Advancing genome editing with artificial intelligence: opportunities, challenges, and future directions

Harnessing bacterial immunity: CRISPR-Cas system as a versatile tool in combating pathogens and revolutionizing medicine

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) technology has emerged as an adaptable instrument for several uses. The CRISPR-Cas system employs Cas proteins and programmable RNA molecules to guide the recognition and cleavage of specific DNA regions, permitting accurate genome editing. It is derived from the bacterial immune system and allows for accurate and efficient modification of DNA sequences. This technique provides unparalleled gene editing, control, and precise alteration opportunities. This review aims to offer a comprehensive update of the core concepts of the CRISPR-Cas system and recent progress, while also providing an overview of the significant applications in diverse fields such as microbiology and medicine. The CRISPR-Cas9 gene editing technique has facilitated substantial advancements in comprehending gene function, simulating diseases, and creating innovative therapeutics. CRISPR-based therapeutics present a hopeful prospect for addressing intricate ailments, including genetic disorders, malignancies, and infectious diseases, as they serve as viable substitutes for conventional pharmaceuticals. In microbiology, this method serves as a diagnostic and therapeutic tool that proves highly efficient in eliminating bacteria that have developed resistance to various antibiotics. Despite its significant potential, CRISPR encounters ethical, safety, and regulatory obstacles that necessitate meticulous deliberation. Concerns regarding off-target effects, poor delivery to target tissues, and unwanted side effects emphasize the necessity to thoroughly examine the technology. It is necessary to balance the advantages and difficulties CRISPR presents. Consequently, more rigorous preclinical and clinical experiments are essential before using it in humans.

Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics

Artificial intelligence (AI) is rapidly transforming numerous aspects of daily life, including clinical practice and biomedical research. In light of this rapid transformation, and in the context of medical genetics, we assembled a group of leaders in the field to respond to the question about how AI is affecting, and especially how AI will affect, medical genetics. The authors who contributed to this collection of essays intentionally represent different areas of expertise, career stages, and geographies, and include diverse types of clinicians, computer scientists, and researchers. The individual pieces cover a wide range of areas related to medical genetics; we expect that these pieces may provide helpful windows into the ways in which AI is being actively studied, used, and considered in medical genetics.

Advancements and implications of artificial intelligence for early detection, diagnosis and tailored treatment of cancer

The complexity and heterogeneity of cancer makes early detection and effective treatment crucial to enhance patient survival and quality of life. The intrinsic creative ability of artificial intelligence (AI) offers improvements in patient screening, diagnosis, and individualized care. Advanced technologies, like computer vision, machine learning, deep learning, and natural language processing, can analyze large datasets and identify patterns that permit early cancer detection, diagnosis, management and incorporation of conclusive treatment plans, ensuring improved quality of life for patients by personalizing care and minimizing unnecessary interventions. Genomics, transcriptomics and proteomics data can be combined with AI algorithms to unveil an extensive overview of cancer biology, assisting in its detailed understanding and will help in identifying new drug targets and developing effective therapies. This can also help to identify personalized molecular signatures which can facilitate tailored interventions addressing the unique aspects of each patient. AI-driven transcriptomics, proteomics, and genomes represents a revolutionary strategy to improve patient outcome by offering precise diagnosis and tailored therapy. The inclusion of AI in oncology may boost efficiency, reduce errors, and save costs, but it cannot take the role of medical professionals. While clinicians and doctors have the final say in all matters, it might serve as their faithful assistant.

Current perspectives in drug targeting intrinsically disordered proteins and biomolecular condensates

Intrinsically disordered proteins (IDPs) and biomolecular condensates are critical for cellular processes and physiological functions. Abnormal biomolecular condensates can cause diseases such as cancer and neurodegenerative disorders. IDPs, including intrinsically disordered regions (IDRs), were previously considered undruggable due to their lack of stable binding pockets. However, recent evidence indicates that targeting them can influence cellular processes. This review explores current strategies to target IDPs and biomolecular condensates, potential improvements, and the challenges and opportunities in this evolving field.

