External Calibration Research Articles

Measuring Erosion of Biodegradable Polymers in Brimonidine Drug Delivery Implants by Quantitative Proton NMR Spectroscopy (q-HNMR)

Erosion of biodegradable polymeric excipients, such as polylactic acid (PLA) and polylactic-co-glycolic acid (PLGA), is generally characterized by microbalance for the remaining mass of PLA and/or PLGA and Gel Permeation Chromatography (GPC) for molecular weight (MW) decrease. For polymer erosion studies of intravitreal sustained release brimonidine implants, however, both microbalance and GPC present several challenges. Mass loss measurement by microbalance does not have specificity for excipient polymers and drug substances. Accuracy of the remaining mass by weighing could also be low due to sample mass loss through retrieval-drying steps, especially at later drug release (DR) time points. When measuring the decrease of polymer MW by GPC, trace amounts of polymeric degradants (oligomers and/or monomers) trapped inside the implants during DR tests may not be measurable due to sensitivity limitations of the GPC detector and column MW range. Previous efforts to measure remained PLGA weight of dexamethasone micro-implants using qNMR with external calibration have been performed, however, these measurements do not account for chemical structure changes (i.e. LA to GA ratio changes from time zero) of PLGA implants during drug release tests. Here, a qNMR method with an internal standard was developed to monitor the following changes in micro-implants during drug release tests: 1. The remaining overall PLA/PLGA mass. 2. The remaining lactic acid (LA), glycolic acid (GA) unit and PLGA's lauryl ester end group percentages. 3. The trace content of PLA/PLGA oligomers as degradants retained in the implants. Unlike microbalance analysis, qNMR has both specificity for drug substance, excipient polymer, and accuracy due to minimal implant loss during sample preparation. Compared to the overall PLA/PLGA remaining mass generally monitored in erosion studies, the percentage of remaining LA, GA, and the ester end group provide more information about the microstructure change (such as hydrophobicity) of PLA/PLGA. Additionally, the qNMR method can complement GPC methods by measuring the change of remaining PLA and PLGA oligomer concentrations in brimonidine implants, with tenfold less sample and no MW cutoff. The qNMR method can be used as a sensitive tool for both polymer excipient characterization and kinetics studies of brimonidine implant erosion.

Development and Validation of a Nomogram for Predicting Sepsis-Induced Coagulopathy in Septic Patients: Mixed Retrospective and Prospective Cohort Study.

Sepsis-induced coagulopathy (SIC) is a common cause of poor prognosis in critically ill patients in the intensive care unit (ICU). However, currently there are no tools specifically designed for predicting the occurrence of SIC in septic patients earlier. This study aimed to develop a predictive nomogram incorporating clinical markers and scoring systems to individually predict the probability of SIC in septic patients. Patients consecutively recruited in the stage between January 2022 and April 2023 constituted the development cohort for retrospective analysis to internally test the nomogram, and patients in the stage between May 2023 to November 2023 constituted the validation cohort for prospective analysis to externally validate the nomogram. Univariate logistic regression analysis of the development cohort was performed firstly, and then multivariate logistic regression analysis was performed using backward stepwise method to determine the best-fitting model and obtain the nomogram from it. The nomogram was validated in an independent external validation cohort, involving discrimination and calibration. A decision curve analysis was also performed to evaluate the net benefit of the insertion decision with this nomogram. A total of 548 and 245 patients, 55.1 and 49.4% with SIC occurrence, were included in the development and validation cohorts, respectively. Predictors contained in the prediction nomogram included shock, platelets, and international normalized ratio (INR). Patients with shock (odds ratio [OR]: 4.499; 95% confidence interval [CI]: 2.730-7.414; p < 0.001), higher INR (OR: 349.384; 95% CI: 62.337-1958.221; p < 0.001), and lower platelet (OR: 0.985; 95% CI: 0.982-0.988; p < 0.001) had higher probabilities of SIC. The development model showed good discrimination, with an area under the receiver operating characteristic curve (AUROC) of 0.879 (95% CI: 0.850-0.908) and good calibration. Application of the nomogram in the validation cohort also gave good discrimination with an AUROC of 0.872 (95% CI: 0.826-0.917) and good calibration. The decision curve analysis of the nomogram provided better net benefit than the alternate options (intervention or no intervention). By incorporating shock, platelets, and INR in the model, this useful nomogram could be accessibly utilized to predict SIC occurrence in septic patients. However, external validation is still required for further generalizability improvement of this nomogram.

