Miracle-dz/UniFAR-FTUs huggingface dataset:
Epidemiology of seasonal coronaviruses: Establishing the context for COVID-19 emergence

Public health preparedness for coronavirus disease 2019 (COVID-19) is challenging in the absence of setting-specific epidemiological data. Here we describe the epidemiology of seasonal human coronaviruses (sCoVs) and other cocirculating viruses in the West of Scotland, UK. We analyzed routine diagnostic data for >70,000 episodes of respiratory illness tested molecularly for multiple respiratory viruses between 2005 and 2017. Statistical associations with patient age and sex differed between CoV-229E, CoV-OC43 and CoV-NL63. Furthermore, the timing and magnitude of sCoV outbreaks did not occur concurrently and coinfections were not reported. With respect to other cocirculating respiratory viruses, we found evidence of positive, rather than negative, interactions with sCoVs. These findings highlight the importance of considering cocirculating viruses in the differential diagnosis of COVID-19. Further work is needed to establish the occurrence/degree of cross-protective immunity conferred across sCoVs and with COVID-19, as well as the role of viral coinfection in COVID-19 disease severity.
Urticarial eruption in COVID‐19 infection

Coronavirus disease (COVID-19) is spreading quickly across the world, until a pandemic condition was announced by the WHO on March. Many clinical manifestations of this virus are described and new symptoms are emerging particularly outside respiratory sphere, such as anosmia and ageusia which are recent ORL published symptoms. About skin manifestation, few cases of rashes on patients with laboratory-confirmed Covid-19 were described in two Chineses cohorts.
A Critical Review of Biological Properties, Delivery Systems and Analytical/Bioanalytical Methods for Determination of Bevacizumab

Abstract Bevacizumab is a chimeric monoclonal human-murine antibody originated from murine monoclonal antibody (muMAb A4.6.1) with the human immunoglobulin IgG1. BVZ binds the extracellular portion of vascular endothelial growth factor receptors (VEGFR), which have tyrosine kinase activity. The mechanism of action of BVZ involves binding to VEGFR, Flt-1 (VEGFR-1) and KDR/Flk-1 (VEGFR-2), inducing homodimerization of two receptor subunits, and, consequently, autophosphorylation of their tyrosine kinase domains located inside the cytoplasm. With the advent of nanostructured systems it is increasingly necessary to look for safe analytical methods, ensuring the reliability of the results obtained by them, becoming essential to ensure the quality of medicines. In this work, the incorporation of bevacizumab in to different drug delivery systems was presented. Moreover, detailed investigation was performed about methods for qualitative and quantitative analyses of bevacizumab, including, biological fluids, and drug delivery systems, were investigated. Most recently high performance liquid chromatography coupled with various detectors, liquid chromatography, mass spectrometry and ELISA were used for this purpose. Thus, this review was performed to evaluate the benefits of bevacizumab carried by nanostructured systems and the analytical methods available for detection and quantification of these drugs.
Latency Compensated Visual-Inertial Odometry for Agile Autonomous Flight

In visual-inertial odometry (VIO), inertial measurement unit (IMU) dead reckoning acts as the dynamic model for flight vehicles while camera vision extracts information about the surrounding environment and determines features or points of interest. With these sensors, the most widely used algorithm for estimating vehicle and feature states for VIO is an extended Kalman filter (EKF). The design of the standard EKF does not inherently allow for time offsets between the timestamps of the IMU and vision data. In fact, sensor-related delays that arise in various realistic conditions are at least partially unknown parameters. A lack of compensation for unknown parameters often leads to a serious impact on the accuracy of VIO systems and systems like them. To compensate for the uncertainties of the unknown time delays, this study incorporates parameter estimation into feature initialization and state estimation. Moreover, computing cross-covariance and estimating delays in online temporal calibration correct residual, Jacobian, and covariance. Results from flight dataset testing validate the improved accuracy of VIO employing latency compensated filtering frameworks. The insights and methods proposed here are ultimately useful in any estimation problem (e.g., multi-sensor fusion scenarios) where compensation for partially unknown time delays can enhance performance.
Cymbopogon Proximus Essential Oil Protects Rats against Isoproterenol-Induced Cardiac Hypertrophy and Fibrosis

Cardiac hypertrophy is an independent risk factor of many cardiovascular diseases. Several cardiovascular protective properties of Cymbopogon proximus have been reported. However, no reports investigating the direct effect of C. proximus essential oil on the heart are available. The goal of this study was to explore the cardioprotective effect of C. proximus on cardiac hypertrophy and fibrosis. Male albino rats were administered C. proximus essential oil in the presence or absence of hypertrophic agonist isoproterenol. Cardiac hypertrophy and fibrosis were assessed using real-time polymerase chain reaction (PCR) and histological examination. Pre- treatment of rats with C. proximus decreased the ratio of heart weight to body weight and gene expression of hypertrophy markers atrial natriuretic peptide (ANP), brain natriuretic peptide (BNP), and β-myosin heavy chain (β-MHC), which were induced by isoproterenol. Moreover, C. proximus prevented the increase in gene expression of fibrosis markers procollagen I and procollagen III and alleviated the collagen volume fraction caused by isoproterenol. The pre- treatment with C. proximus essential oil conferred cardio-protection against isoproterenol- induced cardiac hypertrophy and fibrosis.
Facile removal of phytochromes and efficient recovery of pesticides using heteropore covalent organic framework-based magnetic nanospheres and electrospun films.

