bluuebunny/crossref_metadata_2025 huggingface dataset:
Patient delay and benefit of timely reperfusion in ST-segment elevation myocardial infarction
Authors: Scholz Karl Heinrich , Meyer Thomas , Lengenfelder Björn , Vahlhaus Christian , Tongers Jörn , Schnupp Steffen , Burckhard Rainer , von Beckerath Nicolas , Grusnick Hans-Martin , Jeron Andreas , Winter Klaus Dieter , Maier Sebastian K G , Danner Michael , vom Dahl Jürgen , Neef Stefan , Stefanow Stefan , Friede Tim
Published: May 2021

Background

In patients with ST-segment elevation myocardial infarction (STEMI), it is unknown how patient delay modulates the beneficial effects of timely reperfusion.

Aims

To assess the prognostic significance of a contact-to-balloon time of less than 90 min on in-hospital mortality in different categories of symptom-onset-to-first-medical-contact (S2C) times.

Methods

A total of 20 005 consecutive patients from the Feedback Intervention and Treatment Times in ST-segment Elevation Myocardial Infarction (FITT-STEMI) programme treated with primary percutaneous coronary intervention (PCI) were included.

Results

There were 1554 deaths (7.8%) with a J-shaped relationship between mortality and S2C time. Mortality was 10.0% in patients presenting within 1 hour, and 4.9%, 6.0% and 7.3% in patient groups with longer S2C intervals of 1–2 hours, 2–6 hours and 6–24 hours, respectively. Patients with a short S2C interval of less than 1 hour (S2C<60 min) had the highest survival benefit from timely reperfusion with PCI within 90 min (OR 0.27, 95% CI 0.23 to 0.31, p<0.0001) as compared with the three groups with longer S2C intervals of 1 hour<S2C≤2 hours (OR 0.44, 95% CI 0.33 to 0.59, p<0.0001), 2 hours<S2C≤6 hours (OR 0.49, 95% CI 0.38 to 0.64, p<0.0001) and 6 hours<S2C≤24 hours (OR 0.42, 95% CI 0.30 to 0.58, p<0.0001).

Conclusions

Timely reperfusion with a contact-to-balloon time of less than 90 min is most effective in patients presenting with short S2C intervals of less than 1 hour, but has also beneficial effects in patients with S2C intervals of up to 24 hours.

Trial registration number

NCT00794001.
Antiphospholipid antibodies in COVID-19: a meta-analysis and systematic review
Authors: Taha Muhanad , Samavati Lobelia
Published: May 2021

Background

Many studies reported high prevalence of antiphospholipid antibodies (aPL) in patients with COVID-19 raising questions about its true prevalence and its clinical impact on the disease course.

Methods

We conducted a meta-analysis and a systematic review to examine the prevalence of aPL and its clinical impact in patients with COVID-19.

Results

21 studies with a total of 1159 patients were included in our meta-analysis. Among patients hospitalised with COVID-19, the pooled prevalence rate of one or more aPL (IgM or IgG or IgA of anticardiolipin (aCL) or anti-ß2 glycoprotein (anti-ß2 GPI) or antiphosphatidylserine/prothrombin, or lupus anticoagulant (LA)) was 46.8% (95% CI 36.1% to 57.8%). The most frequent type of aPL found was LA, with pooled prevalence rate of 50.7% (95% CI 34.8% to 66.5%). Critically ill patients with COVID-19 had significantly higher prevalence of aCL (IgM or IgG) (28.8% vs 7.10%, p<0.0001) and anti-ß2 GPI (IgM or IgG) (12.0% vs 5.8%, p<0.0001) as compared with non-critically ill patients. However, there was no association between aPL positivity and mean levels of C reactive protein (mean difference was 32 (95% CI −15 to 79), p=0.18), D-dimer (mean difference was 34 (95% CI −194 to 273), p=0.77), mortality (1.46 (95% CI 0.29 to 7.29), p=0.65), invasive ventilation (1.22 (95% CI 0.51 to 2.91), p=0.65) and venous thromboembolism (1.38 (95% CI 0.57 to 3.37), p=0.48).

Conclusions

aPLs were detected in nearly half of patients with COVID-19, and higher prevalence of aPL was found in severe disease. However, there was no association between aPL positivity and disease outcomes including thrombosis, invasive ventilation and mortality. However, further studies are required to identify the clinical and pathological role of aPL in COVID-19.
Relating natural organic matter conformation, metal complexation, and photophysics
Authors: Stirchak Laura , Donaldson D. James
Published: May 2021

We investigated the relationship between changes in fluorescence intensity and in fluorescence anisotropy for Suwannee River Natural Organic Matter (SRNOM) due to the formation of NOM-metal complexes with divalent and trivalent metals commonly present in both fresh water and sea water environments. We chose metal ions whose complexes give rise to both fluorescence quenching (Fe3+, Cu2+) and fluorescence enhancement (Al3+, Mg2+). Stern–Volmer type analyses quantified the changes in the SRNOM fluorescence as a function of metal concentration. All metals display strong complexation with SRNOM, associated with their effect on fluorescence. Experiments with Fe3+ further show strong effects due to NOM aggregation at all but the lowest metal concentrations studied here. There was little to no change in the conformation of SRNOM as inferred from fluorescence anisotropy caused by increasing metal concentration. These results suggest that there is no correlation between photophysical changes and conformational changes in NOM associated with complexation by the metal ions.
The response ofDaphniato nutrient additions and kokanee abundance in Dworshak Reservoir, Idaho
Authors: Wilson Sean M. , Corsi Matthew P. , Brandt Darren H. , Stark Eric J.
Published: May 2021

