Labeling Procedure Research Articles

A radiohybrid theranostics ligand labeled with fluorine-18 and lutetium-177 for fibroblast activation protein-targeted imaging and radionuclide therapy

PurposeA series of radiotracers targeting fibroblast activation protein (FAP) with great pharmacokinetics have been developed for cancer diagnosis and therapy. Nevertheless, the use of dominant PET tracers, gallium-68–labeled FAPI derivatives, was limited by the short nuclide half-life and production scale, and the therapeutic tracers exhibited rapid clearance and insufficient tumor retention. In this study, we developed a FAP targeting ligand, LuFL, containing organosilicon-based fluoride acceptor (SiFA) and DOTAGA chelator, capable of labeling fluorine-18 and lutetium-177 in one molecular with simple and highly efficient labeling procedure, to achieve cancer theranostics.MethodsThe precursor LuFL (20) and [natLu]Lu-LuFL (21) were successfully synthesized and labeled with fluorine-18 and lutetium-177 using a simple procedure. A series of cellular assays were performed to characterize the binding affinity and FAP specificity. PET imaging, SPECT imaging, and biodistribution studies were conducted to evaluate pharmacokinetics in HT-1080-FAP tumor-bearing nude mice. A comparison study of [177Lu]Lu-LuFL ([177Lu]21) and [177Lu]Lu-FAPI-04 was carried out in HT-1080-FAP xenografts to determine the cancer therapeutic efficacy.ResultsLuFL (20) and [natLu]Lu-LuFL (21) demonstrated excellent binding affinity towards FAP (IC50: 2.29 ± 1.12 nM and 2.53 ± 1.87 nM), compared to that of FAPI-04 (IC50: 6.69 ± 0.88 nM). In vitro cellular studies showed that 18F-/177Lu-labeled 21 displayed high specific uptake and internalization in HT-1080-FAP cells. Micro-PET, SPECT imaging and biodistribution studies with [18F]/[177Lu]21 revealed higher tumor uptake and longer tumor retention than those of [68 Ga]/[177Lu]Ga/Lu-FAPI-04. The radionuclide therapy studies showed significantly greater inhibition of tumor growth for the [177Lu]21 group, than for the control group and the [177Lu]Lu-FAPI-04 group.ConclusionThe novel FAPI-based radiotracer containing SiFA and DOTAGA was developed as a theranostics radiopharmaceutical with simple and short labeling process, and showed promising properties including higher cellular uptake, better FAP binding affinity, higher tumor uptake and prolong retention compared to FAPI-04. Preliminary experiments with 18F- and 177Lu-labeled 21 showed promising tumor imaging properties and favorable anti-tumor efficacy.

Choline Chloride Encapsulated PLGA Nanoparticles Labelled with Lanthanide Metals

Herein, choline chloride was loaded into PLGA nanoparticles to ensure its controlled release. Encapsulation efficiencies (EE) and loading capacities (LC) of them were determined. Also, the produced nanoparticles were analyzed with Zetasizer, FT-IR, SEM, and XRD. The surfaces of the nanoparticles were functionalized by labeling with three different natural lanthanide metals Europium, Gadolinium, and Lutetium to expand diagnostic and therapeutic capabilities with three different imaging tecniques. Metal labeling of the nanoparticles was confirmed with SEM-EDX analysis. EE of the choline chloride nanoparticles were ranging between 62.5%-88.9%. LC of the choline chloride nanoparticles varied from 34.9 to 61.4. The mean size distribution obtained from the Zetasizer analysis of the free nanoparticles was 261.0 ± 7.598 nm. The Z-average size of the encapsulated nanoparticles also varied from 257.5 to 270 nm. The quite negative zeta potential, for example, -17.85 ± 0.165 mV for free NPs showed that the nanoparticles were sufficiently stable. The elemental mapping of the metal labeled NPs verified the labeling procedure. By obtaining therapeutic choline chloride-loaded nanoparticles with different metal labels, theranostic agents with common target and the ability to eliminate limitations with different imaging and detection techniques have been produced.

Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images

Hyperspectral images taken from aircraft or satellites contain information from hundreds of spectral bands, within which lie latent lower-dimensional structures that can be exploited for classifying vegetation and other materials. A disadvantage of working with hyperspectral images is that, due to an inherent trade-off between spectral and spatial resolution, they have a relatively coarse spatial scale, meaning that single pixels may correspond to spatial regions containing multiple materials. This article introduces the Diffusion and Volume maximization-based Image Clustering (D-VIC) algorithm for unsupervised material clustering to address this problem. By directly incorporating pixel purity into its labeling procedure, D-VIC gives greater weight to pixels corresponding to a spatial region containing just a single material. D-VIC is shown to outperform comparable state-of-the-art methods in extensive experiments on a range of hyperspectral images, including land-use maps and highly mixed forest health surveys (in the context of ash dieback disease), implying that it is well-equipped for unsupervised material clustering of spectrally-mixed hyperspectral datasets.

Intracellular and extracellular ion variation measurement in alpha(α)-particle irradiated cells using t-FROZEN! and laser-induced breakdown spectroscopy

Many radiobiological changes occur in unirradiated cells due to concealed signals transmitted from the irradiated cells through the Radiation-induced bystander effect (RIBE). Calcium ions (Ca2+) remain the only ion–molecule extensively studied so far amongst the ions that control cell function. This is partly due to using fluorescent indicators to investigate the signaling process that suffers limitations in labelling procedure and ion specificity. To understand the ion imbalance in irradiated cells and cell medium serving as donor and carrier of the bystander signals. Here, we investigate ion concentrations in the extracellular and intracellular space of α-particle (Am-241) irradiated Chinese hamster ovary (CHO) cells exposed to two different radiation doses (0.3 nGy, and 0.7 nGy). Laser-induced breakdown spectroscopy (LIBS) with our novel t-FROZEN! method separating the cells from its extracellular matrix was employed as a tool. Also, X-ray fluorescence spectroscopy (XRF), inductively coupled plasma-optical emission spectrometry (ICP-OES), and electrical impedance measurements were employed as complementary techniques. Our results show that influx of K+ and outflux of Na+, Mg2+, Cl- and Ca2+ occur in the irradiated cells, compared with controls, leading to an imbalance in the concentration of crucial signaling ions across the cell membrane. Our study has provided vital insights into comprehensive composition of ion-based radiation-induced bystander signals and guide the development of Na+, Mg2+, Cl- and K+ channel targeting drugs.

Boosting research on dental panoramic radiographs: a challenging data set, baselines, and a task central online platform for benchmark

ABSTRACT We address in this study the construction of a public data set of dental panoramic radiographs. Our objects of interest are the teeth, which are segmented and numbered. We benefited from the human-in-the-loop concept to expedite the labelling procedure, using predictions from deep neural networks as provisional labels, later verified by human annotators. Our results demonstrated a 51% labelling time reduction using HITL, saving us more than 390 continuous working hours. In a novel online platform, called OdontoAI, created to work as task central for this novel data set, we released 4,000 images, from which 2,000 have their labels publicly available for model fitting. The labels of the other 2,000 images are private and used for model evaluation on different tasks. To the best of our knowledge, this is the largest-scale publicly available data set for panoramic radiographs, and the OdontoAI is the first platform of its kind in dentistry.

An economic and robust TMT labeling approach for high throughput proteomic and metaproteomic analysis.

Multiplexed quantitative proteomics using tandem mass tag (TMT) is increasingly used in -omic study of complex samples. While TMT-based proteomics has the advantages of the higher quantitative accuracy, fewer missing values, and reduced instrument analysis time, it is limited by the additional reagent cost. In addition, current TMT labeling workflows involve repeated small volume pipetting of reagents in volatile solvents, which may increase the sample-to-sample variations and is not readily suitable for high throughput applications. In this study, we demonstrated that the TMT labeling procedures could be streamlined by using pre-aliquoted dry TMT reagents in a 96 well plate or 12-tube strip. As little as 50μg dry TMT per channel was used to label 6-12μg peptides, yielding high TMT labeling efficiency (∼99%) in both microbiome and mammalian cell line samples. We applied this workflow to analyze 97 samples in a study to evaluate whether ice recrystallization inhibitors improve the cultivability and activity of frozen microbiota. The results demonstrated tight sample clustering corresponding to groups and consistent microbiome responses to prebiotic treatments. This study supports the use of TMT reagents that are pre-aliquoted, dried, and stored for robust quantitative proteomics and metaproteomics in high throughput applications.

