Guangxin Xiang’s research while affiliated with Wenzhou Medical University and other places

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Publications (14)


A label-free and naked-eye fluorescence turn-on assay for one-pot LAMP detection of foodborne pathogens using AuNCs-Cu2+ complex
  • Article

May 2025

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4 Reads

Food Chemistry

Fuyuan Huang

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Paner Jiang

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Yiliang Chen

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UL82 promotes CRC cell proliferation
A qPCR experiment to validate the transfection efficiency of UL82 at the mRNA level; B CCK-8 assay to assess the effect of UL82 transfection on the proliferation ability of SW620 and HCT116 cells; C, D Colony formation assay to evaluate the impact of UL82 transfection on the colony-forming ability of SW620 and HCT116 cells; E, F Flow cytometry analysis to examine the effect of UL82 transfection on the cell cycle of SW620 and HCT116 cells; G Western blot analysis to investigate the effect of UL82 transfection on the levels of cell cycle-related markers in SW620 and HCT116 cells; H Nude mouse xenograft experiment to evaluate the effect of UL82 on CRC cell growth in vivo. ns represents not significant. *, **, and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.
UL82 promotes the expression of the oncogene AGR2
A Heatmap of differentially expressed genes in SW620 cells transfected with UL82 or control plasmid, based on transcriptomic sequencing; B Expression of the top 10 upregulated genes from transcriptomic sequencing in tumor and normal tissues, data sourced from TCGA and GTEX databases; C qPCR analysis of AGR2 mRNA levels in SW620 cells transfected with UL82 or vector; D qPCR analysis of AGR2 mRNA levels in SW620 cells infected with inactivated HCMV (In HCMV) and active HCMV; E Western blot analysis of AGR2 protein levels in SW620 cells transfected with UL82 and infected with HCMV; F IHC analysis of UL82 and AGR2 expression levels in subcutaneous tumors from nude mice inoculated with SW620-UL82 or SW620-Vector cells; G IHC analysis of AGR2 expression levels in HCMV-positive CRC tissues and their paired HCMV-negative adjacent non-cancerous tissues. ns represents not significant. ns represents not significant. *, **, and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.
AGR2 enhances the proliferation and cell cycle of CRC cells
A Western blot analysis of AGR2 expression levels in various CRC cell lines; B qPCR analysis of AGR2 mRNA levels in SW620, HCT116, GP2D, and LS174T cells following AGR overexpression and knockdown; C CCK-8 assay to assess the effect of AGR2 overexpression and knockdown on the proliferation ability of CRC cells; D Colony formation assay to evaluate the impact of AGR2 overexpression and knockdown on the colony-forming ability of CRC cells; E Flow cytometry analysis to examine the effect of AGR2 overexpression and knockdown on the cell cycle of CRC cells; F Western blot analysis to investigate the effect of AGR2 overexpression and knockdown on the levels of cell cycle-related markers in CRC cells. *, **, and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.
UL82 promotes CRC cell proliferation and cell cycle progression through AGR2
A qPCR experiment to validate the knockdown efficiency of AGR2 at the mRNA level; B CCK-8 assay to assess the effect of AGR2 knockdown on the proliferation ability of SW620-UL82 OE and HCT116-UL82 OE cells; C, D Colony formation assay to evaluate the impact of AGR2 knockdown on the colony-forming ability of SW620-UL82 OE and HCT116-UL82 OE cells; E, F Flow cytometry analysis to examine the effect of AGR2 knockdown on the cell cycle of SW620-UL82 OE and HCT116-UL82 OE cells; G Western blot analysis to investigate the effect of AGR2 knockdown on the levels of cell cycle-related markers in SW620-UL82 OE and HCT116-UL82 OE cells.; H In vivo tumor formation assay in nude mice to assess the impact of AGR2 knockdown on CRC cell proliferation. ns represents not significant. *, **, and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.
UL82/DDX5 complex results in the enhanced expression of AGR2
A Immunoprecipitation mass spectrometry identifies UL82 interacting proteins; B Human TFDB database predicts AGR2 promoter binding proteins and intersects with UL82 interacting proteins; C Correlation analysis of DDX5 and PHOX2B with AGR2 expression levels in the GEPIA database; D Co-IP confirms the interaction between UL82 and DDX5; E Immunofluorescence co-localization analysis of UL82 and DDX5 in cells; F Dual-luciferase reporter assay assessing AGR2 transcriptional activity after PHOX2B, DDX5 and UL82 overexpression in 293 T cells; G Dual-luciferase reporter assay assessing AGR2 transcriptional activity after transfection of DDX5 alone and co-transfection of DDX5 and UL82. ++, +, and - represent plasmid amounts of 1 μg, 0.5 μg, and 0 μg, respectively; H, I Application of DDX5 inhibitor FL118 to SW620 and HCT116 cells, with qPCR and Western blot analysis of AGR2 expression levels; J ChIP assay verifies DDX5 binding to the AGR2 promoter region. K Western blot analysis of the effect of UL82 on DDX5 levels. ns represents not significant. *, **, and *** indicate P < 0.05, P < 0.01, and P < 0.001, respectively.

