Comparative Analysis of Biological Signatures between Freshly Preserved and Cryo-Preserved Bone Marrow Mesenchymal Stem Cells

Mesenchymal stem cells (MSCs) can differentiate into multiple connective tissue lineages, including osteoblasts, chondrocytes, and adipocytes. MSCs secrete paracrine molecules that are associated with immunomodulation, anti-fibrotic effects, and angiogenesis. Due to their orchestrative potential, MSCs have been therapeutically applied for several diseases. An important aspect of this process is the delivery of high-quality MSCs to patients at the right time, and cryo-biology and cryo-preservation facilitate the advancement of the logistics thereof. This study aimed to compare the biological signatures between freshly preserved and cryo-preserved MSCs by using big data sourced from the Pharmicell database. From 2011 to 2022, data on approximately 2300 stem cell manufacturing cases were collected. The dataset included approximately 60 variables, including viability, population doubling time (PDT), immunophenotype, and soluble paracrine molecules. In the dataset, 671 cases with no missing data were able to receive approval from an Institutional Review Board and were analyzed. Among the 60 features included in the final dataset, 20 were selected by experts and abstracted into two features by using a principal component analysis. Circular clustering did not introduce any differences between the two MSC preservation methods. This pattern was also observed when using viability, cluster of differentiation (CD) markers, and paracrine molecular indices as inputs for unsupervised analysis. The individual average PDT and cell viability at most passages did not differ according to the preservation method. Most immunophenotypes (except for the CD14 marker) and paracrine molecules did not exhibit different mean levels or concentrations between the frozen and unfrozen MSC groups. Collectively, the biochemical signatures of the cryo-preserved and unfrozen bone marrow MSCs were comparable.

Biological signatures and prediction of an immunosuppressive status-persistent critical illness-among orthopedic trauma patients using machine learning techniques

BACKGROUND

Persistent critical illness (PerCI) is an immunosuppressive status. The underlying pathophysiology driving PerCI remains incompletely understood. The objectives of the study were to identify the biological signature of PerCI development, and to construct a reliable prediction model for patients who had suffered orthopedic trauma using machine learning techniques.

METHODS

This study enrolled 1257 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Lymphocytes were tracked from ICU admission to more than 20 days following admission to examine the dynamic changes over time. Over 40 possible variables were gathered for investigation. Patients were split 80:20 at random into a training cohort (n=1035) and an internal validation cohort (n=222). Four machine learning algorithms, including random forest, gradient boosting machine, decision tree, and support vector machine, and a logistic regression technique were utilized to train and optimize models using data from the training cohort. Patients in the internal validation cohort were used to validate models, and the optimal one was chosen. Patients from two large teaching hospitals were used for external validation (n=113). The key metrics that used to assess the prediction performance of models mainly included discrimination, calibration, and clinical usefulness. To encourage clinical application based on the optimal machine learning-based model, a web-based calculator was developed.

RESULTS

16.0% (201/1257) of all patients had PerCI in the MIMIC-III database. The means of lymphocytes (%) were consistently below the normal reference range across the time among PerCI patients (around 10.0%), whereas in patients without PerCI, the number of lymphocytes continued to increase and began to be in normal range on day 10 following ICU admission. Subgroup analysis demonstrated that patients with PerCI were in a more serious health condition at admission since those patients had worse nutritional status, more electrolyte imbalance and infection-related comorbidities, and more severe illness scores. Eight variables, including albumin, serum calcium, red cell volume distributing width (RDW), blood pH, heart rate, respiratory failure, pneumonia, and the Sepsis-related Organ Failure Assessment (SOFA) score, were significantly associated with PerCI, according to the least absolute shrinkage and selection operator (LASSO) logistic regression model combined with the 10-fold cross-validation. These variables were all included in the modelling. In comparison to other algorithms, the random forest had the optimal prediction ability with the highest area under receiver operating characteristic (AUROC) (0.823, 95% CI: 0.757-0.889), highest Youden index (1.571), and lowest Brier score (0.107). The AUROC in the external validation cohort was also up to 0.800 (95% CI: 0.688-0.912). Based on the risk stratification system, patients in the high-risk group had a 10.0-time greater chance of developing PerCI than those in the low-risk group. A web-based calculator was available at https://starxueshu-perci-prediction-main-9k8eof.streamlitapp.com/.

CONCLUSIONS

Patients with PerCI typically remain in an immunosuppressive status, but those without PerCI gradually regain normal immunity. The dynamic changes of lymphocytes can be a reliable biomarker for PerCI. This work developed a reliable model that may be helpful in improving early diagnosis and targeted intervention of PerCI. Beneficial interventions, such as improving nutritional status and immunity, maintaining electrolyte and acid-base balance, curbing infection, and promoting respiratory recovery, are early warranted to prevent the onset of PerCI, especially among patients in the high-risk group and those with a continuously low level of lymphocytes.

