An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff.

The mental and physical well-being of healthcare workers is being affected by global COVID-19. The pandemic has impacted the mental health of medical staff in numerous ways. However, most studies have examined sleep disorders, depression, anxiety, and post-traumatic problems in healthcare workers during and after the outbreak. The study's objective is to evaluate COVID-19's psychological effects on healthcare professionals of Saudi Arabia. Healthcare professionals from tertiary teaching hospitals were invited to participate in the survey. Almost 610 people participated in the survey, of whom 74.3% were female, and 25.7% were male. The survey included the ratio of Saudi and non-Saudi participants. The study has utilized multiple machine learning algorithms and techniques such as Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The machine learning models offer 99% accuracy for the credentials added to the dataset. The dataset covers several aspects of medical workers, such as profession, working area, years of experience, nationalities, and sleeping patterns. The study concluded that most of the participants who belonged to the medical department faced varying degrees of anxiety and depression. The results reveal considerable rates of anxiety and depression in Saudi frontline workers.

Coronavirus disease 2019 vaccine in pediatric post-kidney transplantation.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and the resulting disease, coronavirus disease 2019 (COVID-19), have spread to millions of persons worldwide. Many vaccines have been developed; however, their efficacy in pediatric solid organ transplant recipients is yet to be determined. METHODS: This is a prospective observational, non-interventional single-center study on the safety and efficacy of a COVID-19 vaccine (BNT162b2) in pediatric kidney transplant recipients. The primary aim of this study was to evaluate immunogenicity according to SARS-CoV-2-specific neutralizing antibody titer after two vaccine doses. The secondary aims were to investigate the safety of the vaccines, solicited local and systemic adverse reactions, incidence of COVID-19 post-vaccination, and effects on transplant graft function. Baseline investigations were conducted on pediatric renal transplant recipients, and recruited participants were advised to have the Comirnaty® mRNA vaccine according to protocol. RESULTS: A total of 48 patients (male, n = 31, 64.6%; female, n = 17, 35.4%), median age 14 [12-16] years were included, and all received two doses of the vaccine. The vaccine had a favorable safety and side-effect profile. The S-antibody titer of all patients ranged between .4 and 2,500 U/ml and was > 50 U/ml in 89% of the patients. No difference in the measured antibody immune response was noted between infected and uninfected children. No major side effects were reported. CONCLUSION: The vaccine had a favorable safety profile in 12- to 15-year-old kidney transplant recipients, producing a greater measured antibody response than that in older transplant recipients.

Mpox Perceptions and Vaccine Advocacy among the Healthcare Workers of Solid Organ Transplant Centers: A Multicenter, Cross-Sectional Survey in Saudi Arabia.

BACKGROUND: In response to the global Mpox outbreaks, this survey aimed to assess the knowledge, perceptions, and advocacy of Mpox vaccines among solid organ transplant healthcare workers (HCWs) in Saudi Arabia. METHODS: A cross-sectional survey was conducted among solid organ transplant HCWs in Saudi Arabia from 15 August to 5 September 2022. A total of 199 responses were received from participants primarily working in the kidney (54.8%) and liver (14.6%) transplant units. RESULTS: The survey found that most participants were aware of the 2022 Mpox outbreak, but the majority were more concerned about COVID-19 than Mpox. While the majority of participants thought laboratory personnel and HCWs in direct contact with Mpox patients should receive the vaccine, less than 60% believed that all HCWs should be vaccinated. Additionally, over half of the participants lacked knowledge of animal-human transmission of the virus. CONCLUSION: The results highlight the need for increased education on Mpox among transplant HCWs in Saudi Arabia, particularly regarding the virus's transmission dynamics and vaccines. This education is crucial to improve HCWs' understanding of this emerging disease, especially given their vulnerability during the COVID-19 pandemic.

