CN107271411A - One kind is directed to serum specificity analysis and modeling method - Google Patents
One kind is directed to serum specificity analysis and modeling method Download PDFInfo
- Publication number
- CN107271411A CN107271411A CN201710405023.4A CN201710405023A CN107271411A CN 107271411 A CN107271411 A CN 107271411A CN 201710405023 A CN201710405023 A CN 201710405023A CN 107271411 A CN107271411 A CN 107271411A
- Authority
- CN
- China
- Prior art keywords
- serum
- mrow
- dimensional
- msub
- point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/64—Fluorescence; Phosphorescence
- G01N21/6428—Measuring fluorescence of fluorescent products of reactions or of fluorochrome labelled reactive substances, e.g. measuring quenching effects, using measuring "optrodes"
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
- G01N21/63—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light optically excited
- G01N21/64—Fluorescence; Phosphorescence
- G01N21/6486—Measuring fluorescence of biological material, e.g. DNA, RNA, cells
Landscapes
- Health & Medical Sciences (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Immunology (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Nuclear Medicine, Radiotherapy & Molecular Imaging (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Pathology (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- Engineering & Computer Science (AREA)
- Chemical Kinetics & Catalysis (AREA)
- Optics & Photonics (AREA)
- Investigating, Analyzing Materials By Fluorescence Or Luminescence (AREA)
Abstract
The present invention proposes a kind of method for the three-dimensional spectrum analysis of serum, three-dimensional fluorescence spectrum is depicted as three-dimensional projection or contour map by this method using matlab softwares, and its complexity based on serum spectral component is proposed to be pre-processed using the method for S G moving-polynomial smoothers to serum spectrum, simultaneously three-dimensional data is parsed using BP neural network, serum spectral prediction model is established, more detailed and accurate serum analysis result is provided for doctor.This method is compared with traditional biochemical detection methods, spectral analysis technique has the advantages that analyze speed is fast, contactless, stable without destruction and result, it can also effectively avoid losing effective physiologic information because three-dimensional data is laid into two-dimensional process, ensure the ingredient more analyzed comprehensively in Sample serum.
Description
Technical field
The present invention relates to medical science field, the method detected in particular to serum spectrum analysis.
Background technology
With the development of society, science and technology is also correspondingly improved constantly, the Diet lifestyle of people also there occurs very
Big change, this causes patient Yu of the diseases such as hyperglycaemia in society, high fat of blood gradually to increase, but also is presented what is increased year by year
Trend, this is accomplished by the relevant physical signs that present medical institutions can be rapidly and accurately diagnosed to be in serum, and this is also
Current life science urgent problem.In terms of the detection technique of present comparative maturity still rests on chemical detection,
Need repeatedly to handle sample, be easily caused in serum contained living matter activity reduction, required for making detection process
Time greatly prolong, bigger problem is that the extension of detection time have impact on testing result accuracy.Nowadays domestic and international medical science
Field, seldom sets foot in the research of serum spectral characteristic, and related paper or science and technology is very few, and this just seriously constrains this
The development of aspect research.
The content of the invention
In order to solve quickly and exactly to test and analyze serum composition, the present invention proposes one kind can be glimmering using three-dimensional
Light spectral technique goes the method for testing and analyzing serum, and proposes to be predicted analysis using genetic algorithm and BP algorithm.
