Levels and Trends of Maternal Mortality and Morbidity by Cause in North Africa and Middle East, 1990 to 2019: An Analysis for the Global Burden of Disease Study 2019

Background: Since 1990, the maternal mortality significantly decreased at global scale as well as the North Africa and Middle East. However, estimates for mortality and morbidity by cause and age at national scale in this region are not available. Method(s): This study is part of the Global Burden of Diseases, Injuries, and Risk Factors study (GBD) 2019. Here we report maternal mortality and morbidity by age and cause across 21 countries in the region from 1990 to 2019. Result(s): Between 1990 and 2019, maternal mortality ratio (MMR) dropped from 148.8 (129.6-171.2) to 94.3 (73.4-121.1) per 100 000 live births in North Africa and Middle East. In 1990, MMR ranged from 6.0 (5.3-6.8) in Kuwait to 502.9 (375.2-655.3) per 100 000 live births in Afghanistan. Respective figures for 2019 were 5.1 (4.0-6.4) in Kuwait to 269.9 (195.8-368.6) in Afghanistan. Percentages of deaths under 25 years was 26.0% in 1990 and 23.8% in 2019. Maternal hemorrhage, indirect maternal deaths, and other maternal disorders rank 1st to 3rd in the entire region. Ultimately, there was an evident decrease in MMR along with increase in socio-demographic index from 1990 to 2019 in all countries in the region and an evident convergence across nations. Conclusion(s): MMR has significantly declined in the region since 1990 and only five countries (Afghanistan, Sudan, Yemen, Morocco, and Algeria) out of 21 nations didn't achieve the Sustainable Development Goal (SDG) target of 70 deaths per 100 000 live births in 2019. Despite the convergence in trends, there are still disparities across countries.Copyright © 2022 Academy of Medical Sciences of I.R. Iran. All rights reserved.

Predicting Crude Oil Future Price Using Traditional and Artificial Intelligence-Based Model: Comparative Analysis

Crude oil is an imperative energy source for the global economy. The future value of crude oil is challenging to anticipate due to its nonstationarity in nature. The focus of this research is to appraise the explosive behavior of crude oil during 2007-2022, including the most recent influential crisis COVID-19 pandemic, to forecast its prices. The crude oil price forecasts by the traditional econometric ARIMA model were compared with modern Artificial Intelligence (AI)-based Long Short-Term Memory Networks (ALSTM). Root mean square error (RMSE) and mean average percent error (MAPE) values have been used to evaluate the accuracy of such approaches. The results showed that the ALSTM model performs better than the traditional econometric ARIMA forecast model while predicting crude oil opening price on the next working day. Crude oil investors can effectively use this as an intraday trading model and more accurately predict the next working day opening price.

Program Development Skills Towards Work Performance of Extension Agent During Movement Control Order (MCO) in Peninsular Malaysia

Coronavirus disease (COVID-19), an infectious disease made Malaysia implemented a Movement Control Order (MCO) as a preventive measure towards the spread of the virus. Malaysia's Gross Domestic Product (GDP) in agriculture sector (2020) is 7.4 per cent, the percentage growth of this sector declined 2.2 per cent from 2.0 per cent in the previous year. The declination of the growth may be related with performance of extension agent during the pandemic due to 1st MCO regulations. In addition, the performance before MCO was high in 2019, hence, the aim of this study is to determine the skills and work performance of extension agents in their Program Development Skills (planning, implementing, monitoring and evaluating - PIME). Specifically, this research intended to determine the level of PIME skills and their work performance, to evaluate the relationship of PIME skills with the work performance of extension agent and to determine the most PIME skills that contributes to the work performance of the extension agent during MCO in Peninsular Malaysia. This study was driven by the Iceberg Model and distributed using a random sampling technique. A total of 362 extension agents from Peninsular Malaysia were participated in this research. Based on the result, all independent variables (PIME skills) indicated a positive correlation towards work performance. The monitoring and evaluating are the skills that significant towards work performance, and the evaluating skill became the highest independent variable that contributes to the work performance of extension agent. About 71.2% variance of work performance is explained by PIME and the balance 28.8% is explained by the other factors. This study suggest that the extension agent should improve their planning and implementing skills to suite with pandemic situation, so that the program that has been planned earlier can be done even though the situation might be challenging © 2023 by the authors. Licensee ResearchersLinks Ltd, England, UK

