Analytical biochemistry最新文献

{"title":"Electrochemical quantification of dopamine using a nanocomposite of silver nanoparticles-potassium modified graphitic carbon nitride (Ag/K-g-CN)","authors":"Smriti Dogra ,&nbsp;Saumya Dwivedi ,&nbsp;Jaspreet Kaur Rajput ,&nbsp;Jigyasa ,&nbsp;Sareen","doi":"10.1016/j.ab.2026.116091","DOIUrl":"10.1016/j.ab.2026.116091","url":null,"abstract":"<div><div>Herein, a selective and sensitive electrochemical sensor based on silver nanoparticles–potassium modified graphitic carbon nitride (Ag/K-g-CN) was developed. The nanocomposite was synthesised via a combined thermal and solvothermal method for the dopamine (DA) detection. To create bulk CN, melamine was thermally polymerised at 550 °C. Subsequently, solvothermal-assisted exfoliation yielded g-CN nanosheets. Next, the porous g-CN was modified with KOH by grinding, dispersing it in an ethanol-water mixture, and calcined it at 300 °C to create K-modified g-CN. Ultrasonication was used to disperse Ag NPs evenly within K-g-CN, yielding a homogeneous Ag/K-g-CN nanocomposite. The Ag/K-g–CN–modified electrode exhibited significant electrochemical responses, demonstrating its successful attachment to the electrode surface. High-resolution transmission electron microscopy (HR-TEM), Fourier-transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), and X-ray diffraction (XRD) were used to investigate the structural and morphological features. The sensor's performance was evaluated using linear sweep voltammetry (LSV) and cyclic voltammetry (CV), with a broad linear range of 0-500 μM, a low detection limit of 0.629 μM, and a sensitivity of 0.432 μA μM⁻¹ cm⁻² for DA. Practical application was demonstrated using spiked recovery studies in 100-fold diluted human serum samples, yielding recoveries of 99–100%, validating the reliability of the proposed sensor.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"713 ","pages":"Article 116091"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EnsemGlyPred: Intelligent prediction system for lysine glycation sites integrating deep semantic features and sequence information","authors":"Yun Zuo,&nbsp;Sifan Zhu","doi":"10.1016/j.ab.2026.116077","DOIUrl":"10.1016/j.ab.2026.116077","url":null,"abstract":"<div><h3>Motivation</h3><div>Protein non-enzymatic glycation plays a crucial role in chronic diseases such as diabetes, atherosclerosis, and neurodegenerative diseases. Accurate identification of lysine glycation sites is essential for elucidating disease pathogenesis and developing diagnostic biomarkers. However, current computational methods face challenges in capturing complex contextual features, integrating multi-source heterogeneous features, and providing biological interpretability.</div></div><div><h3>Results</h3><div>This study proposes EnsemGlyPred, an intelligent prediction system integrating multi-dimensional features with weighted ensemble learning. We constructed a reliable benchmark dataset from the PLMD database after rigorous data processing. A multi-level feature extraction framework was designed, integrating AAC features (amino acid composition), PAAC features (incorporating sequence order), and deep semantic features from ProGen2. Three optimized base classifiers were constructed: AAC-based XGBoost, PAAC-based XGBoost, and ProGen2-based BiLSTM models, with scientifically allocated weights (0.33, 0.4, 0.27) for ensemble prediction.</div><div>Rigorous ten-fold cross-validation and independent testing demonstrated that the ensemble model significantly outperformed existing methods, particularly achieving significant improvement in recall metrics. Systematic ablation experiments confirmed the effectiveness of multi-feature fusion and weighted ensemble strategies. Feature importance analysis revealed the key regulatory role of amino acids in the lysine neighboring region and positively charged residues. t-SNE visualization demonstrated improved discriminative feature space distribution. This research provides a systematic methodological framework for computational prediction of protein post-translational modifications, offering reliable theoretical foundation and important guidance for related fields.</div></div><div><h3>Availability</h3><div>an intelligent online prediction platform (<span><span>http://www.ensemglypred.com</span><svg><path></path></svg></span>) has been developed to facilitate the efficient conduct of academic research. The predictive model EnsemGlyPred and associated datasets are available at: <span><span>https://github.com/pigyoo-1009/EnsemGlyPred</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"713 ","pages":"Article 116077"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment of a homogeneous immunoassay for quantitative detection of total procollagen type 1 N-terminal propeptide","authors":"Shanshan Cheng ,&nbsp;Yuanmin Sun ,&nbsp;Xiaohui Yang ,&nbsp;Hongwei Yang ,&nbsp;Xiaoming Cui ,&nbsp;Xue Li ,&nbsp;Huiqiang Li ,&nbsp;Yang Yu","doi":"10.1016/j.ab.2026.116089","DOIUrl":"10.1016/j.ab.2026.116089","url":null,"abstract":"<div><h3>Objective</h3><div>To establish a homogeneous immunoassay for quantitative detection of total procollagen type 1 n-terminal propeptide (P1NP) in human serum.