Optimal control analysis of Monkeypox disease with the impact of environmental transmission

Monkeypox is an emerging zoonotic viral disease resembling that of smallpox, although it is clinically less severe. Following the COVID-19 outbreak, monkeypox is an additional global health concern. The present study aims to formulate a novel mathematical model to examine various epidemiological aspects and to suggest optimized control strategies for the ongoing outbreak. The environmental viral concentration plays an important role in disease incidence. Therefore, in this study, we consider the impact of the environmental viral concentration on disease dynamics and control. The model is first constructed with constant control measures.The basic mathematical properties including equilibria, stability, and reproduction number of the monkeypox model are presented. Furthermore, using the nonlinear least square method, we estimate the model parameters from the actual cases reported in the USA during a recent outbreak in 2022. Normalized sensitivity analysis is performed to develop the optimal control problem. Based on the sensitivity indices of the model parameters, the model is reformulated by introducing six control variables. Based on theoretical and simulation results, we conclude that considering all suggested control measures simultaneously is the effective and optimal strategy to curtail the infection. We believe that the outcomes of this study will be helpful in understanding the dynamics and prevention of upcoming monkeypox outbreaks. © 2023 the Author(s), licensee AIMS Press.

The state of health in Pakistan and its provinces and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding health trends and estimating the burden of disease at the national and subnational levels helps policy makers track progress and identify disparities in overall health performance. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 provides comprehensive estimates for Pakistan. Comparison of health indicators since 1990 provides valuable insights about Pakistan's ability to strengthen its health-care system, reduce inequalities, improve female and child health outcomes, achieve universal health coverage, and meet the UN Sustainable Development Goals. We present estimates of the burden of disease, injuries, and risk factors for Pakistan provinces and territories from 1990 to 2019 based on GBD 2019 to improve health and health outcomes in the country. Methods We used methods and data inputs from GBD 2019 to estimate socio-demographic index, total fertility rate, cause-specific deaths, years of life lost, years lived with disability, disability-adjusted life-years, healthy life expectancy, and risk factors for 286 causes of death and 369 causes of non-fatal health loss in Pakistan and its four provinces and three territories from 1990 to 2019. To generate estimates for Pakistan at the national and subnational levels, we used 68 location-years of data to estimate Pakistan-specific demographic indicators, 316 location-years of data for Pakistan-specific causes of death, 579 location-years of data for Pakistan-specific non-fatal outcomes, 296 location-years of data for Pakistan-specific risk factors, and 3089 location-years of data for Pakistan-specific covariates. Findings Life expectancy for both sexes in Pakistan increased nationally from 61 center dot 1 (95% uncertainty interval [UI] 60 center dot 0-62 center dot 1) years in 1990 to 65 center dot 9 (63 center dot 8-67 center dot 8) years in 2019;however, these gains were not uniform across the provinces and federal territories. Pakistan saw a narrowing of the difference in healthy life expectancy between the sexes from 1990 to 2019, as health gains for women occurred at faster rates than for men. For women, life expectancy increased by 8 center dot 2% (95% UI 6middot3-13middot8) between 1990 and 2019, whereas the male life expectancy increased by 7 center dot 6% (3 center dot 5-11 center dot 8). Neonatal disorders, followed by ischaemic heart disease, stroke, diarrhoeal diseases, and lower respiratory infections were the leading causes of all-age premature mortality in 2019. Child and maternal malnutrition, air pollution, high systolic blood pressure, dietary risks, and tobacco consumption were the leading all-age risk factors for death and disability-adjusted life-years at the national level in 2019. Five non-communicable diseases-ischaemic heart disease, stroke, congenital defects, cirrhosis, and chronic kidney disease-were among the ten leading causes of years of life lost in Pakistan. Burden varied by socio-demographic index. Notably, Balochistan and Khyber Pakhtunkhwa had the lowest observed gains in life expectancy. Dietary iron deficiency was the leading cause of years lived with disability for both men and women in 1990 and 2019. Low birthweight and short gestation and particulate matter pollution were the leading contributors to overall disease burden in both 1990 and 2019 despite moderate improvements, with a 23 center dot 5% (95% UI 3 center dot 8-39 center dot 2) and 27 center dot 6% (14 center dot 3-38 center dot 6) reduction in age-standardised attributable DALY rates during the study period. Interpretation Our study shows that progress has been made on reducing Pakistan's disease burden since 1990, but geographical, age, and sex disparities persist. Equitable investment in the health system, as well as the prioritisation of high-impact policy interventions and programmes, are needed to save lives and improve health outcomes. Pakistan is facing several domestic and foreign challenges-the Taliban's return to power in Afghanistan, political turmoil, catastrophic flooding, the COVID-19 pandemic-that will shape the trajectory of the country's health and development. Pakistan must address the burden of infectious disease and curb rising rates of non-communicable diseases. Prioritising these three areas will enhance Pakistan's ability to achieve universal health coverage, meet its Sustainable Development Goals, and improve the overall health outcomes.

