Know What You Don’t Know: Assessment of Overlooked Microplastic Particles in FTIR Images

Assessing data analysis routines (DARs) for microplastics (MP) identification in Fourier-transform infrared (FTIR) images left the question ‘Do we overlook any MP particles in our sample?’ widely unanswered. Here, a reference image of microplastics, RefIMP, is presented to answer this question. RefIMP contains over 1200 MP and non-MP particles that serve as a ground truth that a DAR’s result can be compared to. Together with our MatLab® script for MP validation, MPVal, DARs can be evaluated on a particle level instead of isolated spectra. This prevents over-optimistic performance expectations, as testing of three hypotheses illustrates: (I) excessive background masking can cause overlooking of particles, (II) random decision forest models benefit from high-diversity training data, (III) among the model hyperparameters, the classification threshold influences the performance most. A minimum of 7.99% overlooked particles was achieved, most of which were polyethylene and varnish-like. Cellulose was the class most susceptible to over-segmentation. Most false assignments were attributed to confusion of polylactic acid for polymethyl methacrylate and of polypropylene for polyethylene. Moreover, a set of over 9000 transmission FTIR spectra is provided with this work, that can be used to set up DARs or as standard test set

From the Well to the Bottle: Identifying Sources of Microplastics in Mineral Water

Microplastics (MP) have been detected in bottled mineral water across the world. Because only few MP particles have been reported in ground water-sourced drinking water, it is suspected that MP enter the water during bottle cleaning and filling. However, until today, MP entry paths were not revealed. For the first time, this study provides findings of MP from the well to the bottle including the bottle washing process. At four mineral water bottlers, five sample types were taken along the process: raw and deferrized water samples were filtered in situ; clean bottles were sampled right after they left the bottle washer and after filling and capping. Caustic cleaning solutions were sampled from bottle washers and MP particles isolated through enzymatic and chemical treatments. The samples were analyzed for eleven synthetic and natural polymer particles ≥11 µm with Fourier-transform infrared imaging and random decision forests. MP were present in all steps of mineral water bottling, with a sharp increase from <1 MP L−1 to 317 ± 257 MP L−1 attributed to bottle capping. As 81% of MP resembled the PE-based cap sealing material, abrasion from the sealings was identified as the main entry path for MP into bottled mineral water

Tumor markers in the diagnosis of primary bladder cancer. A systematic review

Purpose: We systematically reviewed the available evidence, and obtained and compared summary estimates of the sensitivity and specificity of cytology and the urine based markers bladder tumor antigen, BTA stat (Polymedco, Redmond, Washington), BTA TRAK (Polymedco), NMP22 (Matritech, Cambridge, Massachusetts), telomerase and fibrin degradation product in detecting primary bladder cancer. Materials and Methods: Studies on the diagnosis of primary bladder cancer published from 1990 through November 2001 in English and German were retrieved from MEDLINE and EMBASE data bases. In our research we included studies that evaluated I or more of the markers, used cystoscopy as the reference standard and allowed the construction of a 2x2 contingency table for a per patient analysis. The data plus items on study and clinical characteristics were extracted by 2 observers. Sensitivity and specificity for each marker were estimated using a bivariate random effect meta-analysis. A multivariable analysis was performed to explain study variation. Results: A total of 42 studies were included in our review. Only 2 studies were available on fibrin degradation product, hence a meta-analysis was not possible. Cytology had the best specificity at 94% (95% CI: 90% to 96%). This figure was significantly better than that of the other markers except for telomerase (specificity 86% [71% to 94%]). Telomerase had the best sensitivity (75% [71% to 79%]) but it was not significantly better than that of BTA stat (70% [66% to 74%]). Case control designs yielded lower values for sensitivity for the tumor markers cytology, bladder tumor antigen and BTA stat. Conclusions: Cytology has the best specificity and telomerase the best sensitivity. However, none of the markers studied here is sensitive enough to be recommended for daily routin