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Human cytomegalovirus UL82 promotes cell cycle progression of colorectal cancer by upregulating AGR2
  • Article
  • Full-text available

February 2025

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37 Reads

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1 Citation

Communications Biology

The correlation between persistent human cytomegalovirus (HCMV) infection and poor prognosis in colorectal cancer (CRC) patients has garnered increasing attention. UL82 is a tegument protein of HCMV, and our previous research indicated that the presence of UL82 is significantly associated with reduced overall survival in CRC patients. However, the mechanism by which UL82 affects the prognosis of CRC patients remains unclear. In this study, we investigated the role of UL82 in CRC progression through both in vitro and in vivo experiments, and revealed its downstream regulatory pathways by integrating transcriptomics, metabolomics, and proteomics. Our findings first revealed that UL82 significantly promoted CRC cell proliferation by increasing the proportion of cells in the S phase of the cell cycle. Additionally, UL82 enhanced the expression of the oncogene AGR2, while knockdown of AGR2 abolished the proliferative effect of UL82. Interestingly, UL82 interacted with the transcription factor DDX5, which transcriptionally inhibited AGR2 expression. Furthermore, this UL82-AGR2 axis promoted nucleotide metabolism in CRC cells by enhancing the levels of nucleotide synthesis enzymes DTYMK, RRM2, and TYMS. In conclusion, our study suggests that the UL82/DDX5 complex may promote nucleotide metabolism and cell cycle progression of CRC by upregulating AGR2 and UL82 may serve as a potential prognostic biomarker for CRC patients.

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Identification of Mycobacterium tuberculosis and its Drug Resistance by Targeted Nanopore Sequencing Technology

February 2025

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8 Reads

BIO-PROTOCOL

Tuberculosis (TB) remains the leading cause of human mortality in infectious diseases. Drug-resistant TB, particularly multidrug-resistant TB and extensively drug-resistant TB, poses a pressing clinical and public health challenge. The main causative agents of TB are known as Mycobacterium tuberculosis (MTB), which exhibits a highly complex drug resistance profile. Traditional culture-based phenotypic drug susceptibility testing is time-consuming, and PCR-based assays are restricted to detecting known mutational hotspots. In this study, we present a protocol leveraging high-throughput nanopore sequencing technology in conjunction with multiplex PCR, termed targeted nanopore sequencing, for the identification of MTB and analysis of its drug resistance. Our method for MTB drug resistance assessment offers the benefits of being culture-free, efficient, high-throughput, and highly accurate, which could significantly aid in clinical patient management and the control of TB infections. Key features • Targeted nanopore sequencing detects 18 genes simultaneously linked to antibiotic resistance in MTB. • The method provides broad drug resistance profiles for 14 first- and second-line anti-TB drugs without bacterial culture. • The expedited turnaround time of the process is approximately 7.5 h with a detection limit of 10² bacteria/mL.