Deep Learning Based Automated Chest X-ray Abnormalities Detection

Abnormalities related to the chest are a fairly common occurrence in infants as well as adults. The process of identifying these abnormalities is relatively easy but the task of actually classifying them into specific labels pertaining to specific diseases is a much harder endeavour. COVID-19 sufferers are multiplying at an exponential rate, putting pressure on healthcare systems all around the world. Because of the limited number of testing kits available, it is impractical to test every patient with a respiratory ailment using traditional methods. Thus in such dire circumstances, we propose the use of modern deep learning techniques to help in the detection and classification of a number of different thoracic abnormalities from a chest radiograph. The goal is to be able to automatically identify and localize multiple points of interest in a provided chest X-ray and act as a second level of certainty after the radiologists. On our publically available chest radiograph dataset, our methods resulted in a mean average precision of 0.246 for the detection of 14 different thoracic abnormalities. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Integrated Framework for COVID-19 Classification Based on Ensembles of Deep Features and Entropy Coded GLEO Feature Selection

COVID-19 is a challenging worldwide pandemic disease nowadays that spreads from person to person in a very fast manner. It is necessary to develop an automated technique for COVID-19 identification. This work investigates a new framework that predicts COVID-19 based on X-ray images. The suggested methodology contains core phases as preprocessing, feature extraction, selection and categorization. The Guided and 2D Gaussian filters are utilized for image improvement as a preprocessing phase. The outcome is then passed to 2D-superpixel method for region of interest (ROI). The pre-trained models such as Darknet-53 and Densenet-201 are then applied for features extraction from the segmented images. The entropy coded GLEO features selection is based on the extracted and selected features, and ensemble serially to produce a single feature vector. The single vector is finally supplied as an input to the variations of the SVM classifier for the categorization of the normal/abnormal (COVID-19) X-rays images. The presented approach is evaluated with different measures known as accuracy, recall, F1 Score, and precision. The integrated framework for the proposed system achieves the acceptable accuracies on the SVM Classifiers, which authenticate the proposed approach's effectiveness. © World Scientific Publishing Company.

ECOTOURISM ACTIVITIES, HOMESTAY RECOVERY PLAN AND STRATEGY INDICATORS IN POST COVID-19 KUNDASANG, SABAH, MALAYSIA

The purpose of this article is to discuss strategies and recovery plans for community-based ecotourism and homestays following the Covid-19 pandemic Movement Control Order (MCO), particularly in Kampung Mesilou, Kundasang, Sabah. The spread of the Covid-19 virus has had a significant impact, particularly on the tourism industry in Malaysia and, more specifically, on the ecotourism sector in the state of Sabah. The implementation of MCO in Malaysia, which aims to restrict population movement, has had a negative impact on the tourism sector, as all of them were ordered to halt operations completely. As a result, the question of the strategies and recovery plans implemented by ecotourism and homestay operators to restore the ecotourism sector, particularly in Kampung Mesilou, arises. Therefore, the main research approach in this study is qualitative and based on primary data. The primary data used were the results of in-field informant interviews, which were supplemented by secondary data from journal articles. The study's findings revealed that after the government announced the relaxation of the MCO, the communities in the area took the initiative to re-promote their ecotourism activities widely through social media, etc. One of the entrepreneurs' strategies for attracting tourists in their shorter and medium plans is the addition of new ecotourism products and the improvement of the quality of homestay facilities recently. © 2022 by MIP.

A design concept of big data analytics model for managers in hospitality industries.

