Translational Medicine Research Research Articles

A Futuristic Development in 3D Printing Technique Using Nanomaterials with a Step Toward 4D Printing.

3D bioprinting has shown great promise in tissue engineering and regenerative medicine for creating patient-specific tissue scaffolds and medicinal devices. The quickness, accurate imaging, and design targeting of this emerging technology have excited biomedical engineers and translational medicine researchers. Recently, scaffolds made from 3D bioprinted tissue have become more clinically effective due to nanomaterials and nanotechnology. Because of quantum confinement effects and high surface area/volume ratios, nanomaterials and nanotechnological techniques have unique physical, chemical, and biological features. The use of nanomaterials and 3D bioprinting has led to scaffolds with improved physicochemical and biological properties. Nanotechnology and nanomaterials affect 3D bioprinted tissue engineered scaffolds for regenerative medicine and tissue engineering. Biomaterials and cells that respond to stimuli change the structural shape in 4D bioprinting. With such dynamic designs, tissue architecture can change morphologically. New 4D bioprinting techniques will aid in bioactuation, biorobotics, and biosensing. The potential of 4D bioprinting in biomedical technologies is also discussed in this article.

Next-Generation Electrochemical Biosensing Based on Ternary Nanocomposites of nMoS2@Rgo@MWCNT for Label-Free Cancer Biomarker Detection in Undiluted Human Serum

Sperm protein-17 (Sp17) is a cancer-testicular antigen that is expressed in various types of cancers and has been proposed as a potential biomarker for cancer. However, the detection of Sp17 in human serum is challenging due to its low concentration and the lack of specific and sensitive methods. Hence, there is an urgent need to develop fast, accurate, reliable, and cost-effective disposable biosensors for Sp17 detection. Herein, we report sensing of Sp17 with a disposable carbon cloth-based electrode (CC) modified with a ternary nanocomposite comprising molybdenum disulphide (MoS2), reduced graphene oxide (rGO), and multi-walled carbon nanotube (MWCNT). The nanocomposite was synthesized by a facile hydrothermal method and was well-characterized in terms of structural, morphological, and chemical attributes using X-ray diffraction, transmission electron microscopy, scanning electron microscopy, Raman spectroscopy spectral techniques, and electrochemical studies to prove the robust formation of the electroactive ternary nanocomposite and its suitability for Sp17 detection. The nanocomposite exhibited excellent electrocatalytic activity, biocompatibility, and stability. We immobilized a Sp17-specific antibody on the surface of the nanocomposite-modified electrode and used it as a biosensor for Sp17 detection. The biosensor showed a linear response range from 0.01 to 11 ng mL-1 and a limit of detection (LOD) and limit of quantification (LOQ) of 0.23 ng mL-1 and 0.79 ng mL-1, respectively having sensitivity of 9.97 mA ng mL-1 cm-2. The biosensor was successfully applied to detect Sp17 in human serum samples and showed good accuracy, reproducibility, and selectivity, making it helpful for use in translational medicine research and clinical applications. The biosensor also demonstrated a high correlation with a commercial enzyme-linked immunosorbent assay (ELISA) kit. Our results suggest that the proposed biosensor is a promising tool for Sp17 detection in human serum and could be useful for the diagnosis and prognosis of ovarian cancer and other Sp17-related cancers. Keywords: Carbon cloth; Biosensor; Electrochemistry; Sperm protein 17; Cancer biomarker; Nanocomposite

Combination of eribulin and anlotinib exerts synergistic cytotoxicity in retroperitoneal liposarcoma by inducing endoplasmic reticulum stress

