Cyclic Ion Mobility-Mass Spectrometry and Tandem Collision Induced Unfolding for Quantification of Elusive Protein Biomarkers.

Sensitive analytical techniques that are capable of detecting and quantifying disease-associated biomolecules are indispensable in our efforts to understand disease mechanisms and guide therapeutic intervention through early detection, accurate diagnosis, and effective monitoring of disease. Parkinson's Disease (PD), for example, is one of the most prominent neurodegenerative disorders in the world, but the diagnosis of PD has primarily been based on the observation of clinical symptoms. The protein α-synuclein (α-syn) has emerged as a promising biomarker candidate for PD, but a lack of analytical methods to measure complex disease-associated variants of α-syn has prevented its widespread use as a biomarker. Antibody-based methods such as immunoassays and mass spectrometry-based approaches have been used to measure a limited number of α-syn forms; however, these methods fail to differentiate variants of α-syn that display subtle differences in only the sequence and structure. In this work, we developed a cyclic ion mobility-mass spectrometry method that combines multiple stages of activation and timed ion selection to quantify α-syn variants using both mass- and structure-based measurements. This method can allow for the quantification of several α-syn variants present at physiological levels in biological fluid. Taken together, this approach can be used to galvanize future efforts aimed at understanding the underlying mechanisms of PD and serves as a starting point for the development of future protein-structure-based diagnostics and therapeutic interventions.

Association between Fine Particulate Matter Exposure and Cerebrospinal Fluid Biomarkers of Alzheimer's Disease among a Cognitively Healthy Population-Based Cohort.

BACKGROUND: Epidemiological evidence suggests air pollution adversely affects cognition and increases the risk of Alzheimer's disease (AD), but little is known about the biological effects of fine particulate matter (PM2.5, particulate matter with aerodynamic diameter ≤2.5µm) on early predictors of future disease risk. OBJECTIVES: We investigated the association between 1-, 3-, and 5-y exposure to ambient and traffic-related PM2.5 and cerebrospinal fluid (CSF) biomarkers of AD. METHODS: We conducted a cross-sectional analysis using data from 1,113 cognitively healthy adults (45-75 y of age) from the Emory Healthy Brain Study in Georgia in the United States. CSF biomarker concentrations of Aß42, tTau, and pTau, were collected at enrollment (2016-2020) and analyzed with the Roche Elecsys system. Annual ambient and traffic-related residential PM2.5 concentrations were estimated at a 1-km and 250-m resolution, respectively, and computed for each participant's geocoded address, using three exposure time periods based on specimen collection date. Associations between PM2.5 and CSF biomarker concentrations, considering continuous and dichotomous (dichotomized at clinical cutoffs) outcomes, were estimated with multiple linear/logistic regression, respectively, controlling for potential confounders (age, gender, race, ethnicity, body mass index, and neighborhood socioeconomic status). RESULTS: Interquartile range (IQR; IQR=0.845) increases in 1-y [ß:-0.101; 95% confidence interval (CI): -0.18, -0.02] and 3-y (ß:-0.078; 95% CI: -0.15, -0.00) ambient PM2.5 exposures were negatively associated with Aß42 CSF concentrations. Associations between ambient PM2.5 and Aß42 were similar for 5-y estimates (ß:-0.076; 95% CI: -0.160, 0.005). Dichotomized CSF variables revealed similar associations between ambient PM2.5 and Aß42. Associations with traffic-related PM2.5 were similar but not significant. Associations between PM2.5 exposures and tTau, pTau tTau/Aß42, or pTau/Aß42 levels were mainly null. CONCLUSION: In our study, consistent trends were found between 1-y PM2.5 exposure and decreased CSF Aß42, which suggests an accumulation of amyloid plaques in the brain and an increased risk of developing AD. https://doi.org/10.1289/EHP13503.

Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review.

INTRODUCTION: An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine. METHODS: Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software. RESULTS: Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed. CONCLUSION: Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics.

Two-stage stratified designs with survival outcomes and adjustment for misclassification in predictive biomarkers.

