Evaluation of C4d expression and staining patterns by immunohistochemistry in renal biopsy samples with focal segmental glomerulosclerosis and minimal change disease.

INTRODUCTION: C4d is an activation product of lectin pathway of complement. Glomerular deposition of C4d is associated with poor prognosis in different types of immune-related glomerulonephritis. The present study was conducted to investigate expression level of C4d and its staining pattern in renal biopsy of patients with focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) by immunohistochemistry method. MATERIALS AND METHODS: In this retrospective cross-sectional study, renal biopsy specimens from 46 samples of MCD, 47 samples of FSGS, and 15 samples without glomerular disease as the controls, were subjected to immunohistochemistry staining with C4d. Demographic characteristics and information obtained from light and electron microscopy (EM) of patients were also extracted from their files. RESULTS: C4d positive staining was observed in 97.9 % of FSGS and 43.5 % of MCD samples, which showed a statistically significant difference (P < 0.001). The sensitivity and specificity of C4d expression for diagnosing FSGS were 97.9 % and 56.5 %, respectively. There was no significant correlation between C4d expression and any of the light and electron microscopy findings, including presence of foam cells, mesangial matrix expansion, interstitial fibrosis and tubular atrophy, and basement membrane changes in MCD patients. Also, no significant correlation was observed between C4d expression and clinical symptoms of proteinuria or prolonged high level of creatinine in patients with MCD. DISCUSSION AND CONCLUSION: The expression of C4d marker had a good sensitivity and negative predictive value in the diagnosis of FSGS.

miRNAs and exosomal miRNAs in lung cancer: New emerging players in tumor progression and therapy response.

Non-coding RNAs have shown key roles in cancer and among them, short RNA molecules are known as microRNAs (miRNAs). These molecules have length less than 25 nucleotides and suppress translation and expression. The functional miRNAs are produced in cytoplasm. Lung cancer is a devastating disease that its mortality and morbidity have undergone an increase in recent years. Aggressive behavior leads to undesirable prognosis and tumors demonstrate abnormal proliferation and invasion. In the present review, miRNA functions in lung cancer is described. miRNAs reduce/increase proliferation and metastasis. They modulate cell death and proliferation. Overexpression of oncogenic miRNAs facilitates drug resistance and radio-resistance in lung cancer. Tumor microenvironment components including macrophages and cancer-associated fibroblasts demonstrate interactions with miRNAs in lung cancer. Other factors such as HIF-1α, lncRNAs and circRNAs modulate miRNA expression. miRNAs have also value in the diagnosis of lung cancer. Understanding such interactions can pave the way for developing novel therapeutics in near future for lung cancer patients.

Proteomics to Metabolomics: A New Insight into the Pathogenesis of Hypertensive Nephropathy.

BACKGROUND: Hypertensive nephropathy (HN) is a high-burden disorder and a leading cause of end-stage renal disease. Despite huge investigations, the underlying mechanisms are yet largely unknown. Systems biology is a promising approach to providing a comprehensive insight into this complex disorder. METHODS: Proteome profiles of kidney tubulointerstitium and outer and inner cortex from a rat model of HN were retrieved from the proteomics identification database, and the quality of the datasets was assessed. Proteins that exhibited differential expression were detected and their interactions were analyzed in the kidney sub-compartments. Furthermore, enzymes were linked to the attributed metabolites. Functional enrichment analyses were performed to identify key pathways and processes based on the differentially expressed proteins and predicted metabolites. RESULTS: Proteasome-mediated protein degradation, actin cytoskeleton organization, and Rho GTPase signaling pathway are involved in the pathogenesis of HN. Furthermore, tissue hypoxia and dysregulated energy homeostasis are among the key underlying events. The metabolism of purine and amino acids is also affected in HN. CONCLUSION: Although the proposed pathogenic mechanisms remain to be further validated in experimental studies, this study contributes to the understanding of the molecular mechanisms of HN through a systematic unsupervised approach. Considering the significant alterations of metabolic pathways, HN can be viewed as an "acquired error of metabolism."

miR-802-5p is a key regulator in diabetic kidney disease.