Primary retroperitoneal liposarcoma (RLPS) is a rare heterogeneous tumor occurring within retroperitoneal space, and its overall survival has not improved much in the past few decades. Based on a small-sample clinical practice at our center, patients with RLPS can greatly benefit from anlotinib and eribulin combination. In this study, we investigated the combinational effect of anlotinib and eribulin on RLPS. In vitro experiments revealed that a low dose of anlotinib significantly enhances the cytotoxic effects of eribulin, leading to a remarkable suppression of RLPS cell proliferation, viability, colony formation, migration, and cell-cycle progression compared to individual drug treatments. At the organoid level, the combination treatment causes the spheroids in Matrigel to disintegrate earlier than the single-drug group. In vivo, RLPS patient-derived xenograft (PDX) models demonstrated that the combination of these two drugs can obviously exert a safe and effective anti-tumor effect. Through transcriptome analysis, we uncovered and validated that the synergistic effect mainly is induced by the endoplasmic reticulum stress (ERS) pathway both in vitro and in vivo. Further analyses indicate that anlotinib plus eribulin treatment results in micro-vessel density and PD-L1 expression alterations, suggesting a potential impact on the tumor microenvironment. This study extensively explored the combination regimen at multiple levels and its underlying molecular mechanism in RLPS, thus providing a foundation for translational medicine research.

Exploring the role of tRNA-derived small RNAs (tsRNAs) in disease: implications for HIF-1 pathway modulation.

The tRNA-derived small RNAs (tsRNAs) can be categorized into two main groups: tRNA-derived fragments (tRFs) and tRNA-derived stress-induced RNAs (tiRNAs). Each group possesses specific molecular sizes, nucleotide compositions, and distinct physiological functions. Notably, hypoxia-inducible factor-1 (HIF-1), a transcriptional activator dependent on oxygen, comprises one HIF-1β subunit and one HIF-α subunit (HIF-1α/HIF-2α/HIF-3α). The activation of HIF-1 plays a crucial role in gene transcription, influencing key aspects of cancer biology such as angiogenesis, cell survival, glucose metabolism, and invasion. The involvement of HIF-1α activation has been demonstrated in numerous human diseases, particularly cancer, making HIF-1 an attractive target for potential disease treatments. Through a series of experiments, researchers have identified two tiRNAs that interact with the HIF-1 pathway, impacting disease development: 5'tiRNA-His-GTG in colorectal cancer (CRC) and tiRNA-Val in diabetic retinopathy (DR). Specifically, 5'tiRNA-His-GTG promotes CRC development by targeting LATS2, while tiRNA-Val inhibits Sirt1, leading to HIF-1α accumulation and promoting DR development. Clinical data have further indicated that certain tsRNAs' expression levels are associated with the prognosis and pathological features of CRC patients. In CRC tumor tissues, the expression level of 5'tiRNA-His-GTG is significantly higher compared to normal tissues, and it shows a positive correlation with tumor size. Additionally, KEGG analysis has revealed multiple tRFs involved in regulating the HIF-1 pathway, including tRF-Val-AAC-016 in diabetic foot ulcers (DFU) and tRF-1001 in pathological ocular angiogenesis. This comprehensive article reviews the biological functions and mechanisms of tsRNAs related to the HIF-1 pathway in diseases, providing a promising direction for subsequent translational medicine research.

O12 Macrophages enhance angiogenesis in an induced pluripotent stem cell-derived skin organoid model

Abstract Introduction and aims Hair-bearing skin organoids (SkOs) are a complex three-dimensional (3D) model that faithfully recapitulates human skin as a multicellular organ with appendage structures. The development of such models provides unique potential for investigating human skin development and translational dermatology. Although this model answers many demands of an investigative tool, one obvious limitation is the absence of immune cells in SkOs. Our aim is to generate coculture systems of SkOs with macrophages to investigate how macrophages influence skin development. Methods Our group, and others, generated single-cell (sc)RNA-Seq and spatial transcriptomic datasets to thoroughly characterize human skin cell populations across the human lifespan. We used these datasets to compare the dynamic tempo of induced pluripotent stem cell (iPSC)-derived SkO (n = 84) differentiation with human prenatal (n = 15) and adult skin. For functional assays, we generated 2D angiogenesis coculture assays with macrophages and endothelial cells (n = 3) and 3D coculture of SkOs with macrophages (n = 3). Macrophages, endothelial cells and SkOs were all derived from the same iPSC line following established protocols. SkO vasculature was analysed after immunowholemount staining of CD31+ endothelial cells. Results Analysis of scRNA-Seq data highlighted a paucity in endothelial cell heterogeneity in SkOs compared with prenatal skin. We hypothesized that this could be explained by the absence of immune cells, as spatial transcriptomics microenvironment analysis of prenatal skin demonstrated that endothelial cells collocate with macrophages. With this in mind, we generated functional assays to investigate interactions of macrophages and endothelial cells in skin development. The 2D and 3D assays revealed that presence of macrophages significantly increased endothelial cell sprouting and network formation. Conclusions Our findings suggest a vital role of macrophages in skin morphogenesis, specifically in relation to the guidance of tip cells during angiogenesis. In addition, we present the SkO as an excellent complex model for investigating skin development, cell–cell interactions, and disease modelling for translational research in regenerative medicine and beyond.