The hospitality and tourism sector has long played a significant role in Australia's economy, especially in regional areas. Due to the onslaught of COVID-19, numerous businesses have experienced lockdowns, restrictions, and closures due to the fact that people's activity in restaurants, shopping centers, and recreational destinations was restricted, and many other places went into hibernation. After about 2 years since the outbreak, businesses in this sector are gradually starting to reopen and revitalize themselves, but in order to have better decision support about the future of this sector, thus being able to plan, businesses are suffering from an effective analytics solution due to the lack of broken data trends. Starting from fresh day-to-day real-time big data, the study aims to develop a new data analytics model, adopting the design science research methodology, which can provide invaluable options and techniques to make prediction easier from immediate past datasets. This study introduces an innovative design artifact as a big data solution for hospitality managers to utilize analytics for predictive strategic decision-making in post-COVID situation. The artifact can also be generalized for other sectors with tailoring aspects which are subject to further studies. The proposed artifact is then compared with other design artifacts related to big data solutions where it outperforms them in terms of comprehensiveness. The proposed artifact also shows promises for primarily available UGC in managers' decision support aids.

Detection of SARS-CoV-2 IgG antibodies and inflammatory cytokines in saliva-a pilot study.

Objective: The pandemic caused by SARS-CoV-2 virus continues to have a profound effect worldwide. However, COVID-19 induced oral facial manifestations have not been fully described. We conducted a prospective study to demonstrate feasibility of anti-SARS-CoV-2 IgG and inflammatory cytokine detection in saliva. Our primary objective was to determine whether COVID-19 PCR positive patients with xerostomia or loss of taste had altered serum or saliva cytokine levels compared to COVID-19 PCR positive patients without those oral symptoms. Our secondary objective was to determine the correlation between serum and saliva COVID-19 antibody levels. Materials and methods: For cytokine analysis, saliva and serum were obtained from 17 participants with PCR-confirmed COVID-19 infection at three sequential time points, yielding 48 saliva samples and 19 paired saliva-serum samples from 14 of the 17 patients. For COVID-19 antibody analyses, an additional 27 paired saliva-serum samples from 22 patients were purchased. Results: The saliva antibody assay had 88.64% sensitivity [95% Confidence Interval (CI) 75.44%, 96.21%] to detect SARS-CoV-2 IgG antibodies compared to serum antibody. Among the inflammatory cytokines assessed - IL-6, TNF-α, IFN-γ, IL-10, IL-12p70, IL-1ß, IL-8, IL-13, IL-2, IL-5, IL-7 and IL-17A, xerostomia correlated with lower levels of saliva IL-2 and TNF-α, and elevated levels of serum IL-12p70 and IL-10 (p < 0.05). Loss of taste was observed in patients with elevated serum IL-8 (p < 0.05). Conclusions: Further studies are needed to construct a robust saliva-based COVID-19 assay to assess antibody and inflammatory cytokine response, which has potential utility as a non-invasive monitoring modality during COVID-19 convalescence.

COVID-19 pandemic: What is known till June 2020 and what is yet to know?

Since first cluster of unknown pneumonia from China reported in December 2019 to World Health Organization, more than 10.5 million new cases and more than 0.513 million deaths have been reported till June 30, 2020 in six months' time. World got to know lot of facts about COVID-19 within short period of six months and success stories too concerning its containment. The situation has constantly been unfolding every moment educating people regarding public health and clinical aspects of the infection and disease and its impact on countries and communities. But still lot of information and evidences are required with regard to pharmacological interventions including effective drugs and efficacious vaccine to mitigate the impact of COVID-19 pandemic at all levels. It seems that we have to live with COVID-19 months-years as the virus is going to stay for longer period of time. The option is to continue practice of effective non-pharmacological interventions as to minimize spread of COVID-19 and ensure adequate provision of PPE to healthcare workforce and testing of health-care workers (HCWs) as to alleviate the anxiety of HCW and lessen their depletion by unnecessary quarantine thereby protect their health and reduce in hospital transmission. Copyright © 2020, Kathmandu University. All rights reserved.

