Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine

Artificial intelligence and cheminformatics tools: a contribution to the drug development and chemical science

In the ever-evolving field of drug discovery, the integration of Artificial Intelligence (AI) and Machine Learning (ML) with cheminformatics has proven to be a powerful combination. Cheminformatics, which combines the principles of computer science and chemistry, is used to extract chemical information and search compound databases, while the application of AI and ML allows for the identification of potential hit compounds, optimization of synthesis routes, and prediction of drug efficacy and toxicity. This collaborative approach has led to the discovery, preclinical evaluations and approval of over 70 drugs in recent years. To aid researchers in the pursuit of new drugs, this article presents a comprehensive list of databases, datasets, predictive and generative models, scoring functions and web platforms that have been launched between 2021 and 2022. These resources provide a wealth of information and tools for computer-assisted drug development, and are a valuable asset for those working in the field of cheminformatics. Overall, the integration of AI, ML and cheminformatics has greatly advanced the drug discovery process and continues to hold great potential for the future. As new resources and technologies become available, we can expect to see even more groundbreaking discoveries and advancements in these fields.Communicated by Ramaswamy H. Sarma.

Blood-Based Multiomics-Guided Detection of a Precancerous Pancreatic Tumor

Over a decade ago, longitudinal multiomics analysis was pioneered for early disease detection and individually tailored precision health interventions. However, high sample processing costs, expansive multiomics measurements along with complex data analysis have made this approach to precision/personalized medicine impractical. Here we describe in a case report, a more practical approach that uses fewer measurements, annual sampling, and faster decision making. We also show how this approach offers promise to detect an exceedingly rare and potentially fatal condition before it fully manifests. Specifically, we describe in the present case report how longitudinal multiomics monitoring (LMOM) helped detect a precancerous pancreatic tumor and led to a successful surgical intervention. The patient, enrolled in an annual blood-based LMOM since 2018, had dramatic changes in the June 2021 and 2022 annual metabolomics and proteomics results that prompted further clinical diagnostic testing for pancreatic cancer. Using abdominal magnetic resonance imaging, a 2.6 cm lesion in the tail of the patient's pancreas was detected. The tumor fluid from an aspiration biopsy had 10,000 times that of normal carcinoembryonic antigen levels. After the tumor was surgically resected, histopathological findings confirmed it was a precancerous pancreatic tumor. Postoperative omics testing indicated that most metabolite and protein levels returned to patient's 2018 levels. This case report illustrates the potentials of blood LMOM for precision/personalized medicine, and new ways of thinking medical innovation for a potentially life-saving early diagnosis of pancreatic cancer. Blood LMOM warrants future programmatic translational research with the goals of precision medicine, and individually tailored cancer diagnoses and treatments.

Taking Political Determinants of Planetary Health Seriously: Expanding from P4 to P5 Medicine

Predictive, Personalized, Preventive, and Participatory (P4) Medicine is embedded in the precision medicine conceptual framework to achieve the overarching goal of "the right drug, for the right patient, at the right dose, and at the right time." Science cultures and political determinants of health have normative and instrumental impacts on P4 medicine. Yet, since the age of Enlightenment in the 17th century, science and economics have been disarticulated from politics along the lines of classical liberalism, and with an ahistorical approach that continues into the 21st century. The consequence of this liberal disarticulation is that science is falsely and narrowly understood as an invariably technocratic and objective field. In the aftermath of the Covid-19 pandemic, it is clearer that political determinants of health are the causes-of-causes for disease and health. I propose that we need P5 medicine with a fifth P, political determinants of planetary health. The new "P" can engage not only with instrumental aspects of P4 medicine research and clinical implementation but also with the structural factors that are an integral part of the politics of the P4 medicine. For example, the living legacies of colonialism contribute to the unequal relationships in trade, labor, provision, and production of materials among nation-states and between the Global South and the Global North and shape the class struggles in contemporary society, science, and medicine. A decolonial politics of care in which the political determinants of planetary health are taken seriously is therefore crucial and relevant to building a robust, ethical, responsible, and just P5 medicine in the 21st century.

Insight to Biofabrication of Liver Microtissues for Disease Modeling: Challenges and Opportunities

In the last decade, liver diseases with high mortality rates have become one of the most important health problems in the world. Organ transplantation is currently considered the most effective treatment for compensatory liver failure. An increasing number of patients and shortage of donors has led to the attention of reconstructive medicine methods researchers. The biggest challenge in the development of drugs effective in chronic liver disease is the lack of a suitable preclinical model that can mimic the microenvironment of liver problems. Organoid technology is a rapidly evolving field that enables researchers to reconstruct, evaluate, and manipulate intricate biological processes in vitro. These systems provide a biomimetic model for studying the intercellular interactions necessary for proper organ function and architecture in vivo. Liver organoids, formed by the self-assembly of hepatocytes, are microtissues and can exhibit specific liver characteristics for a long time in vitro. Hepatic organoids are identified as an impressive tool for evaluating potential cures and modeling liver diseases. Modeling various liver diseases, including tumors, fibrosis, non-alcoholic fatty liver, etc., allows the study of the effects of various drugs on these diseases in personalized medicine. Here, we summarize the literature relating to the hepatic stem cell microenvironment and the formation of liver Organoids.

Systems Anesthesiology: Systems of Care Delivery and Optimization in the Operating Room

Anesthesiology presents a challenge to a traditional simplifying approach given the ever-increasing amount of medical data and a more demanding environment. Systems anesthesiology is a modern approach to perioperative care, integrating the complexity of multifactorial knowledge and data to achieve a more adequate representation of reality, while including both patient-related medical aspects as well as economic and organizational challenges. We discuss the value of some innovative technologies such as the emergence of anesthesia information systems, the use of tele-medicine, predictive monitoring, or closed-loop systems as it pertains to the changes in the current standards of care in anesthesiology. Furthermore, we highlight the importance of systems anesthesiology in operating room planning, anesthesia research, and education.

