Frequency of exposure to Toxoplasma gondii in COVID-19 patients. a systematic review and meta-analysis (preprint)

Background Toxoplasmosis is a chronic protozoan parasitic infection that affects nearly one-third of the global population. During the COVID-19 pandemic, cases were observed in patients with COVID-19 and toxoplasmosis. Therefore, this systematic review and meta-analysis aimed to determine the frequency of Toxoplasma gondii exposure in patients with COVID-19. Methods A literature search was conducted in six databases (PubMed, Scopus, Embase, Web of Science, ScienceDirect, and Google Scholar) until March 3, 2024. Study selection, quality assessment, and data extraction were performed independently by three investigators. Statistical analysis was performed using R version 4.3, applying a random-effects model. The quality of the included observational studies was assessed using the "JBI-MAStARI". Results A total of 5,936 studies were retrieved, 13 of which were included in the final meta-analysis. The sample included a total of 2,947 patients with COVID-19 from four countries, of whom approximately 43.26% were men and 49.41% were women. Among the patients, 1,323 showed evidence of exposure to T. gondii through IgG detection, while 1,302 COVID-19 patients were specifically examined for the presence of T. gondii by IgM detection, and 36 positive cases were identified. The frequency of exposure to T. gondii, determined by the presence of IgG in patients with COVID-19, reached 49% (95% CI: 34-63%; 2,947 participants; 13 studies; I2 = 98%, p < 0.01). In addition, the frequency of exposure to T. gondii, evaluated by IgM presence in patients with COVID-19, was 2% (95% CI: 0-6%; 1,302 participants; 6 studies; I2 = 94%, p < 0.01). Conclusion It was shown that almost half of COVID-19 patients had previous exposure to T. gondii through the presence of IgG, and a small percentage, 2%, showed active infection through IgM detection.

The Influence Of Covid-19 On Patient Mobilization And Injury Attributes In The ICU: A Retrospective Analysis Of A Level II Trauma Center (preprint)

The objectives of this study were to determine the effect of COVID-19 on physical therapy (PT) mobilization of traumatically-injured patients and to determine if mobilization affected patient course in the ICU. This retrospective study included patients who were admitted to the ICU of a level II trauma center. The patients were divided into two groups, i.e., those admitted before (n=378) and after (n=499) April 1, 2020 when Georgia's COVID-19 Shelter-in-place order was mandated. The two groups were contrasted on nominal and ratio variables using Chi-square and Student's t-tests. A secondary analysis focused specifically on the after COVID patients examined the extent to which mobilization (n=328) or lack of mobilization (n=171) influenced ICU outcomes (e.g., mortality, readmission). The two groups were contrasted on nominal and ratio variables using Chi-square and Student's -tests. The after COVID patients had higher injury severity as a greater proportion was classified as severely injured (i.e., >15 on Injury Severity Score) compared to the before COVID patients. After COVID patients also had greater cumulative number of comorbidities and experienced greater complications in the ICU. Despite this, there was no difference between patients in receiving a PT consultation or day-to-mobilization. Within the after COVID cohort, those that were mobilized were older, a higher proportion were female, they had greater Glasgow Coma Scale scores, had longer total hospital days, and a lesser mortality rate. Despite shifting patient injury attributes post-COVID-19, a communicable disease, mobilization care remained consistent and effective.

Derivation and Characterization of Novel Cytocompatible Decellularized Tissue Scaffold for Cellular Farming (preprint)

Consumption of conventional animal meat is associated with various zoonotic and chronic diseases which is putting an extra burden on the health sector. Previously, COVID-19, an animal-derived zoonotic disease, exerted detrimental effects globally. Thus, an alternative approach to produce safe and disease-free meat could pave the way to reduce the burden on the health sector. Cellular farming has been suggested and emerged as a potential alternative approach to produce the disease-free cultured meat. However, an appropriate scaffold is key to develop an alternative animal protein in the form of cultured meat or lab-grown meat. Decellularized tissues have been suggested as a potential scaffold for cultured meat production as it provides a suitable environment and niche for the proliferation and growth of the cells and helps to develop the 3D tissues in the laboratory. In the present study, decellularized mushroom tissue has been derived, characterized and the myogenic differentiation capacity of the scaffold was evaluated. The outcome of the study would provide an edible and natural scaffold to produce a safe and disease-free source of animal protein.

