A human pan-disease blood atlas of the circulating proteome

Susumu Kitagawa, Richard Robson and Omar Yaghi pioneered the creation of metal–organic frameworks, which can capture and store molecules such as carbon dioxide.

These immune cells won Nobel fame — can they solve autoimmune disease?

Nature - Issue - nature.com science feeds

By: Rachel Fieldhouse

7 October 2025 at 08:00

Nature, Published online: 07 October 2025; doi:10.1038/d41586-025-03287-y

Regulatory T cells, which help to dampen inflammation, are being used in clinical trials against ailments such as rheumatoid arthritis.

Pathobiology and Genetics

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Christina A Eichstaedt · Andrea Olschewski · Lars Harbaum · Sebastian Cordes · Peter Dorfmüller · Vasile Foris · Wolfgang M Kuebler · Grazyna Kwapiszewska · Soni Pullamsetti · Natascha Sommer · Christoph Tabeling · Norbert Weissmann · Anja Zoll · Katrin Hinderhofer · Ralph T Schermuly

6 October 2025 at 18:00

Pneumologie. 2025 Oct;79(10):701-711. doi: 10.1055/a-2625-4648. Epub 2025 Oct 6.

ABSTRACT

Genetics and pathobiology were addressed at the 7^th World Symposium on Pulmonary Hypertension in Task Forces 2 and 3. The Genetics Task Force also focused on precision medicine approaches, and the Pathobiology working group concentrated heavily on new omics technologies. Therefore, the following not only summarises the current state of knowledge on genetics, genetic testing methods, and molecular pathophysiological changes, but also places it in context and critically discusses it. In addition, the importance of national and international biobanks and cohorts, as well as the active involvement of patients and families, is emphasized.

PMID:41052524 | DOI:10.1055/a-2625-4648

The Potential of AI in Nursing Care: Multicenter Evaluation in Fall Risk Assessment

Journal of Medical Internet Research

By: Ivana Nanevski · Sebastian Jäger · Matthias Schulte-Althoff · Eva-Maria Behnke · Daniel Fürstenau · Felix Biessmann

9 October 2025 at 04:00

Background: With 28%-35% of individuals aged 65 years and older experiencing incidents of falling, falls are the second leading cause of unintentional injury–related deaths globally. Limited availability of clinical staff often impedes the timely detection and prevention of potential falls. Advances in artificial intelligence (AI) could complement existing fall risk assessment and help better allocate nursing care resources. Yet, many studies are based on small datasets from a single institution, which can restrict the generalizability of the model, and do not investigate important aspects in AI model development, such as fairness across demographic groups. Objective: This study aimed to provide a comprehensive empirical evaluation of the potential of AI in nursing care, focusing on the case of fall risk prediction. To account for demographic and contextual differences in fall incidences, we analyze data from a university and a geriatric hospital in Germany. To the best of our knowledge, these are the largest fall risk prediction datasets to date with heterogeneous data distributions. We focus on 3 key objectives. First, does AI help in improving fall risk prediction? Second, how can AI models be trained safely across different hospitals? Finally, are these models fair? Methods: This study used 2 datasets for fall risk prediction: one from a university hospital with 931,726 participants, 10,442 of whom experienced falls, and another from a geriatric hospital with 12,773 participants, 1728 of whom have fallen. State-of-the-art AI models were trained with 3 approaches, including 2 decentralized learning paradigms. First, separate models were trained on data from each hospital; second, models were retrained on the respective other dataset; and federated learning (FL) was applied to both datasets. The performance of these models was compared with the rule-based systems as implemented in clinical practice for fall risk prediction. Additional analyses were conducted to test for model fairness. Results: Our findings demonstrate that AI models consistently outperform rule-based systems across all experimental setups, with the area under the receiver operating characteristic curve of 0.735 (90% CI 0.727-0.744) for the geriatric hospital, and 0.926 (90% CI 0.924-0.928) for the university hospital. FL did not improve the fall risk prediction in this setting. Our fairness analysis ruled out disparities in model performance between different sex groups, but we found fairness infringements across age groups. Conclusions: This study demonstrates that AI models consistently outperform traditional rule-based systems across heterogeneous datasets in predicting fall risk. However, it also reveals the challenges related to demographic shifts and label distribution imbalances, which limited the FL models’ ability to generalize. While the fairness analysis indicated fair results across sex subgroups, age-related disparities emerged. Addressing data imbalances and ensuring broader representation across demographic groups will be crucial for developing more fair and generalizable models.

