About

Ivan Ivanov is a professor associated with the Texas A&M University College of Veterinary Medicine & Biomedical Sciences (VMBS). His work is embedded within an institution that supports collaborations turning discoveries into proactive solutions for animal, human, and environmental health. The college is recognized as the No. 3 ranked veterinary college in the United States and is engaged in cutting-edge basic and clinical research, embracing a 'One Health' approach that recognizes the complex interactions between animal, human, and environmental health. While specific details about his research focus or contributions are not provided in the page text, his association with VMBS indicates involvement in advancing veterinary medicine and biomedical sciences through innovative research and education.

Research topics

• Biology
• Environmental health
• Medicine
• Genetics
• Computer Science
• Biotechnology
• Ecology
• Geography
• Biochemistry
• Evolutionary biology
• Virology
• Computational biology
• Cancer research
• Cell biology
• Environmental science

Selected publications

• Statistical Parametric Mapping Of Force-time Waveforms Of The Countermovement Jump During Prolonged Heavy Rucking

  Medicine & Science in Sports & Exercise · 2025-09-16

  article

  Countermovement jumps (CMJ) performed on force plates have become widely used to detect fatigue, especially in military contexts. While force plates measure many discrete variables, few studies have analyzed the continuous waveforms to identify changes in jump strategy resulting from fatigue. Application of statistical parametric mapping (SPM) to continuous CMJ waveforms allows measurement of spatiotemporal changes that may not be detected by traditional analysis of discrete CMJ variables. PURPOSE: Utilize SPM analysis of CMJ waveforms to identify neuromuscular changes associated with fatigue throughout a prolonged heavy rucking protocol. METHODS: Fit, young adults (Age: 21.9 ± 3.0 y, VO2PEAK: 47.2 ± 6.2 ml·kg-1·min-1, ♂/♀ = 39/28) completed four consecutive 30-min (15 min steady-state at 47% VO2PEAK + 15 min time trial for distance) treadmill rucking blocks carrying 47% of their lean mass. Three maximal effort CMJs without arm swing were performed on a calibrated AMTI force plate and averaged at baseline (PRE) and at 0, 30, and 60 min of recovery (POST, POST30, POST60). The three normalized waveforms were averaged for each subject at every study time point. An SPM two-way (sex x time) repeated measures analysis of variance (ANOVA) was performed to compare changes in force-, velocity-, power-, and displacement-time curves across the physical fatigue protocol. RESULTS: The SPM detected differences between force-, velocity -, and power-time data with main effects for time (p < 0.001 for all) and sex (p < 0.001, p = 0.002, and p < 0.001, respectively). Changes in force, velocity, and power waveforms were observed at all time points in response to the ruck protocol. Pairwise comparisons of PRE to POST, POST30, and POST60 showed differences in force, velocity, and power primarily in the braking and concentric phases. Following the ruck, lower force, greater velocity, and greater power were produced during the braking phase, while lower force, velocity, and power were produced during the concentric phase of the jumps at all recovery time points. CONCLUSION: SPM analysis was able to detect phase-based differences in force, velocity, and power of CMJ waveforms in fit, young adults who performed a high-intensity rucking protocol, demonstrating its utility for assessing fatigue and readiness by considering the whole movement. Supported by: Defense Advanced Research Projects Agency

  PublisherDOI

• Unbiased Selection Of Countermovement Jump Metrics For Fatigue In Prolonged Heavy Load Rucking

