clinical reality yields precision metrics (accuracy, precision, recall, and F1 score) exceeding 95.0%. This study advances the application of cutting-edge matrices-based metabolic phenotyping in practical clinical diagnostics.

From Nanozymes to Multi-Purpose Nanomaterials: The Potential of Metal-Organic Frameworks for Proteomics Applications

Metal-organic frameworks (MOFs) have the potential to revolutionize the biotechnological and medical landscapes due to their easily tunable crystalline porous structure. Herein, the study presents MOFs' potential impact on proteomics, unveiling the diverse roles MOFs can play to boost it. Although MOFs are excellent catalysts in other scientific disciplines, their role as catalysts in proteomics applications remains largely underexplored, despite protein cleavage being of crucial importance in proteomics protocols. Additionally, the study discusses evolving MOF materials that are tailored for proteomics, showcasing their structural diversity and functional advantages compared to other types of materials used for similar applications. MOFs can be developed to seamlessly integrate into proteomics workflows due to their tunable features, contributing to protein separation, peptide enrichment, and ionization for mass spectrometry. This review is meant as a guide to help bridge the gap between material scientists, engineers, and MOF chemists and on the other side researchers in biology or bioinformatics working in proteomics.

Multichannel Nanogenerator-Driven Collaborative Metabolic Fingerprint Diagnostic Strategy for Early Screening and Risk Evaluation of Nonalcoholic Fatty Liver Disease

Nonalcoholic fatty liver disease (NAFLD), along with its progressive forms nonalcoholic steatohepatitis (NASH) and NASH fibrosis, has emerged as a global health crisis. However, the absence of robust screening and risk evaluation tools contributes to the underdiagnosis of NAFLD. Herein, we reported a multichannel nanogenerator-assisted laser desorption/ionization mass spectrometry (LDI-MS) platform for early screening and risk evaluation of NAFLD. Specifically, titanium oxide nanosheets (TiNS) and covalent-organic framework nanosheets (COFNS) were employed as nanogenerators with excellent optical properties and exhibited efficient desorption/ionization during the LDI-MS process. Only ∼0.025 μL of serum without pretreatments and separation, serum metabolic fingerprints (SMFs) can be extracted within seconds. Notably, integrated SMFs from TiNS and COFNS significantly improved diagnostic performance and achieved the area under the curve (AUC) values of 1.000 with 100% sensitivity and 100% specificity for the validation sets of global diagnosis, early diagnosis, high-risk NASH, and NASH fibrosis evaluation. Additionally, four biomarker panels were identified, and their diagnostic AUC values were more than 0.944. Ultimately, key metabolic pathways indicating the change from simple NAFLD to high-risk NASH and NASH fibrosis were uncovered. This work provided a noninvasive and high-throughput screening and risk evaluation strategy for NAFLD healthcare management, thus contributing to the precise treatment of the NALFD.

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.

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.

Precise diagnosis and risk stratification of prostate cancer by comprehensive serum metabolic fingerprints: a prediction model study

OBJECTIVES

Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making.

METHODS

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models.

RESULTS

Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve of 100% and was validated in the external cohort with the area under curve of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk.

CONCLUSIONS

Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.

Metabolomics unveils the exacerbating role of arachidonic acid metabolism in atherosclerosis

Atherosclerosis is a complex vascular disorder characterized by the deposition of lipids, inflammatory cascades, and plaque formation in arterial walls. A thorough understanding of its causes and progression is necessary to develop effective diagnostic and therapeutic strategies. Recent breakthroughs in metabolomics have provided valuable insights into the molecular mechanisms and genetic factors involved in atherosclerosis, leading to innovative approaches for preventing and treating the disease. In our study, we analyzed clinical serum samples from both atherosclerosis patients and animal models using laser desorption ionization mass spectrometry. By employing methods such as orthogonal partial least-squares discrimination analysis (OPLS-DA), heatmaps, and volcano plots, we can accurately classify atherosclerosis (AUC = 0.892) and identify key molecules associated with the disease. Specifically, we observed elevated levels of arachidonic acid and its metabolite, leukotriene B4, in atherosclerosis. By inhibiting arachidonic acid and monitoring its downstream metabolites, we discovered the crucial role of this metabolic pathway in regulating atherosclerosis. Metabolomic research provides detailed insights into the metabolic networks involved in atherosclerosis development and reveals the close connection between abnormal metabolism and the disease. These studies offer new possibilities for precise diagnosis, treatment, and monitoring of disease progression, as well as evaluating the effectiveness of therapeutic interventions.

