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In the analysis of quantitative PCR (qPCR) data, the quantification cycle (Cq) indicates the position of the amplification curve with respect to the cycle axis. Because Cq is directly related to the starting concentration of the target, and the difference in Cq values is related to the starting concentration ratio, the only results of qPCR analysis reported are often Cq, ΔCq or ΔΔCq values. However, reporting of Cq values ignores the fact that Cq values may differ between runs and machines, and, therefore, cannot be compared between laboratories. Moreover, Cq values are highly dependent on the PCR efficiency, which differs between assays and may differ between samples. Interpreting reported Cq values, assuming a 100% efficient PCR, may lead to assumed gene expression ratios that are 100-fold off. This review describes how differences in quantification threshold setting, PCR efficiency, starting material, PCR artefacts, pipetting errors and sampling variation are at the origin of differences and variability in Cq values and discusses the limits to the interpretation of observed Cq values. These issues can be avoided by calculating efficiency-corrected starting concentrations per reaction. The reporting of gene expression ratios and fold difference between treatments can then easily be based on these starting concentrations.

Abstract Background Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA quantities, measured by reverse transcription quantitative PCR (RT-qPCR), have been proposed to stratify clinical risk or determine analytical performance targets. We investigated reproducibility and how setting diagnostic cutoffs altered the clinical sensitivity of coronavirus disease 2019 (COVID-19) testing. Methods Quantitative SARS-CoV-2 RNA distributions [quantification cycle (Cq) and copies/mL] from more than 6000 patients from 3 clinical laboratories in United Kingdom, Belgium, and the Republic of Korea were analyzed. Impact of Cq cutoffs on clinical sensitivity was assessed. The June/July 2020 INSTAND external quality assessment scheme SARS-CoV-2 materials were used to estimate laboratory reported copies/mL and to estimate the variation in copies/mL for a given Cq. Results When the WHO-suggested Cq cutoff of 25 was applied, the clinical sensitivity dropped to about 16%. Clinical sensitivity also dropped to about 27% when a simulated limit of detection of 106 copies/mL was applied. The interlaboratory variation for a given Cq value was >1000 fold in copies/mL (99% CI). Conclusion While RT-qPCR has been instrumental in the response to COVID-19, we recommend Cq (cycle threshold or crossing point) values not be used to set clinical cutoffs or diagnostic performance targets due to poor interlaboratory reproducibility; calibrated copy-based units (used elsewhere in virology) offer more reproducible alternatives. We also report a phenomenon where diagnostic performance may change relative to the effective reproduction number. Our findings indicate that the disparities between patient populations across time are an important consideration when evaluating or deploying diagnostic tests. This is especially relevant to the emergency situation of an evolving pandemic.

Although molecular testing, and RT-qPCR in particular, has been an indispensable component in the scientific armoury targeting SARS-CoV-2, there are numerous falsehoods, misconceptions, assumptions and exaggerated expectations with regards to capability, performance and usefulness of the technology. It is essential that the true strengths and limitations, although publicised for at least twenty years, are restated in the context of the current COVID-19 epidemic. The main objective of this commentary is to address and help stop the unfounded and debilitating speculation surrounding its use.

Coronavirus disease 2019 (COVID-19) caused by the Omicron variant occurred in Shanghai, China, but its clinical characteristics and virology have not been comprehensively described. This retrospective cohort study included adult inpatients (≥18 years) diagnosed with COVID-19 at Changhai Hospital. Laboratory and clinical data were obtained from electronic medical records to investigate the clinical characteristics of COVID-19 and the variations in the patients' laboratory indexes were examined. The symptoms of COVID-19 caused by the Omicron variant were relatively mild. Upper respiratory tract specimens yielded higher positive detection rates than lower respiratory tract and intestinal specimens. Peak COVID-19 viral load was reached at the time of admission; quantification cycle (Cq) values increased to approximately 35 after 8.54 days. viral shedding duration correlated with age and disease severity ( <0.05). The older the patient and the more severe the disease, the longer the duration of viral shedding was. Portion parameters of blood routine, coagulative function, clinical chemistry, and inflammatory factor showed a certain correlation with the SARS-CoV-2 viral load. Virus replication and shedding are rapid in Omicron-positive patients; COVID-19 in these patients is characterized by acute onset, mild symptoms, and fast recovery. Older patients and those with more severe disease demonstrate prolonged virus shedding. Routine hematological indexes can reveal disease severity and help clinically evaluate the patient's condition.

