Safety Applications Research Articles

Global Trends of Exosomes Application in Clinical Trials: A Scoping Review.

Exosomes, nano-sized extracellular vesicles, have emerged as a promising tool for the diagnosis and treatment of various intractable diseases, including chronic wounds and cancers. As our understanding of exosomes continues to grow, their potential as a powerful therapeutic modality in medicine is also expanding. This systematic review aims to examine the progress of exosome-based clinical trials and provide a comprehensive overview of the therapeutic perspectives of exosomes. This systematic review strictly follows PRISMA guidelines and has been registered in PROSPERO, the International Prospective Register of Systematic Reviews. It encompasses articles from January 2000 to January 2023, sourced from bibliographic databases, with targeted search terms targeting exosome applications in clinical trials. During the screening process, strict inclusion and exclusion criteria were applied, including a focus on clinical trials utilizing different cell-derived exosomes for therapeutic purposes. Among the 522 publications initially identified, only 10 studies met the stringent eligibility criteria after meticulous screening. The selection process involved systematically excluding duplicates and irrelevant articles to provide a transparent overview. According to our systematic review, exosomes have promising applications in a variety of medical fields, including cell-free therapies and drug delivery systems for treating a variety of diseases, especially cancers and chronic wounds. To ensure safety, potency, and broader clinical applications, further optimization of exosome extraction, loading, targeting, and administration is necessary. While cell-based therapeutics are increasingly utilizing exosomes, this field is still in its infancy, and ongoing clinical trials will provide valuable insights into the clinical utility of exosomes.

Accurate crowd counting for intelligent video surveillance systems

The paper presents a novel deep learning approach for crowd counting in intelligent video surveillance systems, addressing the growing need for accurate monitoring of public spaces in urban environments. The demand for precise crowd estimation arises from challenges related to security, public safety, and efficiency in urban areas, particularly during large public events. Existing crowd counting techniques, including feature-based object detection and regression-based methods, face limitations in high-density environments due to occlusions, lighting variations, and diverse human figures. To overcome these challenges, the authors propose a new deep encoder-decoder architecture based on VGG16, which incorporates hierarchical feature extraction with spatial and channel attention mechanisms. This architecture enhances the model’s ability to manage variations in crowd density, leveraging adaptive pooling and dilated convolutions to extract meaningful features from dense crowds. The model’s decoder is further refined to handle sparse and crowded scenes through separate density maps, improving its adaptability and accuracy. Evaluations of the proposed model on benchmark datasets, including Shanghai Tech and UCF CC 50, demonstrate superior performance over state-of-the-art methods, with significant improvements in mean absolute error and mean squared error metrics. The paper emphasizes the importance of addressing environmental variability and scale differences in crowded environments and shows that the proposed model is effective in both sparse and dense crowd conditions. This research contributes to the advancement of intelligent video surveillance systems by providing a more accurate and efficient method for crowd counting, with potential applications in public safety, transportation management, and urban planning.

Flower-like nickel oxide nanostructures: Superior ammonia gas sensing and efficient dye removal behavior under UV–visible light illumination

In this study, we fabricated the NiO nanostructures using a straightforward solvothermal technique. The NiO nanoparticles were characterized using various analytical techniques. The FESEM images clearly show the flower-like structure of NiO nanoparticles. These nanostructures were then employed as a sensor to detect the presence of highly toxic ammonia (NH3) gas. The flower-shaped nanostructures of NiO played a vital role in improving sensing performance. Moreover, the NiO sensor displayed rapid response to 100 ppm ammonia at room temperature, with responses/recovery times of 40 s/44 s. This research holds promise as a viable approach for effectively sensing and detecting NH3 gas. The NiO exhibited excellent photocatalytic activity and degradation rates of 80% and 84.75% for Methyl orange (MO) and methylene blue dye (MB) respectively under UV-visible light irradiation. The NiO nanoflower remains unaffected after recycling MB dye degradation. This effective photocatalytic performance might be due to the formation of flower-like morphology. This underscores the potential of NiO as a promising candidate for applications in environmental and healthcare safety applications.

Emerging Low Detection Limit of Optically Activated Gas Sensors Based on 2D and Hybrid Nanostructures.