Engineering Student's Flexibility for Learning During Education Disruption

Due to the recent education disruption, engineering-related module classes have to rapidly and effectively move online because of unpredictable changes. For design-related technical modules, not much literature focused on how students and tutors can adopt the latest technologies in a relatively short span. This paper is an effort to find students' experiences and preferences around various interactive educational tools used in online synchronous teaching, such as interactive live zoom lectures, slide annotations, breakout rooms, recorded videos, and many more, which have been used at the University of Glasgow, Singapore, for the module known as Design and Manufacture 1, during the 2021 COVID-19 crisis and beyond. From this work, we were able to find how an online synchronous learning approach affects design engineering students' learning experience. To understand students' perception of online learning tools to be effective in enhancing their learning during a sudden change in the arrangement of physical classes to online classes due to the pandemic situation. Survey results were collected using google forms at the end of the trimester, which was offered to 65 students enrolled in the module based on the student experience. The response rate is around 70%. The survey result showed that students engaged very well with the technologies and took little time to adjust to online learning. Students found learning very comfortable using the latest online teaching tools during their online learning journey in the design engineering module. © 2022 IEEE.

Face Recognition Attendance System for Online Classes

Due to the increasing need for online lectures due to situations like COVID-19 and various online learning platforms, there is a need for a reliable attendance system for online classes. Our system is developed for deploying an easy and secure way of taking attendance without tedious roll-calls and inaccurate participant lists. The teachers will take screenshots of students in the online meets with their video cameras on. This screenshot will be uploaded to our system which will recognize the students and generate a report of their attendance. Our system will allow students and faculty to view the attendance of each lecture and give feedback if any discrepancy is found. We collected the dataset of face images of 94 students which are then augmented to increase the dataset and then used HOG for face detection. We then applied four algorithms namely VGG-16, MobileNet, InceptionV3, and our own CNN model for face recognition. MobileNet gave us the highest accuracy of 97.14%. We, therefore, deployed this model for our website to recognize faces and generate the attendance report. © 2022 IEEE.

SARS-CoV-2 Illness Severity and Early Hospitalization Outcomes in a Multicenter Prospective Cohort Study Among Veterans

Background. Over 600,000 SARS-CoV-2 infections and 20,000 deaths have occurred among users of the Veterans Health Administration, the US's largest integrated health care system. We explored early outcomes of SARS-COV-2 infection in Veterans. Methods. An ongoing, prospective longitudinal cohort study of Veterans ages >= 18 enrolled 1,826 participants (29.0% inpatient;49.1% vaccinated;68.3% SARS-CoV-2-positive;85.0% male, mean age = 57.1 years) seeking inpatient or outpatient care after SARS-CoV-2 testing at 15 Department of Veterans Affairs medical centers in July 2020 to February 13, 2022. Using multivariable regression, we estimated relationships of baseline demographic characteristics, COVID-19 vaccination, and clinical history to illness severity and cumulative length of hospital stay within 60 days of study entry. Illness severity was defined by a Veterans Affairs adaptation of the WHO COVID-19 severity scale and included 4 levels (mild, moderate, severe, or death). We derived the Charlson co-morbidity index (CCI) and other baseline characteristics from electronic health data and study questionnaires, and reported qualitative SARS-CoV-2 IgG responses using inpatients' study-collected blood specimens. Results. High CCI scores (>= 5) occurred in 47 (42.7%) vaccinated SARS-CoV-2-positive inpatients and 47 (21.2%) unvaccinated. Severe illness occurred in 17 (15.5%) vaccinated inpatients, 37 (16.7%) unvaccinated inpatients, 4 (0.9%) vaccinated outpatients, and 3 (0.7%) unvaccinated outpatients. Eleven (10%) of 110 vaccinated SARS-CoV-2-positive inpatients died, as did 15 (6.8%) of the 222 unvaccinated. In SARS-CoV-2-positive inpatients, a one-step higher CCI was associated with more severe illness (aOR 1.10, 95%CI 1.01-1.20) and more hospitalization days (aIRR 1.06, 95% CI 1.03-1.10), adjusting for vaccination status. Respectively, 93% of vaccinated and 63% of unvaccinated SARS-CoV-2 positive inpatients with baseline antibody results had an anti-spike IgG response. Conclusion. In an ongoing longitudinal cohort study of COVID-19 in US Veterans, comorbidity burden was higher among vaccinated than unvaccinated inpatients and was associated with more severe illness and hospitalization days, independent of vaccination status.

