Journal of Preventive Medicine

Description

In the last ten years, the evaluation of disease risk has seen a significant rise in the use of biomarkers as early warning systems. Indicators of typical biological processes, pathogenic processes, or pharmacological responses to treatment are known as biomarkers. The use and identification of biomarkers in clinical and medical settings has a significant impact on society. Biomarkers' history, various definitions, classifications, characteristics, and discovery are the subject of this review. In addition, a review of the potential uses of biomarkers in the diagnosis, prognosis, and treatment of various diseases over the past decade is provided. The purpose of this review is to encourage readers to investigate new biomarker research and development avenues. Numerous people die annually from cardiovascular diseases, a global health problem. Changes in one's lifestyle and a genetic predisposition are the main factors that lead to Cardiovascular Disease (CVD). Heart transplantation is the only treatment option because the disease is often detected at its final stage in many patients. As a result, every effort should be made to identify the risk early on and implement preventative measures to enhance their quality of life. One of the most important factors in the early diagnosis of cardiovascular diseases is biomarkers. Recently discovered biomarkers that are more specific and highly sensitive have been used for the diagnosis and prognosis of cardiovascular diseases.

Cardiovascular Biomarkers

The various categories of cardiovascular biomarkers, with an emphasis on novel biomarkers, and the biomarkers utilized for various CVD purposes are briefly covered in this review. Additionally, the biomarkers have aided in the identification of COVID-19 patients at increased risk for cardiovascular complications.  Biomarkers are preferable to other approaches for assessing the pathophysiological state of CVDs because they are non-invasive. The intrinsic heterogeneity of NSCLC at the molecular level makes it easier to differentiate between them. To find a small number of NSCLC biomarkers, this paper proposes a novel explainable AI (XAI)-based deep learning framework. An autoencoder that reduces the input feature space, a feed-forward neural network that divides NSCLC cases into LUAD and LUSC, and a biomarker discovery module that makes use of the autoencoder and feed-forward neural network together are the three modules that make up the proposed framework. 52 relevant biomarkers for NSCLC subtype classification were discovered using XAI techniques in the biomarker discovery module. Multiple machine-learning models are constructed with these biomarkers in order to evaluate their classification performance. Multilayer Perceptron achieved an accuracy of 95.74 percent (1.27) with a confidence interval of 95 percent by employing 10-Fold cross-validation. Additionally, the Drug-Gene Interaction Database reveals that 14 of the discovered biomarkers are pharmacologically active. Additionally, the prediction of a patient's likelihood of survival is aided by 28 biomarkers. We find that 45 of the 52 biomarkers we discovered have previously been used to differentiate between the two types of NSCLC. The remaining seven biomarkers have not yet been reported for NSCLC subtyping, and further investigation into their potential contribution to targeted lung cancer therapy may be conducted. Over a decade ago, the goal of the research group called "Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia" (BRINDA) was to make it easier to interpret micronutrient biomarkers in environments with inflammation. Using regression correction, the BRINDA inflammation adjustment method corrects for the conflating effects of inflammation on specific biomarkers of micronutrients. This method has provided important insights for micronutrient research, policy, and programming. However, users may encounter difficulties when applying the BRINDA inflammation adjustment methods to their own data due to the need to develop statistical programming and differing instructions regarding the adjustment strategy for various biomarkers.

Inflammatory Skin Condition

This could mean missing out on opportunities to easily access micronutrient data results during crucial decision-making times. Our research aims to: 1) present a standardized and user-friendly BRINDA adjustment R package and SAS macro, 2) evaluate whether malaria as a binary variable should be included in the BRINDA inflammation adjustment method, and 3) provide an all-in-one summary of the BRINDA method for adjusting multiple micronutrient biomarkers for inflammation. This paper helps users use the BRINDA R package and SAS to streamline their analyses and serves as a practical guide for the BRINDA inflammation adjustment approach. It is essential to identify a small number of gene biomarkers that are able to reliably indicate relevant cellular growth states in bacteria in order to enable the design of genetic circuits for reporting or mitigating stress states. Computational methods that are able to identify robust biomarkers for the purpose of experimental characterisation and verification have been prompted by recent advancements in high-throughput omics technologies, which have made it easier to identify molecular biomarkers specific to specific states in bacteria. This study aimed to develop a knowledge integration strategy for selecting a robust biomarker panel that can be generalized to other datasets and experiments by focusing on the identification of gene expression biomarkers in Bacillus subtilis to detect various stress states. Based on complementary information from a machine learning model, a gene regulatory network, and a co-expression network, we developed a recommendation system that ranks the candidate biomarker panels. In both the dataset used for biomarker identification (mean f1-score achieved at 0.99) and a variety of independent datasets, we discovered a recommended biomarker panel with high stress sensing power for a variety of conditions. Evaluation metrics like the number of associated regulators in a B subtilis Co-Expression Network (CEN) were found to have a significant correlation with stress sensing power. Biomarker genes encode a wide range of biological processes, and GRNs and CENs provide information that is pertinent to these processes. By quantifying meaningful evaluation metrics and stress sensing power, we show that biomarkers with better sensitivity and robustness to a wider range of stress conditions can be identified and a more reliable biomarker panel selection can be made. The inflammatory skin condition known as Atopic Dermatitis (AD) is extremely complicated and diverse. It is highly unlikely that every patient will respond equally to a particular treatment because of AD's highly heterogeneous nature. Patient stratification based on immunologic biomarkers is now more important than ever because of the recent introduction of novel targeted therapies for AD. Various methods of endotyping AD patients based on immunologic profiles and predictive biomarkers have been reviewed as potential tools in the movement toward personalized medicine in AD. Patients will be better categorized and stratified through the use of biomarkers, and new and existing treatments will be more easily compared. The ultimate objective will be to move away from the current standardized "one-drug-fits-all" approach and toward a more individualized "patient endotype–specific" approach.