Designed Nanomaterials-Assisted Proteomics and Metabolomics Analysis for In Vitro Diagnosis

In vitro diagnosis (IVD) is pivotal in modern medicine, enabling early disease detection and treatment optimization. Omics technologies, particularly proteomics and metabolomics, offer profound insights into IVD. Despite its significance, omics analyses for IVD face challenges, including low analyte concentrations and the complexity of biological environments. In addition, the direct omics analysis by mass spectrometry (MS) is often hampered by issues like large sample volume requirements and poor ionization efficiency. Through manipulating their size, surface charge, and functionalization, as well as the nanoparticle-fluid incubation conditions, nanomaterials have emerged as a promising solution to extract biomolecules and enhance the desorption/ionization efficiency in MS detection. This review delves into the last five years of nanomaterial applications in omics, focusing on their role in the enrichment, separation, and ionization analysis of proteins and metabolites for IVD. It aims to provide a comprehensive update on nanomaterial design and application in omics, highlighting their potential to revolutionize IVD.

Urine and serum metabolic profiling combined with machine learning for autoimmune disease discrimination and classification

Precision diagnosis and classification of autoimmune diseases (ADs) is challenging due to the obscure symptoms and pathological causes. Biofluid metabolic analysis has the potential for disease screening, in which high throughput, rapid analysis and minimum sample consumption must be addressed. Herein, we performed metabolomic profiling by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) in urine and serum samples. Combined with machine learning (ML), metabolomic patterns from urine achieved the discrimination and classification of ADs with high accuracy. Furthermore, metabolic disturbances among different ADs were also investigated, and provided information of etiology. These results demonstrated that urine metabolic patterns based on MALDI-MS and ML manifest substantial potential in precision medicine.

Designed Concave Octahedron Heterostructures Decode Distinct Metabolic Patterns of Epithelial Ovarian Tumors

Epithelial ovarian cancer (EOC) is a polyfactorial process associated with alterations in metabolic pathways. A high-performance screening tool for EOC is in high demand to improve prognostic outcome but is still missing. Here, a concave octahedron Mn₂ O₃ /(Co,Mn)(Co,Mn)₂ O₄ (MO/CMO) composite with a heterojunction, rough surface, hollow interior, and sharp corners is developed to record metabolic patterns of ovarian tumors by laser desorption/ionization mass spectrometry (LDI-MS). The MO/CMO composites with multiple physical effects induce enhanced light absorption, preferred charge transfer, increased photothermal conversion, and selective trapping of small molecules. The MO/CMO shows ≈2-5-fold signal enhancement compared to mono- or dual-enhancement counterparts, and ≈10-48-fold compared to the commercialized products. Subsequently, serum metabolic fingerprints of ovarian tumors are revealed by MO/CMO-assisted LDI-MS, achieving high reproducibility of direct serum detection without treatment. Furthermore, machine learning of the metabolic fingerprints distinguishes malignant ovarian tumors from benign controls with the area under the curve value of 0.987. Finally, seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers. The approach guides the future depiction of the state-of-the-art matrix for intensive MS detection and accelerates the growth of nanomaterials-based platforms toward precision diagnosis scenarios.

Engineering the MoS2 /MXene Heterostructure for Precise and Noninvasive Diagnosis of Prostate Cancer with Clinical Specimens

High-throughput metabolic fingerprinting has been deemed one of the most promising strategies for addressing the high false positive rate of prostate cancer (PCa) diagnosis in the prostate-specific antigen (PSA) gray zone. However, the current metabolic fingerprinting remains challenging in achieving high-precision metabolite detection in complex biological samples (e.g., serum and urine). Herein, a novel self-assembly MoS2 /MXene heterostructure nanocomposite with a tailored doping ratio of 10% is presented as a matrix for laser desorption ionization mass spectrometry analysis in clinical biosamples. Notably, owing to the two-dimensional architecture and doping effect, MoS2 /MXene demonstrates favorable laser desorption ionization performance with low adsorption energy, which is evidenced by efficient urinary metabolic fingerprinting with an enhanced area under curve (AUC) diagnosis capability of 0.959 relative to that of serum metabolic fingerprinting (AUC = 0.902) for the diagnosis of PCa in the PSA gray zone. Thus, this MoS2 /MXene heterostructure is anticipated to offer a novel strategy to precisely and noninvasively diagnose PCa in the PSA gray zone.

Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers

BACKGROUND AND AIMS

Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current "gold standard" techniques, non-invasive and highly sensitive screening tests are required for GI cancer diagnosis. Here, we explored the potential of metabolomics for GI cancer detection and the classification of tissue-of-origin, and even the prognosis management.

METHODS

Plasma samples from 37 gastric cancer (GC), 17 colon cancer (CC), and 27 non-cancer (NC) patients were prepared for metabolomics and lipidomics analysis by three MS-based platforms. Univariate, multivariate, and clustering analyses were used for selecting significant metabolic features. ROC curve analysis was based on a series of different binary classifications as well as the true-positive rate (sensitivity) and the false-positive rate (1-specificity).

RESULTS

GI cancers exhibited obvious metabolic perturbation compared with benign diseases. The differentiated metabolites of gastric cancer (GC) and colon cancer (CC) were targeted to same pathways but with different degrees of cellular metabolism reprogramming. The cancer-specific metabolites distinguished the malignant and benign, and classified the cancer types. We also applied this test to before- and after-surgery samples, wherein surgical resection significantly altered the blood-metabolic patterns. There were 15 metabolites significantly altered in GC and CC patients who underwent surgical treatment, and partly returned to normal conditions.

CONCLUSION

Blood-based metabolomics analysis is an efficient strategy for GI cancer screening, especially for malignant and benign diagnoses. The cancer-specific metabolic patterns process the potential for classifying tissue-of-origin in multi-cancer screening. Besides, the circulating metabolites for prognosis management of GI cancer is a promising area of research.

Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests

Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.

SiO2@Au nanoshell-assisted laser desorption/ionization mass spectrometry for coronary heart disease diagnosis

Cardiovascular diseases have threatened human health, amongst which coronary heart disease (CHD) is the third most common cause of death. CHD is considered to be a metabolic disease; however, there is little research on the CHD metabolism. Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) has enabled the development of a suitable nanomaterial that can be used to obtain considerable high-quality metabolic information without complex pretreatment of biological fluid samples. This study combines SiO₂@Au nanoshells with minute plasma to obtain metabolic fingerprints of CHD. The thickness of the SiO₂@Au shell was also optimized to maximize the laser desorption/ionization effect. The results demonstrated 84% sensitivity at 85% specificity for distinguishing CHD patients from controls in the validation cohort.

Diagnosis of Unruptured Intracranial Aneurysm by High-Performance Serum Metabolic Fingerprints

Unruptured intracranial aneurysm (UIA) is a high-risk cerebrovascular saccular dilatation, the effective medical management of which depends on high-performance diagnosis. However, most UIAs are diagnosed incidentally during neurovascular imaging modalities, which are time-consuming and harmful (e.g., radiation). Serum metabolic fingerprints is a promising alternative for early diagnosis of UIA. Here, nanoparticle enhanced laser desorption/ionization mass spectrometry is applied to obtain high-performance UIA-specific serum metabolic fingerprints. Diagnostic performance with an area-under-the-curve (AUC) of 0.842 (95% confidence interval (CI): 0.783-0.891) is achieved by the constructed machine learning (ML) model, including ML algorithm selection and feature selection. Lactate, glutamine, homoarginine, and 3-methylglutaconic acid are identified as the metabolic biomarker panel, which showed satisfactory diagnosis (AUC of 0.812, 95% CI: 0.727-0.897) and effective growth risk assessment (p<0.05, two-tailed t-test) of UIAs. This work aims to promote the diagnostics of UIAs and metabolic biomarker screening for medical management.

Dendritic Plasmonic CuPt Alloys for Closed-Loop Multimode Cancer Therapy with Remarkably Enhanced Efficacy

The outcome of laser-triggered plasmons-induced phototherapy, including photodynamic therapy (PDT) and photothermal therapy (PTT), is significantly limited by the hypoxic tumor microenvironment and the upregulation of heat shock proteins (HSPs) in response to heat stress. Mitochondria, the biological battery of cells, can serve as an important breakthrough to overcome these obstacles. Herein, dendritic triangular pyramidal plasmonic CuPt alloys loaded with heat-sensitive NO donor N, N'-di-sec-butyl-N, N'-dinitroso-1,4-phenylenediamine (BNN) is developed. Under 808 nm laser irradiation, plasmonic CuPt can generate superoxide anion free radicals (·O₂ ^- ) and heat simultaneously. The heat generated can then trigger the release of NO gas, which not only enables gas therapy but also damages the mitochondrial respiratory chain. Impaired mitochondrial respiration leads to reduced oxygen consumption and insufficient intracellular ATP supply, which effectively alleviates tumor hypoxia and undermines the synthesis of HSPs, in turn boosting plasmonic CuPt-based PDT and mild PTT. Additionally, the generated NO and ·O₂ ^- can react to form more cytotoxic peroxynitrite (ONOO^- ). This work describes a plasmonic CuPt@BNN (CPB) triggered closed-loop NO gas, free radicals, and mild photothermal therapy strategy that is highly effective at reciprocally promoting antitumor outcomes.

