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.
Genetic architecture of self-limited delayed puberty and congenital hypogonadotropic hypogonadism Valeria Vezzoli, Faris Hrvat, Giovanni Goggi, Silvia Federici, Biagio Cangiano, et al. Frontiers in Endocrinology, 2023 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, et al. Frontiers in Endocrinology, 2022 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 = <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.
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, Almir Badnjevic 2020 9th Mediterranean Conference on Embedded Computing Meco 2020, 2020 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.
Low-intensity Pulsed Ultrasound Modulates Metabolite Release Reflecting the Inflammatory Status of Colonic Mucosa SEP Chavez, F Hrvat, G Martano, S Timo, L Morosi, C Legrottaglie, G Mori, ... International Conference on Medical and Biological Engineering, 975-985 , 2025 2025.0
MetaboCypher: Analysis of Spatial Metabolomics Through a User-Friendly Application F Hrvat, G Converso, M Wozny, G Martano, S Pineda, C Legrottaglie, ... International Conference on Medical and Biological Engineering, 672-684 , 2025 2025.0
P42-Impact of wfs1b downregulation on GnRH3 neurons development in the zebrafish model I Gentile, F Marelli, S Casafina, L Persani, M Bonomi, V Vezzoli European Journal of Endocrinology 193 (Supplement_1), lvaf168. 105 , 2025 2025.0
P0565 Spatial Metabolic Profiles in Ulcerative colitis patients determine response to therapy F Hrvat, G Converso, M Wozny, G Martano, S Timo, L Morosi, S Pineda, ... Journal of Crohn's and Colitis 19 (Supplement_1), i1136-i1136 , 2025 2025.0
DOP120 Low-intensity Pulsed Ultrasound Modulates Metabolite Release Reflecting the Inflammatory Status of Colonic Mucosa SE Pineda Chavez, F Hrvat, GA Vignolle, Y Gillitschka, L Ciglar, ... Journal of Crohn's and Colitis 19 (Supplement_1), i299-i299 , 2025 2025.0
Impatto della down-regolazione di wfs1b sui neuroni GnRH3 nel modello in vivo zebrafish I Gentile, F Marelli, S Casafina, F Hrvat, L Persani, V Vezzoli, M Bonomi 2025.0
Identification and characterization of WFS1 in the pathogenesis of CHH I Gentile, F Marelli, F Hrvat, L Persani, V Vezzoli, M Bonomi 2024.0
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 2023.0 Citations: 5
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 2023.0 Citations: 26
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 2022.0 Citations: 16
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 2021.0 Citations: 1
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 2021.0
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 2021.0 Citations: 13
Detection of acute inflammation of urinary bladder and acute nephritis of renal pelvis origin using artificial neural network A Aleta, A Džuho, F Hrvat European Medical and Biological Engineering Conference, 363-371 , 2020 2020.0 Citations: 4
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 2020.0 Citations: 52
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 2020.0 Citations: 1
Spahic L Gurbeta Pokvic L Badnjevic A, Artificial Neural Networks for prediction of medical device performance based on conformity assessment data: Infusion and perfusor pumps … F Hrvat IEEE 9th Mediterranean Conference on Embedded Computing (MECO), 08-11 , 2020 2020.0 Citations: 2
Probiotics review and future aspects A Aleta, F Hrvat, A Džuho Int J Innov Sci Res Technol 5 (5), 270-274 , 2020 2020.0 Citations: 9
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 2020.0 Citations: 13
Machine learning regression model for prediction risk of heart diseases F Hrvat, L Spahić, A Aleta
MOST CITED SCHOLAR PUBLICATIONS
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 2020.0 Citations: 52
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 2023.0 Citations: 26
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 2022.0 Citations: 16
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 2021.0 Citations: 13
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 2020.0 Citations: 13
Probiotics review and future aspects A Aleta, F Hrvat, A Džuho Int J Innov Sci Res Technol 5 (5), 270-274 , 2020 2020.0 Citations: 9
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 2023.0 Citations: 5
Detection of acute inflammation of urinary bladder and acute nephritis of renal pelvis origin using artificial neural network A Aleta, A Džuho, F Hrvat European Medical and Biological Engineering Conference, 363-371 , 2020 2020.0 Citations: 4
Spahic L Gurbeta Pokvic L Badnjevic A, Artificial Neural Networks for prediction of medical device performance based on conformity assessment data: Infusion and perfusor pumps … F Hrvat IEEE 9th Mediterranean Conference on Embedded Computing (MECO), 08-11 , 2020 2020.0 Citations: 2
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 2021.0 Citations: 1
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 2020.0 Citations: 1
Low-intensity Pulsed Ultrasound Modulates Metabolite Release Reflecting the Inflammatory Status of Colonic Mucosa SEP Chavez, F Hrvat, G Martano, S Timo, L Morosi, C Legrottaglie, G Mori, ... International Conference on Medical and Biological Engineering, 975-985 , 2025 2025.0
MetaboCypher: Analysis of Spatial Metabolomics Through a User-Friendly Application F Hrvat, G Converso, M Wozny, G Martano, S Pineda, C Legrottaglie, ... International Conference on Medical and Biological Engineering, 672-684 , 2025 2025.0
P42-Impact of wfs1b downregulation on GnRH3 neurons development in the zebrafish model I Gentile, F Marelli, S Casafina, L Persani, M Bonomi, V Vezzoli European Journal of Endocrinology 193 (Supplement_1), lvaf168. 105 , 2025 2025.0
P0565 Spatial Metabolic Profiles in Ulcerative colitis patients determine response to therapy F Hrvat, G Converso, M Wozny, G Martano, S Timo, L Morosi, S Pineda, ... Journal of Crohn's and Colitis 19 (Supplement_1), i1136-i1136 , 2025 2025.0
DOP120 Low-intensity Pulsed Ultrasound Modulates Metabolite Release Reflecting the Inflammatory Status of Colonic Mucosa SE Pineda Chavez, F Hrvat, GA Vignolle, Y Gillitschka, L Ciglar, ... Journal of Crohn's and Colitis 19 (Supplement_1), i299-i299 , 2025 2025.0
Impatto della down-regolazione di wfs1b sui neuroni GnRH3 nel modello in vivo zebrafish I Gentile, F Marelli, S Casafina, F Hrvat, L Persani, V Vezzoli, M Bonomi 2025.0
Identification and characterization of WFS1 in the pathogenesis of CHH I Gentile, F Marelli, F Hrvat, L Persani, V Vezzoli, M Bonomi 2024.0
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 2021.0
Machine learning regression model for prediction risk of heart diseases F Hrvat, L Spahić, A Aleta
