Journal: Journal of Clinical Oncology

Article Title: Incorporating Biomarkers Into Cancer and Aging Research

doi: 10.1200/JCO.2014.55.4261

Figure Lengend Snippet: Summary of Proposed Markers of Functional Age

Article Snippet: Sarcopenia , CT scan , Commercially available software for body composition analysis , Yes , Yes , Baumgartner et al 28 Heymsfield et al 29 Janssen et al 30 Metter et al 31.

Techniques: Functional Assay, Clinical Proteomics, Enzyme-linked Immunosorbent Assay, Southern Blot, Computed Tomography, Software

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Table defining body composition parameters calculated from the five tissue types segmented by DAFS (Voronoi Health Analytics).

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques: Marker

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Representative body composition segmentation performed by DAFS (Voronoi Health Analytics). Representative non-segmented sagittal CT image of the abdomen and pelvis (panel A) and corresponding segmented image (panel B), with subcutaneous adipose tissue (SAT;teal), visceral adipose tissue (VAT;yellow), inter/intramuscular adipose tissue (IMAT; green), skeletal muscle (SKM; red), and bone (purple). Body composition parameters were calculated for the volume defined by the entire lumbar spine (white dashed box, panel B). Representative axial CT at the L3 vertebral body midpoint without segmentation (panel C) and its corresponding automated segmentation (panel D). A magnified view of the paraspinal tissues (panel D, white dashed box) is provided for improved visualization of IMAT (green) within the paraspinal musculature (panel E).

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques:

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Distribution of body composition parameters for each individual lumbar level according to sex. Box-and-whisker plots shows distribution of different body composition parameters (panels A-L) in females (red, n = 384) and males (blue, n = 365). Line within box represents the median, box represents interquartile range, and lines represent maximum and minimum values in our cohort. Significance between sex and among lumbar level was established by repeated measures two-way ANOVA (i.e., FDR q -value < 5%). * indicates q -value < 0.05. ** indicates q -value < 0.005. *** indicates q -value < 0.0005. ns indicates q -value > 0.05.

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques: Whisker Assay

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Distribution of body composition parameters for the volume of the entire lumbar spine according to sex. Box-and-whisker plots shows distribution of different body composition parameters (panels A-L) in females (red, n = 309) and males (blue, n = 308). The line within the box represents the median, the box represents the interquartile range, and vertical lines represent maximum and minimum values in the cohort. The SKMI was not analyzed here, as this measurement pertains to cross-sectional area, not volume. **** indicates a q -value of < 0.0001. ns indicates q -value > 0.05.

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques: Whisker Assay

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Two-tailed Spearman correlation coefficients (ρ) between body composition parameters for the volume of the whole lumbar spine and BMI. Not stratified (panel A, n = 228), stratified by sex (panel B), and stratified by the two most represented self-determined race categories (panel C). Fisher-z-transformed correlations did not differ significantly between groups stratified by sex and self-determined race (i.e., unpaired t-test q -value < 5%).

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques: Two Tailed Test, Transformation Assay

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Hazard ratios (HR) of various body composition metrics in male and female patients, calculated utilizing multivariable Cox regression analysis accounting for patient age and stage. Body composition parameters calculated at a single L3 mid slice level (Panel A, n = 224). Volumetric body composition metrics calculated across the entire lumbar spine (Panel B, n = 194). * indicates a p -value of < 0.05. ** indicates a p -value of < 0.01. *** indicates a p -value of < 0.001. **** indicates a p -value of < 0.0001. ns indicates a p -value of ≥ 0.05. DNC indicates that the model did not converge.

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques:

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Analysis of adipose tissue and muscle body composition metrics with normal male kidney gene expression. Hierarchically clustered Spearman correlation coefficients ( r ≥ 0.5 or r ≤-0.5) heatmap of genes that correlate with normalized inter/intramuscular, visceral, and subcutaneous adipose tissue (IMAT/TAT, VAT/TAT, and SAT/TAT) and skeletal muscle (SKM) (Panel A). Top 10 significantly enriched pathways in the high (total genes = 96, Panel B) and low (total genes = 111, Panel C) SKM clusters. Kaplan-Meier analyses of male (panel D, high expression ( n = 146) and low expression ( n = 178)) and female (panel E, high expression ( n = 81) and low expression ( n = 104)) clear cell renal cell carcinoma (TCGA-KIRC) patients stratified by high and low expression of genes involved in valine, leucine, and isoleucine ( i.e., BCAA) degradation. Statistical significance was assessed using the log-rank test.