A kind of method for the three-dimensional spectrum analysis of serum, comprises the following steps：
Step 1, the method for subject's early morning empty stomach venous puncture obtains human serum sample, then uses pure water mixed diluting
The sample of serum is obtained, allows the light of light source to generate excitation wavelength through exciting light monochromator, the fluorescent material in Sample serum
Fluorescence is produced after being excited；
Step 2, exciting light is at right angles arranged with transmitting light probe, allows fluorescence to act on detector by emission monochromator
On, corresponding electric signal is obtained, it is amplified to record, as shown in Figure 1.Shown again in the form of curve or numeral
Come, the Rayleigh scattering light in three-dimensional fluorescence spectrum is removed using interpolation method, retain fluorescence information useful in fluorescence spectrum；
Step 3, the three-dimensional light after processing is set a song to music face data prediction using S-G polynomial smoothings, two-dimentional S-G is put down
Data vector matrix-expand in sliding method moving window is data matrix, by the non-central point in forms come；
Step 4, threedimensional model is built using parallel factor algorithm (PARAFAC), is then based on the office of correlation analysis
Portion's homing method, extracts the correlation analysis in three-dimensional spectrum, and the method modeled using GA-BP carries out analysis prediction
Further, it is fitted the data point of scattered band using the data point of Rayleigh scattering mountain peak both sides.
The specific region for being to determine scattering first, scattering region is typically distributed across launch wavelength and excited equal to 1 times or 2 times
At wavelength and its adjacent domain (± 10~15nm).Determine after scattering region, by the fluorescence data of the two mountain peak regions
Zero setting, the fluorescence data of scattered band is fitted according to the data of scattered band both sides.Wherein handle three-dimensional fluorescence spectrum number
According to when select low order and smooth enough spline interpolation,
Further, when handling spectroscopic data, one piece of data, continuum method can be chosen before and after the pending point of spectrum
Individual point constitutes a window, and a process points is located at window center, is gone to be fitted the central point of window with this odd number point, will
Window moves one successively from front to back, repeats said process.
Its specific method is one matrix smooth window of selection in three-dimensional fluorescence spectrum so that window includes (2p+1)
The individual data points of × (2q+1), the data point of its window can be expressed as：
(a-p, b-q, x (a-p, b-q)) ... (a-p, b0, x (a-p, b0)) ..., (a-p, bq, x (a-p, bq))
.....
(a0, b-q, x (a0, b-q)) ... (a0, b0, x (a0, b0)) ..., (a0, bq, x (a0, bq))
.....
(ap, b-q, x (ap, b-q)) ... (ap, b0, x (ap, b0)) ..., (ap, bq, x (qp, bq))
Wherein am(m=-p ..., p) is m-th of emission spectrum wavelength, bn(n=-q ..., q) is n-th of excitation spectrum
Wavelength, x (am, bn) (m=-p ..., p, n=-q ..., q) be data point (am, bn) fluorescence intensity.
Further, the fluorescent matrix of excitation-emission is combined with Chemical Measurement second order correction method, fully profit
With the second order advantage of second order correction method, " Chemical Decomposition " is replaced with " mathematics separation ", standard can quickly and be simply obtained
True quantitative result, obtains the matrix form of trilinear model：
Then by the typical iterative process of PARAFAC, relative matrix A and known normal concentration matrix Y are obtained
Between linear concentration relation：Y=A β+E, and to unknown serum sample concentration sealing：Ynew=Anewβ。
Further, chosen wherein in turn to threedimensional model model training using genetic algorithm and BP algorithm (GA-BP)
220-240nm wave band substitutes into the modeling of Genetic Algorithm Model income, until seeing network convergence.
Abstracting method is：First all serum samples are arranged according to the order of concentration from small to large, then since No. 1,
A sample is extracted at interval 5, and for fluorescent absorption spectrum, forecast set is：1-6-11-16-21-26-31-36, totally 8 samples,
Remaining 32 samples are used as training set.
Wherein BP e-learnings flow is as shown in Fig. 2 the topological structure of BP networks, the choosing of its input layer are rubbed in selection 3
The pixel of fluorescence spectrum image is taken, then networking input normalization sample data, with reference to the simulated effect of forecast sample, when pre-
Measured value root-mean-square error reaches that certain index just shifts to an earlier date deconditioning, the BP network models trained is directly exported, such as Fig. 3 institutes
Show.