Optimizing short-term in-class process evaluation: analyzing the effectiveness of teaching interventions in pharmaceutical education using repeated measures analysis

BackgroundThe assessment of the effectiveness of teaching interventions in enhancing students’ understanding of the Pharmaceutical Care Network Europe (PCNE) Classification System is crucial in pharmaceutical education. This is especially true in regions like China, where the integration of the PCNE system into undergraduate teaching is limited, despite its recognized benefits in addressing drug-related problems in clinical pharmacy practice. Therefore, this study aimed to evaluate the effectiveness of teaching interventions in improving students’ understanding of the PCNE Classification System in pharmaceutical education.MethodsUndergraduate pharmacy students participated in a series of sessions focused on the PCNE system, including lectures (t1), case analyses (t2), and practical implementation (t3). The levels of understanding were evaluated using time-course questionnaires. Initially, paired samples t-Tests were used to compare understanding levels between different time points. Subsequently, Repeated Measures Analysis (RMA) was employed. Pearson correlation analysis was conducted to examine the relationship between understanding levels and the usability and likelihood of using the PCNE system, as reported in the questionnaires.ResultsThe paired samples t-Tests indicated insignificant differences between t2 and t3, suggesting limited improvement following the practical implementation of the PCNE system. However, RMA revealed significant time effects on understanding levels in effective respondents and the focused subgroup without prior experience (random intercept models: all p < 0.001; random slope models: all p < 0.001). These results confirmed the effectiveness of all three teaching interventions. Pearson correlation analysis demonstrated significant positive correlations between understanding levels and the usability and likelihood of using the PCNE system at all examined time points. This finding highlighted the reliability of the understanding levels reported in the questionnaires. The homework scores were used as external calibration standards, providing robust external validation of the questionnaire’s validity.ConclusionThe implementation of RMA provided robust evidence of the positive impact of time on understanding levels. This affirmed the effectiveness of all teaching interventions in enhancing students’ comprehension of the PCNE Classification System. By utilizing RMA, potential errors inherent in common statistical methods, such as t-Tests, were mitigated. This ensured a more comprehensive and accurate assessment of the effectiveness of the teaching interventions.

Auto recalibration based on dual-mode sensing for robust optical continuous glucose monitoring

Boronic acid based optical continuous glucose monitoring systems (CGMs) hold promise for long-term glucose monitoring. However, these sensors suffer drifts induced inaccuracy and external calibration. One of the reasons is the denature of boronic acid structures and optical reporters. To solve this problem, a dual-mode glucose sensing method with auto-recalibration capabilities is developed using glucose-selective diboronic acid molecule (DBA) and optical reporter alizarin red S (ARS). DBA binds ARS at a 1:2 ratio and produces distinct changes in both the absorbance (Abs) and the fluorescence (FL) spectra of ARS. Through competitive binding with DBA, glucose induces a compensatory shift in both Abs and FL signals. The two signals were demonstrated to be strongly correlated and were combined to build Abs-FL-glucose 3D calibration curves for glucose determination. The mapping relationship of two signals drift closely with both changes of DBA and ARS concentration. By ascertain the right mapping relationship, the two strongly correlated signals can alert undesirable degradation of sensing components, identify the correct calibration curve, and accurately predict glucose concentration without external calibration. The introduction and adoption of the auto-recalibrating dual-mode sensing strategy could constitute a useful development in the pursuit of next generation long-term calibration-free CGM and wearable sensors technologies with superior precision.

