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.
Odontogenesis
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»
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.
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.
CONSORT flowchart of the participants’ progress through the trial phases [35 (link)]
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
Most recents protocols related to «Odontogenesis»
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
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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(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.
These boundary parameters, , 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, ; (ii) a logarithm, ; or (iii) a linear function, . 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
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 variable | variable group | variable type | mean specification | noise specification | negative log-likelihood |
---|---|---|---|---|---|
humerus medial epicondyle (HME_EF) | epiphyseal fusion | ordinal | power law ordinal | constant | 451.46 |
power law ordinal | linear positive intercept | 451.04 | |||
logarithmic | constant | 482.78 | |||
logarithmic | linear positive intercept | 482.78 | |||
linear | constant | 460.03 | |||
linear | linear positive intercept | 452.04 | |||
tarsal count (TC_Oss) | ossification | ordinal | power law ordinal | constant | 441.14 |
power law ordinal | linear positive intercept | 439.24 | |||
logarithmic | constant | rejected for mean specification not able to be fit | |||
logarithmic | linear positive intercept | rejected for mean specification not able to be fit | |||
linear | constant | 498.58 | |||
linear | linear positive intercept | 452.79 | |||
maxillary first molar (max_M1) | dental development | ordinal | power law ordinal | constant | 338.54 |
power law ordinal | linear positive intercept | 335.88 | |||
logarithmic | constant | 362.05 | |||
logarithmic | linear positive intercept | 362.05 | |||
linear | constant | 432.10 | |||
linear | linear positive intercept | 360.65 | |||
mandibular lateral incisor (man_I2) | dental development | ordinal | power law ordinal | constant | 352.97 |
power law ordinal | linear positive intercept | 358.28 | |||
logarithmic | constant | 365.62 | |||
logarithmic | linear positive intercept | 365.62 | |||
linear | constant | 419.08 | |||
linear | linear positive intercept | rejected for large beta2 | |||
FDL | long bone measurement | continuous | power law | constant | 2464.90 |
power law | linear positive intercept | 2352.01 | |||
RDL | long bone measurement | continuous | power law | constant | 1958.46 |
power law | linear positive intercept | 1887.47 |
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More about "Odontogenesis"
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.