Effectiveness of Virtual Training in the Post-Pandemic Period Using the Kirkpatrick Model (preprint)

Teaching and training through online tools were used by most of the (HEIs) Higher Education Institute worldwide during covid-19 to cater to the needs of students who stay far away from universities/colleges. After the pandemic, the virtual model of education and training became a trend. The students and teachers are also more used to this trend and which is giving more opportunities to both learners and instructors.  One of the notable benefits of virtual teaching and learning platform is that it provides a flexible environment to gain knowledge, skills, and attitude simultaneously along with formal off-line education. This directly influences the employability of outgoing UG and PG students. In this paper, an attempt has been made to assess the perceived effectiveness of virtual training and education among outgoing graduates under the Kirkpatrick model.

The anti-SARS-CoV-2 activity of novel 9, 10-dihydrophenanthrene derivatives: an insight into molecular docking, ADMET analysis, and molecular dynamics simulation.

Originating in Wuhan, the COVID-19 pandemic wave has had a profound impact on the global healthcare system. In this study, we used a 2D QSAR technique, ADMET analysis, molecular docking, and dynamic simulations to sort and evaluate the performance of thirty-nine bioactive analogues of 9,10-dihydrophenanthrene. The primary goal of the study is to use computational approaches to create a greater variety of structural references for the creation of more potent SARS-CoV-2 3Clpro inhibitors. This strategy is to speed up the process of finding active chemicals. Molecular descriptors were calculated using 'PaDEL' and 'ChemDes' software, and then redundant and non-significant descriptors were eliminated by a module in 'QSARINS ver. 2.2.2'. Subsequently, two statistically robust QSAR models were developed by applying multiple linear regression (MLR) methods. The correlation coefficients obtained by the two models are 0.89 and 0.82, respectively. These models were then subjected to internal and external validation tests, Y-randomization, and applicability domain analysis. The best model developed is applied to designate new molecules with good inhibitory activity values against severe acute respiratory syndrome coronavirus 2 (SARS CoV-2). We also examined various pharmacokinetic properties using ADMET analysis. Then, through molecular docking simulations, we used the crystal structure of the main protease of SARS-CoV-2 (3CLpro/Mpro) in a complex with the covalent inhibitor "Narlaprevir" (PDB ID: 7JYC). We also supported our molecular docking predictions with an extended molecular dynamics simulation of a docked ligand-protein complex. We hope that the results obtained in this study can be used as good anti-SARS-CoV-2 inhibitors.

Apremilast Add-On Benefits Over Conventional Drugs (ABCD) in Unstable Non-segmental Vitiligo: A 12-Week Single-Center Randomized Controlled Trial.

Background Apremilast is an oral phosphodiesterase-4 enzyme inhibitor that modulates the immune system by increasing intracellular cyclic adenosine monophosphate levels and inhibiting inflammatory cytokines synthesis. We aimed to compare the efficacy and safety of add-on apremilast in combination therapy with standard treatment in patients with unstable, non-segmental vitiligo. Methods The study was a 12-week randomized, controlled, parallel-group, open-labeled trial. The control group received standard treatment (n=15), and the intervention group received 30 mg apremilast twice daily in addition to standard treatment (n= 16). Time to the first sign of re-pigmentation, halt in progression, and change in vitiligo area scoring index (VASI) score is the primary outcomes. Normality was assessed, and appropriate parametric and nonparametric tests were undertaken. Results Thirty-seven participants were randomized into two groups, and analysis was done on thirty-one participants. Over the treatment duration of 12 weeks, the median time to observe the first sign of re-pigmentation was four weeks in the add-on apremilast group compared to seven weeks in the control group (p=0.018). The halt in progression was observed more in the add-on Apremilast group (93.75%) compared to the control group (66.66%) (p=0.08). The VASI score decreased by 1.24 in the add-on apremilast group and 0.05 in the control group (p= 0.754). Parameters including body surface area, dermatology life quality index, and body mass index reduced significantly, while the visual analog scale increased significantly in the add-on apremilast group. However, results were comparable between groups. Conclusions Treatment with add-on apremilast accelerated clinical improvement. It also reduced disease progression and improved the disease index among participants. However, add-on apremilast had a lower tolerability profile than the control group.

Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection.

BACKGROUND: Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. METHODS: Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p < 0.05). Of these, 62 proteins were validated in an external cohort (p < 0.05, N = 261). RESULTS: We demonstrate that COVID-AKI is associated with increased markers of tubular injury (NGAL) and myocardial injury. Using estimated glomerular filtration (eGFR) measurements taken after discharge, we also find that 25 of the 62 AKI-associated proteins are significantly associated with decreased post-discharge eGFR (adjusted p < 0.05). Proteins most strongly associated with decreased post-discharge eGFR included desmocollin-2, trefoil factor 3, transmembrane emp24 domain-containing protein 10, and cystatin-C indicating tubular dysfunction and injury. CONCLUSIONS: Using clinical and proteomic data, our results suggest that while both acute and long-term COVID-associated kidney dysfunction are associated with markers of tubular dysfunction, AKI is driven by a largely multifactorial process involving hemodynamic instability and myocardial damage.

Acute kidney injury (AKI) is a sudden, sometimes fatal, episode of kidney failure or damage. It is a known complication of COVID-19, albeit through unclear mechanisms. COVID-19 is also associated with kidney dysfunction in the long term, or chronic kidney disease (CKD). There is a need to better understand which patients with COVID-19 are at risk of AKI or CKD. We measure levels of several thousand proteins in the blood of hospitalized COVID-19 patients. We discover and validate sets of proteins associated with severe AKI and CKD in these patients. The markers identified suggest that kidney injury in COVID-19 patients involves damage to kidney cells that reabsorb fluid from urine and reduced blood flow to the heart, causing damage to heart muscles. Our findings might help clinicians to predict kidney injury in patients with COVID-19, and to understand its mechanisms.

Integration of Internet of Things and Blockchain Technologies in Healthcare: A Bibliometric and Network Analysis

This paper has quantitatively analysed the research trends on the integration of the Internet of Things (IoT) and Blockchain Technology (BCT) through bibliometric and network analysis and has also expressed how this integration can be leveraged in healthcare. The research methodology consists of a comprehensive search for scholarly publications on IoT and BCT in healthcare, published during 1999-2021 in Scopus and Web of Science (WoS), leading to 325 articles, followed by bibliometric analysis using Biblioshiny and network analysis using VOSviewer. The bibliometric analysis reveals that there is a surge in research papers since 2020, largely attributed to the outbreak of COVID-19. The research in this area is dominated by India and China, followed by the USA, South Korea, and Australia. The research on the integration of IoT and BCT in healthcare is still evolving. This study adds new avenues of knowledge to the existing literature and gives guidance to future researchers exploring the usage of IoT and BCT to revamp healthcare. This is the first such paper that has done a comprehensive analysis of existing literature in this field by considering papers published in both databases: Scopus and WoS, which could act as a starting point for new researchers.