Gas sensing is essential for detecting and measuring gas concentrations across various environments, with applications in environmental monitoring, industrial safety, and healthcare. The integration of two-dimensional (2D) materials, organic materials, and metal oxides has significantly advanced gas sensor technology, enhancing its sensitivity, selectivity, and response times at room temperature. This review examines the progress in optically activated gas sensors, with emphasis on 2D materials, metal oxides, and organic materials, due to limited studies on their use in optically activated gas sensors, in contrast to other traditional gas-sensing technologies. We detail the unique properties of these materials and their impact on improving the figures of merit (FoMs) of gas sensors. Transition metal dichalcogenides (TMDCs), with their high surface-to-volume ratio and tunable band gap, show exceptional performance in gas detection, especially when activated by UV light. Graphene-based sensors also demonstrate high sensitivity and low detection limits, making them suitable for various applications. Although organic materials and hybrid structures, such as metal-organic frameworks (MoFs) and conducting polymers, face challenges related to stability and sensitivity at room temperature, they hold potential for future advancements. Optically activated gas sensors incorporating metal oxides benefit from photoactive nanomaterials and UV irradiation, further enhancing their performance. This review highlights the potential of the advanced materials in developing the next generation of gas sensors, addressing current research gaps and paving the way for future innovations.

Improving the Accuracy of mmWave Radar for Ethical Patient Monitoring in Mental Health Settings.

Monitoring patient safety in high-risk mental health environments is a challenge for clinical staff. There has been a recent increase in the adoption of contactless sensing solutions for remote patient monitoring. mmWave radar is a technology that has high potential in this field due it its low cost and protection of privacy; however, it is prone to multipath reflections and other sources of environmental noise. This paper discusses some of the challenges in mmWave remote sensing applications for patient safety in mental health wards. In line with these challenges, we propose a novel low-data solution to mitigate the impact of multipath reflections and other sources of noise in mmWave sensing. Our solution uses an unscented Kalman filter for target tracking over time and analyses features of movement to determine whether targets are human or not. We chose a commercial off-the-shelf radar and compared the accuracy and reliability of sensor measurements before and after applying our solution. Our results show a marked decrease in false positives and false negatives during human target tracking, as well as an improvement in spatial location detection in a two-dimensional space. These improvements demonstrate how a simple low-data solution can improve existing mmWave sensors, making them more suitable for patient safety solutions in high-risk environments.

Artificial Intelligence in Maritime Transportation: A Comprehensive Review of Safety and Risk Management Applications

Maritime transportation is crucial for global trade but faces significant risks and operational challenges. Ensuring safety is essential for protecting lives, the environment, and economic stability. This review explores the role of artificial intelligence (AI) in enhancing maritime safety and risk management. Key AI applications include risk analysis, crew resource management, hazardous material handling, predictive maintenance, and navigation systems. AI systems identify potential hazards, provide real-time decision support, monitor hazardous materials, predict equipment failures, and optimize shipping routes. Case studies, such as Wärtsilä’s Fleet Operations Solution and ABB Ability™ Marine Pilot Vision, illustrate the benefits of AI in improving safety and efficiency. Despite these advancements, integrating AI poses challenges related to infrastructure compatibility, data quality, and regulatory issues. Addressing these is essential for successful AI implementation. This review highlights AI’s potential to transform maritime safety, emphasizing the need for innovation, standardized practices, and robust regulatory frameworks to achieve safer and more efficient maritime operations.

MoS2@Fe3O4 prepared by γ-ray in situ reduction for enhancing the fire safety and mechanical properties of EVA/MCA composites

The high flammability of ethylene vinyl acetate copolymers (EVA) limits its important applications in fire safety, and the addition of highly effective flame retardants can improve this deficiency. Melamine cyanurate (MCA) has limited carbon-forming ability and therefore is often combined with other flame retardants to achieve synergistic flame retardant effects and thus improve the flame retardancy of EVA. Therefore, in this study, MoS2@Fe3O4 hybrid materials were synthesized by in-situ growth of molybdenum disulfide (MoS2) and ferric oxide (Fe3O4) and their structures were characterized using various techniques. The hybrids were blended with melamine cyanurate (MCA) into ethylene vinyl acetate copolymer (EVA) by melt blending method to reduce the fire hazard. The EVA composites with 1.0 wt% MoS2@Fe3O4 blends significantly improved the coke residue and limiting oxygen index values to 22.5 % and 23.2 %, while their peak heat release rate, total heat release, and total smoke release were reduced by 62.5 %, 11.7 %, and 35.3 %, respectively, as compared to pure EVA. The results suggest that the use of MoS2@Fe3O4 hybrids in combination with MCA is a promising strategy for the development of advanced flame-retardant materials.