Brief description of the drawings
Fig. 1 is fluorescence spectral measuring schematic diagram
Fig. 2 is BP e-learning flow charts
Fig. 3 is GA-BP modeling process figures
Embodiment
The present invention proposes the data analysing method that a kind of serum composition is quick and precisely analyzed, and provides more detailed for hospital
And accurate clinical diagnosis technology.The method for the three-dimensional modeling that this method is used, is effectively avoided because three-dimensional data is put down
Effective information is lost in paving city two-dimensional process, the information of sample can be analyzed more comprehensively.And fluorescence data is analyzed to have and divided
Analyse speed it is fast, contactless, without destruction and result it is stable the advantages of.Decision-making foundation can be provided with Accurate Diagnosis and treatment for doctor
And technical support.
To reach above-mentioned purpose, implementation of the invention employs following technical scheme：
Step 1, the method for subject's early morning empty stomach venous puncture obtains human serum sample, then uses pure water mixed diluting
The sample of serum is obtained, allows the light of light source to generate excitation wavelength through exciting light monochromator, the fluorescent material in Sample serum
Fluorescence is produced after being excited；
Step 2, exciting light is at right angles arranged with transmitting light probe, allows fluorescence to act on detector by emission monochromator
On, corresponding electric signal is obtained, it is amplified to record, as shown in Figure 1.Shown again in the form of curve or numeral
Come, the Rayleigh scattering light in three-dimensional fluorescence spectrum is removed using interpolation method, retain fluorescence information useful in fluorescence spectrum；
Step 3, the three-dimensional light after processing is set a song to music face data prediction using S-G polynomial smoothings, two-dimentional S-G is put down
Data vector matrix-expand in sliding method moving window is data matrix, by the non-central point in forms come；
Step 4, threedimensional model is built using parallel factor algorithm (PARAFAC), is then based on the office of correlation analysis
Portion's homing method, extracts the correlation analysis in three-dimensional spectrum, and the method modeled using GA-BP carries out analysis prediction
Wherein interpolation method removes the Rayleigh scattering light in three-dimensional fluorescence spectrum, the number including the use of Rayleigh scattering mountain peak both sides
Strong point is fitted the data point of scattered band.
Comprise the following steps that
Step 1：It is determined that the region of scattering, scattering region is typically distributed across launch wavelength equal to 1 times or 2 times of excitation wavelengths
Place and its adjacent domain (± 10~15nm).
Step 2：Determine after scattering region, by the fluorescence data zero setting of the two mountain peak regions, according to scattered band two
The data of side fit the fluorescence data of scattered band.
Step 3：One group of polynomial fitting is found according to known data point to be fitted, from low order and smooth enough
Spline interpolation handles three-dimensional fluorescence spectrum data, and spline interpolation function enters row interpolation, wherein interpolating function using piecewise polynomial
It is both low order piecewise function, is smooth function again.
When handling spectroscopic data, one piece of data can be chosen before and after the pending point of spectrum, continuum method point constitutes one
Individual window, and a process points is located at window center, gone to be fitted the central point of window with this odd number point, by window from going to
Move one successively afterwards, repeat said process.
Its specific method is：
Step 1：A matrix smooth window is chosen in three-dimensional fluorescence spectrum so that window includes (2p+1) × (2q+1)
Individual data point, the data point of its window can be expressed as：
(a-p, b-q, x (a-p, b-q)) ... (a-p, b0, x (a-p, b0)) ..., (a-p, bq, x (a-p, bq))
.....
(a0, b-q, x (a0, b-q)) ... (a0, b0, x (a0,b0)) ..., (a0, bq, x (a0, bq))
.....
(ap, b-q, x (ap, b-q)) ... (ap, b0, x (ap, b0)) ..., (ap, bq, x (aq, bq))
Wherein am(m=-p ..., p) is m-th of emission spectrum wavelength, bn(n=-q ..., q) is n-th of excitation spectrum
Wavelength, x (am, bn) (m=-p ..., p, n=-q ..., q) be data point (am, bn) fluorescence intensity.