Integration of scRNA-seq and bulk RNA-seq constructs a stemness-related signature for predicting prognosis and immunotherapy responses in hepatocellular carcinoma

PURPOSE

Cancer stem cells are associated with unfavorable prognosis in hepatocellular carcinoma (HCC). However, existing stemness-related biomarkers and prognostic models are limited.

METHODS

The stemness-related signatures were derived from taking the union of the results obtained by performing WGCNA and CytoTRACE analysis at the bulk RNA-seq and scRNA-seq levels, respectively. Univariate Cox regression and the LASSO were applied for filtering prognosis-related signatures and selecting variables. Finally, ten gene signatures were identified to construct the prognostic model. We evaluated the differences in survival, genomic alternation, biological processes, and degree of immune cell infiltration in the high- and low-risk groups. pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) database was used to evaluate the protein expressions.

RESULTS

A stemness-related prognostic model was constructed with ten genes including YBX1, CYB5R3, CDC20, RAMP3, LDHA, MTHFS, PTRH2, SRPRB, GNA14, and CLEC3B. Kaplan-Meier and ROC curve analyses showed that the high-risk group had a worse prognosis and the AUC of the model in four datasets was greater than 0.64. Multivariate Cox regression analyses verified that the model was an independent prognostic indicator in predicting overall survival, and a nomogram was then built for clinical utility in predicting the prognosis of HCC. Additionally, chemotherapy drug sensitivity and immunotherapy response analyses revealed that the high-risk group exhibited a higher likelihood of benefiting from these treatments.

CONCLUSION

The novel stemness-related prognostic model is a promising biomarker for estimating overall survival in HCC.

Applications of multi-omics analysis in human diseases

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

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

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

Mass spectrometry-based phosphoproteomics in clinical applications

Protein phosphorylation is an essential post-translational modification that regulates many aspects of cellular physiology, and dysregulation of pivotal phosphorylation events is often responsible for disease onset and progression. Clinical analysis on disease-relevant phosphoproteins, while quite challenging, provides unique information for precision medicine and targeted therapy. Among various approaches, mass spectrometry (MS)-centered characterization features discovery-driven, high-throughput and in-depth identification of phosphorylation events. This review highlights advances in sample preparation and instrument in MS-based phosphoproteomics and recent clinical applications. We emphasize the preeminent data-independent acquisition method in MS as one of the most promising future directions and biofluid-derived extracellular vesicles as an intriguing source of the phosphoproteome for liquid biopsy.

Analysis of Single Biomacromolecules and Viruses: Is It a Myth or Reality?

The beginning of the twenty-first century witnessed novel breakthrough research directions in the life sciences, such as genomics, transcriptomics, translatomics, proteomics, metabolomics, and bioinformatics. A newly developed single-molecule approach addresses the physical and chemical properties and the functional activity of single (individual) biomacromolecules and viral particles. Within the alternative approach, the combination of "single-molecule approaches" is opposed to "omics approaches". This new approach is fundamentally unique in terms of its research object (a single biomacromolecule). Most studies are currently performed using postgenomic technologies that allow the properties of several hundreds of millions or even billions of biomacromolecules to be analyzed. This paper discusses the relevance and theoretical, methodological, and practical issues related to the development potential of a single-molecule approach using methods based on molecular detectors.

Firefly-SVM predictive model for breast cancer subgroup classification with clinicopathological parameters

Background

Breast cancer is a highly predominant destructive disease among women characterised with varied tumour biology, molecular subgroups and diverse clinicopathological specifications. The potentiality of machine learning to transform complex medical data into meaningful knowledge has led to its application in breast cancer detection and prognostic evaluation.

Objective

The emergence of data-driven diagnostic model for assisting clinicians in diagnostic decision making has gained an increasing curiosity in breast cancer identification and analysis. This motivated us to develop a breast cancer data-driven model for subtype classification more accurately.

Method

In this article, we proposed a firefly-support vector machine (SVM) breast cancer predictive model that uses clinicopathological and demographic data gathered from various tertiary care cancer hospitals or oncological centres to distinguish between patients with triple-negative breast cancer (TNBC) and non-triple-negative breast cancer (non-TNBC).

Results

The results of the firefly-support vector machine (firefly-SVM) predictive model were distinguished from the traditional grid search-support vector machine (Grid-SVM) model, particle swarm optimisation-support vector machine (PSO-SVM) and genetic algorithm-support vector machine (GA-SVM) hybrid models through hyperparameter tuning. The findings show that the recommended firefly-SVM classification model outperformed other existing models in terms of prediction accuracy (93.4%, 86.6%, 69.6%) for automated SVM parameter selection. The effectiveness of the prediction model was also evaluated using well-known metrics, such as the F1-score, mean square error, area under the ROC curve, logarithmic loss and precision-recall curve.

Conclusion

Firefly-SVM predictive model may be treated as an alternate tool for breast cancer subgroup classification that would benefit the clinicians for managing the patient with proper treatment and diagnostic outcome.

Systems biology of human aging: A Fibonacci time series model

Fractals are everywhere in nature, particularly at the interfaces where matter or energy must be transferred, since they maximize surface area while minimizing energy losses. Temporal fractals have been well studied at micro scales in human biology, but have received comparatively little attention at broader macro scales. In this paper, we describe a fractal time series model of human aging from a systems biology perspective. This model examines how intrinsic aging rates are shaped by entropy and Fibonacci fractal dynamics, with implications for the emergence of key life cycle traits. This proposition is supported by research findings. The finding of an intrinsic aging rate rooted in Fibonacci fractal dynamics represents a new predictive paradigm in evolutionary biology.