SnF2-Catalyzed Lithiophilic-Lithiophobic Gradient Interface for High-Rate PEO-Based All-Solid-State Batteries.

Polyethylene oxide (PEO)-based all-solid-state lithium metal batteries (ASSLMBs) are strongly hindered by the fast dendrite growth at the Li metal/electrolyte interface, especially under large rates. The above issue stems from the suboptimal interfacial chemistry and poor Li+ transport kinetics during cycling. Herein, a SnF2-catalyzed lithiophilic-lithiophobic gradient solid electrolyte interphase (SCG-SEI) of LixSny/LiF-Li2O is in situ formed. The superior ionic LiF-Li2O rich upper layer (17.1 nm) possesses high interfacial energy and fast Li+ diffusion channels, wherein lithiophilic LixSny alloy layer (8.4 nm) could highly reduce the nucleation overpotential with lower diffusion barrier and promote rapid electron transportation for reversible Li+ plating/stripping. Simultaneously, the insoluble SnF2-coordinated PEO promotes the rapid Li+ ion transport in the bulk phase. As a result, an over 46.7 and 3.5 times improvements for lifespan and critical current density of symmetrical cells are achieved, respectively. Furthermore, LiFePO4-based ASSLMBs deliver a recorded cycling performance at 5 C (over 1000 cycles with a capacity retention of 80.0 %). More importantly, impressive electrochemical performances and safety tests with LiNi0.8Mn0.1Co0.1O2 and pouch cell with LiFePO4, even under extreme conditions (i.e., 100 °C), are also demonstrated, reconfirmed the importance of lithiophilic-lithiophobic gradient interfacial chemistry in the design of high-rate ASSLMBs for safety applications.

SnF2‐Catalyzed Lithiophilic–Lithiophobic Gradient Interface for High‐Rate PEO‐Based All‐Solid‐State Batteries

AbstractPolyethylene oxide (PEO)‐based all‐solid‐state lithium metal batteries (ASSLMBs) are strongly hindered by the fast dendrite growth at the Li metal/electrolyte interface, especially under large rates. The above issue stems from the suboptimal interfacial chemistry and poor Li+ transport kinetics during cycling. Herein, a SnF2‐catalyzed lithiophilic‐lithiophobic gradient solid electrolyte interphase (SCG‐SEI) of LixSny/LiF‐Li2O is in situ formed. The superior ionic LiF‐Li2O rich upper layer (17.1 nm) possesses high interfacial energy and fast Li+ diffusion channels, wherein lithiophilic LixSny alloy layer (8.4 nm) could highly reduce the nucleation overpotential with lower diffusion barrier and promote rapid electron transportation for reversible Li+ plating/stripping. Simultaneously, the insoluble SnF2‐coordinated PEO promotes the rapid Li+ ion transport in the bulk phase. As a result, an over 46.7 and 3.5 times improvements for lifespan and critical current density of symmetrical cells are achieved, respectively. Furthermore, LiFePO4‐based ASSLMBs deliver a recorded cycling performance at 5 C (over 1000 cycles with a capacity retention of 80.0 %). More importantly, impressive electrochemical performances and safety tests with LiNi0.8Mn0.1Co0.1O2 and pouch cell with LiFePO4, even under extreme conditions (i.e., 100 °C), are also demonstrated, reconfirmed the importance of lithiophilic‐lithiophobic gradient interfacial chemistry in the design of high‐rate ASSLMBs for safety applications.

Prune and Quantize Semantic Segmentation Network for Aerial Objects Recognition

Semantic segmentation of aerial and satellite images is crucial for applications in environmental management, urban planning, and traffic safety. While deep learning techniques with convolutional neural networks (CNNs) and attention mechanisms have achieved superior accuracy compared to traditional methods, they often struggle with model complexity and resource constraints. This paper introduces two novel techniques - pruning and quantization - to enhance both the performance and efficiency of semantic segmentation models for remote sensing images (RSIs). Pruning reduces model complexity by eliminating less significant weights, while quantization decreases memory usage by converting weights into a more compact format. We applied these techniques to the DeepLabV3+ model with ResNet18 and ResNet50 backbones and assessed their performance across multiple RSI datasets. Our results show that pruning and quantization effectively balance accuracy and computational efficiency, achieving a mean IoU of 81.24% with a memory footprint of 135.19 MB for pruning, and 81.04% mean IoU with a memory footprint of 33.79 MB for quantization on the ISPRS Vaihingen dataset. These methods offer a viable solution for deploying semantic segmentation models on resource-constrained hardware.

