Background: The lutetium-177 (177Lu) radioisotope has been proposed for use in radio-immunotherapy. Nowadays, 177Lu is mostly generated through neutron activation in nuclear reactors, although cyclotron-based production may also be utilized. Methods: In this study, a small cyclotron located in Karaj city of Iran with a maximum deuteron energy of 14[Formula: see text]MeV was simulated to produce 177Lu through 176Yb target by using SRIM and TALYS codes. In addition to estimating the cross-section values, the production yield of the reaction [Formula: see text]Yb(d,p)[Formula: see text]Yb*[Formula: see text]Lu was calculated at different deuteron energies. Here, the combination model of optical model parameters (OMP) and back-shifted Fermi gas model (BSFGM) with RIPL-3 parameters was performed to estimate the energy level density for the compound nucleus [Formula: see text]Yb, which is particularly important for cross-sections near the threshold. Results: The computed cross-section values of the reaction (d,p) and its competing reaction (d,x) were compared with other published reports. The maximum cross-section value for the (d,p) reaction was estimated to be 125[Formula: see text]mb at 10.5[Formula: see text]MeV, and for the (d,x) reaction to be 121[Formula: see text]mb at 9.5[Formula: see text]MeV. The production yields determined by Pade fitting were 118 and 315[Formula: see text]MBq/[Formula: see text] [Formula: see text]A.h, respectively, at deuteron energies of 10.5 and 14[Formula: see text]MeV. This corresponds to a yield of 318 MBq/[Formula: see text] [Formula: see text]A and 272 MBq/[Formula: see text] [Formula: see text]A for 1[Formula: see text]h of irradiation, respectively. Conclusion: The BSFGM may not provide accurate predictions of the [Formula: see text]Lu/[Formula: see text]Lu branching ratios in isomer estimates. Due to pre-equilibrium contributions, the (d,p) reaction exhibits significant direct/semi-direct components that optical models may not fully capture. Besides, using [Formula: see text]Yb-specific parameters ignores contributions from other Yb isotopes in natural targets, which affects the target’s isotopic purity. A drawback of the model is the underestimation of level densities for highly deformed nuclei. Although RIPL-3 provides the best results, uncertainties can be substantial for nuclei with limited data. This parameter uncertainty necessitates a fixed functional form; the exponential form may not capture all effects of nuclear structure.
This study presents a novel extension of the classical Rosenzweig–MacArthur predator–prey model by incorporating two predator species and an Allee effect in the prey population, along with an eco-epidemiological interaction between the predators. The resulting system captures complex trophic dynamics where the intermediate predator is suppressed by both direct predation on the prey and a parasitic or infection-like pressure from the top predator. The prey population is subject to Allee-type depensation, influencing its ability to recover at low densities a biologically relevant feature in many real ecosystems. Mathematically, the model is formulated through a set of nonlinear ordinary differential equations, analyzed for equilibrium behavior, local and global stability, and bifurcations. The system is nondimensionalized to reduce parameter redundancy and emphasize key ecological mechanisms. Stability and bifurcation analyses are performed to uncover critical thresholds where the system shifts from stable coexistence to periodic or chaotic behavior. We then examine the spatially explicit system and evaluate the Turing instability conditions for the resulting spatio-temporal dynamics. Numerical simulations, including bifurcation diagrams and Lyapunov exponent computations, validate the analytical findings and demonstrate rich dynamical regimes including stable nodes, limit cycles, and strange attractors. Biologically, the model emphasizes the role of Allee effects, predator interference, and infection-like interactions in shaping population persistence and ecosystem stability. The results underscore the importance of managing interspecies interactions and mortality rates to prevent extinction and chaotic outbreaks. This work contributes to the understanding of nonlinear eco-epidemiological systems and provides a basis for future studies incorporating spatial dynamics or stochasticity.
