Resilience is a key factor that reflects a teacher’s ability to utilize their emotional resources and working skills to provide high-quality teaching to children. Resilience-building interventions aim to promote positive psychological functioning and well-being. However, there is lack of evidence on whether these interventions improve the well-being or mental health of teachers in early childhood education (ECE) settings. This review examined the overall effectiveness of resilience-building interventions conducted on teachers working in the ECE field. A systematic approach is used to identify relevant studies that focus on resilience-building in countering work stress among early childhood educators. Findings from this review observed a preference of group approaches and varying durations of interventions. This review highlights the challenges of the group approach which can lead to lengthy interventions and attrition amongst participants. In addition to the concerns regarding response bias from self-report questionnaires, there is also a lack of physiological measures used to evaluate effects on mental health. The large efforts by 11 studies to integrate multiple centres into their intervention and the centre-based assessment performed by four studies highlight the need for a centre-focused approach to build resilience among teachers from various ECE centres. A pilot study is conducted to evaluate the feasibility of an integrated electroencephalography–virtual reality (EEG-VR) approach in building resilience in teachers, where the frontal brain activity can be monitored during a virtual classroom task. Overall, the findings of this review propose the integration of physiological measures to monitor changes in mental health throughout the resilience-building intervention and the use of VR as a tool to design a unique virtual environment. The increase in work-related stress among adults is a raising concern throughout the world. In 2002, the World Health Organization published a monograph describing the alarming mental health problem as one of the leading causes of disability and disease world-wide . This prTeachers in early childhood education (ECE) hold a crucial role in building social–emotional and social–cognitive competence in children . With thDespite the strong relationship between teaching quality and children’s development in ECE, little attention has been given to the well-being of teachers ,23,24. EThe increasing complexity and public expectation of quality childhood education observed in related studies, for example the authors in ,22,26 suRegarding the stressful working environment faced by those in teaching professions, researchers have explored key factors that contribute to teachers remaining in the profession, and how they utilize their resources to develop professionally and build resilience to provide high-quality teaching despite the daily adversities ,31. In tReforms made by policymakers to improve children’s education often emphasize teaching quality, but less attention is given to the well-being and mental health of early childhood educators. Literature has shown a significant link between the well-being of teachers and the development of children. Increased interest in resilience has been hypothesized to promote positive psychological functioning and well-being . ResilieThe aim of this review is to discuss the overall implementation of resilience-building interventions conducted among teachers working in an ECE setting. To identify relevant studies, this systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol.1.The study must include training or interventions to develop resilience or psychosocial well-being of professionals in an ECE setting to withstand or recover from stress or adversity faced at work;2.The study must involve data collection involving in-service teachers working in ECE settings;3.The study must evaluate the outcome of the intervention based on changes in well-being, mental health, or performance outcomes of participants;4.The study is originally written in the English language.The host databases PubMed, Scopus, Web of Science and Google Scholar were used to perform keyword searches for articles, up to and including December 2021. The keywords used to search for articles were: (resilience OR resiliency) AND (work stress) AND (teacher OR educator) AND (intervention OR training OR program OR programme OR module) AND (preschool OR early childhood OR kindergarten). In the identification phase, the keywords were screened in the title and abstract of each article, without restriction on publication date. The articles were screened for research related to resilience interventions conducted in ECE settings. In this review, we define the ECE as education and care-taking of infants to 6-year-old children. Below are the eligibility criteria used to include articles obtained from the database search:The search identified 1324 papers and 4 were removed as duplicates. Papers were screened by the abstract using the eligibility criteria to determine the inclusion of paper. Another 1296 papers were removed for reasons including not being relevant, having no intervention involved, participants not being professionals working in ECE settings or the article not being written in the English language. Twenty-four full-text papers were retrieved related to interventions implemented in an ECE setting to build teacher’s resilience towards work stress. Nine papers were removed as the papers focused on introducing a proposed resilience-building intervention for early childhood educators but with no data collection reported. Hence, a total of 15 papers were reviewed, and 9 are academic theses and 6 are published journal articles. The flow of study selection is shown in This section presents the overview of selected studies included based on the PRISMA protocol. Following the overview, studies were described based on the duration of interventions, implementation of a needs assessment prior to the intervention, characteristics of recruited participants, and the instruments used to measure resilience, well-being, or work stress in teachers. These criteria are used to discuss the preferences of approaches and instruments, and the challenges in conducting resilience-building interventions for ECE professionals, in A total of 15 articles were reviewed in this systematic review. Beginning with the earliest study by Bokaba (2011), the Personal Growth Programme was developed to provide emotional support to teachers as observed from the needs assessment conducted during the early stage of research . The firOne study took a unique approach by assessing the efficacy of implementing an exercise program specifically for employees during their lunch break . Prior tAnother approach that has long been integrated into early childhood development is reflective supervision. Reflective supervision uses the concept of reflection whilst in action as an approach to problem solving . Lepore Christian (2017) recruited 67 kindergarten-to-fifth-grade teachers from six elementary schools to participate in a free web-based training program . The parSimilar to , NilssonAnother unique approach to resilience-building interventions for early childhood educators to address their work stress was implemented by Fulchini (2018) . A biofeThe MindUP curriculum implemented by Killion (2019) used mindfulness training to improve self-efficacy and perceptions of the school climate . The traAnother intervention that implemented a mindfulness approach used the Mindfulness Coach smartphone application that was initially developed and used for PTSD patients . The appLang et al. (2020) implemented the Social Emotional Learning for Teachers (SELF-T) online course, which consists of five lessons where participants are taught to reflect on how stress affects their body and how they respond towards stress, and they are introduced to new stress-reduction strategies . It adopAnother recent intervention by Hatton-Bowers et al. (2020) also conducted a mindfulness- based intervention on early childhood professionals using an online module . The praSusman-Stillman et al. (2020) took a different approach in analysing an intervention for ECE professionals by interviewing the reflective supervisors responsible for conducting reflective supervision/consultation (RS/C) sessions . This inMyTeachingPartner (MTP) program is a professional development intervention used in ECE to promote high-quality teacher–child interactions . SimilarHepburn et al. (2021) conducted a 6-week yoga-based intervention for early career teachers from 49 schools teaching educational levels between prekindergarten and grade 12 [In a recent study, Jones et al. (2021) conducted an intervention involving behavioural activation on early career teachers between kindergarten and 12th grade (K-12) . This inMechelke and Bloomberg (2021) incorporated a personality-based approach using the Enneagram in designing an intervention for teachers . EnneagrThe interventions implemented in the 15 studies varied in terms of duration per session and frequency throughout the intervention  was a three-session intervention based on group discussions . The durThe exercise program carried out at the two preschools selected by Júníusdóttir (2015) varied in terms of duration, as it depended on the managerial implementation . One preThe reflective supervision program conducted by Lepore (2015), although it was not preceded with a needs assessment, stemmed from the concerns of increased mental health referrals from the first-recruited preschool . The posChristian (2017) collected data from six elementary schools within a large urban school district in the Western region of the USA . The 2–2In the three-phase collegial reflection intervention, Nilsson et al. (2017) emphasized limiting it to one meeting only for the first phase and negative evaluation, and continuing the subsequent meetings for the positive evaluation phase to highlight the salutogenic perspective of the chosen issue addressed by participants . A stricThe Spire device used in the biofeedback intervention is a wireless tracker clipped on the clothes near the waistline or chest area . ParticiKillion (2019) fitted the lessons from the MindUP curriculum into weekly sessions for four consecutive weeks . Each seNo restriction of duration was placed on participants using the Mindfulness Coach application intervention . They weParticipants in the SELF-T intervention conducted by Lang et al. (2020) had a period of two weeks to complete a 3 h online course at their own time . LessonsThe online mindful practice module used by Hatton-Bowers et al. (2020) received responses from professionals providing indirect and direct care to children . FocusinThe regular meetings conducted in schools of participants interviewed by Susman-Stillman et al. (2020) were one of the strengths of the intervention, as they provided consistent and specific time and space for teachers to reflect on the challenges they faced during work . No restThe MTP is a continuous intervention where video-recording sessions and coach observations are conducted over a two-week period . It is uIn the yoga-based intervention conducted on early career teachers, the duration of weekly sessions varied depending on the contents covered during the sessions . SessionThe one-to-one sessions conducted in the behavioural activation intervention for ECE professionals were held virtually using the Zoom web conferencing platform . The stuIn the one-day Enneagram training, the participants were first interviewed for 30 min followed by a 6 h Enneagram training given either in-person or using the WebEx platform to all participants . They weWith reference to In order to identify preschools that integrated employees’ exercises in their daily work routines, Júníusdóttir (2015) collected online survey responses from 130 preschools . Out of The ACHIEVER module implemented by Christian (2017) was based on a readiness assessment conducted prior to the intervention on large scale, involving a large urban school district . AnalysiSimilar to , NilssonAlthough Jones et al. (2021) did not conduct a needs assessment of the centre prior to participant recruitment, the intervention itself is designed to assess the participants individually based on their goals and values to facilitate in planning and scheduling their tasks