Clinical and immunological comparison of COVID-19 disease between critical and non-critical courses: a systematic review and meta-analysis.

Introduction: Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which appeared in 2019, has been classified as critical and non-critical according to clinical signs and symptoms. Critical patients require mechanical ventilation and intensive care unit (ICU) admission, whereas non-critical patients require neither mechanical ventilation nor ICU admission. Several factors have been recently identified as effective factors, including blood cell count, enzymes, blood markers, and underlying diseases. By comparing blood markers, comorbidities, co-infections, and their relationship with mortality, we sought to determine differences between critical and non-critical groups. Method: We used Scopus, PubMed, and Web of Science databases for our systematic search. Inclusion criteria include any report describing the clinical course of COVID-19 patients and showing the association of the COVID-19 clinical courses with blood cells, blood markers, and bacterial co-infection changes. Twenty-one publications were eligible for full-text examination between 2019 to 2021. Result: The standard difference in WBC, lymphocyte, and platelet between the two clinical groups was 0.538, -0.670, and -0.421, respectively. Also, the standard difference between the two clinical groups of CRP, ALT, and AST was 0.482, 0.402, and 0.463, respectively. The odds ratios for hypertension and diabetes were significantly different between the two groups. The prevalence of co-infection also in the critical group is higher. Conclusion: In conclusion, our data suggest that critical patients suffer from a suppressed immune system, and the inflammation level, the risk of organ damage, and co-infections are significantly high in the critical group and suggests the use of bacteriostatic instead of bactericides to treat co-infections.

Can modulation of gut microbiota affect anthropometric indices in patients with non-alcoholic fatty liver disease? An umbrella meta-analysis of randomized controlled trials.

Background and aim: Modulating the gut microbiota population by administration of probiotics, prebiotics, and synbiotics has shown to have a variety of health benefits in different populations, particularly those with metabolic disorders. Although the promising effects of these compounds have been observed in the management of patients with non-alcoholic fatty liver disease (NAFLD), the exact effects and the mechanisms of action are yet to be understood. In the present study, we aimed to evaluate how gut microbiota modulation affects anthropometric indices of NAFLD patients to achieve a comprehensive summary of current evidence-based knowledge. Methods: Two researchers independently searched international databases, including PubMed, Scopus, and Web of Science, from inception to June 2023. Meta-analysis studies that evaluated the effects of probiotics, prebiotics, and synbiotics on patients with NAFLD were entered into our umbrella review. The data regarding anthropometric indices, including body mass index, weight, waist circumference (WC), and waist-to-hip ratio (WHR), were extracted by the investigators. The authors used random effect model for conducting the meta-analysis. Subgroup analysis and sensitivity analysis were also performed. Results: A total number of 13 studies were finally included in our study. Based on the final results, BMI was significantly decreased in NAFLD patients by modulation of gut microbiota [effect size (ES): -0.18, 05% CI: -0.25, -0.11, P<0.001]; however, no significant alteration was observed in weight and WC (ES: -1.72, 05% CI: -3.48, 0.03, P=0.055, and ES: -0.24, 05% CI: -0.75, 0.26, P=0.353, respectively). The results of subgroup analysis showed probiotics had the most substantial effect on decreasing BMI (ES: -0.77, 95% CI: -1.16, -0.38, P<0.001) followed by prebiotics (ES: -0.51, 95% CI: -0.76, -0.27, P<0.001) and synbiotics (ES: -0.12, 95% CI: -0.20, -0.04, P=0.001). Conclusion: In conclusion, the present umbrella meta-analysis showed that although modulation of gut microbiota by administration of probiotics, prebiotics, and synbiotics had promising effects on BMI, no significant change was observed in the WC and weight of the patients. No sufficient data were available for other anthropometric indices including waist-to-hip ratio and waist-to-height ratio and future meta-analyses should be done in this regard.

The association between various viral infections and multiple sclerosis: An umbrella review on systematic review and meta-analysis.

