Challenges of Teaching English Language Online: Teachers Perspective

Virtual online teaching was adopted at the Iraqi educational institutions: schools and universities when COVID-19 break out. It was the only alternative to revival the education sector after locking down schools and universities by the condition of the quarantine. The scenario of locking down schools and universities after the spread of COVID-19 pandemic pushed stakeholders and decision makers to switch the educational process to the online mode (Thi1 &Minh, 2021). This sudden shift arose many issues and challenges which was faced by both teachers as well as learners, soon questions about the validity and authenticity of the educational process and outcomes were raised. The current study seeks to uncover hidden issues and challenges which English language teachers at Diyala University encounter at this critical period 2019-2022. A semi-structured interview was utilized as the instrument of this small scale qualitative and descriptive study. The qualitative raw data obtained from the interview transcripts were thematically coded and analyzed. The finding were interpreted in two directions: first: failure of the new online teaching process in obtaining the desired educational outcomes outlined by the ministries of education and higher education due to several reasons: uncontrolled examination session which led to student cheating and resulted in unauthentic grading marks. In addition to internet connection issues which is added to the teachers burden and affect negatively EFL teaching process. Secondly: it was proven that online teaching can be beneficial in the sense that it can save time and effort if new strategies of assessment were adopted, intensive training courses for teachers as well as students were applied, in addition to providing good internet service for both student and teacher. © 2023 Otjivanda Presse.Essen. All rights reserved.

HarDNet and Dual-Code Attention Mechanism based model for medical images segmentation

During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients’quality of life. Author

Comparative Analysis of Laboratory and Radiation Methods of Studies with the Degree of Severity of Patients with COVID-19

The study is devoted to a comparative analysis and retrospective evaluation of laboratory and instrumental data with the severity of lung tissue damage in COVID-19 of patients with COVID-19. An improvement was made in the methodology for interpreting and analyzing dynamic changes associated with COVID-19 on CT images of the lungs. The technique includes the following steps: pre-processing, segmentation with color coding, calculation and evaluation of signs to highlight areas with probable pathology (including combined evaluation of signs). Analysis and interpretation is carried out on the emerging database of patients. At the same time the following indicators are distinguished: the results of the analysis of CT images of the lungs in dynamics;the results of the analysis of clinical and laboratory data (severity course of the disease, temperature, saturation, etc.). The results of laboratory studies are analyzed with an emphasis on the values of the main indicator - interleukin-6. This indicator is a marker of significant and serious changes characterizing the severity of the patient's condition. © 2023 IEEE.

Orchestrating Image Retrieval and Storage Over a Cloud System

Since massive numbers of images are now being communicated from, and stored in different cloud systems, faster retrieval has become extremely important. This is more relevant, especially after COVID-19 in bandwidth-constrained environments. However, to the best of our knowledge, a coherent solution to overcome this problem is yet to be investigated in the literature. In this article, by customizing the Progressive JPEG method, we propose a new Scan Script to ensure Faster Image Retrieval. Furthermore, we also propose a new lossy PJPEG architecture to reduce the file size as a solution to overcome our Scan Script's drawback. In order to achieve an orchestration between them, we improve the scanning of Progressive JPEG's picture payloads to ensure Faster Image Retrieval using the change in bit pixels of distinct Luma and Chroma components ([Formula Omitted], [Formula Omitted], and [Formula Omitted]). The orchestration improves user experience even in bandwidth-constrained cases. We evaluate our proposed orchestration in a real-world setting across two continents encompassing a private cloud. Compared to existing alternatives, our proposed orchestration can improve user waiting time by up to 54% and decrease image size by up to 27%. Our proposed work is tested in cutting-edge cloud apps, ensuring up to 69% quicker loading time.

