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Sancha Dominguez L, Cotos Suárez A, Sánchez Ledesma M, Muñoz Bellido JL. Present and Future Applications of Digital PCR in Infectious Diseases Diagnosis. Diagnostics (Basel) 2024; 14:931. [PMID: 38732345 PMCID: PMC11083499 DOI: 10.3390/diagnostics14090931] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Revised: 04/19/2024] [Accepted: 04/28/2024] [Indexed: 05/13/2024] Open
Abstract
Infectious diseases account for about 3 million deaths per year. The advent of molecular techniques has led to an enormous improvement in their diagnosis, both in terms of sensitivity and specificity and in terms of the speed with which a clinically useful result can be obtained. Digital PCR, or 3rd generation PCR, is based on a series of technical modifications that result in more sensitive techniques, more resistant to the action of inhibitors and capable of direct quantification without the need for standard curves. This review presents the main applications that have been developed for the diagnosis of viral, bacterial, and parasitic infections and the potential prospects for the clinical use of this technology.
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Affiliation(s)
- Laura Sancha Dominguez
- Department of Microbiology, Hospital Universitario de Salamanca, 37007 Salamanca, Spain; (L.S.D.); (A.C.S.)
- Research Group IIMD-16, Institute for Biomedical Research of Salamanca (IBSAL), SACYL, Universidad de Salamanca, CSIC, 37007 Salamanca, Spain
| | - Ana Cotos Suárez
- Department of Microbiology, Hospital Universitario de Salamanca, 37007 Salamanca, Spain; (L.S.D.); (A.C.S.)
- Research Group IIMD-16, Institute for Biomedical Research of Salamanca (IBSAL), SACYL, Universidad de Salamanca, CSIC, 37007 Salamanca, Spain
| | - María Sánchez Ledesma
- Infectious Diseases Unit, Hospital Universitario de Salamanca, 37007 Salamanca, Spain;
| | - Juan Luis Muñoz Bellido
- Department of Microbiology, Hospital Universitario de Salamanca, 37007 Salamanca, Spain; (L.S.D.); (A.C.S.)
- Research Group IIMD-16, Institute for Biomedical Research of Salamanca (IBSAL), SACYL, Universidad de Salamanca, CSIC, 37007 Salamanca, Spain
- Department of Biomedical and Diagnosis Sciences, Faculty of Medicine, Universidad de Salamanca, 37007 Salamanca, Spain
- Center for Research on Tropical Diseases, Universidad de Salamanca (CIETUS), 37007 Salamanca, Spain
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Liu Q, Cao M, Shao N, Qin Y, Liu L, Zhang Q, Yang X. Development and validation of a new model for the early diagnosis of tuberculous meningitis in adults based on simple clinical and laboratory parameters. BMC Infect Dis 2023; 23:901. [PMID: 38129813 PMCID: PMC10740218 DOI: 10.1186/s12879-023-08922-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 12/15/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND The differential diagnosis between tuberculous meningitis (TBM) and viral meningitis (VM) or bacterial meningitis (BM) remains challenging in clinical practice, particularly in resource-limited settings. This study aimed to establish a diagnostic model that can accurately and early distinguish TBM from both VM and BM in adults based on simple clinical and laboratory parameters. METHODS Patients diagnosed with TBM or non-TBM (VM or BM) between January 2012 and October 2021 were retrospectively enrolled from the General Hospital (derivation cohort) and Branch Hospital (validation cohort) of Ningxia Medical University. Demographic characteristics, clinical symptoms, concomitant diseases, and cerebrospinal fluid (CSF) parameters were collated. Univariable logistic analysis was performed in the derivation cohort to identify significant variables (P < 0.05). A multivariable logistic regression model was constructed using these variables. We verified the performance including discrimination, calibration, and applicability of the model in both derivation and validation cohorts. RESULTS A total of 222 patients (70 TBM and 152 non-TBM [75 BM and 77 VM]) and 100 patients (32 TBM and 68 non-TBM [31 BM and 37 VM]) were enrolled as derivation and validation cohorts, respectively. The multivariable logistic regression model showed that disturbance of consciousness for > 5 days, weight loss > 5% of the original weight within 6 months, CSF lymphocyte ratio > 50%, CSF glucose concentration < 2.2 mmol/L, and secondary cerebral infarction were independently correlated with the diagnosis of TBM (P < 0.05). The nomogram model showed excellent discrimination (area under the curve 0.959 vs. 0.962) and great calibration (P-value in the Hosmer-Lemeshow test 0.128 vs. 0.863) in both derivation and validation cohorts. Clinical decision curve analysis showed that the model had good applicability in clinical practice and may benefit the entire population. CONCLUSIONS This multivariable diagnostic model may help clinicians in the early discrimination of TBM from VM and BM in adults based on simple clinical and laboratory parameters.
