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The impacts of technological overlap on international collaboration in China’s green innovation endeavors
Sex-specific trends in the global burden and risk factors of atrial fibrillation and flutter from 1990 to 2021
A novel simultaneous monitoring method for surface roughness and tool wear in milling process
Heartbeat evoked potentials reflect interoceptive awareness during an emotional situation
Shikimic acid protects against doxorubicin-induced cardiotoxicity in rats
Abstract Doxorubicin (DOX) is used to treat a variety of malignancies; however, its cardiotoxicity limits its effectiveness. Shikimic acid (SA) showed several promising biomedical applications. This study investigated the protective effect of SA on DOX-induced cardiotoxicity in male rats. The ADMETlab 2.0 web server was used to predict the pharmacokinetic properties of SA. Molecular docking studies were conducted using AutoDock Vina. Fifty male rats were divided into 4 groups (n = 10); G1 was a negative control; G2 was injected with 4 mg/kg of DOX intraperitoneally (i.p.) once a week for a month; G3 was gavaged by 1/10 of SA LD50 (280 mg/kg) daily for a month, and G4 was injected with DOX as in G2 and with SA as in G3. After a month, hematological, biochemical, molecular, and histopathological investigations were assessed. The results showed that SA treatment led to significant amelioration of the DOX-induced cardiotoxicity in rats by restoring hematological, biochemical, inflammatory biomarkers, antioxidant gene expression, and cardiac histopathological alterations. Importantly, the impact of SA treatment against DOX-promoted cardiac deterioration is by targeting the Nrf-2/Keap-1/HO-1/NQO-1 signaling pathway, which in turn induces the antioxidant agents. These findings suggest that SA treatment could potentially mitigate cardiac toxicity during DOX-based chemotherapy.
Enhancing the shelf life of natural scale inhibitors using bio preservatives
Abstract One of the key challenges in using natural extracts for water treatment is their biodegradability and susceptibility to microbial spoilage, which can limit storage and long-term effectiveness. This study investigates the scale inhibition capabilities of an aqueous extract of Salvia rosmarinus sp through electrochemical measurements, conductivity tests, and morphological examination. Additionally, two natural substances, Rhamnolipid and Chitosan, were evaluated as bio-preservatives to prevent mold growth and enhance the shelf life of the rosemary extract. The reasons for selecting these specific bio-preservatives include their known antimicrobial properties, antioxidant effects, environmental benefits, and suitability for the intended application. For 24 weeks, we conducted a microbial examination and assessed the anti-scaling performance of the extract in combination with the bio-preservatives. The results demonstrate that rosemary extract significantly inhibits CaCO3 scale precipitation, attributed to the presence of carboxylate and hydroxyl groups which effectively chelate cations and disturb the normal crystal growth of the scales. Additionally, the rosemary extract-chitosan mixture exhibits superior antimicrobial and anti-scaling performance compared to the rosemary extract–rhamnolipids combination over six months. It can be concluded that a 1:2 ratio of chitosan to rosemary extract provides an effective eco-friendly scale inhibitor and reduces the growth of pathogenic bacteria and fungi with an extended shelf life. In this context, biosurfactants and polysaccharides present beneficial properties that offer sustainable and biological alternatives to conventional chemical biocides.
Real-time vs. static ultrasound-guided needle cricothyroidotomy: a randomized crossover simulation trial
Green forage impacts on the DNA methylation in the ruminal wall of Italian mediterranean dairy buffaloes
Porcine milk small extracellular vesicles modulate peripheral blood mononuclear cell proteome in vitro
Position-context additive transformer-based model for classifying text data on social media
Abstract In recent years, the continuous increase in the growth of text data on social media has been a major reason to rely on the pre-training method to develop new text classification models specially transformer-based models that have proven worthwhile in most natural language processing tasks. This paper introduces a new Position-Context Additive transformer-based model (PCA model) that consists of two-phases to increase the accuracy of text classification tasks on social media. Phase I aims to develop a new way to extract text characteristics by paying attention to the position and context of each word in the input layer. This is done by integrating the improved word embedding method (the position) with the developed Bi-LSTM network to increase the focus on the connection of each word with the other words around it (the context). As for phase II, it focuses on the development of a transformer-based model based primarily on improving the additive attention mechanism. The PCA model has been tested for the implementation of the classification of health-related social media texts in 6 data sets. Results showed that performance accuracy was improved by an increase in F1-Score between 0.2 and 10.2% in five datasets compared to the best published results. On the other hand, the performance of PCA model was compared with three transformer-based models that proved high accuracy in classifying texts, and experiments also showed that PCA model overcame the other models in 4 datasets to achieve an improvement in F1-score between 0.1 and 2.1%. The results also led us to conclude a direct correlation between the volume of training data and the accuracy of performance as the increase in the volume of training data positively affects F1-Score improvement.