Comparison of the safety and immunogenicity of the BNT-162b2 vaccine and the ChAdOx1 vaccine for solid organ transplant recipients: a prospective study.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and its resulting disease, coronavirus disease 2019 (COVID-19), has spread to millions of people worldwide. Preliminary data from organ transplant recipients have shown reduced seroconversion rates after the administration of different SARS-CoV-2 vaccination platforms. However, it is unknown whether different vaccination platforms provide different levels of protection against SARS-CoV-2. To answer this question, we prospectively studied 431 kidney and liver transplant recipients (kidney: n = 230; liver: n = 201) who received either the ChAdOx1 vaccine (n = 148) or the BNT-162b2 vaccine (n = 283) and underwent an assessment of immunoglobulin M/immunoglobulin G spike antibody levels. The primary objective of the study is to directly compare the efficacy of two different vaccine platforms in solid organ transplant recipients by measuring of immunoglobulin G (IgG) antibodies against the RBD of the spike protein (anti-RBD) two weeks after first and second doses. Our secondary endpoints were solicited specific local or systemic adverse events within 7 days after the receipt of each dose of the vaccine. There was no difference in the primary outcome between the two vaccine platforms in patients who received two vaccine doses. Unresponsiveness was mainly linked to diabetes. The rate of response after the first dose among younger older patients was significantly larger; however, after the second dose this difference did not persist (p = 0.079). Side effects were similar to those that were observed during the pivotal trials.

IoT Framework for a Decision-Making System of Obesity and Overweight Extrapolation among Children, Youths, and Adults.

Approximately 30% of the global population is suffering from obesity and being overweight, which is approximately 2.1 billion people worldwide. The ratio is expected to surpass 40% by 2030 if the current balance continues to grow. The global pandemic due to COVID-19 will also impact the predicted obesity rates. It will cause a significant increase in morbidity and mortality worldwide. Multiple chronic diseases are associated with obesity and several threat elements are associated with obesity. Various challenges are involved in the understanding of risk factors and the ratio of obesity. Therefore, diagnosing obesity in its initial stages might significantly increase the patient's chances of effective treatment. The Internet of Things (IoT) has attained an evolving stage in the development of the contemporary environment of healthcare thanks to advancements in information and communication technologies. Therefore, in this paper, we thoroughly investigated machine learning techniques for making an IoT-enabled system. In the first phase, the proposed system analyzed the performances of random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), logistic regression (LR), and naïve Bayes (NB) algorithms on the obesity dataset. The second phase, on the other hand, introduced an IoT-based framework that adopts a multi-user request system by uploading the data to the cloud for the early diagnosis of obesity. The IoT framework makes the system available to anyone (and everywhere) for precise obesity categorization. This research will help the reader understand the relationships among risk factors with weight changes and their visualizations. Furthermore, it also focuses on how existing datasets can help one study the obesity nature and which classification and regression models perform well in correspondence to others.

Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models

The modern scientific world continuously endeavors to battle and devise solutions for newly arising pandemics. One such pandemic which has turned the world's accustomed routine upside down is COVID-19: it has devastated the world economy and destroyed around 45 million lives, globally. Governments and scientists have been on the front line, striving towards the diagnosis and engineering of a vaccination for the said virus. COVID-19 can be diagnosed using artificial intelligence more accurately than traditional methods using chest X-rays. This research involves an evaluation of the performance of deep learning models for COVID-19 diagnosis using chest X-ray images from a dataset containing the largest number of COVID-19 images ever used in the literature, according to the best of the authors' knowledge. The size of the utilized dataset is about 4.25 times the maximum COVID-19 chest X-ray image dataset used in the explored literature. Further, a CNN model was developed, named the Custom-Model in this study, for evaluation against, and comparison to, the state-of-the-art deep learning models. The intention was not to develop a new high-performing deep learning model, but rather to evaluate the performance of deep learning models on a larger COVID-19 chest X-ray image dataset. Moreover, Xception- and MobilNetV2- based models were also used for evaluation purposes. The criteria for evaluation were based on accuracy, precision, recall, F1 score, ROC curves, AUC, confusion matrix, and macro and weighted averages. Among the deployed models, Xception was the top performer in terms of precision and accuracy, while the MobileNetV2-based model could detect slightly more COVID-19 cases than Xception, and showed slightly fewer false negatives, while giving far more false positives than the other models. Also, the custom CNN model exceeds the MobileNetV2 model in terms of precision. The best accuracy, precision, recall, and F1 score out of these three models were 94.2%, 99%, 95%, and 97%, respectively, as shown by the Xception model. Finally, it was found that the overall accuracy in the current evaluation was curtailed by approximately 2% compared with the average accuracy of previous work on multi-class classification, while a very high precision value was observed, which is of high scientific value.

