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The two types of nano carbons (NCs); carbon nanofibers (CNFs) and carbon nanotubes (CNTs) have been studied in numerous research works, which can extraordinarily enhance the mechanical properties of structural composites (concrete or soil-cement). Their amazing mechanical properties and tremendously high aspect ratios make NCs as the most valuable nanomaterials for nano-reinforcement. Yet, the dispersion of NCs is the major factor that strongly affects the properties of nan composites. A good deal of research has been carried out on chemical methods (chemical agent) to attain homogeneous dispersion of nano carbon in water. While if not precisely done, it can damage or shorten NCs and at the worst can dissolve them. This results in a negative effect on composites. Considering this, NCs can be physically dispersed in water and then mixed with composites. This paper presents a discussion on types of NCs and different dispersion techniques (chemical and physical), and research works in improve of soil properties use NCs.

In this study six unsymmetrical thiourea derivatives, 1-isobutyl-3-cyclohexylthiourea (1), 1-tert-butyl-3-cyclohexylthiourea (2), 1-(3-chlorophenyl)-3-cyclohexylthiourea (3), 1-(1,1-dibutyl)-3-phenylthiourea (4), 1-(2-chlorophenyl)-3-phenylthiourea (5) and 1-(4-chlorophenyl)-3-phenylthiourea (6) were obtained in the laboratory under aerobic conditions. Compounds 3 and 4 are crystalline and their structure was determined for their single crystal. Compounds 3 is monoclinic system with space group P21/n while compound 4 is trigonal, space group R3:H. Compounds (1–6) were tested for their anti-cholinesterase activity against acetylcholinesterase and butyrylcholinesterase (hereafter abbreviated as, AChE and BChE, respectively). Potentials (all compounds) as sensing probes for determination of deadly toxic metal (mercury) using spectrofluorimetric technique were also investigated. Compound 3 exhibited better enzyme inhibition IC50 values of 50, and 60 µg/mL against AChE and BChE with docking score of −10.01, and −8.04 kJ/mol, respectively. The compound also showed moderate sensitivity during fluorescence studies.

Background The increasing prevalence of type 2 diabetes mellitus (T2DM) can have a substantial impact in low- and middle-income countries (LMICs). Community-based programs addressing diet, physical activity, and health behaviors have shown significant benefits on the prevention and management of T2DM, mainly in high-income countries. However, their effects on preventing T2DM in the at-risk population of LMICs have not been thoroughly evaluated. Methods The Cochrane Library (CENTRAL), MEDLINE, EMBASE and two clinical trial registries were searched to identify eligible studies. We applied a 10 years limit (from 01 Jan 2008 to 06 Mar 2018) on English language literature. We included randomized controlled trials (RCTs) with programs focused on lifestyle changes such as weight loss and/or physical activity increase, without pharmacological treatments, which aimed to alter incidence of diabetes or one of the T2DM risk factors, of at least 6 months duration based on follow-up, conducted in LMICs. Results Six RCTs randomizing 2574 people were included. The risk of developing diabetes in the intervention groups reduced more than 40%, RR (0.57 [0.30, 1.06]), for 1921 participants (moderate quality evidence), though it was not statistically significant. Significant differences were observed in weight, body mass index, and waist circumference change in favor of community-based programs from baseline, (MD [95% CI]; − 2.30 [− 3.40, − 1.19], p  < 0.01, I2 = 87%), (MD [95% CI]; − 1.27 [− 2.10, − 0.44], p  < 0.01, I2 = 96%), and (MD [95% CI]; − 1.66 [− 3.17, − 0.15], p  = 0.03, I2 = 95%), respectively. The pooled effect showed a significant reduction in fasting blood glucose and HbA1C measurements in favor of the intervention (MD [95% CI]; − 4.94 [− 8.33, − 1.55], p  < 0.01, I2 = 62%), (MD [95% CI]; − 1.17 [− 1.51, − 0.82], p  < 0.01, I2 = 46%), respectively. No significant difference was observed in 2-h blood glucose values, systolic or diastolic blood pressure change between the two groups. Conclusion Based on available literature, evidence suggests that community-based interventions may reduce the incidence rate of T2DM and may positively affect anthropometric indices and HbA1C. Due to the heterogeneity observed between trials we recommend more well-designed RCTs with longer follow-up durations be executed, to confirm whether community-based interventions lead to reduced T2DM events in the at-risk population of LMIC settings.

In today’s rapidly evolving digital landscape, the surge in smartphone usage is paralleled by an increasing wave of cyberthreats, highlighting the limitations of existing signature-based malware detection methods. To address this problem, our research introduces a Time-Aware Machine Learning (TAML) framework specifically designed for Android malware detection. Our framework extracts the best time-correlated features and then it builds time-aware and time-agnostic machine learning (ML) models. The ML models are trained on the KronoDroid dataset, which contains more than 41,000 benign Android apps and more than 36,000 malicious apps developed between 2008 to 2020. Our experimental evaluation revealed that the Last Modification Date ‘LastModDate’ feature is a critical variable for time-aware classification. Moreover, our empirical analysis reveals that real-device detection outperforms emulator-based detection. Impressively, the time-correlated features boosts the detection performance and achieving an outstanding 99.98% F1 score in a time-agnostic setting. In addition, on each year, our time-aware experiments outperformed the traditional ML detection models. Our time-aware classifier achieved a 91% F1 score on average and a maximum F1 score of 99% of yearly chunk experiments over 12 years. These experimental results affirm the effectiveness of our proposed method in detecting Android malware.

