Artificial Intelligence in Molecular Medicine

Integrated transcriptomic and proteomic analyses reveal the effects of chronic benzene exposure on the central nervous system in mice

Benzene exposure is known to cause serious damage to the human hematopoietic system. However, recent studies have found that chronic benzene exposure may also cause neurological damage, but there were few studies in this issue. The aim of this study was to investigate the mechanism of damage to the central nervous system (CNS) by chronic benzene exposure with a multi-omics analysis. We established a chronic benzene exposure model in C57BL/6J mice by gavage of benzene-corn oil suspension, identified the differentially expressed proteins (DEPs) and differentially expressed genes (DEGs) in mice brain using 4D Label-free proteomic and RNA-seq transcriptomic. We observed that the benzene exposure mice had a significant loss of body weight, reduction in complete blood counts, abnormally high MRI signals in brain white matter, as well as extensive brain edema and neural demyelination. 162 DEPs were identified by the proteome, including 98 up-regulated and 64 down-regulated proteins. KEGG pathway analysis of DEPs showed that they were mainly involved in the neuro-related signaling pathways such as metabolic pathways, pathways of neurodegeneration, chemical carcinogenesis, Alzheimer disease, and autophagy. EPHX1, GSTM1, and LIMK1 were identified as important candidate DEGs/DEPs by integrated proteomic and transcriptomic analyses. We further performed multiple validation of the above DEGs/DEPs using fluorescence quantitative PCR (qPCR), parallel reaction monitoring (PRM), immunohistochemistry, and immunoblotting to confirm the reliability of the multi-omics study. The functions of these DEGs/DEPs were further explored and analyzed, providing a theoretical basis for the mechanism of nerve damage caused by benzene exposure.

Convergence of evolving artificial intelligence and machine learning techniques in precision oncology

The confluence of new technologies with artificial intelligence (AI) and machine learning (ML) analytical techniques is rapidly advancing the field of precision oncology, promising to improve diagnostic approaches and therapeutic strategies for patients with cancer. By analyzing multi-dimensional, multiomic, spatial pathology, and radiomic data, these technologies enable a deeper understanding of the intricate molecular pathways, aiding in the identification of critical nodes within the tumor's biology to optimize treatment selection. The applications of AI/ML in precision oncology are extensive and include the generation of synthetic data, e.g., digital twins, in order to provide the necessary information to design or expedite the conduct of clinical trials. Currently, many operational and technical challenges exist related to data technology, engineering, and storage; algorithm development and structures; quality and quantity of the data and the analytical pipeline; data sharing and generalizability; and the incorporation of these technologies into the current clinical workflow and reimbursement models.

Validation and accuracy of the Hyfe cough monitoring system: a multicenter clinical study

BACKGROUND

The ability to passively and continuously monitor coughing for prolonged periods of time would significantly improve cough management and research. To date there is no automated clinically validated cough monitor that can be routinely used in clinical care and research. Here we describe the validation of such an automated cough monitor.

METHODS

This multicenter observational study compared the results of the Hyfe CoughMonitor wrist-worn device with manually counted coughs in subjects with a variety of etiologies as they went about their usual daily activities. We collected 24 h of continuous sounds from subjects while they simultaneously wore a CoughMonitor and an audio recorder. Coughs were labelled by multiple trained annotators who listened to the continuous audio recordings using validated methodology. The time stamps of these human-detected coughs were compared to those of the CoughMonitor to determine the system's overall performance using event-to-event and hourly rate correlation analyses.

RESULTS

Over the 546 h monitored, 4,454 cough events were recorded; The overall sensitivity was 90.4% (95% CI of 88.3-92.2%). The overall false positive rate was 1.03 false positives per hour (95% CI of 0.84 to 1.24). The overall correlation between manual and CoughMonitor measured hourly coughing was high (Pearson correlation coefficient of 0.99). Two case studies of long-term monitoring of patients with chronic cough are presented.

