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The rapid growth of Internet-enabled applications, such as social media platforms, e-commerce sites, and blogs, has led to a surge in user-generated content. This vast amount of data has made sentiment analysis increasingly valuable. Modern Aspect-Based Sentiment Analysis (ABSA) offers a more detailed approach by identifying sentiment trends related to specific aspects within the text. However, the challenge lies in analyzing reviews that are often short, unstructured, and filled with slang and emotive language, making it difficult to gauge customer opinions accurately. To address these issues, we proposed an effective hybrid approach “RoBERTa-1D-CNN-BiLSTM” for ABSA. Initially, the pre-trained Robustly Optimized BERT approach (RoBERTa) and One Dimensional Convolutional Neural Network (1D-CNN) models are used to extract features at the aspect level from the context of the review, following which classification is performed using Bidirectional Long Short-Term Memory (BiLSTM). The approach is evaluated on three cross-domain standards datasets, yielding an accuracy of 92.33%. The results of the experiments show that it surpasses the current leading methods in sentiment analysis and product recommendation.

As technology advances, most people use social media sites like Twitter, Facebook, and Flickr to share information and communicate with others. The volume of free-text data is growing daily due to the widespread use of these social media platforms. These platforms contain a substantial amount of unstructured information. Patient opinions expressed on social media platforms play a significant role in healthcare improvement and impact health-related policymaking. In this research, we introduce a machine learning approach for the optimal identification of healthcare-related features. This approach is based on a novel synthetic method. Additionally, we employ an entropy-based technique to classify free-text comments from hospital data into positive, negative or neutral. The experimental results and evaluations show 85%, 82.3%, 78.2% and 87% accuracy between ratings of health care. We observed that there is a minor association between our technique, expert opinion and patient interviews. Through the use of machine learning techniques, we achieve an accuracy level that suggests we are capable of providing an accurate and reasonable assessment of the ideal healthcare center for a patient. Our proposed novel framework predicts the healthcare experience at hospitals based on patient reviews posted on social media. This innovative approach outperforms traditional methods, such as surveys and expert opinions.

Background: The heightened morbidity and mortality associated with repeat cardiac surgery are well documented. Redo median sternotomy (MS) and minimally invasive valve surgery are options for patients with prior cardiac surgery who require mitral valve surgery (MVS). We conducted a systematic review and meta-analysis comparing the outcomes of redo MS and minimally invasive MVS (MIMVS) in this population. Methods: We searched PubMed, EMBASE, and Scopus for studies comparing outcomes of redo MS and MIMVS for MVS. To calculate risk ratios (RRs) for binary outcomes and weighted mean differences (MDs) for continuous data, we employed a random-effects model. Results: We included 12 retrospective observational studies, comprising 4157 participants (675 for MIMVS; 3482 for redo MS). Reductions in mortality (RR, 0.54; 95% confidence interval [CI], 0.37–0.80), length of hospital stay (MD, −4.23; 95% CI, −5.77 to −2.68), length of intensive care unit (ICU) stay (MD, −2.02; 95% CI, −3.17 to −0.88), and new-onset acute kidney injury (AKI) risk (odds ratio, 0.34; 95% CI, 0.19 to 0.61) were statistically significant and favored MIMVS (p0.05). Conclusion: The current literature, which primarily consists of retrospective comparisons, underscores certain benefits of MIMVS over redo MS. These include decreased mortality, shorter hospital and ICU stays, and reduced AKI risk. Given the lack of high-quality evidence, prospective randomized control trials with adequate power are necessary to investigate long-term outcomes.

Additive manufacturing (AM) aka three-dimensional (3D) printing has been a well-established and unparalleled technology, which is expanding the boundaries of materials science and is exhibiting an enormous potential to fabricate intricate geometries for healthcare, electronics, and construction sectors. In the contemporary era, the combination of AM technology and stimuli-responsive hydrogels (SRHs) helps to create dynamic and functional structures with extreme accuracy, which are capable of changing their shape, functional, or mechanical properties in response to environmental cues such as humidity, heat, light, pH, magnetic field, electric field, etc. 3D printing of SRHs permits the creation of on-demand dynamically controllable shapes with excellent control over various properties such as self-repair, self-assembly, multi-functionality, etc. These properties accelerate researchers to think of unthinkable applications. Additively manufactured objects have shown excellent potential in applications like tissue engineering, drug delivery, soft robots, sensors, and other biomedical devices. The current review provides recent progress in the 3D printing of SRHs, with more focus on their 3D printing techniques, stimuli mechanisms, shape-morphing behaviors, and their functional applications. Finally, current trends and future roadmap of additively manufactured smart structures for different applications have also been presented, which will be helpful for future research. This review holds great promise for providing fundamental knowledge about SRHs to fabricate structures for diverse applications.