Pharmaceutical Risks of Injection Therapy: The Role of a Nurse

Nurse-administered injections come with a special set of risks, including such pharmaceutical factors as preparation of medicinal products in a vial prior to administration, conditions of their further storage, labelling of packages, and the need for personalised dosing of solutions for injection.The aim of the study was to analyse Russian state standard GOST R 52623.4-2015 and identify factors contributing to the risk of nursing staff’s errors in preparing, storing, and labelling of medicinal products for injection.Materials and methods. The authors analysed GOST R 52623.4-2015 Technologies for Performing Simple Medical Services of Invasive Interventions and assessed the identified risks by the FMEA method (failure modes and effects analysis).Results. The authors identified pharmaceutical risks, causes, and sources of hazardous events that occur during administration of parenteral medicinal products. The standard does not instruct readers on the order of mixing medicines in a vial and on choosing solvents for lyophilised powders. There is no labelling procedure for opened vials in the document. It does not stress the inadmissibility of pre-administration storage of a syringe/dropper with a medicinal product. The document lacks information on the need to warm the aqueous solutions that are stored in a refrigerator and/or administered at a temperature of 36–38 °С as per the package leaflet. The standard does not include a procedure for visual estimation of the volume of a solution drawn into a syringe for individual dosing of medicinal products to children. According to the FMEA risk assessment, the highest risks are posed by the order of mixing medicines in a vial and by the storage of syringes/droppers prior to administration to patients.Conclusions. To improve the safety of injection therapy, it is necessary to supplement the state standard with the identified causes of hazardous events. Nursing staff in medical organisations would benefit from in-house training on prevention of pharmaceutical risks carried out with the help of specialists in pharmaceutics and from wider involvement in discussing pharmacotherapy errors.

Trace detection of bromadiolone and brodifacoum in biological and food samples: A label-free fluorescence immunoassay based on an in situ formation strategy

The widespread use of bromadiolone and brodifacoum poses a threat to public health and initiates poisoning incidents. Conventional colorimetric enzyme-linked immunosorbent assays (ELISAs) suffer from poor sensitivity, making it difficult to meet the trace detection of analytes. In this work, we constructed a sensitive and label-free fluorescence immunoassay based on the in situ formation strategy, for the quantification of bromadiolone and brodifacoum in biological and food samples. Alkaline phosphatase, an essential enzyme in ELISA, catalyzed the substrate sodium ascorbyl phosphate to L-ascorbic acid, which reacted with ο-phenylenediamine and triggered an in situ fluorescence signal with excitation/emission wavelengths of 350/430 nm. To reduce the reaction time, Fe3+ was utilized for the in situ immunoassay system. The fluorescence signal system was introduced to conventional ELISA without any label procedures, thus resulting in higher sensitivity with a 3.8-fold improvement in the half-maximal inhibitory concentration for bromadiolone, compared with colorimetric ELISA. The calculated limits of detection for bromadiolone and brodifacoum were 0.45–0.76 and 0.34–0.58 ng/mL (ng/g) in the human serum and corn samples, respectively, with recovery values ranging from 82.0 % to 93.1 %, and a coefficient of variation lower than 13.4 %. Due to its advantages of high sensitivity, cost-effectiveness, and easy substrate fabrication, this immunoassay may offer promise for applications in poisoning diagnosis and food analysis of bromadiolone and brodifacoum.

15N-labelling of Leaves Combined with GC-MS Analysis as a Tool for Monitoring the Dynamics of Nitrogen Incorporation into Amino Acids.

