Are innovation and new technologies in precision medicine paving a new era in patients centric care?

Effect of pharmacogenomic testing on the clinical treatment of patients with depressive disorder: A randomized clinical trial

BACKGROUND

Pharmacotherapy is one of the primary treatment modalities for depression. However, there is considerable variability in the individual response to antidepressant medications. Personalized medicine guided by pharmacogenomic testing may hold promise in addressing this issue.

METHODS

In this study, 665 depressive patients were randomly enrolled into two groups: the pharmacogenomic testing group (n = 333) and the control group (n = 332). In the testing group, participants underwent pharmacogenomic testing, and clinicians customized the treatment plan with the result, while the control group relied solely on clinicians' experience. The primary outcomes were the proportion of remission and response, assessed with Hamilton Depression Rating Scale (HDRS). The secondary outcomes included changes in HDRS scores over time and frequency of adverse drug reactions by the participants.

RESULTS

At week 8, the pharmacogenomic testing group showed significantly higher remission rates (24.0 % v.s. 15.1 %; RR = 1.117; P = 0.007) and response rates (39.3 % v.s. 25.7 %; RR = 1.225; P < 0.001) compared to the control group. By week 12, the pharmacogenomic testing group continued to demonstrate significant advantages in remission (31.0 % v.s. 20.0 %; RR = 1.159; P = 0.003) and response (48.7 % v.s. 37.3 %; RR = 1.224; P = 0.006). Additionally, adverse drug reactions were less frequent in the pharmacogenomic testing group.

LIMITATIONS

This study is not blind to clinicians and it's a single-center study.

CONCLUSIONS

Pharmacogenomic testing-guided drug therapy can provide greater assistance in the treatment of depression.

Advancing drug discovery with deep attention neural networks

In the dynamic field of drug discovery, deep attention neural networks are revolutionizing our approach to complex data. This review explores the attention mechanism and its extended architectures, including graph attention networks (GATs), transformers, bidirectional encoder representations from transformers (BERT), generative pre-trained transformers (GPTs) and bidirectional and auto-regressive transformers (BART). Delving into their core principles and multifaceted applications, we uncover their pivotal roles in catalyzing de novo drug design, predicting intricate molecular properties and deciphering elusive drug-target interactions. Despite challenges, these attention-based architectures hold unparalleled promise to drive transformative breakthroughs and accelerate progress in pharmaceutical research.

Approaches of wearable and implantable biosensor towards of developing in precision medicine

In the relentless pursuit of precision medicine, the intersection of cutting-edge technology and healthcare has given rise to a transformative era. At the forefront of this revolution stands the burgeoning field of wearable and implantable biosensors, promising a paradigm shift in how we monitor, analyze, and tailor medical interventions. As these miniature marvels seamlessly integrate with the human body, they weave a tapestry of real-time health data, offering unprecedented insights into individual physiological landscapes. This log embarks on a journey into the realm of wearable and implantable biosensors, where the convergence of biology and technology heralds a new dawn in personalized healthcare. Here, we explore the intricate web of innovations, challenges, and the immense potential these bioelectronics sentinels hold in sculpting the future of precision medicine.

Extracellular Proteomic Profiling from the Erythrocytes Infected with Plasmodium Falciparum 3D7 Holds Promise for the Detection of Biomarkers

Plasmodium falciparum (P. falciparum), which causes the most severe form of malaria, if left untreated, has 24 h window in which it can cause severe illness and even death. The aim of this study was to create the most comprehensive and informative secretory-proteome possible by combining high-accuracy and high-sensitivity protein identification technology. In this study, we used Plasmodium falciparum 3D7 (Pf3D7) as the model parasite to develop a label-free quantification proteomic strategy with the main goal of identifying Pf3D7 proteins that are supposed to be secreted outside the infected erythrocytes in the spent media culture during the in-vitro study. The spent culture media supernatant was subjected to differential and ultra-centrifugation steps followed by total protein extraction, estimation, and in-solution digestion using trypsin, digested peptides were analyzed using Nano-LC coupled with ESI for MS/MS. MS/MS spectra were processed using Maxquant software (v2.1.4.0.). Non-infected erythrocytes incubated spent cultured media supernatant were considered as control. Out of discovered 38 proteins, proteins belonging to P. falciparum spp. were EGF-like protein (C0H544), Endoplasmic reticulum chaperone GRP170 (C0H5H0), Small GTP-binding protein sar1 (Q8I1S0), Erythrocyte membrane protein 1, PfEMP1 (Q8I639), aldehyde reductase (Q8ID61), Conserved Plasmodium proteins (Q8IEH3, Q8ILD1), Antigen 332, DBL-like protein (Q8IHN4), Fe-S cluster assembly protein (Q8II78), identified and chosen for further in-depth investigation. This study highlights the value of secretory Plasmodium proteins play crucial roles in various aspects of the disease progression and host-pathogen interactions which can serve as diagnostic markers for malaria infection.

Advancing Point-of-Care Applications with Droplet Microfluidics: From Single-Cell to Multicellular Analysis

Recent advances in single-cell and multicellular microfluidics technology have provided powerful tools for studying cancer biology and immunology. The ability to create controlled microenvironments, perform high-throughput screenings, and monitor cellular interactions at the single-cell level has significantly advanced our understanding of tumor biology and immune responses. We discuss cutting-edge multicellular and single-cell microfluidic technologies and methodologies utilized to investigate cancer-immune cell interactions and assess the effectiveness of immunotherapies. We explore the advantages and limitations of the wide range of 3D spheroid and single-cell microfluidic models recently developed, highlighting the various approaches in device generation and applications in immunotherapy screening for potential opportunities for point-of-care approaches.

Unveiling the genetic tapestry: Rare disease genomics of spinal muscular atrophy and phenylketonuria proteins

Rare diseases, defined by their low prevalence, present significant challenges, including delayed detection, expensive treatments, and limited research. This study delves into the genetic basis of two noteworthy rare diseases in Saudi Arabia: Phenylketonuria (PKU) and Spinal Muscular Atrophy (SMA). PKU, resulting from mutations in the phenylalanine hydroxylase (PAH) gene, exhibits geographical variability and impacts intellectual abilities. SMA, characterized by motor neuron loss, is linked to mutations in the survival of motor neuron 1 (SMN1) gene. Recognizing the importance of unveiling signature genomics in rare diseases, we conducted a quantitative study on PAH and SMN1 proteins of multiple organisms by employing various quantitative techniques to assess genetic variations. The derived signature-genomics contributes to a deeper understanding of these critical genes, paving the way for enhanced diagnostics for disorders associated with PAH and SMN1.

Advancing Precision Medicine: Algebraic Topology and Differential Geometry in Radiology and Computational Pathology

Precision medicine aims to provide personalized care based on individual patient characteristics, rather than guideline-directed therapies for groups of diseases or patient demographics. Images-both radiology- and pathology-derived-are a major source of information on presence, type, and status of disease. Exploring the mathematical relationship of pixels in medical imaging ("radiomics") and cellular-scale structures in digital pathology slides ("pathomics") offers powerful tools for extracting both qualitative and, increasingly, quantitative data. These analytical approaches, however, may be significantly enhanced by applying additional methods arising from fields of mathematics such as differential geometry and algebraic topology that remain underexplored in this context. Geometry's strength lies in its ability to provide precise local measurements, such as curvature, that can be crucial for identifying abnormalities at multiple spatial levels. These measurements can augment the quantitative features extracted in conventional radiomics, leading to more nuanced diagnostics. By contrast, topology serves as a robust shape descriptor, capturing essential features such as connected components and holes. The field of topological data analysis was initially founded to explore the shape of data, with functional network connectivity in the brain being a prominent example. Increasingly, its tools are now being used to explore organizational patterns of physical structures in medical images and digitized pathology slides. By leveraging tools from both differential geometry and algebraic topology, researchers and clinicians may be able to obtain a more comprehensive, multi-layered understanding of medical images and contribute to precision medicine's armamentarium.

