Analytical Challenges Research Articles

Nasal exudate for diagnosis of stroke: fundamental studies through iron fractionation, total iron, and targeted protein determinations

During the last years, there has been an increasing research interest in the analysis of biological fluids requiring non-invasive sampling for biomedical and clinical applications. In this work, we have focused on the nasal exudate with the aim of investigating the potential use of this fluid to know the role of iron in stroke and also for diagnosis. Potential differences in the nasal exudate, collected in swabs, from diagnosed hemorrhagic stroke, ischemic stroke, and control groups were investigated with regard to total iron by inductively coupled plasma-mass spectrometry, iron fractionation studies by size exclusion chromatography together with post-column isotope dilution analysis, and four proteins containing iron (ferritin, transferrin, lactoferrin, and ferroportin) with ELISA kits. All these analyses represent an analytical challenge, considering the rather limited amount of sample (10–40 mg) available, being the nasal exudate extracted from the swab with 300 µL 10 mM Tris/HCl, pH = 7.4. Studies to obtain reliable analytical information, such as the blank contribution of the sampling step, evaluation of the extraction efficiency of the nasal exudate from the swab, and normalization strategies for data treatment, have been carried out. Results showed that despite the limited number of investigated samples, fractionation studies as well as the concentrations of ferritin and ferroportin obtained with ELISA kits showed a differential behavior between the different cohorts.

Navigating regulatory and analytical challenges in live biotherapeutic product development and manufacturing

Navigating regulatory and analytical challenges in live biotherapeutic product development and manufacturing

Secretome Analysis Using Affinity Proteomics and Immunoassays: A Focus on Tumor Biology

The study of the cellular secretome using proteomic techniques continues to capture the attention of the research community across a broad range of topics in biomedical research. Due to their untargeted nature, independence from the model system used, historically superior depth of analysis, as well as comparative affordability, mass spectrometry-based approaches traditionally dominate such analyses. More recently, however, affinity-based proteomic assays have massively gained in analytical depth, which together with their high sensitivity, dynamic range coverage as well as high throughput capabilities render them exquisitely suited to secretome analysis. In this review, we revisit the analytical challenges implied by secretomics and provide an overview of affinity-based proteomic platforms currently available for such analyses, using the study of the tumor secretome as an example for basic and translational research.

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajectories. In the past decade, machine learning (ML) and deep learning (DL) integration has transformed the field, providing advanced models for brain age estimation. However, achieving precise brain age prediction across all ages remains a significant analytical challenge. This comprehensive review scrutinizes advancements in ML- and DL-based brain age prediction, analyzing 52 peer-reviewed studies from 2020 to 2024. It assesses various model architectures, highlighting their effectiveness and nuances in lifespan brain age studies. By comparing ML and DL, strengths in forecasting and methodological limitations are revealed. Finally, key findings from the reviewed articles are summarized and a number of major issues related to ML/DL-based lifespan brain age prediction are discussed. Through this study, we aim at the synthesis of the current state of brain age prediction, emphasizing both advancements and persistent challenges, guiding future research, technological advancements, and improving early intervention strategies for neurodegenerative diseases.

Novel method for monitoring of carcinogenic impurity of N-nitrosamine in nizatidine pharmaceutical products using ultra high-pressure liquid chromatography triple quadrupole mass spectrometry.

Nitrosamine compounds pose a significant concern as potential carcinogens, prompting heightened scrutiny from regulatory bodies, particularly regarding their presence in pharmaceuticals. The detection of unacceptable levels of N-nitrosodiethylamine (NDMA) in ranitidine has led to widespread recalls, driving interest in alternative medications such as nizatidine, which shares a similar pharmacological class and is used to treat various gastrointestinal conditions. Despite fewer reports on NDMA levels in nizatidine, its structural similarity to ranitidine, characterized by a tertiary amine, underscores the potential for NDMA formation. Addressing the analytical challenges associated with nitrosamine detection, this study focuses on developing and validating an ultra-high pressure liquid chromatography triple quadrupole mass spectrometry (UHPLC-MS/MS) method for quantifying NDMA in both nizatidine active pharmaceutical ingredients and tablet formulations. Method validation adheres to International Council for Harmonisation recommendations, with a demonstrated linear range of 0.25-100 ng/mL for NDMA, exhibiting excellent linearity (regression coefficient >0.999) and efficient recovery rates ranging from 95.98% to 109.57%. The method shows high sensitivity, with limits of detection and quantification of 0.25 and 0.5ng/mL, respectively. The developed UHPLC-MS/MS method offers a simple, precise, accurate, and selective approach for monitoring NDMA levels in nizatidine formulations available in Australia, promising enhanced sensitivity and specificity with limits of quantification in the ppb and sub-ppb ranges.

