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Background: Autosomal recessive hypercholesterolemia (ARH) is a rare monogenic disorder resulting from mutations of the LDLRAP1 gene, which leads to elevated LDL-C levels. Here, using whole exome sequencing (WES), we describe a 22-year-old Iranian female who carries a novel nonsense mutation in LDLRAP1. Methods: Genetic investigations were performed for the patient and her family. She showed LDL-C level of 720 mg/dL since the age of 11 years. At the age of 13 years old, aortic valve repair surgery was performed due to severe aortic valve stenosis (AVS). At the age of 17, along with prescription of rosuvastatin plus ezetimibe, coronary angiography displayed the presence of serious stenotic lesions of the coronary arteries and also aortic valve, making the patient eligible for coronary artery bypass grafting (CABG) and aortic valve replacement (AVR). Results: Genetic analysis showed the presence of a previously unreported homozygous LDLRAP1 gene variant, c.649G>T, generating a nonsense mutation at amino acid 217, shortening the ARH protein from 308 to 217 amino acid, which removes AP-2 binding domain of ARH, as an important part in LDL uptake.Conclusion: During a 10-year treatment, we observed a 74% reduction in LDL-C level. Despite the treatment with maximal dose of rosuvastatin plus ezetimibe, the results of coronary angiography demonstrated severe supravalvular aortic stenosis (SVAS) resulted in significant stenotic lesions of the coronary arteries and aortic valve. This highlights the importance of WES in early diagnosis of ARH, and it is proposed to prevent or at least delay the onset of the cardiovascular events.

Background: Autosomal recessive hypercholesterolemia (ARH) is a rare monogenic disorder resulting from mutations of the LDLRAP1 gene, which leads to elevated LDL-C levels. Here, using whole exome sequencing (WES), we describe a 22-year-old Iranian female who carries a novel nonsense mutation in LDLRAP1. Methods: Genetic investigations were performed for the patient and her family. She showed LDL-C level of 720 mg/dL since the age of 11 years. At the age of 13 years old, aortic valve repair surgery was performed due to severe aortic valve stenosis (AVS). At the age of 17, along with prescription of rosuvastatin plus ezetimibe, coronary angiography displayed the presence of serious stenotic lesions of the coronary arteries and also aortic valve, making the patient eligible for coronary artery bypass grafting (CABG) and aortic valve replacement (AVR).Results: Genetic analysis showed the presence of a previously unreported homozygous LDLRAP1 gene variant, c.649G>T, generating a nonsense mutation at amino acid 217, shortening the ARH protein from 308 to 217 amino acid, which removes AP-2 binding domain of ARH, as an important part in LDL uptake.Conclusion: During a 10-year treatment, we observed a 74% reduction in LDL-C level. Despite the treatment with maximal dose of rosuvastatin plus ezetimibe, the results of coronary angiography demonstrated severe supravalvular aortic stenosis (SVAS) resulted in significant stenotic lesions of the coronary arteries and aortic valve. This highlights the importance of WES in early diagnosis of ARH, and it is proposed to prevent or at least delay the onset of the cardiovascular events.

Background: Diabetes mellitus may cause neurodegeneration, but the exact mechanism by which diabetic conditions induce neuronal cell death remains unclear. Tau protein hyperphosphorylation is considered to be a major pathological hallmark of neurodegeneration and can be triggered by diabetes. Various tau-directed kinases, including P38, can be activated upon diabetic stress and induce tau hyperphosphorylation. Despite extensive research efforts and the known importance of tau pathology in neurodegeneration, the exact tau specie(s) and kinases driving neurodegeneration in diabetes mellitus have not been clearly elucidated. Methods: We herein employed protein expression data analysis as well as immunofluorescence and immunoblotting techniques to determine the exact molecular mechanism of tau pathology triggered by diabetes in both in vitro and in vivo systems.Results: We found that P38, a major tau kinase, was increased in Glutamatergic & GABAergic neuron subtypes under diabetic conditions. This rendered them more responsive to oxidative stress caused by diabetes. We observed that oxidative stress activated P38, which in turn directly and indirectly drove tau pathology in the brainstem (enriched by Glutamatergic & GABAergic neurons), which gradually spread to neighboring brain areas. Notably, P38 inhibition suppressed tau pathogenicity and neurodegeneration in diabetic mouse models. Conclusion: The data establish P38 as a central mediator of diabetes mellitus induced tau pathology. Furthermore, the inhibition of P38 at early stages of diabetes-induced stress can inhibit tau pathology. Our findings provide mechanistic insight on the consequences of this metabolic disorder on the nervous system.

Background: Intrauterine Insemination (IUI) outcome prediction is a challenging issue with which the assisted reproductive technology (ART) practitioners are dealing. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. The large number of studies that have been focused on predicting the IVF and ICSI outcome by machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to develop an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking of the most significant features, based on patients' cycle’s characteristics under IUI treatment.Methods: For this purpose, a novel hybrid approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute for 8,360 couples who underwent 11,255 IUI cycles were included. Results: Experimental results show that the proposed method outperforms the compared methods with AUC of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions: The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female BMI.