Labeling plant material such as detached leaves with 15NH4+ is a very instrumental method for the characterization of metabolic pathways of mineral nitrogen assimilation and incorporation into amino acids. A procedure of labeling, followed by amino acid extraction, purification, and derivatization for gas chromatography coupled to mass spectrometry (GC/MS) analysis, is presented. The rationale of heavy isotope abundance calculations and amino acid quantification is detailed. This method is adaptable to various plant species and various kinds of investigations, such as elucidating physiological changes occurring as a result of gene mutations (overexpression or inhibition) in natural variants or genetically modified crops, or characterization of metabolic fluxes in genotypes exhibiting contrasted physiological or developmental adaptive responses to biotic and/or abiotic environmental stresses. Furthermore, the benefit of working on detached organs or pieces of organs is to investigate finely the metabolism of species that are not amenable to laboratory work, such as plants growing in natural environments or under agricultural conditions in the field.

13CO2-labelling and Sampling in Algae for Flux Analysis of Photosynthetic and Central Carbon Metabolism.

The flux in photosynthesis can be studied by performing 13CO2 pulse labelling and analysing the temporal labelling kinetics of metabolic intermediates using gas or liquid chromatography linked to mass spectrometry. Metabolic flux analysis (MFA) is the primary approach for analysing metabolic network function and quantifying intracellular metabolic fluxes. Different MFA approaches differ based on the metabolic state (steady vs. non-steady state) and the use of stable isotope tracers. The main methodology used to investigate metabolic systems is metabolite steady state associated with stable isotope labelling experiments. Specifically, in biological systems like photoautotrophic organisms, isotopic non-stationary 113C metabolic flux analysis at metabolic steady state with transient isotopic labelling (13C-INST-MFA) is required. The common requirement for metabolic steady state, alongside its very short half-timed reactions, complicates robust MFA of photosynthetic metabolism. While custom gas chambers design has addressed these challenges in various model plants, no similar tools were developed for liquid photosynthetic cultures (e.g., algae, cyanobacteria), where diffusion and equilibration of inorganic carbon species in the medium entails a new dimension of complexity. Recently, a novel tailor-made microfluidics labelling system has been introduced, supplying short 13CO2 pulses at steady state, and resolving fluxes across most photosynthetic metabolic pathways in algae. The system involves injecting algal cultures and medium containing pre-equilibrated inorganic 13C into a microfluidic mixer, followed by rapid metabolic quenching, enabling precise seconds-level label pulses. This was complemented by a 13CO2-bubbling-based open labelling system (photobioreactor), allowing long pulses (minutes-hours) required for investigating fluxes into central C metabolism and major products. This combined labelling procedure provides a comprehensive fluxome cover for most algal photosynthetic and central C metabolism pathways, thus allowing comparative flux analyses across algae and plants.

Conformational GPCR BRET Sensors Based on Bioorthogonal Labeling of Noncanonical Amino Acids.

Here we describe the application of genetic code expansion and site-specific incorporation of noncanonical amino acids that serve as anchor points for fluorescent labeling to generate bioluminescence resonance energy transfer (BRET)-based conformational sensors. Using a receptor with an N-terminal NanoLuciferase (Nluc) and a fluorescently labeled noncanonical amino acid in the receptor's extracellular part allows to analyze receptor complex formation, dissociation, and conformational rearrangements over time and in living cells. These BRET sensors can be used to investigate ligand-induced intramolecular (cysteine-rich domain [CRD] dynamics), but also intermolecular (dimer dynamics) receptor rearrangements. With the design of BRET conformational sensors based on the minimally invasive bioorthogonal labeling procedure, we describe a method that can be used in a microtiter plate format and can be easily adopted to investigate ligand-induced dynamics in various membrane receptors.

Generative Reasoning Integrated Label Noise Robust Deep Image Representation Learning.

