Profiling of phthalates, brominated, and organophosphate flame retardants in COVID-19 lockdown house dust; implication on the human health.

In this study, brominated flame retardants (BFRs), phthalates, and organophosphate flame retardants (PFRs) were analyzed in indoor household dust collected during the COVID-19 related strict lockdown (April-July 2020) period. Floor dust samples were collected from 40 households in Jeddah, Saudi Arabia. The levels of most of the analyzed chemicals were visibly high and for certain chemicals multifold high in analyzed samples compared to earlier studies on indoor dust from Jeddah. Bis (2-ethylhexyl) phthalate (DEHP) was the primary chemical in these dust samples, with a median concentration of 769,500 ng/g of dust. Tris (2-butoxy ethyl) phosphate (TBEP) and Decabromodiphenyl ether (BDE 209) contributed the highest among PFRs and BFRs with median levels of 5990 and 940 ng/g of dust, respectively. The estimated daily exposure in the worst case scenario (23,700 ng/kg bw/day) for Saudi children was above the reference dose (20,000 ng/kg bw/day) for DEHP, and the hazardous index (HI) was also >1. The long-term carcinogenic risk was above the 1 × 10-5, indicating a risk to the health of Saudi young children from getting exposed to DEHP from indoor dust. This study draws attention to the increased indoor pollution during the lockdown period when all of the daily activities by adults and children were performed indoors, which negatively impacted human health, as suggested by the calculated risk. However, the current study has limitations and warrants more monitoring studies from different parts of the world to understand the phenomenon. At the same time, this study also highlights another side of COVID-19 related to our lives.

The prevalence of pulmonary embolism among COVID-19 patients underwent CT pulmonary angiography

Background Pulmonary embolism (PE) is a common and fatal complication of COVID-19 infection. COVID-19's main clinical manifestations are not only pneumonia but also coagulation disorders. This study evaluates the prevalence of pulmonary embolism at CT pulmonary angiography (CTA) for positive coronavirus patients as well as the factors associated with PE severity. Materials and methods This is a retrospective cross-sectional study that was conducted at King Faisal Medical Complex (KFMC) in Taif city of Saudi Arabia from June 2020 to June 2021. Data was collected from the picture archiving and communication system (PACs) for a total of 445 positive patients who underwent CT pulmonary angiography and analyzed using SPSS. Results The mean age and gender of the male were 57.3 ± 15.8 years and 64.5%, respectively. The prevalence of pulmonary embolism at CTA among patients with COVID-19 was found to be 8.1%. Bilateral segmental and bilateral subsegmental pulmonary embolism were found to be the most common sites for PE (16.7% for each). Furthermore, shortness of breath (SOB) was found to be the most common reported symptom among the respondents. Lastly, shortness of breath, chest pain, loss of taste or smell, D-dimer, and cardiac troponin were found to be significantly associated with PE (P-value = < 0.001, <0.001, 0.001, <0.001 and 0.037 respectively). Conclusion Present results show that the prevalence of pulmonary embolism among COVID19 patients with CT Pulmonary Angiography at KFMC is relatively low (8.1%) and most of the patients were from the ICU department. Early detection and treatment of COVID-19 patients with PE and APE complications are critical for lowering the mortality rate.

Multi-user routing algorithm for indoor spaces – adapted for social distancing

The development of techniques and systems for indicating routes in outdoor and indoor spaces has received a great deal of attention. While outdoor route planning is based on criteria such as the shortest, fastest route, and the least number of turns, indoor route planning is primarily focused on accessibility and safety. Routing systems for indoor spaces have become the focus of many researchers due to significant differences that make indoor routing is more complex than outdoor routing. An indoor environment is complex, which makes it difficult to find a route due to closed corridors, multiple floors, and other indoor features. Because of the present global scenario following the advent of the COVID-19 pandemic, indoor routing has become increasingly crucial.Wi-Fi, Bluetooth, and RFID are among the technologies used for indoor routing. For these technologies to help and guide users to the optimal route to their chosen destinations, they require accurate information, appropriate processing and modeling, and route density monitoring to ensure social distancing. In this paper, a new multi-user routing algorithm for indoor spaces is proposed. It has been adapted to the need for social distancing, and is based on multiple users, allowing more than one user to take the route or separate routes at one time without causing congestion on the same route. Despite the complexity of the system, the evaluation indicated that the proposed algorithm successfully controlled the flow of moving objects while ensuring social distancing and maintaining low costs.

