The Effect of FOXP3+Regulatory T Cells on Infectious and Inflammatory Diseases

CD4+CD25+FOXP3+regulatory T cells (Tregs) contribute to the maintenance of immune homeostasis and tolerance in the body. The expression levels and functional stability of FOXP3 control the function and plasticity of Tregs. Tregs critically impact infectious diseases, especially by regulating the threshold of immune responses to pathogenic microorganisms. The functional regulatory mechanism and cell-specific surface markers of Tregs in different tissues and inflammatory microenvironments have been investigated in depth, which can provide novel ideas and strategies for immunotherapies targeting infectious diseases.Copyright © 2021. All rights reserved.

A COVID-19 Detection Model Based on Convolutional Neural Network and Residual Learning

A model that can obtain rapid and accurate detection of coronavirus disease 2019 (COVID-19) plays a significant role in treating and preventing the spread of disease transmission. However, designing such a model that can balance the detection accuracy and weight parameters of memory well to deploy a mobile device is challenging. Taking this point into account, this paper fuses the convolutional neural network and residual learning operations to build a multi-class classification model, which improves COVID-19 pneumonia detection performance and keeps a trade-off between the weight parameters and accuracy. The convolutional neural network can extract the COVID-19 feature information by repeated convolutional operations. The residual learning operations alleviate the gradient problems caused by stacking convolutional layers and enhance the ability of feature extraction. The ability further enables the proposed model to acquire effective feature information at a low cost, which can make our model keep small weight parameters. Extensive validation and comparison with other models of COVID-19 pneumonia detection on the well-known COVIDx dataset show that (1) the sensitivity of COVID-19 pneumonia detection is improved from 88.2% (non-COVID-19) and 77.5% (COVID-19) to 95.3% (non-COVID-19) and 96.5% (COVID-19), respectively. The positive predictive value is also respectively increased from 72.8% (non-COVID-19) and 89.0% (COVID-19) to 88.8% (non-COVID-19) and 95.1% (COVID-19). (2) Compared with the weight parameters of the COVIDNet-small network, the value of the proposed model is 13 M, which is slightly higher than that (11.37 M) of the COVIDNet-small network. But, the corresponding accuracy is improved from 85.2% to 93.0%. The above results illustrate the proposed model can gain an efficient balance between accuracy and weight parameters. © 2023 Tech Science Press. All rights reserved.

A Deep Learning Network for Detecting COVID-19 From Chest X-Ray Images

Rapid and accurate detection of COVID-19 plays a significant role in treating and preventing the spread of disease transmission. To this end, we fuse the convolutional neural network and residual learning operation to build a multi-class classification model, which has a few parameters and is more conducive to be deployed on a mobile device. Extensive experiments show that our proposed model gains competitive performance. Compared with the COVIDNet-small network, the sensitivity of COVID-19 pneumonia detection is improved from 88.2% (non-COVID-19) and 77.5% (COVID-19) to 95.3% (non-COVID-19) and 96.5% (COVID-19). Alternatively, the Positive predictive value is increased from 72.8% (non-COVID-19) and 89.0% (COVID-19) to 88.8% (non-COVID-19) and 95.1 % (COVID-19). The accuracy is also improved from 85.2 % to 93.0 %, which is very close to the value (93.3 %) of the COVIDNet-large network. But, the weight parameters (13M) of the proposed model are slightly higher than that (11.37M) of the COVIDNet-small network, but only about one-third of that (37.85M) of the COVIDNet-large network. © 2022 IEEE.

One case of novel coronavirus pneumonia complicated with acute appendicitis in children.

