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Bone Density

Bone Density: A measure of the amount of mineralized tissue in bone.
This is an important indicator of osteoporosis and fracture risk.
Bone density scanning, also called dual-energy x-ray absorptiometery (DEXA), is the most widely used and accurate method of measuring bone density.
With PubCompare.ai, researchers can easily locate and compare protocols from scientific literature, preprints, and patents to identify the most effective methods for optimizing bone density.
This AI-driven tool streamlines the research process and helps drive better outcomes for bone health studies.

Most cited protocols related to «Bone Density»

1
Cardiovascular Trait Association Analysis
To determine whether the identified loci were also associated with any of 22 cardio-metabolic traits, we obtained association data from meta-analysis consortia DIAGRAM (T2D)58 (link), CARDIoGRAM-C4D (CAD)59 (link), ICBP (SBP, DBP)60 (link), GIANT (BMI, height)36 ,37 , GLGC (HDL, LDL, and TG)61 (link), MAGIC (fasting glucose, fasting insulin, fasting insulin adjusted for BMI, and two-hour glucose)62 (link)-64 (link), ADIPOGen (BMI-adjusted adiponectin)65 (link), CKDgen (urine albumin-to-creatinine ratio (UACR), estimated glomerular filtration rate (eGFR), and overall CKD)66 (link),67 (link), ReproGen (age at menarche, age at menopause)68 (link),69 (link), and GEFOS (bone mineral density)70 (link); others provided association data for IgA nephropathy71 (link) (also Kiryluk K, Choi M, Lifton RP, Gharavi AG, unpublished data) and for endometriosis (stage B cases only)72 (link). Proxies (r2>0.80 in CEU) were used when an index SNP was unavailable.
We also searched the National Human Genome Research Institute (NHGRI) GWAS Catalog for previous SNP-trait associations near our lead SNPs73 (link). We supplemented the catalog with additional genome-wide significant SNP-trait associations from the literature13 (link),70 (link),74 (link)-80 (link). We used PLINK to identify SNPs within 500 kb of lead SNPs using 1000 Genomes Project Pilot I genotype data and LD (r2) values from CEU81 (link),82 (link); for rs7759742, HapMap release 22 CEU data81 (link),83 (link) were used. All SNPs within the specified regions were compared with the NHGRI GWAS Catalog16 .
Shungin D., Winkler T.W., Croteau-Chonka D.C., Ferreira T., Locke A.E., Mägi R., Strawbridge R.J., Pers T.H., Fischer K., Justice A.E., Workalemahu T., Wu J.M., Buchkovich M.L., Heard-Costa N.L., Roman T.S., Drong A.W., Song C., Gustafsson S., Day F.R., Esko T., Fall T., Kutalik Z., Luan J., Randall J.C., Scherag A., Vedantam S., Wood A.R., Chen J., Fehrmann R., Karjalainen J., Kahali B., Liu C.T., Schmidt E.M., Absher D., Amin N., Anderson D., Beekman M., Bragg-Gresham J.L., Buyske S., Demirkan A., Ehret G.B., Feitosa M.F., Goel A., Jackson A.U., Johnson T., Kleber M.E., Kristiansson K., Mangino M., Leach I.M., Medina-Gomez C., Palmer C.D., Pasko D., Pechlivanis S., Peters M.J., Prokopenko I., Stančáková A., Sung Y.J., Tanaka T., Teumer A., Van Vliet-Ostaptchouk J.V., Yengo L., Zhang W., Albrecht E., Ärnlöv