Reading Chinese anti-COVID-19 pandemic narratives on facemasks as the art of disaster governance: a semiotic and biopolitical survey

In China and around the world, the global spread of COVID-19 has made wearing a facemask more than a pragmatic or aesthetic individual-level issue: it has instilled in people deontic value. In Chinese anti-epidemic narratives, the semiotic ideology of wearing a facemask has been closely related to collectivism, patriotism and, to a certain degree, nationalism. The facemask not only serves as a protective biomedical device but also as a cultural, political and spatial sign of the line of defence against disorders of the natural system, to establish the order of the social system. This paper argues from the perspective of semiotics and life politics that such mask narratives have effectively helped China prevent the large-scale spread of the epidemic across the nation and have served as a means of collective psychotherapy, paradoxically transforming individual separation into collective spiritual cohesion. Previous semiotic studies of disaster have not paid much attention to plagues or disaster governance discourse, between which biomedicine plays an important role. Thus, this paper aims to shed light on how biomedicine works with politics in coding and decoding the relationship between the natural system of the plague and the social system of governance.

Evusheld prophylaxis increases social interactions and improves anxiety, depression, agoraphobia and quality of life scores

Evusheld is a combination injection of tixagevimab/cilgavimab for pre-exposure COVID-19 prophylaxis and was made available to UK private clinics from October 2022. NICE review is ongoing. Whilst efficacy analysis of Evusheld has focused on the risk-reduction of contracting COVID-19, anecdotal reports suggest additional psychological benefits from Evusheld, although supportive objective data are lacking. In this study, we used 4 well-established psychological health questionnaires to assess different psychological parameters (EQ5D-3 L quality of life (QoL) score, DSM5 Agoraphobia score, Duke's Social Support Index (DSSI) and the hospital anxiety and depression score (HADS)) in blood cancer patients treated with Evusheld at the Genesis Care (GC) Clinic, Cambridge. Patient data (pre-and post-Evusheld) were compared with a control group of GC blood cancer patients who had not received Evusheld. The study was approved by GC and all patients had consented to email contact. Questionnaire replies were anonymised and free-text comments were invited. Questionnaires were completed by 29/40 Evusheld and 54/100 control patients. With EQ5D, Evusheld did not impact mobility, self-care and pain/discomfort scores and patient/ control groups scored at similar levels. EQ5D scores for 'usual activities' and 'anxiety/depression' improved post-Evusheld (patients reporting 'normal activities' increased from 52% to 76% (control = 78%);patients reporting 'no anxiety/ depression' increased from 45% to 66% (control = 65%)). The mean global EQ5D QoL score improved post-Evusheld [69.4% to 72.9% (control = 75.7%)]. With the DSM5 agoraphobia score, Evusheld treatment improved agoraphobia parameters, reducing the mean score from 15.7 to 5.1 (control = 3.7;max = 40) with certain striking changes;72% of pretreatment patients avoided crowded situations all of/most of the time, reducing to 14% post-Evusheld (control = 11%). The DSSI score assessed social/work interactions with external household contacts and post-Evusheld the mean number of interactions over 3 weeks increased from 1.48 to 3.37 (control = 3.77). Pre-Evusheld, 52% of patients had no interactions outside their household, dropping to 20% in the 3 weeks post-Evusheld (control = 17%). Using the HADS 14-point analysis of depression and anxiety revealed on average that each parameter was 25.3% 'significantly improved' and 25.4% 'a little improved' post-Evusheld. Accepting limitations of a small study and potential biases associated with a self-funding patient cohort, Evusheld treatment broadly improved all psychological scores assessed. Free-text comments clearly indicate that Evusheld had a major positive impact on QoL/social mobility for specific patients. The Evusheld patients had higher baseline scores for social isolation, anxiety, depression and agoraphobia compared with control patients, yet Evusheld treatment appeared to improve these parameters to a level similar to control patients.

Combining information technology and human research efforts to lessen personnel burden and costs One success story