A practical approach to estimate analytical method variability from routine testing

The performance of analytical test methods is critical to ensure decisions that affect efficacy and quality of pharmaceutical products are based on accurate and reliable results. As described in USP <1220> and advocated for in ICH Q14, continued verification of critical method attributes linked to bias and precision is essential to ensure method performance throughout the lifecycle of an analytical test method. As continued verification programs for analytical methods within the pharmaceutical industry mature, additional monitoring tools are required to deliver robust and cost-effective verification programs. Herein, a novel methodology is presented to evaluated analytical method variability directly from results generated during routine method execution. The implementation of the methodology is demonstrated for a small molecule liquid chromatographic assay method utilizing a single-point external reference calibration. Approaches to reduce the required data to be collected and broaden the applicability of the methodology to a wide range of analytical methods is described. Finally, the application of the methodology to method development activities is discussed to aid in the identification of variability sources and effectively select replication strategies, thus allowing a holistic understanding of method variability throughout the entirety of the method lifecycle.

Development of a nomogram for predicting malnutrition in elderly hospitalized cancer patients: a cross-sectional study in China.

The Patient-Generated Subjective Global Assessment (PG-SGA) serves as a specialized nutritional assessment instrument designed for cancer patients. Despite its specificity, the complexity and time requirements of this tool, along with the necessity for administration by trained professionals, limit its practicality in clinical settings. Our objective is to identify a straightforward, efficient, and dependable nutritional assessment tool to promote broader adoption in clinical practice. This study encompassed a total of 450 patients diagnosed with cancer. Of these, 315 individuals constituted the training set, and the remaining 135 were allocated to the external validation set. The model variables were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Binary logistic regression outcomes facilitated the development of a nomogram, offering a visual depiction of the predicted probabilities. The predictive accuracy of the nomogram model was evaluated by calculating the area under the Receiver Operating Characteristic (ROC) curve. The LASSO method detected four variables that were included in the final prediction model: age, serum albumin levels (ALB), body mass index (BMI), and activities of daily living (ADL). The area under the curve (AUC) for this prediction model was 0.905. Both the internal and external calibration curves for malnutrition showed that the predictive nomogram model was highly accurate. The study has developed a prediction model that demonstrates remarkable accuracy in forecasting malnutrition. Furthermore, it presents a streamlined nutritional assessment tool aimed at swiftly identifying cancer patients at nutritional risk, thereby facilitating oncologists in delivering targeted nutritional support to these individuals.

Py–GC–MS analysis for microplastics: Unlocking matrix challenges and sample recovery when analyzing wastewater for polypropylene and polystyrene

Matrix interference and recovery when using pyrolysis gas chromatography (Py–GC–MS) to analyze wastewater for polystyrene (PS) and polypropylene (PP) microplastics (MP) was studied. Raw wastewater underwent a sample preparation train commonly applied for such matrix. The train consisted of six discrete steps to reduce the organic matter content without affecting MP in the sample. One large wastewater sample was collected, homogenized, and subdivided into 21 subsamples. Three samples were analyzed without further sample preparation. The remaining samples were divided in sets of three, and each set underwent an increasing number of steps of the procedure, up to the last set, which underwent the full treatment. The matrix effect on the determination of PS and PP was statistically evaluated by comparing in-matrix and external calibration curves at each step. Recovery of MP was assessed after each step by adding deuterated PS to the samples. A main finding was that there was no significant matrix effect for these polymers throughout the preparation train, suggesting that matrix components did not interfere with the analytical method. However, a significant loss of polymer mass was found between the steps, which may result in MPs falling below detection limits. Therefore, Py–GC–MS could be used for MP quantification before analysis by other techniques which require more extensive matrix removal. A downside of this approach is that analyzing such samples without matrix reduction will increase the need for instrumental maintenance.

Chemical Standardization of Milk Thistle (Silybum marianum L.) Extract Using UHPLC-MS/MS and the Method of Standard Addition.