Step 2, a new coordinate is set up in fitting window, with the central point (a of window data0, b0, x (a0, b0))
For the origin of coordinate system, launch wavelength a direction is abscissa, and excitation wavelength b direction is ordinate,
Choose Savizky-Golay fitting of a polynomials binary h order polynomial curved surfaces be：
Wherein d is coefficient matrix, and smooth window has the individual data points of (2p+1) × (2q+1), data point is updated into above formula and obtained
To equation group
Wherein m=-p ,-p+1 ..., 0, p-1, p；N=-q ,-q+1 ..., O, q-1, q build new matrix A:
D optimal value is solved using least-squares algorithm：
The smooth value of each point in calculation window：
Wherein, the fluorescent matrix of excitation-emission is combined with Chemical Measurement second order correction method, makes full use of second order
The second order advantage of bearing calibration, " Chemical Decomposition " is replaced with " mathematics separation ", can quickly and simply obtain accurately fixed
Measure result
The first step, the trilinear model of the fluorescence data of F composition of N number of serum sample is expressed as：
Wherein xijkBe i-th of serum sample at launch wavelength j, excitation wavelength k should intensity, F represents number of components；
aifFor the relative concentration values of f-th of composition in i-th of serum sample；bjfThe relative transmission light for being f-th of component at wavelength j
Spectrum；ckfThe relative excitation spectral value for being f-th of component at wavelength k.
Second step, determines number of components F value, and initialize load moment by the typical iterative process of PARAFAC
Battle array B and C,
3rd step, using X, B and C, according to
AT (i)=(BTB, CTC)-1diag(BTXi..C) formula, estimated matrix A；
Then according to X, A and C, utilize
Formula estimates B；
Then according to X, A and B, utilize
Formula estimates C；
4th step is exactly repeatedly the 3rd step, until
Object functionConvergence,
Finally, linear concentration relation between relative matrix A and known normal concentration matrix Y is obtained：Y=A β+E, and it is right
Unknown serum sample concentration sealing：Ynew=Anewβ。
Also, wherein 220- is chosen in turn to threedimensional model model training using genetic algorithm and BP algorithm (GA-BP)
240nm wave band substitutes into the modeling of Genetic Algorithm Model income, until seeing network convergence.
Abstracting method is：First all serum samples are arranged according to the order of concentration from small to large, then since No. 1,
A sample is extracted at interval 5, and for fluorescent absorption spectrum, forecast set is：1-6-11-16-21-26-31-36, totally 8 samples,
Remaining 32 samples are used as training set.
Wherein BP e-learnings flow is as shown in Fig. 2 the topological structure of BP networks, the choosing of its input layer are rubbed in selection 3
The pixel of fluorescence spectrum image is taken, then networking input normalization sample data, with reference to the simulated effect of forecast sample, when pre-
Measured value root-mean-square error reaches that certain index just shifts to an earlier date deconditioning, the BP network models trained is directly exported, such as Fig. 3 institutes
Show.
Claims (5)
1. a kind of method for the three-dimensional spectrum analysis of serum, it is characterised in that methods described comprises the following steps：
Step 1, the method for subject's early morning empty stomach venous puncture obtains human serum sample, then is obtained with pure water mixed diluting
The sample of serum, allows the light of light source to generate the fluorescent material in excitation wavelength, Sample serum through exciting light monochromator and is swashed
Fluorescence is produced after hair；
Step 2, exciting light is at right angles arranged with transmitting light probe, allows fluorescence to be acted on by emission monochromator on detector,
Corresponding electric signal is obtained, it is amplified to record, then shown in the form of curve or numeral, gone using interpolation method
Except the Rayleigh scattering light in three-dimensional fluorescence spectrum, retain fluorescence information useful in fluorescence spectrum；
Step 3, the three-dimensional light after processing is set a song to music face data prediction using S-G polynomial smoothings, by two-dimentional S-G exponential smoothings
Data vector matrix-expand in moving window is data matrix, by the non-central point in forms come；
Step 4, threedimensional model is built using parallel factor algorithm, is then based on the local regression method of correlation analysis, carries
The correlation analysis in three-dimensional spectrum is taken, the method modeled using GA-BP carries out analysis prediction.