Evaluating Large Language Models and Retrieval-Augmented Generation Enhancement for Delivering Guideline-Adherent Nutrition Information for Cardiovascular Disease Prevention: Cross-Sectional Study

Journal of Medical Internet Research

By: Vijaya Parameswaran · Jenna Bernard · Alec Bernard · Neil Deo · Sean Tsung · Kalle Lyytinen · Christopher Sharp · Fatima Rodriguez · David J Maron · Rajesh Dash

8 October 2025 at 05:45

Background: Cardiovascular disease (CVD) remains the leading cause of death worldwide, yet many web-based sources on cardiovascular (CV) health are inaccessible. Large language models (LLMs) are increasingly used for health-related inquiries and offer an opportunity to produce accessible and scalable CV health information. However, because these models are trained on heterogeneous data, including unverified user-generated content, the quality and reliability of food and nutrition information on CVD prevention remain uncertain. Recent studies have examined LLM use in various health care applications, but their effectiveness for providing nutrition information remains understudied. Although retrieval-augmented generation (RAG) frameworks have been shown to enhance LLM consistency and accuracy, their use in delivering nutrition information for CVD prevention requires further evaluation. Objective: To evaluate the effectiveness of off-the-shelf and RAG-enhanced LLMs in delivering guideline-adherent nutrition information for CVD prevention, we assessed 3 off-the-shelf models (ChatGPT-4o, Perplexity, and Llama 3-70B) and a Llama 3-70B+RAG model. Methods: We curated 30 nutrition questions that comprehensively addressed CVD prevention. These were approved by a registered dietitian providing preventive cardiology services at an academic medical center and were posed 3 times to each model. We developed a 15,074-word knowledge bank incorporating the American Heart Association’s 2021 dietary guidelines and related website content to enhance Meta’s Llama 3-70B model using RAG. The model received this and a few-shot prompt as context, included citations in a Context Source section, and used vector similarity to align responses with guideline content, with the temperature parameter set to 0.5 to enhance consistency. Model responses were evaluated by 3 expert reviewers against benchmark CV guidelines for appropriateness, reliability, readability, harm, and guideline adherence. Mean scores were compared using ANOVA, with statistical significance set at P<.05. interrater agreement was measured using the cohen coefficient and readability estimated flesch-kincaid score. results: llama model scored higher than perplexity gpt-4o models on reliability appropriateness guideline adherence showed no harm.>70%; P<.001 indicated high reviewer agreement. conclusions: the llama model outperformed off-the-shelf models across all measures with no evidence of harm although responses were less readable due to technical language. scored lower on and produced some harmful responses. these findings highlight limitations demonstrate that rag system integration can enhance llm performance in delivering evidence-based dietary information.>

The Role of Data in Public Health and Health Innovation: Perspectives on Social Determinants of Health, Community-Based Data Approaches, and AI

Journal of Medical Internet Research

By: Kaakpema Yelpaala · Michael Christopher Gibbons · Ines Maria Vigil · Jennifer Leaño · Terika McCall · Ijeoma Opara · Anne Zink · Marcella Nunez-Smith · Bhramar Mukherjee · Megan Ranney

8 October 2025 at 03:30

Public health is undergoing profound transformation driven by data from the global health sector and related fields. To address systemic health disparities, scholars and practitioners are increasingly applying a data equity lens, an approach that has become even more urgent as the United States faces the erosion of public health data infrastructure. This paper summarizes insights from an April 2024 convening by the Yale School of Public Health—The Role of Data in Public Health Equity and Innovation—with intersectoral stakeholders from academia, government (local, state, and federal), healthcare, and private industry. The convening included keynote presentations and roundtables regarding the depiction of social determinants of health (SDOH) in data; effects of artificial intelligence (AI) on health data equity; and community-based models for data, providing a framework for cross-cutting discussions. Through a narrative synthesis, themes were identified and synthesized from systematically gathered information from presentations and roundtables. This process led to a set of actionable, cross-cutting recommendations to guide inclusive and impactful data practices for policymakers, public health professionals, and health innovators across diverse contexts: (1) Enable big data and interoperability connecting SDOH and health outcomes; (2) Include diverse, non-technical voices in AI and health discussions; (3) Fund research on data equity and AI in health sciences; (4) Modernize Health Insurance Portability and Accountability Act (HIPAA) with new guidelines for AI and big data; and (5) Research and conceptual frameworks are needed to elucidate interconnections between data equity and health equity.

