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Odontogenesis

Odontogenesis is the biological process of tooth development, involving the formation and growth of teeth.
It is a complex process that begins during embryonic development and continues into adulthood.
Odontogenesis involves the interaction of ectodermal and mesenchymal tissues, leading to the formation of the tooth bud, enamel organ, and dental papilla.
This process is regulated by a carefully orchestrated series of signaling pathways and transcriptional events.
Understaning odontogeneis is crucial for research into dental health, tooth regeneration, and developmental anomalies.
PubCompare.ai can optimize your odontogenesis research by enhancing reproducibility and accurracy, helping you easily locate and identify the best protocols and products from literature, pre-prints, and patents.

Most cited protocols related to «Odontogenesis»

We obtained data from two convenience samples (Figure 1, page 1186). The first sample consisted of 732 adults at three academic health centers in the Research Diagnostic Criteria for TMD (RDC-TMD) Validation Project whom we characterized by using the expanded RDC-TMD assessment protocol.10 (link) Case status was based on consensus by two dentists at each site (among them Y.M.G. and R.O. at the University at Buffalo, N.Y., E.S. at the University of Minnesota, Minneapolis, and E.L.T. at the University of Washington, Seattle) using calibrated technique.10 (link) We recruited putative control participants on the basis of absence of pain in the facial area during the preceding six months. At the time of participants’ enrollment, we evaluated them according to history, clinical examination and panoramic radiographic findings to exclude people with any possibility of odontogenic pain. We used this first sample for initial item development. Among the participants with TMD, we included 65 in a reliability assessment, with an interval of two to seven days between survey administrations to evaluate temporal stability.11 (link)For validity testing, we divided the 732 participants into four groups. We defined the target group, those having pain-related TMD, as those having a diagnosis of pain-related TMD (that is, myofascial pain, arthralgia or both) (as described by Schiffman and colleagues10 (link)). We identified two comparison groups without pain. One of them consisted of healthy control participants, defined as not meeting criteria for a diagnosis of TMD; exclusion criteria at enrollment permitted only low-severity headaches (per International Classification of Headache Disorders, second edition [ICHD-II], criteria12 (link)) that were not affected by masticatory function. The second comparison group consisted of those with a nonpainful TMJ disorder, defined as a TMJ disorder (such as disk displacement or osteoarthrosis) identified via magnetic resonance imaging or computed tomography, and this group served as a comparison for reporting of masticatory system symptoms. Participants in this latter group may have had jaw pain symptoms, but we required that those symptoms be insufficient to meet criteria for a diagnosis of TMD pain. We identified a third comparison group—those with headache in the temple region—by means of an algorithm from the ICHD-II criteria.12 (link) This group was a subset of the healthy control participants and those with nonpainful TMJ disorders, and we selected it on the basis of the absence of a diagnosis of TMD pain. Consequently, these participants represented those with regional headache without TMD pain and served as a comparison for pain symptom reporting. To create groups with similar sample sizes, we randomly selected a subset of the participants with pain-related TMD and retained it for analyses.
Another pain group, that with odontalgia, consisted of 80 participants whose chief complaint was toothache and odontogenic disease confirmed by means of clinical examination and radiographs. We did not determine the presence or absence of TMD in this group owing to logistic limitations, so we used these data for secondary analyses to determine the false-positive rate associated with a competitive pain condition.
Publication 2011
Adult Arthralgia Buffaloes Degenerative Arthritides Dentist Diagnosis Face Headache Headache Disorders Healthy Volunteers Management, Pain Masticatory System Odontogenesis Pain Pain Disorder Panoramic Radiography Physical Examination Temporomandibular Joint Disorders Toothache X-Ray Computed Tomography X-Rays, Diagnostic