Biomarker stratified clinical trial designs are versatile tools to assess biomarker clinical utility and address its relationship with clinical endpoints. Due to imperfect assays and/or classification rules, biomarker status is prone to errors. To account for biomarker misclassification, we consider a two-stage stratified design for survival outcomes with an adjustment for misclassification in predictive biomarkers. Compared to continuous and/or binary outcomes, the test statistics for survival outcomes with an adjustment for biomarker misclassification is much more complicated and needs to take special care. We propose to use the information from the observed biomarker status strata to construct adjusted log-rank statistics for true biomarker status strata. These adjusted log-rank statistics are then used to develop sequential tests for the global (composite) hypothesis and component-wise hypothesis. We discuss the power analysis with the control of the type-I error rate by using the correlations between the adjusted log-rank statistics within and between the design stages. Our method is illustrated with examples of the recent successful development of immunotherapy in nonsmall-cell lung cancer.

The development of a novel zeolite-based assay for efficient and deep plasma proteomic profiling.

Plasma proteins are considered the most informative source of biomarkers for disease diagnosis and monitoring. Mass spectrometry (MS)-based proteomics has been applied to identify biomarkers in plasma, but the complexity of the plasma proteome and the extremely large dynamic range of protein abundances in plasma make the clinical application of plasma proteomics highly challenging. We designed and synthesized zeolite-based nanoparticles to deplete high-abundance plasma proteins. The resulting novel plasma proteomic assay can measure approximately 3000 plasma proteins in a 45 min chromatographic gradient. Compared to those in neat and depleted plasma, the plasma proteins identified by our assay exhibited distinct biological profiles, as validated in several public datasets. A pilot investigation of the proteomic profile of a hepatocellular carcinoma (HCC) cohort identified 15 promising protein features, highlighting the diagnostic value of the plasma proteome in distinguishing individuals with and without HCC. Furthermore, this assay can be easily integrated with all current downstream protein profiling methods and potentially extended to other biofluids. In conclusion, we established a robust and efficient plasma proteomic assay with unprecedented identification depth, paving the way for the translation of plasma proteomics into clinical applications.

A comprehensive guide to extract information from extracellular vesicles: a tutorial review towards novel analytical developments.

In the medical field, extracellular vesicles (EVs) are gaining importance as they act as cells mediators. These are phospholipid bilayer vesicles and contain crucial biochemical information about their mother cells being carrier of different biomolecules such as small molecules, proteins, lipids, and nucleic acids. After release into the extracellular matrix, they enter the systemic circulation and can be found in all human biofluids. Since EVs reflect the state of the cell of origin, there is exponential attention as potential source of new circulating biomarkers for liquid biopsy. The use of EVs in clinical practice faces several challenges that need to be addressed: these include the standardization of lysis protocols, the availability of low-cost reagents and the development of analytical tools capable of detecting biomarkers. The process of lysis is a crucial step that can impact all subsequent analyses, towards the development of novel analytical strategies. To aid researchers to support the evolution of measurement science technology, this tutorial review evaluates and discuss the most commonly protocols used to characterize the contents of EVs, including their advantages and disadvantages in terms of experimental procedures, time and equipment. The purpose of this tutorial review is to offer practical guide to researchers which are intended to develop novel analytical approaches. Some of the most significant applications are considered, highlighting their main characteristics divided per mechanism of action. Finally, comprehensive tables which provide an overview at a glance are provided to readers.

Exploring the Gut Microbiome and Metabolome in Individuals with Alopecia Areata Disease.

