Profile Information Research Articles

Random Forest Analysis of Out-of-Pocket Health Expenditures Associated with Cardiometabolic Diseases, Lifestyle, Lipid Profile, and Genetic Information in São Paulo, Brazil

Random Forest Analysis of Out-of-Pocket Health Expenditures Associated with Cardiometabolic Diseases, Lifestyle, Lipid Profile, and Genetic Information in São Paulo, Brazil

Forensic Comparison of Amphetamine Chemical Profiles by Bayesian Predictive Modelling

ABSTRACTForensic chemists frequently employ statistical profiling approaches to assess the degree of similarity between samples of illicit drugs. Such profiling information can help reveal connections between nodes in distribution networks and manufacturing laboratories. For amphetamine, the routine method of comparing a pair of samples includes the use of a dissimilarity measure based on the Pearson correlation coefficient calculated between their chemical profiles obtained through gas chromatography–mass spectrometry. This simple measure of (dis)similarity has been shown distinguish pairs sharing a common origin (e.g., same production batch) to a reasonable level of accuracy. However, Pearson correlation fails to capture all the relevant notions of similarity between chemical profiles of amphetamine. We present a new statistical method for forensic drug comparison that uses a more sophisticated statistical modelling approach to determine similarity between samples. We show that this leads to improved performance over the correlation‐based approach. The proposed method is easily extendable and has an intuitive interpretation both from chemistry and forensic perspectives, which supports wide applicability to illicit drug profiling in practice.

Unveiling user identity across social media: a novel unsupervised gradient semantic model for accurate and efficient user alignment

AbstractThe field of social network analysis has identified User Alignment (UA) as a crucial area of investigation. The objective of UA is to identify and connect user accounts across diverse social networks, even when there are no explicit interconnections. UA plays a pivotal role in synthesising coherent user profiles and delving into the intricacies of user behaviour across platforms. However, traditional approaches have encountered limitations. Singular embedding techniques have been found to fall short in fully capturing the semantic essence of user profile attributes. Furthermore, classification-based embedding methods lack definitive criteria for categorisation, thereby constraining both the efficacy and applicability of these models. This paper presents a novel unsupervised Gradient Semantic Model for User Alignment (GSMUA) for the purpose of identifying common user identities across social networks. GSMUA categorises user profile information into weak, sub, and strong gradients based on the semantic intensity of attributes. Different gradient semantic levels direct attention to literal features, semantic features, or a combination of both during feature extraction, thereby achieving a full semantic representation of user attributes. In the case of strongly semantic long texts, GSMUA employs Named Entity Recognition (ENR) technology in order to enhance the inefficient handling of such texts. Furthermore, GSMUA compensates for missing user profile attributes by utilising profile information from user neighbours, thereby reducing the negative impact of missing user profile attributes on model performance. Extensive experiments conducted on four pairs of real datasets demonstrate the superiority of our approach. In comparison to the most effective previously developed unsupervised methods, GSMUA demonstrates improvements in hit-precision ranging from 5.32 to 12.17%. When compared to supervised methods, the improvements range from 0.71 to 11.79%.

SOCIAL MEDIA FAKE ACCOUNT IDENTIFICATION USING ML

The proliferation of social media platforms has led to an increase in the creation of fake accounts. These accounts are used for various malicious activities, such as spreading false information, phishing, and identity theft. As a result, there is a growing need for effective methods to identify and eliminate fake accounts. This paper proposes a machine learning-based approach for social media fake account identification. This paper proposes a machine learning-based approach to identify fake accounts on social media platforms. Our method leverages a combination of feature extraction techniques and supervised learning algorithms to classify accounts as genuine or fake. We collect a large dataset of labeled social media accounts, including both genuine and fake profiles, and extract features from various sources such as profile information, network behavior, and content analysis. We experiment with multiple machine learning models, including Support vector machines(SVM),K-Nearest Neighbors Algorithm(KNN),Random forest, Logistic Regression & Artificial Neural Network(ANN) to evaluate their performance in identifying fake accounts. Our proposed method has significant implications for social media platform operators, policymakers, and researchers seeking to combat fake accounts and maintain online trust. The approach can be integrated with existing social media moderation tools to enhance the accuracy and efficiency of fake account identification. Key Words: Support vector machines(SVM),K-Nearest Neighbors Algorithm(KNN),Random forest, Logistic Regression & Artificial Neural Network(ANN),Python.