In this paper, we extend the classical Hastings–Powell tri-trophic food chain model by incorporating two ecologically realistic mechanisms: predator-induced fear in prey and a weak Allee effect. A rigorous mathematical analysis is carried out, including positivity, boundedness, dissipativeness, and persistence of solutions, followed by a detailed investigation of all equilibrium points and their local and global stability properties. Through bifurcation analysis, we identify that govern system transitions. Sensitivity analysis using Partial Rank Correlation Coefficients (PRCC) highlights the most influential parameters for prey, predator, and top predator dynamics. Numerical simulations, conducted via MATLAB and MATCONT, further reveal complex dynamical regimes. In particular, variation of the prey-handling time parameter [Formula: see text] drives the system into chaos, as confirmed by a positive maximum Lyapunov exponent (MLE). Importantly, we demonstrate that chaos can be suppressed by strengthening the weak Allee effect ([Formula: see text]) and predator-induced fear ([Formula: see text]). Two-parameter bifurcation diagrams for [Formula: see text], [Formula: see text], and [Formula: see text] provide deeper insights into the combined ecological effects of these parameters. The results, supported with ecological interpretations, show how behavioral responses and Allee effects can regulate chaotic oscillations and promote system stability, offering useful theoretical insights for ecological management and conservation.
The physical behavior of pharmaceutical molecules in living systems cannot be fully described without considering their interaction with structurally diverse aqueous media. Heterogeneous hydration layers, fluctuating dipole potentials near membrane surfaces, ionic microdomains, and molecular crowding create a dynamic environment in which drug molecules may undergo context-dependent changes in oscillatory properties, dipole orientation, or conformational stability prior to biochemical recognition. Evidence from spectroscopic and biophysical studies indicates that drug molecules exhibit variable physical states depending on the organization of the surrounding liquid, particularly near biomolecular interfaces. The discussion does not propose a new mechanism of drug action. Still, it suggests that interactions between liquid microenvironments and intrinsic molecular properties may condition how a drug is positioned for chemical binding. Recognizing this context-dependent behavior can encourage future work linking pharmaceutical science with liquid-state physics.
In marine fisheries management, the development of suitable harvesting strategies is important for maintaining a substantial proportion of the unharvested fish population. In this paper, we study the spatial dynamics of the fish population put forward by Lundberg and Jonzen, in which the total population is distributed over two habitats, where one area is kept as a reserve and the migration of a certain proportion of adults from the reserve to the harvesting area is allowed. We find out the equilibrium populations, discuss their nature, and derive the conditions for the asymptotic stability of these equilibrium populations of this model represented by a rational system. The results obtained in this paper are useful in obtaining the optimal harvesting strategies with respect to the maximum population growth rates. We also use phase space diagrams to discuss the dynamics of the equilibrium populations.
Nanobiosensors, with their unique physicochemical properties, are transformative tools for diagnosing and monitoring neurodegenerative diseases and mental disorders. This article systematically reviews the latest progress of nanomaterial systems and integrated sensing modalities in neurological disease diagnosis. First, we clarify the multiple functional roles of nanomaterials in biosensors, including signal amplification, interface optimization, and spatial positioning, and compare the applicable scenarios of various sensing principles based on different nanomaterials. Second, we evaluate the design and integration strategies of molecular recognition elements (antibodies, nucleic acid aptamers, molecularly imprinted polymers, and CRISPR-Cas systems) and discuss their synergistic integration mechanisms for improving detection performance. In terms of detection targets, we focus on three applications: high-sensitivity quantification of established protein biomarkers, real-time monitoring of dynamic neurochemicals (dopamine, serotonin, glutamate), and emerging liquid biopsy targets such as exosomal cargo and circulating microRNAs. Finally, to address the core challenges of biofouling, sensitivity-selectivity trade-offs, and multiplex detection in complex matrices, we propose three breakthrough directions for next-generation diagnostics: deep integration of multimodal and multiplexing platforms, closed-loop chemical brain-computer interfaces (cBCIs), and AI-driven predictive diagnostic models, collectively enabling a transition from passive detection to active sensing and intervention for precise, rapid, and non-invasive neurological disease management.