A comprehensive review of neurotransmitter modulation via artificial intelligence: A new frontier in personalized neurobiochemistry

The deployment of artificial intelligence (AI) is revolutionizing neuropharmacology and drug development, allowing the modulation of neurotransmitter systems at the personal level. This review focuses on the neuropharmacology and regulation of neurotransmitters using predictive modeling, closed-loop neuromodulation, and precision drug design. The fusion of AI with applications such as machine learning, deep-learning, and even computational modeling allows for the real-time tracking and enhancement of biological processes within the body. An exemplary application of AI is the use of DeepMind's AlphaFold to design new GABA reuptake inhibitors for epilepsy and anxiety. Likewise, Benevolent AI and IBM Watson have fast-tracked drug repositioning for neurodegenerative conditions. Furthermore, we identified new serotonin reuptake inhibitors for depression through AI screening. In addition, the application of Deep Brain Stimulation (DBS) settings using AI for patients with Parkinson's disease and for patients with major depressive disorder (MDD) using reinforcement learning-based transcranial magnetic stimulation (TMS) leads to better treatment. This review highlights other challenges including algorithm bias, ethical concerns, and limited clinical validation. Their proposal to incorporate AI with optogenetics, CRISPR, neuroprosthesis, and other advanced technologies fosters further exploration and refinement of precision neurotherapeutic approaches. By bridging computational neuroscience with clinical applications, AI has the potential to revolutionize neuropharmacology and improve patient-specific treatment strategies. We addressed critical challenges, such as algorithmic bias and ethical concerns, by proposing bias auditing, diverse datasets, explainable AI, and regulatory frameworks as practical solutions to ensure equitable and transparent AI applications in neurotransmitter modulation.

Advancing fish disease research through CRISPR-Cas genome editing: Recent developments and future perspectives

CRISPR-Cas genome editing technology has transformed genetic research, by enabling unprecedented precision in modifying DNA sequences across various organisms, including fish. This review explores the significant advancements and potential uses of CRISPR-Cas technology in the study and management of fish diseases, which pose serious challenges to aquaculture and wild fish populations. Fish diseases cause significant economic losses and environmental impacts, therefore effective disease control a top priority. The review highlights the pivotal role of CRISPR-Cas in identifying disease-associated genes, which is critical to comprehending the genetic causes of disease susceptibility and resistance. Some studies have reported key genetic factors that influence disease outcomes, using targeted gene knockouts and modifications to pave the way for the development of disease-resistant fish strains. The creation of such genetically engineered fish holds great promise for enhancing aquaculture sustainability by reducing the reliance on antibiotics and other conventional disease control measures. In addition, CRISPR-Cas has facilitated in-depth studies of pathogen-host interactions, offering new insights into the mechanisms by which pathogens infect and proliferate within their hosts. By manipulating both host and pathogen genes, this technology provides a powerful tool for uncovering the molecular underpinnings of these interactions, leading to the development of more effective treatment strategies. While CRISPR-Cas has shown great promise in fish research, its application remains limited to a few species, primarily model organisms and some freshwater fish. In addition, challenges such as off-target effects, ecological risks, and ethical concerns regarding the release of genetically modified organisms into the environment must be carefully addressed. This review also discusses these challenges and emphasizes the need for robust regulatory frameworks and ongoing research to mitigate risks. Looking forward, the integration of CRISPR-Cas with other emerging technologies, such as multi-omics approaches, promises to further advance our understanding and management of fish diseases. This review concludes by envisioning the future directions of CRISPR-Cas applications in fish health, underscoring its potential to its growing in the field.