Role of Tocilizumab in Patients with Moderate-to-Severe Covid-19 Pneumonia

Objectives: Cytokine release syndrome (CRS) is believed to be responsible for death in COVID-19. Tocilizumab is an interleukin (IL)-6 receptor antagonist, IL-6 being identified as a major component of the CRS cascade. The objective of the study was to determine if tocilizumab can prevent mortality and morbidity in moderate-to-severe COVID-19 pneumonia. Method(s): Patients admitted to the ICU between the time period of June 2020-August 2020 were included in this retrospective and cohort study conducted at GCS medical college, hospital and research center. Patients had to be more than 18 years of age and were required to have a positive reverse transcription polymerase chain reaction report for COVID-19. After applying the inclusion/exclusion criteria, 119 patients were considered for final analysis. Tocilizumab was administered as a single dose of 8 mg/kg in 22 patients. Rest of the patients received standard of care regime. The primary outcome was either discharge or death of the patients and the requirement of invasive mechanical ventilation during their hospital stay. The secondary outcome was the length of hospital stay. Appropriate demographic, clinical, and laboratory data were documented. Statistical analysis was done with appropriate clinical tests with significance set at p<0.05. Result(s): Tocilizumab significantly reduced deaths in patients as well as the need for mechanical ventilation with NNT=3 and 5, respectively. The same held true even when the data were adjusted for age, gender, and number of comorbidities. Number of comorbidities had a negative association with mortality and need for mechanical ventilation irrespective of administration of tocilizumab as evidenced by multivariable logistic regression. There was no effect of tocilizumab in shortening the hospital stay in patients. Conclusion(s): Tocilizumab seems to be a promising agent for the treatment of moderate to severe COVID-19 pneumonia and similar agents hold promise for any similar future emerging infections. Copyright © 2022 The Authors.

HealthyGAN: Learning from Unannotated Medical Images to Detect Anomalies Associated with Human Disease

Automated anomaly detection from medical images, such as MRIs and X-rays, can significantly reduce human effort in disease diagnosis. Owing to the complexity of modeling anomalies and the high cost of manual annotation by domain experts (e.g., radiologists), a typical technique in the current medical imaging literature has focused on deriving diagnostic models from healthy subjects only, assuming the model will detect the images from patients as outliers. However, in many real-world scenarios, unannotated datasets with a mix of both healthy and diseased individuals are abundant. Therefore, this paper poses the research question of how to improve unsupervised anomaly detection by utilizing (1) an unannotated set of mixed images, in addition to (2) the set of healthy images as being used in the literature. To answer the question, we propose HealthyGAN, a novel one-directional image-to-image translation method, which learns to translate the images from the mixed dataset to only healthy images. Being one-directional, HealthyGAN relaxes the requirement of cycle-consistency of existing unpaired image-to-image translation methods, which is unattainable with mixed unannotated data. Once the translation is learned, we generate a difference map for any given image by subtracting its translated output. Regions of significant responses in the difference map correspond to potential anomalies (if any). Our HealthyGAN outperforms the conventional state-of-the-art methods by significant margins on two publicly available datasets: COVID-19 and NIH ChestX-ray14, and one institutional dataset collected from Mayo Clinic. The implementation is publicly available at https://github.com/mahfuzmohammad/HealthyGAN.

