CN116559308A - System for non-invasive assessment of neonatal intrauterine nutritional status and use thereof - Google Patents
System for non-invasive assessment of neonatal intrauterine nutritional status and use thereof Download PDFInfo
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- CN116559308A CN116559308A CN202310055143.1A CN202310055143A CN116559308A CN 116559308 A CN116559308 A CN 116559308A CN 202310055143 A CN202310055143 A CN 202310055143A CN 116559308 A CN116559308 A CN 116559308A
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- stool
- neonate
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- intrauterine
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Abstract
The invention provides a system for non-invasively evaluating the intrauterine nutritional status of a neonate and application thereof, wherein the system is used for analyzing the compound component change in the neonate's fetal stool. The invention provides a novel system and a method for noninvasively evaluating the intrauterine nutritional status of a neonate, which can rapidly and conveniently evaluate the intrauterine nutritional status of the neonate in a noninvasive manner.
Description
The present application is the patent division application of application No. 202110611164.8, the name of the invention of the system for non-invasive assessment of the intrauterine nutrition status of the neonate and the application thereof, which is the 6-month 1-day application of 2021.
Technical Field
The invention relates to the field of infant health, in particular to a system for non-invasively evaluating the intrauterine nutritional status of a newborn and application thereof.
Background
Currently, the World Health Organization (WHO) criteria for assessing neonatal in-utero growth and for assessing child growth and nutrition are based on anthropometric Z scoring systems, wherein the indices involved are neonatal weight, length and head circumference divided by gestational age and gender. However, methods for assessing intra-uterine cumulative nutrition in newborns are limited. It is well known that birth weight and length are affected by a variety of factors, such as race, mother's stature, pre-pregnancy weight, birth times, altitude, and other factors. Body weight does not distinguish between fat deposition and tissue moisture. In addition, the length cannot be accurately measured, the measurement accuracy of the length and head is far lower than that of the body weight measurement, and a great deal of training is required. Fetal nutrition is obviously lost in uterus, but body weight, length and head circumference may not be affected; meanwhile, the CANS method based on measuring the nutritional subcutaneous fat and hair condition of newborns has not been generalized, in which factors affecting hair, such as genetics, etc., are numerous; whereas the formation of subcutaneous tissue of the fetus, in particular fat, begins at late gestation and therefore reflects only the nutritional status of late gestation. The detection of biochemical indicators in blood is a "dynamic assessment" method for exploring nutrition in newborns, but it has the disadvantage of detecting transients and invasiveness. As mentioned above, the lack of simple, accurate and non-invasive nutrition assessment of intrauterine newborns is one of the global public health challenges. This would lead to the nutrition of newborns, especially in low income countries, not being assessed and cared for and intervened in a timely and accurate manner, which would be detrimental to their infant health and even to the health of adults. In summary, current methods for assessing neonatal nutrition have limitations and efforts are still being made to find accurate and non-invasive assessment methods.
Disclosure of Invention
In order to solve the above problems, the present invention provides a system for non-invasively evaluating the intrauterine nutritional status of a neonate, which is used for analyzing the change of the compound components in the neonate's fetal stool.
In one embodiment, the system is used to analyze changes in at least 10 compound components in neonatal fetal stool.
In one embodiment, wherein at least 10 compound components in neonatal stool are analyzed, the markers are selected from the group consisting of: arginine, glutamic acid, histidine, hydroxyproline, leucine, leucine_isoleucine, methionine, ornithine, phenylalanine, proline, tryptophan, tyrosine, valine, phosphoserine, betaine, indole-3-aldehyde, indole-3-propionic acid, indoline, piperonic acid, pyrrolidine, 1, 7-dimethylxanthine, 1-methylguanine, caffeine, cortisone, leucyl-proline, N-acetylglutamic acid, phenylacetyl-L-glutamine, propionyl L-carnitine, uric acid, taurocholate, cholesterol glycyrrhizinate, butyrylccarnitine, carnitine c12_0, decanoylccarnitine, carnitine c16_0, hexanoylcarnitine, and pentanoylcarnitine.