The development of deep learning based image representation learning (IRL) methods has attracted great attention for various image understanding problems. Most of these methods require the availability of a set of high quantity and quality of annotated training images, which can be time-consuming, complex and costly to gather. To reduce labeling costs, crowdsourced data, automatic labeling procedures or citizen science projects can be considered. However, such approaches increase the risk of including label noise in training data. It may result in overfitting on noisy labels when discriminative reasoning is employed as in most of the existing methods. This leads to sub-optimal learning procedures, and thus inaccurate characterization of images. To address this issue, in this paper, we introduce a generative reasoning integrated label noise robust deep representation learning (GRID) approach. The proposed GRID approach aims to model the complementary characteristics of discriminative and generative reasoning for IRL under noisy labels. To this end, we first integrate generative reasoning into discriminative reasoning through a supervised variational autoencoder. This allows the proposed GRID approach to automatically detect training samples with noisy labels. Then, through our label noise robust hybrid representation learning strategy, GRID adjusts the whole learning procedure for IRL of these samples through generative reasoning and that of the other samples through discriminative reasoning. Our approach learns discriminative image representations while preventing interference of noisy labels during training independently from the IRL method being selected. Thus, unlike the existing label noise robust methods, GRID does not depend on the type of annotation, label noise, neural network architecture, loss function or learning task, and thus can be directly utilized for various image understanding problems. Experimental results show the effectiveness of the proposed GRID approach compared to the state-of-the-art methods. The code of the proposed approach is publicly available at https://github.com/gencersumbul/GRID.

Fluorescence labeling methods influence the aggregation process of α-syn in vitro differently.

In a previous study, the coexistence of different aggregation pathways of insulin and β-amyloid (Aβ) peptides was demonstrated by correlative stimulated emission depletion (STED) microscopy and atomic force microscopy (AFM). This had been explained by suboptimal proteins labeling strategies that generate heterogeneous populations of aggregating species. However, because of the limited number of proteins considered, the failure of the fluorescent labeling that occurs in a large portion of the aggregating fibrils observed for insulin and Aβ peptides, could not be considered a general phenomenon valid for all molecular systems. Here, we investigated the aggregation process of α-synuclein (α-syn), an amyloidogenic peptide involved in Parkinson's disease, which is significantly larger (MW ∼14 kDa) than insulin and Aβ, previously investigated. The results showed that an unspecific labeling procedure, such as that previously adopted for shorter proteins, reproduced the coexistence of labeled/unlabeled fibers. Therefore, a site-specific labeling method was developed to target a domain of the peptide scarcely involved in the aggregation process. Correlative STED-AFM illustrated that all fibrillar aggregates derived from the aggregation of α-syn at the dye-to-protein ratio of 1 : 22 were fluorescent. These results, demonstrated here for the specific case of α-syn, highlight that the labeling artifacts can be avoided by careful designing the labeling strategy for the molecular system under investigation. The use of a label-free correlative microscopy technique would play a crucial role in the control of the setting of these conditions.

Protocol for Clinical GLP-1 Receptor PET/CT Imaging with [68Ga]Ga-NODAGA-Exendin-4.

Imaging with radiolabeled exendin enables detection and characterization of glucagon-like peptide 1 receptors (GLP-1Rs) in vivo with high specificity. The novel radiotracer [68Ga]Ga-NODAGA-exendin-4 forms a stable complex after a simple and fast labeling procedure. Beta-cell mass in the islets of Langerhans can be visualized using [68Ga]Ga-NODAGA-exendin-4, which is promising for research into diabetes mellitus (DM) pathophysiology. Furthermore, this radiotracer enables very sensitive detection of insulinomas, resulting from vast overexpression of GLP-1Rs, and seems promising for the detection of focal lesions in congenital hyperinsulinism (CHI). Here, we describe the procedures involved in [68Ga]Ga-NODAGA-exendin-4 positron emission tomography (PET)/computed tomography (CT) imaging including the radiolabeling of the NODAGA-exendin conjugate with 68Ga, quality controls, and PET/CT.

