Analytical Variables Research Articles

The variability of measured and calculated low-density lipoprotein (LDL) cholesterol in statin-treated diabetes patients.

The Sampson-NIH and Martin-Hopkins low-density lipoprotein cholesterol (LDL-C) equations are advocated as being superior to the Friedewald calculation. However, their mathematical complexity means they may have different biological and analytical variation when tracking LDL-C in the same patient. This study has established the biological variation (BV) of calculated and directly measured LDL-C (dLDL-C) in patients taking equivalent doses of a long (atorvastatin) and short (simvastatin) half-life statin. It also modelled how analytical imprecision might add to these BVs. In a crossover study of lipid BV involving 26 patients with type 2 diabetes (T2DM) initially taking either simvastatin 40mg or atorvastatin 10mg, fasting lipids were measured 10 times over 5 weeks after a 3 month run-in. The same procedure was then followed for the alternate statin. Outlier removal and CV-ANOVA established the BV of dLDL and each formula. Analytical measurement uncertainty was estimated from 6 months of real-world data. The intra-individual BV of dLDL-C measurement was considerably lower with atorvastatin than simvastatin (CV 1.3%(95% CI 1.1-1.5%) vs. 11.1%(10.2-12.2%), respectively). No equation could distinguish this difference (Friedewald 11.0%(95% CI 10.0-12.1%) vs. 12.9%(11.8-14.2%), Sampson-NIH 10.4%(9.5-11.5%) vs. 11.7% (10.7-12.8%) and Martin-Hopkins 9.3%(8.5-10.3%) vs. 11.3%(10.3-12.4%)). Real-world analytical CVs were 2.6% (Sampson-NIH), 2.6% (Martin-Hopkins) 2.8% (Friedewald) and 2.0% (dLDL-C). Inherent biological LDL-C variability using these formulae is substantially greater than direct measurement in T2DM patients taking atorvastatin. Typical analytical imprecision was also greater. Together, this may fundamentally limit these equations' ability to track true LDL-C changes in patients taking popular statin treatments.

COVID-19 clinical phenotypes in vaccinated and nonvaccinated solid organ transplant recipients: a multicenter validation study

Clinical phenotypes of COVID-19, associated with mortality risk, have been identified in the general population. The present study assesses their applicability in solid organ transplant recipients (SOTR) hospital-admitted by COVID-19. In a cohort of 488 SOTR, nonvaccinated (n = 394) and vaccinated (n = 94) against SARS-CoV-2, we evaluated 16 demographic, clinical, analytical, and radiological variables to identify the clinical phenotypes A, B, and C. The median age was 61.0 (51–69) years, 330 (67.6%) and 158 (32.4%) were men and women, respectively, 415 (85%) had pneumonia, and 161 (33%) had SpO2 < 95% at admission. All-cause mortality occurred in 105 (21.5%) cases. It was higher in nonvaccinated versus vaccinated SOTR (23.4% vs 13.8%, P = 0.04). Patients in the entire cohort were classified into phenotypes A (n = 149, 30.5%), B (n = 187, 38.3%), and C (n = 152, 31.1%), with mortality rates of 8.7%, 16.6%, and 40.1%, respectively, which were similar to those of nonvaccinated SOTR (9.5%, 16.7%, and 52.0%) and lower in vaccinated SOTR (4.4%, 15.8%, and 17.3%, respectively), with difference between nonvaccinated and vaccinated in the phenotype C (P < 0.001). In conclusion, COVID-19 clinical phenotypes are useful in SOTR, and all-cause mortality decreases in vaccinated patients.