Relationships among intensive blood pressure control, morning blood pressure surge and 24-hour blood pressure variability

Abstract Objectives This study aimed to investigate the relationships between cardiovascular outcomes and blood pressure (BP) reduction, 24-hour BP variability (BPV), morning BP surge (MBPS), and visit-to-visit BPV. Methods This study involved 897 participants from the Systolic Blood Pressure International Trial (SPRINT), for whom biochemical and demographic data, medication history, and various blood profile information were collected. Univariate and multivariate linear regression models were used to assess the impact of intensive BP management on 24-hour BPV and the MBPS. Results Among the 897 participants (average age 71.48 ± 9.42 years; 640 males), 453 received intensive treatment. Those in the intensive group were more likely to be on ACE inhibitors, CCBs, or diuretics. This group showed significant reductions in 24-hour systolic and diastolic BP, as well as in both short-term and long-term BPVs, such as average real variability (ARV) and MBPS. The analysis demonstrated strong associations between intensive BP management and improvements in various BPV measures, including systolic ARV, diastolic BP ARV, and cumulative BP loads. A significant correlation was also found between intensive BP lowering and the primary composite outcome of the SPRINT study. Conclusions This study highlighted the significant associations between intensive BP management and reductions in the MBPS and 24-hour BPV, which affect both short-term and long-term health outcomes. The primary benefits of intensive BP control appear to stem from direct BP reduction rather than changes in BPV. However, further extensive randomized controlled trials are needed to confirm these findings.

Subclassification of obesity for precision prediction of cardiometabolic diseases.

Obesity and cardiometabolic disease often, but not always, coincide. Distinguishing subpopulations within which cardiometabolic risk diverges from the risk expected for a given body mass index (BMI) may facilitate precision prevention of cardiometabolic diseases. Accordingly, we performed unsupervised clustering in four European population-based cohorts (N ≈ 173,000). We detected five discordant profiles consisting of individuals with cardiometabolic biomarkers higher or lower than expected given their BMI, which generally increases disease risk, in total representing ~20% of the total population. Persons with discordant profiles differed from concordant individuals in prevalence and future risk of major adverse cardiovascular events (MACE) and type 2 diabetes. Subtle BMI-discordances in biomarkers affected disease risk. For instance, a 10% higher probability of having a discordant lipid profile was associated with a 5% higher risk of MACE (hazard ratio in women 1.05, 95% confidence interval 1.03, 1.06, P = 4.19 × 10-10; hazard ratio in men 1.05, 95% confidence interval 1.04, 1.06, P = 9.33 × 10-14). Multivariate prediction models for MACE and type 2 diabetes performed better when incorporating discordant profile information (likelihood ratio test P < 0.001). This enhancement represents an additional net benefit of 4-15 additional correct interventions and 37-135 additional unnecessary interventions correctly avoided for every 10,000 individuals tested.

Dental age estimation in adults using the Lamendin criteria: Validation of a bayesian model in the Brazilian population

Age estimation provides information for biological profile, which is used as an important data during the process of to aid in the identification of bodies of deceased persons. Lamendin et al. proposed a simplified dental age estimation technique for adults based on the analysis of periodontal recession height, root height, and root translucency height. These characteristics were used to build a multi-population database that served in the development of a Bayesian model, known as the Forensic International Dental Database (FIDB). However, before this research, this model did not incorporate dental information from the Brazilian population. The aim of this article was to validate the application of the FIDB Bayesian statistical model for estimating dental age in samples from the Brazilian population and compare it to Lamendin's technique. A cross-sectional study was conducted using 230 dental elements extracted for therapeutic reasons and stored in a biobank. Two independent and calibrated examiners collected the data, considering the randomization of dental samples. Intra- and inter-examiner reliability analysis included the re-evaluation of 20 % (n = 40) of the sample. For statistical analyses, the data were organized in Microsoft© Excel© 365 version 2204 (Microsoft Corp., Redmond, WA, USA) spreadsheets, and R version 4.0.2 (R Foundation, Vienna, Austria) and IBM® SPSS® version 23.0 (SPSS Inc., IBM, Chicago, IL, USA) software were used. After applying inclusion and exclusion criteria, the final sample comprised 200 intact dental elements from 84 male and 116 female individuals. According to the Kolmogorov-Smirnov test, the data did not show a normal distribution, and the Intraclass Correlation Coefficient indicated that intra- and inter-examiner agreements were adequate. Through the Mann-Whitney U test, statistically significant differences between sexes were not observed. In terms of overall accuracy, the Bayesian model and the original Lamendin´s technique showed a mean absolute error of 9.39 years and 9.95, respectively. Regarding bias, it was generally 0.15 years for the Bayesian model and 5.62 years for the original Lamendin´s technique. The Wilcoxon classification test revealed no statistically significant difference between chronological and estimated ages, but only for the Bayesian model. It can be concluded that the developed Bayesian statistical model was able to estimate the age of Brazilian adults satisfactorily, and overall, the error rates were acceptable.

