A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

RBM24 Suppresses the Tumorigenesis of Glioblastoma by Stabilizing LATS1 mRNA.

The ribose nucleic acid (RNA)-binding motif protein 24 (RBM24) has been recognized as a critical regulatory protein in various types of tumors. However, its specific role in glioblastoma (GBM) has not been thoroughly investigated. The objective of this study is to uncover the role of RBM24 in GBM and understand the underlying mechanism. The expression of RBM24 in GBM was initially analyzed using the Gene Expression Profiling Interactive Analysis (GEPIA). Subsequently, the RBM24 expression levels in clinical samples of GBM were examined, and the survival curves of GBM patients were plotted based on high- and low-expression levels of RBM24 using Kaplan-Meier (KM) plotter. In addition, RBM24 knockdown cell lines and overexpression vectors were created to assess the effects on proliferation, apoptosis, and invasion abilities. Finally, the binding level of RBM24 protein to LATS1 messenger RNA (mRNA) was determined by RNA immunoprecipitation (RIP) assay, and the expression levels of RBM24 and LATS1 were measured through quantitative reverse-transcriptase-polymerase chain reaction (qRT-PCR) and Western blot (WB). Our data revealed a significant decrease in RBM24 mRNA and protein levels in GBM patients, indicating that those with low RBM24 expression had a worse prognosis. Overexpression of RBM24 led to inhibited cell proliferation, reduced invasion, and increased apoptosis in LN229 and U87 cells. In addition, knocking down LATS1 partially reversed the effects of RBM24 on cell proliferation, invasion, and apoptosis in GBM cells. In vivo xenograft model further demonstrated that RBM24 overexpression reduced the growth of subcutaneous tumors in nude mice, accompanied by a decrease in Ki-67 expression and an increase in apoptotic events in tumor tissues. There was also correlation between RBM24 and LATS1 protein expression in the xenograft tumors. RBM24 functions to stabilize LATS1 mRNA, thereby inhibiting the proliferation, suppressing invasion, and promoting apoptosis in GBM cells.

Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks.

Wastewater-based SARS-CoV-2 surveillance enables unbiased and comprehensive monitoring of defined sewersheds. We performed real-time monitoring of hospital wastewater that differentiated Delta and Omicron variants within total SARS-CoV-2-RNA, enabling correlation to COVID-19 cases from three tertiary-care facilities with >2100 inpatient beds in Calgary, Canada. RNA was extracted from hospital wastewater between August/2021 and January/2022, and SARS-CoV-2 quantified using RT-qPCR. Assays targeting R203M and R203K/G204R established the proportional abundance of Delta and Omicron, respectively. Total and variant-specific SARS-CoV-2 in wastewater was compared to data for variant specific COVID-19 hospitalizations, hospital-acquired infections, and outbreaks. Ninety-six percent (188/196) of wastewater samples were SARS-CoV-2 positive. Total SARS-CoV-2 RNA levels in wastewater increased in tandem with total prevalent cases (Delta plus Omicron). Variant-specific assessments showed this increase to be mainly driven by Omicron. Hospital-acquired cases of COVID-19 were associated with large spikes in wastewater SARS-CoV-2 and levels were significantly increased during outbreaks relative to nonoutbreak periods for total SARS-CoV2, Delta and Omicron. SARS-CoV-2 in hospital wastewater was significantly higher during the Omicron-wave irrespective of outbreaks. Wastewater-based monitoring of SARS-CoV-2 and its variants represents a novel tool for passive COVID-19 infection surveillance, case identification, containment, and potentially to mitigate viral spread in hospitals.

Sieve estimation of a class of partially linear transformation models with interval-censored competing risks data.

In this paper, we consider a class of partially linear transformation models with interval-censored competing risks data. Under a semiparametric generalized odds rate specification for the cause-specific cumulative incidence function, we obtain optimal estimators of the large number of parametric and nonparametric model components via maximizing the likelihood function over a joint B-spline and Bernstein polynomial spanned sieve space. Our specification considers a relatively simpler finite-dimensional parameter space, approximating the infinite-dimensional parameter space as n → ∞, thereby allowing us to study the almost sure consistency, and rate of convergence for all parameters, and the asymptotic distributions and efficiency of the finite-dimensional components. We study the finite sample performance of our method through simulation studies under a variety of scenarios. Furthermore, we illustrate our methodology via application to a dataset on HIV-infected individuals from sub-Saharan Africa.

