Antibiotic susceptibility patterns of bacterial isolates of patients with upper respiratory tract infections

Abstract To evaluate the antibiotic susceptibility patterns in URTIs reporting to tertiary hospitals of Lahore. A cross-sectional study employing 259 culture sensitivity reports obtained from tertiary care hospitals of Lahore. Using SPSS, descriptive statistics were used to estimate frequencies and percentages. In URTIs, S. aureus (5%) was the frequent gram-positive isolate followed by MRSA (1.5%) and MSSA (1.5%), while P. aeruginosa (15.8%) was the prevalent gram-negative isolate followed by Klebsiella (13.1%) and E. coli (6.9%). Against P. aeruginosa, ceftazidime (7.7%), cefuroxime/ceftriaxone (4.6%), amoxicillin (4.3%) and ciprofloxacin (4.2%), were tested resistant, while imipenem (11.2%), ciprofloxacin (9.2%), amikacin (9.2%), meropenem/ levofloxacin/gentamicin (8.1%) and piptaz (6.9%) were found sensitive. Against Klebsiella, carbepenems (7.3%), amikacin (6.5%), ciprofloxacin (5.4%) and gentamicin (5%) were tested sensitive, whereas, ceftazidime (8.5%), ceftriaxone (5.8%), cefaclor (5.5%), ampicillin (4.6%), co-amoxiclave (4.2%) and ciftazidime/ciprofloxacin (3.8%) were found resistant. Overall, imipenem (35%), meropenem (30.8%) and amikacin (31.9%) were the three most sensitive antibiotics, while ceftazidime (25.4%), ceftriaxone (19.2%) and ampicillin (18.5%) were the three most resistant antibiotics. Data suggested that P.aeruginosa and Klebsiella, were the most frequent bacterial isolates in URTIs of Lahore. These isolates were resistant to ampicillin, cefuroxime and ceftazidime, but were sensitive to carbapenem and aminoglycosides

Amentoflavone derivatives significantly act towards the main protease (3CLPRO/MPRO) of SARS-CoV-2: in silico admet profiling, molecular docking, molecular dynamics simulation, network pharmacology.

SARS-CoV-2 is the foremost culprit of the novel coronavirus disease 2019 (nCoV-19 and/or simply COVID-19) and poses a threat to the continued life of humans on the planet and create pandemic issue globally. The 3-chymotrypsin-like protease (MPRO or 3CLPRO) is the crucial protease enzyme of SARS-CoV-2, which directly involves the processing and release of translated non-structural proteins (nsps), and therefore involves the development of virus pathogenesis along with outbreak the forecasting of COVID-19 symptoms. Moreover, SARS-CoV-2 infections can be inhibited by plant-derived chemicals like amentoflavone derivatives, which could be used to develop an anti-COVID-19 drug. Our research study is designed to conduct an in silico analysis on derivatives of amentoflavone (isoginkgetin, putraflavone, 4''''''-methylamentoflavone, bilobetin, ginkgetin, sotetsuflavone, sequoiaflavone, heveaflavone, kayaflavone, and sciadopitysin) for targeting the non-structural protein of SARS-CoV-2, and subsequently further validate to confirm their antiviral ability. To conduct all the in silico experiments with the derivatives of amentoflavone against the MPRO protein, both computerized tools and online servers were applied; notably the software used is UCSF Chimera (version 1.14), PyRx, PyMoL, BIOVIA Discovery Studio tool (version 4.5), YASARA (dynamics simulator), and Cytoscape. Besides, as part of the online tools, the SwissDME and pKCSM were employed. The research study was proposed to implement molecular docking investigations utilizing compounds that were found to be effective against the viral primary protease (MPRO). MPRO protein interacted strongly with 10 amentoflavone derivatives. Every time, amentoflavone compounds outperformed the FDA-approved antiviral medicine that is currently underused in COVID-19 in terms of binding affinity (- 8.9, - 9.4, - 9.7, - 9.1, - 9.3, - 9.0, - 9.7, - 9.3, - 8.8, and - 9.0 kcal/mol, respectively). The best-selected derivatives of amentoflavone also possessed potential results in 100 ns molecular dynamic simulation (MDS) validation. It is conceivable that based on our in silico research these selected amentoflavone derivatives more precisely 4''''''-methylamentoflavone, ginkgetin, and sequoiaflavone have potential for serving as promising lead drugs against SARS-CoV-2 infection. In consequence, it is recommended that additional in vitro as well as in vivo research studies have to be conducted to support the conclusions of this current research study.

