Lupine Publishers | Comparative in Silico Analysis of Antigenic Proteins “Nucleocapsid Protein “of SARS-Cov-2 from Different Geographical Locations

 Lupine Publishers | LOJ Pharmacology & Clinical Research

Abstract

SARS-CoV-2 Nucleocapsid protein considered as a vaccine target. Viral nucleocapsid protein is a potential antiviral drug target, serving multiple critical functions during the viral life cycle. However, the structural information of SARS-CoV-2 in different geographical locations considered as an important factor in order to produce a global vaccine for the pandemic. The present investigation relies on the analysis of the similarities and differences of the SARS-CoV-2 Nucleocapsid protein from various geographical locations, considering certain protein parameters, as well as the study of the alignment of the protein sequence of amino acids.

Keywords: Nucleocapsid protein; SARS-CoV-2; geographical locations; bioinformatics

Introduction

SARS-CoV-2 is a single-stranded, positive-sense RNA virus with a 30 kb genome, one of the largest among RNA viruses [1]. The viral envelop of SARS-CoV-2, contains many proteins such as spike protein, and glycoprotein [2]. Another protein incorporated with the viral genome is nucleocapsid protein which is responsible for protection the virus from the host cell environment [3]. The SARSCoV- 2 spike protein is being used as the principal antigen target in vaccine progress. However, the multifaceted molecular details of viral entrance may lead to obstacles with the vaccine reaction [4]. The nucleocapsid (N) protein is a significant antigen for coronavirus, which contribute to RNA package and virus particle discharge [5]. After infection, the N protein enters the host cell along with the viral RNA to facilitate its replication and to process the assembly and release of the virus. SARS-CoV N comprises two distinct RNA-binding domains (N-terminal domain and C-terminal domain) connected by a poorly structured linkage region. Due to positive amino acids, SARS-CoV N-terminal domain and C-terminal domain have been documented to bind to the viral RNA genome [6]. Serological diagnosis has shown that the unique antibodies to the N protein in the serum of SARS patients have a higher sensitivity and longer persistence than those of other structural SARS-CoV proteins. In addition, anti-N antibodies have been observed at an early stage of infection with a high specificity. Thus, any information obtained from the study of this protein, whether in vivo or in vitro, will improve our understanding of COVID-19 and enable us to develop better biological agents for the treatment or diagnosis of diseases [7]. It is becoming clearer how important this protein is, to the multiple stages of the viral life cycle. This studie provide valuable and timely insights specific to the SARS-CoV-2 N protein, a vaccine target that has some distinct advantages over other possible SARSCoV- 2 antigens according to geographical location. Because of the conservation of the N-protein sequence, the increased awareness of its genetics and biochemistry regarding different geographical location, the N-protein SARS-CoV-2 should be considered as a global vaccine candidate for SARS-CoV-2. The present investigation relies on study the similarities and difference of Nucleocapsid protein “of SARS-CoV-2 from different geographical locations considering some protein parameters as well as study the alignment of amino acid sequence of protein.

Materials and Methods

Sequences, alignment, and construction of phylogenetic tree

Amino acids sequences for the Nucleocapsid protein of SARSCoV- 2 from different geographical locations were obtained from the National Center for Biotechnology Information database. The accession numbers of the corresponding database entries and isolation source are listed in Table 1. Multiple sequence alignment of Nucleocapsid protein of SARS-CoV-2 was performed in order to find the evolutionary relationships between sequences from different geographical location to identify shared patterns. Sequences were multiply aligned Jalview software version 2.8 [8]. Phylogenetic trees were constructed with neighbour-joining using MEGA [9]. A distance matrix was generated using the model building Jones-Taylor-Thornton [10]. Graphical way of representing and visualizing consensus data developed of amino acid multiple sequence alignment developed were displayed according to method described by Tom Schneider and Mike Stephens [11].

Computation of amino acid composition and molecular weight of protein sequences

Estimation of the amino acid composition and molecular weight was determined using Isoelectric Point Calculator (IPC), a web service and a standalone program for the accurate estimation of protein and peptide characteristic [12].

Solvent content of protein crystals

From the currently available sequence, of the solvent content of Nucleocapsid protein. The fraction of the crystal volume occupied by solvent was calculated according to Matthews [13].