COVID‐opt‐aiNet: A clinical decision support system for COVID‐19 detection

Coronavirus disease (COVID‐19) has had a major and sometimes lethal effect on global public health. COVID‐19 detection is a difficult task that necessitates the use of intelligent diagnosis algorithms. Numerous studies have suggested the use of artificial intelligence (AI) and machine learning (ML) techniques to detect COVID‐19 infection in patients through chest X‐ray image analysis. The use of medical imaging with different modalities for COVID‐19 detection has become an important means of containing the spread of this disease. However, medical images are not sufficiently adequate for routine clinical use;there is, therefore, an increasing need for AI to be applied to improve the diagnostic performance of medical image analysis. Regrettably, due to the evolving nature of the COVID‐19 global epidemic, the systematic collection of a large data set for deep neural network (DNN)/ML training is problematic. Inspired by these studies, and to aid in the medical diagnosis and control of this contagious disease, we suggest a novel approach that ensembles the feature selection capability of the optimized artificial immune networks (opt‐aiNet) algorithm with deep learning (DL) and ML techniques for better prediction of the disease. In this article, we experimented with a DNN, a convolutional neural network (CNN), bidirectional long‐short‐term memory, a support vector machine (SVM), and logistic regression for the effective detection of COVID‐19 in patients. We illustrate the effectiveness of this proposed technique by using COVID‐19 image datasets with a variety of modalities. An empirical study using the COVID‐19 image dataset demonstrates that the proposed hybrid approaches, named COVID‐opt‐aiNet, improve classification accuracy by up to 98%–99% for SVM, 96%–97% for DNN, and 70.85%–71% for CNN, to name a few examples. Furthermore, statistical analysis ensures the validity of our proposed algorithms. The source code can be downloaded from Github: https://github.com/faizakhan1925/COVID-opt-aiNet.

COVID-opt-aiNet: A clinical decision support system for COVID-19 detection.

Coronavirus disease (COVID-19) has had a major and sometimes lethal effect on global public health. COVID-19 detection is a difficult task that necessitates the use of intelligent diagnosis algorithms. Numerous studies have suggested the use of artificial intelligence (AI) and machine learning (ML) techniques to detect COVID-19 infection in patients through chest X-ray image analysis. The use of medical imaging with different modalities for COVID-19 detection has become an important means of containing the spread of this disease. However, medical images are not sufficiently adequate for routine clinical use; there is, therefore, an increasing need for AI to be applied to improve the diagnostic performance of medical image analysis. Regrettably, due to the evolving nature of the COVID-19 global epidemic, the systematic collection of a large data set for deep neural network (DNN)/ML training is problematic. Inspired by these studies, and to aid in the medical diagnosis and control of this contagious disease, we suggest a novel approach that ensembles the feature selection capability of the optimized artificial immune networks (opt-aiNet) algorithm with deep learning (DL) and ML techniques for better prediction of the disease. In this article, we experimented with a DNN, a convolutional neural network (CNN), bidirectional long-short-term memory, a support vector machine (SVM), and logistic regression for the effective detection of COVID-19 in patients. We illustrate the effectiveness of this proposed technique by using COVID-19 image datasets with a variety of modalities. An empirical study using the COVID-19 image dataset demonstrates that the proposed hybrid approaches, named COVID-opt-aiNet, improve classification accuracy by up to 98%-99% for SVM, 96%-97% for DNN, and 70.85%-71% for CNN, to name a few examples. Furthermore, statistical analysis ensures the validity of our proposed algorithms. The source code can be downloaded from Github: https://github.com/faizakhan1925/COVID-opt-aiNet.

The Impact of the COVID-19 Pandemic on Medical Imaging Case Volumes in Aseer Region: A Retrospective Study.