From Diabetes to Dementia: Identifying Key Genes in the Progression of Cognitive Impairment.

To provide a basis for further research on the molecular mechanisms underlying type 2 diabetes-associated mild cognitive impairment (DCI) using two bioinformatics methods to screen key genes involved in the progression of mild cognitive impairment (MCI) and type 2 diabetes. RNA sequencing data of MCI and normal cognition groups, as well as expression profile and sample information data of clinical characteristic data of GSE63060, which contains 160 MCI samples and 104 normal samples, were downloaded from the GEO database. Hub genes were identified using weighted gene co-expression network analysis (WGCNA). Protein-protein interaction (PPI) analysis, combined with least absolute shrinkage and selection operator (LASSO) and receiver operating characteristic (ROC) curve analyses, was used to verify the genes. Moreover, RNA sequencing and clinical characteristic data for GSE166502 of 13 type 2 diabetes samples and 13 normal controls were downloaded from the GEO database, and the correlation between the screened genes and type 2 diabetes was verified by difference and ROC curve analyses. In addition, we collected clinical biopsies to validate the results. Based on WGCNA, 10 modules were integrated, and six were correlated with MCI. Six hub genes associated with MCI (TOMM7, SNRPG, COX7C, UQCRQ, RPL31, and RPS24) were identified using the LASSO algorithm. The ROC curve was screened by integrating the GEO database, and revealed COX7C, SNRPG, TOMM7, and RPS24 as key genes in the progression of type 2 diabetes. COX7C, SNRPG, TOMM7, and RPS24 are involved in MCI and type 2 diabetes progression. Therefore, the molecular mechanisms of these four genes in the development of type 2 diabetes-associated MCI should be studied.

Comprehensive pan-cancer analysis of FUTs family as prognostic and immunity markers based on multi-omics data

BackgroundThe dysregulation of fucosyltransferases (FUTs) contributes to alterations in fucosylated epitope expression, which serve as distinctive features of cancer cells. Nonetheless, a comprehensive elucidation of the prognostic biological marker and therapeutic target of the FUTs family in pan-cancer remains elusive.MethodsOver 10,000 individuals' profiling information was examined, including information on 750 small molecule drugs, 33 types of cancer, and 24 types of immune cells. We focused on POFUT2's function and applied GSVA (Gene Set Variation Analysis) to calculate the FUT score. Survival and cancer pathways were found to be correlated with this score. After deriving a signature via univariate Cox and LASSO regression, we generated and analyzed the ROC curve and developed a nomogram.ResultsOur comprehensive analysis revealed epigenetic, genomic, and immunogenomic changes in FUTs, particularly POFUT2, resulting in aberrant expression. Elevated frequencies of CNV (Copy number variation), SNV (Single Nucleotide Variant), and hypermethylation were observed in FUTs. Additionally, the survival of patients with various types of cancers may be predicted by FUT expression. Immune response and prognosis in numerous types of cancer were found to be strongly linked to aberrant POFUT2 expression. Pathway analysis unveiled the role of FUTs in apoptosis, epithelial-to-mesenchymal transition (EMT), cell cycle, DNA damage response, RAS/MAPK, TSC/mTOR, PI3K/AKT, AR, ER, and RTK. A prognostic index for patients diagnosed with adrenocortical carcinoma (ACC) was established by applying a risk model incorporating nine FUTs and based on the findings of the GSVA.ConclusionsFUTs, particularly POFUT2, emerge as candidate targets for improving the outcomes of immune therapy. The significance of aberrant MUC12 expression, cancer immune therapy, and patient survival in the context of diverse malignancies is enhanced by the strong correlation observed among these factors. Our five-gene risk signature provides patients with ACC with an independent prognostic indicator, emphasizing the critical function of these genes in inhibiting the immune system's response in ACC.

