RESUMEN

Multivariable Mendelian randomization allows simultaneous estimation of direct causal effects of multiple exposure variables on an outcome. When the exposure variables of interest are quantitative omic features, obtaining complete data can be economically and technically challenging: the measurement cost is high, and the measurement devices may have inherent detection limits. In this paper, we propose a valid and efficient method to handle unmeasured and undetectable values of the exposure variables in a one-sample multivariable Mendelian randomization analysis with individual-level data. We estimate the direct causal effects with maximum likelihood estimation and develop an expectation-maximization algorithm to compute the estimators. We show the advantages of the proposed method through simulation studies and provide an application to the Hispanic Community Health Study/Study of Latinos, which has a large amount of unmeasured exposure data.

RESUMEN

Quantifying and controlling fugitive methane emissions from oil and gas facilities remains essential for addressing climate goals, but the costs associated with monitoring millions of production sites remain prohibitively expensive. Current thinking, supported by measurement and simple dispersion modelling, assumes single-digit parts-per-million instrumentation is required. To investigate instrument response, the inlets of three trace-methane (sub-ppm) analyzers were collocated on a facility designed to release gas of known composition at known flow rates between 0.4 and 5.2 kg CH4 h-1 from simulated oil and gas infrastructure. Methane mixing ratios were measured by each instrument at 1 Hertz resolution over nine hours. While mixing ratios reported by a cavity ring-down spectrometer (CRDS)-based instrument were on average 10.0 ppm (range 1.8 to 83 ppm), a mid-infrared laser absorption spectroscopy (MIRA)-based instrument reported short-lived mixing ratios far larger than expected (range 1.8 to 779 ppm) with a similar nine-hour average to the CRDS (10.1 ppm). We suggest the peaks detected by the MIRA are likely caused by a micrometeorological phenomenon, where vortex shedding has resulted in heterogeneous methane plumes which only the MIRA can observe. Further analysis suggests an instrument like the MIRA (an optical-cavity-based instrument with cavity size ≤10 cm3 measuring at ≥2 Hz with air flow rates in the order of ≤0.3 slpm at distances of ≤20 m from the source) but with a higher detection limit (25 ppm) could detect enough of the high-concentration events to generate representative 20 min-average methane mixing ratios. Even though development of a lower-cost, high-precision, high-accuracy instrument with a 25 ppm detection threshold remains a significant problem, this has implications for the use of instrumentation with higher detection thresholds, resulting in the reduction in cost to measure methane emissions and providing a mechanism for the widespread deployment of effective leak detection and repair programs for all oil and gas infrastructure.

RESUMEN

BACKGROUND: Identifying the impact of environmental mixtures on human health is an important topic. However, such studies face challenges when exposure measurements lie below limit of detection (LOD). While various approaches for accommodating a single exposure subject to LOD have been used, their impact on mixture analysis has not been thoroughly investigated. Our study aims to understand the impact of five popular LOD accommodation approaches on mixture analysis results with multiple exposures subject to LOD, including omitting subjects with any exposures below LOD (complete case analysis); single imputations by LOD/ 2 , and by estimates from a censored accelerated failure time (AFT) model; and multiple imputation (MI) with or without truncation based on LOD. METHODS: In extensive simulation studies with high-dimensional and highly correlated exposures and a continuous health outcome, we examined the performance of each LOD approach on three mixture analysis methods: elastic net regression, weighted quantile sum regression (WQS) and Bayesian kernel machine regression (BKMR). We further analyzed data from the National Health and Nutrition Examination Survey (NHANES) on how persistent organic pollutants (POPs) influenced leukocyte telomere length (LTL). RESULTS: Complete case analysis was inefficient and could result in severe bias for some mixture methods. Imputation by LOD/ 2 showed unstable performance across mixture methods. Conventional MI was associated with consistent mild biases, which can be reduced by using a truncated distribution for imputation. Estimating censored values by AFT models had a minimal impact on the results. In the NHANES analysis, imputation by LOD/ 2 , truncated MI and censored AFT models performed similarly, with a positive overall effect of POPs on LTL while PCB126, PCB169 and furan 2,3,4,7,8-pncdf being the most important exposures. CONCLUSIONS: Our study favored using truncated MI and censored AFT models to accommodate values below LOD for the stability of downstream mixture analysis.

