Development of Prediction Models of COVID-19 Vaccine Uptake among Lebanese and Syrians in a district of Beirut, Lebanon: a population-based study (preprint)

ImportanceVaccines are essential to prevent infection and reduce morbidity of infectious diseases, and vulnerable populations may lack access to vaccination campaigns. Previous evidence has shown that migrants and refugees are particularly vulnerable to exclusion, stigma and discrimination, and low COVID-19 vaccine intention and uptake were observed among refugees globally. ObjectiveTo develop and internally validate prediction models of COVID-19 vaccine uptake by nationality. DesignThis is a nested prognostic population-based cross-sectional analysis. SettingData was collected between June and October 2022 in Sin-El-Fil, a district of Beirut, Lebanon. ParticipantsAll Syrian adults and a random sample of other adults from low-socioeconomic status neighborhoods were invited to participate in the study (n=3,138). A telephone survey with consenting participants (n=2,045) was conducted. ExposuresCandidate predictors of COVID-19 vaccine uptake identified from the literature were collected. Main Outcome and MeasuresThe main outcome was uptake of COVID-19 vaccine. Predictors of COVID-19 vaccine uptake were assessed using LASSO regression for Lebanese and Syrian nationalities, respectively. The models discrimination capabilities are presented using the AUC, and their calibration are presented using the calibration slopes. ResultsOf 2,045 participants, 79% were Lebanese, 18% Syrians and 3% of other nationalities. COVID-19 vaccination uptake was higher among Lebanese (85% (95%CI:82-86) compared to Syrians (47% (95% CI:43-51)) (P<0.001); adjusted odds ratio (aOR) 6.8 (95%CI:5.5-8.4). Predictors of uptake of one or more vaccine dose for Lebanese were older age, presence of an older adult in the household, higher education, greater asset-based wealth index, private healthcare coverage, feeling susceptible to COVID-19, belief in the safety and efficacy of vaccines and previous receipt of flu vaccine. For Syrians they were older age, male, completing school or higher education, receipt of cash assistance, presence of comorbidities, belief in the safety and efficacy of vaccines, previous receipt of flu vaccine, and legal residency status in Lebanon. Conclusions and RelevanceThese findings indicate barriers for vaccine uptake in Syrian migrants and refugees, including legal residency status. They call for urgent action to enable equitable access to vaccines by raising awareness about the importance of vaccination and the targeting of migrant and refugee populations through vaccination campaigns. FundingInternational Development Research Centre (IDRC) - Canada Key PointsO_ST_ABSQuestionC_ST_ABSWhat are the context-specific differential predictors of vaccine uptake among the resident population by nationality in Lebanon? FindingsThis is a nested population-based cross-sectional analysis that examined predictors of COVID-19 vaccine uptake among Lebanese and Syrians living in low-socioeconomic neighborhoods in 2022. Socioeconomic, demographic and health risk perceptions were identified for both Syrians and Lebanese, while additional barriers identified in Syrian refugees included legal residency status in the country. MeaningDespite the availability of vaccines to all residents, there were inequalities in vaccine uptake between Syrians and Lebanese which need to be addressed.

Clinical Characteristics and Serum Cytokines Profiling in Hospitalized COVID-19 Patients in Lebanon.

