You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Background

The purpose of our study was to investigate the potential contribution of germline mutations in NOTCH1, GATA5 and TGFBR1 and TGFBR2 genes in a cohort of Italian patients with familial Bicuspid Aortic Valve (BAV).

Methods

All the coding exons including adjacent intronic as well as 5′ and 3′ untranslated (UTR) sequences of NOTCH1, GATA5, TGFBR1 and TGFBR2 genes were screened by direct gene sequencing in 11 index patients (8 males; age = 42 ± 19 years) with familial BAV defined as two or more affected members.

Results

Two novel mutations, a missense and a nonsense mutation (Exon 5, p.P284L; Exon 26, p.Y1619X), were found in the NOTCH1 gene in two unrelated families. The mutations segregated with the disease in these families, and they were not found on 200 unrelated chromosomes from ethnically matched controls. No pathogenetic mutation was identified in GATA5, TGFBR1 and TGFBR2 genes.

Conclusions

Two novel NOTCH1 mutations were identified in two Italian families with BAV, highlighting the role of a NOTCH1 signaling pathway in BAV and its aortic complications. These findings are of relevance for genetic counseling and clinical care of families presenting with BAV. Future studies are needed in order to unravel the still largely unknown genetics of BAV.

Bicuspid aortic valve (BAV) is the most common congenital cardiac malformation, with a prevalence estimated between 0.5% and 2% [1, 2]. Nearly all patients with BAV develop serious aortic complications, including significant valve regurgitation, severe aortic stenosis, aortic aneurysm and dissection [3, 4]. Therefore, the burden of BAV disease is more significant than any other congenital cardiac abnormalities [4]. Clinical studies have reported high heritability in families of patients with BAV [5–7], suggesting a Mendelian pattern of inheritance and the need for echocardiography screening in first-degree relatives of patients with BAV [8].

A family-based linkage analysis has identified genomic regions on chromosomes 18q, 5q and 13q responsible for BAV and associated cardiac malformations, but the precise genes within these regions were not defined [9].

To date, only mutations in the NOTCH1 gene, a signaling transmembrane receptor (2,556 amino acids) involved in multiple cell aspects of vascular development, have been associated with dominantly inherited BAV in a small number of families [10].

However, the rate of NOTCH1 gene mutations in familial forms of BAV and their association with different clinical complications remains incomplete [11, 12]. Interestingly, McBride et al. demonstrated that mutations in the gene NOTCH1 are found in cases of left ventricular outflow tract (LVOT) malformations including aortic valve stenosis, coarctation of the aorta and hypoplastic left heart syndrome [13]. In particular, a much higher rate of bicuspid aortic valve (BAV) has been found in families with LVOT malformations compared to the general population, suggesting that BAV may be a forme frusta of the more serious LVOT malformations [13].

Recently, a missense mutation in transforming growth factor-beta receptor 2 gene (TGFBR2) was found in a patient with BAV and aortic aneurysm [14], but earlier studies found no mutation in either TGFBR1 or TGFBR2 in patients with familial and sporadic BAV disease [15, 16].

Finally, GATA5 was reported to have an essential role in cardiac morphogenesis and in aortic valve development [17, 18], and targeted deletion of this gene in mice leads to partially penetrant BAV [19]. Accordingly, a very recent study investigated the relationship between GATA5 gene variations and BAV disease identifying rare non-synonymous variations in the functionally important GATA5 transcriptional activation domain [20]. Conversely from NOTCH1 gene, the involvement of GATA5 has not been described in other LVOT malformations.

However, further studies are needed to assess the involvement of NOTCH1, GATA5 and TGFBR1 and TGFBR2 mutations in patients with BAV and its associated aortic complications.

The purpose of our study was to investigate the potential contribution of germline mutations in NOTCH1, GATA5, TGFBR1 and TGFBR2 genes in a cohort of Italian patients with familial BAV.

Study population

A cohort of 11 unrelated Italian patients (8 males; age = 42 ± 19 years) with familial BAV were recruited for this study. Patients with sporadic BAV and/or with coexisting syndromes, such as Marfan syndrome, Turner syndrome and Di George syndrome or unknown syndromes were excluded from the study. Clinical evaluation consisted of medical history, physical examination, 2-dimensional echocardiography and a complete Magnetic Resonance Imaging (MRI) study. A detailed family history was obtained for each proband. Familial BAV was defined if two or more affected relatives had proven BAV diagnosed by echocardiography or cardiac magnetic resonance. Figure 1 shows the pedigree of BAV patients. Patient’s phenotype is reported in Table 1. Two of the probands (18%) had BAV with unknown morphology. Of the 9 probands with known BAV morphology, 7 (63%) had fusion of the right/left coronary commissure whereas 2 (18%) had a valve with anterior-posterior sinus without raphe.We also included a control cohort comprised of 100 ethnically unrelated individuals (60 male; age = 35 ± 16 years) without BAV as shown by echocardiography. This study was conducted with informed consent of every participant subject and was approved by the local Ethical Research Committee.