Uncertainty Analysis in Ecological Risk Assessment: Sensitivity Analysis of Different Exposure Models

Ecological risk assessments are used for decision making regarding releasing new products to market as well as remedial action required to address contamination at legacy sites. Uncertainty analysis is considered a key part of the process (USEPA 1997) but assessments seldom address uncertainty quantitatively. Key sources of uncertainty include: Sampling and analytical variability (soil/ sediment matrix, lab error), Choice of “Indicator” Species as Target Receptors for Different Exposure Pathways, Sample size. Bioaccumulation and Food Chain Modeling involve assumptions and uncertainty including: Linear assumption of bioaccumulation and selection of bioaccumulation factors; Other model inputs such as: Home range size, Dietary percentages, Body Weights, Ingestion Rate; Literature data on effects, Use of No Observed Adverse Effects Level (NOAEL) or Lowest Observed Adverse Effects Level (LOAEL) as a decision point. Toxicity testing where employed brings its own set of assumptions and interpretation that are subject to uncertainty. These include: Use of single organism, Correlation (or lack thereof) with Contaminants of Concern, Contaminant mixtures, and Reference area selection. Keywords: Ecotoxicology, Risk Assessment, Ecology

Secondary use of routinely collected administrative health data for epidemiologic research: Answering research questions using data collected for a different purpose.

The use of routinely collected administrative health data for research can provide unique insights to inform decision-making and, ultimately, support better public health outcomes. Yet, since these data are primarily collected to administer healthcare service delivery, challenges exist when using such data for secondary purposes, namely epidemiologic research. Many of these challenges stem from the researcher's lack of control over the quality and consistency of data collection, and - furthermore - a lessened understanding of the data being analyzed. That said, we assert that these challenges can be partly mitigated through careful, systematic use of these data in epidemiologic research. This article presents considerations derived from experiences analyzing administrative health data (e.g., healthcare practitioner billings, hospitalizations, and prescription medication data) in the Canadian province of British Columbia (population of over 5 million in 2024), though we believe the underlying principles generalize beyond this region. Key considerations were organized around four themes: 1) Know the data and their primary use (understand their scope and limitations); 2) Understand classification and coding systems (appreciate the nuances regarding classification systems, versions, how they are employed in the primary uses of the data, and querying the values); 3) Transform data into meaningful forms (process data and apply identification algorithms, when necessary); 4) Recognize the importance of validity when defining analytic variables (make meaningful inferences based on data/algorithms). Although this article is not an exhaustive list of all considerations, we believe that it will provide pragmatic insights for those interested in leveraging administrative health data for epidemiologic research.

Effect of different pooled qc samples on data quality during an inter-batch experiment in untargeted UHPLC-HRMS analysis on two different MS platforms.

Quality control (QC) samples are commonly used in metabolomics approaches for three main reasons: (i) the initial conditioning of the column; (ii) the correction of analytical drift especially between batches; and (iii) the evaluation of measurement precision. In practice, there are several ways to prepare and conserve QC samples. The most common in untargeted metabolomics is to pool samples after or before extraction, in order to obtain pooled QC samples accounting, respectively, for analytical variance or for both analytical and sample preparation variances. In this study, focusing on untargeted analysis of tea (Camellia sinensis) leaves, we compared three ways of preparing pooled QC samples (two usual and one unusual QC sample preparations) and their efficiency to improve data quality in terms of inter-batch correction, measurement precision, and VIP candidates selection on datasets obtained using two mass spectrometry (MS) technologies (Orbitrap and time of flight (QToF)). We also investigated the effect of data processing modalities, based on the different QC preparations, on data loss and on the global structure of the datasets. Generally, our results show that usual QC sample preparation leads to comparable datasets quality in terms of precision and dispersion on both MS instruments. They also show that QC preparation is crucial for VIP selection; in fact, up to 54% of biomarkers candidates were specific of the QC preparation type used for data processing.