Traditional solid-contact ion-selective electrodes (SC-ISEs) are severely constrained by a long-standing thermodynamic bottleneck, which requires hours of pre-conditioning and stabilization to establish a stable phase-boundary potential. To fundamentally bypass this limitation, we present a paradigm shift in electrochemical ion sensing that exploits dynamic kinetics rather than waiting for thermodynamic equilibrium. In this paper, we report a transient potential profiling method that eliminates the need for equilibration by analyzing the open-circuit voltage decay during the first 60 s of polarization. A discharge step on indicator electrode returns the membrane to a reproducible initial state, allowing for the extraction of a concentration correlated coefficient. Using a calcium ISE with an optimized membrane, the early-stage polarization dynamics were fitted to a single exponential saturation model, predicting the steady state response with an average error of 1.6%. The method achieved high repeatability (intra-day RSD 3.22%), batch to batch reproducibility (4.57%), and recovery rates from 90.7% to 115.0% in real water samples. Validation against ion chromatography showed high agreement (R2 = 0.997). This strategy enabled conditioning free, disposable ISEs for point of care and environmental monitoring.
Early and reliable identification of cardiac disorders from Phonocardiogram (PCG) signals acquired from wearable biosensors is critical to support clinical decision making and reduce subjectivity in auscultation-based assessments. This study proposes a multi-stage hybrid feature selection-classification approach to increase diagnostic accuracy without requiring computationally expensive deep learning (DL) architectures. First, the most statistically discriminative features were identified using mRMR, ReliefF, and Kruskal-Wallis filtering methods. Particle swarm optimization (PSO) and ant colony optimization (ACO) were then applied to optimize the solution space. Finally, the selected feature subsets were tested with k-nearest neighbor (k-NN), support vector machines (SVMs), and Bagged Tree (BT) classifiers. Experimental results show that the proposed method significantly increases the model robustness and generalizability. In particular, the Kruskal-Wallis+k-NN and ReliefF+k-NN combinations achieved competitive performance compared to many DL-based approaches in the literature, with 99.80% accuracy and 99.50% F1-score. Furthermore, hybrid models augmented with PSO and ACO also achieved 99.60% accuracy. The findings demonstrate that well-designed feature selection strategies offer high accuracy and enhanced clinical applicability while using only a small set of handcrafted features and conventional classifiers. Therefore, the proposed framework is a strong candidate for smart stethoscope-based early screening solutions.
Herbal medicines represent a significant global market, yet food safety remains threatened by counterfeit products morphologically resembling authentic samples. Models trained on limited datasets are prone to shortcut learning, relying on superficial features rather than intrinsic morphological characteristics. This study identified size-based shortcut learning as a critical factor degrading the classification of Ziziphus jujuba Mill. var. spinosa and its counterfeit Ziziphus mauritiana Lam., and demonstrated that focal loss alone can effectively mitigate this issue. Models trained on the internal dataset were evaluated on an external dataset acquired with the Herb-X. On the internal test set, all configurations achieved high classification accuracies (≥98%), thereby obscuring meaningful differences in external generalization. However, consistent performance degradation was observed on the external dataset. The cross-entropy model trained on background-removed data dropped to 82.08 ± 10.97%, while size-normalized models recovered to 84.17 ± 10.15% (upsizing) and 88.94 ± 6.76% (downsizing), confirming that suppressing size shortcuts improves external generalization. The focal loss model, without any preprocessing, achieved 90.88 ± 2.71%, reducing the internal-external generalization gap from 16.18 to 8.11 percentage points. Grad-CAM++ and loss analyses confirmed that the focal loss model attended to intrinsic morphological features rather than object size. This study provides a practical, preprocessing-free approach for reliable herbal-medicine authentication in field conditions.
