Biotechnology and applied biochemistry最新文献

We present a novel, cost-effective sensor for carcinoembryonic antigen (CEA) detection utilizing a pencil graphite electrode (PGE) in combination with electrochemical impedance spectroscopy (EIS), which offers high sensitivity and selectivity. Anti-CEA/AuNPs/PGE was successfully illustrated as a label-free impedimetric immunosensor for the detection of CEA. Through EIS, we observed distinct impedance changes upon CEA binding, enabling real-time detection with high reproducibility and low interference from non-target molecules. Due to its satisfying impedimetric response, this new immunosensor demonstrated that it can be used for high-performance detection of CEA with a wide linear range extending from 13.2 to 1 × 10⁵ pg mL^-1, with correlation coefficients (R²) of 0.9923. The PGE's excellent conductive properties and surface stability allowed for the successful detection of CEA at low concentrations, demonstrating a detection limit of 4.4 pg mL^-1, which is competitive with existing, more costly alternatives. The sensor's robust performance in spiked artificial urine samples indicates its potential for practical application in point-of-care cancer diagnostics, especially in resource-limited environments. The developed electrochemical biosensor holds promise for accurately detecting CEA in urine samples, offering a precise technique that could find valuable application in clinical tumor detection.

Multidrug-resistant tumor cells pose significant challenges in cancer treatment. Alternative strategies such as targeted gene silencing and the use of compounds with minimal cytotoxicity toward normal cells are therefore of great interest. Antimicrobial peptides (AMPs) have demonstrated anticancer potential due to their physicochemical properties. In lung cancer, overexpression of AKT serine/threonine kinase 1 (AKT1) promotes abnormal tumor growth and progression. In this study, we synthesized chitosan-based nanoparticles (CSNPs) co-loaded with Pseudomonas aeruginosa peptide from strain P3 (PAP3) (an AMP) and siRNA targeting the AKT1 gene, and evaluated their anticancer activity at the cellular and molecular levels. Characterization of the CSNPs revealed a nanoscale structure, low polydispersity index, and moderate encapsulation efficiency for both peptide and siRNA. Evaluation using L929 cells confirmed PAP3's nontoxic profile, while a dose-dependent anticancer effect against A549 cells was observed. Delivery of encapsulated peptide, siRNA, and their combination increased cell death and induced morphological changes in A549 cells. Gene expression analysis showed upregulation of pro-apoptotic markers (Bax and Caspase-3) and downregulation of the anti-apoptotic marker Bcl2, indicating promising anticancer properties of the engineered compound. In conclusion, co-delivery of PAP3 and AKT1-targeting siRNA via CSNPs demonstrates potential for future anticancer therapies.

In this study, we report biogenic synthesis of silver nanoparticles (AgNPs) using polyextremophile bacteria Deinococcus radiodurans. Optical and structural properties of the green synthesized silver nanoparticles were investigated by various techniques, including Fourier transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), scanning electron microscopy (SEM), energy-dispersive x-ray (EDX) spectroscopy, and UV-visible (UV-Vis) absorption spectroscopy. The AgNPs were entrapped in calcium alginate beads and were used for photo decolorization of various charged pollutant dyes, under solar irradiation. In this study, cationic (methylene blue [MB], methyl green [MG]) and anionic (methyl orange [MO]) dyes were used as model dyes. Both AgNPs in suspension and those entrapped in beads could degrade all the three dyes with 100% degradation efficiency in suspension and slightly lower efficiency with beads. The photocatalytic activity of immobilized AgNPs in fabricated column model demonstrates potential application for the removal of dyes from effluents, contributing ultimately to ecological cleanup process and facilitated in recovery and reprocessing. The nanocomposites retained a significant amount of photocatalytic efficiency even after four reuse cycles, and the degradation efficiency followed the order: MB > MO > MG. Additionally, phytotoxicity and cytotoxicity assay was performed to show significant reduction in toxicity of nanoparticle (NP)-assisted degraded cationic and anionic dyes, thereby substantiating the nontoxic nature of the degraded dye. The efficiency of beads entrapped silver NPs as a viable option, for the degradation of harmful organic dyes from the environment, is established in the present study.