A new framework for exploratory network mediator analysis in omics data.

Omics methods are widely used in basic biology and translational medicine research. More and more omics data are collected to explain the impact of certain risk factors on clinical outcomes. To explain the mechanism of the risk factors, a core question is how to find the genes/proteins/metabolites that mediate their effects on the clinical outcome. Mediation analysis is a modeling framework to study the relationship between risk factors and pathological outcomes, via mediator variables. However, high-dimensional omics data are far more challenging than traditional data: (1) From tens of thousands of genes, can we overcome the curse of dimensionality to reliably select a set of mediators? (2) How do we ensure that the selected mediators are functionally consistent? (3) Many biological mechanisms contain nonlinear effects. How do we include nonlinear effects in the high-dimensional mediation analysis? (4) How do we consider multiple risk factors at the same time? To meet these challenges, we propose a new exploratory mediation analysis framework, medNet, which focuses on finding mediators through predictive modeling. We propose new definitions for predictive exposure, predictive mediator, and predictive network mediator, using a statistical hypothesis testing framework to identify predictive exposures and mediators. Additionally, two heuristic search algorithms are proposed to identify network mediators, essentially subnetworks in the genome-scale biological network that mediate the effects of single or multiple exposures. We applied medNet on a breast cancer data set and a metabolomics data set combined with food intake questionnaire data. It identified functionally consistent network mediators for the exposures' impact on the outcome, facilitating data interpretation.

DNA shape features improve prediction of CRISPR/Cas9 activity

The CRISPR/Cas9 genome editing technology has transformed basic and translational research in biology and medicine. However, the advances are hindered by off-target effects and a paucity in the knowledge of the mechanism of the Cas9 protein. Machine learning models have been proposed for the prediction of Cas9 activity at unintended sites, yet feature engineering plays a major role in the outcome of the predictors. This study evaluates the improvement in the performance of similar predictors upon inclusion of epigenetic and DNA shape feature groups in the conventionally used sequence-based Cas9 target and off-target datasets. The approach involved the utilization of neural networks trained on a diverse range of parameters, allowing us to systematically assess the performance increase for the meticulously designed datasets- (i) sequence only, (ii) sequence and epigenetic features, and (iii) sequence, epigenetic and DNA shape feature datasets.The addition of DNA shape information significantly improved predictive performance, evaluated by Akaike and Bayesian information criteria. The evaluation of individual feature importance by permutation and LIME-based methods also indicates that not only sequence features like mismatches and nucleotide composition, but also base pairing parameters like opening and stretch, that are indicative of distortion in the DNA-RNA hybrid in the presence of mismatches, influence model outcomes.

Tumor Mutation Burden as a Cornerstone in Precision Oncology Landscapes: Effect of Panel Size and Uncertainty in Cutoffs.

Tumor mutation burden (TMB) has profound implications for personalized cancer therapy, particularly immunotherapy. However, the size of the panel and the cutoff values for an accurate determination of TMB are still controversial. In this study, a pan-cancer analysis was performed on 22 cancer types from The Cancer Genome Atlas. The efficiency of gene panels of different sizes and the effect of cutoff values in accurate TMB determination was assessed on a large cohort using Whole Exome Sequencing data (n = 9929 patients) as the gold standard. Gene panels of four different sizes (i.e., 0.44-2.54 Mb) were selected for comparative analyses. The heterogeneity of TMB within and between cancer types is observed to be very high, and it becomes possible to obtain the exact TMB value as the size of the panel increases. In panels with limited size, it is particularly difficult to recognize patients with low TMB. In addition, the use of a general TMB cutoff can be quite misleading. The optimal cutoff value varies between 5 and 20, depending on the TMB distribution of the different tumor types. The use of comprehensive gene panels and the optimization of TMB cutoff values for different cancer types can make TMB a robust biomarker in precision oncology. Moreover, optimization of TMB can help accelerate translational medicine research, and by extension, delivery of personalized cancer care in the future.

