Tool For Diagnosis Research Articles

Low specificity of Aspergillus spp. ELITe MGB assay, and potential risks in management of invasive aspergillosis

ObjectivesIn recent years, commercial molecular tools for diagnosis of invasive aspergillosis have emerged, requiring evaluation to ensure quality. Here we assessed the specificity of Aspergillus spp.-ELITe MGB Assay a commercial assay tergeting 18S gene of Aspergillus spp. Design and methodsAs part of a method validation, we evaluate the specificity of the Aspergillus spp.-ELITe MGB Assay by testing fourteen culture based samples of sequenced non-Aspergillus fungal species. The benefits of a pre-lysis treatment was evaluated in parallel on serial dilutions of an Aspergillus fumigatus strain. ResultsOur findings revealed cross-reactivity in five strains using the 50 copies/mL cut-off recommended by the manufacturer, suggesting potential diagnostic errors and inappropriate management of patients. Pre-lysis treatment does not affect the limit of detection at serial dilution. ConclusionsIn conclusion, the Aspergillus spp. ELITe MGB Assay exhibits limited specificity in culture-based samples, underscoring the importance of careful utilization in laboratories. Further studies are warranted to better comprehend of the impact of this cross-reactivity on clinical samples.

Polarization sensitive optical side leakage radiometry for distributed characterization of anti-resonant hollow-core fibers.

A novel technique referred to as optical side leakage radiometry is proposed and experimentally demonstrated for non-destructive and distributed characterization of anti-resonant hollow-core optical fibers with high spatial resolution. Through in-depth analysis of the leakage light collection, we discover a unique polarization dependence, which is validated by our experiment. By leveraging this effect and employing Fourier filtering, this method enables accurate quantification of propagation attenuations for fundamental and higher order modes (with the uncertainty of <1 dB/km), identification of localized defects (with the resolution of ∼5 cm), and measurement of ultra-low spectral phase birefringence (at the level of 10-7) in two in-house-fabricated nested antiresonant nodeless hollow-core fibers. Such a fiber characterization approach, boasting unprecedently high accuracy and a potentially wide dynamic range, holds the potential to become an indispensable diagnosis tool for monitoring and assisting the manufacture of high-quality anti-resonant hollow-core fiber.

Improved Mel Frequency Cepstral Coefficients for Compressors and Pumps Fault Diagnosis with Deep Learning Models

Compressors and pumps are machines frequently used in petroleum and chemical industries for fluid transportation through flow systems to keep industrial processes running permanently. As their failure can produce costly disruption, developing fault detection and diagnosis tools is essential for accurately detecting and diagnosing faults. This research proposes a bi-dimensional representation of the vibration signal corresponding to the Mel Frequency Cepstral Coefficients (MFCC) and their first two derivatives as features. The pseudo-periodic nature of the fault signature in rotating machines is exploited to put forward an efficient and accurate patch-wise fault classification method. This approach enables the classification of 13 combined types of faults in a multi-stage centrifugal pump and 17 faults in a reciprocating compressor. Classification is performed using the Long Short-Term Memory (LSTM) network, the bidirectional Long Short-Term Memory (BiLSTM) neural network, and the Convolutional Neural Network (CNN). Accurate classification over 99% is attained, showing that the proposed feature extraction procedure correctly classifies a large set of faults simultaneously appearing in such rotating machines.

Analyzing the impact of data visualization applications for diagnosing the health conditions through hesitant fuzzy-based hybrid medical expert system

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia.

Computed tomographic assessment of bronchoesophageal artery hypertrophy and systemic‐to‐pulmonary shunt mimicking a persistent ductus arteriosus in a French Bulldog

AbstractA 5‐year‐old, female French bulldog was evaluated for left‐sided congestive heart failure. A diagnosis of bronchoesophageal artery hypertrophy with systemic‐to‐pulmonary shunt was made. Even though Doppler echocardiographic findings were in favour of a left‐to‐right shunting patent ductus arteriosus, the exploratory thoracotomy highlighted a more complex vascular anomaly. An angiographic computed tomography study allowed to establish the final diagnosis. Bronchial‐to‐pulmonary artery shunts can mimic the presence of a patent ductus arteriosus, therefore computed tomography is considered a valuable additional tool for diagnosis and surgical planning.