Integrative multiomic analyses of dorsal root ganglia in diabetic neuropathic pain using proteomics, phospho-proteomics, and metabolomics

Diabetic peripheral neuropathy (DPN) is characterized by spontaneous pain in the extremities. Incidence of DPN continues to rise with the global diabetes epidemic. However, there remains a lack of safe, effective analgesics to control this chronic painful condition. Dorsal root ganglia (DRG) contain soma of sensory neurons and modulate sensory signal transduction into the central nervous system. In this study, we aimed to gain a deeper understanding of changes in molecular pathways in the DRG of DPN patients with chronic pain. We recently reported transcriptomic changes in the DRG with DPN. Here, we expand upon those results with integrated metabolomic, proteomic, and phospho-proteomic analyses to compare the molecular profiles of DRG from DPN donors and DRG from control donors without diabetes or chronic pain. Our analyses identified decreases of select amino acids and phospholipid metabolites in the DRG from DPN donors, which are important for cellular maintenance. Additionally, our analyses revealed changes suggestive of extracellular matrix (ECM) remodeling and altered mRNA processing. These results reveal new insights into changes in the molecular profiles associated with DPN.

Breast Cancer Subtypes Classification with Hybrid Machine Learning Model

BACKGROUND

 Breast cancer is the most prevailing heterogeneous disease among females characterized with distinct molecular subtypes and varied clinicopathological features. With the emergence of various artificial intelligence techniques especially machine learning, the breast cancer research has attained new heights in cancer detection and prognosis.

OBJECTIVE

 Recent development in computer driven diagnostic system has enabled the clinicians to improve the accuracy in detecting various types of breast tumors. Our study is to develop a computer driven diagnostic system which will enable the clinicians to improve the accuracy in detecting various types of breast tumors.

METHODS

 In this article, we proposed a breast cancer classification model based on the hybridization of machine learning approaches for classifying triple-negative breast cancer and non-triple negative breast cancer patients with clinicopathological features collected from multiple tertiary care hospitals/centers.

RESULTS

 The results of genetic algorithm and support vector machine (GA-SVM) hybrid model was compared with classics feature selection SVM hybrid models like support vector machine-recursive feature elimination (SVM-RFE), LASSO-SVM, Grid-SVM, and linear SVM. The classification results obtained from GA-SVM hybrid model outperformed the other compared models when applied on two distinct hospital-based datasets of patients investigated with breast cancer in North West of African subcontinent. To validate the predictive model accuracy, 10-fold cross-validation method was applied on all models with the same multicentered datasets. The model performance was evaluated with well-known metrics like mean squared error, logarithmic loss, F1-score, area under the ROC curve, and the precision-recall curve.

CONCLUSION

 The hybrid machine learning model can be employed for breast cancer subtypes classification that could help the medical practitioners in better treatment planning and disease outcome.

Integrative Approach to Rhinosinusitis: An Update

Rhinosinusitis is characterized by inflammation of the mucosa involving the paranasal sinuses and the nasal cavity and is one of the most common and significant health care problems, with significant impairment of quality of life. Current standard conventional management of rhinosinusitis commonly uses multiple therapeutic modalities to break the cycle of chronic disease. However, to date, there is no consensus as to the optimal treatment algorithm for patients with chronic rhinosinusitis. There is a growing interest in the use of complementary and integrative medicine for the treatment of rhinosinusitis. This article update focuses on an integrative approach to rhinosinusitis.

The Role of a Polymer-Based E-Nose in the Detection of Head and Neck Cancer from Exhaled Breath

The aim of our study was to assess whether a polymer-based e-nose can distinguish head and neck cancer subjects from healthy controls, as well as from patients with allergic rhinitis. A total number of 45 subjects participated in this study. The first group was composed of 15 patients with histology confirmed diagnosis of head and neck cancer. The second group was made up of 15 patients with diagnoses of allergic rhinitis. The control group consisted of 15 subjects with a negative history of upper airways and/or chest symptoms. Exhaled breath was collected from all participants and sampled by a polymer-based e-nose (Cyranose 320, Sensigent, Pasadena, CA, USA). In the Principal Component Analysis plot, patients with head and neck cancer clustered distinctly from the controls as well as from patients with allergic rhinitis. Using canonical discriminant analysis, the three groups were discriminated, with a cross validated accuracy% of 75.1, p < 0.01. The area under the curve of the receiver operating characteristic curve for the discrimination between head and neck cancer patients and the other groups was 0.87. To conclude, e-nose technology has the potential for application in the diagnosis of head and neck cancer, being an easy, quick, non-invasive and cost-effective tool.

Locality-sensitive hashing enables efficient and scalable signal classification in high-throughput mass spectrometry raw data

Background

Mass spectrometry is an important experimental technique in the field of proteomics. However, analysis of certain mass spectrometry data faces a combination of two challenges: first, even a single experiment produces a large amount of multi-dimensional raw data and, second, signals of interest are not single peaks but patterns of peaks that span along the different dimensions. The rapidly growing amount of mass spectrometry data increases the demand for scalable solutions. Furthermore, existing approaches for signal detection usually rely on strong assumptions concerning the signals properties.

Results

In this study, it is shown that locality-sensitive hashing enables signal classification in mass spectrometry raw data at scale. Through appropriate choice of algorithm parameters it is possible to balance false-positive and false-negative rates. On synthetic data, a superior performance compared to an intensity thresholding approach was achieved. Real data could be strongly reduced without losing relevant information. Our implementation scaled out up to 32 threads and supports acceleration by GPUs.

Conclusions

Locality-sensitive hashing is a desirable approach for signal classification in mass spectrometry raw data.

Availability

Generated data and code are available at https://github.com/hildebrandtlab/mzBucket. Raw data is available at https://zenodo.org/record/5036526.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04833-5.

