Clinical Research Applications Research Articles

Development and Challenges of Cyclic Peptides for Immunomodulation.

Cyclic peptides are polypeptide chains formed by cyclic sequences of amide bonds between protein-derived or non-protein-derived amino acids. Compared to linear peptides, cyclic peptides offer several unique advantages, such as increased stability, stronger affinity, improved selectivity, and reduced toxicity. Cyclic peptide has been proved to have a promising application prospect in the medical field. In addition, this paper mainly describes that cyclic peptides play an important role in anti-cancer, anti-inflammatory, anti-virus, treatment of multiple sclerosis and membranous nephropathy through immunomodulation. In order to know more useful information about cyclic peptides in clinical research and drug application, this paper also summarizes cyclic peptides currently in the clinical trial stage and cyclic peptide drugs approved for marketing in the recent five years. Cyclic peptides have many advantages and great potential in treating various diseases, but there are still many challenges to be solved in the development process of cyclic peptides.

An Optimized Method for LC–MS-Based Quantification of Endogenous Organic Acids: Metabolic Perturbations in Pancreatic Cancer

Accurate and reliable quantification of organic acids with carboxylic acid functional groups in complex biological samples remains a major analytical challenge in clinical chemistry. Issues such as spontaneous decarboxylation during ionization, poor chromatographic resolution, and retention on a reverse-phase column hinder sensitivity, specificity, and reproducibility in multiple-reaction monitoring (MRM)-based LC–MS assays. We report a targeted metabolomics method using phenylenediamine derivatization for quantifying carboxylic acid-containing metabolites (CCMs). This method achieves accurate and sensitive quantification in various biological matrices, with recovery rates from 90% to 105% and CVs ≤ 10%. It shows linearity from 0.1 ng/mL to 10 µg/mL with linear regression coefficients of 0.99 and LODs as low as 0.01 ng/mL. The library included a wide variety of structurally variant CCMs such as amino acids/conjugates, short- to medium-chain organic acids, di/tri-carboxylic acids/conjugates, fatty acids, and some ring-containing CCMs. Comparing CCM profiles of pancreatic cancer cells to normal pancreatic cells identified potential biomarkers and their correlation with key metabolic pathways. This method enables sensitive, specific, and high-throughput quantification of CCMs from small samples, supporting a wide range of applications in basic, clinical, and translational research.

Implementation of Obesity Science Into Clinical Practice: A Scientific Statement From the American Heart Association.

Obesity is a recognized public health epidemic with a prevalence that continues to increase dramatically in nearly all populations, impeding progress in reducing incidence rates of cardiovascular disease. Over the past decade, obesity science has evolved to improve knowledge of its multifactorial causes, identifying important biological causes and sociological determinants of obesity. Treatments for obesity have also continued to develop, with more evidence-based programs for lifestyle modification, new pharmacotherapies, and robust data to support bariatric surgery. Despite these advancements, there continues to be a substantial gap between the scientific evidence and the implementation of research into clinical practice for effective obesity management. Addressing barriers to obesity science implementation requires adopting feasible methodologies and targeting multiple levels (eg, clinician, community, system, policy) to facilitate the delivery of obesity-targeted therapies and maximize the effectiveness of guideline-driven care to at-need patient populations. This scientific statement (1) describes strategies shown to be effective or promising for enhancing translation and clinical application of obesity-based research; (2) identifies key gaps in the implementation of obesity science into clinical practice; and (3) provides guidance and resources for health care professionals, health care systems, and other stakeholders to promote broader implementation and uptake of obesity science for improved population-level obesity management. In addition, advances in implementation science that hold promise to bridge the know-do gap in obesity prevention and treatment are discussed. Last, this scientific statement highlights implications for health research policy and future research to improve patient care models and optimize the delivery and sustainability of equitable obesity-related care.

