Mucormycosis co-infection in COVID-19 patients: An update

Mucormycosis (MCM) is a rare fungal disorder that has recently been increased in parallel with novel COVID-19 infection. MCM with COVID-19 is extremely lethal, particularly in immunocompromised individuals. The collection of available scientific information helps in the management of this co-infection, but still, the main question on COVID-19, whether it is occasional, participatory, concurrent, or coincidental needs to be addressed. Several case reports of these co-infections have been explained as causal associations, but the direct contribution in immunocompromised individuals remains to be explored completely. This review aims to provide an update that serves as a guide for the diagnosis and treatment of MCM patients’ co-infection with COVID-19. The initial report has suggested that COVID-19 patients might be susceptible to developing invasive fungal infections by different species, including MCM as a co-infection. In spite of this, co-infection has been explored only in severe cases with common triangles: diabetes, diabetes ketoacidosis, and corticosteroids. Pathogenic mechanisms in the aggressiveness of MCM infection involves the reduction of phagocytic activity, attainable quantities of ferritin attributed with transferrin in diabetic ketoacidosis, and fungal heme oxygenase, which enhances iron absorption for its metabolism. Therefore, severe COVID-19 cases are associated with increased risk factors of invasive fungal co-infections. In addition, COVID-19 infection leads to reduction in cluster of differentiation, especially CD4+ and CD8+ T cell counts, which may be highly implicated in fungal co-infections. Thus, the progress in MCM management is dependent on a different strategy, including reduction or stopping of implicit predisposing factors, early intake of active antifungal drugs at appropriate doses, and complete elimination via surgical debridement of infected tissues

Transcriptional analysis of Rhazya stricta in response to jasmonic acid

Background: Jasmonic acid (JA) is a signal transducer molecule that plays an important role in plant development and stress response; it can also efficiently stimulate secondary metabolism in plant cells. Results: RNA-Seq technology was applied to identify differentially expressed genes and study the time course of gene expression in Rhazya stricta in response to JA. Of more than 288 million total reads, approximately 27% were mapped to genes in the reference genome. Genes involved during the secondary metabolite pathways were up- or downregulated when treated with JA in R. stricta. Functional annotation and pathway analysis of all up- and downregulated genes identified many biological processes and molecular functions. Jasmonic acid biosynthetic, cell wall organization, and chlorophyll metabolic processes were upregulated at days 2, 6, and 12, respectively. Similarly, the molecular functions of calcium-transporting ATPase activity, ADP binding, and protein kinase activity were also upregulated at days 2, 6, and 12, respectively. Time-dependent transcriptional gene expression analysis showed that JA can induce signaling in the phenylpropanoid and aromatic acid pathways. These pathways are responsible for the production of secondary metabolites, which are essential for the development and environmental defense mechanism of R. stricta during stress conditions. Conclusions: Our results suggested that genes involved in flavonoid biosynthesis and aromatic acid synthesis pathways were upregulated during JA stress. However, monoterpenoid indole alkaloid (MIA) was unaffected by JA treatment. Hence, we can postulate that JA plays an important role in R. stricta during plant development and environmental stress conditions. How to cite: Hajrah, NH, Rabah SO, Alghamdi MK, et al. Transcriptional analysis of Rhazya stricta in response to jasmonic acid. Electron J Biotechnol 2021;50. https://doi.org/10.1016/j.ejbt.2021.01.00

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Next-Gen brain tumor classification: pioneering with deep learning and fine-tuned conditional generative adversarial networks

Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2

Exploring the Power of Deep Learning: Fine-Tuned Vision Transformer for Accurate and Efficient Brain Tumor Detection in MRI Scans

A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of the patient. There are several imaging techniques used for brain tumor detection. Among these techniques, the most common are MRI and CT scans. To overcome the limitations associated with these traditional techniques, computer-aided analysis of brain images has gained attention in recent years as a promising approach for accurate and reliable brain tumor detection. In this study, we proposed a fine-tuned vision transformer model that uses advanced image processing and deep learning techniques to accurately identify the presence of brain tumors in the input data images. The proposed model FT-ViT involves several stages, including the processing of data, patch processing, concatenation, feature selection and learning, and fine tuning. Upon training the model on the CE-MRI dataset containing 5712 brain tumor images, the model could accurately identify the tumors. The FT-Vit model achieved an accuracy of 98.13%. The proposed method offers high accuracy and can significantly reduce the workload of radiologists, making it a practical approach in medical science. However, further research can be conducted to diagnose more complex and rare types of tumors with more accuracy and reliability