Daphnia are a keystone species in lentic systems worldwide and an important prey source for planktivorous fishes, such as kokanee (lacustrine Oncorhynchus nerka). Managers have added nutrients, nitrogen (N) and (or) phosphorus (P), in an attempt to improve the growth or carrying capacity for fish by increasing available prey, such as Daphnia. To be successful, this strategy requires that fish growth be prey limited, and prey availability be nutrient limited. In this study, kokanee in Dworshak Reservoir, Idaho fed preferentially on Daphnia ≥ 0.8 mm TL. The biomass of consumable Daphnia decreased with increasing kokanee abundance, indicating top-down control. However, the biomass of consumable Daphnia was greater at any given level of kokanee abundance when N was added to the reservoir, indicating bottom-up control. These results provide an example where Daphnia biomass was simultaneously controlled by both bottom-up and top-down forces, and that N addition alone can be an effective tool for increasing Daphnia biomass.
Effects of low temperature and nitrogen addition during cold stratification on seed germination of Korean pine (Pinus koraiensis)
Authors: Song Yuan , Gao Xiaoye
Published: May 2021

Pinus koraiensis Siebold & Zucc. seeds are primarily dormant following dispersal, restricting the natural regeneration of populations. The aim of this study was to determine the combined effects of increasing low temperature and nitrogen addition during cold stratification on seed germination. We stratified the seeds at 0 °C, 5 °C, and 10 °C for 3.5 months. At each cold stratification temperature, seeds were subjected to 0 (0 nitrogen), 0.08 (low nitrogen), 0.16 (medium nitrogen), and 0.25 mol·L–1 (high nitrogen) nitrate additions. Seed germination response was tested at 25/15 °C and 10/5 °C. Under 0 nitrogen and high nitrogen levels, increasing cold stratification temperature did not markedly affect seed germination at 25/15 °C, but significantly inhibited seed germination at 10/5 °C. At medium nitrogen levels, seed germination at both 25/15 °C and 10/5 °C significantly decreased. At low nitrogen levels, increasing cold stratification temperature significantly decreased germination percentage at 25/15 °C from 86.7% (0 °C) to 50.2% (10 °C) but did not markedly change seed germination percentage at 10/5 °C. We suggest that the effect of increasing cold stratification temperature on seed germination of P. koraiensis depends on both the level of nitrogen addition during cold stratification and subsequent incubation temperature after cold stratification.
ENHANCING THE COMPETITIVENESS OF BUSINESS AND MANAGEMENT CONSULTING FIRMS THROUGH THE APPLICATION OF BIG DATA AND ANALYTICS
Authors: OYEWO BABAJIDE , TRAN DANG KHOA
Published: May 2021

This study investigates the application of big data analytics (BDA) and its impact on consulting firms’ competitiveness. Descriptive statistics, ordinary least square regression, and moderated regression analysis were used to analyze survey data obtained from 118 business and management consultants in Nigeria. The robustness of the result was evaluated using structural equation modeling. Result shows that the application level of BDA by consulting firms is generally moderate. BDA application has a significant positive impact on organizational competitiveness, although the strength of the relationship is weak. Further, data quality significantly moderates the relationship between BDA and organizational competitiveness. The study concludes that the application of BDA can enhance the competitiveness of consulting firms. However, the extent to which such benefit is realized is dependent on the quality of data applied in the analysis. The study contributes to knowledge by providing empirical evidence that the deployment of BDA can be a source of competitive advantage for consulting firms. The study adds to literature on management accounting in the digital economy and the application of big data to business and management consulting.
Heuristic Bilingual Graph Corpus Network to Improve English Instruction Methodology Based on Statistical Translation Approach
Authors: Fang Hui , Shi Hongmei , Zhang Jiuzhou
Published: May 2021

The number of sentence pairs in the bilingual corpus is a key to translation accuracy in computational machine translations. However, if the amount goes beyond a certain degree, the increasing number of cases has less impact on the translation while the construction of translation systems requires a considerable amount of time and energy, thus preventing the development of a statistical translation by the computer. This article offers a number of classifications for measuring the amount of information for each pair of sentences, using the Heuristic Bilingual Graph Corpus Network (HBGCN) to form an improved method of corpus selection that takes the difference between the first amount of information between the pairs of sentences into account. Using a graphic-based selector method as a training set, they achieve a close translation result through our experiments with the whole body and achieve better results than basic results for the following based on the Document Inverse Frequency (DIF) ranking approach.
An Embedding-Based Topic Model for Document Classification
Authors: Seifollahi Sattar , Piccardi Massimo , Jolfaei Alireza
Published: May 2021

