Search Results (584)

Biosensors are rapidly emerging as a pivotal technology with far-reaching implications in fields such as medical diagnostics, environmental analysis and pharmaceutical research. Among the various biosensing platforms, Graphene Field-Effect Transistor (GFET) biosensors have attracted considerable interest due to their exceptional sensitivity, potential for cost-efficient fabrication, and compatibility with scalable manufacturing processes. This work computationally addresses sensing mechanisms and design strategies associated with GFET-based biosensors, with a focus on the influence of electrolyte gating on device performance, tackling the role of graphene’s quantum capacitance and testing the electrical detection of β2-microglobulin as a case study. Molecular dynamics is used to rationalize the details of the physisorption of a single biomolecule onto the graphene surface, while finite element method simulations are employed to evaluate device sensitivity and figure of merit. Results reveal that incorporating quantum capacitance into the model leads to a Sensitivity-over-FWHM_min figure of merit exceeding 100 L/g being achievable for a β2-microglobulin concentration of 0.001 g/L. These computational outcomes highlight the relevance of quantum-electrostatic effects in GFET biosensor performance and suggest potential routes towards the optimization of graphene-based electronic biodetector engineering. Full article

Background/Objectives: Proteolysis Targeting Chimeras (PROTACs) are heterobifunctional small molecules that induce ubiquitin–proteasome–mediated degradation of target proteins and have matured from proof-of-concept chemistry to a clinically validated therapeutic modality, with the first Phase 3 readout reported in 2025. A systematic bibliometric analysis covering this pivotal-trial era, however, has been lacking. This study aimed to map the historical trajectory, current research front, and emerging frontiers of PROTAC research. Methods: We analyzed 2630 PROTAC-related publications indexed in the Web of Science Core Collection (WoSCC) from 2001 to 2025 using a combined toolkit of CiteSpace, HistCite, the Alluvial Generator, and R (ggplot2), covering co-occurrence networks, burst detection, keyword clustering, citation historiography, alluvial flow analysis, and reference co-citation timeline visualization. Results: China and the USA led global output, and the Chinese Academy of Sciences, China Pharmaceutical University, and Harvard University were the most productive institutions; the Journal of Medicinal Chemistry was the leading publishing venue, and Alessio Ciulli, Jian Jin, and Craig M. Crews anchored the author network. Keyword burst analysis showed that early research centred on E3 ubiquitin ligase recruitment and small-molecule PROTAC design, whereas the current hotspots, resolved through keyword clustering and co-citation timelines, included structural basis and ternary complex design, EGFR-directed degradation, oral bioavailability optimization, applications in multiple myeloma and Alzheimer’s disease, tumour-targeted delivery, and computational/AI-driven design. Conclusions: This study extends the bibliometric record of PROTACs across 2001–2025 and identifies oral bioavailability, E3 ligase repertoire expansion, and CNS-penetrant degrader design as the emerging frontiers likely to shape the next phase of the field. Full article

Systematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain. In recent years, several brain-inspired deep learning architectures, such as long short-term memory networks, graph neural networks, and convolutional neural networks, have been introduced for intelligent decision-making tasks. From a biomimetic perspective, these models are inspired by biological information-processing mechanisms. Convolutional neural networks reflect hierarchical procedures similar to those in the visual cortex, graph neural networks mimic communication among biological neurons, and LSTM networks are motivated by short-term and long-term memory mechanisms in the brain. Inspired by these biomimetic computational principles, this study proposes a novel hybrid deep learning strategy composed of LSTM, convolutional layers and GraphSAGE geometric layers for smart supply chain logistics management. This strategy enables leveraging information pertaining to LSTM-based long-term dependencies, convolutional local patterns and graph-related hidden connections of the supply chain dataset for intelligent decision-making. The GraphSAGE framework helps with scalable graph learning, which enhances predictive accuracy in the case of unseen data. The optimizer in the proposed methodology performs sequential optimization using the biomimetic particle swarm optimizer and the Adam approach (PSO-Adam), considering the hybrid cost function. The prediction of logistics parameters is investigated using five datasets, including DataCo, Shipping, Smart Logistics, Hospital Supply Chain, and Pharmaceutical Supply Chain. The average accuracies of 97.8%, 100%, 96.6%, 98.7% and 99.4% are obtained for practical multi-category logistics parameter forecasts. The evaluation metrics for ten logistics predictions confirm the effectiveness of the proposed intelligent logistics model and highlight the potential of biomimetic geometric networks for complex supply chain decision-making. The model is a cost-efficient approach with consideration of the prediction capabilities, helping to reduce the occurrence of logistics risks, increase the productivity of the supply chain and affect the supply chain visibility, customer satisfaction, and industry reputation. Full article