In one embodiment, wherein at least 10 compound components in neonatal stool are analyzed, selected from any one of the following marker sets:
in one embodiment, wherein at least 10 compound components in neonatal stool are analyzed, selected from the following marker sets:
in one embodiment, there is provided the use of the above system for non-invasive assessment of a neonatal intrauterine nutritional condition.
In one embodiment, the present invention provides a method for non-invasively assessing a neonatal intrauterine nutritional condition by analyzing changes in the composition of compounds in neonatal fetal stool.
In one embodiment, the method evaluates the intrauterine nutritional status of a neonate by analyzing changes in at least 10 compound components in the neonate's fetal stool.
The invention provides a novel system and a method for noninvasively evaluating the intrauterine nutritional status of a neonate, which can rapidly and conveniently evaluate the intrauterine nutritional status of the neonate in a noninvasive manner. The system and the method for noninvasively evaluating the intrauterine nutritional status of the neonate truly realize noninvasive evaluation of the neonate, and overcome the possible damage to the neonate in the prior art for evaluating the intrauterine nutritional status of the neonate. More importantly, the nutrition evaluation method for evaluating the nutrition of the neonate can objectively and accurately evaluate the nutrition condition of the neonate so as to provide more accurate nutrition intervention for the neonate.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present application, the present invention will be further described with reference to the following examples, and it is apparent that the described examples are only some of the examples of the present application, not all the examples. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application. The invention is further described below with reference to examples.
Example 1 participant recruitment and sample collection
1. Participant recruitment and sample collection
Preliminary selection criteria for infants to be included in the study are as follows. (1) Live single-birth infants with gestational age greater than or equal to 38 weeks and Apgar score greater than 8 minutes; (2) Only infants with hospitalization times exceeding 24 hours will be included; (3) Known gestational age (reliable ultrasound to measure crown-hip length before 14 weeks of gestation or to measure twin-tube diameter at the onset of prenatal care between 14 and 20 weeks of gestation). Each hospital underwent strict, standardized procedures, agreeing to the conventional policy of ultrasound for gestational age estimation. To ensure the accuracy of gestational age, the last menstrual period was used as a reference in addition to ultrasound estimation. Estimating that the gestational age differs from the gestational age calculated according to the last menstruation by 7 days or less, and recording as reliable; (4) no major congenital anomalies; (5) absence of gestational disorders. According to anthropometric indexes of newborns and normal Z-score and low Z-score of CANS, newborns are divided into a control group with good nutrition (more than or equal to 38 weeks, weight more than or equal to 2500g, and 1 more than or equal to Z-score > -1), and a case group with malnutrition (more than or equal to 38 weeks, weight less than 2500g, and Z-score less than or equal to-2), wherein 140 newborns with good nutrition and 132 newborns with malnutrition. Physical and chemical indexes of the shit, including the weight of the shit, the pH value of the shit and the water content of the shit, are measured by an analytical instrument. The fetal stool sample after neonatal excretion is collected for the first time, immediately placed into liquid nitrogen, and finally stored at-80 ℃ for metabolomic analysis.
2. Training the operator
To ensure the quality of clinical data and samples, we performed standardized conferences and strict training at local hospitals. Doctors from Beijing gynaecology and obstetrics hospitals with abundant clinical experience have performed strict clinical training on all operators of all clinical hospitals in the hospital of me. In addition, doctors have clinical guidelines for all clinical hospital operators. In addition, all sample collection and examination are also well trained and experienced. And meanwhile, the specialists are regularly organized to conduct guidance and supervision on the clinical site.
3. Ethical examination
All surveys and clinical procedures were discussed and approved by the human research ethics committee of the national institute of health and sciences technology. The Chinese informed consent was drafted and modified according to the opinion of the committee member, and finally the informed consent was formed.
4. Clinical data of pregnant women
In the control group, the age of the pregnant women in the group is 20-43 years, and the average + -SD is 29.1 + -4.51 years. In the case group, the age of the pregnant women in the group is 19-42 years, and the average + -SD is 29.5 + -4.50 years. Factors affecting nutrition have been investigated, including regional, ethnic, delivery mode, hair of pregnant women, spouse's hair, gestational hypertension, gestational diabetes, other endocrine disorders, pregnancy reaction, anemia, umbilical cord adhesion, etc. Hair of pregnant women, hypertension of pregnancy, spouse hair are closely related to nutrition (P < 0.05), and other investigation factors have no significant effect (P > 0.05).