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Publication 2009
Bones Joints Odontogenesis Panoramic Radiography Temporomandibular Joint
The criterion examiners, using questionnaires and a semi-structured interview, reviewed the medical history and pain characteristics in order to rule out possible non-TMD pain conditions and to exclude individuals with co-morbid conditions (see exclusion criterion in Table 1). Participants reporting a history consistent with migraine were not excluded. However, if a participant presented for evaluation while having an active migraine headache, the subject was rescheduled at a later date for the clinical examination. In addition, panoramic radiography and a clinical exam, including assessment for warmth, swelling and redness of the tissue, were used to rule out odontogenic, soft tissue, and hard tissue pathology. Other pathology not targeted for inclusion in the project was ruled out with TMJ MRI and CT. In establishing the reference standard diagnoses, the criterion examiners considered self-report of pain in the last month; effect of jaw function, movement, parafunction and rest on the reported pain over the past month; replication of the reported pain on provocation using clinical tests (see Table 2); and the TMJ CT and MRI studies. The criterion examiners also considered both common and uncommon TMD conditions that were operationalized by the consensus of the criterion examiners (see Table 3).
The criterion examiners performed their evaluations within the following procedural framework. Each of two CEs interviewed and examined each participant blinded to each other’s findings. Using all available clinical information including the imaging studies with the radiologist’s interpretations, they independently rendered their criterion diagnoses. They then compared their findings and, if either CE differed with the other’s findings or diagnoses, the participant was reexamined by both of them to resolve the area of disagreement. If either CE disagreed with the radiologist’s interpretation, the radiologist was consulted for further review of the images with the CEs. The reference standard diagnoses were then established by consensus between the CEs. The study’s requirement of a consensus between 2 independent examiners was designed to reduce the likelihood of diagnostic error. The estimated absolute error associated with a single exam is reported in the Results section.
Publication 2010
Diagnosis DNA Replication Erythema Migraine Disorders Movement Odontogenesis Pain Pain Disorder Panoramic Radiography Physical Examination Radiologist Tissues
Although all student participants in ISCOLE were 9–11 years of age, they differed in their stage of biological maturity. Several methods exist to assess biological maturity, including the assessment of secondary sex characteristics, skeletal age, dental maturation, or somatic maturation [37 ]. All options were considered for use in ISCOLE; however, the method that was deemed most feasible across all countries was to use estimates of somatic maturation. Two methods are employed: 1) percentage of predicted adult stature attained, using the Khamis-Roche method [38 (link)] to predict adult stature, and 2) the maturity offset [39 (link)].
The rationale for using percentage of adult stature attained is that two children of the same age can be the same stature, but one may be closer to their final adult stature, and hence is more advanced in somatic maturation. Given that the final adult stature of the children was not known, it was predicted using their chronological age, stature, body mass and mid-parent stature (average of father’s and mother’s stature) [38 (link)]. Another important indicator of somatic maturation is age at peak height velocity, which is typically assessed from serial measurements of the child throughout adolescence. Age at peak height velocity is a commonly used indicator of somatic maturity [37 ]. Given that ISCOLE is a cross-sectional study, the method of Mirwald and colleagues [39 (link)] was used to predict years from peak height velocity, or the “maturity offset” from age, sex, sitting height, stature and body mass.
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Publication 2013
Adult Adult Children Biopharmaceuticals Body Height Child Diploid Cell Human Body Mothers Odontogenesis Parent Sex Characteristics Skeleton Student
The study was conducted on female patients from the University Dental Clinic of the Pomeranian Medical University in Szczecin, Poland. Patients (Polish women living in the West Pomeranian Voivodeship) between 20 and 45 years of age (median 28.1) diagnosed with myofascial pain with limited mouth opening (Ib) based on DC/TMD criteria were included in the study.
Exclusion criteria included:

Inflammation in the oral cavity that emerged as myospasm or preventive muscle contraction,

Earlier splint therapy—could affect the value of the amplitude in the EMG examination, among other variables throughout a signal acquisition procedure.

Pharmacotherapy (e.g., oral contraception, hormone replacement therapy, and antidepressants) – some hormones and their replacements are known to affect muscle tone and pain intensity;

Systemic diseases (e.g., rheumatic and metabolic diseases)—they can affect muscle tone and pain intensity and range of motion TMJ

Mental illness- they can affect muscle tone and pain intensity, both treated and untreated

Lack of stability in the masticatory organ motor system—this affects muscle tone and pain intensity and range of motion TMJ

Masticatory organ injury—can affect muscle tone and pain intensity and range of motion TMJ, usually due to myospasm/local myalgia/preventive co-contraction

Pregnancy – as trimester-dependent estrogen/progesterone and relaxine interplay may affect muscle tone and pain intensity,

Patients undergoing orthodontic treatment—can affect muscle tone and pain intensity and range of motion TMJ,

Other types of inflammation in the oral cavity (e.g., pulp inflammation or impacted molars) – which usually yield in protective co-contraction,

Fibromyalgia—can affect muscle tone, pain intensity and/ as well as range of motion in TMJ and cervical spine,

Other specific contraindications for use of physical treatments in the MT, e.g. cancer therapy, some older models of artificial pacemakers, etc.