Reconstruction of cancer marker analysis with holistic anatomical precision implicates heterogeneity development during breast tumor progression

Biomarkers are not only of significant importance for cancer diagnosis and selection of treatment plans but also recently increasingly used for the evaluation of malignancy development and tumor heterogeneity. Large-size tumors from clinical patients can be unique and valuable sources for the study of cancer progression, particularly to the extent of intratumoral heterogeneity. In the present study, we obtained a series of post-surgery puncture samples from a breast cancer patient with a 4 × 3.5 × 2 cm tumor in its original size. Immunohistochemistry for Ki-67, COX-2, and CA IX was performed and the expression levels within the breast cancer tumor mass were evaluated in the reconstructed 3D models. To further evaluate the intratumoral heterogeneity, we performed high throughput whole transcriptome sequencing of 12 samples from different spatial positions within the tumor tissue. Comparing the reconstructed 3D distribution of biomarkers with projected tumor growth models, asymmetric and heterogeneous expansion of tumor mass was found to be possibly influenced by factors such as blood supply, inflammation and/or hypoxia stimulations, as suggested from the correlation between the results of Ki-67 and CA IX or COX-2 staining. Furthermore, high-throughput RNA sequencing data provided additional information for profiling the intratumoral heterogeneity and expanded the understanding of cancer progression. Digital technology for medical imaging once properly integrated with molecular pathology examinations will become particularly helpful in dissecting out in-depth information for precision medicine. We prospect that this approach, facilitated by rapidly advancing artificial intelligence, could provide new insights for clinical decision-making in the future. Strategies for the continuous development from the present study for better performance and application were discussed.

Ensemble graph neural networks for fake news detection using user engagement and text features

In this digital world with massive information sharing, spreading misinformation is relatively easy, but its consequences are really enormous. Detecting fake news is a critical challenge in today's digital age, where misinformation can spread rapidly and influence public opinion. This paper proposes an ensemble model (EGNN) that leverages Graph Neural Networks (GNNs) and text features to enhance the accuracy of fake news detection. The model is designed to operate effectively even when labeled data is scarce by utilizing a substantial amount of unlabeled data. We employ multiple GNNs, including a standard GNN, a Graph Attention Network (GAT), and a Bidirectional GCN (BiGCN), to capture user engagement patterns and social context. Additionally, text embeddings are extracted using spaCy, BERT, and a combination of spaCy with user profile information. These embeddings are then classified using a neural network, and the final detection is performed using ensemble techniques such as Majority Voting, Weighted Average, and Stacking. To address class imbalance, we incorporate focal loss alongside the traditional binary cross-entropy loss. Experimental results demonstrate that our proposed EGNN model significantly improves the detection of fake news, providing a robust solution for this pervasive problem.

Conjugation of visual enhancers in lateral flow immunoassay for rapid forensic analysis: A critical review.

During crime scene investigations, numerous traces are secured and may be used as evidence for the evaluation of source and/or activity level propositions. The rapid chemical analysis of a biological trace enables the identification of body fluids and can provide significant donor profiling information, including age, sex, drug abuse, and lifestyle. Such information can be used to provide new leads, exclude from, or restrict the list of possible suspects during the investigative phase. This paper reviews the state-of-the-art labelling techniques to identify the most suitable visual enhancer to be implemented in a lateral flow immunoassay setup for the purpose of trace identification and/or donor profiling. Upon comparison, and with reference to the strengths and limitations of each label, the simplistic one-step analysis of noncompetitive lateral flow immunoassay (LFA) together with the implementation of carbon nanoparticles (CNPs) as visual enhancers is proposed for a sensitive, accurate, and reproducible in situ trace analysis. This approach is versatile and stable over different environmental conditions and external stimuli. The findings of the present comparative analysis may have important implications for future forensic practice. The selection of an appropriate enhancer is crucial for a well-designed LFA that can be implemented at the crime scene for a time- and cost-efficient investigation.

Intergovernmental Impact Analysis in Optimizing the Implementation of the Village Profile and Administration Management Information System in Merjoyo Village, Purwoasri District, Kediri Regency

The Government of Kediri District through the Office of Community Empowerment and Village Administration created the Village Profile and Administration Management Information System (SIMPADE). The existence of the SIMPADE application is expected to help the performance of the village government in the service of making population certificates. This research is a descriptive research with a qualitative approach. The focus of this research is to determine the impact of the application of SIMPADE as a form of village cooperation with the implementing elements of village government affairs in Merjoyo Village, Purwoasri District, Kediri Regency. Data collection techniques through semi-structured interviews, direct observation, and documentation review. Data analysis techniques consist of data reduction, data presentation and conclusion drawing. The results of this study indicate that the impact of the SIMPADE application on the provision and dissemination of service information is quite good because it provides options and has the potential for further development even though there are some obstacles regarding the operation of the application in the field.