Extracts prepared from the seeds of the medicinal plant milk thistle [Silybum marianum (L.) Gaertn. (Asteraceae)] are widely used as dietary supplements due to anti-inflammatory, antitumor, and hepatoprotective effects. Called silymarin, the main components of lipophilic extracts of milk thistle seeds are flavonoids and flavonolignans including silybin A, silybin B, isosilybin A, isosilybin B, silydianin, silychristin, taxifolin, and 2,3-dehydrosilybins. The aim of this study was to develop a method based on UHPLC-MS/MS for the chemical authentication and standardization of milk thistle silymarin. Validation included the method of standard addition to account for the lack of a blank matrix. Potential matrix effects were investigated by analyzing silymarin standards dissolved only in the initial UHPLC mobile phase. Measurements of six flavonolignans and taxifolin in the milk thistle extract using UHPLC-MS/MS with standard addition or external standard calibration produced similar results for all analytes except silydianin and 2,3-dehydrosilybin B, which showed significant peak enhancement during negative ion electrospray due to botanical matrix effects. The UHPLC-MS/MS-based method of standard addition requires <10 min per injection and is suitable for the standardization of silymarin from milk thistle in support of preclinical and clinical studies of safety and efficacy.

Techniques for quantification of organochlorine pesticides from a validated method by using gas chromatography-electron capture detector

Organochlorine pesticides (OCPs) are persistent organic compounds found in aquatic environments worldwide. A well-validated and well-established analytical method is crucial for detecting OCPs in the environment. In this study, an analytical method for quantifying OCPs in water was developed and evaluated. Here, the range of linearity, reproducibility, uncertainty, specificity, method detection limits (MDL), and special emphasis on detection and quantitation limits were assessed. Recovery studies were performed to measure the accuracy and precision of the method. This method exhibited excellent linearity in the range of 2.5–20 μg/L for all compounds. As none of the targeted compounds was detected in the chromatograms of the blank sample with no baseline noise, the limits of detection (LOD) and limits of quantification (LOQ) were determined using the linear regression method, external calibration curve slope, and laboratory fortified blank-based detection. All compounds showed different LOD and LOQ values, depending on the approach used. In particular, endosulfan sulfate, methoxychlor, endrin ketone, H. epoxide, heptachlor, and 4,4ʹ-DDT exhibited high detection limits. The recovery percentage of the 15 compounds at 5 μg/L spiked concentration was between 50 and 150 %, which is consistent with the accuracy of the APHA method. Except for endosulfan sulfate, the relative standard deviations of all other compounds were below 20 %, indicating good precision. This method has also been applied to real water samples. This validation technique is reliable, sensitive, simple, rapid, easy to comprehend, and reproducible. The application of this method in the real water samples was also conducted. Only α-BHC and γ-Chlordane were detected in the water sample.

A machine learning clinic scoring system for hepatocellular carcinoma based on the Surveillance, Epidemiology, and End Results database.