Assessing total cost of care and anticoagulant therapy in commercial health plan members with newly diagnosed non-valvular atrial fibrillation

BACKGROUND: Over 454,000 hospitalizations have been associated with atrial fibrillation (AF) as a primary diagnosis . Guideline treatment of AF may improve outcomes and subsequently, reduce healthcare resource utilization (HCRU) and total cost of care (TCOC). Previous studies have assessed cost and HCRU in the Medicare population, but there is limited published data on the commercial population. OBJECTIVE(S): This study's primary objective was to measure TCOC in newly diagnosed non-valvular atrial fibrillation (NVAF) patients within a commercial health plan. METHOD(S): This retrospective case-control study used a commercial health plan claims database to identify members diagnosed with incident NVAF between January 1, 2018, and December 31, 2018 (first diagnosis was index) with 12-month continuous enrollment pre- and post-index and baseline CHA2DS2-VASc >= 2 (N = 1,717). This study period was chosen to capture pre-COVID-19 data. Members with >= 1 claim for an oral anticoagulant (OAC) on or after the index date (treated cohort) were compared to an untreated cohort. Inverse probability of treatment weighting was used to adjust for differences in baseline characteristics. Costs were assessed for medical and pharmacy utilization over a 12-month period. RESULT(S): Compared to the untreated cohort (n = 860), the treated cohort (n = 857, 49.9%) had higher mean - inpatient (IP) costs ($34,023 vs $25,135), emergency room costs ($3,861 vs $2,375), pharmacy costs ($9,054 vs $5,222) and TCOC costs ($69,489 vs $43,950). A higher IP diagnosis of NVAF was observed in the treated cohort compared to the untreated cohort (18.2% vs 3.6%). Rates of stroke were higher in the treated cohort compared to the untreated cohort (3.27% vs 0.14%). Among those receiving an OAC (treated cohort), 67.7% had a treatment duration of <=180 days during the 12-month follow-up period. CONCLUSION(S): Using nationwide commercial claims data, the study showed TCOC was higher for those treated with OAC compared to patients not treated with OAC which prompted additional analyses to better explain the findings. Although recognized AF management guidelines are available, recommended treatment with OAC in patients at high risk for stroke remains suboptimal (50%) with limited duration of therapy. Key study limitations include small sample size and potential channeling bias. The results provide previously unreported data for a younger population with commercial insurance and contribute to a growing body of data showing a gap in care in patients with NVAF. Study outcomes highlight an opportunity for improved care management and better communication with providers and patients along the care pathway.

Assessment of attitude and hesitancy toward vaccine against COVID-19 in a Pakistani population: A mix methods survey Evaluación de la actitud y la renuencia hacia la vacuna frente a la COVID-19 en una población paquistaní: encuesta de método mixto

Objective The objective of this study was to assess the attitude and hesitancy toward vaccine against COVID-19 in a Pakistani Population. Materials and methods A mix-method, prospective study was conducted and adults (aged ≥18 years) residing in Pakistan were invited to participate. The questionnaire was prepared, hosted in Google Forms and circulated through electronic platforms and was also available to be done in in-person. Data was compiled from 15th September to 30th November 2020. Results The response rate was 80%. A total of 1003 participants were included in the final analysis. Of them, 75% completed survey questionnaire online, while remaining 25% responded in-person. The mean age of the participants was 29.62 ± 10.47 years. The majority of participants were females;60.9% (n = 611). 57.02% (n = 572) of the participants were employed at the time of survey. Overall, 70.68% (n = 709) of the participants had previous experience of vaccines such as the flu vaccine Only 4.9% (n = 49) participants thought that they will be seriously ill from COVID-19 within six months and 39% (n = 392) participants were confident that they will get COVID-19. A total of 71.29% of the participants reported they would consider getting vaccinated once available. There was statistical association between gender and getting vaccinated (P < 0.001). Conclusion This study demonstrated that majority of the participants showed positive attitude toward considering COVID-19 vaccine. However awareness with informed knowledge of efficacy, possible adverse effects and cost would be of added great value to increase the real response of Pakistani population toward COVID-19 vaccination.