High throughput Surface Epitope Immunoaffinity Isolation of Extracellular Vesicles and Downstream Analysis

Abstract Extracellular vesicles (EVs), including exosomes, have significant potential for diagnostic and therapeutic applications. The lack of standardized methods for efficient and high throughput isolation and analysis of EVs, however, has limited their widespread use in clinical practice. Surface epitope immunoaffinity (SEI) isolation utilises affinity ligands, including antibodies, aptamers, or lectins, that target specific surface proteins present on EVs. Paramagnetic bead-SEI isolation represents a fit-for-purpose solution for the reproducible, high throughput isolation of EVs from biofluids and downstream analysis of RNA, protein and lipid biomarkers that is compatible with clinical laboratory workflows. This study evaluates a new surface epitope immunoaffinity isolation method for enriching subpopulations of EVs. EVs were isolated from human plasma using a bead-based SEI method designed for on-bead and downstream analysis of EV-associated RNA and protein biomarkers. Western blot analysis confirmed the presence of EV markers in the captured nanoparticles. Mass spectrometry analysis of the SEI lysate identified over 1500 proteins, with the top 100 including known EV-associated proteins. miRNA sequencing followed by RT-qPCR analysis identified EV-associated miRNA transcripts. Using SEI, EVs were isolated using automated high throughput particle moving instruments, demonstrating equal or higher protein and miRNA yield and recovery compared to manual processing. SEI is a rapid, efficient, and high throughput method for isolating enriched populations of EVs; effectively reducing contamination and enabling the isolation of a specific subpopulation of EVs. In this study, high throughput EV isolation and RNA extraction have been successfully implemented. This technology holds great promise for advancing the field of EV research and facilitating their application for biomarker discovery and clinical research.

Engineering Cyanine- and Hemicyanine-Based Probes for Optical Imaging of Kidney Diseases.

Molecular optical probes paly pivotal roles in in vivo imaging of biomarkers associated to kidney diseases. Relying on structural tunability and high fluorescence quantum yields, versatile optical probes have been constructed on cyanine or hemicyanine-based scaffold in recent years. This review summaries the recent progress on the development of optical probes for imaging of kidney diseases, particularly through near-infrared fluorescence, chemiluminescence and photoacoustic imaging modalities. The chemical design and sensing mechanisms are discussed along with applications in the detection of renal cell carcinoma and acute kidney injury. This progress provides insights and directions for the development of next generation kidney-targeted probes and for pushing their further applications in preclinical and clinical research.

Impact of artificial intelligence on health information literacy: guidance for healthcare professionals

Purpose This paper aims to explore the intricate relationship between artificial intelligence (AI) and health information literacy (HIL), examining the rise of AI in health care, the intersection of AI and HIL and the imperative for promoting AI literacy and integrating it with HIL. By fostering collaboration, education and innovation, stakeholders can navigate the evolving health-care ecosystem with confidence and agency, ultimately improving health-care delivery and outcomes for all. Design/methodology/approach This paper adopts a conceptual approach to explore the intricate relationship between AI and HIL, aiming to provide guidance for health-care professionals navigating the evolving landscape of AI-driven health-care delivery. The methodology used in this paper involves a synthesis of existing literature, theoretical analysis and conceptual modeling to develop insights and recommendations regarding the integration of AI literacy with HIL. Findings Impact of AI on health-care delivery: The integration of AI technologies in health-care is reshaping the industry, offering unparalleled opportunities for improving patient care, optimizing clinical workflows and advancing medical research. Significance of HIL: HIL, encompassing the ability to access, understand and critically evaluate health information, is crucial in the context of AI-driven health-care delivery. It empowers health-care professionals, patients and the broader community to make informed decisions about their health and well-being. Intersection of AI and HIL: The convergence of AI and HIL represents a critical juncture, where technological innovation intersects with human cognition. AI technologies have the potential to revolutionize how health information is generated, disseminated and interpreted, necessitating a deeper understanding of their implications for HIL. Challenges and opportunities: While AI holds tremendous promise for enhancing health-care outcomes, it also introduces new challenges and complexities for individuals navigating the vast landscape of health information. Issues such as algorithmic bias, transparency and accountability pose ethical dilemmas that impact individuals’ ability to critically evaluate and interpret AI-generated health information. Recommendations for health-care professionals: Health-care professionals are encouraged to adopt strategies such as staying informed about developments in AI, continuous education and training in AI literacy, fostering interdisciplinary collaboration and advocating for policies that promote ethical AI practices. Practical implications To enhance AI literacy and integrate it with HIL, health-care professionals are encouraged to adopt several key strategies. First, staying abreast of developments in AI technologies and their applications in health care is essential. This entails actively engaging with conferences, workshops and publications focused on AI in health care and participating in professional networks dedicated to AI and health-care innovation. Second, continuous education and training are paramount for developing critical thinking skills and ethical awareness in evaluating AI-driven health information (Alowais et al., 2023). Health-care organizations should provide opportunities for ongoing professional development in AI literacy, including workshops, online courses and simulation exercises focused on AI applications in clinical practice and research. Originality/value This paper lies in its exploration of the intersection between AI and HIL, offering insights into the evolving health-care landscape. It innovatively synthesizes existing literature, proposes strategies for integrating AI literacy with HIL and provides guidance for health-care professionals to navigate the complexities of AI-driven health-care delivery. By addressing the transformative potential of AI while emphasizing the importance of promoting critical thinking skills and ethical awareness, this paper contributes to advancing understanding in the field and promoting informed decision-making in an increasingly digital health-care environment.