The first case report: diagnosis and management of necrotizing fusobacterium lung abscess via BALF next-generation sequencing

BackgroundFusobacterium necrophorum (F. necrophorum)-induced necrotizing pneumonia is a rare but severe pulmonary infection. Insufficient microbiological detection methods can lead to diagnostic difficulties.MethodsWe report a case of F. necrophorum lung abscess diagnosed by next-generation sequencing (NGS) of bronchoalveolar lavage fluid (BALF).ResultsBALF-NGS detected F. necrophorum, guiding subsequent targeted antibiotic therapy. With active drainage and metronidazole treatment, the patient’s condition was effectively treated.ConclusionBALF-NGS is a valuable tool for the rapid diagnosis of infections caused by difficult-to-culture bacteria. It played a decisive role in the early identification of F. necrophorum, enabling timely and targeted antibiotic intervention. Early diagnosis and appropriate treatment are crucial for the management of F. necrophorum pneumonia.

Advanced detection of cervical cancer biomarkers using engineered filamentous phage nanofibers.

Cervical cancer is a major global health concern, characterized by its high incidence and mortality rates. The detection of tumor markers is crucial for managing cancer, making treatment decisions, and monitoring disease progression. Vascular endothelial growth factor (VEGF) and programmed death-ligand 1 (PDL-1) are key targets in cervical cancer therapy and valuable biomarkers in predicting treatment response and prognosis. In this study, we found that combining the measurement of VEGF and soluble PDL-1 can be used for diagnosing and evaluating the progression of cervical cancer. To explore a more convenient approach for detecting and assessing cervical cancer, we designed and prepared an engineered fd bacteriophage, a human-safe viral nanofiber, equipped with two peptides targeting VEGF and PD-L1. The dual-display phage nanofiber specifically recognizes and binds to both proteins. Utilizing this nanofiber as a novel capture agent, we developed a new enzyme-linked immunosorbent assay (ELISA) method. This method shows significantly enhanced detection sensitivity compared to conventional ELISA methods, which use either anti-VEGF or anti-PD-L1 antibodies as capture agents. Therefore, the phage dual-display nanofiber presents significant potential in detecting cancer markers, evaluating medication efficacy, and advancing immunotherapy drug development. KEY POINTS: • The combined measurement of VEGF and soluble Programmed Death-Ligand 1(sPD-L1) demonstrates an additive effect in the diagnosis of cervical cancer. Fd phage nanofibers have been ingeniously engineered to display peptides that bind to VEGF and PD-L1, enabling the simultaneous detection of both proteins within a single assay • Genetically engineered phage nanofibers, adorned with two distinct peptides, can be utilized for the diagnosis and prognosis of cancer and can be mass-produced cost-effectively through bacterial infections • Employing dual-display fd phage nanofibers as capture probes, the phage ELISA method exhibited significantly enhanced detection sensitivity compared to traditional sandwich ELISA. Furthermore, phage ELISA facilitates the detection of a single protein or the simultaneous detection of multiple proteins, rendering them powerful tools for protein analysis and diagnosis across various fields, including cancer research.

A highly sensitive fluorescent probe RN-NA reveals peroxynitrite as a novel biomarker for primary open angle glaucoma.

To directly quantify peroxynitrite (ONOO-) using a highly sensitive fluorescence resonance energy transfer probe RN-NA, investigate the association between ONOO- and primary open angle glaucoma (POAG), and clarify whether RN-NA could be used as a potential tool for POAG diagnosis. Plasma and aqueous humor (AH) samples were collected from POAG patients (n=100, age: 59.70±6.87y) and age-related cataract (ARC) patients (n=100, age: 61.15±4.60y) admitted to our hospital. Next, RN-NA was used to detect ONOO- in plasma and AH samples, and the relationship between ONOO- level and POAG was analyzed using binary logistic regression. Besides, Pearson correlation analysis was applied to characterize the correlation of the levels of ONOO- with the patients' age, intraocular pressure (IOP), and mean deviation of visual field testing. The ONOO- scavenger MnTMPyP was employed to treat the 3-morpholinosyndnomine (SIN-1)-induced ocular hypertension in mice (n=7, 6-8wk). Finally, the IOP and ONOO- in both eyes were measured 30min after the last drug treatment. ONOO- levels of AH and plasma were significantly higher in the POAG group than in the ARC group (P<0.01). Additionally, ONOO- levels were closely correlated with POAG in a binary logistic regression analysis [odds ratio (OR)=1.008, 95% confidence interval (CI): 1.002-1.013, P<0.01 for AH; OR=1.004, 95%CI: 1.002-1.006, P<0.001 for plasma]. Pearson correlation analysis showed that ONOO- levels in AH or plasma were positively associated with visual field defects (R=0.51, P<0.01 for AH; R=0.45, P<0.001 for plasma), and ONOO- levels in plasma and AH were correlated in the POAG group (R=0.69, P<0.001). However, administering MnTMPyP to mouse eyes reversed the elevated IOP caused by SIN-1 (P<0.05). ONOO- levels in AH and plasma, detected by RN-NA, are significantly related to POAG and positively correlated with visual field defects in POAG patients. Hence, ONOO- is a potential biomarker of POAG, especially advanced POAG. Besides, anti-nitration compounds may be novel ocular hypotensive agents based on the animal study.