Asunto(s)

RESUMEN

Several pharmaceuticals and personal care products (PPCPs) were evaluated using the fish plasma model (FPM) for juvenile Chinook salmon exposed to effluent from a large urban wastewater treatment plant. The FPM compares fish plasma concentrations to therapeutic values determined in human plasma as an indication of potential adverse effects. We used human Cmax values, which are the maximum plasma concentration for a minimum therapeutic dose. Observed and predicted plasma concentrations from juvenile Chinook salmon exposed to a dilution series of whole wastewater effluent were compared to 1%Cmax values to determine Response Ratios (RR) ([plasma]/1%Cmax) for assessment of possible adverse effects. Several PPCPs were found to approach or exceed an RR of 1, indicating potential effects in fish. We also predicted plasma concentrations from measured water concentrations and determined that several of the values were close to or below the analytical reporting limit (RL) indicating potential plasma concentrations for a large number of PPCPs that were below detection. Additionally, the 1%Cmax was less than the RL for several analytes, which could impede predictions of possible effect concentrations. A comparison of observed and predicted plasma concentrations found that observed values were frequently much higher than values predicted with water concentrations, especially for low log10Dow compounds. The observed versus predicted values using the human volume of distribution (Vd), were generally much closer in agreement. These data appear to support the selection of whole-body concentrations to predict plasma values, which relies more on estimating simple partitioning within the fish instead of uptake via water. Overall, these observations highlight the frequently underestimated predicted plasma concentrations and potential to cause adverse effects in fish. Using measured plasma concentrations or predicted values from whole-body concentrations along with improved prediction models and reductions in analytical detection limits will foster more accurate risk assessments of pharmaceutical exposure for fish.

Asunto(s)

RESUMEN

Analysis of microplastics in the environment requires polymer characterization as a confirmation step for suspected microplastic particles found in a sample. Material characterization is costly and can take a long time per particle. When microplastic particle counts are high, many researchers cannot characterize every particle in their sample due to time or monetary constraints. Moreover, characterizing every particle in samples with high plastic particle counts is unnecessary for describing the sample properties. We propose an a priori approach to determine the number of suspected microplastic particles in a sample that should be randomly subsampled for characterization to accurately assess the polymer distribution in the environmental sample. The proposed equation is well-founded in statistics literature and was validated using published microplastic data and simulations for typical microplastic subsampling routines. We report values from the whole equation but also derive a simple way to calculate the necessary particle count for samples or subsamples by taking the error to the power of negative two. Assuming an error of 0.05 (5 %) with a confidence interval of 95 %, an unknown expected proportion, and a sample with many particles (> 100k), the minimum number of particles in a subsample should be 386 particles to accurately characterize the polymer distribution of the sample, given the particles are randomly characterized from the full population of suspected particles. Extending this equation to simultaneously estimate polymer, color, size, and morphology distributions reveals more particles (620) would be needed in the subsample to achieve the same high absolute error threshold for all properties. The above proposal for minimum subsample size also applies to the minimum count that should be present in samples to accurately characterize particle type presence and diversity in a given environmental compartment.