Results

Table 2 is displayed the chemical composition of the Nucleocapsid protein “of SARS-CoV-2 from different geographic location. The preset data revealed that amino acid composition of the tested 13 sequences showed high similarity except for the protein sequence from Spain. Figure 1 represented the overall height of the stack indicates the amino acid sequence conservation at each position of protein, while the height of symbols within the stack indicates the relative frequency of each amino or nucleic acid at that position. In general, sequence logo of Nucleocapsid protein provides that almost the sequence from different location did not differ from each other and revealed high similarity. Regarding Phylogeny estimation for Nucleocapsid protein of SARS-CoV-2 from different geographical locations. Neighbour-joining tree constructed using Mega 10.1.8. The multiple sequence alignments are depicted in Figure 2. Constructed phylogenetic tree is depicted in Figure 3. The current results indicated that; Neighbour-joining tree of Nucleocapsid protein of SARS-CoV-2 displays the comparison of amino acid sequences of Nucleocapsid protein from different location. The results shows that the phylogenetic analysis rooted by two clusters (Figure 3). Cluster 1 represents France, Australia, China, India, South Africa, Morocco, United Kingdom and Spain. In the Cluster 1 Spain considered as the main root. Cluster 2 represents USA, Canada, Argentina, Saudi Arabia and Nigeria. The main root of the cluster 2 is Canada. Figure 4 shows Matthews coefficient , the current results revealed that the parameter value equal to 0.68 for all locations except for Spain its value reaches 0.85. Figure 5 shows the results for Solvent content of protein crystals were near 80 % for all geographical locations but has been observed that the value calculate for Spain was the lowest equal to 44%. As showed in Table 3 molecular weight of Nucleocapsid protein “of SARS-CoV-2 from different geographic location ranges from 36759.93 to 45696.7 Dalton, exhibited low variability in molecular weight.

The current study demonstrated that the Nucleocapsid protein analysis from different location shared a common primary amino acid composition which resulted in a uniform sequence alignment. Previously reports declared that alignments are a powerful way to compare related protein sequences. They can be used to capture various facts about the sequences aligned, such as communal evolutionary descent or shared structural function [14]. The obtain composition information of SARS-CoV-2 Nucleocapsid protein revealed high similarity in different geographic location with mild variability in amino acid concentration. The study compared aminoacid distributions between different locations. When looking at overall amino acid frequencies it could be recognized that byand- large, amino acid frequencies in all investigated sequences mirrored to each other except for the sequence obtained from Spain. The biggest differences arose in all amino acids except for Histidine, Isoleucine, Tryptophan and valine. According to Jackson et al. [15] there are several patterns of sequence variation that are consistently seen in natural proteins.
The first steps in a macromolecular structure determination of protein are to detect the count of molecules in the crystallographic asymmetric unit. The crystal volume per unit of protein molecular weight, known as Matthews coefficient. A substantial percentage of the protein crystals volume is employed by solvent ranged from 27% to 78%, with the most common value being about 43% [16]. The Matthews Coefficient and solvent content are calculated for Nucleocapsid protein “of SARS-CoV-2 from different geographic location which revealed almost the same results or all locations except for Spain. Water plays an important role in the structure of biomolecules and often influences protein function [17]. Water molecules not only affect protein folding, but also mediate biological processes such as enzymatic reactions and molecular recognition. Information about the fraction of water (solvent) plays a significant role in the X-ray structure determination process [18]. The present results revealed that solvent content for Nucleocapsid protein of SARS-CoV-2 from different geographical locations were almost similar except for Spain.

Conclusion

In the current study, the variation of Nucleocapsid protein of SARS-CoV-2 composition and its alignment were investigated for different location, it revealed high similarity for each other and showed a uniform sequence alignment with mild variability in amino acid concentration. The crystal volume per unit of protein molecular weight of Nucleocapsid protein “of SARS-CoV-2 revealed almost the same results or all locations except for Spain

 

 

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Lupine Publishers | Thyroid Function Variability in a Cohort of Healthy Pregnant Women: Effect of BMI, Smoking and Iodized Salt Consumption

 Lupine Publishers | LOJ Pharmacology & Clinical Research

Abstract

Introduction: It remains unclear to what extent intrinsic maternal characteristics, lifestyle or diet and dietary supplements contribute to explain the variability of maternal thyroid function. The aim of this study was to analyse the effect of age, parity, pregestational body mass index (BMI), beta human chorionic gonadotropin (β-hCG), thyroid autoantibodies, smoking habit and use of vitamins/supplements on maternal thyroid function variability throughout pregnancy.