This review examines the emerging role of machine learning (ML) in pharmaceutical chemistry, with emphasis on molecular design, synthetic feasibility, and structure–property–performance (SPP) relationships. By enabling pre-synthesis prediction of physicochemical properties, reaction pathways, and pharmaceutical performance, ML can reduce empirical trial-and-error experimentation and support more efficient exploration of chemical space. A structured narrative review design with PRISMA-aligned systematic search elements was used to evaluate 101 studies, enabling transparent literature identification, eligibility screening, and thematic synthesis across heterogeneous ML applications in pharmaceutical chemistry. This review examines structure–property relationships (SPRs) and property–performance relationships (PPRs), with emphasis on key pharmaceutical endpoints such as solubility, permeability, stability, dissolution, and bioavailability. An integrated SPP framework is proposed to connect molecular structure, intermediate properties, and final performance outcomes while incorporating retrosynthetic analysis and experimental feedback and closed-loop optimization. Recent frontier developments are also discussed, including molecular foundation models, multimodal language–graph models, diffusion-based molecular generation, E(3)-equivariant models, and MolMIM-like latent-space optimization. This review also covers co-folding and joint ligand–protein modeling, Boltz-2-like affinity prediction, AlphaFold 3-related biomolecular interaction modeling, and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction. Key limitations include dataset leakage, benchmark inconsistency, assay variability, conformational and protonation-state effects, reproducibility challenges, regulatory constraints, and the gap between computational prediction and prospective experimental validation. Future progress is expected to depend on hybrid physics–ML models, uncertainty-aware prospective validation, autonomous experimentation, explainable artificial intelligence, and sustainability-aware molecular design. Overall, ML is evolving from a predictive tool into a chemically informed decision-support framework for rational, synthesis-aware, and experimentally validated pharmaceutical development. Full article

Recent advancements in computer vision, particularly in detection, segmentation, and classification, have significantly impacted various domains. However, these advancements are still strongly tied to RGB-based systems, which are insufficient for applications in industries such as waste sorting, pharmaceuticals, and defence, where material characterization beyond shape or visible colour is necessary. Hyperspectral (HS) imaging captures spatial and spectral information for each pixel and therefore offers a promising route for material-level classification. This study evaluates the potential of combining HS imaging with deep learning for plastic material classification. The work includes: (i) the design of an experimental setup with a HS line-scan camera, conveyor, and controlled illumination; (ii) the construction of an object-disjoint dataset of HDPE, PET, PP, and PS samples with semi-automated mask generation and Raman spectroscopy-based labelling; and (iii) the development of P1CH, a lightweight pixel-wise 1D convolutional hyperspectral classifier. On object-disjoint test images, P1CH achieved 97.44% all-pixel accuracy. A boundary sensitivity analysis, reported separately because semi-automated labels are uncertain at material/background interfaces, yielded 99.94% accuracy after excluding a pre-defined two-pixel border band. Additional ablation, baseline, and robustness analyses show that the proposed pixel-wise spectral approach is effective for small fragments, visually similar plastics, and overlapping materials, while black or very dark plastics remain challenging under the present camera and illumination configuration. Full article

Pharmaceutical cold-chain distribution must maintain timely access to temperature-sensitive medicines while limiting the energy demand and carbon emissions associated with refrigerated transport. This study proposes a sustainable dynamic route optimization method that integrates reinforcement learning (RL) with an improved neighborhood search (NS) algorithm to balance delivery timeliness and transportation carbon emissions. The NS algorithm is enhanced with carbon emission and timeliness operators, and RL adaptively adjusts their weights under dynamic events, including traffic congestion, vehicle failure, and order insertion. The method is evaluated using the Solomon Benchmark dataset and a warehouse-to-community-pharmacy last-mile distribution case for chronic-disease medicines. The RL-NS algorithm achieves an average computation time of 45.3 ms and a standard deviation of 2.7, outperforming the comparison algorithms. In the case study, it reduces transportation carbon emissions by approximately 18% and delivery time by approximately 12% relative to traditional routing. By reducing route redundancy and enabling rapid replanning, the method supports lower-emission and potentially more energy-efficient transport operations. The findings demonstrate its relevance to sustainable transportation, sustainable logistics, and resilient pharmaceutical cold-chain management. Full article

