Analytical biochemistry最新文献

Ascorbic acid (Vitamin C) is crucial for bodily functions, including collagen synthesis, immune system support and antioxidant defense. Despite autism spectrum disorder's multifactorial nature involving genetic, environmental and neurological factors, robust evidence exploring the association between ascorbic acid and this disorder is notably lacking. This study introduces an innovative spectrofluorometric method to quantify ascorbic acid in the plasma of healthy children and those with autism spectrum disorder. The method relies on the interaction of ascorbic acid with the fluorescent dye propidium iodide. In acidic conditions, propidium iodide undergoes protonation and selectively binds to the negatively charged ascorbic acid forming an ion-pair complex. This complex alters the molecular structure of propidium iodide inducing chemical fluorescence quenching, that can be utilized for ascorbic acid quantification. The developed method undergoes rigorous validation following ICH guidelines, demonstrating a linear relationship within a concentration range of 4–40 μg/mL, with high precision and accuracy metrics. Analysis of real plasma samples from autistic and healthy children reveals clinically and statistically elevated levels of ascorbic acid in those with autism spectrum disorder.

Neuropeptides play crucial roles in regulating neurological function acting as signaling molecules, which provide new opportunity for developing drugs for the treatment of neurological diseases. Therefore, it is very necessary to develop a rapid and accurate prediction model for neuropeptides. Although a few prediction tools have been developed, there is room for improvement in prediction accuracy by using deep learning approach. In this paper, we establish the NeuroPred-ResSE model based on residual block and squeeze-excitation attention mechanism. Firstly, we extract multi-features by using one-hot coding based on the NT5CT5 sequence, dipeptide deviation from expected mean and natural vector. Then, we integrate residual block and squeeze-excitation attention mechanism, which can capture and identify the most relevant attribute features. Finally, the accuracies of the training set and test set are 97.16 % and 96.60 % based on the 5-fold cross-validation and independent test, respectively, and other evaluation metrics have also obtained satisfactory results. The experimental results show that the performance of the NeuroPred-ResSE model outperforms those of existing state-of-the-art models, and our model is an effective, intelligent and robust prediction tool. The datasets and source codes are available at https://github.com/yunyunliang88/NeuroPred-ResSE.

The development of integrated analytical devices is crucial for advancing next-generation point-of-care platforms. Herein, we describe a facile synthesis of a strongly catalytic and durable Nitrogen-doped graphene oxide decorated platinum cobalt (NGO-PtCo) nanocomposite that is conjugated with target-specific DNA aptamer (i-e. MUC1) and grown on carbon fiber. Benefitting from the combined features of the high electrochemical surface area of N-doped GO, high capacitance and stabilization by Co, and high kinetic performance by Pt, a robust, multifunctional, and flexible nanotransducer surface was created. The designed platform was applied for the specific detection of a blood-based oncomarker, CA15-3. The electrochemical characterization proved that nanosurface provides a highly conductive and proficient immobilization support with a strong bio-affinity towards MUC1 aptamer. The specific interaction between CA15-3 and the aptamer alters the surface properties of the aptasensor and the electroactive signal probe generated a remarkable increase in signal intensity. The sensor exhibited a wide dynamic range of 5.0 × 10⁻² -200 U mL⁻¹, a low limit of detection (LOD) of 4.1 × 10⁻² U mL⁻¹, and good reproducibility. The analysis of spiked serum samples revealed outstanding recoveries of up to 100.03 %, by the proposed aptasensor. The aptasensor design opens new revelations in the reliable detection of tumor biomarkers for timely cancer diagnosis.

