Translational Biomedical Research Research Articles

Advancing Nitrile-Aminothiol Strategy for Dual and Sequential Bioconjugation.

Nitrile-aminothiol conjugation (NATC) stands out as a promising biocompatible ligation technique due to its high chemo-selectivity. Herein we investigated the reactivity and substrate scope of NAT conjugation chemistry, thus developing a novel pH dependent orthogonal NATC as a valuable tool for chemical biology. The study of reaction kinetics elucidated that the combination of heteroaromatic nitrile and aminothiol groups led to the formation of an optimal bioorthogonal pairing, which is pH dependent. This pairing system was effectively utilized for sequential and dual conjugation. Subsequently, these rapid (≈1 h) and high yield (>90%) conjugation strategies were successfully applied to a broad range of complex biomolecules, including oligonucleotides, chelates, small molecules and peptides. The effectiveness of this conjugation chemistry was demonstrated by synthesizing a fluorescently labelled antimicrobial peptide-oligonucleotide complex as a dual conjugate to imaging in live cells. This first-of-its-kind sequential NATC approach unveils unprecedented opportunities in modern chemical biology, showcasing exceptional adaptability in rapidly creating structurally complex bioconjugates. Furthermore, the results highlight its potential for versatile applications across fundamental and translational biomedical research.

Generation of a Bovine Primary Enteroid-Derived Two-Dimensional Monolayer Culture System for Applications in Translational Biomedical Research.

Organoid cell culture systems can recapitulate the complexity observed in tissues, making them useful in studying host-pathogen interactions, evaluating drug efficacy and toxicity, and tissue bioengineering. However, applying these models for the described reasons may be limited because of the three-dimensional (3D) nature of these models. For example, using 3D enteroid culture systems to study digestive diseases is challenging due to the inaccessibility of the intestinal lumen and its secreted substances. Indeed, stimulation of 3D organoids with pathogens requires either luminal microinjection, mechanical disruption of the 3D structure, or generation of apical-out enteroids. Moreover, these organoids cannot be co-cultured with immune and stromal cells, limiting in-depth mechanistic analysis into pathophysiological dynamics. To circumvent this, we optimized a bovine primary cell two-dimensional (2D) enteroid-derived monolayer culture system, allowing co-culture with other relevant cell types. Ileal crypts isolated from healthy adult cattle were cultured to generate 3D organoids that were cryopreserved for future use. A 2D monolayer was created using revived 3D enteroids that were passaged and disrupted to yield single cells, which were seeded on basement membrane extract-coated transwell cell culture inserts, thereby exposing their apical surface. The intestinal monolayer polarity, cellular differentiation, and barrier function were characterized using immunofluorescence microscopy and measuring transepithelial electrical resistance. Stimulation of the apical surface of the monolayer revealed the expected functionality of the monolayer, as demonstrated by cytokine secretion from both apical and basal compartments. The described 2D enteroid-derived monolayer model holds great promise in investigating host-pathogen interactions and intestinal physiology, drug development, and regenerative medicine.

Unlocking the Potential of Human-Induced Pluripotent Stem Cells: Cellular Responses and Secretome Profiles in Peptide Hydrogel 3D Culture.

