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Background: Percutaneous coronary intervention (PCI), also known as coronary angioplasty, is the preferred strategy for treating obstructive coronary artery disease. Existing literature suggests the worsening of clinical outcomes in patients with previous coronary artery bypass grafting (CABG) history. In light of this, a comprehensive systematic review and meta-analysis was performed. Methods: Databases including PubMed, Cochrane Library, and ScienceDirect were utilized for the inclusive systematic search dating from inception to September 01, 2023. The risk of bias assessment was performed using the Newcastle-Ottawa scale for cohort studies, and the Cochrane Risk of Bias Tool for randomized controlled trials. Results: Ultimately, there were 16 eligible studies pooled together, involving a total of 250 684 patients, including 231 552 CABG-naïve patients, and 19 132 patients with a prior history of CABG. Overall, patients with CABG history were associated with significantly greater short-term mortality (P = .004), long-term mortality (P = .005), myocardial infarction (P < .00001), major adverse cardiovascular events (P = .0001), and procedural perforation (P < .00001). Contrastingly, CABG-naïve patients were associated with significantly greater risk of cardiac tamponade (P = .02) and repeat CABG (P = .03). No significant differences in stroke, bleeding, revascularization, or repeat PCI were observed. Conclusion: Comparatively worsened clinical outcomes were observed, as patients with prior CABG history typically exhibit complex coronary anatomy, and have higher rates of comorbidities in comparison to their CABG-naïve counterparts. The refinement of current procedural and surgical techniques, in conjunction with continued research endeavors, are needed in order to effectively address this trend.

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Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis.

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INTRODUCTION: Coronary artery bypass grafting (CABG) is a surgical procedure that restores blood flow to heart muscle by bypassing the blocked or narrowed coronary arteries. On the other hand, subclinical hypothyroidism (SCH) is characterized by an elevated serum concentration of thyroid stimulating hormone with normal levels of serum free thyroxine. With limited research into the impact of SCH on postoperative CABG outcomes, this systematic review and meta-analysis was performed. EVIDENCE ACQUISITION: An electronic search of PubMed, Cochrane Library, and Scopus was performed from inception to April 2023. After the inclusion of five studies, a total of 2,786 patients were pooled in this quantitative synthesis. EVIDENCE SYNTHESIS: It was observed that SCH significantly increased cardiovascular mortality (OR: 2.80; 95% CI: 1.37, 5.72; P=0.005), and all-cause mortality (OR: 2.62; 95% CI: 1.80, 3.80; P<0.00001). However, no significant differences were observed for secondary outcomes, including major adverse cardiac events, incidence of postoperative stroke, and incidence of postoperative myocardial infarction. CONCLUSIONS: To the best of our knowledge, this is the first meta-analysis conducted that evaluates the impact of SCH on outcomes after CABG. The preoperative assessment of thyroid function may be considered prior to cardiovascular procedures, particularly within CABG. However, future comprehensive studies, with individual participant-level data, are necessary in order to arrive at a valid conclusion and recommendation.

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The optimal timing of coronary angiography (CAG) in patients after out-of-hospital cardiac arrest (OHCA) without ST-segment elevation remains controversial. Therefore, we conducted a meta-analysis of randomized control trials to investigate the effectiveness of emergency CAG versus delayed CAG in OHCA patients with a non-ST-segment elevated rhythm. PubMed, Scopus, CINAHL, Cochrane CENTRAL, and JBI databases were searched from inception to September 7, 2022. Our primary end point was survival with a good neurological outcome, whereas the secondary outcomes included short-term survival, mid-term survival, recurrent arrhythmias, myocardial infarction after hospitalization, major bleeding, acute kidney injury, and left ventricular ejection fraction. Nine randomized control trials involving 2,569 patients were included in this analysis. Our meta-analysis showed no significant difference in the improvement of neurological outcome (RR 0.96, 95% Confidence Interval [CI] [0.87, 1.06]), short-term survival (risk ratio [RR] 0.98, 95% CI [0.89, 1.08]), mid-term survival (RR 0.98, 95% CI [0.87, 1.10]), recurrent arrhythmias (RR 1.02, 95% CI [0.50, 2.06]), myocardial infarction (RR 0.66, 95% CI [0.13, 3.30]), major bleeding (RR 0.96, 95% CI [0.55, 1.69]), acute kidney injury (RR 1.20, 95% CI [0.32, 4.49]) and left ventricular ejection fraction (RR 0.89, 95% CI [0.69, 1.15]) in patients who underwent emergency CAG compared with delayed CAG patients. In conclusion, our analysis revealed that emergency CAG had no prognostic superiority over delayed CAG in patients with OHCA without ST-segment elevation.

