Analysis Of Biological Samples Research Articles

Antimalarial analysis of pharmaceutical formulations and biological samples by capillary electrophoresis: the state of the art and applications.

Malaria is a serious public health problem, being an endemic disease in 84 countries, mainly in Africa. This review explores the application of capillary electrophoresis (CE) techniques for analyzing antimalarial drugs, highlighting methods from 2000 to 2023 for the analysis of pharmaceutical formulations and human biological samples. The versatility, selectivity, high efficiency, cost-effectiveness, and high analytical frequency of CE techniques have become attractive choices for pharmaceutical analysis, focusing on quality control and impurity analysis applications. The evolution of achiral and chiral electromigration methods has been described based on the features of each mode of separation: capillary zone electrophoresis (CZE), micellar electrokinetic chromatography, microemulsion electrokinetic chromatography, and capillary electrochromatography. As expected, CZE is reported in most articles owing to its compatibility with drug properties and separation mode. However, it is necessary to perform other separation modes for a few drugs that are present in neutral form. After exhaustive research using different databases and statistical analyses, 27 articles using CE techniques for antimalarial drug analysis were found and are mentioned in this review.

Hydrophilic molecularly imprinted thermal-responsive polymers based sorbent for ambient ionization mass spectrometric analysis of sulfonamide antibiotics from food samples

The thermal-responsive magnetic molecularly imprinted polymer (TrMMIP) sorbent was synthesized by surface imprinting method, and then used for magnetic solid-phase extraction (MSPE) and subsequent integrated into the ion source for elution and ionization. The shrinking-strength states change of the thermal-responsive polymer chain on TrMMIP alters the wettability of the sorbent when the working temperature crosses the lower critical solution temperature (LCST) of the polymer, and thus affects its behavior of in the extraction and clean-up process. The targeted analytes could be effectively extracted due to the high selectivity of MIPs and well dispersibility of polymer chain under the open state. Additionally, a hydrophilic polymer chain wrapped on the sorbent surface further protected target substances from co-elution during cleanup. Analytical methods for sulfonamide antibiotics (SAs) detection in complex food samples (milk, honey, fish) were developed, demonstrating potential for rapid and sensitive SAs analysis in diverse food and biological samples.

Research progress on pretreatment technology for the analysis of amphetamine biological samples.

Sample pretreatment technology is crucial for drug analysis and detection, because the effect of sample pretreatment directly determinates the final analysis results. In recent years, with the continuous innovation of microextraction and other technologies like material preparation technologies and assistant technologies for extraction, the sample pretreatment techniques in the process of drug analysis have become more and more mature and diverse. This article takes amphetamine (AM) or methamphetamine as an example to review the recent development of pretreatment methods for AM-containing biological samples from the perspectives of extraction techniques, extraction media and auxiliary technologies. Extraction techniques are summarized with the categories of contact microextraction, separate microextraction and membrane-based microextraction for better guidance of application according to their features. Prevailing and innovative extraction media including carbon-based material, silicon-based material, metal organic framework, molecularly selective materials, supramolecular solvents and ionic liquids are reviewed. Auxiliary technologies like magnetic field, electric field, microwave, ultrasound and so on which can enhance extraction efficiency and accuracy are also reviewed. In the last, prospects of the future development of pretreatment technology for the analysis of AM biological samples are provided.

Identification and characterization of GSK-9089 metabolites through high resolution-mass spectrometry based in vitro and in vivo rat biological sample analysis