Background: Diabetic kidney disease has substantial burden and limited therapeutic options. An inadequate understanding of the complex gene regulatory circuits underlying this disorder contributes to the insufficiency of current treatment strategies. MicroRNAs (miRNAs) play a crucial role as regulators of functionally related gene networks. Previously, mmu-mir-802-5p was identified as the sole dysregulated miRNA in both the kidney cortex and medulla of diabetic mice. This study aims to investigate the role of miR-802-5p in diabetic kidney disease. Materials and Methods: The validated and predicted targets of miR-802-5p were identified using miRTarBase and TargetScan databases, respectively. The functional role of this miRNA was inferred using gene ontology enrichment analysis. The expression of miR-802-5p and its selected targets were assessed by qPCR. The expression of the angiotensin receptor (Agtr1a) was measured by ELISA. Results: miR-802-5p exhibited dysregulation in both the kidney cortex and medulla of diabetic mice, with two- and four-fold over-expressions, respectively. Functional enrichment analysis of the validated and predicted targets of miR-802-5p revealed its involvement in the renin-angiotensin pathway, inflammation, and kidney development. Differential expression was observed in the Pten transcript and Agtr1a protein among the examined gene targets. Conclusion: These findings suggest that miR-802-5p is a critical regulator of diabetic nephropathy in the cortex and medulla compartments, contributing to disease pathogenesis through the renin-angiotensin axis and inflammatory pathways.

Quantitative control of Ets1 dosage by a multi-enhancer hub promotes Th1 cell differentiation and protects from allergic inflammation.

Multi-enhancer hubs are spatial clusters of enhancers present across numerous developmental programs. Here, we studied the functional relevance of these three-dimensional structures in T cell biology. Mathematical modeling identified a highly connected multi-enhancer hub at the Ets1 locus, comprising a noncoding regulatory element that was a hotspot for sequence variation associated with allergic disease in humans. Deletion of this regulatory element in mice revealed that the multi-enhancer connectivity was dispensable for T cell development but required for CD4+ T helper 1 (Th1) differentiation. These mice were protected from Th1-mediated colitis but exhibited overt allergic responses. Mechanistically, the multi-enhancer hub controlled the dosage of Ets1 that was required for CTCF recruitment and assembly of Th1-specific genome topology. Our findings establish a paradigm wherein multi-enhancer hubs control cellular competence to respond to an inductive cue through quantitative control of gene dosage and provide insight into how sequence variation within noncoding elements at the Ets1 locus predisposes individuals to allergic responses.

A deep learning approach to predict inter-omics interactions in multi-layer networks.

BACKGROUND: Despite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heterogeneous molecular types. Hence, developing strategies to predict inter-omics connections is essential to construct holistic maps of disease. RESULTS: Here, as a novel nonlinear deep learning method, Data Integration with Deep Learning (DIDL) was proposed to predict inter-omics interactions. It consisted of an encoder that performs automatic feature extraction for biomolecules according to existing interactions coupled with a predictor that predicts unforeseen interactions. Applicability of DIDL was assessed on different networks, namely drug-target protein, transcription factor-DNA element, and miRNA-mRNA. Also, validity of the novel predictions was evaluated by literature surveys. According to the results, the DIDL outperformed state-of-the-art methods. For all three networks, the areas under the curve and the precision-recall curve exceeded 0.85 and 0.83, respectively. CONCLUSIONS: DIDL offers several advantages like automatic feature extraction from raw data, end-to-end training, and robustness to network sparsity. In addition, reliance solely on existing inter-layer interactions and independence of biochemical features of interacting molecules make this algorithm applicable for a wide variety of networks. DIDL paves the way to understand the underlying mechanisms of complex disorders through constructing integrative networks.

Systems biology and machine learning approaches identify drug targets in diabetic nephropathy.

Diabetic nephropathy (DN), the leading cause of end-stage renal disease, has become a massive global health burden. Despite considerable efforts, the underlying mechanisms have not yet been comprehensively understood. In this study, a systematic approach was utilized to identify the microRNA signature in DN and to introduce novel drug targets (DTs) in DN. Using microarray profiling followed by qPCR confirmation, 13 and 6 differentially expressed (DE) microRNAs were identified in the kidney cortex and medulla, respectively. The microRNA-target interaction networks for each anatomical compartment were constructed and central nodes were identified. Moreover, enrichment analysis was performed to identify key signaling pathways. To develop a strategy for DT prediction, the human proteome was annotated with 65 biochemical characteristics and 23 network topology parameters. Furthermore, all proteins targeted by at least one FDA-approved drug were identified. Next, mGMDH-AFS, a high-performance machine learning algorithm capable of tolerating massive imbalanced size of the classes, was developed to classify DT and non-DT proteins. The sensitivity, specificity, accuracy, and precision of the proposed method were 90%, 86%, 88%, and 89%, respectively. Moreover, it significantly outperformed the state-of-the-art (P-value ≤ 0.05) and showed very good diagnostic accuracy and high agreement between predicted and observed class labels. The cortex and medulla networks were then analyzed with this validated machine to identify potential DTs. Among the high-rank DT candidates are Egfr, Prkce, clic5, Kit, and Agtr1a which is a current well-known target in DN. In conclusion, a combination of experimental and computational approaches was exploited to provide a holistic insight into the disorder for introducing novel therapeutic targets.