Prevalence of Computer Vision Syndrome During the COVID-19 Pandemic: Systematic Review and Meta-Analysis (preprint)

Background Computer vision syndrome (CVS) has become a significant public health problem, especially in developing countries. Therefore, this study aims to identify the prevalence of CVS during the COVID-19 pandemic. Methods A systematic review and meta-analysis of the literature was conducted using the databases PubMed, Scopus, Web of Science, and Embase up to February 22, 2023, using the search terms "Computer Vision Syndrome" and "COVID-19". Three authors independently performed study selection, quality assessment, and data extraction, and the Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument was used to evaluate study quality. Heterogeneity was assessed using the statistical test I2, and the R version 4.2.3 program was used for statistical analysis. Results A total of 192 studies were retrieved, of which 18 were included in the final meta-analysis. The total sample included 10337 participants from 12 countries. The combined prevalence of CVS was 74% (95% CI: 66, 81). Subgroup analysis based on country revealed a higher prevalence of CVS in Pakistan (99%, 95% CI: 97, 100) and a lower prevalence in Turkey (48%, 95% CI: 44, 52). In addition, subgroup analysis based on study subjects showed a prevalence of 82% (95% CI: 74, 89) for CVS in non-students and 70% (95% CI: 60, 80) among students. Conclusion According to the study, 74% of the participants experienced CVS during the COVID-19 pandemic. Given this finding, it is essential to implement preventive and therapeutic measures to reduce the risk of developing CVS and improve the quality of life of those affected. Trial registration The protocol for this systematic review and meta-analysis was registered in the international registry of systematic reviews, PROSPERO, with registration number CRD42022345965.

COVID-19-related social isolation and symptoms of depression and anxiety in young men in Poland: Does insomnia mediate the relationship?

The need for physical distancing due to COVID-19 mitigation efforts forced prolonged social isolation, which may affect sleep and lead to mental health problems. Previous research has shown that young adults are particularly vulnerable to psychological stress caused by social isolation, the negative psychological impact of the pandemic, and greater frequency and severity of sleep problems. Therefore, the main goal of the present study was to examine whether insomnia could constitute a mediation mechanism that explains the relationship between social isolation experienced during the COVID-19 pandemic and mental health outcomes (depression and anxiety) reported up to 1.5 years later. The study was conducted among young (M±SD; 24.08±3.75) men (N = 1025) in Poland. Data were collected by means of self-report questionnaires, including The Social Isolation Index, The Athens Insomnia Scale, The State-Trait Anxiety Inventory (STAI-S) and Beck's Depression Inventory (BDI-II). The results show that insomnia mediates the relationships between social isolation and both anxiety and depression. The current findings emphasize the role of insomnia in the relationships between social isolation experienced during COVID-19 and negative emotional states. From a clinical perspective, the results suggest that implementing therapeutic components that address social isolation in insomnia treatment programs may prevent the development of depression and anxiety symptoms among young men.

Endogenous fungal endophthalmitis following COVID-19 infection with microbiological and molecular biological correlation - A report of two cases.

This case report describes three eyes of two patients, who were diagnosed to have endogenous fungal endophthalmitis post coronavirus disease 2019 (COVID-19) infection. Both patients underwent vitrectomy with intravitreal anti-fungal injection. Intra-ocular samples confirmed the fungal etiology by conventional microbiological investigations and polymerase chain reaction in both cases. The patients were treated with multiple intravitreal and oral anti-fungal agents; however, vision could not be salvaged.

Prevalence of Malaria and COVID-19 Coinfection: A Systematic Review and Meta-Analysis (preprint)

Background COVID-19, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), has significantly impacted public health worldwide. However, the outcomes can be unfavourable for those living in areas where malaria is endemic and are affected by SARS-CoV-2. Therefore, this systematic review and meta-analysis aimed to determine the prevalence of coinfection between malaria and COVID-19.  Methods A systematic review and meta-analysis of the literature were conducted, utilizing the most important databases in the field, such as PubMed, Scopus, Web of Science, Embase, and Google Scholar, up to April 9, 2023, using the search terms "Malaria" and "COVID-19". Three authors independently carried out the selection of studies, evaluation of their quality, and data extraction. The Joanna Briggs Institute Meta-Analysis of Statistics Assessment and Review Instrument was used to assess the quality of the studies. In addition, heterogeneity was evaluated using the statistical test I2, and the statistical analysis was performed using the R version 4.2.3 program. Results A total of 10227 studies were retrieved, of which 13 were included in the final meta-analysis. The pooled prevalence of coinfection between Malaria and COVID-19 was 45% (95% CI: 16, 76). Subgroup analysis was performed based on country, and the prevalence of coinfection between Malaria and COVID-19 was highest in China (100%, 95% CI: 88, 100) and lowest in the Democratic Republic of Congo (1%, 95% CI: 0, 2). Regarding the subgroup analysis to evaluate the prevalence of different types of Plasmodium spp. in COVID-19 patients, it was found that the combined prevalence of Plasmodium spp. was 53% (95% CI: 15%-88%; 478/1011 cases).  Conclusion The current research revealed a 45% prevalence of coinfection between Malaria and COVID-19. However, additional future studies are necessary to evaluate the impact of COVID-19 in Malaria-endemic areas fully.  Trial registration  The protocol for this systematic review and meta-analysis was registered with PROSPERO under the registration number CRD42023410177.