Transforming Pharmacogenomics and CRISPR Gene Editing with the Power of Artificial Intelligence for Precision Medicine

Background: Advancements in pharmacogenomics, artificial intelligence (AI), and CRISPR gene-editing technology are revolutionizing precision medicine by enabling highly individualized therapeutic strategies. Artificial intelligence-driven computational techniques improve biomarker discovery and drug optimization while pharmacogenomics helps to identify genetic polymorphisms affecting medicine metabolism, efficacy, and toxicity. Genetically editing based on CRISPR presents a precise method for changing gene expression and repairing damaging mutations. This review explores the convergence of these three fields to enhance improved precision medicine. Method: A methodical study of the current literature was performed on the effects of pharmacogenomics on drug response variability, artificial intelligence, and CRISPR in predictive modeling and gene-editing applications. Results: Driven by artificial intelligence, pharmacogenomics allows clinicians to classify patients and select the appropriate medications depending on their DNA profiles. This reduces the side effect risk and increases the therapeutic efficacy. Precision genetic modifications made feasible by CRISPR technology improve therapy outcomes in oncology, metabolic illnesses, neurological diseases, and other fields. The integration of artificial intelligence streamlines genome-editing applications, lowers off-target effects, and increases CRISPR specificity. Notwithstanding these advances, issues including computational biases, moral dilemmas, and legal constraints still arise. Conclusions: The synergy of artificial intelligence, pharmacogenomics, and CRISPR alters precision medicine by letting customized therapeutic interventions. Clinically translating, however, hinges on resolving data privacy concerns, assuring equitable access, and strengthening legal systems. Future research should focus on refining CRISPR gene-editing technologies, enhancing AI-driven pharmacogenomics, and developing moral guidelines for applying these tools in individualized medicine going forward.

A Systematic Integration of Artificial Intelligence Models in Appendicitis Management: A Comprehensive Review

Artificial intelligence (AI) and machine learning (ML) are transforming the management of acute appendicitis by enhancing diagnostic accuracy, optimizing treatment strategies, and improving patient outcomes. This study reviews AI applications across all stages of appendicitis care, from triage to postoperative management, using sources from PubMed/MEDLINE, IEEE Xplore, arXiv, Web of Science, and Scopus, covering publications up to 14 February 2025. AI models have demonstrated potential in triage, enabling rapid differentiation of appendicitis from other causes of abdominal pain. In diagnostics, ML algorithms incorporating clinical, laboratory, imaging, and demographic data have improved accuracy and reduced uncertainty. These tools also predict disease severity, aiding decisions between conservative management and surgery. Radiomics further enhances diagnostic precision by analyzing imaging data. Intraoperatively, AI applications are emerging to support real-time decision-making, assess procedural steps, and improve surgical training. Postoperatively, ML models predict complications such as abscess formation and sepsis, facilitating early interventions and personalized recovery plans. This is the first comprehensive review to examine AI's role across the entire appendicitis treatment process, including triage, diagnosis, severity prediction, intraoperative assistance, and postoperative prognosis. Despite its potential, challenges remain regarding data quality, model interpretability, ethical considerations, and clinical integration. Future efforts should focus on developing end-to-end AI-assisted workflows that enhance diagnosis, treatment, and patient outcomes while ensuring equitable access and clinician oversight.

Learn from artificial intelligence: the pursuit of objectivity

Humans continuously face threats from emerging novel pathogens and antimicrobial resistant bacteria or fungi, which requires urgently and efficient solutions. Alternatively, microbes also produce compounds or chemicals highly valuable to humans of which require continuous refinement and improvement of yields. Artificial intelligence (AI) is a promising tool to search for solutions combatting against diseases and facilitating productivity underpinned by robust research providing accurate information. However, the extent of AI credibility is yet to be fully understood. In terms of human bias, AI could arguably act as a means of ensuring scientific objectivity to increase accuracy and precision, however, whether this is possible or not has not been fully discussed. Human bias and error can be introduced at any step of the research process, including conducting experiments and data processing, through to influencing clinical applications. Despite AI's contribution to advancing knowledge, the question remains, is AI able to achieve objectivity in microbiological research? Here, the benefits, drawbacks, and responsibilities of AI utilization in microbiological research and clinical applications were discussed.