Emergent laparoscopic appendectomy was performed for a boy of occult novel coronavirus pneumonia with a presenting symptom of acute appendicitis at Wuhan Children's Hospital. Postoperative lung computed tomography (CT) indicated a round dense shadow with slightly ground-glass-like margins in the dorsal segment of right lower lung. Pharyngeal swab nucleic acid test was positive for 2019-nCoV and thus a definite diagnosis of COVID-19 was made. Prior to the onset, he had close contacts with his grandmother with a definite diagnosis of COVID -19. It proved that intra-family transmission was an important transmission route for pediatric 2019-nCoV infection. In this case, the respiratory symptoms of COVID-19 were not obvious during an early stage. The major symptoms were nausea, vomiting and abdominal pain. For individuals coming from the epidemic area, with a history of exposure and developing acute surgical conditions, preoperative pulmonary CT scan is necessary for screening COVID-19.Copyright © 2020 by the Chinese Medical Association.

The Exploration of the Future Teaching Mode in Post-pandemic Higher Education

Covid-19 has changed the study life of many people with many courses in higher education being moved online. With the situation continuing like this, it is worthwhile to ask the questions such as: is the current provision of online education effective? Will the pandemic change higher education for ever? And what is the future of higher education post-pandemic? To answer these questions, we have conducted a survey to the students at the University of York. The survey provides some clarifications for the current state of online learning. It is discovered that while the adoption of online learning is continuously increasing, the current provision of online teaching during the pandemic has plenty of room to improve. Most participants believe that blended learning e.g. flipped classroom is the future of education post-pandemic;this is in contrast with a small number of participants who believe the in-class teaching is the future of education. In the process of arriving the conclusion, we have also learned a number of best practices for online learning. It is anticipated that the evidence collected from this study will shed light for university senior management to make strategic decisions in preparation for the future of education post-pandemic. © 2022 ACM.

Travel pattern variation effects on utility factor after COVID-19: An empirical case

Carbon emission is largely reduced during the COVID-19 due to the lockdown. However, the accurate impact in the personal transport sector after the epidemic is still not clear. To accurately measure the travel pattern variation effects on utility factor of plug-in hybrid electric vehicles (PHEV s) due to COVID-19, travel pattern, charging pattern, and utility factors (UF) are compared in a typical city based on actual travel data before and after the pandemic. The result shows that the number of trips and the daily vehicle kilometers travelled decreased significantly during the pandemic while the average daily travel mileage increased quickly after the pandemic and is only 9% lower than that before the pandemic. Some consumers even travel longer with personal vehicles to avoid possible health risks from public transportation. The electricity utility factor after the pandemic is 0.022 larger compared to that before the pandemic due to the variation of travel patterns, a 60-km-range PHEV has a pre-pandemic standard UF of 0.745 and a post-pandemic standard UF of 0.767. Besides, the actual UF is 15% smaller compared to the standard UF due to the actual charging frequency in reality. © 2021 IEEE.

RANBP2 and USP9x regulate nuclear import of adenovirus minor coat protein IIIa.

As intracellular parasites, viruses exploit cellular proteins at every stage of infection. Adenovirus outbreaks are associated with severe acute respiratory illnesses and conjunctivitis, with no specific antiviral therapy available. An adenoviral vaccine based on human adenovirus species D (HAdV-D) is currently in use for COVID-19. Herein, we investigate host interactions of HAdV-D type 37 (HAdV-D37) protein IIIa (pIIIa), identified by affinity purification and mass spectrometry (AP-MS) screens. We demonstrate that viral pIIIa interacts with ubiquitin-specific protease 9x (USP9x) and Ran-binding protein 2 (RANBP2). USP9x binding did not invoke its signature deubiquitination function but rather deregulated pIIIa-RANBP2 interactions. In USP9x-knockout cells, viral genome replication and viral protein expression increased compared to wild type cells, supporting a host-favored mechanism for USP9x. Conversely, RANBP2-knock down reduced pIIIa transport to the nucleus, viral genome replication, and viral protein expression. Also, RANBP2-siRNA pretreated cells appeared to contain fewer mature viral particles. Transmission electron microscopy of USP9x-siRNA pretreated, virus-infected cells revealed larger than typical paracrystalline viral arrays. RANBP2-siRNA pretreatment led to the accumulation of defective assembly products at an early maturation stage. CRM1 nuclear export blockade by leptomycin B led to the retention of pIIIa within cell nuclei and hindered pIIIa-RANBP2 interactions. In-vitro binding analyses indicated that USP9x and RANBP2 bind to C-terminus of pIIIa amino acids 386-563 and 386-510, respectively. Surface plasmon resonance testing showed direct pIIIa interaction with recombinant USP9x and RANBP2 proteins, without competition. Using an alternative and genetically disparate adenovirus type (HAdV-C5), we show that the demonstrated pIIIa interaction is also important for a severe respiratory pathogen. Together, our results suggest that pIIIa hijacks RANBP2 for nuclear import and subsequent virion assembly. USP9x counteracts this interaction and negatively regulates virion synthesis. This analysis extends the scope of known adenovirus-host interactions and has potential implications in designing new antiviral therapeutics.