Estrogen related receptors (ERRs) agonist GSK-9089 (DY-131) reported to pose a potential in increasing exercise endurance. High resolution mass spectrometry (HRMS) based analysis has utmost importance in the detection, identification, or characterization of a molecule including its metabolites in human body. In this study, in vitro metabolism profile of GSK-9089 was investigated after incubation with liver microsomes and S9 fractions. Additionally, in vivo metabolites of the molecule were identified in plasma, urine, and faeces samples of rats. Structures of all the potential metabolites were revealed by employing an in silico tool and HRMS based analysis through data-dependent and data-independent mining strategies. Nine unknown metabolites of GSK-9089 have been identified which were found to be present in a trace amount in in vivo matrices. Most of the in vitro and in vivo phase I metabolites of the molecule were formed after imine bond hydrolysis followed by deamidation, oxidation, and N-oxidation. The molecule underwent phase II metabolism to generate more polar metabolites mainly through glucuronide, sulfate conjugation biotransformation reactions. The in vitro and in vivo metabolites of GSK-9089 could be useful to identify the abuse of this ERRs agonist in the future.

Estimating the household secondary attack rate and serial interval of COVID-19 using social media

We propose a method to estimate the household secondary attack rate (hSAR) of COVID-19 in the United Kingdom based on activity on the social media platform X, formerly known as Twitter. Conventional methods of hSAR estimation are resource intensive, requiring regular contact tracing of COVID-19 cases. Our proposed framework provides a complementary method that does not rely on conventional contact tracing or laboratory involvement, including the collection, processing, and analysis of biological samples. We use a text classifier to identify reports of people tweeting about themselves and/or members of their household having COVID-19 infections. A probabilistic analysis is then performed to estimate the hSAR based on the number of self or household, and self and household tweets of COVID-19 infection. The analysis includes adjustments for a reluctance of Twitter users to tweet about household members, and the possibility that the secondary infection was not acquired within the household. Experimental results for the UK, both monthly and weekly, are reported for the period from January 2020 to February 2022. Our results agree with previously reported hSAR estimates, varying with the primary variants of concern, e.g. delta and omicron. The serial interval (SI) is based on the time between the two tweets that indicate a primary and secondary infection. Experimental results, though larger than the consensus, are qualitatively similar. The estimation of hSAR and SI using social media data constitutes a new tool that may help in characterizing, forecasting and managing outbreaks and pandemics in a faster, affordable, and more efficient manner.

Magnetic-free chiral eigenmode spectroscopy for simultaneous sensitive measurement of optical rotary dispersion and circular dichroism

Chirality, defined by Lord Kelvin, refers to the geometric symmetry property of an object that cannot be superposed onto its mirror image using rotations and translations. The material’s chirality can be probed with light as the optical activity: optical rotary dispersion (ORD) and circular dichroism (CD). It is still challenging to yield extremely sensitive ORD and CD for very weak chirality and measure both simultaneously. Cavity ringdown polarimetry has been reported to improve ORD detection sensitivity with the absence of equally important CD signature, at the price of high cavity finesse near 400, frequency-locking sophistication, and large magnetic field. Here, we report a unique recipe to demonstrate the simultaneous measurement of ORD and the CD by separately observing the chiral eigenmode spectra from a bowtie optical cavity with a finesse about 30, without resorting to frequency locking or magnetic field. We obtain a sensitivity of ∼2.7×10-3deg/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 2.7\ imes 10^{-3} \ ext {deg}/\\sqrt{\ ext {Hz}}$$\\end{document} for ORD, ∼8.1×10-6/Hz\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 8.1 \ imes 10^{-6} /\\sqrt{\ ext {Hz}}$$\\end{document} for CD, and a spectral resolution of 0.04pm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.04~\ ext {pm}$$\\end{document} within a millisecond-scale measurement. We present a cost-effective yet ultrasensitive account for chiral chromatography, the conformational dynamics and chiroptical analysis of biological samples which particularly exhibit weak and narrow spectral signals.