Phospholipid fatty acid (PLFA) analysis is used for characterizing microbial communities based on their lipid profiles. This method avoids biases from PCR or culture, allowing data collection in a natural state. However, PLFA is labor-intensive due to lipid fractionation. Simplified ester-linked fatty acid analysis (ELFA), which skips lipid fractionation, offers an alternative. It utilizes base-catalyzed methylation to derivatize only lipids, not free fatty acids, and found glycolipid and neutral lipid fractions are scarcely present in most bacteria, allowing lipid fractionation to be skipped. ELFA method showed a high correlation to PLFA data (r = 0.99) and higher sensitivity than the PLFA method by 1.5–2.57-fold, mainly due to the higher recovery of lipids, which was 1.5–1.9 times higher than with PLFA. The theoretical limit of detection (LOD) and limit of quantification (LOQ) for the ELFA method indicated that 1.54-fold less sample was needed for analysis than with the PLFA method. Our analysis of three bacterial cultures and a simulated consortium revealed the effectiveness of the ELFA method by its simple procedure and enhanced sensitivity for detecting strain-specific markers, which were not detected in PLFA analysis. Overall, this method could be easily used for the population analysis of synthetic consortia.

Accurate identifications of protein-peptide binding residues are essential for protein-peptide interactions and advancing drug discovery. To address this problem, extensive research efforts have been made to design more discriminative feature representations. However, extracting these explicit features usually depend on third-party tools, resulting in low computational efficacy and suffering from low predictive performance. In this study, we design an end-to-end deep learning-based method, E2EPep, for protein-peptide binding residue prediction using protein sequence only. E2EPep first employs and fine-tunes two state-of-the-art pre-trained protein language models that can extract two different high-latent feature representations from protein sequences relevant for protein structures and functions. A novel feature fusion module is then designed in E2EPep to fuse and optimize the above two feature representations of binding residues. In addition, we have also design E2EPep+, which integrates E2EPep and PepBCL models, to improve the prediction performance. Experimental results on two independent testing data sets demonstrate that E2EPep and E2EPep + could achieve the average AUC values of 0.846 and 0.842 while achieving an average Matthew's correlation coefficient value that is significantly higher than that of existing most of sequence-based methods and comparable to that of the state-of-the-art structure-based predictors. Detailed data analysis shows that the primary strength of E2EPep lies in the effectiveness of feature representation using cross-attention mechanism to fuse the embeddings generated by two fine-tuned protein language models. The standalone package of E2EPep and E2EPep + can be obtained at https://github.com/ckx259/E2EPep.git for academic use only.

Each year, millions of people suffer from foodborne illness due to the consumption of food contaminated with pathogenic bacteria, which severely challenges global health. Therefore, it is essential to recognize foodborne pathogens swiftly and correctly. However, conventional detection techniques for bacterial pathogens are labor-intensive, low selectivity, and time-consuming, highlighting a notable knowledge gap. A novel approach, aptamer-based biosensors (aptasensors) linked to carbon nanomaterials (CNs), has shown the potential to overcome these limitations and provide a more reliable method for detecting bacterial pathogens. Aptamers, short single-stranded DNA (ssDNA)/RNA molecules, serve as bio-recognition elements (BRE) due to their exceptionally high affinity and specificity in identifying foodborne pathogens such as Salmonella spp., Escherichia coli (E. coli), Listeria monocytogenes, Campylobacter jejuni, and other relevant pathogens commonly associated with foodborne illnesses. Carbon nanomaterials' high surface area-to-volume ratio contributes unique characteristics crucial for bacterial sensing, as it improves the binding capacity and signal amplification in the design of aptasensors. Furthermore, aptamers can bind to CNs and create aptasensors with improved signal specificity and sensitivity. Hence, this review intends to critically review the current literature on developing aptamer functionalized CN-based biosensors by transducer optical and electrochemical for detecting foodborne pathogens and explore the advantages and challenges associated with these biosensors. Aptasensors conjugated with CNs offers an efficient tool for identifying foodborne pathogenic bacteria that is both precise and sensitive to potentially replacing complex current techniques that are time-consuming.