Methods: A prospective, observational study was carried out including 339 healthy pregnant women from their first to their third trimester. Clinical and biological variables were registered at each stage. Univariate correlation and multiple linear regression analysis for thyroid parameters were performed in the three trimesters.

Results: Thyrotropin (TSH) in the first trimester (1T) was dependent on free thyroxine (FT4), maternal age, β-hCG and smoking habit. FT4 levels in the 1T were significantly lower in obese women (0.937±0.078ng/dl), compared with overweighed (0.981±0.14ng/dl) and normal weight women (0.989± 0.98ng/dl) (p=0.012). Multiple linear regression showed that FT4 in all trimesters is significantly dependent on pregestational BMI. Additionally, TSH and FT4 in the 1T were significantly related to TSH and FT4 levels in second and third trimesters, respectively. All studied factors influenced TSH with different degree all-over the pregnancy. Dietary supplements did not modify maternal thyroid function.

Conclusion: FT4 availability during the first half of the gestation can be hindered by maternal overweight/obesity. Beside age, obesity and smoking, TSH and FT4 in the 1T are very important regarding thyroid function variability in further stages of the pregnancy.

Introduction

Thyroid hormones are involved in many processes during intrauterine life such as somatic growth, metabolic regulation, and neurodevelopment [1]. Maternal thyroid function has also been related to obstetric outcomes in different studies [2,3]. The developing foetus is completely dependent on maternal supply of these hormones during the first half of the pregnancy [4]; therefore, an adequate maternal thyroid function becomes crucial to achieve a successful intrauterine development [5]. In order to identify maternal thyroid dysfunction at early stages of gestation, it is important to fully understand the physiological changes that happen to the thyroid gland in pregnant women as well as the potential factors that may modulate the concentration of thyroid hormones in maternal serum [6]. Once fecundation occurs, β-hCG increases rapidly in order to guarantee implantation and supporting embryonic growth [7]. The interpretation of thyroid parameters in pregnant women should take into account β-hCG action and thus gestational age and reference ranges need to be trimester-specific [8]. Beta-hCG and thyrotropin (TSH) relationship usually show an inverse correlation in a linear way [9]. However, more recently it has been demonstrated that the thyrotrophic effect of β-hCG can be modulated by individual factors such as maternal age [10], parity, body mass index (BMI) [11], thyroid autoimmunity [12] or even by the sex of the fetus [13]. Additionally, women with subclinical hypothyroidism show an abnormal response to thyroidal stimulation by β-hCG [14]. It is not well known what the relative importance of maternal personal conditions is such as weight, iodine-enriched multivitamin complexes intake [15,16], smoking habit and lifestyle [17] in thyroid function throughout the pregnancy. The aim of our research is to study the potential contribution of different factors in maternal thyroid function variability in pregnant women without maternal or fetal risk factors, as well as to identify profiles of low availability of maternal thyroxine (T4).

Material and Methods

Study subjects

The study included 339 healthy pregnant women assisted at a maternal primary health care clinic, the ASSIR La Riera (Badalona, Spain), recruited at first trimester (1T) of pregnancy (before 10 weeks of amenorrhea) and followed up to the third trimester (3T). Exclusion criteria were based on the presence of pregestational maternal and/or foetal disorders that might represent an obstetric or perinatal risk, as well as pregnant women with a previously known history of thyroid dysfunction, those who were taking thyroid hormone or antithyroid drugs before pregnancy or those recently exposed to iodinated antiseptics or radiologic contrasts. Pregestational body mass index was considered as quantitative variable (kg/m2) and also as qualitative variable (BMI category) with
i) Normal weight (BMI<25kg/m²)
ii) Overweight (BMI 25.0-29.9kg/m²) and
iii) Obesity (BMI ≥30.0kg/m²).
Smoking habit was recorded as smokers, non-smokers, and stopped smokers (women who had quitted it when they became pregnant or less than a year before).
All women provided blood samples at the three trimesters of gestation. All the samples were stored at -80 ºC until the analysis. Beta-HCG was performed in 1T blood analysis, as routine clinical practice. The study design and research aims were approved by the Ethics Committee of the Germans Trias i Pujol University Hospital (HUGTiP) and written consent was obtained from all the participants.