Impact of buffer composition on biochemical, morphological and mechanical parameters: A tare before dielectrophoretic cell separation and isolation

Dielectrophoresis (DEP) represents an electrokinetic approach for discriminating and separating suspended cells based on their intrinsic dielectric characteristics without the need for labeling procedure. A good practice, beyond the physical and engineering components, is the selection of a buffer that does not hinder cellular and biochemical parameters as well as cell recovery. In the present work the impact of four buffers on biochemical, morphological, and mechanical parameters was evaluated in two different cancer cell lines (Caco-2 and K562). Specifically, MTT ([3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide]) assay along with flow cytometry analysis were used to evaluate the occurring changes in terms of cell viability, morphology, and granulocyte stress formation, all factors directly influencing DEP sorting capability. Quantitative real-time PCR (qRT-PCR) was instead employed to evaluate the gene expression levels of interleukin-6 (IL-6) and inducible nitric oxide synthase (iNOS), two well-known markers of inflammation and oxidative stress, respectively. An additional marker representing an index of cellular metabolic status, i.e. the expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) gene, was also evaluated. Among the four buffers considered, two resulted satisfactory in terms of cell viability and growth recovery (24 h), with no significant changes in cell morphology for up to 1 h in suspension. Of note, gene expression analysis showed that in both cell lines the apparently non-cytotoxic buffers significantly modulated IL-6, iNOS, and GAPDH markers, underlining the importance to deeply investigate the molecular and biochemical changes occurring during the analysis, even at apparently non-toxic conditions. The selection of a useful buffer for the separation and analysis of cells without labeling procedures, preserving cell status, represents a key factor for DEP analysis, giving the opportunity to further use cells for additional analysis.

When less is more powerful: Shapley value attributed ablation with augmented learning for practical time series sensor data classification.

Time series sensor data classification tasks often suffer from training data scarcity issue due to the expenses associated with the expert-intervened annotation efforts. For example, Electrocardiogram (ECG) data classification for cardio-vascular disease (CVD) detection requires expensive labeling procedures with the help of cardiologists. Current state-of-the-art algorithms like deep learning models have shown outstanding performance under the general requirement of availability of large set of training examples. In this paper, we propose Shapley Attributed Ablation with Augmented Learning: ShapAAL, which demonstrates that deep learning algorithm with suitably selected subset of the seen examples or ablating the unimportant ones from the given limited training dataset can ensure consistently better classification performance under augmented training. In ShapAAL, additive perturbed training augments the input space to compensate the scarcity in training examples using Residual Network (ResNet) architecture through perturbation-induced inputs, while Shapley attribution seeks the subset from the augmented training space for better learnability with the goal of better general predictive performance, thanks to the "efficiency" and "null player" axioms of transferable utility games upon which Shapley value game is formulated. In ShapAAL, the subset of training examples that contribute positively to a supervised learning setup is derived from the notion of coalition games using Shapley values associated with each of the given inputs' contribution into the model prediction. ShapAAL is a novel push-pull deep architecture where the subset selection through Shapley value attribution pushes the model to lower dimension while augmented training augments the learning capability of the model over unseen data. We perform ablation study to provide the empirical evidence of our claim and we show that proposed ShapAAL method consistently outperforms the current baselines and state-of-the-art algorithms for time series sensor data classification tasks from publicly available UCR time series archive that includes different practical important problems like detection of CVDs from ECG data.

PENINGKATAN KETERAMPILAN ISTRI NELAYAN DESA ALUE AMBANG MELALUI PELATIHAN PEMBUATAN TERASI IKAN HASIL TANGKAP SAMPING

The People of Alue Ambang Village mostly work as fishermen with their main catch were shrimp. Apart from the main catch, the catches of fishermen in Alue Ambang Village were fish by-catch (HTS). Utilization of fish by catch by into shrimp paste is an alternative for increase the income of fishermen’s household. The purpose of this training is to improve the skills of the women of Alue Ambang Village through making the shrimp paste from fish by catch. The activity was carried out in Alue Ambang Village, Teunom District, Aceh Jaya Regency. The activities were carried out in three meetings, the first meeting was the socialization of fermented products, the second meeting was training of making shrimp and the third meeting was socialization of packaging and labeling procedures for shrimph paste products. The training activities were attended by enthusiastic participants from start to finish. Achievements obtained from this activity were participants being able to utilize fish by catch into the product with a higher selling value, being able to produce the shrimp paste based on fish by catch as a household business independently, and being able to understand the importance of using packaging and labels to product development.