An insight into the electro-chemical properties of halogen (F, Cl and Br) doped BP and BN nanocages as anodes in metal-ion batteries.

Here, electro-chemical properties of BN and BP nanocages as anodes in metal-ion batteries are examined. The effect of halogens adoption of BN and BP-NCs on electro-chemical properties of M-IBs are investigated. Results showed that the BP nanocages as anode electrode in M-IBs has higher efficiency than BN nanocages and the K-IB has higher cell voltage than N-IBs. Results indicated that the halogens adoption of BN and BP-NCs are improved the cell voltage of M-IBs. Results proved that the F-doped M-IBs have higher cell voltage than M-IBs. Finally, F-B17P18 as anodes in K-IB is proposed as suitable electrodes.

A systematic integrative approach reveals novel microRNAs in diabetic nephropathy.

BACKGROUND: Despite huge efforts, the underlying molecular mechanisms of diabetic nephropathy (DN) are yet elusive, and holistic views have rarely been generated. Considering the complexity of DN pathogenesis, the integration of datasets from different molecular types to construct a multilayer map of DN can provide a comprehensive insight toward the disease mechanisms and also can generate new knowledge. Here, we have re-analyzed two mRNA microarray datasets related to glomerular and tubulointerstitial compartments of human diabetic kidneys. MATERIALS AND METHODS: The quality of the datasets was confirmed by unsupervised hierarchical clustering and principal component analysis. For each dataset, differentially expressed (DE) genes were identified, and transcription factors (TFs) regulating these genes and kinases phosphorylating the TFs were enriched. Furthermore, microRNAs (miRNAs) targeting the DE genes, TFs, and kinases were detected. Based on the harvested genes for glomeruli and tubulointerstitium, key signaling pathways and biological processes involved in diseases pathogenesis were recognized. In addition, the interaction of different elements in each kidney compartment was depicted in multilayer networks, and topology analysis was performed to identify key nodes. Central miRNAs whose target genes were most likely to be related to DN were selected, and their expressions were quantitatively measured in a streptozotocin-induced DN mouse model. RESULTS: Among the examined miRNAs, miR-208a-3p and miR-496a-3p are, for the first time, found to be significantly overexpressed in the cortex of diabetic kidneys compared to controls. CONCLUSION: We predict that miR-208 is involved in oxygen metabolism and regulation of cellular energy balance. Furthermore, miR-496 potentially regulates protein metabolism and ion transport. However, their exact functions remain to be investigated in future studies. Taken together, starting from transcriptomics data, we have generated multilayer interaction networks and introduced novel players in DN.

Big data to knowledge: common pitfalls in transcriptomics data analysis and representation.

The omics technologies provide an invaluable opportunity to employ a global view towards human diseases. However, the appropriate translation of big data to knowledge remains a major challenge. In this study, we have performed quality control assessments for 91 transcriptomics datasets deposited in gene expression omnibus database and also have evaluated the publications derived from these datasets. This survey shows that drawbacks in the analyses and reports of transcriptomics studies are more common than one may assume. This report is concluded with some suggestions for researchers and reviewers to enhance the minimal requirements for gene expression data generation, analysis and report.