Mutational sequencing analysis

A mutational screening was performed by direct gene sequencing of all coding exons including adjacent intronic as well as 5′ and 3′ untranslated sequences of NOTCH1, GATA5 and TGFBR1, TGFBR2 genes. Genomic DNA was extracted from peripheral blood cells using the QIAGEN BioRobot® EZ1 System. Exons and the flanking intronic sequences were amplified by polymerase chain reaction (PCR) using specific primers as described previously for NOTCH1 and TGFBR1 and TGFBR2[11, 21] and using Primer3 (v. 0.4.0) software (http://frodo.wi.mit.edu/primer3/) based on the cDNA sequences available in GenBank for GATA5 screening.

Approximately, PCR amplifications were performed with a volume of 50 microl mixture containing 1.5 mM MgCl2, 10 mMdNTP, 50 ng genomic DNA, 20 micromol of each primer and 2.5 U Taq DNA polymerase. PCR product was used for PCR sequencing reaction with the CEQ DTCS Quick Start Kit. The sequencing reaction products were purified by precipitation with ethanol, re-suspended in a sample loading solution and analyzed with a CEQ 8800 capillary sequence (Beckman Coulter, Germany) according to the manufacturer’s protocol. Resulting sequences were analyzed using CEQ 8800 software packages and aligned against a reference sequence obtained from GenBank.

For prediction of the functional consequences of mutations on protein sequence, we used software programs available on the internet, namely PolyPhen-2(http://genetics.bwh.harvard.edu/pph/) SIFT (http://sift.jcvi.org/) and Mutation Taster (http://www.mutationtaster.org/). PolyPhen-2 predicts the effect of an amino acid substitution on the structure and function of a protein, using sequence homology. The PolyPhen-2 score represents the probability that a substitution is damaging, so values nearer 1 are more confidently predicted to be deleterious (note that this is the opposite of SIFT).

SIFT uses sequence homology to predict whether an amino acid substitution will affect the protein function. SIFT score <0.05 indicates the amino acid substitution is damaging while scores ≥ 0.05 are predicted to be tolerant. Mutation Taster is a free, web-based application for rapid evaluation of the disease-causing potential of DNA sequence alterations. Mutation Taster integrates information from different biomedical databases and uses established analysis tools. Analyses comprise evolutionary conservation, splice-site changes, loss of protein features and changes that might affect the amount of mRNA.

Detected mutations in probands were confirmed by sequencing in the opposite direction from another two independent PCR products.

The presence of a novel mutation was assessed in 200 ethnically reference alleles derived from volunteers by PCR-based restriction fragment length polymorphism (PCR-RFLP) or by direct gene sequencing.

Clinical and demographic characteristics of each patient are summarized in Table 1. The group comprised 73% males, and coarctation of the aorta (CoA) as coexistent cardiovascular anomalies occurred in 18% of the cohort.

Genetic analysis of NOTCH1gene

A mutational screening was performed by direct gene sequencing of all 34 coding exons including adjacent intronic as well as 5′ and 3′ untranslated sequences of NOTCH1 gene. Seventeen separate NOTCH1 sequence variants listed previously in public dbSNPs or in the literature were found in our patients, while five variants were novel. These included one synonymous variant (Exon 21, p.G1166G), two intronic (IVS11 + 63; IVS32-41) and two novel mutations (Exon 5,p.P284L; Exon 26, p.Y1619X). Details of the specific sequence variants and number of positive patients are summarized in Table 2.

Table 2

Genetic variants within the humanNOTCH1gene identified in familial BAV patients

Genomic position (NG_007458.1)

Position NOTCH1gene

cDNA position (NM_017617.3)

