A Sensitivity Study for Interpreting Nucleic Acid Sequence Screening Regulatory and Guidance Documentation: Toward a Foundational Synthetic Nucleic Acid Sequence Screening Framework.

Objectives: The primary objectives of this study were to develop an objective nucleic acid sequence screening framework and to leverage the framework for an empirical sensitivity study that measures the impact of ambiguities in regulatory and guidance documentation regarding the control of synthetic nucleic acids and screening of nucleic acid orders.

Methods: Foundational risk levels were constructed using the bioinformatic sequencing screening tool UltraSEQ. The risk levels range from high (corresponding to regulated sequences) to low (corresponding to nonregulated sequences of concern) to no-risk.

Practical Questions for Securing Nucleic Acid Synthesis.

Introduction: Affordable and accurate nucleic acid synthesis is foundational to modern biotechnology, but raises security concerns because it facilitates the construction of pathogens and other potentially dangerous biological agents. Nucleic acid synthesis screening can reduce the risk of providing potentially harmful nucleic acids to those without a legitimate use for them. Governments, industry associations, and biosecurity organizations have offered guidance on synthesis screening for a decade, and are now considering how to translate industry best practices into regulatory frameworks.

Shaping the future US bioeconomy through safety, security, sustainability, and social responsibility.

Biomanufacturing practitioners and researchers describe the norms that should govern the growing, global field, to include safety, security, sustainability, and social responsibility. These '4S Principles' should be broadly adopted so that the future of the field may provide the greatest benefits to society.

Safety by design: Biosafety and biosecurity in the age of synthetic genomics.

Technologies to profoundly engineer biology are becoming increasingly affordable, powerful, and accessible to a widening group of actors. While offering tremendous potential to fuel biological research and the bioeconomy, this development also increases the risk of inadvertent or deliberate creation and dissemination of pathogens. Effective regulatory and technological frameworks need to be developed and deployed to manage these emerging biosafety and biosecurity risks.

Making Security Viral: Shifting Engineering Biology Culture and Publishing.

The ability to construct, synthesize, and edit genes and genomes at scale and with speed enables, in synergy with other tools of engineering biology, breakthrough applications with far-reaching implications for society. As SARS-CoV-2 spread around the world in early spring of 2020, researchers rapidly mobilized, using these tools in the development of diagnostics, therapeutics, and vaccines for COVID-19. The sharing of knowledge was crucial to making rapid progress.

Guiding Ethical Principles in Engineering Biology Research.

Engineering biology is being applied toward solving or mitigating some of the greatest challenges facing society. As with many other rapidly advancing technologies, the development of these powerful tools must be considered in the context of ethical uses for personal, societal, and/or environmental advancement. Researchers have a responsibility to consider the diverse outcomes that may result from the knowledge and innovation they contribute to the field.

Next Steps to Grow the Bioeconomy.

The bioeconomy in the United States represents a significant proportion of total economic output and is poised to grow even more rapidly over the next decade. Global competition is increasing, and the United States must work to ensure we maintain global leadership in this field. In this commentary, we outline policy recommendations in 6 topic areas.

Next Steps for Access to Safe, Secure DNA Synthesis.

The DNA synthesis industry has, since the invention of gene-length synthesis, worked proactively to ensure synthesis is carried out securely and safely. Informed by guidance from the U.S.

Defining the Synthetic Biology Supply Chain.

Several recent articles have described risks posed by synthetic biology and spurred vigorous discussion in the scientific, commercial, and government communities about how to best detect, prevent, regulate, and respond to these risks. The Pacific Northwest National Laboratory's (PNNL) deep experience working with dual-use technologies for the nuclear industry has shown that analysis of supply chains can reveal security vulnerabilities and ways to mitigate security risk without hindering beneficial research and commerce. In this article, a team of experts in synthetic biology, data analytics, and national security describe the overall supply chain surrounding synthetic biology to illustrate new insights about the effectiveness of current regulations, the possible need for different screening approaches, and new technical solutions that could help identify or mitigate risks in the synthetic biology supply chain.

Classification of usual interstitial pneumonia in patients with interstitial lung disease: assessment of a machine learning approach using high-dimensional transcriptional data.

Background: Idiopathic pulmonary fibrosis is a progressive fibrotic lung disease that distorts pulmonary architecture, leading to hypoxia, respiratory failure, and death. Diagnosis is difficult because other interstitial lung diseases have similar radiological and histopathological characteristics. A usual interstitial pneumonia pattern is a hallmark of idiopathic pulmonary fibrosis and is essential for its diagnosis.

Machine learning from concept to clinic: reliable detection of BRAF V600E DNA mutations in thyroid nodules using high-dimensional RNA expression data.

The promise of personalized medicine will require rigorously validated molecular diagnostics developed on minimally invasive, clinically relevant samples. Measurement of DNA mutations is increasingly common in clinical settings but only higher-prevalence mutations are cost-effective. Patients with rare variants are at best ignored or, at worst, misdiagnosed.

A prospective assessment defining the limitations of thyroid nodule pathologic evaluation.

Background: Clinical management of thyroid neoplasms is based on light microscopic diagnosis, but its accuracy and precision are poorly defined.

Objective: To assess inter- and intraobserver variability of preoperative cytopathologic and postoperative histopathologic thyroid diagnoses.

Design: Samples were collected in a prospective, multicenter trial validating a gene expression classifier between June 2009 and December 2010.

The impact of benign gene expression classifier test results on the endocrinologist-patient decision to operate on patients with thyroid nodules with indeterminate fine-needle aspiration cytopathology.

Background: Seventy-five percent of thyroid nodules with indeterminate fine-needle aspiration (FNA) cytology are found to be benign postoperatively. A novel genomic test, the Afirma gene expression classifier (AGEC), has been available for clinical use in the United States, since late 2010. In 2010, two modest-sized validation studies showed that the AGEC could identify a benign gene expression signature in indeterminate cytology thyroid FNA samples with a negative predictive value >95%.

Preoperative diagnosis of benign thyroid nodules with indeterminate cytology.

Background: Approximately 15 to 30% of thyroid nodules evaluated by means of fine-needle aspiration are not clearly benign or malignant. Patients with cytologically indeterminate nodules are often referred for diagnostic surgery, though most of these nodules prove to be benign. A novel diagnostic test that measures the expression of 167 genes has shown promise in improving preoperative risk assessment.

Open-target sparse sensing of biological agents using DNA microarray.

Background: Current biosensors are designed to target and react to specific nucleic acid sequences or structural epitopes. These 'target-specific' platforms require creation of new physical capture reagents when new organisms are targeted. An 'open-target' approach to DNA microarray biosensing is proposed and substantiated using laboratory generated data.