Computational approaches to investigate the relationship between periodontitis and cardiovascular diseases for precision medicine.

Periodontitis is a highly prevalent inflammatory illness that leads to the destruction of tooth supporting tissue structures and has been associated with an increased risk of cardiovascular disease (CVD). Precision medicine, an emerging branch of medical treatment, aims can further improve current traditional treatment by personalizing care based on one's environment, genetic makeup, and lifestyle. Genomic databases have paved the way for precision medicine by elucidating the pathophysiology of complex, heritable diseases. Therefore, the investigation of novel periodontitis-linked genes associated with CVD will enhance our understanding of their linkage and related biochemical pathways for targeted therapies. In this article, we highlight possible mechanisms of actions connecting PD and CVD. Furthermore, we delve deeper into certain heritable inflammatory-associated pathways linking the two. The goal is to gather, compare, and assess high-quality scientific literature alongside genomic datasets that seek to establish a link between periodontitis and CVD. The scope is focused on the most up to date and authentic literature published within the last 10 years, indexed and available from PubMed Central, that analyzes periodontitis-associated genes linked to CVD. Based on the comparative analysis criteria, fifty-one genes associated with both periodontitis and CVD were identified and reported. The prevalence of genes associated with both CVD and periodontitis warrants investigation to assess the validity of a potential linkage between the pathophysiology of both diseases.

Artificial intelligence and machine learning approaches using gene expression and variant data for personalized medicine.

Precision medicine uses genetic, environmental and lifestyle factors to more accurately diagnose and treat disease in specific groups of patients, and it is considered one of the most promising medical efforts of our time. The use of genetics is arguably the most data-rich and complex components of precision medicine. The grand challenge today is the successful assimilation of genetics into precision medicine that translates across different ancestries, diverse diseases and other distinct populations, which will require clever use of artificial intelligence (AI) and machine learning (ML) methods. Our goal here was to review and compare scientific objectives, methodologies, datasets, data sources, ethics and gaps of AI/ML approaches used in genomics and precision medicine. We selected high-quality literature published within the last 5 years that were indexed and available through PubMed Central. Our scope was narrowed to articles that reported application of AI/ML algorithms for statistical and predictive analyses using whole genome and/or whole exome sequencing for gene variants, and RNA-seq and microarrays for gene expression. We did not limit our search to specific diseases or data sources. Based on the scope of our review and comparative analysis criteria, we identified 32 different AI/ML approaches applied in variable genomics studies and report widely adapted AI/ML algorithms for predictive diagnostics across several diseases.

IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles.

SUMMARY: In this article, we present IntelliGenes, a novel machine learning (ML) pipeline for the multi-genomics exploration to discover biomarkers significant in disease prediction with high accuracy. IntelliGenes is based on a novel approach, which consists of nexus of conventional statistical techniques and cutting-edge ML algorithms using multi-genomic, clinical, and demographic data. IntelliGenes introduces a new metric, i.e. Intelligent Gene (I-Gene) score to measure the importance of individual biomarkers for prediction of complex traits. I-Gene scores can be utilized to generate I-Gene profiles of individuals to comprehend the intricacies of ML used in disease prediction. IntelliGenes is user-friendly, portable, and a cross-platform application, compatible with Microsoft Windows, macOS, and UNIX operating systems. IntelliGenes not only holds the potential for personalized early detection of common and rare diseases in individuals, but also opens avenues for broader research using novel ML methodologies, ultimately leading to personalized interventions and novel treatment targets. AVAILABILITY AND IMPLEMENTATION: The source code of IntelliGenes is available on GitHub (https://github.com/drzeeshanahmed/intelligenes) and Code Ocean (https://codeocean.com/capsule/8638596/tree/v1).

Plant root mechanisms and their effects on carbon and nutrient accumulation in desert ecosystems under changes in land use and climate.