Personalized prediction of incident hospitalization for cardiovascular disease in patients with hypertension using machine learning.

BACKGROUND: Prognostic information for patients with hypertension is largely based on population averages. The purpose of this study was to compare the performance of four machine learning approaches for personalized prediction of incident hospitalization for cardiovascular disease among newly diagnosed hypertensive patients. METHODS: Using province-wide linked administrative health data in Alberta, we analyzed a cohort of 259,873 newly-diagnosed hypertensive patients from 2009 to 2015 who collectively had 11,863 incident hospitalizations for heart failure, myocardial infarction, and stroke. Linear multi-task logistic regression, neural multi-task logistic regression, random survival forest and Cox proportional hazard models were used to determine the number of event-free survivors at each time-point and to construct individual event-free survival probability curves. The predictive performance was evaluated by root mean squared error, mean absolute error, concordance index, and the Brier score. RESULTS: The random survival forest model has the lowest root mean squared error value at 33.94 and lowest mean absolute error value at 28.37. Machine learning methods provide similar discrimination and calibration in the personalized survival prediction of hospitalizations for cardiovascular events in patients with hypertension. Neural multi-task logistic regression model has the highest concordance index at 0.8149 and lowest Brier score at 0.0242 for the personalized survival prediction. CONCLUSIONS: This is the first personalized survival prediction for cardiovascular diseases among hypertensive patients using administrative data. The four models tested in this analysis exhibited a similar discrimination and calibration ability in predicting personalized survival prediction of hypertension patients.

Improved performance of lateral flow immunoassays for alpha-fetoprotein and vanillin by using silica shell-stabilized gold nanoparticles.

The sensitivity of lateral flow assays (LFA) was increased 30-fold by making use of spherical core-shell gold-silica nanoparticles (AuNP@SiO2 NPs). They can be prepared by silylation of surfactant stabilized AuNPs. The AuNP@SiO2 NPs are highly stable and can be used to label antibodies at virtually any concentration. The detection limit of an LFA for alpha-fetoprotein (AFP) can be decreased from 10 ng·mL-1 to 300 pg·mL-1 which makes it comparable to an enzyme-linked immunosorbent assay. To demonstrate the applicability to an immunoassay, a sandwich assay was developed for vanillin by covalent modification of the AuNP@SiO2 NPs with antibody. By using the method, vanillin can be detected visually in milk powder samples in concentrations as low as 100 ng·g-1. With unique optical property and great stability, this AuNP@SiO2 endows great potential in biosensing applications. Graphical abstract Controlled growth of AuNP@SiO2. The newly prepared AuNP has a negative hydration layer. This layer is further surrounded by a bilayer of CTAB through electrostatic attraction. The hydrophobic inner layer enables the access and assembling of APTES and MTTS. After the hydrolysis of siloxane, a thin layer of silica shell is formed around AuNP.

Lateral flow biosensor for multiplex detection of nitrofuran metabolites based on functionalized magnetic beads.

The use of potential mutagenic nitrofuran antibiotic in food animal production has been banned world-wide. Common methods for nitrofuran detection involve complex extraction procedures. In the present study, magnetic beads functionalized with antibody against nitrofuran derivative were used as both the extraction and color developing media in lateral flow biosensor. Derivatization reagent carboxybenzaldehyde is firstly modified with ractopamine. After reaction with nitrofuran metabolites, the resultant molecule has two functional groups: the metabolite moiety and the ractopamine moiety. Metabolite moiety is captured by the antibody that is coated on magnetic beads. This duplex is then loaded onto biosensor and ractopamine moiety is further captured by the antibody immobilized on the test zone of nitrocellulose membrane. Without tedious organic reagent-based extraction procedure, this biosensor was capable of visually detecting four metabolites simultaneously with a detection limit of 0.1 µg/L. No cross-reactivity was observed in the presence of 50 µg/L interferential components. Graphical abstract Derivatization of nitrofuran metabolites (AHD, AOZ, SEM, or AMOZ) and LFA detection of the derivative products.