Protein position

Number of patients

rsSNP number (dbSNP)

g.26979 T > C

Exon 3

c.312 T > C

p.N104N

6

rs4489420

g.31330 C > T

Exon 5

c.851C > T

p.P284L

1

New

g.31331 G > A

Exon 5

c.852 G > A

p.P284P

4

rs2229975

g.34650 T > C

IVS 9-43

3

rs4880099

g.33525A > G

IVS 9 + 10

2

rs11145767

g.33601 C > A/T

IVS 9 + 86

2

rs113341997

g.33617C > T

IVS 9 + 102

1

rs10781498

g.33620C > T

IVS 9 + 105

1

rs11574887

g.34772C > T

Exon 10

c.1635 C > T

p.D545D

1

rs11574889

g.35062A > G

IVS 11-9

3

rs3124603

g.35367 G > A

IVS 11 + 63

1

New

g.35591C > T

IVS 12 + 94

1

rs62579232

g.36347G > A

IVS 13 + 70

2

rs3812609

g.37307 T > C

Exon 14

c.2265 T > C

p.N755N

4

rs2229971

g.39978G > A

IVS 16-4

3

rs3125001

g.40058C > T

Exon 17

c.2664 C > T

p.H888H

1

rs61751548

g.40085C > T

Exon 17

c.2691 C > T

p.A897A

1

rs11574895

g.42820 C > T

Exon 21

c.3498 C > T

p.G1166G

1

New

g.45953 C > G

Exon 26

c.4856 C > G

p.Y1619X

1

New

g.47532C > T

Exon 27

c.5094 C > T

p.D1698D

3

rs10521

g.48831 T > C

IVS 30-43

2

rs3124594

g.49928C > T

IVS 30-12

1

rs11574908

g.51487 C > T

IVS 32-41

1

New

The novel C > T heterozygous substitution in exon 5, resulting in the substitution of a Proline with a Leucine (P284L) at position 284, was found in the sequencing electropherograms of a 40-year-old male patient (Proband A; Figure 2). The identified missense mutation was absent in 200 ethnically reference alleles derived from volunteers. PolyPhen-2 analysis predicted the p.P284L mutation as probably damaging with a score of 0.993. This result was confirmed by SIFT (score: 0.04).

The missense mutation (p.P284L) identified in our proband, tested by Mutation Taster, induced the loss of an important residue with putative significance for calcium binding to epidermal growth factor (EGF)-like 7 domain.

The proband, at the age of 39 years, presented severe calcified aortic valve and normal diameter of ascending aorta (39 mm). One year later, the patient underwent elective aortic valve and ascending aorta repair (48 mm) replacement. Family history and echocardiography analysis revealed the presence of BAV in his 7-year-old son. Genetic screening of the proband’s child confirmed the presence of p.P284L mutation in exon 5 of NOTCH1 gene.

Moreover, we detected a novel heterozygous C-to-G transition, in exon 26, of nucleotide 4856 leading to a premature stop codon at position 1619 (p.Y1619X) instead of tyrosine in the sequencing electropherograms of a 38-year-old male patient (Proband I; Figure 2). The identified mutation was absent in 200 ethnically reference alleles derived from volunteers. At age 6 months the proband had surgery to correct the CoA, while at age 25 years he underwent elective aortic valve replacement. Family history and echocardiography analysis revealed the presence of BAV in his 5-year-old daughter. Genetic screening of the proband’s child confirmed the presence of p.Y1619X mutation in exon 26 of NOTCH1 gene.

Genetic analysis of GATA5gene

Sequencing of all 6 exons and splice signal sequences of GATA5 in our cohort revealed five synonymous, one non-synonymous and three intronic variants. Details of the specific sequence variants and number of positive patients are summarized in Table 3. We identified one novel intronic variant (C > T; IVS 5–15) in one patient, but it was also present in our control group.

Table 3

Genetic variants within the humanGATA5gene identified in familial BAV patients

Position GATA5gene

HGVS names

Protein position

Number of patients

rsSNP number (dbSNP)

Exon1

c.199 A > C

p.Thr67Pro

5

rs6142775

Exon 2

c.609 C > T

p.Asp203Asp

7

rs41305803

Exon 2

c.678 C > T

p.Leu226Leu

1

rs139428354

IVS 4-14

c.826-14 G > A

1

rs140408446

Exon 4

c.852 G > A

p.Lys284Lys

8

rs6587239

IVS 5-16

c.914-16 C > T

1

New

Exon 5

c.981 G > C

p.Ser327Ser

7

rs6061243

IVS 6-78

c.1039-78 C > G

5

rs113912772

Exon6

c.1128 A > G

p.Pro376Pro

11

rs6061550

NM_080473.4.

Genetic analysis of TGFBR1 and TGFBR2genes

TGFBR1 and TGFBR2 genes were analyzed by direct gene sequencing of all coding exons including adjacent intronic as well as 5′ and 3′ untranslated sequences. No pathogenetic mutation was identified; we found only known common genetic polymorphisms. Details of the specific sequence variants in TGFBR1 and TGFBR2 genes are summarized in Table 4.

Table 4

Genetic variants within the humanTGFBR1andTGFBR2genes identified in familial BAV patients

In our cohort of 11 familial patients with BAV two novel mutations were found in the NOTCH1 gene in two unrelated families. The mutations segregated with the disease in these families, and they were not found in 200 chromosomes from ethnically matched controls.