In recent years, heightened attention has been devoted to unravelling the intricate interplay between genetic and environmental factors shaping the gut microbiota and its significance for human health. This study delves into exploring the plausible connection between Alopecia Areata (AA), an autoimmune disease, and the dynamics of the gut microbiome. Examining a cohort of healthy adults and individuals with AA, both the gut microbiota composition and volatile organic compound (VOC) metabolites from faeces and urine were analysed. While overall microbiota composition showed no significant differences, intra-individual variability revealed distinctions related to age, gender, and pathology status, with AA individuals exhibiting reduced species richness and evenness. Differential abundance analysis identified microbial biomarkers for AA, notably Firmicutes, Lachnospirales, and Blautia, while Coprococcus stood out for healthy individuals. The Data Integration Analysis for Biomarker discovery using Latent Components (DIABLO) method further supported these findings including metabolite biomarkers, such as esters of branched chain fatty acids and branched chain amino acids as predictors for AA, suggesting potential links to oxidative stress. Despite certain limitations, the study highlights the complexity of the gut microbiome and its metabolites in the context of AA, while the biomarkers identified could be useful starting points for upcoming studies.

A review on the new age methodologies for early detection of Alzheimer's and Parkinson's disease.

BACKGROUNDS: Neurodegenerative diseases (NDDs) such as Alzheimer's (AD) and Parkinson's (PD) are often diagnosed late, impeding effective treatment; therefore, early detection is imperative. Modern methodologies can serve a pivotal role in fulfilling the crucial need for timely detection and intervention in this context. OBJECTIVES: Evaluate early detection's significance and summarize key technologies (biomarkers, neuroimaging, AI/ML, genetics, digital health) for enhanced diagnostic strategies in AD and PD. METHODS: This study employs a focused descriptive review approach, encompassing analysis of peer-reviewed articles and clinical trials from existing literature, to provide a nuanced exploration of the subject matter. FINDINGS: This review underscores the efficacy of non-invasive biomarkers, biosensors and emerging promising technologies for advancing early diagnosis of AD and PD. CONCLUSION: The landscape of early NDD detection has been reshaped by technology, yet challenges persist, encompassing the domains of validation and ethics. A collaborative effort between medical professionals, researchers and technologists is imperative to effectively address and combat NDDs.

NT-proBNP detection with a one-step magnetic lateral flow channel assay.

Point-of-care sensors targeting blood marker analysis must be designed to function with very small volumes since acquiring a blood sample through a simple, mostly pain-free finger prick dramatically limits the sample size and comforts the patient. Therefore, we explored the potential of converting a conventional lateral flow assay (LFA) for a significant biomarker into a self-contained and compact polymer channel-based LFA to minimize the sample volume while maintaining the analytical merits. Our primary objective was to eliminate the use of sample-absorbing fleece and membrane materials commonly present in LFAs. Simultaneously, we concentrated on developing a ready-to-deploy one-step LFA format, characterized by dried reagents, facilitating automation and precise sample transport through a pump control system. We targeted the detection of the heart failure biomarker NT-proBNP in only 15 µL human whole blood and therefore implemented strategies that ensure highly sensitive detection. The biosensor combines streptavidin-functionalized magnetic beads (MNPs) as a 3D detection zone and fluorescence nanoparticles as signal labels in a sandwich-based immunoassay. Compared to the currently commercialized LFA, our biosensor demonstrates comparable analytical performance with only a tenth of the sample volume. With a detection limit of 43.1 pg∙mL-1 and a mean error of 18% (n ≥ 3), the biosensor offers high sensitivity and accuracy. The integration of all-dried long-term stable reagents further enhances the convenience and stability of the biosensor. This lateral flow channel platform represents a promising advancement in point-of-care diagnostics for heart failure biomarkers, offering a user-friendly and sensitive platform for rapid and reliable testing with low finger-prick blood sample volumes.

A point-of-care electrochemical biosensor for the rapid and sensitive detection of biomarkers in murine models with LPS-induced sepsis.

Sepsis is a life-threatening condition, which is irreversible if diagnosis and intervention are delayed. The response of the immune cells towards an infection triggers widespread inflammation through the production of cytokines, which may result in multiple organ dysfunction and eventual death. Conventional detection techniques fail to provide a rapid diagnosis because of their limited sensitivity and tedious protocol. This study proposes a point-of-care (POC) electrochemical biosensor that overcomes the limitations of current biosensing technologies in the clinical setting by its integration with electrokinetics, enhancing the sensitivity to picogram level compared with the nanogram limit of current diagnostic technologies. This biosensor promotes the use of a microelectrode strip to address the limitations of conventional photolithographic fabrication methods. Tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and microRNA-155 (miR-155) were monitored in a lipopolysaccharide (LPS)-induced septic mouse model. The optimum target hybridization time in a high conductivity medium was observed to be 60 s leading to the completion of the whole operation within 5 min compared with the 4-h detection time of the traditional enzyme-linked immunosorbent assay (ELISA). The limit of detection (LOD) was calculated to be 0.84, 0.18, and 0.0014 pg mL-1, respectively. This novel sensor may have potential for the early diagnosis of sepsis in the clinical setting.