during the intervention . This isThe 15 interventions varied in terms of the number of centres involved and the teaching level of participants. Four studies conducted the intervention in two or fewer schools ,40,41,53Fulchini (2018) chose one public charter elementary school with low socio-economic status, located in communities of poverty, serving mainly low income and racial or ethnic minority populations . Novice The demography of participants in the intervention reported by Killion (2019) reflects the diversity of teachers, where 8 teachers were teaching preschool, 10 elementary, 1 middle school and 10 taught multiple grades . The jobParticipants interviewed by Susman-Stillman et al. (2020) were reflective supervisors that were trained to provide RS/C to ECE professionals and were also ECE professionals themselves . They han = 196) or the MTP group (n = 205). The teachers had varying years of experience in teaching pre-kindergarten classrooms, from less than one to 38 years [The two-phase longitudinal study by Pianta and Burchinal (2016) recruited teachers from 200 ECE centres . From th38 years .Jones et al. (2021) recruited K-12 teachers from public schools located in districts with critical shortages of teacher placements . The teaFrom the 15 studies reviewed, 6 focused on qualitative analysis in evaluating the outcome of intervention on either resilience, well-being, or work stress levels of the participants ,45,46,53MindUP module intervention based on the changes in sense of self-efficacy, which is a resilience-related measure, and in the perception of school climate by the teachers.In the nine studies that utilized quantitative instruments ,47,49,50Measures on stress-related changes were used in eight of the studies to evaluate the outcomes of intervention ,47,49,50Fulchini (2018) collected the breathing pattern of teachers for 29 days, and burnout level and stress using instruments specific for teachers . The strIn the yoga-based intervention, Hepburn et al. (2021) also took a physiological approach in measuring level of stress using salivary cortisol samples . Paired The interventions were also assessed for their effects on the professional development of participants. The focus of professional development was mostly on strategies and skills garnered to address stress-inducing events , perceptThis section presents the key findings reviewed from the 15 selected studies. Interventions vary in the approach taken to conduct the intervention, duration, and frequency of sessions, with shorter periods observed in interventions implementing an individual approach and longer periods in interventions where participants were involved in group activities. In studies where the participating institutes decided to implement the interventions in their curriculum ,37,38, tConventional assessment tools of stress among early childhood educators used in the literature show that most research relies on self-report instruments. As these assessments are developed for specific psycho-social measures, the use of these instruments to conduct and assess comprehensive resilience-building interventions may require several questionnaires to be answered by participants. This can be observed in the number of items per questionnaire used in the interventions reviewed in From the 15 studies reviewed, 8 studies reported positive effects of the interventions implemented on the mental health or well-being of participants. Participants were reported to have improved emotions ,44,49,50Kwon et al. (2019) raised the concern of using the direct contact approach of collecting self-report information on the teacher’s stress or depressive-related problems . In addiThe group approach is more commonly adopted in the interventions as it boasts experience-sharing and includes more participants per session. Several studies observed a high level of attrition rates among participants. Based on the number of participants analysed after each intervention, as shown in The attrition issues in studies are often due to incomplete participation or missing information. Participants who opted out of the intervention often did so due to work schedules, time constraints and other conflicting commitments ,43,49. BFindings based on visual observations and qualitative analysis limit generalization towards the intended population. The quantitative approach to analysing findings allows for generalization to a broader population and provides causal evidence for the research questions, whereas qualitative analysis provides diversities in the information being explored . This caIn addressing the challenges of building resilience through group approaches and using psychological assessments, as discussed in Amongst the modalities of brain imaging, electroencephalography (EEG) has the advantages of being non-invasive, and having high temporal resolution, low cost, and portability. Stress resilience is a measure of an individual’s vulnerability towards stress and perceiving adverse events as minimally threatening and developing adaptive physiological and psychological responses . ResearcFurthermore, we also propose the integration of virtual environments to mimic real-life scenarios that reflect the challenges faced by different workplace settings. Various workplace settings have been used to design virtual environments for simulation and training purposes ,67,68. VIn an effort towards the proposed direction for resilience-building interventions for early childhood educators, we conducted a pilot study to evaluate the feasibility of monitoring the frontal brain activity of teachers in a virtual classroom. The EEG device is a non-invasive brain-monitoring tool that has been used in various clinical and health science practices, and has the advantage of being portable and having high temporal resolution. It has been used to monitor brain activities in virtual environments to gauge how an individual reacts to a simulated real-life scenario. The use of EEG in resilience-building interventions for teachers in ECE allows for the monitoring of mental health and the effects of the intervention on resilience levels, whereas VR technology allows the intervention to be designed based on the needs and actual settings of the centre itself. Hence, in this pilot study, we monitored the frontal brain activity of teachers’ during a mental task in a virtual classroom and identified its correlation to their resilience scores.The study was conducted on 25 teachers recruited from the National Child Development Research Centre (NCDRC) in Universiti Pendidikan Sultan Idris (UPSI). The participants were healthy working adults involved in the ECE at NCDRC, aged 18 or above, and had normal or corrected-to-normal vision. The participants were screened as healthy by answering “No” to questions on having a diagnosis of hypertension without intake of antihypertensive medication, positive drug anamnesis, history of psychiatric diseases requiring inpatient treatment longer than 2 weeks, neurological disorders, and malignant disease. The ethical approval for this study was obtained from the UPSI Ethics Committee (2020-0121-01), and written informed consent was obtained from all participants prior to the experiment. The CD-RISC questionnaire was used to measure the resilience scores of each participant .1.2 minutes of resting condition in a virtual enclosed room;2.4 minutes of mental task condition in a virtual classroom;3.2 minutes of resting condition in a virtual enclosed room;4.4 minutes of mental task condition in a virtual classroom.The experiment involved using the Oculus Quest headset to display the virtual environment of an enclosed room and a classroom , theta , alpha , beta  and gamma . Alpha brainwaves in the frontal area of the brain have been studied extensively in stress-related literature , where aThe EEG signals are recorded using a 32-channel EEG cap at a sampling rate of 500 Hz. Six electrode channels that represent the frontal area of the brain are chosen  to compute the alpha power for both resting and task conditions, using the following processing steps. From the six electrodes, three pairs of alpha asymmetry indices are computed for Fp2-Fp1 , F4-F3  and F8-F7 .1.Band-pass filtering between 1 and 45 Hz EEG signals to remove power-line noise;2.Automated artifact rejection using the HAPPE pipeline  and MARA3.Reference-free quantification using CSD toolbox ,85,86;4.Computation of relative alpha power  for both5.Computation of alpha asymmetry index using log-transformed alpha-power density values ,80(1)AlThe relative power values for each session are tested using the Shapiro–Wilk and Levene tests for normality and equality of variances. To observe the difference in power values between right and left frontal areas, an independent t-test is used for parametric testing, and a Mann-Whitney U test is used for non-parametric testing, to compare the relative alpha power values of all three pairs of electrodes. Correlation analysis is performed using Spearman’s rank-order rho to identify the association between CD-RISC resilience scores and the three alpha asymmetry indices in both resting and task conditions.A total of 25 teachers amongst the childcare professionals working in the NCDRC, UPSI, passed the health screening assessment and were recruited as participants for this study. The participants were healthy adults with no neurological complications with a mean age of 33.52 ± 5.5 years old. Demographic characteristics of participants are presented in Q1 = 6 ; nQ2 = 7 (score 60.75–68); nQ3 = 6 (score 60.75–68); nQ4 = 5 . One resilience score of the male participants was observed to be an outlier, with a score of 98.9.The CD-RISC scores ranged from 53 to 98.9 . Following the CD-RISC manual guide, the CD-RISC scores can be described using quartile statistics to divide the participants into four groups from lowest to highest resilience level . In thisp > 0.05) between the hemispheres at all three electrode pairs.Relative power of the alpha brainwaves throughout the experiment is calculated for each session, and grand averaged into resting and task conditions. Brain topography of the grand-averaged relative alpha power in 1: rho = 0.38, p = 0.06; task2: rho = 0.40, p = 0.05). This correlation is illustrated in the scatter plot between the resilience score of each participant and their respective alpha asymmetry index at F4-F3, averaged from the two task sessions  in 1: rho = −0.02, p = 0.90; rest2: rho = 0.11, p = 0.61).Correlation analysis between the alpha asymmetry indices of all three electrode pairs and resilience scores indicated a positive correlation at the F4-F3 electrode location (as shown in Alpha brainwaves reflect the resting state and are inverse to the cortical activities at the brain area monitored ,89. A hiThis systematic review provides an overall view of the implementation of resilience-building interventions among early childhood educators to improve their mental health and well-being. The group approach is more commonly practised in the interventions, along with psychological assessments to measure mental health or well-being. The main challenges of the group approach are its lengthy period and simultaneous involvement of educators during meetings, which may affect the teaching workload of colleagues. Differences in time availability and level of motivation to attend the meetings are also factors that contribute to the success of intervention. There is also an equal need for interventions that address centre-focused issues and ones that can be implemented at multiple centres with similar population demographics. To address these challenges, we propose an integrated EEG-VR approach to build resilience in ECE teachers. The use of VR allows the training to be designed based on the needs of the respective ECE institutes, whereas the monitoring of brain activity using the EEG provides a more personalized delivery of the intervention for the teachers. These advantages can be observed in the preliminary findings, where the frontal brain activity reflected in alpha asymmetry indices correlated with the resilience level of teachers when performing mental tasks but not during resting. By incorporating other endogenous and exogenous factors of individuals, this approach allows the resilience-building intervention to be planned based on the strengths and limitations of the teachers.