Gender and personalized profile information influence online ratings of Canadian academic ophthalmologists

ObjectiveTo determine the characteristics associated with higher online ratings of academic ophthalmologists in Canada. DesignRetrospective cross-sectional study. MethodsAll ophthalmologists affiliated with Canadian ophthalmology departments were queried in March 2023 using WebMDs. Online ratings and physician profile details were extracted and descriptively analyzed using nonparametric tests with significance at p < 0.05. Subgroup analysis was conducted using ≥ 4-star rated profiles. ResultsEight hundred and ninety-nine department faculty from 15 institutions were considered, and 660 ophthalmologists with active, rated profiles were included. A total of 27,823 online ratings with a median of 4.14 stars (out of 5) were observed. Most profiles were of men (74.1%). Women received lower overall ratings compared to men (median = 4.08 vs. 4.20; p = 0.021), and lower number of reviews (median = 23 vs. 34; p < 0.001). Most profiles included office addresses (87.9%), private practice affiliation (79.8%), and contact information (51.1%). There was a positive correlations between higher ratings and profiles that included biographies (rho = 0.13; p = 0.001), languages spoken (rho = 0.15; p < 0.001), educational background (rho = 0.13; p < 0.001), areas of expertise (rho = 0.10; p = 0.010), awards (rho = 0.12; p = 0.002), and among physicians indicating they accept new patients (rho = 0.15; p < 0.001) and accommodate virtual visits (rho = 0.09; p = 0.020). ConclusionsCanadian ophthalmologists having certain personal information on their online profiles tended to have higher ratings, despite weak associations, possibly due to wider public outreach. Women had fewer and lower overall ratings compared to men. Further research about online ratings’ influence on physician selection and physician career satisfaction is needed.

Penerapan Metode Field Trip Observation untuk Meningkatkan Wawasan Siswa di MTs Muhammadiyah 10 Gresik

The aim of this research is to examine the implementation of the field trip observation method in the learning process of students, focusing on their ability to adapt and modify themselves in their surroundings, and to identify the strengths and weaknesses of this method. This qualitative study employs ethnographic or field research methodology, with teachers and students under observation as the primary data sources. Secondary data sources include school profile information, field trip destination observation data, and observations of field trip learning strategies. Data collection techniques involve documentation, interviews, and observations. Data analysis proceeds incrementally, leading to conclusions. Findings indicate that using field trip observation methods enhances students' insights, exposes them to new experiences, and alleviates boredom associated with classroom learning. However, this method requires thorough planning and significant financial resources to implement effectively.

The international dissemination of China’s English academic journals on Twitter: Structure, theme and impact

Objective/Significance: To measure the international communication capacity of China’s English academic journal papers based on social media platforms. Method/Process: Social network analysis, content analysis and emotion analysis were used to describe the diffusion structure of China’s English academic journal papers on Twitter from the aspects of network structure and identify important node users, explore the identity characteristics and emotion characteristics of users as well as diffusion capability of journals of different disciplines. Conclusion/Discovery: Journal articles in the fields of natural sciences and engineering as well as humanities and social sciences exhibit a strong positive correlation between their diffusion structure and network size. Networks with a mixed diffusion structure typically have larger network sizes. The users occupying key positions in the diffusion network typically have profile information that is academically relevant. The retweets and comments are mainly statements without emotional inclination, and the comments are mainly summary descriptions of the papers. The distribution of diffusion width, diffusion strength and diffusion speed of each discipline is significantly different. There is a significant positive correlation between the diffusion structure of papers and their diffusion capabilities. Innovation/Value: To reveal the diffusion structure and characteristics of China’s English academic journal papers on social media platforms, so as to provide reference for expanding the visibility of China’s English academic journals and improving the international dissemination capacity of China’s English journals.

Detecting Fake Accounts on Instagram Using Machine Learning and Hybrid Optimization Algorithms

In this paper, we propose a hybrid method for detecting fake accounts on Instagram by using the Binary Grey Wolf Optimization (BGWO) and Particle Swarm Optimization (PSO) algorithms. By combining these two algorithms, we aim to leverage their complementary strengths and enhance the overall optimization performance. We evaluate the proposed hybrid method using four classifiers: Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Logistic Regression (LR). The dataset for the experiments contains 65,329 Instagram accounts. We extract features from each account, including profile information, posting behavior, and engagement metrics. The Binary Grey Wolf and Particle Swarm Optimizations, when combined to form a hybrid method (BGWOPSO), improved the performance in accurately detecting fake accounts on Instagram.

