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Preclinical assessment of a ganglioside-targeted therapy for Parkinson’s disease with the first-in-class adaptive peptide AmyP53
Innovative ZnO-W18O49 nanocomposites and ZnWO4 nanostructures for water treatment
Association between hypothermia and hyperthermia and 28-day mortality in pediatric intensive care unit patients: a retrospective cohort study
MRI distortion correction is associated with improved local control in stereotactic radiotherapy for brain metastases
Abstract Distortions in brain MRI caused by gradient nonlinearities may reach several millimeters, thus distortion correction is strongly recommended for radiotherapy treatment planning. However, the significance of MRI distortion correction on actual clinical outcomes has not been described yet. Therefore, we investigated the impact of planning MRI distortion correction on subsequent local control in a historic series of 419 brain metastases in 189 patients treated with stereotactic radiotherapy between 01/2003 and 04/2015. Local control was evaluated using a volumetric extension of the RANO-BM criteria. The predictive significance of distortion correction was assessed using competing risk analysis. In this cohort, 2D distortion-corrected MRIs had been used for treatment planning in 52.5% (220/419) of lesions, while uncorrected MRIs had been employed in 47.5% (199/419) of metastases. 2D distortion correction was associated with improved local control (Cumulative incidence of local progression at 12 months: 14.3% vs. 21.2% and at 24 months: 18.7% vs. 28.6%, p = 0.038). In multivariate analysis, adjusting for histology, baseline tumor volume, interval between MRI and treatment delivery, year of planning MRI, biologically effective dose and adjuvant Whole-brain radiotherapy, use of distortion correction remained significantly associated with improved local control (HR 0.55, p = 0.020). This is the first study to clinically evaluate the impact of MRI gradient nonlinearity distortion correction on local control in stereotactic radiotherapy for brain metastases. In this historic series, we found significantly higher local control when using 2D corrected vs. uncorrected MRI studies for treatment planning. These results stress the importance of assuring that MR images used for radiotherapy treatment planning are properly distortion-corrected.
Evaluation of the winning technical and tactical actions of the best female table tennis players in the years 1970-2021
U shaped relationship between mean arterial pressure and 28 day mortality in ICU patients with acute myocardial infarction
Disproportionality analysis of post-marketing safety concerns associated with selumetinib in the FDA adverse event reporting system
Network pharmacology and experimental validation reveal dexmedetomidine’s protective mechanisms against acute liver injury in mice
Measuring the effect of repetitive stretching on the deformability of human red blood cells using optical tweezers
The facile conversion of waste biomass into few-layer graphene oxide
Abstract Carbon-based materials are highly sought after due to their superior properties, making them valuable for high-performance applications. However, most carbon-based materials are derived from fossil sources, and their synthesis often involves hazardous chemicals. Therefore, it is essential to develop sustainable methods for synthesising these materials from renewable resources, using fewer solvents, catalytic reagents, and generating minimal waste. In this study, few-layer graphene oxide (GO) was directly synthesised from waste biomass, without the formation of an amorphous intermediate, and its use as a fire retardant in two bioplastics was evaluated. Waste birch wood biomass was converted directly into graphitic carbon using manganese nitrate as a catalyst, with varying concentrations (0.003 to 0.1 mol-metal/g-wood) and treatment durations (1 and 2 h). The catalyst was doped through vacuum soaking and mild heating (90 °C), which facilitated the formation of graphitic carbon at relatively lower temperatures (< 1000 °C), eliminating the need for producing amorphous biochar prior to graphitisation. After pyrolysis at 900 °C and 950 °C for 2 h, the sample containing 0.005 mol-metal/g-wood, treated at 950 °C, exhibited the highest degree of graphitisation. This sample was further processed in a planetary ball mill with melamine as a dispersant for 30 min. Characterisation showed a broad absorption peak at 230 nm and the presence of semi-transparent sheets (3–8 layers), indicating the presence of GO. To evaluate its performance as a fire retardant, 2 wt% of the synthesised GO was added to polyamide 11 and wheat gluten bioplastics, which were then subjected to cone calorimeter tests. The results showed a 42% and 33% reduction in the peak heat release rate for polyamide 11 and wheat gluten, respectively, compared to their neat counterparts. The flame retardancy index further indicated that GO had a more significant impact on improving the fire safety of wheat gluten compared to polyamide 11. This study highlights a sustainable method for the preparation of few-layer GO at lower temperatures than contemporary methods, making the process more energy-efficient, environmentally friendly, and cost-effective. Additionally, the effectiveness of few-layer GO as a fire-retardant additive for enhancing the fire safety of bioplastics has been demonstrated.