Countermeasures against economic crisis from COVID-19 pandemic in China: An analysis of effectiveness and trade-offs.

The effectiveness of different countermeasures to economic crisis from the public health emergency is still inadequately understood. We establish an illustrative scenario, specifying the shocks of COVID-19 pandemic and countermeasures applying a general equilibrium model to analyze the effectiveness of countermeasures with a particular focus on trade-offs in the impacts of monetary and fiscal policies. We find that both monetary and fiscal countermeasures could effectively mitigate the economic damages to GDP and employment. However, they would also produce adverse side-effects such as an increase in consumer price by 1.05% and 0.57%, respectively, and a decline in exports by 2.61% and 1.05%, respectively. Monetary policies would exacerbate the damages to external demand by supply-side shocks of the pandemic, but they are more suitable for mitigating demand-side shocks. While fiscal policies would benefit nearly all producing sectors, monetary policies would mainly affect export-oriented manufacturing sectors negatively.

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.

COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as 'hybrid images' (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.

Syntheses, crystal structures, antioxidant, in silico DNA and SARS-CoV-2 interaction studies of triorganotin(IV) carboxylates

Triorganotin(IV) carboxylate complexes R3SnL, where R = C4H9 (1), CH3 (2) and L = 2-chlorophenyl ethanoate, were synthesized and characterized by elemental analysis, FT-IR, NMR (1H, 13C, 119Sn) and X-ray single crystal analysis. The solid state analyses confirmed a bridging bidentate coordination mode for the carboxylate ligand rendering the tin ion a penta-coordinated centre in the synthesized complexes. NMR spectra revealed a change in the coordination number (5→4) for tin when in the solution. The structural geometry and the electronic properties of complexes were calculated by using the density functional theory (DFT) method at B3LYP level 6-31G(d, p) and Lanl2DZ basis sets. A fairly good agreement was found between the observed and theoretical bond length and bond angle values for the complex (1) and (2). The in vitro antioxidant potential of the complexes was investigated by DPPH, ferrous ion chelation, ferric ion reducing, total antioxidant and hydroxyl free radical scavenging assays. The nature of the tin bonded R groups has apparently a significant impact on the antioxidant activity of the complexes. Molecular docking studies suggest intercalation as possible mode of complex-DNA interactions. Docking studies also confirm that interactions of the two complexes with some active site residues of SARS-CoV-2 nucleocapsid protein and angiotensin-converting enzyme 2 (ACE2) are probable.

Coronavirus Disease-19: Disease Severity and Outcomes of Solid Organ Transplant Recipients: Different Spectrums of Disease in Different Populations?

BACKGROUND: Coronavirus disease-19 (COVID-19) is associated with significant mortality. The elderly, patients with comorbidities, and solid organ transplant (SOT) recipients are particularly at risk. We observed a low incidence of severe disease in our population and aimed to determine the outcomes of COVID-19 (disease severity/intensive care unit [ICU] admissions/mortality) in SOT recipients. METHODS: All SOT recipients diagnosed with COVID-19 were included. Their demographic and clinical data were recorded from the hospital electronic system. Patients were assigned to 1 of 4 stages of disease severity: stage A = asymptomatic, stage B = mild, stage C = moderate, and stage D = severe. RESULTS: Of the 3052 SOT recipients, 67 were diagnosed with COVID-19. The mean age was 52 years, and 69% were male. There were approximately 25% patients in stage A, 28% in stage B, 34% in stage C, and 12% in stage D. Patients in stages C and D were older than those in stage A (P = 0.04) or stage B (P = 0.03). Lactic dehydrogenase (P < 0.01) and D-dimer (P < 0.01) levels were higher across the stages. Approximately 70% of patients were admitted for a median duration of 9 days and the median follow-up was 35 days. Acute kidney injury occurred in 19% of patients, and 45% required supplementary oxygen. The symptomatic patients were treated with Hydroxychloroquine (83%), Azithromycin (89%), and Tocilizumab (23%). Around 15% of patients were admitted to ICU and 2 patients have died. CONCLUSIONS: Most SOT recipients developed mild to moderate COVID-19 infection; few required ICU admission and 2 patients have died. Remaining patients have recovered and have been discharged from the hospital.