J., Arscott G.M., Bandinelli S., Barrett A., Bellis C., Bennett A.J., Berne C., Blüher M., Böhringer S., Bonnet F., Böttcher Y., Bruinenberg M., Carba D.B., Caspersen I.H., Clarke R., Daw E.W., Deelen J., Deelman E., Delgado G., Doney A.S., Eklund N., Erdos M.R., Estrada K., Eury E., Friedrich N., Garcia M.E., Giedraitis V., Gigante B., Go A.S., Golay A., Grallert H., Grammer T.B., Gräßler J., Grewal J., Groves C.J., Haller T., Hallmans G., Hartman C.A., Hassinen M., Hayward C., Heikkilä K., Herzig K.H., Helmer Q., Hillege H.L., Holmen O., Hunt S.C., Isaacs A., Ittermann T., James A.L., Johansson I., Juliusdottir T., Kalafati I.P., Kinnunen L., Koenig W., Kooner I.K., Kratzer W., Lamina C., Leander K., Lee N.R., Lichtner P., Lind L., Lindström J., Lobbens S., Lorentzon M., Mach F., Magnusson P.K., Mahajan A., McArdle W.L., Menni C., Merger S., Mihailov E., Milani L., Mills R., Moayyeri A., Monda K.L., Mooijaart S.P., Mühleisen T.W., Mulas A., Müller G., Müller-Nurasyid M., Nagaraja R., Nalls M.A., Narisu N., Glorioso N., Nolte I.M., Olden M., Rayner N.W., Renstrom F., Ried J.S., Robertson N.R., Rose L.M., Sanna S., Scharnagl H., Scholtens S., Sennblad B., Seufferlein T., Sitlani C.M., Smith A.V., Stirrups K., Stringham H.M., Sundström J., Swertz M.A., Swift A.J., Syvänen A.C., Tayo B.O., Thorand B., Thorleifsson G., Tomaschitz A., Troffa C., van Oort F.V., Verweij N., Vonk J.M., Waite L.L., Wennauer R., Wilsgaard T., Wojczynski M.K., Wong A., Zhang Q., Zhao J.H., Brennan E.P., Choi M., Eriksson P., Folkersen L., Franco-Cereceda A., Gharavi A.G., Hedman Å.K., Hivert M.F., Huang J., Kanoni S., Karpe F., Keildson S., Kiryluk K., Liang L., Lifton R.P., Ma B., McKnight A.J., McPherson R., Metspalu A., Min J.L., Moffatt M.F., Montgomery G.W., Murabito J.M., Nicholson G., Nyholt D.R., Olsson C., Perry J.R., Reinmaa E., Salem R.M., Sandholm N., Schadt E.E., Scott R.A., Stolk L., Vallejo E.E., Westra H.J., Zondervan K.T., Amouyel P., Arveiler D., Bakker S.J., Beilby J., Bergman R.N., Blangero J., Brown M.J., Burnier M., Campbell H., Chakravarti A., Chines P.S., Claudi-Boehm S., Collins F.S., Crawford D.C., Danesh J., de Faire U., de Geus E.J., Dörr M., Erbel R., Eriksson J.G., Farrall M., Ferrannini E., Ferrières J., Forouhi N.G., Forrester T., Franco O.H., Gansevoort R.T., Gieger C., Gudnason V., Haiman C.A., Harris T.B., Hattersley A.T., Heliövaara M., Hicks A.A., Hingorani A.D., Hoffmann W., Hofman A., Homuth G., Humphries S.E., Hyppönen E., Illig T., Jarvelin M.R., Johansen B., Jousilahti P., Jula A.M., Kaprio J., Kee F., Keinanen-Kiukaanniemi S.M., Kooner J.S., Kooperberg C., Kovacs P., Kraja A.T., Kumari M., Kuulasmaa K., Kuusisto J., Lakka T.A., Langenberg C., Le Marchand L., Lehtimäki T., Lyssenko V., Männistö S., Marette A., Matise T.C., McKenzie C.A., McKnight B., Musk A.W., Möhlenkamp S., Morris A.D., Nelis M., Ohlsson C., Oldehinkel A.J., Ong K.K., Palmer L.J., Penninx B.W., Peters A., Pramstaller P.P., Raitakari O.T., Rankinen T., Rao D., Rice T.K., Ridker P.M., Ritchie M.D., Rudan I., Salomaa V., Samani N.J., Saramies J., Sarzynski M.A., Schwarz P.E., Shuldiner