Validation of Sample Preparation Methods for Microplastic Analysis in Wastewater Matrices—Reproducibility and Standardization

There is a growing interest in monitoring microplastics in the environment, corresponding to increased public concerns regarding their potential adverse effects on ecosystems. Monitoring microplastics in the environment is difficult due to the complex matrices that can prevent reliable analysis if samples are not properly prepared first. Unfortunately, sample preparation methods are not yet standardized, and the various efforts to validate them overlook key aspects. The goal of this study was to develop a sample preparation method for wastewater samples, which removes natural organic matter without altering the properties of microplastics. Three protocols, based on KOH, H2O2, and Fenton reactions, were chosen out of ten protocols after a literature review and pre-experiments. In order to investigate the effects of these reagents on seven polymers (PS, PE, PET, PP, PA, PVC, and PLA), this study employed µFTIR, laser diffraction-based particle size analysis, as well as TD-Pyr-GC/MS. Furthermore, the study discussed issues and inconsistencies with the Fenton reactions reported in the literature in previous validation efforts. The findings of this study suggest that both H2O2 and Fenton reactions are most effective in terms of organic matter removal from microplastic samples while not affecting the tested polymers, whereas KOH dissolved most PLA and PET particles

A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma

Pseudoprogression (PSP) detection in glioblastoma remains challenging and has important clinical implications. We investigated the potential of machine learning (ML) in improving the performance of PET using O-(2-[18F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. Contrast-enhanced MRI suspected PSP/TP and all patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. We trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a hold-out validation set. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. Of the 44 patients included in this preliminary study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p = 0.014 and p = 0.033, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. This preliminary study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performanc

MACHINE LEARNING-BASED EVALUATION OF STATIC AND DYNAMIC FET-PET FOR THE DETECTION OF PSEUDOPROGRESSION IN PATIENTS WITH IDH-WILDTYPE GLIOBLASTOMA

BACKGROUND: Pseudoprogression (PSP) detection in glioblastoma has important clinical implications and remains a challenging task. With the significant advances provided by machine learning (ML) in health care, we investigated the potential of ML in improving the performance of PET using O-(2-[(18)F]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. METHODS: We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. All patients presented imaging findings suspected of PSP/TP on contrast-enhanced MRI. For further diagnostic evaluation, patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. To develop a robust ML model, we trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a training cohort and validated the results on a separate test cohort. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. RESULTS: Of the 44 patients included in this study, 14 patients were diagnosed with PSP. The mean (TBR(mean)) and maximum tumor-to-brain ratios (TBR(max)) were significantly higher in the TP group as compared to the PSP group (p=0.010 and p=0.047, respectively). The area under the ROC curve (AUC) for TBR(max) and TBR(mean) was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBR(max). CONCLUSIONS: This study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance compared to conventional ROC analysis

Dynamic O-(2-[18F]fluoroethyl)-L-tyrosine PET imaging for the detection of checkpoint inhibitor-related pseudoprogression in melanoma brain metastases

Identifying patients with pseudoprogression, which is characterized by an initial increase of contrast-enhancing lesions that resolve or at least stabilize spontaneously on follow-up imaging without any treatment change, is critical for avoiding premature termination of potentially effective treatment. With the advent and success of checkpoint inhibitors such as ipilimumab, nivolumab, or pembrolizumab in particular, detecting pseudoprogression in patients with malignant melanoma has become a major challenge in clinical practice given a frequency as high as 7%–10% of cases.1,2 Diagnosing progressive disease and excluding pseudoprogression in melanoma metastases using the immune-related Response Criteria (irRC)2 require the initial increase of at least 25% in lesions load to be confirmed by follow-up imaging at least 4 weeks later.2 However, particularly for brain metastases from malignant melanoma, follow-up imaging might not be feasible for patients with clinical deterioration at the time of initial increase of lesions load. These patients might not be able to wait 4 weeks for a follow-up investigation to decide o

