Pulmonary Sclerosing Pneumocytomas Mimicking Lung Cancer.

Pulmonary sclerosing pneumocytomas are benign tumors. These tumors are often found incidentally and can be challenging to distinguish from lung malignancies. Here, we describe the case of a 31-year-old woman who presented with an incidental finding of a lung nodule in the lingula. She was asymptomatic and had no history of cancer. Positron emission tomography showed [18F] fluorodeoxyglucose (FDG) uptake in the nodule but no FDG-avid mediastinal lymphadenopathy. In view of these findings, a bronchoscopy was performed, and biopsy samples were taken. The final pathological diagnosis revealed a sclerosing pneumocytoma.

Probability Analysis of a Stochastic Non-Autonomous SIQRC Model with Inference

When an individual with confirmed or suspected COVID-19 is quarantined or isolated, the virus can linger for up to an hour in the air. We developed a mathematical model for COVID-19 by adding the point where a person becomes infectious and begins to show symptoms of COVID-19 after being exposed to an infected environment or the surrounding air. It was proven that the proposed stochastic COVID-19 model is biologically well-justifiable by showing the existence, uniqueness, and positivity of the solution. We also explored the model for a unique global solution and derived the necessary conditions for the persistence and extinction of the COVID-19 epidemic. For the persistence of the disease, we observed that Rs0>1, and it was noticed that, for Rs<1, the COVID-19 infection will tend to eliminate itself from the population. Supplementary graphs representing the solutions of the model were produced to justify the obtained results based on the analysis. This study has the potential to establish a strong theoretical basis for the understanding of infectious diseases that re-emerge frequently. Our work was also intended to provide general techniques for developing the Lyapunov functions that will help the readers explore the stationary distribution of stochastic models having perturbations of the nonlinear type in particular.

A vigorous study of fractional order mathematical model for SARS-CoV-2 epidemic with Mittag-Leffler kernel

SARS-CoV-2 and its variants have been investigated using a variety of mathematical models. In contrast to multi-strain models, SARS-CoV-2 models exhibit a memory effect that is often overlooked and more realistic. Atangana-Baleanu's fractional-order operator is discussed in this manuscript for the analysis of the transmission dynamics of SARS-CoV-2. We investigated the transmission mechanism of the SARS-CoV-2 virus using the non-local Atangana-Baleanu fractional-order approach taking into account the different phases of infection and transmission routes. Using conventional ordinary derivative operators, our first step will be to develop a model for the proposed study. As part of the extension, we will incorporate fractional order derivatives into the model where the used operator is the fractional order operator of order

Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning.

Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 and its variants accompanied by pneumonia and tuberculosis can be fatal in some cases and lives could be saved through early detection and appropriate intervention for the advanced cases. Thus CXRs can be used for an automated severity grading of pulmonary diseases that can aid radiologists in making better and informed diagnoses. In this article, we propose a single framework for disease classification and severity scoring produced by segmenting the lungs into six regions. We present a modified progressive learning technique in which the amount of augmentations at each step is capped. Our base network in the framework is first trained using modified progressive learning and can then be tweaked for new data sets. Furthermore, the segmentation task makes use of an attention map generated within and by the network itself. This attention mechanism allows to achieve segmentation results that are on par with networks having an order of magnitude or more parameters. We also propose severity score grading for 4 thoracic diseases that can provide a single-digit score corresponding to the spread of opacity in different lung segments with the help of radiologists. The proposed framework is evaluated using the BRAX data set for segmentation and classification into six classes with severity grading for a subset of the classes. On the BRAX validation data set, we achieve F1 scores of 0.924 and 0.939 without and with fine-tuning, respectively. A mean matching score of 80.8% is obtained for severity score grading while an average area under receiver operating characteristic curve of 0.88 is achieved for classification.