Hepatocellular carcinoma (HCC) poses a global threat to life; however, numerical tools to predict the clinical prognosis of these patients remain scarce. The primary objective of this study is to establish a clinical scoring system for evaluating the overall survival (OS) rate and cancer-specific survival (CSS) rate in HCC patients. From the Surveillance, Epidemiology, and End Results (SEER) Program, we identified 45,827 primary HCC patients. These cases were randomly allocated to a training cohort (22,914 patients) and a validation cohort (22,913 patients). Univariate and multivariate Cox regression analyses, coupled with Kaplan-Meier methods, were employed to evaluate prognosis-related clinical and demographic features. Factors demonstrating prognostic significance were used to construct the model. The model's stability and accuracy were assessed through C-index, receiver operating characteristic (ROC) curves, calibration curves, and clinical decision curve analysis (DCA), while comparisons were made with the American Joint Committee on Cancer (AJCC) staging. Ultimately, machine learning (ML) quantified the variables in the model to establish a clinical scoring system. Univariate and multivariate Cox regression analyses identified 11 demographic and clinical-pathological features as independent prognostic indicators for both CSS and OS using. Two models, each incorporating the 11 features, were developed, both of which demonstrated significant prognostic relevance. The C-index for predicting CSS and OS surpassed that of the AJCC staging system. The area under the curve (AUC) in time-dependent ROC consistently exceeded 0.74 in both the training and validation sets. Furthermore, internal and external calibration plots indicated that the model predictions aligned closely with observed outcomes. Additionally, DCA demonstrated the superiority of the model over the AJCC staging system, yielding greater clinical net benefit. Ultimately, the quantified clinical scoring system could efficiently discriminate between high and low-risk patients. A ML clinical scoring system trained on a large-scale dataset exhibits good predictive and risk stratification performance in the cohorts. Such a clinical scoring system is readily integrable into clinical practice and will be valuable in enhancing the accuracy and efficiency of HCC management.

Quantification of linear alkylbenzene sulphonates in complex sludge samples: Influence of matrix effects in calibration methods

Linear Alkylbenzene Sulfonates (LAS) are among the priority organic pollutants in sludge owing to their high concentrations, due to its use as a major cleaning agent (surfactant) in laundry detergents and household cleaning products, and to the concentration limits of 2,600 mg/kg outlined in the EU directive draft for land application of sludge. In this work, a method has been optimized and validated for the accurate quantification of LAS homologues C10-C13 in different types of samples in the sludge treatment and disposal process including primary, secondary, and digested sludge, compost, and soil. The type of matrix had a significant impact on the method’s validation process, resulting in the need of the use of distinct dilution factors for each matrix. Overall, the procedure propose involves a cost-effective and user-friendly technique that combines both extraction and clean-up in a single step (ultrasound-assisted extraction and dispersive solid phase extraction). Analytical determination is conducted using liquid chromatography-tandem mass spectrometry. The method demonstrated excellent accuracy (relative recoveries exceeding 84.5 % across all types of sludge samples) and the limit of quantification enables the measurement of LAS concentrations up to 1000 times lower than the concentration limits stipulated in the EU directive draft. For an accurate quantification, three calibration approaches were tested (external calibration, standard addition, and matrix-matched calibration). Standard addition was the preferred method for quantitative analysis. It helps mitigate matrix effects that could otherwise distort the accuracy of analyte measurements, making it crucial for quality control, especially in monitoring applications where matrix effects may introduce bias into concentration calculations.

Isotope dilution with isotopically labeled biomass: An effective alternative for quantitative metabolomics

BackgroundState-of-the-art quantitative metabolomics relies on isotope dilution using internal standards (IS) derived from fully 13C labeled biomass. By spiking samples and external standards with known amounts of IS, the spike characterization demands are kept to a minimum. In fact, it is sufficient to experimentally assess the isotopic enrichment of the IS. This study develops the yeast derived IS toolbox further, (1) by characterizing the concentration levels of hydrophilic metabolites in a yeast fermentation batch and (2) by exploring the analytical figures of merit of one-point IS versus multipoint external calibration using IS, the established gold-standard for quantitative metabolomics. ResultsIndependent reverse isotope dilution experiments using different chromatographic methods over a period of several months, delivered a list of 83 13C-labeled metabolites with fully characterized concentration and their uncertainty, covering 5 orders of magnitude, from the nanomolar to the low millimolar range. The 13C-labeled yeast-derived IS showed excellent intermediate stability with 92 % of molecules showing inter-method RSDs ≤30 % (75 % of molecules showed RSDs ≤15 %) over a timeframe of five months. One-point internal standardization with the characterized labeled biomass achieved figures of merit equivalent to multipoint calibrations for the majority of metabolites. SignificanceThe proposed calibration workflow rationalizes time and standard expenditure and is particularly beneficial for laboratories dealing with wide-target assays and small analysis batches. The present assessment serves as a seminal study for further developments of the concept towards absolute quantification from archive high-resolution MS data of U13C-biomass-spiked samples and the implementation of quick biomass recalibration with each experiment, promising seamless transition between internal standards derived from different fermentation batches.