A.R., Staessen J.A., Steinthorsdottir V., Stolk R.P., Strauch K., Tönjes A., Tremblay A., Tremoli E., Vohl M.C., Völker U., Vollenweider P., Wilson J.F., Witteman J.C., Adair L.S., Bochud M., Boehm B.O., Bornstein S.R., Bouchard C., Cauchi S., Caulfield M.J., Chambers J.C., Chasman D.I., Cooper R.S., Dedoussis G., Ferrucci L., Froguel P., Grabe H.J., Hamsten A., Hui J., Hveem K., Jöckel K.H., Kivimaki M., Kuh D., Laakso M., Liu Y., März W., Munroe P.B., Njølstad I., Oostra B.A., Palmer C.N., Pedersen N.L., Perola M., Pérusse L., Peters U., Power C., Quertermous T., Rauramaa R., Rivadeneira F., Saaristo T.E., Saleheen D., Sinisalo J., Slagboom P.E., Snieder H., Spector T.D., Stefansson K., Stumvoll M., Tuomilehto J., Uitterlinden A.G., Uusitupa M., van der Harst P., Veronesi G., Walker M., Wareham N.J., Watkins H., Wichmann H.E., Abecasis G.R., Assimes T.L., Berndt S.I., Boehnke M., Borecki I.B., Deloukas P., Franke L., Frayling T.M., Groop L.C., Hunter D.J., Kaplan R.C., O’Connell J.R., Qi L., Schlessinger D., Strachan D.P., Thorsteinsdottir U., van Duijn C.M., Willer C.J., Visscher P.M., Yang J., Hirschhorn J.N., Zillikens M.C., McCarthy M.I., Speliotes E.K., North K.E., Fox C.S., Barroso I., Franks P.W., Ingelsson E., Heid I.M., Loos R.J., Cupples L.A., Morris A.P., Lindgren C.M, & Mohlke K.L. (2015). New genetic loci link adipose and insulin biology to body fat distribution. Nature, 518(7538), 187-196.
Publication 2014
ADIPOQ protein, human Albumins Bone Density Creatinine Endometriosis Genome Genome-Wide Association Study Genotype Gigantism Glomerular Filtration Rate Glucose HapMap Insulin Menarche Menopause Single Nucleotide Polymorphism Urine
2
Measuring Body Composition with DEXA and BIA
Total body fat mass and total bone-free lean mass (kg) were acquired from total body scans using fan-beamed dual-energy x-ray absorptiometry (Hologic, Waltham, MA or Lunar, Madison, WI) using standardized protocols (35 (link),36 (link)). Appendicular lean mass (ALM) was the sum of lean mass from both arms and legs. Participants missing lean mass measurements for an arm or leg were excluded. The validity and reproducibility of dual-energy x-ray absorptiometry have been reported previously. In Invecchiare in Chianti, body composition was measured using peripheral quantitative computed tomography of the calf. For Invecchiare in Chianti, estimated ALM was available only in men and was derived from equations from Osteoporotic Fractures in Men Study that included height, weight, waist circumference, fat area, muscle area, and muscle density. In Age, Gene/Environment Susceptibility-Reykjavik Study, body composition was measured with bioelectrical impedance (Xitron Hydra ECF/ICF Bio-Impedance Spectrum Analyzer).
Studenski S.A., Peters K.W., Alley D.E., Cawthon P.M., McLean R.R., Harris T.B., Ferrucci L., Guralnik J.M., Fragala M.S., Kenny A.M., Kiel D.P., Kritchevsky S.B., Shardell M.D., Dam T.T, & Vassileva M.T. (2014). The FNIH Sarcopenia Project: Rationale, Study Description, Conference Recommendations, and Final Estimates. The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 69(5), 547-558.