Asunto(s)

RESUMEN

We developed a state-of-the-art, high-sensitivity, low-stray-light standoff deep-ultraviolet (DUV) Raman spectrometer for the trace detection of resonance Raman-enhanced chemical species. As an excitation source for Raman measurements, we utilized our recently developed, second-generation, miniaturized, diode-pumped, solid-state neodymium-doped gadolinium orthovanadate (Nd:GdVO4) laser that generates quasi-continuous wave 228 nm light. This 228 nm excitation enhances the Raman intensities of vibrations of NOx groups in explosive molecules, aromatic groups in biological molecules, and various aromatic hydrocarbons. Our DUV Raman spectrograph utilizes a custom DUV f/8 Cassegrain telescope with an ∼200 mm diameter primary mirror, high-efficiency DUV transmission gratings, custom DUV mirrors, and a custom 228 nm Rayleigh rejection filter. We utilized our new standoff DUV Raman spectrometer to measure high signal-to-noise ratio spectra of ∼50 µg/cm2 drop-cast explosives: ammonium nitrate (AN), trinitrotoluene, pentaerythritol tetranitrate as well as aromatic biological molecules: lysozyme, tryptophan, tyrosine, deoxycytidine monophosphate, deoxyadenosine monophosphate at an ∼3 m distance within 10-30 s accumulation times. We roughly estimate the average ultraviolet resonance Raman (UVRR) detection limits for the relatively homogeneous drop-cast films of explosives and biological molecules to be ∼1 µg/cm2 when utilizing a continuous raster scanning that averages Raman signal over ∼1 cm2 sample area to avoid quick analyte depletion due to ultraviolet (UV) photolysis. We determined 3 m standoff UVRR detection limits for drop-cast AN films and identified factors impacting UVRR detection limits such as analyte photochemistry and analyte morphology. We found a detection limit of ∼0.5 µg/cm2 for drop-cast AN films on glass substrates when the Raman signal is averaged over ∼0.5 cm2 of sample surface using a continuous raster scan. For a step raster scan, when the probed sample area is limited to the laser spot size, the detection limit is approximately tenfold higher (∼5 µg/cm2) due to the impact of UV photochemistry.

RESUMEN

Mendelian randomization has been widely used to assess the causal effect of a heritable exposure variable on an outcome of interest, using genetic variants as instrumental variables. In practice, data on the exposure variable can be incomplete due to high cost of measurement and technical limits of detection. In this paper, we propose a valid and efficient method to handle both unmeasured and undetectable values of the exposure variable in one-sample Mendelian randomization analysis with individual-level data. We estimate the causal effect of the exposure variable on the outcome using maximum likelihood estimation and develop an expectation maximization algorithm for the computation of the estimator. Simulation studies show that the proposed method performs well in making inference on the causal effect. We apply our method to the Hispanic Community Health Study/Study of Latinos, a community-based prospective cohort study, and estimate the causal effect of several metabolites on phenotypes of interest.

Asunto(s)

RESUMEN

Organochlorine pesticides (OCPs) are persistent organic pollutants (POPs) widely used in agriculture and industry, causing serious health and ecological consequences upon exposure. This review offers a thorough overview of OCPs analysis emphasizing the necessity of ongoing work to enhance the identification and monitoring of these POPs in environmental and human samples. The benefits and drawbacks of the various OCPs analysis techniques including gas chromatography-mass spectrometry (GC-MS), gas chromatography-electron capture detector (GC-ECD), and liquid chromatography-mass spectrometry (LC-MS) are discussed. Challenges associated with validation and optimization criteria, including accuracy, precision, limit of detection (LOD), and limit of quantitation (LOQ), must be met for a method to be regarded as accurate and reliable. Suitable quality control measures, such as method blanks and procedural blanks, are emphasized. The LOD and LOQ are critical quality control measure for efficient quantification of these compounds, and researchers have explored various techniques for their calculation. Matrix interference, solubility, volatility, and partition coefficient influence OCPs occurrences and are discussed in this review. Validation experiments, as stated by European Commission in document SANTE/11813/2017, showed that the acceptance criteria for method validation of OCP analytes include ≤20 % for high precision, and 70-120 % for recovery. This may ultimately be vital for determining the human health risk effects of exposure to OCP and for formulating sensible environmental and public health regulations.

