Generate Calibration Curves Research Articles

Biological effectiveness of combined exposure to neutrons and gamma radiation applied in two orders of sequence: Relevance for biological dosimetry after nuclear emergencies

ObjectiveAfter a nuclear detonation, people will be exposed to varying mixtures of neutrons and gamma radiation and the biological effectiveness of mixed beams is not well known. Additionally, it is not known how far both radiation types interact, a question that is relevant for generating calibration curves for the purpose of biological dosimetry. The objective of this study was to investigate the potential impact of two different combinations of neutron and gamma radiation on gene expression and dicentric chromosomes in peripheral blood mononuclear cells (PBMC). MethodsWhole blood from 3 human donors was exposed to neutrons with an energy spectrum similar to that of the Hiroshima uranium bomb, to gamma radiation from a 60Co source and to a 50:50 combination of both radiations, given in two orders of sequence. In all cases the total doses were 0.5, 0.75 and 1.0 Gy. Dicentric chromosomes were analyzed by light microscopy and the expression of six known radiation-responsive genes BBC3, CDKN1A, FDXR, GADD45A, MDM2, and XPC were analyzed by RT-qPCR. ResultsPer unit dose, exposure to neutrons lead to a higher level of dicentrics and gene expression as compared to gamma radiation. Dose-response relationships for both endpoints were linear, allowing calculating the expected outcome of combined exposure by arithmetic. For dicentric chromosomes, the RBE values for 60Co→neutrons, neutrons→60Co and neutrons were 4.05, 3.62 and 7.30 respectively. For gene expression the RBE values were gene-specific, but showed values in the range of 1.14–3.01 for 60Co→neutrons, 1.33–2.68 for neutrons→60Co and 1.39–3.91 for neutrons. ConclusionsThe results demonstrate that combined exposure to neutrons and gamma radiation, regardless of the order of sequence, leads to an additive response at both endpoints. This indicates that calibration curves for mixed beams can be constructed from dose response relationships of the single beam components.

An optimised method to isotopically label pure synthetic peptides 'in-house' for absolute quantification in bottom-up proteomics.

Heavy-labelled internal standards increasingly represent the gold standard for absolute quantitation in mass spectrometry (MS)-based bottom-up proteomics. The biggest drawbacks of using these standards are that they have high costs and lengthy lead times. We describe an efficient, low-cost optimised method to enable 'in-house' heavy labelling of synthetic tryptic peptides for absolute quantification using tandem LC-MS/MS mass spectrometry. Our methodology uses 18O water in a trypsin-catalysed oxygen exchange reaction at the carboxyl terminus with the overall aim of reducing the costs and lead time associated with sourcing heavy standards from commercial vendors. Step-by-step instructions are provided on how to execute this protocol with high-throughput adaptations utilising a 96-well plate and a liquid-handling robot. Detailed notes on experimental setup, tips for troubleshooting and suggested improvements to maximise labelling efficiencies are highlighted to achieve the best results. Under optimum conditions, labelling efficiencies of peptides can reach from 95% to 100%. The application of the 'in-house' labelled standards in generating calibration curves to quantify endogenous peptide concentrations is just as effective as using the synthetically sourced standards while also having great cost reduction implications as well as saving time spent waiting for peptides to arrive. The protocol is highly adaptable and can be customized to fit the specific setup of any laboratory, maximizing achievable labelling efficiencies.

Multivariate calibration in smartphones using digital images for the determination of pH of drinking water

The execution of calibration models was previously restricted to computers, but with technological advances, applications on mobile devices have made this process more accessible. Photometrix PRO stands out as an innovative tool for creating calibration models from digital images. The colorimetric chart, widely used to determine the pH of solutions by visual analysis, can present subjectivity in interpretation. This study aims to develop and evaluate calibration models (PLS) using Photometrix PRO to accurately correlate the visual information of a colorimetric pH chart from the company AlfaKit. The application was used to acquire images, generate calibration curves and obtain pH values from prediction and validation sets. The curves presented R2, RMSEC and RMSECV that indicated good fit of the models. The t-test, with 95% confidence, showed that there was no significant difference between the results of the models and the colorimetric method. The Residual Prediction Deviation (RPD) ranged from 1.12 (RGB) to 5.37 (RGBHSVLI), making the model (RGBHSVLI) suitable for quality control. The study successfully demonstrated the feasibility and effectiveness of smartphone-based multivariate calibration (PLS) for determining pH in drinking water.