Unveiling the potential of gene editing techniques in revolutionizing Cancer treatment: A comprehensive overview

Gene editing techniques have emerged as powerful tools in biomedical research, offering precise manipulation of genetic material with the potential to revolutionize cancer treatment strategies. This review provides a comprehensive overview of the current landscape of gene editing technologies, including CRISPR-Cas systems, base editing, prime editing, and synthetic gene circuits, highlighting their applications and potential in cancer therapy. It discusses the mechanisms, advantages, and limitations of each gene editing approach, emphasizing their transformative impact on targeting oncogenes, tumor suppressor genes, and drug resistance mechanisms in various cancer types. The review delves into population-level interventions and precision prevention strategies enabled by gene editing technologies, including gene drives, synthetic gene circuits, and precision prevention tools, for controlling cancer-causing genes, targeting pre-cancerous lesions, and implementing personalized preventive measures. Ethical considerations, regulatory challenges, and future directions in gene editing research for cancer treatment are also addressed. This review highlights how gene editing could revolutionize precision medicine by enhancing patient care and advancing cancer treatments with targeted, personalized methods. For these benefits to be fully realized, collaboration among researchers, doctors, regulators, and patient advocates is crucial in fighting cancer and meeting clinical needs.

Advances in DNA/RNA Sequencing and Their Applications in Acute Myeloid Leukemia (AML)

Acute myeloid leukemia (AML) is an aggressive malignancy that poses significant challenges due to high rates of relapse and resistance to treatment, particularly in older populations. While therapeutic advances have been made, survival outcomes remain suboptimal. The evolution of DNA and RNA sequencing technologies, including whole-genome sequencing (WGS), whole-exome sequencing (WES), and RNA sequencing (RNA-Seq), has significantly enhanced our understanding of AML at the molecular level. These technologies have led to the discovery of driver mutations and transcriptomic alterations critical for improving diagnosis, prognosis, and personalized therapy development. Furthermore, single-cell RNA sequencing (scRNA-Seq) has uncovered rare subpopulations of leukemia stem cells (LSCs) contributing to disease progression and relapse. However, widespread clinical integration of these tools remains limited by costs, data complexity, and ethical challenges. This review explores recent advancements in DNA/RNA sequencing in AML and highlights both the potential and limitations of these techniques in clinical practice.

CRISPR: New promising biotechnological tool in wastewater treatment

The increasing demand for water resources with increase in population has sparked interest in reusing produced water, especially in water-scarce regions. The clustered regularly interspaced short palindromic repeats (CRISPR) technology is an emerging genome editing tool that has the potential to trigger significant impact with broad application scope in wastewater treatment. We provide a comprehensive overview of the scope of CRISPR-Cas based tool for treating wastewater that may bring new scope in wastewater management in future in controlling harmful contaminants and pathogens. As an advanced versatile genome engineering tool, focusing on particular genes and enzymes that are accountable for pathogen identification, regulation of antibiotic/antimicrobial resistance, and enhancing processes for wastewater bioremediation constitute the primary focal points of research associated with this technology. The technology is highly recommended for targeted mutations to incorporate desirable microalgal characteristics and the development of strains capable of withstanding various wastewater stresses. However, concerns about gene leakage from strains with modified genome and off target mutations should be considered during field application. A comprehensive interdisciplinary approach involving various fields and an intense research focus concerning delivery systems, target genes, detection, environmental conditions, and monitoring at both lab and ground level should be considered to ensure its successful application in sustainable and safe wastewater treatment.