In recent years, more sophisticated DNA technologies for genotyping have enabled considerable progress in various fields such as clinical genetics, archaeogenetics and forensic genetics. DNA samples previously rejected as too challenging to analyze due to low amounts of degraded DNA can now provide useful information. To increase the chances of success with the new methodologies, it is crucial to know the fragment size of the template DNA molecules, and whether the DNA in a sample is mostly single or double stranded. With this knowledge, an appropriate library preparation method can be chosen, and the DNA shearing parameters of the protocol can be adjusted to the DNA fragment size in the sample. In this study, we first developed and evaluated a user-friendly fluorometry-based protocol for estimation of DNA strandedness. We also evaluated different capillary electrophoresis methods for estimation of DNA fragmentation levels. Next, we applied the developed methodologies to a broad variety of DNA samples processed with different DNA extraction protocols. Our findings show that both the applied DNA extraction method and the sample type affect the DNA strandedness and fragmentation. The established protocols and the gained knowledge will be applicable for future sequencing-based high-density SNP genotyping in various fields.

In this paper, we introduced a novel phase-transfer strategy tailored for the efficient batch detection of ascorbic acid in vitamin C tablets. This method entailed the reaction between ascorbic acid and an excess of potassium permanganate. Subsequent reaction of the residual potassium permanganate with sodium oxalate in an acidic medium led to the generation of carbon dioxide. The quantification of the produced carbon dioxide was achieved using headspace GC, enabling the indirect measurement of ascorbic acid. The obtained findings revealed that the headspace method exhibited satisfied precision with a relative standard deviation of less than 2.11 % and high sensitivity with a limit of quantitation of 0.27 μmol. These results firmly establish the reliability of this innovative approach for determining ascorbic acid. In addition, the highly automated feature of headspace method significantly enhances the efficiency of batch sample detection and reduces the errors caused by human operation. Thus, the adoption of the transformed phase strategy has demonstrated its effectiveness in assessing ascorbic acid, especially for large-scale sample analysis in industrial applications, owing to its efficiency, precision, and sensitivity.

Lateral-flow immunoassays (LFAs) can be used to diagnose urinary tract infections caused by Escherichia coli (E. coli) at the point of care. Unfortunately, urine samples containing dilute concentrations of E. coli can yield false negative results on LFAs. Our laboratory was first to implement aqueous two-phase systems (ATPSs) to preconcentrate samples into smaller volumes prior to their application on LFAs. This is achieved by manipulating the ratio of the volume of the top phase to that of the bottom phase (volume ratio; VR) and concentrating biomarkers in the bottom phase which, when applied to LFAs in fixed volumes, leads to corresponding improvements in sensitivity. This work is the first demonstration that the same LOD can be achieved irrespective of the VR when the entire bottom phase is added to LFAs. A custom 3D-printed device was also developed to decrease liquid handling steps. Across different VRs expected from patient urine variability, this diagnostic workflow successfully detected E. coli concentrations down to 2 × 10⁵ colony-forming units (cfu) mL⁻¹ in synthetic urine, demonstrating consistent 10-fold improvements in sensitivity compared to trials conducted without ATPS preconcentration. This method successfully addresses the variability of patient samples while remaining easy to use at the point of care.

The Peptide therapeutics market was evaluated to be around USD 45.67 BN in 2023 and is projected to witness massive growth at a CAGR of around 5.63 % from 2024 to 2032 (USD 80.4 BN). Generic peptides are expected to reach USD 27.1 billion by 2032 after the patent monopoly of the pioneer peptides expires, and generic peptides become accessible. The generic manufacturers are venturing into peptide-based therapeutics for the aforementioned reasons. There is an abundance of material accessible regarding the characterization of peptides, which can be quite confusing for researchers. The FDA believes that an ANDA applicant may now demonstrate that the active component in a proposed generic synthetic peptide drug product is the “same” as the active ingredient in a peptide of rDNA origin that has previously been approved. To ensure the efficacy, safety, and quality of peptide therapies during development, regulatory bodies demand comprehensive characterization utilizing several orthogonal methodologies. This article elaborates the peptide characterization by segmenting into different segments as per the critical quality attribute from identification of the peptide to the physicochemical property of the peptide therapeutics which will be required to demonstrate the sameness with reference product based on the size of the peptide chain and molecular weight of the peptides. Article insights briefly on each individual technique and the orthogonal techniques for each test were explained. The impurities requirements in the generic peptides as per the regulatory requirement were also discussed.