Gene Vectors Research Articles

Multifunctional Microneedle Patches for Perivascular Gene Delivery and Treatment of Vascular Intimal Hyperplasia.

Gene therapy has emerged as a promising approach to address challenging cardiovascular diseases. Extensive efforts have been focused on developing highly efficient gene vectors with precise delivery techniques to enhance its effectiveness. In this study, we present multifunctional dopamine-gelatin microneedle patches with gene therapy capabilities to achieve perivascular gene delivery for intimal hyperplasia treatment. These patches that were fabricated through freeze-drying of gelatin are with recombinant adeno-associated virus (rAAVs)-carrying tips and dopamine coating backing layers. The lyophilized gelatin could not only effectively preserve the therapeutic activity of rAAVs but could also demonstrate the capability to penetrate the adventitia for efficient delivery. The incorporation of dopamine facilitated patch adhesion and extended the release duration. Based on these advantages, we have demonstrated that the rAAVs-loaded microneedle patches (AMNPs) behave satisfactorily in perivascular gene delivery to inhibit carotid artery restenosis in rats. These features indicate that the AMNPs are clinically valuable in the treatment of vascular intimal hyperplasia diseases.

Ancient Text Translation Model Optimized with GujiBERT and Entropy-SkipBERT

To cope with the challenges posed by the complex linguistic structure and lexical polysemy in ancient texts, this study proposes a two-stage translation model. First, we combine GujiBERT, GCN, and LSTM to categorize ancient texts into historical and non-historical categories. This categorization lays the foundation for the subsequent translation task. To improve the efficiency of word vector generation and reduce the limitations of the traditional Word2Vec model, we integrated the entropy weight method in the hopping lattice training process and spliced the word vectors with GujiBERT. This improved method improves the efficiency of word vector generation and enhances the model’s ability to accurately represent lexical polysemy and grammatical structure in ancient documents through dependency weighting. In training the translation model, we used a different dataset for each text category, significantly improving the translation accuracy. Experimental results show that our categorization model improves the accuracy by 5% compared to GujiBERT. In contrast, the Entropy-SkipBERT improves the BLEU scores by 0.7 and 0.4 on historical and non-historical datasets. Ultimately, the proposed two-stage model improves the BLEU scores by 2.7 over the baseline model.

Engineering an Ultrasound-Responsive Glycopolymersome for Hepatocyte-Specific Gene Delivery.

The ability to design liver-targeted gene delivery vectors is plagued with difficulties ranging from carrier-mediated cellular toxicity to challenges in encapsulating sensitive nucleic acids. Herein, we present an ultrasound-responsive glycopolymersome strategy for in situ loading of nucleic acids and achieving hepatocyte-specific gene delivery. This glycopolymersome is self-assembled from a block copolymer, N-acetylgalactosamine-grafted poly(glutamic acid)-block-poly(ε-caprolactone) (PGAGalNAc-b-PCL). GalNAc is introduced to afford liver targeting through the selective binding to the asialoglycoprotein receptor overexpressed on hepatocytes. External ultrasound is utilized to assist in encapsulating nucleic acids within the hydrophilic lumen of glycopolymersomes by exploiting their ultrasound responsiveness nature. Biological studies confirmed the successful encapsulation of plasmid DNA (pDNA) and small interfering RNA (siRNA), rapid nuclear internalization, and efficient gene transfection. These findings collectively demonstrated that this ultrasound-responsive glycopolymersome could be exploited as a novel safe and efficient gene vector targeting hepatocytes.