</div></div><div><h3>Methods</h3><div>The assay was established after screening raw materials and optimizing the reaction conditions, and the performance of the assay was evaluated according to the clinical guidelines. Meanwhile, the detection results of the established assay were compared with those of the Roche electrochemiluminescence immunoassay.</div></div><div><h3>Results</h3><div>The coefficient of variation was 4.20 % ∼9.37 %, and the intermediate precision was 6.20 % ∼9.80 %. The limit of blank (LoB) was 1.14 ng/mL, and the limit of quantification (LoQ) was 3.83 ng/mL. The linear range was 5.88 ng/mL ∼1447.65 ng/mL. The correlation coefficient (<em>r</em>) between the results of the established total P1NP-LiCA and the Roche ECLIA was 0.8969.</div></div><div><h3>Conclusions</h3><div>A homogeneous quantitative immunoassay for total P1NP was developed, which could be used as a practical and cost-effective alternative for routine clinical use in assessing bone formation markers.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"713 ","pages":"Article 116089"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"msCNN-PLM-FAD: Enhanced predicting FAD binding sites by using protein language representation combined with multiple separable windows convolutional neural networks","authors":"Dinh-Quy Nguyen ,&nbsp;Viet-Thanh Nguyen ,&nbsp;Cam-Hong Ly ,&nbsp;Yu-Yen Ou ,&nbsp;Quang-Thai Ho","doi":"10.1016/j.ab.2026.116092","DOIUrl":"10.1016/j.ab.2026.116092","url":null,"abstract":"<div><div>The FAD binding sites classification problem is crucial because it is directly related to many diseases, such as flavoprotein-associated diseases, developmental disorders, digestive and lipid metabolism abnormalities, anemia, cancer, cardiovascular diseases, and neurological diseases. Researchers have conducted numerous studies to classify FAD binding sites, achieving varying levels of effectiveness. Nevertheless, significant potential for enhancement persists. Our study proposes msCNN-PLM-FAD, an innovative computational model predicting FAD binding sites in electron transport proteins using a sliding window feature extraction technique and pretrained protein language models (PLMs). Our approach combines a convolutional neural network-based window scanning technique able to capture various sequence features relevant to FAD binding sites with embeddings from the ESM PLM. Built on a dataset of 12,850 samples, the model outperformed previous studies, particularly in class-balance metrics, with an area under the curve (AUC) of 0.9614 and a Matthews correlation coefficient (MCC) of 0.7491, specificity of 0.9836, and accuracy of 0.9795, while maintaining a sensitivity of 0.8704. The results of this study demonstrate the potential to improve the accuracy of FAD binding site prediction, thereby promoting the development of drugs for diseases associated with FAD deficiency as well as therapeutic approaches that target bacterial FAD synthesis without affecting human health.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"713 ","pages":"Article 116092"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning assisted cell electrical signal analysis in impedance cytometry","authors":"Houchen Zhou ,&nbsp;Zheng Zhou ,&nbsp;Longlong Wang ,&nbsp;Wenlai Tang ,&nbsp;Shijie Wei ,&nbsp;Shu Zhu","doi":"10.1016/j.ab.2026.116093","DOIUrl":"10.1016/j.ab.2026.116093","url":null,"abstract":"<div><div>In this study, we developed BioFluxNet, a 1D CNN-based algorithm for automated analysis of raw electrical signals in impedance cytometry to directly classify cell types and quantify cell counts. The network comprises three functional blocks: a feature extraction module with five alternating convolutional, normalization, activation, and pooling layers; a classification block with a single linear layer; and a counting block incorporating two linear layers and an activation layer. Raw signal streams of particles and cells, collected from an impedance cytometry featuring a serpentine microchannel and four pairs of face-to-face electrodes, are stored as training and testing sets, and then used to train and test BioFluxNet. Results demonstrate that the well-trained network achieves robust classification of raw signal streams from diverse particles and cells (including blood and tumor cells), while simultaneously enabling accurate counting of particles or cells form corresponding signal streams. Compared to conventional signal processing methods, BioFluxNet eliminates many time-consuming signal processing steps, reduces manual intervention, and minimizes subjectivity of operators. The proposed deep learning framework offers a rapid, automated solution for electrical signal analysis in impedance cytometry, showcasing broad applicability in cell characterization and related biomedical fields.