Synergistically biomimetic platform that enables droplets to be self-propelled

Droplet transport still faces numerous challenges, such as a limited transport distance, large volume loss, and liquid contamination. Inspired by the principle of ‘synergistic biomimetics’, we propose a design for a platform that enables droplets to be self-propelled. The orchid leaf-like three-dimensional driving structure provides driving forces for the liquid droplets, whereas the lotus leaf-like superhydrophobic surface prevents liquid adhesion, and the bamboo-like nodes enable long-distance transport. During droplet transport, no external energy input is required, no fluid adhesion or residue is induced, and no contamination or mass loss of the fluid is caused. We explore the influence of various types and parameters of wedge structures on droplet transportation, the deceleration of droplet speed at nodal points, and the distribution of internal pressure. The results indicate that the transport platform exhibits insensitivity to pH value and temperature. It allows droplets to be transported with varying curvatures in a spatial environment, making it applicable in tasks like target collection, as well as load, fused, anti-gravity, and long-distance transport. The maximum droplet transport speed reached (58 ± 5) mm·s−1, whereas the transport distance extended to (136 ± 4) mm. The developed platform holds significant application prospects in the fields of biomedicine and chemistry, such as high-throughput screening of drugs, genomic bioanalysis, microfluidic chip technology for drug delivery, and analysis of biological samples.

Optimizing and Predicting Antidepressant Efficacy in Patients with Major Depressive Disorder Using Multi-Omics Analysis and the Opade AI Prediction Tools.

According to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability. Given the complexity and clinical heterogeneity of these disorders, a shift from "canonical treatment" to personalized medicine with improved patient stratification is needed. OPADE is a non-profit study that researches biomarkers in MDD to tailor personalized drug treatments, integrating genetics, epigenetics, microbiome, immune response, and clinical data for analysis. A total of 350 patients between 14 and 50 years will be recruited in 6 Countries (Italy, Colombia, Spain, The Netherlands, Turkey) for 24 months. Real-time electroencephalogram (EEG) and patient cognitive assessment will be correlated with biological sample analysis. A patient empowerment tool will be deployed to ensure patient commitment and to translate patient stories into data. The resulting data will be used to train the artificial intelligence/machine learning (AI/ML) predictive tool.

Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images

Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.

Lipidomics Study of Type 1 Diabetic Rats Using Online Phase Transition Trapping-Supercritical Fluid Extraction-Chromatography Coupled with Quadrupole Time-of-Flight Tandem Mass Spectrometry.

Chromatography-mass spectrometry-based lipidomics represents an essential tool for elucidating lipid dysfunction mechanisms and is extensively employed in investigating disease mechanisms and identifying biomarkers. However, the detection of low-abundance lipids in biological matrices, along with cumbersome operational procedures, complicates comprehensive lipidomic analyses, necessitating the development of highly sensitive, environmentally friendly, and automated methods. In this study, an online phase transition trapping-supercritical fluid extraction-chromatography-mass spectrometry (PTT-SFEC-MS/MS) method was developed and successfully applied to plasma lipidomics analysis in Type 1 diabetes (T1D) rats. The PTT strategy captured entire extracts at the column head by converting CO2 from a supercritical state to a gaseous state, thereby preventing peak spreading, enhancing peak shape for precise quantification, and boosting sensitivity without any sample loss. This method utilized only 5 μL of plasma and accomplished sample extraction, separation, and detection within 27 min. Ultimately, 77 differential lipids were identified, including glycerophospholipids, sphingolipids, and glycerolipids, in T1D rat plasma. The results indicated that the progression of the disease might be linked to alterations in glycerophospholipid and sphingolipid metabolism. Our findings demonstrated a green, highly efficient, and automated method for the lipidomics analysis of biological samples, providing a scientific foundation for understanding the pathogenesis and diagnosis of T1D.

Unbiased method for spectral analysis of cells with great diversity of autofluorescence spectra.