Scalable multimodal approach for face generation and super-resolution using a conditional diffusion model

Multimodal Conditioned face image generation and face super-resolution are significant areas of research. To achieve optimal results, this paper utilizes diffusion models as the primary engine for these tasks. This paper presents two main contributions: (1) “Speaking the Language of Faces” (SLF): a flexible, modular, fusion-less and architecturally simple multimodal system. (2) A Scalability scheme and a sensitivity analysis which can assist practitioners in system parameter estimation and feature selection. SLF consists of two main components: a feature vector generator (encoder), and an image generator (decoder) utilizing a conditional diffusion model. SLF can accept various inputs, including low-resolution images, speech signals, person attributes (age, gender, ethnicity), or any combination of these. Moreover, Scalability based on conditional scale values is utilized. The implementation of SLF has confirmed its versatility (e.g., speech to face image generation, conditioned face super-resolution). We trained multiple system versions to conduct a sensitivity analysis and to determine the influence of each individual feature on the output image. Consequently, speaker embeddings have proven to be sufficient audio features for our task. It was also found that the effects of audio signals are profound and are more pronounced than those of the low resolution images (8 × 8), whose effects are still significant. The effect of gender, ethnicity and age were found to be moderate. On another note, conditional scale values significantly impact the system’s behavior and performance.

Microplastics from face masks: Unraveling combined toxicity with environmental hazards and their impacts on food safety.

Microplastics (MPs) refer to tiny plastic particles, typically smaller than 5mm in size. Due to increased mask usage during COVID-19, improper disposal has led to masks entering the environment and releasing MPs into the surroundings. MPs can absorb environmental hazards and transfer them to humans and animals via the food chain, yet their impacts on food safety and human health are largely neglected. This review summarizes the release process of MPs from face masks, influencing factors, and impacts on food safety. Highlights are given to the prevalence of MPs and their combined toxicities with other environmental hazards. Control strategies are also explored. The release of MPs from face masks is affected by environmental factors like pH, UV light, temperature, ionic strength, and weathering. Due to the chemical active surface and large surface area, MPs can act as vectors for heavy metals, toxins, pesticides, antibiotics and antibiotic resistance genes, and foodborne pathogens through different mechanisms, such as electrostatic interaction, precipitation, and bioaccumulation. After being adsorbed by MPs, the toxicity of these environmental hazards, such as oxidative stress, cell apoptosis, and disruption of metabolic energy levels, can be magnified. However, there is a lack of comprehensive research on both the combined toxicities of MPs and environmental hazards, as well as their corresponding control strategies. Future research should prioritize understanding the interaction of MPs with other hazards in the food chain, their combined toxicity, and integrating MPs detection and degradation methods with other hazards.

Capsule Attention Network for Hyperspectral Image Classification

While many neural networks have been proposed for hyperspectral image classification, current backbones cannot achieve accurate results due to the insufficient representation by scalar features and always cause a cumbersome calculation burden. To solve the problem, we propose the capsule attention network (CAN), which combines an activity vector with an attention mechanism to improve HSI classification. In particular, we consider two attention mechanisms to improve the effectiveness of the activity vectors. First, an attention-based feature extraction (AFE) module is proposed to preprocess the spectral-spatial features of HSI data, which effectively mines useful information before the generation of the activity vectors. Second, we propose a self-weighted mechanism (SWM) to distinguish the importance of different capsule convolutions, which enhances the representation of the primary activity vectors. Experiments on four well-known HSI datasets have shown our CAN surpasses state-of-the-art (SOTA) methods on three widely used metrics with a much lower computational burden.

Site-Specific Integration by Circular Donor Improves CRISPR/Cas9-Mediated Homologous Recombination in Human Cell Lines

The technology for obtaining the high-efficiency expression of target proteins through site-specific recombination has made progress. However, using the CRISPR/Cas9 system for site-specific integration of long fragments and the expression of active proteins remains a challenge. This study optimized the linear DNA circularization system, eliminated the prokaryotic plasmid backbone on the traditional foreign gene vector, and generated a homologous arm-free circular donor template with a single guide RNA target site (sgRNA TS). This strategy significantly increased the co-transfection efficiency of the 1.6 kb template and Cas9 plasmid by 1.15-fold, and the average knock-in (KI) efficiency of the 4.7 kb long-fragment template for the two target gene sites increased by 1.3-fold. Subsequently, we used rhBCHE as a reporter gene to efficiently integrate the 5.4 kb fragment containing the gene of interest (GOI) into specific sites in the HEK293T cell line to detect the expression of the circular template at different target sites. Overall, this study further verifies that the length of the circular donor is more conducive to non-homologous integration, and more importantly, we provide a simple and optimized strategy for the construction of long-fragment site integration cell lines.