Laboratory procedures

Serum measurements TSH were performed on the automated Abbott Architect TSH Ref. 7K62 (Abbott Diagnostic Division, Longford, Ireland). Free T4 (FT4) was measured by the Abbott Architect FT4 Ref. 7K65 (Abbott Diagnostic Division, Longford, Ireland). Total β-hCG was measured by the automated Abbott Architecht Re. 7K78 (Abbott Diagnostic Division, Longford, Ireland). Serum anti-thyroid peroxidase antibodies (anti-TPOAbs) measurements were performed on the automated Abbott Architect Ref. 2K47 (Abbott Diagnostic Division, Longford, Ireland). Serum anti-thyroglobulin antibodies (anti-TgAbs) measurements were performed on the automated Abbott Architect Ref. 2K46 (Abbott Diagnostic Division, Longford, Ireland).

Statistical analysis

Normal distribution of quantitative variables was assessed by the Kolmogorov-Smirnov test. Variables were expressed as mean and standard deviation (SD) or as median and interquartile range (P25-75) as appropriate. Quantitative variables were shown as the mean ± SD and qualitative variables as percentages. The contrast hypothesis for two samples was evaluated with the Student’s t-test, and for more than two samples, with an analysis of variance (ANOVA) test. The chi-square test was applied in case of categorical variables. The correlation between variables was determined using the Spearman test, designing multiple linear regression models in those cases where it was desired to predict the variance adjusted for other variables, besides the main variable. In all cases, the rejection level for a null hypothesis was alpha below 0.05. All data were analysed using SPSS 20.0 (IBM SPSS Statistics).

Results

Clinical and demographic variables

All participants were classified as healthy pregnant women after a general medical examination. Table 1 shows the clinical variables of the participants. There were significant differences in maternal age according to parity (30.2±5.0 years for parity 0; 32.4±5.3 years for parity 1; 33.6±4.7 years for parity 2 and 33.0±5.0 years for parity above 2; p<0.001) and level of education (30.9±5.9 years in low level; 31.8±5.1 in middle level and 33.3±3.9 years in high level of education; p=0.016). There were significant differences in smoking habit during pregnancy according to the level of education, (36.2% of smokers in women with low level of education, 20.8% in middle level group and 6.5% in women with high level of education; p<0.001).

Maternal thyroid function

The maternal values of TSH and FT4 in the three trimesters of pregnancy are summarized in Tables 2 & 3 respectively. TSH range values were 1.54±1.06 mUI/L for 1T, 1.71±0.87 mUI/L for second trimester (2T) and 2.03±0.96 mUI/L for 3T and FT4 range values were 0.98±0.11ng/dL for 1T, 0.80±0.07ng/dL for 2T and 0.79±0.08ng/dL for 3T. When univariate analyses were performed, TSH concentrations in 1T and 2T were significantly higher in women up to 30 years of age, compared with those older than 30. Additionally, women with low parity showed significantly higher TSH concentrations in the three trimesters in comparison with women with two or more previous pregnancies. Regarding FT4, women of 30 years of age and below showed significantly higher FT4 levels in 2T and 3T (Table 3). Additionally, FT4 was statistically lower in overweighed and obese pregnant women in comparison with women of normal weight, but the concentrations did not vary in relation with parity. The concentrations of FT4 in 1T and 2T were significantly higher in those who consumed iodised salt compared with those who did not.

Thyroid autoimmunity

The frequency of pregnant women positive for anti-TPOAbs was 10.6%, 7.8% and 7% in the 1T, 2T and 3T respectively. The prevalence of anti-TgAbs was 12.2%, 10% and 6.2% at 1T, 2T and 3T respectively. The Spearman’s correlation coefficient between anti- TPOAbs and anti-TgAbs was 0.47; 0.45 and 0.42 in the 1T, 2T and 3T respectively (p<0.001 in all the cases). The TSH concentration at each trimester was significantly higher in those pregnant women who were anti-TPOAbs positive, but these differences were not significant in case of anti-TgAbs positive (Table 4). The FT4 levels in the 1T were lower in women with positive anti-TPOAbs. (Table 4). TSH and FT4 concentrations in the three trimesters of gestation according to thyroid immune status.