Autofluorescence is an intrinsic feature of cells, caused by the natural emission of light by photo-excitatory molecular content, which can complicate analysis of flow cytometry data. Different cell types have different autofluorescence spectra and, even within one cell type, heterogeneity of autofluorescence spectra can be present, for example, as a consequence of activation status or metabolic changes. By using full spectrum flow cytometry, the emission spectrum of a fluorochrome is captured by a set of photo detectors across a range of wavelengths, creating an unique signature for that fluorochrome. This signature is then used to identify, or unmix, that fluorochrome's unique spectrum from a multicolor sample containing different fluorescent molecules. Importantly, this means that this technology can also be used to identify intrinsic autofluorescence signal of an unstained sample, which can be used for unmixing purposes and to separate the autofluorescence signal from the fluorophore signals. However, this only works if the sample has a singular, relatively homogeneous and bright autofluorescence spectrum. To analyze samples with heterogeneous autofluorescence spectral profiles, we setup an unbiased workflow to more quickly identify differing autofluorescence spectra present in a sample to include as "autofluorescence signatures" during the unmixing of the full stained samples. First, clusters of cells with similar autofluorescence spectra are identified by unbiased dimensional reduction and clustering of unstained cells. Then, unique autofluorescence clusters are determined and are used to improve the unmixing accuracy of the full stained sample. Independent of the intensity of the autofluorescence and immunophenotyping of cell subsets, this unbiased method allows for the identification of most of the distinct autofluorescence spectra present in a sample, leading to less confounding autofluorescence spillover and spread into extrinsic phenotyping markers. Furthermore, this method is equally useful for spectral analysis of different biological samples, including tissue cell suspensions, peripheral blood mononuclear cells, and in vitro cultures of (primary) cells.

Challenges in the diagnosis and control of female genital schistosomiasis in sub-Saharan Africa: an exemplar case report associated with mixed and putative hybrid schistosome infection in Nsanje District, Southern Malawi

Female genital schistosomiasis (FGS) remains an often overlooked chronic complication of urogenital schistosomiasis in adolescent girls and women. Moreover, the role of zoonotic or hybrid schistosome infection(s) is poorly appreciated, but is increasingly becoming an emerging public health concern in sub-Saharan Africa. In Southern Malawi, during the “Hybridization in UroGenital Schistosomiasis (HUGS)” study visit, we describe the case of a 33-year-old woman with suspected FGS who partook in a detailed external assessment with internal cervical examination using a portable colposcope. She provided several biological samples for analysis with traditional and molecular parasitological methods—urine, cervicovaginal lavage (CVL), cervical swabs, and external mass and cervical biopsies—alongside provision of detailed demographic information after a thorough medical history questionnaire and an in-depth interview. These samples were screened for the presence of Schistosoma ova on microscopy and DNA genotyping using a novel real-time PCR assay in parallel to pre-published probe-based PCR assays capable of identifying and discriminating up to six named Schistosoma species. A further molecular screen of sexually transmitted infections (STIs) including Trichomonas vaginalis, Chlamydia spp., and human papilloma virus (HPV) was conducted on her genital swab and CVL. Overt FGS was diagnosed on clinical colposcopy alongside inspection of the cervical biopsy by microscopy, real-time PCR, and histopathology. The urine filtration, microscopy and real-time PCR of the CVL and swab were negative. This evidences the typical diagnostic challenge, and cases such as this will pose an unmet need in satisfactory patient management. In addition to Schistosoma haematobium, the presence of the zoonotic species Schistosoma mattheei and concurrent STIs raise questions as to the long-term effectiveness of the current control strategies of the National Control Programme to eliminate schistosomiasis as a public health problem. Improved availability of and regular accessibility to praziquantel treatment for women at risk such as this are urgently needed. Furthermore, targeted health education, increased community awareness, and dovetailing of synergistic activities and strategies with other health stakeholders such as those in sexual and reproductive health, as well as HIV/AIDS programs in the Ministry of Health, are needed here and in neighboring countries.