Potent targeted larvicidal activities of marine-derived Bacillus sp. bacterial extracts on mosquito vectors
Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks
Insights into putative alginate lyases from epipelagic and mesopelagic communities of the global ocean
Identification of novel COL4A5 variants and prenatal diagnosis in three large families
Italian screening protocol and genotypes characterization for HCV elimination (2022–2023) in Ferrara’s province: a real-world study
Abstract Hepatitis C virus (HCV) is a worldwide health hazard, and in chronic form (nowadays affecting 50 million people – World Health Organization data) can be lethal. To forestall it, preventive screening is a mandatory approach. Since 2021, Italy conducts a national-wide screening program to eliminate the virus from its population. The team perfected an innovative method throughout the period between 2022 and 2023, to answer that medical necessity and map HCV genotypes. The medical protocol has introduced a dedicated double invitation model for the adherents, with consequential pre-prepared medical consumables allocated. The population was divided in three separate groups: born between 1969 and 1989, addiction services, and prison. Two screening levels were carried out: anti-HCV antibodies (indirect chemiluminescence immunoassay) and quantitative HCV RNA reverse transcription. 51,283 adherents were registered: 447 resulted positive to the first screening round, and 88 to the second (393 and 77 patients respectively from population born between 1969 and 1989). The medical protocol introduced allowed to substantially increase patients and medical staff compliance to HCV screening (adherence: 51.83%, the highest in Italy). HCV genotypes’ distribution was mapped by patients’ age, biological sex and origin over five groups comprehending subtypes 1a(35.06% of the positive population), 1b(27.27%), 2a/2c(10.39%), 3a(22.08%), 4a/4c/4d(5.19%).
Disulfidptosis related immune genes drive prognostic model development and tumor microenvironment characterization in bladder urothelial carcinoma
A new approach for the detection of genetic alterations utilizing modified loop-mediated isothermal amplification reaction (LAMP)
Abstract The increasing use of genetic testing for personalised therapy, highlights the need for rapid, reliable diagnostics. Current methods are hindered by complex workflows, requiring advanced equipment, skilled personnel, and invasive tissue sampling. Loop-mediated isothermal amplification (LAMP) has emerged as a more efficient alternative to traditional PCR. LAMP eliminates thermal cycling, allowing faster, more cost-effective tests, and is less sensitive to inhibitors, enabling testing from minimally processed samples. Although LAMP is newer and has a longer assay development time than PCR, its potential in oncology, particularly for detecting genetic changes, is promising. We have developed a LAMP-based method for detecting genetic variations, optimized for point-of-care testing. This technique uses modified primers with alterations at the 3’ end of either F2 or B2 primers, ensuring specificity for altered sequences. The assay only produces a positive signal when the genetic variant is present, distinguishing it from wild-type DNA. Our findings demonstrate that this method has high specificity and sensitivity, even in samples with both wild-type and mutated material. Paired with a portable device, this LAMP-based diagnostic method could revolutionize genetic alteration detection, offering quicker results and improving treatment outcomes, particularly for targeted therapies.
RETRACTED ARTICLE: Predicting the compressive strength of polymer-infused bricks: A machine learning approach with SHAP interpretability
Abstract The rapid increase in global waste production, particularly Polymer wastes, poses significant environmental challenges because of its nonbiodegradable nature and harmful effects on both vegetation and aquatic life. To address this issue, innovative construction approaches have emerged, such as repurposing waste Polymers into building materials. This study explores the development of eco-friendly bricks incorporating cement, fly ash, M sand, and polypropylene (PP) fibers derived from waste Polymers. The primary innovation lies in leveraging advanced machine learning techniques, namely, artificial neural networks (ANN), support vector machines (SVM), Random Forest and AdaBoost to predict the compressive strength of these Polymer-infused bricks. The polymer bricks’ compressive strength was recorded as the output parameter, with cement, fly ash, M sand, PP waste, and age serving as the input parameters. Machine learning models often function as black boxes, thereby providing limited interpretability; however, our approach addresses this limitation by employing the SHapley Additive exPlanations (SHAP) interpretation method. This enables us to explain the influence of different input variables on the predicted outcomes, thus making the models more transparent and explainable. The performance of each model was evaluated rigorously using various metrics, including Taylor diagrams and accuracy matrices. Among the compared models, the ANN and RF demonstrated superior accuracy which is in close agreement with the experimental results. ANN model achieves R 2 values of 0.99674 and 0.99576 in training and testing respectively, whereas RMSE value of 0.0151 (Training) and 0.01915 (Testing). This underscores the reliability of the ANN model in estimating compressive strength. Age, fly ash were found to be the most important variable in predicting the output as determined through SHAP analysis. This study not only highlights the potential of machine learning to enhance the accuracy of predictive models for sustainable construction materials and demonstrates a novel application of SHAP to improve the interpretability of machine learning models in the context of Polymer waste repurposing.