Governance and responses of health and surveillance systems to COVID-19 in BRICS countries: A scoping review protocol
The capacity of countries to respond to COVID-19 has varied, worsening inequities. Structural factors (such as socio-economic inequalities and health system financing conditions) and political-institutional factors (such as the leadership and directionality of government action) have a major influence on the structure and outcomes of national responses. An effective COVID-19 response requires strong national governance, coordination strategies, and integration between health system actions and public health surveillance measures. This scoping review aims to synthesize the evidence on the governance and responses of the BRICS countries to the COVID-19 pandemic within a context of structural inequalities, identifying lessons and gaps in the current literature. This protocol was guided by the scoping review methodology, developed by the Joanna Briggs Institute, and used the PRISMA-P reporting guidelines. Searches will be carried out on the BVS Portal, Pubmed, Web of Science, Scopus, Embase and Dimensions databases. Inclusion criteria include secondary sources in the form of scientific publications, complemented by government and institutional documents published between 2020 and 2024. Exclusion criteria exclude publications that do not involve the selected countries, focus only on local or regional level without contemplating national-level elements, and/or address with COVID-19 solely from a clinical perspective. The scoping review process will select and organize retrieved data using the Rayyan software. Six independent reviewers will select the articles, working in three pairs. The results will be described, analyzed, and categorized through a descriptive synthesis, correlating them with the research objectives and questions. Registration This protocol is registered on the Open Science Framework (OSF) (available at https://doi.org/10.17605/OSF.IO/SN5ZY) and Figshare (available at https://doi.org/10.6084/m9.figshare.25908340) platforms.
New adjusted missing value imputation in multiple regression with simple random sampling and rank set sampling methods
This research compared the efficiency of several adjusted missing value imputation methods in multiple regression analysis. The four imputation methods were the following: regression-ratio quartile1,3 (R-RQ1,3) imputation of Al-Omari, Jemain and Ibrahim; adjusted regression-chain ratio quartile1,3 (AR-CRQ1,3) imputation of Kadilar and Cinji; adjusted regression-multivariate ratio quatile1,3 (AR-MRQ1,3) imputation of Feng, Ni, and Zou; and adjusted regression-multivariate chain ratio quartile1,3 (AR-MCRQ1,3) imputation of Lu for each simple random sampling (SRS) and rank set sampling (RSS). The performance measures mean square error (MSE) and mean absolute percentage error (MAPE). The study showed that the AR-MRQ1 method with SRS provided the minimum mean square error for small error variance. However, the AR-MCRQ3 provided the minimum mean square error for a large error variance. Considering all error variance in mean absolute percentage error, the AR-MCRQ1 provided the minimum mean absolute percentage error. The AR-MRQ1 method with RSS provided the minimum mean square error for a small error variance. However, the AR-MCRQ3 provided the minimum mean square error for medium and large error variance. Regarding the mean absolute percentage error measure, the AR-MRQ1 provided the minimum mean absolute percentage error for a small error variance. However, the AR-MCRQ1 provided the minimum mean absolute percentage error for medium and large error variance. For both SRS and RSS, AR-MCRQ1 was the best method for missing value imputation in multiple regression analysis, followed by AR-MCRQ3. Moreover, the RSS estimators provided smaller MSE and MAPE than the SRS estimators. Therefore, the RSS estimators were more efficient than the SRS estimators.