A UNIFIED DENSITY-DRIVEN FRAMEWORK FOR EFFECTIVE DATA DENOISING AND ROBUST ABSTENTION

The success of Deep Neural Networks (DNNs) highly depends on data quality. Moreover, predictive uncertainty reduces reliability of DNNs for real-world applications. In this paper, we aim to address these two issues by proposing a unified filtering framework leveraging underlying data density, that effectively denoises training data as well as avoids predicting confusing samples. Our proposed framework differentiates noise from clean data samples without modifying existing DNN architectures or loss functions. Extensive experiments on multiple benchmark datasets and recent COVIDx dataset demonstrate the effectiveness of our framework over state-of-the-art (SOTA) methods in denoising training data and abstaining uncertain test data. © 2021 IEEE.

Improving Representation Consistency with Pairwise Loss for Masked Face Recognition

Given the coronavirus disease (COVID-19) pandemic, people need to wear masks to protect themselves and reduce the spread of COVID, which brings new challenge to the traditional face recognition task. Since features like the nose and mouth, which are well distinguishable, are hidden under the mask, traditional methods are no longer simply applicable, even though they once achieved a high degree of accuracy. In response to this problem, the Masked Face Recognition Challenge & Workshop (MFR) was held in conjunction with the International Conference on Computer Vision (ICCV) 2021. This article details a method that combining the classic ArcFace and pairwise loss to target the new masked face recognition task. So far, our method has achieved the second place in the competition.