Since its emergence, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains a public health threat worldwide. While the majority of patients recover in 3-4 weeks, complications in severely ill patients, including acute respiratory distress syndrome, cardiac injury, thrombosis, and sepsis, can lead to death. Several biomarkers, in addition to cytokine release syndrome (CRS), have been associated with severe and fatal outcomes in coronavirus disease 2019 (COVID-19) patients. The aim of this study is to assess clinical characteristics and cytokines profiles in hospitalized COVID-19 patients in Lebanon. A total of 51 hospitalized COVID-19 patients were recruited between February 2021 and May 2022. Clinical data and sera were collected at two time points: at hospital presentation (T0) and last collected results during hospitalization (T1). Our results showed that 49% of participants were >60 years with males accounting for the majority (72.5%). Hypertension, followed by diabetes and dyslipidemia, were the most frequent comorbid conditions among study participants accounting for 56.9% and 31.4%, respectively. Chronic obstructive pulmonary disease (COPD) was the only significantly different comorbid condition between intensive care unit (ICU) and non-ICU patients. Our results also showed that the median level of D-dimer was significantly elevated among patients in ICU and those who died compared to non-ICU patients and those who survived. Moreover, C-reactive protein (CRP) levels were significantly higher at T0 compared to T1 in ICU and non-ICU patients. The median level of IL-12p70 was significantly higher in patients >60 years compared to those ≤60 years (p = 0.0209). Our data are in agreement with previous reports suggesting the importance of IL-6, CRP, and IL-12p70 in the assessment of risk of severe disease and mortality.

Photoacoustic Imaging of COVID-19 Vaccine Site Inflammation of Autoimmune Disease Patients

Based on the observations made in rheumatology clinics, autoimmune disease (AD) patients on immunosuppressive (IS) medications have variable vaccine site inflammation responses, whose study may help predict the long-term efficacy of the vaccine in this at-risk population. However, the quantitative assessment of the inflammation of the vaccine site is technically challenging. In this study analyzing AD patients on IS medications and normal control subjects, we imaged the inflammation of the vaccine site 24 h after mRNA COVID-19 vaccinations were administered using both the emerging photoacoustic imaging (PAI) method and the established Doppler ultrasound (US) method. A total of 15 subjects were involved, including 6 AD patients on IS and 9 normal control subjects, and the results from the two groups were compared. Compared to the results obtained from the control subjects, the AD patients on IS medications showed statistically significant reductions in vaccine site inflammation, indicating that immunosuppressed AD patients also experience local inflammation after mRNA vaccination but not in as clinically apparent of a manner when compared to non-immunosuppressed non-AD individuals. Both PAI and Doppler US were able to detect mRNA COVID-19 vaccine-induced local inflammation. PAI, based on the optical absorption contrast, shows better sensitivity in assessing and quantifying the spatially distributed inflammation in soft tissues at the vaccine site.

Photoacoustic Imaging of COVID-19 Vaccine Site Inflammation of Autoimmune Disease Patients.

Based on the observations made in rheumatology clinics, autoimmune disease (AD) patients on immunosuppressive (IS) medications have variable vaccine site inflammation responses, whose study may help predict the long-term efficacy of the vaccine in this at-risk population. However, the quantitative assessment of the inflammation of the vaccine site is technically challenging. In this study analyzing AD patients on IS medications and normal control subjects, we imaged the inflammation of the vaccine site 24 h after mRNA COVID-19 vaccinations were administered using both the emerging photoacoustic imaging (PAI) method and the established Doppler ultrasound (US) method. A total of 15 subjects were involved, including 6 AD patients on IS and 9 normal control subjects, and the results from the two groups were compared. Compared to the results obtained from the control subjects, the AD patients on IS medications showed statistically significant reductions in vaccine site inflammation, indicating that immunosuppressed AD patients also experience local inflammation after mRNA vaccination but not in as clinically apparent of a manner when compared to non-immunosuppressed non-AD individuals. Both PAI and Doppler US were able to detect mRNA COVID-19 vaccine-induced local inflammation. PAI, based on the optical absorption contrast, shows better sensitivity in assessing and quantifying the spatially distributed inflammation in soft tissues at the vaccine site.

COVID-19 Infections and Predictors of Sickness Related Absences Among Healthcare Workers: The Experience of a Tertiary Care Center With the COVID-19 Pandemic.

