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Ameliorative effects of Berberine chloride against 5-fluorouracil-induced cardiotoxicity in Sprague Dawley rats
Abstract 5-Fluorouracil (5-FU) is the most commonly used chemotherapeutic agent for solid malignancies. Although it has crucial therapeutic effects, it ranks as the second most cardiotoxic antineoplastic agent. Berberine (BBR) is a quaternary benzylisoquinoline alkaloid with promising antioxidant properties. The current study aimed to assess the palliative effect of BBR on 5-FU-induced cardiotoxicity in rats. Fifty male Sprague Dawley rats were randomly divided into five groups: negative control, which received 2% DMSO orally (PO) for 2 weeks; cardiotoxic, which received a single intraperitoneal (IP) injection of 5-FU (150 mg/kg); groups 3 and 4, which received a single IP injection of 5-FU (150 mg/kg) followed by BBR (50 mg/kg and 100 mg/kg, respectively) PO for 2 weeks; and a BBR-only group, which received BBR (100 mg/kg) PO for 2 weeks. On the 14th day, all groups underwent ECG evaluation. Blood and heart samples were collected 24 h after the last dose for further investigations. 5-FU induced significant alterations in the ECG pattern and caused a significant increase in cardiac troponin I (cTnI), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), and malondialdehyde (MDA). Moreover, it led to decreased levels of superoxide dismutase (SOD), reduced glutathione (GSH), and total antioxidant capacity (TAC). Our data suggest that BBR could mitigate 5-FU-induced cardiotoxicity by modulating cardiac injury markers, normalizing cTnI, CK-MB, and LDH levels, reducing oxidative stress by lowering MDA levels, and increasing SOD, GSH, and TAC levels. Additionally, BBR inhibited apoptotic events by suppressing caspase-3 activation and upregulating Bcl-2 expression, reduced the inflammatory response by downregulating cyclooxygenase-2 (COX-2) and tumor necrosis factor-alpha (TNF-α) expression levels, and decreased the risk of thrombosis by increasing endothelial nitric oxide synthase (eNOS) expression levels. In conclusion, BBR exerts ameliorative effects against 5-FU-induced cardiotoxicity in rats.
Nanoengineered cotton wipes for antiviral protection and environmental compatibility
Abstract Nanotechnology-based modifications enable the development of high-performance materials, expanding their applications beyond conventional uses. This study presents the production of sustainable antiviral cotton wipes through the nanoengineering of cotton fibers and investigates their mineralization behavior in compost and marine environments. Silver (Ag) nanoparticles, averaging 22 nm in diameter, were synthesized in situ using the inherent reducing agents present in raw cotton fiber and embedded within the fiber matrix. The modified cotton fibers were incorporated into nonwoven wipes using a hydroentanglement process at 20 wt%, yielding cotton wipes containing 225 mg/kg of Ag nanoparticles. The Ag-nanoengineered cotton wipes demonstrated a 99.68% reduction in virus titer against Feline calicivirus in a surface time-kill test using ready-to-use, pre-saturated wipes. Mineralization analyses indicated that both control and Ag-engineered cotton wipes followed first-order decay kinetics in compost and marine environments, with no significant difference in overall mineralization behavior. Ag-nanoengineered cotton wipes exhibited slightly lower mineralization rates, extended induction periods, and delayed maximum mineralization rates compared to control cotton wipes. Nanoengineering increased the half-life of cotton wipes by 19% in compost and 8% in marine conditions, suggesting complete mineralization within one month in compost and two months in marine environments.
A high load torsion pendulum of trifilar suspension magnetic damper for inertial sensor test
The cycle gene is essential for both daily responses and seasonal reproduction in the Northern house mosquito, Culex pipiens
Identifying the most sensitive growth stages of soybean to defoliation
Comparative analysis of Monte Carlo simulations and experimental evaluation of PMMA reinforced with hgo for gamma radiation shielding
Lensless magneto-optical imaging
Abstract Magneto-optical methods, which utilize the interaction of polarized light with the magnetization of the sample in reflection through the magneto-optical Kerr effect or in transmission through the accordant Faraday effect, present prominent and widespread optical microscopy techniques for studying magnetic microstructures. In non-magnetic light microscopy, several alternatives to lens-based imaging have been developed, which offer various advantages, including an improved ratio of field-of-view to magnification. Selected lensless methods also provide access to both intensity and phase information of the probing light field, which presents an additional information channel obtainable from the studied sample. In a proof-of-principle study we verify that the reconstructed magneto-optical intensity obtained from a lensless multiplane recording scheme is in full qualitative agreement with conventional lens-based Faraday microscopy. The additional phase information, not accessible with conventional methods, offers direct access to domain information through the imaginary part of the Faraday or Kerr component in the studied material and allows domain imaging even in a crossed analyzer position or without the use of an analyzer. These findings will open the path to exploit the various established advantages of lensless microscopy for the magneto-optical investigation of magnetic materials.