Precision Medicine Approaches in Cardiology and Personalized Therapies for Improved Patient Outcomes: A systematic review

Personalized medicine is a novel and rapidly evolving approach to clinical practice that involves making decisions about disease prediction, prevention, diagnosis, and treatment by utilizing modern technologies. The concepts of precision medicine have grown as a result of ongoing developments in genomic analysis, molecular diagnostics, and technology. These advancements have enabled a deeper understanding and interpretation of the human genome, allowing for a personalized approach to clinical care. The primary objective of this research is to assess personalized medicine in terms of its indications, advantages, practical clinical uses, potential future directions, problems, and effects on healthcare. An extensive analysis of the scientific literature regarding this topic demonstrated the new medical approach's relevance and usefulness, as well as the fact that personalized medicine is becoming increasingly prevalent in various sectors. The online, internationally indexed databases PubMed and Cochrane Reviews were used to conduct searches for and critically evaluate the most relevant published research including original papers and reviews in the scientific literature. The findings suggest that precision medicine has a lot of potential and its implementation lowers the incidence of stroke as well as coronary heart disease and improves patient health in cardiology.

Building a Competency Framework to Integrate Inter-disciplinary Precision Medicine Capabilities into the Medical Technology and Pharmaceutical Industry

INTRODUCTION

Integration of precision medicine (PM) competencies across the Medical Technology and Pharmaceutical industry is critical to enable industry professionals to understand and develop the skills needed to navigate the opportunities arising from rapid scientific and technological innovation in PM. Our objective was to identify the key competency domains required by industry professionals to enable them to upskill themselves in PM-related aspects of their roles.

METHODS

A desktop research review of current literature, curriculum, and healthcare trends identified a core set of domains and subdomains related to PM competencies that were consistent across multiple disciplines and competency frameworks. A survey was used to confirm the applicability of these domains to the cross-functional and multi-disciplinary work practices of industry professionals. Companies were requested to trial the domains to determine their relevance in practice and feedback was obtained.

RESULTS

Four PM-relevant domains were identified from the literature review: medical science and technology; translational and clinical application; governance and regulation and professional practice. Survey results refined these domains, and case studies within companies confirmed the potential for this framework to be used as an adjunct to current role specific competency frameworks to provide a specific focus on needed PM capabilities.

CONCLUSION

The framework was well accepted by local industry as a supplement to role specific competency frameworks to provide a structure on how to integrate new and evolving technologies into their current workforce development planning and build a continuous learning and cross-disciplinary mindset.

Definitions of digital biomarkers: a systematic mapping of the biomedical literature

BACKGROUND

Technological devices such as smartphones, wearables and virtual assistants enable health data collection, serving as digital alternatives to conventional biomarkers. We aimed to provide a systematic overview of emerging literature on 'digital biomarkers,' covering definitions, features and citations in biomedical research.

METHODS

We analysed all articles in PubMed that used 'digital biomarker(s)' in title or abstract, considering any study involving humans and any review, editorial, perspective or opinion-based articles up to 8 March 2023. We systematically extracted characteristics of publications and research studies, and any definitions and features of 'digital biomarkers' mentioned. We described the most influential literature on digital biomarkers and their definitions using thematic categorisations of definitions considering the Food and Drug Administration Biomarkers, EndpointS and other Tools framework (ie, data type, data collection method, purpose of biomarker), analysing structural similarity of definitions by performing text and citation analyses.

RESULTS

We identified 415 articles using 'digital biomarker' between 2014 and 2023 (median 2021). The majority (283 articles; 68%) were primary research. Notably, 287 articles (69%) did not provide a definition of digital biomarkers. Among the 128 articles with definitions, there were 127 different ones. Of these, 78 considered data collection, 56 data type, 50 purpose and 23 included all three components. Those 128 articles with a definition had a median of 6 citations, with the top 10 each presenting distinct definitions.

CONCLUSIONS

The definitions of digital biomarkers vary significantly, indicating a lack of consensus in this emerging field. Our overview highlights key defining characteristics, which could guide the development of a more harmonised accepted definition.

Vascular access device type for systemic anti-cancer therapies in cancer patients: A scoping review

BACKGROUND

Patients with cancer can expect to receive numerous invasive vascular access procedures for intravenous therapy and clinical diagnostics. Due to the increased incidence and prevalence of cancer globally there will be significantly more people who require first-line intravenous chemotherapy over the next ten years.

METHODS

Our objective was to determine the types of evidence that exist for the vascular access device (VAD) type for the delivery of systemic anti-cancer therapy (SACT) in cancer patients. We used JBI scoping review methodology to identify the types of VADs used for SACT and with a specific search strategy included articles from 2012-2022 published in the English language. We identify (i) type of VADs used for SACT delivery (ii) the type of insertion and post-insertion complications (iii) the geographical location and clinical environment (iv) and whether VAD choice impacts on quality of life (QOL). Findings were presented using the PAGER framework.

MAIN FINDINGS

Our search strategy identified 10,390 titles, of these, 5318 duplicates were removed. The remaining 5072 sources were screened for eligibility, 240 articles met the inclusion criteria. The most common design include retrospective study designs (n = 91) followed by prospective study designs (n = 31). We found 28 interventional studies with 21 registered in a clinical trial registry and identified no core outcome sets papers specific to VAD for SACT. The most prevalent publications were those that featured two or more VAD types (n = 70), followed by tunnelled intravenous VADs (n = 67). Of 38 unique complications identified, the most frequent catheter related complication was catheter related thrombosis (n = 178, 74%), followed by infection (n = 170, 71%). The county where the most publications originated from was China (n = 62) with one randomized controlled multicenter study from a comprehensive cancer centre. Of the thirty three studies that included QOL we found 4 which reported on body image. No QOL measurement tools specific to the process of SACT administration via VAD are available INTERPRETATION: Our findings suggest a systematic review and meta-analysis of VAD use for intravenous SACT can be considered. However, the development of a core outcome set for SACT should be prioritised. Funding for high quality programs of research for VAD in cancer are needed. Comprehensive cancer centres should lead this research agenda.

Pulmonary Diseases in Older Patients: Understanding and Addressing the Challenges

As the global population ages, pulmonary diseases among older people have emerged as a significant and growing public health concern. The increasing incidence of these conditions has led to higher rates of morbidity and mortality among older adults. This perspective study offers a thorough overview of the prevalent pulmonary diseases affecting the elderly demographic. It delves into the challenges encountered during the diagnosis and management of these conditions in older individuals, considering factors such as comorbidities, functional limitations, and medication complexities. Furthermore, innovative strategies and personalized interventions such as precision medicine, advanced therapies, telemedicine solutions, and patient-centered support systems aimed at enhancing the care provided to older individuals grappling with pulmonary disorders are thoroughly explored. By addressing the unique needs and complexities of this vulnerable population, healthcare systems can strive towards improving outcomes and enhancing the quality of life for elderly individuals affected by pulmonary diseases.