Medi-Cinema: A Pilot Study on Cinematherapy and Cancer as A New Psychological Approach on 30 Gynecological Oncological Patients

Background: Several subjects affected by cancer experience a significant level of multidimensional disease. This longitudinal study aims to evaluate the effectiveness of psycho-oncological support using Cinema as an emotional mediator and to promote perceived well-being by personalized psychological treatment. Methods: Thirty women diagnosed with gynecological cancer watched 12 movies and participated in a psychotherapy group co-conducted by two psychotherapists. Patients completed nine questionnaires at T0 (baseline), T1 (3 months) and T2 (6 months). Results: Patients observed significant improvements (CORE-OM: p < 0.001) in psychological well-being. The results showed statistically significant differences, even in several other dimensions, such as Anxiety (STAY-Y1-2: p < 0.001), Empathy (BEES, p < 0.001), Coping (COPE: p < 0.001), QoL (QLQ-C30, p: 0.026), couple relationship (DAS, Satisfaction: p: 0.013; Cohesion: p: 0.004) and alexithymia (TAS-20, Difficulty Identifying Feeling: p: 0.002; Externally-Oriented Thinking: p: 0.003). Conclusions: The data show that cinema, as an innovative psychological approach, could be a valid instrument to support patients in oncological pathways as well as facilitating the process of recognizing themselves in other patients and communicating about their own feelings.

Coding recognition of the dose-effect interdependence of small biomolecules encrypted on paired chromatographic-based microassay arrays

The discovery of small biomolecules has suffered from the lack of a comprehensive framework to express the intrinsic correlation between bioactivity and the contribution from small molecules in complex samples with molecular and bioactivity diversity. Here, by mapping a sample’s 2D-HPTLC fingerprint to microplates, paired chromatographic-based microassay arrays are created, which can be used as quasi-chips to characterize multiple attributes of chromatographic components; as the array differential expression of the bioactivity and molecular attributes of irregular chromatographic spots for dose–effect interdependent encoding; and also as the automatic-collimated array mosaics of the multi-attributes of each component itself encrypted by its chromatographic fingerprint. Based on this homologous framework, we propose a correlating recognition strategy for small biomolecules through their self-consistent chromatographic behavior characteristics. In the approach, the small biomolecule recognition in diverse compounds is transformed into a constraint satisfaction problem, which is addressed through examining the dose–effect interdependence of the homologous 2D code pairs by an array matching algorithm, instead of preparing diverse compound monomers of complex test samples for identification item-by-item. Furthermore, considering the dose–effect interdependent 2D code pairs as links and the digital-specific quasimolecular ions as nodes, an extendable self-consistent framework that correlates mammalian cell phenotypic and target-based bioassays with small biomolecules is established. Therefore, the small molecule contributions and the correlations of bioactivities, as well as their pathways, can be comprehensively revealed, so as to improve the reliability and efficiency of screening. This strategy was successfully applied to galangal, and demonstrated the high-throughput digital preliminary screening of small biomolecules in a natural product.

P4 Medicine as a model for precision periodontal care

Objectives

P4 Medicine is based on a proactive approach for clinical patient care incorporating the four “pillars” of prediction, prevention, personalization, and participation for patient management. The purpose of this review is to demonstrate how the concepts of P4 medicine can be incorporated into the management of periodontal diseases (particularly periodontitis) termed P4 periodontics.

Methods

This is a narrative review that used current literature to explore how P4 periodontics can be aligned with the 2018 Classification of Periodontal Diseases, current periodontal treatment paradigms, and periodontal regenerative technologies.

Results

The proposed model of P4 periodontics is highly aligned with the 2018 Classification of Periodontal Diseases and represents a logical extension of this classification into treatment paradigms. Each stage of periodontitis can be related to a holistic approach to clinical management. The role of “big data” in future P4 periodontics is discussed and the concepts of a treat-to-target focus for treatment outcomes are proposed as part of personalized periodontics. Personalized regenerative and rejuvenative periodontal therapies will refocus our thinking from risk management to regenerative solutions to manage the effects of disease and aging.

Conclusions

P4 Periodontics allows us to focus not only on early prevention and intervention but also allow for personalized late-stage reversal of the disease trajectory and the use of personalized regenerative procedures to reconstruct damaged tissues and restore them to health.

Clinical Significance

P4 Periodontics is a novel means of viewing a holistic, integrative, and proactive approach to periodontal treatment.

Laying the groundwork for the Biobank of Rare Malignant Neoplasms at the service of the Hellenic Network of Precision Medicine on Cancer

Biobanks constitute an integral part of precision medicine. They provide a repository of biospecimens that may be used to elucidate the pathophysiology, support diagnoses, and guide the treatment of diseases. The pilot biobank of rare malignant neoplasms has been established in the context of the Hellenic Network of Precision Medicine on Cancer and aims to enhance future clinical and/or research studies in Greece by collecting, processing, and storing rare malignant neoplasm samples with associated data. The biobank currently comprises 553 samples; 384 samples of hematopoietic and lymphoid tissue malignancies, 72 samples of pediatric brain tumors and 97 samples of malignant skin neoplasms. In this article, sample collections and their individual significance in clinical research are described in detail along with computational methods developed specifically for this project. A concise review of the Greek biobanking landscape is also delineated, in addition to recommended technologies, methodologies and protocols that were integrated during the creation of the biobank. This project is expected to re‑enforce current clinical and research studies, introduce advances in clinical and genetic research and potentially aid in future targeted drug discovery. It is our belief that the future of medical research is entwined with accessible, effective, and ethical biobanking and that our project will facilitate research planning in the '‑omic' era by contributing high‑quality samples along with their associated data.

