Serum proteomic analysis focused on fibrosis in patients with hepatitis C virus infection

<p>Abstract</p> <p>Background</p> <p>Despite its widespread use to assess fibrosis, liver biopsy has several important drawbacks, including that is it semi-quantitative, invasive, and limited by sampling and observer variability. Non-invasive serum biomarkers may more accurately reflect the fibrogenetic process. To identify potential biomarkers of fibrosis, we compared serum protein expression profiles in patients with chronic hepatitis C (CHC) virus infection and fibrosis.</p> <p>Methods</p> <p>Twenty-one patients with no or mild fibrosis (METAVIR stage F0, F1) and 23 with advanced fibrosis (F3, F4) were retrospectively identified from a pedigreed database of 1600 CHC patients. All samples were carefully phenotyped and matched for age, gender, race, body mass index, genotype, duration of infection, alcohol use, and viral load. Expression profiling was performed in a blinded fashion using a 2D polyacrylamide gel electrophoresis/LC-MS/MS platform. Partial least squares discriminant analysis and likelihood ratio statistics were used to rank individual differences in protein expression between the 2 groups.</p> <p>Results</p> <p>Seven individual protein spots were identified as either significantly increased (α₂-macroglobulin, haptoglobin, albumin) or decreased (complement C-4, serum retinol binding protein, apolipoprotein A-1, and two isoforms of apolipoprotein A-IV) with advanced fibrosis. Three individual proteins, haptoglobin, apolipoprotein A-1, and α₂-macroglobulin, are included in existing non-invasive serum marker panels.</p> <p>Conclusion</p> <p>Biomarkers identified through expression profiling may facilitate the development of more accurate marker algorithms to better quantitate hepatic fibrosis and monitor disease progression.</p

CNN Based Water Stress Detection in Chickpea Using UAV Based Hyperspectral Imaging

Water is an important agronomic input, which plays a vital role in the health and yield of the crop. Water deficiency results in abiotic stress, early detection of water stress help in recovering the health of the crop. Hyperspectral imaging (HSI) sensors acquire rich spectral information of the objects in hundreds of narrow bands, are capable of identifying the change in canopy water content, which is crucial in predicting irrigation requirements of the crop. Due to the wide field of coverages, short revisiting periods, and high spectral resolutions, Unmanned Aerial Vehicle (UAV) based HSI techniques are suitable in precision agriculture. In this paper, water stress detection in chickpea canopy is presented using hyperspectral (HS) images acquired from UAV. The drought classification was performed in two ways, i. by considering selected water-sensitive bands, and ii. by considering the whole spectral bands of the HS images. A 3D-2D convolutional neural network (CNN) model is used to classify well-watered canopy from water-stressed one, and its performance is compared with that of a Support Vector Machine (SVM) and a 2D+1D CNN model in identifying water stress. We obtained the best classification accuracy of 95.44%, which shows the potential of HSI in successfully detecting water stress in chickpea. © 2021 IEEE

Efficient Processing Methodology for UAV Flight Path Detection

Unmanned Areal Vehicle (UAV) based imagery is an emerging technology that has penetrated numerous verticals such as remote sensing, precision agriculture, land surveying. Various types of sensors are mounted onto UAV, and the images of the area of interest are captured. To get a complete distortionless areal view of the area, an orthomosaic is created using the captured images on which further analysis is done. But the traditional orthomosaic creation techniques are tedious, time-consuming, and also computationally complex. In this paper, a novel algorithm is proposed which speeds up the region of interest (ROI) detection significantly. In this method, the UAV flight path is divided into multiple Sub-Paths, and each path is processed parallelly. This method is universal and drastically improves the processing speed for any set of UAV images. It is observed that the algorithm reduces the computation time by around 75%

Presence of diabetes-specific autoimmunity in women with gestational diabetes mellitus (GDM) predicts impaired glucose regulation at follow-up

Purpose: Gestational diabetes mellitus (GDM) is the most frequent complication of pregnancy; around 10% of GDM cases may be determined by autoimmunity, and our aims were to establish the role of autoimmunity in a population of Sardinian women affected by GDM, to find predictive factors for autoimmune GDM, and to determine type 1 diabetes (T1D) auto-antibodies (Aabs) together with glucose tolerance after a mean 21.2 months of follow-up. Methods: We consecutively recruited 143 women affected by GDM and 60 without GDM; clinical data and pregnancy outcomes were obtained by outpatient visit or phone recall. T1D auto-antibodies GADA, IA2-A, IAA, ZnT8-A were measured in the whole population at baseline, and in the Aab-positive women at follow-up. Results: The overall prevalence of autoimmunity was 6.4% (13/203). No significant difference was found in the prevalence of auto-antibodies between GDM (5.6%) and control (8.3%) women, neither in antibody titres. Highest titres for GADA and ZnT8-A were observed in the control group; no phenotypic factors were predictive for autoimmune GDM. Diabetes-related autoantibodies were still present in all the GDM women at follow-up, and their presence was associated with a 2.65 (p < 0.0016) relative risk (RR) of glucose impairment. Conclusion: We observed a low prevalence (5.6%) of diabetes-related autoimmunity in our GDM cohort, consistent with the prevalence reported in previous studies. It was not possible to uncover features predictive of autoimmune GDM. However, given the significant risk of a persistent impaired glycemic regulation at follow-up, it is advisable to control for glucose tolerance in GDM women with diabetes-related autoimmunity

Presence of diabetes-specific autoimmunity in women with gestational diabetes mellitus (GDM) predicts impaired glucose regulation at follow-up

Gestational diabetes mellitus (GDM) is the most frequent complication of pregnancy; around 10% of GDM cases may be determined by autoimmunity, and our aims were to establish the role of autoimmunity in a population of Sardinian women affected by GDM, to find predictive factors for autoimmune GDM, and to determine type 1 diabetes (T1D) auto-antibodies (Aabs) together with glucose tolerance after a mean 21.2 months of follow-up