5. Clinical data of neonate
5.1. Malnourished neonates and well-nourished neonates are sex and distribution.
The proportion of female malnutritional newborns was 56.9%, the proportion of male malnutritional newborns was 39.1%, and the number of female malnutritional newborns was significantly higher than that of male newborns (P < 0.05).
5.2 anthropometry of neonates
Anthropometry involves body weight, body length, head circumference, abdomen circumference, leg length and chest circumference. The average values of these indicators for the malnourished newborn were 3255.0g, 53.0cm, 33.5cm, 32.0cm, 25.0cm and 34.0cm, respectively, and the average values of the corresponding indicators for the malnourished newborn were 2410.0g, 49.0cm, 31.5cm, 28.0cm, 22.0cm and 30.0cm, respectively. Statistical analysis was performed using the independent sample Mann-Whitney U test. These measurements for malnourished newborns were significantly reduced compared to well-nourished newborns (P < 0.05).
5.3. CAN assessment of neonates
Neonatal CAN assessment mainly measures subcutaneous fat, involving cheek, chin, neck, chest, abdomen, arm, thigh, calf, back, buttocks, etc. The average values of the corresponding subcutaneous fat for the nutritionally good individuals were 32.0mm, 12.9mm, 11.0mm, 19.9mm, 13.3mm, 12.0mm, 9.3mm, 10.5mm, 8.6mm, 10.1mm and 10.7mm, respectively. The average values of the corresponding body parts of the malnourished newborns were 23.0mm, 11.9mm, 10.1mm, 11.4mm, 9.9mm, 9.0mm, 6.9mm, 8.7mm, 6.6mm, 6.9mm and 8.4mm, respectively. Statistical analysis was performed using the independent sample Mann-Whitney U test. The subcutaneous fat in the various parts of the body of the malnourished neonate (cheek, neck, chest, abdomen, arms, thigh, calf, back and buttocks) was significantly reduced compared to the well nourished neonate, except for the body parts of the examination and chin (P < 0.05).
The nutrition accumulated in the uterus of newborns is assessed by simple physical/chemical characteristics of the fetal feces.
1. Physical/chemical characteristics of the stool
The weight of the malnourished neonate is between 0.20g and 8.65g, and the average weight is 1.4g; the weight of the neonate with good nutrition is between 0.12g and 20.81g, and the average weight is 1.6g; the pH value of the neonatal stool with good nutrition is between 4.29 and 8.53, and the average value is 5.9. The pH value of the neonatal stool of the malnutrition group is between 4.36 and 7.45; in addition, the water content of the neonatal stool with good nutrition is 41.67-93.67%, and the water content of the neonatal stool with poor nutrition is 4.28-92.53%.
2. Physical and chemical properties of the stool
2.1. Statistical analysis of stool color
Recording the color of the stool according to the stool classification standard card, and adopting chi-square test to analyze statistical significance. There was no statistical significance between the two groups (P > 0.05).
2.2. Statistical analysis of stool color
The stool volume was recorded with a stool color grading standard card and analyzed for statistical significance by T-test. The stool card classification is semi-quantitative, and the stool color of the two neonates of the good and bad nutrition groups has no statistical significance (P > 0.05), so the stool color cannot distinguish the two nutrition groups.
2.3. Statistical analysis of stool consistency
The fetal stool viscosity was recorded by glass rod stretching and analyzed for statistical significance by chi-square test. The fetal fecal viscosity of newborns in both the dystrophy and the dystrophy groups was not statistically significant (P > 0.05).
2.4. Weight of stool, pH and Water content statistical analysis (%)
The weight, pH and water content (%) of the stool were statistically analyzed by chi-square test. Neither the dystrophy nor the dystrophy group have statistical significance (P > 0.05) on these characteristics of neonatal fetal feces.