Overview of RIAMs for multi-scenario, ultrasensitive and on-site airborne virus surveillance
a Description of the importance of monitoring airborne virus. (i) Schematic representation of virus-laden aerosols/droplets generated by an infected individual and potential aerosol transmission pathway, (ii) Table of the median half-life of common airborne respiratory viruses. RH: relative humidity. RSV: respiratory syncytial virus. b Schematic diagram of the comprehensive airborne viruses monitoring solution provided by RIAMs, including (i) offline surveillance of airborne respiratory viruses and (ii) continuous surveillance of airborne respiratory viruses. c Schematic diagram of the versatile and ultrasensitive QF paper-centered workflows of in-situ PCR chemistry. QF paper: quartz filter paper. d Exploded schematic of the microfluidic cartridge for fully integrated and automated viral nucleic acid detection. e A series of basic elements of mesoscopic design paradigm, including IN, OUT, MIX for various basic fluidic operations. f Images of the microfluidic cartridge and the compact control and detection instrument.
Performance assessment of in-situ four-plex PCR assay within the microfluidic cartridge for ultrasensitive detection of SARS-COV-2, Influenza A, B, and RSV
a Screenshots of Supplementary Movie 1 showing the microfluidic cartridge workflow and biochemical principles. b Sensitivity of the in-situ four-plex PCR system. The four target RNAs from 1000 copies/mL to 5 copies/mL were tested, demonstrating an ultra-high sensitivity of 10 copies/mL for SARS-CoV-2, and 5 copies/mL for Influenza A, Influenza B, and RSV. Error bars represent mean ± SD (n = 3 independent experiments on different simplified microdevices). c Linear fitting between the logarithm of virus input concentration of the four target RNAs and the corresponding Ct values. High linearity suggests that the in-situ multiplex PCR system can be used for the quantification of those four target RNAs. Error bars represent mean ± SD (n = 3 independent experiments on different simplified microdevices). d Evaluation of the specificity of the in-situ four-plex RT-qPCR system, showing no cross-reaction or non-specific amplifications of the four-plex PCR assay. Error bars represent mean ± SD (n = 3 independent experiments on different simplified microdevices). e LoDs determination of the microfluidic cartridge assay for ultrasensitive and simultaneous detection of SARS-CoV-2, Influenza A, B, and RSV, measured using pseudovirus quality reference materials of the four target viruses. Error bars represent mean ± SD (n = 3 independent experiments performed on different microfluidic cartridges). NC: negative control. f LoD determination for in-situ RT-qPCR to detect Omicron viral RNA, including the fitted relationship between the logarithm of virus input concentration and Ct value. Error bars represent mean ± SD (n = 3 independent experiments performed on different microfluidic cartridges). g Amplification curves for 10 copies/mL of Omicron at a primer concentration of 0.75 μM. Error bars represent mean ± SD (n = 3 independent experiments performed on different microfluidic cartridges). NC: negative control.
M-RIAMs for comprehensive analysis of virus-laden aerosol samples
a Workflow of M-RIAMs from aerosol sample collection to on-site or centralized nucleic acid testing. b Physical collection efficiency curves of the aerosol sampler when collecting polystyrene latex microspheres with different sizes ranging from 0.5, 0.8, 1.0, 1.5, and 2.0 μm. Error bars represent mean ± SD (n = 3 collection efficiency values from independent aerosol particle generation and collection experiments). c Working noise of the aerosol sampler with and without silencer at different distances. Error bars represent mean ± SD (n = 3 independently measured noise values). d Performance assessment of M-RIAMs to detect mock samples to test potential impurities in real aerosol samples. Error