Objectives: Develop a coding system to extract EHR data and establish research validity to lessen need for manual data extraction Methods: As part of a data collection project for COVID + patients requiring ICU care, we established data elements able to be extracted from the Epic electronic health record (EHR). Collaboration between Information Technology (IT), research and clinical personnel established where data elements were located within the EHR and what data could be extracted with minimal manual assistance and uploaded to a research database. Coding was developed using Structured Query Language (SQL) with best practices (includes indexes, execution plans, optimized range keys, avoiding large reads inside read-write transactions as instructed by the Epic consultant). Accuracy of extracted data was evaluated by manual validation of data against Epic records via random selection of patient data within the cohort. Result(s): From July-December 2022, coding was developed which extracted over 130 fields of data from 3093 COVID patients across 5 INOVA ICU sites (demographic, physiologic, lab, interventions, outcome). Prior efforts at data extraction of these elements from research personnel (ZS) who previously performed this task noted an average of 4 hours/patient to complete coded fields. Coded data was also noted to be more accurate when accessed by the same personnel to manually extracted fields. Assuming 4 hrs/pt, manual extraction would require 12,372 hours, which equates to over 6 full time human research personnel. Data coding required 446 hours. Coded data extraction can be almost immediate once fields requested are established, decreasing personnel costs and effort significantly. Conclusion(s): Reduction in need for manual data collection using automated coding extraction can reduce costs, personnel time and enhance research efforts. Sharing coding mapping to other EPIC sites or use of similar methods may improve timeliness of ongoing data extraction and will be useful to develop earlywarning and patient-centered care algorithms to improve care.

Perceptions of pre-service teachers on breakout room micro-teaching with Zoom

Due to the emergence of Covid-19, many educators moved from a face-to-face teaching environment to an online microteaching setting using Zoom. This study explores pre-service teachers' perspectives on microteaching within Zoom's breakout rooms. The authors approached this study from a positivist-postpositivist perspective employing a mixed-methods methodology. The exploratory sequential mixed-method design employed here combines qualitative and quantitative data. Analysis entailed open coding of data from Zoom recordings and statistical analysis of a post-course survey. Convenience sampling of pre-service teachers (PSTs) from a teacher education teaching method course provided the data sets. Findings indicate that microteaching activities within breakout rooms facilitated an environment where pre-service teachers engaged and conversed with peers while developing teaching skills. PSTs valued breakout room interactions, though males and females valued different aspects. Finally, although the findings suggest that microteaching in Zoom's breakout rooms is effective, the findings indicate that the pre-service teachers desired a return to the classroom. This research extends previous research on online microteaching student experiences by providing recommendations regarding microteaching via video conferences.

A resource of human coronavirus protein-coding sequences in a flexible, multipurpose Gateway Entry clone collection.

The COVID-19 pandemic has catalyzed unprecedented scientific data and reagent sharing and collaboration, which enabled understanding the virology of the SARS-CoV-2 virus and vaccine development at record speed. The pandemic, however, has also raised awareness of the danger posed by the family of coronaviruses, of which 7 are known to infect humans and dozens have been identified in reservoir species, such as bats, rodents, or livestock. To facilitate understanding the commonalities and specifics of coronavirus infections and aspects of viral biology that determine their level of lethality to the human host, we have generated a collection of freely available clones encoding nearly all human coronavirus proteins known to date. We hope that this flexible, Gateway-compatible vector collection will encourage further research into the interactions of coronaviruses with their human host, to increase preparedness for future zoonotic viral outbreaks.

The Struggle to Balance Work and Family Life During the COVID-19 Pandemic: Insights Based on the Situations of Working Women in Delhi.

The COVID-19 pandemic generated economic contraction across the world. In India, the stringent lockdown led to extreme distress. The unprecedented situation adversely affected the women's efforts to balance professional life with family life because of a disproportionate increase in their domestic work burden and a shift in their workstation to home. Since every job cannot be performed remotely, women employed in healthcare services, banks and media witnessed additional risks of commuting and physical interaction at the workplace. Based on personal interviews of women in the Delhi-NCR region, the study aims to explore the commonalities and variances in the challenges experienced by the women engaged in diverse occupations. Using the qualitative methodology of flexible coding, the study finds that a relatively larger section of women travelling to their office during the pandemic, rather than those working from home, had an effective familial support system that helped them navigate this tough time.

Cardiovascular complications of diabetes: role of non-coding RNAs in the crosstalk between immune and cardiovascular systems.