Publication 2014
Bioelectrical Impedance Body Composition Body Fat Bone Density Dual-Energy X-Ray Absorptiometry Fracture, Bone Genetic Predisposition to Disease Hydra Muscle Tissue Silver Waist Circumference Whole Body Imaging X-Ray Computed Tomography
3
Gender-specific Grip Strength Centiles
Our main analyses used all available data, including values for individuals who had had grip strength measured at more than one age. We produced gender-specific cross-sectional centiles for grip strength using the Box-Cox Cole and Green (BCCG) distribution (also known as the LMS method [27] (link)) implemented in the Generalised Additive Models for Location, Scale and Shape (GAMLSS) library [28] for the statistical program, R [29] . We used restricted cubic splines to model the relationship between age and each of the three model parameters: the median, variation and skewness. We identified the optimum number of degrees of freedom for each parameter using the GAMLSS command find.hyper. We anticipated a smooth relationship with age and therefore used a maximum number of degrees of freedom of seven and increased the standard penalty. We looked for evidence of kurtosis in the grip strength values by using the Box-Cox power exponential distribution. We modelled the mean and SD of grip at each age using the normal distribution in GAMLSS.
We defined a T-score for grip strength as an individual’s value expressed as a multiple of the number of standard deviations below the peak mean value encountered in young adult life. This is the same as the approach applied to measurements of bone density in the diagnosis of osteoporosis [30] (link), except we used gender-specific peak mean values for grip strength. We explored the gender-specific prevalence of weak grip strength in mid and late adult life in two ways. Firstly, using a T-score for grip strength of equal to or less than −2 as used previously [31] (link), and secondly using a T-score of equal to or less than −2.5, as widely used in the diagnosis of osteoporosis.
We carried out sensitivity analyses by producing further sets of centile curves and comparing these to our main findings. We restricted the data to the first observation for each individual. We produced dynamometer-specific sets of centile curves by allowing the median, variation and skewness curves to vary by dynamometer type. Similarly we considered the impact of the position of grip strength measurement: standing or sitting, with the latter divided into those who were sitting as per protocol and those who chose to sit or were unable to stand. Finally we checked if any one study was unduly influencing the results obtained by excluding each study in turn. To compare each additional model to the main findings, we examined absolute differences for the 10th, median and 90th centiles; we considered that a 10 percent difference or less in the centile values at any given age provided evidence of acceptably similar findings. We carried out data management using Stata version 12.0 [32] .
Dodds R.M., Syddall H.E., Cooper R., Benzeval M., Deary I.J., Dennison E.M., Der G., Gale C.R., Inskip H.M., Jagger C., Kirkwood T.B., Lawlor D.A., Robinson S.M., Starr J.M., Steptoe A., Tilling K., Kuh D., Cooper C, & Sayer A.A. (2014). Grip Strength across the Life Course: Normative Data from Twelve British Studies. PLoS ONE, 9(12), e113637.
Publication 2014
Adult Bone Density cDNA Library Cuboid Bone Debility Diagnosis Grasp Hypersensitivity Osteoporosis Young Adult
4
Comprehensive Multi-Site Physiological Assessment Protocol
After arriving at one of the three participating sites, respondents were escorted by project staff to the clinic where they were checked in, and were then escorted to the room where they would stay overnight. In most cases, respondents arrived mid-afternoon of Day 1 of their visit and ended their stay by noon of Day 2. On Day 1, with staff assistance, they completed the medical history, the bone densitometry scan, and physical exam, each of which required 30–45 minutes. They were also given the self-administered questionnaire (SAQ) to complete that evening (see www.midus.wisc.edu for copies of assessment instruments, which are included under descriptions of the MIDUS II projects) Clinic nursing staff began collecting the 12 hour urine specimen (collection period 7 p.m. to 7 a.m.). On Day 2 nursing staff collected the fasting blood specimen and completed the 12 hour urine specimen collection.