RESUMEN

BACKGROUND: The presence of antimicrobial drugs residues in animal products at levels higher than the maximum residue level (MRL) may have adverse effects on consumer health such as allergic reactions and resistance development. Therefore, it is necessary to monitor animal products for the presence of antimicrobial residues. OBJECTIVES: The aim of this study was to evaluate the detection limit of microbial inhibition assay (MIA) in microplate by using of Bacillus licheniformis as indicator microorganism for two antibiotics, enrofloxacin (ENR) and sulfamethazine (SMT), in broiler chicken's kidney, liver and muscle tissue samples. METHODS: Spiked tissues samples for the two antibiotics were analysed separately by this method. The results of the assay were evaluated by the determination of the absorbance after mean 3.47 h of incubation at 45°C. RESULTS: Results showed that the detection limits of MIA for ENR and SMT in kidney (124.03 and 23.21 µg/kg, respectively) and liver (90.02 and 62.03 µg/kg, respectively) as well as SMT in muscle (46.95 µg/kg) were lower than EU (European Union) - MRL, whereas the detection limit for ENR in muscle was slightly higher than MRL (136.3 µg/kg compared to 100 µg/kg MRL). Furthermore, the MIA in the current study was found to be more sensitive to SMT than ENR (92% and 88% sensitivity rate, respectively). No false-positive was observed in the assay. CONCLUSIONS: Based on the results, the MIA investigated in this study had the potential to detect ENR and SMT residues in broiler chicken kidney, liver and muscle tissues at levels below or close to EU - MRL but offered lower capability for the detection of ENR compared with SMT in kidney and muscle tissue samples.

Asunto(s)

RESUMEN

Titanium-containing nanoparticles (NPs) and submicrometer particles (µPs) in the environment can come from natural or anthropogenic sources. In this study, we investigate the use of single-particle inductively coupled plasma time-of-flight mass spectrometry (spICP-TOFMS) to measure and classify individual Ti-containing particles as either engineered (Ti-eng) or naturally occurring (Ti-nat) based on elemental composition and multielement mass ratios. We analyze mixtures of four Ti-containing particle types: anthropogenic food-grade TiO2 particles and particles from rutile, ilmenite, and biotite mineral samples. Through characterization of neat particle suspensions, we develop a decision-tree-based classification scheme to distinguish Ti-eng from Ti-nat particles and to classify individual Ti-nat particles by mineral type. Engineered TiO2 and rutile particles have the same major-element composition. To distinguish Ti-eng particles from rutile, we developed particle-type detection limits based on the average crustal abundance ratio of titanium to niobium. For our measurements, the average Ti mass needed to classify Ti-eng particles is 9.3 fg, which corresponds to a diameter of 211 nm for TiO2. From neat suspensions, we demonstrate classification rates of 55%, 32%, 75%, and 72% for Ti-eng, rutile, ilmenite, and biotite particles, respectively. Our classification approach minimizes false-positive classifications, with rates below 5% for all particle types. Individual Ti-eng particles can be accurately classified at the submicron size range, while the Ti-nat particles are classified in the nanoregime (diameter < 100 nm). Efficacy of our classification approach is demonstrated through the analysis of controlled mixtures of Ti-eng and Ti-nat and the analysis of natural streamwater spiked with Ti-eng particles. In control mixtures, Ti-eng particles can be measured and classified at particle-number concentrations (PNCs) 60-times lower than that of Ti-nat particles and across a PNC range of at least 3 orders of magnitude. In the streamwater sample, Ti-eng particles are classified at environmentally relevant PNCs that are 44-times lower than the background Ti-nat PNC and 2850-times lower than the total PNC.