CONCLUSION

The present analysis of cough events demonstrated that the Hyfe CoughMonitor accurately reflects them with a high sensitivity and a low false positive rate. Future studies should focus on its potential role in the management of patients with cough in clinical practice.Registration Clinicaltrials.gov, NCT05723159.

Artificial intelligence in drug development

Drug development is a complex and time-consuming endeavor that traditionally relies on the experience of drug developers and trial-and-error experimentation. The advent of artificial intelligence (AI) technologies, particularly emerging large language models and generative AI, is poised to redefine this paradigm. The integration of AI-driven methodologies into the drug development pipeline has already heralded subtle yet meaningful enhancements in both the efficiency and effectiveness of this process. Here we present an overview of recent advancements in AI applications across the entire drug development workflow, encompassing the identification of disease targets, drug discovery, preclinical and clinical studies, and post-market surveillance. Lastly, we critically examine the prevailing challenges to highlight promising future research directions in AI-augmented drug development.

ChromTR: chromosome detection in raw metaphase cell images via deformable transformers

Chromosome karyotyping is a critical way to diagnose various hematological malignancies and genetic diseases, of which chromosome detection in raw metaphase cell images is the most critical and challenging step. In this work, focusing on the joint optimization of chromosome localization and classification, we propose ChromTR to accurately detect and classify 24 classes of chromosomes in raw metaphase cell images. ChromTR incorporates semantic feature learning and class distribution learning into a unified DETR-based detection framework. Specifically, we first propose a Semantic Feature Learning Network (SFLN) for semantic feature extraction and chromosome foreground region segmentation with object-wise supervision. Next, we construct a Semantic-Aware Transformer (SAT) with two parallel encoders and a Semantic-Aware decoder to integrate global visual and semantic features. To provide a prediction with a precise chromosome number and category distribution, a Category Distribution Reasoning Module (CDRM) is built for foreground-background objects and chromosome class distribution reasoning. We evaluate ChromTR on 1404 newly collected R-band metaphase images and the public G-band dataset AutoKary2022. Our proposed ChromTR outperforms all previous chromosome detection methods with an average precision of 92.56% in R-band chromosome detection, surpassing the baseline method by 3.02%. In a clinical test, ChromTR is also confident in tackling normal and numerically abnormal karyotypes. When extended to the chromosome enumeration task, ChromTR also demonstrates state-of-the-art performances on R-band and G-band two metaphase image datasets. Given these superior performances to other methods, our proposed method has been applied to assist clinical karyotype diagnosis.

[The future of medicine: an informed look into the "crystal ball"]

This article explores potential future scenarios for the medical field based on current trends, technological advancements, and social dynamics. By examining advances in artificial intelligence, immersive technologies, genomics, and digital health infrastructure, the paper envisions a healthcare system poised for transformative change. The anticipated role of AI as a digital assistant in diagnostics, resource management, and personalized medicine is highlighted, alongside the implications for clinical workflows. Immersive technologies, such as VR and AR, promise enhancements in medical education, patient care, and therapeutic interventions. Advances in genomics and gene editing technologies such as CRISPR further open possibilities for personalized treatment regimens and potential cures for genetic diseases. However, these innovations introduce new ethical challenges around privacy, data security, and clinical accountability. The article also addresses the healthcare implications of climate change, aging populations, and global conflicts, urging preparedness and resilience within healthcare systems. Taken together, it emphasizes the need for a balanced approach to innovation, integrating ethical considerations to foster a future where medicine remains empathetic and human-centered, even as it becomes more data-driven and technologically complex.

GFPrint™: A machine learning tool for transforming genetic data into clinical insights

The increasing availability of massive genetic sequencing data in the clinical setting has triggered the need for appropriate tools to help fully exploit the wealth of information these data possess. GFPrint™ is a proprietary streaming algorithm designed to meet that need. By extracting the most relevant functional features, GFPrint™ transforms high-dimensional, noisy genetic sequencing data into an embedded representation, allowing unsupervised models to create data clusters that can be re-mapped to the original clinical information. Ultimately, this allows the identification of genes and pathways relevant to disease onset and progression. GFPrint™ has been tested and validated using two cancer genomic datasets publicly available. Analysis of the TCGA dataset has identified panels of genes whose mutations appear to negatively influence survival in non-metastatic colorectal cancer (15 genes), epidermoid non-small cell lung cancer (167 genes) and pheochromocytoma (313 genes) patients. Likewise, analysis of the Broad Institute dataset has identified 75 genes involved in pathways related to extracellular matrix reorganization whose mutations appear to dictate a worse prognosis for breast cancer patients. GFPrint™ is accessible through a secure web portal and can be used in any therapeutic area where the genetic profile of patients influences disease evolution.