After breakfast, project staff carried out an experimental protocol assessing physiological response to, and recovery from, cognitive and orthostatic challenges similar to stressors people experience in their daily lives. The protocol consisted of a series of two randomized 6 minute cognitive challenges, one involving a math task and the other a Stroop-like test (decision-making about stimuli in which letters and colors are in conflict), followed by a 6 minute orthostatic (standing) challenge. Each challenge was followed by a 6 minute recovery period. Physiological reactivity throughout the experimental protocol was monitored via measures of blood pressure, heart rate variability and respiration, and salivary cortisol. Completed SAQs were then collected, and respondents were debriefed. At the UW-Madison data collection site, information was given about completing objective sleep assessments, to be returned by mail, after returning home. At the end of their visits, respondents were given a report about their blood pressure, body mass index (BMI), and waist-hip ratio. They were sent letters reporting cholesterol, HAlc, and bone density 1–2 months after the clinic visit.
To ensure consistency across sites and optimize the pace and quality of data collection, project staff and clinic nursing staff at all three sites followed standardized procedures that were detailed in a general Manual of Procedures, as well as more specific Guidelines for Collecting and Processing Biomarkers, and a Psychophysiology Manual. An administrative database was used to facilitate management and tracking of cross-project participation as well as tracking of participation at the three Project 4 sites. This information allowed review of participation information and quality control assessments, including identifying areas where additional staff training was required. Monthly conference calls with staff and investigators from all sites provided a forum to discuss issues or problems. Prior to these calls, each site generated a “Progress Report”, using report queries built into the administrative database; the reports were circulated for review by all on the conference call.
Love G.D., Seeman T.E., Weinstein M, & Ryff C.D. (2010). Bioindicators in the MIDUS National Study: Protocol, Measures, Sample, and Comparative Context. Journal of aging and health, 22(8), 1059-1080.
Publication 2010
Biological Markers BLOOD Blood Pressure Bone Density Bones Cholesterol Clinic Visits Cognition Conferences Densitometry Hydrocortisone Index, Body Mass Life Experiences Nursing Staff Physical Examination physiology Rate, Heart Respiration Sleep Urine Specimen Collection Waist-Hip Ratio
5
NHANES DXA Measurement Protocols
The whole body DXA exams in NHANES were acquired according to the procedures recommended by the manufacturer on a QDR 4500A fan beam densitometer (Hologic, Inc., Bedford, MA). All subjects changed into paper gowns and were asked to remove all jewelry and other personal effects that could interfere with the DXA exam. The DXA exams were reviewed and analyzed by the University of California, San Francisco Department of Radiology Bone Density Group using industry standard techniques. Analysis of all exams was performed using Hologic Discovery software version 12.1 in its default configuration. Exams that contained artifacts which could affect the accuracy of the DXA results, such as prosthetic devices, implants or other extraneous objects had the regional and global DXA results for these exams set to missing in the dataset. The precision of the DXA instrument used in the NHANES study has been reported on elsewhere [5] (link), [6] (link), [7] (link).
Body composition measurements are technology and calibration dependent and hence results provided by different instruments vary widely. The DXA instruments used in the NHANES survey employed the calibration proposed by Schoeller et al. [8] (link), whereby DXA lean mass results were calibrated to lean mass measured in 7 independent studies utilizing total body water (4 studies), hydrodensitometry (1 study), and four compartment measures (2 studies). The seven independent studies involved a total of 1195 subjects (602 male, 593 female). The BMD and BMC results were calibrated by the DXA manufacturer and maintained by an internal reference system that periodically measures bone and soft tissue equivalent reference standards during the patient measurement.