Wearable healthcare technologies are transforming the healthcare landscape by enabling remote, real-time health data collection, supporting early diagnosis, personalizing treatment plans, and reducing healthcare costs and medical burdens. Central to these advancements are wearable sensors, which continuously capture physiological data such as heart rate, temperature, activity levels, and biomarker concentrations. However, the large volume and complexity of this data demand effective processing to extract meaningful medical insights. Artificial intelligence (AI) and machine learning (ML) have significantly enhanced the capabilities of wearable sensors by enabling advanced data analysis, pattern recognition, and predictive modeling. AI-enhanced wearable sensors can detect early signs of health issues, such as heart attacks, chronic diseases, and mental health conditions like stress, often before clinical symptoms become apparent. This review examines the integration of AI/ML models with wearable sensors across physical activity recognition, stress assessment, cardiovascular monitoring, personal exposure monitoring, and sweat biomarker detection. Unlike prior application-centered reviews, we emphasize methodological and translational evaluation by comparing task formulations, sensing modalities, dataset scale, validation protocols, performance metrics, and deployment constraints across domains. We further discuss advanced architectures, multimodal fusion, explainable AI, edge deployment, privacy and regulatory considerations, and the translational gap between research prototypes and clinically deployable wearable AI systems.
Food allergies are one of the most critical food safety issues, with epidemiological studies confirming a global increase. In this context, effective and sensitive analytical methods play a crucial role in ensuring allergen-free food products. To face this issue, electrochemical biosensors offer powerful, sensitive, selective, and cost-effective alternatives to conventional methods for food allergen analysis while enabling rapid on-site detection. In this study, we developed a sandwich electrochemical magneto-genoassay aimed at the parallel detection of soy (Glycine max) and mustard (Sinapis alba) allergens, suitable for implementation on multichannel instrumentation. The assay involves the functionalization of magnetic microbeads functionalized with peptide nucleic acid-based (PNA) capture probes, capable of undergoing target-induced bio-orthogonal ligation with biotin-labelled signalling probes. Carbon nanotubes-modified screen-printed carbon electrodes were exploited for the voltammetric readout. We demonstrated the effectiveness of functional PNA probes by comparing their performance with those achieved using analogous DNA probes. The developed method exhibited excellent selectivity in terms of cross-reactivity, sensitivity, and precision, achieving detection limits of 16 and 19 pM for soy and mustard, respectively. Finally, by successfully applying the biosensor platform to genomic DNA extracted from plant-based food ingredients, we demonstrated its potential as a valuable tool in food safety risk management.
Phenylephrine is a widely used α1-adrenergic agonist employed as a decongestant and vasoconstrictor in numerous pharmaceutical formulations. Considering its widespread use and its relevance in biological monitoring and anti-doping control, the development of rapid, sensitive, and reliable analytical methods for its determination has attracted significant attention. A paper-based colorimetric sensor based on Prussian blue nanoparticles was developed for the determination of phenylephrine. Prussian blue nanoparticles were synthesized by the precipitation method, and their structural, morphological, and surface properties were systematically characterized using complementary analytical techniques. The sensing mechanism is based on the reduction in Prussian blue to its colorless form in the presence of phenylephrine, resulting in a decrease in absorbance intensity. Under optimized conditions (pH 6.5 and 5 min incubation time), the colorimetric sensor exhibited a linear response toward phenylephrine over the concentration range of 5-150 µg mL-1, with a limit of detection of 1.56 µg mL-1 (R2 = 0.9986). The sensing system was further integrated into a paper-based platform, enabling visual detection of phenylephrine. Digital image analysis using ImageJ showed a linear response over 5-150 µg mL-1 (R2 = 0.9884) and a detection limit of 5.37 µg mL-1. The sensor's practical applicability was validated using artificial urine samples, yielding recovery values of 95.87-97.5% and relative standard deviations of 1.15-2.13%. Unlike conventional methods requiring multi-step reactions, this study introduces, for the first time, a simple paper-based colorimetric sensor for phenylephrine detection based on the direct Prussian blue-Prussian white redox transition integrated with digital image analysis.
Biosensors are analytical devices that integrate a biological or synthetic recognition element (e [...]
