Biogenesis of bacterial cellulose/xanthan/CeO2NPs composite films for active food packaging.

The increasing significance of biopolymer-based food packaging can be attributed to its biodegradability and independence from petroleum-derived materials. Concurrently, metal oxide nanoparticles (NPs) have gained prominence as effective antimicrobial agents against both wild-type and antibiotic-resistant microbes. In this study, cerium oxide or ceria, CeO2, nanoparticles with an average diameter of 50 nm were synthesized via a green method utilizing Vibrio sp. VLC cell lysate supernatant. The synthesized CeO2 NPs displayed remarkable antimicrobial properties, inhibiting the growth of Escherichia coli and Staphylococcus aureus by 93.7 % and 98 %, respectively. To enhance the potential of bacterial cellulose (BC) for advanced applications, we developed a BC/xanthan/CeO2 nanocomposite using both ex situ and in situ techniques. The integration of CeO2 NPs within the nanocomposite structure not only improved the inherent properties of BC, but also rendered it suitable for use in active food packaging systems. The nanocomposite exhibited no significant cytotoxicity on the human dermal fibroblast (HDF) cells, confirming its safety. Nanocomposites containing biogenically synthesized CeO2 NPs demonstrated exceptional efficacy for reducing microbial contamination. Bread samples coated with nanocomposite films displayed no signs of microbial growth. These results support the application of BC/xanthan/CeO2 nanocomposites as suitable and effective coating materials for antimicrobial food packaging applications.

Application of Meta-Heuristic Algorithms for Training Neural Networks and Deep Learning Architectures: A Comprehensive Review.

The learning process and hyper-parameter optimization of artificial neural networks (ANNs) and deep learning (DL) architectures is considered one of the most challenging machine learning problems. Several past studies have used gradient-based back propagation methods to train DL architectures. However, gradient-based methods have major drawbacks such as stucking at local minimums in multi-objective cost functions, expensive execution time due to calculating gradient information with thousands of iterations and needing the cost functions to be continuous. Since training the ANNs and DLs is an NP-hard optimization problem, their structure and parameters optimization using the meta-heuristic (MH) algorithms has been considerably raised. MH algorithms can accurately formulate the optimal estimation of DL components (such as hyper-parameter, weights, number of layers, number of neurons, learning rate, etc.). This paper provides a comprehensive review of the optimization of ANNs and DLs using MH algorithms. In this paper, we have reviewed the latest developments in the use of MH algorithms in the DL and ANN methods, presented their disadvantages and advantages, and pointed out some research directions to fill the gaps between MHs and DL methods. Moreover, it has been explained that the evolutionary hybrid architecture still has limited applicability in the literature. Also, this paper classifies the latest MH algorithms in the literature to demonstrate their effectiveness in DL and ANN training for various applications. Most researchers tend to extend novel hybrid algorithms by combining MHs to optimize the hyper-parameters of DLs and ANNs. The development of hybrid MHs helps improving algorithms performance and capable of solving complex optimization problems. In general, the optimal performance of the MHs should be able to achieve a suitable trade-off between exploration and exploitation features. Hence, this paper tries to summarize various MH algorithms in terms of the convergence trend, exploration, exploitation, and the ability to avoid local minima. The integration of MH with DLs is expected to accelerate the training process in the coming few years. However, relevant publications in this way are still rare.

GIS-based spatio-temporal analysis and modeling of COVID-19 incidence rates in Europe.

The COVID-19 epidemic has emerged as one of the most severe public health crises worldwide, especially in Europe. Until early July 2021, reported infected cases exceeded 180 million, with almost 4 million associated deaths worldwide, almost a third of which are in continental Europe. We analyzed the spatio-temporal distribution of the disease incidence and mortality rates considering specific periods in this continent. Further, we applied Global Moran's I to examine the spatio-temporal distribution patterns of COVID-19 incidence rates and Getis-Ord Gi* hotspot analysis to represent high-risk areas of the disease. Additionally, we compiled a set of 40 demographic, socioeconomic, environmental, transportation, health, and behavioral indicators as potential explanatory variables to investigate the spatial variations of COVID-19 cumulative incidence rates (CIRs). Ordinary Least Squares (OLS), Spatial Lag model (SLM), Spatial Error Model (SLM), Geographically Weighted Regression (GWR), and Multiscale Geographically Weighted Regression (MGWR) regression models were implemented to examine the spatial dependence and non-stationary relationships. Based on our findings, the spatio-temporal distribution pattern of COVID-19 CIRs was highly clustered and the most high-risk clusters of the disease were situated in central and western Europe. Moreover, poverty and the elderly population were selected as the most influential variables due to their significant relationship with COVID-19 CIRs. Considering the non-stationary relationship between variables, MGWR could describe almost 69% of COVID-19 CIRs variations in Europe. Since this spatio-temporal research is conducted on a continental scale, spatial information obtained from the models could provide general insights to authorities for further targeted policies.

