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Purpose: To determine the incidence, characteristics, disposition, and economic burden of emergency department (ED) visits for ophthalmic trauma in the United States (US)., Design: Retrospective study., Methods: The Nationwide Emergency Department Sample was used to calculate and characterize ED visits for ophthalmic trauma in the US from 2009 to 2018. Linear regression was used to estimate trends in annual incidence and mean inflation-adjusted ED charges. Logistic regression was used to assess variables associated with inpatient admission., Results: There were over 7.3 million ED visits for ophthalmic trauma in the US over the 10 years, with an annual incidence of 233 per 100,000 population. Patients were predominantly male (65 %), 21-44 years old (39 %), and from low-income households (56 %). Only 1 % of patients were hospitalized. Older age, male sex, metropolitan teaching hospitals, and trauma centers were associated with significantly higher odds of inpatient admission. The mean inflation-adjusted ED charge per visit more than doubled over the decade ($1,333 to $3,187) with total charges exceeding $14 billion. Superficial injuries (44 %) and eyelid/orbit wounds (20 %) accounted for the majority of visits. Orbital floor fractures (4 %) and open globe injuries (2 %) accounted for a minority of visits but were responsible for most admissions (49 % and 29 %, respectively) and the highest mean ED charge ($7,157 and $6,808, respectively)., Conclusions: Ophthalmic trauma represents an increasingly significant burden to EDs in the US. Preventive efforts should target young males from low socioeconomic backgrounds. Strategies to improve outpatient access and redirect non-urgent injuries may help alleviate costs., Competing Interests: Declaration of Competing Interest The authors declare there is no conflict of interests., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Efflux proteins are the transport proteins expressed in the plasma membrane, which are involved in the movement of unwanted toxic substances through specific efflux pumps. Several studies based on computational approaches have been proposed to predict transport proteins and thereby to understand the mechanism of the movement of ions across cell membranes. However, few methods were developed to identify efflux proteins. This paper presents an approach based on the contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) with the Support Vector Machine (SVM) classifier. BERT is the most effective pre-trained language model that performs exceptionally well on several Natural Language Processing (NLP) tasks. Therefore, the contextualized representations from BERT were implemented to incorporate multiple interpretations of identical amino acids in the sequence. A dataset of efflux proteins with annotations was first established. The feature vectors were extracted by transferring protein data through the hidden layers of the pre-trained model. Our proposed method was trained on complete training datasets to identify efflux proteins and achieved the accuracies of 94.15% and 87.13% in the independent tests on membrane and transport datasets, respectively. This study opens a research avenue for the implementation of contextualized word embeddings in Bioinformatics and Computational Biology., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Motivation: Primary and secondary active transport are two types of active transport that involve using energy to move the substances. Active transport mechanisms do use proteins to assist in transport and play essential roles to regulate the traffic of ions or small molecules across a cell membrane against the concentration gradient. In this study, the two main types of proteins involved in such transport are classified from transmembrane transport proteins. We propose a Support Vector Machine (SVM) with contextualized word embeddings from Bidirectional Encoder Representations from Transformers (BERT) to represent protein sequences. BERT is a powerful model in transfer learning, a deep learning language representation model developed by Google and one of the highest performing pre-trained model for Natural Language Processing (NLP) tasks. The idea of transfer learning with pre-trained model from BERT is applied to extract fixed feature vectors from the hidden layers and learn contextual relations between amino acids in the protein sequence. Therefore, the contextualized word representations of proteins are introduced to effectively model complex structures of amino acids in the sequence and the variations of these amino acids in the context. By generating context information, we capture multiple meanings for the same amino acid to reveal the importance of specific residues in the protein sequence., Results: The performance of the proposed method is evaluated using five-fold cross-validation and independent test. The proposed method achieves an accuracy of 85.44 %, 88.74 % and 