Quantification of impact of COVID-19 pandemic on cancer screening programmes - a case study from Argentina, Bangladesh, Colombia, Morocco, Sri Lanka, and Thailand.

It is quite well documented that the COVID-19 pandemic disrupted cancer screening services in all countries, irrespective of their resources and healthcare settings. While quantitative estimates on reduction in volume of screening tests or diagnostic evaluation are readily available from the high-income countries, very little data are available from the low- and middle-income countries (LMICs). From the CanScreen5 global cancer screening data repository we identified six LMICs through purposive sampling based on the availability of cancer screening data at least for the years 2019 and 2020. These countries represented those in high human development index (HDI) categories (Argentina, Colombia, Sri Lanka, and Thailand) and medium HDI categories (Bangladesh and Morocco). No data were available from low HDI countries to perform similar analysis. The reduction in the volume of tests in 2020 compared to the previous year ranged from 14.1% in Bangladesh to 72.9% in Argentina (regional programme) for cervical screening, from 14.2% in Bangladesh to 49.4% in Morocco for breast cancer screening and 30.7% in Thailand for colorectal cancer screening. Number of colposcopies was reduced in 2020 compared to previous year by 88.9% in Argentina, 38.2% in Colombia, 27.4% in Bangladesh, and 52.2% in Morocco. The reduction in detection rates of CIN 2 or worse lesions ranged from 20.7% in Morocco to 45.4% in Argentina. Reduction of breast cancer detection by 19.1% was reported from Morocco. No association of the impact of pandemic could be seen with HDI categories. Quantifying the impact of service disruptions in screening and diagnostic tests will allow the programmes to strategize how to ramp up services to clear the backlogs in screening and more crucially in further evaluation of screen positives. The data can be used to estimate the impact on stage distribution and avoidable mortality from these common cancers.

Anti-COVID-19 Credentials of Chitosan Composites and Derivatives: Future Scope?

Chitosan derivatives and composites are the next generation polymers for biomedical applications. With their humble origins from the second most abundant naturally available polymer chitin, chitosan is currently one of the most promising polymer systems, with wide biological applications. This current review gives a bird's eye view of the antimicrobial applications of chitosan composites and derivatives. The antiviral activity and the mechanisms behind the inhibitory activity of these components have been reviewed. Specifically, the anti-COVID-19 aspects of chitosan composites and their derivatives have been compiled from the existing scattered reports and presented. Defeating COVID-19 is the battle of this century, and the chitosan derivative-based combat strategies naturally become very attractive. The challenges ahead and future recommendations have been addressed.