Decoupling Deep Learning for Enhanced Image Recognition Interpretability

The quest for enhancing the interpretability of neural networks has become a prominent focus in recent research endeavors. Prototype-based neural networks have emerged as a promising avenue for imbuing models with interpretability by gauging the similarity between image components and category prototypes to inform decision-making. However, these networks face challenges as they share similarity activations during both the inference and explanation processes, creating a tradeoff between accuracy and interpretability. To address this issue and ensure that a network achieves high accuracy and robust interpretability in the classification process, this article introduces a groundbreaking prototype-based neural network termed the “Decoupling Prototypical Network” (DProtoNet). This novel architecture comprises encoder, inference, and interpretation modules. In the encoder module, we introduce decoupling feature masks to facilitate the generation of feature vectors and prototypes, enhancing the generalization capabilities of the model. The inference module leverages these feature vectors and prototypes to make predictions based on similarity comparisons, thereby preserving an interpretable inference structure. Meanwhile, the interpretation module advances the field by presenting a novel approach: a “multiple dynamic masks decoder” that replaces conventional upsampling similarity activations. This decoder operates by perturbing images with mask vectors of varying sizes and learning saliency maps through consistent activation. This methodology offers a precise and innovative means of interpreting prototype-based networks. DProtoNet effectively separates the inference and explanation components within prototype-based networks. By eliminating the constraints imposed by shared similarity activations during the inference and explanation phases, our approach concurrently elevates accuracy and interpretability. Experimental evaluations on diverse public natural datasets, including CUB-200-2011, Stanford Cars, and medical datasets like RSNA and iChallenge-PM, corroborate the substantial enhancements achieved by our method compared to previous state-of-the-art approaches. Furthermore, ablation studies are conducted to provide additional evidence of the effectiveness of our proposed components.

Genetic determinants of antidepressant and antipsychotic drug response.

Today, more than 90% of inpatients hospitalized with Major Depression or Schizophrenia are treated with psychotropic drugs. Since none of the treatment options is causal, response rates are modest and the course of recovery is very heterogeneous. Genetic studies on the etiology and pathogenesis of major psychiatric disorders over the past decades have been largely unsuccessful. Likewise, genetic studies to predict response to psychopharmacological treatment have also not been particularly successful. In this project we have recruited 902 inpatients with ICD-10 diagnoses of schizophrenic ("F2 patients") or depressive disorders ("F3 patients"). The study assessed today's acute inpatient treatment regimens with up to 8 repeated measurements regarding the time course of recovery and adverse side effects. The genotyping included 100 candidate genes with genotypic patterns computed from 549 Single Nucleotide Polymorphisms (SNPs). To predict response to psychopharmacological treatment, we relied on a multidimensional approach to analyzing genetic diversity in combination with multilayer Neural Nets (NNs). Central to this new method were the "gene vectors" that (1) assessed the multidimensional genotypic patterns observed with genes; and (2) evaluated the correlations between genes. By means of these methods, we searched for combinations of multidimensional genotypic patterns that were characteristic of treatment responders while being rare among non-responders. The chosen method of approach provided a powerful technique to detail the complex structures of SNP data that are not detectable by conventional association methods. Molecular-genetic NNs enabled correct classification of 100% "non-responders", along with 94.7% correctly classified "responders" among the F2 patients, and 82.6% correctly classified "responders" among the F3 patients. The F2 and F3 classifiers were not disjoint but showed an overlap of 29.6% and 35.7% between the diagnostic groups, thus indicating that clinical diagnoses may not constitute etiologic entities. Our results suggested that patients may have an unspecific physical-genetic disposition that enables, facilitates, impedes or prevents recovery from major psychiatric disorders by setting various thresholds for exogenous triggers that initiate improvement ("recovery disposition"). Even though this disposition is not causally linked to recovery, it can nonetheless be clinically used in the sense of a "surrogate". Indeed, clinicians are also interested in reliable tools that can "do the job", despite the fact that etiology and pathogenesis of the treated disorders remain unknown.