Association between the Erythrocyte Membrane Fatty Acid Profile and Cognitive Function in the Overweight and Obese Population Aged from 45 to 75 Years Old

Dietary fatty acid intake is closely related to the cognitive function of the overweight and obese population. However, few studies have specified the correlation between exact fatty acids and cognitive functions in different body mass index (BMI) groups. We aimed to explain these relationships and reference guiding principles for the fatty acid intake of the overweight and obese population. Normal weight, overweight, and obese participants were recruited to receive a cognitive function assessment and dietary survey, dietary fatty acids intake was calculated, and the erythrocyte membrane fatty acid profile was tested by performing a gas chromatography analysis. The percentages of saturated fatty acids (SFAs) in the obese group were higher, while monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) were lower than in the normal weight and overweight groups. In the erythrocyte membrane, the increase of n-3 PUFAs was accompanied by cognitive decline in the overweight group, which could be a protective factor for cognitive function in the obese group. High n-6 PUFAs intake could exacerbate the cognitive decline in the obese population. Dietary fatty acid intake had different effects on the cognitive function of overweight and obese people, especially the protective effect of n-3 PUFAs; more precise dietary advice is needed to prevent cognitive impairment.

Systems approaches to investigate the role of NF-κB signaling in aging

The nuclear factor-κB (NF-κB) signaling pathway is one of the most well-studied pathways related to inflammation, and its involvement in aging has attracted considerable attention. As aging is a complex phenomenon and is the result of a multi-step process, the involvement of the NF-κB pathway in aging remains unclear. To elucidate the role of NF-κB in the regulation of aging, different systems biology approaches have been employed. A multi-omics data-driven approach can be used to interpret and clarify unknown mechanisms but cannot generate mechanistic regulatory structures alone. In contrast, combining this approach with a mathematical modeling approach can identify the mechanistics of the phenomena of interest. The development of single-cell technologies has also helped clarify the heterogeneity of the NF-κB response and underlying mechanisms. Here, we review advances in the understanding of the regulation of aging by NF-κB by focusing on omics approaches, single-cell analysis, and mathematical modeling of the NF-κB network.

Pursuit of precision medicine: Systems biology approaches in Alzheimer's disease mouse models

Alzheimer's disease (AD) is a complex disease that is mediated by numerous factors and manifests in various forms. A systems biology approach to studying AD involves analyses of various body systems, biological scales, environmental elements, and clinical outcomes to understand the genotype to phenotype relationship that potentially drives AD development. Currently, there are many research investigations probing how modifiable and nonmodifiable factors impact AD symptom presentation. This review specifically focuses on how imaging modalities can be integrated into systems biology approaches using model mouse populations to link brain level functional and structural changes to disease onset and progression. Combining imaging and omics data promotes the classification of AD into subtypes and paves the way for precision medicine solutions to prevent and treat AD.

Diagnosis and prognosis models for hepatocellular carcinoma patient's management based on tumor mutation burden

Introduction

The development and prognosis of HCC involve complex molecular mechanisms, which affect the effectiveness of its treatment strategies. Tumor mutational burden (TMB) is related to the efficacy of immunotherapy, but the prognostic role of TMB-related genes in HCC has not yet been determined clearly.

Objectives

In this study, we identified TMB-specific genes with good prognostic value to build diagnostic and prognostic models and provide guidance for the treatment of HCC patients.

Methods

Weighted gene co-expression network analysis (WGCNA) was applied to identify the TMB-specific genes. And LASSO method and Cox regression were used in establishing the prognostic model.

Results

The prognostic model based on SMG5 and MRPL9 showed patients with higher prognostic risk had a remarkedly poorer survival probability than their counterparts with lower prognostic risk in both a TCGA cohort (P < 0.001, HR = 1.93) and an ICGC cohort (P < 0.001, HR = 3.58). In addition, higher infiltrating fractions of memory B cells, M0 macrophages, neutrophils, activated memory CD4 + T cells, follicular helper T cells and regulatory T cells and higher expression of B7H3, CTLA4, PD1, and TIM3 were present in the high-risk group than in the low-risk group (P < 0.05). Patients with high prognostic risk had higher resistance to some chemotherapy and targeted drugs, such as methotrexate, vinblastine and erlotinib, than those with low prognostic risk (P < 0.05). And a diagnostic model considering two genes was able to accurately distinguish patients with HCC from normal samples and those with dysplastic nodules. In addition, knockdown of SMG5 and MRPL9 was determined to significantly inhibit cell proliferation and migration in HCC.

Conclusion

Our study helps to select patients suitable for chemotherapy, targeted drugs and immunotherapy and provide new ideas for developing treatment strategies to improve disease outcomes in HCC patients.

Pharmacotherapy for Seizures in Tuberous Sclerosis Complex

Epilepsy is one of the main symptoms affecting the lives of individuals with tuberous sclerosis complex (TSC), causing a high rate of morbidity. Individuals with TSC can present with various types of seizures, epilepsies, and epilepsy syndromes that can coexist or appear in relation to age. Focal epilepsy is the most frequent epilepsy type with two developmental and epileptic encephalopathies: infantile spasms syndrome and Lennox-Gastaut syndrome. Active screening and early management of epilepsy is recommended in individuals with TSC to limit its consequences and its impact on quality of life, cognitive outcome and the economic burden of the disease. The progress in the knowledge of the mechanisms underlying epilepsy in TSC has paved the way for new concepts in the management of epilepsy related to TSC. In addition, we are moving from traditional "reactive" and therapeutic choices with current antiseizure medications used after the onset of seizures, to a proactive approach, aimed at predicting and preventing epileptogenesis and the onset of epilepsy with vigabatrin, and to personalized treatments with mechanistic therapies, namely mechanistic/mammalian target of rapamycin inhibitors. Indeed, epilepsy linked to TSC is one of the only epilepsies for which a predictive and preventive approach can delay seizure onset and improve seizure response. However, the efficacy of such interventions on long-term cognitive and psychiatric outcomes is still under investigation.

