ABSTRACT
BACKGROUND: One million individuals in the USA die from acute myocardial infarction (MI), which currently affects 3 million people globally. The available data about the early and late outcomes of both biodegradable polymer drug-eluting stents (BP-DES) and durable polymer drug-eluting stents exhibit inconsistency. We performed a meta-analysis comparing the safety and efficacy of BP-DES with DP-DES. METHODS: PubMed, Google Scholar, EMBASE, Cochrane, Ovid Medline, and Clinical Trials.gov databases were used to find out studies comparing BP-DES to DP-DES. All the analyses used the random-effects model. RESULTS: A total of 18 studies were incorporated in this meta-analysis that involved 28,874 patients, out of which 11,997 received the BP Stent, and the rest of 16,578 received the DP stent. Thorough analyses revealed that the risk of all-cause death was significantly higher in the BP-DES group (5.4% vs 2.7%) (RR 1.22, p 0.02) for two years or less than two-year follow-up. For studies with more than two years of follow-up, all-cause death was 9.07% (599/6603) in BP-DES and 9.47% (531/5602) in the DP-DES group but failed to achieve statistically significant levels (RR 0.97, p 0.58). CONCLUSIONS: The study revealed no clinically significant (P value was > 0.05) differences in all-cause death, cardiac death, target lesion revascularization (TLR), late stent thrombosis, device-oriented composite endpoint/target lesion failure (DOCE/TLF), myocardial infarction (MI), target vessel MI, target vessel revascularization (TVR), target vessel infarction (TVI) between BP-DES and DP-DES for more than two years of follow-up. Additionally, all-cause death was only outcomes which found to have a statistically significant difference for less than two years of follow-up, while remaining were statistically non-significant.
ABSTRACT
Tandem solar cells (TSCs) perform a better adaptation of the incident photons in different-energy-level bandgap materials, and overcome the Shockley-Queisser limit, but they require advanced control over the management of light for optimum performance. Nanomaterials and nanostructures offer a vastly improved control over the management of light. Through different optimization techniques, researchers can gain valuable insights regarding the optimization of various parameters of nano-optical designs. Over the past years, the number of studies on this topic has been continuously increasing. The present study reviews various current state-of-the-art optical designs, and provides an overview of the optimization techniques and numerical modeling of TSCs. This paper collected and analyzed different studies published within the years 2015-2022, using systematic literature review techniques, such as specific protocol screening and a search strategy. Seven different optical designs were extracted, along with their advanced local and global optimization methods, which offer a solution to the optical limitations of TSCs.
ABSTRACT
Sentiments are the key factors that lead to influence our behavior. Sentiment analysis is a technique that analyzes people's behaviors, attitudes, and emotions toward a service, product, topic, or event. Since 2020, no country has remained untouched by COVID-19, and the governing bodies of most countries have been applying several anti-pandemic countermeasures to combat it. In this regard, it becomes tremendously important to analyze people's sentiments when tackling infectious diseases similar to COVID-19. The countermeasures taken by any country to control the pandemic leave a direct and crucial impact on each sector of public life, and every individual reacts to them differently. It is necessary to consider these reactions to implement appropriate messaging and decisive policies. Pakistan has done enough to control this virus's spread like every other country. This research aims to perform a sentimental analysis on the famous microblogging social platform, Twitter, to get insights into public sentiments and the attitudes displayed towards the precautionary steps taken by the Government of Pakistan in the years 2020 and 2021. These steps or countermeasures include the closure of educational institutes, suspension of flight operations, lockdown of business activities, enforcement of several standard operating procedures (SOPs), and the commencement of the vaccination program. We implemented four approaches for the analysis, including the Valence Aware Dictionary and sEntiment Reasoner (VADER), TextBlob, Flair, and Bidirectional Encoder Representations from Transformers (BERT). The first two techniques are lexicon-based. Flair is a pre-trained embedding-based approach, whereas BERT is a transformer-based model. BERT was fine-tuned and trained on a labeled dataset, achieving a validation accuracy of 92%. We observed that the polarity score kept varying from month to month in both years for all countermeasures. This score was analyzed with real-time events occurring in the country, which helped understand the public's sentiment and led to the possible formation of a notable conclusion. All implemented approaches showed independent performances. However, we noticed from the classification results of both TextBlob and the fine-tuned BERT model that neutral sentiment was dominant in the data, followed by positive sentiment.
ABSTRACT
The Internet of Things (IoT) is a complete ecosystem encompassing various communication technologies, sensors, hardware, and software. IoT cutting-edge technologies and Artificial Intelligence (AI) have enhanced the traditional healthcare system considerably. The conventional healthcare system faces many challenges, including avoidable long wait times, high costs, a conventional method of payment, unnecessary long travel to medical centers, and mandatory periodic doctor visits. A Smart healthcare system, Internet of Things (IoT), and AI are arguably the best-suited tailor-made solutions for all the flaws related to traditional healthcare systems. The primary goal of this study is to determine the impact of IoT, AI, various communication technologies, sensor networks, and disease detection/diagnosis in Cardiac healthcare through a systematic analysis of scholarly articles. Hence, a total of 104 fundamental studies are analyzed for the research questions purposefully defined for this systematic study. The review results show that deep learning emerges as a promising technology along with the combination of IoT in the domain of E-Cardiac care with enhanced accuracy and real-time clinical monitoring. This study also pins down the key benefits and significant challenges for E-Cardiology in the domains of IoT and AI. It further identifies the gaps and future research directions related to E-Cardiology, monitoring various Cardiac parameters, and diagnosis patterns.
Subject(s)
Artificial Intelligence , Ecosystem , Wireless Technology , Delivery of Health Care , TechnologyABSTRACT
Phytoremediation is a cost-effective and eco-friendly process to remove heavy metals from water. A small-scale experiment was conducted to determine the phytoremediation efficiency of two macrophytes, i.e. Pistia stratiotes and Eichhornia crassipes for the removal of chromium and copper from the prepared metal solutions of different concentrations (2, 4, 6, 8â mg/L) for a period of one month. Both aquatic macrophytes showed different efficiency for the removal and accumulation of Chromium (Cr) and Copper (Cu). The dry biomass of both macrophytes increased over the period of time. The average accumulation of chromium in roots and leaves of P. stratiotes was 85â mg, 56â mg and copper was 96â mg and 70â mg, respectively whereas the average accumulation of chromium in roots and leaves of E. crassipes was 90â mg, 53â mg and copper was 86â mg, 50â mg, respectively. Higher absorbance of metals was observed in roots as compared to the leaves of both macrophytes. The concentration of metals in the solution significantly decreased from day 1 to day 30. The results of one-way ANOVA showed that removal of Cr was significantly (p < .05) greater in E. crassipes and removal of Cu was significantly (p < .05) greater in P. stratiotes.
