COVID-19 affects several human genes, each with its own p-value. The combination of drugs associated with these genes with small p-values may lead to an estimation of the combined p-value between COVID-19 and some drug combinations, thereby increasing the effectiveness of these combinations in defeating the disease. Based on human genes, we introduced a new machine learning method that offers an effective drug combination with low combined p-values between them and COVID-19. This study follows an improved approach to systematic reviews, called the Systematic Review and Artificial Intelligence Network Meta-Analysis (RAIN), registered within PROSPERO (CRD42021256797), in which, the PRISMA criterion is still considered. Drugs used in the treatment of COVID-19 were searched in the databases of ScienceDirect, Web of Science (WoS), ProQuest, Embase, Medline (PubMed), and Scopus. In addition, using artificial intelligence and the measurement of the p-value between human genes affected by COVID-19 and drugs that have been suggested by clinical experts, and reported within the identified research papers, suitable drug combinations are proposed for the treatment of COVID-19. During the systematic review process, 39 studies were selected. Our analysis shows that most of the reported drugs, such as azithromycin and hydroxyl-chloroquine on their own, do not have much of an effect on the recovery of COVID-19 patients. Based on the result of the new artificial intelligence, on the other hand, at a significance level of less than 0.05, the combination of the two drugs therapeutic corticosteroid + camostat with a significance level of 0.02, remdesivir + azithromycin with a significance level of 0.03, and interleukin 1 receptor antagonist protein + camostat with a significance level 0.02 are considered far more effective for the treatment of COVID-19 and are therefore recommended. Additionally, at a significance level of less than 0.01, the combination of interleukin 1 receptor antagonist protein + camostat + azithromycin + tocilizumab + oseltamivir with a significance level of 0.006, and the combination of interleukin 1 receptor antagonist protein + camostat + chloroquine + favipiravir + tocilizumab7 with corticosteroid + camostat + oseltamivir + remdesivir + tocilizumab at a significant level of 0.009 are effective in the treatment of patients with COVID-19 and are also recommended. The results of this study provide sets of effective drug combinations for the treatment of patients with COVID-19. In addition, the new artificial intelligence used in the RAIN method could provide a forward-looking approach to clinical trial studies, which could also be used effectively in the treatment of diseases such as cancer.
Background: Familial Hypercholesterolemia (FH) is a genetic disorder in lipoprotein metabolism caused by mutations that increase LDL and total cholesterol levels. High LDL and cholesterol levels increase atherosclerosis risk. FH mutations impact the LDL receptor (LDLR) gene, apolipoprotein B, and PCSK9. About 20% of FH cases have a polygenic basis that affects LDL levels. We decided to conduct a systematic review of the available research in this field to provide a thorough genes/proteins network meta-analysis on the impact of drug combinations on the management of heterozygous Familial Hypercholesterolemia (HeFH). This paper reviews and analyzes the literature on the effects of medication combinations on HeFH management. This study investigates articles that analyzed the management and adjuvants of HeFH to recommend forceful drug combinations. Methods: This systematic review and network meta-analysis analyzed the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar databases without a lower time limit and up to July 2022. The current study consists of three fundamental stages. Firstly, drug combinations are recommended by reinforcement learning. In the second stage, we used a systematic review to analyze RL's outcomes in diverse populations (with a variety of ages, sex, etc.). Natural Language Processing (NLP) employs context to search these articles. We contrasted manual and NLP-based searches and discovered that NLP could find articles based on MeSH, not simply words. In stage three, we analyze RL outcomes using network meta-analysis. Results: This study uses the RAIN method to investigate the most effective medication combination for managing Heterozygous Familial Hypercholesterolemia (HeFH). Results from the method indicate that the best-recommended scenario is 2.7 times more efficient than the prescription of Ezetimibe as the initial scenario. Conclusion: Our systematic review and network meta-analysis review indicate that a drug combination of Ezetimibe, Pravastatin, and Simvastatin is highly effective. However, additional high-quality clinical trials are required to determine the efficacy and safety of other treatments.