Abstract A034: Addressing the challenge of pre-analytical variables in circulating protein biomarker analysis for liquid biopsy

Abstract While liquid biopsy using plasma proteins is a promising avenue for early cancer detection, diagnosis, prognosis, and disease monitoring, the clinical translation of plasma proteomics has been limited due to the complexity of the proteome and the impact of pre-analytical variation on its integrity. In recent years, advancements in plasma proteomic technologies, such as Olink, have helped address the challenge of proteome complexity. However, challenges with pre- analytical variables have persisted. During blood collection and whole blood storage, ex vivo blood cell activation and lysis can occur, releasing proteins into the plasma and obscuring relevant in vivo abundances. This is especially true for cancer where many key biomarkers are expressed in white blood cells and platelets. To address these issues, Streck developed Protein Plus BCT, which stabilizes blood cells and minimizes plasma proteome changes following prolonged whole blood storage. To demonstrate its functionality and compatibility with Olink assays, blood was collected from 5 donors into EDTA, ACD-A, and Protein Plus BCT. Plasma was isolated at draw time (&amp;lt; 2 hours) and following 24, 72, and 120 hours of ambient whole blood storage using a double spin protocol (per Instructions for Use). Plasma samples were analyzed using an Olink Explore 384-plex panel. After just 24 hours of whole blood storage, plasma isolated from EDTA or ACD-A had 4.2 and 2.2 times more proteins that are significantly elevated in abundance relative to draw time, respectively, than plasma isolated from Protein Plus BCT. For the key cancer biomarker IL-8, which is associated with worse clinical outcomes and poor response to immunotherapy, Protein Plus BCT maintains draw-time levels such that the mean NPX difference relative to draw time never exceeds -0.18, or a 0.88-fold change for up to 120 hours. Comparatively, samples collected into EDTA and ACD-A show dramatic increases in IL-8 levels with mean NPX differences of 6.34 and 6.62, respectively, corresponding to 80.1- and 98.3-fold increases after 120 hours of whole blood storage. For Galectin-3, an inflammatory biomarker elevated in many cancer types (i.e., lung, breast, and ovarian), the mean NPX difference relative to draw time for samples drawn into EDTA is 1.46 after 24 hours and increases to 2.27 after 120 hours of whole blood storage (a 2.75-fold and 4.8-fold increase, respectively). In samples collected into Protein Plus BCT, the mean NPX difference, relative to draw time, for Galectin-3 never exceeds 0.05 (a 1.04-fold increase) even after 120 hours of whole blood storage. Taken together, these data suggest that Protein Plus BCT extends the storage of whole blood at ambient temperature without compromising the integrity of the sample or the proteomic results, providing researchers and assay developers valuable flexibility in the transport, storage, and processing of their samples. Protein Plus BCT is for Research Use Only. Not for use in diagnostic procedures. Protein Plus BCT should only be used for research or the development of new assays. Citation Format: Rachel M Miller, Jing Li. Addressing the challenge of pre-analytical variables in circulating protein biomarker analysis for liquid biopsy [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A034.

Abstract 4125999: Elderly patients with HF with reduced ejection fraction and renal failure: are SGLT2i a good option?

SGLT-2 receptor inhibitors (SGLT-2i) have been shown to reduce cardiovascular mortality and hospitalisations for heart failure (HF) in patients with HF with reduced ejection fraction (HFrEF) and to have a nephroprotective role in patients with chronic kidney disease (CKD). However, the available evidence in patients older than 75 years and with CKD is limited. We conducted a retrospective, single-centre study including patients aged &gt;75 years diagnosed with HFrEF (defined as ejection fraction &lt;40%), CKD (eGFR &lt;60mL/min) and with an indication for treatment with SGLT-2i for HF between November 2019 and November 2022. Clinical, analytical, electrocardiographic and echocardiographic variables were collected. A total of 173 patients were included with a mean follow-up of 31.6 months (SD ±13.2). Mean age 84.7 years (SD ±4.9), 66.5% were male, 85.5% were hypertensive, 34.7% diabetic and 39.3% dyslipidaemic. Peripheral vascular disease was present in 11.6%. Mean glomerular filtration rate was 45.9 mL/min and only 2.3% were on haemodialysis. LVEF at inclusion was 29.5% (SD ±7.9), with 53.1% being of ischaemic aetiology and the most frequent functional class being NYHA II (53.2%). Thirty-seven percent had atrial fibrillation and 34.1% had complete left bundle branch block. At the end of follow-up 80.3% were on beta-blockers, 48.6% on ACE inhibitors, 37.6% on aldosterone antagonists, 23.7% on ARNI and only 11% on SGLT-2i. Survival analysis was performed using Cox logistic regression (multivariate analysis), where SGLT-2i was shown to be the only protective factor for all-cause mortality (HR 0.3 [0.1-0.9]). Male sex (HR 2.2 [1.3-3.9]) and diabetes (HR 1.6 [1.0-2.6]) were associated with higher mortality. The attached figure (figure 1) shows the Kaplan-Meier survival curves for total mortality in those patients with and without SGLT-2i (p log-rank 0.05). In our population, SGLT2i showed a significant reduction in total mortality in patients older than 75 years, with HFrEF and CKD. However, the treatment rate in this subgroup of patients is very low. Further implementation of SGLT2i treatment in this type of patient could be of significant clinical and prognostic benefit.