Engineered exosomes in emerging cell-free therapy.

The discovery and use of exosomes ushered in a new era of cell-free therapy. Exosomes are a subgroup of extracellular vesicles that show great potential in disease treatment. Engineered exosomes. with their improved functions have attracted intense interests of their application in translational medicine research. However, the technology of engineering exosomes still faces many challenges which have been the great limitation for their clinical application. This review summarizes the current status of research on engineered exosomes and the difficulties encountered in recent years, with a view to providing new approaches and ideas for future exosome modification and new drug development.

Recruitment, Retention, and Training of Citizen Scientists in Translational Medicine Research: A Citizen Science Initiative on Non-Alcoholic Fatty Liver Disease.

Citizen science is a participatory science approach in which members of the public (citizens) collaborate with scientists and professional researchers and become involved in research and innovation activities, resulting in the co-creation of scientific knowledge and innovation. Citizen science has been widely applied in research, particularly in the social sciences, environmental sciences, information and communication technologies, and public health. However, the application of this approach inclinical sciences, particularly in translational medicine research, is still nascent. This exploratory study involved members of the public (citizen scientists) in a translational medicineexperiment on non-alcoholic fatty liver disease that incorporated a lifestyle and weight-loss intervention. The aim of this paper is to report successful methods and approaches for the recruitment, retention, and training of citizen scientists. For the citizen scientists' recruitment, online calls placed on the websites of our research project and biomedical research centerand targeted emails were the most helpful. Of the 14 members of the public who expressed interest in our study, six were recruited as citizen scientists. Citizen scientists were mostly female (n = 5, 83%), white (n = 3, 50%), over 50 years of age (n = 4, 67%), educated to postgraduate level (n = 5, 83%), and either retired or not in employment (n = 5, 83%). The retention rate was 83% (n = 5), and the dropout rate was 17% (n = 1). We arranged instructor-led interactive online training sessions (an hour-long one-on-one session and two-hour group sessions). Research skills training covered ethics in research and qualitative and quantitative data analysis. Citizen scientists were given several incentives, such as reimbursement of travel and care costs, selection as citizen scientists of the month, publications of their blogs and perspective articles, and co-authorship and acknowledgement in papers and project deliverables. To conclude, members of the public (particularly middle-aged white women with postgraduate education) are interested in becoming citizen scientists in translational medicine research. Their retention rate is higher, and they can contribute to different research activities. However, they need training to develop their research skills and expertise. The training should be simple, comprehensive, and flexible to accommodate the schedules of individual citizen scientists. They deserve incentives as they work on a voluntary basis.

Developments and Trends of Nanotechnology Application in Sepsis: A Comprehensive Review Based on Knowledge Visualization Analysis.

Sepsis, a common life-threatening clinical condition, continues to have high morbidity and mortality rates, despite advancements in management. In response, significant research efforts have been directed toward developing effective strategies. Within this scope, nanotechnology has emerged as a particularly promising field, attracting significant interest for its potential to enhance disease diagnosis and treatment. While several reviews have highlighted the use of nanoparticles in sepsis, comprehensive studies that summarize and analyze the hotspots and research trends are lacking. To identify and further promote the development of nanotechnology in sepsis, a bibliometric analysis was conducted on the relevant literature, assessing research trends and hotspots in the application of nanomaterials for sepsis. Next, a comprehensive review of the subjectively recognized research hotspots in sepsis, including nanotechnology-enhanced biosensors and nanoscale imaging for sepsis diagnostics, and nanoplatforms designed for antimicrobial, immunomodulatory, and detoxification strategies in sepsis therapy, is elucidated, while the potential side effects and toxicity risks of these nanomaterials were discussed. Particular attention is given to biomimetic nanoparticles, which mimic the biological functions of source cells like erythrocytes, immune cells, and platelets to evade immune responses and effectively deliver therapeutic agents, demonstrating substantial translational potential. Finally, current challenges and future perspectives of nanotechnology applications in sepsis with a view to maximizing their great potential in the research of translational medicine are also discussed.