Targeted proteomic approach for quantification of collagen type I and type III in formalin-fixed paraffin-embedded tissue

Collagen is the most abundant protein in mammals and a major structural component of the extracellular matrix (ECM). Changes to ECM composition occur as a result of numerous physiological and pathophysiological causes, and a common means to evaluate these changes is the collagen 3 (Col3) to collagen 1 (Col1) ratio. Current methods to measure the Col3/1 ratio suffer from a lack of specificity and often under- or over-estimate collagen composition and quantity. This manuscript presents a targeted liquid chromatography tandem mass spectrometry (LC–MS/MS) method for quantification of Col3 and Col1 in FFPE tissues. Using surrogate peptides to generate calibration curves, Col3 and Col1 are readily quantified in FFPE tissue sections with high accuracy and precision. The method is applied to several tissue types from both human and reindeer sources, demonstrating its generalizability. In addition, the targeted LC–MS/MS method permits quantitation of the hydroxyprolinated form of Col3, which has significant implications for understanding not only the quantity of Col3 in tissue, but also understanding of the pathophysiology underlying many causes of ECM changes. This manuscript presents a straightforward, accurate, precise, and generalizable method for quantifying the Col3/1 ratio in a variety of tissue types and organisms.

Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.

The reaserch of artificial intelligence (AI) model for predicting spinal refracture is limited to bone mineral density, X-ray and some conventional laboratory indicators, which has its own limitations. Besides, it lacks specific indicators related to osteoporosis and imaging factors that can better reflect bone quality, such as computed tomography (CT). To construct a novel predicting model based on bone turn-over markers and CT to identify patients who were more inclined to suffer spine refracture. CT images and clinical information of 383 patients (training set = 240 cases of osteoporotic vertebral compression fractures (OVCF), validation set = 63, test set = 80) were retrospectively collected from January 2015 to October 2022 at three medical centers. The U-net model was adopted to automatically segment ROI. Three-dimensional (3D) cropping of all spine regions was used to achieve the final ROI regions including 3D_Full and 3D_RoiOnly. We used the Densenet 121-3D model to model the cropped region and simultaneously build a T-NIPT prediction model. Diagnostics of deep learning models were assessed by constructing ROC curves. We generated calibration curves to assess the calibration performance. Additionally, decision curve analysis (DCA) was used to assess the clinical utility of the predictive models. The performance of the test model is comparable to its performance on the training set (dice coefficients of 0.798, an mIOU of 0.755, an SA of 0.767, and an OS of 0.017). Univariable and multivariable analysis indicate that T_P1NT was an independent risk factor for refracture. The performance of predicting refractures in different ROI regions showed that 3D_Full model exhibits the highest calibration performance, with a Hosmer-Lemeshow goodness-of-fit (HL) test statistic exceeding 0.05. The analysis of the training and test sets showed that the 3D_Full model, which integrates clinical and deep learning results, demonstrated superior performance with significant improvement (p-value < 0.05) compared to using clinical features independently or using only 3D_RoiOnly. T_P1NT was an independent risk factor of refracture. Our 3D-FULL model showed better performance in predicting high-risk population of spine refracture than other models and junior doctors do. This model can be applicable to real-world translation due to its automatic segmentation and detection.

Data-science-guided calibration curve prediction of an MLCT-based ee determination assay for chiral amines

Circular dichroism (CD) based enantiomeric excess (ee) determination assays are optical alternatives to chromatographic ee determination in high-throughput screening (HTS) applications. However, the implementation of these assays requires calibration experiments using enantioenriched materials. We present a data-driven approach that circumvents the need for chiral resolution and calibration experiments for an octahedral Fe(II) complex (1) used for the ee determination of α-chiral primary amines. By computationally parameterizing the imine ligands formed in the assay conditions, a model of the circular dichroism (CD) response of the Fe(II) assembly was developed. Using this model, calibration curves were generated for four analytes and compared to experimentally generated curves. In a single-blind ee determination study, the ee values of unknown samples were determined within 9% mean absolute error, which rivals the error using experimentally generated calibration curves.