bars represent mean ± SD (n = 8 independently collected aerosol samples). ND: not detected. e Performance re-assessment of M-RIAMs that adds a piece of filter cotton to the air inlet of the cyclone sampler to detect mock samples collected in the parking lots. Error bars represent mean ± SD (n = 8 independently collected aerosol samples). ND: not detected. f Schematic diagram of sampling sites in wards for SARS-CoV-2 patients. g Comparison of the virus concentration of aerosol samples collected before and after indoor ventilation conditions in the same patient ward. Solid lines represent the mean values. (n = 4 independently collected aerosol samples). h Violin plot of Ct values of positive test results at seven environmental sampling sites, revealing that aerosol samples show lower mean Ct values, i.e. better environmental virus risk assessment. i Virus concentration in wards where aerosol samples were collected. j Positive detection rates of the seven environmental samples, highlighting the highest positive detection rate of aerosol samples. k Comparison of positive detection rates of environmental samples from the same patient ward collected and tested on day 1 and day 3. l Positive detection rates of environmental samples in two wards for severe COVID-19 patients and two wards for mild COVID-19 patients indicating that aerosol samples can better reflect the patients’ disease status (in terms of individual viral shedding).
The structure and working principle of S-RIAMs and R-RIAMs
a Structural overview of S-RIAMs and exploded schematic of its functional parts, including a cyclone bioaerosol sampler unit (i), a microfluidic cartridge sampling unit (ii), microfluidic cartridges loading tray (iii), and a control and detection system for performing fluid actuation, PCR temperature control, and fluorescence signal read-out (iv). b Optical image of S-RIAMs showing its 3D dimensions. c Optical image of R-RIAMs showing its 3D dimensions. d Schematic illustration of the microfluidic cartridge sampling process. e Ct values of viral RNA samples before and after transfer through the sampling system, verifying that there is no adsorption of viral RNA within the sampling systems inside the S-RIAMs and R-SIAMs. Error bars represent mean ± SD (n = 3 independently transferred or not-transferred samples).
Performance evaluation and real-world deployment of S-RIAMs for ultra-sensitive and continuous airborne virus surveillance
a Photograph of the experimental setup of the generation and simultaneous detection of SARS-CoV-2 aerosols in a biosafety cabinet. b A histogram of Ct values of mock aerosol samples with varying viral concentrations in the aerosolized liquid stock. Error bars represent mean ± SD (n = 3 independently aerosolized and collected samples). c The fitted relationship between the logarithm of virus input concentration and Ct values. Error bars represent mean ± SD (n = 3 independently aerosolized and collected samples). d Results of 6-day continuous aerosol COVID-19 monitoring using S-RIAMs in an office workplace affected by the COVID-19 epidemic, showing the positive detection rates and the absolute amount of airborne virus in the space. Solid lines represent mean values. (n = 8 independently collected and tested aerosol samples). e Comparison of the positive detection rates and the virus concentrations on day 1 and day 4 of airborne viruses monitoring results around infected individuals within the student dormitory affected by the COVID-19 pandemic, indicating a gradual increase in the number of infected people. Solid lines represent mean values. (n = 10 independently collected and tested aerosol samples). f A scatter plot of the virus concentrations of aerosol RSV samples collected from neonatal RSV-positive areas, including 10 all-positive aerosol samples from 5 neonatal RSV-positive wards.
Multi-scenario surveillance of respiratory viruses in aerosols with sub-single-copy spatial resolution