Deserts represent key carbon reservoirs, yet as these systems are threatened this has implications for biodiversity and climate change. This review focuses on how these changes affect desert ecosystems, particularly plant root systems and their impact on carbon and mineral nutrient stocks. Desert plants have diverse root architectures shaped by water acquisition strategies, affecting plant biomass and overall carbon and nutrient stocks. Climate change can disrupt desert plant communities, with droughts impacting both shallow and deep-rooted plants as groundwater levels fluctuate. Vegetation management practices, like grazing, significantly influence plant communities, soil composition, root microorganisms, biomass, and nutrient stocks. Shallow-rooted plants are particularly susceptible to climate change and human interference. To safeguard desert ecosystems, understanding root architecture and deep soil layers is crucial. Implementing strategic management practices such as reducing grazing pressure, maintaining moderate harvesting levels, and adopting moderate fertilization can help preserve plant-soil systems. Employing socio-ecological approaches for community restoration enhances carbon and nutrient retention, limits desert expansion, and reduces CO2 emissions. This review underscores the importance of investigating belowground plant processes and their role in shaping desert landscapes, emphasizing the urgent need for a comprehensive understanding of desert ecosystems.

Genomic approaches to identify and investigate genes associated with atrial fibrillation and heart failure susceptibility.

Atrial fibrillation (AF) and heart failure (HF) contribute to about 45% of all cardiovascular disease (CVD) deaths in the USA and around the globe. Due to the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. To improve the deciphering of CVD mechanisms, we need to deeply investigate well-known and identify novel genes that are responsible for CVD development. With the advancements in sequencing technologies, genomic data have been generated at an unprecedented pace to foster translational research. Correct application of bioinformatics using genomic data holds the potential to reveal the genetic underpinnings of various health conditions. It can help in the identification of causal variants for AF, HF, and other CVDs by moving beyond the one-gene one-disease model through the integration of common and rare variant association, the expressed genome, and characterization of comorbidities and phenotypic traits derived from the clinical information. In this study, we examined and discussed variable genomic approaches investigating genes associated with AF, HF, and other CVDs. We collected, reviewed, and compared high-quality scientific literature published between 2009 and 2022 and accessible through PubMed/NCBI. While selecting relevant literature, we mainly focused on identifying genomic approaches involving the integration of genomic data; analysis of common and rare genetic variants; metadata and phenotypic details; and multi-ethnic studies including individuals from ethnic minorities, and European, Asian, and American ancestries. We found 190 genes associated with AF and 26 genes linked to HF. Seven genes had implications in both AF and HF, which are SYNPO2L, TTN, MTSS1, SCN5A, PITX2, KLHL3, and AGAP5. We listed our conclusion, which include detailed information about genes and SNPs associated with AF and HF.

Tuning active sites on biochars for remediation of mercury-contaminated soil: A comprehensive review.

Mercury (Hg) contamination is acknowledged as a global issue and has generated concerns globally due to its toxicity and persistence. Tunable surface-active sites (SASs) are one of the key features of efficient BCs for Hg remediation, and detailed documentation of their interactions with metal ions in soil medium is essential to support the applications of functionalized BC for Hg remediation. Although a specific active site exhibits identical behavior during the adsorption process, a systematic documentation of their syntheses and interactions with various metal ions in soil medium is crucial to promote the applications of functionalized biochars in Hg remediation. Hence, we summarized the BC's impact on Hg mobility in soils and discussed the potential mechanisms and role of various SASs of BC for Hg remediation, including oxygen-, nitrogen-, sulfur-, and X (chlorine, bromine, iodine)- functional groups (FGs), surface area, pores and pH. The review also categorized synthesis routes to introduce oxygen, nitrogen, and sulfur to BC surfaces to enhance their Hg adsorptive properties. Last but not the least, the direct mechanisms (e.g., Hg- BC binding) and indirect mechanisms (i.e., BC has a significant impact on the cycling of sulfur and thus the Hg-soil binding) that can be used to explain the adverse effects of BC on plants and microorganisms, as well as other related consequences and risk reduction strategies were highlighted. The future perspective will focus on functional BC for multiple heavy metal remediation and other potential applications; hence, future work should focus on designing intelligent/artificial BC for multiple purposes.

Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine.

Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. Rightful application of artificial intelligence (AI) and machine learning (ML) approaches can lead to new insights into CVDs for providing better personalized treatments with predictive analysis and deep phenotyping. In this study we focused on implementing AI/ML techniques on RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. The study involved generating RNA-seq data derived from the serum of consented CVD patients. Next, we processed the sequenced data using our RNA-seq pipeline and applied GVViZ for gene-disease data annotation and expression analysis. To achieve our research objectives, we developed a new Findable, Accessible, Intelligent, and Reproducible (FAIR) approach that includes a five-level biostatistical evaluation, primarily based on the Random Forest (RF) algorithm. During our AI/ML analysis, we have fitted, trained, and implemented our model to classify and distinguish high-risk CVD patients based on their age, gender, and race. With the successful execution of our model, we predicted the association of highly significant HF, AF, and other CVDs genes with demographic variables.