The NHANES data sets contained whole body DXA measurements of bone mineral content (BMC, g), areal bone mineral density (BMD, g/cm2), fat mass (g) and lean mass including BMC (g) and percent fat, calculated as (fat mass divided by total mass) ×100 along with demographic information for each subject. The above measurements were also available for a number of pre-defined anatomical regions, including the head, arms, legs, trunk, pelvic regions, sub-total whole body (excluding only the head) and whole body. From these whole body measures the following derivative values were calculated: FMI (fat mass/height2), lean mass/height2, appendicular lean mass/height2. For adults, only total body reference values and the above derivative reference values were generated. For children, (subjects less than 20 years of age), total body and sub-total body reference values and selected derivative reference values were generated.
There is increasing realization that fat distribution may be as important as total fat mass, so two indices of fat mass distribution, %fat of the trunk divided by %fat of the legs and fat mass of the trunk divided by fat mass of the limbs (fat mass of arms plus legs) were included in this analysis for adults. These indices may have a role in defining metabolic syndrome or lipodystorphy [9] (link), [10] (link).
Kelly T.L., Wilson K.E, & Heymsfield S.B. (2009). Dual Energy X-Ray Absorptiometry Body Composition Reference Values from NHANES. PLoS ONE, 4(9), e7038.
Publication 2009
Adult Arm, Upper Body Regions Bone Density Bones Child Females Head Human Body Leg Males Measure, Body Metabolic Syndrome X Patients Pelvis Prosthesis Radiography Tissues Water, Body

Most recents protocols related to «Bone Density»

1
Osteoporosis Fracture Transcriptome Analysis
The mRNA expression profile microarray data were obtained from the GEO dataset: GSE7158 series included 12 OP (low bone density) samples and 14 NC (high bone density) samples; GSE56814 series included 31 pre-treatment samples (15 OP and 16 NC) and 43 prognostic samples, and GSE56815 series included 40 pre-treatment samples (20 OP and 20 NC) and 40 post-treatment samples. For GSE56814 and GSE56815, sample data before treatment were used for difference analysis. As this study involves only a bioinformatics analysis of the GEO data set, no ethical approval was required. The inclusion criteria included age >18 years, osteoporosis fractures grades 1–4 according to OF classification, pathological fractures: osteoporotic fractures, fracture of at least 1 vertebral body, fractures of thoracic or lumbar vertebral body. The exclusion criteria included pathological neoplastic fractures, osteoporosis fractures (OF) grade 5 according to OF classification and AO type B and C fractures.
Deng Y., Wang Y., Shi Q, & Jiang Y. (2023). Identification of hub genes associated with osteoporosis development by comprehensive bioinformatics analysis. Frontiers in Genetics, 14, 1028681.
Publication 2023
Bone Density Fracture, Bone Human Body Lumbar Region Microarray Analysis Neoplasms Osteopenia Osteoporotic Fractures Pathological Fracture RNA, Messenger Specimen Handling Spinal Fractures Vertebrae, Lumbar Vertebral Body
2
Microstructural Bone Evaluation via micro-CT

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Publication 2023
Anisoptera Bone Density Bone Tissue X-Ray Computed Tomography
3
Ex vivo µCT Analysis of Digit Regeneration

Ex vivo μCT images of the digits were obtained using a Bruker SkyScan 1,172 scanner (Bruker, Kontich, Belgium) at a pixel size of 4 µm with 0.2 rotation angle and five frame averaging using a custom 0.25 mm aluminum filter. The X-ray source used was 50 kV, 201 μA, and 10 W, as described previously (Tower et al., 2022 (link)). All samples were reconstructed using NRecon with smoothing correction disabled, a beam hardening correction of 24%, and a dynamic range of 0.00–0.339. Reconstructed digits were exported as 8-bit BMP output files, rotated transaxially in DataViewer, and binarized and 3D analyzed in CTAn. Global thresholds were used for all young mice (8-week old) data sets with minimum