Utilizing metagenomic next-generation sequencing for pathogen detection and diagnosis in lower respiratory tract infections in real-world clinical practice.

Infectious etiologies of lower respiratory tract infections (LRTIs) by the conventional microbiology tests (CMTs) can be challenging. Metagenomic next-generation sequencing (mNGS) has great potential in clinical use for its comprehensiveness in identifying pathogens, particularly those difficult-to-culture organisms. We analyzed a total of 205 clinical samples from 201 patients with suspected LRTIs using mNGS in parallel with CMTs. mNGS results were used to guide treatment adjustments for patients who had negative CMT results. The efficacy of treatment was subsequently evaluated in these patients. mNGS-detected microorganisms in 91.7% (188/205) of the clinical samples, whereas CMTs demonstrated a lower detection rate, identifying microorganisms in only 37.6% (77/205) of samples. Compared to CMT results, mNGS exhibited a detection sensitivity of 93.5% and 95.4% in all 205 clinical samples and 180 bronchoalveolar lavage fluid (BALF) samples, respectively. A total of 114 patients (114/201; 56.7%) showed negative CMT results, among which 92 received treatment adjustments guided by their positive mNGS results. Notably, 67.4% (62/92) of patients demonstrated effective treatment, while 25% (23/92) experienced a stabilized condition. Subgroup analysis of cancer patients revealed that 41.9% (13/31) exhibited an effective response to treatment, and 35.5% (11/31) maintained a stable condition following medication adjustments guided by mNGS. mNGS demonstrated great potential in identifying microorganisms of clinical significance in LRTIs. The rapid turnaround time and reduced susceptibility to the impact of antimicrobial administration make mNGS a valuable supplementary tool for diagnosis and treatment decision-making for suspected LRTIs in clinical practice.

An assessment system for clinical and biological interpretability in ulcerative colitis.

Ulcerative colitis (UC) is a serious inflammatory bowel disease (IBD) with high morbidity and mortality worldwide. As the traditional diagnostic techniques have various limitations in the practice and diagnosis of early ulcerative colitis, it is necessary to develop new diagnostic models from molecular biology to supplement the existing methods. In this study, we developed a machine learning-based synthesis to construct an artificial intelligence diagnostic model for ulcerative colitis, and the correctness of the model is verified using an external independent dataset. According to the significantly expressed genes related to the occurrence of UC in the model, an unsupervised quantitative ulcerative colitis related score (UCRScore) based on principal coordinate analysis was established. The UCRScore is not only highly generalizable across UC bulk cohorts at different stages, but also highly generalizable across single-cell datasets, with the same effect in terms of cell numbers, activation pathways and mechanisms. As an important role of screening genes in disease occurrence, based on connectivity map analysis, 5 potential targeting molecular compounds were identified, which can be used as an additional supplement to the therapeutic of UC. Overall, this study provides a potential tool for differential diagnosis and assessment of bio-pathological changes in UC at the macroscopic level, providing an opportunity to optimize the diagnosis and treatment of UC.

Chiral active particles are sensitive reporters to environmental geometry

Chiral active particles (CAPs) are self-propelling particles that break time-reversal symmetry by orbiting or spinning, leading to intriguing behaviors. Here, we examined the dynamics of CAPs moving in 2D lattices of disk obstacles through active Brownian dynamics simulations and granular experiments with grass seeds. We find that the effective diffusivity of the CAPs is sensitive to the structure of the obstacle lattice, a feature absent in achiral active particles. We further studied the transport of CAPs in obstacle arrays under an external field and found a reentrant directional locking effect, which can be used to sort CAPs with different activities. Finally, we demonstrated that parallelogram lattices of obstacles without mirror symmetry can separate clockwise and counter-clockwise CAPs. The mechanisms of the above three novel phenomena are qualitatively explained. As such, our work provides a basis for designing chirality-based tools for single-cell diagnosis and separation, and active particle-based environmental sensors.