Perinatal COVID-19 Infection and Outcomes: A Retrospective Observational Study from a Low–Middle Income Setting

Aim: Coronavirus disease 2019 (COVID-19) pandemic is an ongoing emergency with limited data on perinatal outcomes. The aim of the study was to describe key maternal, perinatal, and neonatal outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection from low–middle income settings. Materials and methods: We conducted a retrospective observational study in a tertiary level public hospital in India. All pregnant women admitted from May 2020 to July 2020 were included in the study. Maternal demography, medical and obstetric complications, clinical characteristics, and investigations were described. Symptomatic infected women were compared with the asymptomatic group for important outcomes. Key perinatal outcomes such as early pregnancy losses, fetal distress, stillbirths, and placental changes were evaluated. Neonatal characteristics of SARS-CoV-2 positive and negative pregnancies were described and compared. Results: Among the 702 pregnant women enrolled, the incidence of SARS-CoV-2 infection was 16.2%, with the majority being asymptomatic. Infected women had an increased mortality, while symptomatic women had a significant risk of stillbirth. Mean placental weight of infected women was significantly higher. Neonatal infection rate was 9.1%, with 50% manifesting mild respiratory symptoms without any mortality. Conclusion: This study provides a comprehensive description of important antenatal, intrapartum and neonatal complications and outcomes in a low–middle income setting characterized by high disease burden and an overwhelmed health care system. Clinical significance: Incidence of SARS-CoV-2 infection in pregnancy was 16.2%. The symptomatic infected women had increased stillbirth and mortality. Neonatal transmission was seen in 9.1% with good survival.

GLASSY LUNGS, DRUGS AND MELENIC RUNS, A CASE REPORT

CASE: A 73-year-old male with a history of prostate cancer, hypertension and hyperthyroidism presented with one week of worsening dyspnea, productive cough and pleurisy. He also endorsed new orthopnea and melena over the last three days. Home medications included abiraterone, prednisone, methimazole and amlodipine. On admission, vitals were notable for tachycardia, tachypnea and hypoxia (82% on room air and 90% on 3L by nasal canula (NC)). Initials labs showed WBC count 17.4, Hemoglobin 7.1, proBNP 256, two negative COVID-19 PCR tests, negative respiratory virus panel and normal TSH and PSA. CTPE was negative for pulmonary embolism but showed new diffuse ground glass opacities. The patient was started on broad spectrum antibiotics and IV diuretics for possible pneumonia and new heart failure. However, the patient's respiratory status continued to decline, now requiring 6L by NC. Hemoglobin also continued to drop precipitously. A broad rheumatologic and infectious workup was largely negative with findings notable for a positive ANA, CRP 74, LDH 359 and an undetectable haptoglobin. A urinalysis was positive for protein and blood. At this time, empiric treatment for pneumocystis pneumonia was initiated with a plan for bronchoscopy. The bronchoscopy with bronchoalveolar lavage (BAL) revealed diffuse alveolar hemorrhage (DAH) with studies negative for infection or malignancy. An upper endoscopy did not reveal any gastrointestinal source of bleeding but rather favored a pulmonary source due to some red blood in the esophagus and coffee ground material in the stomach. Given these findings, a diagnosis of “Methimazole induced vasculitis with DAH” was made, a diagnosis of exclusion. The patient was started on pulse steroids for three days and his methimazole was held. By day four, the patient reported improvement and his oxygen was decreased to 2L. He was subsequently discharged on a steroid taper. At his two-week follow-up, the patient had improving respiratory status and repeat labs showed an improved and stable hemoglobin, and normal haptoglobin. IMPACT/DISCUSSION: This case illustrates a rare but life-threatening complication of methimazole use. Common offenders of drug-induced DAH include propylthiouracil, carbimazole and hydralazine. This complication is reported in 15-37% of patients on propylthiouracil but only 0-3% of patients on methimazole. A third of patients with DAH do not present with hemoptysis making this diagnosis challenging. Lab findings can also be largely nonspecific making a thorough history, imaging and interdisciplinary collaboration key in identifying this adverse effect early on to prevent mortality. CONCLUSION: Include drug-induced DAH on the differential for patients presenting with respiratory failure in the setting of new anemia, melena or hemoptysis. Stopping the offending drug and initiating steroids is the treatment of choice. Consider empiric PCP treatment and BAL for patients with severe hypoxia, ground glass opacities and immunosuppression.