Diabetes mellitus, a group of metabolic disorders characterized by high levels of blood glucose caused by insulin defect or impairment, is a major risk factor for cardiovascular diseases and related mortality. Patients with diabetes experience a state of chronic or intermittent hyperglycemia resulting in damage to the vasculature, leading to micro- and macro-vascular diseases. These conditions are associated with low-grade chronic inflammation and accelerated atherosclerosis. Several classes of leukocytes have been implicated in diabetic cardiovascular impairment. Although the molecular pathways through which diabetes elicits an inflammatory response have attracted significant attention, how they contribute to altering cardiovascular homeostasis is still incompletely understood. In this respect, non-coding RNAs (ncRNAs) are a still largely under-investigated class of transcripts that may play a fundamental role. This review article gathers the current knowledge on the function of ncRNAs in the crosstalk between immune and cardiovascular cells in the context of diabetic complications, highlighting the influence of biological sex in such mechanisms and exploring the potential role of ncRNAs as biomarkers and targets for treatments. The discussion closes by offering an overview of the ncRNAs involved in the increased cardiovascular risk suffered by patients with diabetes facing Sars-CoV-2 infection.

A Qualitative Investigation on COVID-19 Vaccine Hesitancy in Neurodivergent Communities.

Vaccine hesitancy is a major barrier to vaccination, hindering the success of vaccine efforts and thereby increasing public health risk to viral diseases, including COVID-19. Neurodivergent (ND) individuals, including individuals with an intellectual and/or developmental disability, have demonstrated a heightened risk of hospitalization and death due to COVID-19, highlighting the need for further research specifically on ND communities. We conducted a qualitative analysis using in-depth interviews with medical professionals, non-medical health professionals and communicators, and ND individuals or their caregivers. Using a thematic coding analysis methodology, trained coders identified major themes according to 24 distinct codes spanning across the categories of (1) barriers to vaccination; (2) facilitators to vaccination; and (3) suggestions for improving vaccine confidence. Qualitative findings identify misinformation, perception of vaccine risk, sensory sensitivities, and structural hardship as the most significant barriers to COVID-19 vaccination. We highlight the importance of accommodations to vaccination for the ND community alongside coordinated efforts by healthcare leaders to direct their communities to accurate sources of medical information. This work will inform the direction of future research on vaccine hesitancy, and the development of programs specific to the ND community's access to vaccines.

Android Based Smart Appointment System (SAS) for Booking and Interacting with Teacher for Counselling

An appointment system is going to be popular nowadays. The necessity of these types of systems is increasing day by day specially in education sector. Worldwide COVID-19 pandemic provoke the demand of these types of application. In this research paper, an Android-based appointment is built for booking an appointment and communicating with the teacher. To use this system both student and teacher have to an android device with connection of the internet. A single android application will be used for both types of users. Students can get the information of all teachers and book an appointment with teachers and teachers can accept or decline this appointment. Java programming language is used for this system and Google's Firebase is used for the database. In addition, the modern coding Architecture pattern MVVM (Model- View-View Model) followed to build this system. Hopefully, this system saves valuable time and makes the teacher-student interaction journey easier. © 2023 IEEE.

Work in Progress: A Virtual Educational Robotics Coding Club Framework to Improve K-6 Students Emotional Engagement in STEM

Educational robots allow students deepen their knowledge of mathematics and scientific concepts. Educational Robotic coding clubs provide a learning environment for K-6 students that promotes coding through STEM digital literacy. Students in educationally disadvantaged families may not have the educational and financial capital to engage in STEM learning. Closures of schools and afterschool services during the COVID-19 pandemic increased this digital divide. This research proposes a framework for delivering a virtual robotic coding club in an educationally disadvantaged community. The framework develops young people's emotional engagement in STEM through robotic coding. Synchronous online classes were delivered into family homes using Zoom. Results demonstrate that children achieved emotional engagement as reported through high levels of enjoyment and increased interest after participating in the programme. The research shows promise in increasing children's STEM skills and knowledge, and in improving positive attitudes towards STEM for children and parents. © 2023 IEEE.