Polarimetric calibration of linear dual-pol SAR when corner reflectors are unavailable

Dual-pol synthetic aperture radar (SAR) has a higher resolution and broader swath than the quad-pol system. However, calibration of dual-pol SAR is challenging because only two-channel observations exist: HH&VH and HV&VV. In external calibration, the timely and periodic deployment of corner reflectors (CRs) increases costs when calibration factors vary over time. The effectiveness of such calibration can be increased when the target scattering in the scene is known. This study proposes a novel method that considers a distortion-free image as a benchmark to calibrate the receiving distortion of other products even within several years. The high-correlation targets in the time-series images present stable scattering mechanisms and provide a robust reference to estimate the radiometric calibration factor and channel imbalance (CI). The vegetation with azimuth symmetry is proposed to evaluate the crosstalk (XT) further. The novel technique is tested on dual-pol data of Gaofen-3 and Sentinel-1A. Results confirm that the residual XT is lower than − 35 dB, and the CI error is better than 0.25 dB with a 5° phase error after calibration. Our novel method can calibrate dual-pol data without using CR and reduce the workload in external calibration. The source codes are provided on the website for benchmarking and reproducibility purposes (http://jszy.whu.edu.cn/sl/zh_CN/jxzy/428540/list/index.htm).

A novel surgical scheme for hepatectomy in hepatocellular carcinoma patients with clinically significant portal hypertension

ObjectiveClinically significant portal hypertension (CSPH) seriously affects the feasibility and safety of surgical treatment for hepatocellular carcinoma (HCC) patients. The aim of this study was to establish a new surgical scheme defining risk classification of post-hepatectomy liver failure (PHLF) to facilitate the surgical decision-making and identify suitable candidates for individual hepatectomy among HCC patients with CSPH.BackgroundsHepatectomy is the preferred treatment for HCC. Surgeons must maintain a balance between the expected oncological outcomes of HCC removal and short-term risks of severe PHLF and morbidity. CSPH aggravates liver decompensation and increases the risk of severe PHLF thus complicating hepatectomy for HCC.MethodsMultivariate logistic regression and stochastic forest algorithm were performed, then the independent risk factors of severe PHLF were included in a nomogram to determine the risk of severe PHLF. Further, a conditional inference tree (CTREE) through recursive partitioning analysis validated supplement the misdiagnostic threshold of the nomogram.ResultsThis study included 924 patients, of whom 137 patients (14.8%) suffered from mild-CSPH and 66 patients suffered from (7.1%) with severe-CSPH confirmed preoperatively. Our data showed that preoperative prolonged prothrombin time, total bilirubin, indocyanine green retention rate at 15 min, CSPH grade, and standard future liver remnant volume were independent predictors of severe PHLF. By incorporating these factors, the nomogram achieved good prediction performance in assessing severe PHLF risk, and its concordance statistic was 0.891, 0.850 and 0.872 in the training cohort, internal validation cohort and external validation cohort, respectively, and good calibration curves were obtained. Moreover, the calculations of total points of diagnostic errors with 95% CI were concentrated in 110.5 (range 76.9-178.5). It showed a low risk of severe PHLF (2.3%), indicating hepatectomy is feasible when the points fall below 76.9, while the risk of severe PHLF is extremely high (93.8%) and hepatectomy should be rigorously restricted at scores over 178.5. Patients with points within the misdiagnosis threshold were further examined using CTREE according to a hierarchic order of factors represented by the presence of CSPH grade, ICG-R15, and sFLR.ConclusionThis new surgical scheme established in our study is practical to stratify risk classification in assessing severe PHLF, thereby facilitating surgical decision-making and identifying suitable candidates for individual hepatectomy.