Cortical functional mechanisms in emotional cognitive tasks in first-episode, drug-naïve with major depressive disorder: A fNIRS study.

BACKGROUND: Previous research has revealed that patients with major depressive disorder (MDD) have negative biases in various aspects of information processing, and these biases are mainly manifested in recognizing facial expressions. However, the link between this emotional cognitive inhibition and neural activation mechanisms in cortical brain regions remains poorly understood. Therefore, this study employed functional near-infrared spectroscopy (fNIRS) to explore the potential impaired regions and neural mechanisms associated with facial emotion cognition in MDD patients. METHODS: 37 MDD patients and 34 healthy controls (HC) were recruited to participate in three sets of cognitive tasks for emotion recognition, and the cortical activation in the brain was synchronously recorded using multi-channel fNIRS. RESULTS: During tasks requiring the motions identification of sad versus happy emotional states, MDD patients exhibit altered activation in both the left frontopolar cortex (FPC) and the right dorsolateral prefrontal cortex (DLPFC). Notably, the FPC demonstrates a higher level of internal coherence and broader correlation with other cortical areas. Moreover, MDD patients showed lower accuracy in distinguishing emotional cues associated with sadness versus those associated with neutral and happy emotions. LIMITATIONS: The study had a relatively small sample size, and it specifically examined only three prevalent facial expressions. CONCLUSION: Facial expression recognition in MDD patients is characterized by negative cognitive interpretation of expressions, which are associated with various cortical altered activations. Neuroimaging further suggests that the cognitive inhibition of emotion signal recognition in everyday interpersonal interactions in MDD patients may primarily be influenced by activation in the left FPC.

Lateral Flow Biosensor for On-Site Multiplex Detection of Viruses Based on One-Step Reverse Transcription and Strand Displacement Amplification.

Respiratory pathogens pose a huge threat to public health, especially the highly mutant RNA viruses. Therefore, reliable, on-site, rapid diagnosis of such pathogens is an urgent need. Traditional assays such as nucleic acid amplification tests (NAATs) have good sensitivity and specificity, but these assays require complex sample pre-treatment and a long test time. Herein, we present an on-site biosensor for rapid and multiplex detection of RNA pathogens. Samples with viruses are first lysed in a lysis buffer containing carrier RNA to release the target RNAs. Then, the lysate is used for amplification by one-step reverse transcription and single-direction isothermal strand displacement amplification (SDA). The yield single-strand DNAs (ssDNAs) are visually detected by a lateral flow biosensor. With a secondary signal amplification system, as low as 20 copies/µL of virus can be detected in this study. This assay avoids the process of nucleic acid purification, making it equipment-independent and easier to operate, so it is more suitable for on-site molecular diagnostic applications.

A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease.

Coronary artery disease is one of the most common types of cardiovascular disease. Death from coronary heart disease is influenced by genetic factors in both women and men. In this article, we propose a novel Bayesian variable selection framework for the identification of important genetic variants associated with coronary artery disease disease status. Instead of treating each feature independently as in conventional Bayesian variable selection methods, we propose an innovative prior for the inclusion probabilities of genetic variants that accounts for their ordering structure. We assume that neighboring variants are more likely to be selected together as they tend to be highly correlated and have similar biological functions. Additionally, we propose to group participating subjects based on underlying population structure and fit separate regressions, so that the regression coefficients can better reflect different disease risks in different population groups. Our approach borrows strength across regression models through an innovative prior inspired by the Markov random fields. The proposed framework can improve variable selection and prediction performances as demonstrated in the simulation studies. We also apply the proposed framework to the CATHeterization GENetics data with binary Coronary artery disease disease status.

A bibliometric analysis of publications on obsessive-compulsive disorder using VOSviewer.