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

Improving equity, efficiency and adherence to referral in Pakistan's eye health programmes: Pre- and post-pandemic onset

Background Over one billion people worldwide live with avoidable blindness or vision impairment. Eye Health Programmes tackle this by providing screening, primary eye care, refractive correction, and referral to hospital eye services. One point where patients can be lost in the treatment journey is adherence to hospital referral. Context Peek Vision's software solutions have been used in Pakistan with the goal of increasing eye health programme coverage and effectiveness. This involved collaboration between health system stakeholders, international partners, local community leaders, social organizers and “Lady Health Workers”. Results From the beginning of the programmes in November 2018, to the end of December 2021, 393,759 people have been screened, 26% of whom (n = 101,236) needed refractive services or secondary eye care, and so were referred onwards to the triage centers or hospital services. Except for a short period affected heavily by COVID-19 pandemic, the programmes reached an increasing number of people over time: screening coverage improved from 774 people per month to over 28,300 people per month. Gathering and discussing data regularly with stakeholders and implementers has enabled continuous improvement to service delivery. The quality of screening and adherence to hospital visits, gender balance differences and waiting time to hospital visits were also improved. Overall attendance to hospital appointments improved in 2020 compared to 2019 from 45% (95% CI: 42–48%) to 78% (95% CI: 76–80%) in women, and from 48% (95% CI: 45–52%) to 70% (95% CI: 68–73%) in men. These patients also accessed treatment more quickly: 30-day hospital referral adherence improved from 12% in 2019 to 66% in 2020. This approach helped to utilize refractive services more efficiently, reducing false positive referrals to triage from 10.6 to 5.9%. Hospital-based services were also utilized more efficiently, as primary eye care services and refractive services were mainly delivered at the primary healthcare level. Discussion Despite various challenges, we demonstrate how data-driven decisions can lead to health programme systems changes, including patient counseling and appointment reminders, which can effectively improve adherence to referral, allowing programmes to better meet their community's needs.

Use of CRISPR in Infection Control.

One of the greatest threats to the global world is infectious diseases. The morbidity and fatality of infectious diseases cause 17 million deaths annually. The recent COVID-19 pandemic describes the uncertain potential of these diseases. Understanding the pathogenesis of infectious agents, including bacteria, viruses, fungi, etc. and the evolution of rapid diagnostic techniques and treatments has become a pressing priority to improve infectious disease outcomes worldwide. Clustered regularly interspaced short palindromic repeats (CRISPR) constitute the adaptive immune system of archaea and bacteria along with CRISPR-associated (Cas) proteins that recognize and destroy foreign DNA acting as molecular scissors. Since their discovery, CRISPR systems are classified into 6 types and 22 subtypes. Type II, V, and VI are used for diagnostic purposes. Utilizing the CRISPR-Cas system's capabilities will aid promote the development of novel and improved diagnostics as well as innovative delivery systems and the prevention and treatment of infectious diseases.

Methylprednisolone Treatment Versus Standard Supportive Care for Adult COVID-19 Mechanically Ventilated, Acute Respiratory Distress Syndrome Patients.

A myriad of symptoms presented by severely ill mechanically ventilated COVID-19 patients has added pressure on the caregivers to explore therapeutic options. Systemic steroids have been reported to therapeutically benefit patients, with elevated inflammatory markers, during the severe acute respiratory syndrome, and the Middle East respiratory syndrome outbreak. COVID-19 disease is characterized by inflammation of the respiratory system and acute respiratory distress syndrome. Given the lack of specific treatment for COVID-19, the current study aimed to evaluate the therapeutic benefit of methylprednisolone as an add-on treatment for mechanically ventilated hospitalized COVID-19 patients with severe COVID pneumonia. Data were collected retrospectively from the electronic patient medical records, and interrater reliability was determined to limit selection bias. Descriptive and inferential statistical methods were used to analyze the data. The variables were cross-tabulated with the clinical outcome, and the chi-square test was used to determine the association between the outcomes and other independent variables. Sixty-one percent (43/70) of the COVID-19 ARDS patients received standard supportive care, and the remainder were administered, methylprednisolone (minimum 40 mg daily to a maximum 40 mg q 6 h). A 28-day all-cause mortality rate, in the methylprednisolone group, was 18% (5/27, p < 0.01) significantly lower, compared to the group receiving standard supportive care (51%, 22/43). The median number of days, for the hospital length of stay (18 days), ICU length of stay (9.5 days), and the number of days intubated (6 days) for the methylprednisolone-treated group, was significantly lower (p < 0.01) when compared with the standard supportive care group. Methylprednisolone treatment also reduced the C-reactive protein levels, compared to the standard care group on day 7. Our results strengthen the evidence for the role of steroids in reducing mortality, ICU length of stay, and ventilator days in mechanically ventilated COVID-19 patients with respiratory distress syndrome.

Prognostic accuracy of time to sputum culture conversion in predicting cure in extensively drug-resistant tuberculosis patients: a multicentre retrospective observational study.