Deciphering genomic signatures associating human dental oral craniofacial diseases with cardiovascular diseases using machine learning approaches.

OBJECTIVES: Periodontal diseases are chronic, inflammatory disorders that involve the destruction of supporting tissues surrounding the teeth which leads to permanent damage and substantially heightens systemic exposure. If left untreated, dental, oral, and craniofacial diseases (DOCs), especially periodontitis, can increase an individual's risk in developing complex traits including cardiovascular diseases (CVDs). In this study, we are focused on systematically investigating causality between periodontitis with CVDs with the application of artificial intelligence (AI), machine learning (ML) algorithms, and state-of-the-art bioinformatics approaches using RNA-seq-driven gene expression data of CVD patients. MATERIALS AND METHODS: In this study, we built a cohort of CVD patients, collected their blood samples, and performed RNA-seq and gene expression analysis to generate transcriptomic profiles. We proposed a nexus of AI/ML approaches for the identification of significant biomarkers, and predictive analysis. We implemented recursive feature elimination, Pearson correlation, chi-square, and analysis of variance to detect significant biomarkers, and utilized random forest and support vector machines for predictive analysis. RESULTS: Our AI/ML analyses have led us to the preliminary conclusion that GAS5, GPX1, HLA-B, and SNHG6 are the potential gene markers that can be used to explain the causal relationship between periodontitis and CVDs. CONCLUSIONS: CVDs are relatively common in patients with periodontal disease, and an increased risk of CVD is associated with periodontal disease independent of gender. Genetic susceptibility contributing to periodontitis and CVDs have been suggested to some extent, based on the similar degree of heritability shared between both complex diseases.

Proactive policies are the key to reversing desertification in the main stream of the Tarim River in the past 30 years.

The main stream of the Tarim River is a crucial part of China's ecological security strategy within the "two screens and three belts" framework, serving the "northern sand control belt" and the ecological civilization corridor. Evaluating and understanding desertification dynamics and driving factors is essential for achieving the United Nations Sustainable Development Goals and promoting ecological civilization. This study, based on the United Nations Convention to Combat Desertification, quantified the desertification process of the Tarim River's main stream from 1990 to 2020 using Landsat series satellite imagery from Google Earth Engine. Additionally, we employed panel data models to analyze the driving roles of climate change, socio-economic factors, and policies in desertification.Results indicate that between 1990 and 2020, desertification in the main stream of the Tarim River initially intensified, with 15% of the area experiencing desertification between 1990 and 2000. However, this trend was subsequently reversed, with only 0.6% of the area undergoing desertification over the 30-year period. Extensive economic development (-37.2%) and population growth (-48.7%) were the predominant factors exacerbating desertification in the initial period (1990-2000), whereas positive policies (55.3%) were the key to reversing desertification in the main stream of the Tarim River over the past 30 years.We also explored whether policies necessarily play a positive role in combating and preventing desertification. Based on this, we suggest actively engaging in interdisciplinary research and linking research outcomes with practical situations to formulate scientifically sound policies and measures for desertification prevention and control.

Dual electro-optic frequency comb photonic thermometry.

We report a precision realization of photonic thermometry using dual-comb spectroscopy to interrogate a π-phase-shifted fiber Bragg grating. We achieve readout stability of 7.5 mK at 1 s and resolve temperature changes of similar magnitude-sufficient for most industrial applications. Our dual-comb approach enables rapid sensing of dynamic temperature, and our scalable and reconfigurable electro-optic generation scheme enables a broad sensing range without laser tuning. Reproducibility on the International Temperature Scale of 1990 is tested, and ultimately limited by the frequency reference and check-thermometer stability. Our demonstration opens the door for a universal interrogator deployable to multiple photonic devices in parallel to potentially unravel complex multi-physical quantity measurements.

Strigolactones can be a potential tool to fight environmental stresses in arid lands.