Nanotherapeutic approaches for delivery of long non-coding RNAs: an updated review with emphasis on cancer.

The long noncoding RNAs (lncRNAs) comprise a wide range of RNA species whose length exceeds 200 nucleotides, which regulate the expression of genes and cellular functions in a wide range of organisms. Several diseases, including malignancy, have been associated with lncRNA dysregulation. Due to their functions in cancer development and progression, lncRNAs have emerged as promising biomarkers and therapeutic targets in cancer diagnosis and treatment. Several studies have investigated the anti-cancer properties of lncRNAs; however, only a few lncRNAs have been found to exhibit tumor suppressor properties. Furthermore, their length and poor stability make them difficult to synthesize. Thus, to overcome the instability of lncRNAs, poor specificity, and their off-target effects, researchers have constructed nanocarriers that encapsulate lncRNAs. Recently, translational medicine research has focused on delivering lncRNAs into tumor cells, including cancer cells, through nano-drug delivery systems in vivo. The developed nanocarriers can protect, target, and release lncRNAs under controlled conditions without appreciable adverse effects. To deliver lncRNAs to cancer cells, various nanocarriers, such as exosomes, microbubbles, polymer nanoparticles, 1,2-dioleyl-3-trimethylammoniumpropane chloride nanocarriers, and virus-like particles, have been successfully developed. Despite this, every nanocarrier has its own advantages and disadvantages when it comes to delivering nucleic acids effectively and safely. This article examines the current status of nanocarriers for lncRNA delivery in cancer therapy, focusing on their potential to enhance cancer treatment.

Editorial: The Artificial Intelligence in Translational Medicine and Biomedical Research, How Future Can be Shaped?!!

The new era of translational medicine is facing a special and unique transition as the Artificial Intelligence (AI) and Machine Learning (ML) advance. The latest findings in many laboratories around the globes are promising and paved the way toward extraordinary innovative development in the field of medicine and medical research. In medical research, gradual and consistent advancement, from rudimentary origins, were initial key steps toward medical discoveries and innovation. However, with the AI inclusion into these research approaches, the traditional translational medicine is pacing up. Creating a great hope for filling many sophisticated gaps in knowledge that has been a challenge by the traditional methodology. Such advancements in AI and its involvement in biomedicine and healthcare are forging the framework for a flourishing and visionary translational medicine and biomedical research. Lately, following the discoveries made by a collaborative team of scientists from the University of Vermont and Tufts University (The xenobots), enormous research and investigation started to glance in the field. Attempts toward drug discovery, wound recoveries and self-proliferating stem cells were made. Most recently, scientists at Tufts University and Harvard University’s Wyss Institute have designed and created living small robots from cells of human trachea. In an in vitro world, these created cells could induce tissue damage recovery. The AI based medical diagnosis is also a revolutionary step toward proper diagnosis and consequently, medical intervention. The AI and ML are becoming so intelligent to an extent not to only diagnose the disease but also predict disease prognosis in future. The narrow AI (WatsonX) that was introduced by the IBM company can play a major role in assisting clinical decisions and image analysis. The challenge that faced the globe after the COVID-19 outbreak with multiple false negativity and false positivity outcomes in the diagnose necessitated the support of the AI in this field. Additionally, the Neuralink that is introduced by Neuralink Corp. is taking a historical step after gaining the approval from FDA to start human trials. Such dramatical change in the brain interface using AI can be a solution for many diseases that human couldn’t treat. Restoring brain activity and autonomy through integration of an AI to human nervous system is a mind-boggling promise that AI can give to medicine and medical science. Finally, these new approaches and advancement in AI and ML are prone to proper design and supervision, as well as, performing randomized clinical trials prior to the use in real-life medical interventions. Nonetheless, research and experimentations for AI inclusion in medicine are still an ongoing process, yet the opportunities are realistic that AI can be of great benefit in supporting medicine and biomedical research, specifically, where there is gap in knowledge and inability of human capacity to resolve them. There is no doubt that AI will be a significant party of the healthcare system in the future and a core medical support.

Cross-sectional network analysis of plasma proteins/metabolites correlated with pathogenesis and therapeutic response in acute promyelocytic leukemia.