BACKGROUND: There was a lack of information about prognostic accuracy of time to sputum culture conversion (SCC) in forecasting cure among extensively drug-resistant tuberculosis (XDR-TB) patients. Therefore, this study evaluated the prognostic accuracy of SCC at various time points in forecasting cure among XDR-TB patients. METHODS: This retrospective observational study included 355 eligible pulmonary XDR-TB patients treated at 27 centers in Pakistan between 01-05-2010 and 30-06-2017. The baseline and follow-up information of patients from treatment initiation until the end of treatment were retrieved from electronic nominal recording and reporting system. Time to SCC was analyzed by Kaplan-Meier method, and differences between groups were compared through log-rank test. Predictors of time to SCC and cure were respectively evaluated by multivariate Cox proportional hazards and binary logistic regression analyses. A p-value < 0.05 was considered statistically significant. RESULTS: A total of 226 (63.6%) and 146 (41.1%) patients respectively achieved SCC and cure. Median time to SCC was significantly shorter in patients who achieved cure, 3 months (95% confidence interval [CI]: 2.47-3.53), than those who did not (median: 10 months, 95% CI: 5.24-14.76) (p-value < 0.001, Log-rank test). Patient's age > 40 years (hazards ratio [HR] = 0.632, p-value = 0.004), baseline sputum grading of scanty, + 1 (HR = 0.511, p-value = 0.002), + 2, + 3 (HR = 0.523, p-value = 0.001) and use of high dose isoniazid (HR = 0.463, p-value = 0.004) were significantly associated with early SCC. Only SCC at 6 month of treatment had statistically significant association with cure (odds ratio = 15.603, p-value < 0.001). In predicting cure, the sensitivities of SCC at 2, 4 and 6 months were respectively 41.8% (95%CI: 33.7-50.2), 69.9% (95%CI: 61.7-77.2) and 84.9% (95%CI: 78.1-90.3), specificities were respectively, 82.8% (95%CI: 76.9-87.6), 74.6% (95%CI: 68.2-80.4) and 69.4% (95%CI: 62.6-75.5) and prognostic accuracies were respectively 65.9% (95%CI: 60.7-70.8), 72.7% (95%CI: 67.7-77.2) and 75.8% (95%CI: 71.0-80.1). CONCLUSION: In forecasting cure, SCC at month 6 of treatment performed better than SCC at 2 and 4 months. However, it would be too long for clinicians to wait for 6 months to decide about the regimen efficacy. Therefore, with somewhat comparable prognostic accuracy to that SCC at 6 month, using SCC at 4 month of treatment as a prognostic marker in predicting cure among XDR-TB patients can decrease the clinicians waiting time to decide about the regimen efficacy.

Clinical and laboratory characteristics of patients hospitalised with COVID-19: clinical outcomes in Abu Dhabi, United Arab Emirates.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in December 2019. The severity of coronavirus disease 2019 (COVID-19) ranges from asymptomatic to severe and potentially fatal. We aimed to describe the clinical and laboratory features and outcomes of hospitalised patients with COVID-19 within the Abu Dhabi Healthcare Services Facilities (SEHA). METHODS: Our retrospective analysis of patient data collected from electronic health records (EHRs) available from the SEHA health information system included all patients admitted from 1 March to 31 May 2020 with a laboratory-confirmed PCR diagnosis of SARS-CoV-2 infection. Data of clinical features, co-morbidities, laboratory markers, length of hospital stay, treatment received and mortality were analysed according to severe versus non-severe disease. RESULTS: The study included 9390 patients. Patients were divided into severe and non-severe groups. Seven hundred twenty-one (7.68%) patients required intensive care, whereas the remaining patients (92.32%) had mild or moderate disease. The mean patient age of our cohort (41.8 years) was lower than the global average. Our population had male predominance, and it included various nationalities. The major co-morbidities were hypertension, diabetes mellitus and chronic kidney disease. Laboratory tests revealed significant differences in lactate dehydrogenase, ferritin, C-reactive protein, interleukin-6 and creatinine levels and the neutrophil count between the severe and non-severe groups. The most common anti-viral therapy was the combination of Hydroxychloroquine and Favipiravir. The overall in-hospital mortality rate was 1.63%, although the rate was 19.56% in the severe group. The mortality rate was higher in adults younger than 30 years than in those older than 60 years (2.3% vs. 0.95%). CONCLUSIONS: Our analysis suggested that Abu Dhabi had lower COVID-19 morbidity and mortalities rates were less than the reported rates then in China, Italy and the US. The affected population was relatively young, and it had an international representation. Globally, Abu Dhabi had one of the highest testing rates in relation to the population volume. We believe the early identification of patients and their younger age resulted in more favourable outcomes.