Generative artificial intelligence in medicine

Nature Medicine

By: Zhen Ling Teo · Arun James Thirunavukarasu · Kabilan Elangovan · Haoran Cheng · Prasanth Moova · Brian Soetikno · Christopher Nielsen · Andreas Pollreisz · Darren Shu Jeng Ting · Robert J. T. Morris · Nigam H. Shah · Curtis P. Langlotz · Daniel Shu Wei Ting

6 October 2025 at 08:00

Nature Medicine, Published online: 06 October 2025; doi:10.1038/s41591-025-03983-2

This Review summarizes recent technical advancements in generative AI, outlines how new models might improve healthcare and discusses validation approaches—using lessons from recent successes and failures in the field.

Single-cell and multi-omics analysis identifies TRIM9 as a key ubiquitination regulator in pancreatic cancer

(Multiomics OR Omics) AND (Pancreatic)

By: Liang Chen · Xiaomei Ying · Chenfeng Ma · Qikai Tang · Shuai Chen

6 October 2025 at 18:00

Front Immunol. 2025 Sep 19;16:1631708. doi: 10.3389/fimmu.2025.1631708. eCollection 2025.

ABSTRACT

This study investigates the role of ubiquitination-related genes in pancreatic cancer (PC) using single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multi-omics approaches. scRNA-seq data (GSE155698) from PC samples identified 12 cell types, with endothelial cells exhibiting high ubiquitination scores (High_ubiquitin-Endo) and enriched interactions with fibroblasts/macrophages via WNT, NOTCH, and integrin pathways. Spatial transcriptomics (GSE235315) validated cell-type localization. Mendelian randomization (SMR) analysis prioritized TRIM9 as a PC-protective gene, downregulated in tumors and correlated with better survival. WGCNA revealed TRIM9-co-expressed modules linked to prognosis. A machine learning-based prognostic model (CoxBoost+RSF) integrating seven genes (TSPAN6, TSC1, RNF167, PBXIP1, LRRC49, KATNAL2, IGF2BP2) stratified patients into high/low-risk groups with distinct survival, mutation burdens, and immune infiltration. TRIM9 overexpression suppressed PC cell proliferation/migration in vitro, while knockdown enhanced malignancy. Mechanistically, TRIM9 promoted K11-linked ubiquitination and proteasomal degradation of HNRNPU, dependent on its RING domain. In vivo, TRIM9 overexpression reduced tumor growth, rescued by HNRNPU co-expression. Integrated analyses highlight TRIM9 as a tumor suppressor and prognostic biomarker, mediated via ubiquitination-dependent regulation of HNRNPU stability. This work provides insights into ubiquitination-driven PC pathogenesis and therapeutic targeting.

PMID:41050689 | PMC:PMC12491318 | DOI:10.3389/fimmu.2025.1631708

A computational medicine framework integrating multi-omics, systems biology, and artificial neural networks for Alzheimer's disease therapeutic discovery

(Multiomics OR Omics) AND (Pancreatic)

By: Yisheng Yang · Yizhu Diao · Lulu Jiang · Fanlu Li · Liye Chen · Ming Ni · Zheng Wang · Hai Fang

6 October 2025 at 18:00

Acta Pharm Sin B. 2025 Sep;15(9):4411-4426. doi: 10.1016/j.apsb.2025.07.018. Epub 2025 Jul 16.