Non-targeted analysis of food safety requires selective removal of interference matrices and highly efficient recovery of chemical hazards. Porous materials such as covalent organic frameworks (COFs) show great promise in selective adsorption of matrix molecules via size selectivity. Considering the complexity of interference matrices, we prepared crystalline heteropore COFs whose two kinds of pores have comparable sizes to those of several common phytochromes, main interference matrices in vegetable sample analysis. By controlling the growth of COFs on the surface of Fe3O4 nanoparticles or by utilizing a facile co-electrospinning method, heteropore COFs-based magnetic nanospheres or electrospun nanofiber films were prepared, respectively. Both the nanospheres and the films maintain the dual-pore structures of COFs and show good stability and excellent reusability. Via simple magnetic separation or immersion operation, respectively, they were successfully used for the complete removal of phytochromes and highly efficient recovery of 15 pesticides from the extracts of four vegetable samples, the recoveries are in the range of 83.10~114.00% and 60.52%~107.35%, respectively. Film-based immersion operation gives better sample pretreatment performance than film-based filtration one. This work highlights the great application potentials of heteropore COFs in sample pretreatment for non-targeted analysis, thus opening up a new way to achieve high performance sample preparation in many fields such as food safety analysis, environment monitoring and so on.
AK-DL: A Shallow Neural Network Model for Diagnosing Actinic Keratosis with Better Performance than Deep Neural Networks

Actinic keratosis (AK) is one of the most common precancerous skin lesions, which is easily confused with benign keratosis (BK). At present, the diagnosis of AK mainly depends on histopathological examination, and ignorance can easily occur in the early stage, thus missing the opportunity for treatment. In this study, we designed a shallow convolutional neural network (CNN) named actinic keratosis deep learning (AK-DL) and further developed an intelligent diagnostic system for AK based on the iOS platform. After data preprocessing, the AK-DL model was trained and tested with AK and BK images from dataset HAM10000. We further compared it with mainstream deep CNN models, such as AlexNet, GoogLeNet, and ResNet, as well as traditional medical image processing algorithms. Our results showed that the performance of AK-DL was better than the mainstream deep CNN models and traditional medical image processing algorithms based on the AK dataset. The recognition accuracy of AK-DL was 0.925, the area under the receiver operating characteristic curve (AUC) was 0.887, and the training time was only 123.0 s. An iOS app of intelligent diagnostic system was developed based on the AK-DL model for accurate and automatic diagnosis of AK. Our results indicate that it is better to employ a shallow CNN in the recognition of AK.
Palliative care for rare advanced lung diseases in underserved Appalachia: Study protocol for a randomized controlled trial.

AIM To pilot test a home end-of-life and palliative care intervention for family caregivers and patients with rare advanced lung diseases and estimate effect-size for the power analysis in a future clinical trial. DESIGN This study uses a parallel randomized control trial. Families are randomly assigned to the intervention group or the control group in a 1:1 fashion. METHODS The study population includes patients with rare advanced lung diseases and their family caregivers who are involved in patients' home care. The control group receives standard care through their hospital or outpatient clinics. The intervention group receives standard care plus 2-weekly home end-of-life and palliative care coaching by experienced community nurses. Primary outcome is breathlessness measured by shortness of breath scale. Secondary outcomes are: (1) caregivers' anxiety and depression measures; (2) the presence of patient's signed advance directives in the medical record or not; and (3) Helpfulness of intervention measured by self-report Helpfulness scale. The study was funded in October 2018 and received ethical Institutional Review Board approval in February 2019. DISCUSSION West Virginia has one of the highest incidence rates of lung disease deaths in the nation. However, there is inadequate home end-of-life and palliative care for this underserved population. This is an initial interventional study of nurse-led coaching home-based palliative care for rare advanced lung diseases in rural Appalachia. Developing research collaboration with clinicians is essential for enrollment. Enrollment was successful due to regular meetings with pulmonologists who screened patients per the study inclusion criteria in their specialty clinic and made direct referrals to the research assistants. Results of this study will be used in the future trial. IMPACT The findings will contribute to the evidence-based home nursing care, planning for family/patient preferences and supportive end-of-life palliative care for managing advanced lung diseases at home.
Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service