BACKGROUND: Environmental stresses pose a significant threat to plant growth and ecosystem productivity, particularly in arid lands that are more susceptible to climate change. Strigolactones (SLs), carotenoid-derived plant hormones, have emerged as a potential tool for mitigating environmental stresses. METHODS: This review aimed to gather information on SLs' role in enhancing plant tolerance to ecological stresses and their possible use in improving the resistance mechanisms of arid land plant species to intense aridity in the face of climate change. RESULTS: Roots exude SLs under different environmental stresses, including macronutrient deficiency, especially phosphorus (P), which facilitates a symbiotic association with arbuscular mycorrhiza fungi (AMF). SLs, in association with AMF, improve root system architecture, nutrient acquisition, water uptake, stomatal conductance, antioxidant mechanisms, morphological traits, and overall stress tolerance in plants. Transcriptomic analysis revealed that SL-mediated acclimatization to abiotic stresses involves multiple hormonal pathways, including abscisic acid (ABA), cytokinins (CK), gibberellic acid (GA), and auxin. However, most of the experiments have been conducted on crops, and little attention has been paid to the dominant vegetation in arid lands that plays a crucial role in reducing soil erosion, desertification, and land degradation. All the environmental gradients (nutrient starvation, drought, salinity, and temperature) that trigger SL biosynthesis/exudation prevail in arid regions. The above-mentioned functions of SLs can potentially be used to improve vegetation restoration and sustainable agriculture. CONCLUSIONS: Present review concluded that knowledge on SL-mediated tolerance in plants is developed, but still in-depth research is needed on downstream signaling components in plants, SL molecular mechanisms and physiological interactions, efficient methods of synthetic SLs production, and their effective application in field conditions. This review also invites researchers to explore the possible application of SLs in improving the survival rate of indigenous vegetation in arid lands, which can potentially help combat land degradation problems.

How workplace-based assessments guide learning in postgraduate education: A scoping review.

INTRODUCTION: Competency-based medical education (CBME) led to the widespread adoption of workplace-based assessment (WBA) with the promise of achieving assessment for learning. Despite this, studies have illustrated tensions between the summative and formative role of WBA which undermine learning goals. Models of workplace-based learning (WBL) provide insight, however, these models excluded WBA. This scoping review synthesizes the primary literature addressing the role of WBA to guide learning in postgraduate medical education, with the goal of identifying gaps to address in future studies. METHODS: The search was applied to OVID Medline, Web of Science, ERIC and CINAHL databases, articles up to September 2020 were included. Titles and abstracts were screened by two reviewers, followed by a full text review. Two members independently extracted and analysed quantitative and qualitative data using a descriptive-analytic technique rooted in Billett's four premises of WBL. Themes were synthesized and discussed until consensus. RESULTS: All 33 papers focused on the perception of learning through WBA. The majority applied qualitative methodology (70%), and 12 studies (36%) made explicit reference to theory. Aligning with Billett's first premise, results reinforce that learning always occurs in the workplace. WBA helped guide learning goals and enhanced feedback frequency and specificity. Billett's remaining premises provided an important lens to understand how tensions that existed in WBL have been exacerbated with frequent WBA. As individuals engage in both work and WBA, they are slowly transforming the workplace. Culture and context frame individual experiences and the perceived authenticity of WBA. Finally, individuals will have different goals, and learn different things, from the same experience. CONCLUSION: Analysing WBA literature through the lens of WBL theory allows us to reframe previously described tensions. We propose that future studies attend to learning theory, and demonstrate alignment with philosophical position, to advance our understanding of assessment-for-learning in the workplace.

SVS WIfI score as a predictor of amputation after onset of CLI: Validation in an Irish tertiary vascular unit.

INTRODUCTION: Critical limb ischemia (CLI) in diabetic patients is defined by non-healing foot ulcer or rest pain for more than 2 weeks with ankle pressure of less than 40 mmHg. The SVS WIfI classification system stratifies CLI on the basis of perfusion, extent of wound and superadded infection to provide a composite score which guides further management and predicts final prognosis OBJECTIVE: The aim of the study was to use the SVS WIfI scoring system to predict the need for early revascularization versus early amputation depending on the composite WIfI score at presentation. METHODOLOGY: This was a retrospective observational study. Data was collected on patients admitted with CLI, in the last 2 years, to calculate composite WIfI score. The WIfI categories according to risk of limb loss were identified with endpoint being major or minor amputation. RESULTS: Among the 87 patients reviewed, 35 patients (40%) required major amputation, and 29 of those underwent vascular intervention (83%) as part of their care. Median age of the cohort was 72 and 71% were male patients. Comparative analysis between major amputations and minor amputation showed the median score on initial clinical presentation to be 7 in major amputation and 5 in minor amputations (p < 0.0001). CONCLUSION: The composite WIFi score (a summation of the Wound, Ischaemia, and Infection sub-scores) was a good predictor of need for an amputation WIfI scoring system is a useful tool and should be used early in the management of infected ischaemic limbs.