The primary global response to the SARS-CoV-2 pandemic has been to bring to the clinic as rapidly as possible a number of vaccines that are predicted to enhance immunity to this viral infection. While the rapidity with which these vaccines have been developed and tested (at least for short-term efficacy and safety) is commendable, it should be acknowledged that this has occurred despite the lack of research into, and understanding of, the immune elements important for natural host protection against the virus, making this endeavor a somewhat unique one in medical history. In contrast, as pointed out in the review below, there were already important past observations that suggested that respiratory infections at mucosal surfaces were susceptible to immune clearance by mechanisms not typical of infections caused by systemic (blood-borne) pathogens. Accordingly, it was likely to be important to understand the role for both innate and acquired immunity in response to viral infection, as well as the optimum acquired immune resistance mechanisms for viral clearance . This information was needed both to guide vaccine development and to monitor its success. We have known that many pathogens enter into a quasi-symbiotic relationship with the host, with each undergoing sequential change in response to alterations the other makes to its presence. The subsequent evolution of viral variants which has caused such widespread concern over the last 3–6 months as host immunity develops was an entirely predictable response. What is still not known is whether there will be other unexpected side-effects of the deployment of novel vaccines in humans which have yet to be characterized, and, if so, how and if these can be avoided. We conclude by remarking that to ignore a substantial body of well-attested immunological research in favour of expediency is a poor way to proceed. For the past 18 months, the world has been ravaged by a pandemic caused by a coronavirus infection originating in late 2019 in Wuhan, China. By mid-2020 it became apparent that there had arisen global consensus that the way forward from the socioeconomic and medical morass which had occurred was through rapid development and implementation of a universal vaccination program. However, unlike past precedents, this was to take place in the relative absence of detailed knowledge and investigation into the nature of natural host resistance to the pathogen concerned, and by “speed-tracking” novel vaccine designs to clinical use, again in the absence of detailed knowledge of possible short-term and longer-term implications of this vaccine’s administration. In a recent review the (then) current status of understanding of immunity to SARS-CoV-2, and how this might affect future approaches to protective vaccination, was discussed [Mammalian immunity has both an innate and adaptive arm. Protection mediated by innate immunity, the sole immune mechanism for 95% of the species on earth, develops quickly 1–2 days), with some evidence also implying an immune memory, “training”, affording an enhanced protection from reinfection (with the same pathogen) and even enhanced immunity to novel pathogens [ days, wide novo mutations that remain uncorrected may also accumulate in the DNA of transcribed non-Ig genes, and possibly lead to further cell damage in infected tissue [d tissue .The mechanism(s) involved in training innate immunity likely involve epigenetic changes  which results in more rapid activation of the genes implicated in response to pathogens . EpigeneDefects in innate immunity, as well as acquired immunity, are particularly evident in the elderly ,16,17,18Adaptive (acquired) T and B lymphocyte mediated immunity, while certainly primarily responsible for immunologic memory, takes some 10–14 days post pathogen exposure to become active, but in general shows much greater diversity for pathogen recognition than does innate immunity. Given the experience in understanding how acquired immune mechanisms can be brought to play to enhance pathogen resistance by deliberate vaccination, and the numerous successes evident in global disease control reported as a result, it is not surprising that over the last 12–18 months effort has been directed to making this the key strategy employed against the current pandemic. The discussion below reviews what we have learned about the importance of antibody (B cell mediated) and T effector immunity in providing protection following natural infection, or following vaccination, and how the pathogen has, in turn, responded to naturally acquired or vaccine-induced heightened host resistance. In addition, a brief synopsis of some unexpected adverse effects already noted with vaccines currently “in play”, and how this might affect the future direction of vaccinology will be mentioned.Entry of virus into the cells of infected individuals was shown early on to depend on the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 . AccordiThe sophistication of dissecting Ig response to the RBD to assess efficacy in protection is highlighted by more recent analysis of epitope binding. Thirty-eight RBD-binding neutralizing Abs with known structures, mostly isolated from virus-infected patients, were grouped into five general clusters, which were, in turn, able to document distinct non-neutralizing faces on the RBD. A maximum of up to 4 of these neutralizing Abs could bind to the RBD simultaneously, with significant implications for vaccine design ,30. ThesDespite these data, it should be acknowledged that clinical manifestations of SARS-CoV-2 infection are not the same in children, and nor is the immune response engendered following infection. Children are largely spared from severe respiratory disease but may develop a multisystem inflammatory syndrome similar to Kawasaki’s disease . SARS-CoIt has been known for many years, that the best form of protective immunity for pathogens invading by the nasal or oral route are local secretory IgA responses . Recent A separate study measured humoral responses to SARS-CoV-2, including analysis of the presence of specific neutralizing antibodies in the serum, saliva, and bronchoalveolar fluid of 159 patients following natural infection with SARS-CoV-2. Again, early viral specific humoral responses were dominated by IgA antibodies with peaks during the third week post-infection, with IgA contributing to virus neutralization to a greater extent than IgG or IgM antibodies. While anti-viral IgA serum concentrations decreased after 1 month neutralizing IgA remained detectable in saliva for up to 10 weeks . The samLimited research studies have been reported on vaccine induced mucosal immunity in animals. A chimpanzee adenovirus-vectored vaccine encoding a prefusion stabilized spike protein (ChAd-SARS-CoV-2-S) was studied following intramuscular (im) injection for protection against SARS-CoV-2 infection in mice expressing the human angiotensin-converting enzyme 2 receptor . A singlFurther insight into the importance of mucosal immunity (and its induction) in effective immunity to SARS-CoV-2 infection comes from studies with children. Unlike other respiratory viruses where disease manifestations are often more severe in children, infection of children with SARS-CoV-2 generally follows a more benign course. Pierce et al.  found th+ T cells were also induced of monofunctional, polyfunctional and cytotoxic phenotypes, with, to date, little documented clinical significance. A more exhaustive study of CD8 T cell immunity following natural infection was also reported recently [It has been known for some time that activated T lymphocytes are crucial for protective immunity to viral infections. This is consistent with what we understand about the quite different antigen recognition by B versus T cells. The latter recognize cell surface MHC-presented epitopes altered following viral infection and thus can act to destroy potential “viral factories” before living viral replication is completed within the infected cell. While monitoring of (serum) Ig may make for easier assessment of the development of an immune response to a pathogen, it may, as inferred above, provide little information about the development of protective immunity in the infected host. The issue is further discussed in some detail in a recent review . Antibodrecently . A highlrecently .Some attempt to gain more insight into the mechanism(s) of T cell resistance comes from animal studies by Zhuang and co-workers . Their dIn the introduction ,10,11,12A recent study comparing innate immune response to Influenza and SARS-CoV-2 in nasal washes from infected adults suggested an important difference in innate immunity following SARS-CoV-2 infection , with deStudies in a naturally infected cohort of individuals who had recovered from mild SARS-CoV-2 infection showed evidence for SARS-CoV-2 specific IgG, neutralizing antibodies, and memory B and memory T cells persisting beyond months . Memory How does the development of a protective Ig response compare