A high-throughput supramolecular solvent method for benzophenone quantification in juice packs via liquid chromatography-mass spectrometry

Benzophenones (BPs) are among the most frequently detected photoinitiators in liquid packaged food, mainly using liquid chromatography tandem mass spectrometry (LC-MS/MS). However, sample treatments in these methods are demanding in terms of resources and expenses, are little or not sustainable, and prevent high throughput sample processing. In this paper, supramolecular solvents with properties of restricted access materials (SUPRAS-RAM) were firstly used to make simpler, faster and more sustainable the sample treatment in the determination of BPs in juices. The SUPRAS-RAM formed instantaneously in the juice by the addition of hexanol (75 µL) and tetrahydrofuran (600 µL). The BPs were extracted after vortexing the mixture for 2 min. Macromolecules in the sample, including carbohydrates, fibers and proteins were not extracted in the SUPRAS-RAM by physical or chemical mechanisms, so the extract could be directly injected into the LC-MS/MS system. Detection limits were in the range of 0.013–0.414ng mL−1. Average recoveries of BPs in juices (orange, tomato, pineapple, apple) at three concentration levels (1, 5 and 20ng mL−1) were 70.4–118 %, with relative standard deviations in the interval 0.05–9 %. Matrix effects were all in the range of ±20 %. Unlikely most of the reported methods for BPs based on LC-MS/MS, external calibration gave reliable quantification. Three BPs were detected and/or quantified in the ten juice samples analysed; namely benzophenone, 2-hydroxy-4-methoxybenzophenone and 4-methylbenzophenone. The characteristics of the sample treatment here proposed (i.e. low solvent consumption, integration of BP extraction and sample clean-up and a simple and fast procedure, and the effective removal of interferences) render this method competitive for BPs quantification with respect to previously reported methods.

Accurate extrinsic calibration for the invisible-light 1D laser rangefinder and camera

Abstract A combined sensor, comprising a camera and a one-dimensional laser rangefinder (1D LRF), has wide application across engineering sectors, notably in aerospace. This combined sensor is pivotal for earth observation and deep space exploration. To achieve precise and stable external parameters for this combined sensor, an accurate external calibration method is proposed. Initially, a technique for localized registration of laser spots is introduced to ensure precise determination of their positions, addressing the challenge of laser invisibility in a 1D LRF. Subsequently, a data evaluation criterion known as the data synthesis criterion is presented, addressing the issue of limited constraints in traditional calibration methods. This criterion evaluates relative errors encompassing 1D LRF ranging values, camera external parameters, and laser spot positions. Finally, based on the proposed criteria, a robust extrinsic calibration method is introduced that automatically filters observation data with significant errors and utilizes the growth rate of camera spatial resolution as the termination condition. The efficacy of the proposed method is confirmed through simulation experiments and real-world data experiments.

Evaluation of Two Digital Wound Area Measurement Methods Using a Non-Randomized, Single-Center, Controlled Clinical Trial

A prospective, single-center, non-randomized, pre-marketing clinical investigation was conducted with a single group of subjects to collect skin lesion images. These images were subsequently utilized to compare the results obtained from a traditional method of wound size measurement with two novel methods developed using Machine Learning (ML) approaches. Both proposed methods automatically calculate the wound area from an image. One method employs a two-dimensional system with the assistance of an external calibrator, while the other utilizes an Augmented Reality (AR) system, eliminating the need for a physical calibration object. To validate the correlation between these methods, a gold standard measurement with digital planimetry was employed. A total of 67 wound images were obtained from 41 patients between 22 November 2022 and 10 February 2023. The conducted pre-marketing clinical investigation demonstrated that the ML algorithms are safe for both the intended user and the intended target population. They exhibit a high correlation with the gold standard method and are more accurate than traditional methods. Additionally, they meet the manufacturer’s expected use. The study validated the performance, safety, and usability of the implemented methods as a valuable tool in the measurement of skin lesions.