Downregulation of salivary miR-3928 as a potential biomarker in patients with oral squamous cell carcinoma and oral lichen planus.

OBJECTIVES: Recent studies highlighted the role of miR expressed in saliva as reliable diagnostic and prognostic tools in the long-term monitoring of cancer processes such as oral squamous carcinoma (OSCC). Based on a few previous studies, it seems the miR-3928 can be considered a master regulator in carcinogenesis, and it can be therapeutically exploited. This is the first study that compared oral potentially malignant disorder (OLP) and malignant (OSCC) lesions for miR-3928 expression. MATERIALS AND METHODS: In this cross-sectional study, saliva samples from 30 healthy control individuals, 30 patients with erosive/atrophic oral lichen planus, and 31 patients with OSCC were collected. The evaluation of miR-3928 expression by q-PCR and its correlation with clinicopathological indices were analyzed by Shapiro-Wilk, Kruskal-Wallis, Pearson's χ2 , and Mann-Whitney tests. The p-value less than .05 indicated statistically significant results. RESULTS: Based on nonparametric Kruskal-Wallis test results, there was a statistically significant difference between the ages of three study groups (p < .05). This test demonstrated a statistically significant difference between the average of miR-3928 expression in three study groups (p < .05). The result of the χ2  test showed a statistically significant difference in miR-3928 expression between patients with OLP (p = .01) and also patients with OSCC (p < .0001) in comparison to the control group. CONCLUSIONS: The salivary miR-3928 can play a tumor suppressive role in the pathobiology of OSCC, and it is significantly downregulated in patients. According to the potential tumor suppressive role of miR-3928 in the OSCC process, we can consider this microRNA as a biomarker for future early diagnosis, screening, and potential target therapy.

Biomarkers identification in follicular fluid in relation to live birth in in vitro fertilization of women with polycystic ovary syndrome in different subtypes by using UPLC-MS method.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common infertility disorder which affects reproductive-aged women. However, metabolic change profiles of follicular fluid (FF) in lean and obese women diagnosed with and without PCOS remains unclear. METHODS: 95 infertile women were divided into four subgroups: LC (lean control), OC (overweight control), LP (lean PCOS), and OP (overweight PCOS). The FF samples were collected during oocyte retrieval and assayed by ultra-performance liquid chromatography coupled with mass spectrometry (UPLC-MS) metabolomics. RESULTS: A total of 236 metabolites were identified by metabolic analysis. The pathway enrichment analysis revealed that the glycerophospholipid metabolism (impact = 0.11182), ether lipid metabolism (impact = 0.14458), and primary bile acid biosynthesis (impact = 0.03267) were related to metabolic pathway between PCOS and control. Correlation analyses showed that epitestosterone sulfate was found positively correlated with fertilization rate in PCOS, while falcarindione, lucidone C. and notoginsenoside I was found to be negatively correlated. The combined four biomarkers including lucidone C, epitestosterone sulfate, falcarindione, and notoginsenoside I was better in predicting live birth rate, with AUC of 0.779. CONCLUSION: The follicular fluid of women with PCOS showed unique metabolic characteristics. Our study provides better identification of PCOS follicular fluid metabolic dynamics, which may serve as potential biomarkers of live birth.

Comprehensive profiling of amino acids and derivatives in biological samples: A robust UHPLC-MS/MS method for investigating acute lung injury.