ABSTRACT

The translation of genetic findings from genome-wide association studies into actionable therapeutics persists as a critical challenge in Alzheimer's disease (AD) research. Here, we present PI4AD, a computational medicine framework that integrates multi-omics data, systems biology, and artificial neural networks for therapeutic discovery. This framework leverages multi-omic and network evidence to deliver three core functionalities: clinical target prioritisation; self-organising prioritisation map construction, distinguishing AD-specific targets from those linked to neuropsychiatric disorders; and pathway crosstalk-informed therapeutic discovery. PI4AD successfully recovers clinically validated targets like APP and ESR1, confirming its prioritisation efficacy. Its artificial neural network component identifies disease-specific molecular signatures, while pathway crosstalk analysis reveals critical nodal genes (e.g., HRAS and MAPK1), drug repurposing candidates, and clinically relevant network modules. By validating targets, elucidating disease-specific therapeutic potentials, and exploring crosstalk mechanisms, PI4AD bridges genetic insights with pathway-level biology, establishing a systems genetics foundation for rational therapeutic development. Importantly, its emphasis on Ras-centred pathways-implicated in synaptic dysfunction and neuroinflammation-provides a strategy to disrupt AD progression, complementing conventional amyloid/tau-focused paradigms, with the future potential to redefine treatment strategies in conjunction with mRNA therapeutics and thereby advance translational medicine in neurodegeneration.

PMID:41049755 | PMC:PMC12491700 | DOI:10.1016/j.apsb.2025.07.018

MOFNet: a deep learning framework for multi-omics data fusion in cancer subtype classification

(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)

By: Guangji Zhang · Chunxiao Zhang · Pengpai Li · Duanchen Sun · Zhixia Yang · Zhi-Ping Liu

1 October 2025 at 18:00

Mol Omics. 2025 Oct 1. doi: 10.1039/d5mo00221d. Online ahead of print.

ABSTRACT

BACKGROUND: cancer exhibits high molecular and clinical heterogeneity, making accurate subtyping essential for personalized treatment. Traditional single-omics approaches often fail to capture this complexity. Multi-omics integration offers a more holistic understanding, but many existing methods either lack interpretability or fail to model cross-omics correlations effectively.

METHODS: we developed MOFNet, a novel supervised deep learning framework for multi-omics integration, incorporating a similarity graph pooling (SGO) module and a view correlation discovery network (VCDN). MOFNet processes omics data-including mRNA expression, DNA methylation, and miRNA expression-via omics-specific graph learning and cross-omics label space fusion. Three cancer types-breast cancer (BRCA), low-grade glioma (LGG), and stomach adenocarcinoma (STAD)-were analyzed using datasets from the cancer genome atlas (TCGA). Statistical evaluation was performed using accuracy, weighted F1 score, and macro F1 score across stratified training/testing splits.

RESULTS: MOFNet achieved superior performance across all datasets. For BRCA, it obtained an accuracy of 85.17%, F1_weighted of 85.36%, and macro F1 of 80.93%, outperforming all baseline models by up to 18.25%. In LGG and STAD, MOFNet also showed robust gains, with maximum improvements of 23.72% and 21.56%, respectively. Omics ablation studies demonstrated enhanced performance with multi-omics integration. Functional enrichment analysis revealed that MOFNet-identified key features were involved in biologically relevant pathways such as cell cycle regulation, synaptic signaling, and ion transport.

CONCLUSIONS: MOFNet enables scalable and interpretable multi-omics data fusion for cancer subtype classification, significantly improving predictive accuracy while retaining only 25% of input features. The integration of SGO and VCDN modules offers both biological interpretability and computational efficiency. These results suggest MOFNet's promising application in precision oncology and biomarker discovery.

PMID:41031935 | DOI:10.1039/d5mo00221d

The tumour microenvironment in pancreatic cancer - new clinical challenges, but more opportunities

(Multiomics OR Omics) AND (Pancreatic)

By: Heng-Chung Kung · Kevin W Zheng · Jacquelyn W Zimmerman · Lei Zheng

3 October 2025 at 18:00

Nat Rev Clin Oncol. 2025 Oct 3. doi: 10.1038/s41571-025-01077-z. Online ahead of print.

