Analysis of the application of a gene chip method for detecting Mycobacterium tuberculosis drug resistance in clinical specimens: a retrospective study

Advances in AI-assisted biochip technology for biomedicine

The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields.

Identification of Mycobacterium tuberculosis Resistance to Common Antibiotics: An Overview of Current Methods and Techniques

Multidrug-resistant tuberculosis (MDR-TB) is an essential cause of tuberculosis treatment failure and death of tuberculosis patients. The rapid and reliable profiling of Mycobacterium tuberculosis (MTB) drug resistance in the early stage is a critical research area for public health. Then, most traditional approaches for detecting MTB are time-consuming and costly, leading to the inappropriate therapeutic schedule resting on the ambiguous information of MTB drug resistance, increasing patient economic burden, morbidity, and mortality. Therefore, novel diagnosis methods are frequently required to meet the emerging challenges of MTB drug resistance distinguish. Considering the difficulty in treating MDR-TB, it is urgently required for the development of rapid and accurate methods in the identification of drug resistance profiles of MTB in clinical diagnosis. This review discussed recent advances in MTB drug resistance detection, focusing on developing emerging approaches and their applications in tangled clinical situations. In particular, a brief overview of antibiotic resistance to MTB was present, referred to as intrinsic bacterial resistance, consisting of cell wall barriers and efflux pumping action and acquired resistance caused by genetic mutations. Then, different drug susceptibility test (DST) methods were described, including phenotype DST, genotype DST and novel DST methods. The phenotype DST includes nitrate reductase assay, RocheTM solid ratio method, and liquid culture method and genotype DST includes fluorescent PCR, GeneXpert, PCR reverse dot hybridization, ddPCR, next-generation sequencing and gene chips. Then, novel DST methods were described, including metabolism testing, cell-free DNA probe, CRISPR assay, and spectral analysis technique. The limitations, challenges, and perspectives of different techniques for drug resistance are also discussed. These methods significantly improve the detection sensitivity and accuracy of multidrug-resistant tuberculosis (MRT) and can effectively curb the incidence of drug-resistant tuberculosis and accelerate the process of tuberculosis eradication.

Comparison of 3 diagnostic methods for pulmonary tuberculosis in suspected patients with negative sputum smear or no sputum

STUDY DESIGN

To explore the diagnostic value of 3 methods for sputum smear-negative and non-sputum patients with suspected pulmonary tuberculosis (TB).

METHODS

This prospective study enrolled sputum smear-negative and non-sputum patients with suspected TB admitted to Jiangxi Chest Hospital between January 2020 and December 2022. The 3 methods were bronchoalveolar lavage fluid (BALF)-acid-fast bacillus (AFB) smear, GeneXpert MTB/RIF, and gene chip for Mycobacterium strain identification. The diagnostic performance of the 3 tests was evaluated with BALF Mycobacterium culture + BALF-AFB smear + GeneXpert MTB/RIF + Gene chip as the gold standard.

RESULTS

A total of 456 samples were collected from 114 patients with suspected TB. Twenty-four patients were diagnosed with TB. The combination of GeneXpert MTB/RIF and gene chip for Mycobacterium strain identification yielded the highest area under the receiver operating characteristics curve (AUC) of 0.953 and had sensitivity of 90.57%, specificity of 100%, positive predictive value (PPV) of 100%, negative predictive value (NPV) of 92.42%, accuracy of 95.61%. GeneXpert MTB/RIF achieved AUC of 0.906, sensitivity of 81.13%, specificity of 100%, PPV of 100%, NPV of 85.92%, accuracy of 91.23%. BALF-AFB smear had AUC of 0.519, sensitivity of 3.77%, specificity of 100%, PPV of 100%, NPV of 54.46%, and accuracy of 55.26%. The combination of GeneXpert MTB/RIF and gene chip for Mycobacterium strain identification yielded the highest κ of 0.911, while BALF-AFB smear had the lowest κ value of 0.040.

CONCLUSION

For TB in sputum smear-negative and non-sputum patients using BALF Mycobacterium culture + BALF-AFB smear + GeneXpert MTB/RIF + Gene chip as the gold standard, BALF-AFB smear showed low diagnostic performance, while, though GeneXpert MTB/RIF and gene chip had good diagnostic performance, combining GeneXpert MTB/RIF and gene chip improved the diagnostic value to a great extent.

Advances and Challenges in Cytomegalovirus Detection Methods for Liver Transplant Donors

Cytomegalovirus (CMV) infection is a highly prevalent opportunistic infection among liver transplant recipients. When the liver donor is infected with CMV, there is a risk of transmission to the recipient, leading to CMV infection. To improve the postoperative outcome of liver transplantation, it is crucial to shift the focus of CMV detection to the donor and achieve early diagnosis, as well as implement effective preventative and therapeutic measures. However, the commonly used CMV detection methods in the past had limitations that prevented their early and accurate diagnosis in liver transplant donors. This review focuses on the latest advancements in CMV detection methods that can potentially be applied to liver transplant donors. The objective is to compare and evaluate their clinical utility, thereby providing guidance and support for rapid and accurate diagnosis of CMV infection in the clinic. The clustered regularly interspaced short palindromic repeats-associated proteins (CRISPR-Cas) system-based assay emerges as a promising method for detecting the virus, offering great prospects for early and expedient CMV infection diagnosis in clinical settings.