ZnO-Enhanced Reduced Graphene Oxide Electrodes from Cocoa Shell: Nanoarchitectonics Platform for Photoelectrocatalytic Detection of Methylene Blue.

In this study, we report the successful preparation of reduced graphene oxide modified zinc oxide (rGO-ZnO) composites from cocoa shells. Synthesis of rGO-ZnO was carried out using the Hummer method and thermal reduction. The electrode material was comprehensively characterized using fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy & Energy Dispersive X-ray (SEM-EDX). The photoelectrocatalytic performance of the prepared composite electrodes was evaluated using various electrochemical techniques, including Linear Sweep Voltammetry (LSV), Cyclic Voltammetry (CV), and Multi Pulse Amperometry (MPA). The FTIR analysis of rGO-ZnO exhibited distinct bands corresponding to C-O at 1022 cm-1, C=C at 1600 cm-1, and Zn-O at 455 cm-1. The XRD analysis revealed characteristic peaks at 26.6º, 29.2º, 36.2º, 44.04º, 47.58º, and 64.4º, confirming the presence of key crystalline phases. SEM-EDX analysis of rGO-ZnO revealed a rough surface morphology with bright white and black regions, signifying the coexistence of ZnO and rGO with carbon, oxygen, and zinc contents of 78.98%, 17.46%, and 3.56%, respectively. The investigations involved the photoelectrochemical profiles of methylene blue organic dyes at different concentrations, ranging from 0.5 ppm to 3.0 ppm. The acquired findings offer valuable understanding into the photoelectrocatalytic effectiveness of the composite electrodes containing rGO-ZnO, suggesting their potential use in potential scenarios involving the revitalization of the environment in industrial water systems.

Knowledge, Attitudes, and Anxiety of Midwives Toward Maternal Care During the COVID-19 Pandemic: A Descriptive Study on Midwives Who Experience the Perinatal and Breastfeeding Phases.

Background: People all over the world, including pregnant women, have experienced stress and anxiety due to the coronavirus disease 2019 (COVID-19) pandemic. Providing services during the pandemic is something that midwives in primary health care cannot avoid. This study assesses a midwife's knowledge, attitude, and anxiety toward providing maternal care during the pandemic. Material and Method: A cross-sectional design was used in this study, which involved 183 midwives who provided midwifery services at hospitals and health centers and had experience with the perinatal and breastfeeding phases. Using the Google Forms tool, respondents completed a questionnaire regarding their knowledge, attitudes, and anxiety toward maternal care during the COVID-19 pandemic. Results: One hundred eighty-two midwives (99.50%) understood maternal care well during the COVID-19 pandemic. In addition, the maternal care provided during the COVID-19 pandemic was viewed favorably by almost all the participants. The percentage of midwives working in primary care in the perinatal phase who had moderate-to-severe anxiety levels during the COVID-19 pandemic was 17 women (27.42%). Conclusions: The knowledge and attitudes of midwives about the COVID-19 pandemic in this study were good, and there was a smaller percentage of midwives with an extreme level of anxiety.

Performance of rotation forest ensemble classifier and feature extractor in predicting protein interactions using amino acid sequences.

BACKGROUND: There are two significant problems associated with predicting protein-protein interactions using the sequences of amino acids. The first problem is representing each sequence as a feature vector, and the second is designing a model that can identify the protein interactions. Thus, effective feature extraction methods can lead to improved model performance. In this study, we used two types of feature extraction methods-global encoding and pseudo-substitution matrix representation (PseudoSMR)-to represent the sequences of amino acids in human proteins and Human Immunodeficiency Virus type 1 (HIV-1) to address the classification problem of predicting protein-protein interactions. We also compared principal component analysis (PCA) with independent principal component analysis (IPCA) as methods for transforming Rotation Forest. RESULTS: The results show that using global encoding and PseudoSMR as a feature extraction method successfully represents the amino acid sequence for the Rotation Forest classifier with PCA or with IPCA. This can be seen from the comparison of the results of evaluation metrics, which were >73% across the six different parameters. The accuracy of both methods was >74%. The results for the other model performance criteria, such as sensitivity, specificity, precision, and F1-score, were all >73%. The data used in this study can be accessed using the following link: https://www.dsc.ui.ac.id/research/amino-acid-pred/. CONCLUSIONS: Both global encoding and PseudoSMR can successfully represent the sequences of amino acids. Rotation Forest (PCA) performed better than Rotation Forest (IPCA) in terms of predicting protein-protein interactions between HIV-1 and human proteins. Both the Rotation Forest (PCA) classifier and the Rotation Forest IPCA classifier performed better than other classifiers, such as Gradient Boosting, K-Nearest Neighbor, Logistic Regression, Random Forest, and Support Vector Machine (SVM). Rotation Forest (PCA) and Rotation Forest (IPCA) have accuracy, sensitivity, specificity, precision, and F1-score values >70% while the other classifiers have values <70%.