The severe respiratory dysfunctions associated with acute lung injury (ALI) and its sequelae have a high morbidity and mortality rate, are multifactorial, and lack a viable treatment. Considering the critical function that amino acids and derivatives play in the genesis of illnesses and the regulation of metabolic processes, monitoring the levels of metabolites associated with amino acids in biological matrices is necessary and interesting to study their pathological mechanisms. Exploring the dynamics of amino acids and derivatives level and searching for biomarkers provides improved clinical ideas for the diagnosis and treatment of ALI. Therefore, we developed an ultra-high-performance liquid chromatography-electrospray tandem mass spectrometry (UHPLC-MS/MS) method that can simultaneously determine the amino acid and derivatives metabolic levels to study amino acid profiles in different biological samples to facilitate clinical research of ALI. In this study, 48 amino acids and derivatives, including neurotransmitters, polyamines, purines, and other types, were quantified simultaneously in a fast, high-throughput, sensitive, and reliable manner within a 15-minute run time without derivatization. No relevant studies have been reported to quantify these 48 amino acid metabolites in three biological samples simultaneously. Satisfactory linearity (R > 0.995), inter-day and intra-day accuracy (85.17-112.67 % and 85.29-111.60 %, respectively), inter-day and intra-day precision (RSD < 13.80 % and RSD < 12.01 %, respectively), matrix effects (81.00 %-118.00 %), recovery (85.09 %-114.65 %) and stability (RSD < 14.72 %) were all demonstrated by the optimized method's successful validation for all analytes. In addition, the suggested method was effectively implemented in plasma, urine, and lung tissue from normal mice and mice with ALI, with the aim of finding potential biomarkers associated with ALI. Potential biomarkers were screened through multivariate statistical analysis and volcanic map analysis, and the changes of markers in ALI were again identified through heat map analysis and correlation analysis with biochemical indicators, which provided ideas and references for subsequent mechanism studies. Here, the technique created in this work offers a quick and dependable way to perform an integrated analysis of amino acids in a variety of biological materials, which can provide research ideas for understanding the physiopathological state of various diseases.

Recent Advances in Biosensors for Diagnosis of Autoimmune Diseases.

Over the last decade, autoimmune diseases (ADs) have undergone a significant increase because of genetic and/or environmental factors; therefore, their simple and fast diagnosis is of high importance. The conventional diagnostic techniques for ADs require tedious sample preparation, sophisticated instruments, a dedicated laboratory, and qualified personnel. For these reasons, biosensors could represent a useful alternative to these methods. Biosensors are considered to be promising tools that can be used in clinical analysis for an early diagnosis due to their high sensitivity, simplicity, low cost, possible miniaturization (POCT), and potential ability for real-time analysis. In this review, recently developed biosensors for the detection of autoimmune disease biomarkers are discussed. In the first part, we focus on the main AD biomarkers and the current methods of their detection. Then, we discuss the principles and different types of biosensors. Finally, we overview the characteristics of biosensors based on different bioreceptors reported in the literature.

Salivary and serum biomarkers to evaluate psychological disorders in burning mouth syndrome: A systematic review and meta-analysis.

BACKGROUND: Burning mouth syndrome is a chronic pain syndrome mainly characterized by an intensive burning sensation of tongue. Previous studies have suggested that saliva/serum biomarkers in burning mouth syndrome might be associated with psychological disorders. The aim of systematic review was to observe whether the biomarkers in serum/saliva could be an alternative method to evaluate the psychological disorders in patients with burning mouth syndrome. MATERIALS AND METHODS: The PubMed, Embase, and Cochrane Library databases were searched for papers published up to March 15, 2023. Risk of bias was measured by using the Newcastle-Ottawa Scale. RevMan was used for meta-analysis. RESULTS: A total of 467 articles were screened, which of 12 studies were included. These studies collected 43 different biomarkers in saliva and 35 in serum. Of these biomarkers, only three (cortisol, α-amylase, and IL-6) were analyzed in two or more studies. Only salivary cortisol levels were significantly higher in the patient group compared to the controls (Mean Difference = 1.39; 95% CI [0.80-1.97]; p < 0.001). Moreover, cortisol might be relevant to psychological scores, especially anxiety. CONCLUSION: Different papers have investigated salivary and serum biomarkers in burning mouth syndrome patients with controversial results. This meta-analysis showed that cortisol levels in saliva may be a potential biomarker to assess the psychological disorders in burning mouth syndrome patients.