Leveraging HLS to Design a Versatile & High-Performance Classic McEliece Accelerator

By harnessing fundamental quantum properties, a large-scale quantum computer could undermine currently deployed public-key algorithms. The post-quantum, code-based cryptosystem Classic McEliece (CM) addresses this security concern. However, its large public key size (up to 1.3MB) poses various hardware implementation challenges. In this paper, we focus on the high memory bandwidth requirements of the CM encoding function, in the context of heterogeneous CPU-FPGA devices. More concretely, we target the acceleration of public-key loading and processing from any globally-shared or accelerator-private memory system. We present a novel and constant-time accelerator eEnc that exploits the elevated parallelization potential of FPGA devices to yield high-performance results. Our accelerator implements the encoding and the random error vector generation functions, which comprise the main computational load of Encapsulation. Two accelerator design variants are introduced, providing different hardware tradeoffs. Regarding intra-accelerator data communication, and unlike other state-of-the-art (SOTA) works, we combine a streaming protocol with task-level parallelization to remove the need to store the public key in accelerator-private memories. Our proposed design shows new record execution times over its SOTA counterparts, ranging on average from 3.5 × up to 7.7 × across the five security level parameter sets. Our end-to-end implementation in a Zynq SoC shows an average speedup of 2.2 × compared to a 64-bit vectorized CM software-baseline. The elevated logic resource consumption, characteristic of HLS designs, can be readily adjusted with a performance tradeoff.

FastAd: A versatile toolkit for rapid generation of single adenoviruses or diverse adenoviral vector libraries

Adenoviruses (Ads) are potent gene delivery vectors for in vitro and in vivo applications. However, current methods for their construction are time-consuming and inefficient, limiting their rapid production and utility in generating complex genetic libraries. Here, we introduce FastAd, a rapid and easy-to-use technology for inserting recombinant “donor” DNA directly into infectious “receiver” Ads in mammalian cells by the concerted action of two efficient recombinases: Cre and Bxb1. Subsequently, the resulting mixed recombinant Ad population is subjected to negative selections by flippase (FLP) recombinase to remove viruses that missed the initial recombination. With this approach, recombinant Ad production time is reduced from two months to 10 days or less. FastAd can be applied for inserting complex genetic DNA libraries into Ad genomes, as evidenced by the generation of barcode libraries with over 3 million unique clones from a T25 flask-scale of transfection. Furthermore, we leveraged FastAd to construct an Ad library containing a comprehensive genome-wide CRISPR/Cas9 guide RNA (gRNA) library and demonstrated its effectiveness in uncovering novel virus-host interactions. In summary, FastAd enables the rapid generation of single Ad vectors or complex genetic libraries, facilitating not only novel applications of Ad vectors but also research in foundational Ad biology.