  Medicine & Science in Sports & Exercise · 2025-09-16

  article

  The countermovement jump (CMJ) is a non-invasive, easily administered method to assess neuromuscular fatigue. However, prior studies have analyzed multiple correlated CMJ metrics without a clear rationale. To optimize the utility of the CMJ for fatigue detection and streamline its application, identifying a subset of uncorrelated metrics tailored to the fatigue context is essential. PURPOSE: This study aimed to statistically filter discrete CMJ metrics to optimize the detection of physical fatigue status. METHODS: Sixty-seven healthy, fit adults (Age: 21.9 ± 3.0 y, VO2PEAK: 47.2 ± 6.2 ml·kg-1·min-1, ♂/♀ = 39/28) completed four 30-min treadmill rucking blocks carrying 47% of their lean mass. Each block consisted of two 15-min phases: 1) at a speed equal to 47% VO2PEAK, and 2) to cover the maximum distance possible while self-adjusting speed. Three maximal effort CMJs without arm swing were performed on a calibrated AMTI force plate and averaged at baseline (PRE); between blocks (IB1-3); and at 0, 30, and 60 min of recovery (POST, POST30, POST60). Feature selection via differential analysis (p-value <10E-24, fold change > |1|) alongside correlation-based hierarchical clustering reduced 28 discrete CMJ parameters to a subset of key performance indicators. Linear mixed-effects models with a random subject term determined the influence of time and sex on the selected metrics (α = 0.05). RESULTS: Momentum (MOM), peak concentric velocity (PCV), mean concentric velocity (MCV), mean braking power (MBP), and mean braking force (MBF) were identified as most responsive to the fatigue protocol. Raw metric values and percent changes declined progressively from IB1, with large to extreme effect sizes (0.8 - >2.0) observed at POST, POST30, and POST60. MBP showed the largest percent change across recovery time points (%∆ at POST, POST30, and POST60: -11.53, -11.56, -14.96). MOM, PCV, and MCV were significantly higher in males compared to females based on absolute but not relative change. CONCLUSION: This study provides evidence for an approach to selecting specific CMJ metrics to assess physical fatigue progression. These findings can help practitioners optimize the detection of fatigue by focusing CMJ testing on the most useful indicators, enhancing training and recovery strategies in athletic and military contexts. Supported by: This work was made possible in part by Defense Advanced Research Projects Agency (DARPA) under Grant Number HR0011-22-2-0040. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of DARPA.

  PublisherDOI

• Gut microbial community and host intestinal gene expression with combined fish oil and soluble corn fiber compared with corn oil and maltodextrin: A randomized crossover trial in healthy older individuals

  American Journal of Clinical Nutrition · 2025-06-19 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• METHODOLOGICAL AND APPLIED ASPECTS OF ARTIFICIAL INTELLIGENCE IN ENERGY CONSUMPTION PREDICTION

  Environment Technology Resources Proceedings of the International Scientific and Practical Conference · 2025-06-08

  articleOpen access1st authorCorresponding

  This research analyzes the application of modern methods for energy consumption forecasting in the residential sector. Traditional statistical models show limitations in modeling complex consumer behaviors, while technologies based on machine learning (ML) and artificial intelligence (AI) demonstrate significantly improved accuracy and adaptability. The study encompasses a wide spectrum of methodologies – from conventional statistical approaches to cutting-edge generative algorithms, evaluating their applicability for personalized household solutions. Comparative analysis highlights the advantages of AI-based technologies in terms of precision and adaptability, positioning them as optimal for integration into intelligent energy consumption management systems. The results provide a foundation for improving energy efficiency and resource optimization in the context of the growing application of smart technologies in the residential sector.

  PublisherOA PDFDOI

• Integrated analysis of gut metabolome, microbiome, and exfoliome data in an equine model of intestinal injury

  Microbiome · 2024-04-15 · 4 citations

  articleOpen access

  BACKGROUND: The equine gastrointestinal (GI) microbiome has been described in the context of various diseases. The observed changes, however, have not been linked to host function and therefore it remains unclear how specific changes in the microbiome alter cellular and molecular pathways within the GI tract. Further, non-invasive techniques to examine the host gene expression profile of the GI mucosa have been described in horses but not evaluated in response to interventions. Therefore, the objectives of our study were to (1) profile gene expression and metabolomic changes in an equine model of non-steroidal anti-inflammatory drug (NSAID)-induced intestinal inflammation and (2) apply computational data integration methods to examine host-microbiota interactions. METHODS: Twenty horses were randomly assigned to 1 of 2 groups (n = 10): control (placebo paste) or NSAID (phenylbutazone 4.4 mg/kg orally once daily for 9 days). Fecal samples were collected on days 0 and 10 and analyzed with respect to microbiota (16S rDNA gene sequencing), metabolomic (untargeted metabolites), and host exfoliated cell transcriptomic (exfoliome) changes. Data were analyzed and integrated using a variety of computational techniques, and underlying regulatory mechanisms were inferred from features that were commonly identified by all computational approaches. RESULTS: Phenylbutazone induced alterations in the microbiota, metabolome, and host transcriptome. Data integration identified correlation of specific bacterial genera with expression of several genes and metabolites that were linked to oxidative stress. Concomitant microbiota and metabolite changes resulted in the initiation of endoplasmic reticulum stress and unfolded protein response within the intestinal mucosa. CONCLUSIONS: Results of integrative analysis identified an important role for oxidative stress, and subsequent cell signaling responses, in a large animal model of GI inflammation. The computational approaches for combining non-invasive platforms for unbiased assessment of host GI responses (e.g., exfoliomics) with metabolomic and microbiota changes have broad application for the field of gastroenterology. Video Abstract.