threshold value set to 0 and maximum threshold value of 67 and a global threshold for aged mice (18-month old) data sets with minimum threshold value set to 0 and maximum threshold value of 79. For analysis of hypomineralized tissue at day 14 we used CTAn (Bruker, Kontich, Belgium, RRID:SCR_021338) and the Binary Selection preview window to identify newly regenerated bone. Hypomineralized tissue was isolated by identifying mineralized areas below 67 (the 6-month old mouse threshold for bone in the digit) in the grayscale data stack. To exclude mineralized bone the Binary Selection preview window was used to set a global threshold with a lower bound of 35 and an upper bound of 67. The Morphological Operations plugin was used to remove the partial volume effect in the 3D binarized image using Opening, Round Kernel, and Radius of 1. The volume of hypomineralized tissue was quantified using 3D analysis. The taper of the digit morphology was quantified as previously described (Tower et al., 2022 (link)). Briefly, we identified the start of newly regenerated bone using CTAn and measuring the area of the newly regenerated bone from the P3 cortical bone stump to the distal tip. The bone area was recorded and graphically represented along the length of the newly regenerated bone for control and OAA treated samples (Tower et al., 2022 (link)). Bone mineral density (BMD) was calculated as previously described (Hoffseth et al., 2021a (link)). Briefly, we calibrated attenuated X-ray data values from digit data sets to known mineral density standards of 0.25 and 0.75 mg calcium hydroxyapatite (CaHA) known as “phantoms” to determine the density of CaHA g/cm-3 in mineralized tissue (Hoffseth et al., 2021a (link)).
Jaramillo J., Taylor C., McCarley R., Berger M., Busse E, & Sammarco M.C. (2023). Oxaloacetate enhances and accelerates regeneration in young mice by promoting proliferation and mineralization. Frontiers in Cell and Developmental Biology, 11, 1117836.
Publication 2023
Aluminum Amputation Stumps BMP8B protein, human Bone Density Bones Compact Bone Digital Radiography Durapatite Fingers Minerals Mus Radiography Radius Reading Frames Sclerosis Tissues
4
Radiographic Assessment of Stemless Shoulder Implants

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Publication 2023
Axilla Bone Density Bones Cortex, Cerebral Fellowships Humerus Osteopenia Plant Tubers Radiography Shoulder Surgeons
5
Micro-CT Analysis of Cranial Defects
The computed tomography (CT) scans of all cranial specimens were performed using the micro-CT (SCANCO, Viva CT40, Switzerland). The 3D image reconstruction was performed using the accompanying SCANCO analysis software, and the bone volume-to-total volume ratio (BV/TV) and bone mineral density (BMD) of the defect sites were identified.
The skull specimens were paraffinized as described in Section ‘Effects of Ng-m-SAIB on the polarization of macrophages in vivo’. After dewaxing and hydrating, the paraffinized sections were stained with a hematoxylin–eosin (H&E) staining kit (Solarbio, G1120, China) and a Masson staining kit (Solarbio, G1304, China), following their respective manufacturer’s instructions. The ALP immunofluorescence staining was performed as described previously [34 (link)] using the ALP antibody (Servicebio, GB112527, 1:200, China).
Li J., Song J., Meng D., Yi Y., Zhang T., Shu Y, & Wu X. (2023). Electrospun naringin-loaded microsphere/sucrose acetate isobutyrate system promotes macrophage polarization toward M2 and facilitates osteoporotic bone defect repair. Regenerative Biomaterials, 10, rbad006.
Publication 2023
Bone Density Bones Cranium Eosin Fluorescent Antibody Technique Immunoglobulins Macrophage Activation Radionuclide Imaging Reconstructive Surgical Procedures X-Ray Computed Tomography

Top products related to «Bone Density»

1
Skyscan 1176 by Bruker
Sourced in Belgium, Germany, United States, Spain
The SkyScan 1176 is a high-resolution in vivo micro-CT scanner designed for small animal imaging. It provides fast, high-quality 3D imaging of small samples, including small animals, plant specimens, and materials.