On the contrary, analysis of GATA5, TGFBR1 and TGFBR2 genes identified only known genetic variants. Our findings confirm the important role of NOTCH1 as conserved intracellular regulator in the pathogenesis of congenital valve disease and in its complications. Indeed, the finding of two independent de novo mutations in this gene in a small number of affected subjects is compelling evidence of disease causation.

NOTCH1 encodes for a transmembrane protein that activates a signaling pathway with a major role in cardiac embryogenesis, including aortic and pulmonary valve development as well as the development and maintenance of the aorta and other great vessels [22]. Murine model studies indicate that Notch signaling promotes the epithelial-to mesenchyme transition that gives rise to the primordial cardiac valve, which later is sculpted into mature valves [23]. Moreover, a recent study showed that heterozygous Notch1-null (Notch1(+/)(−) mice had greater than fivefold more aortic valve calcification than age- and sex-matched wild type littermates [24].

Indeed, in vitro studies showed that Notch1 signaling repressed valvular Bmp2 expression, and de-repression of Bmp2 was involved in calcification induced by Notch1 inhibition. Thus, Notch1 signaling appears to prevent aortic valve calcification in part by repressing Bmp2 expression within the valve [24].

Defective NOTCH signaling might also contribute to aortic valve calcification by repressing the activity of the hairy-related family of transcription factors (Hrt), and thereby also repressing the Runx2/Cbfa1 pathway, a central transcriptional regulator of osteoblast cell fate [24, 25].

In addition, there is increasing evidence of interaction between the Notch1 pathway and transforming growth factor beta (TGFβ) [26]. This is particularly important in light of evidence showing that TGFβ plays an important role in vascular remodeling and alterations in TGFβ signaling activity might enhance matrix degradation [27, 28].

In 2005, the discovery of a nonsense (p.R1108X) and a frameshift mutation (p.H1505del) in two families represented the first demonstration of NOTCH1 germline mutations as a cause of BAV and severe valve calcification in non-syndromic autosomal-dominant human pedigrees [10].

Subsequently, two novel mutations (p.T596M and p.P1797H) located in highly conserved regions of the protein were identified in patients with sporadic BAV, providing evidence that mutations in NOTCH1 do not only play a role in familial BAV, but can also be observed in approximately 4% of sporadic cases [11]. Furthermore, a targeted mutational analysis of NOTCH1 demonstrated the presence of two novel mutations (p.A1343V and p.P1390T) among patients with BAV with sporadic and concomitant thoracic aortic aneurysms, providing evidence that mutations in NOTCH1 may also confer susceptibility to aortic aneurysm formation [12].

In a very recent paper, Kent et al. suggested a NOTCH1-dependent mechanism that produces stenotic, insufficient and/or calcified aortic valves with a rare aneurysm, and a NOTCH1-independent mechanism that produces highly penetrant ascending aortic aneurysm (AscAA) in the presence of a non-calcified and often normally functioning BAV [29]. Our data appear to agree with and fit well into the proposal made in this article by Kent et al., since we found two novel mutations in these patients with valve malformation, calcification, and dysfunction as predominant phenotype caused by altered NOTCH1 signaling.

Conversely, we did not identify any pathogenetic mutations in GATA5. The non-synonymous variant Thr67Pro located in the transcriptional activator domain encoded by exon 1, detected with a frequency of 45% in our population, is a common genetic polymorphism previously described [20, 30].

Regarding to TGFBR1 and TGFBR2 genes, in contrast with a recent paper showing a missense mutation in TGFBR2 gene in a patient with BAV and aortic aneurysm [14], our findings support the studies suggesting that mutations in these genes are rarely identified in patients with familial BAV [15, 16].

Although the small number of patients (11) might be an important study limitation, we have identified two novel NOTCH1 mutations in two Italian families with BAV. Our findings confirm the critical role of NOCTH1 in the development of aortic valve and aortic complications. Genetic screening for NOTCH1 mutation may be considered for diagnosis and family screening as part of the standard management of patients with family history of BAV. However, the etiology of this common valve disease is largely unknown. Future studies based on new genetic screening strategies, the so-called next-generation sequencing, are needed in order to unravel the still largely unknown genetics of BAV.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

IF participated in the design of the study, carried out the molecular genetic studies, performed the statistical analysis and drafted the manuscript. MGA conceived the study, participated in its design and coordination and critically revised the manuscript. PP carried out the molecular genetic studies. MM, AAL and PF participated in design of the study and in the enrollment of the patients. NB and CV performed the genetic analysis and critically revised the manuscript. All authors read and approved the final manuscript.

Pre-publication history

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.