Background(Importance)One of the most dangerous kinds of skin cancer, Melanoma, develops in the cells (melanocytes) that make melanin, the pigment responsible for giving your skin its color. As well as developing everywhere on the body, including the eyes, Melanoma can sporadic occur internally, such as in the nose or throat. It is unknown what causes all melanomas, although exposure to ultraviolet (UV) radiation from the sun, tanning salons, and lamps increases the risk of getting them. As a result, radiation exposure increases the chance of obtaining Melanoma. Limiting your exposure to UV radiation can help reduce your risk of Melanoma.(Objective)Due to the unknown nature of this disease and its severe impact on human genes, the use of safe and effective drug combinations for treatment is very important. Proposed drug combinations should be administered with the greatest positive effect on the genes involved. Therefore, it is important to suggest an effective drug combination that can significantly affect the genes involved.Method(Data sources)This systematic review and network meta-analysis searched various databases, including Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar, without a lower time limit and up until July 2022, for articles focused on drug combinations for managing Melanoma. The study utilized a network meta-analysis to explore the effectiveness of the proposed medication combination on genes and proteins that may act as potential targets for improving Melanoma treatment.ResultsThe results of this study show that the p-value between the proposed drug combination and Melanoma was 1.12E-08. This is while the p-value of Melanoma and only one drug has a maximum value of 0.0149. Therefore, the proposed drug combination’s effectiveness for treating Melanoma has increased 74 times. A systematic review has investigated the validity of the proposed drug combinations, human genes network meta-analysis, and prescription drug information.ConclusionThe findings from this systematic review and meta-analysis suggest that the proposed drug combination reduces the p-value between Melanoma and genes that could potentially be targeted to slow the progression of the disease, ultimately improving its management. Therefore, selecting the appropriate drug combination is critical for enhancing community health and reducing per capita treatment expenses.HighlightsMelanoma is one of the most aggressive kinds of skin cancer, and it begins in the cells (melanocytes) responsible for producing melanin.Therapy must make use of pharmacological combinations that are both safe and effective.Any proposed medication combinations must be delivered in a way that will have the maximum possible beneficial impact on the genes at play.In this research, an effective pharmacological combination for the treatment of melanoma illness is investigated.The results suggest that the suggested treatment combination is beneficial in the treatment of Melanoma, as it reduces the p-value between the disease and the genes identified as potential targets for therapy. This indicates that the proposed treatment approach has the potential to improve the management of Melanoma.
Objectives: (Importance) Cerebrovascular accident (Stroke) is a term used in medicine to describe cutting off blood supply to a portion of the brain, which causes tissue damage in the brain. Clots of blood that form in the brain's blood vessels and ruptures in the brain's blood vessels are the root causes of cerebrovascular accidents. Dizziness, numbness, weakness on one side of the body, and difficulties communicating verbally, writing, or comprehending language are the symptoms of this condition. Smoking, being older and having high blood pressure, diabetes, high cholesterol, heart disease, a history of cerebrovascular accident in the family, atherosclerosis (which is the buildup of fatty material and plaque inside the coronary arteries), or high cholesterol all contribute to an increased risk of having a cerebrovascular accident. (Objective) This paper analyzes available studies on Cerebrovascular accident medication combinations. Evidence acquisition: (Data sources) This systematic review and network meta-analysis analyzed the Science Direct, Embase, Scopus, PubMed, Web of Science (ISI), and Google Scholar databases without a lower time limit and up to July 2022. A network meta-analysis examines the efficacy of this drug combination on genes/proteins that serve as progression targets for cerebrovascular accidents. Results and Conclusion: In scenarios 1 through 3, the p-values for the suggested medication combination and Cerebrovascular accident were 0.036633, 0.007763, and 0.003638, respectively. Scenario I is the combination of medications initially indicated for treating a cerebrovascular accident. The recommended combination of medications for cerebrovascular accidents is ten times more effective. This systematic review and network meta-analysis demonstrate that the recommended medication combination decreases the p-value between cerebrovascular accidents and the genes as potential progression targets, thereby enhancing the treatment for cerebrovascular accidents. The optimal combination of medications improves community health and decreases per-person management costs.
Background: The World Health Organization (WHO) describes Monkeypox as a viral zoonosis, or an animal-to-human virus transmission, with symptoms comparable to those of past smallpox patients but clinically less severe. This study's objective is to assess the results of previous investigations on the best drug combinations for treating Monkeypox. Method: The pharmacological combinations used to treat monkeypox sickness have been researched in two stages for this systematic review and network meta-analysis. To begin with, a certain machine learning technique is used to extract the medication combinations from the researched articles offered on science databases, including Scopus, PubMed, Web of Science (ISI), Science Direct, Embase, and Google Scholar. Second, the tested medicine combinations will have been proven. Results: The results of this study show that the p-value between the proposed drug combination and Monkeypox for scenarios 1 to 5 were 0.108, 0.042, 0.023, 0.018, and 0.015, respectively. Scenario i is the combination of the first i suggested drugs for treating Monkeypox. This has led to a 720 percent increase in the proposed drug combination's efficacy in treating Monkeypox. Conclusion: The suggested drug combination decreases the p-value between MonkeyPox and the genes as potential targets for Monkeypox progression, which leads to an improvement in the treatment of Monkeypox. Therefore, using the right combination of drugs is important in improving the community's health and reducing per capita treatment costs. Keywords: Genes, Biological data, Drug Combination, Systematic Review, Network Meta-analysis, Machine Learning, MonkeyPox.
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