Digital Twins-Based Internet of Robotic Things for Remote Health Monitoring of COVID-19 Patients

The deadly coronavirus disease (COVID-19) has highlighted the importance of remote health monitoring (RHM). The digital twins (DTs) paradigm enables RHM by creating a virtual replica that receives data from the physical asset, representing its real-world behavior. However, DTs use passive internet of things (IoT) sensors, which limit their potential to a specific location or entity. This problem can be addressed by using the internet of robotic things (IoRT), which combines robotics and IoT, allowing the robotic things (RTs) to navigate in a particular environment and connect to IoT devices in the vicinity. Implementing DTs in IoRT, creates a virtual replica (virtual twin) that receives real-time data from the physical RT (physical twin) to mirror its status. However, DTs require a user interface for real-time interaction and visualization. Virtual reality (VR) can be used as an interface due to its natural ability to visualize and interact with DTs. This research proposes a real-time system for RHM of COVID-19 patients using the DTs-based IoRT and VR-based user interface. It also presents and evaluates robot navigation performance, which is vital for remote monitoring. The virtual twin (VT) operates the physical twin (PT) in the real environment (RE), which collects data from the patient-mounted sensors and transmits it to the control service to visualize in VR for medical examination. The system prevents direct interaction of medical staff with contaminated patients, protecting them from infection and stress. The experimental results verify the monitoring data quality (accuracy, completeness, timeliness) and high accuracy of PT’s navigation. Author

A threefold approach including quantum chemical, molecular docking and molecular dynamic studies to explore the natural compounds from Centaurea jacea as the potential inhibitors for COVID-19.

In the current report, we studied the possible inhibitors of COVID-19 from bioactive constituents of Centaurea jacea using a threefold approach consisting of quantum chemical, molecular docking and molecular dynamic techniques. Centaurea jacea is a perennial herb often used in folk medicines of dermatological complaints and fever. Moreover, anticancer, antioxidant, antibacterial and antiviral properties of its bioactive compounds are also reported. The Mpro (Main proteases) was docked with different compounds of Centaurea jacea through molecular docking. All the studied compounds including apigenin, axillarin, Centaureidin, Cirsiliol, Eupatorin and Isokaempferide, show suitable binding affinities to the binding site of SARS-CoV-2 main protease with their binding energies -6.7 kcal/mol, -7.4 kcal/mol, -7.0 kcal/mol, -5.8 kcal/mol, -6.2 kcal/mol and -6.8 kcal/mol, respectively. Among all studied compounds, axillarin was found to have maximum inhibitor efficiency followed by Centaureidin, Isokaempferide, Apigenin, Eupatorin and Cirsiliol. Our results suggested that axillarin binds with the most crucial catalytic residues CYS145 and HIS41 of the Mpro, moreover axillarin shows 5 hydrogen bond interactions and 5 hydrophobic interactions with various residues of Mpro. Furthermore, the molecular dynamic calculations over 60 ns (6×106 femtosecond) time scale also shown significant insights into the binding effects of axillarin with Mpro of SARS-CoV-2 by imitating protein like aqueous environment. From molecular dynamic calculations, the RMSD and RMSF computations indicate the stability and dynamics of the best docked complex in aqueous environment. The ADME properties and toxicity prediction analysis of axillarin also recommended it as safe drug candidate. Further, in vivo and in vitro investigations are essential to ensure the anti SARS-CoV-2 activity of all bioactive compounds particularly axillarin to encourage preventive use of Centaurea jacea against COVID-19 infections.

