Faris Hrvat

@auxologico.it

IRCCS Istituto Auxologico Italiano

EDUCATION

Bachelor Degree in Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, International Burch University
Master Degree in Genetics and Bioengineering, Faculty of Engineering and Natural Sciences, International Burch University
Master Degree in Medical Biotechnology and Molecular Medicine, Faculty of Medicine, University of Milan

RESEARCH INTERESTS

Mainly interested in the field of research on endocrinology, use of artificial intelligence in medicine, neural networks, neurosciences, bioinformatics.

Scopus Publications

106

Scholar Citations

Scholar h-index

Scholar i10-index

Scopus Publications

Heart Disease Prediction Using Logistic Regression Machine Learning Model
Faris Hrvat, Lemana Spahić, and Amina Aleta
Springer Nature Switzerland

Genetic architecture of self-limited delayed puberty and congenital hypogonadotropic hypogonadism
Valeria Vezzoli, Faris Hrvat, Giovanni Goggi, Silvia Federici, Biagio Cangiano, Richard Quinton, Luca Persani, and Marco Bonomi
Frontiers Media SA
Distinguishing between self limited delayed puberty (SLDP) and congenital hypogonadotropic hypogonadism (CHH) may be tricky as they share clinical and biochemical characteristics. and appear to lie within the same clinical spectrum. However, one is classically transient (SDLP) while the second is typically a lifetime condition (CHH). The natural history and long-term outcomes of these two conditions differ significantly and thus command distinctive approaches and management. Because the first presentation of SDLP and CHH is very similar (delayed puberty with low LH and FSH and low sex hormones), the scientific community is scrambling to identify diagnostic tests that can allow a correct differential diagnosis among these two conditions, without having to rely on the presence or absence of phenotypic red flags for CHH that clinicians anyway seem to find hard to process. Despite the heterogeneity of genetic defects so far reported in DP, genetic analysis through next-generation sequencing technology (NGS) had the potential to contribute to the differential diagnostic process between SLDP and CHH. In this review we will provide an up-to-date overview of the genetic architecture of these two conditions and debate the benefits and the bias of performing genetic analysis seeking to effectively differentiate between these two conditions.

Genetic and phenotypic differences between sexes in congenital hypogonadotropic hypogonadism (CHH): Large cohort analysis from a single tertiary centre
Silvia Federici, Biagio Cangiano, Giovanni Goggi, Dario Messetti, Elisabetta Veronica Munari, Myriam Amer, Luca Giovanelli, Faris Hrvat, Valeria Vezzoli, Luca Persani,et al.
Frontiers Media SA
BackgroundCongenital hypogonadotropic hypogonadism (CHH) is a condition with a strong genetic background, caused by a deficient production, secretion, or action of gonadotropin-releasing hormone (GnRH). Published data on CHH cohorts indicate a male predominance, although this is not supported by our current understandings.AimsIn order to unravel the possible causes or contributors to such epidemiological sex difference, the aim of our study is to investigate differences in genetic background and clinical presentation between males and females in a large cohort of CHH patients.Materials and methodsWe enrolled 338 CHH patients with absent or arrested pubertal development, referred to our Center from 01/2016. Data collection included clinical assessment at diagnosis and genetic analysis performed by next generation sequencing (NGS), employing a custom panel of 28 candidate genes.ResultsAmong 338 patients 94 were female (F) and 244 male (M), with a ratio of 1:2.6. We found that 36.09% (122/338) of patients harbored potentially pathogenic rare genetic variants (RVs) with no significant differences between sexes; on the other hand, a significantly higher frequency of oligogenicity was observed in females (F 9,57% 9/94 vs M 3,69% 9/244, P = 0.034). The prevalence of non-reproductive phenotypic features was significantly higher (P = 0.01) in males (53/228, 23.2%) than in females (10/93, 10.8%): in particular, kidney abnormalities affected only male patients and midline defects had a significantly higher prevalence in males (P = 0.010). Finally, BMI SDS was -0.04 ± 1.09 in females and 0.69 ± 1.51 in males, with a statistically significant difference between groups (P = &lt;0.001).ConclusionOur data confirm the male predominance in CHH and identify some differences with regard to the clinical presentation between males and females that could indicate a variable expression of genetic rare variants and a dimorphic modulation of phenotype according to metabolic/behavioral factors, which will need to be substantiated and investigated by further studies.

Comparison of Biofilm Category Determination Using TCP Method Depending on Signal Molecule Adherence
Faris Hrvat, Osman Hasanić, Amina Aleta, Amel Spahić, Amra Džuho, and Mirsada Hukić
Springer International Publishing

Artificial Intelligence in Nanotechnology: Recent Trends, Challenges and Future Perspectives
Faris Hrvat, Amina Aleta, Amra Džuho, Osman Hasanić, and Lemana Spahić Bećirović
Springer International Publishing

First Report on Public Opinion Regarding COVID-19 Vaccination in Bosnia and Herzegovina
Faris Hrvat, Amina Aleta, Amra Džuho, Osman Hasanić, and Lemana Spahić Bećirović
Springer International Publishing

Detection of Acute Inflammation of Urinary Bladder and Acute Nephritis of Renal Pelvis Origin Using Artificial Neural Network
Amina Aleta, Amra Džuho, and Faris Hrvat
Springer International Publishing

ISO/IEC 15189 Implementation in Microbiology Laboratory - General Concepts
Faris Hrvat, Selma Cifric, Amina Aleta, Amra Dzuho, Leja Gurbeta Pokvic, and Almir Badnjevic
IEEE
Microbiology laboratory is a type of medical laboratory and should be safe and efficient environment. Even it is not a mandatory for the accreditation in most of the countries, ISO/IEC 15189 remains the most common reference for quality of work in medical laboratories. It is mostly based on good laboratory practices and is oriented to support accurate clinical decisions. ISO/IEC 15189 has potential to become very effective instrument for development and improvement of medical laboratories. Results from laboratory should guide the majority of clinical decisions and help in providing adequate patient care. This article provides a simple approach to meet the minimum requirements set. To achieve intended goal and strictly follow the requirements proposed in the standard, the trained and well-motivated laboratory staff is necessary to implement the system. The objective of this article is for it to be used as a guideline for evaluation and implementation of the ISO 15189.