Digging into the intrinsic capacity concept: Can it be applied to Alzheimer's disease?

Historically, aging research has largely centered on disease pathology rather than promoting healthy aging. The World Health Organization's (WHO) policy framework (2015-2030) underscores the significance of fostering the contributions of older individuals to their families, communities, and economies. The WHO has introduced the concept of intrinsic capacity (IC) as a key metric for healthy aging, encompassing five primary domains: locomotion, vitality, sensory, cognitive, and psychological. Past AD research, constrained by methodological limitations, has focused on single outcome measures, sidelining the complexity of the disease. Our current scientific milieu, however, is primed to adopt the IC concept. This is due to three critical considerations: (I) the decline in IC is linked to neurocognitive disorders, including AD, (II) cognition, a key component of IC, is deeply affected in AD, and (III) the cognitive decline associated with AD involves multiple factors and pathophysiological pathways. Our study explores the application of the IC concept to AD patients, offering a comprehensive model that could revolutionize the disease's diagnosis and prognosis. There is a dearth of information on the biological characteristics of IC, which are a result of complex interactions within biological systems. Employing a systems biology approach, integrating omics technologies, could aid in unraveling these interactions and understanding IC from a holistic viewpoint. This comprehensive analysis of IC could be leveraged in clinical settings, equipping healthcare providers to assess AD patients' health status more effectively and devise personalized therapeutic interventions in accordance with the precision medicine paradigm. We aimed to determine whether the IC concept could be extended from older individuals to patients with AD, thereby presenting a model that could significantly enhance the diagnosis and prognosis of this disease.

A mathematically rigorous algorithm to define, compute and assess relevance of the probable dissociation constants in characterizing a biochemical network

Metabolism results from enzymatic- and non-enzymatic interactions of several molecules, is easily parameterized with the dissociation constant and occurs via biochemical networks. The dissociation constant is an empirically determined parameter and cannot be used directly to investigate in silico models of biochemical networks. Here, we develop and present an algorithm to define, compute and assess the relevance of the probable dissociation constant for every reaction of a biochemical network. The reactants and reactions of this network are modelled by a stoichiometry number matrix. The algorithm computes the null space and then serially generates subspaces by combinatorially summing the spanning vectors that are non-trivial and unique. This is done until the terms of each row either monotonically diverge or form an alternating sequence whose terms can be partitioned into subsets with almost the same number of oppositely signed terms. For a selected null space-generated subspace the algorithm utilizes several statistical and mathematical descriptors to select and bin terms from each row into distinct outcome-specific subsets. The terms of each subset are summed, mapped to the real-valued open interval [Formula: see text] and used to populate a reaction-specific outcome vector. The p1-norm for this vector is then the probable dissociation constant for this reaction. These steps are continued until every reaction of a modelled network is unambiguously annotated. The assertions presented are complemented by computational studies of a biochemical network for aerobic glycolysis. The fundamental premise of this work is that every row of a null space-generated subspace is a valid reaction and can therefore, be modelled as a reaction-specific sequence vector with a dimension that corresponds to the cardinality of the subspace after excluding all trivial- and redundant-vectors. A major finding of this study is that the row-wise sum or the sum of the terms contained in each reaction-specific sequence vector is mapped unambiguously to a positive real number. This means that the probable dissociation constants, for all reactions, can be directly computed from the stoichiometry number matrix and are suitable indicators of outcome for every reaction of the modelled biochemical network. Additionally, we find that the unambiguous annotation for a biochemical network will require a minimum number of iterations and will determine computational complexity.

A Comprehensive Review of Inhaled Nitric Oxide Therapy: Current Trends, Challenges, and Future Directions

This comprehensive review explores the multifaceted landscape of inhaled nitric oxide (iNO) therapy, tracing its historical evolution, mechanisms of action, clinical applications, challenges, and future directions. The nitric oxide signaling pathway, characterized by vasodilatory effects and anti-inflammatory properties, forms the foundation of iNO's therapeutic efficacy. Clinical applications are found in neonatal respiratory distress syndrome, pulmonary hypertension, and acute respiratory distress syndrome, showcasing its versatility. However, challenges, including cost considerations, technical intricacies, safety concerns, and resistance, highlight the nuanced landscape surrounding iNO therapy. Implications for clinical practice underscore the need for a tailored and evidence-based approach, considering individual patient characteristics and indications. Recommendations for future research emphasize ongoing exploration, novel indications, and the development of targeted therapies. In conclusion, this review positions iNO as a dynamic and adaptable intervention, poised to reshape therapeutic strategies and enhance patient outcomes in critical care.

Novel Strategies Using Sagacious Targeting for Site-Specific Drug Delivery in Breast Cancer Treatment: Clinical Potential and Applications

For more than a decade, researchers have been working to achieve new strategies and smart targeting drug delivery techniques and technologies to treat breast cancer (BC). Nanotechnology presents a hopeful strategy for targeted drug delivery into the building of new therapeutics using the properties of nanomaterials. Nanoparticles are of high regard in the field of diagnosis and the treatment of cancer. The use of these nanoparticles as an encouraging approach in the treatment of various cancers has drawn the interest of researchers in recent years. In order to achieve the maximum therapeutic effectiveness in the treatment of BC, combination therapy has also been adopted, leading to minimal side effects and thus an enhancement in the quality of life for patients. This review article compares, discusses and criticizes the approaches to treat BC using novel design strategies and smart targeting of site-specific drug delivery systems.

Green synthesis of Ag and Cu-doped Bismuth oxide nanoparticles: Revealing synergistic antimicrobial and selective cytotoxic potentials for biomedical advancements

BACKGROUND

Nanotechnology has emerged as a transformative realm of exploration across diverse scientific domains. A particular focus lies on metal oxide nanoparticles, which boast distinctive physicochemical attributes on the nanoscale. Of note, green synthesis has emerged as a promising avenue, leveraging plant extracts as both reduction and capping agents. This approach offers environmentally friendly and cost-effective avenues for generating monodispersed nanoparticles with precise morphologies.

METHODS

In this investigation, we embarked on the synthesis of Bismuth oxide nanoparticles, both in their pure form and doped with silver (Ag) and copper (Cu). This synthesis harnessed the potential of Biebersteinia multifida extract as a versatile reducing agent. To comprehensively characterize the synthesized nanoparticles, a suite of analytical techniques was employed, including energy-dispersive X-ray spectroscopy, field-emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), UV-Vis spectroscopy, and Raman spectroscopy.

RESULTS

The synthesized nanoparticles underwent a rigorous assessment. Their antibacterial attributes were probed, revealing a pronounced enhancement in antibiofilm activity against Pseudomonas aeruginosa and Staphylococcus aureus bacteria upon metal nanoparticle doping. Furthermore, their potential for combating cancer was scrutinized, with the nanoparticles exhibiting selective cytotoxicity towards cancer cells, U87, compared to normal 3T3 cells. Notably, among the doped nanoparticles, Cu-doped variants demonstrated the highest potency, further underscoring their promising potential.