Assessment of Chest X-Ray Image-Based COVID-19 Detection using Deep Transfer Learning Models

Wuhan is the city in China where COVID-19 was first discovered, and the disease quickly spread throughout the world, affecting over 215 million people. Vaccination has been tried to control the disease effects. Many data scientists contributed and analyzed the disease using chest X-Rays and Computed Tomography (CT) scans in order to control it. The data collected from Chest X-rays have been proven to be extremely effective for screening COVID-19 patients, particularly in terms of resolving overcapacity in emergency departments and urgent-care centers. Our proposed approach towards COVID-19 research contribution consists of four transfer learning models i.e., MobileNet, DenseNet201, InceptionNetV2 and NasNetMobile. Grayscale images of chest X-Rays that have been preprocessed are fed into these models as input data. The dataset used in the proposed framework is the COVID19 Radiography Database, which is available to all researchers on the Kaggle platform and contains four different types of chest X-ray images i.e., COVID-19, Pneumonia, Opacity and Normal. For multiclass classification that is MobileNet, DenseNet201, InceptionNetV3 and NasNetMobile the models showed an impressive accuracy of 91.26%, 90.38%, 89.27, and 87.74, while for binary class classification, the prediction capability of our used models is 97.03%, 96.78%, 95.18% and 95.40% respectively. © 2022 IEEE.

Exploring the factors for open innovation in post-COVID-19 conditions by fuzzy Delphi-ISM-MICMAC approach

PurposeOpen innovation has attracted the attention of experts and business entities for the sustainable survivability of firms, especially in the post-COVID-19 era. The food and beverage industry has been facing sustainable survivability problems. It is important to identify and evaluate the factors of open innovation from the perspectives of the food and beverage industry. This study serves that purpose by identifying and evaluating the factors of open innovation in the post-COVID-19 era with a special reference to Pakistan's economy.Design/methodology/approachThe present study integrates the Fuzzy Delphi Method (FDM), Interpretive Structural Modelling (ISM) and Matrice d' Impacts Croises Multiplication Applique a Classement (MICMAC) methods to analyze the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan. Firstly, based on an extensive literature review of the most relevant studies, the factors affecting open innovation have been identified and finalized using FDM and experts' opinions. Secondly, the hierarchical framework has also been prepared by implementing the ISM approach. Thirdly, the MICMAC approach was employed to evaluate the factors to examine the driving and dependence powers of the factors of open innovation adoption.FindingsThe study identified 17 factors of open innovation adoption in Pakistan's food and beverage industry and 16 factors were finalized using FDM. The ISM-MICMAC matrix unveiled that awareness seminars and training, along with a lack of executive commitments, were strong factors with high driving power, but these factors proved to be weakly dependent powers regarding the other factors. Moreover, a lack of innovation strategy, R&D and non-supportive organizational culture exhibited low driving power but strong dependent power.Practical implicationsThe findings of the study could help firms and business entities understand the driving and dependent factors involved in open innovation for the sustainable survivability of the food and beverage industry. The study provides strong reasons to believe that an open innovation strategy, along with stakeholder collaboration, the adoption of rules and regulations and managerial commitment, could stimulate open innovation. Moreover, governments should promote the business sector, especially the food and beverage industry, to facilitate the sector while also providing awareness seminars and training, creating environments conducive to reducing innovation costs.Originality/valueSome previous studies have analyzed the factors involved in green innovation from the perspective of the manufacturing industry and environmental protection. The present study is a pioneer study to examine the factors involved in the adoption of open innovation in the food and beverage industry in Pakistan from the perspective of the post-COVID-19 era. For this purpose, the present study uses an integrated Fuzzy Delphi-ISM-MICMAC approach for the analysis.