Third example evaluation of intrauterine nutrition of neonates by means of fetal Metabolic analysis
1. Pretreatment of fetal stool
In the case of the grinding beads, 150. Mu.L of acetonitrile was added to 10mg of fetal stool, and then the mixture was ground by grinding the beads (25 Hz. Times.1 min. Times.2). 120. Mu.L of the homogenate was placed in an EP tube and vortexed for 30s. And then centrifuged at 14,000rpm for 10 minutes at 4 ℃. The supernatant was obtained, and the internal labeling solution was added to the supernatant, followed by freeze-drying for LC-MS analysis.
2. Metabonomics analysis of fetal feces
Fetal stool metabolism curves were obtained using a Waters acquisition ultra-high performance liquid chromatograph (UPLC) coupled to a Waters Q-TOF Premier mass spectrometer (Waters corp., milford, MA, USA). In cationic mode, the chromatographic separation was carried out on a Waters BEH C8 column (50 mm. Times.2.1 mm,1.7 μm) (Waters, milford, mass.). Mobile phase a was water containing 0.1% formic acid, while mobile phase B was acetonitrile containing 0.1% formic acid. The linear gradient increased from 5% b to 40% b over 2 minutes, increased from 40% b to 100% b over 6 minutes and held for 2 minutes, then decreased to 5% over 10.1 minutes and held for 2 minutes to reach equilibrium. In negative ion mode, the chromatographic separation was performed on a ACQUITY UPLC HSST3 chromatographic column (100 mm. Times.2.1 mm,1.8 μm) (Waters, milford, mass.). Mobile phase a was 6.5mM NH containing 4 HCO 3 And mobile phase B is water containing 6.5mM NH 4 HCO 3 Is a solvent for acetonitrile. The linear gradient was 0% b in 1 minute, increased from 0% to 40% b in 2 minutes, then increased to 100% in 13 minutes and held for 5 minutes, decreased to 0% b at 22.1 minutes, and held at equilibrium for 2.9 minutes.
3. Quality evaluation
In the positive ion mode, 126 qualitative metabolites were detected. The reproducibility of the samples was assessed using QC, with 89 metabolites with RSD (70.63%) less than 30%. In negative ion mode, 50 qualitative metabolites were detected. The reproducibility of the samples was assessed using QC, with RSD (94.34%) for 50 metabolites less than 30%. Thus, it shows that the measurement has good consistency.
4. Fetal stool metabolic profile between malnutrition and well-nourished newborns
Fetal stool metabolism analysis was performed by A-monitoring PLS-DA. The score plot was used to analyze the distribution of metabolites in the fetal stool. The results indicate that there is a significant segregation phenomenon and therefore a significant metabolic disturbance in the malnourished neonate compared to the well nourished neonate.
5. Analysis of differences in metabolites in malnutrition neonates and in well-nourished neonates' fetal stool.
The 126 qualitative metabolites in the fetal stool of the malnourished neonate and the well-nourished neonate were detected in positive and negative modes. The specific analysis method is as follows.
5.1. Fetal stool sample pretreatment
About 10mg of the stool sample was weighed, and grinding beads were added, followed by 150L of acetonitrile, and grinding was performed by beating (25 Hz. Times.1 min. Times.2). 120. Mu.L of the homogenate was taken in an EP tube and vortexed for 30s to mix. Centrifugation (14000 rpm. Times.10 min. Times.4 oC). Taking supernatant, adding an internal standard solution, and freeze-drying. Before analysis, the solution was reconstituted with 20% acetonitrile and water, and the LC-MS analysis was awaited. Mixing and loading.
lc-MS whole group data acquisition
Instrument: vanquish UPLC-Q actual (Thermo Fisher Scientific, rockford, ill., USA).
Before analyzing an actual sample, a blank sample balancing system is used; in the process of analyzing the actual samples, 1 QC is required to be operated for monitoring the pretreatment and the stability of the instrument operation every 10 samples.
5.2.1 chromatographic separation conditions
Positive ion mode
Chromatographic column: waters BEH C8 column (specification: 50 mm. Times.2.1 mm,1.7 μm) (Waters, milford, mass.) column temperature: 60 ℃, flow rate: 0.4ml/min.
Mobile phase: water was added with 0.1% formic acid (phase a) and acetonitrile was added with 0.1% formic acid (phase B).