Examining the Association of Air Quality on Covid-19 Patient Outcomes Using Area Level Data from the Electronic Health Record

Background: Within the coronavirus 2019 (COVID-19) pandemic, literature has found worsened patient outcomes and increased virus transmissibility associated with reduced air quality. This factor, a structural social determinant of health (SDOH), has shown great promise as a link between air quality and patient outcomes during the COVID-19 pandemic. Researching SDOH within our patient populations is often difficult and limited by poor documentation or extensive questionnaires or surveys. The use of demographic data derived from the electronic health record (EHR) to more accurately represent SDOH holds great promise. The use of area-level determinants of health outcomes has been shown to serve as a good surrogate for individual exposures. We posit that an area level measure of air quality, the county-level Air Quality Index (AQI), will be associated with disease worsening in intensive care unit (ICU) patients being treated for COVID-19. Method(s): We will calculate AQI using a combination of open-source records available via the United States Environmental Protection Agency (EPA) and manual calculations using geospatial informatics systems (GIS) methods. Subjects will be identified as adult (> 18 years) patients admitted to Vanderbilt University Medical Center's ICUs between January 1, 2020, and March 31, 2022 with a positive SARS-CoV-2 laboratory analysis result. We will exclude patients without a home address listed. Patient demographic and hospital data from ICU admission to 28 days following admission will include: age, sex, home address, race, insurance type, primary language, employment status, highest level of education, and hospital course data. Together these will be collated to produce our primary outcome variable of WHO Clinical Progression Scale score. These validated scores range from 0 (uninfected) to 10 (dead) to track clinically meaningful progression of COVID-19 infected patients. Our AQI variable will be obtained from the EPA available county-level monitoring station spatial data combined with open-source state/county center point spatial data. These data contain historic cataloguing to determine air quality at both specific time points and averages over time. Where a county's average yearly AQI is not available due to lack of a monitoring station, we will use spatial data tools to calculate an average based on data from nearby stations. We will utilize yearly averages of AQI in the year prior to COVID-19 diagnosis to describe overall impact of air quality on patients' respiratory outcomes as opposed to single day exposures. Linkage of patient data to AQI database will be performed using patient addresses. Discussion(s): By combining area level data with electronic health record (EHR) data, we will be positioned to understand the contribution of environmental and social determinants of health on patient outcomes. Our long-term goal is to elucidate which social and environmental determinants of health are associated with worse outcomes from COVID-19 and other respiratory viruses, using data extracted from the EHR.

Efficient intelligent compression and recognition system-based vision computing for computed tomography COVID images

Computed tomography (CT) image-based medical recognition is extensively used for COVID recognition as it improves recognition and scanning rate. A method for intelligent compression and recognition system-based vision computing for CT COVID (ICRS-VC-COVID) was developed. The proposed system first preprocesses lung CT COVID images. Segmentation is then used to split the image into two regions: nonregion of interest (NROI) with fractal lossy compression and region of interest with context tree weighting lossless. Subsequently, a fast discrete curvelet transform (FDCT) is applied. Finally, vector quantization is implemented through the encoder, channel, and decoder. Two experiments were conducted to test the proposed ICRS-VC-COVID. The first evaluated the segmentation compression, FDCT, wavelet transform, and discrete curvelet transform (DCT). The second evaluated the FDCT, wavelet transform, and DCT with segmentation. It demonstrates a significant improvement in performance parameters, such as mean square error, peak signal-to-noise ratio, and compression ratio. At similar computational complexity, the proposed ICRS-VC-COVID is superior to some existing techniques. Moreover, at the same bit rate, it significantly improves the quality of the image. Thus, the proposed method can enable lung CT COVID images to be applied for disease recognition with low computational power and space. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [DOI: 10.1117/1.JEI.32.2.021404] © 2023 SPIE. All rights reserved.

Evaluating the Use of Patient Reported Outcome Measures to Assess Intra-Pandemic Psoriatic Arthritis Disease Activity and Comparing Them with Pre-Pandemic Clinical Assessments