Topic modeling is an unsupervised learning task that discovers the hidden topics in a collection of documents. In turn, the discovered topics can be used for summarizing, organizing, and understanding the documents in the collection. Most of the existing techniques for topic modeling are derivatives of the Latent Dirichlet Allocation which uses a bag-of-word assumption for the documents. However, bag-of-words models completely dismiss the relationships between the words. For this reason, this article presents a two-stage algorithm for topic modelling that leverages word embeddings and word co-occurrence. In the first stage, we determine the topic-word distributions by soft-clustering a random set of embedded
n
-grams from the documents. In the second stage, we determine the document-topic distributions by sampling the topics of each document from the topic-word distributions. This approach leverages the distributional properties of word embeddings instead of using the bag-of-words assumption. Experimental results on various data sets from an Australian compensation organization show the remarkable comparative effectiveness of the proposed algorithm in a task of document classification.
A GDPR-compliant Ecosystem for Speech Recognition with Transfer, Federated, and Evolutionary Learning
Authors: Jiang Di , Tan Conghui , Peng Jinhua , Chen Chaotao , Wu Xueyang , Zhao Weiwei , Song Yuanfeng , Tong Yongxin , Liu Chang , Xu Qian , Yang Qiang , Deng Li
Published: May 2021

Automatic Speech Recognition (ASR) is playing a vital role in a wide range of real-world applications. However, Commercial ASR solutions are typically “one-size-fits-all” products and clients are inevitably faced with the risk of severe performance degradation in field test. Meanwhile, with new data regulations such as the European Union’s General Data Protection Regulation (GDPR) coming into force, ASR vendors, which traditionally utilize the speech training data in a centralized approach, are becoming increasingly helpless to solve this problem, since accessing clients’ speech data is prohibited. Here, we show that by seamlessly integrating three machine learning paradigms (i.e.,
T
ransfer learning,
F
ederated learning, and
E
volutionary learning (TFE)), we can successfully build a win-win ecosystem for ASR clients and vendors and solve all the aforementioned problems plaguing them. Through large-scale quantitative experiments, we show that with TFE, the clients can enjoy far better ASR solutions than the “one-size-fits-all” counterpart, and the vendors can exploit the abundance of clients’ data to effectively refine their own ASR products.
Vector-Quantization-Based Topic Modeling
Authors: Gupta Amulya , Zhang Zhu
Published: May 2021

With the purpose of learning and utilizing explicit and dense topic embeddings, we propose three variations of novel vector-quantization-based topic models (VQ-TMs): (1) Hard VQ-TM, (2) Soft VQ-TM, and (3) Multi-View Soft VQ-TM. The model family capitalize on vector quantization techniques, embedded input documents, and viewing words as mixtures of topics. Guided by a comprehensive set of evaluation metrics, we conduct systematic quantitative and qualitative empirical studies, and demonstrate the superior performance of VQ-TMs compared to important baseline models. Through a unique case study on code generation from natural language descriptions, we further illustrate the power of VQ-TMs in downstream tasks.
Ethical Machine Learning in Healthcare
Authors: Chen Irene Y. , Pierson Emma , Rose Sherri , Joshi Shalmali , Ferryman Kadija , Ghassemi Marzyeh
Published: May 2021

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
The Contribution of Biophysics and Structural Biology to Current Advances in COVID-19
Authors: Barrantes Francisco J.
Published: May 2021

Critical to viral infection are the multiple interactions between viral proteins and host-cell counterparts. The first such interaction is the recognition of viral envelope proteins by surface receptors that normally fulfil other physiological roles, a hijacking mechanism perfected over the course of evolution. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of coronavirus disease 2019 (COVID-19), has successfully adopted this strategy using its spike glycoprotein to dock on the membrane-bound metalloprotease angiotensin-converting enzyme 2 (ACE2). The crystal structures of several SARS-CoV-2 proteins alone or in complex with their receptors or other ligands were recently solved at an unprecedented pace. This accomplishment is partly due to the increasing availability of data on other coronaviruses and ACE2 over the past 18 years. Likewise, other key intervening actors and mechanisms of viral infection were elucidated with the aid of biophysical approaches. An understanding of the various structurally important motifs of the interacting partners provides key mechanistic information for the development of structure-based designer drugs able to inhibit various steps of the infective cycle, including neutralizing antibodies, small organic drugs, and vaccines. This review analyzes current progress and the outlook for future structural studies.
Discounting and Global Environmental Change
Authors: Polasky Stephen , Dampha Nfamara K.
Published: May 2021

Discounting plays a central role in decisions about global environmental change that affect the well-being of future generations. Discounting the future more heavily tilts decisions toward the present, making it less likely that society will undertake actions to mitigate climate change or other global environmental change. This article reviews the standard economics approach to discounting that emerges from solving for optimal savings and investment through time. Discounting depends on the pure rate of time preference and differences in consumption levels across time, giving rise to different marginal utilities of consumption. Discounting for problems like climate change that impact future generations involves an ethical dimension. This article includes discussions of ethical dimensions of discounting related to intragenerational and intergenerational equity and also covers behavioral economics, ecological economics, and mainstream economics approaches to discounting. The article closes with a review of the debates over discounting in climate change policy.
Enhanced Electrochemical Performance of Hydrothermally Exfoliated Hexagonal Boron Nitride Nanosheets for Applications in Electrochemistry
Authors: Sharma Kanika , Puri Nitin K.
Published: May 2021