Fast neonicotinoid quantification in honey using the one-point internal calibration approach

Neonicotinoids, a highly effective class of insecticides used worldwide, have been identified as a major cause of concern for biodiversity. To assess the ecological and environmental consequences of neonicotinoids' use, reliable analytical methodologies, including calibration approaches, are needed. Here, we compared the performance of internal calibration (IC) using a single concentration of stable isotope-labeled standard (SIL) with classical multipoint external calibration (EC) for the quantification of six neonicotinoids in honey. IC showed acceptable levels of trueness (86.3% - 116.0%) and precision (1.4% - 20.8%), although slight biases were observed at very low concentrations compared to EC. When applied to 32 original honey samples, both approaches showed strong agreement (R2 > 0.998) with proportional biases lower than 5%. These results highlight the possibility of implementing IC to simplify quantification in liquid chromatography-mass spectrometry-based pesticide applications.

Ambient ionization mass spectrometry provides screening of selective androgen receptor modulators

Ambient ionization mass spectrometry allows for analysis of samples in their natural state, i.e., with no sample pre-treatment. It can be viewed as a fast, simple, and economical analysis, but its main disadvantages include a lower analytical performance due to the presence of complex sample matrix and the lack of chromatographic separation prior to the introduction of the sample into the mass spectrometer. Here we present an application of two ambient ionization mass spectrometry techniques, i.e., Desorption Atmospheric Pressure Photoionization and Dielectric Barrier Discharge Ionization, for the analysis of known Selective Androgen Receptor Modulators, which represent common compounds of abuse in professional and semiprofessional sport. Eight real samples of illegal food supplements, seized by the local law enforcement, were used to test the performance of the ambient mass spectrometry and the results were validated against a newly developed targeted LC-UV-MS/MS method performed in multiple reaction monitoring mode with an external calibration for each analyte. In order to decide whether or not the compound can be declared as present, we proposed a system of rules for the interpretation of the obtained spectra. The criteria are based on mass spectrum matching (5–10 ppm accuracy from the theoretical exact mass and a correct isotopic pattern), duration of the mass signal (three or five consecutive scans, depending on the instrumentation used), and intensity above the background noise (threefold increase in intensity and absolute intensity above 5E4 or 1E5, depending on the instrumentation). When applying these criteria, good agreement was found between the tested methods. Ambient ionization techniques were effective at detecting SARMs at pharmacologically relevant doses, i.e., approximately above 1 mg per capsule, although they may fail to detect lower levels or isomeric species. It is demonstrated that when adhering to a set of clear and consistent rules, ambient mass spectrometry can be employed as a qualitative technique for the screening of illegal SARMs with sufficient confidence and without the necessity to perform a regular LC-MS analysis.

Self-calibration strategies for reducing systematic slope measurement errors of autocollimators in deflectometric profilometry.

Deflectometric profilometers are used to precisely measure the form of beam shaping optics of synchrotrons and X-ray free-electron lasers. They often utilize autocollimators which measure slope by evaluating the displacement of a reticle image on a detector. Based on our privileged access to the raw image data of an autocollimator, novel strategies to reduce the systematic measurement errors by using a set of overlapping images of the reticle obtained at different positions on the detector are discussed. It is demonstrated that imaging properties such as, for example, geometrical distortions and vignetting, can be extracted from this redundant set of images without recourse to external calibration facilities. This approach is based on the fact that the properties of the reticle itself do not change - all changes in the reticle image are due to the imaging process. Firstly, by combining interpolation and correlation, it is possible to determine the shift of a reticle image relative to a reference image with minimal error propagation. Secondly, the intensity of the reticle image is analysed as a function of its position on the CCD and a vignetting correction is calculated. Thirdly, the size ofthe reticle image is analysed as a function of its position and an imaging distortion correction is derived. It is demonstrated that, for different measurement ranges and aperture diameters of the autocollimator, reductions in the systematic errors of up to a factor of four to five can be achieved without recourse to external measurements.