Utilization of experimental design in the formulation and optimization of hyaluronic acid–based nanoemulgel loaded with a turmeric–curry leaf oil nanoemulsion for gingivitis

AbstractNumerous problems affect oral health, and intensive research is focused on essential oil–based nanoemulsions that might treat prevent or these problems. Nanoemulsions are delivery systems that enhance the distribution and solubility of lipid medications to targeted locations. Turmeric (Tur)- and curry leaf oil (CrO)–based nanoemulsions (CrO-Tur-self-nanoemulsifying drug delivery systems [SNEDDS]) were developed with the goal of improving oral health and preventing or treating gingivitis. They could be valuable because of their antibacterial and anti-inflammatory capabilities. CrO-Tur-SNEDDS formulations were produced using the response surface Box-Behnken design with different concentrations of CrO (120, 180, and 250 mg), Tur (20, 35, and 50 mg), and Smix 2:1 (400, 500, and 600 mg). The optimized formulation had a bacterial growth inhibition zone of up to 20 mm, droplet size of less than 140 nm, drug-loading efficiency of 93%, and IL-6 serum levels of between 950 ± 10 and 3000 ± 25 U/ml. The optimal formulation, which contained 240 mg of CrO, 42.5 mg of Tur, and 600 mg of Smix 2:1, was created using the acceptable design. Additionally, the best CrO-Tur-SNEDDS formulation was incorporated into a hyaluronic acid gel, and thereafter it had improved ex-vivo transbuccal permeability, sustained in-vitro release of Tur, and large bacterial growth suppression zones. The optimal formulation loaded into an emulgel had lower levels of IL-6 in the serum than the other formulations evaluated in rats. Therefore, this investigation showed that a CrO-Tur-SNEDDS could provide strong protection against gingivitis caused by microbial infections

Functional dissection of polymicrobial synergy between Porphyromonas gingivalis and Streptococcus gordonii

Whole proteome proteomics data for P. gingivalis in the presence or absence of 4 aminobenzoate/para-amino benzoic acid (pABA)Many human infections are polymicrobial in origin, and synergistic interactions among community inhabitants control colonization and pathogenic potential (Murray et al., 2014). However, few interspecies interactions have been functionally dissected at the molecular level or characterized on a systems level. Periodontitis, which is the sixth most prevalent infectious disease worldwide (Kassebaum et al., 2014), is associated with a dysbiotic microbial community, and the keystone pathogen Porphyromonas gingivalis forms synergistic communities with the accessory pathogen Streptococcus gordonii (Lamont and Hajishengallis, 2015). P. gingivalis and S. gordonii communicate through co-adhesion and metabolite perception, and close association between P. gingivalis and S. gordonii results in significant changes in the expressed proteomes of both organisms (Kuboniwa et al., 2012, Hendrickson et al., 2012). Here we show that streptococcal 4 aminobenzoate/para-amino benzoic acid (pABA) is required for maximal accumulation of P. gingivalis in communities with S. gordonii. Exogenous pABA upregulates production of fimbrial interspecies adhesins and of a tyrosine phosphorylation-dependent signaling system in P. gingivalis. Consequently, fimbrial-dependent attachment and invasion of epithelial cells by P. gingivalis is also increased by pABA. Moreover, trans-omics studies performed by proteomics and metabolomics showed that pABA induces metabolic shifts within P. gingivalis, predominantly folate derivative biosynthesis. In a murine oral infection model, pABA increased colonization and survival of P. gingivalis, but did not increase virulence. The results establish streptococcal pABA as a major component of the interspecies S. gordonii-P. gingivalis interaction which regulates distinct aspects of polymicrobial synergy