2. the method according to claim 1 for the three-dimensional spectrum analysis of serum, it is characterised in that described interpolation method is gone
Except the Rayleigh scattering light in three-dimensional fluorescence spectrum includes, the number of scattered band is fitted using the data point of Rayleigh scattering mountain peak both sides
Strong point, is specifically the functional value of any according to function on certain interval, optimal function expression is fitted, then by interval
On other point be updated in function expression, be used as these point approximation.
3. the method according to claim 1 for the three-dimensional spectrum analysis of serum, it is characterised in that wherein described S-G
Polynomial smoothing includes, and one piece of data is chosen before and after the pending point of spectrum, and continuum method point constitutes a window, and
And a process points is located at window center, gone to be fitted the central point of window with this odd number point, window is moved successively from front to back
It is dynamic one, repeat said process.
4. according to claim 1 for the method for the three-dimensional spectrum analysis of serum, it is characterised in that described parallel factor
Algorithm includes, and the fluorescent matrix of excitation-emission is combined with Chemical Measurement second order correction method, second order correction is made full use of
The second order advantage of method, " Chemical Decomposition " is replaced with " mathematics separation ", can quickly and simply obtain accurately quantitative knot
Really, the matrix form of trilinear model is obtained：
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
<mi>k</mi>
</mrow>
</msub>
<mo>=</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>f</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>F</mi>
</munderover>
<msub>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mi>f</mi>
</mrow>
</msub>
<msub>
<mi>b</mi>
<mrow>
<mi>j</mi>
<mi>f</mi>
</mrow>
</msub>
<msub>
<mi>c</mi>
<mrow>
<mi>k</mi>
<mi>f</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>e</mi>
<mrow>
<mi>i</mi>
<mi>j</mi>
<mi>k</mi>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mi>N</mi>
<mo>;</mo>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2...</mn>
<mi>J</mi>
<mo>;</mo>
<mi>k</mi>
<mo>=</mo>
<mn>1</mn>
<mo>,</mo>
<mn>2</mn>
<mo>,</mo>
<mn>...</mn>
<mi>K</mi>
<mo>)</mo>
</mrow>
</mrow>
Then by the typical iterative process of PARAFAC, obtain between relative matrix A and known normal concentration matrix Y
Linear concentration relation：Y=A β+E, and to unknown serum sample concentration sealing：Ynew=Anewβ。
5. the method according to claim 1 for the three-dimensional spectrum analysis of serum, it is characterised in that described GA-BP is built
Mould includes, and using genetic algorithm and BP algorithm in turn to threedimensional model model training, the wave band for choosing wherein 220-240nm is substituted into
Genetic Algorithm Model income is modeled, until network convergence.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710405023.4A CN107271411A (en) | 2017-06-01 | 2017-06-01 | One kind is directed to serum specificity analysis and modeling method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710405023.4A CN107271411A (en) | 2017-06-01 | 2017-06-01 | One kind is directed to serum specificity analysis and modeling method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107271411A true CN107271411A (en) | 2017-10-20 |
Family
ID=60064398
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710405023.4A Pending CN107271411A (en) | 2017-06-01 | 2017-06-01 | One kind is directed to serum specificity analysis and modeling method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107271411A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108801304A (en) * | 2018-06-13 | 2018-11-13 | 武汉理工大学 | A method of improving Rayleigh scattering many reference amounts distributed measurement precision |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9523640B2 (en) * | 2010-11-03 | 2016-12-20 | Reametrix, Inc. | Method of fluorescent measurement of samples, and devices therefrom |
