Continuous Biomarker Research Articles

Recent advances in gold nanostructure-based biosensors in detecting diabetes biomarkers.

Diabetes mellitus (DM) is a prevalent disorder with an urgent need for continuous, precise, and on-site biomarker monitoring devices. The continuous monitoring of DM biomarkers from different biological matrices will become routine in the future, thanks to the promising biosensor design. Lately, employing different nanomaterials in biosensor receptor parts has had a great impact on smart DM monitoring. Among them, gold nanostructures (AuNSs) have arisen as highly potential materials in fabricating precise DM biosensors due to their unique properties. The present study provides an update on the applications of AuNSs in biosensors for detecting glucose as well as other DM biomarkers, such as glycated hemoglobin (HbA1c), glycated albumin (GA), insulin, insulin antibodies, uric acid, lactate, and glutamic acid decarboxylase antibodies (GADA), with a focus on the most important factors in biosensor performance such as sensitivity, selectivity, response time, and stability. Specified values of limit of detection (LOD), linear concentrations, reproducibility%, recovery%, and assay time were used to compare studies. In conclusion, AuNSs, owing to the wide electrochemical potential window and low electrical resistivity, are valuable tools in biosensor design, alongside other biological reagents and/or nanomaterials.

Interval-specific likelihood ratios and probability-based models for interpreting combined CSF biomarkers for Alzheimer’s disease

BackgroundIn Alzheimer’s disease (AD) diagnosis, a cerebrospinal fluid (CSF) biomarker panel is commonly interpreted with binary cutoff values. However, these values are not generic and do not reflect the disease continuum. We explored the use of interval-specific likelihood ratios (LRs) and probability-based models for AD using a CSF biomarker panel. MethodsCSF biomarker (Aβ1-42, tTau and pTau181) data for both a clinical discovery cohort of 241 patients (measured with INNOTEST) and a clinical validation cohort of 129 patients (measured with EUROIMMUN), both including AD and non-AD dementia/cognitive complaints were retrospectively retrieved in a single-center study. Interval-specific LRs for AD were calculated and validated for univariate and combined (Aβ1-42/tTau and pTau181) biomarkers, and a continuous bivariate probability-based model for AD, plotting Aβ1-42/tTau versus pTau181 was constructed and validated. ResultsLR for AD increased as individual CSF biomarker values deviated from normal. Interval-specific LRs of a combined biomarker model showed that once one biomarker became abnormal, LRs increased even further when another biomarker largely deviated from normal, as replicated in the validation cohort. A bivariate probability-based model predicted AD with a validated accuracy of 88% on a continuous scale. ConclusionsInterval-specific LRs in a combined biomarker model and prediction of AD using a continuous bivariate biomarker probability-based model, offer a more meaningful interpretation of CSF AD biomarkers on a (semi-)continuous scale with respect to the post-test probability of AD across different assays and cohorts.

Embroidered Interdigitated Electrodes (IDTs) with Wireless Readout for Continuous Biomarker Monitoring.

Non-invasive continuous health monitoring has become feasible with the advancement of biosensors. While monitoring certain biomarkers such as heart rate or skin temperature are now at a certain maturity, monitoring molecular biomarkers is still challenging. Progress has been shown in sampling, measurement, and interpretation of data toward non-invasive molecular sensors that can be integrated into daily wearable items. Toward this goal, this paper explores the potential of embroidered interdigitated transducer (IDT)-based sensors for non-invasive, continuous monitoring of human biomarkers, particularly glucose levels, in human sweat. The study employs innovative embroidery techniques to create flexible fabric-based sensors with gold-coated IDTs. In controlled experiments, we have shown the variation of glucose concentration in water can be wirelessly detected by tracking the resonant frequency of the embroidered sensors. The current sensors operate at 1.8 GHz to 2 GHz and respond to the change in glucose concentration with a sensitivity of 0.17 MHz/(mg/dL). The embroidered IDT-based sensors with wireless sensing will be a new measurement modality for molecular wearable sensors. The establishment of a wireless sensing mechanism for embroidered IDT-based sensors will be followed by an investigation of sweat for molecular detection. This will require adding functionalities for sampling and interpretation of acquired data. We envisage the embroidered IDT-based sensors offer a unique approach for seamless integration into clothing, paving the way for personalised, continuous health data capture.