AI-Assisted Identification of Primary and Secondary Metabolomic Markers for Postoperative Delirium

Despite considerable investigative efforts, the molecular mechanisms of postoperative delirium (POD) remain unresolved. The present investigation employs innovative methodologies for identifying potential primary and secondary metabolic markers of POD by analyzing serum metabolomic profiles utilizing the genetic algorithm and artificial neural networks. The primary metabolomic markers constitute a combination of metabolites that optimally distinguish between POD and non-POD groups of patients. Our analysis revealed L-lactic acid, inositol, and methylcysteine as the most salient primary markers upon which the prediction accuracy of POD manifestation achieved AUC = 99%. The secondary metabolomic markers represent metabolites that exhibit perturbed correlational patterns within the POD group. We identified 54 metabolites as the secondary markers of POD, incorporating neurotransmitters such as gamma-aminobutyric acid (GABA) and serotonin. These findings imply a systemic disruption in metabolic processes in patients with POD. The deployment of gene network reconstruction techniques facilitated the postulation of hypotheses describing the role of established genomic POD markers in the molecular-genetic mechanisms of metabolic pathways dysregulation, and involving the identified primary and secondary metabolomic markers. This study not only expands the understanding of POD pathogenesis but also introduces a novel technology for the bioinformatic analysis of metabolomic data that could aid in uncovering potential primary and secondary markers in diverse research domains.

Use of artificial intelligence algorithms to analyse systemic sclerosis-interstitial lung disease imaging features

The use of artificial intelligence (AI) in high-resolution computed tomography (HRCT) for diagnosing systemic sclerosis-associated interstitial lung disease (SSc-ILD) is relatively limited. This study aimed to analyse lung HRCT images of patients with systemic sclerosis with interstitial lung disease (SSc-ILD) using artificial intelligence (AI), conduct correlation analysis with clinical manifestations and prognosis, and explore the features and prognosis of SSc-ILD. Overall, 72 lung HRCT images and clinical data of 58 patients with SSC-ILD were collected. ILD lesion type, location, and volume on HRCT images were identified and evaluated using AI. The imaging characteristics of diffuse SSC (dSSc)-ILD and limited SSc-ILD (lSSc-ILD) were statistically analysed. Furthermore, the correlations between lesion type, clinical indicators, and prognosis were investigated. dSSc and lSSc were more prevalent in patients with a disease duration of < 1 and ≥ 5 years, respectively. SSc-ILD mainly comprises non-specific interstitial pneumonia (NSIP), usual interstitial pneumonia (UIP), and unclassifiable idiopathic interstitial pneumonia. HRCT reveals various lesion types in the early stages of the disease, with an increase in the number of lesion types as the disease progresses. Lesions appearing as grid, ground-glass, and nodular shadows were dispersed throughout both lungs, while those appearing as consolidation shadows and honeycomb were distributed across the lungs. Ground-glass opacity lesion type was absent on HRCT images of patients with SSc-ILD and pulmonary hypertension. This study showed that AI can efficiently analyse imaging characteristics of SSc-ILD, demonstrating its potential to learn from complex images with high generalisation ability.