Molecular docking, dynamics, and quantum chemical study of vanillylacetone and beta-hydroxy ketone derivatives against Mproof SARS-CoV-2

This study is carried out to find novel active drug candidates which can effectively bind to key residues of main protease (Mpro) of SARS-CoV-2. We performed molecular docking of fifty-seven (57) ligands from two classes: vanillylacetone and its derivatives and beta-hydroxy ketone derivatives against Mpro of SARS-CoV-2. We also docked three antiviral drugs as reference/benchmark drugs including remdesivir (RDV), chloroquine (CQ), and hydroxychloroquine (HCQ) against Mpro for comparison of inhibition tendencies of selected ligands. Binding energies of our reference drugs are as: CQ = -5.1 kcal mol-1 (with predicted inhibition constant (Ki pred) = 177 mu mol), HCQ = -5.7 kcal mol-1 (Ki pred = 64.07 mu mol) and RDV -6.3 kcal mol-1 (Ki pred = 13.95 mu mol). We got remarkable results for our docked ligands as 79% of total ligands indicated binding energies better than CQ, 39 % better than both HCQ and CQ, and 19 % better than all reference drugs. More interestingly interaction analysis of eight best-docked ligands showed that they interacted with desired key residues of Mpro. We further selected the four best-docked ligands L1 = -6.6 kcal mol-1 (Ki pred=13.95 mu mol), L6 = -7.0 kcal mol-1 (Ki pred = 7.08 mu mol), L34 = -6.0 kcal mol-1 (Ki pred = 38.54 mu mol), and L50 = -6.6 kcal mol-1 (Ki pred=13.95 mu mol) for further analysis by quantum chemical study, molecular dynamic (MD) simulations and ADMET analysis. We have also carried out MD-simulations of six more docked ligand L2, L14, L20, L36, L46 and L48 some of which were showing weak binding affinities and some average binding affinities to check their simulation behavior. Their RMSD, RMSF and binding free energy results were also quite satisfying. We believe the current investigation will evoke the scientific community and highlights the potential of selected compounds for potential use as antiviral compounds against Mpro of SARS-CoV-2.

Assessment of Psychological Impact of Covid Pandemic on Frontline workers at Points of Entry,Pakistan : Nadia Noreen

Background The unprecedented public health crisis of the COVID-19 pandemic has caused heightened levels of stress and fear among health care workers.With the advent of COVID-19 in Pakistan,frontline workers of POEs have been under physical and psychological pressure including a high risk of infection, abnormal levels of workload, prolonged working hours, lack of personal protective equipment for safety from contagion, isolation, exhaustion, and lack of contact with family.The study aims to assess the impact of Covid-19 on the mental health of frontline healthcare workers. Methods A descriptive study was conducted among HCWs across points of entry from 1st October 2020 to 31st December 2020.Data was collected using a structured questionnaire.Depression, anxiety, and stress scale (DASS-21)was used for the assessment of depression, stress, anxiety. Descriptive analysis of socio-demographic and professional factors was done. Multivariable logistic regression analysis (MLRA) was performed using SPSS version 23.0. Results A total of 628 participants (586 males and 42 females) completed questionnaire.The mean age of the participants was 42.6 ± 45.9 years. The majority of the respondents were married (94.3%). The frequency of depression, anxiety, and stress in the HCWs was 12.1%, 42.3%, and 22.1 %, respectively. Multivariable logistic regression analysis found that the depression in HCWs was significantly associated with the profession and age (P < 0.001). The anxiety in HCWs was associated with their age and gender (P < 0.005). The stress in HCWs was significantly associated with their age (P < 0.05). Conclusions The HCWs at the Points of entry across Pakistan showed mild to moderate symptoms of DAS. The COVID-19 pandemic has caused a heavy psychological impact among the frontline healthcare professionals. Timely psychological counseling and early psychological intervention need to be implemented for HCWs to alleviate their anxiety and stress and improve their general mental health. Key messages • The COVID-19 pandemic has caused a heavy psychological impact. • Timely psychological counseling and early psychological intervention.