Analytical Insights: Ce-Ms Unveiled

Hyphenated techniques in the field of analytical chemistry involve combining different analytical methods to enhance the analysis of samples. One common approach is to combine chromatographic techniques with spectroscopic techniques. This makes it possible to comprehend the component included in a mixture on a more thorough level. It contains the various techniques such as GC-MS, LC-MS, LC-NMR, CE-MS, and LC-IR etc. Overall hyphenated technique offer improved analysis of samples in terms of specificity, accuracy, and precisions by combining multiple analytical methods, scientists can obtain more comprehensive information about the components. Capillary electrophoresis-mass spectrometry (CE-MS) has emerged as a powerful analytical tool for the comprehensive analysis of complex biological samples. This review provides a comprehensive overview of the latest advances in sophisticated hyphenated analytical techniques within the realm of CE-MS. It highlights the synergistic combination of capillary electrophoresis with mass spectrometry, showcasing its applications in proteomics, metabolomics, pharmaceutical analysis, and clinical diagnostics.

Enhanced Detection of Lamotrigine Through HPLC Methodology in Bioanalysis

Background: Lamotrigine, an anticonvulsant drug, requires sensitive detection methods in biological and pharmaceutical matrices. Current methods struggle with interference and sensitivity, necessitating the development of improved analytical techniques. Objective: This study aims to develop and validate a highly sensitive HPLC method enhanced by salicylaldehyde derivatization for detecting lamotrigine in pharmaceutical and biological samples, and to differentiate it from Piracetam when co-administered. Methods: A reverse-phase HPLC method was employed, utilizing salicylaldehyde as a derivatizing agent to induce a bathochromic shift in lamotrigine from 315 nm to 415 nm. Optimization of the derivatization reaction included adjustments of pH, reagent concentration, temperature, and time. The standard addition method was applied to evaluate the recovery rates from spiked samples. Results: The optimized method demonstrated a linear calibration range of 1-5 µg/ml with a determination coefficient (R²) of 0.998. Recovery of lamotrigine from pharmaceutical preparations averaged 96.5%, while recovery from deprotonated serum and urine samples of healthy volunteers was 96% with a relative standard deviation (RSD) of 6%. Conclusion: The developed method offers a robust and highly sensitive approach to detect and distinguish lamotrigine in the presence of Piracetam, proving effective for both pharmaceutical and biological sample analysis.

Intelligent near-infrared light-activatable DNA machine with DNA wire nano-scaffold-integrated fast domino-like driving amplification for high-performance imaging in live biological samples

While there is significant potential for DNA machine-built enzyme-free fluorescence biosensors in the imaging analysis of live biological samples, they persist certain shortcomings. These encompass a deficiency of signal enrichment within a singular interface, uncontrolled premature activation during bio-delivery, and a slow reaction rate due to free nucleic acid collisions. In this contribution, we are committed to resolving the above challenges. Firstly, a single-interface-integrated domino-like driving amplification is constructed. In this conception, a specific target acts as the domino promotor (namely the energy source), initiating a cascading chain reaction that grafts onto a singular interface. Next, an 808 nm near-infrared (NIR) light-excited up-converting luminescence-induced light-activatable biosensing technique is introduced. By locking the target-specific identification segment with a photo-cleavage connector, the up-converted ultraviolet emission can activate target binding in a completely controlled manner. Moreover, a fast reaction rate is achieved by confining nucleic acid collisions within the surface of a DNA wire nano-scaffold, leading to a substantial enhancement in local contact concentration (30.8-fold increase, alongside a 15 times elevation in rate). When a non-coding microRNA (miRNA-221) is positioned as the model low-abundance target for proof-of-concept validation, our intelligent DNA machine demonstrates ultra-high sensitivity (with a limit of detection down to 62.65 fM) and good specificity for this hepatic malignant tumor-associated biomarker in solution detection. Going further, it is worth highlighting that the biosensing system can be employed to carry out high-performance imaging analysis in live bio-samples (ranging from the cellular level to the nude mouse body), thereby propelling the field of DNA machines in disease diagnosis.

Direct Glycan Analysis of Biological Samples and Intact Glycoproteins by Integrating Machine Learning-Driven Surface-Enhanced Raman Scattering and Boronic Acid Arrays.