Identification of Amino Acids and Polyphenolic Metabolites in Human Plasma by UHPLC-ESI-QTOF-MS/MS, after the Chronic Intake of a Functional Meal in an Elderly Population.

Novel foods especially formulated and targeted for the elderly population should provide sufficient nutrients and bioactive ingredients to counteract the natural age-related deterioration of various organs and tissues. Dietary protein and phenolic compounds achieve this goal; however, older adults have alterations in their gastrointestinal system that may impact their bioavailability and few studies have been aimed at this population. Since phenolic compounds are the subject of multiple biotransformations by host and microbiome enzymes during the digestion process, identification of their bioavailable forms in human plasma or tissues represents a considerable analytical challenge. In this study, UHPLC-ESI-QTOF/MS-MS, chemometrics, and multivariate statistical methods were used to identify the amino acids and phenolic compounds that were increased in the plasma of elderly adults after a 30-day intervention in which they had consumed an especially formulated muffin and beverage containing Brosimum alicastrum Sw. seed flour. A large interindividual variation was observed regarding the amino acids and phenolic metabolites identified in the plasma samples, before and after the intervention. Three phenolic metabolites were significantly increased in the population after the intervention: protocatechuic acid, 5-(methoxy-4'-hydroxyphenyl) valerolactone, and phloretic acid. These metabolites, as well as others that were not significantly increased (although they did increase in several individuals), are probably the product of the microbiota metabolism of the major phenolic compounds present in the B. alicastrum Sw. seed flour and other food ingredients. A significant decrease in 4-ethyl-phenol, a biomarker of stress, was observed in the samples. Results showed that the incorporation of foods rich in phenolic compounds into the regular diet of older adults contributes to the increase in bioactive compounds in plasma, that could substantially benefit their mental, cardiovascular, and digestive health.

Particle Swarm Optimization for a redundant repairable machining system with working vacations and impatience in a multi-phase random environment

With the increasing reliance on cloud computing as the foundational manufacturing systems with intricate dynamics, featuring multiple service areas, varying job arrival rates, diverse service requirements, and the interplay of failures and impatience, significant analytical challenges arise. Queueing networks offer a powerful stochastic modeling framework to capture such complex dynamics. This paper develops a novel, exhaustive queueing model for a finite-capacity redundant multi-server system operating in a multi-phase random environment. The proposed model uniquely integrates real-world factors, including server breakdowns and repairs, waiting servers, synchronous working vacations, and state dependent balking and reneging, into a single queueing model, representing a significant advancement in the field. Using the matrix-analytic method, we establish the steady-state solution and derive key performance metrics. Numerical experiments and sensitivity analyses elucidate the impact of system parameters on performance measures. Additionally, a cost model is formulated, enabling cost optimization analysis using direct search method and Particle Swarm Optimization (PSO) to identify efficient operating configurations.

Breaking the data barrier: a review of deep learning techniques for democratizing AI with small datasets