Assessing the influence of celebrity and government endorsements on bitcoin's price volatility

The global market capitalisation of bitcoin has exponentially increased in recent years and there are concerns that the current prices of bitcoin do not reflect the true and fair underlying value of this particular type of digital asset. Applying Cue utilisation theory and signalling theory, and using a panel data on bitcoin prices from Bloomberg between 1st November 2019 and 31st May 2021, we examine the association between celebrity and government endorsements and volatility in bitcoin prices. We find that positive celebrity tweets and positive government sentiments towards bitcoin are significantly positively associated with positive changes in its prices. Our findings imply that although celebrity endorsements may cause a temporary 'exponential rise' in bitcoin prices, investors need to carefully diversify their portfolio to maximise their risk-return relationship.

Cluster detection for spatio-temporal dengue cases at Selangor districts using multi-EigenSpot algorithm

Detecting clusters for spatio-temporal cases are becoming important to help hotspots detection for any seasonal outbreaks' cases such as dengue, covid-19, malaria etc. Cluster detection is classified into three types of clustering groups, which are spatial clustering, temporal clustering, and spatio-temporal clustering. In this study, spatio-temporal clustering is carried out to dengue datasets that were obtained from the Ministry of Health (MoH), Malaysia. Generally, the datasets were analyzed based on dengue cases reported for Selangor districts in years 2009 until 2013 to detect abnormal regions between the study areas. In health organization and epidemiology sectors, detection of cluster disease plays an important role to understand disease etiology and improve public health interventions strategy. Parametric assumptions commonly implemented in most of algorithm in cluster detections. However, the main limitation of the parametric assumptions are restrictions on the datasets' quality and type of clusters shapes. This study aims to detect the spatio-temporal clustering or hotspot regions of dengue cases for the districts of Selangor, Malaysia using a nonparametric algorithm (Multi-EigenSpot) to detect dengue clusters. The algorithm was deployed to the datasets using MATLAB software. This study has found that the most likely clusters were detected more efficiently when the algorithm removed the low-risk regions and low-risk time-point during scanning window search to avoid any false detection clusters. Different scope of clustering and geometric form of scanning window has significant contribution to the detected clusters. The finding in this study indicates that Petaling district is the most likely clusters which contributed the most of the reported dengue cases in Malaysia. © 2022 Author(s).

Severe Presentation of Neuromyelitis Optica with Positive Antibody Following COVID-19 Vaccination

Objective: To report a case of antibody-positive neuromyelitis optica (NMO) after COVID-19 vaccination. Background: Neuromyelitis optica spectrum disorder is a rare demyelinating disorder of the central nervous system characterized by longitudinally extensive transverse myelitis (LETM) and severe, sometimes bilateral optic neuritis. The majority of cases have serologic or CSF antibody for aquaporin-4 (AQP4). Design/Methods: Case report Results: A 19 year old woman with no prior medical history presented with two days of progressive, severe weakness and sensory changes first in the arms, then legs. On the morning of presentation, she woke with urinary incontinence. She had received COVID-19 vaccination (Moderna) fifteen days preceding her onset of symptoms. Examination revealed sinus tachycardia, MRC grade 3-4/5 power in the arms, 0/5 in the legs with approximately T4 sensory level. Cervical spine MRI revealed T2 prolongation in the spinal cord extending from the cervicomedullary junction to the conus medullaris. CSF revealed neutrophilic pleocytosis with increased IgG synthesis rate and positive CSF AQP4 antibody;serum AQP4 and MOG antibodies were negative. Bilateral, saddle pulmonary emboli were discovered shortly after admission. Her NMO was treated with high-dose intravenous methylprednisolone, plasmapheresis, and rituximab. Conclusions: This case describes a severe, new presentation of antibody-positive neuromyelitis optica following vaccination against COVID-19.