Asunto(s)

RESUMEN

Mass spectrometry is a well-established analytical technique for studying the masses of atoms, molecules, or fragments of molecules. One of the key metrics of mass spectrometers is the limit of detection e.g., the minimum amount of signal from an analyte that can be reliably distinguished from noise. Detection limits have improved greatly over the last 30-40 years to the point that nanogram per litre and even picogram per litre detections are commonly reported. There is however, a difference between detection limits obtained with a single, pure compound in a pure solvent and those obtained from real life samples/matrixes. Determining a practical detection limit for mass spectrometry is difficult because it depends on multiple factors, such as the compound under test, the matrix, data processing methods and spectrometer type. Here we show the improvements in reported limits of detection on mass spectrometers over time using industry and literature data. The limit of detection for glycine and dichlorodiphenyltrichloroethane were taken from multiple published articles spanning a period of 45 years. The limits of detection were plotted against the article's year of publication to assess whether the trend in improvement in sensitivity resembles Moore's Law of computing (essentially doubling every two years). The results show that advancements in detection limits in mass spectrometry are close to, but not quite at a rate equivalent to Moore's Law and the improvements in detection limits reported from industry seem to be greater than those reported in the academic literature.

RESUMEN

A critical analysis of the known theories of functioning of H+-selective electrodes (H+-SEs) based on neutral amine-type carriers is given. A model of specific ion association is proposed, according to which, in membranes plasticized with 2-nitrophenyloctyl ether, the protonated ionophore and cation-exchanger form much stronger ion pairs with inorganic ions extracted from the sample solution than with each other, and simple equations that describe the lower and upper limit detection (pHUDL and pHLDL) are obtained. A feasible and reliable method for quantifying the pKa values of ionophores in the membrane phase from potentiometric data is substantiated. The efficiency of using single-ion partition coefficients and ion pair formation constants for a priori quantitative description of the H+-SE response in solutions of various compositions has been demonstrated for the first time. It is shown that the width of the dynamic response range of such electrodes depends on the nature of the tertiary amino group, and the reasons for the observed effect are discussed.

Asunto(s)

RESUMEN

We present here a technique for setting up detection limits on any fluorescent microscope in conjunction with the fluorophore Nile Red for microplastic identification. Our method also describes a rigorous morphology-specific identification key for microplastics to reduce subjectivity between researchers. The detection limits were established for nine common polymer types and five natural substrates which could result in false-positive signals when using Nile Red for microplastic identification. This method was then applied to real freshwater samples and identified particles were validated with micro-FTIR or Raman spectroscopy. This approach may reduce subjectivity in microplastic identification and counting and enhances transparency, repeatability and harmonization within microplastic research community.•Instructions for calibration of detection limits for microplastics on fluorescence microscope systems described•Microplastic identification key developed and tested to reduce false positive detection•Lower subjectivity for microplastic identification obtained using the detection limits & identification key.

RESUMEN

Emerging materials integrated into high performance flexible electronics to detect environmental contaminants have received extensive attention worldwide. The accurate detection of widespread organophosphorus (OP) compounds in the environment is crucial due to their high toxicity even at low concentrations, which leads to acute health concerns. Therefore, developing rapid, highly sensitive, reliable, and facile analytical sensing techniques is necessary to monitor environmental, ecological, and food safety risks. Although enzyme-based sensors have better sensitivity, their practical usage is hindered due to their low specificity and stability. Therefore, among various detection methods of OP compounds, this review article focuses on the progress made in the development of enzyme-free electrochemical sensors as an effective nostrum. Further, the novel materials used in these sensors and their properties, synthesis methodologies, sensing strategies, analytical methods, detection limits, and stability are discussed. Finally, this article summarizes potential avenues for future prospective electrochemical sensors and the current challenges of enhancing the performance, stability, and shelf life.