Dielectric Characterization of Ex-Vivo Breast Tissues: Differentiation of Tumor Types through Permittivity Measurements.

Early analysis and diagnosis of breast tumors is essential for either quickly launching a treatment or for seeing the evolution of patients who, for instance, have already undergone chemotherapy treatment. Once tissues are excised, histological analysis is the most frequent tool used to characterize benign or malignant tumors. Dielectric microwave spectroscopy makes use of an open-ended coaxial probe in the 1-8 GHz frequency range to quickly identify the type of tumor (ductal carcinoma, lobular carcinoma, mucinous carcinoma and fibroadenoma). The experiment was undertaken with data from 70 patients who had already undergone chemotherapy treatment, which helped to electrically map the histological tissues with their electric permittivity. Thus, the variations in the permittivity of different types of tumors reveal distinctive patterns: benign tumors have permittivity values lower than 35, while malignant ones range between 40 and 60. For example, at a frequency of 2 GHz, the measured permittivity was 45.6 for ductal carcinoma, 33.1 for lobular carcinoma, 59.5 for mucinous carcinoma, and 27.6 for benign tumors. This differentiation remains consistent in a frequency range of 1 to 4.5 GHz. These results highlight the effectiveness of these measurements in the classification of breast tumors, providing a valuable tool for quick and accurate diagnosis and effective treatment.

Value of combined serum CEA, CA72-4, and CA19-9 marker detection in diagnosis of colorectal cancer.

The aim of this study was to examine whether the combination of serum tumor markers (carcinoembryonic antigen [CEA], carbohydrate antigen [CA]72-4, CA19-9) improves sensitivity and accuracy in the diagnosis of colorectal cancer and precancerous lesion tubular adenoma. An automatic electrochemiluminescence immunoassay with matched kits (ECLIA) was performed on a Roche Cobas e411 analyzer to determine the levels of serum CEA, CA72-4, and CA19-9 in 35 patients with early colorectal cancer, 87 patients with tubular adenoma, and 58healthy people undergoing colonoscopy after positive fecal immunochemical test (FIT) in a colorectal cancer screening program 2021 January to April. The values of these three tumor markers in the diagnosis of colorectal cancer and tubular adenoma were analyzed. 180 patients (92 female and 88 male) were included into the study. We compared serum CEA, CA72-4 and CA19-9 markers among 3 groups: healthy people (mean age 64,0 ±8,6), patients with tubular adenoma (mean age 62,7 ± 6,4) and colorectal cancer (mean age 59,2 ±6,2). The levels of serum CEA, CA72-4, and CA19-9 were higher in the colorectal cancer group than in the tubular adenoma group and healthy subjects, and these differences were significant (p < 0.05). The combination of CEA, CA72-4, and CA19-9had a higher diagnostic value for colorectal cancer compared to single markers, and the positive detection rate was 54.3%. The diagnostic power when using all three markers was the best, and applied for colorectal cancer and tubular adenoma. The combination of CA72-4, CEA, and CA19-9 markers increases the sensitivity and accuracy in the diagnosis of colorectal cancer and can thus be considered an important tool for early colorectal diagnosis.

Artificial intelligence-enhanced electrocardiography : Will it revolutionize diagnosis and management of our patients?

The use of artificial intelligence (AI) in healthcare has made significant progress in the last 10years. Many experts believe that utilization of AI technologies, especially deep learning, will bring about drastic changes in how physicians understand, diagnose, and treat diseases. One aspect of this development is AI-enhanced electrocardiography (ECG) analysis. It involves not only optimizing the traditional ECG analysis by the physician and improving the accuracy of automatic interpretation by the ECG device but also introducing entirely new diagnostic options enabled by AI. Examples include assessing left ventricular function, predicting atrial fibrillation, and diagnosing both cardiac and noncardiac conditions. Through AI, the ECG becomes acomprehensive tool for screening, diagnosis, and patient management, potentially revolutionizing clinical practices. This paper provides an overview of the current state of this development, discusses existing limitations, and explores the challenges that may arise for healthcare professionals in this context.