Identifying and Analyze the Face Mask Detection for the Person during Covid

In December 2019, a disease known as COVID-19 outbroke in some part of China which killed a bunch of people in that area. Till Mid-March, the disease spread all over the world killing more people. Due to this, World Health Organization (WHO) had to declare this decease as a pandemic. Therefore, scientists from all over the world were working very hard, giving their best everyday in order to make an anti-dote of this disease but it was expected to take alot of time and it even took. Now, government allowed the citizens to continue their normal way of living but still they made some protocols which was to be followed such as sanitization of hands on a regular interval, maintaining two-yard distance from each other, wearing masks, etc. Considering those protocols, we have evolved a Face-masks Detection System with a view to be useful in figuring out whether or not a person is wearing a mask or not inside the public locations which include Temples, Airports, and so on. The face masks detection database contains a mask and further to the facial snap shots, we've used OpenCV to carry out actual-time face detection from live streaming thru webcam. We have used the database to create a COVID-19 face mask detector from a computer view the use of Python, OpenCV, and Tensor Flow and Cameras. We present an in-intensity reading software that could come across conditions where a face mask isn't always used well. Our software incorporates a two-section configuration of the Convolutional Neural Network (CNN) that could hit upon hidden and unidentified faces and may point-out with pre-hooked up CCTV cameras. This allows in monitoring safety violations, promote using face mask, and ensures a safe running environment. © 2021 IEEE.

Prevalence of Diabetes Mellitus and Impaired Glucose Tolerance in Patients with Covid-19

Objective: The aim of this study is to determine the prevalence of diabetes mellitus and impaired glucose tolerance in patients with COVID-19. Study Design:Cross-sectional study Place and Duration:Conducted at department of Medicine, Khyber Teaching Hospital (KTH),Peshawer and Avicenna Teaching Hospital, Lahorefor the duration from July 2020 to December 2020. Methods: There were one hundred and fifteen patients of both genders had coronavirus disease were included in this study.Patients ranged in age from 25 to 80 years.After obtaining informed written permission, we collected detailed demographic information on all of the registered patients, including their age, gender, BMI, educational attainment and place of residence. All of the patients had their blood tested for corona disease using RT-PCR. After screening positive (fasting capillary glucose >100 mg/dl and 200 mg/dl) and each sixth consecutive negative (fasting capillary glucose <100 mg/dl) subjects, the 75-g oral glucose tolerance test was administered. The SPSS 23.0 software was used for analyzing of data. Results:Included patients had mean age 59.4±12.55 years with mean body mass index 29.12±11.76 kg/m2. There were 70 (60.9%) male patients and 45 (38.1%) females. Majority of the patients were illiterate 65 (56.5%) and 49 (42.6%) patients were from urban areas. Most common co-morbidities were hypertension, hyperlipidemia, chronic kidney disease and coronary artery disease. We found 62 (53.9%) patients had diabetes mellitus in which majority of the cases were pre-existing. Frequency of impaired glucose tolerance was found among 26 (22.6%) cases in which majority of the cases had pancreatic cancer. 28 (24.4%) cases had intubation. Overall mortality was found among 18 (15.3%) cases. Conclusion:This research found that people with diabetes and poor glucose metabolism are more likely to have severe Covid-19. A previously undiagnosed symptom of primary infection has been linked to a disorder in glucose metabolism. Pathways through which SARS-CoV-2 affects glucose metabolism must be investigated if disease aetiology is to be fully understood.

Analyzing and Battling the Emerging Variants of Covid-19 Using Artificial Neural Network and Blockchain

The fast expansion of the COVID-19 epidemic has revealed the shortcomings of current healthcare institutions in dealing with public emergency situations. One of the big reasons of Covid-19 spread is the lack of standard track and trace mechanisms in healthcare infrastructures. Furthermore, throughout the epidemic, the transmission of disinformation has accelerated, and existing platforms lacking capability of verifying the veracity of information, resulting to social unrest and illogical conduct. Therefore, building a track and trace system is critical to ensuring that data collected by the government and the public entities is accurate and dependable. It is obvious that implementing state-of-the-art predictive models like Artificial Neural Network and Blockchain-based traceable mechanisms can help to prevent the spreads of the new variants. In this paper, we proposed a Blockchain based traceable model to track and trace the infected cases so to help an effective planning to prevent the spread. © 2021 IEEE.