2
Vivact 40 by Scanco
Sourced in Switzerland, United States
The VivaCT 40 is a high-resolution micro-computed tomography (microCT) system designed for 3D imaging and analysis of small samples. It provides high-quality, non-destructive imaging capabilities for a variety of applications.
3
Lunar prodigy by GE Healthcare
Sourced in United States, United Kingdom, Germany, Belgium, Japan, Morocco
The Lunar Prodigy is a bone densitometry system designed for the assessment of bone mineral density (BMD) and body composition. It utilizes dual-energy X-ray absorptiometry (DXA) technology to provide accurate and reproducible measurements.
4
Skyscan 1172 by Bruker
Sourced in Belgium, United States, Germany, Switzerland, United Kingdom
The Skyscan 1172 is a high-resolution desktop micro-CT scanner designed for non-destructive 3D imaging and analysis of a wide range of small samples. It provides high-quality X-ray imaging and data processing capabilities for various applications.
5
Lunar idxa by GE Healthcare
Sourced in United States, United Kingdom, Japan, Germany, Belgium, Denmark
The Lunar iDXA is a dual-energy X-ray absorptiometry (DXA) system used for the measurement of bone mineral density and body composition. It provides accurate and precise assessments of bone, lean, and fat mass.
6
μct40 by Scanco
Sourced in Switzerland, United States, Germany
The μCT40 is a micro-computed tomography (micro-CT) imaging system. It is designed to capture high-resolution, three-dimensional images of small samples. The μCT40 utilizes X-ray technology to generate detailed scans of the internal structures of objects.
7
Qdr 4500a by Hologic
Sourced in United States, United Kingdom, New Zealand
The QDR 4500A is a dual-energy X-ray absorptiometry (DXA) system designed for bone density measurements. It is a diagnostic medical device used to assess bone mineral density (BMD) and evaluate the risk of osteoporosis.
8
Nrecon software by Bruker
Sourced in Belgium, United States, Germany, Switzerland
NRecon software is a reconstruction tool developed by Bruker for tomographic data. It provides a user-friendly interface for reconstructing volumetric data from 2D projection images.
9
Ctan software by Bruker
Sourced in Belgium, United States, Germany
CTAn software is a tool for the analysis and visualization of 3D data from micro-computed tomography (micro-CT) imaging. It provides core functions for image processing, analysis, and quantification of various morphological parameters.
10
Qdr 4500 by Hologic
Sourced in United States, United Kingdom, Morocco
The QDR 4500 is a dual-energy X-ray absorptiometry (DXA) system designed for bone mineral density (BMD) measurements. It is a diagnostic device used to assess bone health and detect conditions such as osteoporosis.

More about "Bone Density"

Bone density, also known as bone mineral density (BMD), is a crucial indicator of overall bone health and a key factor in assessing the risk of osteoporosis and fractures.
This metric measures the amount of mineralized tissue present in the bones, which can be influenced by various factors such as age, gender, physical activity, and dietary intake.
Dual-energy X-ray absorptiometry (DEXA or DXA) is the gold standard method for measuring bone density, providing accurate and precise results.
Instruments like the SkyScan 1176, VivaCT 40, Lunar Prodigy, Skyscan 1172, Lunar iDXA, and μCT40 are commonly used in research and clinical settings to perform these scans.
The QDR 4500A and QDR 4500 are also well-established DEXA systems for assessing bone density.
Beyond the hardware, software tools such as NRecon and CTAn play a vital role in the analysis and interpretation of bone density data.
These programs allow researchers to visualize, quantify, and compare bone characteristics across different studies and populations.
By leveraging the capabilities of PubCompare.ai, researchers can easily locate and compare bone density measurement protocols from scientific literature, preprints, and patents.
This AI-driven tool streamlines the research process, enabling the identification of the most effective methods for optimizing bone density and supporting better outcomes in bone health studies.