Two-dimensional (2D) morphology of hexagonal boron nitride (h-BN), owing to its peculiar characteristics of non-toxicity and uniquely featured oxidation resistance has attracted extensive attention in electrochemical applications. Here, we report a facile top-down approach for the successful synthesis of hexagonal boron nitride nanosheets (h-BNNS) through a low-temperature hydrothermal method. The structural and spectroscopic characterizations have been performed using XRD, FT-IR, Raman and UV-Visible Spectroscopy that reveals incorporation of maximum induced strain, multifunctional groups and formation of few layers h-BNNS. The nanosheets morphology is confirmed by series of characterizations (SEM, TEM, and AFM) revealing the large lateral size and relatively low surface roughness of h-BNNS. The electrodes for electrochemical characterizations are prepared using the electrophoretic deposition (EPD) technique onto ITO substrates with the help of magnesium nitrate [Mg(NO3)2·6H2O] as a mediator. Electrochemical Studies has been performed in phosphate buffer saline (PBS) with Ferro-ferricyanide [Fe(CN)6]3−/4− as a redox couple. Cyclic voltammogram of h-BNNS (Ipa = 1.29 mA and Ipc = −1.23 mA) indicates 69.96% enhancement in redox peaks current and 71.04% rise in electro-active surface area with respect to Bulk h-BN. The electrochemical studies uncover the potential of h-BNNS in the development of electroanalytical devices in applications such as sensing, anticorrosion, energy conversion and energy storage applications owing to their enhanced redox peaks, large electroactive surface area, and reduced charge transfer resistance as well as the admittance of Warburg element.
Substituted o-Aminophenols as Redox-Mediators in the Thiol Oxidation to Unsymmetrical Disulfides
Authors: Burmistrova Daria A. , Galustyan Andrey , Smolyaninov Ivan V. , Berberova Nadezhda T.
Published: May 2021

A number of substituted o-aminophenols has been investigated as redox mediators of the thiol oxidation to disulfides. The electrooxidation of o-aminophenols leads to the corresponding o-iminobenzoquinones. These compounds react with thiols in the solution with a formation of disulfides. It was established that the use of 4,6-di-tert-butyl-2-(tert-butylamino)phenol as a redox mediator can reduce the overpotential of the thiol oxidation by 0.2–1.4 V depending on the nature of the coupling thiols. The unsymmetrical disulfides with alkyl, aryl, and heteroaryl substituents were obtained as the result of the indirect electrosynthesis.
Evaluating the effects of oral contraceptive use on biomarkers and body composition during a competitive season in collegiate female soccer players
Authors: Bozzini Brittany N. , McFadden Bridget A. , Elliott-Sale Kirsty J. , Swinton Paul A. , Arent Shawn M.
Published: May 2021

This study highlights the influence of OC use on physiological changes that occur over a 4-mo intense, competitive season and the differential systemic exposure to biomarkers, specifically those of inflammation, stress, anabolism, and energy balance, between OC-using and nonusing soccer players. Additionally, this study provides insight into changes in body composition with prolonged training between female athletes with and without OC use.
A Multimodality Information Synchronization Scheme for a Multisource Information System in the Electric Grid
Authors: Zhang Pan , Lai Fenggang , Du Jing , Lu Wenda , Yu Xiao
Published: May 2021

A multisource and heterogeneous database is an important problem that disturbs the use of the electric power information system. The existing database synchronization scheme has some problems in practical applications, such as high resource loss and poor portability. This paper presents a high-efficiency database synchronization scheme for the electric power information system. The database is monitored, and its changes are captured by the shadow table and trigger method. Thus, data could be exchanged in trusted networks and nontrusted networks. In addition, a predetermined strategy is used to avoid data conflicts and ensure consistency and reliability of data synchronization. The above method is applied in the protection system of power networks. The results show that the synchronization scheme can effectively ensure the security of the system and has higher synchronization efficiency.
Cross-Platform Drilling 3D Visualization System Based on WebGL
Authors: Liu Shanshan , Feng Yueli , Wang Xiaoqiu , Yan Pengyin
Published: May 2021

This study develops a novel drilling 3D visualization solution based on WebGL, termed as WebDrillingViz, and introduces the system architecture design and software programming implementation. The software is part of the Engineering Technology Internet of Things (IoT) System, interfacing with other software, and also capable of direct hardware interfacing for data retrieval and system control. It is fully web-based, used real time, and used in RTOC (Real-Time Operating Center) of IoT system, which is a software system for drilling process remote monitor and decision. WebDrillingViz uses the most frontier HTML5 technology to realize a brand-new drilling 3D visualization system. The front end is designed in single-page application (SPA) mode and adopts technologies such as angular, bootstrap, and WebGL. The front-end uses single page application (SPA) mode, Angular, Bootstrap, WebGL and other technologies are used. The back-end data services provide data interface support for front-end visualization applications based on HTTP protocol which uses NodeJS, a lightweight development platform suitable for cloud platform, and Restify to realize a REST JSON API. Both sides are using the same object-oriented oriented development language—TypeScript. The front-end develops an easy-to-extend 3D visualization class library based on WebGL for drilling. It is encapsulated as Angular modularization to form an Angular component, which can be used standalone or integrated into other Angular applications. At the same time, the back-end microservice architecture combined with container and cloud technology is easy to maintain, deploy, and expand and has the advantages of being lightweight, cross-platform, flexible, and efficient. Using HTML5 standard and Bootstrap's responsive layout achieves cross-platform, which can support different operating systems and screen sizes. The system has better robustness and maintainability, thanks to the object-oriented and strong typing characteristics of TypeScript. Practical application shows that WebDrillingViz is efficient, capable of visualization of large drilling 3D scene, and compatible with mainstream devices, such as Windows, Linux, macOS, iOS, and Android. The use of open standards-based modern web technologies and data format enables a more lightweight and economical solution. WebGL, Angular, NodeJS, and TypeScript formed a powerful technology stack, which can be used as an excellent reference for other browser-based visualization development.
Framework for Educational Domain-Based Multichatbot Communication System
Authors: Memon Zojan , Aghian Hamideh , Sarfraz Muhammad Shahzad , Hussain Jalbani Akhtar , Oskouei Rozita Jamili , Jalbani Khuda Bux , Hussain Jalbani Ghulam
Published: May 2021