Justifiably, while big data is the primary interest of research and public discourse, it is essential to acknowledge that small data remains prevalent. The same technological and societal forces that generate big datasets also produce a more significant number of small datasets. Contrary to the notion that more data is inherently superior, real-world constraints such as budget limitations and increased analytical complexity present critical challenges. Quality versus quantity trade-offs necessitate strategic decision-making, where small data often leads to quicker, more accurate, and cost-effective insights. Concentrating AI research, particularly in deep learning (DL), on big datasets exacerbates AI inequality, as tech giants such as Meta, Amazon, Apple, Netflix and Google (MAANG) can easily lead AI research due to their access to vast datasets, creating a barrier for small and mid-sized enterprises that lack similar access. This article addresses this imbalance by exploring DL techniques optimized for small datasets, offering a comprehensive review of historic and state-of-the-art DL models developed specifically for small datasets. This study aims to highlight the feasibility and benefits of these approaches, promoting a more inclusive and equitable AI landscape. Through a PRISMA-based literature search, 175+ relevant articles are identified and subsequently analysed based on various attributes, such as publisher, country, utilization of small dataset technique, dataset size, and performance. This article also delves into current DL models and highlights open research problems, offering recommendations for future investigations. Additionally, the article highlights the importance of developing DL models that effectively utilize small datasets, particularly in domains where data acquisition is difficult and expensive.

Degradation strategies for structural characterization of insoluble synthetic polymers by mass spectrometry.

With the advent of soft ionization techniques such as electrospray (ESI) and matrix-assisted laser desorption/ionization (MALDI) to produce intact gas-phase ions from nonvolatile macromolecules, mass spectrometry has become an essential technique in the field of polymeric materials. However, (co)polymers of very high molecular weight or with reticulated architectures still escape ESI or MALDI, mainly due to solubility issues. Strategies developed to tackle such an analytical challenge all rely on sample degradation to produce low-mass species amenable to existing ionization methods. Yet, chain degradation needs to be partial and controlled to generate sufficiently large species that still contain topological or architectural information. The present article reviews the different analytical degradation strategies implemented to perform mass spectrometry of these challenging synthetic polymers, covering thermal degradation approaches in sources developed in the 2000s, off-line sample pre-treatments for controlled chemical degradation of polymeric substrates, and most recent achievements employing reactive ionization modes to perform chemolysis on-line with MS.

Engineering a GPCR-based yeast biosensor for a highly sensitive melatonin detection from fermented beverages

Melatonin is a multifunctional molecule with diverse biological roles that holds great value as a health-promoting bioactive molecule in any food product and yeast’s ability to produce it has been extensively demonstrated in the last decade. However, its quantification presents costly analytical challenges due to the usual low concentrations found as the result of yeast metabolism. This study addresses these analytical challenges by optimizing a yeast biosensor based on G protein-coupled receptors (GPCR) for melatonin detection and quantitation. Strategic genetic modifications were employed to significantly enhance its sensitivity and fluorescent signal output, making it suitable for detection of yeast-produced melatonin. The optimized biosensor demonstrated significantly improved sensitivity and fluorescence, enabling the screening of 101 yeast strains and the detection of melatonin in various wine samples. This biosensor’s efficacy in quantifying melatonin in yeast growth media underscores its utility in exploring melatonin production dynamics and potential applications in functional food development. This study provides a new analytical approach that allows a rapid and cost-effective melatonin analysis to reach deeper insights into the bioactivity of melatonin in fermented products and its implications for human health. These findings highlight the broader potential of biosensor technology in streamlining analytical processes in fermentation science.

Sequence-specific nanoparticle barcode strategy for multiplex human enterovirus typing

Human enteroviruses (HEV) can cause a range of diseases from mild to potentially life-threatening. Identification and genotyping of HEV are crucial for disease management. Existing typing methods, however, have inherent limitations. Developing alternative methods to detect HEV with more virus types, high accuracy, and sensitivity in an accessible manner presents a technological and analytical challenge. Here, a sequence-specific nanoparticle barcode (SSNB) method is presented for simultaneous detection of 10 HEV types. This method significantly increases sensitivity, enhancing detection by 10-106 times over the traditional multiplex hybrid genotyping (MHG) method, by resolving cross-interference between the multiple primer sets. Furthermore, the SSNB method demonstrates a 100% specificity in accurately distinguishing between 10 different HEV types and other prevalent clinical viruses. In an analysis of 70 clinical throat swab samples, the SSNB method shows slightly higher detection rate for positive samples (50%) compared to the RT-PCR method (48.6%). Additionally, further assessment of the typing accuracy for samples identified as positive by SSNB using sequencing method reveals a concordance rate of 100%. The combined high sensitivity and specificity level of the methodology, together with the capability for multiple type analysis and compatibility with clinical workflow, make this approach a promising tool for clinical settings.