PATH-46. DETECTION OF CDKN2A/B HOMOZYGOUS DELETION BY DNA METHYLATION AND DNA NEXT GENERATION SEQUENCING: A COMPARISON

Abstract CDKN2A/B homozygous deletion status is critical for grading of several central nervous system (CNS) tumors, including IDH mutant astrocytoma, pleomorphic xanthoastrocytoma, and meningioma. CDKN2A/B status can be assed using variety of methods, but the accuracy varies due to technical and analytical variables. Here we sought to compare sensitivity of CDKN2A/B homozygous deletion detection by NGS and DNA methylation array derived copy number analysis. We retrospectively analyzed 100 CNS tumors diagnosed at NYU Langone Health between 2020 and 2023. All tumors were profiled by whole genome DNA methylation profiling using the Illumina EPIC array. Copy numbers were analyzed using the ‘conumee’ R package and visual inspection. Next-generation sequencing (NGS) analysis was performed by NYU Langone Genome PACT, a 510(k) FDA cleared (K202304) matched tumor-normal 607 gene panel. Variant allele frequency of canonical driver mutations was used to molecularly estimate tumor cell content. Discrepant cases were assed using targeted qPCR. Our cohort of 100 CNS tumors included glioblastoma (N=75), IDH mutant astrocytoma (N=14), oligodendroglioma (N=4), pleomorphic xanthoastrocytoma (N=2), meningioma (N=1), infantile hemispheric glioma (N=1), central neurocytoma (N=1), diffuse midline glioma K27-altered (N=1), and anaplastic pilocytic astrocytoma (N=1). By DNA methylation, CDKN2A/B homozygous deletion was detected in 54 cases, hemizygous deletion was detected in 13 cases, and no deletion was detected in 33 cases. By NGS, CDKN2A/B homozygous deletion was detected in 31 cases, hemizygous deletion was detected in 28, and no deletion was detected in 41 cases. DNA methylation detected a homozygous deletion in 23 cases that were not detected by NGS. All cases of homozygous deletion detected by NGS were also detected by DNA methylation. DNA methylation derived copy number assessment is more sensitive for CDKN2A/B homozygous deletion than next generation sequencing.

Diagnostic Value of Serum Sodium Level and Neutrophil-to-Lymphocyte Ratio in Predicting Severity of Acute Appendicitis: A Retrospective Cross-Sectional Two-Center Study.