Determinants of anxiety and depression among university teachers during third wave of COVID-19.

BACKGROUND: To estimate the determinants of anxiety and depression among university teachers in Lahore, Pakistan, during COVID-19. METHODS: A cross-sectional study was conducted by enrolling 668 teachers from the universities of Lahore, Pakistan. Data were collected using a questionnaire. Chi-square for significance and logistic regression for the association were used. RESULTS: Majorly, the university teachers, with an average age of 35.29 years, had regular jobs (72.8%), job experience of > 6 years (51.2%) and good self-reported health (55.4%). The majority of the teachers were working as lecturers (59.6%), lecturing in arts (33.5%) or general science (42.5%) departments, having MPhil (37.9%) or master (28.9%) degrees, and teaching via synchronous video (59.3%) mode. Anxiety and depression, severe and extremely severe, were higher among lecturers, MPhil or master degree holders, teachers lecturing arts and general science subjects, and in those on contract employment. Anxiety was significantly associated with academic departments; arts (OR;2.5, p = 0.001) and general science (OR;2.9, p = 0.001), poor health status (OR;4.4, p = 0.018), and contractual employment (OR;1.8, p = 0.003). Depression was associated with academic departments; arts (OR;2.7, p = 0.001) and general science (OR;2.5, p = 0.001), and health status (OR;2.3, p = 0.001). CONCLUSION: Among university teachers, anxiety and depression, severe and extremely severe, were prevalent among lecturers having MPhil or master degrees, belonging to arts and general science departments, and among contract employees. Anxiety and depression were significantly associated with academic disciplines, lower cadre, and poor health status.

Feasibility and acceptability of home-based neonatal hyperbilirubinemia screening by community health workers using transcutaneous bilimeters in Bangladesh.

BACKGROUND: Universal screening for neonatal hyperbilirubinemia risk assessment is recommended by the American Academy of Pediatrics to reduce related morbidity. In Bangladesh and in many low- and middle-income countries, there is no screening for neonatal hyperbilirubinemia. Furthermore, neonatal hyperbilirubinemia may not be recognized as a medically significant condition by caregivers and community members. We aimed to evaluate the acceptability and operational feasibility of community health worker (CHW)-led, home-based, non-invasive neonatal hyperbilirubinemia screening using a transcutaneous bilimeter in Shakhipur, a rural subdistrict in Bangladesh. METHODS: We employed a two-step process. In the formative phase, we conducted eight focus group discussions with parents and grandparents of infants and eight key informant interviews with public and private healthcare providers and managers to explore their current knowledge, perceptions, practices, and challenges regarding identification and management of neonatal hyperbilirubinemia. Next, we piloted a prenatal sensitization intervention and home-based screening by CHWs using transcutaneous bilimeters and evaluated the acceptability and operational feasibility of this approach through focus group discussions and key informant interviews with parents, grandparents and CHWs. RESULTS: Formative findings identified misconceptions regarding neonatal hyperbilirubinemia causes and health risks among caregivers in rural Bangladesh. CHWs were comfortable with adoption, maintenance and use of the device in routine home visits. Transcutaneous bilimeter-based screening was also widely accepted by caregivers and family members due to its noninvasive technique and immediate display of findings at home. Prenatal sensitization of caregivers and family members helped to create a supportive environment in the family and empowered mothers as primary caregivers. CONCLUSION: Adopting household neonatal hyperbilirubinemia screening in the postnatal period by CHWs using a transcutaneous bilimeter is an acceptable approach by both CHWs and families and may increase rates of screening to prevent morbidity and mortality.

Leveraging machine learning to analyze sentiment from COVID-19 tweets: A global perspective.