Human-induced pluripotent stem cells (hiPSCs) have shown great potential for human health, but their growth and properties have been significantly limited by the traditional monolayer (2D) cell culture method for more than 15 years. Three-dimensional (3D) culture technology has demonstrated tremendous advantages over 2D. In particular, the 3D PGmatrix hiPSC derived from a peptide hydrogel offers a breakthrough pathway for the maintenance and expansion of physiologically relevant hiPSC 3D colonies (spheroids). In this study, the impact of 3D culture conditions in PGmatrix hiPSC on cell performance, integrity, and secretome profiles was determined across two commonly used hiPSC cell lines derived from fibroblast cells (hiPSC-F) and peripheral blood mononuclear cells (hiPSC-P) in the two most popular hiPSC culture media (mTeSR1 and essential eight (E8)). The 3D culture conditions varied in hydrogel strength, 3D embedded matrix, and 3D suspension matrix. The results showed that hiPSCs cultured in 3D PGmatrix hiPSC demonstrated the ability to maintain a consistently high cell viability that was above 95% across all the 3D conditions with cell expansion rates of 10-20-fold, depending on the 3D conditions and cell lines. The RT-qPCR analysis suggested that pluripotent gene markers are stable and not significantly affected by the cell lines or 3D PGmatrix conditions tested in this study. Mass spectrometry-based analysis of secretome from hiPSCs cultured in 3D PGmatrix hiPSC revealed a significantly higher quantity of unique proteins, including extracellular vesicle (EV)-related proteins and growth factors, compared to those in the 2D culture. Moreover, this is the first evidence to identify that hiPSCs in a medium with a rich supplement (i.e., mTeSR1) released more growth-regulating factors, while in a medium with fewer supplements (i.e., E8) hiPSCs secreted more survival growth factors and extracellular proteins. These findings offer insights into how these differences may impact hiPSC behavior, and they deepen our understanding of how hiPSCs respond to 3D culture conditions, aiding the optimization of hiPSC properties in translational biomedical research toward clinical applications.

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.

Single-cell Mayo Map (scMayoMap): an easy-to-use tool for cell type annotation in single-cell RNA-sequencing data analysis

BackgroundSingle-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed.ResultsWe curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap, an easy-to-use single-cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. Additionally, the scMayoMapDatabase can be integrated with other tools and further improve their performance.ConclusionsscMayoMap and scMayoMapDatabase will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.

Yearly variation coupled with social interactions shape the skin microbiome in free-ranging rhesus macaques.

While skin microbes are known to mediate human health and disease, there has been minimal research on the interactions between skin microbiota, social behavior, and year-to-year effects in non-human primates-important animal models for translational biomedical research. To examine these relationships, we analyzed skin microbes from 78 rhesus macaques living on Cayo Santiago Island, Puerto Rico. We considered age, sex, and social group membership, and characterized social behavior by assessing dominance rank and patterns of grooming as compared to nonsocial behaviors. To measure the effects of a shifting environment, we sampled skin microbiota (based on sequence analysis of the 16S rRNA V4 region) and assessed weather across sampling periods between 2013 and 2015. We hypothesized that, first, monkeys with similar social behavior and/or in the same social group would possess similar skin microbial composition due, in part, to physical contact, and, second, microbial diversity would differ across sampling periods. We found significant phylum-level differences between social groups in the core microbiome as well as an association between total grooming rates and alpha diversity in the complete microbiome, but no association between microbial diversity and measures of rank or other nonsocial behaviors. We also identified alpha and beta diversity differences in microbiota and differential taxa abundance across two sampling periods. Our findings indicate that social dynamics interact with yearly environmental changes to shape the skin microbiota in rhesus macaques, with potential implications for understanding the factors affecting the microbiome in humans, which share many biological and social characteristics with these animals. IMPORTANCE Primate studies are valuable for translational and evolutionary insights into the human microbiome. The majority of primate microbiome studies focus on the gut, so less is known about the factors impacting the microbes on skin and how their links affect health and behavior. Here, we probe the impact of social interactions and the yearly environmental changes on food-provisioned, free-ranging monkeys living on a small island. We expected animals that lived together and groomed each other would have more similar microbes on their skin, but surprisingly found that the external environment was a stronger influence on skin microbiome composition. These findings have implications for our understanding of the human skin microbiome, including potential manipulations to improve health and treat disease.