Background and Objectives: The best way to distinguish complicated acute appendicitis (CAA) from uncomplicated acute appendicitis (UCAA) is still under debate. The aim of this study was to investigate the potential use of the serum sodium (Na+) level and the neutrophil-to-lymphocyte ratio (NLR) to distinguish CAA from UCAA and to evaluate whether CAA is more reliably diagnosed using these two variables together. Materials and Methods: This was a retrospective, cross-sectional, two-center study of patients diagnosed with AA between 1 January 2016 and 31 December 2023. The demographic and analytical variables were analyzed. The NLR was defined as the quotient between the absolute values of neutrophils and lymphocytes. Hyponatremia was defined as a serum Na+ level of ≤135 mmol/L. The sensitivity and specificity of the NLR and the serum Na+ level in the diagnosis of CAA were determined by assessing the receiver operating characteristic curves. Results: Among the patients who underwent an appendectomy, 3066 histologically confirmed AA cases were identified and included in this study. The registered data revealed that 348 (11.3%) patients had CAA, and the remaining 2718 (88.7%) patients had UCAA. The mean ages were 49.47 ± 18.97 and 38.16 ± 14.50, respectively (p < 0.001). The analysis revealed an exponential correlation between the NLR and the serum Na+ level with a moderate degree of agreement with CAA (Cohen's Kappa: 0.461, p < 0.001). For CAA, using the NLR and the serum Na+ level, the areas under the curve and the cutoffs were 0.664, 4.2 with a confidence interval (CI) of 0.647-0.681 and 0.727, 135 mmol/L with a CI of 0.711-0.742, respectively; all these values were significant with a p-value of <0.001. Conclusions: Although the serum Na+ level is a more effective marker than the NLR, using these two variables together can help detect high-risk patients who may benefit from early management by limiting delays in surgery.

A multimodal understanding of the role of sound and music in gendered toy marketing.

Literature in music theory and psychology shows that, even in isolation, musical sounds can reliably encode gender-loaded messages. Musical material can be imbued with many ideological dimensions and gender is just one of them. Nonetheless, studies of the gendering of music within multimodal communicative events are sparse and lack an encompassing theoretical framework. The present study attempts to address this literature gap by employing a critical quantitative analysis of music in gendered toy marketing, which integrated a content analytical approach with multimodal affective and music-focused perceptual responses. Ratings were collected on a set of 606 commercials spanning a ten-year time frame and strong gender polarization was observed in nearly all of the collected variables. Gendered music styles in toy commercials exhibit synergistic design choices, as music in masculine-targeted adverts was substantially more abrasive-louder, more inharmonious, and more distorted-than in feminine-targeted ones. Thus, toy advertising music appeared deliberately and consistently in line with traditional gender norms. In addition, music perceptual scales and voice-related content analytical variables explain quite well the heavily polarized affective ratings. This study presents a empirical understanding of the gendering of music as constructed within multimodal discourse, reiterating the importance of the sociocultural underpinnings of music cognition. We provided a public repository with all code and data necessary to reproduce the results of this study on github.com/marinelliluca/music-role-gender-marketing.

Unraveling Uncertainty: The Impact of Biological and Analytical Variation on the Prediction Uncertainty of Categorical Prediction Models.

Interest in prediction models, including machine learning (ML) models, based on laboratory data has increased tremendously. Uncertainty in laboratory measurements and predictions based on such data are inherently intertwined. This study developed a framework for assessing the impact of biological and analytical variation on the prediction uncertainty of categorical prediction models. Practical application was demonstrated for the prediction of renal function loss (Chronic Kidney Disease Epidemiology Collaboration [CKD-EPI] equation) and 31-day mortality (advanced ML model) in 6360 emergency department patients. Model outcome was calculated in 100 000 simulations of variation in laboratory parameters. Subsequently, the percentage of discordant predictions was calculated with the original prediction as reference. Simulations were repeated assuming increasing levels of analytical variation. For the ML model, area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity were 0.90, 0.44, and 0.96, respectively. At base analytical variation, the median [2.5th-97.5th percentiles] percentage of discordant predictions was 0% [0%-28.8%]. In addition, 7.2% of patients had >5% discordant predictions. At 6× base analytical variation, the median [2.5th-97.5th percentiles] percentage of discordant predictions was 0% [0%-38.8%]. In addition, 11.7% of patients had >5% discordant predictions. However, the impact of analytical variation was limited compared with biological variation. AUROC, sensitivity, and specificity were not affected by variation in laboratory parameters. The impact of biological and analytical variation on the prediction uncertainty of categorical prediction models, including ML models, can be estimated by the occurrence of discordant predictions in a simulation model. Nevertheless, discordant predictions at the individual level do not necessarily affect model performance at the population level.

