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.

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.

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 provide practical guidelines for managing and treating RTIs. This study describes a method for rapidly detecting bacterial pathogens that cause respiratory tract infections via multi-channel loop-mediated isothermal amplification (LAMP). LAMP is a sensitive and specific diagnostic tool that rapidly detects bacterial nucleic acids with high accuracy and reliability. The proposed method offers a significant advantage over traditional bacterial culturing methods, which are time-consuming and often require greater sensitivity for detecting low levels of bacterial nucleic acids. This article presents representative results of K. pneumoniae infection and its multiple co-infections using LAMP to detect samples (sputum, bronchial lavage fluid, and alveolar lavage fluid) from the lower respiratory tract. In summary, the multi-channel LAMP method provides a rapid and efficient means of identifying single and multiple bacterial pathogens in clinical samples, which can help prevent the spread of bacterial pathogens and aid in the appropriate treatment of RTIs.

Quantitative Polymerase Chain Reaction (qPCR)-Based Rapid Diagnosis of Helicobacter pylori Infection and Antibiotic Resistance.

Helicobacter pylori is a major human pathogen that infects approximately half of the global population and is becoming a serious health threat due to its increasing antibiotic resistance. It is the causative agent of chronic active gastritis, peptic ulcer disease, and gastric cancer and has been classified as a Group I Carcinogen by the International Agency for Research on Cancer. Therefore, the rapid and accurate diagnosis of H. pylori and the determination of its antibiotic resistance are important for the efficient eradication of this bacterial pathogen. Currently, H. pylori diagnosis methods mainly include the urea breath test (UBT), the antigen test, the serum antibody test, gastroscopy, the rapid urease test (RUT), and bacterial culture. Among them, the first three detection methods are noninvasive, meaning they are easy tests to conduct. However, bacteria cannot be retrieved through these techniques; thus, drug resistance testing cannot be performed. The last three are invasive examinations, but they are costly, require high skills, and have the potential to cause damage to patients. Therefore, a noninvasive, rapid, and simultaneous method for H. pylori detection and drug resistance testing is very important for efficiently eradicating H. pylori in clinical practice. This protocol aims to present a specific procedure involving the string test in combination with quantitative polymerase chain reaction (qPCR) for the rapid detection of H. pylori infection and antibiotic resistance. Unlike bacterial cultures, this method allows for easy, rapid, noninvasive diagnosis of H. pylori infection status and drug resistance. Specifically, we used qPCR to detect rea for H. pylori infection and mutations in the 23S rRNA and gyrA genes, which encode resistance against clarithromycin and levofloxacin, respectively. Compared to routinely used culturing techniques, this protocol provides a noninvasive, low-cost, and time-saving technique to detect H. pylori infection and determine its antibiotic resistance using qPCR.

Recent advances in surface enhanced Raman spectroscopy for bacterial pathogen identifications.

BACKGROUND: The rapid and reliable detection of pathogenic bacteria at an early stage is a highly significant research field for public health. However, most traditional approaches for pathogen identification are time-consuming and labour-intensive, which may cause physicians making inappropriate treatment decisions based on an incomplete diagnosis of patients with unknown infections, leading to increased morbidity and mortality. Therefore, novel methods are constantly required to face the emerging challenges of bacterial detection and identification. In particular, Raman spectroscopy (RS) is becoming an attractive method for rapid and accurate detection of bacterial pathogens in recent years, among which the newly developed surface-enhanced Raman spectroscopy (SERS) shows the most promising potential. AIM OF REVIEW: Recent advances in pathogen detection and diagnosis of bacterial infections were discussed with focuses on the development of the SERS approaches and its applications in complex clinical settings. KEY SCIENTIFIC CONCEPTS OF REVIEW: The current review describes bacterial classification using surface enhanced Raman spectroscopy (SERS) for developing a rapid and more accurate method for the identification of bacterial pathogens in clinical diagnosis. The initial part of this review gives a brief overview of the mechanism of SERS technology and development of the SERS approach to detect bacterial pathogens in complex samples. The development of the label-based and label-free SERS strategies and several novel SERS-compatible technologies in clinical applications, as well as the analytical procedures and examples of chemometric methods for SERS, are introduced. The computational challenges of pre-processing spectra and the highlights of the limitations and perspectives of the SERS technique are also discussed.Taken together, this systematic review provides an overall summary of the SERS technique and its application potential for direct bacterial diagnosis in clinical samples such as blood, urine and sputum, etc.