Artificial Intelligence and Its Application in Endodontics: A Review.

AIM AND BACKGROUND: Artificial intelligence (AI) since it was introduced into dentistry, has become an important and valuable tool in many fields. It was applied in different specialties with different uses, for example, in diagnosis of oral cancer, periodontal disease and dental caries, and in the treatment planning and predicting the outcome of orthognathic surgeries. The aim of this comprehensive review is to report on the application and performance of AI models designed for application in the field of endodontics. MATERIALS AND METHODS: PubMed, Web of Science, and Google Scholar were searched to collect the most relevant articles using terms, such as AI, endodontics, and dentistry. This review included 56 papers related to AI and its application in endodontics. RESULT: The applications of AI were in detecting and diagnosing periapical lesions, assessing root fractures, working length determination, prediction for postoperative pain, studying root canal anatomy and decision-making in endodontics for retreatment. The accuracy of AI in performing these tasks can reach up to 90%. CONCLUSION: Artificial intelligence has valuable applications in the field of modern endodontics with promising results. Larger and multicenter data sets can give external validity to the AI models. CLINICAL SIGNIFICANCE: In the field of dentistry, AI models are specifically crafted to contribute to the diagnosis of oral diseases, ranging from common issues such as dental caries to more complex conditions like periodontal diseases and oral cancer. AI models can help in diagnosis, treatment planning, and in patient management in endodontics. Along with the modern tools like cone-beam computed tomography (CBCT), AI can be a valuable aid to the clinician. How to cite this article: Ahmed ZH, Almuharib AM, Abdulkarim AA, et al. Artificial Intelligence and Its Application in Endodontics: A Review. J Contemp Dent Pract 2023;24(11):912-917.

Essential Role of NLRP3 Inflammasome in Mediating IL-1ß Production and the Pathobiology of Staphylococcus aureus Endophthalmitis.

Staphylococcal endophthalmitis is one of the leading causes of blindness following ocular surgery and trauma. Dysregulated inflammation during bacterial endophthalmitis causes host-induced inflammatory damage and vision loss if it remains unchecked. Emerging evidence indicates that inflammasome plays a critical role in regulating innate immunity in various infectious and inflammatory diseases. However, the role of the inflammasome in endophthalmitis remains elusive. Here, using a mouse model of Staphylococcus (S) aureus endophthalmitis, we show that NLRP3/ASC/Caspase-1 signaling regulates IL-1ß production in endophthalmitis. We also show that S. aureus and its cell wall components and toxins induce the activation of the NLRP3 inflammasome complex in mouse eyes. Moreover, we found that both infiltrating neutrophils and retinal microglia contribute toward NLRP3 activation and IL-1ß production in S. aureus-infected eyes. Furthermore, our data using NLRP3-/- and IL-1ß-/- mice revealed that NLRP3 and IL-1ß deficiency leads to increased intraocular bacterial burden and retinal tissue damage. Altogether, our study demonstrated an essential role of NLRP3 inflammasome activation in regulating innate immune responses in bacterial endophthalmitis.

Psychotropic prescribing rates and pharmacogenomic testing implications for autism in the Canadian primary care sentinel surveillance network.