Cytotoxicity and genotoxic impacts of LAPONITE® on murine adipose stem cells

This study comprehensively characterized LAPONITE® and evaluated its cytotoxic and genotoxic effects on rat adipose-derived stem cells (ADSCs). Chemical analyses included Fourier transform infrared spectroscopy, thermogravimetric analysis, X-ray diffraction, and scanning electron microscopy. Cytotoxic screening using the Artemia salina lethality test indicated low toxicity. ADSCs exhibited mesenchymal stem cell chracteristics. LAPONITE® demonstrated osteogenic induction capability. MTT assays revealed increased cell viability across concentrations and exposure times. The comet assay for genotoxicity showed no significant differences in damage index between experimental groups, except for a distinction in the frequency of damage between LAPONITE® concentrations at the longest exposure time. This comprehensive assessment provides valuable insights into the safety and potential applications of LAPONITE® in regenerative medicine.

Optical Image Sensors for Smart Analytical Chemiluminescence Biosensors.

Optical biosensors have emerged as a powerful tool in analytical biochemistry, offering high sensitivity and specificity in the detection of various biomolecules. This article explores the advancements in the integration of optical biosensors with microfluidic technologies, creating lab-on-a-chip (LOC) platforms that enable rapid, efficient, and miniaturized analysis at the point of need. These LOC platforms leverage optical phenomena such as chemiluminescence and electrochemiluminescence to achieve real-time detection and quantification of analytes, making them ideal for applications in medical diagnostics, environmental monitoring, and food safety. Various optical detectors used for detecting chemiluminescence are reviewed, including single-point detectors such as photomultiplier tubes (PMT) and avalanche photodiodes (APD), and pixelated detectors such as charge-coupled devices (CCD) and complementary metal-oxide-semiconductor (CMOS) sensors. A significant advancement discussed in this review is the integration of optical biosensors with pixelated image sensors, particularly CMOS image sensors. These sensors provide numerous advantages over traditional single-point detectors, including high-resolution imaging, spatially resolved measurements, and the ability to simultaneously detect multiple analytes. Their compact size, low power consumption, and cost-effectiveness further enhance their suitability for portable and point-of-care diagnostic devices. In the future, the integration of machine learning algorithms with these technologies promises to enhance data analysis and interpretation, driving the development of more sophisticated, efficient, and accessible diagnostic tools for diverse applications.

Signal-enhanced electrochemical sensor employing MWCNTs/CMK-3/AuNPs and Au@Pd core-shell structure for sensitive determinationof AFB1 in complex matrix.

A sandwich electrochemical sensor was fabricated based on multi-walled carbon nanotubes/ordered mesoporous carbon/AuNP (MWCNTs/CMK-3/AuNP) nanocomposites and porous core-shell nanoparticles Au@PdNPs to achieve rapid and sensitive detection of AFB1 in complex matrices. MWCNTs/CMK-3/AuNP nanocomposite, which was prepared by self-assembly method, served as a substrate material to increase the aptamer loading and improve the conductivity and electrocatalytic activity of the electrode for the first signal amplification. Then, Au@PdNPs, which were synthesized by one-pot aqueous phase method, were applied as nanocarriers loaded with plenty of capture probe antibody (Ab) and signal molecule toluidine blue (Tb) to form the Au@PdNPs-Ab-Tb bioconjugates for secondary signal amplification. The sensing system could still significantly improve the signal output intensity even in the presence of ultra-low concentration target compounddue to the dual signal amplification of MWCNTs/CMK-3/AuNP nanocomposites and Au@PdNPs-Ab-Tb. The method exhibited high selectivity, low detection limit (9.13fg/mL), and strong stability to differentiate AFB1 from other mycotoxins. Furthermore, the sensor has been successfully applied to the quantitative determination of AFB1 in corn, malt, and six herbs, which has potential applications in food safety, quality control, and environmental monitoring.

