Harnessing Machine Learning to Improve Healthcare Monitoring with FAERS

This research study investigates the potential of machine learning techniques to improve healthcare monitoring through the utilization of data from the FDA Adverse Event Reporting System (FAERS). The objective is to explore specific applications of machine learning in healthcare monitoring with FAERS and highlight their findings. The study reveals several significant ways in which machine learning can contribute to enhancing healthcare monitoring using FAERS.Machine learning algorithms can detect potential safety signals at an early stage by analyzing FAERS data. By employing anomaly detection and temporal pattern analysis techniques, these models can identify emerging safety concerns that were previously unknown or underreported. This early detection enables timely action to mitigate risks associated with medications or medical products.Machine learning models can assist in pharmacovigilance triage, addressing the challenge posed by the large number of adverse event reports within FAERS. By developing ranking and classification models, adverse events can be prioritized based on severity, novelty, or potential impact. This automation of the triage process enables pharmacovigilance teams to efficiently identify and investigate critical safety concerns.Machine learning models can automate the classification and coding of adverse events, which are often present in unstructured text within FAERS reports. Through the application of Natural Language Processing (NLP) techniques, such as named entity recognition and text classification, relevant information can be extracted, enhancing the efficiency and accuracy of adverse event coding.Machine learning algorithms can refine and validate signals generated from FAERS data by incorporating additional data sources, such as electronic health records, social media, or clinical trials data. This integration provides a more comprehensive understanding of potential risks and helps filter out false positives, facilitating the identification of signals requiring further investigation.Machine learning enables real-time surveillance of FAERS data, allowing for the identification of safety concerns as they occur. Continuous monitoring and real-time analysis of incoming reports enable machine learning models to trigger alerts or notifications to relevant stakeholders, promoting timely intervention to minimize patient harm.The study demonstrates the use of machine learning models to conduct comparative safety analyses by combining FAERS data with other healthcare databases. These models assist in identifying safety differences between medications, patient populations, or dosing regimens, enabling healthcare providers and regulators to make informed decisions regarding treatment choices.While machine learning is a powerful tool in healthcare monitoring, its implementation should be complemented by human expertise and domain knowledge. The interpretation and validation of results generated by machine learning models necessitate the involvement of healthcare professionals and pharmacovigilance experts to ensure accurate and meaningful insights.This research study illustrates the diverse applications of machine learning in improving healthcare monitoring using FAERS data. The findings highlight the potential of machine learning in early safety signal detection, pharmacovigilance triage, adverse event classification and coding, signal refinement and validation, real-time surveillance and alerting, and comparative safety analysis. The study emphasizes the importance of combining machine learning with human expertise to achieve effective and reliable healthcare monitoring

A concatenation approach-based disease prediction model for sustainable health care system

In the present world, due to many factors like environmental changes, food styles, and living habits, human health is constantly affected by different diseases, which causes a huge amount of data to be managed in health care. Some diseases become life-threatening if they are not cured at the starting stage. Thus, it is a complex task for the healthcare system to design a well-trained disease prediction model for accurately identifying diseases. Deep learning models are the most widely used in disease prediction research, but their performance is inferior to conventional models. In order to overcome this issue, this work introduces the concatenation of Inception V3 and Xception deep learning convolutional neural network models. The proposed model extracts the main features and produces the prediction result more accurately than traditional predictive models. This work analyses the performance of the proposed model in terms of accuracy, precision, recall, and f1-score. It compares the proposed model to existing techniques such as Stacked Denoising Auto-Encoder (SDAE), Logistic Regression (LR), MLP, MLP with attention mechanism (MLP-A), Support Vector Machine (SVM), Multi Neural Network (MNN), and Hybrid Convolutional Neural Network (CNN)-Random Forest (RF)

Phyto-fabrication of AgNPs using leaf extract of Vitex trifolia: potential to antibacterial, antioxidant, dye degradation, and their evaluation of non-toxicity to Chlorella vulgaris