Gradient: the initial gradient was 5% B for 0.5min, then increased linearly to 40% B in 1.5min, increased linearly to 100% B in 6min and maintained for 2min, and decreased back to the initial gradient of 5% B at 10.1min, equilibrated for 2min.
Negative ion mode
Chromatographic column: ACQUITY UPLC HSS T3 (specification: 100 mm. Times.2.1 mm,1.8 μm) (Waters, milford, mass.) column temperature: 50 ℃, flow rate: 0.35ml/min.
Mobile phase: phase A was water-added 6.5mM NH 4 HCO 3 The method comprises the steps of carrying out a first treatment on the surface of the Phase B was an aqueous solution of 95% methanol and 6.5mM NH4HCO 3.
Gradient: the initial gradient was 0% B for 1min, increased to 40% B over 2min, then linearly increased to 100% B over 13min and maintained for 5min, decreased back to the initial gradient of 0% B at 22.1min, equilibrated for 2.9min.
5.2.2 Mass Spectrometry data acquisition parameters
MS full scanning range positive ion m/z 80-1200, spray voltage 3.50kV; negative ion 80-1200, spray voltage 3.00kV. The capillary temperature was 300 ℃, the auxiliary heating gas temperature was 350 ℃, and the sheath gas and auxiliary gas flow rates were 45and 10 (arbitrary units), respectively. The resolution is set to 7e4.
5.2.3 data processing software
Metabolite peak table extraction using data integration processing software tracefilter 3.2 (Thermo Fisher Scientific, USA).
The Wilcoxon Mann-Whitney test was used to analyze differences in metabolites between the dystrophic neonatal group and the dystrophic neonatal group. The malnourished neonatal group had a significant increase in 17 metabolites and a significant decrease in 19 metabolites (P < 0.05) compared to the control group, see table 1.
TABLE 1 metabolic product differences in fetal feces for malnourished and well-nourished newborns
Average value of ∈is reduced (malnutrition compared with good nutrition)
Average value of ∈is increased (malnutrition compared with good nutrition)
6. Candidate marker panel for assessing neonatal nutrition accumulation in fetal stool
Subject operating characteristics (ROC) curves play a central role in biomarker assessment. ROC curve analysis was performed using SPSS 18. The training set is used to build the ROC model. The area under the ROC curve (AUC) was used as a measure of sensitivity and specificity of the biomarker. By searching for sensitivity and specificity, the optimal critical point for each biomarker can be determined. The test is then assembled in an ROC model to evaluate its diagnostic capabilities. ROC analysis was performed on candidate metabolite biomarkers. From the differential metabolites 10-15 markers were randomly selected as candidate markers, and 69 sets of markers were analyzed, together with 13 sets of markers, with AUC >0.95, see in particular table 2.
7. Determining a set of potential markers in the fetal stool to assess intrauterine cumulative nutrition of a neonate
130 newborns in the 172 enqueues will be analyzed using the random number table method to block the original packet. Candidate biomarkers for metabolites have been validated by target analysis to assess both well-and poorly-nourished newborns. The 69 sets of markers were selected and validated in 130 newborns. ROC analysis is used to evaluate the value of the obtained metabolites. Of the 13 sets of markers with AUC >0.95, the highest AUC was 0.88, indicating that this set of candidate markers can be potential markers for highly accurate assessment of malnutrition in newborns. This group of potential markers includes 14 markers, glutamate, proline, tryptophan, tyrosine, valine, 1, 7-dimethylxanthine, 1-methylguanine, cortisone, leucyl-proline, phenylacetyl-L-glutamine, propionyl L-carnitine, butyryl carnitine, carnitine c16_0 and valeryl carnitine, respectively. One set of candidate markers includes 10 markers with AUC of 0.81 including glutamic acid, tryptophan, tyrosine, valine, 1, 7-dimethylxanthine, cortisone, phenylacetyl-L-glutamine, butyrylcarnitine, pentanoylcarnitine, and hexanoylcarnitine. Of these 10 markers, 9 were identical to the set of markers using AUC 0.88, with only one caproyl carnitine being different. This set of markers is three-fold less than the set of markers for AUC 0.88, so it may be a better choice with fewer markers and greater efficiency. See in particular table 2.