On the use of submerged piezoelectric MFCs for dual viscosity sensing and energy harvesting

We demonstrate the potential of unimorph beams equipped with macro fiber composites to integrate viscosity sensing and energy harvesting functionalities into a single device. The viscosity sensing component involves measuring the quality factor and resonance frequency peak associated with the second out-of-plane mode of vibration. We showcase the suitability of the present sensing device for liquids of wide range of kinematic viscosities from 1 to 1543.5 cSt. Following the quality factor-based sensing approach, the sensor reached a sensitivity of S = −0.0019/cSt. The amplitude-based sensing mechanism demonstrated a sensitivity of S = −3.3 mV/cSt. The viscosities of motor oil and glycerin, determined using the generated calibration curves, are found in close agreement with those obtained from a conventional rotational viscometer. The proposed sensing device holds great promise for online viscosity measurement, particularly for high-viscosity fluids, simple design, ease of operation, and partial self-sensing capability, thanks to energy harvesting.

Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke.

We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations. We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram's net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions. A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit. Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort.

Highly sensitive therapeutic drug monitoring of infliximab in serum by targeted mass spectrometry in comparison to ELISA data

BackgroundPresently, antibody concentration measurements for patients undergoing treatment are predominantly determined by ELISA, which still comes with known disadvantages. Therefore, our aim was to establish a targeted mass-spectrometric assay enabling the reproducible absolute quantification of peptides from the hypervariable and interaction regions of infliximab.MethodsPeptides of infliximab were measured post-trypsin digestion and subsequent separation on a Vanquish Horizon UHPLC coupled to a TSQ Altis Triple-Quad mass spectrometer. Normalization and absolute quantification were conducted using stable isotope-synthesized peptides. Calibration curves covering a range of 0.25-50 µg/ml were employed for quantitation.ResultsWe demonstrated the substantial influence of peptide selection, choice of hydrolase for digestion, and digestion time on absolute peptide yield (28–44% for peptide 1 and 64–97% for peptide 2). Furthermore, we showed that the generated calibration curves for absolute quantification were highly reproducible and robust (LLOQ1 0.72 µg/ml and LLOQ2 1.00 µg/ml) over several months. In comparison to ELISA values, the absolute values obtained by mass spectrometry often yielded lower results for both targeted peptides.ConclusionsIn this study, a semi-automated workflow was employed and tested with 8 patients and corresponding replicates (n = 3–4). We demonstrated the robust implementation of calibration curves for the absolute quantification of infliximab in patient samples, with coefficients of variation ranging from 0.5 to 9%. Taken together, we have developed a platform enabling the rapid (2 days of sample preparation and 30 min of measurement time per sample) and robust quantification of Infliximab antibody concentration in patients. The use of mass spectrometry also facilitates the straightforward expansion of the method to include additional antibody peptides.

Enhancing LOD determination in gas chromatography: Validating the Hubaux-Vos method for gas concentration measurement

The limit of detection (LOD) is a crucial measure in analytical methods, representing the smallest amount of a substance that can be distinguished from background noise. In the realm of gas chromatography (GC), however, determining LOD can be quite subjective, leading to significant variability among researchers. In this study, we validate the Hubaux-Vos method, an International Standards Organization(ISO)-approved approach for determining LOD in gas concentration measurements, using a GC equipped with a discharge ionization detector (DID) and a dynamic dilution system. We employ a gas mixture certified reference material (CRM) of CO, CH4, and CO2 at various concentrations to generate calibration curves for each gas. Subsequently, we estimate the LODs for each gas using the Hubaux-Vos method. Surprisingly, our findings indicate a notable difference between the LODs calculated using the Hubaux-Vos method and those confirmed through experiments. This highlights the importance of critically examining the theoretical foundations of LOD determination. We strongly recommend researchers to scrutinize the principles guiding LOD determination. The method proposed in this study offers an effective way to rigorously validate theoretical approaches for estimating LODs in gas concentration measurements using GC.

Real-Time Measurement of Antiglaucoma Drugs in Porcine Eyes Using Boron-Doped Diamond Microelectrodes.