The treatment of PML/RARA+ acute promyelocytic leukemia (APL) with all-trans-retinoic acid and arsenic trioxide (ATRA/ATO) has been recognized as a model for translational medicine research. Though an altered microenvironment is a general cancer hallmark, how APL blasts shape their plasma composition is poorly understood. Here, we reported a cross-sectional correlation network to interpret multilayered datasets on clinical parameters, proteomes, and metabolomes of paired plasma samples from patients with APL before or after ATRA/ATO induction therapy. Our study revealed the two prominent features of the APL plasma, suggesting a possible involvement of APL blasts in modulating plasma composition. One was characterized by altered secretory protein and metabolite profiles correlating with heightened proliferation and energy consumption in APL blasts, and the other featured APL plasma-enriched proteins or enzymes catalyzing plasma-altered metabolites that were potential trans-regulatory targets of PML/RARA. Furthermore, results indicated heightened interferon-gamma signaling characterizing a tumor-suppressing function of the immune system at the first hematological complete remission stage, which likely resulted from therapy-induced cell death or senescence and ensuing supraphysiological levels of intracellular proteins. Overall, our work sheds new light on the pathophysiology and treatment of APL and provides an information-rich reference data cohort for the exploratory and translational study of leukemia microenvironment.

Generation of canine induced pluripotent stem cells under feeder-free conditions using Sendai virus vector encoding six canine reprogramming factors

Although it is in its early stages, canine induced pluripotent stem cells (ciPSCs) hold great potential for innovative translational research in regenerative medicine, developmental biology, drug screening, and disease modeling. However, almost all ciPSCs were generated from fibroblasts, and available canine cell sources for reprogramming are still limited. Furthermore, no report is available to generate ciPSCs under feeder-free conditions because of their low reprogramming efficiency. Here, we reanalyzed canine pluripotency-associated genes and designed canine LIN28A, NANOG, OCT3/4, SOX2, KLF4, and C-MYC encoding Sendai virus vector, called 159cf. and 162cf. We demonstrated that not only canine fibroblasts but also canine urine-derived cells, which can be isolated using a noninvasive and straightforward method, were successfully reprogrammed with or without feeder cells. ciPSCs existed in undifferentiated states, differentiating into the three germ layers invitro and invivo. We successfully generated ciPSCs under feeder-free conditions, which can promote studies in veterinary and consequently human regenerative medicines.

Biobanking in the hospital of a multidisciplinary research medical center as a potential for a wide research range. Part II. Specifics and first results of developing a described collection of biomaterial

In connection with the development of genetic and other types of research in translational medicine, collections containing a significant number of biospecimens and large data sets are required. Since in-depth examination of patients is most often carried out during hospitalization, the creation of collections based on electronic medical records (EMR) data is promising. In this regard, the project "Total biobanking in the hospital of the National Medical Research Center for Therapy and Preventive Medicine" was initiated, aimed at total biobanking of blood and related products from patients in inpatient departments and the creation of a unified system that combines information on biosamples, clinical and other types of data.Aim. To describe the collection of biospecimens collected within the project "Total biobanking in the hospital of the National Medical Research Center for Therapy and Preventive Medicine" for August 2023, and to analyze the process of biospecimen description.Material and methods. Recruitment of patients is carried out on the basis of inpatient departments of the National Medical Research Center for Therapy and Preventive Medicine, only if the patient signs informed consent. After blood is taken, the biomaterial enters the biobank, where sample preparation and storage is carried out, as well as data about each biosample is entered into a special FreezerPRO system. At the same time, clinical data about the patient is collected using EMR, which are combined in the Medialog medical information system.Results. A special software for data exchange between the medical information system and the biobank has been developed, which combines information about patients entering the Medialog medical information system and the FreezerPro biospecimen management system, which contains detailed information about biospecimens. As of August 25, 2023, biomaterial from 3996 patients was collected, which was accompanied by a detailed clinical summary. In particular, data is available on diagnoses (for 100% of patients), laboratory test data (100%), paraclinical data, in particular, electrocardiography (100%), echocardiography (76,8%), 24-hour Holter monitoring (59,8%), esophagogastroduodenoscopy (27,3%), etc. The most common diseases were hypertension (76,0%), atherosclerosis (69,9%), coronary artery disease (52,9%), arrhythmias (50,6%), diabetes (19,2%). In addition, 25,0% of patients had prior stenting.Conclusion. Total biobanking within the hospital of a multidisciplinary research medical center allows for the rapid and high-quality collection of biomaterial and related data. A collection of biospecimens with comprehensive and detailed clinical annotations has been collected. A primary analysis of the collection was carried, which showed that it can be used for a wide range of studies.