TABLE 2 results of evaluation of different metabolite marker sets in neonatal infant feces
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8. Conclusion(s)
1. In the invention, the shit is verified that: a new index for non-invasively evaluating the nutritional status of newborns.
2. In the invention, the shit is verified that: a new index for non-invasively evaluating the nutritional status of newborns. A group of 14 markers was found in the fetal stool, which were glutamic acid, proline, tryptophan, tyrosine, valine, 1, 7-dimethylxanthine, 1-methylguanine, cortisone, leucyl-proline, phenylacetyl-L-glutamine, propionyl L-carnitine, butyryl-carnitine, carnitine C16_0 and valeryl-carnitine. Another group of 10 markers in the fetal stool included glutamic acid, tryptophan, tyrosine, valine, 1, 7-dimethylxanthine, cortisone, phenylacetyl-L-glutamine, butyrylcarnitine, valerylcarnitine and caproyl carnitine. These potential markers are mainly related to the metabolism of fats and proteins, which are essential nutrients for the human body. We therefore believe that these two sets of markers in the fetal stool can well assess the accumulation of nutrition in utero in newborns.
It is to be understood that this invention is not limited to the particular methodology, protocols, and materials described, as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims.
Those skilled in the art will also recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are also encompassed by the appended claims.
Claims (9)
1. A method of non-invasively assessing the nutritional status of a neonate's uterus, the method comprising assessing the nutritional status of a neonate's uterus by fetal urine metabolome analysis.
2. The method of claim 1, wherein the method evaluates intrauterine nutritional status of the neonate by determining compound composition changes in the neonate's fetal feces by fetal feces metabonomic analysis.
3. The method of claim 2, wherein analyzing at least 10 compound components in neonatal stool is selected from the group consisting of: arginine, glutamic acid, histidine, hydroxyproline, leucine, leucine_isoleucine, methionine, ornithine, phenylalanine, proline, tryptophan, tyrosine, valine, phosphoserine, betaine, indole-3-aldehyde, indole-3-propionic acid, indoline, piperonic acid, pyrrolidine, 1, 7-dimethylxanthine, 1-methylguanine, caffeine, cortisone, leucyl-proline, N-acetylglutamic acid, phenylacetyl-L-glutamine, propionyl L-carnitine, uric acid, taurocholate, cholesterol glycyrrhizinate, butyrylccarnitine, carnitine c12_0, decanoylccarnitine, carnitine c16_0, hexanoylcarnitine, and pentanoylcarnitine.
4. A method according to claim 3, wherein the analysis of at least 10 compound components in neonatal infant stool is selected from any one of the following marker sets:
5. the system of claim 4, wherein analyzing at least 10 compound components in neonatal stool is selected from the group of markers consisting of:
6. use of a system for non-invasively assessing a neonatal intrauterine nutritional condition in a method according to any of claims 1-5.
7. The system of claim 6, which evaluates intrauterine nutritional status of a neonate by analyzing changes in at least 10 compound components in the neonate's fetal stool.
8. The system of claim 7, wherein analyzing at least 10 compound components in neonatal stool is selected from the group consisting of: arginine, glutamic acid, histidine, hydroxyproline, leucine, leucine_isoleucine, methionine, ornithine, phenylalanine, proline, tryptophan, tyrosine, valine, phosphoserine, betaine, indole-3-aldehyde, indole-3-propionic acid, indoline, piperonic acid, pyrrolidine, 1, 7-dimethylxanthine, 1-methylguanine, caffeine, cortisone, leucyl-proline, N-acetylglutamic acid, phenylacetyl-L-glutamine, propionyl L-carnitine, uric acid, taurocholate, cholesterol glycyrrhizinate, butyrylccarnitine, carnitine c12_0, decanoylccarnitine, carnitine c16_0, hexanoylcarnitine, and pentanoylcarnitine.
9. The system of claim 8, wherein analyzing at least 10 compound components in neonatal stool is selected from the group of markers consisting of:
wherein at least 10 compound components of neonatal stool are analyzed selected from the following marker sets:
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