The study assessed the bactericidal effects of green rapid biogenic synthesis of Vitex trifolia leaves AgNPs on MDR bacteria. The synthesis of AgNPs is indicated by a color change from yellow to dark brown. The ultra-visible spectrophotometer displays AgNPs at 430 nm max. This demonstrates that ions (Ag+) were converted to silver (Ag), indicating the formation of silver nanoparticles. The synthesized nanoparticles were confirmed by their crystalline nature, shape, size, and functional groups via Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), energy dispersive X- ray spectroscopy (EDAX), and transmission electron microscopy (TEM). Biomolecules contain aqueous Vitex extract for capping and reducing the AgNPs. The nanoparticles have a face-centered cubic structure (FCC) crystallized. The antibacterial activity against Staphylococcus aureus, Vibrio cholerae, and Klebsiella pneumoniae exhibited a maximum zone of growth inhibition at 75 µg/mL. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of AgNPs against the clinically isolated pathogen S. aureus were 3.12 µg/mL and 4.5 µg/mL. Furthermore, time-dependent killing kinetic experiments showed that a 6 h AgNPs treatment was sufficient to fully inhibit all bacterial growth. AgNPs at a concentration of 250 µg/mL demonstrated antioxidant activity as measured by the FRAP and DPPH tests (85% and 90%, respectively). AgNPs demonstrated efficient photocatalytic activity in the degradation of methylene blue (MB) and achieved their highest photocatalytic activity (95%) after 2.30 h. Besides, the synthesis of AgNPs was targeted towards C. vulgaris algae, and exhibited deleterious effects even at larger concentrations. The chosen AgNPs concentration reduced chlorophyll, impeded algal development, and damaged the whole membrane system, as evidenced by the increased electrolyte leakage and malondialdehyde (MDA) and glutathione s-transferase (GSH) content after AgNPs exposure. Our report demonstrates that AgNPs V. trifolia have promising antibacterial, antioxidant, and potential dye degradation activities and can be employed in biomedical applications.</p

Phytomediated synthesis of copper oxide nanoparticles from floating fern Salvinia cucullata Roxb. and their antibacterial, antioxidant, and anticancer potential

The plant-based green synthesis method is creating more interest in safety, low-cost, biocompatibility, and eco-friendly approaches to producing nanoparticles biologically than chemical and physical methods of synthesis. The copper oxide nanoparticles (CuONPs) attract researchers due to the wide range of industrial, pharmacological, and biomedical applications that depend on the size, charge, and shape of the nanoparticles. This research work aimed to synthesize CuO nanoparticles using aqueous extract of Salvinia cucullata, characterization, and assessment of in vitro antibacterial, antioxidant, and anticancer activity. The characterization of CuONPs was performed by different techniques which include UV spectroscopy, FTIR, XRD, TEM, SEM, EDXS, and Zeta size and potential analysis. UV spectroscopy confirms the synthesis of CuONPs with an absorbance peak of 266 nm. The monoclinic crystalline nature of CuONPs was revealed by XRD analysis, and this pattern exhibited similar diffraction peaks with no significant shift in peak position. The functional groups of phytocomponents present in the aqueous extract of S. cucullata which act as reducing and stabilizing agents responsible for CuONPs synthesis were analyzed by the FTIR technique. The spherical shape of the synthesized CuONPs was ascertained by SEM and TEM image techniques, and the selected area for electron diffraction shows the crystal orientation of the CuONPs. EDXS shows that only copper and oxygen were present and the synthesized CuONPs are in pure form. The average size and charge of the synthesized CuONPs were analyzed by PSA as 67.5 nm and − 2.2 mV. The antibacterial potential of synthesized CuONPs was tested against both gram- positive and gram-negative bacterial strains. Among all eight strains, the maximum and minimum zone of inhibition was observed against Enterococcus spp. and Aeromonas hydrophila (maximum inhibition against gram-positive strains than gram-negative strains). The antioxidant and anticancer activity of CuONPs were tested against DPPH and MCF-7 breast cancer cell line and are observed with increased activity in a dose-depending manner with the IC50 value of 81.21 μg/ml and 68.16 μg/ml concentration. The green synthesized CuONPs were characterized and exhibited antibacterial, antioxidant, and anticancer activity. Thus, CuONPs can be utilized for broad-spectrum biomedical applications.</p