Miniaturized Hyperspectral Imager Utilizing a Reconfigurable Filter Array for Both High Spatial and Spectral Resolutions.

Miniaturized hyperspectral imaging based on filter arrays has attracted much attention in consumer applications, such as food safety and biomedical applications. In this Letter, we demonstrate a miniaturized hyperspectral imager using a reconfigurable filter array to tackle the existing trade-off issue between the spectral and spatial resolutions. Utilizing tens of intermediate states of a vanadium dioxide cavity, we increase the total number of physical spectral channels by tens of times from a 2 × 2 mosaic filter unit, providing both high spatial and spectral resolutions for spectral imaging. The reconfigurable filter has a good spectral resolvability of 10 nm in the visible range with a wavelength inaccuracy of less than 2.1 nm. Hyperspectral imaging is demonstrated with a frame rate of 4.5 Hz.

Discrimination of Explosive Residues by Standoff Sensing Using Anodic Aluminum Oxide Microcantilever Laser Absorption Spectroscopy with Kernel-Based Machine Learning.

Standoff laser absorption spectroscopy (LAS) has attracted considerable interest across many applications for environmental safety. Herein, we propose an anodic aluminum oxide (AAO) microcantilever LAS combined with machine learning (ML) for sensitive and selective standoff discrimination of explosive residues. A nanoporous AAO microcantilever with a thickness of <1 μm was fabricated using a micromachining process; its spring constant (18.95 mN/m) was approximately one-third of that of a typical Si microcantilever (53.41 mN/m) with the same dimensions. The standoff infrared (IR) spectra of pentaerythritol tetranitrate, cyclotrimethylene trinitramine, and trinitrotoluene were measured using our AAO microcantilever LAS over a wide range of wavelengths, and they closely matched the spectra obtained using standard Fourier transform infrared spectroscopy. The standoff IR spectra were fed into ML models, such as kernel extreme learning machines (KELMs), support vector machines (SVMs), random forest (RF), and backpropagation neural networks (BPNNs). Among these four ML models, the kernel-based ML models (KELM and SVM) were found to be efficient learning models able to satisfy both a high prediction accuracy (KELM: 94.4%, SVM: 95.8%) and short hyperparameter optimization time (KELM: 5.9 s, SVM: 7.6 s). Thus, the AAO microcantilever LAS with kernel-based learners could emerge as an efficient sensing method for safety monitoring.

Signal amplification strategy based on target-controlled release of mediator for ultrasensitive self-powered biosensing of acetamiprid

Self-powered biosensors with high sensitivity have garnered significant interest for their potential applications in the realm of portable sensing. Herein, a self-powered biosensor with a novel signal amplification strategy was developed by integrating target-controlled release of mediator with an enzyme biofuel cell for the ultrasensitive detection of acetamiprid (ACE). Zeolitic imidazolate framework-67 was utilized as both a nanocontainer for capturing the electron mediator 2,2′-azidobis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and a precursor for the synthesis of cobalt nanoparticles/nitrogen, sulfur-codoped carbon nanotubes (Co NPs/NS-CNTs), which were employed as the electrode material for constructing both the glucose oxidase-based bioanode and the laccase-based biocathode. The target analyte ACE can specifically bind to its aptamer, leading to the release of ABTS, which cyclically participates in the catalytic reaction of the biocathode, thereby amplifying the electrochemical signal. By leveraging the benefits of ABTS cyclic catalysis and the effective electrocatalysis of bioelectrodes based on Co NPs/NS-CNTs, the self-powered biosensor has a broad detection range of 0.1–1000 fM and a low detection limit of 25 aM toward ACE. The proposed signal amplification approach presents a promising strategy for enhancing sensitivity and enabling portable analysis in applications of food safety, environmental monitoring, and medical diagnostics.