A prognostic and predictive computational pathology immune signature for ductal carcinoma in situ: retrospective results from a cohort within the UK/ANZ DCIS trial

The density of tumour-infiltrating lymphocytes (TILs) could be prognostic in ductal carcinoma in situ (DCIS). However, manual TIL quantification is time-consuming and suffers from interobserver and intraobserver variability. In this study, we developed a TIL-based computational pathology biomarker and evaluated its association with the risk of recurrence and benefit of adjuvant treatment in a clinical trial cohort. In this retrospective cohort study, a computational pathology pipeline was developed to generate a TIL-based biomarker (CPath TIL categories). Subsequently, the signature underwent a masked independent validation on H&E-stained whole-section images of 755 patients with DCIS from the UK/ANZ DCIS randomised controlled trial. Specifically, continuous biomarker CPath TIL score was calculated as the average TIL density in the DCIS microenvironment and dichotomised into binary biomarker CPath TIL categories (CPath TIL-high vs CPath TIL-low) using the median value as a cutoff. The primary outcome was ipsilateral breast event (IBE; either recurrence of DCIS [DCIS-IBE] or invasive progression [I-IBE]). The Cox proportional hazards model was used to estimate the hazard ratio (HR). CPath TIL-score was evaluable in 718 (95%) of 755 patients (151 IBEs). Patients with CPath TIL-high DCIS had a greater risk of IBE than those with CPath TIL-low DCIS (HR 2·10 [95% CI 1·39-3·18]; p=0·0004). The risk of I-IBE was greater in patients with CPath TIL-high DCIS than those with CPath TIL-low DCIS (3·09 [1·56-6·14]; p=0·0013), and the risk of DCIS-IBE was non-significantly higher in those with CPath TIL-high DCIS (1·61 [0·95-2·72]; p=0·077). A significant interaction (pinteraction=0·025) between CPath TIL categories and radiotherapy was observed with a greater magnitude of radiotherapy benefit in preventing IBE in CPath TIL-high DCIS (0·32 [0·19-0·54]) than CPath TIL-low DCIS (0·40 [0·20-0·81]). High TIL density is associated with higher recurrence risk-particularly of invasive recurrence-and greater radiotherapy benefit in patients with DCIS. Our TIL-based computational pathology signature has a prognostic and predictive role in DCIS. National Cancer Institute under award number U01CA269181, Cancer Research UK (C569/A12061; C569/A16891), and the Breast Cancer Research Foundation, New York (NY, USA).

Nonparametric receiver operating characteristic curve analysis with an imperfect gold standard.

This article addresses the challenge of estimating receiver operating characteristic (ROC) curves and the areas under these curves (AUC) in the context of an imperfect gold standard, a common issue in diagnostic accuracy studies. We delve into the nonparametric identification and estimation of ROC curves and AUCs when the reference standard for disease status is prone to error. Our approach hinges on the known or estimable accuracy of this imperfect reference standard and the conditional independent assumption, under which we demonstrate the identifiability of ROC curves and propose a nonparametric estimation method. In cases where the accuracy of the imperfect reference standard remains unknown, we establish that while ROC curves are unidentifiable, the sign of the difference between two AUCs is identifiable. This insight leads us to develop a hypothesis-testing method for assessing the relative superiority of AUCs. Compared to the existing methods, the proposed methods are nonparametric so that they do not rely on the parametric model assumptions. In addition, they are applicable to both the ROC/AUC analysis of continuous biomarkers and the AUC analysis of ordinal biomarkers. Our theoretical results and simulation studies validate the proposed methods, which we further illustrate through application in two real-world diagnostic studies.