A LUNG WITHIN A LUNG? A CASE REPORT OF MASSIVE HEMOPTYSIS SECONDARY TO PULMONARY SEQUESTRATION

TOPIC: Lung Pathology TYPE: Medical Student/Resident Case Reports INTRODUCTION: Bronchopulmonary sequestration (BPS), is defined as a non-functional mass of lung tissue with systemic arterial blood supply, but without normal tracheobronchial communication. BPS often presents in adulthood with recurrent pulmonary infections and even fatal hemoptysis. We present a case of a 53-year-old African American male with large volume hemoptysis while hiking in upstate New York, who was found to have right lower lobe intralobar pulmonary sequestration. CASE PRESENTATION: A 53-year-old African American male with a past medical history of hypertension, hyperlipidemia, and diabetes mellitus presented with a subacute cough for one month and one episode of large volume hemoptysis while hiking. Review of systems was negative for fever, chills, chest pain, pleurisy, history of tuberculosis, weight loss, and anticoagulation use. He admitted to recreational marajuana use, but denied smoking cigarettes. Initially he was tachypneic, saturating 91% on room air, but hemodynamically stable. Computed tomography angiography (CTA) revealed right lower lobe intralobar pulmonary sequestration with internal locules of air suggesting superimposed infection. Flexible bronchoscopy revealed fresh blood in the right mainstem and lower lobe. Thoracotomy was followed by a lower lobectomy, which confirmed the diagnosis as it demonstrated an aberrant arterial supply from the descending thoracic aorta. There was no envelopment of pleura around the lung fed by the large pulsatile artery. DISCUSSION: Pulmonary airway malformations are the most common type of congenital abnormalities of the lower respiratory tract, diagnosed 1 in 10,000 to 35,000 live births [1]. An intralobar sequestration (IS) is located in a normal lobe and an extralobar sequestration (ES) is outside, with both having their own visceral pleura. Hybrid lesions are most common and have features of both. ES presents early with associated congenital malformations, whereas IS presents later with hemoptysis and recurrent pulmonary infections [2]. BPS can appear as other pathologies on CT such as a mass lesion, cyst, cavity lesion and localized emphysema [3,4]. Lobectomy should be prompt to prevent complications, such as fatal hemoptysis [5]. Shorter recovery times have been noted with video-assisted thoracoscopic surgery (VATS) [6,7]. Postoperative complications include empyema, hemoptysis, prolonged air leak, and fistula formation. Berna et al described their surgical approach in 25 patients in 2011, and all patients were doing well at long-term follow-up [7]. CONCLUSIONS: BPS can be fatal despite its rarity and innocent presentation. Radiographic ambiguity and nonspecific symptoms may delay appropriate management. It is imperative to include BPS on the initial query when managing hemoptysis. In the era of COVID-19, where hemoptysis may herald a deadly disease, understanding the broad differential of hemoptysis expedites appropriate management. REFERENCE #1: [1] Durell J, Thakkar H, Gould S, Fowler D, Lakhoo K. Pathology of asymptomatic, prenatally diagnosed cystic lung malformations. J Pediatr Surg. 2016;51(2):231-235. doi:10.1016/j.jpedsurg.2015.10.061 REFERENCE #2: [2] Van Raemdonck D, De Boeck K, Devlieger H, et al. Pulmonary sequestration: a comparison between pediatric and adult patients. Eur J Cardiothorac Surg. 2001;19(4):388-395. doi:10.1016/s1010-7940(01)00603-0 REFERENCE #3: [3] Wei Y, Li F. Pulmonary sequestration: a retrospective analysis of 2625 cases in China. Eur J Cardiothorac Surg. 2011;40(1):e39-e42. doi:10.1016/j.ejcts.2011.01.080[4] Qi W, Zhao J, Shi G, Yang F. Intralobar pulmonary sequestration displayed as localized emphysema on computed tomography image. J Cardiothorac Surg. 2017;12(1):83. Published 2017 Sep 8. doi:10.1186/s13019-017-0646-9[5] Rubin EM, Garcia H, Horowitz MD, Guerra JJ Jr. Fatal massive hemoptysis secondary to intralobar sequestration. Chest. 1994 Sep;106(3):954-5. doi: 10.1378/chest.106.3.954. PMID: 8082388.[6] Sun X, Xiao Y. Pulmonary sequestration in dult patients: a retrospective study. Eur J Cardiothorac Surg. 2015;48(2):279-282. doi:10.1093/ejcts/ezu397[7] Polaczek, M., Baranska, I., Szolkowska, M., Zych, J., Rudzinski, P., Szopinski, J., Orlowski, T., & Roszkowski-Sliz, K. (2017). Clinical presentation and characteristics of 25 adult cases of pulmonary sequestration. Journal of thoracic disease, 9(3), 762–767. https://doi.org/10.21037/jtd.2017.03.107 DISCLOSURES: No relevant relationships by Christian Castaneda, source=Web Response no disclosure on file for Rammohan Gumpeni;No relevant relationships by Sophia Ji, source=Web Response No relevant relationships by Parmjyot Singh, source=Web Response No relevant relationships by Anthony Smith, source=Web Response

A review of automated diagnosis of COVID-19 based on scanning images

The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming of the conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted. Therefore, researchers of the computer vision area have developed many automatic diagnosing models based on machine learning or deep learning to assist the radiologists and improve the diagnosing accuracy. In this paper, we present a review of these recently emerging automatic diagnosing models. 70 models proposed from February 14, 2020, to July 21, 2020, are involved. We analyzed the models from the perspective of preprocessing, feature extraction, classification, and evaluation. Based on the limitation of existing models, we pointed out that domain adaption in transfer learning and interpretability promotion would be the possible future directions. © 2020 ACM.