Weighted pain-related behaviors in pigs undergoing castration based on multilevel logistic regression algorithm

Pigs are largely raised for food and translational biomedical research. In several intentional and unintentional situations pigs experience pain. In 2020, the Unesp-Botucatu pig composite acute pain scale (UPAPS) was validated according to the COSMIN guidelines for quick and simple assessment of pain in pigs. Recently, an algorithm has been used to estimate the importance of different behaviors in sheep and horses, however, the statistical weight of each pain-related behavior is still unknown for pigs. Herein, we aimed to investigate whether UPAPS pain-related behaviors in pigs have different statistical weights and to rank their importance. A database from a previous study was used, containing the behavioral records of 45 pigs in the perioperative period of castration. The pigs were filmed between 16 and 24 h before the castration, 3.5 and 4 h after the castration, 1.5–2 h after intramuscular administration of analgesics, and 24 h after the end of the castration. Two experienced observers individually and randomly assessed all videos, without knowing which time-point they were observing (blind analysis). At the end of each video observation, the observers recorded whether they would indicate analgesia, if they considered the pigs were suffering pain, according to their clinical experience, and scored the UPAPS. The intra- and inter-observer reliability were calculated with intraclass correlation coefficient and weighted kappa. A multilevel binomial logistic regression algorithm was constructed to estimate the statistical weights of each UPAPS behavior. The intra- and inter-observer reliability values ranged from 0.71 to 0.88. According to the algorithm, 10 of 19 slope coefficients of the behaviors were significant, evidencing a difference in weight for each pain-related behavior in pigs undergoing castration. The change in posture ‘protecting the affected area’ was the heaviest pain-related behavior based on the algorithm. We conclude that the pain-related behaviors in pigs from the UPAPS have different statistical weights. The algorithm has the potential to indicate the degree of importance of different pain behaviors in pigs undergoing castration and, therefore, to select the most relevant pain behaviors to be targeted when assessing pain in pigs.

Older adults (OA) in clinical trials: Where are they? Representation of OA in clinical trials conducted at the national cancer institute (NCI) clinical center.

e18517 Background: Clinical trials are a significant source of evidence for diagnostic and management strategies in cancer patients. Diversity, equity, and inclusion are necessary to provide access to clinical trials and improve care. Despite the increase of OA in the United States (US), OA are still under-represented in clinical trials, with a lack of information on clinical outcomes and safety. The NCI provides oncologic care on research studies conducted at the Clinical Center (CC), the research hospital of the National Institutes of Health (NIH) where third-party payers are neither notified nor invoiced for treatments received. Participants come from all over the US and are primarily referred by their primary oncologists. We sought to evaluate the participation of OA in clinical trials at the NCI-CC. Methods: In this retrospective analysis, OA were defined as participants aged ≥65 years. The demographic data of OA enrolled from 2005 to 2019 were retrieved from the Biomedical Translational Research Information System (BTRIS), the NIH clinical research data repository. Data on the incidence of new cancer diagnoses in the US population for the same period (2005-2019) were obtained from the Surveillance, Epidemiology, and End Results (SEER) program. We compared the proportions of OA enrolled in NCI-CC clinical trials with the corresponding data from SEER program. We further stratified the OA into four age subcategories: from 65 to 69, from 70 to 74, from 75 to 79, and older than 80 years of age. No inferential statistical analyses were performed. Results: A total of 37,326 participants were enrolled from 2005 to 2019 on NCI-CC clinical trials. The number of OA = 7,934 (21.3%). According to the SEER program, 11,017,070 people were diagnosed with cancer from 2005 to 2019, and 5,971,607 (54.2%) were OA. Notably, the proportions of OA enrolled in NCI-CC was lower than the SEER data in all the subcategories. Patients ≥75 years accounted for the most under-represented (Table). Conclusions: OA are under-represented in clinical trials conducted at the NCI-CC from 2005 to 2019, emphasizing the need to “geriatricize” clinical trials. Adaptations of trial design such as expansion of eligibility criteria, utilization of geriatric assessment tools, and inclusion of OA-specific enrollment targets may reduce barriers to enrollment. Outreach initiatives to the oncology community to reinforce the need for increased referral of OA along with incorporation of telehealth for trial screening and assessments could also be considered to expand enrollment and increase the evidence base to guide treatment of OA with cancer. [Table: see text]