The relationship between stress during pregnancy with leptin and cortisol blood concentrations and complications of pregnancy in the mother

Objective: Pregnancy is one of the most stressful periods a woman experiences in her life. The present study was an attempt to determine the relationship between maternal stress during pregnancy and cortisol plus maternal serum leptin concentrations as well as pregnancy outcomes. Material and Methods: This longitudinal study was conducted on 90 pregnant women in Miandoab city between 2015 and 2016. The samples were chosen from mothers with a gestational age of 24 to 28 weeks. The participants were asked to complete Cohen's Perceived Stress Scale (PSS) and a demographic questionnaire and blood samples were taken from them. The mothers were then tracked with four-week intervals until the time of delivery and were asked to complete Cohen's PSS each time along with a questionnaire related to maternal outcomes. Again, a blood sample was taken at the time of delivery. Data analysis was performed using SPSS 16. Descriptive statistics, Pearson's correlation coefficient, and the t-test were employed for analysis. Results: A significant relationship was found between maternal stress and preeclampsia (p=0.008). The relationships between preterm childbirth and maternal cortisol concentrations in weeks 24-28 (p=0.015), and between preterm childbirth and maternal leptin concentrations at the time of delivery (p=0.007) were also found to be significant. Conclusion: Pregnancy and labor, as physically and mentally stressful events, can affect women's physiologic and psychological indicators. As a consequence, during pregnancy, the cortisol and leptin index changes in response to the activity of the hypothalamic-pituitary axis and autonomic nervous system under stress.

Deterministic Measurement Matrix for Compressive Imaging Using Novel Transform Functions.

Efficient source coding is desired for any data storage and transmission. It could be enabled by adopting a transform inspired by natural phenomena. Based on the mechanical vibration models, a family of bases applicable to data compression is constructed. The eigenvectors of vibrating thin square plates, which are composed of sinusoidal and hyperbolic functions, are used to construct these real-orthonormal bases. Our analyses show that their attributes and performance are comparable to those of the widely used Discrete Cosine Transform. Since compressive sensing is a way to build the data compression directly into the acquisition, we propose to apply the set of low-frequency atoms of these bases that carry the main portion of the signal energy as sensing patterns. Thus, the measurement ensemble contains the high-energy Sinusoidal-Hyperbolic Transform's (SHT's) coefficients of the scene under-view. This sampling method leads to high fidelity reconstruction along with good efficiency in encoding and decoding. The dictionary matrix that is made by the SHTs and a non-trigonometric sparsifying basis like Hadamard is well-conditioned for the pursuit algorithm. Both subjective and objective evaluation of the reconstruction results validates the effectiveness of our method. Compared to the random Gaussian sensing patterns, for the same Compression Ratio (CR), the proposed sampling method results in images with significantly higher fidelity. The sensing patterns are also shown to be robust against Gaussian and Poisson noise. Application of our scheme to image compression is also discussed.

Using a Veterans Affairs Posttraumatic Stress Disorder Group Therapy Program With Refugees.

The PTSD Recovery Program, an intervention based on guidelines for the treatment of combat Veterans diagnosed with posttraumatic stress disorder (PTSD) that includes group therapy as adjunctive treatment to medication and individual therapy, was used for the treatment of PTSD in refugees at a clinic in central Texas. Eighteen clients diagnosed with PTSD completed 10 weekly group therapy sessions in addition to individual therapy and medication use. An in-service presentation educated providers on the PTSD Recovery Program and the group therapy intervention. Data were collected using a pre- and postintervention questionnaire. Statistical analysis supports the effectiveness of the PTSD Recovery Program as an adjunctive treatment for PTSD in the refugee population. Participant statements and provider satisfaction are included as qualitative data. Participant statements about symptom improvement, as well as providers' reported satisfaction with the PTSD Recovery Program, support this intervention as an effective adjunctive treatment for PTSD in the refugee population. [Journal of Psychosocial Nursing and Mental Health Services, 57(5), 21-28.].

A Serological Survey of Chlamydia trachomatis and its Related Factors in Individuals with High-Risk Sexual Behavior.