Background: Unambiguous clinical interpretation of PCR results for urinary tract infections (UTIs) remains a challenge. Here we compare and correlate multiplex qPCR results (quantification cycle values) with traditional microbial culture results (colony forming units) for clinical samples. Methods: Serial dilutions [108 to 100 colony forming units (CFU)/mL] were performed on five Gram-negative and two Gram-positive UTI-causing bacterial pathogens. For each dilution, quantitative cultures on solid media to confirm CFU/mL values and a real-time PCR UTI panel employing a nanofluidic Open ArrayTM platform producing quantification cycle (Cq) values were performed. Cq values were correlated with CFU/mL values, generating a semi-quantitative interpretive scale for clinical samples. The clinical utility of the scale was then assessed using PCR and culture data from 168 clinical urine samples. Results: For Gram-negative bacteria, Cq values of <23, 23 to 28, and >28 corresponded with ≥105 CFU/mL, <105 CFU/mL and negative cultures, respectively. For Gram-positive bacteria, Cq values of <26, 26 to 30, and >30 corresponded with ≥105 CFU/mL, <105 CFU/mL and negative cultures, respectively. Among 168 urine specimens (including 138 Gram-negative and 30 Gram-positive bacteria), there was 83.3% agreement (n = 140/168) and 16.6% non-agreement (n = 28/168) between culture CFU/mL and qPCR Cq. Gram-negative bacteria had higher agreement (87.6%, 121/138) than Gram-positive bacteria (63.3%, 19/30). Conclusions: This study demonstrates that qPCR Cq results can be directly correlated with traditional urine quantitative culture results and reliably identify the clinically relevant cutoff of 105 CFU/mL for detected uropathogens.

Diagnosis of Pneumocystis jirovecii pneumonia relies on nucleic acid quantification in respiratory samples. Lack of standardization among molecular assays results in significant differences among assays/centers. To further promote standardization, we compared four thermocyclers and six master mixes for the detection of P. jirovecii. Whole nucleic acid (WNA) was extracted from broncho-alveolar lavages. Positive and negative sample extracts were pooled to get enough homogeneous materials. Three master mixes were tested to detect DNA by qPCR (D1, D2, and D3), and three to detect WNA by reverse transcriptase qPCR (W1, W2, and W3) manufactured by Roche, Eurogentec, Applied Biosystem, Invitrogen and Thermofischer Scientific. Experiments were performed on four thermocyclers (Roche LightCycler 480, Qiagen Rotor-Gene Q, Applied Biosystem ABI7500, and QuantStudio). Comparison of quantitative cycle (Cq) values between the methods targeting WNA versus DNA showed lower Cq values for WNA, independently of thermocycler and master mix. For high and low fungal loads, ∆Cq values between DNA and WNA amplification were 6.97 (±2.95) and 5.81 (±3.30), respectively (p < 0.0001). Regarding DNA detection, lower Cqs were obtained with D1 compared to D2 and D3, with median ∆Cq values of 2.6 (p = 0.015) and 2.9 (p = 0.039) respectively. Regarding WNA detection, no mix was superior to the others. PCR efficiency was not significantly different according to the qPCR platform (p = 0.14). This study confirmed the superiority of WNA over DNA detection. A calibration method (e.g., an international standard) for accurate comparative assessment of fungal load seems necessary.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is a rapid, reliable and widely used method of studying gene expression profiles that requires appropriate normalization for accurate and reliable results. Reference genes are usually used to normalize mRNA levels; however, the expression levels of these reference genes may vary between cell types, developmental stages, species and experimental conditions. Therefore, a normalization strategy is an important precondition for reliable conclusions, with endogenous controls requiring determination for every experimental system. In the present study, 18 reference genes used in various prior studies were analyzed to determine their applicability in bladder cancer. A total of 35 matched malignant and non-malignant bladder cancer (specifically transitional cell carcinoma) tissue specimens were examined. RNA and cDNA quality was stringently controlled. Candidate reference genes were assessed using SYBR-Green RT-qPCR. mRNA abundance was compared and reference genes with distinct ranges of expression to possible target genes were excluded. Genes that were differentially expressed in matched non-cancerous and cancerous samples were also excluded, using quantification cycle analysis. Subsequently, the stability of the selected reference genes was analyzed using three different methods: geNorm, NormFinder and BestKeeper. The rarely used ribosomal protein S23 (RPS23) was the most stable single reference gene, with RPS23, tumor protein, translationally controlled 1 and RPS13 comprising the optimal reference gene set for all the bladder samples. These stable reference genes should be employed in normalization and quantification of transcript levels in future expression studies of bladder cancer-associated genes.