after natural infection versus vaccination? In particular, what implications are there for vaccine induced immunity to infection, given the (relatively) rapid antigenic drift in SARS-CoV-2 reported over the last 8 months . MutatioAnalysis of antibody and memory B cell responses of a cohort of 20 volunteers given either of two mRNA vaccines against SARS-CoV-2 showed the plasma neutralizing activity and relative numbers of RBD-specific memory B cells was similar in vaccinated and naturally infected cohorts. However, activity against SARS-CoV-2 variants was reduced significantly . A similA more comprehensive summary using 506,768 SARS-CoV-2 genome isolates, including mutations of the S-RBD from patients, was reported to explore the efficacy of immunity to the growing list of SARS-CoV-2 variants, also concluded that most variants were associated with increased binding to ACEII, and thus likely greater infectiousness. Many new RBD mutants were characterized which could affect neutralizing Ig binding to the RBD, including mutations now described in the California variant B.1.427, and the Mexico variant B.1.1.222, which markedly enhances the infectivity of the latter. The authors conclude that “the genetic evolution of SARS-CoV-2 on the RBD, which may be regulated by host gene editing, viral proofreading, random genetic drift, and natural selection, (can) give rise to more infectious variants that will potentially compromise existing vaccines and antibody therapies” ,74. A siAs mentioned previously, it seems self-evident that a systematic analysis of T cell epitopes recognized by subjects following natural infection, correlated with disease outcome, is essential to guide interpretation of monitoring patient responses, and to develop vaccines that might prove efficacious in protection. A USA government-led clinical trial  was desiFurther studies have attempted to explore the nature of recognition of viral variants in vaccinated/infected versus naïve subjects, in order to compare the data with that observed from comparison of Ig responses in similar groups (see above). Subjects receiving either Pfizer-BioNTech (BNT162b2) or Moderna (mRNA-1273) mRNA-based SARS-CoV-2 vaccine were characterized, and found to show broad T cell responses to the SARS-CoV-2-S protein, with only 4/23 targeted peptides potentially affected by mutations in the UK (B.1.1.7) and South African (B.1.351) variants. CD4+ T cells from vaccine recipients recognized the 2 variant spike proteins as effectively as they recognized the ancestral virus S-protein from the ancestral virus, in contrast to antibody data discussed above. Interestingly, a 3-fold increase in the CD4+ T cell responses to influenza S-peptides was seen after vaccination, implying a cross-protection (following SARS-CoV-2 vaccination) against some endemic coronaviruses . Others The importance of understanding T cell immunity to SARS-CoV-2 is confirmed by a recent report investigating the logistics of using in vitro expanded SARS-CoV-2 immune T cells in adoptive immunotherapy in immunocompromised subjects . This gr+ T cell responses. There are a number of plausible hypotheses, including, but not limited to, possible differences in innate responses to the two candidates; and the mRNA sequence design  which might have contributed to the potency and reactogenicity (minor adverse effects) of the vaccines. It is beyond doubt, that more in-depth knowledge on the in vivo delivery efficiency and the particular innate immune effects of the different mRNA vaccines will contribute to the design of even safer and more effective mRNA vaccines in the future.It seems apropos to conclude the present discussion with some thoughts on adverse effects noted from SARS-CoV-2 vaccination. Heralding the rapid and novel introduction of mRNA vaccines into the clinical armamentarium, a new formulation of synthetic mRNA strands encoding the SARS-CoV-2-S glycoprotein, packaged in lipid nanoparticles to deliver mRNA to cells, Verbeke et al. suggest we stand on the threshold of a “new dawn” in vaccinology . HoweverAlthough still not fully understood, even the most effective of vaccines does not prevent illness 100% of the time-vaccine breakthrough occurs, though generally with less disease severity.Since it takes some 10–14 days post-vaccination to develop immunity, if an individual was infected shortly before/after vaccination, it is predicted they may still become infected. In addition (see above) infection may occur with a variant for which the current vaccines are not providing effective immunization for.While to date no unusual patterns have been detected in the data of people infected post-vaccination, CDC has an ongoing goal to identify any unusual patterns, such as trends in age or sex, the vaccines involved, underlying health conditions, and whether particular SARS-CoV-2 variants are causing sickness.There is abundant data now to suggest that vaccines help protect people who are vaccinated from getting COVID-19 or from getting severely ill from SARS-CoV-2. Nevertheless, because people can still get sick and possibly spread the virus to others after being fully vaccinated, the current recommendation remains that people continue to take simple public health measures to protect themselves and others-see also . Indeed,The need to address these issues is highlighted by the recent CDC briefing, held with the plan to alleviate many concerns within the public over what may be (in the longer-scheme of things) minor issues . As a brOthers have focused attention on clearly documented adverse effects from SARS-CoV-2 vaccines already in use. First and foremost, amongst these is vaccine-induced immune thrombotic thrombocytopenia (VITT) in the aftermath of vaccination with the adenoviral vector SARS-CoV-2 vaccine ChAdOx1 nCoV-19 . Rare paAdditional groups have focused on the theoretical risk associated with the current vaccines, arguing that their “rush into service” has ignored potential concerns with their use, particularly the concern regarding induction of autoimmune reactivity . As an eIn conclusion, we recall that pandemics of the type we have experienced over the past 18 months are far from new. Similar pandemics have punctuated human history over many millennia, often contributing to episodes of severe attenuation of populations, and in some instances leading even to the collapse of empires. In the past, however, the societal role for coping with such pandemics has been limited-confined in the main to alleviating suffering that resulted from the disease, and perhaps enabling the segregation of infective victims wherever possible. There was obviously no possibility at this stage of any globally coordinated response, nor of any large-scale intervention, to minimize transmission and thus accelerate the end of a pandemic. The advantage we possess now is scientific knowledge of the type we have discussed in this paper. If such knowledge is utilized honestly and dispassionately, a new utopia beckons; otherwise, we may be no better off now than in earlier epochs, and by some reckoning perhaps even worse.All pandemics are of course self-limiting, eventually ending through the development of herd immunity to the infective agent. The global response to the current pandemic, involving severe and often punitive limitations of individual freedom is clearly unprecedented in history. The justification for such draconian measures is based on the assertion that scientific advances have made available a shortcut to the natural process of immunity that would greatly reduce the total number of infections, and the number of deaths. We show in this review that some aspects of the scientific arguments being currently deployed are either deficient or seriously flawed.There is hope that we are now approaching an entrenchment phase in our response to the SARS-CoV-2 pandemic, with more widespread uptake of vaccines, protection of vulnerable population cohorts , and adherence to better public health measures continuing to improve the overall outlook. At all levels, politically, sociologically, ethically, scientifically and medically, there have been instances of major mismanagement and misunderstanding, coupled with gross errors of judgement, which have clearly cost lives. As reviewed above, it can be argued that we still have failed to recognize the importance of implementation of basic science knowledge, both new research and understanding old observations, which even now would likely improve the future course of the disease. We need to remain vigilant in the face of having implemented so many previously untried and untested therapies for the appearance of new signs and symptoms in treated patients which are early indications of adverse events, for which VITT may be merely the tip of a large iceberg. As the philosopher, George Santayana once said “Those who cannot remember the past are condemned to repeat it”. We need to make sure that the valuable lessons learned, at all levels, over the last 18 months are not forgotten.