Integrative Transcriptomic and Target Metabolite Analysis as a New Tool for Designing Metabolic Engineering in Yeast

Precision fermentation processes, especially when using edited microorganisms, demand accuracy in the bioengineering process to maximize the desired outcome and to avoid adverse effects. The selection of target sites to edit using CRISPR/Cas9 can be complex, resulting in non-controlled consequences. Therefore, the use of multi-omics strategies can help in the design, selection and efficiency of genetic editing. In this study, we present a multi-omics approach based on targeted metabolite analysis and transcriptomics for the designing of CRISPR/Cas9 in baker's yeast as a more efficient strategy to select editing regions. Multi-omics shows potential to reveal new metabolic bottlenecks and to elucidate new metabolic fluxes, which could be a key factor in minimizing the metabolic burden in edited microorganisms. In our model, we focus our attention on the isoprenoid synthesis due to their industrial interest. Targeted metabolite detection combined with a transcriptomic analysis revealed hydroxymethylglutaryl-CoA reductases (HMGs) as the best target gene to induce an increase in isoprenoid synthesis. Thus, an extra copy of HMG1 was introduced using, for the first time, the synthetic UADH1 promoter. The multi-omics analysis of the recombinant strain results in an accurate assessment of yeast behavior during the most important growth phases, highlighting the metabolic burden, Crabtree effect or the diauxic shift during culture.

Current Knowledge on CRISPR Strategies Against Antimicrobial-Resistant Bacteria

CRISPR/Cas systems have emerged as valuable tools to approach the problem of antimicrobial resistance by either sensitizing or lysing resistant bacteria or by aiding in antibiotic development, with successful applications across diverse organisms, including bacteria and fungi. CRISPR/Cas systems can target plasmids or the bacterial chromosome of AMR-bacteria, and it is especially necessary to have an efficient entry into the target cells, which can be achieved through nanoparticles or bacteriophages. Regarding antibiotic development and production, though the use of CRISPR/Cas in this field is still modest, there is an untapped reservoir of bacterial and fungal natural products, with over 95% yet to be characterized. In Streptomyces, a key antibiotic-producing bacterial genus, CRISPR/Cas has been successfully used to activate silent biosynthetic gene clusters, leading to the discovery of new antibiotics. CRISPR/Cas is also applicable to non-model bacteria and different species of fungi, making it a versatile tool for natural products discovery. Moreover, CRISPR/Cas-based studies offer insights into metabolic regulation and biosynthetic pathways in both bacteria and fungi, highlighting its utility in understanding genetic regulation and improving industrial strains. In this work, we review ongoing innovations on ways to treat antimicrobial resistances and on antibiotic discovery using CRISPR/Cas platforms, highlighting the role of bacteria and fungi in these processes.

Opportunities, challenges and future perspectives of using bioinformatics and artificial intelligence techniques on tropical disease identification using omics data

Tropical diseases can often be caused by viruses, bacteria, parasites, and fungi. They can be spread over vectors. Analysis of multiple omics data types can be utilized in providing comprehensive insights into biological system functions and disease progression. To this end, bioinformatics tools and diverse AI techniques are pivotal in identifying and understanding tropical diseases through the analysis of omics data. In this article, we provide a thorough review of opportunities, challenges, and future directions of utilizing Bioinformatics tools and AI-assisted models on tropical disease identification using various omics data types. We conducted the review from 2015 to 2024 considering reliable databases of peer-reviewed journals and conference articles. Several keywords were taken for the article searching and around 40 articles were reviewed. According to the review, we observed that utilization of omics data with Bioinformatics tools like BLAST, and Clustal Omega can make significant outcomes in tropical disease identification. Further, the integration of multiple omics data improves biomarker identification, and disease predictions including disease outbreak predictions. Moreover, AI-assisted models can improve the precision, cost-effectiveness, and efficiency of CRISPR-based gene editing, optimizing gRNA design, and supporting advanced genetic correction. Several AI-assisted models including XAI can be used to identify diseases and repurpose therapeutic targets and biomarkers efficiently. Furthermore, recent advancements including Transformer-based models such as BERT and GPT-4, have been mainly applied for sequence analysis and functional genomics. Finally, the most recent GeneViT model, utilizing Vision Transformers, and other AI techniques like Generative Adversarial Networks, Federated Learning, Transfer Learning, Reinforcement Learning, Automated ML and Attention Mechanism have shown significant performance in disease classification using omics data.