92.84 % for Class-1, Class-2, and Class-3, respectively. Experimental results show that this approach can outperform from other feature extraction methods using context information, effectively classify two types of active transport and improve the overall performance., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Sirtuins are a family of proteins that play a key role in regulating a wide range of cellular processes including DNA regulation, metabolism, aging/longevity, cell survival, apoptosis, and stress resistance. Sirtuins are protein deacetylases and include in the class III family of histone deacetylase enzymes (HDACs). The class III HDACs contains seven members of the sirtuin family from SIRT1 to SIRT7. The seven members of the sirtuin family have various substrates and are present in nearly all subcellular localizations including the nucleus, cytoplasm, and mitochondria. In this study, a deep neural network approach using one-dimensional Convolutional Neural Networks (CNN) was proposed to build a prediction model that can accurately identify the outcome of the sirtuin protein by targeting their subcellular localizations. Therefore, the function and localization of sirtuin targets were analyzed and annotated to compartmentalize into distinct subcellular localizations. We further reduced the sequence similarity between protein sequences and three feature extraction methods were applied in datasets. Finally, the proposed method has been tested and compared with various machine-learning algorithms. The proposed method is validated on two independent datasets and showed an average of up to 85.77 % sensitivity, 97.32 % specificity, and 0.82 MCC for seven members of the sirtuin family of proteins., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

The recent emergence of novel coronavirus disease (COVID-19) triggered by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has resulted in substantial mortality worldwide. Presently, there is no approved treatment for COVID-19. Consequently, the clinical, scientific, and regulatory authorities have joint efforts to reduce the severe impact of COVID-19. To date, there is minimal arsenal with no definite curative drugs, licensed-vaccines, or therapeutic conducts to combat the COVID-19 infections. Keeping in view the threats of this pandemic, various global organizations, physicians, researchers, and scientists, are trying to recognize the epidemiological characteristics and pathogenic mechanisms of COVID-19 to discover potential treatment regimens, vaccines, and therapeutic modes for future anticipation. Herein, we summarize a contemporary overview of curative invasions and vaccines for COVID-19 based on the earlier information and considerate of similar earlier RNA coronaviruses. The information reviewed here establishes a paramount intellectual basis to promote ongoing research to develop vaccines and curative agents. Thus, this review suggests the furthermost accessible frontiers in the vaccine development to tackle or combat the COVID-19/SARS-CoV-2., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objective: To determine and compare epidemiology, patient demographics, and socioeconomic factors of ophthalmic procedures in the United States., Participants: All ophthalmic ambulatory procedures performed from 2012 to 2014., Methods: The State Ambulatory Surgery Databases (SASD), a collection of data from 29 participating states that represent two-thirds of the U.S. population, were used for analysis. All ophthalmic procedures performed in ambulatory settings from 2012 to 2014 were included. Incidence and demographics for each procedure were assessed, with descriptive statistics determined to highlight key differences in epidemiology and patient characteristics for each procedure. Population-based rates of procedures for each racial group were calculated using national census data., Results: Over a period of 3 years, 3 934 004 ophthalmic procedures were performed, with majority being ambulatory (98.5%). Most common procedures were lens and cataract removal (66.5%), followed by intraocular procedures (11.4%) and procedures involving eyelids, cornea, and conjunctiva (8.2%). All ambulatory procedures had a higher number of female patients except retinal tear and detachment repairs (male patients, 59.4%). Most procedures had a higher rate in blacks compared with whites and other races, and Medicare was the most common mode of payment., Conclusions: Approximately 1.3 million ambulatory ophthalmic procedures were performed every year between 2012 and 2014 in the 29 reporting states of the United States. Low-income groups and certain minorities, apart from blacks, having a lower rate of procedure suggest that disparities exist in provision of eye care. The factors leading to these differences need to be further studied in order to devise strategies to provide equitable care., (Copyright © 2019 Canadian Ophthalmological Society. Published by Elsevier Inc. All rights reserved.)