A Preliminary Study on Machine Learning-Based Evaluation of Static and Dynamic FET-PET for the Detection of Pseudoprogression in Patients with IDH-Wildtype Glioblastoma

Simple Summary Pseudoprogression detection in glioblastoma patients remains a challenging task. Although pseudoprogression has only a moderate prevalence of 10-30% following first-line treatment of glioblastoma patients, it bears critical implications for affected patients. Non-invasive techniques, such as amino acid PET imaging using the tracer O-(2-[F-18]-fluoroethyl)-L-tyrosine (FET), expose features that have been shown to provide useful information to distinguish tumor progression from pseudoprogression. The usefulness of FET-PET in IDH-wildtype glioblastoma exclusively, however, has not been investigated so far. Recently, machine learning (ML) algorithms have been shown to offer great potential particularly when multiparametric data is available. In this preliminary study, a Linear Discriminant Analysis-based ML algorithm was deployed in a cohort of newly diagnosed IDH-wildtype glioblastoma patients (n = 44) and demonstrated a significantly better diagnostic performance than conventional ROC analysis. This preliminary study is the first to assess the performance of ML in FET-PET for diagnosing pseudoprogression exclusively in IDH-wildtype glioblastoma and demonstrates its potential. Pseudoprogression (PSP) detection in glioblastoma remains challenging and has important clinical implications. We investigated the potential of machine learning (ML) in improving the performance of PET using O-(2-[F-18]-fluoroethyl)-L-tyrosine (FET) for differentiation of tumor progression from PSP in IDH-wildtype glioblastoma. We retrospectively evaluated the PET data of patients with newly diagnosed IDH-wildtype glioblastoma following chemoradiation. Contrast-enhanced MRI suspected PSP/TP and all patients underwent subsequently an additional dynamic FET-PET scan. The modified Response Assessment in Neuro-Oncology (RANO) criteria served to diagnose PSP. We trained a Linear Discriminant Analysis (LDA)-based classifier using FET-PET derived features on a hold-out validation set. The results of the ML model were compared with a conventional FET-PET analysis using the receiver-operating-characteristic (ROC) curve. Of the 44 patients included in this preliminary study, 14 patients were diagnosed with PSP. The mean (TBRmean) and maximum tumor-to-brain ratios (TBRmax) were significantly higher in the TP group as compared to the PSP group (p = 0.014 and p = 0.033, respectively). The area under the ROC curve (AUC) for TBRmax and TBRmean was 0.68 and 0.74, respectively. Using the LDA-based algorithm, the AUC (0.93) was significantly higher than the AUC for TBRmax. This preliminary study shows that in IDH-wildtype glioblastoma, ML-based PSP detection leads to better diagnostic performance

Removal of Iron, Manganese, Cadmium, and Nickel Ions Using Brewers’ Spent Grain

The human-made pollution of surface and ground waters is becoming an inevitable and persistently urgent problem for humankind and life in general, as these pollutants are also distributed by their natural circulation. For example, from mining activities and metallurgy, toxic heavy metals pollute the environment and present material risk for human health and the environment. Bioadsorbers are an intriguing way to efficiently capture and eliminate these hazards, as they are environmentally friendly, cheap, abundant, and efficient. In this study, we present brewers’ spent grain (BSG) as an efficient adsorber for toxic heavy metal ions, based on the examples of iron, manganese, cadmium, and nickel ions. We uncover the adsorption properties of two different BSGs and investigate thoroughly their chemical and physical properties as well as their efficiency as adsorbers for simulated and real surface waters. As a result, we found that the adsorption behavior of BSG types differs despite almost identical chemistry. Elemental mapping reveals that all components of BSG contribute to the adsorption. Further, both types are not only able to purify water to reach acceptable levels of cleanness, but also yield outstanding adsorption performance for iron ions of 0.2 mmol/g and for manganese, cadmium, and nickel ions of 0.1 mmol/g