CONCLUSION

In conclusion, the present study underscores the efficacy of green synthesized Bismuth oxide nanoparticles, particularly those doped with Ag and Cu, in augmenting antibacterial efficacy, bolstering biofilm inhibition, and manifesting selective cytotoxicity against cancer cells. These findings portend a promising trajectory for these nanoparticles in the spheres of biomedicine and therapeutics. As we look ahead, a deeper elucidation of their mechanistic underpinnings and in vivo investigations are essential to fully unlock their potential for forthcoming biomedical applications.

Navigating the Immune Maze: Pioneering Strategies for Unshackling Cancer Immunotherapy Resistance

Cancer immunotherapy has ushered in a transformative era in oncology, offering unprecedented promise and opportunities. Despite its remarkable breakthroughs, the field continues to grapple with the persistent challenge of treatment resistance. This resistance not only undermines the widespread efficacy of these pioneering treatments, but also underscores the pressing need for further research. Our exploration into the intricate realm of cancer immunotherapy resistance reveals various mechanisms at play, from primary and secondary resistance to the significant impact of genetic and epigenetic factors, as well as the crucial role of the tumor microenvironment (TME). Furthermore, we stress the importance of devising innovative strategies to counteract this resistance, such as employing combination therapies, tailoring immune checkpoints, and implementing real-time monitoring. By championing these state-of-the-art methods, we anticipate a paradigm that blends personalized healthcare with improved treatment options and is firmly committed to patient welfare. Through a comprehensive and multifaceted approach, we strive to tackle the challenges of resistance, aspiring to elevate cancer immunotherapy as a beacon of hope for patients around the world.

The Advent of Artificial Intelligence in Diabetic Foot Medicine: A New Horizon, a New Order, or a False Dawn?

In a world where automation is becoming increasingly common, easier collection of mass of data and powerful computer processing has meant a transformation in the field of artificial intelligence (AI). The diabetic foot is a multifactorial problem; its issues render it suitable for analysis, interrogation, and development of AI. The latter has the potential to deliver many solutions to issues of delayed diagnosis, compliance, and defining preventative treatments. We describe the use of AI and the development of artificial neural networks that may supplement the failed networks in the diabetic foot. The potential of this technology, current developing applications, and their limitations for diabetic foot care are suggested.

Revolutionizing orthopedics: a comprehensive review of robot-assisted surgery, clinical outcomes, and the future of patient care

Robotic-assisted orthopedic surgery (RAOS) is revolutionizing the field, offering the potential for increased accuracy and precision and improved patient outcomes. This comprehensive review explores the historical perspective, current robotic systems, advantages and limitations, clinical outcomes, patient satisfaction, future developments, and innovation in RAOS. Based on systematic reviews, meta-analyses, and recent studies, this article highlights the most significant findings and compares RAOS to conventional techniques. As robotic-assisted surgery continues to evolve, clinicians and researchers must stay informed and adapt their practices to provide optimal patient care. Evidence from published studies corroborates these claims, highlighting superior component positioning, decreased incidence of complications, and heightened patient satisfaction. However, challenges such as costs, learning curves, and technical issues must be resolved to fully capitalize on these advantages.

Attention is all you need: utilizing attention in AI-enabled drug discovery

Recently, attention mechanism and derived models have gained significant traction in drug development due to their outstanding performance and interpretability in handling complex data structures. This review offers an in-depth exploration of the principles underlying attention-based models and their advantages in drug discovery. We further elaborate on their applications in various aspects of drug development, from molecular screening and target binding to property prediction and molecule generation. Finally, we discuss the current challenges faced in the application of attention mechanisms and Artificial Intelligence technologies, including data quality, model interpretability and computational resource constraints, along with future directions for research. Given the accelerating pace of technological advancement, we believe that attention-based models will have an increasingly prominent role in future drug discovery. We anticipate that these models will usher in revolutionary breakthroughs in the pharmaceutical domain, significantly accelerating the pace of drug development.

Transform diabetes care with precision medicine

Background and Aims

Diabetes is a global concern. This article took a closer look at diabetes and precision medicine.

Methods

A literature search of studies related to the use of precision medicine in diabetes care was conducted in various databases (PubMed, Google Scholar, and Scopus).

Results

Precision medicine encompasses the integration of a wide array of personal data, including clinical, lifestyle, genetic, and various biomarker information. Its goal is to facilitate tailored treatment approaches using contemporary diagnostic and therapeutic techniques that specifically target patients based on their genetic makeup, molecular markers, phenotypic traits, or psychosocial characteristics. This article not only highlights significant advancements but also addresses key challenges, particularly focusing on the technologies that contribute to the realization of personalized and precise diabetes care.

Conclusion

For the successful implementation of precision diabetes medicine, collaboration and coordination among multiple stakeholders are crucial.

ReDirection: an R-package to compute the probable dissociation constant for every reaction of a user-defined biochemical network

Biochemical networks integrate enzyme-mediated substrate conversions with non-enzymatic complex formation and disassembly to accomplish complex biochemical and physiological functions. The choice of parameters and constraints used in most of these studies is numerically motivated and network-specific. Although sound in theory, the outcomes that result depart significantly from the intracellular milieu and are less likely to retain relevance in a clinical setting. There is a need for a computational tool which is biochemically relevant, mathematically rigorous, and unbiased, and can ascribe functionality to and generate potentially testable hypotheses for a user-defined biochemical network. Here, we present "ReDirection," an R-package which computes the probable dissociation constant for every reaction of a biochemical network directly from a null space-generated subspace of the stoichiometry number matrix of the modeled network. "ReDirection" delineates this subspace by excluding all trivial and redundant or duplicate occurrences of non-trivial vectors, combinatorially summing the vectors that remain and verifying that the upper or lower bounds of the sequence of terms formed by each row of this subspace belong to the open real-valued intervals - ∞ , - 1 or 1 , ∞ or whether the number of terms that are differently signed are almost equal. "ReDirection" iterates these steps until these bounds are consistent and unambiguous for all reactions of the modeled biochemical network. Thereafter, "ReDirection" filters the terms from each row of this subspace, bins them to outcome-specific subsets, sums and maps this to an outcome-specific reaction vector, and computes the p1-norm, which is the probable dissociation constant for a reaction. "ReDirection" works on first principles, does not discriminate between enzymatic and non-enzymatic reactions, offers a biochemically relevant and mathematically rigorous environment to explore user-defined biochemical networks under baseline and perturbed conditions, and can be used to address empirically intractable biochemical problems. The utility and relevance of "ReDirection" are highlighted by numerical studies on stoichiometric number models of biochemical networks of galactose metabolism and heme and cholesterol biosynthesis. "ReDirection" is freely available and accessible from the comprehensive R archive network (CRAN) with the URL (https://cran.r-project.org/package=ReDirection).

Biomaterials-mediated CRISPR/Cas9 delivery: recent challenges and opportunities in gene therapy

The use of biomaterials in delivering CRISPR/Cas9 for gene therapy in infectious diseases holds tremendous potential. This innovative approach combines the advantages of CRISPR/Cas9 with the protective properties of biomaterials, enabling accurate and efficient gene editing while enhancing safety. Biomaterials play a vital role in shielding CRISPR/Cas9 components, such as lipid nanoparticles or viral vectors, from immunological processes and degradation, extending their effectiveness. By utilizing the flexibility of biomaterials, tailored systems can be designed to address specific genetic diseases, paving the way for personalized therapeutics. Furthermore, this delivery method offers promising avenues in combating viral illnesses by precisely modifying pathogen genomes, and reducing their pathogenicity. Biomaterials facilitate site-specific gene modifications, ensuring effective delivery to infected cells while minimizing off-target effects. However, challenges remain, including optimizing delivery efficiency, reducing off-target effects, ensuring long-term safety, and establishing scalable production techniques. Thorough research, pre-clinical investigations, and rigorous safety evaluations are imperative for successful translation from the laboratory to clinical applications. In this review, we discussed how CRISPR/Cas9 delivery using biomaterials revolutionizes gene therapy and infectious disease treatment, offering precise and safe editing capabilities with the potential to significantly improve human health and quality of life.