Reporter Alleles in hiPSCs: Visual Cues on Development and Disease

Reporter alleles are essential for advancing research with human induced pluripotent stem cells (hiPSCs), notably in developmental biology and disease modeling. This study investigates the state-of-the-art gene-editing techniques tailored for generating reporter alleles in hiPSCs, emphasizing their effectiveness in investigating cellular dynamics and disease mechanisms. Various methodologies, including the application of CRISPR/Cas9 technology, are discussed for accurately integrating reporter genes into the specific genomic loci. The synthesis of findings from the studies utilizing these reporter alleles reveals insights into developmental processes, genetic disorder modeling, and therapeutic screening, consolidating the existing knowledge. These hiPSC-derived models demonstrate remarkable versatility in replicating human diseases and evaluating drug efficacy, thereby accelerating translational research. Furthermore, this review addresses challenges and future directions in refining the reporter allele design and application to bolster their reliability and relevance in biomedical research. Overall, this investigation offers a comprehensive perspective on the methodologies, applications, and implications of reporter alleles in hiPSC-based studies, underscoring their essential role in advancing both fundamental scientific understanding and clinical practice.

Mpox in sports: A comprehensive framework for anticipatory planning and risk mitigation in football based on lessons from COVID-19

The World Health Organization's declaration of mpox (formerly known as monkeypox) as a public health emergency of international concern (PHEIC) in July 2022, followed by its resurgence in 2024, has sparked concerns about its potential impact on sports, especially contact sports such as football. Although mpox is not a pandemic (as of late September 2024), the coronavirus disease 2019 (COVID-19) experience offers valuable lessons for proactive planning in sports. Our conceptual framework has been designed to draw insightful lessons from the COVID-19 pandemic to assist sports organizations in planning for and preventing similar situations. We aimed to draw lessons from COVID-19 to help sports organizations enhance practical preparedness through effective planning and mitigation strategies. Accordingly, we sought to assess the potential impact of mpox on sports, with a focus on football (soccer), and to develop strategies for prevention, management, and preparedness based on epidemiological insights and lessons from COVID-19 pandemic experience. Here we review mpox's pathophysiology and possibility of transmission in sports settings and discuss tailored strategies, including risk assessments, testing protocols, hygiene measures, and return-to-play policies. This review highlights key differences between mpox and COVID-19 in transmission, incubation, and management, emphasizing the need for customized prevention and control measures in sports. We propose innovative risk assessment methods using global positioning system tracking and machine learning for contact analysis, alongside tailored testing and hygiene protocols. We emphasize the importance of proactive planning, noting improved preparedness in the sports community compared to the early days of COVID-19. In conclusion, our proposed framework provides sports organizations with practical tools to manage potential risks associated with mpox, ensuring the continuity of activities while prioritizing public health.

Leveraging State-of-the-Art AI Algorithms in Personalized Oncology: From Transcriptomics to Treatment

BACKGROUND

Continuous breakthroughs in computational algorithms have positioned AI-based models as some of the most sophisticated technologies in the healthcare system. AI shows dynamic contributions in advancing various medical fields involving data interpretation and monitoring, imaging screening and diagnosis, and treatment response and survival prediction. Despite advances in clinical oncology, more effort must be employed to tailor therapeutic plans based on each patient's unique transcriptomic profile within the precision/personalized oncology frame. Furthermore, the standard analysis method is not compatible with the comprehensive deciphering of significant data streams, thus precluding the prediction of accurate treatment options.

METHODOLOGY

We proposed a novel approach that includes obtaining different tumour tissues and preparing RNA samples for comprehensive transcriptomic interpretation using specifically trained, programmed, and optimized AI-based models for extracting large data volumes, refining, and analyzing them. Next, the transcriptomic results will be scanned against an expansive drug library to predict the response of each target to the tested drugs. The obtained target-drug combination/s will be then validated using in vitro and in vivo experimental models. Finally, the best treatment combination option/s will be introduced to the patient. We also provided a comprehensive review discussing AI models' recent innovations and implementations to aid in molecular diagnosis and treatment planning.