NETosis is a form of neutrophil death leading to the release of extracellular chromatin and the assembling of proteins, including antiviral proteins, primed by an initial pathogenic stimulus. Under certain specific conditions, neutrophils can exhibit a double-edged activity. This event has been implicated in COVID-19 among other conditions. Neutrophil extracellular traps (NETs) are involved in the pathogenesis of COVID-19 by promoting a pro-inflammatory and a procoagulant state leading to multiorgan failure. This particular form of host defense promoted by neutrophils is closely related to the well-known cytokine storm observed in severe COVID-19 patients. These two elements therefore represent possible targets for treatment of severe SARS-CoV-2 infections. Coronaviridae. The HKU1, the NL63, the OC43 and the 229E are four common endemic coronaviruses causing diseases of low intensity. These four coronaviruses are called non-severe acute respiratory syndrome (SARS)-like coronaviruses (The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is responsible for the ongoing pandemic and is associated with significant morbidity and mortality. The number of confirmed cases approaches 194 million and the number of deaths worldwide has reached the four million mark. The most affected continents were America and Europe. Coronaviruses (CoVs) are single-stranded RNA enveloped viruses. They belong to the subfamily aviruses . Three haviruses . Lastly,aviruses . Other vThis virus is mainly transmitted via the respiratory tract and is airborne via microdroplets. SARS-CoV-2 has the capacity to infiltrate the lower respiratory tract and generate a series of respiratory and systemic complications . Older pOne of the current challenges is to estimate the percentage of asymptomatic subjects who may unknowingly carry and transmit the virus. As the pandemic is currently worldwide, it will be unrealistic to eradicate the virus, especially because of the non-specific nature of the disease and the rapid emergence of variants which can in some way escape the immune response induced by a previous strain or the current vaccines available . Beside In vitro investigations”; “Ex vivo investigations”; “In vivo investigations”; “Clinical trials”. One article may be included in different categories. Only a subset of the papers is cited due to space restriction in the citation list and redundant information, especially for the reviews. Nevertheless, they can all be found in the We performed a review of the literature about NETs in COVID-19, by doing an electronic search on PubMed. Other search engines have not been consulted. We used the following keywords: “SARS-COV-2”; “COVID-19”; “COVID”; “COVID19” in combination with “NETosis”; “neutrophil extracellular traps”; “NETs”. Two researchers (CG and JD) independently screened all titles and abstracts identified from the literature search to determine potentially eligible manuscripts. After a first screening, articles were categorized as follows: “Review”; Cells involved in innate immunity represent the first line defense of the body against an infective pathogen. Their role is to neutralize these pathogens and to trigger adaptive immunity in case of persistent infection. Given the growing evidence of their role in the antiviral response, neutrophils could be considered as the main “foot soldiers” of innate immunity in association with other blood elements such as platelets . NeutropNETosis has also been described in the pathophysiology of viral infections other than COVID-19.In normal conditions, NETosis is a regulated form of neutrophil death, which participates in the host’s immune defenses by the formation of traps to prevent the pathogen from spreading in the organism. This host defense mechanism has been reported in many infectious diseases such as infections by the respiratory syncytial virus (RSV), human immunodeficiency virus (HIV) or Chikungunya virus (CHIKV) . The stiThis pathway is generally induced by bacterial stimulation and does not involve nicotinamide adenine dinucleotide phosphate (NADPH) oxidase and the formation of reactive oxygen species (ROS), unlike the suicide pathway. This response is also faster because it occurs within 30 min compared to 3 h for the suicide pathway . ROS havAn important point is the clearance of NETs because many pathological conditions, especially vascular disorders leading to the occlusion of micro-vessels, can occur due to NET clearance deficiency. NETs are usually degraded by plasma DNases (DNase 1 and DNase 3) and are then eliminated by macrophages.In fact, NETs have double-edged-sword activities. They are not only involved in the response to viral infections since they can be found in certain diseases where their presence is a sign of an inadequate immune response, leading to tissue damage. These include non-infectious inflammatory diseases, autoimmune diseases and other non-autoimmune diseases . The mosNETs can also trigger the formation of an inflammatory cascade leading to cancer metastasis, tissues damaging or multiple organ dysfunctions which are more often observed in the pulmonary, cardiovascular and renal systems .The understanding of the pathophysiology of COVID-19 remains incomplete, particularly in regard to the multi-organ failure it may cause. When the virus is detected in the lower respiratory tract, the immune machinery is set up with the activation of innate immunity . Symptom+ T-cells, CD8+ T-cells and with an increase in T-helper 17 (Th17) proinflammatory cells  and the arrestin pathway. In turn, NETs are able to induce CXCL-8 production by macrophages via the TLR9/NF-κB pathway . To evaluate NETs levels in blood, researchers used three well-established markers, namely DNA-free, MPO-DNA and citrullinated histone H3 (Cit-H3) . Among tet al. have discovered that disulfiram is able to inhibit Gasdermin G which reduces the formation of NETs. A significant reduction in mortality in a mouse model of pneumonia treated with disulfiram vs placebo was demonstrated  in conjunction with standard therapies . DNase 1In addition to therapies directly targeting the formation of NETs or their degradation, it is also possible to target the different pathways involved in the amplification loops with NETs. As already mentioned, IL-1β is integrated into an amplification loop with neutrophils, leading to sustained NETosis. Anakinra, an IL-1 receptor antagonist, has been shown to reduce the formation of NETs in studies of pyogenic arthritis, pyoderma gangrenosum and acne (PAPA) syndrome . AnakinrIn addition to IL-1, CXCL-8 is also involved in an amplification loop with macrophages. BMS-986253 is a monoclonal anti-CXCL-8 antibody and is currently being studied for its potential impact on COVID-19 Similarly, reparixin, a CXCR1 and CXCR2 receptor antagonist, is currently in clinical trials for its use in COVID-19 . AlfaroIL-6 is also known to be a key element in the cytokine storm observed in severe COVID-19 patients. Several studies have therefore tested products able to inhibit the IL-6 pathway. These include direct IL-6 inhibitors such as siltuximab or IL-6 receptor antagonists such as tocilizumab and sarilumab. Of note, tocilizumab has been approved in China for use in COVID-19 patients with severe pneumonia. Tocilizumab reduces the likelihood of using mechanical ventilation but does not increase survival . SarilumJAK inhibitors such as tofacitinib, baricitinib and ruxolitinib have also been identified as possible therapeutic agents for reducing the burden of severe COVID-19 in patients. The JAK family consists of JAK1, JAK2, JAK3, and tyrosine kinase 2 (TYK2), and the different JAK inhibitors are targeted against one or more of these JAK members. Tofacitinib, which inhibit JAK1, JAK2, and JAK3, and baricitinib and ruxolitinib, both acting against JAK1 and JAK2 are currently under investigation for COVID-19 . RecentlThe main parenteral anticoagulant used in the treatment and prophylaxis of thrombotic events in COVID-19 is low molecular weight heparin (LMWH) and in some severe cases admitted to ICU, unfractionated heparin (UFH) . In addiHeparin may reduce P-selectin expression reducing the recruitment of neutrophils at the inflammatory site. Heparin is able to inhibit cathepsin G and NE, which are important inflammatory promoters as outlined in cystic fibrosis and ARDS . They alA significant number of clinical trials are underway to evaluate potential therapies targeting NETs in COVID-19. Disulfiram is investigated in two studies for which no results are currently available . HoweverOf the seven studies investigating the use of dornase alfa in the treatment of COVID-19, 2 have completed patient enrollment (NCT04387786 and NCT04402970). The results for these two clinical trials are not yet available but it should be noted that NCT04387786 is an observational study involving only five patients, so the results should be interpreted with caution . SeveralOf the colchicine studies, five out of the six clinical trials listed in Regarding the clinical trials investigating the potential effect of anakinra, NCT04680949 is still active but no longer recruiting patients. NCT04443881 has completed recruitment but results are not yet available. NCT04462757 has been terminated due to a lack of patients in the target population of the study. Finally, the clinical trial NCT04364009 was stopped prematurely due to efficacy and patient safety issues . SeveralOf the canakinumab studies, only NCT04365153 and NCT04362813 have completed recruitment .The study on BMS-986253 is still under recruitment.Of the three tocilizumab clinical trials mentioned in Only study NCT04415151 with tofacitinib is still enrolling, the other two studies have completed recruitment. Concerning the baricitinib studies, only NCT04401579 and NCT04421027 have finished their recruitment, the other studies are still under recruitment. Concerning the ruxolitinib studies, only NCT04338958 is still under recruitment, the others have completed their recruitment . The JAKThis review has highlighted the importance of NETs in a pathology such as COVID-19 although their impact was already known in some autoimmune diseases such as systemic lupus erythematosus. The pathophysiology of COVID-19 is complex and involves different systems including the complement, the inflammatory and the coagulation systems, a well-known triad in ARDS. All of these systems interact with each other with the starting point being the deregulation of the inflammatory system and the appearance of the cytokine storm. This leads to multisystem failure, especially in the case of disseminated intravascular coagulation and lung disorders. The management of hospitalized COVID-19 patients is challenging and may require drugs acting on different pathways to minimize the burden of the disease, especially in the more severe cases. Therapies acting on NETs may play an important role, as it is the cornerstone of many subsequent complications. This review focuses only on the NETosis aspect of the triad involved in COVID-19. This is due to the increasing importance of the involvement of NETs in the disease. This aspect is nevertheless indistinguishable from the other elements with which NETs interact. As mentioned, several treatments targeting NETs are currently being evaluated, at various stages of development. Some of these treatments already exist for other indications. Although the vaccine strategy is advancing rapidly in many countries, this is not the case worldwide, some countries are still very affected by the disease. The importance of having treatments that can act on the severity of the response to the infection are therefore essential. Moreover, the appearance of different variants could compromise the effectiveness of the different vaccines.