Based on growing demand for assisted reproduction technology, improved predictive models are required to optimize in vitro fertilization/intracytoplasmatic sperm injection strategies, prioritizing single embryo transfer. There are still several obstacles to overcome for the purpose of improving assisted reproductive success, such as intra- and inter-observer subjectivity in embryonic selection, high occurrence of multiple pregnancies, maternal and neonatal complications. Here, we compare studies that used several variables that impact the success of assisted reproduction, such as blastocyst morphology and morphokinetic aspects of embryo development as well as characteristics of the patients submitted to assisted reproduction, in order to predict embryo quality, implantation or live birth. Thereby, we emphasize the proposal of an artificial intelligence-based platform for a more objective method to predict live birth.
Pan-Drug Resistant Acinetobacter baumannii, but Not Other Strains, Are Resistant to the Bee Venom Peptide Melittin

Acinetobacter baumannii is a prevalent pathogen in hospital settings with increasing importance in infections associated with biofilm production. Due to a rapid increase in its drug resistance and the failure of commonly available antibiotics to treat A. baumannii infections, this bacterium has become a critical public health issue. For these multi-drug resistant A. baumannii, polymyxin antibiotics are considered the only option for the treatment of severe infections. Concerning, several polymyxin-resistant A. baumannii strains have been isolated over the last few years. This study utilized pan drug-resistant (PDR) strains of A. baumannii isolated in Brazil, along with susceptible (S) and extreme drug-resistant (XDR) strains in order to evaluate the in vitro activity of melittin, an antimicrobial peptide, in comparison to polymyxin and another antibiotic, imipenem. From a broth microdilution method, the determined minimum inhibitory concentration showed that S and XDR strains were susceptible to melittin. In contrast, PDR A. baumannii was resistant to all treatments. Treatment with the peptide was also observed to inhibit biofilm formation of a susceptible strain and appeared to cause permanent membrane damage. A subpopulation of PDR showed membrane damage, however, it was not sufficient to stop bacterial growth, suggesting that alterations involved with antibiotic resistance could also influence melittin resistance. Presumably, mutations in the PDR that have arisen to confer resistance to widely used therapeutics also confer resistance to melittin. Our results demonstrate the potential of melittin to be used in the control of bacterial infections and suggest that antimicrobial peptides can serve as the basis for the development of new treatments.
Diastolic dysfunction in severe aortic stenosis: Old but still gold

A simple, widely applicable and reproducible risk predictor may improve patient care, risk stratification, and clinical decision making for symptomatic severe aortic stenosis (sAS) patients undergoing transcatheter aortic valve implantation (TAVI). Baseline severe left ventricular diastolic dysfunction identified by E/A ratio in sAS patients undergoing TAVI is associated with increased post‐procedural all‐cause mortality or heart failure hospitalization but is not an independent predictor of outcome. Large studies employing multimodality imaging of cardiac function/structure and accounting for sex and comorbidities will be necessary to validate DD parameters (or their combination) in sAS patients undergoing TAVI.
REMEMBER WHEN, ON THE INTERNET, NOBODY KNEW WHO YOU WERE?

Social Networking Services have seen an unprecedented adoption rate in the world of information technologies. This adherence has been so strong that even new psychiatric pathologies have been risen around them. Notwithstanding the numerous benefits of using social networking services, these systems live from information share between their users, and very often this information is private. Although social network services have numerous privacy and security settings, and management features, many users choose consciously or unintentionally to make excessive or uncontrolled sharing of personal information with social network services. This supposed controlled sharing of personal information on social media can put people at risk, or even in threatening situations, either to the individual himself or to others, linked to his network or family. An exploratory study was conducted through a focus group involving 12 college students attending the first year of an undergraduate program. This study aims, particularly, to understand how college students perceive their own online exposition, and the importance that this has on their privacy and the security of information shared, in social networking services. The data gathered from the focus group was analyzed with an online content analysis software – using the Leximancer platform. From the content analysis of the focus group several important concepts emerged: social network companies, location information, information sharing and distribution, user awareness changes. Findings suggest students are getting a new approach to privacy concerns over social media. Even though they are aware of the risks inherent in over-sharing private data, when faced with “to share or not to share” very often they tend to overlook the privacy concerns in favour of the social exposition. The obtained items and concepts were also used to develop a questionnaire, to be answered by a population of college students, in a subsequent study.
A Monte Carlo based scatter removal method for non-isocentric cone-beam CT acquisitions using a deep convolutional autoencoder