Spatiotemporal metabolomic approaches to the cancer-immunity panorama: a methodological perspective

Metabolic reprogramming drives the development of an immunosuppressive tumor microenvironment (TME) through various pathways, contributing to cancer progression and reducing the effectiveness of anticancer immunotherapy. However, our understanding of the metabolic landscape within the tumor-immune context has been limited by conventional metabolic measurements, which have not provided comprehensive insights into the spatiotemporal heterogeneity of metabolism within TME. The emergence of single-cell, spatial, and in vivo metabolomic technologies has now enabled detailed and unbiased analysis, revealing unprecedented spatiotemporal heterogeneity that is particularly valuable in the field of cancer immunology. This review summarizes the methodologies of metabolomics and metabolic regulomics that can be applied to the study of cancer-immunity across single-cell, spatial, and in vivo dimensions, and systematically assesses their benefits and limitations.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

Colorectal cancer

Despite decreased incidence rates in average-age onset patients in high-income economies, colorectal cancer is the third most diagnosed cancer in the world, with increasing rates in emerging economies. Furthermore, early onset colorectal cancer (age ≤50 years) is of increasing concern globally. Over the past decade, research advances have increased biological knowledge, treatment options, and overall survival rates. The increase in life expectancy is attributed to an increase in effective systemic therapy, improved treatment selection, and expanded locoregional surgical options. Ongoing developments are focused on the role of sphincter preservation, precision oncology for molecular alterations, use of circulating tumour DNA, analysis of the gut microbiome, as well as the role of locoregional strategies for colorectal cancer liver metastases. This overview is to provide a general multidisciplinary perspective of clinical advances in colorectal cancer.

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation simplifies long-read variant calling with DeepVariant.

Application of Metabolomics across the Spectrum of Pulmonary and Critical Care Medicine

In recent years, metabolomics, the systematic study of small-molecule metabolites in biological samples, has yielded fresh insights into the molecular determinants of pulmonary diseases and critical illness. The purpose of this article is to orient the reader to this emerging field by discussing the fundamental tenets underlying metabolomics research, the tools and techniques that serve as foundational methodologies, and the various statistical approaches to analysis of metabolomics datasets. We present several examples of metabolomics applied to pulmonary and critical care medicine to illustrate the potential of this avenue of research to deepen our understanding of pathophysiology. We conclude by reviewing recent advances in the field and future research directions that stand to further the goal of personalizing medicine to improve patient care.

Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

James German and the Quest to Understand Human RECQ Helicase Deficiencies

James German's work to establish the natural history and cancer risk associated with Bloom syndrome (BS) has had a strong influence on the generation of scientists and clinicians working to understand other RECQ deficiencies and heritable cancer predisposition syndromes. I summarize work by us and others below, inspired by James German's precedents with BS, to understand and compare BS with the other heritable RECQ deficiency syndromes with a focus on Werner syndrome (WS). What we know, unanswered questions and new opportunities are discussed, as are potential ways to treat or modify WS-associated disease mechanisms and pathways.

Unraveling the role of plasma proteins in dementia: insights from two cohort studies in the UK, with causal evidence from Mendelian randomization

Population-based proteomics offer a groundbreaking avenue to predict dementia onset. This study employed a proteome-wide, data-driven approach to investigate protein-dementia associations in 229 incident all-cause dementia (ACD) among 3,249 participants from the English Longitudinal Study of Ageing (ELSA) over a median 9.8-year follow-up, then validated in 1,506 incident ACD among 52,745 individuals from the UK Biobank (UKB) over median 13.7 years. NEFL and RPS6KB1 were robustly associated with incident ACD; MMP12 was associated with vascular dementia in ELSA. Additional markers EDA2R and KIM1 (HAVCR1) were identified from sensitivity analyses. Combining NEFL and RPS6KB1 with other factors yielded high predictive accuracy (area under the curve (AUC)=0.871) for incident ACD. Replication in the UKB confirmed associations between identified proteins with various dementia subtypes. Results from reverse Mendelian Randomization also supported the role of several proteins as early dementia biomarkers. These findings underscore proteomics' potential in identifying novel risk screening targets for dementia.

What does artificial intelligence mean in rheumatology?

Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician's diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

The future of artificial intelligence in clinical nutrition

PURPOSE OF REVIEW

Artificial intelligence has reached the clinical nutrition field. To perform personalized medicine, numerous tools can be used. In this review, we describe how the physician can utilize the growing healthcare databases to develop deep learning and machine learning algorithms, thus helping to improve screening, assessment, prediction of clinical events and outcomes related to clinical nutrition.

RECENT FINDINGS

Artificial intelligence can be applied to all the fields of clinical nutrition. Improving screening tools, identifying malnourished cancer patients or obesity using large databases has been achieved. In intensive care, machine learning has been able to predict enteral feeding intolerance, diarrhea, or refeeding hypophosphatemia. The outcome of patients with cancer can also be improved. Microbiota and metabolomics profiles are better integrated with the clinical condition using machine learning. However, ethical considerations and limitations of the use of artificial intelligence should be considered.

SUMMARY

Artificial intelligence is here to support the decision-making process of health professionals. Knowing not only its limitations but also its power will allow precision medicine in clinical nutrition as well as in the rest of the medical practice.

Ethics and artificial intelligence

The relationship between ethics and artificial intelligence in medicine is a crucial and complex topic that falls within its broader context. Ethics in medical artificial intelligence (AI) involves ensuring that technologies are safe, fair, and respect patient privacy. This includes concerns about the accuracy of diagnoses provided by artificial intelligence, fairness in patient treatment, and protection of personal health data. Advances in artificial intelligence can significantly improve healthcare, from more accurate diagnoses to personalized treatments. However, it is essential that developments in medical artificial intelligence are carried out with strong ethical consideration, involving healthcare professionals, artificial intelligence experts, patients, and ethics specialists to guide and oversee their implementation. Finally, transparency in artificial intelligence algorithms and ongoing training for medical professionals are fundamental.

Bladder Cancer and Artificial Intelligence: Emerging Applications

Bladder cancer is a common and heterogeneous disease that poses a significant burden to the patient and health care system. Major unmet needs include effective early detection strategy, imprecision of risk stratification, and treatment-associated morbidities. The existing clinical paradigm is imprecise, which results in missed tumors, suboptimal therapy, and disease progression. Artificial intelligence holds immense potential to address many unmet needs in bladder cancer, including early detection, risk stratification, treatment planning, quality assessment, and outcome prediction. Despite recent advances, extensive work remains to affirm the efficacy of artificial intelligence as a decision-making tool for bladder cancer management.

New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology

Until recently the application of artificial intelligence (AI) in precision oncology was confined to activities in drug development and had limited impact on the personalisation of therapy. Now, a number of approaches have been proposed for the personalisation of drug and cell therapies with AI applied to therapy design, planning and delivery at the patient's bedside. Some drug and cell-based therapies are already tuneable to the individual to optimise efficacy, to reduce toxicity, to adapt the dosing regime, to design combination therapy approaches and, preclinically, even to personalise the receptor design of cell therapies. Developments in AI-based healthcare are accelerating through the adoption of foundation models, and generalist medical AI models have been proposed. The application of these approaches in therapy design is already being explored and realistic short-term advances include the application to the personalised design and delivery of drugs and cell therapies. With this pace of development, the limiting step to adoption will likely be the capacity and appropriateness of regulatory frameworks. This article explores emerging concepts and new ideas for the regulation of AI-enabled personalised cancer therapies in the context of existing and in development governance frameworks.

Status quo and future directions of digitalization in gynecology and obstetrics in Germany: a survey of the commission Digital Medicine of the German Society for Gynecology and Obstetrics

PURPOSE

Digitalization plays a critical role and is beginning to impact every part of the patient journey, from drug discovery and data collection to treatment and patient-reported outcomes. We aimed to evaluate the status quo and future directions of digital medicine in the specialty of gynecology and obstetrics in Germany.

METHODS

An anonymous questionnaire was distributed via the German Society of Gynecology and Obstetrics newsletter in December 2022. The questionnaire covered the domains baseline demographic information, telemedicine, digital health applications (DIGAs), and future expectations.