All women underwent an intra-oral and extra-oral dental examinations performed by orofacial pain trained dentist. The aim was to exclude odontogenic, periodontal and articular causes of TMD pain. Women meeting the above criteria constituted the study group (G1, n = 82). The patients qualified for the study underwent instrumental diagnostics (sEMG of the masseter muscles at rest and exercise, linear measurement of the range of mandibular mobility) and the level of pain intensity was assessed on the NRS numerical scale (Fig. 1).

CONSORT flowchart of the participants’ progress through the trial phases [35 (link)]

The control group (G2, n = 104) consisted of healthy women Patients (Polish women living in the West Pomeranian Voivodeship), aged 20 to 45 (median 29), without claimed TMDs and pain disorders (based on the extra-oral and intra-oral dental examination, DC/TMD criteria, NRS scale), in whom SEMG tests were performed, and TMJ mobility measurements were performed.
The research project was approved by the Bioethics Committee of the Pomeranian Medical University in Szczecin (no. KB – 0012/102/13). Information on the clinical trial registration is available at www.ClinicalTrials.gov (NCT05021874). All participants gave their written consent. The study complies with the CONSORT guidelines for reporting randomized controlled trials [27 ].
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Publication 2023
Antidepressive Agents Arthralgia Cervical Vertebrae Contraceptives, Oral Dental Health Services Dental Pulp Dentist Diagnosis Early Therapy Estrogens Fibromyalgia Hormones Inflammation Injuries Malignant Neoplasms Mandible Masticatory System Metabolic Diseases Molar Motion Sickness Muscle Contraction Muscles, Masseter Muscle Tonus Myalgia Odontogenesis Oral Cavity Oral Examination Orofacial Pain Pacemaker, Artificial Cardiac Pain Pain Disorder Patients Periodontium Pharmacotherapy Physical Examination Progesterone Range of Motion, Articular Severity, Pain Splints Surgical Replantation Therapeutics Therapy, Hormone Replacement Woman

Most recents protocols related to «Odontogenesis»

Differential abundance analysis (DAA) was used to assess the impact of time point or stage of dental development and caries at T3 on the resistome. Prior to DAA, the data were filtered by removing low abundance ARGs (cutoff: 0.01% counts) and ARGs present in only one sample. To assess the impact of time point, we used a linear mixed effects model (MaAslin2), where time point was treated as a fixed effect and individual as a random effect, to account for repeated sampling per person. Analysis was performed on ARGs present in at least 20% of the samples that had been normalised by total sum scaling (TSS) and log transformed. To ensure robust biological interpretation75 (link), we used two DAA methods (MaAslin2 v1.0.0 and ANCOM-BC v2.1.2), to investigate the impact of caries (mild, moderate, severe) on ARGs at T3. We then assessed if treating caries (restorations) impacted ARGs where caries status (caries free or affected) and restorative status (restored [yes] or unrestored [no]) were treated as fixed effects. Default parameters were used in both packages; however, alpha was adjusted to 0.25 in ANCOM-BC to be consistent with MaAsLin2.
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Publication 2023
Biopharmaceuticals Dental Caries Odontogenesis
This study was undertaken with approval from the University of Adelaide Human Ethics Research Committee (H-2013-097 and H-78-2003). Parents provided written informed consent for the use of samples and the data for this research.
Our cohort of 221 Australian children consisted of 108 twin pairs, 2 unpaired twins and one set of triplets identified from the 550 twin/triplet families enrolled in the Tooth Emergence and Oral Health study. Parents/caregivers were required to complete a series of questionnaires as part of the study. The sex of participants was assigned based on parental report. Sex-specific effects were not detected from analysis of ARG diversity or gene abundance. Hence, sex was not included in further analysis of the resistome. A standard medical history was taken at the clinical examination at T3. See Table S5, Supplementary Information for key population characteristics. At T3, the severity of caries was assessed using ICDAS II. In the 66 CA children, the mean ICDAS II score was 1 ± 1.8, while the remaining 145 were caries-free (ICDAS II score = 0).
From the 221 children, 542 oral biofilm samples were initially identified for genomic analysis, of which a total of 12 were excluded, making the final sample size 530. Two were excluded due to antibiotic use (within the past three months). Three were excluded due to extreme sequence depth variation from the mean. This included two with low sequence depth, T1657B_14042014_Q2_Q3 (0.339702 million target reads) and T1472B_25022007_D1_D2 (6.447160 million target reads), and one unusually high sequence depth sample, T1658A_6012009_D1_D2 (117.369 million target reads). We excluded seven samples that contained over 65% host DNA. The eligible samples were from 93 monozygous (MZ), 66 dizygous (DZ), and 59 opposite sex DZ (OSDZ) twins plus and one set of DZ/OSDZ/DZ triplets. One hundred and seventeen children (53%) were sampled at all three time points, 73 (35%) were sampled at 2 time points and 28 (12%) participants sampled at one time point only. While all twins/triplets were samples at the same time, not all individuals had a sample available that met the requirements for stage of dental development for all time points. For this reason, in addition to the post-sequencing removal of 12 samples, there was inconsistent sampling over time.
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Publication 2023
Antibiotics Biofilms Child Dental Caries Genes Genetic Diversity Genome Homo sapiens Odontogenesis Parent Physical Examination Tooth Triplets Twins