The primary cone-beam computed tomography (CBCT) imaging beam scatters inside the patient and produces a contaminating photon fluence that is registered by the detector. Scattered photons cause artifacts in the image reconstruction, and are partially responsible for the inferior image quality compared to diagnostic fan-beam CT. In this work, a deep convolutional autoencoder (DCAE) and projection-based scatter removal algorithm were constructed for the ImagingRingTM system on rails (IRr), which allows for non-isocentric acquisitions around virtual rotation centers with its independently rotatable source and detector arms. A Monte Carlo model was developed to simulate (i) a non-isocentric training dataset of ≈1200 projection pairs (primary + scatter) from 27 digital head-and-neck cancer patients around five different virtual rotation centers (DCAENONISO), and (ii) an isocentric dataset existing of ≈1200 projection pairs around the physical rotation center (DCAEISO). The scatter removal performance of both DCAE networks was investigated in two digital anthropomorphic phantom simulations and due to superior performance only the DCAENONISO was applied on eight real patient acquisitions. Measures for the quantitative error, the signal-to-noise ratio, and the similarity were evaluated for two simulated digital head-and-neck patients, and the contrast-to-noise ratio (CNR) was investigated between muscle and adipose tissue in the real patient image reconstructions. Image quality metrics were compared between the uncorrected data, the currently implemented heuristic scatter correction data, and the DCAE corrected image reconstruction. The DCAENONISO corrected image reconstructions of two digital patient simulations showed superior image quality metrics compared to the uncorrected and corrected image reconstructions using a heuristic scatter removal. The proposed DCAENONISO scatter correction in this study was successfully demonstrated in real non-isocentric patient CBCT acquisitions and achieved statistically significant higher CNRs compared to the uncorrected or the heuristic corrected image data. This paper presents for the first time a projection-based scatter removal algorithm for isocentric and non-isocentric CBCT imaging using a deep convolutional autoencoder trained on Monte Carlo composed datasets. The algorithm was successfully applied to real patient data.
Resistance Training and Handball Players’ Isokinetic, Isometric and Maximal Strength, Muscle Power and Throwing Ball Velocity: A Systematic Review and Meta-Analysis

Purpose: Handball (Team Handball) is an intermittent and strenuous contact sport, the successful performance of which depends on frequent body contacts, and the ability to make repeated explosive muscular contractions required for jumping, acceleration, sprinting, turning, changing pace, and throwing a ball. Many studies have investigated the effect of resistance training (RT) in handball players, however with conflicting results. Therefore, our objective was to investigate the impact of RT on maximal strength (isometric and isokinetic strength), the power of both lower and upper limbs, and throwing velocity, in handball players. Methods: A comprehensive literature search yielded a pool of 18 studies, which were retained in the systematic review and meta-analysis. Results: A total of 275 handball players were included. The overall effect size (ES) of RT was 0.996 ([95%CI 0.827–1.165], p = 0.0000). At the multivariate meta-regression, the effect of publication year was significant, as well as the effects of country, gender, and level. The impact of RT on isokinetic strength was not significant (ES 0.079 [95%CI −0.060–0.219], p = 0.265), whereas the impact of RT on throwing (ES 1.360 [95%CI 0.992–1.728], p = 0.000) was significant, as well as the effects of RT on isometric strength (ES 0.398 [95%CI 0.096–0.700], p = 0.010), on maximal strength (ES 1.824 [95%CI 1.305–2.343], p = 0.000), and on power (ES 0.892 [95%CI 0.656–1.128], p = 0.000). Conclusions: RT has a significant impact in handball players. Handball coaches could design conditioning protocols and programs based on our results. However, due to a number of shortcomings, including the high, statistically significant heterogeneity among studies and the evidence of publication bias, further high-quality investigations are needed.
Targeting Aldehyde Dehydrogenases to Eliminate Cancer Stem Cells in Gynecologic Malignancies

Gynecologic cancers cause over 600,000 deaths annually in women worldwide. The development of chemoresistance after initial rounds of chemotherapy contributes to tumor relapse and death due to gynecologic malignancies. In this regard, cancer stem cells (CSCs), a subpopulation of stem cells with the ability to undergo self-renewal and clonal evolution, play a key role in tumor progression and drug resistance. Aldehyde dehydrogenases (ALDH) are a group of enzymes shown to be robust CSC markers in gynecologic and other malignancies. These enzymes also play functional roles in CSCs, including detoxification of aldehydes, scavenging of reactive oxygen species (ROS), and retinoic acid (RA) signaling, making ALDH an attractive therapeutic target in various clinical scenarios. In this review, we discuss the critical roles of the ALDH in driving stemness in different gynecologic malignancies. We review inhibitors of ALDH, both general and isoform-specific, which have been used to target CSCs in gynecologic cancers. Many of these inhibitors have been shown to be effective in preclinical models of gynecologic malignancies, supporting further development in the clinic. Furthermore, ALDH inhibitors, including 673A and CM037, synergize with chemotherapy to reduce tumor growth. Thus, ALDH-targeted therapies hold promise for improving patient outcomes in gynecologic malignancies.