ABSTRACT

Patients with advanced-stage pancreatic ductal adenocarcinoma (PDAC) predominantly receive chemotherapy, and despite initial responses in some patients, most will have disease progression and often dismal outcomes. This lack of clinical effectiveness partly reflects not only cancer cell-intrinsic factors but also the presence of a tumour microenvironment (TME) that precludes access of both systemic therapies and circulating immune cells to the primary tumour, as well as supporting the growth of PDAC cells. Combined with improved preclinical models of PDAC, advances in single-cell spatial multi-omics and machine learning-based models have provided novel methods of untangling the complexities of the TME. In this Review, we focus on the desmoplastic stroma and both the intratumoural and intertumoural heterogeneity of PDAC, with an emphasis on cancer-associated fibroblasts and their surrounding immune cell niches. We describe new approaches in converting the immunologically 'cold' PDAC TME into a 'hot' TME by priming T cell activation, overcoming T cell exhaustion and unravelling myeloid cell-mediated immunosuppression. Furthermore, we explore integrated targets involving the TME, such as points of convergence among tumour, stromal and immune cell metabolism as well as oncogenic KRAS signalling. Finally, building on our experience with failed clinical trials in the past, we consider how this evolving comprehensive understanding of the TME will ensure future success in developing more effective therapies for patients with PDAC.

PMID:41044427 | DOI:10.1038/s41571-025-01077-z

AI has designed thousands of potential antibiotics. Will any work?

Nature - Issue - nature.com science feeds

By: Rachel Fieldhouse

3 October 2025 at 08:00

Nature, Published online: 03 October 2025; doi:10.1038/d41586-025-03201-6

Machine learning can speed up the discovery of potential antibiotics, but challenges remain.

Clinical validation of an AI-based blood testing device for diagnosis and prognosis of acute infection and sepsis

30 September 2025 at 08:00

Nature Medicine, Published online: 30 September 2025; doi:10.1038/s41591-025-03933-y

In a prospective study enrolling 1,222 patients from 22 emergency departments, a device using a machine-learning-based signature of blood mRNAs demonstrated clinically acceptable performance to diagnose bacterial and viral infections and to predict the all-cause need for critical care interventions within 7 days, with benchmark to established biomarkers and risk scores.

Clinical Decision Support Systems Using Home Blood Pressure Readings to Manage Patients With Hypertension: Scoping Review

Journal of Medical Internet Research

By: Aminath Shiwaza Moosa · Jun Jie Benjamin Seng · Chirk Jenn Ng

4 October 2025 at 04:30

Background: Home blood pressure (HBP) is an important parameter that guides clinicians in managing hypertension in patients. However, in using these records to manage patients, physicians face challenges, particularly regarding access, integration, and interpretation of the records when making clinical decisions. Clinical decision support systems (CDSSs) have been proposed to address these challenges; however, current literature reveals significant heterogeneity and gaps in CDSSs used for hypertension management. Objective: This study aimed to summarize existing studies on CDSSs that use HBP readings to manage patients with hypertension. Methods: We conducted a scoping review, with searches performed in PubMed, Embase, and Scopus on April 1, 2024. The results were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. Studies that used CDSSs integrated with HBP monitoring among adult patients with hypertension in outpatient settings were included. Non-English studies were excluded. Outcomes assessed included the theoretical frameworks used for CDSS development, CDSS components (data capture, processing, and output), clinical outcomes, user experiences, and implementation processes. Results: Of the 5023 articles screened, 33 (0.66%) were included. Most of the studies were conducted in the United States (16/33, 49%) and were randomized controlled trials (21/33, 64%). Nearly two-thirds of the CDSSs (21/33, 64%) were computerized. Only 1 (3%) of the 33 studies reported using a theoretical framework for CDSS development. HBP recording and uploading were predominantly automatic (23/33, 70%). All computerized CDSSs (21/33, 64%) used rule-based algorithms, and most (19/21, 91%) incorporated alert triggers for results outside the reference range. More than a third of the studies (13/33, 39%) were based on hypertension guidelines. Among studies that reported outcomes, most reported improved blood pressure (25/29, 86%) and adjustment in antihypertensive medications (16/19, 84%). Patients and clinicians appreciated the convenience and remote monitoring (10/33, 30%) but reported challenges with usability and access to computerized CDSSs (2/21, 10%). Of the studies using noncomputerized CDSSs (12/33, 36%), all incorporated patient education, while nearly two-thirds of the studies using computerized CDSSs (13/21, 62%) did the same. Clinician training was reported in 5% (1/21) of the computerized CDSSs and 25% (3/12) of the noncomputerized CDSSs. Conclusions: While CDSSs hold promise for improving hypertension management, gaps remain in their development and implementation. Future efforts should focus on integrating robust frameworks; aligning with guidelines; enhancing manual data integration; and addressing usability to maximize effectiveness, adoption, and user satisfaction. Trial Registration: Open Science Framework 26zmn; https://osf.io/26zmn