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Affiliation(s)
- Qiang Liu
- Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory, Yinchuan, 750004, Ningxia Province, China
- Graduate College of Ningxia Medical University, Yinchuan, 750004, Ningxia Province, China
| | - Meiling Cao
- Department of Internal Medicine, The Inner Mongolia Autonomous Region, The People's Hospital of Wushen Banner, Erdos, 017000, China
| | - Na Shao
- Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory, Yinchuan, 750004, Ningxia Province, China
| | - Yixin Qin
- Department of Neurology, The First People's Hospital of Yinchuan, Yinchuan, 750004, Ningxia Province, China
| | - Lu Liu
- Graduate College of Ningxia Medical University, Yinchuan, 750004, Ningxia Province, China
| | - Qing Zhang
- Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory, Yinchuan, 750004, Ningxia Province, China.
| | - Xiao Yang
- Department of Neurology, General Hospital of Ningxia Medical University, Ningxia Key Laboratory of Cerebrocranial Diseases, Incubation Base of National Key Laboratory, Yinchuan, 750004, Ningxia Province, China.
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Luo Y, Xue Y, Lin Q, Mao L, Tang G, Song H, Liu W, Wu S, Liu W, Zhou Y, Xu L, Xiong Z, Wang T, Yuan X, Gan Y, Sun Z, Wang F. Diagnostic Model for Discrimination Between Tuberculous Meningitis and Bacterial Meningitis. Front Immunol 2021; 12:731876. [PMID: 34867952 PMCID: PMC8632769 DOI: 10.3389/fimmu.2021.731876] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Accepted: 10/07/2021] [Indexed: 11/15/2022] Open
Abstract
Background The differential diagnosis between tuberculous meningitis (TBM) and bacterial meningitis (BM) remains challenging in clinical practice. This study aimed to establish a diagnostic model that could accurately distinguish TBM from BM. Methods Patients with TBM or BM were recruited between January 2017 and January 2021 at Tongji Hospital (Qiaokou cohort) and Sino-French New City Hospital (Caidian cohort). The detection for indicators involved in cerebrospinal fluid (CSF) and T-SPOT assay were performed simultaneously. Multivariate logistic regression was used to create a diagnostic model. Results A total of 174 patients (76 TBM and 98 BM) and another 105 cases (39 TBM and 66 BM) were enrolled from Qiaokou cohort and Caidian cohort, respectively. Significantly higher level of CSF lymphocyte proportion while significantly lower levels of CSF chlorine, nucleated cell count, and neutrophil proportion were observed in TBM group when comparing with those in BM group. However, receiver operating characteristic (ROC) curve analysis showed that the areas under the ROC curve (AUCs) produced by these indicators were all under 0.8. Meanwhile, tuberculosis-specific antigen/phytohemagglutinin (TBAg/PHA) ratio yielded an AUC of 0.889 (95% CI, 0.840–0.938) in distinguishing TBM from BM, with a sensitivity of 68.42% (95% CI, 57.30%–77.77%) and a specificity of 92.86% (95% CI, 85.98%–96.50%) when a cutoff value of 0.163 was used. Consequently, we successfully established a diagnostic model based on the combination of TBAg/PHA ratio, CSF chlorine, CSF nucleated cell count, and CSF lymphocyte proportion for discrimination between TBM and BM. The established model showed good performance in differentiating TBM from BM (AUC: 0.949; 95% CI, 0.921–0.978), with 81.58% (95% CI, 71.42%–88.70%) sensitivity and 91.84% (95% CI, 84.71%–95.81%) specificity. The performance of the diagnostic model obtained in Qiaokou cohort was further validated in Caidian cohort. The diagnostic model in Caidian cohort produced an AUC of 0.923 (95% CI, 0.867–0.980) with 79.49% (95% CI, 64.47%–89.22%) sensitivity and 90.91% (95% CI, 81.55%–95.77%) specificity. Conclusions The diagnostic model established based on the combination of four indicators had excellent utility in the discrimination between TBM and BM.
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Affiliation(s)
- Ying Luo
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ying Xue
- Department of Immunology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qun Lin
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.,Laboratory Medicine Center, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China
| | - Liyan Mao
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Guoxing Tang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Huijuan Song
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wei Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shiji Wu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Weiyong Liu
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Zhou
- Department of Laboratory Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Lingqing Xu
- Qingyuan People's Hospital, The Sixth Affiliated Hospital of Guangzhou Medical University, Qingyuan, China
| | - Zhigang Xiong
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ting Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Xu Yuan
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yong Gan
- Department of Social Medicine and Health Management, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziyong Sun
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Wang
- Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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