Opinion mining towards blockchain technology adoption for accessing digital library resources

PurposeBlockchain technology is a distributed and decentralized public digital ledger, which is employed to save dynamic transaction data and static records across several computers so that each record could not be modified retroactively without the collusion of the network and alteration of all subsequent blocks. Recently, it has become immensely popular in digital resource sharing in different research areas such as healthcare, smart cities, cryptocurrency and libraries. Since the current eLibrary systems are vulnerable to issues such as unauthorized access, plagiarism, etc., there is a lack of access control system that can efficiently address these issues.Design/methodology/approachThe authors designed a conceptual model for evaluating the users' intention in the use of blockchain-based digital libraries, which can facilitate the resource organization and provide additional security to interactive processes between users. To conduct our survey, the authors devised and shared two versions, English and Chinese, among 298 participants. Moreover, 7 PhD students participated in the pre-testing of the questioner design. The authors analyzed the demographic data using the Jamovi software and SmartPLS in order to generate the path modeling.FindingsThis study revealed that blockchain technology adaption in eLibraries is essential for enhancing the quality of services, infrastructure and resources for libraries. The study’s results show that optimism, informativeness, perceived usefulness, perceived ease of use, attitude and intention to use blockchain technology for accessing digital resources in libraries.Originality/valueThis study contributes to the adoption of blockchain technology in the digital library. To the best of the authors’ knowledge, this work is the first empirical attempt to provide a new perspective of developing digital libraries based on security policies. This model shows the underpinning knowledge to manage digital resources, which can facilitate the design phases and enhance the management costs in eLibraries.

Assessment of mortality from COVID-19 in a multicultural multi-ethnic patient population.

BACKGROUND: Studies indicate that ethnicity and socioeconomic disparity are significant facilitators for COVID-19 mortality. The United Arab Emirates, distinctly has a population of almost 12% citizens and the rest, immigrants, are mainly unskilled labourers. The disparate socio-economic structure, crowded housing conditions, and multi-ethnic population offer a unique set of challenges in COVID-19 management. METHODS: Patient characteristics, comorbidities, and clinical outcomes data from the electronic patient medical records were retrospectively extracted from the hospital information system of the two designated public COVID-19 referral hospitals. Chi-square test, logistic regression, and odds ratio were used to analyse the variables. RESULTS: From, the total of 3072 patients, less than one-fifth were females; the Asian population (71.2%);followed by Middle Eastern Arabs (23.3%) were the most infected by the virus. Diabetes Mellitus (26.8%), hypertension (25.7%) and heart disease (9.6%) were the most prevalent comorbidities observed among COVID-19 patients. Kidney disease as comorbidity significantly diminished the survival rates (Crude OR 9.6, 95% CI (5.6-16.6), p < 0.001) and (Adjusted OR 5.7 95% CI (3.0 - 10.8), p < 0.001), as compared to those patients without kidney disease. Similarly, the higher age of patients between 51 and 65 years, significantly decreased the odds for survival (Crude OR 14.1 95% CI (3.4-58.4), p < 0.001) and (Adjusted OR 12.3 95% CI (2.9 - 52.4), p < 0.001). Patient age beyond 66 years, further significantly decreased the odds for survival (Crude OR 36.1 95% CI (8.5-154.1), p < 0.001), and (Adjusted OR 26.6 95% CI (5.7 - 123.8), p < 0.001). CONCLUSION: Our study indicates that older ages above 51 years and kidney disease increased mortality significantly in COVID-19 patients. Ethnicity was not significantly associated with mortality in the UAE population. Our findings are important in the management of the COVID-19 disease in the region with similar economic, social, cultural, and ethnic backgrounds.

Repurposing drug molecule against SARS-Cov-2 (COVID-19) through molecular docking and dynamics: a quick approach to pick FDA-approved drugs.