β-hCG in the first trimester

Beta-hCG levels were significantly lower in overweighed and obese pregnant women (152337.47±78237.95mIU/mL in normal weighed women; 139169.68±121041.64mIU/mL in overweighed and 102129.23±46361.49mIU/mL in obese women, respectively; p=0.005). No correlation was found between maternal age and β-hCG, but a negative correlation between pregestational BMI and β-hCG was found (r=-0.205; p=0.001). When the sample was split out according to BMI categories, there was a strong negative correlation between maternal age and β-hCG in overweighed women (r=-0.438; p<0.001).
Pregnant women who had smoked before gestation showed lower β-hCG levels compared with those who did not smoke (122112.47±110104.65mIU/mL vs. 150427.48±74808.07mIU/ mL respectively; p=0.014) and was greater in case of smoking during the pregnancy (100365.15±53551.61mIU/mL vs. 153156.03±93571.64mIU/mL respectively, p<0.001).

Maternal age

Correlations between thyroid function and maternal age, according to different clinical conditions are shown in Table 5. TSH concentrations in 1T and 2T were significantly higher in women up to 30 years than in those above 30 (1.80±1.30mIU/ dl vs, 1.37±0.82mIU/dl in 1T; p=0.001 and 1.86±0.97mIU/dl vs. 1.61±0.79mIU/dl in 2T; p=0.036). Maternal age showed a negative correlation with TSH concentrations in 1T and 2T. FT4 concentrations were significantly higher in young women (up to 30) when compared to those older than 30 years old in 2T and 3T (0.99±0.10ng/dl vs. 0.97±0.11ng/dl for 1T, p=0.159; 0.82±0.07ng/ dl vs. 0.79±0.72ng/dl in 2T, p=0.002; and 0.80±0.08ng/dl vs. 0.78±0.08ng/dl in 3T, p=0.012).

Pregestational body mass index (BMI)

BMI showed a negative correlation with FT4 in the three trimesters of gestation (Table 5). This negative correlation was significant in women above 30 years old and in women who did not smoke neither during nor before pregnancy. FT4 concentrations were significantly higher in young women (up to 30) when compared to those older than 30 years old, and this difference increased particularly in the first trimester in the case of obese women (BMI≥30kg/m2) (0.97±0.07ng/dl vs 0.90±0.07ng/dl for the first trimester, p=0.003; 0.80±0.05 ng/dl vs 0.75±0.06ng/dl in the second trimester, p=0.011; and 0.79±0.07ng/dl vs 0.73± 0.07ng/dl in the third trimester, p=0.035). Besides, the negative correlation between maternal age and FT4 became stronger in obese women (Table 4).

Multiple linear regression of thyroid function parameters

We searched for maternal variables that may explain the variability of TSH and FT4 within normal reference ranges in each trimester of pregnancy, designing multiple linear regression models for each parameter (TSH, FT4) in the three trimesters. The best model for TSH at 1T included FT4 at 1T (p<0.001), parity (p<0.001), anti-TPOAbs (p<0.001), gestational age (p=0.008), β-hCG (p=0.014), and current smoking habit (p=0.020), with R2=0.240; p<0.001. For TSH in 2T, the predictive variables were TSH at 1T (p<0.001), β-hCG (p<0.001), gestational age (p=0.001), and parity (p= 0.06), with R2=0.621; p< 0.001. And for TSH in 3T, the predictive variables were TSH at 2T (p<0.001) and TSH at 1T (p=0.003) and parity (p=0.049), with R2=0.685; p<0.001. For FT4, the best model at 1T included TSH at 1T (p<0.001), β-hCG (p=0.005), pregestational BMI (p=0.005) and maternal age (p=0.027) with R2=0.151; p<0.001. For FT4 in 2T, the predictive variables were FT4 in 1T (p<0.001), TSH in 2T (p=0.005), pregestational BMI (p=0.007) and maternal age (p=0.024), with R2=0.356; p<0.001. And for FT4 in 3T, the predictive variables were FT4 at 2T (p<0.001) and FT4 at 1T trimester (p=0.001) and pregestational BMI (p=0.021) with R2=0.550; p<0.001.