Duo: A Signature Based Method to Batch-Analyze Functional Similarities of Proteins

With the rapid advancement of sequencing technology, handling of large sequencing data to analyze for protein coding capacity and functionality of predicted proteins has become an urgent demand. There is a lack of simple and effective tools to functionally annotate large number of unknown proteins in a personalized and customized workflow. To address this, we developed Duo, which batch-analyze functional similarities of predicted proteins. Duo can screen query proteins with specific characteristics based on highly flexible and customizable reference inputs from the user. In the current study, Duo was applied to screen for virulence associated proteins in the genome-sequence of Salmonella Typhimurium. Based on the analysis, recommendation for choice of Seed_database in order to get a reasonable number of predicted proteins for further analysis, and recommendation for preparing a Reference_proteins set for Duo was given. Delta-bitscore analysis was shown to be useful tool to focus the follow-up on predicted proteins. A successful screen for virulence proteins in the bacterial genome-sequence was further performed in a selection of 32 pathogenic bacteria, documenting the ability of Duo to work on a broad collection of bacteria. We anticipate that Duo will be a useful auxiliary tool for personalized and customized protein function research in the future.

Challenges in IBD Research: Precision Medicine

Precision medicine is part of five focus areas of the Challenges in IBD research document, which also includes preclinical human IBD mechanisms, environmental triggers, novel technologies, and pragmatic clinical research. The Challenges in IBD Research document provides a comprehensive overview of current gaps in inflammatory bowel diseases (IBD) research and delivers actionable approaches to address them. It is the result of a multidisciplinary input from scientists, clinicians, patients, and funders, and represents a valuable resource for patient centric research prioritization. In particular, the precision medicine section is focused on highlighting the main gap areas that must be addressed to get closer to treatments tailored to the biological and clinical characteristics of each patient, which is the aim of precision medicine. The main gaps were identified in: 1) understanding and predicting the natural history of IBD: disease susceptibility, activity, and behavior; 2) predicting disease course and treatment response; and 3) optimizing current and developing new molecular technologies. Suggested approaches to bridge these gaps include prospective longitudinal cohort studies to identify and validate precision biomarkers for prognostication of disease course, and prediction and monitoring of treatment response. To achieve this, harmonization across studies is key as well as development of standardized methods and infrastructure. The implementation of state-of-the-art molecular technologies, systems biology and machine learning approaches for multi-omics and clinical data integration and analysis will be also fundamental. Finally, randomized biomarker-stratified trials will be critical to evaluate the clinical utility of validated signatures and biomarkers in improving patient outcomes and cost-effective care.

Biomarkers of Whole Grain Intake

There is growing evidence that whole grain (WG) intake may prevent many chronic diseases. However, there are mixed results on this topic in human studies as a result of a lack of accurate tools to assess the intake of WGs and individual metabolic variation. To better understand the effects of WGs on health maintenance and the risk of chronic disease, there is an urgent need to identify the biomarkers for WG intake. The molecular signatures of WG intake remain undefined. This perspective gives an overview of the current knowledge, challenges, and future directions on the biomarkers of WG intake.

The DSM5/RDoC debate on the future of mental health research: implication for studies on human stress and presentation of the signature bank

In 2008, the National Institute of Mental Health (NIMH) announced that in the next few decades, it will be essential to study the various biological, psychological and social "signatures" of mental disorders. Along with this new "signature" approach to mental health disorders, modifications of DSM were introduced. One major modification consisted of incorporating a dimensional approach to mental disorders, which involved analyzing, using a transnosological approach, various factors that are commonly observed across different types of mental disorders. Although this new methodology led to interesting discussions of the DSM5 working groups, it has not been incorporated in the last version of the DSM5. Consequently, the NIMH launched the "Research Domain Criteria" (RDoC) framework in order to provide new ways of classifying mental illnesses based on dimensions of observable behavioral and neurobiological measures. The NIMH emphasizes that it is important to consider the benefits of dimensional measures from the perspective of psychopathology and environmental influences, and it is also important to build these dimensions on neurobiological data. The goal of this paper is to present the perspectives of DSM5 and RDoC to the science of mental health disorders and the impact of this debate on the future of human stress research. The second goal is to present the "Signature Bank" developed by the Institut Universitaire en Santé Mentale de Montréal (IUSMM) that has been developed in line with a dimensional and transnosological approach to mental illness.