There are limited data concerning the prevalence of post-traumatic stress disorder (PTSD) among teachers. Therefore, the present study estimated the prevalence of PTSD among mainland Chinese teachers during the COVID-19 pandemic and to construct a model with mediation and moderation effects to explain the PTSD. Data collection was conducted in schools in the Jiangxi province between October and November 2020 among k-12 schoolteachers. An online survey, including five different psychometric scales, was used to collect data. All participants were assessed for PTSD using the Chinese version of the PTSD Checklist for DSM-5 (PCL-5). Hayes’ PROCESS Model 8 was used to examine the potential factors explaining a higher PTSD scores. A total of 2603 teachers from k-12 schools participated. With the cutoff score at 31, the prevalence of PTSD was 12.3% but decreased to 1.0% when the cutoff score was at 49. Nomophobia moderated the effects of Fear of COVID-19 Scale on PTSD. The findings suggest that fear of COVID-19 among teachers leads to PTSD via psychological distress, highlighting the moderating effect of nomophobia in this association. Based on the study’s findings, psychological interventions and educational training are needed to reduce fear among teachers at higher risk of developing PTSD. The novel coronavirus disease-2019 (COVID-19) outbreak which emerged in December 2019 in Wuhan (China) rapidly spread overseas, leading to it being declared a Public Health Emergency of International Concern (PHEIC) on 30 January 2020, by the World Health Organization (WHO) Emergency Committee , death dSeveral cohorts, irrespective of their occupation , have suffered psychological stress, experienced reduced autonomy, and felt job insecurity due to the COVID-19 pandemic ,12,13. CPost-traumatic stress disorder (PSTD) is a psychological disorder that can occur when individuals experience a traumatic incident such as natural calamities or infectious disease outbreaks such as SARS or COVID-19 ,18. Its Prior studies have shown that participants with high levels of psychological distress often develop PTSD symptoms ,5. This The term ‘nomophobia’ refers to the fear of not having a mobile phone or mobile phone contact . During Moreover, fear of COVID-19 may be elevated when receiving scary information concerning COVID-19 via social media on smartphones. Consequently, when a person has higher levels of nomophobia, they may frequently check information and/or social media on a smartphone and obtain COVID-19 information that makes them scared. This may enhance the effect of fear of COVID-19 on PTSD. In order to test the speculation asserted here, this present study (i) estimated the prevalence of PTSD among mainland Chinese teachers during the COVID-19 pandemic, and (ii) constructed a moderated mediation model to explain PTSD. More specifically, the model tested whether the association between fear of COVID-19 and PTSD is mediated by psychological distress and moderated by nomophobia; whether the association between fear of COVID-19 and psychological distress is moderated by nomophobia.Sojump. A non-probability sampling strategy was employed for data collection. The research team first sought help from the principals of k-12 schools in Jiangxi province. After that, the principals who accepted the invitation to participate provided the online survey link to the schoolteachers. In order to minimize the potential biases caused by a small number of schools , the present study attempted to invite as many schools as possible. Finally, a total of 188 schools participated. However, school principals who accepted the present study’s invitation might have high levels of confidence in their teachers’ mental health. The online survey was voluntary and anonymous. Informed consent was provided by all the participants using the e-forms. The Sojump platform prompted participants to complete all items. Therefore, there were no missing data in the study.The data collection period was between 26 October and November 2020 via the online survey platform The participants were recruited from Jiangxi as it is a province close to Wuhan, where the COVID-19 outbreak originated. After the lockdown in Wuhan, some Wuhan residents traveled to Jiangxi province, which led to Jiangxi being one of the first regions in the world to experience a large-scale COVID-19 outbreak. Moreover, Jiangxi province is located in inner China, and its educational infrastructure is less developed than in the coastal provinces due to its less developed local economy. Therefore, much less attention was paid to the teacher populations by the authorities. More specifically, the psychological resources and psychological counseling services during the outbreak of the pandemic (March to June 2020) were scarce in Jiangxi. Only after September, 2020 , did Jiangxi province introduce a public welfare hotline for psychological assistance, which teachers and students were encouraged to use.d = Z √P(1 − P)/n, where n = sample size needed; Z = Z statistic for a level of confidence (set at 1.96 in the present study); P = Expected proportion (set at 0.01 in the present study), and d = Precision (set at 0.004 in the present study). The calculation indicated that 2377 participants were needed.This study was approved by the Institutional Review Board of the Jianxi Psychological Consultant Association. The sample size needed for the present study was calculated using the following equation: Chinese PTSD Checklist for DSM-5 (PCL-5) has 20 items that assess the 20 DSM-5 symptoms of PTSD with good reliability and validity [not at all) to 4 (extremely). The PCL-5 is easy to administer and assesses all symptom clusters of DSM-5 PTSD over the past month. The total score of the PCL-5 is calculated by adding the scores of each item [The validity ,32. It uach item . The stuach item . Moreoveach item . Fung etChinese Fear of COVID-19 Scale (FCV-19S) assesses individuals’ fear of COVID-19 [strongly disagree) to 5 (strongly agree). A higher score of the FCV-19S represents a greater level of fear of COVID-19. An example item is “I cannot sleep because I’m worrying about getting COVID-19” [The seven-item COVID-19 . Items aOVID-19” . Higher Chinese Nomophobia Questionnaire (NMPQ) was developed to assess the dimensions of nomophobia [“I would feel uncomfortable without constant access to information through my smartphone”. The NMPQ comprises four factors . Items are responded to on seven-point scale from 1 (strongly disagree) to 7 (strongly agree) is applied to each NMPQ item. The responses are summed up to find a total score. Higher scores indicate greater severity of nomophobia [The 20-item mophobia . An exammophobia . The CroChinese Depression, Anxiety, Stress Scale (DASS-21) is a self-report scale assessing negative affect comprising three seven-item subscales . Items are responded to on a four-point scale from 0  to 3 (“applies to me very much or most of the time”). Scores for each subscale are obtained by summing the responses of the all the items [The he items . Higher All the instruments were adopted from the previous studies and these scales have demonstrated good internal and test–retest reliability in a Chinese sample ,35,38,39Descriptive statistics of means and frequencies were first applied to the data to understand the participants’ characteristics, including their background information and all the information assessed using the standardized scales. Pearson correlations were then carried out to examine the associations between every two studied variables. Finally, study used PROCESS Model 8  suggested by Hayes  to examiThe participants who agreed to participate in the study (N = 2603) completed all items of the online survey. The sample size is sufficient, as it is larger than the calculated size . Among participants, approximately one-quarter were males (28.4%), and almost all participants taught in public schools (99.5%). Slightly more than half of the participants had 10 or fewer years of teaching experience (56.2%). On average, the fear of COVID-19 score was 15.40 , the nomophobia score was 69.14 , the psychological distress score was 30.03 , and the score for PTSD was 14.46 . Using the cutoff of 31, the prevalence of PTSD was 12.3%, but 1% when using the cutoff score of 49 see .r = 0.26 to 0.58; all p-values < 0.001).p < 0.001) and PTSD . The nomophobia score  and DASS-21  significantly explained the PTSD score. Moreover, the results of moderated direct effect indicated that nomophobia moderated the positive association between fear of COVID-19 and PTSD . The indirect effect of fear of COVID-19 on PTSD via psychological distress was significant   .To the best of the authors’ knowledge, the present study is the first to examine PTSD among teachers from k-12 schools during the COVID-19 pandemic. Considering the need to address the scarcity of research into PTSD among schoolteachers, the study shed light on the prevalence of PTSD resulting from COVID-19. The prevalence of COVID-19 related PTSD was 12.3%, using the cutoff score of 31, which decreased to 1% with a cutoff score of 49%. This is lower than that reported in a cross-sectional study that examined the prevalence of PTSD among teachers (24.4%) three months after the Lushan earthquake , and morThe present study’s findings demonstrated significant positive correlations between fear of COVID-19 and psychological distress. However, it should be kept in mind that the level of stress could be linked to uncertainty about the possibility of children becoming infected in schools. Although there are no similar studies among teachers, this relationship concurs with prior studies involving different populations . For insAdditionally, the findings indicated significant positive relationships between fear of COVID-19 and PTSD. Prior studies have shown a consistent association between fear of COVID-19 and PTSD ,24. The In addition, the present study analyzed the association between fear of COVID-19 and PTSD and found that the association was mediated by psychological distress. The study’s mediation analysis indicated that teachers with a fear of COVID-19 were more likely to have psychological distress, which ultimately led to PTSD. The fear of COVID-19 can affect experiences of stigmatization and social exclusion of confirmed patients, survivors, their families, and others associated with the disease, which increases the risk of developing psychological distress . FurtherMoreover, the present study indicated that nomophobia played a moderating role in the direct effect between fear of COVID-19 and PTSD. Based on the nomophobia score, nomophobia moderated the association of fear of COVID-19 on PTSD. A potential reason is that an individual with high levels of nomophobia and fear of COVID-19 may use a smartphone to search for coping methods to deal with the fear of COVID-19. For instance, a teacher has higher levels of nomophobia under the situation of COVID-19 as indicated by the fear of COVID-19, the teacher may be motivated to retain, protect, and build various resources to survive and maintain well-being. However, such protection may work only for a serious condition  but not a mild condition  . Thus, sThe present study has some limitations. First, the use of cross-sectional design cannot provide strong evidence for causality. Therefore, future research should employ longitudinal designs to overcome this. Secondly, this study used the convenience sampling method and was unable to address response bias, which limits the representativeness of the present sample. In order to obtain more generalizable result, future studies are advised to use more nationally representative sample groups. Thirdly, reported PTSD use may be vulnerable to inherent bias due to self-report. As individuals with persistent PTSD recall the traumatic event, their recall may be biased by their judgement, and they selectively retrieve information that is consistent with these judgements . TherefoThe present study suggests that more should be done to lessen the risk of PTSD among k-12 schoolteachers. These findings suggest that educational authorities should allocate reasonable resources to identifying and helping k-12 schoolteachers who are at high risk of developing PTSD. There is a need for trauma-informed educational practices and adaptive formative assessment tools to support teachers’ mental health during the ongoing COVID-19 pandemic period. The k-12 school administrators need to support teachers’ mental health needs through trauma-informed teaching, which requires ensuring safety, establishing trustworthiness, maximizing choice, maximizing collaboration, and prioritizing empowerment .The present cross-sectional study provided novel data concerning post-traumatic psychological distress related to COVID-19 in China, and suggests a need for more general psychological support, especially in educational settings. The study also found that fear of COVID-19 among teachers appears to result in PTSD via psychological distress, highlighting the moderating effect of nomophobia in this association. The study also provided the foundational evidence needed to help formulate psychological interventions to improve teachers’ mental health and psychological adaptability during the COVID-19 pandemic, as well as similar pandemics in the future.