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Publication 2023
Birth Bones Dental Caries Dental Health Services Ethics Committees, Research Females Fillcanal Injuries Jaw Koreans Males Mandible Maxilla Odontogenesis Panoramic Radiography Patients Roman Catholics Third Molars Tooth Tooth Components Tooth Root X-Rays, Diagnostic
For continuous variables, we assume that the observed response, w (the y-axis variable figure 1a), is normally distributed with a mean of h(x,c) and a s.d. of ψ(x,κ) , wN(h,ψ). For the parametrization of the mean, we use a scaled, offset power law, h(x,c)=c2xc1+c3. For the parametrization of the s.d., we consider either constant noise, ψ(x,κ)=κ1 (homoscedastic) or linear positive noise, ψ(x,κ)=κ1[1+κ2x] (heteroscedastic). To ensure that the standard deviation is always positive, we require all parameters to be positive; since the intercept for the heteroscedastic model must be positive, we refer to this noise model as ‘linear positive intercept.’ Figure 1a shows the FDL value (w) as a function of age (x). The red dots are pairs of values for individuals of known age. The solid line is the function h(x,c) that resulted from a maximum likelihood, univariate fit to the known age data for the heteroscedastic model. The shaded region shows the noise level as a function of age ( h±ψ ).

(a) FDL versus known age with a heteroscedastic maximum-likelihood fit. Red dots are observations, the black line is the mean response, and the blue shaded region marks the noise bounds. (b) Maxillary first molar (max_M1) developmental score versus known age. (c) Probability of observing the dental developmental stage of 7 (v = 7) as a function of age for max_M1. The grey band that extends from the middle to bottom plot marks the range of ages for which v = 7 is observed in the data. The black curve is the predicted probability the model preferred by cross-validation (power law for the mean and heteroscedastic noise). The red dots are the observed proportions in the underlying data, which are calculated by binning observations by known age value and calculating the proportion of observations in each bin for which v = 7.