Education is an area where innovation moves slowly. In this study, we will propose a framework with a novel approach that will support the development of a multi-interactive chatbot’s system for an educational area using AIML 2.0. The system will facilitate the students for their learning towards an outcome-based education domain. The proposed framework will be composed of a user module which consists of user and user interface, chat agents module which will respond to the user query, chatbot KB which will act as the brain for the chatbot system, and socket system for establishing the communication link. Finally, the proposed system will be evaluated using a confusion matrix. The multichatbot communication system will support text-based dialogues on a limited set of questions related to education. However, the system will be implemented in java. The outcomes of this research will be useful for the education sector where these intelligent systems will help the students in schools, universities, and other training scenarios.
Long-Term Rainfall Information Forecast by Utilizing Constrained Amount of Observation through Artificial Neural Network Approach
Authors: Akiner Muhammed E.
Published: May 2021

Estimating models are becoming increasingly crucial in highlighting the nonlinear connections of the massive level of rough information and chaotic components. The study demonstrates a modern approach utilizing a created artificial neural network (ANN) method that may be an alternative strategy to conventional factual procedures for advancing rainfall estimate execution. A case study was presented for Turkey’s Düzce and Bolu neighboring territories located on the Black Sea’s southern coast. This study’s primary aim is to create an ANN model unique in the field to generate satisfactory results even with limited data. The proposed technique is being used to estimate rainfall and make predictions regarding future precipitation. Bolu daily average rainfall by month data and a limited number of Düzce rainfall data were used. Missing forecasts and potential rainfall projections will be examined in the fundamental research. This research further focuses on ANN computational concepts and develops a neural network for rainfall time series forecasting. The emphasis of this study was a feed-forward backpropagation network. The Levenberg–Marquardt algorithm (LMA) was implemented for training a two-layer feed-forward ANN for the missing rainfall data prediction part of this research. The inaccessible rainfall parameters for Düzce were determined for the years 1995 to 2009. From 2010 to 2020, a two-layer feed-forward ANN was trained using the gradient descent algorithm to forecast daily average rainfall data by month. The findings reported in this study guide researchers interested in implementing the ANN forecast model for an extended period of missing rainfall data.
Disturbance Observer‐Based Robust Formation‐Containment of Discrete‐Time Multiagent Systems with Exogenous Disturbances
Authors: Xu Chengjie , Li Bofan , Yuan Yi
Published: May 2021

This paper investigates robust formation‐containment control of discrete‐time multiagent systems (MASs) with exogenous disturbances. Based on the discrete‐time disturbance observer method, both state feedback and output feedback control protocols are proposed. Formation‐containment conditions are obtained and convergency analysis is given according to Lyapunov stability theory. And, the corresponding control gains are obtained by solving some discrete‐time algebraic Riccati equations. Numerical simulations are presented to illustrate the theoretical findings.
Identification of Novel Gene Signature Associated with Cell Glycolysis to Predict Survival in Hepatocellular Carcinoma Patients
Authors: Wang Ming , Jiang Feng , Wei Ke , Mao Erli , Yin Guoyong , Wu Chuyan
Published: May 2021

Purpose. As hepatocellular carcinoma (HCC) is a complex disease, it is hard to classify HCC with a specific biomarker. This study used data from TCGA to create a genetic signature for predicting the prognosis of HCC patients. Methods. In a group of HCC patients (n = 424) from TCGA, mRNA profiling was carried out. To recognize gene sets that differed significantly between HCC and normal tissues, an enrichment study of genes was carried out. Cox relative hazard regression models have been used to identify genes that are significantly associated with overall survival. To test the function of a prognostic risk parameter, the following multivariate Cox regression analysis was used. The log-rank test and Kaplan–Meier survival estimates were used to test the significance of risk parameters for predictive prognoses. Results. Eight genes have been identified as having a significant link to overall survival (PAM, NUP155, GOT2, KDELR3, PKM, NSDHL, ENO1, and SRD5A3). The 377 HCC patients were divided into eight-gene signature-based high/low-risk subgroups. The eight-gene signature’s prognostic ability was unaffected by a number of factors. Conclusion. To predict the survival of patients with HCC, an eight-gene signature associated with cellular glycolysis was then identified. The findings shed light on cellular glycolysis processes and the diagnosis of patients with low HCC prognoses.
Some Fixed-Point Theorems over a Generalized
F
-Metric Space
Authors: Roy Kushal , Alaeidizaji Hossein , Saha Mantu , Mohammadi Babak , Parvaneh Vahid
Published: May 2021

