Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice, affecting millions of individuals worldwide. The management of AF presents significant challenges due to its complex nature and diverse patient profiles. In recent years, the integration of artificial intelligence (AI) and machine learning technologies has emerged as a promising approach to enhance various aspects of AF management. This comprehensive review aims to explore the evolving role of AI in AF management, with a focus on its potential applications in diagnosis, risk prediction, treatment selection, and patient monitoring. Real academic references were employed to provide evidence-based insights into the impact of AI on AF management.

This comprehensive review delves into the intricate relationship between Sodium-GlucoseCo-Transporter 2 (SGLT2) inhibitors and AF, highlighting the multifaceted actions of thesedrugs and their significant cardiovascular benefits. Mechanisms: SGLT2 inhibitors inducesubstantial hemodynamic changes, impacting blood pressure, adipose tissue function, andinflammation. They enhance glycemic control while potentially reducing oxidative stress andinflammation, and they modulate atrial electrophysiology, collectively contributing to the reductionof AF risk. Clinical Evidence: This review synthesizes a wealth of clinical evidence,drawing from observational studies and randomized controlled trials, consistently demonstratinga correlation between SGLT2 inhibitors, particularly dapagliflozin and empagliflozin, anda decreased risk of AF. This protective effect is most pronounced in patients with type 2 diabetesand heart failure, underscoring their potential as therapeutic agents for high-risk populations.Potential Benefits: SGLT2 inhibitors offer a spectrum of potential benefits, includingsubstantial reductions in AF risk, particularly in patients with type 2 diabetes and heart failure.These drugs have consistently demonstrated benefits in reducing heart failure hospitalizations,cardiovascular mortality, and stroke risk, which indirectly contribute to better AF management.Furthermore, SGLT2 inhibitors exhibit renoprotective properties by slowing theprogression of chronic kidney disease and reducing albuminuria, offering substantial benefitsfor AF patients with comorbid kidney disease. Metabolic improvements involving weight loss,blood pressure reduction, and favorable changes in lipid profiles contribute to enhanced cardiovascular health and reduced AF risk. Originally designed for diabetes management, thesemedications offer a holistic approach to AF risk reduction, particularly in patients with type 2diabetes and heart failure.

This paper considers a hybrid relay network consisting of the source, the amplify-and-forward (AF) relay, the decode-and-forward (DF) relay, and the destination. We propose the optimal power allocation schemes between two different relays which maximize the achievable rate under a sum relay power constraint for given channel gains and transmit power from source. By solving the optimization problem to maximize the achievable rate for each relay network, the transmit power values in closed-form are derived. When the channel gains are the same, the optimal power allocation scheme for AF-DF relay network proves that a more power should be allocated at the first relay to maximize the achievable rate. In case of the DF-AF relay network, we derive the optimal power allocation scheme for the possible four cases. Under the same SNR condition at the first hop, we show that the achievable rate of AF-DF relay network is greater than that of DF-AF relay network when the channel gain between two relays is higher than that between the second relay and destination. Simulation results show that the proposed power allocation schemes provide a higher achievable rate than the equal power allocation schemes.

This paper studies a multihop amplify-and-forward (AF) simultaneous wireless information and power transfer (SWIPT) relaying system. Each relaying node harvests energy using power splitting (PS) scheme from a part of its received signal to amplify and forward the rest the received signal to the next relay. Based on this system model and signal flow, we derived and solved the convex energy minimization problem with optimal PS ratio. The influence of processing cost was then investigated for the AF-SWIPT system with the decode-and-forward SWIPT as benchmark, where AF-SWIPT was found to be superior.

Current prenatal genetic evaluation showed a significantly increase in non-invasive screening and the reduction of invasive diagnostic procedures. To evaluate the diagnostic efficacy on detecting common aneuploidies, structural chromosomal rearrangements and pathogenic copy number variants (pCNV), we performed a retrospective analysis on a case series initially analyzed by aneuvysion fluorescence in situ hybridization (FISH) and karyotyping then followed by array comparative genomic hybridization (aCGH). Of the 386 cases retrieved from the past decade, common aneuploidies were detected in 137 cases (35.5%), other chromosomal structural rearrangements were detected in four cases (1%), and pCNV were detected in five cases (1.3%). The relative frequencies for common aneuploidies suggested a under detection of sex chromosome aneuploidies. Approximately 9.5% of cases with common aneuploidies showed a mosaic pattern. Inconsistent results between FISH and karyotyping were noted in cases with pseudo-mosaicism introduced by culture artifact or variable cellular proliferation from cells with mosaic karyotypic complements under in vitro cell culture. Based on findings from this case series, cell-based FISH and karyotyping should be performed to detect common aneuploidies, structural chromosomal abnormalities, and mosaic pattern. DNA-based aCGH and reflex FISH should be performed to detect and confirm genomic imbalances and pCNV. Practice points to ensure the diagnostic accuracy and efficacy were summarized.