Perancangan Sistem Informasi Sekolah SD Negeri 4 Kerta Payangan Menggunakan Metode Design Thinkin

School information systems are one of the most important needs in educational institutions. Building a school information system is the right step to help provide more relevant information and also in processing data in schools into useful information. However, there are some schools that have not implemented it such as SD Negeri 4 Kerta, In providing conventional information that is done manually where all data or documents are stored in hardcopy form, as well as in processing data such as school profile information, academic and school finances. The purpose of this study is to determine the design of the school information system user interface to be a solution to these problems. This research uses the Design Thinking method implemented to design a website-based school information system for SD Negeri 4 Kerta. The user interface design of the school information system is built in the form of a prototype using the figma application. This study used interview methods and literature studies in collecting data and related information. And the result of designing the user interface of the SD Negeri 4 Kerta school information system is to design a website to provide school information and to process school data and process the financial section.

Research on the use of communication big data and AI artificial intelligence technology to construct telecom fraud prevention behavior portrait

A solid foundation for behavior portrait construction in the fight against telecom fraud is the goal of this research. The study explores the integration of communication AI and Big Data technologies, focusing on the perspective of artificial intelligence. By using insights obtained from a telecom fraud detection model that relies on users’ behavior variations expressed through time-varying signatures, the goal of this study is to enhance fraud prevention strategies in the telecom industry. Through the examination of call detail records and customer profile information, the TeleGuard AI Fraud Prevention Framework (TGAI-FPF) aims to recognize suspicious trends and variations that are potentially suggestive of fraudulent actions. The purpose of the model is to generate behavior portraits that are capable of capturing the distinctive aspects of fraudulent conduct in telecom networks. This will be accomplished through the utilization of advanced analytics and machine learning algorithms. The study highlights the significance of leveraging big data analytics and artificial intelligence technologies to efficiently detect and thwart fraudulent activity in the telecom industry. The results of this study should fortify the defenses of telecom networks against growing fraudulent schemes and help in the development of preventative measures to combat fraud. This is the anticipated manner in which the results will add.

Development and validation of a multi-marker liquid bead array assay for the simultaneous detection of PIK3CA and ESR1 hotspot mutations in single circulating tumor cells (CTCs)

BackgroundPIK3CA and ESR1 mutations are associated with progression and therapy resistance in metastatic breast cancer (MBC). CTCs are highly heterogeneous and their analysis at single cell level can provide unique information for mutational profiling and the existence of different sub-clones related to tumor progression. We have developed a novel multi-marker liquid bead array assay based on combination of an enzymatic mutation enrichment method, multiplex PCR-based assay, and liquid bead array technology for the simultaneous detection of PIK3CA and ESR1 hotspot mutations in liquid biopsy samples. We focus on single CTCs, however the assay can be used for bulk CTC and ctDNA analysis. Materials and methodsSingle CTCs were isolated from an ER+/HER2+ MBC patient from CellSearch® cartridges using the VyCAP Puncher System and subjected to whole genome amplification followed by nuclease-assisted minor-allele enrichment with probe-overlap (NaME-PrO) enrichment. The assay was validated for analytical sensitivity and specificity for the simultaneous detection of PIK3CA (E545K, E542K, H1047R, H1047L) and ESR1 (Y537S, Y537C, Y537N, D538G, L536H) mutations in single CTCs, while its clinical performance was evaluated on 22 single CTCs and three single white blood cells (WBCs). ResultsThe developed multi-marker liquid bead array assay is novel, highly specific and sensitive for both mutation panels. The assay can reliably detect mutation-allelic-frequencies (MAFs) as low as 0.1 %. The presence of PIK3CA and ESR1 mutations was detected in 13.6 % and 72.7 % of single CTCs, respectively. The developed assay is sample-saving since it requires only 2 μL of amplified DNA to check for nine hotspot PIK3CA and ESR1 mutations in a single cell. The developed liquid bead array assay (Luminex, US), based on a 96 microwell plate format, enables the simultaneous analysis of 96 single cells. ConclusionsThe developed novel multi-marker liquid bead array assay for the simultaneous detection of PIK3CA and ESR1 hotspot mutations in single CTCs is highly specific, highly sensitive, high-throughput, and sample-, cost-, and time-saving. This multi-marker liquid bead array assay can be extended to detect up to 100 mutations in many genes at once and can be applied for bulk CTC and ctDNA analysis.