Chronic diseases such as cardiovascular disorders, diabetes, neurological conditions, and kidney disease continue to rise worldwide. These conditions create a growing demand for continuous, non-invasive, and personalized health monitoring technologies. Wearable biosensors meet this need by enabling real-time physiological and biochemical measurements outside traditional clinical settings. Among wearable biosensors, those based on biofluids like sweat, tears, and saliva provide a painless alternative to blood sampling. These fluids also grant access to metabolites, electrolytes, hormones, proteins, and disease related biomarkers that reflect systemic health status. Advanced sensing technology allow us to continuously track health status by analyzing key biomarkers in these accessible biofluids. This review summarizes recent advances in non-invasive wearable biosensors and focuses on their sensing principles which includes biorecognition elements, signal transduction mechanisms, and data acquisition strategies. We also discussed key sensing modalities, including electrochemical, optical, thermal, and piezoelectric approaches, highlighting their advantages for wearable integration and performance in biofluid sensing. Finally the review also outlines recent developments and applications of these systems in biofluid sensing. In the end we highlights existing challenges, potential solutions, and future directions toward clinically deployable, AI-assisted precision healthcare systems.
One of the major obstacles in early cancer detection in dogs is the limited sensitivity in detecting circulating tumor DNAs (ctDNAs) with low abundances. Standard next-generation sequencing (NGS) without error correction typically achieves detection limits around ~1% mutant allele frequency (MAF). We sought to improve the detection sensitivity using a sequential CRISPR-EspCas9 enrichment strategy in which iterative in vitro cleavage (IVC) was combined with PCR amplification to selectively deplete wild-type DNA and enrich rare tumor mutations. Applying the strategy to genomic DNA and cell-free DNA mimics from canine mammary gland tumor cell lines demonstrated that IVC enrichment enabled the detection of cancer-associated PIK3CA H1047R mutations that were undetectable by conventional Sanger sequencing. To evaluate detection sensitivity, we characterized enrichment using synthetic templates for PIK3CA H1047R and other cancer-related mutations, BRAF V596E, and KRAS G12C. We observed that three iterations of sequential IVC achieved ~160, ~15, and ~2.2-fold enrichment for PIK3CA H1047R, BRAF V596E, and KRAS G12C, respectively. Under the present synthetic-template conditions, the analytical LOD reached 0.001% MAF for PIK3CA and 0.01% MAF for BRAF, whereas KRAS showed only modest enrichment and remained practically limited under the current guide design. Together, the results show that the CRISPR-EspCas9 IVC strategy enables selective enrichment of low-frequency single-nucleotide mutant alleles. We anticipate that the finding could be utilized to develop a highly sensitive veterinary liquid biopsy application with further optimization and validation using canine plasma cfDNA.
Genetically encoded biosensors are now central tools, deployed either as intracellular reporters to advance basic research, or as whole-cell reagents that detect analytes in diverse sample-types. Across the diversity of molecular scaffolds and modes of operation, biosensors serve a common functional purpose: translating ligand presence into a readable signal. Despite this shared logic, biosensor development as a field of practice remains fragmented: different scaffolds and modalities are advanced in separate, often lab-specific pipelines with diverse assays, metrics, and design practices. Moreover, libraries, selection histories and performance data generated during routine campaigns rarely outlive the projects that produced them. In this perspective, we focus on this fragmentation as a field-level bottleneck and argue that it deserves explicit attention in its own right. We discuss how modest, incremental steps—such as structured development records, adherence to high-information screening formats, library annotation, and community-level deposition infrastructure—could make biosensor development more reproducible, more comparable, and easier to build on across projects and laboratories. We further argue that such infrastructure will become increasingly valuable as computational protein design matures—not as a competing approach, but as the source of diverse, comparable, and context-annotated experimental data that sequence-function models and design benchmarks ultimately depend on.