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"713 ","pages":"Article 116093"},"PeriodicalIF":2.5,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147321174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recent advances in ZnO-based electrochemical sensors for food safety monitoring","authors":"Md Maruf Ahmed ,&nbsp;Qin Xu","doi":"10.1016/j.ab.2026.116049","DOIUrl":"10.1016/j.ab.2026.116049","url":null,"abstract":"<div><div>Food safety is a paramount worldwide issue that directly affects human health and well-being, underscoring the necessity for rapid and accurate detection strategies. Conventional analytical techniques, including chromatography, mass spectrometry, and immunoassays, are highly sensitive and reliable; however, they are costly, laborious, and impractical for real-time monitoring. Conversely, electrochemical sensors have benefits for food safety monitoring, including simplicity, quick response, cost-effectiveness, and portability, making them suitable for continuous monitoring. Optimal sensing performance necessitates the design and development of functional electrode materials with superior electrocatalytic activity. Over the past decade, ZnO nanomaterials have been widely used in electrochemical sensor technology due to their multifunctional properties, including high electron mobility, biocompatibility, and tunable architecture. This review summarizes recent advancements in ZnO-based electrochemical sensors for detecting food toxins. The synthesis methodologies and material characteristics are initially examined, followed by a review of their electrochemical sensing systems. The functionalization strategies for improving ZnO's sensing capabilities have been discussed. This study highlights the existing obstacles and potential opportunities for the advancement of ZnO-based electrochemical sensors in food safety monitoring.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"712 ","pages":"Article 116049"},"PeriodicalIF":2.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145948399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and characterization of monoclonal antibodies against inhibin α subunit as a potential cancer biomarker","authors":"Kaoutar Aalilouch ,&nbsp;Khalida Sabeur ,&nbsp;Ikhlass El Berbri ,&nbsp;Faouzi Kichou ,&nbsp;Najet Safini ,&nbsp;Mehdi Elharrak ,&nbsp;Ouafaa Fassi Fihri","doi":"10.1016/j.ab.2026.116066","DOIUrl":"10.1016/j.ab.2026.116066","url":null,"abstract":"<div><div>Inhibin is a dimeric glycoprotein hormone consisting of α and β subunits. Inhibin has gained significant interest as a biomarker for ovarian and testicular malignancies. While existing inhibin immunoassays have demonstrated efficacy, assays specifically targeting the free α subunit are not yet available. This study aimed to generate monoclonal antibodies against inhibin-α using hybridoma technology, targeting distinct epitopes within the N-region of the α subunit. These epitopes were chosen using bioinformatic analysis to ensure strong antigenicity and successful antibody production. To screen the antibodies, an ELISA was developed using plates coated with recombinant inhibin-α protein. This ELISA confirmed that the antibodies were reactive and could effectively detect inhibin-α <em>in vitro</em>. The antibodies were further evaluated for specificity and immunoreactivity using Western blotting, and IHC methods. Western blot analysis showed that the antibodies specifically recognized the free inhibin-α, demonstrating high specificity under both reducing and non-reducing conditions. IHC studies on normal mouse tissue sections further confirmed the localization of inhibin-α in granulosa, theca, Sertoli, and Leydig cells. The developed antibodies showed significant potential for use <em>in vitro</em> assays for cancer diagnosis. Further validation is needed to ensure their successful clinical application.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"712 ","pages":"Article 116066"},"PeriodicalIF":2.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146083778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a chemiluminescence immunoassay for proGRP in human serum","authors":"Xiang Chen ,&nbsp;Lin Ma ,&nbsp;Zhonghu Bai ,&nbsp;Jiong Wu","doi":"10.1016/j.ab.2026.116074","DOIUrl":"10.1016/j.ab.2026.116074","url":null,"abstract":"<div><div>Progastrin-releasing peptide is a crucial serum biomarker for the diagnosis, differential diagnosis, therapeutic monitoring, and prognostic evaluation of small cell lung cancer. In this study, a double-antibody sandwich immunoassay for the biomarker was developed. Key factors in the method development were systematically investigated, including magnetic particle coating, conjugate labeling, concentrations of magnetic particles and conjugates, and serum sample volume. The established assay has a total analysis time of approximately 13 min, with a limit of blank of 3.53 pg/mL. The repeatability and within-laboratory precision coefficients of variation were less than 8%. Assay accuracy was unaffected by common endogenous interferents at pathological concentrations or by the presence of gastrin-releasing peptide in the serum. Additionally, thermal accelerated stability testing over 7 days confirmed the robust stability of the reagents. Method comparison further demonstrated that the clinical results obtained by the established method showed high consistency with those of the Roche assay, fully meeting the requirements for clinical application.