A practical guide to estimating treatment effects in patients with rheumatic diseases using real-world data.

Randomized controlled trials are considered the gold standard in study methodology. However, due to their study design and inclusion criteria, these studies may not capture the heterogeneity of real-world patient populations. In contrast, the lack of randomization and the presence of both measured and unmeasured confounding factors could bias the estimated treatment effect when using observational data. While causal inference methods allow for the estimation of treatment effects, their mathematical complexity may hinder their application in clinical research. We present a practical, nontechnical guide using a common statistical package (Stata) and a motivational simulated dataset that mirrors real-world observational data from patients with rheumatic diseases. We demonstrate regression analysis, regression adjustment, inverse-probability weighting, propensity score (PS) matching and two robust estimation methods. Although the methods applied to control for confounding factors produced similar results, the commonly used one-to-one PS matching method could yield biased results if not thoroughly assessed. The guide we propose aims to facilitate the use of readily available methods in a common statistical package. It may contribute to robust and transparent epidemiological and statistical methods, thereby enhancing effectiveness research using observational data in rheumatology.

Glucagon-like peptide-1 receptor agonists: new strategies and therapeutic targets to treat atherosclerotic cardiovascular disease.

Atherosclerotic cardiovascular disease (ASCVD) is a leading cause of cardiovascular mortality and is increasingly prevalent in our population. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) can safely and effectively lower glucose levels while concurrently managing the full spectrum of ASCVD risk factors and improving patients' long-term prognosis. Several cardiovascular outcome trials (CVOTs) have been carried out to further investigate the cardiovascular benefits of GLP-1RAs. Analyzing data from CVOTs can provide insights into the pathophysiologic mechanisms by which GLP-1RAs are linked to ASCVD and define the use of GLP-1RAs in clinical practice. Here, we discussed various mechanisms hypothesized in previous animal and preclinical human studies, including blockade of the production of adhesion molecules and inflammatory factors, induction of endothelial cells' synthesis of nitric oxide, protection of mitochondrial function and restriction of oxidative stress, suppression of NOD-like receptor thermal protein domain associated protein three inflammasome, reduction of foam cell formation and macrophage inflammation, and amelioration of vascular smooth muscle cell dysfunction, to help explain the cardiovascular benefits of GLP-1RAs in CVOTs. This paper provides an overview of the clinical research, molecular processes, and possible therapeutic applications of GLP-1RAs in ASCVD, while also addressing current limitations in the literature and suggesting future research directions.

Potential Diagnostic and Clinical Significance of Selected Genetic Alterations in Glioblastoma.

Glioblastoma is currently considered the most common and, unfortunately, also the most aggressive primary brain tumor, with the highest morbidity and mortality rates. The average survival of patients diagnosed with glioblastoma is 14 months, and only 2% of patients survive 3 years after surgery. Based on our clinical experience and knowledge from extensive clinical studies, survival is mainly related to the molecular biological properties of glioblastoma, which are of interest to the general medical community. Our study examined a total of 71 retrospective studies published from 2016 through 2022 and available on PubMed that deal with mutations of selected genes in the pathophysiology of GBM. In conclusion, we can find other mutations within a given gene group that have different effects on the prognosis and quality of survival of a patient with glioblastoma. These mutations, together with the associated mutations of other genes, as well as intratumoral heterogeneity itself, offer enormous potential for further clinical research and possible application in therapeutic practice.

The digital revolution in pathology: Towards a smarter approach to research and treatment.