Demographic, socioeconomic and regional disparities in the coverage of water, sanitation and hygiene facilities in four South Asian Countries
Background Ensuring an adequate water, sanitation, and hygiene (WASH) is crucial for upholding public health and achieving Sustainable Development Goals (SDG-6). The main goal of this study was to review the existing WASH facilities, mapping for regional comparisons, and identify the significant socioeconomic and demographic factors associated with WASH facilities in Afghanistan, Bangladesh, Nepal, and Pakistan. Methods This study employed a quantitative research design using the most recent Multiple Indicator Cluster Survey (MICS) data from Afghanistan (2022–23, n = 23,213), Bangladesh (2019, n = 61,242), Nepal (2019, n = 12,655), and Pakistan (2017–19, n = 96,105). Data analysis was done using descriptive statistics and multivariate logistic regression model. Besides, spatial mapping was used for regional comparison, the Generalized Variance Inflation Factor (GVIF) was applied for checking multicollinearity, and the Receiver Operating Characteristic (ROC) curve was used to evaluate model performance. Results This study revealed substantial disparities in the coverage of WASH facilities across four countries. Nepal had the highest coverage (75.33%), followed by Pakistan (59.47%), Bangladesh (50.28%) and Afghanistan (33.54%). Wealthier households were associated with higher odds of WASH facilities compared to the poor: Afghanistan (aOR = 7.83; 95% CI: 6.58–9.32; p < 0.001), Bangladesh (aOR = 5.75; 95% CI: 5.34–6.20; p < 0.001), Nepal (aOR = 5.80; 95% CI: 4.52–7.44; p < 0.001), and Pakistan (aOR = 9.64; 95% CI: 8.79-10.58; p < 0.001). In addition, place of residence, education of household head, access to the media, age of household head, and family size emerged as significant determinants of WASH facilities across all four countries. Conclusion The findings suggest that wealthier households, those with educated heads, and those in urban areas have higher coverage of WASH facilities. To ensure adequate WASH facilities, policymakers should focus on rural areas, lower-income groups, less educated household heads, and should conduct awareness campaigns.
Classification of pulmonary diseases from chest radiographs using deep transfer learning
Pulmonary diseases are the leading causes of disabilities and deaths worldwide. Early diagnosis of pulmonary diseases can reduce the fatality rate. Chest radiographs are commonly used to diagnose pulmonary diseases. In clinical practice, diagnosing pulmonary diseases using chest radiographs is challenging due to Overlapping and complex anatomical Structures, variability in radiographs, and their quality. The availability of a medical specialist with extensive professional experience is profoundly required. With the use of Convolutional Neural Networks in the medical field, diagnosis can be improved by automatically detecting and classifying these diseases. This paper has explored the effectiveness of Convolutional Neural Networks and transfer learning to improve the predictive outcomes of fifteen different pulmonary diseases using chest radiographs. Our proposed deep transfer learning-based computational model achieved promising results as compared to existing state-of-the-art methods. Our model reported an overall specificity of 97.92%, a sensitivity of 97.30%, a precision of 97.94%, and an Area under the Curve of 97.61%. It has been observed that the promising results of our proposed model will be valuable tool for practitioners in decision-making and efficiently diagnosing various pulmonary diseases.
STAGE framework: A stock dynamic anomaly detection and trend prediction model based on graph attention network and sparse spatiotemporal convolutional network
As the financial market becomes increasingly complex, stock prediction and anomaly data detection have emerged as crucial tasks in financial risk management. However, existing methods exhibit significant limitations in handling the intricate relationships between stocks and addressing anomalous data. This paper proposes the STAGE framework, which integrates the Graph Attention Network (GAT), Variational Autoencoder (VAE), and Sparse Spatiotemporal Convolutional Network (STCN), to enhance the accuracy of stock prediction and the robustness of anomaly data detection. Experimental results show that the complete STAGE framework achieved an accuracy of 85% after 20 training epochs, which is 10% to 20% higher than models with key algorithms removed. In the anomaly detection task, the STAGE framework further improved the accuracy to 95%, demonstrating fast convergence and stability. This framework offers an innovative solution for stock prediction, adapting to the complex dynamics of real-world markets.