The escalating global demand for biopharmaceuticals is placing increasing strain on conventional production systems, highlighting the need for innovative and sustainable alternatives. Industrial byproducts, produced extensively across pharmaceutical and allied sectors, remain an underexploited resource with significant potential to reduce production costs and strengthen circular economy integration. This review systematically explores the sources and classification of industrial wastes relevant to biopharmaceutical manufacturing, while addressing critical regulatory, safety, and quality considerations for their adoption. Emerging biotechnological strategies-such as microbial fermentation, enzymatic biotransformation, and synthetic biology-driven metabolic engineering-are evaluated for their ability to convert industrial residues into high-value therapeutic products. Representative case studies demonstrate the feasibility of these approaches, including the utilization of agro-industrial waste for therapeutic enzymes, marine-derived residues for bioactive compounds, and fermentation byproducts for vaccine components. Environmental and economic implications are assessed through life cycle analysis (LCA) and cost-benefit evaluations, underscoring the alignment of waste valorization with sustainable manufacturing principles. Despite these opportunities, technological limitations, stringent quality and standardization requirements, and complex policy and ethical challenges remain substantial barriers. Future perspectives highlight the role of green bioprocessing, artificial intelligence (AI), and automation in optimizing waste-to-medicine pathways, alongside the long-term vision of achieving zero-waste biopharmaceutical production. By positioning industrial byproducts as valuable feedstocks, this review underscores their transformative potential in driving sustainable, resilient, and responsible healthcare manufacturing.

Cow dung is a low-cost lignocellulosic biomass generated in large quantities across India, yet remains underutilized and contributes to environmental pollution when improperly managed. In this study, cellulose was isolated from cow dung using two different approaches a green, microbially-assisted natural extraction process under dark conditions with mild chemical uses, and a chemically driven Soxhlet-assisted method employing alkali oxidative pretreatment. The physicochemical characteristics of the isolated cellulose were examined using the Van Soest compositional protocol, FTIR spectroscopy, UV-Vis analysis, CHNS elemental profiling, and SEM imaging. The Soxhlet route produced a cellulose yield of 3.65% ± 0.2%, with purity of 28.68% cellulose and 3.68% lignin, whereas the natural method resulted in a yield of 3.55% ± 0.3%, with purity of 26.31% cellulose and 8.9% lignin. Soxhlet extraction enabled more effective delignification and improved fiber defibrillation, while the natural method, despite of lower lignin removal rate, preserved the structural integrity of the cellulose and offered substantial sustainability advantages by reducing chemical consumption and energy requirements. These findings highlight cow dung as a viable renewable feedstock for cellulose-based biomaterials and demonstrate that low-resource, environmentally benign extraction strategies can support decentralized and rural circular bio-economy initiatives.

Wearable Human Activity Recognition (HAR) models often degrade across users and sensor placements due to domain shifts. This paper presents the Multi-Sensor Adaptive Feature Alignment Network (MSAFAN), integrating Sensor-Specific Normalization Layer (SSNL), Hybrid Polynomial Feature Transformation (HPFT), Conditional Alignment Loss (CAL), and Entropy-Guided Pseudo-Labeling (EGPL) for class-wise adaptation and robust cross-sensor generalization. Evaluated on four benchmark datasets: BAR, DSADS, PAMAP2, and MM-DOS, the MSAFAN improves macro-F1 by 8.4% and accuracy by 10.3% while reducing computational cost by 26% over state-of-the-art UDA models. The framework achieves stable convergence, efficient adaptation, and scalable performance, confirming its suitability for real-time deployment in edge AI and wearable computing applications.

Kidney injury molecule-1 (KIM-1) is a Type I transmembrane glycoprotein and is a potential biomarker for detecting kidney damage, as its urinary levels fluctuate in cases of acute kidney injury. In this study, an electrochemical immunosensor was developed for the first time using a quartz tuning fork (QTF) working electrode to detect the KIM-1 biomarker. The gold-tipped QTF electrode surface was modified with 11-mercaptoundecanoic acid (11-MUA) to form a self-assembled monolayer (SAM). To construct the biosensor, extensive optimization studies were conducted on the fabrication parameters, followed by characterization and real urine sample testing to evaluate its applicability. Electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) methods were utilized in all electrochemical experiments. Morphological changes on the QTF electrode surface were examined using atomic force microscopy (AFM) and scanning electron microscopy (SEM). The developed electrochemical KIM-1 immunosensor demonstrated highly promising performance, exhibiting an exceptionally wide detection range (0.05-250 fg/mL). Furthermore, the dissociation constant (Kd) of the interaction between KIM-1 and its antibody was successfully calculated using the Hill equation, on the basis of the QTF-based system.