From book to bedside? A critical perspective on the debate about "translational bioethics".

The concept of "translational bioethics" has received considerable attention in recent years. Most publications draw an analogy to translational medicine and describe bioethical research that aims at implementing and evaluating ethical interventions. However, current accounts of translational bioethics are often rather vague and seem to differ with regard to conceptual and methodological assumptions. It is not clear and scarcely analyzed what exactly "translation" in the field of bioethics means, in particular regarding goals and processes so that it is justified to appeal to translational medicine. In this article, we thus explore possible analogies and disanalogies between translational medicine and translational bioethics to establish whether the often occurring reference to concepts of translational medicine in the field of bioethics can be justified by substantial analogies. We will first provide an account of different models of translational medicine. In a second step, we will propose an analytic definition that explicitly articulates the essential characteristics of "translational research" irrespective of the research field (i.e., biomedicine, bioethics). Subsequently, we will explore whether and in how far general characteristics and phases of translational research in medicine can be applied to translational research in bioethics. Based on our analyses, we will come to the skeptical conclusion that at present there are considerable conceptual disanalogies and unsolved conceptual problems that disallow using "translational bioethics" in a meaningful analogy to respective accounts in biomedicine. Nevertheless, we will demonstrate that some insights gained by the conceptual accounts of translational medicine can contribute to advance current research activities in bioethics.

Current status of cardiovascular translational medicine research: from high throughput multi omics to integrative bioinformatics

Current status of cardiovascular translational medicine research: from high throughput multi omics to integrative bioinformatics

Genome-wide analysis identifies non-reference transposable element polymorphisms associated with Parkinson’s disease

Parkinson’s disease (PD) is a common neurodegenerative disease that primarily affects the elderly, significantly impacting patients’ health and quality of life. While most genetic studies on PD have focused on single nucleotide polymorphisms, the effects of other forms of genomic variation in PD are yet to be fully elucidated. Transposable elements (TEs) are one of the main sources of human genome structural variation, with known associations with many human diseases. However, their potential connection to PD remains unclear. In this study, we investigated non-reference TE polymorphisms in three independent PD cohorts and explored their associations with both PD risk and progression. Our findings revealed that one non-reference TE is associated with the risk of PD, while two TEs are associated with disease progression. Furthermore, through expression quantitative trait locus (eQTL) analysis, we identified 18 cis TE-eQTLs in an interaction model and 290 cis TE-eQTLs in a non-interaction model. Several non-reference TE polymorphisms are correlated with specific PD-gene expression patterns in trans. These results indicate the feasibility of delving into the genetics of PD through the study of complex genomic variations. Advances in genomics research have the potential to deepen our understanding of this disease and pave the way for further translational medicine research in PD.

PC3T: a signature-driven predictor of chemical compounds for cellular transition

Cellular transitions hold great promise in translational medicine research. However, therapeutic applications are limited by the low efficiency and safety concerns of using transcription factors. Small molecules provide a temporal and highly tunable approach to overcome these issues. Here, we present PC3T, a computational framework to enrich molecules that induce desired cellular transitions, and PC3T was able to consistently enrich small molecules that had been experimentally validated in both bulk and single-cell datasets. We then predicted small molecule reprogramming of fibroblasts into hepatic progenitor-like cells (HPLCs). The converted cells exhibited epithelial cell-like morphology and HPLC-like gene expression pattern. Hepatic functions were also observed, such as glycogen storage and lipid accumulation. Finally, we collected and manually curated a cell state transition resource containing 224 time-course gene expression datasets and 153 cell types. Our framework, together with the data resource, is freely available at http://pc3t.idrug.net.cn/. We believe that PC3T is a powerful tool to promote chemical-induced cell state transitions.