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A document's keywords provide high-level descriptions of the content that summarize the document's central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document's content that can reflect the authors' main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors' provided keywords. The average similarity of our approach with author-assigned keywords is 51%.

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Readability is an active field of research in the late nineteenth century and vigorously persuaded to date. The recent boom in data-driven machine learning has created a viable path forward for readability classification and ranking. The evaluation of text readability is a time-honoured issue with even more relevance in today's information-rich world. This paper addresses the task of readability assessment for the English language. Given the input sentences, the objective is to predict its level of readability, which corresponds to the level of literacy anticipated from the target readers. This readability aspect plays a crucial role in drafting and comprehending processes of English language learning. Selecting and presenting a suitable collection of sentences for English Language Learners may play a vital role in enhancing their learning curve. In this research, we have used 30,000 English sentences for experimentation. Additionally, they have been annotated into seven different readability levels using Flesch Kincaid. Later, various experiments were conducted using five Machine Learning algorithms, i.e., KNN, SVM, LR, NB, and ANN. The classification models render excellent and stable results. The ANN model obtained an F-score of 0.95% on the test set. The developed model may be used in education setup for tasks such as language learning, assessing the reading and writing abilities of a learner.

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OBJECTIVES: Widespread acceptance of the COVID-19 vaccine will be the next important step in fighting the novel coronavirus disease. Though the Pakistani government has successfully implemented robust policies to overcome the COVID-19 pandemic; however, studies assessing public intention to get COVID-19 vaccination (IGCV) are limited. The aim of this study is to deal with this literature gap and has also expanded the conceptual framework of planned behaviour theory. We have introduced three new considerations (risk perceptions of the pandemic, perceived benefits of the vaccine, and unavailability of vaccine) to have a better understanding of the influencing factors that encourage or discourage public IGCV. METHODS: Results are based on a sample collected from 754 households using an inclusive questionnaire survey. Hypotheses are tested by utilizing the structural equation modelling approach. RESULTS: The results disclose that the intention factors, that is, attitude, risk perceptions of the pandemic, and perceived benefits of the vaccine, impart positive effects on public IGCV. In contrast, the cost of the vaccine and the unavailability of the vaccine have negative effects. Notably, environmental concern has an insignificant effect. CONCLUSIONS: Research findings emphasize the importance of publicizing the devastating impacts of COVID-19 on society and the environment, ensuring vaccination availability at an accessible price while simultaneously improving public healthcare practices.

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Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%.

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Objective: In patients with heart failure (HF), anxiety and depression are commonly observed and confer an adverse outcome. The first-in-class member of angiotensin receptor-neprilysin inhibitor (ARNI), sacubitril/valsartan has been demonstrated to improve functional class and decrease mortality in patients with heart failure with reduced ejection fraction (HFrEF) and reduce the readmission of heart failure with preserved ejection fraction (HFpEF). However, its effects on anxiety and depression levels remain unknown.Methods: Sacubitril/valsartan was started on 764 symptomatic patients with HFrEF and HFpEF who were receiving guideline-directed medical therapy (GDMT) with an angiotensin-converting enzyme inhibitor (ACEI) or angiotensin receptor blocker (ARB). Patients were evaluated using Hamilton's depression rating scale (HDRS) and the hospital anxiety and depression scale (HADS) for their levels of depression and anxiety before and after treatment at a six-month follow-up.Results: A significant reduction in HADS and HDRS scores was observed in patients with HFrEF (9.7 ± 1.3 to 6.4 ± 0.7, p = 0.032 and 19.2 ± 2.2 to 8.9 ± 1.6, p < 0.001, respectively) compared with HFpEF (p = 0.161 and 0.273, respectively). The six-minute walk test (6-MWT) significantly increased HFrEF from 195 ± 68 to 321 ± 97 (p < 0.001). There was an overall improvement in the functional class of all patients.Conclusion: Patients with HFrEF have the additional advantage of using sacubitril/valsartan in the form of decreased anxiety and depression symptoms in addition to an improvement in functional class. However, patients with HFpEF did not exhibit significant improvement in their psychological scores.