The primary treatment for glaucoma, the most common cause of intermediate vision impairment, involves administering ocular hypotensive drugs in the form of topical eye drops. Observing real-time changes in the drugs that pass through the cornea and reach the anterior chamber of the eye is crucial for improving and developing safe, reliable, and effective medical treatments. Traditional methods for measuring temporal changes in drug concentrations in the aqueous humor employ separation analyzers such as LC-MS/MS. However, this technique requires multiple measurements on the eyes of various test subjects to track changes over time with a high temporal resolution. To address this issue, we have developed a measurement method that employs boron-doped diamond (BDD) microelectrodes to monitor real-time drug concentrations in the anterior chamber of the eye. First, we confirmed the electrochemical reactivity of 13 antiglaucoma drugs in a phosphate buffer solution with a pH of 7.4. Next, we optimized the method for continuous measurement of timolol maleate (TIM), a sympathetic beta-receptor antagonist, and generated calibration curves for each BDD microelectrode using aqueous humor collected from enucleated porcine eyes. We successfully demonstrated the continuous ex vivo monitoring of TIM concentrations in the anterior chambers of these enucleated porcine eyes. The results indicate that changes in intracameral TIM concentrations can be monitored through electrochemical measurements using BDD microelectrodes. This technique holds promise for future advancements in optimizing glaucoma treatment and drug administration strategies.

Robust Gold Electrode Fabrication in a Microfluidic System

Electrode fabrication in microfluidic devices is often avoided due to the smaller footprint of the platform, associated complexity in the process, and absence of a well-established method. In this article a glass microchip with chemically modified and vapor deposited gold internal electrode was fabricated and used for biomolecule immobilization and electrochemical applications. The silanol (-OH) on glass surface of the microdevice was modified with (3-mercaptopropyl)trimethoxysilane prior to gold deposition to achieve a strong attachment of the metal film using the gold-thiol binding. The practical utility of the microdevice with gold surface was demonstrated by performing quantitative enzyme linked immunosorbent assay (ELISA) of human interferon gamma (IFN-γ) antigen, yielding a limit of detection (LOD) of 0.15 pg/mL compared to 4 pg/mL in the microplate assay. Additionally, the functionality of the internal electrode was established by performing cyclic voltammetry (CV) and linear sweep voltammetry (LSV) of potassium ferricyanide (III) in potassium chloride (KCl) solution using the proposed gold patterned electrode in the microchip, offering limits of detection of 0.14 and 0.11 mM for CV and LSV respectively. Both the ELISA and electrochemical assays were validated by generating calibration curves and using a control sample for the electroanalytical sensing applications. The measurements yielded experimental results that agreed with control concentration with 7% relative standard deviation (RSD). The promising reproducibility of the electrode in fabrication with no/minimum failure rate and its outstanding performance in immunosensor applications as well as an electron exchange surface makes our technique of gold electrode fabrication in microchips a state of the art.

Agricultural and Veterinary Pesticides Residues in Human Food

Introduction: Expanding utilization of pesticides can provoke serious issues due to their biomagnification and persistent nature. Eventual contamination of fresh daily-consumed food by pesticide constitutes a human food security problem. Methods: The present investigation planned to check out whether the most hazardous pesticides could be found in a varied collection of wheat, vegetables and fruit, on the other hand, we attempted to know if some banned and no more commercialized pesticides could be even detected. Effectively, we have chosen to analyze wheat, potato, tomato, apple, peach and grape, so, after the extraction procedure of pesticides, we used gas chromatography-mass spectrometry (GC-MS) to identify some hazardous compounds (organochlorines and organophosphorus). These compounds were used as standards to generate calibration curves and estimate precisely their concentration in the tested samples using selected ion monitoring (SIM) mode. Results: The results have shown the presence of an important quantity of pesticides that are no more used (banned) for decades for agriculture purposes but they are still used as veterinary drugs, such as lindane (0.2 ppm grape, 0.32 ppm tomato) and fenchlorphos (0.5 ppm grape). Conclusion: This survey should be routinely executed by scientists and concerned authorities to control pesticides circulation with their exact amounts in food and other environmental matrices as well as eventual contamination with another toxic element.

Identification of immune infiltration and cuproptosis-related molecular clusters in tuberculosis.

Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis (Mtb) infection. Cuproptosis is a novel cell death mechanism correlated with various diseases. This study sought to elucidate the role of cuproptosis-related genes (CRGs) in TB. Based on the GSE83456 dataset, we analyzed the expression profiles of CRGs and immune cell infiltration in TB. Based on CRGs, the molecular clusters and related immune cell infiltration were explored using 92 TB samples. The Weighted Gene Co-expression Network Analysis (WGCNA) algorithm was utilized to identify the co-expression modules and cluster-specific differentially expressed genes. Subsequently, the optimal machine learning model was determined by comparing the performance of the random forest (RF), support vector machine (SVM), generalized linear model (GLM), and eXtreme Gradient Boosting (XGB). The predictive performance of the machine learning model was assessed by generating calibration curves and decision curve analysis and validated in an external dataset. 11 CRGs were identified as differentially expressed cuproptosis genes. Significant differences in immune cells were observed in TB patients. Two cuproptosis-related molecular clusters expressed genes were identified. Distinct clusters were identified based on the differential expression of CRGs and immune cells. Besides, significant differences in biological functions and pathway activities were observed between the two clusters. A nomogram was generated to facilitate clinical implementation. Next, calibration curves were generated, and decision curve analysis was conducted to validate the accuracy of our model in predicting TB subtypes. XGB machine learning model yielded the best performance in distinguishing TB patients with different clusters. The top five genes from the XGB model were selected as predictor genes. The XGB model exhibited satisfactory performance during validation in an external dataset. Further analysis revealed that these five model-related genes were significantly associated with latent and active TB. Our study provided hitherto undocumented evidence of the relationship between cuproptosis and TB and established an optimal machine learning model to evaluate the TB subtypes and latent and active TB patients.

Estimating individual minimum calibration for deep-learning with predictive performance recovery: An example case of gait surface classification from wearable sensor gait data

Clinical datasets often comprise multiple data points or trials sampled from a single participant. When these datasets are used to train machine learning models, the method used to extract train and test sets must be carefully chosen. Using the standard machine learning approach (random-wise split), different trials from the same participant may appear in both training and test sets. This has led to schemes capable of segregating data points from a same participant into a single set (subject-wise split). Past investigations have demonstrated that models trained in this manner underperform compared to those trained using random-split schemes. Additional training of models via a small subset of trials, known as calibration, bridges the gap in performance across split schemes; however, the amount of calibration trials required to achieve strong model performance is unclear. Thus, this study aims to investigate the relationship between calibration training set size and prediction accuracy on the calibration test set. A database of 30 young, healthy adults performing multiple walking trials across nine different surfaces while fit with inertial measurement unit sensors on the lower limbs was used to develop a deep-learning classifier. For subject-wise trained models, calibration on a single gait cycle per surface yielded a 70% increase in F1-score, the harmonic mean of precision and recall, while 10 gait cycles per surface were sufficient to match the performance of a random-wise trained model. Code to generate calibration curves may be found at (https://github.com/GuillaumeLam/PaCalC).

Role of androgen receptor signaling pathway-related lncRNAs in the prognosis and immune infiltration of breast cancer

Androgen receptor (AR) is strong association with breast cancer (BRCA). We aimed to investigate the effect of the androgen receptor signaling pathway-related long non-coding RNAs (ARSP-related lncRNAs) on the process of subtype classification and the tumor microenvironment (TME) of breast cancer (BRCA). Our study screen ARSP-related lncRNAs for the construction of a risk model. The single-sample gene set enrichment analysis (ssGSEA) method was used to detect the differences between the immune responses generated by the patients belonging to the low- and high-risk groups. The relationship between the ARSP-related lncRNAs and TME was explored following the process of cluster analysis. The univariate Cox analysis and the Lasso regression analysis method was used to screen nine of these lncRNAs to develop a risk model. It was observed that risk score could function as an independent prognostic factor, affecting the prognoses of patients suffering from BRCA. The validity of the model was assessed by analyzing the generated calibration curves and a nomogram. Additionally, the effect of the risk score on the extent of immune cell infiltration realized in TME was explored. M2 macrophages correlated positively, whereas NK cells, CD4+ T cells, and naive B cells correlated negatively with the risk score. Results obtained using the cluster analysis indicated that immune scores correlated with clustered subtypes. Finally, the risk score and cluster subtypes were analyzed to study the sensitivity of the patients toward different drugs to identify the appropriate therapeutic agents. The prognoses of patients suffering from BRCA can be accurately predicted by ARSP-related lncRNAs.

Toolbox Accelerating Glycomics (TAG): Improving Large-Scale Serum Glycomics and Refinement to Identify SALSA-Modified and Rare Glycans.