Multiple Sclerosis (MS) is one of the immune-mediated demyelinating disorders. Multiple components, including the environment and genetics, are possible factors in the pathogenesis of MS. Also, it can be said that infections are a key component of the host's response to MS development. Finally, we evaluated the relationship between different pathogens and MS disease in this umbrella research. We systematically collected and analysed multiple meta-analyses focused on one particular topic. We utilised the Scopus, PubMed, and Web of Science databases starting with inception until 30 May 2023. The methodological quality of the analysed meta-analysis has been determined based on Assessing the Methodological Quality of Systematic Reviews 2 and Grade, and graph construction and statistical analysis were conducted using Comprehensive Meta-Analysis. The Confidence Interval of effect size was 95% in meta-analyses, and p < 0.05 indicated a statistically meaningful relationship. The included studies evaluated the association between MS and 12 viruses containing SARS-CoV-2, Epstein-Barr virus (EBV), Hepatitis B virus, varicella-zoster virus (VZV), human herpesvirus 6 (HHV-6), HHV-7, HHV-8, HSV-1, HSV-2, Cytomegalovirus, Human Papillomavirus, and influenza. SARS-CoV-2, with a 3.74 odds ratio, has a significantly more potent negative effect on MS among viral infections. After that, EBV, HHV-6, HSV-2, and VZV, respectively, with 3.33, 2.81, 1.76, and 1.72 odds ratios, had a significantly negative relationship with MS (p < 0.05). Although the theoretical evidence mostly indicates that EBV has the greatest effect on MS, recent epidemiological studies have challenged this conclusion and put forward possibilities that SARS-CoV-2 is the culprit. Hence, it was necessary to investigate the effects of SARS-CoV-2 and EBV on MS.

3D Printing for Cardiovascular Surgery and Intervention: A Review Article.

3D printing technology can be applied to practically every aspect of modern life, fulfilling the needs of people from various backgrounds. The utilization of 3D printing in the context of adult heart disease can be succinctly categorized into 3 primary domains: preoperative strategizing or simulation, medical instruction, and clinical consultations. 3D-printed model utilization improves surgical planning and intraoperative decision-making and minimizes surgical risks, and it has demonstrated its efficacy as an innovative educational tool for aspiring surgeons with limited practical exposure. Despite all the applications of 3D printing, it has not yet been shown to improve long-term outcomes, including safety. There are no data on the outcomes of controlled trials available. To appropriately diagnose heart disease, 3D-printed models of the heart can provide a better understanding of the intracardiac anatomy and provide all the information needed for operative planning. Experientially, 3D printing provides a wide range of perceptions for understanding lower extremity arteries' spatial geometry and anatomical features of pathology. Practicing cardiac surgery processes using objects printed using 3D imaging data can become the norm rather than the exception, leading to improved accuracy and quality of treatment. This study aimed to review the various applications of 3D printing technology in cardiac surgery and intervention.

Global evaluation of the antibacterial activity of Ceftolozane/Tazobactam against ESBLs-producing Escherichia coli and Klebsiella pneumoniae: a systematic review and meta-analysis.

Background: Ceftolozane/Tazobactam is a ß-lactam/ß-lactamase inhibitor combination with a high range of efficacy and broad-spectrum action against multidrug-resistant bacterial strains. Objectives: The present study aimed to analyze the in vitro activity of Ceftolozane/Tazobactam against extended-spectrum ß-lactamases (ESBLs)-producing Escherichia coli (ESBLs-EC) and Klebsiella pneumonia (ESBLs-KP) in the published literature to provide international data on the antimicrobial stewardship programs. Design: Systematic review and meta-analysis. Methods: A systematic literature search was conducted on the Web of Science, Embase, PubMed, Scopus, and Google Scholar electronic databases from the beginning of databases to December 2022 to cover all published articles relevant to our scope. Results: At last, 31 publications that met our inclusion criteria were selected for data extraction and analysis by Comprehensive Meta-Analysis Software. The pooled prevalence of Ceftolozane/Tazobactam susceptibility for ESBLs-EC and ESBLs-KP was estimated at 91.3% [95% confidence interval (CI): 90.1-92.5%] and 65.6% (95% CI: 60.8-70.2%), respectively. There was significant heterogeneity among the 31 studies for ESBLs-EC (χ2 = 91.621; p < 0.001; I2 = 67.256%) and ESBLs-KP (χ2 = 348.72; p < 0.001; I2 = 91.4%). Most clinical isolates of ESBLs-EC had MIC50 and MIC90 at a concentration of 0.5 and 2 µg/mL [minimum inhibitory concentration (MIC) at which 50% and 90% of isolates were inhibited], respectively. In contrast, most clinical isolates of ESBLs-KP had MIC50 and MIC90 at a concentration of 1 and 32 µg/mL, respectively. Conclusion: Based on the meta-analysis results, Ceftolozane/Tazobactam has a more promising in vitro antibacterial activity against ESBLs-EC isolates from different clinical sources than ESBLs-KP isolates. Therefore, Ceftolozane/Tazobactam can be a useful therapeutic drug as an alternative to carbapenems. Randomized clinical trials are needed to provide clinical evidence to support these observations.