Identification of mitochondria-related biomarkers for acute respiratory distress syndrome
Efficient enamel subsurface lesion remineralisation and dentine tubule occlusion by high concentration CPP-ACP: a randomised, cross-over in situ study
Development and validation of a gut motility based model for predicting bowel preparation quality
A holistic methodology for evaluating flood vulnerability, generating flood risk map and conducting detailed flood inundation assessment
Abstract Flood risk assessment (FRA) is a process of evaluating potential flood damage by considering vulnerability of exposed elements and consequences of flood events through risk analysis which recommends the mitigation measures to reduce the impact of floods. This flood risk analysis is a technique used to identify and rank the level of flood risk through modeling and spatial analysis. In the present study, Musi River in the Osmansagar basin is taken in to consideration to evaluate the flood risk, which is located at Hyderabad. The input data collected for the study encompasses Hydrological and Meteorological datasets from Gandipet Guage station in Hyderabad, raster grid data for Osmansagar basin along with several indicators data influencing flood vulnerability. The primary research objective is to conduct a quantitative assessment of the Flood vulnerability index (FVI), to develop a comprehensive flood risk map and to evaluate the magnitude of damaging flood parameters, inundated volume and to analyze the regions inundated in the study area. In risk analysis, FVI determines the degree of which an area is susceptible to the negative impact of flood through various influencing indicators, Flood hazard map segregate the regions based on flood risk level through spatial analysis in Arc-GIS. A part of this study includes an integrated methodology for assessing flood inundation using Quantum Geographic Information Systems (QGIS) data modelling for spatial analysis, Hydraulic Engineering Center’s River Analysis System (HEC-RAS) hydraulic modelling for unsteady flow analysis and a machine learning technique i.e. XGBoost, to enhance the accuracy and efficiency of flood risk assessment. Subsequently, inundation map produced using HEC-RAS is superimposed with building footprints to identify vulnerable structures. The results obtained by risk analysis using hydraulic modeling, GIS analysis, and machine learning technique illustrates the flood vulnerability, areas having high flood risk and inundated volume along with predicted flood levels for next 10 years. These findings demonstrate the efficiency of the holistic approach in identifying vulnerability, flood-prone areas and evaluating potential impacts on infrastructure and communities. The outcomes of the study assist the decision-makers to gain valuable insights into flood risk management strategies.
Sensory properties of fermented Zamné (Senegalia macrostachya seeds) and their influence on the broth quality and sensory profile
Relationship between height of cheer basket toss and specific physical ability of bases from a kinematic perspective
Sleep mediates the association between stroke and all cause mortality in the NHANES cohort
Time series analysis of urethral obstruction in male cats in a veterinary teaching hospital in São paulo, Brazil
Thermodynamic analysis and intelligent modeling of statin drugs solubility in supercritical carbon dioxide
Prediction of aggregation in monoclonal antibodies from molecular surface curvature
Abstract Protein aggregation is one of the key challenges in the biopharmaceutical industry as its control is crucial in achieving long-term stability and efficacy of biopharmaceuticals. Attempts have been made to develop regression models for predicting the aggregation of monoclonal antibodies in solution using machine learning methods. These efforts have yielded varying levels of success, with current state-of-the-art AI approaches achieving good prediction accuracies ( $$r=0.86$$ ). Here, we demonstrate the prediction of aggregation rate in monoclonal antibodies with beyond state-of-the-art reliability using a coupled AI-MD-Molecular surface curvature modelling platform. The scientific novelty of this approach lies in using local geometrical surface curvature of proteins as the core element for protein stability analysis. By combining local surface curvature and hydrophobicity, as derived from time-dependent MD simulations, we are able to construct aggregation predictive features that, when coupled with linear regression machine learning techniques, give a high prediction accuracy ( $$r=0.91$$ ) on a dataset of 20 molecules. More generally, this approach shows significant potential for quantitative in silico screening and prediction of protein aggregation, which is of great scientific and industrial relevance, particularly in biopharmaceutics.