Solfeggio-frequency music exposure reverses cognitive and endocrine deficits evoked by a 24-h light exposure in adult zebrafish

Music therapy has long been used as a non-pharmacological intervention to improve cognitive function and mood in humans. Mounting rodent evidence also supports beneficial impact of music exposure on animal cognitive performance. The zebrafish (Danio rerio) is an important emerging aquatic animal model in translational biomedical and neuroscience research. Here, we evaluate the effects of intermittent (2-h or 6-h twice daily) and continuous (24-h) solfeggio-frequency music exposure on behavioral, cognitive and endocrine parameters in adult zebrafish whose circadian rhythm was disturbed by a 24-h light exposure. Overall, a 24-h light exposure stress evokes overt cognitive deficits in the inhibitory avoidance test and elevates zebrafish whole-body cortisol levels. However, these effects were reversed by solfeggio-frequency music exposure for 2 or 6 h twice daily, and by continuous 24-h exposure. Collectively, these findings suggest a positive modulation of cognitive and endocrine responses in adult zebrafish by environmental enrichment via the long-term exposure to music, and reinforces zebrafish as a robust, sensitive model organism for neurocognitive and neuroendocrine research.

Bacterial Cholecystitis and Cholangiohepatitis in Common Marmosets (Callithrix jacchus).

The common marmoset (Callithrix jacchus), a New World NHP, has emerged as important animal model in multiple areas of translational biomedical research. The quality of translational research in marmosets depends on early diagnosis, treatment, and prevention of their spontaneous diseases. Here, we characterize an outbreak of infectious cholangiohepatitis that affected 7 adult common marmosets in a single building over a 10-mo period. Marmosets presented for acute onset of lethargy, dull mentation, weight loss, dehydration, hyporexia, and hypothermia. Blood chemistries at presentation revealed markedly elevated hepatic and biliary enzymes, but mild neutrophilia was detected in only 1 of the 7. Affected marmosets were unresponsive to rigorous treatment and died or were euthanized within 48 h of presentation. Gross and histopathologic examinations revealed severe, necrosuppurative cholangiohepatitis and proliferative cholecystitis with bacterial colonies and an absence of gallstones. Perimortem and postmortem cultures revealed single or dual isolates of Escherichia coli and Pseudomonas aeruginosa. Other postmortem findings included bile duct hyperplasia, periportal hepatitis, bile peritonitis, ulcerative gastroenteritis, and typhlitis. Environmental contamination of water supply equipment with Pseudomonas spp. was identified as the source of infection, but pathogenesis remains unclear. This type of severe, infectious cholangiohepatitis with proliferative cholecystitis with Pseudomonas spp. had not been reported previously in marmosets, and we identified and here describe several contributing factors in addition to contaminated drinking water.

VSOLassoBag: a variable-selection oriented LASSO bagging algorithm for biomarker discovery in omic-based translational research

Screening biomolecular markers from high-dimensional biological data is one of the long-standing tasks for biomedical translational research. With its advantages in both feature shrinkage and biological interpretability, Least Absolute Shrinkage and Selection Operator (LASSO) algorithm is one of the most popular methods for the scenarios of clinical biomarker development. However, in practice, applying LASSO on omics-based data with high dimensions and low-sample size may usually result in an excess number of predictive variables, leading to the overfitting of the model. Here, we present VSOLassoBag, a wrapped LASSO approach by integrating an ensemble learning strategy to help select efficient and stable variables with high confidence from omics-based data. Using a bagging strategy in combination with a parametric method or inflection point search method, VSOLassoBag can integrate and vote variables generated from multiple LASSO models to determine the optimal candidates. The application of VSOLassoBag on both simulation datasets and real-world datasets shows that the algorithm can effectively identify markers for either case-control binary classification or prognosis prediction. In addition, by comparing with multiple existing algorithms, VSOLassoBag shows a comparable performance under different scenarios while resulting in fewer features than others. In summary, VSOLassoBag, which is available at https://seqworld.com/VSOLassoBag/ under the GPL v3 license, provides an alternative strategy for selecting reliable biomarkers from high-dimensional omics data. For user's convenience, we implement VSOLassoBag as an R package that provides multithreading computing configurations.