BACKGROUND: Little has been published on predictors of prolonged sick leaves during the COVID-19 pandemic. This study aims to determine the rate of COVID-19 infections among healthcare workers (HCWs) and to identify the predictors of longer sick leave days. METHODS: We identified predictors of longer sick leave using linear regression analysis in a cross-sectional study design. RESULTS: Thirty-three percent of the total workforce contracted COVID-19. On average, HCWs took 12.5 sick leave days after COVID-19 infection. The regression analysis revealed that older employees, nurses, and those who caught COVID-19 earlier in the pandemic were more likely to take longer sick leave. CONCLUSIONS: Age, job position, and month of infection predicted sick leave duration among HCWs in our sample. Results imply that transmission was most likely community-based. Public health interventions should consider these factors when planning for future pandemics.

Response to COVID-19 in Lebanon: update, challenges and lessons learned.

The COVID-19 pandemic remains a public health problem threatening national and global health security. The socio-economic impact of COVID-19 was more severe on developing countries including Lebanon, especially due to the fragile healthcare system, weak surveillance infrastructure and lack of comprehensive emergency preparedness and response plans. Lebanon has been struggling with plethora of challenges at the social, economic, financial, political and healthcare levels prior to the COVID-19 pandemic. The COVID-19 pandemic in Lebanon revealed gaps and challenges across the spectrum of preparedness and response to emergencies. Despite these challenges, the Lebanese response was successful in delaying the steep surge of COVID-19 cases and hospitalisations through imposing strict public health and social measures. The deployment of the national vaccination plan in Lebanon in February 2021 coincided with the reduction in the number of cases and hospitalisation rates. The aim of this manuscript is to advance the epidemiologic evolution of COVID-19 in Lebanon pre- and post-vaccination, the challenges affecting the response and recovery, and the lessons learned.

Genomic surveillance of SARS-CoV-2 in COVID-19 vaccinated healthcare workers in Lebanon.

BACKGROUND: The emergence of SARS-CoV-2 variants including the Delta and Omicron along with waning of vaccine-induced immunity over time contributed to increased rates of breakthrough infection specifically among healthcare workers (HCWs). SARS-CoV-2 genomic surveillance is an important tool for timely detection and characterization of circulating variants as well as monitoring the emergence of new strains. Our study is the first national SARS-CoV-2 genomic surveillance among HCWs in Lebanon. METHODS: We collected 250 nasopharyngeal swabs from HCWs across Lebanon between December 2021 and January 2022. Data on the date of positive PCR, vaccination status, specific occupation, and hospitalization status of participants were collected. Extracted viral RNA from nasopharyngeal swabs was converted to cDNA, library prepped using the coronaHIT method, followed by whole genome sequencing on the Illumina NextSeq 500 platform. RESULTS: A total of 133 (57.1%) samples belonging to the Omicron (BA.1.1) sub-lineage were identified, as well as 44 (18.9%) samples belonging to the BA.1 sub-lineage, 28 (12%) belonging to the BA.2 sub-lineage, and only 15 (6.6%) samples belonging to the Delta variant sub-lineage B.1.617.2. These results show that Lebanon followed the global trend in terms of circulating SARS-CoV-2 variants with Delta rapidly replaced by the Omicron variant. CONCLUSION: This study underscores the importance of continuous genomic surveillance programs in Lebanon for the timely detection and characterization of circulating variants. The latter is critical to guide public health policy making and to timely implement public health interventions.

Comparison of Different Antiviral Regimens in the Treatment of Patients with Severe COVID-19: A Retrospective Cohort.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes respiratory disorders, with disease severity ranging from asymptomatic to critical manifestations. The current retrospective study compared the efficacies of different antiviral regimens used in patients suffering from severe COVID-19 disease from 19 January 2020 to December 2021 in a single center in Saudi Arabia. In total, 188 patients were enrolled in the current study, including 158 patients treated with different antiviral regimens, and 30 who did not receive any antiviral treatment. Different antiviral regimens, including favipiravir, remdesivir, oseltamivir, favipiravir/remdesivir, and favipiravir/oseltamivir were adopted. The effects of using different antivirals and antibiotics on the survival rate were evaluated, as well as the presence of comorbidities. Among all severely affected patients, 39/188 (20.7%) survived. Both age and comorbidities, including diabetes and hypertension, were significantly correlated with high case fatality following SARS-CoV-2 infection. Remdesivir alone and the combination of favipiravir and remdesivir increased the survival rate. Surprisingly, both imipenem and linezolid helped in the deterioration of disease outcome in the patients. A negative correlation was detected between increased mortality and the use of favipiravir and the use of either imipenem or linezolid. Among the compared antiviral regimens used in the treatment of severe COVID-19, remdesivir was found to be an effective antiviral that reduces COVID-19 case fatality. Antibiotic treatment using imipenem and/or linezolid should be carefully re-evaluated.