  PublisherOA PDFDOI

• Single-nucleus transcriptomics of epicardial adipose tissue from female pigs reveals effects of exercise training on resident innate and adaptive immune cells

  Cell Communication and Signaling · 2024-04-26 · 7 citations

  articleOpen access

  BACKGROUND: Coronary artery disease (CAD) is a leading cause of death in women. Epicardial adipose tissue (EAT) secretes cytokines to modulate coronary artery function, and the release of fatty acids from EAT serves as a readily available energy source for cardiomyocytes. However, despite having beneficial functions, excessive amounts of EAT can cause the secretion of proinflammatory molecules that increase the instability of atherosclerotic plaques and contribute to CAD progression. Although exercise mitigates CAD, the mechanisms by which exercise impacts EAT are unknown. The Yucatan pig is an excellent translational model for the effects of exercise on cardiac function. Therefore, we sought to determine if chronic aerobic exercise promotes an anti-inflammatory microenvironment in EAT from female Yucatan pigs. METHODS: Sexually mature, female Yucatan pigs (n = 7 total) were assigned to sedentary (Sed, n = 3) or exercise (Ex, n = 4) treatments, and coronary arteries were occluded (O) with an ameroid to mimic CAD or remained non-occluded (N). EAT was collected for bulk (n = 7 total) and single nucleus transcriptomic sequencing (n = 2 total, 1 per exercise treatment). RESULTS: Based on the bulk transcriptomic analysis, exercise upregulated S100 family, G-protein coupled receptor, and CREB signaling in neurons canonical pathways in EAT. The top networks in EAT affected by exercise as measured by bulk RNA sequencing were SRC kinase family, fibroblast growth factor receptor, Jak-Stat, and vascular endothelial growth factor. Single nucleus transcriptomic analysis revealed that exercise increased the interaction between immune, endothelial, and mesenchymal cells in the insulin-like growth factor pathway and between endothelial and other cell types in the platelet endothelial cell adhesion molecule 1 pathway. Sub-clustering revealed nine cell types in EAT, with fibroblast and macrophage populations predominant in O-Ex EAT and T cell populations predominant in N-Ex EAT. Unlike the findings for exercise alone as a treatment, there were not increased interactions between endothelial and mesenchymal cells in O-Ex EAT. Coronary artery occlusion impacted the most genes in T cells and endothelial cells. Genes related to fatty acid metabolism were the most highly upregulated in non-immune cells from O-Ex EAT. Sub-clustering of endothelial cells revealed that N-Ex EAT separated from other treatments. CONCLUSIONS: According to bulk transcriptomics, exercise upregulated pathways and networks related to growth factors and immune cell communication. Based on single nucleus transcriptomics, aerobic exercise increased cell-to-cell interaction amongst immune, mesenchymal, and endothelial cells in female EAT. Yet, exercise was minimally effective at reversing alterations in gene expression in endothelial and mesenchymal cells in EAT surrounding occluded arteries. These findings lay the foundation for future work focused on the impact of exercise on cell types in EAT.