Asunto(s)

RESUMEN

Methane emissions from natural gas are of ever-increasing importance as we struggle to reach Paris climate targets. Locating and measuring emissions from natural gas can be particularly difficult as they are often widely distributed across supply chains. Satellites are increasingly used to measure these emissions, with some such as TROPOMI giving daily coverage worldwide, making locating and quantifying these emissions easier. However, there is little understanding of the real-world detection limits of TROPOMI, which can cause emissions to go undetected or be misattributed. This paper uses TROPOMI and meteorological data to calculate, and create a map of, the minimum detection limits of the TROPOMI satellite sensor across North America for different campaign lengths. We then compared these to emission inventories to determine the quantity of emissions that can be captured by TROPOMI. We find that minimum detection limits vary from 500-8800 kg/h/pixel in a single overpass to 50-1200 kg/h/pixel for a yearlong campaign. This leads to 0.04 % of a year's emissions being captured in a single (day) measurement to 14.4 % in a 1-year measurement campaign. Assuming gas sites contain super-emitters, emissions of between 4.5 % - 10.1 % from a single measurement and 35.6 % - 41.1 % for a yearlong campaign are captured.

RESUMEN

Metabarcoding of environmental DNA constitutes a state-of-the-art tool for environmental studies. One fundamental principle implicit in most metabarcoding studies is that individual sample amplicons can still be identified after being pooled with others-based on their unique combinations of tags-during the so-called demultiplexing step that follows sequencing. Nevertheless, it has been recognized that tags can sometimes be changed (i.e., tag jumping), which ultimately leads to sample crosstalk. Here, using four DNA metabarcoding data sets derived from the analysis of soils and sediments, we show that tag jumping follows very specific and systematic patterns. Specifically, we find a strong correlation between the number of reads in blank samples and their topological position in the tag matrix (described by vertical and horizontal vectors). This observed spatial pattern of artefactual sequences could be explained by polymerase activity, which leads to the exchange of the 3' tag of single stranded tagged sequences through the formation of heteroduplexes with mixed barcodes. Importantly, tag jumping substantially distorted our data sets-despite our use of methods suggested to minimize this error. We developed a topological model to estimate the noise based on the counts in our blanks, which suggested that 40%-80% of the taxa in our soil and sedimentary samples were likely false positives introduced through tag jumping. We highlight that the amount of false positive detections caused by tag jumping strongly biased our community analyses.

Asunto(s)

RESUMEN

Nanoscale fabrication and characterization techniques critically underpin a vast range of fields, including nanoelectronics and nanobiotechnology. Focused ion beam (FIB) techniques are appealing due to their high spatial resolution and widespread use for processing of nanostructured materials. Here, we introduce FIB-induced fluorescence spectroscopy (FIB-FS) as a nanoscale technique for spectroscopic detection of atoms sputtered by an ion beam. We use semiconductor heterostructures to demonstrate nanoscale lateral and depth resolution and show that it is limited by ion-induced intermixing of nanostructured materials. Sensitivity is demonstrated qualitatively by depth profiling of 3.5, 5, and 8 nm quantum wells and quantitatively by detection of trace-level impurities present at parts-per-million levels. The utility of the FIB-FS technique is demonstrated by characterization of quantum wells and Li-ion batteries. Our work introduces FIB-FS as a high-resolution, high-sensitivity, 3D analysis and tomography technique that combines the versatility of FIB nanofabrication techniques with the power of diffraction-unlimited fluorescence spectroscopy.

Asunto(s)

RESUMEN

Carbon black (CB) is a nanomaterial with numerous industrial applications and high potential for integration into nano-enabled water treatment devices. However, few analytical techniques are capable of measuring CB in water at environmentally relevant concentrations. Therefore, we intended to establish a quantification method for CB with lower detection limits through utilization of trace metal impurities as analytical tracers. Various metal impurities were investigated in six commercial CB materials, and the Monarch 1000 CB was chosen as a model for further testing. The La impurity was chosen as a tracer for spICP-MS analysis based on measured concentration, low detection limits, and lack of polyatomic interferences. CB stability in water and adhesion to the spICP-MS introduction system presented a challenge that was mitigated by the addition of a nonionic surfactant to the matrix. Following optimization, the limit of detection (64 µg/L) and quantification (122 µg/L) for Monarch 1000 CB demonstrated the applicability of this approach to samples expected to contain trace amounts of CB. When compared against gravimetric analysis and UV-visible absorption spectroscopy, spICP-MS quantification exhibited similar sensitivity but with the ability to detect concentrations an order of magnitude lower. Method detection and sensitivity was unaffected when dissolved La was spiked into CB samples at environmentally relevant concentrations. Additionally, a more complex synthetic matrix representative of drinking water caused no appreciable impact to CB quantification. In comparison to existing quantification techniques, this method has achieved competitive sensitivity, a wide working range for quantification, and high selectivity for tracing possible release of CB materials with known metal contents.