October 2024

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76 Reads

Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-COV-2, Influenza A, B, and respiratory syncytial virus with a sensitivity of 10 copies/mL. When integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single-copy spatial resolution of 0.83 copies/m³ for airborne virus surveillance with an air flow rate of 400 L/min and a sampling time of 30 minutes. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission.



Nanopore targeted sequencing process for drug resistance assay of Mycobacterium tuberculosis (MTB). (A) The drug resistance-associated genes marked by black triangles with their positions in the genome of the reference strain MTB H37Rv, including 18 highly recommended genes which were gyrB, gyrA, rpoB, mmpR5, rpsL, rplC, atpE, rrs, rrl, fabG1, inhA, rpsA, tlyA, katG, pncA, eis, embB, and ubiA, respectively. (B) Process of high-throughput nanopore sequencing combined with single-tube multiplex PCR-based target enrichment. After DNA extraction, the targeted regions of drug resistance-associated genes were amplified by multiplex PCR, followed by barcoding and adapter ligation, and the prepared library was then loaded onto the flow cell on the nanopore sequencer, which generated long-read sequence data finally analyzed with bioinformatics. The turnaround time of the NTS process was approximately 7.5 h.
Development of nanopore targeted sequencing with multiplex PCR for drug resistance assay of Mycobacterium tuberculosis (MTB). (A) Electrophoresis of multiplex PCR-based amplicons from five MTB samples and a negative control. The bands of electrophoretic lanes 1–5 from bottom to top were rpsL (560 bp), mmpR5 (600 bp), rplC (652 bp), atpE (744 bp), tlyA (827 bp), pncA (971 bp), ubiA (1,041 bp), gyrB (1,210 bp), rrl (1,275 bp), eis (1,322 bp), fabG1 and inhA (1,387 bp), rpsA (1,444 bp), gyrA (1,544 bp), katG (1,653 bp), rrs (1741 bp), rpoB (1845 bp), and embB (1925 bp), respectively. The ladder sizes of DNA markers (M) were from 600 bp to 4,000 bp. The negative control was nuclease-free water. (B) Bar chart of 18 drug resistance-associated genes arranged according to the sizes of gene fragments against the sequencing depth of each gene after nanopore sequencing. (C) Distribution of 18 drug resistance-associated genes in MTB genome with their corresponding sequencing depths. (D) NTS-based drug resistance data are available for isolates after using the TBProfiler. Each row is a drug, and the columns with filled cells represent the set of isolates possessing drug resistance information. The box below shows the number of isolates in the set. The bar plot in the left panel shows the number of isolates with resistance information for each drug.
Accuracy of multiple amplicons sequenced by nanopore targeted sequencing (NTS). (A) The density distribution comparison of raw sequence accuracy of multiple amplicons between ONT flow cell R9.4.1 and R10.4.1. (B) Per base sequence quality of the single-base Q-value corresponding to the base position in the multiple amplicons from ONT flow cell R10.4.1. The column area of the box-whisker plot represents the range of 25–75% of Q-value, and the area between the upper and lower black lines represents the range of 10–90%. The blue line represents the average Q-value in the position, and the red line represents the median of the Q-value in the position. The background color divides the picture into three parts: good base quality (green), medium base quality (yellow), and poor base quality (red).
Statistics of clinical samples and their processes of inclusion in nanopore targeted sequencing (NTS) assay. (A) Analysis workflow of the 99 clinical samples included in the study. (B) Contingency tables of MTB culture with NTS and Xpert MTB/RIF sets. Abbreviations: PPV, positive predictive value; NPV, negative predictive value; TCR, the total coincidence rate. (C) NTS-based drug resistance data are available for clinical samples after using the TBProfiler, with a total of 20 drug-resistant samples identified. Each row is a drug, and the columns with filled cells represent the set of samples possessing drug resistance information. The box below shows the number of samples in the set. The bar plot in the left panel shows the number of samples with resistance information for each drug. (D) The mutation at position 955 of the embB gene is detected using NTS and verified using Sanger sequencing.
High-throughput nanopore targeted sequencing for efficient drug resistance assay of Mycobacterium tuberculosis

May 2024

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44 Reads

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5 Citations

Drug-resistant tuberculosis (TB), especially multidrug-resistant tuberculosis (MDR-TB) and extensively drug-resistant tuberculosis (XDR-TB), is one of the urgent clinical problems and public health challenges. Culture-based phenotypic drug susceptibility testing (pDST) is time-consuming, and PCR-based assays are limited to hotspot mutations. In this study, we developed and validated a convenient and efficient approach based on high-throughput nanopore sequencing technology combined with multiplex PCR, namely nanopore targeted sequencing (NTS), to simultaneously sequence 18 genes associated with antibiotic resistance in Mycobacterium tuberculosis (MTB). The analytical performance of NTS was evaluated, and 99 clinical samples were collected to assess its clinical performance. The NTS results showed that MTB and its drug resistance were successfully identified in approximately 7.5 h. Furthermore, compared to the pDST and Xpert MTB/RIF assays, NTS provided much more drug resistance information, covering 14 anti-TB drugs, and it identified 20 clinical cases of drug-resistant MTB. The mutations underlying these drug-resistant cases were all verified using Sanger sequencing. Our approach for this TB drug resistance assay offers several advantages, including being culture-free, efficient, high-throughput, and highly accurate, which would be very helpful for clinical patient management and TB infection control.