Applications of multi-omics analysis in human diseases

Multi-omics usually refers to the crossover application of multiple high-throughput screening technologies represented by genomics, transcriptomics, single-cell transcriptomics, proteomics and metabolomics, spatial transcriptomics, and so on, which play a great role in promoting the study of human diseases. Most of the current reviews focus on describing the development of multi-omics technologies, data integration, and application to a particular disease; however, few of them provide a comprehensive and systematic introduction of multi-omics. This review outlines the existing technical categories of multi-omics, cautions for experimental design, focuses on the integrated analysis methods of multi-omics, especially the approach of machine learning and deep learning in multi-omics data integration and the corresponding tools, and the application of multi-omics in medical researches (e.g., cancer, neurodegenerative diseases, aging, and drug target discovery) as well as the corresponding open-source analysis tools and databases, and finally, discusses the challenges and future directions of multi-omics integration and application in precision medicine. With the development of high-throughput technologies and data integration algorithms, as important directions of multi-omics for future disease research, single-cell multi-omics and spatial multi-omics also provided a detailed introduction. This review will provide important guidance for researchers, especially who are just entering into multi-omics medical research.

A bibliometric analysis of the global impact of metaproteomics research

Background

Metaproteomics is a subfield in meta-omics that is used to characterize the proteome of a microbial community. Despite its importance and the plethora of publications in different research area, scientists struggle to fully comprehend its functional impact on the study of microbiomes. In this study, bibliometric analyses are used to evaluate the current state of metaproteomic research globally as well as evaluate the specific contribution of Africa to this burgeoning research area. In this study, we use bibliometric analyses to evaluate the current state of metaproteomic research globally, identify research frontiers and hotspots, and further predict future trends in metaproteomics. The specific contribution of Africa to this research area was evaluated.

Methods

Relevant documents from 2004 to 2022 were extracted from the Scopus database. The documents were subjected to bibliometric analyses and visualization using VOS viewer and Biblioshiny package in R. Factors such as the trends in publication, country and institutional cooperation networks, leading scientific journals, author's productivity, and keywords analyses were conducted. The African publications were ranked using Field-Weighted Citation Impact (FWCI) scores.

Results

A total of 1,138 documents were included and the number of publications increased drastically from 2004 to 2022 with more publications (170) reported in 2021. In terms of publishers, Frontiers in Microbiology had the highest number of total publications (62). The United States of America (USA), Germany, China, and Canada, together with other European countries were the most productive. Institution-wise, the Helmholtz Zentrum für Umweltforschung, Germany had more publications while Max Plank Institute had the highest total collaborative link strength. Jehmlich N. was the most productive author whereas Hettich RL had the highest h-index of 63. Regarding Africa, only 2.2% of the overall publications were from the continent with more publication outputs from South Africa. More than half of the publications from the continent had an FWCI score ≥ 1.

Conclusion

The scientific outputs of metaproteomics are rapidly evolving with developed countries leading the way. Although Africa showed prospects for future progress, this could only be accelerated by providing funding, increased collaborations, and mentorship programs.

Advancements in portable instruments based on affinity-capture-migration and affinity-capture-separation for use in clinical testing and life science applications

The shift from testing at centralized diagnostic laboratories to remote locations is being driven by the development of point-of-care (POC) instruments and represents a transformative moment in medicine. POC instruments address the need for rapid results that can inform faster therapeutic decisions and interventions. These instruments are especially valuable in the field, such as in an ambulance, or in remote and rural locations. The development of telehealth, enabled by advancements in digital technologies like smartphones and cloud computing, is also aiding in this evolution, allowing medical professionals to provide care remotely, potentially reducing healthcare costs and improving patient longevity. One notable POC device is the lateral flow immunoassay (LFIA), which played a major role in addressing the COVID-19 pandemic due to its ease of use, rapid analysis time, and low cost. However, LFIA tests exhibit relatively low analytical sensitivity and provide semi-quantitative information, indicating either a positive, negative, or inconclusive result, which can be attributed to its one-dimensional format. Immunoaffinity capillary electrophoresis (IACE), on the other hand, offers a two-dimensional format that includes an affinity-capture step of one or more matrix constituents followed by release and electrophoretic separation. The method provides greater analytical sensitivity, and quantitative information, thereby reducing the rate of false positives, false negatives, and inconclusive results. Combining LFIA and IACE technologies can thus provide an effective and economical solution for screening, confirming results, and monitoring patient progress, representing a key strategy in advancing diagnostics in healthcare.

How can we deliver on the promise of precision medicine in oncology and beyond? A practical roadmap for action

Background

Precision medicine (PM) is a form of personalized medicine that recognizes that individuals with the same condition may have different underlying factors and uses molecular information to provide tailored treatments. This approach can improve treatment outcomes and transform lives through favorable risk/benefit ratios, avoidance of ineffective interventions, and possible cost savings, as evidenced in the field of lung cancer and other oncology/therapeutic settings, including cardiac disease, diabetes, and rare diseases. However, the potential benefits of PM have yet to be fully realized.

Discussion

There are many barriers to the implementation of PM in clinical practice, including fragmentation of the PM landscape, siloed approaches to address shared challenges, unwarranted variation in availability and access to PM, lack of standardization, and limited understanding of patients' experience and needs throughout the PM pathway. We believe that a diverse, intersectoral multistakeholder collaboration, with three main pillars of activity: generation of data to demonstrate the benefit of PM, education to support informed decision-making, and addressing barriers across the patient pathway, is necessary to reach the shared goal of making PM an accessible and sustainable reality. Besides healthcare providers, researchers, policymakers/regulators/payers, and industry representatives, patients in particular must be equal partners and should be central to the PM approach-from early research through to clinical trials and approval of new treatments-to ensure it represents their entire experience and identifies barriers, solutions, and opportunities at the point of delivery.

Conclusion

We propose a practical and iterative roadmap to advance PM and call for all stakeholders across the healthcare system to employ a collaborative, cocreated, patient-centered methodology to close gaps and fully realize the potential of PM.

Partnering with First Nations in Northern British Columbia Canada to Reduce Inequity in Access to Genomic Research

Indigenous-led, culturally safe health research and infrastructure are essential to address existing inequities and disparities for Indigenous Peoples globally. Biobanking, genomic research, and self-governance could reduce the existing divide and increase Indigenous participation in health research. While genomic research advances medicine, barriers persist for Indigenous patients to benefit. In northern BC, Canada, the Northern Biobank Initiative (NBI), with guidance from a Northern First Nations Biobank Advisory Committee (NFNBAC), has engaged in consultations with First Nations on biobanking and genomic research. Key informant interviews and focus groups conducted with First Nations leaders, Elders, Knowledge Keepers, and community members established culturally safe ways of biobanking and exploring genomic research. Strong support for a Northern British Columbia First Nations Biobank (NBCFNB) that will promote choice, inclusion, and access to health research opportunities emerged. The acceptance and enthusiasm for the development of this NBCFNB and its governance table highlight the shift towards Indigenous ownership and support of health research and its benefits. With engagement and partnership, community awareness, multigenerational involvement, and support from diverse and experienced healthcare leaders, the NBCFNB will establish this culturally safe, locally driven, and critically important research priority that may serve as an example for diverse Indigenous groups when designing their unique biobanking or genomic research opportunities.