Frequent monitoring of glycan patterns is a critical step in studying glycan-mediated cellular processes. However, the current glycan analysis tools are resource-intensive and less suitable for routine use in standard laboratories. We developed a novel glycan detection platform by integrating surface-enhanced Raman spectroscopy (SERS), boronic acid (BA) receptors, and machine learning tools. This sensor monitors the molecular fingerprint spectra of BA binding to cis-diol-containing glycans. Different types of BA receptors could yield different stereoselective reactions toward different glycans and exhibit unique vibrational spectra. By integration of the Raman spectra collected from different BA receptors, the structural information can be enriched, eventually improving the accuracy of glycan classification and quantification. Here, we established a SERS-based sensor incorporating multiple different BA receptors. This sensing platform could directly analyze the biological samples, including whole milk and intact glycoproteins (fetuin and asialofetuin), without tedious glycan release and purification steps. The results demonstrate the platform's ability to classify milk oligosaccharides with remarkable classification accuracy, despite the presence of other non-glycan constituents in the background. This sensor could also directly quantify sialylation levels of a fetuin/asialofetuin mixture without glycan release procedures. Moreover, by selecting appropriate BA receptors, the sensor exhibits an excellent performance of differentiating between α2,3 and α2,6 linkages of sialic acids. This low-cost, rapid, and highly accessible sensor will provide the scientific community with an invaluable tool for routine glycan screening in standard laboratories.

CRIMINAL LIABILITY FOR ANALYSING GENOMIC DATA WITHOUT OWNER'S CONSENT: A COMPARATIVE STUDY

Background: In the rapidly evolving landscape of genomics and biotechnology, the United Arab Emirates (UAE) has launched the Emirates Genome Program to harness the potential of genomic technologies for advancing healthcare services. Central to this initiative is the informed and voluntary participation of citizens in genetic research aimed at contributing to national health objectives through genetic data utilisation. Notably, the enactment of UAE Decree-Law No. (49) of 2023 underscores the importance of safeguarding genomic privacy as a foundational element for data security and individual rights. This study addresses the challenge of striking a delicate balance between individual genetic rights and the pressing scientific and medical needs of genomic research. It aims to analyse the right to genomic privacy and examine crimes associated with the unauthorised analysis of biological samples. Methods: This research employed an analytical legal methodology and a comparative approach to explore the crime of disclosing genomic data. By analysing Decree-Law No. (49) of 2023 and other relevant Emirati legislation, we examined the legal framework governing genomic research and data protection in the UAE. Comparative legal analysis was then conducted between Emirati and French laws to identify similarities and differences in approaches to genomic data disclosure crimes. The study also considered international standards and ethical principles to provide a comprehensive, multidisciplinary understanding of the intersection of law, ethics, and science in genomic privacy. Results and conclusions: This study’s findings underscore the necessity of establishing a robust legal framework that safeguards individual rights and ensures the confidentiality of genetic data. Such measures are crucial for fostering public trust in genomic research and aligning the UAE's genomic endeavours with rigorous ethical and legal standards. Ultimately, Decree-Law No. (49) of 2023 exemplifies the state's commitment to promoting ethical and legal practices in genomic research, thereby facilitating sustainable advancements in medical science.

Innovative Solid-Phase Extraction Strategies for Improving the Advanced Chromatographic Determination of Drugs in Challenging Biological Samples.

In the past few decades, considerable scientific strides have been made in the subject of drug analysis in human biological samples. However, the risk caused by incorrect drug plasma levels in patients still remains an important concern. This review paper attempts to investigate the advances made over the last ten years in common sample preparation techniques (SPT) for biological samples based on solid sorbents, including solid-phase extraction (SPE) and solid-phase micro-extraction (SPME), and in particular in the field of molecularly imprinted polymers (MIPs), including non-stimuli-responsive and stimuli-responsive adsorbents. This class of materials is known as 'smart adsorbents', exhibiting tailored responses to various stimuli such as magnetic fields, pH, temperature, and light. Details are provided on how these advanced SPT are changing the landscape of modern drug analysis in their coupling with liquid chromatography-mass spectrometry (LC-MS) analytical techniques, a general term that includes high-performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography (UHPLC), as well as any variation of MS, such as tandem (MS/MS), multiple-stage (MSn), and high-resolution (HRMS) mass spectrometry. Some notes are also provided on coupling with less-performing techniques, such as high-performance liquid chromatography with ultraviolet (HPLC-UV) and diode array detection (HPLC-DAD) detection. Finally, we provide a general review of the difficulties and benefits of the proposed approaches and the future prospects of this research area.