Transforming satellite imagery into vector maps using modified GANs

Vector maps find widespread utility across diverse domains due to their capacity to not only store but also represent discrete data boundaries such as building footprints, disaster impact analysis, digitization, urban planning, location points, transport links, and more. Although extensive research exists on identifying building footprints and road types from satellite imagery, the generation of vector maps from such imagery remains an area with limited exploration. Furthermore, conventional map generation techniques rely on labor-intensive manual feature extraction or rule-based approaches, which impose inherent limitations. To surmount these limitations, we propose a novel method called HPix, which utilizes modified Generative Adversarial Networks (GANs) to generate vector tile map from satellite images. HPix incorporates two hierarchical frameworks: one operating at the global level and the other at the local level, resulting in a comprehensive model. Through empirical evaluations, our proposed approach showcases its effectiveness in producing highly accurate and visually captivating vector tile maps derived from satellite images. We achieved a pixel-level accuracy of 61.04% and an SSIM score of 0.75, outperforming all other existing methods. We further extend our study’s application to include mapping of road intersections and building footprints cluster based on their area. We also show usability of our proposed architecture as a general-purpose solutions in other tasks like edges-to-photo, BW-to-color, or labels-to-street scene. GitHub:https://github.com/aditya-taparia/Satellite-Image-to-Vector-Map.

Common SYNE2 Genetic Variant Associated With Atrial Fibrillation Lowers Expression of Nesprin-2α1 With Downstream Effects on Nuclear and Electrophysiological Traits.

Atrial fibrillation GWAS (genome-wide association studies) identified significant associations for rs1152591 and linked variants in the SYNE2 gene encoding Nesprin-2, which connects the nuclear membrane with the cytoskeleton. Reporter gene vector transfection and CRISPR-Cas9 editing were used to identify the causal variant regulating the expression of SYNE2α1. After SYNE2 knockdown or SYNE2α1 overexpression in human stem cell-derived cardiomyocytes, nuclear phenotypes were assessed by imaging and atomic force microscopy. Gene expression was assessed by RNAseq and gene set enrichment analysis. Fura-2 AM staining assessed calcium transients. Optical mapping assessed action potential duration and conduction velocity. The risk allele of rs1152591 had lower promoter and enhancer activity and was significantly associated with lower expression of the short SYNE2α1 isoform in human stem cell-derived cardiomyocytes, without an effect on the expression of the full-length SYNE2 mRNA. SYNE2α1 overexpression had dominant negative effects on the nucleus with its overexpression or SYNE2 knockdown leading to increased nuclear area and decreased nuclear stiffness. Gene expression results from SYNE2α1 overexpression demonstrated both concordant and nonconcordant effects with SYNE2 knockdown. SYNE2α1 overexpression had a gain of function on electrophysiology, leading to significantly faster calcium reuptake and decreased assessed action potential duration, while SYNE2 knockdown showed both shortened assessed action potential duration and decreased conduction velocity. rs1152591 was identified as a causal atrial fibrillation variant, with the risk allele decreasing SYNE2α1 expression. Downstream effects of SYNE2α1 overexpression include changes in nuclear stiffness and electrophysiology, which may contribute to the mechanism for the risk allele's association with AF.

Regional dynamic point cloud completion network

Point cloud completion network often encodes points into a global feature vector, then predicts the complete point cloud through the vector generation process. However, this method may not accurately capture complex shapes, as global feature vectors struggle to recover their detailed structure. In this paper, we present a novel shape completion network, namely RD-Net, that innovatively focuses on the interaction of information between points to provide both local and global information for generating fine-grained complete shape. Specifically, we propose a stored iteration-based method for point cloud sampling that quickly captures representative points within the point cloud. Subsequently, in order to better predict the shape and structure of the missing part, we design an iterative edge-convolution module. It uses a CNN-like hierarchy for feature extraction and learning context information. Moreover, we design a two-stage reconstruction process for latent vector decoding. We first employ a feature-points-based multi-scale generating decoder to estimate the missing point cloud hierarchically. This is followed by a self-attention mechanism that refines the generated shape and effectively generates structural details. By combining these innovations, RD-Net achieves a 2% reduction in CD error compared to the state-of-the-art method on the ShapeNet-part dataset.

Hardware optimization for effective switching power reduction during data compression in GOLOMB rice coding.