Artificial Neural Networks for Prediction of Medical Device Performance based on Conformity Assessment Data: Infusion and perfusor pumps case study
Faris Hrvat, Lemana Spahic, Lejla Gurbeta Pokvic, and Almir Badnjevic
IEEE
This paper presents the results of development of Artificial Neural Networks (ANNs) for prediction of medical device performance based on conformity assessment data. Conformity assessment data of medical devices was obtained from periodical inspections conducted by ISO 17020 accredited laboratory during 2015–2019 period. For the development of ANNs, 1738 samples of conformity assessment of infusion and perfusor pumps was used. Out of the overall number of samples, 1391 (80%) of them were used during system development and 346 (20%) samples were used for subsequent validation of system performance. During system development, the impact on overall system accuracy of different number of neurons in hidden layer and the activation functions was tested. Also, two neural network architectures were tested: feedforward and feedback. The results show that feedforward neural network architecture with 10 neurons in single hidden layer has the best performance. The overall accuracy of that neural network is 98.06% for performance prediction of perfusor pumps and 98.83% for performance prediction of infusion pumps. The recurrent neural network resulted in accuracy of 98.41% for both infusion pumps and perfusor pumps. The results show that conformity assessment data obtained through yearly inspections of medical devices can successfully be used for prediction of performance of single medical device. This is very important in increasing the safety and accuracy of diagnosis and treatment of patients.

RECENT SCHOLAR PUBLICATIONS

Heart Disease Prediction Using Logistic Regression Machine Learning Model
F Hrvat, L Spahić, A Aleta
Mediterranean Conference on Medical and Biological Engineering and Computing 2023

Genetic architecture of self-limited delayed puberty and congenital hypogonadotropic hypogonadism
V Vezzoli, F Hrvat, G Goggi, S Federici, B Cangiano, R Quinton, L Persani, ...
Frontiers in Endocrinology 13, 1069741 2023

Genetic and phenotypic differences between sexes in congenital hypogonadotropic hypogonadism (CHH): Large cohort analysis from a single tertiary centre
S Federici, B Cangiano, G Goggi, D Messetti, EV Munari, M Amer, ...
Frontiers in Endocrinology 13, 965074 2022

First Report on Public Opinion Regarding COVID-19 Vaccination in Bosnia and Herzegovina
F Hrvat, A Aleta, A Džuho, O Hasanić, LS Bećirović
International Conference on Medical and Biological Engineering, 907-920 2021

Comparison of Biofilm Category Determination Using TCP Method Depending on Signal Molecule Adherence
F Hrvat, O Hasanić, A Aleta, A Spahić, A Džuho, M Hukić
International Conference on Medical and Biological Engineering, 575-581 2021

Artificial intelligence in nanotechnology: recent trends, challenges and future perspectives
F Hrvat, A Aleta, A Džuho, O Hasanić, L Spahić Bećirović
International Conference on Medical and Biological Engineering, 690-702 2021

Detection of acute inflammation of urinary bladder and acute nephritis of renal pelvis origin using artificial neural network
A Aleta, A Džuho, F Hrvat
8th European Medical and Biological Engineering Conference: Proceedings of 2021

Artificial Neural Networks for prediction of medical device performance based on conformity assessment data: Infusion and perfusor pumps case study
F Hrvat, L Spahić, LG Pokvić, A Badnjević
2020 9th Mediterranean conference on embedded computing (MECO), 1-4 2020

ISO/IEC 15189 Implementation in Microbiology Laboratory-General Concepts
F Hrvat, S Cifric, A Aleta, A Dzuho, LG Pokvic, A Badnjevic
2020 IEEE International Workshop on Metrology for Industry 4.0 & IoT, 611-616 2020

Spahic L Gurbeta Pokvic L Badnjevic A
F Hrvat
Artificial Neural Networks for prediction of medical device performance 2020

Probiotics review and future aspects
A Aleta, F Hrvat, A Džuho
Int J Innov Sci Res Technol 5 (5), 270-274 2020

Artificial Intelligence for prediction of medical device performance: Infusion and perfusor pumps case study
F Hrvat, L Spahić, LG Pokvić, A Badnjević
9th Mediterranean Conference on Embedded Computing MECO 2020

Machine learning regression model for prediction risk of heart diseases
F Hrvat, L Spahić, A Aleta

MOST CITED SCHOLAR PUBLICATIONS

Spahic L Gurbeta Pokvic L Badnjevic A
F Hrvat
Artificial Neural Networks for prediction of medical device performance 2020
Citations: 12

Probiotics review and future aspects
A Aleta, F Hrvat, A Džuho
Int J Innov Sci Res Technol 5 (5), 270-274 2020
Citations: 9