Risks and benefits of human germline genome editing: An ethical analysis

Non-Obstructive Azoospermia and Intracytoplasmic Sperm Injection: Unveiling the Chances of Success and Possible Consequences for Offspring

Non-obstructive azoospermia (NOA) is found in up to 15% of infertile men. While several causes for NOA have been identified, the exact etiology remains unknown in many patients. Advances in assisted reproductive technology, including intracytoplasmic sperm injection (ICSI) and testicular sperm retrieval, have provided hope for these patients. This review summarizes the chances of success with ICSI for NOA patients and examines preoperative factors and laboratory techniques associated with positive outcomes. Furthermore, we reviewed possible consequences for offspring by the use of ICSI with testicular sperm retrieved from NOA patients and the interventions that could potentially mitigate risks. Testicular sperm retrieved from NOA patients may exhibit increased chromosomal abnormalities, and although lower fertilization and pregnancy rates are reported in NOA patients compared to other forms of infertility, the available evidence does not suggest a significant increase in miscarriage rate, congenital malformation, or developmental delay in their offspring compared to the offspring of patients with less severe forms of infertility or the offspring of fertile men. However, due to limited data, NOA patients should receive specialized reproductive care and personalized management. Counseling of NOA patients is essential before initiating any fertility enhancement treatment not only to mitigate health risks associated with NOA but also to enhance the chances of successful outcomes and minimize possible risks to the offspring.

Advances and challenges in gene therapy strategies for pediatric cancer: a comprehensive update

Pediatric cancers represent a tragic but also promising area for gene therapy. Although conventional treatments have improved survival rates, there is still a need for targeted and less toxic interventions. This article critically analyzes recent advances in gene therapy for pediatric malignancies and discusses the challenges that remain. We explore the innovative vectors and delivery systems that have emerged, such as adeno-associated viruses and non-viral platforms, which show promise in addressing the unique pathophysiology of pediatric tumors. Specifically, we examine the field of chimeric antigen receptor (CAR) T-cell therapies and their adaptation for solid tumors, which historically have been more challenging to treat than hematologic malignancies. We also discuss the genetic and epigenetic complexities inherent to pediatric cancers, such as tumor heterogeneity and the dynamic tumor microenvironment, which pose significant hurdles for gene therapy. Ethical considerations specific to pediatric populations, including consent and long-term follow-up, are also analyzed. Additionally, we scrutinize the translation of research from preclinical models that often fail to mimic pediatric cancer biology to the regulatory landscapes that can either support or hinder innovation. In summary, this article provides an up-to-date overview of gene therapy in pediatric oncology, highlighting both the rapid scientific progress and the substantial obstacles that need to be addressed. Through this lens, we propose a roadmap for future research that prioritizes the safety, efficacy, and complex ethical considerations involved in treating pediatric patients. Our ultimate goal is to move from incremental advancements to transformative therapies.

Initial heritable genome editing: mapping a responsible pathway from basic research to the clinic

Following the Second Summit on Human Gene Editing in Hong Kong in 2018, where the birth of two girls with germline genome editing was revealed, the need for a responsible pathway to the clinical application of human germline genome editing has been repeatedly emphasised. This paper aims to contribute to the ongoing discussion on research ethics issues in germline genome editing by exploring key issues related to the initial applications of CRISPR in reproductive medicine. Following an overview of the current discussion on bringing germline genome editing into clinical practice, we outline the specific challenges associated with such interventions and the features that distinguish them from conventional clinical testing of new medical treatments. We then review proposed ethical requirements for initial heritable genome editing, such as the absence of reasonable alternatives, the existence of sufficient and reliable preclinical data, appropriate informed consent, requirements related to safety, and long-term follow-up.

Ethical Perspectives of Therapeutic Human Genome Editing From Multiple and Diverse Viewpoints: A Scoping Review

Human genome editing has been increasingly explored to determine if it can be used to eradicate genetic diseases like sickle cell disease, but it has also been surrounded by a wide variety of ethical dilemmas. The purpose of this review was to conduct a scoping review of the ethics of therapeutic human genome editing in terms of philosophy, theology, public perspectives, and research ethics. A systemized search of PubMed, Embase, Ovid MEDLINE, and Web of Science was conducted. The initial search resulted in 4,445 articles, and after removing 1,750 duplicates and screening the remaining 2,695 articles, 27 final articles were selected for the final analysis. From a philosophical and theological standpoint, therapeutic human genome editing was generally ethically acceptable. Worldwide public perspectives were also in agreement except for the Oceanic region, which disagreed mainly due to the possible effects on future generations. Lastly, human research ethics revealed that women were not always included in informed consent, and that child autonomy needs to be preserved. Further research is needed to determine adverse effects on the mother, fetus, and future generations.

AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity

BACKGROUND

More and more Cas9 variants with higher specificity are developed to avoid the off-target effect, which brings a significant volume of experimental data. Conventional machine learning performs poorly on these datasets, while the methods based on deep learning often lack interpretability, which makes researchers have to trade-off accuracy and interpretability. It is necessary to develop a method that can not only match deep learning-based methods in performance but also with good interpretability that can be comparable to conventional machine learning methods.

RESULTS

To overcome these problems, we propose an intrinsically interpretable method called AttCRISPR based on deep learning to predict the on-target activity. The advantage of AttCRISPR lies in using the ensemble learning strategy to stack available encoding-based methods and embedding-based methods with strong interpretability. Comparison with the state-of-the-art methods using WT-SpCas9, eSpCas9(1.1), SpCas9-HF1 datasets, AttCRISPR can achieve an average Spearman value of 0.872, 0.867, 0.867, respectively on several public datasets, which is superior to these methods. Furthermore, benefits from two attention modules-one spatial and one temporal, AttCRISPR has good interpretability. Through these modules, we can understand the decisions made by AttCRISPR at both global and local levels without other post hoc explanations techniques.

CONCLUSION

With the trained models, we reveal the preference for each position-dependent nucleotide on the sgRNA (short guide RNA) sequence in each dataset at a global level. And at a local level, we prove that the interpretability of AttCRISPR can be used to guide the researchers to design sgRNA with higher activity.

CRISPRpred(SEQ): a sequence-based method for sgRNA on target activity prediction using traditional machine learning

BACKGROUND

The latest works on CRISPR genome editing tools mainly employs deep learning techniques. However, deep learning models lack explainability and they are harder to reproduce. We were motivated to build an accurate genome editing tool using sequence-based features and traditional machine learning that can compete with deep learning models.

RESULTS

In this paper, we present CRISPRpred(SEQ), a method for sgRNA on-target activity prediction that leverages only traditional machine learning techniques and hand-crafted features extracted from sgRNA sequences. We compare the results of CRISPRpred(SEQ) with that of DeepCRISPR, the current state-of-the-art, which uses a deep learning pipeline. Despite using only traditional machine learning methods, we have been able to beat DeepCRISPR for the three out of four cell lines in the benchmark dataset convincingly (2.174%, 6.905% and 8.119% improvement for the three cell lines).

CONCLUSION

CRISPRpred(SEQ) has been able to convincingly beat DeepCRISPR in 3 out of 4 cell lines. We believe that by exploring further, one can design better features only using the sgRNA sequences and can come up with a better method leveraging only traditional machine learning algorithms that can fully beat the deep learning models.