Ultraviolet (UV) light is emerging as an important tool for biosensing, biomedical signal readout, and dose monitoring because of its strong and selective interactions with nucleic acids, proteins, and other biological components. This review summarizes recent progress in UV sensing-guided biomedical systems, with emphasis on three interconnected directions: label-free and surface-weighted imaging, wearable and embedded UV dosimetry, and sensor-assisted therapeutic guidance. Representative examples include ultraviolet photoacoustic microscopy (UV-PAM) for label-free nuclear imaging, microscopy with ultraviolet surface excitation (MUSE) for rapid slide-free histology-like readout, epidermal and flexible UV dosimeters for skin-level exposure quantification, and UV therapeutic platforms that are increasingly supported by sensing, dosimetry, and feedback for safer dose delivery. Across these applications, we emphasize the shared biosensing principles of signal generation, optical or acoustic transduction, quantitative readout, calibration, and feedback-informed decision support. We also discuss the role of artificial intelligence in virtual staining, image enhancement, domain correction, dose prediction, and decision support. The review concludes with key translational challenges in standardization, uncertainty quantification, multimodal integration, and feedback-driven system design. Overall, this sensing-centered perspective helps define the role of UV technologies more clearly within biosensors-oriented biomedical engineering.
Epilepsy can be effectively controlled with appropriately selected antiepileptic drugs and carefully titrated dosage regimens. Although lamotrigine exhibits favorable pharmacokinetic properties following oral administration, fluctuations in plasma concentration may still occur due to interindividual variability, irregular dosing, and pharmacokinetic interactions. In this study, a subcutaneous implant capable of monitoring plasma lamotrigine levels and adjusting drug delivery accordingly was developed to maintain stable therapeutic concentrations. The proposed system combines intermittent drug release with continuous concentration monitoring using an enzymatic biosensor. A pharmacokinetic model based on first-order absorption and elimination kinetics was implemented in MATLAB/Simulink using clinical lamotrigine concentration data obtained from patients receiving chronic therapy. In the closed-loop configuration, biosensor measurements were used as feedback for a proportional-integral (PI) controller that adjusted the implant release rate in real time. System performance was evaluated using in silico simulations. The open-loop system produced rapid concentration peaks (Cmax ≈ 0.06 mmol/L) followed by a decline below the therapeutic threshold within approximately 80 min. In contrast, the closed-loop system achieved lower peak concentrations (Cmax ≈ 0.045 mmol/L) and maintained plasma concentrations within the therapeutic range of 0.02-0.03 mmol/L with reduced fluctuations. These findings support further investigation of biosensor-guided closed-loop lamotrigine delivery systems.
Two-dimensional-material-based FET biosensors have gained attention for being label-free and having ultra-sensitive detection capability. The high carrier mobility and large surface-to-volume ratio of 2D materials enable low detection limits under buffer conditions; however, practical detection still faces many challenges. Current reviews have largely summarized materials, functionalization routes, or target classes separately, but a clearer framework linking interface design, device architecture, and practical sensing performance is still needed. In this review, we examine how interfacial engineering and device architecture govern signal transduction and sensing behavior in 2D material FET biosensors. We also analyze the major barriers to real-sample detection, including Debye screening, nonspecific adsorption, and signal drift, together with commonly used mitigation strategies. On this basis, an "interface-device-performance" framework is discussed as a conceptual approach for understanding the relationship between molecular recognition, electrical response, and sensing performance. This review mainly focuses on the key challenges of 2D material FET biosensors in practical medical applications, discusses the differences between material and application perspectives, and examines the major factors limiting clinical translation.
The development of new technologies enabling rapid, frequent, and reagent-free monitoring of kidney function is recognized as being of paramount importance. In this work, mid-(MIR) and near-infrared (NIR) spectroscopy were compared for the prediction of key renal biomarkers—creatinine, urea and albumin—using 54 serum solutions mimicking the biochemical profiles of five stages of chronic kidney disease (CKD). MIR spectra were acquired in a high-throughput microplate platform after a simple dehydration step, while the NIR spectra were obtained directly from liquid serum using a fiber optic probe. After evaluating several spectral pre-processing methods and targeted spectral regions, excellent regression models (R2 > 0.9 for the best models) were obtained for the three biomarkers. MIR provided highly accurate urea predictions, whereas optimized NIR sub-regions enabled excellent estimation of creatinine and albumin. Both MIR and NIR, associated with supervised classification methods, enabled us to successfully distinguish healthy from diseased profiles and to identify the diseases state with AUC > 0.93. These findings highlight the complementary value of MIR and NIR spectroscopy for kidney disease assessment and their potential integration into point-of-care diagnostic systems.