Loss-less data compression becomes the need of the hour for effective data compression and computation in VLSI test vector generation and testing in addition to hardware AI/ML computations. Golomb code is one of the effective technique for lossless data compression and it becomes valid only when the divisor can be expressed as power of two. This work aims to increase compression ratio by further encoding the unary part of the Golomb Rice (GR) code so as to decrease the amount of bits used, it mainly focuses on optimizing the hardware for encoding side. The algorithm was developed and coded in Verilog and simulated using Modelsim. This code was then synthesised in Cadence Encounter RTL Synthesiser. The modifications carried out show around 6% to 19% reduction in bits used for a linearly distributed data set. Worst-case delays have been reduced by 3% to 8%. Area reduction varies from 22% to 36% for different methods. Simulation for Power consumption shows nearly 7% reduction in switching power. This ideally suggest the usage of Golomb Rice coding technique for test vector compression and data computation for multiple data types, which should ideally have a geometrical distribution.

The impact and application of artificial intelligence technology on mental health counseling services for college students

With the intensification of social pressure and the enhancement of mental health awareness, the mental health issues of college students have become increasingly prominent, attracting social attention. Mental health counseling services, as an important way to alleviate students’ psychological stress, are facing the dual challenges of a shortage of professionals and growing service demands. In recent years, the application of artificial intelligence (AI) technology in the field of mental health has gradually risen, and its advantages in data analysis, pattern recognition, and natural language processing provide new solutions for mental health counseling services. However, existing research still faces problems such as insufficient understanding and limited emotional interaction capabilities in practical applications. This paper delves into the application of AI technology in mental health counseling services for college students and innovates and improves upon the deficiencies in existing research. The study focuses on two main areas: First, word vector generation technologies based on statistics and language models are used according to different application scenarios, and their effectiveness in the analysis of mental health counseling texts is compared. Second, an improved Seq2Seq model is proposed to enhance the emotional understanding and interaction capabilities of emotional dialogue generation algorithms in mental health counseling. This study not only provides technological support for college mental health counseling services but also opens new research directions and perspectives for the application of AI in the field of mental health.

A p21 reporter iPSC line for evaluating CRISPR-Cas9 and vector-induced stress responses.

CRISPR-Cas9 editing triggers activation of the TP53-p21 pathway, but the impacts of different editing components and delivery methods have not been fully explored. In this study, we introduce a p21-mNeonGreen reporter iPSC line to monitor TP53-p21 pathway activation. This reporter enables dynamic tracking of p21 expression via flow cytometry, revealing a strong correlation between p21 expression and indel frequencies, and highlighting its utility in guide RNA screening. Our findings show that p21 activation is significantly more pronounced with double-stranded oligodeoxynucleotides (ODNs) or adeno-associated viral vectors (AAVs) compared to their single-stranded counterparts. Lentiviral vectors (LVs) and integrase-defective lentiviral vectors induce notably lower p21 expression than AAVs, suggesting their suitability for gene therapy in sensitive cells such as hematopoietic stem cells or immune cells. Additionally, specific viral promoters like SFFV significantly amplify p21 activation, emphasizing the critical role of promoter selection in vector development. Thus, the p21-mNeonGreen reporter iPSC line is a valuable tool for assessing the potential adverse effects of gene editing methodologies and vectors. Highlights Established a p21-mNeonGreen reporter iPSC line to track activation of the TP53-p21 pathway. Found a direct correlation between p21-mNeonGreen expression and indel frequencies, aiding in gRNA screening. Showed that LVs are preferable over AAVs for certain cells due to lower p21 activation, with viral promoter choice impacting p21 response.