Deep top-down proteomics revealed significant proteoform-level differences between metastatic and nonmetastatic colorectal cancer cells.

Understanding cancer metastasis at the proteoform level is crucial for discovering previously unknown protein biomarkers for cancer diagnosis and drug development. We present the first top-down proteomics (TDP) study of a pair of isogenic human nonmetastatic and metastatic colorectal cancer (CRC) cell lines (SW480 and SW620). We identified 23,622 proteoforms of 2332 proteins from the two cell lines, representing nearly fivefold improvement in the number of proteoform identifications (IDs) compared to previous TDP datasets of human cancer cells. We revealed substantial differences between the SW480 and SW620 cell lines regarding proteoform and single amino acid variant (SAAV) profiles. Quantitative TDP unveiled differentially expressed proteoforms between the two cell lines, and the corresponding genes had diversified functions and were closely related to cancer. Our study represents a pivotal advance in TDP toward the characterization of human proteome in a proteoform-specific manner, which will transform basic and translational biomedical research.

Aberrant Expression and Prognostic Potential of IL-37 in Human Lung Adenocarcinoma.

Interleukin-37 (IL-37) is a relatively new IL-1 family cytokine that, due to its immunoregulatory properties, has lately gained increasing attention in basic and translational biomedical research. Emerging evidence supports the implication of this protein in any human disorder in which immune homeostasis is compromised, including cancer. The aim of this study was to explore the prognostic and/or diagnostic potential of IL-37 and its receptor SIGIRR (single immunoglobulin IL-1-related receptor) in human tumors. We utilized a series of bioinformatics tools and -omics datasets to unravel possible associations of IL-37 and SIGIRR expression levels and genetic aberrations with tumor development, histopathological parameters, distribution of tumor-infiltrating immune cells, and survival rates of patients. Our data revealed that amongst the 17 human malignancies investigated, IL-37 exhibits higher expression levels in tumors of lung adenocarcinoma (LUAD). Moreover, the expression profiles of IL-37 and SIGIRR are associated with LUAD development and tumor stage, whereas their high mRNA levels are favorable prognostic factors for the overall survival of patients. What is more, IL-37 correlates positively with a LUAD-associated transcriptomic signature, and its nucleotide changes and expression levels are linked with distinct infiltration patterns of certain cell subsets known to control LUAD anti-tumor immune responses. Our data indicate the potential value of IL-37 and its receptor SIGIRR to serve as biomarkers and/or immune-checkpoint therapeutic targets for LUAD patients. Further, the data highlight the urgent need for further exploration of this cytokine and the underlying pathogenetic mechanisms to fully elucidate its implication in LUAD development and progression.

Long‐Lived Second Near‐Infrared Luminescent Probes: An Emerging Role in Time‐Resolved Luminescence Bioimaging and Biosensing

Luminescence bioimaging and biosensing in the second near‐infrared (NIR‐II) spectral window (1,000–1,700 nm wavelengths) is a newly emerging technique that is extensively used in fundamental research and clinical practice. Owing to its advantages, including deep tissue penetration and excellent temporal resolution, this technique has shown tremendous promise for target‐specific imaging and noninvasive monitoring. However, developing bioimaging modalities that enable target‐specific imaging with high resolution and sensitivity remains a great challenge today. Compared with NIR‐II fluorescence imaging, NIR‐II luminescence lifetime imaging with advantages such as lower background noises, higher sensitivity, and higher spatial resolution has opened up new avenues for deep tissue imaging and biomedical applications. Herein, various types of long‐lived NIR‐II luminescent probes, including complex dyes, rare‐earth‐doped nanoparticles, organic compound‐loaded nanoparticles, inorganic‐based dots and nanotubes, as well as the main types of time‐resolved luminescence techniques including time‐domain (e.g., time‐correlated single‐photon counting, time‐gated detection, pulse sampling, etc.) and frequency‐domain systems are first summarized. The unique advantages of long‐lived NIR‐II luminescent probes and their critical roles in bioimaging and biosensing are also highlighted. Finally, the present challenges and future directions of long‐lived NIR‐II luminescent probes in biomedical research, translational research, and (pre‐)clinical studies are discussed.