Volatilomic profiles of gastric juice in gastric cancer patients.

Volatilomics is a powerful tool capable of providing novel biomarkers for the diagnosis of gastric cancer. The main objective of this study was to characterize the volatilomic signatures of gastric juice in order to identify potential alterations induced by gastric cancer. Gas chromatography with mass spectrometric detection, coupled with headspace solid phase microextraction as the pre-concentration technique, was used to identify volatile organic compounds (VOCs) released by gastric juice samples collected from 78 gastric cancer patients and two cohorts of controls (80 and 96 subjects) from four different locations (Latvia, Ukraine, Brazil, and Colombia). 1440 distinct compounds were identified in samples obtained from patients and 1422 in samples provided by controls. However, only 6% of the VOCs exhibited an incidence higher than 20%. Amongst the volatiles emitted, 18 showed differences in their headspace concentrations above gastric juice of cancer patients and controls. Ten of these (1-propanol, 2,3-butanedione, 2-pentanone, benzeneacetaldehyde, 3-methylbutanal, butylated hydroxytoluene, 2-pentyl-furan, 2-ethylhexanal, 2-methylpropanal and phenol) appeared at significantly higher levels in the headspace of the gastric juice samples obtained from patients; whereas, eight species showed lower abundance in patients than found in controls. Given that the difference in the volatilomic signatures can be explained by cancer-related changes in the activity of certain enzymes or pathways, the former set can be considered potential biomarkers for gastric cancer, which may assist in developing non-invasive breath tests for the diagnosis of this disease. Further studies are required to elucidate further the mechanisms that underlie the changes in the volatilomic profile as a result of gastric cancer.

Novel electrochemical platform based on C3N4-graphene composite for the detection of neuron-specific enolase as a biomarker for lung cancer.

Lung cancer remains the leading cause of cancer mortality worldwide. Small cell lung cancer (SCLC) accounts for 10-15% of cases and has an overall 5-years survival rate of only 15%. Neuron-specific enolase (NSE) has been identified as a useful biomarker for early SCLC diagnosis and therapeutic monitoring. This work reports an electrochemical immunosensing platform based on a graphene-graphitic carbon nitride (g-C3N4) nanocomposite for ultrasensitive NSE detection. The g-C3N4 nanosheets and graphene nanosheets were synthesized via liquid exfoliation and integrated through self-assembly to form the nanocomposite. This nanocomposite was used to modify screen-printed carbon electrodes followed by covalent immobilization of anti-NSE antibodies. The unique properties of the graphene-g-C3N4 composite facilitated efficient antibody loading while also enhancing electron transfer efficiency and electrochemical response. Systematic optimization of experimental parameters was performed. The immunosensor exhibited a wide linear detection range of 10 pg/mL to 100 ng/mL and low limit of detection of 3 pg/mL for NSE along with excellent selectivity against interferences. Real serum matrix analysis validated the applicability of the developed platform for sensitive and accurate NSE quantifica-tion at clinically relevant levels. This novel graphene-g-C3N4 nanocomposite based electro-chemical immunoassay demonstrates great promise for early diagnosis of SCLC.

Cancer diagnosis using label-free SERS-based exosome analysis.

Exosomes, carrying distinctive biomolecules reflective of their parent cell's status and origin, show promise as liquid biopsy biomarkers for cancer diagnosis. However, their clinical translation remains challenging due to their relatively low concentration in body fluids. Surface-Enhanced Raman spectroscopy (SERS) has recently gained significant attention as a label-free and sensitive technique for exosome analysis. This review explores label-free SERS for exosome detection, covering exosome isolation and characterization methods, advancements in SERS substrates, and fingerprint analysis techniques using machine learning. Furthermore, we emphasize the challenges and offer insights into the future prospects of SERS-based exosome analysis to enhance cancer diagnosis.