Assessing Multi-Attribute Characterization of Enveloped and Non-Enveloped Viral Particles by Capillary Electrophoresis.

Virus-based biopharmaceutical products are used in clinical applications such as vaccines, gene therapy, and immunotherapy. However, their manufacturing remains a challenge, hampered by the lack of appropriate analytical tools for purification monitoring or characterization of the final product. This paper describes the implementation of a highly sensitive method, capillary electrophoresis (CE)-sodium dodecyl sulfate (SDS) combined with a laser-induced fluorescence (LIF) detector to monitor the impact of various bioprocess steps on the quality of different viral vectors. The fluorescence labelling procedure uses the (3-(2-furoyl) quinoline-2-carboxaldehyde dye, and the CE-SDS LIF method enables the evaluation of in-process besides final product samples. This method outperforms other analytical methods, such as SDS-polyacrylamide gel electrophoresis with Sypro Ruby staining, in terms of sensitivity, resolution, and high-throughput capability. Notably, this CE-SDS LIF method was also successfully implemented to characterize enveloped viruses such as Maraba virus and lentivirus, whose development as biopharmaceuticals is now restricted by the lack of suitable analytical tools. This method was also qualified for quantification of rAAV2 according to the International Council for Harmonisation guidelines. Overall, our work shows that CE-SDS LIF is a precise and sensitive analytical platform for in-process sample analysis and quantification of different virus-based targets, with a great potential for application in biomanufacturing.

The harmonization issue in laboratory medicine: the commitment of CCLM.

The analytical quality of the clinical laboratory results has shown a significant improvement over the past decades, thanks to the joint efforts of different stakeholders, while the comparability among the results produced by different laboratories and methods still presents some critical issues. During these years, Clinical Chemistry and Laboratory Medicine (CCLM) published several paperson the harmonization issue over all steps in the Total Testing Process, training an important number of laboratory professionals in evaluating and monitoring all the criticisms inherent to the pre-analytical, as well as analytical and post analytical phases: from the consensus statement on the most informative testing in emergency setting, to the prevention and detection of hemolysis or to patients identification and tube labeling procedures, as far as to different approaches to harmonize hormones measurements or to describe new reference methods or to harmonize the laboratory report. During these years the commitment of the journal, devoted to the harmonization processes has allowed to improve the awareness on the topic and to provide specific instruments to monitor the rate of errors and to improve patients safety.

Charge-Dependent Signal Changes for Label-Free Electrochemiluminescence Immunoassays.

Label-free electrochemiluminescence (ECL) immunoassays (lf-ECLIA), based on biomarker-induced ECL signal changes, have attracted increasing attention due to the simple, rapid, and low-cost detection of biomarkers without secondary antibodies and complicated labeling procedures. However, the interaction rule and mechanism between analytical interfaces and biomarkers have rarely been explored. Herein, the interactions between biomarkers and analytical interfaces constructed by assembly of a nanoluminophore and antibody-functionalized gold nanoparticles on an indium tin oxide electrode were studied. The nanoluminophore was synthesized by mixing Cu2+/l-cysteine chelate and N-(4-Aminobutyl)-N-ethylisoluminol-bifunctionalized gold nanoparticles with chitosan. It was found that positively charged biomarkers increased the ECL intensity, whereas negatively charged biomarkers decreased the ECL intensity. The assembly pH influenced the biomarker charges, which determined the ECL enhancement or inhibition. The detection pH only affected the ECL intensity but not the ECL changing trends. Based on the ECL signal changes, a charge-dependent lf-ECLIA was established, which exhibited inhibition responses to negatively charged human immunoglobulin G and copeptin and enhancement responses to positively charged cardiac troponin I, heart-type fatty acid binding protein, brain natriuretic peptide, and SARS-CoV-2 N protein. The linear range was 0.1-1000 pg/mL, and the detection limits were distributed in 0.024-0.091 pg/mL. Besides, a mechanism of the charge-dependent ECL enhancement and inhibition effects is proposed, which is very important for the development of new lf-ECLIA methodologies.