A personalized pharmaco-epistatic network model of precision medicine

Precision medicine, the utilization of targeted treatments to address an individual's disease, relies on knowledge about the genetic cause of that individual's drug response. Here, we present a functional graph (FunGraph) theory to chart comprehensive pharmacogenetic architecture for each and every patient. FunGraph is the combination of functional mapping - a dynamic model for genetic mapping and evolutionary game theory guiding interactive strategies. It coalesces all pharmacogenetic factors into multilayer and multiplex networks that fully capture bidirectional, signed and weighted epistasis. It can visualize and interrogate how epistasis moves in the cell and how this movement leads to patient- and context-specific genetic architecture in response to organismic physiology. We discuss the future implementation of FunGraph to achieve precision medicine. Teaser: We present a functional graph (FunGraph) theory to draw a complete picture of pharmacogenetic architecture underlying interindividual variability in drug response. FunGraph can characterize how each gene acts and interacts with every other gene to mediate therapeutic response.

Potential Impact of Polymorphisms in Toll-like Receptors 2, 3, 4, 7, 9, miR-146a, miR-155, and miR-196a Genes on Osteoarthritis Susceptibility

Osteoarthritis (OA) is a progressive inflammatory disease of synovial joints and a leading cause of disability among adults. Inflammation-related genes, including genes for Toll-like receptors (TLRs), are tightly controlled by several microRNAs that, in addition to their pivotal role in the epigenetic regulation of target genes, are ligands for TLR activation and downstream signaling. Thus, we evaluated the association between OA risk and genetic variants in TLR2, TLR3, TLR4, TLR7, TLR9, and microRNAs that regulate TLRs signaling miR146a, miR155, and miR196a2. Our study group consisted of 95 surgically treated OA patients and a control group of 104 healthy individuals. Genetic polymorphisms were determined using TaqMan real-time PCR assays (Applied Biosystems). Adjusted logistic regression analysis demonstrated that polymorphisms in TLR4 rs4986790 (OR = 2.964, p = 0.006), TLR4 rs4986791 (OR = 8.766, p = 0.00001), and TLR7 rs385389 (OR = 1.579, p = 0.012) increased OA risk, while miR-196a2 rs11614913 (OR = 0.619, p = 0.034) was significantly associated with decreased OA risk. Our findings indicate that polymorphisms in the TLR4 and TLR7 genes might increase OA risk and suggest a novel association of miR-196a2 polymorphism with decreased OA susceptibility. The modulation of TLRs and miRNAs and their cross-talk might be an attractive target for a personalized approach to OA management.

Opportunities and challenges in application of artificial intelligence in pharmacology

Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.

Uncovering the potential of innovation ecosystems in the healthcare sector after the COVID-19 crisis

Industry 4.0 technologies are expected to enhance healthcare quality at the minimum cost feasible by using innovative solutions based on a fruitful exchange of knowledge and resources among institutions, firms and academia. These collaborative mechanisms are likely to occur in an innovation ecosystem where different stakeholders and resources interact to provide ground-breaking solutions to the market. The paper proposes a framework for studying the creation and development of innovation ecosystems in the healthcare sector by using a set of interrelated dimensions including, technology, value, and capabilities within a Triple-Helix model guided by focal actors. The model is applied to an exemplary Italian innovation ecosystem providing cloud and artificial intelligence-based solutions to general practitioners (GPs) under the focal role of the Italian association of GPs. Primary and secondary data are examined starting from the innovation ecosystem's origins and continuing until the COVID-19 crisis. The findings show that the pandemic represented the turning point that altered the ecosystem's dimensions in order to find immediate solutions for monitoring health conditions and organizing the booking of swabs and vaccines. The data triangulation points out the technical, organizational, and administrative barriers hindering the widespread adoption of these solutions at the national and regional levels, revealing several implications for health policy.

Starring Role of Biomarkers and Anticancer Agents as a Major Driver in Precision Medicine of Cancer Therapy

Precision medicine is the most modern contemporary medicine approach today, based on great amount of data on people's health, individual characteristics, and life circumstances, and employs the most effective ways to prevent and cure diseases. Precision medicine in cancer is the most precise and viable treatment for every cancer patient based on the disease's genetic profile. Precision medicine changes the standard one size fits all medication model, which focuses on average responses to care. Consolidating modern methodologies for streamlining and checking anticancer drugs can have long-term effects on understanding the results. Precision medicine can help explicit anticancer treatments using various drugs and even in discovery, thus becoming the paradigm of future cancer medicine. Cancer biomarkers are significant in precision medicine, and findings of different biomarkers make this field more promising and challenging. Naturally, genetic instability and the collection of extra changes in malignant growth cells are ways cancer cells adapt and survive in a hostile environment, for example, one made by these treatment modalities. Precision medicine centers on recognizing the best treatment for individual patients, dependent on their malignant growth and genetic characterization. This new era of genomics progressively referred to as precision medicine, has ignited a new episode in the relationship between genomics and anticancer drug development.

A Comparison of Patients' and Physicians' Knowledge and Expectations Regarding Precision Oncology Tests

(1) Background: As genomic testing is becoming a part of the mainstream oncology practice, it is vital to ensure that our patients fully understand the implications of these tests. This study aimed to compare the attitudes and expectations of cancer patients with those of their physicians regarding the role of biomarker testing in clinical decision making. (2) Methods: Two separate, complimentary, self-administered questionnaires for patients with cancer and their physicians, respectively, were collected in Calgary, Alberta, Canada. Out of 117, 113 completed patient surveys were included in the statistical analysis, constituting a 96.4% response rate. These surveys were subsequently matched with those of their corresponding oncologists to determine the concordance rates. (3) Results: Overall, patients demonstrated a good understanding of general cancer biology (80.0%) and diagnostic processes (90.0%) associated with precision oncology. Most patients wanted their tumours to be tested to guide treatment, and the oncologists broadly shared these views (concordance 65.1%). However, there were discrepancies between the knowledge and expectations regarding the applications of test results on actual diagnosis and prognosis between patients and their oncologists (concordance 26.1% and 36.0%, respectively). While only 28.0% of patients thought they had enough knowledge to make informed decisions, the majority (68.0%) said they needed more information. (4) Conclusion: Our study shows that patients and cancer physicians do not always agree with the roles and applications of genomic tests, which could lead to misplaced expectations and poor health outcomes. More research is needed to devise strategies to improve education and communication to align these expectations and improve the quality of clinical decision making.

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes' standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages.

Artificial intelligence and machine learning in cancer imaging

An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and testing prior to its adoption into healthcare systems. This multidisciplinary review highlights key developments in the field. We discuss the challenges and opportunities of AI and ML in cancer imaging; considerations for the development of algorithms into tools that can be widely used and disseminated; and the development of the ecosystem needed to promote growth of AI and ML in cancer imaging.