Abstract The objective of this study was to conduct a systematic review and meta-analysis of relevant randomized control trials (RCTs) to determine the role of ibuprofen, as well as the optimum dose and duration of therapy, in preventing the incidence of heterotopic ossification (HO) after primary total hip arthroplasty (THA). A literature search was performed using the PubMed/MEDLINE and Cochrane Library databases for RCTs that compared the use of ibuprofen versus placebo as prophylaxis for HO in patients after THA. The main outcomes for this study were overall occurrence of HO, occurrence according to the Brooker classification, and gastrointestinal complications. A total of 27 potential articles were identified from the database. Eventually, four trials with 1,153 patients were included in the final analysis. When compared with placebo, the use of ibuprofen is associated with a reduction in the incidence of HO at the 3- and 12-month follow-up appointments, as well as the incidence of Brooker II and III HO (p < 0.05). However, there was no significant difference between the ibuprofen and placebo groups in terms of treatment discontinuation due to gastrointestinal complications or the incidence of Brooker I and IV HO (p > 0.05). The existing data indicates that ibuprofen is safe and efficacious in reducing the total incidence of HO along with Brooker II and III HO at follow-up. However, due to the small number of studies, the conclusions are limited; therefore, more high-quality clinical trials are required to develop guidelines for optimal dose and duration of therapy.

The increasing prevalence of obesity globally, which as well as affecting people's daily lives and increasing the risk of obesity complications, also threatens the health of animal organisms simultaneously. It’s been reported that Yi-Qi-Yang-Yin-Ye had remarkable efficacy in the treatment of obesity. The specific underlying mechanism of action of Yi-Qi-Yang-Yin-Ye in treating obesity, however, remains ambiguous. Therefore, the innovative approach, which is network pharmacology combined with molecular docking and molecular dynamics simulations, was employed in the current research to explore the potential mechanism and promote further development in the treatment of obesity. The active ingredients and related targets of Yi-Qi-Yang-Yin-Ye and related targets of obesity were summarized from extensive public databases. Furthermore, network topology analysis and pathway enrichment analysis were performed to explore the complicated interactions between drug and targets. Finally, accurate validation methods composed of molecular docking and molecular dynamics simulations were conducted to elucidate the binding affinity of Yi-Qi-Yang-Yin-Ye with obesity-related targets. As a result, 13 main active ingredients and 5 core targets of Yi-Qi-Yang-Yin-Ye against obesity in rats were acquired through primary screening of network topology analysis. Pathway enrichment analysis demonstrated that intersectional targets were involved in multiple signaling pathways, where PI3K-Akt signaling pathway, MAPK signaling pathway, and Insulin resistance were the main pathways of Yi-Qi-Yang-Yin-Ye in treating obesity in rats. Finally, molecular docking indicated that the seven critical active ingredients displayed great binding affinity to the hub targets. Furthermore, molecular dynamics simulations further screened and obtained that five critical active ingredients acting on the Mapk1 target for Yi-Qi-Yang-Yin-Ye against obesity in rats. The innovative approach and the results achieved have further contributed to and revealed the molecular mechanisms for treating obesity, providing an alternative for treating obesity in animals and humans.

The aging analysis of nuclear power plant components and materials is crucial for the power plant’s safety, and for its upgradation and life extension. The health monitoring of power plant materials, such as steel, is mostly done by employing conventional non-destructive evaluation (NDE) techniques. Considering the limitations associated with conventional NDE techniques, thermoelectric power based NDE (TNDE) is being explored. This paper presents the first ever development and successful demonstration of TNDE in Pakistan. Moreover, the aging analysis of carbon steel grade SA 106B is also being presented for the first time. The extraction-steam pipe samples (new and aged) of SA 106B, from a 46-years-old operating nuclear power plant, are being analyzed and the sensitivity of TNDE to aging induced changes in microstructure is demonstrated. Further, the aged SA 106B is simulated via heat treatment and the equipment is calibrated for its utilization in the nuclear power plant. The correlation between thermoelectric power (TEP) and Vicker hardness (HV) [TEP (µV/oC) = ~2x10-5 Hardness (HV) + 0.008] has been analyzed. It is revealed that the technique has the potential to analyze mechanical properties of aged extraction steam piping without cutting and taking the samples to the laboratory.