The seventh human coronavirus was discovered and reported primarily in Wuhan, China. After intense seasons with repercussions in all areas of humanity, the pandemic demonstrates a new perspective. In Brazil, the pandemic concept had impacts in vast areas, including healthcare hospitals. This present study aims to describe and synthesize data from a determined period from the year 2021 that correlate the symptoms of passive and/or active patients for COVID-19 and their respective results of IgG/IgM serological tests in hospitals in the city of Cruzeiro, São Paulo, Brazil. The form had been applied to 333 people and obtained conclusive results and several symptoms were presented; in addition, asymptomatic cases were also analyzed and directed in the genomic study of variants of concern, as well as vaccination data in the study region.

Abstract Globally there is a substantial burden of mental health problems among children and adolescents. Task-shifting/task-sharing mental health services to non-specialists, e.g. teachers in school settings, provide a unique opportunity for the implementation of mental health interventions at scale in low- and middle-income countries (LMICs). There is scant information to guide the large-scale implementation of school-based mental health programme in LMICs. This article describes pathways for large-scale implementation of a School Mental Health Program (SMHP) in the Eastern Mediterranean Region (EMR). A collaborative learning group (CLG) comprising stakeholders involved in implementing the SMHP including policymakers, programme managers and researchers from EMR countries was established. Participants in the CLG applied the theory of change (ToC) methodology to identify sets of preconditions, assumptions and hypothesized pathways for improving the mental health outcomes of school-aged children in public schools through implementation of the SMHP. The proposed pathways were then validated through multiple regional and national ToC workshops held between January 2017 and September 2019, as the SMHP was being rolled out in three EMR countries: Egypt, Pakistan and Iran. Preconditions, strategies and programmatic/contextual adaptations that apply across these three countries were drawn from qualitative narrative summaries of programme implementation processes and facilitated discussions during biannual CLG meetings. The ToC for large-scale implementation of the SMHP in the EMR suggests that identifying national champions, formulating dedicated cross-sectoral (including the health and education sector) implementation teams, sustained policy advocacy and stakeholders engagement across multiple levels, and effective co-ordination among education and health systems especially at the local level are among the critical factors for large-scale programme implementation. The pathways described in this paper are useful for facilitating effective implementation of the SMHP at scale and provide a theory-based framework for evaluating the SMHP and similar programmes in the EMR and other LMICs.

Background It is unclear whether screening for sepsis using an electronic alert in hospitalized ward patients improves outcomes. The objective of the Stepped-wedge Cluster Randomized Trial of Electronic Early Notification of Sepsis in Hospitalized Ward Patients (SCREEN) trial is to evaluate whether an electronic screening for sepsis compared to no screening among hospitalized ward patients reduces all-cause 90-day in-hospital mortality. Methods and design This study is designed as a stepped-wedge cluster randomized trial in which the unit of randomization or cluster is the hospital ward. An electronic alert for sepsis was developed in the electronic medical record (EMR), with the feature of being active (visible to treating team) or masked (inactive in EMR frontend for the treating team but active in the backend of the EMR). Forty-five clusters in 5 hospitals are randomized into 9 sequences of 5 clusters each to receive the intervention (active alert) over 10 periods, 2 months each, the first being the baseline period. Data are extracted from EMR and are compared between the intervention (active alert) and control group (masked alert). During the study period, some of the hospital wards were allocated to manage patients with COVID-19. The primary outcome of all-cause hospital mortality by day 90 will be compared using a generalized linear mixed model with a binary distribution and a log-link function to estimate the relative risk as a measure of effect. We will include two levels of random effects to account for nested clustering within wards and periods and two levels of fixed effects: hospitals and COVID-19 ward status in addition to the intervention. Results will be expressed as relative risk with a 95% confidence interval. Conclusion The SCREEN trial provides an opportunity for a novel trial design and analysis of routinely collected and entered data to evaluate the effectiveness of an intervention (alert) for a common medical problem (sepsis in ward patients). In this statistical analysis plan, we outline details of the planned analyses in advance of trial completion. Prior specification of the statistical methods and outcome analysis will facilitate unbiased analyses of these important clinical data. Trial registration ClinicalTrials.gov NCT04078594 . Registered on September 6, 2019

It is unclear whether screening for sepsis using an electronic alert in hospitalized ward patients improves outcomes. The objective of the Stepped-wedge Cluster Randomized Trial of Electronic Early Notification of Sepsis in Hospitalized Ward Patients (SCREEN) trial is to evaluate whether an electronic screening for sepsis compared to no screening among hospitalized ward patients reduces all-cause 90-day in-hospital mortality. This study is designed as a stepped-wedge cluster randomized trial in which the unit of randomization or cluster is the hospital ward. An electronic alert for sepsis was developed in the electronic medical record (EMR), with the feature of being active (visible to treating team) or masked (inactive in EMR frontend for the treating team but active in the backend of the EMR). Forty-five clusters in 5 hospitals are randomized into 9 sequences of 5 clusters each to receive the intervention (active alert) over 10 periods, 2 months each, the first being the baseline period. Data are extracted from EMR and are compared between the intervention (active alert) and control group (masked alert). During the study period, some of the hospital wards were allocated to manage patients with COVID-19. The primary outcome of all-cause hospital mortality by day 90 will be compared using a generalized linear mixed model with a binary distribution and a log-link function to estimate the relative risk as a measure of effect. We will include two levels of random effects to account for nested clustering within wards and periods and two levels of fixed effects: hospitals and COVID-19 ward status in addition to the intervention. Results will be expressed as relative risk with a 95% confidence interval. The SCREEN trial provides an opportunity for a novel trial design and analysis of routinely collected and entered data to evaluate the effectiveness of an intervention (alert) for a common medical problem (sepsis in ward patients). In this statistical analysis plan, we outline details of the planned analyses in advance of trial completion. Prior specification of the statistical methods and outcome analysis will facilitate unbiased analyses of these important clinical data.