Background/Aims In our department, patient reported outcome measures (PROMs), including RAPID-3 and PSAID12, were employed during the COVID-19 pandemic in asynchronous consultations for patients with psoriatic arthritis (PsA). We compared pre-pandemic DAS28-CRP with intrapandemic PROMs to assess changes in disease activity since the pandemic. Whilst previous studies have primarily compared PsA PROMs with clinician-assessed scores (e.g. PASDAS), we compare PsA PROMs with clinicians' overall assessment of disease activity;this judgement considers PROMs, serology studies and individual patient feedback. Finally, we assess whether patients with PROMs indicating active disease were followed up appropriately. Methods Clinician-assessed scores were collected between 01/01/2019-01/03/ 2020 (''pre-pandemic''). Between 01/12/2020-31/03/2022 (''intrapandemic''), patient data from electronic surveys were analysed in a secure database for calculation of PROMs. These data, alongside blood results and patient comments, informed clinicians' triage decisions. Clinical outcome data were collected from electronic patient records;>=3 months follow-up appointment allocation was the target for patients with active disease (moderate/high disease activity). Data analysis was performed using r (version 4.2.2). Results In our pre-pandemic cohort (n=393), 79.8% of patients were in remission (per DAS28-CRP). Conversely, the intra-pandemic cohort (n=231) showed remission rates of 14.3% (per PSAID12) and 0% (RAPID-3). Indeed, 33.7% (based on PSAID12) vs 75.8% (RAPID-3) had moderate/ high disease activity. These results were validated in a paired cohort (n=38, score recorded in both windows). Disease activity worsened during the pandemic for 63.2% (PSAID12) and 97.4% (RAPID-3) of patients. PSAID-12 correlated positively with RAPID-3 (r=0.52, p<0.001), especially when RAPID-3 >=6.5 (r=0.75, p<0.001). When comparing PROMs with clinicians' assessment of PsA activity in our paired cohort, PSAID12 and RAPID-3 accurately reflected disease status in 70.6% and 58.8% of patients respectively. 3/9 and 9/27 patients with active disease, based on PSAID12 and RAPID-3 respectively, were seen within three months. Conversely, 7/10 patients who clinicians had deemed to have active disease were seen within three months. Conclusion Despite approximately 80% of patients being in pre-pandemic remission, the majority reported active intra-pandemic PsA. Whilst RAPID-3 skewed patients towards active disease, PSAID12 skewed patients towards remission/low disease activity. PSAID-12 and RAPID- 3 have been previously correlated;however, here we suggest that they could be used interchangeably in patients with high disease activity. PSAID-12 was a better predictor of clinicians' assessment of disease activity, although neither PROM correlated well with >=3 months followup appointment allocation. Although RAPID-3 and PSAID12 helped inform clinicians' decisions, neither alone sufficiently reflects patients' disease states. Remote management is practicable, but future studies should validate these findings across a larger cohort and assess the utility of different PROMs across PsA disease activity categories. Furthermore, multivariate analysis is warranted to ascertain which (combination of) variable(s) (e.g., PROMs, serology results, tender/ swollen joint count) best correlates with clinician judgement.

Evaluation of Homologous and Heterologous Vaccine against Sars-Cov-2 in Systemic Lupus Erythematosus Patients: Data from Safer Study

Objectives: To evaluate the immunogenicity of ChAdOx1, Coronavac and BNT162B2 vaccines in SLE patients, including homologous and heterologous immunizations. Method(s): The 'Safety and efficacy on COVID-19 Vaccine in Rheumatic Disease-SAFER study' is a Brazilian multicentric longitudinal phase IV study to evaluate COVID-19 Vaccine in immune-mediated rheumatic diseases (IMRD) in real life, started on May 2021. SLE patients (according to the 2012 SLICC classification criteria), older than 18 years of age were recruited after 2 or 3 doses of vaccine against COVID-19 (ChAdOx1, BNT162b2 and CoronaVac) and were evaluated at baseline and on the 28th day after each dose. Homologous immunization was considered if they received three doses of the same vaccine and heterologous if a different one was applied. IgG antibody against SARS-CoV-2 spike receptor-binding domain were measured by chemiluminescence (SARS-CoV-2-IgG-II Quant assay, Abbott-Laboratories) at baseline and 28 days after the first, 2nd and 3rd doses (Seropositivity IgGSpike>= 7.1BAU/mL). Statistical analysis: ANOVA and pairwise comparisons tests Results: 316 SLE patients were included (255 heterologous and 61 homologous immunization), 89.2% were female and the mean age was 37.6 +/- 11.2 years. The two groups were homogeneous regarding demographical data, disease activity and immunosuppressive treatment. 49.7% used corticosteroids (alpha 5 mg/day in 52.3%), 83.5% antimalarials, 22.8% azathioprine and 20.3% mycophenolate mofetil. 207 patients received the first two doses with CoronaVac, 128 ChadOx-1 and 32 BNT162b2. Regarding the first two doses of the same vaccine, there was no difference in IgG titers over time between CoronaVac or ChadOx-1 (p = 0.313). IgG titers increased in all vaccine groups, with difference only after 2nd dose: 4.96 +/- 1.71BAU/mL CoronaVac vs. 6.00 +/- 1.99BAU/mL ChadOx-1 vs. 7.31 +/- 1.49BAU/mL BNT162b2 (p alpha 0.001). There was no difference in IgG titers over time between homologous or heterologous vaccine schedule (p = 0.872). IgG titers also increased in all groups, with difference only after 2nd dose: 5.49 +/- 1.96BAU/mL heterologous vs. 6.30 +/- 2.10BAU/mL homologous (p = 0.009). Conclusion(s): Induction of immunogenicity occurred in different vaccine regimens in SLE patients. Future research to explore different heterologous schemes in IMRD must be performed.

