Universal digital high-resolution melting for the detection of pulmonary mold infections.

Invasive mold infections (IMIs) are associated with high morbidity, particularly in immunocompromised patients, with mortality rates between 40% and 80%. Early initiation of appropriate antifungal therapy can substantially improve outcomes, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high-resolution melting (U-dHRM) analysis may enable rapid and robust diagnoses of IMI. A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these pathogen curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. U-dHRM achieved 97% overall fungal organism identification accuracy and a turnaround time of ~4 hrs. U-dHRM detected pathogenic molds (Aspergillus, Mucorales, Lomentospora, and Fusarium) in 73% of 30 samples classified as IMI, including mixed infections. Specificity was optimized by requiring the number of pathogenic mold curves detected in a sample to be >8 and a sample volume to be 1 mL, which resulted in 100% specificity in 21 at-risk patients without IMI. U-dHRM showed promise as a separate or combination diagnostic approach to standard mycological tests. U-dHRM's speed, ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples, and detect emerging opportunistic pathogens may aid treatment decisions, improving patient outcomes. IMPORTANCE: Improvements in diagnostics for invasive mold infections are urgently needed. This work presents a new molecular detection approach that addresses technical and workflow challenges to provide fast pathogen detection, identification, and quantification that could inform treatment to improve patient outcomes.

Decoding human cytomegalovirus for the development of innovative diagnostics to detect congenital infection.

Cytomegalovirus is the most common cause of congenital infectious disease and the leading nongenetic etiology of sensorineural hearing loss. Although most infected neonates are asymptomatic at birth, congenital cytomegalovirus infection is responsible for nearly 400 infant deaths annually in the United States and may lead to significant long-term neurodevelopmental impairments in survivors. The resulting financial and social burdens of congenital cytomegalovirus infection have led many medical centers to initiate targeted testing after birth, with a growing advocacy to advance universal newborn screening. While no cures or vaccines are currently available to eliminate or prevent cytomegalovirus infection, much has been learned over the last five years regarding disease pathophysiology and viral replication cycles that may enable the development of innovative diagnostics and therapeutics. This Review will detail our current understanding of congenital cytomegalovirus infection, while focusing our discussion on routine and emerging diagnostics for viral detection, quantification, and long-term prognostication. IMPACT: This review highlights our current understanding of the fetal transmission of human cytomegalovirus. It details clinical signs and physical findings of congenital cytomegalovirus infection. This submission discusses currently available cytomegalovirus diagnostics and introduces emerging platforms that promise improved sensitivity, specificity, limit of detection, viral quantification, detection of genomic antiviral resistance, and infection staging (primary, latency, reactivation, reinfection).

Universal Digital High-Resolution Melt Analysis for the Diagnosis of Bacteremia.

Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires approximately 15 hours to detect the presence of a pathogen. We, therefore, assessed the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 17 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 88% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1-mL sample input and sample-to-answer time of 6 hours. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection.

An update on current and novel molecular diagnostics for the diagnosis of invasive fungal infections.

BACKGROUND: Invasive fungal infections cause millions of infections annually, but diagnosis remains challenging. There is an increased need for low-cost, easy to use, highly sensitive and specific molecular assays that can differentiate between colonized and pathogenic organisms from different clinical specimens. AREAS COVERED: We reviewed the literature evaluating the current state of molecular diagnostics for invasive fungal infections, focusing on current and novel molecular tests such as polymerase chain reaction (PCR), digital PCR, high-resolution melt (HRM), and metagenomics/next generation sequencing (mNGS). EXPERT OPINION: PCR is highly sensitive and specific, although performance can be impacted by prior/concurrent antifungal use. PCR assays can identify mutations associated with antifungal resistance, non-Aspergillus mold infections, and infections from endemic fungi. HRM is a rapid and highly sensitive diagnostic modality that can identify a wide range of fungal pathogens, including down to the species level, but multiplex assays are limited and HRM is currently unavailable in most healthcare settings, although universal HRM is working to overcome this limitation. mNGS offers a promising approach for rapid and hypothesis-free diagnosis of a wide range of fungal pathogens, although some drawbacks include limited access, variable performance across platforms, the expertise and costs associated with this method, and long turnaround times in real-world settings.

Universal Digital High Resolution Melt for the detection of pulmonary mold infections.

Background: Invasive mold infections (IMIs) such as aspergillosis, mucormycosis, fusariosis, and lomentosporiosis are associated with high morbidity and mortality, particularly in immunocompromised patients, with mortality rates as high as 40% to 80%. Outcomes could be substantially improved with early initiation of appropriate antifungal therapy, yet early diagnosis remains difficult to establish and often requires multidisciplinary teams evaluating clinical and radiological findings plus supportive mycological findings. Universal digital high resolution melting analysis (U-dHRM) may enable rapid and robust diagnosis of IMI. This technology aims to accomplish timely pathogen detection at the single genome level by conducting broad-based amplification of microbial barcoding genes in a digital polymerase chain reaction (dPCR) format, followed by high-resolution melting of the DNA amplicons in each digital reaction to generate organism-specific melt curve signatures that are identified by machine learning. Methods: A universal fungal assay was developed for U-dHRM and used to generate a database of melt curve signatures for 19 clinically relevant fungal pathogens. A machine learning algorithm (ML) was trained to automatically classify these 19 fungal melt curves and detect novel melt curves. Performance was assessed on 73 clinical bronchoalveolar lavage (BAL) samples from patients suspected of IMI. Novel curves were identified by micropipetting U-dHRM reactions and Sanger sequencing amplicons. Results: U-dHRM achieved an average of 97% fungal organism identification accuracy and a turn-around-time of 4hrs. Pathogenic molds (Aspergillus, Mucorales, Lomentospora and Fusarium) were detected by U-dHRM in 73% of BALF samples suspected of IMI. Mixtures of pathogenic molds were detected in 19%. U-dHRM demonstrated good sensitivity for IMI, as defined by current diagnostic criteria, when clinical findings were also considered. Conclusions: U-dHRM showed promising performance as a separate or combination diagnostic approach to standard mycological tests. The speed of U-dHRM and its ability to simultaneously identify and quantify clinically relevant mold pathogens in polymicrobial samples as well as detect emerging opportunistic pathogens may provide information that could aid in treatment decisions and improve patient outcomes.