Quantitative ethnomedicinal study of indigenous knowledge on medicinal plants used by the tribal communities of Central Kurram, Khyber Pakhtunkhwa, Pakistan

Background: The use of plants for different ethnobotanical purposes is a common practice in the remote areas of developing countries, particularly in reference to human and animal healthcare. For this aim, it is important to document ethnomedicinal use of plants for human and livestock healthcare from unexplored regions. Objective: The current study aimed to document the use of medicinal plants and to assess their conservation status. We hypothesized that Central Kurram, due to its remoteness and maintenance of traditions would show distinct differences in medicinal plant use in comparison to other areas of Pakistan. Method: The data was collected through semi-structured interviews and was analyzed using various quantitative indices including use value (UV), relative frequency of citation (RFC), use report (UR), fidelity level (FL), informant consensus factor (ICF) and family importance value (FIV). Plant samples were collected identified and then processed as voucher specimens following standard ethnobotanical practice. Results: One hundred twenty participants including 80 men and 40 women were interviewed. The participants reported a total of 106 plant species, belonging to 96 genera and 50 families. There were two families of pteridophytes (2 species), 2 families of gymnosperm (4 species) and 100 species belonging to 46 families of angiosperms. The local population used therapeutic plants to heal 114 different diseases in 19 aliment categories in the study area.A total of 106 species belonging to 50 families were documented as used to treat different types of illness. The UV ranged from 0.01 (Artemisia scoparia and Malva sylvestris) to 0.75 (Conyza canadensis). The RFC varied from 0.025 (Hyoscyamus niger and Senecio crysanthemoides) to 1.992 (Ephedra intermedia). The species with 100% FL were Astragalus stocksii and Artemisia scoparia, while the FCI ranged from 0 to 1 for insecticides and acoustic disorders. The conservation assessment revealed that 49 plant species were vulnerable, followed by rare (34 spp.), infrequent (7 spp.), Dominant (5spp.) And 5 endangered species. Conclusion: The current study showed that Central Kurram has a significant diversity of medicinal plant, and the use of medicinal plants and plant-based remedies is still common in the area. A total of 106 medicinal plant species, belonging to 50 families were documented for the treatment of 114 disorders. The residents used medicinal plants in treatment of important diseases such as Covid-19, cancer, dysentery, as diuretic, wound healing, and sexual diseases. © 2022, Ilia State University, Institute of Botany, Department of Ethnobotany. All rights reserved.

Assessing Public Opinion Regarding COVID-19 Vaccinations in Pakistan: Knowledge, and Perceptions of General Public

The COVID-19 infodemic can be counteracted by clear and consistent communication of scientific evidence and improved health literacy between the public and informants. For complete eradication of COVID-19, several vaccines are approved in various countries for public use by regulatory authorities. Assessing public perception regarding COVID-19 vaccination is an important area of research. In the current study, we aim to evaluate the opinions of individuals from multiple localities about COVID-19 and its vaccination through an online survey. Participants of the study were divided into different groups based on age, profession, demography, and income, and their opinions were calculated in percentage. In age group analysis we reported the highest willingness, 62.8 % (n=22) in age group 30-40, followed by 60 % (n=3) in age group >50, 58.6 % (n=244) in age group 20-30, 57.95 % (n=51) was in age group 15-20 and the least willingness, 33.33 % (n=4) in age group 40-50. The highest disagreement regarding vaccination of 60 % was found in age group >50, followed by 33.3 % in the age group 40-50, 14.7 % in the age group 20-30, 11.4 % in the age group 15-20 and 30-40. Similarly based on profession, maximum acceptability, 59.1 % (n=262) was reported in students, followed by a businessman (68.7 %, n=11), professional workers (3.5 %, n=20). Likewise, in demographic analysis, individuals from Khyber Pakhtunkhwa (KP) (61.3 %, n=200) were found more enthusiastic for vaccination, followed by AJK (58.33 %, n=7) and Islamabad (58.1 %, n=32). In the same way, people with income range $435-$621/month showed the highest willingness (65.7 %, n=69) regarding vaccination, followed by income group ($621 or more $s/month). Surprisingly, individuals from the low-income group were found more interested in vaccination as compared to the higher-income group. Comparatively low interest of high-income group individuals may be due to more exposure to conspiracy theories shared on social media. © Pakistan Academy of Sciences.