The Role of Oral Health in the Acquisition and Severity of SARS-CoV-2: A Retrospective Chart Review.

Objective: Studies have shown that gingival crevices may be a significant route for SARS-CoV-2 entry. However, the role of oral health in the acquisition and severity of COVID-19 is not known. Design: A retrospective analysis was performed using electronic health record data from a large urban academic medical center between 12/1/2019 and 8/24/2020. A total of 387 COVID-19 positive cases were identified and matched 1:1 by age, sex, and race to 387 controls without COVID-19 diagnoses. Demographics, number of missing teeth and alveolar crestal height were determined from radiographs and medical/dental charts. In a subgroup of 107 cases and controls, we also examined the rate of change in alveolar crestal height. A conditional logistic regression model was utilized to assess association between alveolar crestal height and missing teeth with COVID-19 status and with hospitalization status among COVID-19 cases. Results: Increased alveolar bone loss, OR = 4.302 (2.510 - 7.376), fewer missing teeth, OR = 0.897 (0.835-0.965) and lack of smoking history distinguished COVID-19 cases from controls. After adjusting for time between examinations, cases with COVID-19 had greater alveolar bone loss compared to controls (0.641 ± 0.613 mm vs 0.260 ± 0.631 mm, p < 0.01.) Among cases with COVID-19, increased number of missing teeth OR = 2.1871 (1.146- 4.174) was significantly associated with hospitalization. Conclusions: Alveolar bone loss and missing teeth are positively associated with the acquisition and severity of COVID-19 disease, respectively.

High Patient Satisfaction and Acceptability of Tele-Neurology Consultations at a Regional Tertiary Referral Hospital in Kenya During the COVID-19 Pandemic

Objective: To demonstrate the effectiveness and usability of a novel tele-neurology service in Nairobi. Background: There is severe shortage of neurology healthcare workers in low-/lower-middle income countries (LLMICs), especially in Africa. Tele-neurology consultations (TNC), necessitated widely due to the COVID-19 pandemic, have been demonstrated to be effective in bridging neurology service gaps, but there is little evidence of TNC effectiveness in LLMICs. Design/Methods: We conducted a prospective cross-sectional study, enrolling neurology patients referred to our tertiary referral neurology outpatients center over 12 months from October 2020. We measured satisfaction and acceptability using Likert scales, and compared TNC to face-to-face (F2F) consultations. TNC were delivered as per 2020 British and American guidelines. Descriptive data are presented as median (inter-quartile range) and statistical comparisons made using paired student t-test. Results: From 219 enrolled patients, 66.7% (146/219) responded [74% (108/146) had both F2F and TNC]: age 40.9 (30.6-55.2) years;63.0% (92/146) female;2.7% (4/146) from neighboring countries;follow-up period with neurologist (DSS) 6.8 (1.5-29.8) months;and most common presentations were headache [30.8% (45/146)], seizure [26.0% (38/146)] and neurodegenerative [15.1% (22/146)] disorders. For TNC, >90%: (i) found it just as comfortable as F2F (p=0.35) and not in violation of their privacy;(ii) saved time [3.0 (2.0-4.0) hours], travel [11.0 (7.2-21.1) km] and cost [$10 (5-20)];(iii) felt satisfied with the care and that their neurological concerns were adequately addressed;and (iv) would use TNC again. Conversely, 15.1% (22/146) did not agree with TNC being as effective as F2F, including the neurologist identifying all their health problems satisfactorily (p=0.03). In total, our TNC service saved our patients $6,125, 1,143 hours, and 25,506km of travel, equating to 3.5 tons (21 trees) of carbon dioxide emissions. Conclusions: Our study demonstrates that our regionally unique TNC service is an acceptable, efficient, effective, and environmentally-friendly care delivery model in our resource-poor setting.