Correlation of TILs indices generated by visual review and digital computational algorithm with outcomes from preoperative chemotherapy for TNBC in TBCRC 030.

555 Background: Predictive biomarkers for response to neoadjuvant chemotherapy (NAC) in early-stage triple negative breast cancer (TNBC) are needed. Stromal tumor infiltrating lymphocytes (TILs) are prognostic and predictive in TNBC, typically scored by visual examination (VE) of H&E stained slides. Evaluation of image-based surrogate signatures for TILs and other immune profiles by computational assessment (CA) of slides may offer greater precision and more comprehensive assessment of the immune microenvironment. This study was designed to evaluate TILs by VE and CA methods and investigate through blinded assessment the predictive capacity of immune activation. Methods: The phase II study TBCRC 030 randomized patients (pts) with BRCA1/2-proficient stage I-III TNBC to cisplatin (C) or paclitaxel (T) NAC, with primary endpoint response at surgery. TILs were visually scored on a continuous scale by a single experienced pathologist on digitized H&E images of pre-treatment core needle biopsies (CNB). CA used the 4D QPOR platform’s statistical physics and tumor biology based algorithm to quantify cell cycle aberrations and microenvironment dynamics including TILs, immune response (IHI), and combined immune response and cell cycle signature (CmbI) as continuous biomarkers from digitized CNB images. Evaluation of TILs by VE and CA were each correlated with response (RCB 0/1) or non-response (RCB 2/3) to NAC. Receiver operator characteristic (ROC) curves with area under the curve (AUC) measures and odds ratios (OR) with 95% confidence intervals were used to assess the predictive performance of each biomarker. Results: Of 139 evaluable pts, 121 had usable data for TILs by both VE and CA (59 received C, 62 received T). Descriptive statistics for biomarkers by response are shown in the Table. Notably TILs score by VE was significantly predictive of response, as was CA CmbI. TILs by VE had OR 1.86 (95% CI 1.24, 2.87, p=0.031) and AUC 0.69 (95% CI 0.57, 0.66), whereas CA CmbI had OR 1.54 (95% CI 1.00, 2.42, p=0.053) and AUC 0.62 (95% CI 0.51, 0.73). CA TILs and IHI were not predictive of response. TILs by VE predicted response to T (OR 2.91 95% CI 1.56, 6.16, p=0.002) but not C (OR 1.22 95% CI 0.67, 2.18 p=0.49). Conclusions: In this analysis of TBCRC 030, VE of baseline TILs and CA CmbI were predictive of response to NAC for TNBC, however CA did not outperform VE. TILs predicted response to specific chemotherapy. Automated computational evaluation of TILs or the broader immune microenvironment is an emerging field which may allow accurate and reproducible assessment of these important biomarkers. Clinical trial information: NCT01982448 . [Table: see text]

A Holocene history of climate, fire, landscape evolution, and human activity in northeastern Iceland