A deep learning architecture for multi-class lung diseases classification using chest X-ray (CXR) images

In 2019, the world experienced the rapid outbreak of the Covid-19 pandemic creating an alarming situation worldwide. The virus targets the respiratory system causing pneumonia with other symptoms such as fatigue, dry cough, and fever which can be mistakenly diagnosed as pneumonia, lung cancer, or TB. Thus, the early diagnosis of COVID-19 is critical since the disease can provoke patients’ mortality. Chest X-ray (CXR) is commonly employed in healthcare sector where both quick and precise diagnosis can be supplied. Deep learning algorithms have proved extraordinary capabilities in terms of lung diseases detection and classification. They facilitate and expedite the diagnosis process and save time for the medical practitioners. In this paper, a deep learning (DL) architecture for multi-class classification of Pneumonia, Lung Cancer, tuberculosis (TB), Lung Opacity, and most recently COVID-19 is proposed. Tremendous CXR images of 3615 COVID-19, 6012 Lung opacity, 5870 Pneumonia, 20,000 lung cancer, 1400 tuberculosis, and 10,192 normal images were resized, normalized, and randomly split to fit the DL requirements. In terms of classification, we utilized a pre-trained model, VGG19 followed by three blocks of convolutional neural network (CNN) as a feature extraction and fully connected network at the classification stage. The experimental results revealed that our proposed VGG19 + CNN outperformed other existing work with 96.48 % accuracy, 93.75 % recall, 97.56 % precision, 95.62 % F1 score, and 99.82 % area under the curve (AUC). The proposed model delivered superior performance allowing healthcare practitioners to diagnose and treat patients more quickly and efficiently.

Validation and Cultural Adaptation of the Parent Attitudes about Childhood Vaccines (PACV) Questionnaire in Arabic Language Widely Spoken in a Region with a High Prevalence of COVID-19 Vaccine Hesitancy.

The parents' attitude toward vaccinating children and adolescents against coronavirus disease 2019 (COVID-19) remains inconsistent and needs further elucidation. The high rates of COVID-19 vaccine hesitancy in the Middle East and North Africa (MENA) region require intensive research to understand the determinants of this phenomenon. This study aimed to validate a version of the Parent Attitudes about Childhood Vaccines (PACV) tool in Arabic, the most widely spoken language in the MENA. The study objectives included the investigation of Arab-speaking parents' views regarding COVID-19 vaccination of their children. Parents living in Egypt with at least one child aged 5-18 years were eligible to participate in the study that was conducted through an online survey with 15 PACV items. The PACV tool was translated into Arabic using forward and backward translation. To assess the psychometric properties of the Arabic version of PACV, Pearson's correlation coefficient and exploratory and confirmatory factor analysis (EFA and CFA) were performed. A total of 223 parents participated in the study: 59.82% aged 30-39 years, 69.20% were females, 46.19% were university-educated, and 40.63% had one child. The overall Cronbach's alpha for the Arabic version of PACV was 0.799. The EFA of the 15 items showed that three domains were most conceptually equivalent. All items had a positive significant correlation with the mean score of each subscale except for item 4 (r = 0.016, p = 0.811). Regression analyses results indicated that education, previous COVID-19 infection, vaccine status of parents, and PACV score were significantly associated with the intention of the parents to vaccinate their children against COVID-19. The CFA results showed that most of the factor loadings were statistically significant (p < 0.010) except for items 4 and 7. However, the root mean square error of approximation (RMSEA = 0.080) and the standardized root mean squared residual (SRMR = 0.080) indicated that the model had a reasonable fit, and the three factors were good in reproducing each correlation. Our study results indicated the validity and reliability of the PACV instrument in Arabic language. Consequently, the PACV can be used to assess COVID-19 vaccine hesitancy in a majority of MENA countries for better delineation of this highly prevalent phenomenon in the region.