Asunto(s)

RESUMEN

In a standard spin echo, the time evolution due to homonuclear couplings is not reversed, leading to echo time (TE)-dependent modulation of the signal amplitude and signal loss in the case of overlapping multiplet resonances. This has an adverse effect on quantification of several important metabolites such as glutamate and glutamine. Here, we propose a J-refocused variant of the sLASER sequence (J-sLASER) to improve quantification of J-coupled metabolites at ultrahigh field (UHF). The use of the sLASER sequence is particularly advantageous at UHF as it minimizes chemical shift displacement error and results in relatively homogenous refocusing. We simulated the MRS signal from brain metabolites over a broad range of TE values with sLASER and J-sLASER, and showed that the signal of J-coupled metabolites was increased with J-sLASER with TE values up to ~80 ms. We further simulated "brain-like" spectra with both sequences at the shortest TE available on our scanner. We showed that, despite the slightly longer TE, the J-sLASER sequence results in significantly lower Cramer-Rao lower bounds (CRLBs) for J-coupled metabolites compared with those obtained with sLASER. Following phantom validation, we acquired spectra from two brain regions in 10 healthy volunteers (age 38 ± 15 years) using both sequences. We showed that using J-sLASER results in a decrease of CRLBs for J-coupled metabolites. In particular, we measured a robust ~38% decrease in the mean CRLB (glutamine) in parietal white matter and posterior cingulate cortex (PCC). We further showed, in 10 additional healthy volunteers (age 34 ± 15 years), that metabolite quantification following two separate acquisitions with J-sLASER in the PCC was repeatable. The improvement in quantification of glutamine may in turn improve the independent quantification of glutamate, the main excitatory neurotransmitter in the brain, and will simultaneously help to track possible modulations of glutamine, which is a key player in the glutamatergic cycle in astrocytes.

Asunto(s)

RESUMEN

In disease diagnosis, individuals are usually assumed to be one of the two basic types, healthy or diseased, as typically based on an established gold standard. Candidate markers for diagnosing a disease often are much cheaper and less invasive than the gold standard but must be evaluated against the gold standard for their sensitivity and specificity to accurately diagnose the disease. When candidate diagnostic markers are fully measured, receiver operating characteristic curves have been the standard approaches for assessing diagnostic accuracy. However, full measurements of diagnostic markers may not be available above or below certain limits due to various practical and technical limitations. For example, in the diagnosis of Alzheimer disease using cerebrospinal fluid biomarkers, the Roche Elecsys® immunoassays have a measuring range for multiple cerebrospinal fluid molecular concentrations. Many cognitive tests used in diagnosing dementia due to Alzheimer disease are also subject to detection limits, often referred to as the floor and ceiling effects in the neuropsychological literature. We propose a new statistical methodology for estimating the diagnostic accuracy when a diagnostic marker is subject to detection limits by dividing the entire study sample into two sub-samples by a threshold of the diagnostic marker. We then propose a family of estimators to the area under the receiver operating characteristic curve by combining a conditional nonparametric estimator and another conditional semi-parametric estimator derived from Cox's proportional hazards model. We derive the variance to the proposed estimators, and further, assess the performance of the proposed estimators as a function of possible thresholds through an extensive simulation study, and recommend the optimum thresholds. Finally, we apply the proposed methodology to assess the ability of several cerebrospinal fluid biomarkers and cognitive tests in diagnosing early stage Alzheimer disease dementia.

Asunto(s)