Atmospheric rivers (ARs) are long, narrow synoptic scale weather features important for Earth’s hydrological cycle typically transporting water vapor poleward, delivering precipitation important for local climates. Understanding ARs in a warming climate is problematic because the AR response to climate change is tied to how the feature is defined. The Atmospheric River Tracking Method Intercomparison Project (ARTMIP) provides insights into this problem by comparing 16 atmospheric river detection tools (ARDTs) to a common data set consisting of high resolution climate change simulations from a global atmospheric general circulation model. ARDTs mostly show increases in frequency and intensity, but the scale of the response is largely dependent on algorithmic criteria. Across ARDTs, bulk characteristics suggest intensity and spatial footprint are inversely correlated, and most focus regions experience increases in precipitation volume coming from extreme ARs. The spread of the AR precipitation response under climate change is large and dependent on ARDT selection. Plain Language Summary Atmospheric rivers (ARs) are long and narrow weather features often referred to as “rivers in the sky.” They often transport water from lower latitudes to higher latitudes typically across climate zones and produce precipitation necessary for local climates. Understanding ARs in a warming climate is challenging because of the variety of ways an AR can be defined on gridded data sets. Unlike weather features such as tropical cyclones where identification methodologies are similar, algorithms that determine the characteristics of ARs vary depending on the science question posed. Because there is no real consensus on AR identification methodology, we aim to quantify the algorithmic uncertainty in AR metrics and precipitation. We compare 16 different ways of defining an AR on gridded data sets and present the range of possibilities in which an AR could change under global warming. Generally, ARs are projected to increase but the amount of that increase is a function of the algorithm. Across all algorithms and focus regions, AR precipitation is projected to become more extreme. Key Points High‐resolution historical and future simulations are used to evaluate atmospheric river detection tools (ARDT) uncertainty ARDTs mostly show increases in frequency and intensity of future atmospheric rivers (ARs) but the scale of response is dependent on algorithmic restrictiveness Most regions experience an increase in precipitation volume coming from extreme ARs

We recently identified a brief time period during postnatal development when the mammalian heart retains significant regenerative potential after amputation of the ventricular apex. However, one major unresolved question is whether the neonatal mouse heart can also regenerate in response to myocardial ischemia, the most common antecedent of heart failure in humans. Here, we induced ischemic myocardial infarction (MI) in 1-d-old mice and found that this results in extensive myocardial necrosis and systolic dysfunction. Remarkably, the neonatal heart mounted a robust regenerative response, through proliferation of preexisting cardiomyocytes, resulting in full functional recovery within 21 d. Moreover, we show that the miR-15 family of microRNAs modulates neonatal heart regeneration through inhibition of postnatal cardiomyocyte proliferation. Finally, we demonstrate that inhibition of the miR-15 family from an early postnatal age until adulthood increases myocyte proliferation in the adult heart and improves left ventricular systolic function after adult MI. We conclude that the neonatal mammalian heart can regenerate after myocardial infarction through proliferation of preexisting cardiomyocytes and that the miR-15 family contributes to postnatal loss of cardiac regenerative capacity.

Cell mass and chemical composition are important aggregate cellular properties that are especially relevant to physiological processes, such as growth control and tissue homeostasis. Despite their importance, it has been difficult to measure these features quantitatively at the individual cell level in intact tissue. Here, we introduce normalized Raman imaging (NoRI), a stimulated Raman scattering (SRS) microscopy method that provides the local concentrations of protein, lipid, and water from live or fixed tissue samples with high spatial resolution. Using NoRI, we demonstrate that protein, lipid, and water concentrations at the single cell are maintained in a tight range in cells under the same physiological conditions and are altered in different physiological states, such as cell cycle stages, attachment to substrates of different stiffness, or by entering senescence. In animal tissues, protein and lipid concentration varies with cell types, yet an unexpected cell-to-cell heterogeneity was found in cerebellar Purkinje cells. The protein and lipid concentration profile provides means to quantitatively compare disease-related pathology, as demonstrated using models of Alzheimer’s disease. This demonstration shows that NoRI is a broadly applicable technique for probing the biological regulation of protein mass, lipid mass, and water mass for studies of cellular and tissue growth, homeostasis, and disease.