Relationship Between Pulmonary Vascular Dilatation and Clinical Symptoms on Chest Computed Tomography in Patients with Confirmed COVID-19

BACKGROUND: Chest computed tomography (CT) is important in establishing a diagnosis, including detecting pulmonary vascular dilatation as a radiological feature of COVID-19, and consequently in providing comprehensive treatment. AIM: This study aimed to analyze the relationship between pulmonary vascular dilatation and clinical symptoms on chest CT in patients with confirmed COVID-19. PATIENTS AND METHODS: This retrospective and cross-sectional study was conducted at the Radiology Department of Dr. Wahidin Sudirohusodo Hospital and Hasanuddin University Hospital, Makassar, Indonesia, from July to September 2021 in a total of 231 patients with confirmed COVID-19. The Chi-squared correlation test was used to analyze the data, with p < 0.05 considered significant. RESULT(S): Pulmonary vascular dilatation was observed in 31 (37.8%) of the 82 patients with confirmed COVID-19 with mild-to-moderate clinical symptoms and in 51 (69.8%) of the 73 patients with confirmed COVID-19 with severe-to-critical clinical symptoms. The incidence of pulmonary vascular dilatation increased in the patients with confirmed COVID-19 with severe-to-critical clinical symptoms. The chief complaints of most patients were cough, shortness of breath, and fever. In the patients with mild-to-moderate clinical symptoms, the most common chief complaint was cough (n = 53;64.63%), while in those with severe-to-critical clinical symptoms, the most common chief complaint was shortness of breath (n = 60;82.19%). CONCLUSION(S): Based on chest CT findings, pulmonary vascular dilatation is related to clinical symptoms in patients with confirmed COVID-19.Copyright © 2023 Sri Asriyani, Nikmatia Latief, Andi Alfian Zainuddin, Muzakkir Amir, Bachtiar Murtala, Hendra Toreh.

The Compatibility of Chest CT Scan with RT-PCR in Suspected COVID-19 Patients

BACKGROUND: Thoracic computed tomography (CT) scan plays a role in detecting and assessing the progression of COVID-19. It can evaluate the response to the therapy given. In diagnosis, the CT scan of the chest may complement the limitations of reverse transcription polymerase chain reaction (RT-PCR). Several recent studies have discussed the importance of CT scans in COVID-19 patients with false-negative RT-PCR results. The sensitivity of chest CT scan in the diagnosis of COVID-19 is reportedly around 98%. AIM: This study aimed to determine the compatibility of CT scan of the thorax with RT-PCR in suspected COVID-19 patients. MATERIALS AND METHODS: This research was conducted in the Radiology Department of the Wahidin Sudirohusodo Hospital Makassar from April to December 2020 with 350 patients. The method used was a 2 x 2 table diagnostic test. RESULT(S): The study included 188 male patients (53.7%) and 162 female patients (46.2%). The most common age group was 46-65 years (35.4%). The most common types of lesions were ground-glass opacity (163 cases), consolidation (128 cases), and fibrosis (124 cases), mostly found in the inferior lobe with a predominantly peripheral or subpleural distribution. The sensitivity of the CT scan to the PCR examination was 86%, and the specificity was 91%. CONCLUSION(S): Thoracic CT scan was a good modality in establishing the diagnosis of COVID-19. CT scan of the chest with abnormalities could confirm the diagnosis in 88% of cases based on RT-PCR examination. It excluded the diagnosis in 91% based on the RT-PCR examination. The accuracy of the thoracic CT scan was 88% with RT-PCR as the reference value.Copyright © 2023 Sri Asriyani, Albert Alexander Alfonso, Mirna Muis, Andi Alfian Zainuddin, Irawaty Djaharuddin, Muhammad Ilyas.

Design of Mobile Application for SME Business Sustainability during Post Covid-19

In the 21st century, mobile and portable devices have become an integral part of people's lives, assisting them in managing daily tasks, whether for work or personal reasons. Furthermore, this technology will benefit people, particularly those who run small to medium-sized businesses (SMEs), by allowing them to manage their firms' using smartphones and other portable devices from anywhere and at any time. However, many SMEs in Malaysia do not own a mobile app. During this Covid-19 pandemic, the country's economic growth rate has slowed. It has impacted negatively on all types of businesses on a massive scale. Several major retailers have temporarily closed their doors. Aside from that, poor footfalls are causing problems for medium and small-sized enterprises. They have entirely lost their businesses due to the absence of a mobile app. They might still earn profits if they had their smartphone app because the delivery sectors were still operating as usual. The primary purpose of this study is to create importance of using electronic commerce mobile applications among SME business owners. This ecommerce mobile application was developed using Android studio with Kotlin programming language. UI of this application was designed with simple widget and layout. Google Firebase platform was connected to Android studio application which is used to store user details and verify whenever user try to login. © 2022 IEEE.

Machine Learning Facemask Detection Models for COVID-19

With the breakout of the global pandemic known as COVID-19 it has forever changed ways of doing everyday things. Moreover, the discovery of new variants it has compelled regulatory authorities make the use of face mask in public places mandatory. Public places such as the public transport, shopping mall and universities where crowds of people come into contact with one another. It further exacerbates the issue by confining the masses in an indoor premise. As part of the enforcing the mandatory sop protocol work force or manpower is allocated that serve as gatekeepers to ensure the use of face mask. Due to the number of people at public places it increases the probability of human error. The solution is to incorporate the use of Artificial Intelligence that would use effective machine learning models to train and develop an effective and accurate facemask detection system. This study takes note of the existing system and develops one using the open-source library called TensorFlow to provide it with different variations of datasets that would simulate real world scenarios. With the implementation of the face mask detection system, it aims to replace manpower and allow artificial intelligence to conduct unsupervised operation that would be more efficient and effective. © 2022 IEEE.

The effects of shift work, nutritional status, and stress level on nurse brain-derived neurotrophic factor (BDNF) gene levels during the covid-19 pandemic in central Jakarta, Indonesia

Introduction: The study aimed was to analyze the decrease in BDNF gene levels in unit work, shift work, nutritional status, and stress levels of nurses during the Covid-19 pandemic. The number of patients confirmed by the Covid-19 virus receiving treatment at hospitals in Indonesia has increased significantly. This condition causes increased fatigue that can lead to higher stress on nurses. Low serum levels of brain-derived neurotrophic factor (BDNF) were found in workers with high-stress levels. This stress is triggered by several factors such as sleep disturbances, workload, and conflicts that occur in the workplace. Method: This research used an analytic observational method with a cross-sectional design during July-September 2020 at the Cempaka Putih Islamic Hospital Jakarta, in Central Jakarta, Indonesia. A sample of 89 nurses recruited by the purposive sampling technique who met the inclusion criteria obtained, namely not having a systemic disorder, not suffering from psychiatric disorders, not consuming alcohol, and not smoking. The data were collected using anthropometric measurements and intravenous blood collection to check BDNF gene mRNA expression using real-time PCR. Results: The results showed that the mean BDNF levels on respondents with low, medium, and high-stress levels were 11.68752, 8.65197, and 7.35583, respectively. There was a significant relationship between shift work (P-Value = 0.007) and stress levels (P-value = 0.000) of nurses and decreased BDNF gene's level during the Covid-19 pandemic. However, nutritional status was not significantly (P-Value = 0.163) related to BDNF gene's level.