Investigation of ENO2 as a promising novel marker for the progression of colorectal cancer with microsatellite instability-high

May 2024

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27 Reads

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1 Citation

BMC Cancer

Background Microsatellite instability-high (MSI-H) has emerged as a significant biological characteristic of colorectal cancer (CRC). Studies reported that MSI-H CRC generally had a better prognosis than microsatellite stable (MSS)/microsatellite instability-low (MSI-L) CRC, but some MSI-H CRC patients exhibited distinctive molecular characteristics and experienced a less favorable prognosis. In this study, our objective was to explore the metabolic transcript-related subtypes of MSI-H CRC and identify a biomarker for predicting survival outcomes. Methods Single-cell RNA sequencing (scRNA-seq) data of MSI-H CRC patients were obtained from the Gene Expression Omnibus (GEO) database. By utilizing the copy number variation (CNV) score, a malignant cell subpopulation was identified at the single-cell level. The metabolic landscape of various cell types was examined using metabolic pathway gene sets. Subsequently, functional experiments were conducted to investigate the biological significance of the hub gene in MSI-H CRC. Finally, the predictive potential of the hub gene was assessed using a nomogram. Results This study revealed a malignant tumor cell subpopulation from the single-cell RNA sequencing (scRNA-seq) data. MSI-H CRC was clustered into two subtypes based on the expression profiles of metabolism-related genes, and ENO2 was identified as a hub gene. Functional experiments with ENO2 knockdown and overexpression demonstrated its role in promoting CRC cell migration, invasion, glycolysis, and epithelial-mesenchymal transition (EMT) in vitro. High expression of ENO2 in MSI-H CRC patients was associated with worse clinical outcomes, including increased tumor invasion depth (p = 0.007) and greater likelihood of perineural invasion (p = 0.015). Furthermore, the nomogram and calibration curves based on ENO2 showed potential prognosis predictive performance. Conclusion Our findings suggest that ENO2 serves as a novel prognostic biomarker and is associated with the progression of MSI-H CRC.


Multi-scenario surveillance of respiratory viruses in aerosols with a sub-single molecule spatial resolution

March 2024

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57 Reads

Highly sensitive airborne virus monitoring is critical for preventing and containing epidemics. However, the detection of airborne viruses at ultra-low concentrations remains challenging due to the lack of ultra-sensitive methods and easy-to-deployment equipment. Here, we present an integrated microfluidic cartridge that can accurately detect SARS-CoV-2 and various respiratory viruses with a sensitivity of 10 copies/mL. When seamlessly integrated with a high-flow aerosol sampler, our microdevice can achieve a sub-single molecule spatial resolution of 0.83 copies/m ³ for airborne virus surveillance. We then designed a series of virus-in-aerosols monitoring systems (RIAMs), including versions of a multi-site sampling RIAMs (M-RIAMs), a stationary real-time RIAMs (S-RIAMs), and a roaming real-time RIAMs (R-RIAMs) for different application scenarios. Using M-RIAMs, we performed a comprehensive evaluation of 210 environmental samples from COVID-19 patient wards, including 30 aerosol samples. The highest positive detection rate of aerosol samples (60%) proved the aerosol-based SARS-CoV-2 monitoring represents an effective method for spatial risk assessment. The detection of 78 aerosol samples in real-world settings via S-RIAMs confirmed its reliability for ultra-sensitive and continuous airborne virus monitoring. Therefore, RIAMs shows the potential as an effective solution for mitigating the risk of airborne virus transmission.



Citations (7)


... By utilizing such therapies, it is possible to minimize the risk of off-target side effects, making it a highly promising avenue for research. Some studies have investigated the effects of inhibiting Agr2 using short hairpin RNA (shRNA) or small interfering RNA (siRNA) molecules [144,145]. In a colorectal cancer cell model, silencing Agr2 contributed to metformin-dependent sensitization of cells to chemotherapy [80]. ...