Abstract. Paleoclimate reconstructions across Iceland provide a template for past changes in climate across the northern North Atlantic, a crucial region due to its position relative to the global northward heat transport system and its vulnerability to climate change. The roles of orbitally driven summer cooling, volcanism, and human impact as triggers of local environmental changes in the Holocene of Iceland remain debated. While there are indications that human impact may have reduced environmental resilience during late Holocene summer cooling, it is still difficult to resolve to what extent human and natural factors affected Iceland's late Holocene landscape instability. Here, we present a continuous Holocene fire record of northeastern Iceland from proxies archived in Stóra Viðarvatn sediment. We use pyrogenic polycyclic aromatic hydrocarbons (pyroPAHs) to trace shifts in fire regimes, paired with continuous biomarker and bulk geochemical records of soil erosion, lake productivity, and human presence. The molecular composition of pyroPAHs and a wind pattern reconstruction indicate a naturally driven fire signal that is mostly regional. Generally low fire frequency during most of the Holocene significantly increased at 3 ka and again after 1.5 ka BP before known human settlement in Iceland. We propose that shifts in vegetation type caused by cooling summers over the past 3 kyr, in addition to changes in atmospheric circulation, such as shifts in North Atlantic Oscillation (NAO) regime, led to increased aridity and biomass flammability. Our results show no evidence of faecal biomarkers associated with human activity during or after human colonisation in the 9th century CE. Instead, faecal biomarkers follow the pattern described by erosional proxies, pointing toward a negligible human presence and/or a diluted signal in the lake's catchment. However, low post-colonisation levels of pyroPAHs, in contrast to an increasing flux of erosional bulk proxies, suggest that farming and animal husbandry may have suppressed fire frequency by reducing the spread and flammability of fire-prone vegetation (e.g. heathlands). Overall, our results describe a fire frequency heavily influenced by long-term changes in climate through the Holocene. They also suggest that human colonisation had contrasting effects on the local environment by lowering its resilience to soil erosion while increasing its resilience to fire.

Medical diagnostic accuracy measures: an innovative approach based on the area under predictive values curves

ABSTRACT Positive and negative estimates are commonly used by clinicians to evaluate the likelihood of a disease stage being present based on test results. The predicted values are dependent on the prevalence of the underlying illness. However, for certain diseases or clinical conditions, the prevalence is unknown or different from one region to another or from one population to another, leading to an erroneous diagnosis. This article introduces innovative post-test diagnostic precision measures for continuous tests or biomarkers based on the combined areas under the predictive value curves for all possible prevalence values. The proposed measures do not vary as a function of the prevalence of the disease. They can be used to compare different diagnostic tests and/or biomarkers’ abilities for rule-in, rule-out, and overall accuracy based on the combined areas under the predictive value curves. The relationship of the proposed measures to other diagnostic accuracy measures is discussed. We illustrate the proposed measures numerically and use a real data example on breast cancer.

Chronoamperometric interrogation of an electrochemical aptamer-based sensor with tetrahedral DNA nanostructure pendulums for continuous biomarker measurements

Tetrahedral DNA nanostructure (TDN) is highly promising in developing electrochemical aptamer-based (E-AB) sensors for biomolecular detection, owing to its inherit programmability, spatial orientation and structural robustness. However, current interrogation strategies applied for TDN-based E-AB sensors, including enzyme-based amperometry, voltammetry, and electrochemical impedance spectroscopy, either require complicated probe design or suffer from limited applicability or selectivity. In this study, a TDN pendulum-empowered E-AB sensor interrogated by chronoamperometry for reagent-free and continuous monitoring of a blood clotting enzyme, thrombin, was developed. TDN pendulums with extended aptamer sequences at three vertices were immobilized on a gold electrode via a thiolated double-stranded DNA (dsDNA) at the fourth vertex, and their motion is modulated by the bonding of target thrombin to aptamers. We observed a significantly amplified signalling output on our sensor based on the TDN pendulum compared to E-AB sensors modified with linear pendulums. Moreover, our sensor achieved highly selective and rapidly responsive measurement of thrombin in both PBS and artificial urine, with a wide dynamic range from 1 pM to 10 nM. This study shows chronoamperometry-enabled continuous biomarker monitoring on a sub-second timescale with a drift-free baseline, demonstrating a novel approach to accurately detect molecular dynamics in real time.