OBJECTIVE: To estimate prescribing rates of psychotropic drugs to individuals with autism and the proportion of these individuals who could benefit from pharmacogenetic testing. METHODS: Prescribing data for 92 psychotropic drugs, including 31 antidepressants, 22 antipsychotics, 14 mood stabilizer/antiepileptics, 17 anxiolytic/hypnotics and eight antiadrenergic/psychostimulant were retrieved from medical records of 787 (613 males) autistic individuals who sought treatment from a primary care office enrolled in the Canadian Primary Care Sentinel Surveillance Network between 2012 and 2014. Each prescribed drug was cross-referenced with pharmacogenomic-based prescribing guidelines published by the Clinical Pharmacogenetics Implementation Consortium, the Dutch Pharmacogenetics Working Group, and the Canadian Pharmacogenomics Network for Drug Safety. RESULTS: More than half (58%) of the participants were prescribed a psychotropic drug and 37% were prescribed two or more psychotropic drugs concurrently. Among the 83 psychotropic drugs examined, 54 (65%) were prescribed to one or more participants during the study's observation period. The ten most frequently prescribed psychotropics were methylphenidate (16.3%), risperidone (12.8%), lorazepam (12.1%), fluoxetine (7.9%), sertraline (7.1%), quetiapine (6.9%), aripiprazole (6.1%), lisdexamfetamine (5.8%), citalopram (5.6%) and clonazepam (4.8%). Seventeen (32%) of the 54 psychotropic drugs prescribed were linked to a pharmacogenomic-based prescribing guideline, including risperidone, sertraline, aripiprazole and citalopram. CONCLUSIONS: Our findings suggest primary care providers in Canada prescribe a wide range of psychotropics to their patients with autism, some of which may benefit from the integration of pharmacogenomic information into their treatment planning.

100 Years of evolving gene-disease complexities and scientific debutants.

It's been over 100 years since the word `gene' is around and progressively evolving in several scientific directions. Time-to-time technological advancements have heavily revolutionized the field of genomics, especially when it's about, e.g. triple code development, gene number proposition, genetic mapping, data banks, gene-disease maps, catalogs of human genes and genetic disorders, CRISPR/Cas9, big data and next generation sequencing, etc. In this manuscript, we present the progress of genomics from pea plant genetics to the human genome project and highlight the molecular, technical and computational developments. Studying genome and epigenome led to the fundamentals of development and progression of human diseases, which includes chromosomal, monogenic, multifactorial and mitochondrial diseases. World Health Organization has classified, standardized and maintained all human diseases, when many academic and commercial online systems are sharing information about genes and linking to associated diseases. To efficiently fathom the wealth of this biological data, there is a crucial need to generate appropriate gene annotation repositories and resources. Our focus has been how many gene-disease databases are available worldwide and which sources are authentic, timely updated and recommended for research and clinical purposes. In this manuscript, we have discussed and compared 43 such databases and bioinformatics applications, which enable users to connect, explore and, if possible, download gene-disease data.

RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients.

BACKGROUND: Heart failure (HF) is one of the most common complications of cardiovascular diseases (CVDs) and among the leading causes of death in the US. Many other CVDs can lead to increased mortality as well. Investigating the genetic epidemiology and susceptibility to CVDs is a central focus of cardiology and biomedical life sciences. Several studies have explored expression of key CVD genes specially in HF, yet new targets and biomarkers for early diagnosis are still missing to support personalized treatment. Lack of gender-specific cardiac biomarker thresholds in men and women may be the reason for CVD underdiagnosis in women, and potentially increased morbidity and mortality as a result, or conversely, an overdiagnosis in men. In this context, it is important to analyze the expression and enrichment of genes with associated phenotypes and disease-causing variants among high-risk CVD populations. METHODS: We performed RNA sequencing focusing on key CVD genes with a great number of genetic associations to HF. Peripheral blood samples were collected from a broad age range of adult male and female CVD patients. These patients were clinically diagnosed with CVDs and CMS/HCC HF, as well as including cardiomyopathy, hypertension, obesity, diabetes, asthma, high cholesterol, hernia, chronic kidney, joint pain, dizziness and giddiness, osteopenia of multiple sites, chest pain, osteoarthritis, and other diseases. RESULTS: We report RNA-seq driven case-control study to analyze patterns of expression in genes and differentiating the pathways, which differ between healthy and diseased patients. Our in-depth gene expression and enrichment analysis of RNA-seq data from patients with mostly HF and other CVDs on differentially expressed genes and CVD annotated genes revealed 4,885 differentially expressed genes (DEGs) and regulation of 41 genes known for HF and 23 genes related to other CVDs, with 15 DEGs as significantly expressed including four genes already known (FLNA, CST3, LGALS3, and HBA1) for HF and CVDs with the enrichment of many pathways. Furthermore, gender and ethnic group specific analysis showed shared and unique genes between the genders, and among different races. Broadening the scope of the results in clinical settings, we have linked the CVD genes with ICD codes. CONCLUSIONS: Many pathways were found to be enriched, and gender-specific analysis showed shared and unique genes between the genders. Additional testing of these genes may lead to the development of new clinical tools to improve diagnosis and prognosis of CVD patients.