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OBJECTIVE: The disruption of dual antiplatelet therapy (DAPT) causes more adverse events after percutaneous coronary intervention (PCI). However, incidence and predictors of DAPT non-compliance are unknown in chronic coronary syndrome patients when compared between planned and ad hoc PCI. METHODS: This investigation was aimed to assess the incidence, predictors, outcomes, and primary mode of non-compliance of DAPT in patients with chronic coronary syndrome undergoing their first PCI. We analyzed the patients between planned (group 1) and ad hoc (group 2) PCI. RESULTS: There were a total of 628 participants in this investigation (370 were in planned PCI and 270 in the ad hoc PCI group). Out of 628 patients, by one month, 10% left DAPT in planned PCI group and 19.7% in ad hoc PCI group (aOR: 0.451, 95% CI: 0.285-0.713, p = 0.001). At 12 months, DAPT non-compliance was significantly more in ad hoc PCI group (52.7% vs. 47.8%; aOR: 0.647 95% CI: 0.470-0.891, p = 0.008). Age > 65 years (p < 0.001), low education status (p = 0.012), residents of rural areas (p < 0.001), ad hoc PCI group (p = 0.036), and angina class II (p = 0.038) were predictors for DAPT non-compliance in this cohort. CONCLUSION: Approximately 5 out of 10 patients disrupt DAPT due to non-compliance. This investigation provides an insight on additional predictors of non-compliance to DAPT, helping us to identify and address specific patient-related factors for disruption.

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From the past half of a century, identification of the relevant documents is deemed an active area of research due to the rapid increase of data on the web. The traditional models to retrieve relevant documents are based on bibliographic information such as Bibliographic coupling, Co-citations, and Direct citations. However, in the recent past, the scientific community has started to employ textual features to improve existing models' accuracy. In our previous study, we found that analysis of citations at a deep level (i.e., content level) can play a paramount role in finding more relevant documents than surface level (i.e., just bibliography details). We found that cited and citing papers have a high degree of relevancy when in-text citations frequency of the cited paper is more than five times in the citing paper's text. This paper is an extension of our previous study in terms of its evaluation of a comprehensive dataset. Moreover, the study results are also compared with other state-of-the-art approaches i.e., content, metadata, and bibliography. For evaluation, a user study is conducted on selected papers from 1,200 documents (comprise about 16,000 references) of an online journal, Journal of Computer Science (J.UCS). The evaluation results indicate that in-text citation frequency has attained higher precision in finding relevant papers than other state-of-the-art techniques such as content, bibliographic coupling, and metadata-based techniques. The use of in-text citation may help in enhancing the quality of existing information systems and digital libraries. Further, more sophisticated measure may be redefined be considering the use of in-text citations.