The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC , which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections. According to the most recent World Health Organization (WHO) data on 9 December 2021, the cumulative number of confirmed cases of COVID-19 disease globally reached 267,184,623. The number of deaths reached 5,277,327 cases  from  due to the pixel-value representation required in image processing , which cFurther, the deep learning network requires fixed-sized data; thus, the sizes of all CT images are rescaled to 224 × 224 to meet the input size requirement . MoreoveGiven the shortage of CT scans of COVID-19 patients, training a Convolutional Neural Network (CNN) from scratch may be challenging. To overcome this difficulty, we use transfer learning techniques and a range of pre-trained models . The priSixteen widely used CNN models are chosen to perform transfer learning for the COVID-19 recognition task: VGG16, InceptionV3, ResNet50, ResNet152V2, ResNet101, ResNet101V2, DenseNet201, MobileNetV3 small, MobileNet, MobileNetV2, VGG19, ResNet50V2, XceptionNet, InceptionResNetV2, NASNet, and EfficientNet. The reason for selecting these models is that they are effective in computer vision. Many of them have been reported to function well in medical diagnostics ,37.An average pooling layer, which produces a down-sampled feature map by averaging the values of all pixels in each batch of the feature map, and the calculating procedure is shown in A flattened layer to convert the down-sampled feature map to a one-dimensional array;A fully connected layer with 64 filters and a Rectified Linear Unit (ReLU) activation to connect each neuron in layers before and after. ReLU helps to solve the problem of vanishing gradients. It is calculated using the equation below.A dropout layer with a 0.5 dropout ratio to mitigate model overfitting problems;An output layer with Softmax activation to identify whether a CT-Scan is positive or negative for COVID-19 diagnosis ,40,41,42These models have been pre-trained on the ImageNet dataset for classification purposes. ImageNet is a freely accessible image database that contains 14 million photos classified into 20,000 categories . Due to These newly added layers within the modified models are trained using the COVID-19 dataset. After each training epoch, the models are validated against a validation set. Since this research examines a binary classification problem (COVID-19 and non-COVID-19 classes), the number of neurons at the output layer was, thus, set to two. The accuracy of disease prediction is critical in the medical field since erroneous choices result in high expenses and risks to human life. The drawback of using the predictions of several deep learning classifiers independently is that they have a significant degree of variation. Because each model is designed differently and trained independently, they update their weights separately and provide inconsistent results when asked to categorize the same data. These problems may be addressed by the ensemble of individual models, which will decrease variance, and the ensemble model will be more generalizable than the individual models .The ensemble deep neural network for COVID-19 recognition (EDNC) models are proposed based on the combination of three pre-trained models. Three out of sixteen best-performing pre-trained models are chosen to execute the ensemble. Similarly to the individual transfer learning model, feature extraction layers in each of the three pre-trained models were set to be untrainable, preventing the weights from being changed in new model training. The next section details the architectures of three types of combined models.The primary goal of the feature-ensemble deep neural network for COVID-19 recognition (F-EDNC) is to group more characteristics from the input to the model. Thus, this proposed feature-ensemble technique produces a dataset that combines all desired features in the same CT scan input. It comprises three pre-trained models with the highest accuracy for identifying COVID-19 images. Assume Therefore, the feature ensemble from different pre-trained models can be represented as follows.These three models were trimmed after feature extraction layers with average pooling layers added. Following that, an ensemble layer was created to merge the outputs of these three feature extraction layers to obtain more precise data with respect to feature information. Then, three layers were added to complete the feature ensemble model: a flatten layer, a fully connected layer, and an output layer. The loss function we used for our model is categorical cross-entropy, which can be represented as the following equation:The architecture of the F-EDNC model is illustrated in The fully connected-ensemble deep neural network for COVID-19 (FC-EDNC) combines the fully connected layers of three pre-trained models to create an ensemble model with 386 trainable parameters. The output of fully connected layer Therefore, the fully connected layer ensemble from different pre-trained models can be represented as follows.The output of fully connected layers is concatenated to generate a more accurate probability of identifying COVID-19 CT scans. The architecture of the FC-EDNC model is indicated in The output-ensemble deep neural network for COVID-19 (O-EDNC) approach is accomplished by assemble three pre-trained models at the output layer with 14 trainable parameters. This method can be represented as follows:Thus, the output layer ensemble from different pre-trained models can be represented as follows.This method assumes that the ensembled model may learn more characteristics in this merged output to make more precise predictions. The architecture of the O-EDNC model is shown in We proposed a CNN model and built it from scratch to compare it with the pre-trained and ensemble models in terms of model performance, training time, and model complexity in the COVID-19 recognition task. This proposed CNN model, CANet, is constructed using three convolutional and max-pooling layers, as shown in Three layers  and a Softmax activation function are added to complete this CNN architecture. The model is trained and assessed in 50 epochs using the same COVID-19 dataset as the transfer learning models.A web application is built based on the Flask framework, enabling users to upload their CT images easily and to obtain their COVID-19 results quickly. The architecture of the frontend and backend of COVID-19 recognition system is shown in The following steps detail the functionality of the web application.Step 1. The user visits the web application and uploads a CT image.Step 2. The submitted picture is sent to the backend to which the proposed F-EDNC model is supplied. The image is resized to 224*224 and is converted to a NumPy array containing the pixel intensities before feeding into the model.model.load function to process the input image.Step 3. The F-EDNC model has been saved in HDF5 format in the backend and is loaded by the predict function with the NumPy size array , which contains the two classes of probability. The highest probability class is then retrieved.Step 4. The output is calculated by the Step 5. The result is displayed at the front end.Technologies such as Python, Keras, Tensorflow, NumPy, and Pandas are used in building the backend model. The Flask framework routes the web page and hosts the web server in Python. The advantage of using Flask is that it can build a web application in one single python file; moreover, it reduces the work of coding in JavaScript and jQuery. To develop a web application that recognizes CT scan images, we create two routes on the flask application: an index page route for the users to upload their image file and a predicted route to predict the saved model. Furthermore, Bootstrap was utilized as a CSS stylesheet in building a webpage. Bootstrap is a CSS framework that provides some pre-built CSS classes. It can help incorporate responsive web pages in the web application so that our web pages can work well on mobile browsers.The networks are implemented on Jupiter notebook with Python 3.7 , TensorFlow 2.4.0 , Keras 2.3.1 , and Scikit-Learn 0.20.4 . They are trained on a PC with Intel CPU Xeon E5–2680 v2, 16 GB RAM , and Nvidia GPU RTX2070S . The web application is developed with Flask 1.0.2 .A confusion matrix is a table as shown in True Positive (TP): The prediction and the actual outputs are both positive.False Positive (FP): The prediction is positive, but the actual output is negative.True Negative (TN): Both the prediction and the real result are negative.False Negative (FN): There is a negative prediction, while the actual result is positive. The following five metrics were used to evaluate the model’s performance :Accuracy refers to the ratio of correct to incorrect predictions.Precision indicates the accuracy of which a model classifies a sample as positive.Sensitivity refers to a model’s ability to recognize positive samples.F1-Scores measures precision and recall in a balanced manner.Specificity counts the number of negative samples that have been identified as such.The main dataset is randomly partitioned into training, validation, and test subsets with 60%, 20%, and 20%, respectively. The proposed models are trained using training data and validated against the validation set after each training cycle. Then, we use the testing dataset to evaluate the models and quantify the performance of the models using evaluation metrics.As observed in It can be observed from Accuracy and loss curves for sixteen pre-trained models during training and validation periods are shown in Three ensemble strategies have been applied for recognizing COVID-19 CT images. It has been shown in It can be observed from the confusion matrix in False discovery rate (FDR) means the percentage of all false discoveries, for example, the percentage of false discoveries in the calculation of all discoveries. The formula of FDR is as follows.EDNC illustrates much better learning curves than individual pre-trained models, as shown in The CANet model shows small gaps between validation and training value in the accuracy and loss curve; however, there are oscillations observed in the validation curve, which indicated that the size of the validation sample is too small for CANet to learn. According to the above results, it can conclude that F-EDNC performs best in categorizing chest CT scan images.To provide a more accurate assessment of model performance than a single validation (hold-out), we implement the process  five times on main dataset, with the findings averaged to obtain a more consistent and reliable result. It can be observed from We deployed our COVID-19 recognition system in a localhost web application for users to use. The F-EDNC model was chosen to be deployed because it has the highest average of accuracy, precision, sensitivity, specificity, and F1-score.https://github.com/rgiol/code, access date: 4 January 2022).As shown in Using deep learning approaches for recognizing COVID-19 disease is a hot topic that has sparked much attention recently. In this field, exciting results have been shown and continue to emerge while simultaneously utilizing various neural networks. CT scan images are among the critical dataset types used to identify COVID-19 symptoms. Numerous deep learning models have been created