Mycobacterium avium complex (MAC) infection in severely immunocompromised people living with HIV: Findings from a five-year cohort.

We sought to clarify the current incidence, risk factors and symptoms of disseminated Mycobacterium avium complex (dMAC) infection in admitted people living with HIV in a hospital in the Southeast of Spain. 5-years observational, retrospective and single-centre study. Demographic, clinical and analytical variables, along with microbiological, treatment and follow-up were collected. Five cases of dMAC infection in severely immunocompromised people living with HIV people living were found. dMAC was diagnosed in 22.7% of patients under 100 CD4. All patients presented with fever and clinical manifestations of pneumonia, lymphadenopathy, or gastrointestinal symptoms. Despite low CD4 levels and high viral loads in some cases, primary prophylaxis had not been previously administered. Until 2018, U.S. American guidelines recommended antimycobacterial prophylaxis for patients with low CD4 cell counts, a practice not adopted in Europe. Untreated dMAC infection is associated with high morbidity and mortality rates. dMAC infection represents a prevalent disease in severely immunosuppressed people living with HIV. dMAC requires a high index of suspicion in this population, in order to perform mycobacterial cultures from different samples.

Biological Variation of Hemostasis Analytes in Atrial Fibrillation Patients Using Dabigatran.

Analytical criteria for laboratory analysis based on biological variation are considered state-of-the-art. While biological variance should ideally be measured in patient populations for whom the tests are relevant, data are mostly only available from healthy individuals. We determined the biological variance of activated partial thromboplasmin time (APTT), prothrombin time (PT), fibrinogen, and trough dabigatran levels in patients with atrial fibrillation (AF) who were treated with dabigatran. Between 2019 and 2022, patients with AF treated >3 months with dabigatran were included. Blood was collected monthly up to 10 times for the measurement of APTT, PT, fibrinogen, and trough dabigatran levels. Between-subject variance (CVG), within-subject variance (CVI), and analytical variance (CVA) were calculated. Eighteen participants (median age 65.8 years, 22.2% women) were included, with 130 samples in total. For APTT, the CVG was 11.5%, the CVI 8.8%, and the CVA 1.1%. For PT, these values were 5.2%, 4.0%, and 1.0% and for fibrinogen 13.6%, 11.8%, and 1.6%, respectively. For the dabigatran levels, the percentages were 37.9%, 33.0%, and 3.4%, respectively. We assessed the biological variance of APTT, PT, fibrinogen, and dabigatran in a patient population with long-term dabigatran use. The analytical performances of coagulation laboratory tests in patients with AF treated with dabigatran were comparable to those in healthy volunteers.CCMO Registration Number: NL67304.078.18.

Estimating Reference Change Values Using Routine Patient Data: A Novel Pathology Database Approach.

The reference change value (RCV) is calculated by combining the within-subject biological variation (CVI) and local analytical variation (CVA). These calculations do not account for the variation seen in preanalytical conditions in routine practice or CVI in patients presenting for treatment. As a result, the RCVs may not reflect routine practice or align with clinicians' experiences. We propose a novel RCV approach based on routine patient data that is potentially more clinically relevant. This study used the refineR algorithm to determine RCVs using serial patient data extracted from a local Laboratory Information System (LIS). The model was applied to biomarkers with a range of result ratio distributions varying from normal to log-normal. Results were compared against conventional formula-based RCVs using CVI estimates from a state-of-the-art biological variation study. Monte Carlo simulations were also used to validate the LIS data approach. The RCVs estimated from LIS data were: 11-deoxycortisol (men): -70%/+196%, 17-hydroxyprogesterone (men): -49%/+100%, albumin: -10%/+11%, androstenedione (men): -47%/+96%, cortisol (men): -54%/+51%, cortisone (men): -32%/+51%, creatinine: -16%/+14%, phosphate (women): -23%/+29%, phosphate (men): -27%/+29%, testosterone (men): -38%/+60%. The formula-based RCV estimates showed similar but slightly lower results, and the Monte Carlo simulations confirmed the applicability of the new approach. RCVs may be estimated from patient results without prior assumptions about the shape of the ratios between serial results. Laboratories can determine RCVs based on local practice and population.