Clinical Scores, Biomarkers and IT Tools in Lung Cancer Screening-Can an Integrated Approach Overcome Current Challenges?

As most lung cancer (LC) cases are still detected at advanced and incurable stages, there are increasing efforts to foster detection at earlier stages by low dose computed tomography (LDCT) based LC screening. In this scoping review, we describe current advances in candidate selection for screening (selection phase), technical aspects (screening), and probability evaluation of malignancy of CT-detected pulmonary nodules (PN management). Literature was non-systematically assessed and reviewed for suitability by the authors. For the selection phase, we describe current eligibility criteria for screening, along with their limitations and potential refinements through advanced clinical scores and biomarker assessments. For LC screening, we discuss how the accuracy of computerized tomography (CT) scan reading might be augmented by IT tools, helping radiologists to cope with increasing workloads. For PN management, we evaluate the precision of follow-up scans by semi-automatic volume measurements of CT-detected PN. Moreover, we present an integrative approach to evaluate the probability of PN malignancy to enable safe decisions on further management. As a clear limitation, additional validation studies are required for most innovative diagnostic approaches presented in this article, but the integration of clinical risk models, current imaging techniques, and advancing biomarker research has the potential to improve the LC screening performance generally.

Activity-Based Self-Enriched SERS Sensor for Blood Metabolite Monitoring

The monitoring of metabolites in biofluids provides critical clues for disease diagnosis and evaluation. Yet, the quantitative detection of metabolites remains challenging for surface-enhanced Raman spectroscopy (SERS) due to poor reproducibility in preparation and manipulation of SERS nanoprobes. Herein, we develop an activity-based, slippery liquid-infused porous surface SERS (abSLIPSERS) sensor for facile quantification of metabolites with unmodified naked metal nanoparticles (NPs) by integrating biocatalysis-boronate oxidation cascades with SLIPS-driven self-concentration and delivering. Upon mixing the target metabolite with a specific oxidase, a H₂O₂-sensitive phenylboronate probe, and the naked Au NPs, H₂O₂ produced from the biocatalytic reaction oxidizes the phenylboronate probe to phenol, resulting in a ratiometric SERS response. Meanwhile, the SLIPS enables the complete enrichment of molecules and NPs within an evaporating liquid droplet, delivering the probes to the SERS-active sites for Raman amplification. Compared with conventional SERS biosensors, abSLIPSERS avoids multistep synthesis and biofunctionalization of nanoprobes, which significantly simplifies the detection workflow and improves the reproducibility. The abSLIPSERS sensor also shows tunable dynamic range beyond 4 orders of magnitude and allows quantifying any other metabolites with specific enzymes. We demonstrate abSLIPSERS sensing of lactate, glucose, and choline in human serum for exploring energy metabolism in lung cancer. This study opens up a new opportunity for future point-of-care testing of circulating metabolites by SERS and will help to facilitate the translation of SERS bioanalysis to clinical settings.

Bodywide ecological interventions on cancer

Historically, cancer research and therapy have focused on malignant cells and their tumor microenvironment. However, the vascular, lymphatic and nervous systems establish long-range communication between the tumor and the host. This communication is mediated by metabolites generated by the host or the gut microbiota, as well by systemic neuroendocrine, pro-inflammatory and immune circuitries-all of which dictate the trajectory of malignant disease through molecularly defined biological mechanisms. Moreover, aging, co-morbidities and co-medications have a major impact on the development, progression and therapeutic response of patients with cancer. In this Perspective, we advocate for a whole-body 'ecological' exploration of malignant disease. We surmise that accumulating knowledge on the intricate relationship between the host and the tumor will shape rational strategies for systemic, bodywide interventions that will eventually improve tumor control, as well as quality of life, in patients with cancer.