Glycans are involved in many fundamental cellular processes such as growth, differentiation, and morphogenesis. However, their broad structural diversity makes analysis difficult. Glycomics via mass spectrometry has focused on the composition of glycans, but informatics analysis has not kept pace with the development of instrumentation and measurement techniques. We developed Toolbox Accelerating Glycomics (TAG), in which glycans can be added manually to the glycan list that can be freely designed with labels and sialic acid modifications, and fast processing is possible. In the present work, we improved TAG for large-scale analysis such as cohort analysis of serum samples. The sialic acid linkage-specific alkylamidation (SALSA) method converts differences in linkages such as α2,3- and α2,6-linkages of sialic acids into differences in mass. Glycans modified by SALSA and several structures discovered in recent years were added to the glycan list. A routine to generate calibration curves has been implemented to explore quantitation. These improvements are based on redefinitions of residues and glycans in the TAG List to incorporate information on glycans that could not be attributed because it was not assumed in the previous version of TAG. These functions were verified through analysis of purchased sera and 74 spectra with linearity at the level of R2 &gt; 0.8 with 81 estimated glycan structures obtained including some candidate of rare glycans such as those with the N,N'-diacetyllactosediamine structure, suggesting they can be applied to large-scale analyses.

Quantification of microbial growth pattern on growth medium using image processing program and evaluation of antifungal activities of wood vinegar

Digitized growth pattern images of wood-rotting fungi Fomitopsis palustris (FOM) and Trametes versicolor (TRA) were used with a video processing program with a new formula to calculate the numerical results of microbial inhibition by an active substance (wood vinegar). First, images of daily growth pattern were collected to generate calibration curves for each fungus. FOM stopped growing in 7 days, whereas TRA stopped growing in 11 days. The calibration curves FOM and TRA showed R2 = 0.9899 and 0.8880, respectively. The new methods allow numerical comparison by adding different concentrations of active components (wood vinegar) to assess the inhibition effect of microbial growth. Using the formula allowed the results of inhibition effect to be collected as numerical data. Through the image data, it was possible to present daily inhibitory efficacy data in detail, numerically. Based on these results, microbial growth pattern could be evaluated.

Data-Driven Prediction of Circular Dichroism-Based Calibration Curves for the Rapid Screening of Chiral Primary Amine Enantiomeric Excess Values.

Here, we describe the prediction of the circular dichroism (CD) response of a three-component chiroptical sensor for enantiomeric excess (ee) determination of chiral amines using a multivariate fit to electronic and steric parameters. These computationally derived parameters can be computed for nearly any amine and correlate well with the CD response of the 12 amines comprising the training set. The resulting model was used to accurately predict the CD response of a test set of chiral amines. Theoretical calibration curves were then created and used to determine the ee of solutions of unknown ee. Using this method, the error in ee determination differed by less than 10% compared to experimentally generated calibration curves.

Harmonization of SARS-CoV-2 reverse transcription quantitative PCR tests to the first WHO international standard for SARS-CoV-2 RNA.

BackgroundCycle threshold (Ct) values from SARS-CoV-2 reverse transcription quantitative PCR (RT-qPCR) tests are used to measure viral burden. Calibration to the First WHO International Standard for SARS-CoV-2 RNA may improve quantitative inter-assay agreement.MethodsWHO standard was tested using four emergency use authorized RT-qPCRs to generate calibration curves and evaluate Ct value differences. Harmonization of two assays, Cepheid Xpert Xpress SARS-CoV-2 targeting E and nucleocapsid (N2) [Xpert (E) and Xpert (N2)] and a laboratory-developed test targeting E [LDT (E)], was assessed using 93 positive upper respiratory samples. Platform (target) pairs were compared via Bland-Altman analysis and Passing-Bablok regression.ResultsCt values with the WHO standard were comparable across platforms and targets, except Xpert (N2) for which the mean difference was a median of 3.68 cycles (Interquartile Range, IQR = 3.23 to 3.76 cycles) greater than other platform (target) pairs. Using clinical samples, the mean difference of Xpert (N2) to LDT (E) was 3.64 cycles (95% Confidence Interval, CI =1.51 to 5.76). After calibration, the mean difference of Xpert (N2) to LDT (E) was 0.08 log10 IU/mL (95% CI = -0.56 to 0.71) and the regression was y = 1.00x * 0.08 (95% CI slope = 0.93 to 1.07, 95% CI intercept = 0.28 to 0.42).ConclusionsCalibration to the WHO standard resulted in the harmonization of two RT-qPCR tests, whereas analysis by Ct value alone may have led to erroneous quantitation. Harmonization to the WHO standard has the potential to improve the generalizability of clinical associations with SARS-CoV-2 RNA levels.