Systematic review of polyherbal combinations used in metabolic syndrome

Background: Metabolic syndrome (MetS) is a multifactorial disease, whose main stay of prevention and management is life-style modification which is difficult to attain. Combination of herbs have proven more efficacious in multi-targeted diseases, as compared to individual herbs owing to the effect enhancing and side-effect neutralizing properties of herbs, which forms the basis of polyherbal therapies This led us to review literature on the efficacy of herbal combinations in MetS. Methods: Electronic search of literature was conducted by using Cinnahl, Pubmed central, Cochrane and Web of Science, whereas, Google scholar was used as secondary search tool. The key words used were metabolic syndrome, herbal/poly herbal, metabolic syndrome, clinical trial and the timings were limited between 2005-2020. Results: After filtering and removing duplications by using PRISMA guidelines, search results were limited to 41 studies, out of which 24 studies were evaluated for combinations used in animal models and 15 in clinical trials related to metabolic syndrome. SPICE and SPIDER models were used to assess the clinical trials, whereas, a checklist and a qualitative and a semi-quantitative questionnaire was formulated to report the findings for animal based studies. Taxonomic classification of Poly herbal combinations used in animal and clinical studies was designed. Conclusion: With this study we have identified the potential polyherbal combinations along with a proposed method to validate animal studies through systematic qualitative and quantitative review. This will help researchers to study various herbal combinations in MetS, in the drug development process and will give a future direction to research on prevention and management of MetS through polyherbal combinations

Impact Of Missing Data Imputation On The Fairness And Accuracy Of Graph Node Classifiers

Analysis of the fairness of machine learning (ML) algorithms recently attracted many researchers' interest. Most ML methods show bias toward protected groups, which limits the applicability of ML models in many applications like crime rate prediction etc. Since the data may have missing values which, if not appropriately handled, are known to further harmfully affect fairness. Many imputation methods are proposed to deal with missing data. However, the effect of missing data imputation on fairness is not studied well. In this paper, we analyze the effect on fairness in the context of graph data (node attributes) imputation using different embedding and neural network methods. Extensive experiments on six datasets demonstrate severe fairness issues in missing data imputation under graph node classification. We also find that the choice of the imputation method affects both fairness and accuracy. Our results provide valuable insights into graph data fairness and how to handle missingness in graphs efficiently. This work also provides directions regarding theoretical studies on fairness in graph data.Comment: Accepted at IEEE International Conference on Big Data (IEEE Big Data

Isolation and Characteristics of Biotechnologically Important Antagonistic Thermophilic Bacteria from Rhizosphere of Haloxylon salicornicum

Rhizobacteria are an active part of microbial population in the rhizosphere of plants. In this study, twenty rhizobacteria were isolated from the rhizosphere of a perennial grass, Haloxylon salicornicum, found in Cholistan desert, an arid landmass near Bahawalpur Pakistan, in one set of experimental conditions. Colony characteristics, biochemical and molecular analyses of these isolates were performed. All isolates were bacilli, gram positive with off-white colonies and exhibited typical bacilli colony morphology. None of the isolates was gelatinase, urease, indole, H2S and catalase producer. Eleven isolates were amylase producers and 8 isolates were acid producers. All isolates fermented glucose, 3 fermented lactose and 19 fermented fructose. Molecular data revealed that out of twenty isolates, 14 isolates showed 91–99% identity with Brevibacillus borstelensis, 4 with Bacillus subtilis (97–98%) and 2 with Bacillus licheniformis (94–99%) through BLAST analysis. All identified bacterial isolates cladded with their respective groups in the phylogenetic tree. Many (11–15 out of 20) of the isolates were more effective in inhibiting growth of the tested bacterial strains as compared to the positive control (Ampicillin 50 μg/disc). We conclude that bacilli are the predominant form populating rhizosphere of this desert grass. Among the isolated bacteria Brevibacillus borstelensis, Bacillus subtilis and Bacillus licheniformis are the most predominant species

Inventory of glaciers and glacial lakes of the Central Karakoram National Park (CKNP – Pakistan)

This study presents a map reporting valuable information on the cryosphere of the Central Karakoram National Park (CKNP, the largest protected area of Pakistan and the highest park in the world). All the information is provided considering the CKNP as a whole, and in detail by dividing it into five basins (i.e. Shigar, Hunza, Shyok, Upper Indus, and Gilgit). The glacier inventory reports 608 ice bodies covering 3680 km2 ( 3c35% of the CKNP area), with a total glacier volume of ca. 532 km3. In addition, we modeled the meltwater from glacier ice ablation over the period 23 July to 9 August 2011. The total melt amount is ca. 1.5 km3. Finally, we considered glacial lakes (202 water-bodies, covering 4 km2). For these latter glacier features, we also analyzed their potentially dangerous conditions and two lakes were found having such conditions

Inventory of glaciers and glacial lakes of the central Karakoram National Park (Pakistan) as a contribution to know and manage mountain freshwater resource.