  PublisherOA PDFDOI

• Diet therapy abates mutant APC and KRas effects by reshaping plasma membrane cholesterol nanodomains

  Biophysical Journal · 2024-12-20 · 3 citations

  articleOpen access
  PublisherOA PDFDOI

• A Randomized Pilot Study of the Combined Effects of Fish Oil and Fermentable Dietary Fiber on the Gut Microbiome

  Current Developments in Nutrition · 2024-06-29

  articleOpen access

  Objectives: Preclinical studies show that the concurrent consumption of dietary fiber and n-3 polyunsaturated fatty acids from fish oil reduce colon tumor formation. However, no controlled dietary intervention has explored the combined effects of these two dietary components on CRC risk factors in humans. This pilot study investigated the synergistic effects of fish oil and fermentable fiber on the gut microbiome composition in adult humans. Methods: In a randomized controlled crossover pilot study in 30 men and women ages 50-75, we tested the combined effects of supplemental fermentable fiber (35 g/d soluble corn fiber) and eicosapentaenoic acid, and docosahexaenoic acid (EPA+DHA as fish oil, 6 g/d) compared to similar doses of maltodextrin and corn oil. Blood samples were collected at baseline and end of each 30-day intervention period. Serum was used to measure phospholipid fatty acids. Participants collected stool samples at the beginning, middle, and end of each period, which were used to assess the microbiome via (16S rRNAseq) and short chain fatty acids (SCFA) via GC-MS. Results: Serum DHA and EPA concentrations were statistically significantly higher after fish oil and fiber vs the comparison supplements (P < 0.0001), suggesting good adherence to the intervention. Microbiome analysis revealed significant differences in beta-diversity (P = 0.001). Twenty-six of 116 genera analyzed, were significantly different between the two intervention (FDR < 0.05) and are known SCFA producers. Although fecal butyrate concentrations were not statistically significant different between intervention periods (P = 0.9), the bacteria identified play a key role in fermenting dietary fiber and producing butyrate. Conclusions: Intervention differences in the microbial ecosystem underscore the role of dietary components in modulating the gut microbiota. The insights from this study lay a foundation for future research focused on dietary interventions for CRC prevention, emphasizing the importance of diet-gut microbiome interactions. Funding Sources: Study supported by NCI. Supplements provided by Tate & Lyle Solutions USA LLC and Omega3 Innovations.

  PublisherOA PDFDOI

• Supplementary Figures 1-6 from Loss of Aryl Hydrocarbon Receptor Promotes Colon Tumorigenesis in &lt;i&gt;Apc&lt;sup&gt;S580/+&lt;/sup&gt;; Kras&lt;sup&gt;G12D/+&lt;/sup&gt;&lt;/i&gt; Mice

  2023-04-03

  preprintOpen access

  &lt;p&gt;S1. Characterization of genotypes. S2. Loss of AhR increases non-phosphorylated β-catenin. S3. Intestinal pathology analysis from ACK and HACK mice. S4. AhR deletion promotes cell proliferation. S5. Intestinal pathology analysis from ACKG and HACKG mice. S6. Wnt signaling in Lgr5 haploinsufficient mice.&lt;/p&gt;

  PublisherDOI

• Supplementary Table S1-S4 from Single-cell RNA Sequencing Reveals How the Aryl Hydrocarbon Receptor Shapes Cellular Differentiation Potency in the Mouse Colon

  2023-04-03

  supplementary-materialsOpen access

  &lt;p&gt;Supplementary Table S1. Summary of biomarker genes identified and used in this study. Supplementary Table S2. Results of Kolmogorov-Smirnov tests associated with the velocity length of WT and Ahr KO samples. Supplementary Table S3. CellChat-identified significant ligand-receptor pairs in WT crypt cells. Supplementary Table S4. CellChat-identified significant ligand-receptor pairs in Ahr KO crypt cells.&lt;/p&gt;

  PublisherDOI

Recent grants

• INTEGRATED HEALTH SCIENCE

  NIH · $1.8M · 2018

• NIH Grant P30ES023512

  NIH · $4.6M · 2018

Frequent coauthors

• Robert S. Chapkin

  Texas A&M University

  83 shared

• Edward R. Dougherty

  Salve Regina University

  76 shared

• Laurie A. Davidson

  58 shared

• Noushin Ghaffari

  Prairie View A&M University

  25 shared

• Xiaoning Qian

  Texas A&M University

  24 shared

• Joanne R. Lupton

  20 shared

• Chen Zhao

  20 shared

• Jennifer S. Goldsby

  Texas A&M University

  19 shared