Background: Obsessive-compulsive disorder (OCD) is one of the top ten disabling diseases seriously affecting the health of population. Recently, studies on this disease significantly increased. However, only a few bibliometric analyses concerning this area have been reported. In this study, we used bibliometrics and visualization tools to examine the current state, hot topics and future trends in OCD research. Methods: Scientific publications regarding OCD were retrieved from the Web of Science Core Collection (WoSCC) database. The features of OCD research were further analyzed using VOSviewer. Results: A total of 24,552 publications and 65,296 authors in the field of OCD were retrieved from 2000 to 2022, showing an overall upward trend in publications over the past 22 years. One hundred and thirteen countries around the world had participated in the research. Among these countries, the developed countries such as the United States, England, and Canada were the crucial productive nations in this subject. As for institutions, the Harvard University, the University of London, and the University of California system were the leading institutions. Authors including Storch EA, Mataix-Cols D, and Stein DJ were the prolific authors. 1,949 journals are contributing to the OCD field, of which the top three are Biological Psychiatry (831 articles), European Neuropsychopharmacology (776 articles) and Psychiatric Research (648 articles). Research hotspots of OCD included pathogenesis, epidemiology, comorbidities, clinical features, and evaluation methods. COVID-19, mental health, functional connectivity, and genome-wide association were emerging trends in the field of OCD. Conclusion: This study integrates the bibliometric information on the current research status and emerging trends in OCD from a macro perspective. The findings can provide valuable insights into further research on OCD.

Model detection for semiparametric accelerated failure additive model with right-censored data.

Censored data frequently appeared in applications across a variety of different areas like epidemiology or medical research. Traditionally statistical inference on this data mechanism was based on some pre-assigned models that will suffer from the risk of model-misspecification. This article proposes a two-folded shrinkage procedure for simultaneous structure identification and variable selection of the semiparametric accelerated failure additive model with right-censored data, in which the nonparametric functions are addressed by spline approximation. Under some regularity conditions, the consistency of model structure identification is theoretically established in the sense that the proposed method can automatically separate the linear and zero components from the nonlinear ones with probability approaching to one. Detailed issues in computation and turning parameter selection are also discussed. Finally, we illustrate the proposed method by some simulation studies and two real data applications to the primary biliary cirrhosis data and skin cutaneous melanoma data.

Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism.

Wastewater-based surveillance (WBS) of infectious diseases is a powerful tool for understanding community COVID-19 disease burden and informing public health policy. The potential of WBS for understanding COVID-19's impact in non-healthcare settings has not been explored to the same degree. Here we examined how SARS-CoV-2 measured from municipal wastewater treatment plants (WWTPs) correlates with workforce absenteeism. SARS-CoV-2 RNA N1 and N2 were quantified three times per week by RT-qPCR in samples collected at three WWTPs servicing Calgary and surrounding areas, Canada (1.4 million residents) between June 2020 and March 2022. Wastewater trends were compared to workforce absenteeism using data from the largest employer in the city (>15,000 staff). Absences were classified as being COVID-19-related, COVID-19-confirmed, and unrelated to COVID-19. Poisson regression was performed to generate a prediction model for COVID-19 absenteeism based on wastewater data. SARS-CoV-2 RNA was detected in 95.5 % (85/89) of weeks assessed. During this period 6592 COVID-19-related absences (1896 confirmed) and 4524 unrelated absences COVID-19 cases were recorded. A generalized linear regression using a Poisson distribution was performed to predict COVID-19-confirmed absences out of the total number of absent employees using wastewater data as a leading indicator (P < 0.0001). The Poisson regression with wastewater as a one-week leading signal has an Akaike information criterion (AIC) of 858, compared to a null model (excluding wastewater predictor) with an AIC of 1895. The likelihood-ratio test comparing the model with wastewater signal with the null model shows statistical significance (P < 0.0001). We also assessed the variation of predictions when the regression model was applied to new data, with the predicted values and corresponding confidence intervals closely tracking actual absenteeism data. Wastewater-based surveillance has the potential to be used by employers to anticipate workforce requirements and optimize human resource allocation in response to trackable respiratory illnesses like COVID-19.

Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community.