Reference:

Agr2 in cancer and beyond: unraveling its role during protein synthesis, ER stress, and as a predictive biomarker
Human cytomegalovirus UL82 promotes cell cycle progression of colorectal cancer by upregulating AGR2

Communications Biology

... 6 WGS has emerged as a transformative approach in pathogen diagnostics, providing high-resolution genotyping and precise pathogen identification. 7,8 Unlike traditional DST methods, WGS provides a comprehensive analysis of MTB genomic DNA to prediction of drug resistance mechanisms across multiple antimicrobials. 9 While WGS has primarily been applied in economically developed regions with low TB burden, its potential in determining resistance to first-line drugs is increasingly being recognized. ...

High-throughput nanopore targeted sequencing for efficient drug resistance assay of Mycobacterium tuberculosis

... ENO2, primarily located in mature neurons, was the first enzyme identified in mammals and has been reported to show elevated expression in tumors like glioblastoma (36), neuroendocrine prostate carcinoma (37), and renal cell carcinoma (38). Recent evidence also supports ENO2's role in shaping the tumor immune microenvironment, particularly through neutrophil recruitment (39). In contrast to prior studies, our data reveal that ENO2's association with dendritic cell infiltration in COAD may enhance antigen presentation, providing a mechanistic rationale for its inclusion in mRNA vaccine design. ...

Investigation of ENO2 as a promising novel marker for the progression of colorectal cancer with microsatellite instability-high

BMC Cancer

... 34 However, one tube can detect only one target at a time, whether for EVs or EV-A71. 35 Furthermore, when performing LAMP-CRISPR detection in two steps, opening the tube can allow the target virus that might be present in the air to enter the sample or reaction system, leading to false positive results. The limitation underscores the need for advancements in multiplex capabilities and specificity to enhance diagnostic potential. ...

Infectious Disease Diagnosis and Pathogen Identification Platform Based on Multiplex Recombinase Polymerase Amplification-Assisted CRISPR-Cas12a System
  • Citing Article
  • October 2023

ACS Infectious Diseases

... MAVS mainly participates in the IFN signaling pathway [22] . Mfn1, Mfn2 [23] , TOM70 [24] , VDAC1 [25] , TRADD, FADD [26] , Atg5 and Atg12 [27] regulate innate immunity through the modulation of MAVS-mediated signaling. Speci cally, Mfn1 and Mfn2 regulate mitochondrial fusion [28] . ...

Mitofusin 1-Mediated Redistribution of Mitochondrial Antiviral Signaling Protein Promotes Type 1 Interferon Response in Human Cytomegalovirus Infection

Microbiology Spectrum

... Recent advances include the integration of machine learning (ML) and deep learning (DL) models to analyze factors influencing pancreatic fistula rates and INP-related mortality, enhancing risk stratification and personalized management [56,77,78]. Recent advances in ML research have demonstrated that random forest-based models exhibit superior predictive performance for severe acute pancreatitis and have the potential to facilitate personalized therapeutic strategies [79]. Furthermore, ML models utilizing gradient boosting decision trees surpass conventional clinical scoring systems, including the systemic inflammatory response syndrome score and bedside index for severity in acute pancreatitis score, in predicting sepsis among acute pancreatitis patients, thereby may serve as an effective tool for early identification of high-risk patients and timely clinical intervention optimization [80]. ...

Development and Evaluation of Machine Learning Models and Nomogram for the Prediction of Severe Acute Pancreatitis
  • Citing Article
  • January 2023

Journal of Gastroenterology and Hepatology

... The cell atlas was visualized using Uniform Manifold Approximation and Projection (UMAP) by "RunUMAP" [40]. 16:188 Cell cluster annotation Distinct cell clusters were identified and annotated using established cell markers from the literature [18,[41][42][43][44][45]. The COSG method (v0.9.0), based on cosine similarity, was employed for accurate and efficient marker gene identification [46]. ...

Single-Cell Transcriptome Reveals the Metabolic and Clinical Features of a Highly Malignant Cell Subpopulation in Pancreatic Ductal Adenocarcinoma