A Lung Cancer Detection with Pre-Trained CNN Models

Lung cancer is a common cancer in Malaysia, affecting the majority of male citizens. The early detection of lung cancer will decrease its death rate. The only way to detect lung cancer is with a CT scan, and it also requires the doctor to check the scan to confirm the disease. In another way, the computer's support for the detection and diagnosis tool will assist doctors in determining lung cancer more accurately and efficiently. There are three main objectives for this research work. The first target is to study state-of-the-art research work to detect and recognize lung cancer from CT scan images. Then, the article will aim to adopt pre-trained convolutional neural network models in lung cancer detection. It also evaluates the performance of convolutional models on lung cancer imagery data. Then, the pre-trained models with a few added layers and modifications to parameters such as epochs, batch size, optimizer, etc. to conduct model training in this article. After that, Python Pylidc is used in image pre-processing to filter the dataset. Overall, pre-trained models such as ResNet-50, VGG-16, Xception, and MobileNet achieve above-state-of-the-art performance in classifying lung cancer from CT scan images in the range of 78% to 86% accuracy. The best detection accuracy result is the pre-trained VGG-16 model with the addition of some fully connected layers, 16 batch sizes, and the Adam optimizer, which achieved 86.71%.

Ischemic Stroke Classification Using VGG-16 Convolutional Neural Networks: A Study on Moroccan MRI Scans

This study presents a comprehensive exploration of deep learning models for precise brain ischemic stroke classification using medical data from Morocco. Following the OSEMN approach, our methodology leverages transfer learning with the VGG-16 architecture and employs data augmentation techniques to enhance model performance. Our developed model achieved a remarkable validation accuracy of 90%, surpassing alternative state-of-theart models (ResNet50: 87.0%, InceptionV3: 82.0%, VGG-19: 81.0%). Notably, all models were rigorously evaluated on the same meticulously curated dataset, ensuring fair and consistent comparisons. The investigation underscores VGG-16’s superior performance in distinguishing stroke cases, highlighting its potential as a robust tool for accurate diagnosis. Comparative analyses among popular deep learning architectures not only demonstrate our model’s efficacy but also emphasize the importance of selecting the right architecture for medical image classification tasks. These findings contribute to the growing evidence supporting advanced deep learning techniques in medical imaging. Achieving a validation accuracy of 90%, our model holds significant promise for real-world healthcare applications, showcasing the critical role of cutting-edge technologies in advancing diagnostic accuracy and the transformative potential of deep learning in the medical field.

Advanced Machine Learning for Brain Tumor Detection: Leveraging Region of Interest Analysis in MRI Scans

This study introduces an advanced brain tumor detection method leveraging Region of Interest (ROI) analysis in MRI images to enhance diagnostic accuracy and efficiency. By applying sophisticated image processing and machine learning algorithms, our approach significantly improves the identification of tumor presence with reduced computational demands. The results show marked advancements over traditional methods, offering a promising tool for early and precise brain tumor diagnosis

ChatGPT's innovative application in blood morphology recognition.

Recently, the rapid advancement in generative artificial intelligence (AI) technology, such as ChatGPT-4, has sparked discussions, particularly in image recognition. Accurate results are critical for hematological diagnosis, particularly for blood morphology identification. Despite advanced hematology analyzers, reliance on professional hematopathologists for manual identification remains in cases of abnormal or rare conditions, a process prone to human subjectivity and potential errors. Consequently, this study aimed to investigate the potential of ChatGPT-4 to assist with blood morphology identification. We conducted a retrospective study using blood images obtained from the American Society of Hematology (ASH). These images comprised a range of normal and abnormal morphologies. Each sample was analyzed by expert technicians (control group) and classified using ChatGPT-4 (test group). Preliminary results showed that ChatGPT-4 could identify normal blood cells with an accuracy of 88%, exceeding the accuracy of identifying abnormal blood cells at a rate of 54%. Regarding identifying abnormal cells, the accuracy of ChatGPT-4 was slightly higher than that of the manual method, which was 49.5%. This study shows that although generative AI shows the potential for blood type identification, it has not yet reached the point where it can replace the professional judgment of medical staff. The results showed that ChatGPT-4 is excellent for identifying red blood cell morphology, particularly inclusion bodies. It can be used as an auxiliary tool for clinical diagnosis. However, the overall recognition accuracy must be further improved. Our study produced innovative results in this field, establishing a foundation for future studies and highlighting the potential of generative AI in aiding blood morphology recognition. Future research should focus on enhancing the effectiveness of AI to improve overall standards of medical care.

Analytical and clinical validation of a multiplex PCR assay for detection of Neisseria gonorrhoeae and Chlamydia trachomatis including simultaneous LGV serotyping on an automated high-throughput PCR system.