Targeted screening and profiling of massive components of colistimethate sodium by two-dimensional-liquid chromatography-mass spectrometry based on self-constructed compound database

In-depth study of the components of polymyxins is the key to controlling the quality of this class of antibiotics. Similarities and variations of components present significant analytical challenges. A two-dimensional (2D) liquid chromatography-mass spectrometric (LC-MS) method was established for screening and comprehensive profiling of compositions of the antibiotic colistimethate sodium (CMS). A high concentration of phosphate buffer mobile phase was used in the first-dimensional LC system to get the components well separated. For efficient and high-accuracy screening of CMS, a targeted method based on a self-constructed high resolution mass spectrum database of CMS components was established. The database was built based on the commercial MassHunter Personal Compound Database and Library (PCDL) software and its accuracy of the compound matching result was verified with 6 known components before being applied to genuine sample screening. On this basis, the unknown peaks in the CMS chromatograms were deduced and assigned. The molecular formula, group composition and origins of a total of 99 compounds, of which the combined area percentage accounted for more than 95% of CMS components, were deduced by this 2D-LC-MS method combined with the MassHunter PCDL. This profiling method was highly efficient and could distinguish hundreds of components within 3 h, providing reliable results for quality control of this kind of complex drugs.

The role of modern spectroscopy and chromatography in actinide and lanthanide chemistry for nuclear forensics

The field of nuclear forensics relies heavily on the accurate identification and characterization of actinides and lanthanides to trace the origins and history of nuclear materials. Modern spectroscopy and chromatography play a pivotal role in this domain, offering precise analytical techniques to detect and quantify these elements in various samples. This review provides a comprehensive examination of the contributions of spectroscopy and chromatography to actinide and lanthanide chemistry within the context of nuclear forensics. It begins with an overview of the fundamental principles of these methods, followed by detailed discussions on the specific spectroscopic techniques such as mass spectrometry (including ICP-MS and TIMS), X-ray absorption spectroscopy (XANES and EXAFS), and laser-induced breakdown spectroscopy (LIBS). The chromatographic methods covered include high-performance liquid chromatography (HPLC), gas chromatography (GC), and capillary electrophoresis (CE). The integration of these techniques for enhanced forensic analysis is explored through case studies and examples demonstrating their combined use. Furthermore, recent advances, innovations, and future trends in the field are discussed, highlighting the move towards automation and high-throughput analysis. The review concludes with an exploration of the analytical challenges and limitations, along with recommendations for future research directions. This comprehensive review underscores the critical role of advanced spectroscopy and chromatography in advancing the capabilities of nuclear forensics, ensuring robust and reliable forensic investigations.

THz-TDS as a PAT tool for monitoring blend homogeneity in pharmaceutical manufacturing of solid oral dosage forms: A proof-of-concept study

The process analytical technology (PAT) framework is well established and integral to facilitate process understanding, enable a transition from batch to continuous manufacturing, and improve product quality. Near-infrared (NIR) spectroscopy has been established as a standard PAT tool for many process analytical challenges, including monitoring powder blend homogeneity. However, alternative technologies for monitoring powder blending are of interest due to the importance of the blending step in manufacturing solid oral dosage forms. Terahertz time-domain spectroscopy (THz-TDS) is therefore explored in this study as an alternative tool for monitoring blend homogeneity with the potential for endpoint control in a batch blending process. Powder blends of microcrystalline cellulose (MCC) and dibasic calcium phosphate dihydrate and blends of MCC and granulated α-lactose monohydrate were investigated non-invasively at various compositions using THz-TDS in transmission mode for acquiring spectra from samples enclosed in the blending container. It was found that attenuation- and phase-related parameters acquired with THz-TDS could reliably resolve physical changes related to the homogeneity of the blend. Further evaluations revealed that changes in the bulk density of the blend, in addition to the intrinsic optical properties of the materials, played a critical role in the observed trends for both systems. In contrast, the scattering contribution of the powder was mainly crucial for the attenuation-related parameter in blends with materials of high refractive indices. Finally, THz-TDS measurements were acquired throughout a blending process mimicking a continuous acquisition. The method could follow blending dynamics and resulted in reasonable predictive errors of the content of 0.5 – 2.5 %. Relative standard deviations for high content blends (20 %) were acceptable (3 – 7 %) whereas at low contents (5 %) significantly higher values (9 – 35 %) were found. Based on these findings, THz-TDS is a feasible PAT tool for monitoring blend homogeneity and controlling high content blend processes, although precision and accuracy is considered to improve with a more suitable interface.