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective machine learning (ML) technique for classifying public sentiments, to analyze the variations of public sentiment across the globe, and to find the critical contributing factors to sentiment variations. To attain the objectives, 12,000 tweets, 3000 each from the USA, UK, and Bangladesh, were rigorously annotated by three independent reviewers. Based on the labeled tweets, four different boosting ML models, namely, CatBoost, gradient boost, AdaBoost, and XGBoost, are investigated. Next, the top performed ML model predicted sentiment of 300,000 data (100,000 from each country). The public perceptions have been analyzed based on the labeled data. As an outcome, the CatBoost model showed the highest (85.8%) F1-score, followed by gradient boost (84.3%), AdaBoost (78.9%), and XGBoost (83.1%). Second, it was revealed that during the time of the COVID-19 pandemic, the sentiments of the people of the three countries mainly were negative, followed by positive and neutral. Finally, this study identified a few critical concerns that impact primarily varying public sentiment around the globe: lockdown, quarantine, hospital, mask, vaccine, and the like.

COVID-19 Vaccines: How Efficient and Equitable Was the Initial Vaccination Process?

With nearly 11 billion doses of the COVID-19 vaccine being administered, stark differences in the vaccination rates persist. Vaccine distribution initiatives such as COVAX and African Vaccine Acquisition Trust (AVAT) were formed to ensure equitable vaccine delivery. This review evaluates the initial COVID-19 vaccination efforts and the impact of different vaccine distribution initiatives on equitable vaccination coverage in the early phase. We conducted a descriptive and trend analysis with sub-groups by various context parameters of data on COVID-19 vaccination from December 2020 till February 2022, from four public databases including UNICEF, WHO, COVID-19 Task Force and Our World in Data to examine COVID-19 vaccine distribution progress and the contributions of vaccine procurement initiatives. We found that High Income Countries (HICs) had much higher vaccination rate (78.4%) than Lower-Middle-Income Countries (LMICs) (55.5%) and Low-Income Countries (LICs) (10.9%). Large differentials (>80% to <10%) in the vaccination rates of eligible population of adults in LMICs and LICs existed. Differentials in the total vaccine doses delivered to each country ranged from 355.6% to 4.8% of the total population. In LICs, 53.3% of the total doses were obtained via COVAX, 30.9% by bilateral/multilateral agreements, 6.5% by donations and 3.8% by AVAT. In LMICs, 56.4% of total vaccines procured were via bilateral/multilateral agreements, 21.4% by COVAX, 4.2% by donations and 0.5% by AVAT. COVAX delivered 1 billion doses by January 2022 which constituted 53.2% and 21.4% of procured doses in LICs and LMICs. In LICs and LMICs, 6.5% and 4.2% of total doses were acquired through donations while 30.9% and 56.4% of doses were purchased. Despite global efforts, significant disparities were present in COVID-19 vaccination efforts amongst countries of different income groups. Future efforts should focus on addressing vaccine inequities explicitly and in improving global vaccine distribution.

Knowledge, Attitudes, Perceptions, and Acceptance of COVID-19 Vaccination among Pharmacy and Non-Pharmacy Students.

University students are a sub-group of the population at high risk of COVID-19 infection, and their judgments on vaccination affect the public attitudes towards vaccination. Thus, the present study aimed to assess the knowledge, attitudes, perceptions, and acceptance of the COVID-19 vaccine among pharmacy and non-pharmacy students. A cross-sectional study was conducted by enrolling pharmacy (375) and non-pharmacy (225) students from the universities in Lahore. Chi-square analysis was used for significant frequency distributions and a 5-point Likert scale was used to score attitude, perception, and acceptance. The majority of the students were aged between 19-24 years, hailing from urban and middle-class families with good self-reported health. The preferred vaccine was Pfizer, followed by Sinopharm and Sinovac. The major source of information was social media, followed by government campaigns and family members. The pharmacy students demonstrated better knowledge and positive attitudes toward COVID-19 vaccination. The non-pharmacy students scored higher for the questions based on scientific leads, myths, and baffling conspiracies. The non-pharmacy students showed higher hesitancy/barrier total scores related to their trust in the health system, COVID-19 vaccine storage, and efficacy. Data suggested that pharmacy students exhibited better knowledge, positive attitudes, and perceptions about COVID-19 vaccination. Overall, vaccine efficacy and safety were mutual concerns. Nonetheless, non-pharmacy students were hesitant due to mistrust in the health system of Pakistan.