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques: Expressing

Journal: Scientific Reports

Article Title: Volumetric body composition analysis of the Cancer Genome Atlas reveals novel body composition traits and molecular markers Associated with Renal Carcinoma outcomes

doi: 10.1038/s41598-024-76280-6

Figure Lengend Snippet: Analysis of adipose tissue and muscle body composition metrics with normal female kidney gene expression. Hierarchical clustered Spearman correlation coefficients ( r ≥ 0.5 or r ≤ -0.5) heatmap of genes that correlate with normalized inter/intramuscular, visceral, and subcutaneous adipose tissue (IMAT/TAT, VAT/TAT, and SAT/TAT) and skeletal muscle (SKM) identifies genes that cluster predominantly with high versus low SAT/TAT and IMAT/TAT (Panel A). Top 10 significantly enriched pathways in the high (total genes = 350, Panel B) and low (total genes = 366, Panel C) SAT/TAT and IMAT/TAT clusters identify acid secretion and oxidative phosphorylation (OXPHOS) as significantly enriched pathways in the “Low SAT” group. Kaplan-Meier analyses of male (panel D, high expression ( n = 132) and low expression ( n = 192)) and female (panel E, high expression ( n = 73) and low expression n = (112)) clear cell renal cell carcinoma (TCGA-KIRC) patients stratified by high and low expression of F-type ATPases (complex V of OXPHOS; see Supplemental Fig. ). Statistical significance assessed using the log-rank test.

Article Snippet: Karteek Popuri, Vincent Chow, Da Ma, and M. Faisal Beg developed the body composition software from Voronoi Health Analytics, Inc.

Techniques: Expressing

Journal: Nucleic Acids Research

Article Title: DNA sequence templates adjacent nucleosome and ORC sites at gene amplification origins in Drosophila

doi: 10.1093/nar/gkv766

Figure Lengend Snippet: Nucleosome position at DAFC-66D is largely determined by primary DNA sequence. ( A ) Comparison of observed nucleosome occupancy at DAFC-66D in follicle cells in stage 10 (dark blue) with predicted nucleosome occupancy (light blue) based on nucleosome DNA sequence preferences ( , ). Red asterisks indicate three predicted nucleosome positions that were less occupied in vivo than predicted. ( B and C ) Expanded view of predicted and observed nucleosome occupancy at ACE3 ( B ) and Ori-β ( C ), with nucleotide composition plotted above (see color key). Nucleosome occupied sites are relatively GC rich (black and blue), while nucleosome depleted regions in ACE3 and Ori-β contain extended poly A:T tracts (red and green) that correspond to ORC binding sites.

Article Snippet: Nucleotide composition at DAFC-66D was determined over a sliding 50 bp window using the nucleotide composition analysis software of MacVector (version 13.5.2) ( ).

Techniques: Sequencing, Comparison, In Vivo, Binding Assay

Journal: Nutrients

Article Title: The Clinical and Cost-Effectiveness of an Individualized Nutritional CAre (INCA) Bundle versus Standard Care for Adults with Pressure Injuries Receiving Home Nursing Services: A Protocol for a Cluster Randomized and Pragmatic Clinical Trial with an Economic Evaluation

doi: 10.3390/nu16020299

Figure Lengend Snippet: Outcomes and the methods of measurement to be used in the cluster RCT.

Article Snippet: , Change in nutritional intake , Assessed by the change in average of energy, protein, and selected micronutrient intake derived from 3DFR. Three-day food records will be filled by the participants or caregivers at selected time points. If the family or participant is unable to fill out the record, photographs of meals can be taken by the subject or caregiver and sent to the CRC. Trained personnel will use food composition analysis software (DietPlan 7, Forestfield Software Ltd., UK) to determine the intake, adjusted to per unit kilogram weight of the participant The energy, protein, and micronutrient intake of the study subjects on enteral tube feeding will be determined by a calculation of the goal feeding regimen that the study subject has been prescribed prior to the start of the study. , t 0 baseline, t 1 30 days, and t 1 60 days Time frame: 60 days , Dietitian, Nurse.

Techniques: Imaging, Infection, Derivative Assay, Software, Sterility