Multimodal functional deep learning for multiomics data.

With rapidly evolving high-throughput technologies and consistently decreasing costs, collecting multimodal omics data in large-scale studies has become feasible. Although studying multiomics provides a new comprehensive approach in understanding the complex biological mechanisms of human diseases, the high dimensionality of omics data and the complexity of the interactions among various omics levels in contributing to disease phenotypes present tremendous analytical challenges. There is a great need of novel analytical methods to address these challenges and to facilitate multiomics analyses. In this paper, we propose a multimodal functional deep learning (MFDL) method for the analysis of high-dimensional multiomics data. The MFDL method models the complex relationships between multiomics variants and disease phenotypes through the hierarchical structure of deep neural networks and handles high-dimensional omics data using the functional data analysis technique. Furthermore, MFDL leverages the structure of the multimodal model to capture interactions between different types of omics data. Through simulation studies and real-data applications, we demonstrate the advantages of MFDL in terms of prediction accuracy and its robustness to the high dimensionality and noise within the data.

Occurrence of Microplastics in the Atmosphere: An Overview on Sources, Analytical Challenges, and Human Health Effects

The rapid spread and accumulation of microplastics (MPs) in environmental ecosystems result from extensive plastic usage. MPs have been found in both indoor and outdoor air. Outdoor MP levels vary widely across global cities, with reported ranges from 36 to 118 MPs m−2 day−1. However, differing measurement units complicate comparisons. Indoor MPs are particularly concerning due to the significant amount of time people spend indoors. For instance, MP concentrations in workplaces like reception areas and nail salons were found to be 309 ± 214 and 46 ± 55 MPs m−3, respectively. Technological limitations hinder the identification of MPs, with methods like µ-ATR-FTIR, µ-FTIR, and µ-Raman identifying MPs of different sizes. MPs smaller than 0.3 µm pose a health risk as they can be internalized in lung cells, while MPs larger than 10 µm are too large to enter alveolar macrophages. This review highlights the current understanding of airborne MPs, focusing on their sources, transport, and deposition mechanisms. It aims to provide a foundation for further studies to deeply assess the presence, abundance, and occurrence of MPs in aerosols, a subject that remains underexplored.

On-line measurement of COD and nitrate in water against stochastic background interference based on ultraviolet–visible spectroscopy and physics-informed multi-task learning

Traditional ultraviolet–visible spectroscopic quantitative analytical methods face challenges in simultaneous and long-term accurate measurement of chemical oxygen demand (COD) and nitrate due to spectral overlap and the interference from stochastic background caused by turbidity and chromaticity in water. Addressing these limitations, a compact dual optical path spectrum detection sensor is introduced, and a novel ultraviolet–visible spectroscopic quantitative analysis model based on physics-informed multi-task learning (PI-MTL) is designed. Incorporating a physics-informed block, the PI-MTL model integrates pre-existing physical knowledge for enhanced feature extraction specific to each task. A multi-task loss wrapper strategy is also employed, facilitating comprehensive loss evaluation and adaptation to stochastic backgrounds. This novel approach significantly outperforms conventional models in COD and nitrate measurement under stochastic background interference, achieving impressive prediction R2 values of 0.941 for COD and 0.9575 for nitrate, while reducing root mean squared error (RMSE) by 60.89 % for COD and 77.3 % for nitrate in comparison to the conventional chemometric model partial least squares regression (PLSR), and by 30.59 % and 65.96 %, respectively, in comparison to a benchmark convolutional neural network (CNN) model. The promising results emphasize its potential as a spectroscopic instrument designed for online multi-parameter water quality monitoring against stochastic background interference, enabling long-term accurate measurement of COD and nitrate levels.