Deep-Risk: Deep Learning-Based Mortality Risk Predictive Models for COVID-19.

The SARS-CoV-2 virus has proliferated around the world and caused panic to all people as it claimed many lives. Since COVID-19 is highly contagious and spreads quickly, an early diagnosis is essential. Identifying the COVID-19 patients' mortality risk factors is essential for reducing this risk among infected individuals. For the timely examination of large datasets, new computing approaches must be created. Many machine learning (ML) techniques have been developed to predict the mortality risk factors and severity for COVID-19 patients. Contrary to expectations, deep learning approaches as well as ML algorithms have not been widely applied in predicting the mortality and severity from COVID-19. Furthermore, the accuracy achieved by ML algorithms is less than the anticipated values. In this work, three supervised deep learning predictive models are utilized to predict the mortality risk and severity for COVID-19 patients. The first one, which we refer to as CV-CNN, is built using a convolutional neural network (CNN); it is trained using a clinical dataset of 12,020 patients and is based on the 10-fold cross-validation (CV) approach for training and validation. The second predictive model, which we refer to as CV-LSTM + CNN, is developed by combining the long short-term memory (LSTM) approach with a CNN model. It is also trained using the clinical dataset based on the 10-fold CV approach for training and validation. The first two predictive models use the clinical dataset in its original CSV form. The last one, which we refer to as IMG-CNN, is a CNN model and is trained alternatively using the converted images of the clinical dataset, where each image corresponds to a data row from the original clinical dataset. The experimental results revealed that the IMG-CNN predictive model outperforms the other two with an average accuracy of 94.14%, a precision of 100%, a recall of 91.0%, a specificity of 100%, an F1-score of 95.3%, an AUC of 93.6%, and a loss of 0.22.

Retinal Manifestations in Rhinoorbito-cerebral Mucormycosis: A Cross-sectional Study

Introduction: Rhino-orbito-cerebral Mucormycosis (ROCM) is an uncommon but devastating fungal infection caused by Mucoraceae family fungi, which are angiotropic and filamentous, with significantly high morbidity and mortality despite treatment. Post Coronavirus Disease-2019 (COVID-19), there was a sudden surge in ROCM cases nationwide due to immunologically and metabolically compromised status. Aim: To describe retinal manifestations in ROCM in a tertiary eye care centre of Northern India. Materials and Methods: An analytic, cross-sectional and hospital-based study was conducted in Regional Institute of Ophthalmology, PGIMS Rohtak, Haryana, India, from May 2021 to September 2021. This study was conducted on 200 admitted patients of RCOM in the institute, which was only designated Nodal centre in Haryana, India. Detailed history was recorded in every patient regarding presenting symptoms, history of COVID-19, hospital stay, oxygen inhalation, steroid intake and immunisation. Thorough ocular examination was done in every patient including visual acuity, ocular movements and pupillary reactions. Dilated fundus examination was done by Indirect Ophthalmoscopy (IDO) for posterior segment evaluation. Contrast Enhanced Magnetic Resonance Imaging (CE-MRI) brain with orbit and Paranasal Sinus (PNS) was done in every patient to see the extent of spread and planning further management. Results: Out of 200 patients of ROCM, majority of patients (64/200) were of 51-60 year age group (32%) followed by 41-50 year age group (28%). Out of 200 cases of ROCM, 146 patients (73%) had history of COVID-19 infection in past and 134 (67%) patients had history of hospital stay during COVID-19 infection. Oxygen (O2) supplementation was given to 98 patients either at home or during hospital stay. History of steroid intake was present in 34 patients and 46 patients received injection Remedsivir. Only 12 patients had vaccine against COVID-19 and none of them were fully vaccinated. Most common presenting symptom was unilateral nasal stiffness (22%) followed by loss of vision (17%). Most common predisposing factor was Diabetes Mellitus (DM) in 78 patients (39%) followed by steroid intake in 34 patients (17%). Out of 200 patients, only 60 patients had retinal manifestations and most common was Central Retinal Artery Occlusion (CRAO) (35/60) and the main mechanism is the direct infiltration of central retinal artery due to angioinvasion of fungi from the orbit. Conclusion: CE-MRI brain with orbit is an important tool in diagnosing and monitoring progression of RCOM but it cannot provide information regarding retinal findings like CRAO, central retinal venous occlusion (CRVO), disc pallor and optic atrophy. Thus, the fundus examination of every ROCM patient should be emphasised, as it not only helps in categorising ROCM but also tells about the visual potential of affected eye. Patients with CRAO and combined vascular occlusion should be considered for exenteration on urgent basis, so that intracranial spread can be prevented and patient's life can be saved.