Progress and prospects of nanozymes for enhanced antitumor therapy

Nanozymes are nanomaterials with mimicked enzymatic activity, whose catalytic activity can be designed by changing their physical parameters and chemical composition. With the development of biomedical and material science, artificially created nanozymes have high biocompatibility and can catalyze specific biochemical reactions under biological conditions, thus playing a vital role in regulating physiological activities. Under pathological conditions, natural enzymes are limited in their catalytic capacity by the varying reaction conditions. In contrast, compared to natural enzymes, nanozymes have advantages such as high stability, simplicity of modification, targeting ability, and versatility. As a result, the novel role of nanozymes in medicine, especially in tumor therapy, is gaining increasing attention. In this review, function and application of various nanozymes in the treatment of cancer are summarized. Future exploration paths of nanozymes in cancer therapies based on new insights arising from recent research are outlined.

Neighboring mutation-mediated enhancement of dengue virus infectivity and spread

Frequent turnover of dengue virus (DENV) clades is one of the major forces driving DENV persistence and prevalence. In this study, we assess the fitness advantage of nine stable substitutions within the envelope (E) protein of DENV serotypes. Two tandem neighboring substitutions, threonine to lysine at the 226th (T226K) and glycine to glutamic acid at the 228th (G228E) residues in the DENV2 Asian I genotype, enhance virus infectivity in either mosquitoes or mammalian hosts, thereby promoting clades turnover and dengue epidemics. Mechanistic studies indicate that the substitution-mediated polarity changes in these two residues increase the binding affinity of E for host C-type lectins. Accordingly, we predict that a G228E substitution could potentially result in a forthcoming epidemic of the DENV2 Cosmopolitan genotype. Investigations into the substitutions associated with DENV fitness in hosts may offer mechanistic insights into dengue prevalence, thus providing a warning of potential epidemics in the future.

Artificial intelligence in pancreatic cancer

Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

Nodular and Micronodular Basal Cell Carcinoma Subtypes Are Different Tumors Based on Their Morphological Architecture and Their Interaction with the Surrounding Stroma

Basal cell carcinoma (BCC) is the most frequent cancer of the skin and comprises low-risk and high-risk subtypes. We selected a low-risk subtype, namely, nodular (N), and a high-risk subtype, namely, micronodular (MN), with the aim to identify differences between them using a classical morphometric approach through a gray-level co-occurrence matrix and histogram analysis, as well as an approach based on deep learning semantic segmentation. From whole-slide images, pathologists selected 216 N and 201 MN BCC images. The two groups were then manually segmented and compared based on four morphological areas: center of the BCC islands (tumor, T), peripheral palisading of the BCC islands (touching tumor, TT), peritumoral cleft (PC) and surrounding stroma (S). We found that the TT pattern varied the least, while the PC pattern varied the most between the two subtypes. The combination of two distinct analysis approaches yielded fresh insights into the characterization of BCC, and thus, we were able to describe two different morphological patterns for the T component of the two subtypes.

Nanostructured Layer of Silver for Detection of Small Biomolecules in Surface-Assisted Laser Desorption Ionization Mass Spectrometry

A facile approach for the synthesis of a silver nanostructured layer for application in surface-assisted laser desorption/ionization mass spectrometry of low-molecular-weight biomolecules was developed using electrochemical deposition. The deposition was carried out using the following silver salts: trifluoroacetate, acetate and nitrate, varying the voltage and time. The plate based on trifluoroacetate at 10 V for 15 min showed intense SALDI-MS responses for standards of various classes of compounds: fatty acids, cyclitols, saccharides and lipids at a concentration of 1 nmol/spot, with values of the signal-to-noise ratio ≥50. The values of the limit of detection were 0.71 µM for adonitol, 2.08 µM for glucose and 0.39 µM for palmitic acid per spot. SEM analysis of the plate showed anisotropic flower-like microstructures with nanostructures on their surface. The reduced chemical background in the low-mass region can probably be explained by the absence of stabilizers and reducing agents during the synthesis. The plate synthesized with the developed approach showed potential for future use in the analysis of low-molecular-weight compounds of biological relevance. The absence of the need for the utilization of sophisticated equipment and the synthesis time (10 min) may benefit large-scale applications of the layer for the detection of various types of small biomolecules.

Serum fingerprinting by slippery liquid-infused porous SERS for non-invasive lung cancer detection

Direct and label-free analysis of clinical serum samples using slippery liquid-infused porous-enhanced Raman spectroscopy (SLIPSERS) enables the rapid non-invasive identification of lung cancer.