Omics Data and Data Representations for Deep Learning-Based Predictive Modeling

Medical discoveries mainly depend on the capability to process and analyze biological datasets, which inundate the scientific community and are still expanding as the cost of next-generation sequencing technologies is decreasing. Deep learning (DL) is a viable method to exploit this massive data stream since it has advanced quickly with there being successive innovations. However, an obstacle to scientific progress emerges: the difficulty of applying DL to biology, and this because both fields are evolving at a breakneck pace, thus making it hard for an individual to occupy the front lines of both of them. This paper aims to bridge the gap and help computer scientists bring their valuable expertise into the life sciences. This work provides an overview of the most common types of biological data and data representations that are used to train DL models, with additional information on the models themselves and the various tasks that are being tackled. This is the essential information a DL expert with no background in biology needs in order to participate in DL-based research projects in biomedicine, biotechnology, and drug discovery. Alternatively, this study could be also useful to researchers in biology to understand and utilize the power of DL to gain better insights into and extract important information from the omics data.

Artificial intelligence and machine learning in precision medicine: A paradigm shift in big data analysis

The integration of artificial intelligence in precision medicine has revolutionized healthcare delivery. Precision medicine identifies the phenotype of particular patients with less-common responses to treatment. Recent studies have demonstrated that translational research exploring the convergence between artificial intelligence and precision medicine will help solve the most difficult challenges facing precision medicine. Here, we discuss different aspects of artificial intelligence in precision medicine that improve healthcare delivery. First, we discuss how artificial intelligence changes the landscape of precision medicine and the evolution of artificial intelligence in precision medicine. Second, we highlight the synergies between artificial intelligence and precision medicine and promises of artificial intelligence and precision medicine in healthcare delivery. Third, we briefly explain the promise of big data analytics and the integration of nanomaterials in precision medicine. Last, we highlight the challenges and opportunities of artificial intelligence in precision medicine.

Towards Better Pharmaceutical Provision in Europe—Who Decides the Future?

Significant progress has been achieved in human health in the European Union in recent years. New medicines, vaccines, and treatments have been developed to tackle some of the leading causes of disease and life-threatening illnesses. It is clear that investment in research and development (R&D) for innovative medicines and treatments is essential for making progress in preventing and treating diseases. Ahead of the legislative process, which should begin by the end of 2022, discussions focus on how Europe can best promote the huge potential benefits of new science and technology within the regulatory framework. The challenges in European healthcare were spelled out by the panellists at the roundtable organised by European Alliance for Personalised Medicine (EAPM). Outcomes from panellists’ discussions have been summarized and re-arranged in this paper under five headings: innovation, unmet medical need, access, security of supply, adapting to progress, and efficiency. Some of the conclusions that emerged from the panel are a call for a better overall holistic vision of the future of pharmaceuticals and health in Europe and a collaborative effort among all stakeholders, seeing the delivery of medicines as part of a broader picture of healthcare.

Managing the introduction of genomic applications into the National Health Service: A special challenge for health technology assessment in Italy

In recent years, the rapid proliferation of genomic tests for use in clinical practice has prompted healthcare systems to use a health technology assessment (HTA) approach to distinguish valuable from unwarranted applications. In this study, we narratively review the Italian HTA mechanisms for medical devices (MDs), both at the national and regional levels, and discuss the opportunity and benefits of extending them to genomic technologies, for which a dedicated assessment path was advocated by the National Plan for Public Health Genomics in 2017. We found that the National Health Technology Assessment Program for MDs, completed in 2019, had developed a structured pathway for the HTA of MDs; it established a hub-and-spoke structure, run by a governmental institution, and put in place transparent methodological procedures to cover all four HTA phases (i.e., proposal and prioritization, assessment, appraisal, and dissemination). However, several factors have hindered its adoption, and the regions are at different stages of its implementation. For these reasons, efforts should be made to ensure its effective deployment, both at national and regional levels. In addition, we argue that to harmonize the institutional roles and methodological procedures adopted, the time has come to concentrate resources on a single pathway for the assessment of all technologies that include both MDs and genomic applications.

Stratification of rheumatoid arthritis cohort using Ayurveda based deep phenotyping approach identifies novel genes in a GWAS

Background and aim

Genome wide association studies have scaled up both in terms of sample size and range of complex disorders investigated, but these have explained relatively little phenotypic variance. Of the several reasons, phenotypic heterogeneity seems to be a likely contributor for missing out genetic associations of large effects. Ayurveda, the traditional Indian system of medicine is one such tool which adopts a holistic deep phenotyping approach and classifies individuals based on their body constitution/prakriti. We hypothesized that Ayurveda based phenotypic stratification of healthy and diseased individuals will allow us to achieve much desired homogeneous cohorts which would facilitate detection of genetic association of large effects. In this proof of concept study, we performed a genome wide association testing of clinically diagnosed rheumatoid arthritis patients and healthy controls, who were re-phenotyped into Vata, Pitta and Kapha predominant prakriti sub-groups.

Experimental procedure

Genotypes of rheumatoid arthritis cases (Vata = 49; Pitta = 117; Kapha = 78) and controls (Vata = 33; Pitta = 175; Kapha = 85) were retrieved from the total genotype data, used in a recent genome-wide association study performed in our laboratory. A total of 528461 SNPs were included after quality control. Prakriti-wise genome-wide association analysis was employed.

Results and conclusion

This study identified (i) prakriti-specific novel disease risk genes of high effect sizes; (ii) putative candidates of novel therapeutic potential; and (iii) a good correlation between genetic findings and clinical knowledge in Ayurveda. Adopting Ayurveda based deep phenotyping may facilitate explaining hitherto undiscovered heritability in complex traits and may propel much needed progress in personalized medicine.

Predictors of response to secukinumab in patients with psoriatic arthritis and axial manifestations: a post-hoc analysis of the MAXIMISE trial

Objectives

To investigate patient characteristics predictive of response to secukinumab in patients with psoriatic arthritis (PsA) with axial manifestations.

Methods

In a post-hoc analysis from the MAXIMISE trial (NCT02721966) in patients with PsA and axial manifestations, we tested the hypothesis that the OR of the effect of treatment on the primary endpoint of the trial (Assessment of SpondyloArthritis international Society (ASAS) 20 responder status at week 12) would be different depending on 12 prespecified predictor variables. We applied a two-model logistic regression approach, a main effects and an interaction model.

Results

The OR (95% CI) for ASAS20 response for the presence of nail dystrophy was 3.2 (95% CI 0.93 to 10.99) in the secukinumab 150 mg group and 5.0 (95% CI 1.47 to 17.19) in the secukinumab 300 mg group compared with the placebo group (p=0.029). Odds of being a responder were similar in men and women in the secukinumab groups, though men fared worse than women in the placebo group (p=0.039). Current smokers were less likely to be ASAS20 responders compared with never smokers regardless of the treatment group (p=0.589).

Conclusion

Nail dystrophy was identified as a predictor of response to secukinumab in patients with PsA with axial manifestations in the MAXIMISE trial. These findings may be explained by the nail-entheseal concept as part of the axial phenotype in PsA.