Determining factor of enzyme conjugates, bridge heterology and analytical variables of immunogens in prednisolone ELISA

In this study, adipic acid dihydrazide (ADH), carbohydrazide (CH), ethylenediamine (EDA), and urea (U) were used as spacer molecules, covalently linking prednisolone (PSL) to carrier proteins for immunogen preparation, and PSL to enzymes for enzyme conjugate preparation using N-hydroxysuccinimide (NHS)-mediated carbodiimide reactions. The resulting immunogens were used to generate antiserum in New Zealand white rabbits. Antibodies produced against immunogens with various spacers were tested for immunoreactivity with enzyme conjugates, both with and without spacers, in a total of twenty different combinations. All combinations demonstrated binding and were subsequently evaluated through displacement studies. Sensitivity and specificity tests revealed that the combination of PSL-21-HS-U-BSA-antibody with PSL-21-HS-HRP enzyme conjugate exhibited the best sensitivity (0.032 ng/mL) and limited cross-reactivity with other steroids. This combination was further examined for analytical parameters, showing recovery rates of 93.87–101.19 % for PSL from spiked human serum samples, with intra- and inter-assay CVs of <8.88 %. The serum PSL values obtained by this method showed strong correlation with a commercially available ELISA kit (r2 = 0.97, n = 78).

Effects of model-overfit on model-assisted forest inventory in boreal forests with remote sensing data

Abstract While remote sensing can be an effective tool in building a forest inventory, field measurements and model fitting can be both expensive and challenging. One strategy to reduce forest inventory costs is to leverage forest inventory models fitted to a different population (external models), although the effectiveness of external models is poorly understood. One concern is that models may predict well to the sample data, but poorly to the population—which is termed ‘overfitting’. The effect of overfit may be especially problematic in attempts to predict for a different population (a forest area not covered by any sample plots). Assessing overfit is difficult and its consequence for estimation are not well understood, especially in the context of prediction using external models. This study assesses how overfitting affects model-assisted forest inventory estimation when using internal and external models. We used field and remotely sensed data (Sentinel-2 images and airborne laser scanning data) from two forest areas in Finland. We evaluated four modeling approaches: ordinary least square regression (OLS), random forest, k-nearest neighbors, and gaussian process regression. Both analytical and bootstrap variance estimators were used to evaluate model-assisted estimation performance. Internal models, especially OLS, were the most affected by model overfitting, leading to bias in the population means and underestimation of variance. Estimates using external models provided unbiased means and realistic intervals except in the case of deliberate excessive overfitting. The bootstrap variance estimator was found to be more robust to overfit than the analytical variance estimator for the internal model, but was not helpful for the external model. Internal models should be parsimonious to generalize well to the population and avoid bias. The bootstrap estimator of variance is recommended for internal models, especially if there is concern about model overfitting.

Review: Current Laboratory and Point-of-Care Pharyngitis Diagnostic Testing and Knowledge Gaps.

Pharyngitis is an inflammatory condition of the pharynx and/or tonsils commonly seen in both children and adults. Viruses and bacteria represent the most common encountered etiologic agents-yeast/fungi and parasites are infrequently implicated. Some of these are predominantly observed in unique populations (eg, immunocompromised or unvaccinated individuals). This manuscript (part 2 of 3) summarizes the current state of laboratory and point-of-care diagnostic testing and highlights the expanding role of nucleic acid amplification in the expedited diagnosis and management of patients with acute pharyngitis. It discusses preanalytical, analytical, and postanalytical variables that impact the performance of culture, rapid antigen, and nucleic acid amplification testing. Finally, it sets the stage for part 3, which discusses the emerging role of biomarkers in the management of individuals with acute pharyngitis.

Assay variables and early clinical evaluation of low-angle light scattering for platelet function analysis.