BACKGROUND: Chlamydia trachomatis is the most common bacterial sexually transmitted infection (STI) throughout the world and its annual incidence is reported to be around 50 million cases. High-risk sexual behaviors are among the predisposing factors for STIs. OBJECTIVE: The present study aimed to determine the seroprevalence of Chlamydia trachomatis and the factors affecting it in patients with high-risk sexual behaviors who had attended clinic of high risk sexual behavior of Urmia between October 2015 and June 2016. MATERIAL AND METHODS: This was a cross-sectional study conducted at Behavior Disorders Clinic in Urmia. One hundred and seventy six patients who had attended the clinic were selected using convenient sampling. A questionnaire was filled out by the participants and a 5 cc sample of their blood was collected to determine their serological level of antibodies. The participants' blood serum samples were analyzed by ELISA. Descriptive tests, T-test, and Chi-square test were run to analyze the data. All the statistical analyses were done using Statistical Package for the Social Sciences (SPSS) software, version 23. RESULTS: The results indicated that 52.3% of the attendees of the Behavior Disorders Clinic in Urmia were single, 78.4% had a sexual partner except from their spouse, 31.8% had multiple sexual partners, and 8.52% had a high-risk partner. The results also revealed that there was a significant relationship between gender and presence of anti-Chlamydia antibodies (p=0.02). However, no significant relationship was observed between other demographic characteristics or history of high-risk behaviors and presence of anti- Chlamydia antibodies. CONCLUSION: Since the frequency of positive IgM was 11.9% in the present study, and Chlamydia trachomatis infections are mostly asymptomatic, early diagnosis and treatment of this pathogen can have a very important role in public health improvement. Screening individuals with high-risk sexual behavior is recommended.

Rare 48, XYYY syndrome: case report and review of the literature.

48, XYYY syndrome is a rare condition. A male with 32-year-old and three Y chromosomes is described. This syndrome is phenotypically similar to Klinefelter syndrome. In this patient, Semi-Klinefelter characteristics such as tall stature, teeth dysmorphology, long length of fingers, partial deformity of the joints, likewise mental health problems were obvious.

Heterozygosity analysis of polycystic kidney disease 1 gene microsatellite markers for linkage analysis of autosomal dominant polycystic kidney disease type 1 in the Iranian population.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD) is the most common genetic cause of end-stage renal disease. Although imaging techniques are a means of accurate diagnosis when the cysts appear in the third or fourth decades of the patient's life, they are of little value for early diagnosis. Genetic tests are required for preimplantation genetic diagnosis, decision-making for kidney donation to an affected relative. Although mutation of the polycystic kidney disease (PKD1) gene is solely responsible for the most cases of ADPKD, direct genetic testing is limited by the large size of this gene and the presence of many mutations without hot spots. Therefore, indirect diagnosis with linkage analysis using informative microsatellite markers has been suggested. MATERIALS AND METHODS: In this study, we assessed the informativeness of the PKD1 gene markers D16S475, D16S291, and D16S3252 in Iranian population. Using specific primers, fluorescent polymerase chain reaction (PCR) was performed on genomic DNA extracted from fifty unrelated individuals. PCR products were analyzed by the ALFexpress DNA sequencer system, and the number and frequency of alleles were determined to calculate the heterozygosity (HET) and polymorphism information content (PIC) values. RESULTS: We found that the HET and PIC values for the D16S475 marker are 0.92 and 0.91, respectively. These two values are 0.82 and 0.80 for D16S291 and 0.50 and 0.47 for D16S3252, respectively. CONCLUSION: Based on this data, D16S475 and D16S291 are highly and D16S3252 is moderately informative for indirect genetic diagnosis of PKD1 mutations in this population.

Identification of Appropriate Housekeeping Genes for Gene Expression Analysis in Long-term Hypoxia-treated Kidney Cells.

BACKGROUND: Selection of stably expressing housekeeping genes (HKGs) is a crucial step in gene expression analysis. However, there are no universal HKGs for all experiments, and they should be determined by each biologic condition. The aim of this study was to detect appropriate HKGs for kidney cells cultured in long-term hypoxia. MATERIALS AND METHODS: Based on a screening step using a microarray data available from gene expression omnibus database, a set of candidate HKGs were chosen to be assessed in human kidney cells cultured in hypoxic or normoxic conditions for about 2 weeks in a time course manner. The stability of gene expression was assessed by refFinder, a web-based tool that integrates four computational programs (geNorm, Normfinder, BestKeeper, and the comparative ΔΔCt method). RESULTS: GAPDH and ACTB were the most stable genes in hypoxia treated cells whereas, B2M and ACTB were the best HKGs in cells cultured in normoxia. When both hypoxia and normoxia treated cells from all time points were evaluated together, GAPDH and ACTB equally showed the most stability. CONCLUSION: As in relative quantification of real-time polymerase chain reaction data, the same HKGs should be selected for all groups, we believe that GAPDH and ACTB are suitable HKGs for studies on the effect of hypoxia on cultured kidney cells.