Exploring Attitudes and Obstacles Around Digital Public Health Tools: Insights From a Statewide Cross-Sectional Survey on Washington’s Vaccine Verification System

Journal of Medical Internet Research

By: Andrea R Molino · Debra Revere · Rebecca A Hills · Adam S Elder · Laura M West · Bryant T Karras · Chris Baumgartner · Janet G Baseman

3 October 2025 at 23:00

Background: Development and use of digital public health tools surged during the COVID-19 pandemic. Among these tools, vaccine verification systems emerged as alternatives to paper vaccine records, aiming to help limit the spread of disease. In November 2021, the Washington State Department of Health launched “WA Verify,” a QR code–based vaccine verification system built on the SMART Health Card framework, providing residents with a convenient way to store and share proof of vaccination digitally. However, WA Verify was developed and deployed before assessments and public input regarding potential adoption challenges—such as concerns about privacy, surveillance, data sharing, trust in the technology, and the managing organizations—could be completed. Objective: This analysis used statewide survey data from Washington to identify and characterize barriers and facilitators to the adoption of WA Verify, and to understand how factors such as data privacy, security, attitudes toward public health policies and communication, and technological proficiency may influence acceptance and uptake of digital public health tools. Methods: A cross-sectional statewide survey was distributed between September 2022 and January 2023 to a random sample of 5000 Washington households. Respondents were categorized into 3 groups based on their responses indicating WA Verify “users,” “potential users,” or “unlikely users.” Comparisons were made between groups regarding experiences with and opinions on COVID-19 vaccine and test verification, public health policies, communication, digital tools, technological proficiency, sociodemographic characteristics, and health history. Poststratification weights were applied to reduce nonresponse bias. Results: Of the 1401 respondents, 359 (25.6% unweighted, 25.8% weighted) were users, 662 (47.3% unweighted, 49.8% weighted) were potential users, and 380 (27.1% unweighted, 24.4% weighted) were unlikely users. All percentages reported are based on weighted data. Compared with users and potential users, unlikely users were more likely to oppose policies requiring proof of COVID-19 vaccination or negative test results (users: 6.0%, potential users: 13.6%, unlikely users: 65.9%). Unlikely users were more likely to cite concerns about personal health data security and phone hacking or tracking, though these concerns were also notable among potential users and users. Users and potential users were more likely to perceive a digital vaccine verification system as convenient (users: 96.5%, potential users: 92.3%, unlikely users: 38.1%) and indicated openness to receiving relevant information from a range of sources. Unlikely users were more likely to report not owning a smartphone and demonstrated lower technological proficiency (users: 12.3%, potential users: 15.9%, unlikely users: 32.3%), indicating a technological divide between groups. Conclusions: While nearly three-quarters of respondents had either already adopted or were willing to adopt a tool like WA Verify, concerns about data security, lower technological proficiency, and distrust of public health characterized those least likely to adopt such tools. Identifying barriers to adoption among “unlikely users” is essential for developing effective communication strategies—such as targeted marketing and community engagement—to improve adoption and ensure equitable access to public health technologies.

Scaling DNA synthesis with a microchip-based massively parallel synthesis system

Nature Biotechnology - Issue - nature.com science feeds

By: Xiandi Zhang · Xiang’er Jiang · Yun Wang · Qinzhen Chen · Hao Jiang · Hu Zhang · Antoni Beltran · Weiya Yang · Tai Chen · Chenglong Liang · Ning Cheng · Yun Huang · Guqiao Ding · Chengwang Xie · Nanfeng Gao · Juntao Liu · Wei Xu · Jinlei Huang · Dong Cai · Lihao Zhu · Songjin Mo · Mengzhe Shen · Wenwei Zhang · Ben Lehner · Ming Ni · Jian Wang · Xun Xu · Yue Shen

1 October 2025 at 08:00

Nature Biotechnology, Published online: 01 October 2025; doi:10.1038/s41587-025-02844-0

A microchip-based DNA synthesis method enables scalable production of complex DNA constructs.

Reading view