Advancing clinical genomics and precision medicine with GVViZ: FAIR bioinformatics platform for variable gene-disease annotation, visualization, and expression analysis.

BACKGROUND: Genetic disposition is considered critical for identifying subjects at high risk for disease development. Investigating disease-causing and high and low expressed genes can support finding the root causes of uncertainties in patient care. However, independent and timely high-throughput next-generation sequencing data analysis is still a challenge for non-computational biologists and geneticists. RESULTS: In this manuscript, we present a findable, accessible, interactive, and reusable (FAIR) bioinformatics platform, i.e., GVViZ (visualizing genes with disease-causing variants). GVViZ is a user-friendly, cross-platform, and database application for RNA-seq-driven variable and complex gene-disease data annotation and expression analysis with a dynamic heat map visualization. GVViZ has the potential to find patterns across millions of features and extract actionable information, which can support the early detection of complex disorders and the development of new therapies for personalized patient care. The execution of GVViZ is based on a set of simple instructions that users without a computational background can follow to design and perform customized data analysis. It can assimilate patients' transcriptomics data with the public, proprietary, and our in-house developed gene-disease databases to query, easily explore, and access information on gene annotation and classified disease phenotypes with greater visibility and customization. To test its performance and understand the clinical and scientific impact of GVViZ, we present GVViZ analysis for different chronic diseases and conditions, including Alzheimer's disease, arthritis, asthma, diabetes mellitus, heart failure, hypertension, obesity, osteoporosis, and multiple cancer disorders. The results are visualized using GVViZ and can be exported as image (PNF/TIFF) and text (CSV) files that include gene names, Ensembl (ENSG) IDs, quantified abundances, expressed transcript lengths, and annotated oncology and non-oncology diseases. CONCLUSIONS: We emphasize that automated and interactive visualization should be an indispensable component of modern RNA-seq analysis, which is currently not the case. However, experts in clinics and researchers in life sciences can use GVViZ to visualize and interpret the transcriptomics data, making it a powerful tool to study the dynamics of gene expression and regulation. Furthermore, with successful deployment in clinical settings, GVViZ has the potential to enable high-throughput correlations between patient diagnoses based on clinical and transcriptomics data.

Impact of intracoronary assessments on revascularization decisions: A contemporary evaluation.

OBJECTIVES: To investigate the real-world implementation of intracoronary assessment (ICA) techniques and evaluate their impact on clinical decisions regarding the management of coronary artery disease (CAD) in contemporary practice. BACKGROUND: Coronary angiogram is the gold standard used to diagnose vessel stenosis and guide percutaneous coronary intervention (PCI); however, it is limited by its two-dimensional imaging capabilities. ICA techniques like intravascular ultrasound and optical coherence tomography capture the vessel in three-dimensional images. Comparatively, fractional flow reserve provides information on the physiologic significance of coronary stenosis. Both techniques may improve PCI outcomes if they routinely change physician behavior. METHODS: Patients who underwent ICA between August 2015 and March 2020 were included in the study. The primary outcome was the clinical impact of ICA on physician clinical decision making of a stenotic vessel. The secondary outcome was the clinical changes that occurred following ICA. RESULTS: A total of 1135 patients were included in the study. Physiologic assessment (PA) and image assessment (IA) were performed in 61.4% and 38.6% respectively. Management plans were changed in 38.1% and 23.9% of patients who received PA and IA. Over half of the management change resulted in physicians deciding to not intervene on the stenotic vessel. One-year outcome of these decisions showed no significant increase in major adverse cardiac events (hazard ratio [HR], 0.68; 95% confidence interval [CI], 0.40-1.15; p = 0.15) or unplanned revascularization (HR, 0.78; 95% CI, 0.35-1.74; p = 0.55) suggesting reliance on PA/IA data did not increase risk. CONCLUSION: Selected ICA alters physician management of CAD in one-third of patients being evaluated for revascularization-typically leading to fewer interventions. All cause death is numerally lower in patients that received a change in management. However, the 1-year outcome of these altered decisions does not appear to be significantly different.