Immunologic and pathologic characterization of a novel swine biomedical research model for eosinophilic esophagitis.

Eosinophilic esophagitis (EoE) is a chronic allergy-mediated condition with an increasing incidence in both children and adults. Despite EoE's strong impact on human health and welfare, there is a large unmet need for treatments with only one recently FDA-approved medication for EoE. The goal of this study was to establish swine as a relevant large animal model for translational biomedical research in EoE with the potential to facilitate development of therapeutics. We recently showed that after intraperitoneal sensitization and oral challenge with the food allergen hen egg white protein (HEWP), swine develop esophageal eosinophilia-a hallmark of human EoE. Herein, we used a similar sensitization and challenge treatment and evaluated immunological and pathological markers associated with human EoE. Our data demonstrate that the incorporated sensitization and challenge treatment induces (i) a systemic T-helper 2 and IgE response, (ii) a local expression of eotaxin-1 and other allergy-related immune markers, (iii) esophageal eosinophilia (>15 eosinophils/0.24 mm2), and (iv) esophageal endoscopic findings including linear furrows and white exudates. Thereby, we demonstrate that our sensitization and oral challenge protocol not only induces the underlying immune markers but also the micro- and macro-pathological hallmarks of human EoE. This swine model for EoE represents a novel relevant large animal model that can drive translational biomedical research to develop urgently needed treatment strategies for EoE.

The Chemical Probes Portal: an expert review-based public resource to empower chemical probe assessment, selection and use.

We describe the Chemical Probes Portal (https://www.chemicalprobes.org/), an expert review-based public resource to empower chemical probe assessment, selection and use. Chemical probes are high-quality small-molecule reagents, often inhibitors, that are important for exploring protein function and biological mechanisms, and for validating targets for drug discovery. The publication, dissemination and use of chemical probes provide an important means to accelerate the functional annotation of proteins, the study of proteins in cell biology, physiology, and disease pathology, and to inform and enable subsequent pioneering drug discovery and development efforts. However, the widespread use of small-molecule compounds that are claimed as chemical probes but are lacking sufficient quality, especially being inadequately selective for the desired target or even broadly promiscuous in behaviour, has resulted in many erroneous conclusions in the biomedical literature. The Chemical Probes Portal was established as a public resource to aid the selection and best-practice use of chemical probes in basic and translational biomedical research. We describe the background, principles and content of the Portal and its technical development, as well as examples of its applications and use. The Chemical Probes Portal is a community resource and we therefore describe how researchers can be involved in its content and development.

Cancer prediction using RNA sequencing and deep learning

Numerous areas of medical services, including as imaging diagnostics, advanced pathology, emergency clinic confirmation prediction, drug plan, grouping of cancer and stromal cells, specialist help, and so forth, have profited from the utilization of deep learning. Cancer prognosis involves predicting the course of the disease, the likelihood that it will spread and recur, and the likelihood that patients will survive. The clinical management of cancer patients will considerably benefit from the precision of cancer prognosis prediction. To better forecast cancer prognosis, modern statistical analysis and Deep learning techniques are being applied, as well as biomedical translational research being improved. In recent years, the processing capacity has significantly increased and the innovation of artificial insight, especially deep learning, has advanced quickly. Cancer is the leading reason for death in people. As a result, cancer detection is essential for early diagnosis and provides the best chance for treating cancer patients in a secure and efficient manner. It is, however, the trickiest way to increase the likelihood of the person surviving. RNA sequencing has significantly advanced in the last few decades and is now a crucial method for transcriptome profiling.