In this study, we reported valuable information on the cryosphere of the Central Karakoram National Park (CKNP, the largest protected area of Pakistan and the highest park all over the world). In fact, in addition to the glacier inventory, we also estimated the glacier volume and we modeled the amount of meltwater derived from glacier ice ablation during a 18-day summer period (23 July–9 August 2011, time window where also field melt measurements were performed thus enabling a crosscheck of the obtained results). Moreover, glacial lakes were considered as well; for these latter glacier features we also analyzed their potentially dangerous conditions. All these information are given considering the CKNP as a whole and in detail by dividing it into five basins (i.e. Shigar, Hunza, Shyok, Upper Indus and Gilgit). As regards the CKNP as a whole, 608 glaciers are found with a total area of 3682.1 ± 61.0 km2, ~35% of the CKNP area. Analyzing in detail the five basins included in the CKNP area, they reflect the overall conditions regarding glacier distribution per size class, terminus elevation, length, and thickness. The widest basin (for number of ice bodies, glacier extent and ice volume) is the Shigar basin, where the largest glaciers are present (among which Baltoro Glacier), and the smallest one is the Gilgit basin. Finally, the highest number of debris-covered glaciers is located in the Shyok basin (62 glaciers). During 18 days in summer 2011, we quantified a total water magnitude of 1.54 km3 derived from ice melting. Even if we considered a relatively short period, this water volume equals ~11% of the reservoir capacity of the Tarbela Dam. In addition to glacier information, we provided glacial lake occurrence, as these ephemeral water bodies can develop into actual glacial risk conditions, which makes it important to list them and to survey them over time. The information reported in this study would provide base for future monitoring of glacial lakes and GLOFs and for planning and prioritizing disaster mitigation efforts in the park. In fact, even if the Potentially Dangerous Glacial Lakes (PDGLs) identified in the park territory are only 2, they are located in a high vulnerable and fragile area and the recent history suggests us to survey over time these water bodies to avoid losses of human lives and destructions of villages and communities. Moreover, many other supraglacial lakes identified in the park area could develop into conditions of PDGLs thus suggesting to prosecute the lake monitoring and to develop early strategies for risk mitigations and disaster management

Characterization of Computed Tomography Radiomic Features using Texture Phantoms

Radiomics treats images as quantitative data and promises to improve cancer prediction in radiology and therapy response assessment in radiation oncology. However, there are a number of fundamental problems that need to be solved in order to potentially apply radiomic features in clinic. The first basic step in computed tomography (CT) radiomic analysis is the acquisition of images using selectable image acquisition and reconstruction parameters. Radiomic features have shown large variability due to variation of these parameters. Therefore, it is important to develop methods to address these variability issues in radiomic features due to each CT parameter. To this end, texture phantoms provide a stable geometry and Hounsfield Units (HU) to characterize the radiomic features with respect to image acquisition and reconstruction parameters. In this project, normalization methods were developed to address the variability issues in CT Radiomics using texture phantoms. In the first part of this project, variability in radiomic features due to voxel size variation was addressed. A voxel size resampling method is presented as a preprocessing step for imaging data acquired with variable voxel sizes. After resampling, variability due to variable voxel size in 42 radiomic features was reduced significantly. Voxel size normalization is presented to address the intrinsic dependence of some key radiomic features. After normalization, 10 features became robust as a function of voxel size. Some of these features were identified as predictive biomarkers in diagnostic imaging or useful in response assessment in radiation therapy. However, these key features were found to be intrinsically dependent on voxel size (which also implies dependence on lesion volume). The normalization factors are also developed to address the intrinsic dependence of texture features on the number of gray levels. After normalization, the variability due to gray levels in 17 texture features was reduced significantly. In the second part of the project, voxel size and gray level (GL) normalizations developed based on phantom studies, were tested on the actual lung cancer tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were studied and compared with phantom scans acquired on 8 different CT scanners. Eight out of 10 features showed high (Rs \u3e 0.9) and low (Rs \u3c 0.5) Spearman rank correlations with voxel size before and after normalizations, respectively. Likewise, texture features were unstable (ICC \u3c 0.6) and highly stable (ICC \u3e 0.9) before and after gray level normalizations, respectively. This work showed that voxel size and GL normalizations derived from texture phantom also apply to lung cancer tumors. This work highlights the importance and utility of investigating the robustness of CT radiomic features using CT texture phantoms. Another contribution of this work is to develop correction factors to address the variability issues in radiomic features due to reconstruction kernels. Reconstruction kernels and tube current contribute to noise texture in CT. Most of texture features were sensitive to correlated noise texture due to reconstruction kernels. In this work, noise power spectra (NPS) was measured on 5 CT scanners using standard ACR phantom to quantify the correlated noise texture. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and the region of interest (ROI) maximum intensity as correction factors. Most texture features were radiation dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percent improvements in robustness of 19 features were in the range of 30% to 78% after corrections. In conclusion, most texture features are sensitive to imaging parameters such as reconstruction kernels, reconstruction Field of View (FOV), and slice thickness. All reconstruction parameters contribute to inherent noise in CT images. The problem can be partly solved by quantifying noise texture in CT radiomics using a texture phantom and an ACR phantom. Texture phantoms should be a pre-requisite to patient studies as they provide stable geometry and HU distribution to characterize the radiomic features and provide ground truths for multi-institutional validation studies