Colorimetric and fluorescent probes for cysteine detection: Applications in food safety and cellular imaging

In this study, three BODIPY-based fluorescent probes were designed and synthesized. The ultraviolet-visible spectra, fluorescence spectra, smartphone color recognition application and bioimaging were utilized to evaluate the capacity of the probes. By comparing key parameters, BDP-SIN had optimal performances including fastest response (10 min), highest signal-to-noise ratio (815 times) and lowest limit of detection (LOD = 49 nM). The recovery rate ranged from 92.04 % to 103.25 %. Meanwhile, BDP-SIN was triumphantly employed for determination of Cys in different daily food samples. Moreover, the test strips and microporous filter membrane loaded with BDP-SIN were developed for the portable real-time visualization and quantitative detection of Cys in food samples, which the contents ranged from 0.27 μM to 0.49 μM. Besides, BDP-SIN could image Cys in the living cells and mice. The novelty of this work was that developed an effective tool for researching the roles of Cys in food industry and living organisms.

Biotemplated Janus Magnetic Microrobots Based on Diatomite for Highly Efficient Detection of Salmonella.

Foodborne illnesses caused by Salmonella bacteria pose a significant threat to public health. It is still challenging to detect them effectively. Herein, biotemplated Janus disk-shaped magnetic microrobots (BJDMs) based on diatomite are developed for the highly efficient detection of Salmonella in milk. The BJDMs were loaded with aptamer, which can be magnetically actuated in the swarm to capture Salmonella in a linear range of 5.8 × 102 to 5.8 × 105 CFU/mL in 30 min, with a detection limit as low as 58 CFU/mL. In addition, the silica surface of BJDMs exhibited a large specific surface area to adsorb DNA from captured Salmonella, and the specificity was also confirmed via tests of a mixture of diverse foodborne bacteria. These diatomite-based microrobots hold the advantages of mass production and low cost and could also be extended toward the detection of other types of bacterial toxins via loading different probes. Therefore, this work offers a reliable strategy to construct robust platforms for rapid biological detection in practical applications of food safety.

Highly durable yarn-based strain sensor with enhanced underwater monitoring capabilities and rapid drowning detection for safety applications

Flexible strain sensors have garnered significant attention for their outstanding performance in fields such as telemedicine, motion management, and human–machine interfaces. However, in the demanding marine environments, these advanced sensors face challenges related to mechanical weakness, stability, and durability under wet conditions, which are critical for expanding their range of practical applications. In this study, a stable and durable yarn sensor was developed specifically tailored for underwater activity monitoring. This was achieved by using polydopamine to immobilize silver nanoparticles, thereby creating a highly conductive layer, followed by applying an impregnation coating to establish a hydrophobic layer. The resulting composite structure featured a micro-convex layer of silver nanoparticles, which provided a durable hydrophobic surface resistant to mechanical wear and capable of maintaining its electrical properties even under high-velocity water impacts. More significantly, a FWAS was engineered, which is activated by pre-stretching the yarn sensor using a water-soluble vinylon yarn. The FWAS automatically alters its resistance within 1.3 s upon detecting someone falling into the water, thereby sending out an accurate distress signal to the external world by transmitting Morse code signals via gesture movements. This innovation provides a straightforward and effective approach for timely rescue operations from drowning, demonstrating the versatility of yarn-based flexible strain sensors for use not only in air but also in underwater scenarios.

Investigation of the toxicity and safety concerns of transgenic maize seeds expressing immunogenic F and HN protein genes against Newcastle disease virus.

The presented study investigated the potential toxicity and safety concerns associated with transgenic maize seeds expressing immunogenic F and HN protein genes against Newcastle disease virus (NDV). The experiment involved feeding Sprague-Dawley rats with transgenic maize seeds formulated into standard diets at levels of 30% (w/w) for a duration of 90days. The rats were divided into three groups, with 10 rats per group. We assessed various parameters including overall appearance, feed consumption, body weight, organ weight, hematological parameters, serum chemistry, and histopathology. The results of these assessments were compared between the control group and the treatment groups. The study findings revealed that there were no significant differences between the control and treatment groups in terms of overall appearance, feed consumption, body weight, organ weight, hematological parameters, serum chemistry, microscopic histopathology, and gross appearance of tissues. These observations suggest that the consumption of transgenic maize seeds did not lead to any treatment-related adverse effects or toxicological issues. Furthermore, the transgenic maize seeds were found to be nutritionally equivalent to their non-transgenic counterpart. Overall, no physiological, pathological, or molecular toxicity was observed in the Rats fed with transgenic feed.However, it is important to note that this study focused specifically on the parameters measured and the outcomes observed in Sprague-Dawley rats, and further research and studies are necessary to fully evaluate the safety and potential applications of transgenic edible vaccines in humans or other animals.