Polylactic acid micro/nanoplastic-induced hepatotoxicity: Investigating food and air sources via multi-omics

Micro/nanoplastics (MNPs) are detected in human liver, and pose significant risks to human health. Oral exposure to MNPs derived from non-biodegradable plastics can induce toxicity in mouse liver. Similarly, nasal exposure to non-biodegradable plastics can cause airway dysbiosis in mice. However, the hepatotoxicity induced by foodborne and airborne biodegradable MNPs remains poorly understood. Here we show the hepatotoxic effects of biodegradable polylactic acid (PLA) MNPs through multi-omics analysis of various biological samples from mice, including gut, fecal, nasal, lung, liver, and blood samples. Our results show that both foodborne and airborne PLA MNPs compromise liver function, disrupt serum antioxidant activity, and cause liver pathology. Specifically, foodborne MNPs lead to gut microbial dysbiosis, metabolic alterations in the gut and serum, and liver transcriptomic changes. Airborne MNPs affect nasal and lung microbiota, alter lung and serum metabolites, and disrupt liver transcriptomics. The gut Lachnospiraceae_NK4A136_group is a potential biomarker for foodborne PLA MNP exposure, while nasal unclassified_Muribaculaceae and lung Klebsiella are potential biomarkers for airborne PLA MNP exposure. The relevant results suggest that foodborne PLA MNPs could affect the “gut microbiota-gut-liver” axis and induce hepatoxicity, while airborne PLA MNPs could disrupt the “airway microbiota-lung-liver” axis and cause hepatoxicity. These findings have implications for diagnosing PLA MNPs-induced hepatotoxicity and managing biodegradable materials in the environment. Our current study could be a starting point for biodegradable MNPs-induced hepatotoxicity. More research is needed to verify and inhibit the pathways that are crucial to MNPs-induced hepatotoxicity.

Triple Sensing Modes for Triggered β-Galactosidase Activity Assays Based on Kaempferol-Deduced Silicon Nanoparticles and Biological Imaging of MCF-7 Breast Cancer Cells.

β-Galactosidase (β-Gala) is an essential biomarker enzyme for early detection of breast tumors and cellular senescence. Creating an accurate way to monitor β-Gala activity is critical for biological research and early cancer detection. This work used fluorometric, colorimetric, and paper-based color sensing approaches to determine β-Gala activity effectively. Via the sensing performance, the catalytic activity of β-Gala resulted in silicon nanoparticles (SiNPs), fluorescent indicators obtained via a one-pot hydrothermal process. As a standard enzymatic hydrolysis product of the substrate, kaempferol 3-O-β-d-galactopyranoside (KOβDG) caused the fluorometric signal to be attenuated on kaempferol-silicon nanoparticles (K-SiNPs). The sensing methods demonstrated a satisfactory linear response in sensing β-Gala and a low detection limit. The findings showed the low limit of detection (LOD) as 0.00057 and 0.098 U/mL for fluorometric and colorimetric, respectively. The designed probe was then used to evaluate the catalytic activity of β-Gala in yogurt and human serum, with recoveries ranging from 98.33 to 107.9%. The designed sensing approach was also applied to biological sample analysis. In contrast, breast cancer cells (MCF-7) were used as a model to test the in vitro toxicity and molecular fluorescence imaging potential of K-SiNPs. Hence, our fluorescent K-SiNPs can be used in the clinic to diagnose breast cellular carcinoma, since they can accurately measure the presence of invasive ductal carcinoma in serologic tests.