In this article, the concept of sequential

F

-metric spaces has been introduced as a generalization of usual metric spaces,

b

-metric spaces,

J
S

-metric spaces, and mainly

F

-metric spaces. Some topological properties of such spaces have been discussed here. By considering this notion, we prove fixed-point theorems for some classes of contractive mappings over such spaces. Examples have been given in order to examine the validity of the underlying space and in support of our fixed-point theorems. Moreover, our fixed-point theorem is applied to obtain solution of a system of linear algebraic equations.
Study on PPG Biometric Recognition Based on Multifeature Extraction and Naive Bayes Classifier
Authors: Yang Junfeng , Huang Yuwen , Zhang Ruili , Huang Fuxian , Meng Qinggang , Feng Shixin
Published: May 2021

Nowadays, the method of simple-feature extraction has been extensively studied and is used in PPG biometric recognition; some promising results have been reported. However, some useful information is often lost in the process of PPG signal denoising; the time-domain, frequency-domain, or wavelet feature extracted is often partial, which cannot fully express the raw PPG signal; and it is also difficult to choose the appropriate matching method. Therefore, to make up for these shortcomings, a method of PPG biometric recognition based on multifeature extraction and naive Bayes classifier is proposed. First, in the preprocessing of the raw PPG data, the sliding window method is used to rerepresent the raw data. Second, the feature-extraction methods based on time-domain, frequency-domain, and wavelet are analysed in detail, then these methods are used to extract the time-domain, frequency-domain, and wavelet features, and the features are concatenated into a multifeature. Finally, the multifeature is normalized and combined with classifiers and Euclidean distance for matching and decision-making. Extensive experiments are conducted on three PPG datasets, it is found that the proposed method can achieve a recognition rate of 98.65%, 97.76%, and 99.69% on the respective sets, and the results demonstrate that the proposed method is not inferior to several state-of-the-art methods.
Evidence-Based Guideline on Critical Patient Transport and Handover to ICU
Authors: Agizew Tesfaye Belaneh , Ashagrie Henos Enyew , Kassahun Habtamu Getinet , Temesgen Mamaru Mollalign
Published: May 2021

The perioperative period is a time in which significant physiological change occurs. Improper transfer of information at this point can lead to medical errors. Planning and preparation for critical patient transport to ICU is vital to prevent adverse events. Critical patient transport to ICU must be as safe as possible and should not cause additional risks. It needs good communication, planning, and appropriate staffing with standard monitoring. Evidence shows inconsistency and variability on the use of standardized protocols during critical patient transfer and handover to the ICU. There is a variety of controversial approaches about the need of sedation, use of end-tidal CO2 monitoring, and manual versus mechanical ventilation based on different evidence. The objective of this review was to recommend safer options of critical patient transfer to the ICU that help reduce patient morbidity and mortality. Methods. Google Scholars, PubMed through HINARI, and other search engines were used to search high-quality evidence that help reach appropriate conclusions. Discussion. Critical patient transfer and handover to ICU is a complex procedure that needs experienced hands, availability of appropriate team members, standard monitoring, and necessary emergency and patient-specific medications. Appropriate and adequate transfer of patient information to the receiving team decreases patient morbidity and mortality when the transfer team uses standardized checklist. Conclusion. Involvement of senior physicians, use of standard monitoring, and appropriate transfer of information have been shown to decrease critical patient morbidity and mortality.
Biostimulatory Effects of Low-Intensity Pulsed Ultrasound on Rate of Orthodontic Tooth Movement and Associated Pain, Applied at 3-Week Intervals: A Split-Mouth Study
Authors: Qamruddin Irfan , Alam Mohammad Khursheed , Mahroof Verda , Karim Meenaz , Fida Mubassar , Khamis Mohd Fadhli , Husein Adam
Published: May 2021

Objective. Low-intensity pulsed ultrasound (LIPUS) is a noninvasive modality to stimulate bone remodeling (BR) and the healing of hard and soft tissues. This research evaluates the biostimulatory effect of LIPUS on the rate of orthodontic tooth movement (OTM) and associated pain, when applied at 3-week intervals. Methods. Twenty-two patients (11 males and 11 females; mean age 19.18 ± 2.00 years) having Angle’s Class II division 1 malocclusion needing bilateral extractions of maxillary first bicuspids were recruited for this split-mouth randomized clinical trial. After the initial stage of alignment and leveling with contemporary edgewise MBT (McLaughlin–Bennett–Trevisi) prescription brackets (Ortho Organizers, Carlsbad, Calif) of 22 mil, followed by extractions of premolars bilaterally, 6 mm nickel-titanium spring was used to retract the canines separately by applying 150 g force on 0.019 × 0.025-in stainless steel working archwires. LIPUS (1.1 MHz frequency and 30 mW/cm2 intensity output) was applied for 20 minutes extraorally and reapplied after 3 weeks for 2 more successive visits over the root of maxillary canine on the experimental side whereas the other side was placebo. A numerical rating scale- (NRS-) based questionnaire was given to the patients on each visit to record their weekly pain experience. Impressions were also made at each visit before the application of LIPUS (T1, T2, and T3). Models were scanned with a CAD/CAM scanner (Planmeca, Helsinki, Finland). Mann–Whitney U test was applied for comparison of canine movement and pain intensity between both the groups. Results. No significant difference in the rate of canine movement was found among the experimental (0.90 mm ± 0.33 mm) and placebo groups (0.81 mm ± 0.32 mm). There was no difference in pain reduction between experimental and placebo groups (

p
>
0.05

). Conclusion. Single-dose application of LIPUS at 3-week intervals is ineffective in stimulating the OTM and reducing associated treatment pain.
Slot Machine RTP Optimization Using Variable Neighborhood Search
Authors: Kamanas Pantelis-Arsenios , Sifaleras Angelo , Samaras Nikolaos
Published: May 2021