-
2017
- 2017-06-01 CN CN201710405023.4A patent/CN107271411A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9523640B2 (en) * | 2010-11-03 | 2016-12-20 | Reametrix, Inc. | Method of fluorescent measurement of samples, and devices therefrom |
Non-Patent Citations (4)
| Title |
|---|
| 朱卫华: "人体血清光谱特性分析与建模研究", 《中国博士学位论文全文数据库 基础科学辑》 * |
| 杜树新等: "基于Savizky-Golay多项式的三维荧光光谱的曲面平滑方法", 《光谱学与光谱分析》 * |
| 王书涛等: "基于三维荧光与GA-RBF神经网络对茶叶中氯菊酯农药残留的检测", 《发光学报》 * |
| 盖云等: "化学计量学方法在三维荧光光谱分析中的应用", 《光谱学与光谱分析》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108801304A (en) * | 2018-06-13 | 2018-11-13 | 武汉理工大学 | A method of improving Rayleigh scattering many reference amounts distributed measurement precision |
| CN108801304B (en) * | 2018-06-13 | 2019-11-26 | 武汉理工大学 | A method of improving Rayleigh scattering many reference amounts distributed measurement precision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Ibrahim et al. | The case control study: the problem and the prospect | |
| CN103854305B (en) | A kind of Model Transfer method based on multi-scale Modeling | |
| Petersen et al. | Reprint of “influence of analytical bias and imprecision on the number of false positive results using guideline-driven medical decision limits” | |
| Bremhorst et al. | Fertility progression in Germany: An analysis using flexible nonparametric cure survival models | |
| US20210295515A1 (en) | Method and system for determining concentration of an analyte in a sample of a bodily fluid, and method and system for generating a software-implemented module | |
| JP2014528080A (en) | Biochemical data analysis system and method | |
| Amoros et al. | A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma | |
| Cho et al. | A framework for performing data-driven modeling of tumor growth with radiotherapy treatment | |
| Buys et al. | Optical technologies and molecular imaging for cervical neoplasia: a program project update | |
| CN107271411A (en) | One kind is directed to serum specificity analysis and modeling method | |
| Goebell et al. | Assessing the quality of studies on the diagnostic accuracy of tumor markers | |
| Teixeira et al. | Performance of the quantitative food frequency questionnaire used in the Brazilian center of the prospective study Natural History of Human Papillomavirus Infection in Men: The HIM Study | |
| CN104350378B (en) | Method and apparatus for the performance of measure spectrum system | |
| Pak et al. | A multistate model for correlated interval-censored life history data in caries research | |
| JP3902999B2 (en) | Optical scattering characteristic estimation apparatus and operation method thereof | |
| Blettner et al. | Critical reading of epidemiological papers: a guide | |
| Patole | Systematic Reviews and Meta-Analyses of Non-randomised Studies | |
| Tadesse et al. | Identification of differentially expressed genes in high-density oligonucleotide arrays accounting for the quantification limits of the technology | |
| Faulkner et al. | The role of epigenetic and biological biomarkers in the diagnosis of periodontal disease: a systematic review approach | |
| Ganiatsou et al. | WEaning Age FiNder (WEAN): a tool for estimating weaning age from stable isotope ratios of dentinal collagen | |
| Stock et al. | Estimation of disease prevalence, true positive rate, and false positive rate of two screening tests when disease verification is applied on only screen-positives: a hierarchical model using multi-center data | |
| Villalba‐Hernández et al. | Periodontitis detection using Raman spectroscopy, support vector machine, and salivary biomarkers | |
| CN111103242A (en) | Serum characteristic analysis and modeling method | |
| Petelczyc et al. | Maximal oxygen uptake prediction from submaximal bicycle ergometry using a differential model | |
| Nordstrom | The need for validation standards in medical imaging |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20171020 |
|
| RJ01 | Rejection of invention patent application after publication |