Sentiment Analysis of COVID-19 Tweets Using Adaptive Neuro-Fuzzy Inference System Models

In today's digital era, Twitter's data has been the focus point among researchers as it provides specific data in a wide variety of fields. Furthermore, Twitter's daily usage has surged throughout the coronavirus disease (COVID-19) period, presenting a unique opportunity to analyze the content and sentiment of COVID-19 tweets. In this paper, a new approach is proposed for the automatic sentiment classification of COVID-19 tweets using the adaptive neuro-fuzzy inference system (ANFIS) models. The entire process includes data collection, pre-processing, word embedding, sentiment analysis, and classification. Many experiments were accomplished to prove the validity and efficiency of the approach using datasets COVID-19 tweets, and it accomplished the data reduction process to achieve considerable size reduction with the preservation of significant dataset's attributes. The experimental results indicate that fuzzy deep learning achieves the best accuracy (i.e., 0.916) with word embeddings.

Rheumatologists' knowledge and perception of COVID-19 and related vaccines: the vaXurvey2 online survey.

The study aimed to explore the experience of coronavirus disease-2019 (COVID-19) infection and vaccine adverse events (AEs) among rheumatologists. A validated questionnaire was distributed as a Google form to rheumatologists across the country via social networking sites from late December 2021 till early January 2022. The questionnaire included questions regarding participants' socio-demographic details, COVID-19 infection and vaccination details with special emphasis on AEs. Out of 246 responses, 228 were valid. 200 (81.3%) responders had received the vaccine. The mean age of the 228 participants was 37.9 ± 8.5 years, 196 were females and 32 males (F:M 6.1:1) from 18 governorates across the country. Comorbidities were present in 54 subjects (27%). There was a history of highly suspicious or confirmed COVID-19 infection in 66.7% that were all managed at home. The COVID-19 vaccine was received by 200 and a booster dose of 18.5%. Obesity and musculoskeletal involvement co-morbidities were present only in those with AEs (9.1% and 5.5% respectively). AEs were present in 82%; 66.7% had injection-site tenderness, 50% fatigue, 35.5% fever, 15% chills, 42.5% myalgia, 14.5% arthralgia, 8% low back pain, headache 31%, dizziness 10%, sleepliness 16% and 15% developed post-vaccine. There were no differences according to the geolocation regarding the occurrence of COVID-19 infection (p = 0.19) or AEs post-vaccine (p = 0.58). The adverse events were mostly mild to moderate and tolerable which makes this work in agreement with other studies that support the broad safety of the vaccine in favor of the global benefit from mass vaccination.

Multimodality imaging in COVID19 patient with large mobile RV thrombus protruding into the pulmonary trunk with bilateral pulmonary embolism.