Review on the Evaluation and Development of Artificial Intelligence for COVID-19 Containment.

Artificial intelligence has significantly enhanced the research paradigm and spectrum with a substantiated promise of continuous applicability in the real world domain. Artificial intelligence, the driving force of the current technological revolution, has been used in many frontiers, including education, security, gaming, finance, robotics, autonomous systems, entertainment, and most importantly the healthcare sector. With the rise of the COVID-19 pandemic, several prediction and detection methods using artificial intelligence have been employed to understand, forecast, handle, and curtail the ensuing threats. In this study, the most recent related publications, methodologies and medical reports were investigated with the purpose of studying artificial intelligence's role in the pandemic. This study presents a comprehensive review of artificial intelligence with specific attention to machine learning, deep learning, image processing, object detection, image segmentation, and few-shot learning studies that were utilized in several tasks related to COVID-19. In particular, genetic analysis, medical image analysis, clinical data analysis, sound analysis, biomedical data classification, socio-demographic data analysis, anomaly detection, health monitoring, personal protective equipment (PPE) observation, social control, and COVID-19 patients' mortality risk approaches were used in this study to forecast the threatening factors of COVID-19. This study demonstrates that artificial-intelligence-based algorithms integrated into Internet of Things wearable devices were quite effective and efficient in COVID-19 detection and forecasting insights which were actionable through wide usage. The results produced by the study prove that artificial intelligence is a promising arena of research that can be applied for disease prognosis, disease forecasting, drug discovery, and to the development of the healthcare sector on a global scale. We prove that artificial intelligence indeed played a significantly important role in helping to fight against COVID-19, and the insightful knowledge provided here could be extremely beneficial for practitioners and research experts in the healthcare domain to implement the artificial-intelligence-based systems in curbing the next pandemic or healthcare disaster.

Adaptation and Change in Practices in Neuro-Intervention during COVID-19 Pandemic: Experience from Department of Interventional Neurology at Referral Tertiary Care Hospital in Bangladesh

Background: Different adaptation and changes have been practiced during COVID-19 pandemic. Objective: In this paper we investigated the impact of the COVID-19 pandemic on service delivery in the department of interventional neurology and adaptation of the department to the changed environment. Methodology: This was a retrospective analysis of diagnostic digital subtraction angiography (DSA) procedures done from January 2018 to December 2020 and were analyzed to detect significant breaks in time trend. Results: A total of 358, 426 and 251 patients got admitted for DSA in consecutive three years from 2018 to 2020 respectively. There was a sudden drop in the number of DSA procedures from 30 to 50 patients per month in the pre-COVID era before March 2020 to less than 10 patients per month during the COVID period of March to June 2020. However, the situation gradually improved following the introduction of RT-PCR test for SARS CoV-2 in June 2020. A poison regression showed a significant increase in monthly DSA procedures in the year 2019 compared to the year 2018, but a significant decrease in the pandemic year of 2020. Conclusion: By incorporating COVID-19 testing as a pre-requisite test before DSA procedures, the department of interventional neurology recovered from experiencing a significant drop in the number of performed DSA procedures in the initial periods of the pandemic to reaching a level observed in the pre-COVID era.

Robust and efficient COVID-19 detection techniques: A machine learning approach.

The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus.

DEVELOPMENT OF MOBILE VIRTUAL FIELD TRIPS IN IJEN CRATER GEOSITES BASED ON 3600 AUTO STEREOSCOPIC AND GEOSPATIAL TECHNOLOGY AS GEOGRAPHY LEARNING MEDIA

The Covid-19 pandemic limits the space for teachers and students in the Geography learning process, thus affecting the achievement of the competencies and capabilities of geography students. The purpose of this research is to develop Ijen Geosites Mobile virtual field trip (M-VFTs) media to help facilitate Geography learning. This study aims to develop Ijen Crater Geosites based on Mobile Virtual Field Trips media based on 3600 Auto Stereoscopic and Geospatial Technology. This research is included in research and development by adopting the PLOMP model. The results obtained consist of making M-VFTs related to the Ijen Geosites study with interactive informative access that can be easily accessed by users. The results of this study can relate the risk of limitations in obtaining information during actual visits, especially in learning geography, so that it can help students learn to be active, independent and meaningful through observation and exploration activities.

COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets.

The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes. Worldwide promotion of the COVID-19 vaccine additionally generates a huge amount of discussions on social media platforms about diverse factors of vaccines including protection and efficacy. Twitter is considered one of the most well-known social media platforms which have been widely used to share a public opinion on vaccine-related problems in the COVID-19 pandemic. However, there is a lack of research work to analyze the public perception of COVID-19 vaccines systematically from a gender perspective. In this paper, we perform an in-depth analysis of the coronavirus vaccine-related tweets to understand the people's sentiment towards various vaccine brands corresponding to the gender level. The proposed method focuses on the effect of COVID-19 vaccines on gender by taking into account descriptive, diagnostic, predictive, and prescriptive analytics on the Twitter dataset. We also conduct experiments with deep learning models to determine the sentiment polarities of tweets, which are positive, neutral, and negative. The results reveal that LSTM performs better compared to other models with an accuracy rate of 85.7%.

A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic.

Artificial intelligence (AI) and machine learning (ML) have caused a paradigm shift in healthcare that can be used for decision support and forecasting by exploring medical data. Recent studies have shown that AI and ML can be used to fight COVID-19. The objective of this article is to summarize the recent AI- and ML-based studies that have addressed the pandemic. From an initial set of 634 articles, a total of 49 articles were finally selected through an inclusion-exclusion process. In this article, we have explored the objectives of the existing studies (i.e., the role of AI/ML in fighting the COVID-19 pandemic); the context of the studies (i.e., whether it was focused on a specific country-context or with a global perspective; the type and volume of the dataset; and the methodology, algorithms, and techniques adopted in the prediction or diagnosis processes). We have mapped the algorithms and techniques with the data type by highlighting their prediction/classification accuracy. From our analysis, we categorized the objectives of the studies into four groups: disease detection, epidemic forecasting, sustainable development, and disease diagnosis. We observed that most of these studies used deep learning algorithms on image-data, more specifically on chest X-rays and CT scans. We have identified six future research opportunities that we have summarized in this paper. Impact Statement: Artificial intelligence (AI) and machine learning(ML) methods have been widely used to assist in the fight against COVID-19 pandemic. A very few in-depth literature reviews have been conducted to synthesize the knowledge and identify future research agenda including a previously published review on data science for COVID-19 in this article. In this article, we synthesized reviewed recent literature that focuses on the usages and applications of AI and ML to fight against COVID-19. We have identified seven future research directions that would guide researchers to conduct future research. The most significant of these are: develop new treatment options, explore the contextual effect and variation in research outcomes, support the health care workforce, and explore the effect and variation in research outcomes based on different types of data.

Introducing pulse oximetry in routine IMCI services in Bangladesh: A context-driven approach to influence policy and programme through stakeholder engagement.