Analysing omics data sets with weighted nodes networks (WNNets)

Current trends in biomedical research indicate data integration as a fundamental step towards precision medicine. In this context, network models allow representing and analysing complex biological processes. However, although effective in unveiling network properties, these models fail in considering the individual, biochemical variations occurring at molecular level. As a consequence, the analysis of these models partially loses its predictive power. To overcome these limitations, Weighted Nodes Networks (WNNets) were developed. WNNets allow to easily and effectively weigh nodes using experimental information from multiple conditions. In this study, the characteristics of WNNets were described and a proteomics data set was modelled and analysed. Results suggested that degree, an established centrality index, may offer a novel perspective about the functional role of nodes in WNNets. Indeed, degree allowed retrieving significant differences between experimental conditions, highlighting relevant proteins, and provided a novel interpretation for degree itself, opening new perspectives in experimental data modelling and analysis. Overall, WNNets may be used to model any high-throughput experimental data set requiring weighted nodes. Finally, improving the power of the analysis by using centralities such as betweenness may provide further biological insights and unveil novel, interesting characteristics of WNNets.

Predicting Postoperative Complications in Cancer Patients: A Survey Bridging Classical and Machine Learning Contributions to Postsurgical Risk Analysis

Simple Summary

Structured survey on the predictive analysis of postoperative complications in oncology, bridging classic risk scores with machine learning advances, and further establishing principles to guide the design of cohort studies and the predictive modeling of postsurgical risks.

Abstract

Postoperative complications can impose a significant burden, increasing morbidity, mortality, and the in-hospital length of stay. Today, the number of studies available on the prognostication of postsurgical complications in cancer patients is growing and has already created a considerable set of dispersed contributions. This work provides a comprehensive survey on postoperative risk analysis, integrating principles from classic risk scores and machine-learning approaches within a coherent frame. A qualitative comparison is offered, taking into consideration the available cohort data and the targeted postsurgical outcomes of morbidity (such as the occurrence, nature or severity of postsurgical complications and hospitalization needs) and mortality. This work further establishes a taxonomy to assess the adequacy of cohort studies and guide the development and assessment of new learning approaches for the study and prediction of postoperative complications.

Precision Health Care Elements, Definitions, and Strategies for Patients with Diabetes: A Literature Review

Diabetes is a prevalent disease with a high risk of complications. The number of people with diabetes worldwide was reported to increase every year. However, new integrated individualized health care related to diabetes is insufficiently developed. Purpose: The objective of this study was to conduct a literature review and discover precision health care elements, definitions, and strategies. Methods: This study involved a 2-stage process. The first stage comprised a systematic literature search, evidence evaluation, and article extraction. The second stage involved discovering precision health care elements and defining and developing strategies for the management of patients with diabetes. Results: Of 1337 articles, we selected 35 relevant articles for identifying elements and definitions of precision health care for diabetes, including personalized genetic or lifestyle factors, biodata- or evidence-based practice, glycemic target, patient preferences, glycemic control, interdisciplinary collaboration practice, self-management, and patient priority direct care. Moreover, strategies were developed to apply precision health care for diabetes treatment based on eight elements. Conclusions: We discovered precision health care elements and defined and developed strategies of precision health care for patients with diabetes. precision health care is based on team foundation, personalized glycemic target, and control as well as patient preferences and priority, thus providing references for future research and clinical practice.

A field guide to U.S. healthcare reform: The evolution to value-based healthcare

OBJECTIVE

A consolidated state-of-the-art review of U.S. healthcare reform efforts that documents the evolution towards value-based healthcare (VBH) is lacking in peer-review literature. This field guide attempts to clarify working definitions and conceptual boundaries within the lexicon of U.S. healthcare reform efforts that predated and have common thematic perspectives within the evolving VBH reform paradigm.

DATA SOURCES

Pubmed/MEDLINE/Google search.

REVIEW METHODS

Pubmed/MEDLINE/Google search was performed during August 1, 2020-January14, 2021 for U.S. healthcare reform terms, legislative and government agency publications. Those citing relevant legislative, regulatory, philosophical and technological advancements integral to the development and function of VBH were catalogued according to the targeted stakeholders and evolving reform strategy or technology.

CONCLUSIONS

Eight healthcare reform paradigms were identified as influential precursors to VBH: Patient-Centered Care Model, Patient-Centered Medical Home, Population Health, Personalized Medicine, P4 Medicine, Precision Medicine, Managed Care, and Accountable Care. Several of these models have similar nomenclature and, confusingly, many have multiple interpretations of the terms used to describe these models. However, consistent stakeholders identified within these paradigms are key to VBH; notably the patient, the physician and the payer (the "Big 3"). Demonstrable healthcare spending reductions have been best achieved when the Big 3 stakeholder interests are aligned within healthcare reform legislation. The definition of "Value" within each reform model was found to be based upon the perspective of the targeted stakeholder. Within VBH, the perspectives of the Big 3 stakeholders form a multidimensional meaning of "Value" that can be represented by the equation Value = Patient Experience Management 3 .