Inflammation related to coronavirus disease (COVID)-19 can promote a pro-thrombotic process and subsequent thrombosis. We report a confirmed COVID-19 case in a 51-year-old patient who presented with chest pain and severe hypoxemia. Although the right heart chambers are unusual locations for a thrombus, an echocardiogram demonstrated a large mobile right ventricular thrombus protruding into the right ventricle outflow tract. A computed tomography angiogram and cardiac magnetic resonance image showed the extension of the thrombus into the pulmonary trunk. A continuous intravenous unfractionated heparin infusion resulted in a dramatic clinical and echocardiographic improvement. .

COVID-19 related posttraumatic stress disorder in children and adolescents in Saudi Arabia.

INTRODUCTION: The COVID-19 pandemic resulted in quarantine/lockdown measures in most countries. Quarantine may create intense psychological problems including post-traumatic stress disorder (PTSD) especially for the vulnerable critically developing children/adolescents. Few studies evaluated PTSD associated with infectious disasters but no Saudi study investigated PTSD associated with COVID-19 in children/adolescents. This study was undertaken to screen for PTSD in children/adolescent in Saudi Arabia to identify its prevalence/risk factors during COVID-19 pandemic and its quarantine. METHODS: A cross-sectional survey was conducted after 2 months form start of quarantine for COVID-19 pandemic utilizing the original English version and an Arabic translated version for the University of California at Los Angeles Brief COVID-19 Screen for Child/Adolescent PTSD that can be parent-reported or self-completed by older children/adolescents. Participants (Saudi citizens/non-Saudi residents) were approached online via social media. RESULTS: Five hundred and thirty seven participants were enrolled. The participants were 262 boys and 275 girls with a mean age of 12.25±3.77 years. Symptoms of no, minimal, mild and potential PTSD were identified in 15.5%, 44.1%, 27.4% and 13.0% of children/adolescents, respectively. The age, gender, school grade, and residence were not predictive of PTSD symptoms. Univariate analysis of risk factors for PTSD revealed that work of a close relative around people who might be infected was significantly different between groups of PTSD symptoms, but this difference disappeared during multivariate analysis. Children/adolescents of Saudi citizens had significantly lower median total PTSD score than children/adolescents of expatriate families (p = 0.002). CONCLUSION: PTSD associated with the COVID-19 and its resultant quarantine shouldn't be overlooked in different populations as it is expected in a considerable proportion of children/adolescents with variable prevalence, risk factors and severity. Parents/healthcare providers must be aware of PTSD associated with COVID-19 or similar disasters, so, they can provide children/adolescent with effective coping mechanisms.

Formulation and Evaluation of Alcohol-Free Hand Sanitizer Gels to Prevent the Spread of Infections during Pandemics.

Hand hygiene is an essential factor to prevent or minimize the spread of infections. The ability to prepare an alcohol-free hand sanitizer (AFHS) with antimicrobial properties is crucial, especially during pandemics, when there are high demands and a low supply chain for ethanol and isopropanol. The objective of this study was to prepare AFHS gels based on natural materials that contain essential oils (EOs) that would be effective against a broad spectrum of pathogens. The results showed that the organoleptic characteristics of all prepared hand sanitizer gels were considered acceptable. The pH of the formulations was slightly acidic (circa 3.9) owing to the presence of aloe vera in large proportions (90% v/v), which is known for its acidity. The spreadability for all tested formulations was in the acceptable range. The antimicrobial effectiveness test demonstrated that the prepared hand sanitizer gels had antimicrobial activities against different gram-positive and gram-negative bacteria and Candida albicans yeast. The highest antibacterial effect was observed with tea tree oil hand sanitizers, which lack activity against the yeast, while clove oil hand sanitizers showed effectiveness against all microorganisms, including Candida albicans. The lavender hand sanitizer exhibited the least antimicrobial efficiency. The acceptability study on 20 human volunteers showed that the hand sanitizer gel containing 1.25% (v/v) clove oil did not produce any signs of skin irritation. This study suggested that the prepared natural hand sanitizer gel with 1.25% (v/v) clove oil can be a potential alternative to commonly used alcohol-based hand sanitizers (ABHS).