by Md Abdur Razzak, Muhammad Nazrul Islam, Md Shadman Aadeeb, Tasfia Tasnim
BackgroundCervical cancer is a malignancy among women worldwide, which is responsible for innumerable deaths every year. The primary objective of this review study is to offer a comprehensive and synthesized overview of the existing literature concerning digital interventions in cervical cancer care. As such, we aim to uncover prevalent research gaps and highlight prospective avenues for future investigations.
MethodsThis study adopted a Systematic Literature Review (SLR) methodology where a total of 26 articles were reviewed from an initial set of 1110 articles following an inclusion-exclusion criterion.
ResultsThe review highlights a deficiency in existing studies that address awareness dissemination, screening facilitation, and treatment provision for cervical cancer. The review also reveals future research opportunities like explore innovative approaches using emerging technologies to enhance awareness campaigns and treatment accessibility, consider diverse study contexts, develop sophisticated machine learning models for screening, incorporate additional features in machine learning research, investigate the impact of treatments across different stages of cervical cancer, and create more user-friendly applications for cervical cancer care.
ConclusionsThe findings of this study can contribute to mitigating the adverse effects of cervical cancer and improving patient outcomes. It also highlights the untapped potential of Artificial Intelligence and Machine Learning, which could significantly impact our society.
by Tasnim F. Imran, Ali A. Khan, Phinnara Has, Alexis Jacobson, Stephanie Bogin, Mahnoor Khalid, Asim Khan, Samuel Kim, Sebhat Erqou, Gaurav Choudhary, Karen Aspry, Wen-Chih Wu
BackgroundAtherosclerotic cardiovascular disease (ASCVD) is the leading cause of mortality worldwide. Atherosclerosis occurs due to accumulation of low-density lipoprotein cholesterol (LDL-c) in the arterial system. Thus, lipid lowering therapy is essential for both primary and secondary prevention. Proprotein convertase subtilisn/kexin type 9 (PCSK9) inhibitors (Evolocumab, Alirocumab) and small interfering RNA (siRNA) therapy (Inclisiran) have been demonstrated to lower LDL-c and ASCVD events in conjunction with maximally tolerated statin therapy. However, the degree of LDL-c reduction and the impact on reducing major adverse cardiac events, including their impact on mortality, remains unclear.
ObjectiveThe purpose of this study is to examine the effects of PCSK9 inhibitors and small interfering RNA (siRNA) therapy on LDL-c reduction and major adverse cardiac events (MACE) and mortality by conducting a meta-analysis of randomized controlled trials.
MethodsUsing Pubmed, Embase, Cochrane Library and clinicaltrials.gov until April 2023, we extracted randomized controlled trials (RCTs) of PCSK9 inhibitors (Evolocumab, Alirocumab) and siRNA therapy (Inclisiran) for lipid lowering and risk of MACE. Using random-effects models, we pooled the relative risks and 95% CIs and weighted least-squares mean difference in LDL-c levels. We estimated odds ratios with 95% CIs among MACE subtypes and all-cause mortality. Fixed-effect model was used, and heterogeneity was assessed using the I2 statistic.
ResultsIn all, 54 studies with 87,669 participants (142,262 person-years) met criteria for inclusion. LDL-c percent change was reported in 47 studies (n = 62,634) evaluating two PCSK9 inhibitors and siRNA therapy. Of those, 21 studies (n = 41,361) included treatment with Evolocumab (140mg), 22 (n = 11,751) included Alirocumab (75mg), and 4 studies (n = 9,522) included Inclisiran (284mg and 300mg). Compared with placebo, after a median of 24 weeks (IQR 12–52), Evolocumab reduced LDL-c by -61.09% (95% CI: -64.81, -57.38, p Conclusion
PCSK9 inhibitors (Evolocumab, Alirocumab) and siRNA therapy (Inclisiran) significantly reduced LDL-c by >40% in high-risk individuals. Additionally, both Alirocumab and Evolocumab reduced the risk of MACE, and Alirocumab reduced cardiovascular and all-cause mortality.