Wastewater-based surveillance (WBS) has been established as a powerful tool that can guide health policy at multiple levels of government. However, this approach has not been well assessed at more granular scales, including large work sites such as University campuses. Between August 2021 and April 2022, we explored the occurrence of SARS-CoV-2 RNA in wastewater using qPCR assays from multiple complimentary sewer catchments and residential buildings spanning the University of Calgary's campus and how this compared to levels from the municipal wastewater treatment plant servicing the campus. Real-time contact tracing data was used to evaluate an association between wastewater SARS-CoV-2 burden and clinically confirmed cases and to assess the potential of WBS as a tool for disease monitoring across worksites. Concentrations of wastewater SARS-CoV-2 N1 and N2 RNA varied significantly across six sampling sites - regardless of several normalization strategies - with certain catchments consistently demonstrating values 1-2 orders higher than the others. Relative to clinical cases identified in specific sewersheds, WBS provided one-week leading indicator. Additionally, our comprehensive monitoring strategy enabled an estimation of the total burden of SARS-CoV-2 for the campus per capita, which was significantly lower than the surrounding community (p≤0.001). Allele-specific qPCR assays confirmed that variants across campus were representative of the community at large, and at no time did emerging variants first debut on campus. This study demonstrates how WBS can be efficiently applied to locate hotspots of disease activity at a very granular scale, and predict disease burden across large, complex worksites.

A simple colorimetric detection of DNA methylation.

In this work, we describe a simple colorimetric method to detect DNA methylation. Adenomatous polyposis coli (APC) with a small CpG region containing methylated cytosine (methylated APC) was synthesized and tested. Methylated APC was first captured and enriched by anti-5-methylcytosine monoclonal antibody conjugated magnetic microspheres (MMPs). Then a probe partly complementary to the APC sequence was added, resulting in the formation of DNA duplexes. The microsphere-captured probe was then released by heat denaturation and added into unmodified gold nanoparticle (AuNP) solution. Colorimetric detection was performed by salt-induced aggregation. The limit of detection is 80 fmol. Semi-quantitative analysis was done with a UV/Vis spectrophotometer by recording the absorbance of AuNP solution at 520 nm. Thus, this method provides a simple, rapid and quantitative tool for DNA methylation detection.

Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver.

Approaches systematically characterizing interactions via transcriptomic data usually follow two systems: (i) coexpression network analyses focusing on correlations between genes and (ii) linear regressions (usually regularized) to select multiple genes jointly. Both suffer from the problem of stability: A slight change of parameterization or dataset could lead to marked alterations of outcomes. Here, we propose Stabilized COre gene and Pathway Election (SCOPE), a tool integrating bootstrapped least absolute shrinkage and selection operator and coexpression analysis, leading to robust outcomes insensitive to variations in data. By applying SCOPE to six cancer expression datasets (BRCA, COAD, KIRC, LUAD, PRAD, and THCA) in The Cancer Genome Atlas, we identified core genes capturing interaction effects in crucial pan-cancer pathways related to genome instability and DNA damage response. Moreover, we highlighted the pivotal role of CD63 as an oncogenic driver and a potential therapeutic target in kidney cancer. SCOPE enables stabilized investigations toward complex interactions using transcriptome data.

Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater.

The ribonucleic acid (RNA) of the severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) is detectable in municipal wastewater as infected individuals can shed the virus in their feces. Viral concentration in wastewater can inform the severity of the COVID-19 pandemic but observations can be noisy and sparse and hence hamper the epidemiological interpretation. Motivated by a Canadian nationwide wastewater surveillance data set, unlike previous studies, we propose a novel Bayesian statistical framework based on the theories of functional data analysis to tackle the challenges embedded in the longitudinal wastewater monitoring data. By employing this framework to analyze the large-scale data set from the nationwide wastewater surveillance program covering 15 sampling sites across Canada, we successfully detect the true trends of viral concentration out of noisy and sparsely observed viral concentrations, and accurately forecast the future trajectory of viral concentrations in wastewater. Along with the excellent performance assessment using simulated data, this study shows that the proposed novel framework is a useful statistical tool and has a significant potential in supporting the epidemiological interpretation of noisy viral concentration measurements from wastewater samples in a real-life setting.

Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population.