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Publication 2023
Epistropheus Maxilla Molar Odontogenesis
For ordinal variables, it is the latent (unobserved) response that is normally distributed, with mean g(x,b) and s.d. γ(x,β), vN(h,γ). The observed ordinal response, v, is related to the latent response, v*, via a vector of boundary parameters, τ , per v={0if<vτ1mifτm<vτm+1MifτM<v.
These boundary parameters, τm , are part of the model parametrization and included in the maximum-likelihood fitting. The assumption that the noise is normally distributed makes this a probit model. This assumption of a probit link function is an entirely distinct assumption from the choice of the mean and noise responses. For statistical identifiability reasons (see electronic supplementary material, §1.3), we allow three specifications of the mean: (i) an unscaled, unshifted power law, g(x,b)=xb1 ; (ii) a logarithm, g(x,b)=logx ; or (iii) a linear function, g(x,b)=x . The response functions are unscaled and unshifted to ensure statistical identifiability (see electronic supplementary material, §1.3). Often, in previous work, one of these specifications of the mean is assumed without being checked, usually either the logarithm or the linear specification (e.g. [2 ,24 (link)]). We adopt the same noise specifications for ordinal variables as for continuous variables. In total, therefore, there are 3*2 = 6 distinct models that we cross-validate for univariate ordinal fits (see §2.13 and table 1). Figure 1b shows the ordinal value of max_M1 as a function of known age. It is more challenging to visualize ordinal fits; in figure 1c we visualize the fit for one ordinal response, v = 7, which refers to the dental developmental stage of 7.

Variable information and the associated cross-validated results for Step 1 of the cross-validation (univariate models). The model with the smallest negative log-likelihood is considered the best fit (italicized). For each ordinal variable, six distinct models were assessed (three choices for the parametrization of the mean and two for the noise). For each continuous variable, two distinct models were assessed (one choice for the parametrization of the mean and two for the noise). The ‘constant’ noise specification is the homoscedastic model and the ‘linear positive intercept’ noise specification is the heteroscedastic model. The heteroscedastic model was preferred by cross-validation for five of the six models.

response variablevariable groupvariable typemean specificationnoise specificationnegative log-likelihood
humerus medial epicondyle (HME_EF)epiphyseal fusionordinalpower law ordinalconstant451.46
power law ordinallinear positive intercept451.04
logarithmicconstant482.78
logarithmiclinear positive intercept482.78
linearconstant460.03
linearlinear positive intercept452.04
tarsal count (TC_Oss)ossificationordinalpower law ordinalconstant441.14
power law ordinallinear positive intercept439.24
logarithmicconstantrejected for mean specification not able to be fit
logarithmiclinear positive interceptrejected for mean specification not able to be fit
linearconstant498.58
linearlinear positive intercept452.79
maxillary first molar (max_M1)dental developmentordinalpower law ordinalconstant338.54
power law ordinallinear positive intercept335.88
logarithmicconstant362.05
logarithmiclinear positive intercept362.05
linearconstant432.10
linearlinear positive intercept360.65
mandibular lateral incisor (man_I2)dental developmentordinalpower law ordinalconstant352.97
power law ordinallinear positive intercept358.28
logarithmicconstant365.62
logarithmiclinear positive intercept365.62
linearconstant419.08
linearlinear positive interceptrejected for large beta2
FDLlong bone measurementcontinuouspower lawconstant2464.90
power lawlinear positive intercept2352.01
RDLlong bone measurementcontinuouspower lawconstant1958.46
power lawlinear positive intercept1887.47
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Publication 2023
Bone and Bones Cloning Vectors Dental Health Services Epiphyses Incisor Molar Odontogenesis

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More about "Odontogenesis"

Odontogenesis, the intricate process of tooth development, involves the formation and growth of teeth, beginning in embryonic stages and continuing throughout adulthood.
This complex process relies on the interplay between ectodermal and mesenchymal tissues, leading to the creation of the tooth bud, enamel organ, and dental papilla.
Tightly regulated signaling pathways and transcriptional events orchestrate this developmental journey.
Understanding the nuances of odontogenesis is pivotal for research into dental health, tooth regeneration, and addressing developmental anomalies.
Key factors like dexamethasone, β-glycerophosphate, ascorbic acid, and fetal bovine serum (FBS) play crucial roles in regulating this process.
Alizarin Red S and TRIzol reagent are commonly used to assess mineralization and gene expression, respectively.
Optimizing your odontogenesis research can be achieved through the use of AI-driven tools like PubCompare.ai, which enhances reproducibility and accuracy.
This platform helps you easily locate and identify the best protocols and products from literature, preprints, and patents, streamlining your research workflow.
By incorporating synonyms, related terms, and abbreviations, you can expand your understanding of this captivating field and drive breakthroughs in dental science.
Eexplore the depth of odontogenesis and unlock new possibilities with the power of PubCompare.ai.