Artificial-Intelligence-Assisted Discovery of Genetic Factors for Precision Medicine of Antiplatelet Therapy in Diabetic Peripheral Artery Disease

The Role of BTBD7 in Normal Development and Tumor Progression

BTB/POZ domain-containing protein 7 (BTBD7) has a relative molecular weight of 126KD and contains two conserved BTB/POZ protein sequences. BTBD7 has been shown to play an essential role in normal human development, precancerous lesions, heat-stress response, and tumor progression. BTBD7 promotes branching morphogenesis during development and participates in the salivary gland, lung, and tooth formation. Furthermore, many studies have shown that aberrant expression of BTBD7 promotes heat stress response and the progression of precancerous lesions. BTBD7 has also been found to play an important role in cancer. High expression of BTBD7 affects tumor progression by regulating multiple pathways. Therefore, a complete understanding of BTBD7 is crucial for exploring human development and tumor progression. This paper reviews the research progress of BTBD7, which lays a foundation for the application of BTBD7 in regenerative medicine and as a biomarker for tumor prediction or potential therapeutic target.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review

PURPOSE

The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients.

METHODS

Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification.

SUMMARY

We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Mono or Dual Antiplatelet Therapy for Treating Patients with Peripheral Artery Disease after Lower Extremity Revascularization: A Systematic Review and Meta-Analysis

The efficacy of dual antiplatelet therapy (DAPT) for patients with peripheral artery disease (PAD) after lower-limb intervention remains controversial. Currently, the prescription of DAPT after an intervention is not fully recommended in guidelines due to limited evidence. This study compares and analyzes the prognosis for symptomatic PAD patients receiving DAPT versus monotherapy after lower-limb revascularization. Up to November 2021, PubMed/MEDLINE, Embase, and Cochrane databases were searched to identify studies reporting the efficacy, duration, and bleeding complications when either DAPT or monotherapy were used to treat PAD patients after revascularization. Three randomized controlled trials and seven nonrandomized controlled trials were included in our study. In total, 74,651 patients made up these ten studies. DAPT in PAD patients after intervention was associated with lower rates of all-cause mortality (HR = 0.86; 95% CI, 0.79−0.94; p < 0.01), major adverse limb events (HR = 0.60; 95% CI, 0.47−0.78; p < 0.01), and major amputation (HR = 0.78; 95% CI, 0.64−0.96) when follow-up was for more than 1-year. DAPT was not associated with major bleeding events when compared with monotherapy (OR = 1.22; 95% CI, 0.69−2.18; p = 0.50) but was associated with a higher rate of minor bleeding as a complication (OR = 2.54; 95% CI, 1.59−4.08; p < 0.01). More prospective randomized studies are needed to provide further solid evidence regarding the important issue of prescribing DAPT.

Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review

Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.