For effective infection control measures for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG), a reliable tool for screening and diagnosis is essential. Here, we aimed to establish and validate a multiplex PCR assay on an automated system using a dual-target approach for the detection of CT/NG and differentiation between lymphogranuloma venereum (LGV) and non-LGV from genital and extra-genital specimens. Published primer/probe sets (CT: pmpH, cryptic plasmid; NG: porA, opa) were modified for the cobas 5800/6800/8800. Standards quantified by digital PCR were used to determine linearity and lower limit of detection (LLoD; eSwab, urine). For clinical validation, prospective samples (n = 319) were compared with a CE-marked in vitro diagnostics (CE-IVD) assay. LLoDs ranged from 21.8 to 244 digital copies (dcp)/mL and 10.8 to 277 dcp/mL in swab and urine, respectively. A simple linear regression analysis yielded slopes ranging from -4.338 to -2.834 and Pearson correlation coefficients from 0.956 to 0.994. Inter- and intra-run variability was <0.5 and <1 cycle threshold (ct), respectively. No cross-reactivity was observed (n = 42). Clinical validation showed a sensitivity of 94.74% (95% confidence interval (CI): 87.23%-97.93%) and 95.51% (95% CI: 89.01%-98.24%), a specificity of 99.59% (95% CI: 97.71%-99.98%) and 99.57% (95% CI: 97.58%-99.98%), positive predictive values of 89.91% (estimated prevalence: 3.7%; 95% CI: 80.91%-95.6%) and 88.61% (estimated prevalence: 3.4%; 95% CI: 80.18%-94.34%), and negative predictive values of 99.81% (95% CI: 98.14%-100%) and 99.85% (95% CI: 98.14%-100%) for the detection of CT and NG, respectively. In conclusion, we established a dual-target, internally controlled PCR on an automated system for the detectiwon of CT/NG from genital and extra-genital specimens. Depending on local regulations, the assay can be used as a screening or a confirmatory/typing assay.IMPORTANCEChlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) represent a major global health burden, with the World Health Organization estimating that >128 million and >82 million people, respectively, were newly infected in 2020. For effective infection control measures, a reliable tool for sensitive diagnosis and screening of CT/NG is essential. We established a multiplex PCR assay for the detection of CT/NG and simultaneous discrimination between lymphogranuloma venereum (LGV) and non-LGV strains, which has been validated for genital and extra-genital specimens on a fully automated system. To increase assay sensitivity, a dual-target approach has been chosen for both pathogens. This strategy reduces false-positive results in oropharyngeal swabs due to the detection of commensal N. species that may harbor NG DNA fragments targeted in the PCR due to horizontal gene transmission following previous infection. In sum, the established assay provides a powerful tool for use as either a screening/diagnostic or a typing/confirmatory assay.

Development and application of targeted multiplex polymerase chain reaction to help diagnose infectious uveitis

Abstract&#x0D; Introduction &amp; Objectives : Clinical pattern recognition is paramount in uveitis diagnosis. Pathogen detection from ocular fluid samples is often necessary to support infectious uveitis diagnosis, particularly in cases presenting with atypical clinical appearance. This study aims to present the process of developing targeted multiplex PCR and its application in infectious uveitis.&#x0D; Methods : This was a cross sectional study to evaluate the diagnostic performance of targeted multiplex PCR in infectious uveitis. We obtained ocular fluid samples and reviewed medical records of uveitis patients who underwent ocular fluid analysis at Cipto Mangunkusumo Hospital from February 2022 to March 2023. PCR detection threshold values (DNA copies/mL) were 10.9 for Mycobacterium tuberculosis (Mtb), 672 for Epstein-Barr virus, 4.77 for Cytomegalovirus, 6.37 for Toxoplasma gondii, and 5.53 for Herpes simplex virus. With every two-fold increase of pathogen selection, this method requires a half volume of extracted DNA template from aqueous/vitreous samples than uniplex PCR. The ophthalmologist selected the pathogen combination to be detected, allowing for a tailored examination.&#x0D; Results : Forty-seven aqueous or vitreous samples were analyzed. The positivity rate was 23.4% (11/47) with Mtb yielded the highest positivity (7/34; 20.6%). With final diagnosis as a reference, targeted multiplex PCR resulted in 32.3% sensitivity, 93.8% specificity, 90.9% positive predictive value and 19.2% negative predictive value.&#x0D; Conclusion : With its high specificity, targeted multiplex PCR is useful as a confirmatory but not screening tool in uveitis diagnosis. Ocular fluid analysis is an important part of stepwise diagnostic approach in uveitis.