Coping with endogeneity and unobserved heterogeneity in real-time clustering critical crash occurrences nested within weather and road surface conditions.

The knot of endogeneity and unobserved heterogeneity are causes of rendering parameter estimates inconsistent in real-time crash prediction. This study intends to alleviate the potential sources of these issues in detecting critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes through a 402-mile Interstate-80 in Wyoming. Among different types of endogeneity, the problem of errors-in-variables and simultaneity was respectively mitigated by conflating disaggregated real-time traffic observations with aggregated environmental conditions and removing secondary crashes from the dataset. The possibility of omitted variables and unobserved heterogeneity were dealt by using random intercepts in hierarchical modeling under Bayesian inference. Three models were calibrated. Model-1 treated all predictors as fixed parameters. Model-2 and Model-3, respectively, considered weather and road surface conditions as random intercepts. Model-2 outperformed the others where the Intraclass Correlation Coefficients confirmed that the crash dataset is more nested within weather conditions. Results indicated that critical crashes require more interaction between vehicles, and shaping backward shockwave reduces their risk on Interstate-80 with a comparatively less traffic volume. Furthermore, considering different categories of weather and road surface conditions, critical crashes are more likely to occur on dry pavement and cloudy conditions compared to the wet surface and rainy days.

Practical advantage of crossed random intercepts under Bayesian hierarchical modeling to tackle unobserved heterogeneity in clustering critical versus non-critical crashes.

Traditional hierarchical modeling has been proposed to account for unobserved heterogeneity in the crash analysis. Previous studies investigated the grouping of individual observations between different clusters by considering a single random factor at level-2 of data structure. This approach, however, hinders exploring the possible crossed effects of additional random factors at the level-2 of data hierarchy on the response variable. The current study aims to expand the previous attempts by introducing the concept of Cross-Classified Random Effects Modeling (CCREM) and utilizing crossed random intercepts to account for the crossed effects of two random factors. Aligned with the Connected Vehicle Pilot Deployment Program on Interstate-80 (I-80), this paper intends to cluster critical crashes, involving fatal or incapacitating injuries, versus non-critical crashes through a 402-mile I-80 in Wyoming during the first five months of 2017. Aggregated environmental conditions were conflated with disaggregated real-time traffic observations. Concerning road surface conditions and longitudinal grade categories, four Logistic Regression models were calibrated under Bayesian Inference. Model-1 considered these two factors as fixed parameters; however, in each of Model-2 and Model-3, one of these factors was treated as a random intercept. Model-4 considered both factors as random intercepts and investigated their crossed effect on the critical crash probability. Model-4 outperformed the others and showed that the maximum probability of critical crashes arises on dry pavements and steep downgrades. In contrast, the combined effect of wet pavements and less steep downgrades is associated with the minimum risk of critical crashes. It was revealed that the probability of critical crashes varies at any given value of real-time traffic-related predictors according to different combinations of longitudinal grade and road surface conditions. This finding indicates an essential need for Active Traffic Management to timely apply interventions not only based on real-time traffic-related predictors but also according to various combinations of environmental conditions.

Gut microbiome and multiple sclerosis: New insights and perspective.

The human gastrointestinal microbiota, also known as the gut microbiota living in the human gastrointestinal tract, has been shown to have a significant impact on several human disorders including rheumatoid arthritis, diabetes, obesity, and multiple sclerosis (MS). MS is an inflammatory disease characterized by the destruction of the spinal cord and nerve cells in the brain due to an attack of immune cells, causing a wide range of harmful symptoms related to inflammation in the central nervous system (CNS). Despite extensive studies on MS that have shown that many external and genetic factors are involved in its pathogenesis, the exact role of external factors in the pathophysiology of MS is still unclear. Recent studies on MS and experimental autoimmune encephalomyelitis (EAE), an animal model of encephalitis, have shown that intestinal microbiota may play a key role in the pathogenesis of MS. Therefore, modification of the intestinal microbiome could be a promising strategy for the future treatment of MS. In this study, the characteristics of intestinal microbiota, the relationship between intestine and brain despite the blood-brain barrier, various factors involved in intestinal microbiota modification, changes in intestinal microbial composition in MS, intestinal microbiome modification strategies, and possible use of intestinal microbiome and factors affecting it have been discussed.