Acceptability of donor funding for clinical trials in the UK: a qualitative empirical ethics study using focus groups to elicit the views of research patient public involvement group members, research ethics committee chairs and clinical researchers

OBJECTIVES

The Plutocratic Proposal is a novel method of funding early phase clinical trials where a single donor funds the entire trial and in so doing secures a place on it. The aim of this study was to identify and explore concerns that may be raised by UK research ethics committees (RECs) when reviewing clinical trials funded in this way.

DESIGN

Empirical ethics combining ethical analysis and qualitative data from three focus groups held online using Frith's symbiotic approach. Data were analysed using inductive thematic approach informed by the study aims and ethical analysis.

PARTICIPANTS

22 participants were recruited: 8 research patient public involvement group members, 7 REC chairs and 7 clinical researchers. All were based in the UK.

RESULTS

With one exception, participants thought the Plutocratic Proposal may be 'all things considered' acceptable, providing their concerns were met, primary of which was upholding scientific integrity. Other concerns discussed related to the acceptability of the donor securing a place on the trial including: whether this was an unfair distribution of benefits, disclosing the identity of the donor as the funder, protecting the donor from exploitation and funding a single study with multiple donors on the same terms. Some misgivings fell outside the usual REC purview: detrimental impact of donors of bad character, establishing the trustworthiness of the matching agency and its processes and optimising research funding and resources. Despite their concerns, participants recognised that because the donor funds the whole trial, others would also potentially benefit from participating.

CONCLUSIONS

We identified concerns about the Plutocratic Proposal. UK RECs may be open to approving studies if these can be addressed. Existing governance processes will do some of this work, but additional REC guidance, particularly in relation to donors securing a place on the trial, may be necessary to help RECs navigate ethical concerns consistently.

A 3-miRNA Signature Enables Risk Stratification in Glioblastoma Multiforme Patients with Different Clinical Outcomes

Malignant gliomas constitute a complex disease phenotype that demands optimum decision-making as they are highly heterogeneous. Such inter-individual variability also renders optimum patient stratification extremely difficult. microRNA (hsa-miR-20a, hsa-miR-21, hsa-miR-21) expression levels were determined by RT-qPCR, upon FFPE tissue sample collection of glioblastoma multiforme patients (n = 37). In silico validation was then performed through discriminant analysis. Immunohistochemistry images from biopsy material were utilized by a hybrid deep learning system to further cross validate the distinctive capability of patient risk groups. Our standard-of-care treated patient cohort demonstrates no age- or sex- dependence. The expression values of the 3-miRNA signature between the low- (OS > 12 months) and high-risk (OS < 12 months) groups yield a p-value of <0.0001, enabling risk stratification. Risk stratification is validated by a. our random forest model that efficiently classifies (AUC = 97%) patients into two risk groups (low- vs. high-risk) by learning their 3-miRNA expression values, and b. our deep learning scheme, which recognizes those patterns that differentiate the images in question. Molecular-clinical correlations were drawn to classify low- (OS > 12 months) vs. high-risk (OS < 12 months) glioblastoma multiforme patients. Our 3-microRNA signature (hsa-miR-20a, hsa-miR-21, hsa-miR-10a) may further empower glioblastoma multiforme prognostic evaluation in clinical practice and enrich drug repurposing pipelines.

Effects of a video intervention on physicians' acceptance of pain apps: a randomised controlled trial

OBJECTIVES

The aim of our study was to determine and enhance physicians' acceptance, performance expectancy and credibility of health apps for chronic pain patients. We further investigated predictors of acceptance.

DESIGN

Randomised experimental trial with a parallel-group repeated measures design.

SETTING AND PARTICIPANTS

248 physicians working in various, mainly outpatient settings in Germany.

INTERVENTION AND OUTCOME

Physicians were randomly assigned to either an experimental group (short video about health apps) or a control group (short video about chronic pain). Primary outcome measure was acceptance. Performance expectancy and credibility of health apps were secondary outcomes. In addition, we assessed 101 medical students to evaluate the effectiveness of the video intervention in young professionals.

RESULTS

In general, physicians' acceptance of health apps for chronic pain patients was moderate (M=9.51, SD=3.53, scale ranges from 3 to 15). All primary and secondary outcomes were enhanced by the video intervention: A repeated-measures analysis of variance yielded a significant interaction effect for acceptance (F(1, 246)=15.28, p=0.01), performance expectancy (F(1, 246)=6.10, p=0.01) and credibility (F(1, 246)=25.61, p<0.001). The same pattern of results was evident among medical students. Linear regression analysis revealed credibility (β=0.34, p<0.001) and performance expectancy (β=0.30, p<0.001) as the two strongest factors influencing acceptance, followed by scepticism (β=-0.18, p<0.001) and intuitive appeal (β=0.11, p=0.03).

CONCLUSIONS AND RECOMMENDATIONS

Physicians' acceptance of health apps was moderate, and was strengthened by a 3 min video. Besides performance expectancy, credibility seems to be a promising factor associated with acceptance. Future research should focus on ways to implement acceptability-increasing interventions into routine care.

A Non-Hazardous Deparaffinization Protocol Enables Quantitative Proteomics of Core Needle Biopsy-Sized Formalin-Fixed and Paraffin-Embedded (FFPE) Tissue Specimens

Most human tumor tissues that are obtained for pathology and diagnostic purposes are formalin-fixed and paraffin-embedded (FFPE). To perform quantitative proteomics of FFPE samples, paraffin has to be removed and formalin-induced crosslinks have to be reversed prior to proteolytic digestion. A central component of almost all deparaffinization protocols is xylene, a toxic and highly flammable solvent that has been reported to negatively affect protein extraction and quantitative proteome analysis. Here, we present a 'green' xylene-free protocol for accelerated sample preparation of FFPE tissues based on paraffin-removal with hot water. Combined with tissue homogenization using disposable micropestles and a modified protein aggregation capture (PAC) digestion protocol, our workflow enables streamlined and reproducible quantitative proteomic profiling of FFPE tissue. Label-free quantitation of FFPE cores from human ductal breast carcinoma in situ (DCIS) xenografts with a volume of only 0.79 mm³ showed a high correlation between replicates (r² = 0.992) with a median %CV of 16.9%. Importantly, this small volume is already compatible with tissue micro array (TMA) cores and core needle biopsies, while our results and its ease-of-use indicate that further downsizing is feasible. Finally, our FFPE workflow does not require costly equipment and can be established in every standard clinical laboratory.

Medical Students' Perceptions towards Digitization and Artificial Intelligence: A Mixed-Methods Study

Digital technologies in health care, including artificial intelligence (AI) and robotics, constantly increase. The aim of this study was to explore attitudes of 2020 medical students’ generation towards various aspects of eHealth technologies with the focus on AI using an exploratory sequential mixed-method analysis. Data from semi-structured interviews with 28 students from five medical faculties were used to construct an online survey send to about 80,000 medical students in Germany. Most students expressed positive attitudes towards digital applications in medicine. Students with a problem-based curriculum (PBC) in contrast to those with a science-based curriculum (SBC) and male undergraduate students think that AI solutions result in better diagnosis than those from physicians (p < 0.001). Male undergraduate students had the most positive view of AI (p < 0.002). Around 38% of the students felt ill-prepared and could not answer AI-related questions because digitization in medicine and AI are not a formal part of the medical curriculum. AI rating regarding the usefulness in diagnostics differed significantly between groups. Higher emphasis in medical curriculum of digital solutions in patient care is postulated.