COVID-19 has had a significant impact on global health systems. The aim of this study was to evaluate how imaging volumes and imaging types in radiology departments have been affected by the COVID-19 pandemic across different locations. METHODS: Imaging volumes in the Aseer region (in the south of Saudi Arabia) across main hospitals were reviewed retrospectively including all cases referred from different locations (outpatient, inpatient and emergency departments). Data for years 2019 and 2020 were compared. The mean monthly cases were compared using a t-test. RESULTS: The total imaging volumes in 2019 were 205,805 compared to 159,107 in 2020 with a 22.7% overall reduction. A substantial decline was observed in both the April to June and the July to September periods of approximately 42.9% and 44.4%, respectively. With respect to location, between April and June, the greatest decline was observed in outpatient departments (76% decline), followed by emergency departments (25% decline), and the least impact was observed in inpatient departments, with only 6.8% decline over the same period. According to modality type, the greatest decreases were reported in nuclear medicine, ultrasound, MRI, and mammography, by 100%, 76%, 74%, and 66%, respectively. Our results show a statistically significant (p-value ≤ 0.05) decrease of cases in 2020 compared to 2019, except for mammography procedures. CONCLUSION: There has been a significant decline in radiology volumes due to COVID-19. The overall reduction in radiology volumes was dependent on the stage/period of lockdown, location, and imaging modality.

Measuring Awareness of Infection Control Guidelines for Patients with COVID-19 in Radiology Departments in Saudi Arabia.

Due to the contagious nature of the COVID-19 virus, healthcare workers are at a great risk of infection. Since medical imaging plays a significant part in the healthcare system and is often used in the diagnosis of potential COVID-19 patients, the radiology personnel are at a very high risk of becoming infected. PURPOSE: This study aims to assess the enforcement of infection control guidelines for patients with COVID-19 during medical imaging procedures and raise awareness of infection control in different hospitals in Saudi Arabia. METHODS: A total of 128 responses were collected from four hospitals across Saudi Arabia using a new structured questionnaire, which was created for health workers by the WHO specifically for this purpose. Data were collected during the COVID-19 pandemic in April 2020. RESULTS: Most participants correctly followed the guidelines of the WHO and Centers for Disease Control and Prevention (CDC) on infection control in the X-ray and general radiology departments. Guideline awareness was higher among magnetic resonance imaging (MRI) and computerised tomography (CT) radiographers, which reduced the risk of future infections. Out of the total respondents, 98.4% stated that they had received formal training in hand hygiene. Only 40.6% of participants, however, knew that respiratory droplets are the primary mode of transmission of the virus from person to person. CONCLUSION: The knowledge of healthcare professionals in the radiology department regarding infection control needs to be continually assessed. A focus on educational interventions on infection control is required in order to maintain well-informed medical staff.

Polycyclic Aromatic Hydrocarbons in Indoor Dust Collected during the COVID-19 Pandemic Lockdown in Saudi Arabia: Status, Sources and Human Health Risks.

To control the spread of coronavirus disease (COVID-19), Saudi Arabia's government imposed a strict lockdown during March-July 2020. As a result, the public was confined to indoors, and most of their daily activities were happening in their indoor places, which might have resulted in lower indoor environment quality. Polycyclic aromatic hydrocarbons (PAHs) were analyzed in household dust (n = 40) collected from different residential districts of Jeddah, Saudi Arabia, during the lockdown period. PAHs' levels were two folds higher than the previously reported PAHs in indoor dust from this region. We detected low molecular weight (LMW) with two to four aromatic ring PAHs in all the samples with a significant contribution from Phenanthrene (Phe), present at an average concentration of 1590 ng/g of dust. Although high molecular weight (HMW) (5-6 aromatic ring) PAHs were detected at lower concentrations than LMW PAHs, however, they contributed >90% in the carcinogenic index of PAHs. The estimated daily intake (EDI) of specific PAHs was above the reference dose (RfD) for young children in high-end exposure and the calculated Incremental Lifetime Cancer Risk (ILCR) was >1.00 × 10-4 for both Saudi adults and young children. The study highlighted that indoor pollution has increased significantly during lockdown due to the increased indoor activities and inversely affect human health. This study also warrants to conduct more studies involving different chemicals to understand the indoor environment quality during strict lockdown conditions.