and effectively deployed for identifying COVID-19.The majority of studies utilized a dataset of only a few hundred COVID-19 images, which is inadequate for developing accurate and robust deep learning methods. Insufficient data may affect the performance of proposed methods.In most studies, there was a data imbalance problem, with one class having more images than the other. This affects the accuracy of models.Additionally, there are still some other pre-trained models that have not been utilized in COVID-19 classification.The impact of different ensemble methods has not received adequate attention in COVID-19 research. It should be emphasized that these techniques are beneficial in both improving performances and dealing with uncertainty associated with deep learning models.In none of the studies was there a webpage set up for users to upload images and to obtain COVID-19 predictions.In this study, twenty state-of-the-art techniques were selected for comparison purposes. After reviewing their methods and findings, we believe that there are still several research gaps in COVID-19 recognition compared to our study. Some of the most critical ones are listed as follows:In contrast, our study used a perfectly balanced CT scan dataset with more than two thousand chest CT images. Sixteen pre-trained deep learning models have been investigated, including those not employed in the COVID-19 detection area. Furthermore, three ensemble models have been proposed to recognize COVID-19 CT images. The findings of each model are summarized in This paper applies transfer learning methodology to modify and build sixteen deep learning models for COVID-19 recognition with the help of chest CT scans. Three ensemble deep neural networks  were proposed further to enhance the performance of those sixteen deep learning models with a dataset containing 2458 CT scans. CANet, a self-build CNN model, has been designed and trained on the same dataset. The performances of the proposed EDNC have been evaluated and compared to CANet and the sixteen modified pre-trained models. The results have shown that EDNC outperformed the pre-trained models and CANet in COVID-19 image classification performance.Among the results, F-EDNC achieves an accuracy of 97.75%, a sensitivity of 97.95%, a precision of 97.55%, a specificity of 97.56%, and an F1 score of 97.75%. Additionally, the proposed F-EDNC is deployed through a web application, enabling users to easily use the COVID-19 recognition system. Despite the excellent performance of the proposed COVID-19 recognition system, this study has several limitations. Firstly, if a user conducts the process of deriving a 2D image from a 3D CT scan, the classification result may vary depending on the selection of the 2D image. Secondly, this study has not utilized other preprocessing techniques such as image enhancement. In future work, image enhancement technology may be used to determine whether there is room for the improvement of results. In this study, the proposed EDNC significantly improved COVID-19 recognition performance, indicating the possibility of a completely automated and quick diagnosis of COVID-19 using deep learning. This finding will save time and money for health-care professionals in screening COVID-19 infections.


This paper outlines a system for detecting printing errors and misidentifications on hard disk drive sliders, which may contribute to shipping tracking problems and incorrect product delivery to end users. A deep-learning-based technique is proposed for determining the printed identity of a slider serial number from images captured by a digital camera. Our approach starts with image preprocessing methods that deal with differences in lighting and printing positions and then progresses to deep learning character detection based on the You-Only-Look-Once (YOLO) v4 algorithm and finally character classification. For character classification, four convolutional neural networks (CNN) were compared for accuracy and effectiveness: DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201. Experimenting on almost 15,000 photographs yielded accuracy greater than 99% on four CNN networks, proving the feasibility of the proposed technique. The EfficientNet-B0 network outperformed highly qualified human readers with the best recovery rate (98.4%) and fastest inference time (256.91 ms). The head slider is a magnetic head sensor mounted to the tip of a suspension arm, also known as a head gimbal assembly, of a hard disk drive (HDD) that assists in reading and writing components while flying above the hard disk magnetic media surface . A slideIn large-scale industrial production, an optical character recognition (OCR) system-based computer vision approach is widely used to read serial numbers on the head slider acquired from more than a hundred slider attachment machines B, with aMachine learning based on OCR systems is increasingly being introduced in several tasks. Huang et al. suggested a technique for serial number identification of bank cards using a Normalization-Cooperated Gradient Feature (NCGF) and Recurrent Neural Network (RNN) based on Long Short-Term Memory (LSTM), with 90.04% digit string recognition precision . Jang etCaldeira et al. proposed a convolutional neural network (CNN) classification based on an OCR architecture for implementations of machine learning based on OCR systems in auto ID systems to identify printing ID errors and misidentifications on steel coils . Their pIn this article, we propose a novel concatenated method for recognizing serial numbers on HDD head sliders in an industrial production context C. We proCNNs have emerged as the dominant machine learning technique for detecting virtual objects. Since the original Lenet-5 , VGG-19 Deep CNN Darknet-19  was propThe concatenated approach is proposed as a novel technique that consists of two concatenated deep learning models, one for character detection and another for character classification. The excellent performance of the hybrid algorithms for developing an object detection and classification approach for classifying species and gender mosquito vectors  was prevThe defect images  of a slider’s serial number are usually rejected by the industrial standard OCR protocol. Typically, such images were taken by industrial digital cameras  from 100 head reader/writer machines (150 captured images/machine). Normally, the defect images were identified manually in order to retrieve all potential good sliders from the rejection. During image capture procedures in a typical industrial environment, there are two possible variants that are uncontrollable in the experiment setting A. First,https://www.facebook.com/groups/cira.core.comm, (accessed on 9 September 2021).to train the character-detection-based CNN model (YOLO v4)  were obtained after the model training by choosing the best performance of a character detection model. Finally, the character detection model was tested with D1-weighted with validation data containing 400 images  was used for performing the training of the character classification model. For classification model training, the augmented training dataset of 36 classes of 630,000 characters was imported into the in-house deep learning framework (CiRA CORE platform) .Experiments were carried out to evaluate the performance of the proposed method for automated character identification and serial number classification from defect images. The proposed model was employed to test 6000 defect images for automatic character recognition and cropping using the D1-weighted model , which is the inference time of the OCR-based computer vision. Inference time (per image) was compared in four different CNN networks, i.e., DarkNet-19, EfficientNet-B0, ResNet-50, and DenseNet-201.Receiver Operating Characteristic (ROC) assessments were also conducted to evaluate the proposed method’s accuracy in the top four groups , which are the characters considered to have the greatest detection errors in the manufacturing process . AccordiWe implemented the system by using the concept of an application program interface (API) service call via the cloud infrastructure to facilitate in the recovery of defects from a standard OCR-based computer vision system in order to minimize computing hardware costs to assist the production lines. To incorporate the proposed system for automatic serial number classification from defect images in production lines, operating costs such as response time  and high-performance computer hardware must be considered. However, the communication time between a typical OCR-based computer vision system and the server can have a limited influence on response time. Nonetheless, the proposed approach can eventually replace traditional OCR-based computer vision when hardware costs are reduced, and the investment is worthwhile. In most OCR processes, checksum measurement is used to ensure that the reading is accurate. In this study, a checksum calculation is employed to verify the results of the proposed model. We can determine the serial number’s checksum as follows:Serial numbers (12 characters) predicted by the proposed method must be checked. When the checksum does not equal zero, an reading error is indicated. To evaluate the benefit of the proposed approach for identifying defective serial number images, we obtained 5000 images from 100 head reader/writer machines on the production line (50 captured images/machine). The recovery rate was estimated as follows:The overall number of images captured included both identifiable and unidentifiable images. The recovery rates of the four candidate CNN models and human reading are compared to inference time as shown in To ensure the generality of our model, five-fold cross validation was performed to validate if our chosen model consistently provided general accuracy with no prediction bias ,25. CrosFuture research should focus on improving the proposed process. First, the proposed method’s generalizability is still minimal. Despite the fact that the proposed model was trained using dataset images that included all practicable and uncontrollable variants in the real industrial context, the proposed model was only trained on a single font type. When the font styles of the serial numbers differ and vary from those of the training dataset, misrecognized font issues can be exacerbated. To boost the generalization of the model, model adaptation should be done by using other font forms of serial numbers in the training process. Nonetheless, misrecognized fonts occurred in the standard OCR process . FurtherIn this paper, we introduce a concatenated method for character recognition to reduce serial number identification failures on hard disk drive head sliders from the traditional OCR-system-based computer vision. On all four CNN networks, the proposed model offered precision greater than 99%; furthermore, the EfficientNet-B0 network had the highest recovery rate, which was preferable to human reading. The inference times of DarkNet-19 and EfficientNet-B0 were less than 300 ms, which was more than 15 times faster than the inference time of a human reading. As a result, Our proposed method outperformed human reading in character classification. When hardware costs are minimized and the investment is worthwhile, the proposed solution could potentially replace conventional OCR-based computer vision.