Central Nodes in Protein Interaction Networks Drive Critical Functions in Transforming Growth Factor Beta-1 Stimulated Kidney Cells.

OBJECTIVE: Despite the huge efforts, chronic kidney disease (CKD) remains as an unsolved problem in medicine. Many studies have shown a central role for transforming growth factor beta-1 (TGFß-1) and its downstream signaling cascades in the pathogenesis of CKD. In this study, we have reanalyzed a microarray dataset to recognize critical signaling pathways controlled by TGFß-1. MATERIALS AND METHODS: This study is a bioinformatics reanalysis for a microarray data. The GSE23338 dataset was downloaded from the gene expression omnibus (GEO) database which assesses the mRNA expression profile of TGFß-1 treated human kidney cells after 24 and 48 hours incubation. The protein interaction networks for differentially expressed (DE) genes in both time points were constructed and enriched. In addition, by network topology analysis, genes with high centrality were identified and then pathway enrichment analysis was performed with either the total network genes or with the central nodes. RESULTS: We found 110 and 170 genes differentially expressed in the time points 24 and 48 hours, respectively. As the genes in each time point had few interactions, the networks were enriched by adding previously known genes interacting with the differentially expressed ones. In terms of degree, betweenness, and closeness centrality parameters 62 and 60 nodes were considered to be central in the enriched networks of 24 hours and 48 hours treatment, respectively. Pathway enrichment analysis with the central nodes was more informative than those with all network nodes or even initial DE genes, revealing key signaling pathways. CONCLUSION: We here introduced a method for the analysis of microarray data that integrates the expression pattern of genes with their topological properties in protein interaction networks. This holistic novel approach allows extracting knowledge from raw bulk omics data.

Nodes with high centrality in protein interaction networks are responsible for driving signaling pathways in diabetic nephropathy.

In spite of huge efforts, chronic diseases remain an unresolved problem in medicine. Systems biology could assist to develop more efficient therapies through providing quantitative holistic sights to these complex disorders. In this study, we have re-analyzed a microarray dataset to identify critical signaling pathways related to diabetic nephropathy. GSE1009 dataset was downloaded from Gene Expression Omnibus database and the gene expression profile of glomeruli from diabetic nephropathy patients and those from healthy individuals were compared. The protein-protein interaction network for differentially expressed genes was constructed and enriched. In addition, topology of the network was analyzed to identify the genes with high centrality parameters and then pathway enrichment analysis was performed. We found 49 genes to be variably expressed between the two groups. The network of these genes had few interactions so it was enriched and a network with 137 nodes was constructed. Based on different parameters, 34 nodes were considered to have high centrality in this network. Pathway enrichment analysis with these central genes identified 62 inter-connected signaling pathways related to diabetic nephropathy. Interestingly, the central nodes were more informative for pathway enrichment analysis compared to all network nodes and also 49 differentially expressed genes. In conclusion, we here show that central nodes in protein interaction networks tend to be present in pathways that co-occur in a biological state. Also, this study suggests a computational method for inferring underlying mechanisms of complex disorders from raw high-throughput data.

GNSS Signal Tracking Performance Improvement for Highly Dynamic Receivers by Gyroscopic Mounting Crystal Oscillator.

In this paper, the efficiency of the gyroscopic mounting method is studied for a highly dynamic GNSS receiver's reference oscillator for reducing signal loss. Analyses are performed separately in two phases, atmospheric and upper atmospheric flights. Results show that the proposed mounting reduces signal loss, especially in parts of the trajectory where its probability is the highest. This reduction effect appears especially for crystal oscillators with a low elevation angle g-sensitivity vector. The gyroscopic mounting influences frequency deviation or jitter caused by dynamic loads on replica carrier and affects the frequency locked loop (FLL) as the dominant tracking loop in highly dynamic GNSS receivers. In terms of steady-state load, the proposed mounting mostly reduces the frequency deviation below the one-sigma threshold of FLL (1σ(FLL)). The mounting method can also reduce the frequency jitter caused by sinusoidal vibrations and reduces the probability of signal loss in parts of the trajectory where the other error sources accompany this vibration load. In the case of random vibration, which is the main disturbance source of FLL, gyroscopic mounting is even able to suppress the disturbances greater than the three-sigma threshold of FLL (3σ(FLL)). In this way, signal tracking performance can be improved by the gyroscopic mounting method for highly dynamic GNSS receivers.