Protein complex heterogeneity and topology revealed by electron capture charge reduction and surface induced dissociation.

We illustrate the utility of native mass spectrometry (nMS) combined with a fast, tunable gas-phase charge reduction, electron capture charge reduction (ECCR), for the characterization of protein complex topology and glycoprotein heterogeneity. ECCR efficiently reduces the charge states of tetradecameric GroEL, illustrating Orbitrap m/z measurements to greater than 100,000 m/z. For pentameric C-reactive protein and tetradecameric GroEL, our novel device combining ECCR with surface induced dissociation (SID) reduces the charge states and yields more topologically informative fragmentation. This is the first demonstration that ECCR yields more native-like SID fragmentation. ECCR also significantly improved mass and glycan heterogeneity measurements of heavily glycosylated SARS-CoV-2 spike protein trimer and thyroglobulin dimer. Protein glycosylation is important for structural and functional properties and plays essential roles in many biological processes. The immense heterogeneity in glycosylation sites and glycan structure poses significant analytical challenges that hinder a mechanistic understanding of the biological role of glycosylation. Without ECCR, average mass determination of glycoprotein complexes is available only through charge detection mass spectrometry or mass photometry. With narrow m/z selection windows followed by ECCR, multiple glycoform m/z values are apparent, providing quick global glycoform profiling and providing a future path for glycan localization on individual intact glycoforms.

A Quasi Real-Time Evaluation of High-Resolution Mass Spectra of Complex Chlorinated Paraffin Mixtures and Their Transformation Products.

Chlorinated paraffins (CPs) are complex mixtures of polychlorinated n-alkanes with multiple carbon- (C-, nC = 9-30) and chlorine homologues (Cl-, nCl = 3-18). The mass spectrometric analysis of CPs is time-consuming and challenging, especially when interferences between CPs, their transformation products, or from the matrix are numerous. These analytical challenges and the lack of appropriate and accessible data evaluation tools are obstacles to their analysis. CP-Hunter is a web-based, open-access data processing platform for the automatic analysis of mass spectra of CPs and their transformation products. Extracts of two consumer plastic materials and sewage sludge were evaluated with CP-Hunter. C- and Cl-homologue distributions were obtained in quasi-real-time and the posterior calculated fingerprints were in agreement with the ones obtained by traditional methods. However, the data extraction and evaluation time were now reduced from several minutes to seconds. The implemented signal deconvolution method, i.e., to resolve mass spectrometric interferences, provides robust results, even when severe matrix effects are present. CP-Hunter facilitates the untargeted analysis of unknown products and the detection and elimination of false positive signals. Finally, data evaluation with CP-Hunter is performed locally without the transfer of data to external servers. The tool is safe, public, and accessible at https://cphunter.cheminfo.org/.

Modeling the management of patients with human epidermal growth factor receptor 2-positive breast cancer with liquid biopsy: the future of precision medicine.

In the evolving landscape of human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC) management, liquid biopsy offers unprecedented opportunities for guiding clinical decisions. Here, we review the most recent findings on liquid biopsy applications in HER2-positive BC and its potential role in addressing challenges specific to this BC subtype. Recent studies have highlighted the significance of liquid biopsy analytes, primarily circulating tumor DNA (ctDNA) and circulating tumor cells (CTCs), in stratifying patients' prognosis, predicting treatment response, and monitoring tumor evolution in both early and advanced stages of BC. Liquid biopsy holds promise in studying minimal residual disease to detect and potentially treat disease recurrence before it manifests clinically. Additionally, liquid biopsy may have significant implication in the management of brain metastasis, a major challenge in HER2-positive BC, and could redefine parameters for determining HER2 positivity. Combining ctDNA and CTCs is crucial for a comprehensive understanding of HER2-positive tumors, as they provide complementary insights. Research efforts are needed to address analytical challenges, validate, and broaden the application of liquid biopsy in HER2-positive BC. This effort will ultimately facilitate its integration into clinical practice, optimizing the care of patients with HER2-positive tumors.