This work presents a Variable Neighborhood Search (VNS) approach for solving the Return-To-Player (RTP) optimization problem. A large number of software companies in the gaming industry seek to solve the RTP optimization problem in order to develop modern virtual casino gambling machines. These slot machines have a number of reels (e.g., three or more) that spin once a button is pushed. Each slot machine is required to have an RTP in a particular range according to the legislation of each country. By using a VNS framework that guides two local search operators, we show how to control the distribution of the symbols in the reels in order to achieve the desired RTP. In this study, optimization refers only to base game, the core of slot machine games, and not in bonus games, since a bonus game is triggered once two, three, or more specific symbols occur in the gaming monitor. Although other researchers have tried to solve the RTP problem in the past, this is the first time that a VNS methodology is proposed for this problem in the literature with good computational results.
Utilizing Colpocleisis to Repair a Vesicovaginal Fistula in a Cervical Cancer Patient with History of Pelvic Radiation: A Case Report and Literature Review
Authors: Dahman Ahmad , McClelland Daniel , Zaslau Stanley , Turner Valerie Galvan , Duenas Omar , Shapiro Robert
Published: May 2021

Background. Vesicovaginal fistula is a rare and distressing urological condition. It is especially prevalent in developing countries with the predominant etiology secondary to obstructed labor. Radiation therapy in female patients with cervical cancer is a risk factor for vesicovaginal fistula formation in the United States. Case Presentation. A 53-year-old woman with a history of cervical cancer and radiation presented with continuous urinary incontinence. Following diagnostic vaginoscopy, a 1 cm vesicovaginal fistula was diagnosed at the vaginal apex. The patient elected for surgical repair. She subsequently underwent successful transvaginal fistula closure using colpocleisis to optimally address the systemic factors of poor wound healing associated with irradiated tissue. Because of the adjacent tissue having been compromised by pelvic radiation, we opted to use a biologic graft made of human cadaveric pericardial tissue (CPT) instead of a native tissue flap to provide additional support for the fistula repair. Conclusion. A transvaginal approach for surgical repair of vesicovaginal fistula can be successful in patients with a prior history of pelvic radiation. Transvaginal colpocleisis is a viable option to augment vesicovaginal fistula repair for patients with significant comorbidities when sexual intercourse is no longer desired.
New Enhancement beyond Radiation Field Improves Survival Prediction in Patients with Post-Treatment High-Grade Glioma
Authors: Yuan Tao , Ji Xiaoli , Liu Yawu , Gao Guodong , Ren Jia-Liang , Huang Deyou , Quan Guanmin
Published: May 2021

The imaging signs which can accurately predict survival prognosis after standard treatment of high-grade glioma (HGG) are highly desirable. This study aims to explore the role of new enhancement beyond radiation field (NERF) in the survival prediction in patients with post-treatment HGG. The present study included 142 pathologically confirmed HGG patients who had received standard treatment. NERF, as well as other conventional MR findings and clinical variables, were included in univariate and multivariate analyses for evaluating their impactions on progression-free survival (PFS) and overall survival (OS). Univariate analysis showed that histological grade (

p
=
0.008

) and NERF (

p
=
0.001

) were the prognostic variables for poor PFS, whereas histological grade (

p
=
0.017

), NERF (

p
=
0.001

), and new subventricular zone enhancement (nSVZE) (

p
=
0.001

) were prognostic variables for poor OS. The multivariate analysis showed that NERF (HR 3.93; 95% CI 1.93–8.01;

p
=
0.001

) and nSVZE (HR 3.92; 95% CI 1.95–7.89;

p
=
0.001

) were the prognostic variables for poor OS. However, only nSVZE was (HR 3.29; 95% CI 2.04–5.28;

p
=
0.001

) the prognostic variable for poor PFS. When combining the NERF with the clinical and other MR variables, the highest AUC (0.924) and specificity (0.899) for predicting poor OS were achieved. The location of new developed enhancements relevant to high dose radiation field appears to be the main determinant of their prognostic value. Our results suggest that the new enhancement beyond radiation field can improve the survival prediction in patients with HGG after standard treatment.
Efficacy and Safety of Dual Antiplatelet Therapy in Patients Undergoing Coronary Stent Implantation: A Systematic Review and Network Meta-Analysis
Authors: Xu Yi , Shen Yimin , Chen Delong , Zhao Pengfei , Jiang Jun
Published: May 2021