Background/Objectives: Cyclotides are plant-derived macrocyclic peptides distinguished by their head-to-tail cyclized backbone and cystine knot motif, which confer remarkable stability against thermal, enzymatic, and chemical degradation. These features, combined with a compact and rigid structure, position cyclotides as promising scaffolds for future antibacterial agents in response to the escalating threat of multidrug-resistant (MDR) pathogens and the stagnation of conventional antibiotic discovery pipelines. This review summarizes the structural features, antibacterial mechanisms, bioengineering strategies, and translational potential of cyclotides against MDR infections. Methods: A narrative review of the literature was conducted using recent original research articles and reviews on cyclotide structure, antibacterial activity, bioengineering, computational modeling, and pharmaceutical applications. Results: Cyclotides exhibit potent antimicrobial activity, primarily through membrane disruption mediated by amphipathic surfaces and affinity for anionic bacterial membranes. Some variants also demonstrate anti-virulence and antibiofilm properties, broadening their therapeutic relevance for difficult-to-treat infections. Bioengineering approaches, including epitope grafting and rational design, have improved selectivity and potency while reducing cytotoxicity. Advances in computational modeling, molecular dynamics, and artificial intelligence have accelerated the prediction and optimization of antimicrobial activity, toxicity, and pharmacokinetic properties. Conclusions: Innovations in synthesis, including recombinant expression and enzymatic ligation, are helping overcome translational barriers related to cost and scalability. Although challenges remain in oral bioavailability and systemic delivery, strategies such as lipidation and scaffold modification support the development of cyclotide-based therapeutics as adaptable platforms for peptide drug discovery. Full article

Pharmaceutical and microbial pollution in urban rivers is an emerging concern, particularly in developing regions with limited wastewater treatment capacity, posing risks to human health and ecosystems. This study evaluated the risk profiles of selected pharmaceutical compounds and bacterial indicators in the Sand River, South Africa, and computed their ecological risks, antimicrobial resistance (AMR), and human health risk assessment. Surface water samples were collected from three sites during the wet season and analyzed for target antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs) using High-Performance Liquid Chromatography (HPLC) with a photodiode array (PDA) detector, while total coliforms (TCs) and Escherichia coli (E. coli) were enumerated using the Colilert system. Ciprofloxacin, sulfamethoxazole, and erythromycin were the most abundant pharmaceuticals, with maximum concentrations of 2.50 µg/L, 2.76 µg/L, and 2.53 µg/L, respectively. TC and E. coli levels exceeded regulatory thresholds, indicating severe microbial contamination. Risk quotient analysis identified ciprofloxacin, erythromycin, and trimethoprim as high-risk compounds for potential resistance selection (RQ ≥ 1), while ciprofloxacin and erythromycin posed significant ecological risks to fish. Although non-carcinogenic health risk assessment remained below concern (HI < 1), children showed higher exposure levels. These findings underscore the urgent need for improved pharmaceutical waste management and wastewater treatment infrastructure. Full article

Continuous manufacturing has emerged as the prevailing paradigm in the modern pharmaceutical industry, imposing stringent demands for efficient, real-time inspection methods. Furthermore, deploying high-performance deep learning models on industrial edge devices remains challenging due to computational constraints and the difficulty of detecting micro-defects (e.g., micro-cracks and spots). This paper proposes EMN-net, a lightweight defect detection model built upon the YOLOv8n architecture. The proposed algorithm integrates a MobileNetV3 backbone, the Efficient Local Attention (ELA) mechanism and the Normalized Wasserstein Distance (NWD) loss function to balance computational efficiency with sensitivity toward micro-defects. Evaluated on a self-built industrial tablet dataset expanded to 3086 images, EMN-net achieves an mAP50 of 97.8%, representing a 2.5% improvement over the baseline YOLOv8n. the computational complexity is reduced to 4.4 GFLOPs, while the inference throughput reaches 118 FPS, satisfying the real-time requirements of high-speed production lines. Additionally, the model exhibits improved robustness under simulated motion blur and sensor noise. EMN-net presents a balanced automated visual inspection solution for edge devices in continuous pharmaceutical manufacturing. Full article