Background: Pneumonia is the leading cause of under-five child deaths globally and in Bangladesh. Hypoxaemia or low (<90%) oxygen concentration in the arterial blood is one of the strongest predictors of child mortality from pneumonia and other acute respiratory infections. Since 2014, the World Health Organization recommends using pulse oximetry devices in Integrated Management of Childhood Illness (IMCI) services (outpatient child health services), but it was not routinely used in most health facilities in Bangladesh until 2018. This paper describes the stakeholder engagement process embedded in an implementation research study to influence national policy and programmes to introduce pulse oximetry in routine IMCI services in Bangladesh. Methods: Based on literature review and expert consultations, we developed a conceptual framework, which guided the planning and implementation of a 4-step stakeholder engagement process. Desk review, key informant interviews, consultative workshops and onsite demonstration were the key methods to involve and engage a wide range of stakeholders. In the first step, a comprehensive desk review and key informant interviews were conducted to identify stakeholder organisations and scored them based on their power and interest levels regarding IMCI implementation in Bangladesh. In the second step, two national level, two district level and five sub-district level sensitisation workshops were organised to orient all stakeholder organisations having high power or high interest regarding the importance of using pulse oximetry for pneumonia assessment and classification. In the third step, national and district level high power-high interest stakeholder organisations were involved in developing a joint action plan for introducing pulse oximetry in routine IMCI services. In the fourth step, led by a formal working group under the leadership of the Ministry of Health, we updated the national IMCI implementation package, including all guidelines, training manuals, services registers and referral forms in English and Bangla. Subsequently, we demonstrated its use in real-life settings involving various levels of (national, district and sub-district) stakeholders and worked alongside the government leaders towards carefully resuming activities despite the COVID-19 pandemic. Results: Our engagement process contributed to the national decision to introduce pulse oximetry in routine child health services and update the national IMCI implementation package demonstrating country ownership, government leadership and multi-partner involvement, which are steppingstones towards scalability and sustainability. However, our experience clearly delineates that stakeholder engagement is a context-driven, time-consuming, resource-intensive, iterative, mercurial process that demands meticulous planning, prioritisation, inclusiveness, and adaptability. It is also influenced by the expertise, experience and positionality of the facilitating organization. Conclusions: Our experience has demonstrated the value and potential of the approach that we adopted for stakeholder engagement. However, the approach needs to be conceptualised coupled with the allocation of adequate resources and time commitment to implement it effectively.

Antiviral potential of garlic (Allium sativum) and its organosulfur compounds: A systematic update of pre-clinical and clinical data.

BACKGROUND: Garlic (Allium sativum L.) is a common herb consumed worldwide as functional food and traditional remedy for the prevention of infectious diseases since ancient time. Garlic and its active organosulfur compounds (OSCs) have been reported to alleviate a number of viral infections in pre-clinical and clinical investigations. However, so far no systematic review on its antiviral effects and the underlying molecular mechanisms exists. SCOPE AND APPROACH: The aim of this review is to systematically summarize pre-clinical and clinical investigations on antiviral effects of garlic and its OSCs as well as to further analyse recent findings on the mechanisms that underpin these antiviral actions. PubMed, Cochrane library, Google Scholar and Science Direct databases were searched and articles up to June 2020 were included in this review. KEY FINDINGS AND CONCLUSIONS: Pre-clinical data demonstrated that garlic and its OSCs have potential antiviral activity against different human, animal and plant pathogenic viruses through blocking viral entry into host cells, inhibiting viral RNA polymerase, reverse transcriptase, DNA synthesis and immediate-early gene 1(IEG1) transcription, as well as through downregulating the extracellular-signal-regulated kinase (ERK)/mitogen activated protein kinase (MAPK) signaling pathway. The alleviation of viral infection was also shown to link with immunomodulatory effects of garlic and its OSCs. Clinical studies further demonstrated a prophylactic effect of garlic in the prevention of widespread viral infections in humans through enhancing the immune response. This review highlights that garlic possesses significant antiviral activity and can be used prophylactically in the prevention of viral infections.

UVC-PURGE: A Novel Cost-Effective Disinfection Robot for Combating COVID-19 Pandemic.

During the COVID-19 pandemic, surface disinfection using prevailing chemical disinfection methods had several limitations. Due to cost-inefficiency and the inability to disinfect shaded places, static UVC lamps cannot address these limitations properly. Moreover, the average market price of the prevailing UVC robots is huge, approximately 55,165 USD. In this research firstly, a requirement elicitation study was conducted using a semi-structured interview approach to reveal the requirements to develop a cost-effective UVC robot. Secondly, a semi-autonomous robot named UVC-PURGE was developed based on the revealed requirements. Thirdly, a two-phased evaluation study was undertaken to validate the effectiveness of UVC-PURGE to inactivate the SARS-CoV-2 virus and the capability of semi-autonomous navigation in the first phase and to evaluate the usability of the system through a hybrid approach of SUPR-Q forms and subjective evaluation of the user feedback in the second phase. Pre-treatment swab testing revealed the presence of both Gram-positive and Gram-Negative bacteria at 17 out of 20 test surfaces in the conducted tests. After the UVC irradiation of the robot, the microbial load was detected in only 2 (1D and 1H) out of 17 test surfaces with significant reductions (95.33% in 1D and 90.9% in 1H) of microbial load. Moreover, the usability evaluation yields an above-average SUPR-Q score of 81.91% with significant scores in all the criteria (usability, trust, loyalty, and appearance) and the number of positive themes from the subjective evaluation using thematic analysis is twice the number of negative themes. Additionally, compared with the prevailing UVC disinfection robots in the market, UVC-PURGE is cost-effective with a price of less than 800 USD. Moreover, small form factor along with the real time camera feedback in the developed system helps the user to navigate in congested places easily. The developed robot can be used in any indoor environment in this prevailing pandemic situation and it can also provide cost-effective disinfection in medical facilities against the long-term residual effect of COVID-19 in the post-pandemic era.