RESULTS

The expected transcriptomic analysis generated by the AI-based algorithms will provide an inclusive genomic profile for each patient, containing statistical and bioinformatics analyses, identification of the dysregulated pathways, detection of the targeted genes, and recognition of molecular biomarkers. Subjecting these results to the prediction and pairing AI-based processes will result in statistical graphs presenting each target's likely response rate to various treatment options. Different in vitro and in vivo investigations will further validate the selection of the target drug/s pairs.

CONCLUSIONS

Leveraging AI models will provide more rigorous manipulation of large-scale datasets on specific cancer care paths. Such a strategy would shape treatment according to each patient's demand, thus fortifying the avenue of personalized/precision medicine. Undoubtedly, this will assist in improving the oncology domain and alleviate the burden of clinicians in the coming decade.

Relieving metabolic burden to improve robustness and bioproduction by industrial microorganisms

Metabolic burden is defined by the influence of genetic manipulation and environmental perturbations on the distribution of cellular resources. The rewiring of microbial metabolism for bio-based chemical production often leads to a metabolic burden, followed by adverse physiological effects, such as impaired cell growth and low product yields. Alleviating the burden imposed by undesirable metabolic changes has become an increasingly attractive approach for constructing robust microbial cell factories. In this review, we provide a brief overview of metabolic burden engineering, focusing specifically on recent developments and strategies for diminishing the burden while improving robustness and yield. A variety of examples are presented to showcase the promise of metabolic burden engineering in facilitating the design and construction of robust microbial cell factories. Finally, challenges and limitations encountered in metabolic burden engineering are discussed.

Cardiac Xenotransplantation: A Narrative Review

Cardiac xenotransplantation (cXT) has emerged as a solution to heart donor scarcity, prompting an exploration of its scientific, ethical, and regulatory facets. The review begins with genetic modifications enhancing pig hearts for human transplantation, navigating through immunological challenges, rejection mechanisms, and immune responses. Key areas include preclinical milestones, complement cascade roles, and genetic engineering to address hyperacute rejection. Physiological counterbalance systems, like human thrombomodulin and endothelial protein C receptor upregulation in porcine xenografts, highlight efforts for graft survival enhancement. Evaluating pig and baboon donors and challenges with non-human primates illuminates complexities in donor species selection. Ethical considerations, encompassing animal rights, welfare, and zoonotic disease risks, are critically examined in the cXT context. The review delves into immune control mechanisms with aggressive immunosuppression and clustered regularly interspaced palindromic repeats associated protein 9 (CRISPR/Cas9) technology, elucidating hyperacute rejection, complement activation, and antibody-mediated rejection intricacies. CRISPR/Cas9's role in creating pig endothelial cells expressing human inhibitor molecules is explored for rejection mitigation. Ethical and regulatory aspects emphasize the role of committees and international guidelines. A forward-looking perspective envisions precision medical genetics, artificial intelligence, and individualized heart cultivation within pigs as transformative elements in cXT's future is also explored. This comprehensive analysis offers insights for researchers, clinicians, and policymakers, addressing the current state, and future prospects of cXT.

Exploring opportunities of Artificial Intelligence in aquaculture to meet increasing food demand

The increasing global population drives a rising demand for food, particularly fish as a preferred protein source, straining capture fisheries. Overfishing has depleted wild stocks, emphasizing the need for advanced aquaculture technologies. Unlike agriculture, aquaculture has not seen substantial technological advancements. Artificial Intelligence (AI) tools like Internet of Things (IoT), machine learning, cameras, and algorithms offer solutions to reduce human intervention, enhance productivity, and monitor fish health, feed optimization, and water resource management. However, challenges such as data collection, standardization, model accuracy, interpretability, and integration with existing aquaculture systems persist. This review explores the adoption of AI techniques and tools to advance the aquaculture industry and bridge the gap between food supply and demand.

Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering

Genome editing is a crucial technology for obtaining desired phenotypes in a variety of species, ranging from microbes to plants, animals, and humans. With the advent of CRISPR-Cas technology, it has become possible to edit the intended sequence by modifying the target recognition sequence in guide RNA (gRNA). By expressing multiple gRNAs simultaneously, it is possible to edit multiple targets at the same time, allowing for the simultaneous introduction of various functions into the cell. This can significantly reduce the time and cost of obtaining engineered microbial strains for specific traits. In this review, we investigate the resolution of multiplex genome editing and its application in engineering microorganisms, including bacteria and yeast. Furthermore, we examine how recent advancements in artificial intelligence technology could assist in microbial genome editing and engineering. Based on these insights, we present our perspectives on the future evolution and potential impact of multiplex genome editing technologies in the agriculture and food industry.

Recent Therapeutic Gene Editing Applications to Genetic Disorders

Recent years have witnessed unprecedented progress in therapeutic gene editing, revolutionizing the approach to treating genetic disorders. In this comprehensive review, we discuss the progression of milestones leading to the emergence of the clustered regularly interspaced short palindromic repeats (CRISPR)-based technology as a powerful tool for precise and targeted modifications of the human genome. CRISPR-Cas9 nuclease, base editing, and prime editing have taken center stage, demonstrating remarkable precision and efficacy in targeted ex vivo and in vivo genomic modifications. Enhanced delivery systems, including viral vectors and nanoparticles, have further improved the efficiency and safety of therapeutic gene editing, advancing their clinical translatability. The exploration of CRISPR-Cas systems beyond the commonly used Cas9, such as the development of Cas12 and Cas13 variants, has expanded the repertoire of gene editing tools, enabling more intricate modifications and therapeutic interventions. Outstandingly, prime editing represents a significant leap forward, given its unparalleled versatility and minimization of off-target effects. These innovations have paved the way for therapeutic gene editing in a multitude of previously incurable genetic disorders, ranging from monogenic diseases to complex polygenic conditions. This review highlights the latest innovative studies in the field, emphasizing breakthrough technologies in preclinical and clinical trials, and their applications in the realm of precision medicine. However, challenges such as off-target effects and ethical considerations remain, necessitating continued research to refine safety profiles and ethical frameworks.

Research Hotspots and Trends of NK Cell Immunotherapy for Acute Myeloid Leukemia: A Bibliometric Analysis From 2000 to 2023

BACKGROUND

Natural killer (NK) cell immunotherapy has shown promising therapeutic potential for acute myeloid leukemia (AML), especially with advancements in chimeric antigen receptor-engineered NK cells (CAR-NK) and artificial intelligence (AI). Despite these developments, the field lacks comprehensive bibliometric analyses to identify research hotspots and trends, which could guide future precision treatments.

METHODS

A bibliometric analysis of NK cell immunotherapy for AML was conducted using literature from 2000 to 2023 retrieved from the Web of Science Core Collection database. Data visualization tools like CiteSpace, VOSviewer, and RStudio were employed to analyze publication trends, country contributions, institutional collaborations, influential authors, and research themes.

RESULTS

The analysis identified 1513 studies, with the United States and China leading global contributions. Notable institutions include the University of Minnesota and MD Anderson Cancer Center. Hot topics include allogeneic NK therapy, CAR-NK cell therapy, and memory-like NK cells. Emerging trends highlight the integration of intelligent NK cells and combinatory therapies, offering promising avenues for AML treatment. Despite progress, challenges such as NK cell expansion, activation, and resistance mechanisms remain critical areas for research.

CONCLUSION

This study provides a comprehensive overview of the research landscape, highlighting the transformative potential of NK cell immunotherapy in AML. It underscores the need for international collaboration and continued innovation to overcome existing challenges and advance precision therapies.