Particle-laden pipe flows are ubiquitous in food, chemical and pharmaceutical processes, where solid particles significantly alter fluid deformation and mixing. Understanding these transport mechanisms is critical for process optimisation. A Lagrangian analysis framework based on a SPH-DEM simulation is proposed to compute finite-time Lyapunov exponent (FTLE) fields and extract Lagrangian coherent structures (LCSs) for non-Newtonian particle-laden pipe flows. The method directly exploits the inherently Lagrangian particle trajectories and computes the FTLE fields using the SPH interpolation scheme, avoiding the costly numerical integration required by conventional Eulerian approaches. Subsequently, LCSs are extracted via a ridge detection algorithm and the combined FTLE is introduced to quantify mixing intensity. The framework is validated against the Kármán vortex street benchmark, showing good agreement with experiment and numerical results. Then the validated framework is applied to non-Newtonian particle-laden pipe flows for a wide range (0 vol.%~30 vol.%) of particle loading. Results reveal a critical concentration range of 20 vol.%~30 vol.%, where the cross-sectionally average combined FTLE increases with concentration up to 20 vol.%, indicating enhanced mixing, but decreases beyond 30 vol.% as particle–particle interactions suppress near-wall fluid deformation. These findings provide a robust Lagrangian tool and new quantitative insights for optimising mixing and transport in industrial particulate flows, such as in food processing pipelines and chemical reactors. Full article

This research aimed to develop a rapid, non-destructive, and accurate method for detecting adulteration in turmeric using Visible–Near-Infrared (UV/Vis and NIR) spectroscopy combined with machine learning algorithms. Spectral data from the samples were collected and analyzed in two ranges: 170–870 nm (UV/Vis) and 900–2170 nm (NIR). Four supervised learning algorithms, including Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), the Multilayer Perceptron (MLP) neural network, and Decision Tree, were evaluated for modeling. To quantitatively assess model performance, we employed not only the accuracy metric but also complementary performance indicators including precision, recall, and the F1-score to provide a more comprehensive evaluation of classification effectiveness. The models developed in the 900–2170 nm spectral range demonstrated highly significant performance, with most models achieving 100% accuracy on the independent test set. To reduce data dimensionality and enhance computational efficiency, a hybrid feature selection method combining SVM with five algorithms—League Championship Algorithm (LCA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Imperialist Competitive Algorithm (ICA)—was employed. Upon evaluation of each method, the SVM-LCA was selected as the optimal feature selection technique. This algorithm successfully extracted the most effective wavelengths with the highest correlation and lowest error, which maintained or improved the accuracy of the classification models. This study confirms the high potential of UV/Vis and NIR spectroscopy as rapid, non-destructive, and precise tools for detecting adulteration in turmeric. The findings can pave the way for the development of intelligent quality control systems in the food and pharmaceutical industries, playing a crucial role in ensuring consumer health and safety. Full article

Nyaope is a highly addictive street drug that is widely used in South Africa, particularly in urban and peri-urban settings. Although it is traditionally consumed by smoking, increasing injection use has raised serious public health concerns due to an elevated risk of bloodborne viral infections and other drug-related health complications. The composition of nyaope is highly variable, frequently adulterated, and continually evolving, thus highlighting the need for detailed chemical characterization to support forensic investigations and public health interventions. An exploratory study design was conducted using eight nyaope samples seized from six sites within the City of Tshwane Metropolitan Municipality that were provided by the South African Police Service Forensic Science Chemistry Laboratory (SAPS-FSCL). Samples were analyzed using Ultra-High-Performance Liquid Chromatography coupled to Quadrupole-Time-of-Flight Mass Spectrometry (UHPLC-qTOF-MS) operated in data-dependent acquisition mode under positive ionization. Raw data from the methanolic extracts of nyaope was converted to mzML format and processed using SIRIUS software for compound annotation based on isotope pattern ranking and fragmentation analysis. Chemical profiling revealed multiple opiate-related compounds, including noscapine, heroin, papaverine, and codeine. Molecular networking revealed chemically diverse yet structurally related metabolites consistent with a poppy-derived botanical origin. In addition, multiple synthetic pharmaceutical adulterants were detected. Notably, one sample contained formaline, a toxic rodenticide structurally related to protopine, highlighting the risk of misidentification using less advanced analytical approaches. This study demonstrates the value of advanced computational metabolomics, including molecular networking and machine-learning-assisted mass spectrometry interpretation, for comprehensive characterization of complex illicit drug mixtures. These approaches enhance forensic accuracy and support informed public health and law-enforcement responses. Full article