Real-Time Face Mask Detection to Ensure COVID-19 Precautionary Measures in the Developing Countries

Recently, the rapid transmission of Coronavirus 2019 (COVID-19) is causing a significant health crisis worldwide. The World Health Organization (WHO) issued several guidelines for protection against the spreading of COVID-19. According to the WHO, the most effective preventive measure against COVID-19 is wearing a mask in public and crowded areas. It is quite difficult to manually monitor and determine people with masks and no masks. In this paper, different deep learning architectures were used for better results evaluations. After extensive experimentation, we selected a custom model having the best performance to identify whether people wear a face mask or not and allowing an easy deployment on a small device such as a Jetson Nano. The experimental evaluation is performed on the custom dataset that is developed from the website (See data collection section) after applying different masks on those images. The proposed model in comparison with other methods produced higher accuracy (99% for training accuracy and 99% for validation accuracy). Moreover, the proposed method can be deployed on resource-constrained devices.

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection

Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus transmission generally occurs through a small respiratory droplet ejected from the mouth or nose of an infected person to another person. To reduce and prevent the spread of COVID-19 transmission, the World Health Organization (WHO) advises the public to wear face masks as one of the most practical and effective prevention methods. Early face mask detection is very important to prevent the spread of COVID-19. For this purpose, we investigate several deep learning-based architectures such as VGG16, VGG19, InceptionV3, ResNet-101, ResNet-50, EfficientNet, MobileNetV1, and MobileNetV2. After these experiments, we propose an efficient and effective model for face mask detection with the potential to be deployable over edge devices. Our proposed model is based on MobileNetV2 architecture that extracts salient features from the input data that are then passed to an autoencoder to form more representations prior to the classification layer. The proposed model also adopts extensive data augmentation techniques (e.g., rotation, flip, Gaussian blur, sharping, emboss, skew, and shear) to increase the number of samples for effective training. The performance of our proposed model is evaluated on three publicly available datasets and achieved the highest performance as compared to other state-of-the-art models.

Assessing the Impact of COVID-19 on the Mental Health of Healthcare Workers in Three Metropolitan Cities of Pakistan.

PURPOSE: The COVID-19 (coronavirus disease-2019) has been associated with psychological distress during its rapid rise period in Pakistan. The present study aimed to assess the mental health of healthcare workers (HCWs) in the three metropolitan cities of Pakistan. METHODS: A cross-sectional, web-based study was conducted in 276 HCWs from April 10, 2020, to June 5, 2020. Depression, anxiety, and stress scale (DASS-21) were used for the mental health assessment of the HCWs. Multivariable logistic regression analysis (MLRA) was performed to measure the association between the demographics and the occurrence of depression, anxiety, and stress (DAS). RESULTS: The frequency of DAS in the HCWs was 10.1%, 25.4%, and 7.3%, respectively. The MLRA showed that the depression in HCWs was significantly associated with the profession (P<0.001). The anxiety in HCWs was significantly associated with their age (P=0.005), profession (P<0.05), and residence (P<0.05). The stress in HCWs was significantly associated with their age (P<0.05). LIMITATION: This study was conducted in the early phase of the COVID-19 pandemic, when the number of COVID-19 cases was on the rise in Pakistan and it only represents a definite period (April to June 2020). CONCLUSION: The symptoms of DAS are present in the HCWs of Pakistan and to manage the psychological health of HCWs, there is a need for the initiation of psychological well-being programs.