RESULTS

In all, 91 participants completed the survey. Median age was 34 years; 67.4% (60 of 89) were female and 32.6% (29 of 89) were male. About 10% (9 of 88) have prescribed DIGAs to date and 14% (12 of 86) offer telemedical appointments. Among those who do not use digital medicine, very few plan to do so in the near future. Reasons include missing software interfaces, lack of time to try out new things, lack of knowledge, lack of monetary compensation (66.3%), and employee concerns. A majority agreed that digitalization will help to save time and improve patient care and that intelligent algorithms will aid clinicians in providing patient care to women.

CONCLUSIONS

The status quo and future directions of digital medicine in gynecology and obstetrics in Germany are characterized by contradicting expectations regarding the benefits of digital medicine and its actual implementation in clinical routine. This represents an important call to action to meet the requirements of modern patient care.

Prenatal and postnatal genetic testing toward personalized care: The non-invasive perinatal testing

This article investigates how non-invasive prenatal testing and the incorporation of genomic sequencing into newborn screening postnatally are transforming perinatal care. They improve the accuracy of prenatal and neonatal screening, allowing for early interventions and personalized therapies. Non-invasive prenatal testing before birth and saliva-sample-based newborn genomic sequencing after birth can be collectively referred to as non-invasive perinatal testing. Non-invasive prenatal testing is particularly useful for aneuploidy, whereas performance markers worsen as DNA abnormalities shrink in size. Screening for clinically actionable diseases in childhood would be crucial to personalized medical therapy, as the postnatal period remains appropriate for screening for the great majority of monogenic disorders. While genomic data can help diagnose uncommon diseases, challenges like ethics and equity necessitate joint approaches for appropriate integration in this revolutionary journey toward personalized care.

DNA origami: a tool to evaluate and harness transcription factors

Alongside other players, such as CpG methylation and the "histone code," transcription factors (TFs) represent a key feature of gene regulation. TFs are implicated in critical cellular processes, ranging from cell death, growth, and differentiation, up to intranuclear signaling of steroid and other hormones, physical entities, and hypoxia regulation. Notwithstanding an extensive body of research in this field, several questions and therapeutic options remain unanswered and unexplored, respectively. Of note, many of these TFs represent therapeutic targets, which are either difficult to be pharmacologically tackled or are still not drugged via traditional approaches, such as small-molecule inhibition. Upon providing a brief overview of TFs, we focus herein on how synthetic biology/medicine could assist in their study as well as their therapeutic targeting. Specifically, we contend that DNA origami, i.e., a novel synthetic DNA nanotechnological approach, represents an excellent synthetic biology/medicine tool to accomplish the above goals, since it can harness several vital characteristics of DNA: DNA polymerization, DNA complementarity, DNA "programmability," and DNA "editability." In doing so, DNA origami can be applied to study TF dynamics during DNA transcription, to elucidate xeno-nucleic acids with distinct scaffolds and unconventional base pairs, and to use TFs as competitors of oncogene-engaged promoters. Overall, because of their potential for high-throughput design and their favorable pharmacodynamic and pharmacokinetic properties, DNA origami can be a novel armory for TF-related drug design. Last, we discuss future trends in the field, such as RNA origami and innovative DNA origami-based therapeutic delivery approaches.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

Local read haplotagging enables accurate long-read small variant calling

Long-read sequencing technology has enabled variant detection in difficult-to-map regions of the genome and enabled rapid genetic diagnosis in clinical settings. Rapidly evolving third-generation sequencing platforms like Pacific Biosciences (PacBio) and Oxford nanopore technologies (ONT) are introducing newer platforms and data types. It has been demonstrated that variant calling methods based on deep neural networks can use local haplotyping information with long-reads to improve the genotyping accuracy. However, using local haplotype information creates an overhead as variant calling needs to be performed multiple times which ultimately makes it difficult to extend to new data types and platforms as they get introduced. In this work, we have developed a local haplotype approximate method that enables state-of-the-art variant calling performance with multiple sequencing platforms including PacBio Revio system, ONT R10.4 simplex and duplex data. This addition of local haplotype approximation makes DeepVariant a universal variant calling solution for long-read sequencing platforms.