Dental Calculus – An emerging bio resource for past SARS CoV2 detection, studying its evolution and relationship with oral microflora

The most grievous threat to human health has been witnessed worldwide with the recent outbreak of Corona virus disease 2019 (COVID-19). There is mounting evidence available regarding theconnect of COVID -19 and oral cavity, particularly periodontal disease. The current review provides an update on the diagnostic potential of dental calculus and how this bio resource may help in providing us huge amount of diagnostic regarding the causative virus. Contemporary standard method of diagnosis via nasopharyngeal swabs (NPS) is tedious, may enhance the risk of aerosol contamination by inducing sneezing and detects the presence of active infection only.However,dental calculus being a mineralized deposit serves as a reservoir for biomoleculesand provides detection of past SARS CoV2 infection. Further, the abundance of information that can be obtained from this remarkable mineralized deposit on teeth regarding the viral genome, its evolution and interactions with the oral microflora shall enhance the understanding of the viral disease process and its connection with the periodontal disease. Additional diagnostic information, which may be obtained from this simple bio reservoir can complement the contemporary diagnostic strategies adopted in the management of COVID-19pandemic and enhance our existing knowledge for developing improvised novel approaches to mitigate the effects of mutated variants of the infectious agent.

Chest X-ray findings and severity score of patients with respiratory symptoms in COVID-19 intensive care unit of a teaching hospital

Background: Chest X-rays are important for tracking the progression of lung abnormalities, particularly in critically ill COVID-19 patients in the intensive care units (ICU). We aimed to assess correlation of chest X-ray findings with the COVID-19 disease severity and outcome. Methods: This prospective observational study was conducted at B & C Teaching Hospital, Birtamode from 15 March to 15 July 2021. All diagnosed COVID-19 patients admitted in the ICU for respiratory distress with oxygen saturation < 90% and evaluated by portable X-rays were enrolled. The radiographic findings were evaluated for the distribution and patterns of affection, and the total severity score was calculated using RALE scales of 0 - 8 and Brixia scales of 0 - 18. The scores were then compared between the expired and improved patients. Results: The age of patients (mean +or- SD) was 48.2 +or- 13.84 years. During the first four days of hospitalization, there was no significant difference in Brixia score (p = 0.793) or RALE score (p = 0.842) between expired and improved patients. The differences in both (Brixia and RALE) scores grew with each passing day (p < 0.05). The Brixia and RALE severity scores had a strong positive correlation at various stages of disease in both expired and improved patients. (r > 0.75, p < 0.001). Conclusion: In resource limited setting, the severity scores (RALE or Brixia) can be used as a quantitative method of the extent of COVID-19 pneumonia, correlating with an increased risk of ICU admission.

Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19.

BACKGROUND AND OBJECTIVES: AKI treated with dialysis initiation is a common complication of coronavirus disease 2019 (COVID-19) among hospitalized patients. However, dialysis supplies and personnel are often limited. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using data from adult patients hospitalized with COVID-19 from five hospitals from the Mount Sinai Health System who were admitted between March 10 and December 26, 2020, we developed and validated several models (logistic regression, Least Absolute Shrinkage and Selection Operator (LASSO), random forest, and eXtreme GradientBoosting [XGBoost; with and without imputation]) for predicting treatment with dialysis or death at various time horizons (1, 3, 5, and 7 days) after hospital admission. Patients admitted to the Mount Sinai Hospital were used for internal validation, whereas the other hospitals formed part of the external validation cohort. Features included demographics, comorbidities, and laboratory and vital signs within 12 hours of hospital admission. RESULTS: A total of 6093 patients (2442 in training and 3651 in external validation) were included in the final cohort. Of the different modeling approaches used, XGBoost without imputation had the highest area under the receiver operating characteristic (AUROC) curve on internal validation (range of 0.93-0.98) and area under the precision-recall curve (AUPRC; range of 0.78-0.82) for all time points. XGBoost without imputation also had the highest test parameters on external validation (AUROC range of 0.85-0.87, and AUPRC range of 0.27-0.54) across all time windows. XGBoost without imputation outperformed all models with higher precision and recall (mean difference in AUROC of 0.04; mean difference in AUPRC of 0.15). Features of creatinine, BUN, and red cell distribution width were major drivers of the model's prediction. CONCLUSIONS: An XGBoost model without imputation for prediction of a composite outcome of either death or dialysis in patients positive for COVID-19 had the best performance, as compared with standard and other machine learning models. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_07_09_CJN17311120.mp3.