Viral overexpression of human alpha-synuclein in mouse substantia nigra dopamine neurons results in hyperdopaminergia but no neurodegeneration

Loss of select neuronal populations such as midbrain dopamine (DA) neurons is a pathological hallmark of Parkinson's disease (PD). The small neuronal protein α-synuclein has been related both genetically and neuropathologically to PD, yet how and if it contributes to selective vulnerability remains elusive. Here, we describe the generation of a novel adeno-associated viral vector (AAV) for Cre-dependent overexpression of wild-type human α-synuclein. Our strategy allows us to restrict α-synuclein to select neuronal populations and hence investigate the cell-autonomous effects of elevated α-synuclein in genetically-defined cell types. Since DA neurons in the substantia nigra pars compacta (SNc) are particularly vulnerable in PD, we investigated in more detail the effects of increased α-synuclein in these cells. AAV-mediated overexpression of wildtype human α-synuclein in SNc DA neurons increased the levels of α-synuclein within these cells and augmented phosphorylation of α-synuclein at serine-129, which is considered a pathological feature of PD and other synucleinopathies. However, despite abundant α-synuclein overexpression and hyperphosphorylation we did not observe any dopaminergic neurodegeneration up to 90 days post virus infusion. In contrast, we noticed that overexpression of α-synuclein resulted in increased locomotor activity and elevated striatal DA levels suggesting that α-synuclein enhanced dopaminergic activity. We therefore conclude that cell-autonomous effects of elevated α-synuclein are not sufficient to trigger acute dopaminergic neurodegeneration.

Adenovirus-Mediated Expression of Dengue Virus 2 Envelope Ferritin Nanoparticles Induced Virus-Specific Immune Responses in BALB/c Mice

This study provides a preliminary background for the development of a viral vector vaccine for the dengue virus using genetic material encoded by dengue envelope ferritin nanoparticles. Adenoviruses were generated for the recombinant envelope of dengue virus 2 (DENV2) and the envelope human ferritin heavy chain using a two-vector adenovirus system. The primary immunostimulatory activity of the two viruses was analyzed in mice to determine the effect of envelope ferritin nanoparticles. Transfection of a shuttle vector delivered the target gene and packaging vector carrying the packaging signal, and recombinant adenoviruses (rAds) were generated and purified using an ultracentrifugation method. Transduction efficiencies of the generated adenoviruses were confirmed in A549 cells. Purified adenoviruses (8 × 106 PFU/mL) were immunized intramuscularly into 6 weeks old BALB/c mice. Subsequently, the DENV2-specific IgG titer was evaluated 1 and 4 weeks after immunization. Envelope ferritin-immunized mice showed a significant IgG response compared to envelope-only immunized mice at 1 and 4 weeks after immunization, revealing the persistence of the dengue virus-specific IgG response. This method demonstrated the capability of the viral vector vaccine to be used as a carrier for ferritin nanoparticles, instead of direct immunization with ferritin nanoparticles.

A GMO Experiment on Two-Thirds of the World’s Population: Reaction to Ulrich’s Commentary on Lee and Broudy (2024)

While Professor A. Ulrich (2024) raised justifiable questions about the interpretation of microscopic images from incubated COVID-19 injectables from Pfizer and Moderna, her message treats the world-wide genetic modification experiment with COVID-19 injectables on humans — an experiment on us without our consent — as normal. I will not discuss the physico-chemistry of the 10 trillion lipid nanoparticles she claims are in every dose of the injectables. She acknowledges that “many of the lipid constituents . . . tend to induce inflammation” (p. 1244.3) but says they account for all the self-assembling structures documented in what she describes as the “reliable” and “fully consistent” findings (Ulrich, p. 1244.7) of Lee and Broudy (2024). Though I believe the self-assembling structures also merit in-depth study, the technology underlying them, must be contained in the concoctions injected into the arms of two-thirds of the world’s population without proper preclinical testing and without the informed consent required by the Nuremberg Code. My focus is on that violation of medical ethics turning humans into genetically modified organisms. Here I illustrate in her words that such a genetic experiment on humans is underway. Instead of refuting any of the evidence in Lee and Broudy (2024) —that whatever the injectables may actually contain they are harming the human population of the world — she actually admits in closing that those “risks will be exacerbated with the next generation of self-amplifying or self-replicating RNA vectors for vaccines” (1244.8).