Use of Biosensors within the Oral Environment for Systemic Health Monitoring—A Systematic Review

Scientific advances in biosensor technology are leading to the potential of wearable biosensors for salivary biomarker detection. This review aims to identify the current status of intraoral biosensor technology that can be used to monitor systemic diseases. A total of 11 studies were identified for inclusion, which included nine different devices, including modified mouthguards, retainers, toothbrushes, and dental floss. Out of the 11 studies, 8 studied continuous biomarker monitoring, and the remaining 3 were point-of-care applications. A total of seven biomarkers were studied, six of which investigated the intraoral detection of salivary glucose levels using glucose oxidase enzyme. All the sensors demonstrated excellent sensitivity (minimum R = 0.9928) and selectivity. The study designs were proof of concept, with five studies including in vivo components. We concluded that while there are established links between salivary biomarkers and systemic health, there is a lack of mature intraoral biosensor research. Refinement of biosensor design and data analysis is required to improve patient acceptability by promoting more discrete, real-time, low-cost, and wireless devices. Further research that utilises the biosensor technology in large controlled clinical trials will be required to confirm clinical applicability before intraoral biosensor technology can be integrated into routine health monitoring.

Dynamic risk prediction for cervical precancer screening with continuous and binary longitudinal biomarkers

Dynamic risk prediction for cervical precancer screening with continuous and binary longitudinal biomarkers

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements. We hypothesize that regression-based DL outperforms classification-based DL. Therefore, we develop and evaluate a self-supervised attention-based weakly supervised regression method that predicts continuous biomarkers directly from 11,671 images of patients across nine cancer types. We test our method for multiple clinically and biologically relevant biomarkers: homologous recombination deficiency score, a clinically used pan-cancer biomarker, as well as markers of key biological processes in the tumor microenvironment. Using regression significantly enhances the accuracy of biomarker prediction, while also improving the predictions’ correspondence to regions of known clinical relevance over classification. In a large cohort of colorectal cancer patients, regression-based prediction scores provide a higher prognostic value than classification-based scores. Our open-source regression approach offers a promising alternative for continuous biomarker analysis in computational pathology.

Comprehensive modelling of prostate cancer progression: integrating continuous and binary biomarkers with event time data and missing covariates

Longitudinal studies involve recording observations at scheduled visits or time points for individuals until a predetermined event, like reaching satisfactory tumor shrinkage in cancer studies. Furthermore, dropout in longitudinal studies leads to incomplete data, which significantly increases the risk of bias. An amended joint shared-random effects model (SREM) is proposed for mixed continuous and binary longitudinal measurements and a time-to-event (TTE) outcome, incorporating missing covariates. In the proposed model, a conditional model is applied for the mixed continuous and binary longitudinal outcomes; a mixed effect model is considered for the continuous longitudinal outcome. For the binary longitudinal outcome, given the continuous longitudinal outcome, a logistic mixed effect model is considered. These models share common random effects with the model for the event time outcome. The model formulation is based on Bayesian statistical thinking via Markov Chain Monte Carlo (MCMC). The proposed joint modelling is applied to contribute to the understanding of the progression of prostate cancer (PCa) by considering a generalized linear mixed effects model for time-varying covariates that incorporate ignorable missingness. The association between prostate-specific antigen (PSA) with alkaline phosphatase (ALP) and tumor status has been studied with mixed conclusions.

Optimal design for inference on the threshold of a biomarker.

Enrichment designs with a continuous biomarker require the estimation of a threshold to determine the subpopulation benefitting from the treatment. This article provides the optimal allocation for inference in a two-stage enrichment design for treatment comparisons when a continuous biomarker is suspected to affect patient response. Several design criteria, associated with different trial objectives, are optimized under balanced or Neyman allocation and under equality of the first two empirical biomarker's moments. Moreover, we propose a new covariate-adaptive randomization procedure that converges to the optimum with the fastest available rate. Theoretical and simulation results show that this strategy improves the efficiency of a two-stage enrichment clinical trial, especially with smaller sample sizes and under heterogeneous responses.

Nucleic acid-based wearable and implantable electrochemical sensors.