Wastewater-based epidemiology (WBE) is an emerging surveillance tool that has been used to monitor the ongoing COVID-19 pandemic by tracking SARS-CoV-2 RNA shed into wastewater. WBE was performed to monitor the occurrence and spread of SARS-CoV-2 from three wastewater treatment plants (WWTP) and six neighborhoods in the city of Calgary, Canada (population 1.44 million). A total of 222 WWTP and 192 neighborhood samples were collected from June 2020 to May 2021, encompassing the end of the first-wave (June 2020), the second-wave (November end to December 2020) and the third-wave of the COVID-19 pandemic (mid-April to May 2021). Flow-weighted 24-hour composite samples were processed to extract RNA that was then analyzed for two SARS-CoV-2-specific regions of the nucleocapsid gene, N1 and N2, using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Using this approach SARS-CoV-2 RNA was detected in 98.06% (406/414) of wastewater samples. SARS-CoV-2 RNA abundance was compared to clinically diagnosed COVID-19 cases organized by the three-digit postal code of affected individuals' primary residences, enabling correlation analysis at neighborhood, WWTP and city-wide scales. Strong correlations were observed between N1 & N2 gene signals in wastewater and new daily cases for WWTPs and neighborhoods. Similarly, when flow rates at Calgary's three WWTPs were used to normalize observed concentrations of SARS-CoV-2 RNA and combine them into a city-wide signal, this was strongly correlated with regionally diagnosed COVID-19 cases and clinical test percent positivity rate. Linked census data demonstrated disproportionate SARS-CoV-2 in wastewater from areas of the city with lower socioeconomic status and more racialized communities. WBE across a range of urban scales was demonstrated to be an effective mechanism of COVID-19 surveillance.

[Studies on the polysaccharide SGPS2 from the fruits of Siraitia grosvenorii].

OBJECTIVE: To study the structure of the polysaccharides constituents in the fruits of Siraitia grosvenorii (Swingle) C. Jeffrey. METHODS: SGPS2 was purified by DEAE-cellulose and Sephadex G-200 column chromatography, the component and structure of SGPS2 were analyzed on the basis of spectral and chemical studies by HPLC, IR analysis, partial hydrolysis with acid, methylation analysis, GC and 13C-NMR. RESULTS: The molecular weight of SGPS2 was 650 000. Polysaccharide was composed of L- made up of (1 --> 2, 4) linked rhamnose, (1 --> 4) linked rhamnose residues in main chain; and (1 --> 2) linked rhamnose, (1 --> 3) linked rhamnose in side chains. Terminal residues attached main chain to rhamnose. CONCLUSION: SGPS2 was composed of rhamnose and curonic acid.

A signal-enhanced and sensitive lateral flow aptasensor for the rapid detection of PDGF-BB.

Platelet-derived growth factor BB (PDGF-BB) is a potential biomarker of tumor angiogenesis. For the first time, we developed a highly sensitive aptasensor for PDGF-BB with an enhanced test line signal by using two different gold nanoparticles (AuNPs). Herein, we describe a highly sensitive biosensor for PDGF-BB detection that combines biotinylated aptamer on a sample pad and poly thymine-Cy3-AuNP-monoclonal antibody complexes against PDGF-BB immobilized on conjugate pad A. Streptavidin (SA) and rabbit anti-mouse polyclonal antibody were also immobilized in the nitrocellulose membrane at the test and control zones, respectively. When the target PDGF-BB protein was added, it first bound the aptamer, and later the monoclonal antibody to form a biotinylated complex that was captured by SA, resulting in a visual red line on the test zone. In addition, to enhance the sensitivity, another monoclonal antibody against Cy3 was conjugated on AuNP B and immobilized on conjugate pad B to form a AuNPs (A&B)-antibody-(PDGF-BB-Cy3)-aptamer-biotin-SA complex on the test line when a loading buffer was subsequently added. This approach showed a linear response to PDGF-BB from 3 ng mL-1 to 300 ng mL-1 with a limit of detection as low as 1 ng mL-1 obtained in 10 minutes. Our biosensor displayed results through red lines readable by the naked eye. Interestingly, our approach has been successfully applied for real sample verification, proving its applicability for cancer monitoring and diagnosis.