Universal digital high resolution melt analysis for the diagnosis of bacteremia.

Fast and accurate diagnosis of bloodstream infection is necessary to inform treatment decisions for septic patients, who face hourly increases in mortality risk. Blood culture remains the gold standard test but typically requires ∼15 hours to detect the presence of a pathogen. Here, we assess the potential for universal digital high-resolution melt (U-dHRM) analysis to accomplish faster broad-based bacterial detection, load quantification, and species-level identification directly from whole blood. Analytical validation studies demonstrated strong agreement between U-dHRM load measurement and quantitative blood culture, indicating that U-dHRM detection is highly specific to intact organisms. In a pilot clinical study of 21 whole blood samples from pediatric patients undergoing simultaneous blood culture testing, U-dHRM achieved 100% concordance when compared with blood culture and 90.5% concordance when compared with clinical adjudication. Moreover, U-dHRM identified the causative pathogen to the species level in all cases where the organism was represented in the melt curve database. These results were achieved with a 1 mL sample input and sample-to-answer time of 6 hrs. Overall, this pilot study suggests that U-dHRM may be a promising method to address the challenges of quickly and accurately diagnosing a bloodstream infection. Universal digital high resolution melt analysis for the diagnosis of bacteremia: April Aralar, Tyler Goshia, Nanda Ramchandar, Shelley M. Lawrence, Aparajita Karmakar, Ankit Sharma, Mridu Sinha, David Pride, Peiting Kuo, Khrissa Lecrone, Megan Chiu, Karen Mestan, Eniko Sajti, Michelle Vanderpool, Sarah Lazar, Melanie Crabtree, Yordanos Tesfai, Stephanie I. Fraley.

Perovskite Nanoparticles Toxicity Study on Airway Epithelial Cells.

Research on the toxicity of nanoparticles has developed over recent years due to their increasing prevalence in common everyday materials. Various nanoparticles have been reported to promote and induce mucus secretion, which could potentially lead to airway damages and respiratory complications. Lanthanum strontium manganite (LSM) is a nanoparticle widely used in solar oxidized fuel cells (SOFCs) due to its high electrical conductivity, high electrochemical activity for O2 reduction reaction, high thermal stability and compatibility of SOFC electrolytes, and most importantly, its microstructural stability and long-term performance. Very few studies have been conducted on LMS's toxicity, thus its effect on airway cells was investigated in this study. After treating trachea cells with increasing concentrations of LSM ranging up to 500 µg/ml, we found that it has a moderate effect on cell viability, ROS production, cytochrome C, and caspase 3 expression. Despite its minimal impact on stated apoptosis-inducing characteristics, LSM illustrated an inhibiting effect on mucus secretion. We obtained a decreasing trend in mucus secretion with an increased concentration of the LSM treatment. Overall, LSM's advancement in SOFCs necessitated a toxicity study, and although it does not show a significant toxicity to trachea cells, LSM reduces mucus secretion, and can potentially interfere with airway clearance.

High-throughput label-free microcontact printing graphene-based biosensor for valley fever.

The highly prevalent and virulent disease in the Western Hemisphere Coccidioidomycosis, also known as Valley Fever, can cause serious illness such as severe pneumonia with respiratory failure. It can also take on a disseminated form where the infection spreads throughout the body. Thus, a serious impetus exists to develop effective detection of the disease that can also operate in a rapid and high-throughput fashion. Here, we report the assembly of a highly sensitive biosensor using reduced graphene oxide (rGO) with Coccidioides(cocci) antibodies as the target analytes. The facile design made possible by the scalable microcontact printing (µCP) surface patterning technique which enables rapid, ultrasensitive detection. It provides a wide linear range and sub picomolar (2.5 pg/ml) detection, while also delivering high selectivity and reproducibility. This work demonstrates an important advancement in the development of a sensitive label-free rGO biosensor for Coccidioidomycosis detection. This result also provides the potential application of direct pathogen diagnosis for the future biosensor development.

CeO2 nanoparticles attenuate airway mucus secretion induced by TiO2 nanoparticles.

Nanotoxicity studies associated with various nanoparticles (NPs) have attracted intense research interest due to the broader applications of nanoparticles in our daily lives. The exposure of nanoparticles can lead to hypersecretion and accumulation of airway mucus which are closely associated with many respiratory diseases. Titanium dioxide (TiO2), one of the PM10 components, is a major NP that is widely utilized in many commercial products. Our previous study established the connection between induced airway mucus secretion and TiO2 NPs. However, the countermeasure to reduce the harmful effects of TiO2 NPs, especially airway mucus secretion, remains unexplored. One of the potential candidates to reduce airway mucus secretion is cerium oxide (CeO2) NPs. It has been reported that CeO2 NPs can protect cells by diminishing ROS and inflammatory responses. Herein, our study shows that CeO2 NPs are able to reduce cytosolic Ca2+ changes and mitochondrial damage caused by TiO2 NPs. Our results provide the evidence that hypersecretion of mucus and apoptosis progression induced by TiO2 NPs can be attenuated by CeO2 NPs. This study highlights the potential capacity of CeO2 NPs as a supplementary material for TiO2 NPs applications in the future.