The rapid advancements in nucleic acid-based electrochemical sensors for implantable and wearable applications have marked a significant leap forward in the domain of personal healthcare over the last decade. This technology promises to revolutionize personalized healthcare by facilitating the early diagnosis of diseases, monitoring of disease progression, and tailoring of individual treatment plans. This review navigates through the latest developments in this field, focusing on the strategies for nucleic acid sensing that enable real-time and continuous biomarker analysis directly in various biofluids, such as blood, interstitial fluid, sweat, and saliva. The review delves into various nucleic acid sensing strategies, emphasizing the innovative designs of biorecognition elements and signal transduction mechanisms that enable implantable and wearable applications. Special perspective is given to enhance nucleic acid-based sensor selectivity and sensitivity, which are crucial for the accurate detection of low-level biomarkers. The integration of such sensors into implantable and wearable platforms, including microneedle arrays and flexible electronic systems, actualizes their use in on-body devices for health monitoring. We also tackle the technical challenges encountered in the development of these sensors, such as ensuring long-term stability, managing the complexity of biofluid dynamics, and fulfilling the need for real-time, continuous, and reagentless detection. In conclusion, the review highlights the importance of these sensors in the future of medical engineering, offering insights into design considerations and future research directions to overcome existing limitations and fully realize the potential of nucleic acid-based electrochemical sensors for healthcare applications.

Spiers Memorial Lecture: Challenges and prospects in organic photonics and electronics.

While a substantial amount of research activity has been conducted in fields related to organic photonics and electronics, including the development of devices such as organic field-effect transistors, organic photovoltaics, and organic light-emitting diodes for applications encompassing organic thermoelectrics, organic batteries, excitonic organic materials for photochemical and optoelectronic applications, and organic thermoelectrics, this perspective review will primarily concentrate on the emerging and rapidly expanding domain of organic bioelectronics and neuromorphics. Here we present the most recent research findings on organic transistors capable of sensing biological biomarkers down at the single-molecule level (i.e., oncoproteins, genomes, etc.) for the early diagnosis of pathological states and to mimic biological synapses, paving the way to neuromorphic applications that surpass the limitations of the traditional von Neumann computing architecture. Both organic bioelectronics and neuromorphics exhibit several challenges but will revolutionize human life, considering the development of artificial synapses to counteract neurodegenerative disorders and the development of ultrasensitive biosensors for the early diagnosis of cancer to prevent its development. Moreover, organic bioelectronics for sensing applications have also triggered the development of several wearable, flexible and stretchable biodevices for continuous biomarker monitoring.

Modeling multiple correlated end-organ disease trajectories: A tutorial for multistate and joint models with applications in diabetes complications.

State-of-the-art biostatistics methods allow for the simultaneous modeling of several correlated non-fatal disease processes over time, but there is no clear guidance on the optimal analysis in most settings. An example occurs in diabetes, where it is not known with certainty how microvascular complications of the eyes, kidneys, and nerves co-develop over time. In this article, we propose and contrast two general model frameworks for studying complications (sequential state and parallel trajectory frameworks) and review multivariate methods for their analysis, focusing on multistate and joint modeling. We illustrate these methods in a tutorial format using the long-term follow-up from the Diabetes Control and Complications Trial and Epidemiology of Diabetes Interventions and Complications study public data repository. A formal comparison of prediction error and discrimination is included. Multistate models are particularly advantageous for determining the order and timing of complications, but require discretization of the longitudinal outcomes and possibly a very complex state space process. Intermittent observation of the states must be accounted for, and discretization is a probable disadvantage in this setting. In contrast, joint models can account for variations of continuous biomarkers over time and are particularly designed for modeling complex association structures between the complications and for performing dynamic predictions of an outcome of interest to inform clinical decisions (eg, a late-stage complication). We found that both models have helpful features that can better-inform our understanding of the complex trajectories that complications may take and can therefore help with decision making for patients presenting with diabetes complications.