The recently developed platelet aggregation technique based on low-angle light scattering (LaSca) in diluted platelet-rich plasma (PRP) requires only a small sample volume and provides information about platelet aggregation and shape change. This study aimed to investigate the influence of preanalytical and analytical variables and to validate the method in a real-life pediatric hematology hospital setting. Platelet aggregation was induced by ADP in diluted PRP in the presence of 2mM calcium at 23°C. The study included healthy adults (n = 30), healthy children (n = 20), and pediatric patients with suspected or diagnosed platelet function abnormalities (n = 25). The assay parameters were stable for at least 3h after isolation of PRP and were sensitive to plasma dilution in the range of 2-8%. The initial aggregation velocity was significantly reduced in pediatric patients compared with healthy children (p < 0.05). ADP-induced light transmission amplitude was moderately correlated with LaSca amplitude of aggregation in healthy children (p = 0.52, p < 0.05) but not in pediatric patients. We standardized the protocol for platelet aggregation assessment by LaSca and characterized the influence of preanalytical and analytical variables on it.

Evaluation of inter- and intra-variability in gut health markers in healthy adults using an optimised faecal sampling and processing method.

Despite advances in gut health research, the variability of important gut markers within individuals over time remains underexplored. We investigated the intra-individual variation of various faecal gut health markers using an optimised processing protocol aimed at reducing variability. Faecal samples from ten healthy adults over three consecutive days demonstrated marker-specific intra-individual coefficients of variation (CV%), namely: stool consistency (16.5%), water content (5.7%), pH (3.9%), total SCFAs (17.2%), total BCFAs (27.4%), total bacteria and fungi copies (40.6% and 66.7%), calprotectin and myeloperoxidase (63.8% and 106.5%), and untargeted metabolites (on average 40%). For thirteen microbiota genera, including Bifidobacterium and Akkermansia, variability exceeded 30%, whereas microbiota diversity was less variable (Phylogenetic Diversity 3.3%, Inverse Simpson 17.2%). Mill-homogenisation of frozen faeces significantly reduced the replicates CV% for total SCFAs (20.4-7.5%) and total BCFAs (15.9-7.8%), and untargeted metabolites compared to faecal hammering only, without altering mean concentrations. Our results show the potential need for repeated sampling to accurately represent specific gut health markers. We also demonstrated the effectiveness of optimised preprocessing of human stool samples in reducing overall analytical variability.

Development and validation of an in vitro model to study thrombin generation on the surface of catheters in platelet-poor and platelet-rich plasma

IntroductionCoagulation activation on medical devices remains a significant problem as it can lead to dramatic thromboembolic complications. Understanding its poorly described mechanisms and finding optimal pharmacological prevention means is crucial to improve patient safety. MethodsWe developed an in vitro model to study thrombin generation (TG) initiated by the contact of plasma with the surface of catheters. Interventional cardiology catheters were cut into segments and inserted in the bottom of multi-well plates; TG was then measured with the calibrated automated thrombogram (CAT). Model performance (analytical, intra- and inter-individual variability) was investigated and compared with activation of thrombin generation by tissue factor (TF) or contact pathway activator (ellagic acid), in the presence (PRP) and absence (PPP) of platelets. Model response to unfractionated heparin (UFH) was also assessed. ResultsTG was greater when measured in presence of catheter segments, compared to conditions without activators. The analytical variability of the model was good (CV ≤ 5 %), both with PPP and PRP. Intra-individual variability was between 15 and 30 % with PPP and between 10 and 15 % with PRP. Inter-individual variability was between 15 and 30 % with both kinds of plasma samples. The analytical performance of the catheter-initiated TG model was equivalent to that observed when TG was initiated with TF or ellagic acid. Catheter-initiated TG was measurable until 0.1 IU/mL UFH with PPP and until 1.0 IU/mL UFH with PRP, highlighting the crucial requirement of platelets. ConclusionOur model is suitable for studying TG initiated with catheters. Inhibition of TG by UFH is overestimated in the absence of platelets.