Biosensors have emerged as a rapidly evolving area of research, offering transformative potential across biomedical diagnostics, environmental monitoring, and pharmaceutical applications. Among the diverse range of biosensing technologies, graphene-based surface plasmonic resonance (SPR) biosensors have attracted particular interest due to their exceptional sensitivity, scalability for mass production, and cost-effective fabrication processes. This study explores the operational principles and current design methodologies of graphene-based SPR biosensors, with a special emphasis on the role of electrolyte gating and its impact on sensor performance. Furthermore, the influence of graphene’s quantum capacitance is investigated as a critical parameter for improving the accuracy and reliability of performance predictions in the proposed sensor configuration. Computational analysis of sensitivity and key performance metrics was conducted. Notably, key performance metrics of the sensor improved upon incorporating quantum capacitance effects into the simulation framework. At a β₂-microglobulin concentration of 0.00118 g/L, the sensitivity increased to 174 GHz·g/L, the figure of merit reached 0.55 L/g, the quality factor was 0.01, the signal-to-noise ratio (SNR) rose to 0.008, and the detection accuracy (DA) reached 0.08 L/THz, demonstrating the significant impact of quantum capacitance on the sensor’s performance. These findings highlight the potential of quantum-electrostatic considerations to enhance the precision and efficacy of graphene-based SPR biosensors, paving the way for the development of next-generation biosensing platforms with improved analytical capabilities. Unlike conventional graphene SPR biosensors, which primarily detect refractive index changes near the graphene surface, our model explicitly considers the electrostatic effect of biomolecules on graphene’s Fermi energy. By modelling β2-microglobulin as a charged species, we compute the resulting electric double layer and incorporate quantum capacitance in series. This amplifies the charge-induced modulation of graphene’s optical conductivity, and, combined with a graphene perfect absorber design, leads to enhanced plasmonic resonance shifts. Consequently, our approach achieves higher sensitivity and more precise detection of biomolecular interactions compared to traditional simulations. Full article

Cardiometabolic diseases, encompassing obesity, insulin resistance, type 2 diabetes (T2D), metabolic dysfunction-associated steatotic liver disease (MASLD), hypertension, and atherosclerotic cardiovascular disease (ASCVD), represent a vast continuum driven by multi-organ network dysregulation. Clinical risk assessment remains dominated by late-stage measures (e.g., fasting glucose, HbA1c, standard lipids). While these assessments predominate the literature and clinical trial endpoints, each incompletely capture early mechanistic risk, inter-individual heterogeneity, and differential response to interventions. Multiomics (genomics, epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, microbiomics, and extracellular vesicle/exosome cargo profiling) expands the biomarker landscape but introduces translational barriers: high dimensionality, cohort heterogeneity, limited causal inference, and insufficient validation pipelines. AI-driven systems biology platforms can support cardiometabolic biomarker discovery and therapeutic translation by enabling systems-level biological inference across heterogeneous datasets, prioritizing mechanism and traceability over purely correlation-based models. GATC Health’s Operon™ platform is described as a proprietary, AI-driven internal scientific computing platform designed to support therapeutic discovery and development decision-making across the pharmaceutical lifecycle, including evaluation of drug efficacy, safety, off-target effects, pharmacokinetics (PK), pharmacodynamics (PD), and overall development risk. Operon evolved from earlier generations of GATC Health’s internal multiomic modeling systems (formerly referred to as the Multiomics Advanced Technology, MAT) and incorporates expanded data types, orchestration layers, validation workflows, and productization frameworks. Operon is operated by GATC scientists and generates structured, productized outputs (e.g., formal assessments, analyses, and decision frameworks) that are reviewed by experts. Operon methodologies have undergone internal validation and independent academic evaluation under blinded conditions, with reported classification performance (true positive rate 86% and true negative rate 91%) in controlled evaluation settings; these performance metrics should not be interpreted as guarantees of clinical success. This review provides a T2D-centered cardiometabolic biomarker landscape with cardiovascular extension and outlines how Operon-enabled multiomic integration and scenario-based simulation can support early screening, endotype stratification, mechanistic interpretation, and precision intervention design, including AI-guided polypharmacology strategies. Full article