Clinical characteristics and outcomes of 85 intensive care patients with Covid-19 in South London: A single centre observational study

BackgroundIn March 2020, Covid-19 secondary to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) was declared a global pandemic.MethodsThis retrospective observational study included patients with Covid-19, managed in a single intensive care unit (ICU). We collected data on patient characteristics, laboratory and radiological findings and ICU management. Data are reported as median (interquartile range). Binary logistic regression modelling was used to identify variables at ICU admission associated with mortality.Results85 patients (age 57.3 years [49.4?64.2], 75.3% male) were followed up for 34 days (26?40). The commonest comorbidities were hypertension (51.8%), obesity (48.7%), and type 2 diabetes (31.8%). Covid-19 presented with shortness of breath (89.4%), fever (82.4%), and cough (81.2%), first noted 8 days (6?10) prior to ICU admission. PaO2/FiO2-ratios at ICU admission were 8.28?kPa (7.04?11.7). Bilateral infiltrates on chest X-ray, lymphopenia, and raised C-reactive protein and ferritin were typical. 81.2% received invasive mechanical ventilation (IMV). Acute kidney injury occurred in 62.4% with renal replacement therapy required in 20.0%. By the end of the follow-up period, 44.7% had died, 30.6% had been discharged from hospital, 14.1% had been discharged from ICU but remained in hospital and 10.6% remained in ICU. ICU length of stay was 14 days (9?23). Age was the only variable at admission which was associated with mortality. PaO2/FiO2-ratio, driving pressure and peak ferritin and neutrophil count over the first 72-hours of IMV all correlated with mortality.ConclusionsWe report the clinical characteristics, ICU practices and outcomes of a South London cohort with Covid-19, and have identified factors which correlate with mortality. By sharing our insight, we hope to further understanding of this novel disease.

The moral and political economy of the pandemic in Bangladesh: Weak states and strong societies during Covid-19.

As the Covid-19 pandemic spread in 2020, the government of Bangladesh ordered a lockdown and promised a program of relief. Citizens complied at first, but soon returned to economic and social life; relief proved slow and uncertain, and citizens could not rely on government assistance. The government tacitly and then officially permitted the lockdown to end, despite a rising Covid-19 caseload. This article draws on theories about state capacity to make and enforce policy to understand why Bangladesh proved unable to sustain a lockdown deemed necessary to contain the pandemic in this densely populated, low income country. Drawing on original qualitative mobile phone-based research in six selected communities, this article examines how the state exercised its capacities for coercion, control over lower factions within political society, and sought to preserve and enhance its legitimacy. It concludes that despite a) the growth in the capacity of the Bangladeshi state in the past decade and b) strong political incentives to manage the pandemic without harm to economic wellbeing, the pressures to sustain legitimacy with the masses forced the state and its frontline actors to tolerate lockdown rule-breaking, conceding that the immediate livelihood needs of the poor masses overrode national public health concerns. Chronically unable to enforce its authority over local political elites, the state failed to ensure a fair and timely distribution of relief. The weakness of the Bangladeshi state contrasts with the strength of widely shared 'moral economy' views within society, which provided powerful ethical and political justification for citizens' failures to comply with the lockdown, and for officials' forbearance in its enforcement. The Covid-19 pandemic highlights both the importance of state capacity in managing novel shocks from within the global system, and the challenges in settings where weak states are embedded in strong societies.