Highly-sensitive and rapid detection of COVID-19 in wastewater: a comprehensive review based on graphene field effect transistor biosensor

The novel Corona Virus Disease 2019 (COVID-19) has been spread from the Wuhan city of China has now affected many countries;it is still circulating worldwide. Consecutive studies of finding the RNA of this virus in sewage systems increase renewed interest about COVID-19 faucal transmission and its pathogenic issue on sanitation and wastewater systems. Municipal wastewater is typically remarked as one of the major end routes of different types of emerging contaminants such as pharmaceuticals, endocrine disruptors, antibiotics, micro plastics, pesticide and heavy metal residues associated with antimicrobial resistance. Currently all available, antibodies based and molecular base testing have some limitations for this purpose: whole coronavirus particles instead of pure antigen proteins need to be tested in a short time and take control of the pandemic of COVID-19. The current study helped in understanding, concept and demonstrated the potential of graphene Field Effect Transistor (FET) technology for sensitive and rapid detection of corona viruses. Therefore, extra trustworthy, quick response, economical and broadly accessible analytical devices or diagnostic approaches are crucially required. We have critically reviewed and argued the biomarkers and indicators used for COVID-19 diagnostics or SARS-CoV-2 detection. In this regard, ultrasensitive graphene FET biosensors are powerful tools in early diagnosis of COVID-19 infection via targeting virus S1 protein to assess the clinical progress and offer awareness on severity and critical trends of infection.

A diagnostic challenge in COVID-19 pandemic: PCP or COVID-19

This case report describes the clinical course and management of a 67-year gentleman presented with shortness of breath, fever and cough. History is significant for diabetes and hypertension. Baseline imaging and routine blood tests were consistent with respiratory tract infection. The patient clinically deteriorated, inflammatory markers increased by and the ABG showed type 1 respiratory failure. He was admitted to the ICU, and due to severe hypoxemia, he was intubated and mechanically ventilated. Treatment with broad-spectrum intravenous (IV) antibiotics was initiated but no clinical or radiological improvement was observed. Chest CT scan showed bilateral opacity/unification of the mid-inferior lungs in patch and interstitial pneumonia (Figs 1A and B). On clinical suspicion and repeated negative COVID-19 swabs, negative blood and urine cultures, treatment with PCP was initiated. There was excellent clinical improvement. Patient was extubated and then weaned off oxygen. His HIV status was negative, but his sputum and CRP showed PCP.

REMOTE PATIENT MONITORING SYSTEM (RPMS) MODEL TO IMPROVE EMERGENCY SERVICES IN PAKISTAN DURING COVID 19 - A CONCEPTUAL FRAMEWORK USING ICT TOWARDS OVERCOMING HEALTHCARE PROBLEMS IN PANDEMIC HISTORY

The history of the world during the pandemic era shows a worse kind of treatment to the effected people. The year 2020 is known as a terrifying year due to pandemics in the world. It badly exposes the healthcare system's weakness especially in the developing countries like Pakistan. Ambulance services played a major role in transporting affected people during pandemics. However, an independent survey shows that around 130 million people have very limited access to these facilities in Pakistan. The majority of the population of Pakistan lives in remote and rural areas and is deprived of these services. In this regard, remote monitoring of patients while they are in transit is very crucial. Considering the importance of providing better healthcare services to patients, we propose a model for remote patient monitoring systems (RPMS) integrated with emergency services in Pakistan. This healthcare model continuously keeps track of people including patients and nearest ambulances (the in-context emergency service and the key resource) for helping patients by transporting them to the appropriate healthcare center, as per needed assistance on the go. It helps reducing response time and yet increasing golden time by directly engaging the ambulance from the nearest location (using spatial and temporal features) and bypassing the call center to save the inevitable consumption of time shattered by conventional method. The represented model may enhance the availability of emergency healthcare facilities by reducing service time and allow efficient use of resources. © 2021 Asian Research Publishing Network (ARPN). All Rights Reserved.