Evaluating the Effectiveness of Personalized Medicine With Software

We present methodological advances in understanding the effectiveness of personalized medicine models and supply easy-to-use open-source software. Personalized medicine involves the systematic use of individual patient characteristics to determine which treatment option is most likely to result in a better average outcome for the patient. Why is personalized medicine not done more in practice? One of many reasons is because practitioners do not have any easy way to holistically evaluate whether their personalization procedure does better than the standard of care, termed improvement. Our software, "Personalized Treatment Evaluator" (the R package PTE), provides inference for improvement out-of-sample in many clinical scenarios. We also extend current methodology by allowing evaluation of improvement in the case where the endpoint is binary or survival. In the software, the practitioner inputs 1) data from a single-stage randomized trial with one continuous, incidence or survival endpoint and 2) an educated guess of a functional form of a model for the endpoint constructed from domain knowledge. The bootstrap is then employed on data unseen during model fitting to provide confidence intervals for the improvement for the average future patient (assuming future patients are similar to the patients in the trial). One may also test against a null scenario where the hypothesized personalization are not more useful than a standard of care. We demonstrate our method's promise on simulated data as well as on data from a randomized comparative trial investigating two treatments for depression.

Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables

Diabetic foot ulcers (DFUs) are a life-changing complication of diabetes that can lead to amputation. There is increasing evidence that long-term management with wearables can reduce incidence and recurrence of this condition. Temperature asymmetry measurements can alert to DFU development, but measurements of dynamic information, such as rate of temperature change, are under investigated. We present a new wearable device for temperature monitoring at the foot that is personalised to account for anatomical variations at the foot. We validate this device on 13 participants with diabetes (no neuropathy) (group name D) and 12 control participants (group name C), during sitting and standing. We extract dynamic temperature parameters from four sites on each foot to compare the rate of temperature change. During sitting the time constant of temperature rise after shoe donning was significantly (p < 0.05) faster at the hallux (p = 0.032, 370.4 s (C), 279.1 s (D)) and 5th metatarsal head (p = 0.011, 481.9 s (C), 356.6 s (D)) in participants with diabetes compared to controls. No significant differences at the other sites or during standing were identified. These results suggest that temperature rise time is faster at parts of the foot in those who have developed diabetes. Elevated temperatures are known to be a risk factor of DFUs and measurement of time constants may provide information on their development. This work suggests that temperature rise time measured at the plantar surface may be an indicative biomarker for differences in soft tissue biomechanics and vascularisation during diabetes onset and progression.

A new hierarchical approach to multi-level model abstraction for simplifying ODE models of biological networks and a case study: The G1/S Checkpoint/DNA damage signalling pathways of mammalian cell cycle

Model reduction is an important topic in studies of biological systems. By reducing the complexity of large models through multi-level models while keeping the essence (biological meaning) of the model, model reduction can help answer many important questions about these systems. In this paper, we present a new reduction method based on hierarchical representation and a lumping approach. We used G1/S checkpoint pathway represented in Ordinary Differential Equations (ODE) in Iwamoto et al. (2011) as a case study to present this reduction method. The approach consists of two parts; the first part represents the biological network as a hierarchy (multiple levels) based on protein binding relations, which allowed us to model the biological network at different levels of abstraction. The second part applies different levels (level 1, 2 and 3) of lumping the species together depending on the level of the hierarchy, resulting in a reduced and transformed model for each level. The model at each level is a representation of the whole system and can address questions pertinent to that level. We develop and simulate reduced models for levels-1, 2 and 3 of lumping for the G1/S checkpoint pathway and evaluate the biological plausibility of the proposed method by comparing the results with the original ODE model of Iwamoto et al. (2011). The results for continuous dynamics of the G1/S checkpoint pathway with or without DNA-damage for reduced models of level- 1, 2 and 3 of lumping are in very good agreement and consistent with the original model results and with biological findings. Therefore, the reduced models (level-1, 2 and 3) can be used to study cell cycle progression in G1 and the effects of DNA damage on it. It is suitable for reducing complex ODE biological network models while retaining accurate continuous dynamics of the system. The 3 levels of the reduced models respectively achieved 20%, 26% and 31% reduction of the base model. Moreover, the reduced model is more efficient to run (30%, 44% and 52% time reduction for the three levels) and generate solutions than the original ODE model. Simplification of complex mathematical models is possible and the proposed reduction method has the potential to make an impact across many fields of biomedical research.

Neuroadaptability and Habit: Modern Medicine and Ayurveda

In our increasingly stressed world, especially with the COVID-19 pandemic, the activation of the threat network in everyday situations can adversely affect our mental and physical health. Neurophysiological response to these threats/challenges depends on the type of challenge and the individual’s neuroadaptability. Neuroadaptability is defined as the ability of the nervous system to alter responsiveness over time to reoccurring stimuli. Neuroadaptability differs from neuroplasticity, which is more inclusive and refers to the ability of the nervous system to change and learn from any experience. We examine neuroadaptability and how it affects health from the perspective of modern medicine and Ayurveda.

Healthy Aging Biomarkers: The INSPIRE's Contribution

The find solutions for optimizing healthy aging and increase health span is one of the main challenges for our society. A novel healthcare model based on integration and a shift on research and care towards the maintenance of optimal functional levels are now seen as priorities by the WHO. To address this issue, an integrative global strategy mixing longitudinal and experimental cohorts with an innovative transverse understanding of physiological functioning is missing. While the current approach to the biology of aging is mainly focused on parenchymal cells, we propose that age-related loss of function is largely determined by three elements which constitute the general ground supporting the different specific parenchyma: i.e. the stroma, the immune system and metabolism. Such strategy that is implemented in INSPIRE projects can strongly help to find a composite biomarker capable of predicting changes in capacity across the life course with thresholds signalling frailty and care dependence.

Ayurgenomics and Modern Medicine

Within the disciplines of modern medicine, P4 medicine is emerging as a new field which focuses on the whole patient. The development of Ayurgenomics could greatly enrich P4 medicine by providing a clear theoretical understanding of the whole patient and a practical application of ancient and modern preventative and therapeutic practices to improve mental and physical health. One of the most difficult challenges today is understanding the ancient concepts of Ayurveda in terms of modern science. To date, a number of researchers have attempted this task, of which one of the most successful outcomes is the creation of the new field of Ayurgenomics. Ayurgenomics integrates concepts in Ayurveda, such as Prakriti, with modern genetics research. It correlates the combination of three doshas, Vata, Pitta and Kapha, with the expression of specific genes and physiological characteristics. It also helps to interpret Ayurveda as an ancient science of epigenetics which assesses the current state of the doshas, and uses specific personalized diet and lifestyle recommendations to improve a patient’s health. This review provides a current update of this emerging field.