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"712 ","pages":"Article 116074"},"PeriodicalIF":2.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146123426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing digital and real-time PCR platforms for detecting residual iPSCs and virus-producing cells in manufacturing","authors":"Un Na Koh ,&nbsp;Si-Keun Lim","doi":"10.1016/j.ab.2026.116053","DOIUrl":"10.1016/j.ab.2026.116053","url":null,"abstract":"<div><div>Digital PCR (dPCR) enables absolute nucleic acid quantification and has been widely adopted for quality control (QC) applications in cell therapy manufacturing. Ensuring patient safety during cell therapy manufacturing requires reliable detection of trace residual cells, such as undifferentiated induced pluripotent stem cells (iPSCs) and virus-producing cells. This study compared qPCR (CFX96 Opus System, Bio-Rad) with two dPCR platforms—the QX200 Droplet Digital PCR System (Bio-Rad) and the QIAcuity Digital PCR System (QIAGEN). For QC evaluation, iPSCs mixed with differentiated cardiomyocytes (CMs) or neural progenitor cells (NPCs) were analyzed using <em>TDGF1</em>, <em>OCT4</em>, and <em>NANOG</em>, while virus-producing 293T cells in CAR-T preparations were targeted using <em>gag</em> and VSVG sequences within the lentiviral packaging plasmid. Mixed samples were serially diluted from 1:1 to 1:10⁶ to evaluate performance across a wide concentration range. Both dPCR platforms and qPCR showed comparable sensitivity and linearity across most dilution points. However, qPCR exhibited more frequent signal loss at low template concentrations. In contrast, dPCR showed reduced variability across dilution intervals, and lower coefficients of variation (CV), indicating more stable quantification at low target levels. Despite minor differences in absolute copy number, both dPCR systems demonstrated comparable analytical performance. These results indicate that, although overall sensitivity and linearity were similar between qPCR and dPCR, dPCR provides more consistent quantification across dilution ranges, supporting its suitability for detecting low-abundance residual cells in cell therapy manufacturing.</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"712 ","pages":"Article 116053"},"PeriodicalIF":2.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146016843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Word2Vec-ResNet Transfer Learning model for promoter prediction with dimensionality reduction and cross-domain knowledge integration","authors":"Jiale Fu,&nbsp;Xiao Liu","doi":"10.1016/j.ab.2026.116076","DOIUrl":"10.1016/j.ab.2026.116076","url":null,"abstract":"<div><div>Promoter prediction is critical for deciphering transcriptional regulatory mechanisms. However, traditional one-hot encoding strategies suffer from dimensionality explosion with vocabulary expansion, while single-domain knowledge constraints limit predictive performance. Therefore, we propose a promoter prediction method (Word2Vec-ResNet) that innovatively integrates natural language processing (NLP) techniques with cross-domain transfer learning. By pretraining word embeddings on source domain data and transferring the pretrained embedding table to the target domain, the method effectively reduces the dimensionality of nucleotide sequence encoding while leveraging inter-domain knowledge to enhance model generalization. Comprehensive experiments on promoter datasets of four representative organisms (<em>Bacillus subtilis</em>, <em>Escherichia coli</em>, <em>Saccharomyces cerevisiae</em>, and <em>Drosophila melanogaster</em>) demonstrate that the proposed method achieves significant performance improvements: compared with one-hot encoding, its average encoding dimension is reduced by 97.6%; compared with baseline methods, the prediction accuracy is increased by an average of 18.12% (with the ratio of the training set to the test set being 8:2).</div></div>","PeriodicalId":7830,"journal":{"name":"Analytical biochemistry","volume":"712 ","pages":"Article 116076"},"PeriodicalIF":2.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146140949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}