A novel coronavirus known as severe acute respiratory syndrome is rapidly spreading worldwide. The international health authorities are putting all their efforts on quick diagnosis and placing the patients in quarantine. Although different vaccines have come for quick use as prophylactics, drug repurposing seems to be of paramount importance because of inefficient therapeutic options and clinical trial limitations. Here, we used structure-based drug designing approach to find and check the efficacy of the possible drug that can inhibit coronavirus main protease which is involved in polypeptide processing to functional protein. We performed virtual screening, molecular docking and molecular dynamics simulations of the FDA-approved drugs against the main protease of SARS-CoV-2. Using well-defined computational methods, we identified amprenavir, cefoperazone, riboflavin, diosmin, nadide and troxerutin approved for human therapeutic uses, as COVID-19 main protease inhibitors. These drugs bind to the SARS-CoV-2 main protease conserved residues of substrate-binding pocket and formed a remarkable number of non-covalent interactions. We have found diosmin as an inhibitor which binds covalently to the COVID-19 main protease. This study provides enough evidences for therapeutic use of these drugs in controlling COVID-19 after experimental validation and clinical demonstration.

Bitcoin and Altcoins Price Dependency: Resilience and Portfolio Allocation in COVID-19 Outbreak

The main aim of this article is to examine the inter-relationships among the top cryptocurrencies on the crypto stock market in the presence and absence of the COVID-19 pandemic. The nine chosen cryptocurrencies are Bitcoin, Ethereum, Ripple, Litecoin, Eos, BitcoinCash, Binance, Stellar, and Tron and their daily closing price data are captured from coinmarketcap over the period from 13 September 2017 to 21 September 2020. All of the cryptocurrencies are integrated of order 1 i.e., I(1). There is strong evidence of a long-run relationship between Bitcoin and altcoins irrespective of whether it is pre-pandemic or pandemic period. It has also been found that these cryptocurrencies’ prices and their inter-relationship are resilient to the pandemic. It is recommended that when the investors create investment plans and strategies they may highly consider Bitcoin and altcoins jointly as they give sustainability and resilience in the long run against the geopolitical risks and even in the tough time of the COVID-19 pandemic.

Therapeutic plasma exchange for coronavirus disease-2019 triggered cytokine release syndrome; a retrospective propensity matched control study.

BACKGROUND: Cytokine release syndrome (CRS) plays a pivotal role in the pathophysiology and progression of Coronavirus disease-2019 (COVID-19). Therapeutic plasma exchange (TPE) by removing the pathogenic cytokines is hypothesized to dampen CRS. OBJECTIVE: To evaluate the outcomes of the patients with COVID-19 having CRS being treated with TPE compared to controls on the standard of care. METHODOLOGY: Retrospective propensity score-matched analysis in a single centre from 1st April to 31st July 2020. We retrospectively analyzed data of 280 hospitalized patients developing CRS initially. PSM was used to minimize bias from non-randomized treatment assignment. Using PSM 1:1, 90 patients were selected and assigned to 2 equal groups. Forced matching was done for disease severity, routine standard care and advanced supportive care. Many other Co-variates were matched. Primary outcome was 28 days overall survival. Secondary outcomes were duration of hospitalization, CRS resolution time and timing of viral clearance on Polymerase chain reaction testing. RESULTS: After PS-matching, the selected cohort had a median age of 60 years (range 32-73 in TPE, 37-75 in controls), p = 0.325 and all were males. Median symptoms duration was 7 days (range 3-22 days' TPE and 3-20 days controls), p = 0.266. Disease severity in both groups was 6 (6.6%) moderate, 40 (44.4%) severe and 44 (49%) critical. Overall, 28-day survival was significantly superior in the TPE group (91.1%), 95% CI 78.33-97.76; as compared to PS-matched controls (61.5%), 95% CI 51.29-78.76 (log rank 0.002), p<0.001. Median duration of hospitalization was significantly reduced in the TPE treated group (10 days vs 15 days) (p< 0.01). CRS resolution time was also significantly reduced in the TPE group (6 days vs. 12 days) (p< 0.001). In 71 patients who underwent TPE, the mortality was 0 (n = 43) if TPE was done within the first 12 days of illness while it was 17.9% (deaths 5, n = 28 who received it after 12th day (p = 0.0045). CONCLUSION: An earlier use of TPE was associated with improved overall survival, early CRS resolution and time to discharge compared to SOC for COVID-19 triggered CRS in this selected cohort of PS-matched male patients from one major hospital in Pakistan.