Artificial intelligence (AI) applications in oncology are at the forefront of transforming healthcare during the Fourth Industrial Revolution, driven by the digital data explosion. This review provides an accessible introduction to the field of AI, presenting a concise yet structured overview of the foundations of AI, including expert systems, classical machine learning, and deep learning, along with their contextual application in clinical research and healthcare. We delve into the current applications of AI in oncology, with a particular focus on diagnostic imaging and pathology. Numerous AI tools have already received regulatory approval, and more are under active development, bringing clear benefits but not without challenges. We discuss the importance of data security, the need for transparent and interpretable models, and the ethical considerations that must guide AI development in healthcare. By providing a perspective on the opportunities and challenges, this review aims to inform and guide researchers, clinicians, and policymakers in the adoption of AI in oncology.

Early Serial Assessment of Aggregate Vasoactive Support and Mortality in Cardiogenic Shock: Insights From the Critical Care Cardiology Trials Network Registry.

Associations of early changes in vasoactive support with cardiogenic shock (CS) mortality remain incompletely defined. The Critical Care Cardiology Trials Network is a multicenter registry of cardiac intensive care units. Patients admitted with CS (2018-2023) had vasoactive dosing assessed at 4 and 24 hours from cardiac intensive care unit admission and quantified by the vasoactive-inotropic score (VIS). Prognostic associations of VIS at both time points, as well as change in VIS from 4 to 24 hours, were examined. Interaction testing was performed based on mechanical circulatory support status. Among 3665 patients, 82% had a change in VIS <10, with 7% and 11% having a ≥10-point increase and decrease from 4 to 24 hours, respectively. The 4 and 24-hour VIS were each associated with cardiac intensive care unit mortality (13%-45% and 11%-73% for VIS <10 to ≥40, respectively; Ptrend <0.0001 for each). Stratifying by the 4-hour VIS, changes in VIS from 4 to 24 hours had a graded association with mortality, ranging from a 2- to >4-fold difference in mortality comparing those with a ≥10-point increase to ≥10-point decrease in VIS (Ptrend <0.0001). The change in VIS alone provided good discrimination of cardiac intensive care unit mortality (C-statistic, 0.72 [95% CI, 0.70-0.75]) and improved discrimination of the 24-hour Sequential Organ Failure Assessment score (0.72 [95% CI, 0.69-0.74] to 0.76 [95% CI, 0.74-0.78]) and the clinician-assessed Society for Cardiovascular Angiography and Interventions shock stage (0.72 [95% CI, 0.70-0.74] to 0.77 [95% CI, 0.75-0.79]). Although present in both groups, the mortality risk associated with VIS was attenuated in patients managed with versus without mechanical circulatory support (odds ratio per 10-point higher 24-hour VIS, 1.36 [95% CI, 1.23-1.49] versus 1.84 [95% CI, 1.69-2.01]; Pinteraction <0.0001). Early changes in the magnitude of vasoactive support in CS are associated with a gradient of risk for mortality. These data suggest that early VIS trajectory may improve CS prognostication, with the potential to be leveraged for clinical decision-making and research applications in CS.

Imaging the pulmonary vasculature in acute respiratory distress syndrome

Acute respiratory distress syndrome (ARDS) is characterized by a redistribution of regional lung perfusion that impairs gas exchange. While speculative, experimental evidence suggests that perfusion redistribution may contribute to regional inflammation and modify disease progression. Unfortunately, tools to visualize and quantify lung perfusion in patients with ARDS are lacking. This review explores recent advances in perfusion imaging techniques that aim to understand the pulmonary circulation in ARDS. Dynamic contrast-enhanced computed tomography captures first-pass kinetics of intravenously injected dye during continuous scan acquisitions. Different contrast characteristics and kinetic modeling have improved its topographic measurement of pulmonary perfusion with high spatial and temporal resolution. Dual-energy computed tomography can map the pulmonary blood volume of the whole lung with limited radiation exposure, enabling its application in clinical research. Electrical impedance tomography can obtain serial topographic assessments of perfusion at the bedside in response to treatments such as inhaled nitric oxide and prone position. Ongoing technological improvements and emerging techniques will enhance lung perfusion imaging and aid its incorporation into the care of patients with ARDS.

The Post-Transcriptional Regulatory Element of Hepatitis B Virus: From Discovery to Therapy.