Selecting the most important self-assessed features for predicting conversion to mild cognitive impairment with random forest and permutation-based methods

Alzheimer's Disease is a complex, multifactorial, and comorbid condition. The asymptomatic behavior in the early stages makes the identification of the disease onset particularly challenging. Mild cognitive impairment (MCI) is an intermediary stage between the expected decline of normal aging and the pathological decline associated with dementia. The identification of risk factors for MCI is thus sorely needed. Self-reported personal information such as age, education, income level, sleep, diet, physical exercise, etc. is called to play a key role not only in the early identification of MCI but also in the design of personalized interventions and the promotion of patients empowerment. In this study, we leverage a large longitudinal study on healthy aging in Spain, to identify the most important self-reported features for future conversion to MCI. Using machine learning (random forest) and permutation-based methods we select the set of most important self-reported variables for MCI conversion which includes among others, subjective cognitive decline, educational level, working experience, social life, and diet. Subjective cognitive decline stands as the most important feature for future conversion to MCI across different feature selection techniques.

Novel bio-lab-on-a-tip for electrochemical glucose sensing in commercial beverages

The development of portable and user-friendly sensing platforms is a hot topic in the field of analytical chemistry. Among others, electroanalytical approaches exhibit a high amenability for reaching this purpose, i.e. the commercial strips for diabetes care are an obvious success. However, providing fully-integrated and reagent-free methods is always a leitmotiv. In this work, we evaluated the use of a disposable pipette tip, opportunely configured to demonstrate the first example of an electrochemical biosystem in a pipette tip, namely bio-lab-on-a-tip. The combination of a pipette tip, wire electrodes, enzyme, and cotton wool filter, allows the fabrication of a novel electroanalytical platform that does not need expertise-required tasks. To demonstrate the feasibility of this novel method, glucose is detected in beverages by means of chronoamperometry. The experimental setup, entirely built inside the pipette tip, is able to 1) block impurities/interferences from matrix, 2) load/release reagents for the bio-assay, 3) reduce the operating task to zero, and 4) perform electrochemical detection. With optimized experimental parameters, the bio-lab-on-a-tip is able to detect glucose linearly up to 10 mM with a detection limit of 170 μM. The effectiveness of the platform was confirmed by testing commercial beverages, e.g. Coca-Cola and Coca-Cola Zero, with high accuracy. In addition, the shelf-life of the novel device was evaluated, highlighting the role of cotton wool filter for providing a suitable environment for glucose oxidase stability. The novel concept can be easily generalized for further applications in the field of non-invasive clinical diagnostics and in-situ environmental monitoring.

Isoproterenol Triggers ROS/P53/S100-A9 Positive Feedback to Aggravate Myocardial Damage Associated with Complement Activation

Negative feelings caused by external stress can continually agonize adrenergic receptors via promoting catecholamine secretion, causing cardiovascular disease. This study examines the mechanism by which persistent β-adrenergic receptor agonism induces myocardial injury. A rat model of cardiac injury was herein established using isoproterenol (5 mg/kg, continuous intraperitoneal injection for 3 days), and multiomics technology combined with metabolomics and proteomics was used to explore the mechanism by which persistent β-adrenergic receptor agonism induces myocardial injury. The mechanism underlying this phenomenon was further verified at the cellular level. Isoproterenol-induced persistent β-adrenergic receptor agonism promoted the release of reactive oxygen species, and P53, S100-A9, and complement 3 were shown to be involved in complement system activation pathways. Our data have demonstrated that isoproterenol could trigger ROS/P53/S100-A9 positive feedback to aggravate myocardial damage associated with complement activation.

Reverse diauxie phenotype in Pseudomonas aeruginosa biofilm revealed by exometabolomics and label-free proteomics

Microorganisms enhance fitness by prioritizing catabolism of available carbon sources using a process known as carbon catabolite repression (CCR). Planktonically grown Pseudomonas aeruginosa is known to prioritize the consumption of organic acids including lactic acid over catabolism of glucose using a CCR strategy termed "reverse diauxie." P. aeruginosa is an opportunistic pathogen with well-documented biofilm phenotypes that are distinct from its planktonic phenotypes. Reverse diauxie has been described in planktonic cultures, but it has not been documented explicitly in P. aeruginosa biofilms. Here a combination of exometabolomics and label-free proteomics was used to analyze planktonic and biofilm phenotypes for reverse diauxie. P. aeruginosa biofilm cultures preferentially consumed lactic acid over glucose, and in addition, the cultures catabolized the substrates completely and did not exhibit the acetate secreting "overflow" metabolism that is typical of many model microorganisms. The biofilm phenotype was enabled by changes in protein abundances, including lactate dehydrogenase, fumarate hydratase, GTP cyclohydrolase, L-ornithine N(5)-monooxygenase, and superoxide dismutase. These results are noteworthy because reverse diauxie-mediated catabolism of organic acids necessitates a terminal electron acceptor like O2, which is typically in low supply in biofilms due to diffusion limitation. Label-free proteomics identified dozens of proteins associated with biofilm formation including 16 that have not been previously reported, highlighting both the advantages of the methodology utilized here and the complexity of the proteomic adaptation for P. aeruginosa biofilms. Documenting the reverse diauxic phenotype in P. aeruginosa biofilms is foundational for understanding cellular nutrient and energy fluxes, which ultimately control growth and virulence.