Role of non-coding RNAs in tuberculosis and their potential for clinical applications.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains the leading cause of mortality due to infectious diseases, only surpassed in 2020 by COVID-19. Despite the development in diagnostics, therapeutics, and evaluation of new vaccines for TB, this infectious disease remains uncontrollable due to the emergence of multidrug-resistant (MDR) and extremely drug-resistant (XDR) TB, among other factors. The development in transcriptomics (RNomics) has enabled the study of gene expression in TB. It is considered that non-coding RNAs (ncRNAs) from host [microRNAs (miRNAs)] and Mtb [small RNAs (sRNAs)] are important elements in TB pathogenesis, immune resistance, and susceptibility. Many studies have shown the importance of host miRNAs in regulating immune response against Mtb via in vitro and in vivo mice models. The bacterial sRNAs play a major role in survival, adaptation, and virulence. Here, we review the characterization and function of host and bacteria ncRNAs in TB and their potential use in clinical applications as diagnostic, prognostic, and therapeutic biomarkers.

Expression pattern of long non-coding RNAs MALAT1 and MEG3 in COVID-19 patients.

BACKGROUND: COVID-19 is a novel infectious disease for which no specific treatment exists. It is likely that a combination of genetic and non-genetic factors predispose to it. Expression levels of genes that are involved in the interaction with SARS-CoV-2 or the host response are thought to play a role in disease susceptibility and severity. It is crucial to explore biomarkers for disease severity and outcome. Herein, we studied the expression levels and effects of long non-coding metastasis-associated lung adenocarcinoma transcript 1 (lnc-MALAT1) and long non-coding maternally expressed gene 3 (lnc-MEG3) in COVID-19 patients. The study enrolled 35 hospitalized and 35 non-hospitalized COVID-19 patients, and 35 healthy controls. A chest computed tomography (CT) scan, complete blood count (CBC), ferritin, C-reactive protein (CRP), D-dimer and analysis of lnc-MALAT1 and lnc-MEG3 expression were done. RESULTS: There was a significant relation between ferritin, CRP, D-dimer levels, oxygen saturation, CT-CORADS score and disease severity. Lnc-MALAT1 was significantly higher but lnc-MEG3 was significantly lower in patients vs. controls, and in hospitalized vs. non-hospitalized patients. Elevated MALAT1 and reduced MEG3 levels were significantly associated with more elevated ferritin, CRP, D-dimer levels, lower oxygen saturation, higher CT-CORADS score and poor survival. Moreover, MALAT1 and MEG3 levels displayed higher sensitivity and specificity as predictors of COVID-19 severity compared with other prognostic biochemical markers such as ferritin, CRP, and D-dimer. CONCLUSIONS: MALAT1 levels are higher, whereas MEG3 levels are lower in COVID-19 patients. Both are linked to disease severity and mortality and could emerge as predictive biomarkers for COVID-19 severity and therapeutic targets.