The post-transcriptional regulatory element (PRE) is present in all HBV mRNAs and plays a major role in their stability, nuclear export, and enhancement of viral gene expression. Understanding PRE's structure, function, and mode of action is essential to leverage its potential as a therapeutic target. A wide range of PRE-based reagents and tools have been developed and assessed in preclinical and clinical settings for therapeutic and biotechnology applications. This manuscript aims to provide a systematic review of the characteristics and mechanism of action of PRE, as well as elucidating its current applications in basic and clinical research. Finally, we discuss the promising opportunities that PRE may provide to antiviral development, viral biology, and potentially beyond.

IGAMT: Privacy-Preserving Electronic Health Record Synthesization with Heterogeneity and Irregularity

Integrating electronic health records (EHR) into machine learning-driven clinical research and hospital applications is important, as it harnesses extensive and high-quality patient data to enhance outcome predictions and treatment personalization. Nonetheless, due to privacy and security concerns, the secondary purpose of EHR data is consistently governed and regulated, primarily for research intentions, thereby constraining researchers' access to EHR data. Generating synthetic EHR data with deep learning methods is a viable and promising approach to mitigate privacy concerns, offering not only a supplementary resource for downstream applications but also sidestepping the confidentiality risks associated with real patient data. While prior efforts have concentrated on EHR data synthesis, significant challenges persist in the domain of generating synthetic EHR data: balancing the heterogeneity of real EHR including temporal and non-temporal features, addressing the missing values and irregular measures, and ensuring the privacy of the real data used for model training. Existing works in this domain only focused on solving one or two aforementioned challenges. In this work, we propose IGAMT, an innovative framework to generate privacy-preserved synthetic EHR data that not only maintain high quality with heterogeneous features, missing values, and irregular measures but also balances the privacy-utility trade-off. Extensive experiments prove that IGAMT significantly outperforms baseline architectures in terms of visual resemblance and comparable performance in downstream applications. Ablation case studies also prove the effectiveness of the techniques applied in IGAMT.

ALISTER – Application for lipid stability evaluation and research

Background and aimsIn lipidomic and metabolomic studies, pre-analytical pitfalls enhance the risk of misusing resources such as time and money, as samples that are analyzed may not yield accurate or reliable data due to poor sample handling. Guidance and pre-analytic know-how are necessary for translation of omics technologies into routine clinical testing. The present work aims to enable decision making regarding sample stability in every phase of lipidomics- and metabolomics-centered studies. Materials and methodsData of multiple pre-analytic studies were aggregated into a database. Flexible approaches for evaluating these data were implemented in an RShiny-based web-application, tailored towards broad applicability in clinical and bioanalytic research. ResultsOur “Application for lipid stability evaluation & research” - ALISTER facilitates decision making on blood sample stability during lipidomic and metabolomic studies, such as biomarker research, analysis of biobank samples and clinical testing. The interactive tool gives sampling recommendations when planning sample collection or aids in the assessment of sample quality of experiments retrospectively. ConclusionALISTER is available for use under https://itmp.shinyapps.io/alister/. The application enables and simplifies data-driven decision making concerning pre-analytic blood sample handling and fits the needs of clinical investigations from multiple perspectives.

Addiction neurobiologists should study resilience

The study of addiction has historically focused on identifying the differences which make susceptible individuals fall prey to drug use and abuse. This has resulted in a deep understanding of the learning, reward, and habit-formation circuits which drive initial drug reinforcement, as well as exploration of the cognitive control and anxiety systems which are important for craving and relapse to drug use. However, most human drug users and a smaller proportion of laboratory animals are resilient to addiction, meaning that they can use addictive drugs without developing substance use disorders. Evidence from the neurobiology of resilience in stress models suggests that the mechanisms of resilience are distinct from those of vulnerability, and thus may be missed if resilience is not investigated in a targeted manner. In this review, we briefly review the limitations in our knowledge which have resulted from a focus on addiction vulnerability. We next introduce the formal construct of resilience and its historical application in neurobiology and clinical research. Finally, we present evidence for the presence of resilience mechanisms in studies of addiction neuroscience and argue for an increase in the focused study of resilience to substance abuse. By intentionally identifying compensatory resilience mechanisms in addiction, rather than only focusing on mechanisms of susceptibility, more effective therapeutic approaches may be discovered.

