RESUMO
Host defense or antimicrobial peptides (AMPs) are promising candidates for protecting host against microbial pathogens for example bacteria, virus, fungi, yeast. Defensins are the type of AMPs that act as potential therapeutic drug agent and perform vital role in various biological process. Conventional Experiments to identify defensin peptides (DPs) are time consuming and expensive. Thus, the shortcomings of wet lab experiments are leveraged by computational methods to accurately predict the functional types of DPs. In this paper, we aim to propose a novel multi-class ensemble-based prediction model called StackDPPred for identifying the properties of DPs. The peptide sequences are encoded using split amino acid composition (SAAC), segmented position specific scoring matrix (SegPSSM), histogram of oriented gradients-based PSSM (HOGPSSM) and feature extraction based graphical and statistical (FEGS) descriptors. Next, principal component analysis (PCA) is used to select the best subset of attributes. After that, the optimized features are fed into single machine learning and stacking-based ensemble classifiers. Furthermore, the ablation study demonstrates the robustness and efficacy of the stacking approach using reduced features for predicting DPs and their families. The proposed StackDPPred method improves the overall accuracy by 13.41% and 7.62% compared to existing DPs predictors iDPF-PseRAAC and iDEF-PseRAAC, respectively on validation test. Additionally, we applied the local interpretable model-agnostic explanations (LIME) algorithm to understand the contribution of selected features to the overall prediction. We believe, StackDPPred could serve as a valuable tool accelerating the screening of large-scale DPs and peptide-based drug discovery process.
RESUMO
This laboratory experiment was conducted with the objective of augmenting the mechanical properties of glass ionomer cement (GIC) via altering the composition of GIC luting powder through the introduction of micron-sized silanized glass fibres (GFs). Experimental GICs were prepared through the addition of two concentrations of GFs (0.5% and 1.0% by weight) to the powder of commercially available GIC luting materials. The effect of GF in set GIC was internally evaluated using micro-CT while the mechanical attributes such as nano hardness (nH), elastic modulus (EM), compressive strength (CS), and diametral tensile strength (DTS) were gauged. Additionally, the physical properties such as water solubility and sorption, contact angle (CA), and film thickness were evaluated. Reinforced Ketac Cem Radiopaque (KCR) GIC with 0.5 wt.% GF achieved improved nH, EM, CS, and DTS without affecting the film thickness, CA or internal porosity of the set GIC cement. In contrast, both GF-GIC formulations of Medicem (MC) GIC showed the detrimental effect of the GF incorporation. Reinforcing KCR GIC with 0.5 wt.% silanized GFs could improve the physical and mechanical attributes of luting material. Silanized GF, with optimal concentration within the GIC powder, can be used as a functional additive in KCR GIC with promising results.
RESUMO
BACKGROUND: Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder requiring multimodal intervention and an army of multidisciplinary teams for a proper rehabilitation plan. Accordingly, multiple practice guidelines have been published for different disciplines. However, systematic evidence to detect and intervene must be updated regularly. Our main objective is to compare and summarize the recommendations made in the clinical practice guidelines (CPGs) for ASD in children released from November 2015 to March 2022. METHODS: CPGs were subjected to a systematic review. We developed the inclusion and exclusion criteria and health-related questions, then searched and screened for CPGs utilizing bibliographic and CPG databases. Each of the CPGs used in the study were critically evaluated using the Appraisal of Guidelines for REsearch and Evaluation II (AGREE II) instrument. In a realistic comparison table, we summarized the recommendations. RESULTS: Four eligible CPGs were appraised: Australian Autism CRC (ACRC); Ministry of Health New Zealand (NZ); National Institute for Health and Care Excellence (NICE); and Scottish Intercollegiate Guidelines Network, Healthcare Improvement Scotland (SIGN-HIS). The overall assessments of all four CPGs scored greater than 80%; these findings were consistent with the high scores in the six domains of AGREE II, including: (1) scope and purpose, (2) stakeholder involvement, (3) rigor of development, (4) clarity of presentation, (5) applicability, and (6) editorial independence domains. Domain (3) scored 84%, 93%, 86%, and 85%; domain (5) 92%, 89%, 54%, and 85%; and domain (6) 92%, 96%, 88%, and 92% for ACRC, NICE, NZ, and SIGN-HIS, respectively. Overall, there were no serious conflicts between the clinical recommendations of the four CPGs, but some were more comprehensive and elaborative than others. CONCLUSIONS: All four assessed evidence-based CPGs demonstrated high methodological quality and relevance for use in practice.