Digital biomarkers of mobility and quality of life in Parkinson's disease based on a wearable motion analysis LAB

Background and objectiveFunctional mobility, an indicator of the quality of life (QoL), requires fast and flexible changes during motion, which are limited in Parkinson's disease (PD). Recent body-worn sensors have emerged in the last decades as potential solutions to produce digital biomarkers able to quantify mobility outside routine consultations and during real-life scenarios for multiple days at a time. The proposed research aims to study the ability of a wearable motion analysis lab, developed by our team, to produce digital biomarkers of mobility and QoL levels in patients with PD. MethodsA cross-sectional study was followed, including 40 patients stratified into three subgroups according to a clinic motor examination and a QoL questionnaire. ResultsThe achieved outcomes demonstrate the ability of the proposed high-tech solution to measure prototypical gait impairments and discriminate motor condition (AUC=0,890) and patients' QoL levels (AUC=0,950). Also, from the measured multiple gait-associated parameters, we identified the variables with the most potential to be applied as digital biomarkers of mobility (67 % of the metrics) and QoL (72 % of the metrics) in PD. ConclusionsOverall, we confirmed our hypothesis of using our body-worn sensor-based solution for passive or active monitoring of mobility and QoL in PD to produce objective, feasible, and continuous digital biomarkers.

Cutoff estimation and construction of their confidence intervals for continuous biomarkers under ternary umbrella and tree stochastic ordering settings.

Tuberculosis (TB) studies often involve four different states under consideration, namely, "healthy," "latent infection," "pulmonary active disease," and "extra-pulmonary active disease." While highly accurate clinical diagnosis tests do exist, they are expensive and generally not accessible in regions where they are most needed; thus, there is an interest in assessing the accuracy of new and easily obtainable biomarkers. For some such biomarkers, the typical stochastic ordering assumption might not be justified for all disease classes under study, and usual ROC methodologies that involve ROC surfaces and hypersurfaces are inadequate. Different types of orderings may be appropriate depending on the setting, and these may involve a number of ambiguously ordered groups that stochastically exhibit larger (or lower) marker scores than the remaining groups. Recently, there has been scientific interest on ROC methods that can accommodate these so-called "tree" or "umbrella" orderings. However, there is limited work discussing the estimation of cutoffs in such settings. In this article, we discuss the estimation and inference around optimized cutoffs when accounting for such configurations. We explore different cutoff alternatives and provide parametric, flexible parametric, and non-parametric kernel-based approaches for estimation and inference. We evaluate our approaches using simulations and illustrate them through a real data set that involves TB patients.

Association of urine glyphosate levels with renal injury biomarkers in children living close to major vegetable-producing regions in China

Glyphosate (GLY)-based herbicides exposure contributes to renal dysfunction in experimental conditions, but the effects on humans are rarely reported. Biomonitoring is practically relevant for evaluating the association of urine GLY levels and renal damage in children living close to vegetable-cultivating regions. In this study, we collected the first-morning void urine samples of 239 healthy children (aged 3–12, 48.12 % boys) living near major vegetable-producing regions in March–May and August 2023 in Shandong Province, China. Urine levels of GLY and kidney injury-associated biomarkers were determined using ELISA kits to assess their correlation. GLY was detected in 92.05 % of urine samples (220 out of 239 participants) and the geometric concentration (GM) was 7.429 μg/L (range: 0.625 to 38.267 μg/L). Binary logistic regression and multivariate regression analysis revealed GLY detectability and levels positively correlated with home ventilation and self-producing vegetable intake of the subjects, as well as sampling periods. Moreover, a statistically significant concentration association with urine GLY was found for kidney injury-associated biomarkers (NGAL and KIM-1) (R2 = 0.923 and 0.855, respectively). Additionally, risk assessment revealed that the maximum value of probable daily intake was 0.150 mg/kg bw/day, accounting for 30.1 % of the established Acceptable Daily Intake of GLY. This study unveils a positive correlation between continuous GLY-based herbicide exposure and renal injury biomarkers of children. A large-scale epidemiological study is warranted for comprehensively assessing the effects of GLY-based herbicides on kidney function of the entire public.