A simple and accurate ratiometric sensor for determination of dopamine based on dual-emission carbon dots.

Dopamine (DA) is a neuromodulatory molecule that plays critical roles in many biological processes. The dysfunctions of the DA system are closely associated with several nervous system diseases. Therefore, it is urgent to establish a simple and accurate method for DA analysis. In this study, an economic and accurate DA ratiometric sensor was established using dual-emission carbon dots (DE-CDs). DE-CDs were first synthesized by the one-step solvothermal method and two separate fluorescence emission peaks at 340 and 500nm were observed under the excitation of 310nm. In the presence of Hg2+, the fluorescence signal at 340nm was significantly quenched, while the signal at 500nm keeps stable. Upon adding DA, the quenched signal at 340nm was significantly recovered, whereas the signal at 500nm remains stable. Therefore, a novel ratiometric sensor for DA analysis was established. This method shows a good linear range from 500nM to 100μM, and the detection limit was calculated to be 80nM. Moreover, this established method shows excellent specificity and could be applied in real sample analysis, showing great potential for application in clinical research.

UK medical students’ self-reported knowledge and harm assessment of psychedelics and their application in clinical research: a cross-sectional study

ObjectiveTo capture UK medical students’ self-reported knowledge and harm assessment of psychedelics and to explore the factors associated with support for changing the legal status of psychedelics to facilitate further...

Perspectives of Certified Nurse-Midwives and Physicians on the Structural and Institutional Barriers that Contribute to the Reproductive Inequities of Black Birthing People in the San Francisco Bay Area.

Black birthing people in the United States disproportionately endure inequitable experiences and outcomes during pregnancy and childbirth via structural, interpersonal, and obstetric racism. In this study, the researchers explore provider perspectives of how racism is perpetuated in institutional perinatal and reproductive health care. Critical Race Theory, Reproductive Justice, and midwifery theory were operationalized through secondary thematic analysis of existing qualitative data from the Community Racial Equity and Training Interventions and Evaluation of Current and Future Healthcare Clinicians Study. Twenty-four perinatal providers (certified nurse-midwives [n = 7] and physicians [n = 17]) voluntarily participated in interviews. A comparative approach was used to determine how professional identity and model of care influence physicians' and certified nurse-midwives' perceptions of equity. Thematic analysis produced 5 themes: racism as a comorbidity, health care systems' inability to address the needs of Black birthing people, health care systems prioritizing providers over patients are failed systems, patients are the experts in the optimal health care model, and benefits of interprofessional teams grounded in Reproductive Justice. Additionally, both physicians and midwives expressed a need for a new care model. With these findings, our team proposes a modification of the midwifery model for application by all provider types that could radically shift the experience and outcomes of perinatal and reproductive health care and reduce mortality. Using a human rights approach to care, a Reproductive Justice-Public Health Critical Race praxis-informed midwifery model may be operationalized by all perinatal and reproductive health care providers. This novel model reflects an iterative process that may offer institutions and providers methods to build on past research supporting midwifery-centered care for improving outcomes for all patients by specifically focusing on improving care of Black birthing people. The implications of this work offer broad application in current clinical practice, quality improvement, research, technology, and patient resources.

Basic principles of artificial intelligence in dermatology explained using melanoma.

The use of artificial intelligence (AI) continues to establish itself in the most diverse areas of medicine at an increasingly fast pace. Nevertheless, many healthcare professionals lack the basic technical understanding of how this technology works, which severely limits its application in clinical settings and research. Thus, we would like to discuss the functioning and classification of AI using melanoma as an example in this review to build an understanding of the technology behind AI. For this purpose, elaborate illustrations are used that quickly reveal the technology involved. Previous reviews tend to focus on the potential applications of AI, thereby missing the opportunity to develop a deeper understanding of the subject matter that is so important for clinical application. Malignant melanoma has become a significant burden for healthcare systems. If discovered early, a better prognosis can be expected, which is why skin cancer screening has become increasingly popular and is supported by health insurance. The number of experts remains finite, reducing their availability and leading to longer waiting times. Therefore, innovative ideas need to be implemented to provide the necessary care. Thus, machine learning offers the ability to recognize melanomas from images at a level comparable to experienced dermatologists under optimized conditions.