quantitative-polymerase-chain-reaction-qpcr-based-rapid-diagnosis

Quantitative Polymerase Chain Reaction (qPCR)-Based Rapid Diagnosis of <em>Helicobacter pylori</em> Infection and Antibiotic Resistance

Helicobacter pylori is a major human pathogen that&#160;infects approximately half of the global population and is becoming a serious health threat due to ...

rapid-detection-bacterial-pathogens-causing-lower-respiratory-tract

Rapid Detection of Bacterial Pathogens Causing Lower Respiratory Tract Infections via Microfluidic-Chip-Based Loop-Mediated Isothermal Amplification

Respiratory tract infections (RTIs) are among the most common problems in clinical settings. Rapid and accurate identification of bacterial pathogens will ...

detection-helicobacter-pylori-infection-antibiotic-resistance-via

Detection of <em>Helicobacter pylori</em> Infection and Antibiotic Resistance via Stool Quantitative Polymerase Chain Reaction Analysis

Helicobacter pylori (H. pylori) is widely prevalent worldwide, with approximately 50% of the global population having a history of H. pylori infection. In ...

Southern Medical University

Medical Technology School of Xuzhou Medical University

Xuzhou Medical University

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms.

Bacterial species within the Acinetobacter baumannii-calcoaceticus (Acb) complex are very similar and are difficult to discriminate. Misidentification of these species in human infection may lead to severe consequences in clinical settings. Therefore, it is important to accurately discriminate these pathogens within the Acb complex. Raman spectroscopy is a simple method that has been widely studied for bacterial identification with high similarities. In this study, we combined surfaced-enhanced Raman spectroscopy (SERS) with a set of machine learning algorithms for identifying species within the Acb complex. According to the results, the support vector machine (SVM) model achieved the best prediction accuracy at 98.33% with a fivefold cross-validation rate of 96.73%. Taken together, this study confirms that the SERS-SVM method provides a convenient way to discriminate between A. baumannii, Acinetobacter pittii, and Acinetobacter nosocomialis in the Acb complex, which shows an application potential for species identification of Acinetobacter baumannii-calcoaceticus complex in clinical settings in near future.

Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis.

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gastric diseases. This study introduces an innovative non-invasive approach to differentiate gastric disease stage using gastric fluid samples through machine-learning-assisted surface-enhanced Raman spectroscopy (SERS). This method effectively identifies different stages of gastric lesions. The XGBoost algorithm demonstrates the highest accuracy of 96.88% and 91.67%, respectively, in distinguishing chronic non-atrophic gastritis from intestinal metaplasia and different subtypes of gastritis (mild, moderate, and severe). Through blinded testing validation, the model can achieve more than 80% accuracy. These findings offer new possibilities for rapid, cost-effective, and minimally invasive diagnosis of gastric diseases.

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples.

WHO classified  as a Group I carcinogen for gastric cancer as early as 1994. However, despite the high prevalence of  infection, only about 3 % of infected individuals eventually develop gastric cancer, with the highly virulent  strains expressing cytotoxin-associated protein (CagA) and vacuolating cytotoxin (VacA) being critical factors in gastric carcinogenesis. It is well known that  infection is divided into two types in terms of the presence and absence of CagA and VacA toxins in serum, that is, carcinogenic Type I infection (CagA+/VacA+, CagA+/VacA-, CagA-/VacA+) and non-carcinogenic Type II infection (CagA-/VacA-). Currently, detecting the two carcinogenic toxins in active modes is mainly done by diagnosing their serological antibodies. However, the method is restricted by expensive reagents and intricate procedures. Therefore, establishing a rapid, accurate, and cost-effective way for serological profiling of carcinogenic  infection holds significant implications for effectively guiding  eradication and gastric cancer prevention. In this study, we developed a novel method by combining surface-enhanced Raman spectroscopy with the deep learning algorithm convolutional neural network to create a model for distinguishing between serum samples with Type I and Type II  infections. This method holds the potential to facilitate rapid screening of  infections with high risks of carcinogenesis at the population level, which can have long-term benefits in reducing gastric cancer incidence when used for guiding the eradication of  infections.

Rapid profiling of carcinogenic types of  infection via deep learning analysis of label-free SERS spectra of human serum.

To assess the risk factors associated with Helicobacter pylori infection in the urban Chinese population.

Risk factors associated with Helicobacter pylori infection in the urban population of China: A nationwide, multi-center, cross-sectional study.

Helicobacter pylori (H. pylori) infection has long been a significant global public health concern, with antibiotic resistance becoming increasingly severe. Gastric fluid qPCR detection can provide information on H. pylori infection and antibiotic resistance.

Detection of Helicobacter pylori Infection and Antibiotic Resistance via String Test Coupled With qPCR Method: A Comparative Methodological Study.

 (), linked to gastric cancer, lacks regional data on infection rates, resistance, and risks in Dongying, China.

ArticleTotal : 3	Year
Publication title Cited by 8	2023
Publication title	2024
Publication title	2025

Article	Year
Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms. Laboratory investigation; a journal of technical methods and pathology\| PubMed ID: 38135155	2024
Differentiation of closely-related species within Acinetobacter baumannii-calcoaceticus complex via Raman spectroscopy: a comparative machine learning analysis. World journal of microbiology & biotechnology\| PubMed ID: 38538920	2024
Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-colle... Biosensors & bioelectronics\| PubMed ID: 38943854	2024
Rapid profiling of carcinogenic types of infection via deep learning analysis of label-free SERS spectra of human serum. Computational and structural biotechnology journal\| PubMed ID: 39329094	2024
Risk factors associated with Helicobacter pylori infection in the urban population of China: A nationwide, multi-center, cross-sectional study. International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases\| PubMed ID: 40096882	2025
Detection of Helicobacter pylori Infection and Antibiotic Resistance via String Test Coupled With qPCR Method: A Comparative Methodological Study. Journal of gastroenterology and hepatology\| PubMed ID: 40170571	2025
Current rates of infection and antibiotic resistance in the eastern coast of China: a single center study. Frontiers in cellular and infection microbiology\| PubMed ID: 40292221	2025

Yu-Ting Si

Biography

JoVE Journal Publications

Other Publications