A guide to open science practices for animal research.

Translational biomedical research relies on animal experiments and provides the underlying proof of practice for clinical trials, which places an increased duty of care on translational researchers to derive the maximum possible output from every experiment performed. The implementation of open science practices has the potential to initiate a change in research culture that could improve the transparency and quality of translational research in general, as well as increasing the audience and scientific reach of published research. However, open science has become a buzzword in the scientific community that can often miss mark when it comes to practical implementation. In this Essay, we provide a guide to open science practices that can be applied throughout the research process, from study design, through data collection and analysis, to publication and dissemination, to help scientists improve the transparency and quality of their work. As open science practices continue to evolve, we also provide an online toolbox of resources that we will update continually.

Making translational value: Identifying ‘good targets’ for clinical research on gene editing and induced pluripotent stem cell technologies

Biomedical translational researchers aim to develop knowledge and techniques arising from research in the life sciences into clinical applications. Using the examples of induced pluripotent stem cells (iPSC) and gene editing, this paper examines how translational researchers identify and justify which particular conditions or patient populations make ‘good targets’ for translational research with particular technologies. Drawing on empirical data from qualitative interviews with academic and commercial researchers working on clinical translation of iPSC and gene editing in the UK, this study illustrates how particular combinations of technology and disease (for example iPSC-derived cells as a therapy for Parkinson's disease or gene editing for Cystic Fibrosis) were evaluated and justified as worth pursuing. The results show that translational researchers anticipate the ways in which their therapies-in-the making will be evaluated by other groups including regulators, physicians, patients, and bodies charged with health technology assessment. Each of these groups have their own understandings of what is valuable in a novel health technology and their own criteria for evaluation. As a result, translational researchers must supplement justifications that draw on scientific and industrial logics, with accounts that recognise other forms of worth, including market and civic registers of justification. These findings give an insight into the factors shaping contemporary biomedical translational research. The current regulatory and health technology adoption frameworks exert a strong influence, with elements such as ‘safety’ or ‘unmet need’ being common to most justification. However there was also sufficient flexibility to allow different competing definitions of what safety or unmet need might look like.

Analysis of demographic characteristics and disparities of enrollment in cancer clinical research at the U.S. National Cancer Institute (NCI): A 15-year experience.

6529 Background: Racial, gender, and socioeconomic disparities in cancer clinical trials participation have been well documented. The intramural program of the NCI in the National Institutes of Health (NIH) Bethesda campus conducts clinical research where participants are referred by their physician or self-referred, treated without charge and receive aid in travel expenses. We sought to describe the demographics of participants and identify potential disparities. Methods: We used the clinical research data repository of the NIH, Biomedical Translational Research Information System (BTRIS), to extract self-reported demographic data of enrollees in NCI protocols from 2005-2020. Comparisons were made with the U.S. population of patients with cancer as captured in the Surveillance, Epidemiology, and End Results (SEER) Program (2005-2018). The average household income per zip code was derived from the American Community Survey at five-year increments from 2011, 2015, and 2019. Results: We obtained data on 40,007 participants; 38,531 (95%) came from the U.S., of which 18,606 (48%) from DC, Maryland, or Virginia (DMV). Median age at consent was 54 years (range 2 – 89). Proportions of basic demographic attributes and corresponding values from SEER data were presented. Notably, Hispanic male, non-Hispanic female, and white female participants were under-represented in NCI clinical research compared to SEER data. African Americans were under-represented in the non-DMV population (5%) vs. the DMV (19%). Also, for patients from the DMV area, median income was consistently less for African American participants compared to White and Asian participants. Conclusions: The identified disparities may be partially due to the specific research activities available at the NCI, but strategies aiming at increasing the participation and representation of under-represented groups are required. [Table: see text]