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Frequently used items mining is a significant subject of data mining studies. In the last ten years, due to innovative development, the quantity of data has grown exponentially. For frequent Itemset (FIs) mining applications, it imposes new challenges. Misconceived information may be found in recent algorithms, including both threshold and size based algorithms. Threshold value plays a central role in generating frequent itemsets from the given dataset. Selecting a support threshold value is very complicated for those unaware of the dataset's characteristics. The performance of algorithms for finding FIs without the support threshold is, however, deficient due to heavy computation. Therefore, we have proposed a method to discover FIs without the support threshold, called Top-k frequent itemsets mining (TKFIM). It uses class equivalence and set-theory concepts for mining FIs. The proposed procedure does not miss any FIs; thus, accurate frequent patterns are mined. Furthermore, the results are compared with state-of-the-art techniques such as Top-k miner and Build Once and Mine Once (BOMO). It is found that the proposed TKFIM has outperformed the results of these approaches in terms of execution and performance, achieving 92.70, 35.87, 28.53, and 81.27 percent gain on Top-k miner using Chess, Mushroom, and Connect and T1014D100K datasets, respectively. Similarly, it has achieved a performance gain of 97.14, 100, 78.10, 99.70 percent on BOMO using Chess, Mushroom, Connect, and T1014D100K datasets, respectively. Therefore, it is argued that the proposed procedure may be adopted on a large dataset for better performance.

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In this note, we give fixed point results in fractal generation (Julia sets and Mandelbrot sets) by using Noor iteration scheme with s-convexity. Researchers have already presented fixed point results in Mann and Ishikawa orbits that are examples of one-step and two-step feedback processes respectively. In this paper we present fixed point results in Noor orbit, which is a three-step iterative procedure.

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Primary renal lymphoma is a rare entity representing less than 1% of lesions in the kidney. We present the case of a 42-year-old male who was evaluated for pain and a mass in the abdomen. The computed tomogram of the abdomen showed a large lobulated homogeneously enhancing mass lesion of about 14×12×18 cm, involving the whole of the left kidney and encasing the left renal vessels and ureter. The patient underwent a biopsy, and the histopathology was diffuse large B cell lymphoma, positive for LCA, CD20, PAX 5, Bcl 2 and negative for SIgM, CD33, CD34, CD5, Tdt, with MIB 1 labelling index of 40%. He received chemotherapy with rituximab, cyclophosphamide, doxorubicin, vincristine and prednisolone (R CHOP) for eight cycles followed by radiation to the residual mass and achieved complete remission. Currently, he is alive and in remission at 28 months.

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UNLABELLED: Clinical studies have shown that neuromodulation therapies, such as spinal cord stimulation (SCS) and transcutaneous electrical nerve stimulation (TENS), reduce symptoms of chronic neuropathic and visceral pain. The neural mechanisms underlying SCS and TENS therapy are poorly understood. The present study was designed to compare the effects of SCS and TENS on spinal neuronal responses to noxious stimuli applied to the heart and esophagus. Direct stimulation of an intercostal nerve (ICNS) was used to simulate the effects of TENS. Extracellular potentials of left thoracic (T3) spinal neurons were recorded in pentobarbital anesthetized, paralyzed, and ventilated male rats. SCS (50 Hz, 0.2 ms, 3-5 minutes) at a clinical relevant intensity (90% of motor threshold) was applied on the C1-C2 or C8-T1 ipsilateral spinal segments. Intercostal nerve stimulation (ICNS) at T3 spinal level was performed using the same parameters as SCS. Intrapericardial injection of bradykinin (IB, 10 microg/mL, 0.2 mL, 1 minute) was used as the noxious cardiac stimulus. Noxious thoracic esophageal distension (ED, 0.4 mL, 20 seconds) was produced by water inflation of a latex balloon. C1-C2 SCS suppressed excitatory responses of 16/22 T3 spinal neurons to IB and 25/30 neurons to ED. C8-T1 SCS suppressed excitatory responses of 10/15 spinal neurons to IB and 17/23 neurons to ED. ICNS suppressed excitatory responses of 9/12 spinal neurons to IB and 17/22 neurons to ED. These data showed that SCS and ICNS modulated excitatory responses of T3 spinal neurons to noxious stimulation of the heart and esophagus. PERSPECTIVE: Neuromodulation of noxious cardiac and esophageal inputs onto thoracic spinal neurons by spinal cord and intercostal nerves stimulation observed in the present study may help account for therapeutic effects on thoracic visceral pain by activating the spinal dorsal column or somatic afferents.

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