Discussion

Our study shows the complex interplay between maternal age, pregestational BMI, parity and smoking habit as modulators of thyroid function variability in healthy pregnant women. In our cohort, univariate analyses identified differences in 1T FT4 levels according to BMI, smoking habit and β-hCG. We also found that maternal age negatively correlated with TSH concentrations and this relationship was influenced by thyroid autoimmunity, smoking habit and the intake of iodised salt. The variations of TSH concentrations in 1T, and to a lesser extent in the 2T and 3T, were also related to β-hCG. In addition, women older than 30 showed lower concentrations of TSH in 1T and 2T compared to those under 30, thus accounting for a negative correlation between TSH and maternal age. Moreover, FT4 was significantly lower in women of 30 and above, with a statistically significant negative correlation. Pregnant women older than 30 show lower TSH concentrations for a given FT4 value, which may indicate a less effective direct stimulatory action of β-hCG, as if some decreased sensitivity of maternal thyroid gland to β-hCG was ongoing with age. However, TSH concentrations were also significantly lower in women with high parity in our sample and the negative correlation between TSH and maternal age did not persist when the sample was stratified according to parity, rendering maternal age as a likely confounder. Parity >2, but not maternal age, has been associated with lower thyroidal response to β-hCG in other studies [11,14]. Some studies have thoroughly analysed those variables with substantial effect on serum β-hCG levels during pregnancy, such as gestational age at assessment, maternal age, weight and cigarette smoking [18] and their relation to thyroid function tests. We found a strong negative effect of BMI and smoking habit on β-hCG concentration in 1T, in consonance with other authors [11]. Since obesity has been associated with a lower thyroidal response to β-hCG stimulation [11], the low levels of FT4 that we found in women with high BMI or who had smoked before the pregnancy might be interpreted as a poor β-hCG-mediated stimulation [19].

TSH seems to be positively related to the degree of obesity [20], meanwhile fat accumulation has been associated with lower FT4[21]. In our study, the negative effect of BMI on FT4 levels is steadily present in the three trimesters. It has been suggested that women with abdominal obesity show a high conversion of T4 to T3 due to increased deiodinase activity, as a compensatory mechanism to improve energy expenditure [22]. Additionally, the presence of anti-TPOAbs was associated with higher levels of TSH in the three trimesters of gestation and a lower level of FT4 in the 1T, while anti-TgAbs did not affect TSH nor FT4 concentrations. These results reinforce the importance of anti-TPOAbs-positive as a marker of impaired thyroid response to β-hCG stimulation [11,6].

There is solid evidence that current smokers have significantly lower serum TSH levels than non-smokers, probably in relation with leptin levels [23]. This effect is dose-dependent, disappears slowly after smoking cessation and is not associated with iodine intake [24]. Our results show a difference in mean TSH levels in 2T greater in current smokers than in stopped smokers. However, this difference did not reach statistical significance for TSH in 1T nor in 3T, which might be due to the interaction with potential confounders such as BMI or parity [25]. Finally, pregnant women who consumed iodised salt showed higher levels of FT4 in 1T and 2T and did not show the negative effect of BMI or maternal age on FT4, particularly in the 1T of pregnancy. Long-term iodized salt consumption before pregnancy significantly improves maternal thyroid economy and reduces the risk of maternal thyroid insufficiency during gestation, probably because of optimized intrathyroidal iodine stores [26]. A prior study of our group found that iodised salt (consumed at least one year before becoming pregnant) may be as effective as other forms of iodoprophylaxis in terms of infant neurocognitive development [27-29]. Our study was focused on healthy pregnant women, and even the obese or smoker participants had no other comorbidities to be considered at risk to develop any obstetric or perinatal complication. Maternal thyroid function in the first trimester can be useful for predicting thyroid function throughout the pregnancy [30], together with BMI and smoking habit as modulating factors. Therefore, preconceptional identification and modification of preventable maternal conditions that could potentially undermine thyroid function -such as those latter BMI, smoking-, especially at early stages of pregnancy is important.

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