Accurate prediction of protein secondary structure is a critical step toward understanding protein function and facilitating structure‑based drug discovery. We present a template‑independent, single sequence method utilizing a shallow feed‑forward artificial neural network (ANN) with one hot (binary) amino acid encoding and a sliding window input. The network is trained and evaluated on two datasets: (i) a curated, nonhomologous Protein Data Bank (PDB) set with a strict $\le 25\%$ maximum pairwise sequence identity, annotated with STRIDE; and (ii) a homologous human papillomavirus (HPV) set (L1, L2, E1-E7) whose labels are obtained from the Proteus predictor and used solely for a system specific, post hoc analysis. To improve transparency regarding generalization, we report the all‑vs‑all sequence identity distribution for the nonhomologous set (matrix and histogram). The model achieves competitive Q3 accuracy on the nonhomologous PDB benchmark and yields $82.2\%$ Q3‑agreement (Proteus) on the HPV case study. We explicitly frame the HPV evaluation as agreement with a labeling tool rather than accuracy versus experiment. Despite its simplicity and lack of evolutionary profiles, the ANN demonstrates robust sequence-only performance, offering a lightweight baseline that is easy to reproduce and deploy on the CPU. We discuss limitations (dataset size, lack of cross‑tool bake‑offs, absence of long‑range features) and delineate concrete avenues for future work.

The industrial-scale manufacturing and purification of melittin are of great interest due to its significant antibacterial and anticancer properties. Here, for the first time, prepromelittin (ppMEL), the precursor of melittin-a key component of bee venom-was extracellularly produced by Komagataella phaffii, and an alternating current magnetic field-assisted three-phase partitioning (ACMF-TPP) method was used to concentrate and partially purify the recombinant ppMEL (rppMEL). ACMF-TPP, a novel variation of the traditional TPP method widely used for biomolecular purification, was employed to purify recombinant protein from culture liquid more rapidly and in larger quantities. First, the amplified ppMEL gene was inserted into pPICZαA. So, the obtained pPICZαA-ppMEL was cloned into E. coli. Then, K. phaffii cells were transformed with linearized recombinant DNA, and a yeast clone was cultivated for extracellular rppMEL production. The optimal conditions of ACMF-TPP for the purification and concentration of rppMEL were the following parameters: pH 6.7, 70% w/v ammonium sulfate concentration, a 1:1 ratio of culture liquid to 2-propanol, 100% power, a 10-minute magnetic field duration, and a 70% duty cycle. As a result, it was observed that rppMEL primarily precipitated at the interface and that the difference in recovery efficiency (32.5% for TPP and 66.1% for ACMF-TPP, respectively) was statistically significant. The ACMF-TPP approach exhibited more efficacy than the TPP, demonstrating superior potential for separating and purifying other biomolecules. Moreover, this approach offers a cost-effective and scalable solution for the precise isolation, purification, and concentration of mixture components.

Microorganisms drive essential ecosystem functions by mediating carbon, nitrogen, sulfur, and phosphorus transformations that regulate productivity and shape climate feedbacks. Rapid methodological advances now allow precise linkage of microbial identity, in situ activity, and ecosystem processes across spatial and temporal scales. High-resolution approaches-including long-read metagenomics and Hi-C-generate near-complete metagenome-assembled genomes (MAGs) from diverse environments, enabling reconstruction of microbial and viral-host interaction networks. Activity-resolved tools such as quantitative stable isotope probing (qSIP) and bioorthogonal non-canonical amino acid tagging (BONCAT), combined with fluorescence-activated cell sorting (FACS), yield taxon-specific growth and substrate assimilation rates within hours. Single-cell isotope techniques, including Raman-SIP and nanoSIMS, deliver nanometer-scale metabolic insights. Spatial meta-omics platforms, such as MetaFISH and MALDI-MSI, map metabolites alongside microbial identities with micrometer-level precision. Meanwhile, autonomous sequencing systems, including environmental sample processors and nanopore adaptive sampling, enable real-time (<24 h) ecological surveillance. Integrating these multimodal datasets into trait-based frameworks has reduced uncertainty in carbon flux predictions by nearly 20%. This review synthesizes these innovations, outlines optimized analytical pipelines, and proposes a framework for embedding eco-omics into predictive ecosystem and climate models, supporting evidence-driven management aligned with Climate Action and Life on Land.