Introduction. This network meta-analysis aimed to evaluate the efficacy and safety of different dual antiplatelet therapies (DAPTs) after percutaneous coronary intervention (PCI) with drug-eluting stents (DESs). Methods. Randomized controlled trials (RCTs) comparing longer-term (>12 months) DAPT (L-DAPT), 12-month DAPT (DAPT 12Mo), 6-month DAPT (DAPT 6Mo), 3-month DAPT followed by aspirin monotherapy (DAPT 3Mo + ASA), 3-month DAPT followed by a P2Y12 receptor inhibitor monotherapy (DAPT 3Mo + P2Y12), or 1-month DAPT with a P2Y12 receptor inhibitor monotherapy (DAPT 1Mo + P2Y12) were searched. Primary endpoints were all-cause mortality, cardiac death, myocardial infarction (MI), major bleeding, any bleeding, definite or probable stent thrombosis (ST), and net adverse clinical events (NACE). This Bayesian network meta-analysis was performed with the random-effects model. Results. Twenty-four RCTs (n = 81339) were included. In comparison with L-DAPT, DAPT 6Mo (OR: 0.50, 95% CI: 0.29–0.83), DAPT 3Mo + P2Y12 (OR: 0.38, 95% CI: 0.18–0.82), DAPT 3Mo + ASA (OR: 0.44, 95% CI: 0.17–0.98), and DAPT 1Mo + P2Y12 (OR: 0.45, 95% CI: 0.14–0.93) were associated with a lower risk of major bleeding. DAPT 3Mo + P2Y12 (OR: 0.58, 95% CI: 0.38–0.88) reduced the risk of any bleeding when compared with DAPT 12Mo. L-DAPT decreased the risk of MI and definite or probable stent ST when compared with DAPT 6Mo. DAPT 3Mo + P2Y12 decreased the risk of NACE in comparison with DAPT 6Mo and DAPT 12Mo. No significant difference in all-cause mortality and cardiac death was observed. In patients with acute coronary syndrome, DAPT 6Mo was comparable to DAPT 12Mo. Conclusion. Short-term (1–3 months) DAPT is noninferior to DAPT 6Mo after DESs implantation, while L-DAPT reduces MI and definite or probable ST rates. DAPT 3Mo + P2Y12 might be a reasonable trade-off in patients with high risk of bleeding accompanied by ischemia.
Varicella-Zoster Meningitis in an Immunocompetent Male without Fever or Rash
Authors: Raghunathan Raju , Khalil Qasim , Mooty Mohamad
Published: May 2021

Varicella-Zoster virus (VZV) is a human herpesvirus that primarily causes chickenpox and can reactivate later in life. Chickenpox occurs mostly in children and is characterized by a typical generalized vesicular rash. Following the primary infection, VZV can remain latent and can reactivate decades later to produce Zoster, being more common in the elderly as well as immunosuppressed individuals. The diagnosis of both the primary and reactivation is mostly clinical from the typical rash. However, when presentations are atypical, it leads to diagnostic challenges. We report an unusual case of VZ reactivation in an immunocompetent young adult presenting without fever, zoster rash, or neuralgia. The diagnosis was established by a positive polymerase chain reaction (PCR) performed on cerebrospinal fluid samples. The patient was treated with acyclovir and responded very well. The diagnosis of VZ meningitis is challenging in the absence of typical features of Zoster rash and requires a high index of suspicion.
Bismuth-Remaining Cupellation Fire Assay Preconcentration Combined with Inductively Coupled Plasma Mass Spectrometry for the Simultaneous Determination of Ultratrace Au, Pt, Pd, Ru, Rh, and Ir in Geologic Samples
Authors: Ni Wenshan , Mao Xiangju , Yao Mingxing , Sun Qiliang , Guo Xiaorui , Zhang Hongli , Liu Lu
Published: May 2021

In this work, a novel method of bismuth fire assay (Bi-FA) combined with inductively coupled plasma mass spectrometry (ICP-MS) simultaneous determination of ultratrace gold (Au), platinum (Pt), palladium (Pd), ruthenium (Ru), rhodium (Rh), and iridium (Ir) in geologic samples was established. Bismuth oxide (Bi2O3) was used as noble metal elements fire assay collector, and Bi-remaining protection cupellation was employed to generate Bi granule. After the Bi granule was microwave-digested by aqua regia (40%, v/v), 197Au, 195Pt, 106Pd, 101Ru, 103Rh, and 193Ir in the sample solution were determined by ICP-MS. Using Bi as cupellation protector, volatile Ru could be collected effectively and without volatilization loss during microwave digestion and decompression. Moreover, the toxicity of Bi was exceptionally low compared to toxic nickel oxide and lead oxide in nickel sulfide/lead fire assay; thus Bi-FA was a green environmental analysis method. The mineral composition and decomposition character of chromite, black shale, and polymetallic ore were investigated, and pretreatment procedures were optimized for such special samples. Besides, the influence of mass spectrum interference of coexisting elements was discussed. Under the optimal conditions, excellent curve fittings of Au, Pt, Pd, Ru, Rh, and Ir were obtained between 0.01 and 100 ng·mL−1, with the correlation coefficients exceeding 0.9995. The detection limits were from 0.002 ng·g−1 to 0.025 ng·g−1. The developed method was applied to analyze the Chinese Certified Reference Materials (CRMs) and the determined values were in good agreement with the certified values.
Efficient Automated Disease Diagnosis Using Machine Learning Models
Authors: Kumar Naresh , Narayan Das Nripendra , Gupta Deepali , Gupta Kamali , Bindra Jatin
Published: May 2021

Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagnosis model is designed using the machine learning models. In this paper, we have selected three critical diseases such as coronavirus, heart disease, and diabetes. In the proposed model, the data are entered into an android app, the analysis is then performed in a real-time database using a pretrained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown in the android app. Logistic regression is used to carry out computation for prediction. Early detection can help in identifying the risk of coronavirus, heart disease, and diabetes. Comparative analysis indicates that the proposed model can help doctors to give timely medications for treatment.