A Comprehensive Review of the Development of Carbohydrate Macromolecules and Copper Oxide Nanocomposite Films in Food Nanopackaging.

Background. Food nanopackaging helps maintain food quality against physical, chemical, and storage instability factors. Copper oxide nanoparticles (CuONPs) can improve biopolymers' mechanical features and barrier properties. This will lead to antimicrobial and antioxidant activities in food packaging to extend the shelf life. Scope and Approach. Edible coatings based on carbohydrate biopolymers have improved the quality of packaging. Several studies have addressed the role of carbohydrate biopolymers and incorporated nanoparticles to enhance food packets' quality as active nanopackaging. Combined with nanoparticles, these biopolymers create film coatings with an excellent barrier property against transmissions of gases such as O2 and CO2. Key Findings and Conclusions. This review describes the CuO-biopolymer composites, including chitosan, agar, cellulose, carboxymethylcellulose, cellulose nanowhiskers, carrageenan, alginate, starch, and polylactic acid, as food packaging films. Here, we reviewed different fabrication techniques of CuO biocomposites and the impact of CuONPs on the physical, mechanical, barrier, thermal stability, antioxidant, and antimicrobial properties of carbohydrate-based films.

Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms.

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.

Brain tumors and circulating micrornas: a systematic review and diagnostic meta-analysis.

PURPOSE: Brain tumors (BT) are among the most prevalent cancers in recent years. Various studies have examined the diagnostic role of microRNAs in different diseases; however, their diagnostic role in BT has not been comprehensively investigated. This meta-analysis was performed to assess microRNAs in the blood of patients with BTs accurately. METHODS: Twenty-six eligible studies were included for analysis. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), area under curve (AUC), Q*index, summary receiver-operating characteristic (SROC) were assessed using the Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.3.3 software. RESULTS: The diagnostic accuracy of microRNA was high in identifying BT based on the pooled sensitivity 0.82 (95%CI: 0.816-0.84), specificity 0.82 (95%CI: 0.817-0.84), PLR 5.101 (95%CI: 3.99-6.51), NLR 0.187 (95%CI: 0.149-0.236), DOR 34.07 (95%CI: 22.56-51.43) as well as AUC (0.92), and Q*-index (0.86). Subgroup analyses were performed for sample types (serum/plasma), reference genes (RNU6, miR-39, and miR-24), and region to determine the diagnostic power of microRNAs in the diagnosis of BT using pooled sensitivity, specificity, PLR, NLR, AUC, and DOR. CONCLUSION: This meta-analysis suggested that circulating microRNAs might be potential markers for noninvasive early detection of BT.

Urinary Angiogenin as a Marker for Bladder Cancer: A Meta-Analysis.

AIMS: Bladder cancer (BCa) is a common cancer in North America and Europe that carries considerable morbidity and mortality. A reliable biomarker for early detection of the bladder is crucial for improving the prognosis of BCA. In this meta-analysis, we examine the diagnostic role of the angiogenin (ANG) protein in patients' urine with bladder neoplasm. METHODS: We performed a systematic literature search using ScienceDirect, Web of Science, PubMed/MEDLINE, Scopus, Google Scholar, and Embase, up to 10th October 2020 databases. Meta-Disc V.1.4 and Comprehensive Meta-Analysis V.2.2 software calculated the pooled specificity, sensitivity, area under the curve (AUC), diagnostic odds ratio (DOR), positive likelihood ratio (LR+), negative likelihood ratio (LR-), Q ∗ index, and summary receiver-operating characteristic (SROC) for the role of ANG as a urinary biomarker for BCa patients. RESULTS: Four case-control studies were included with 656 participants (417 cases and 239 controls) in this meta-analysis. The pooled sensitivity of 0.71 (95% CI: 0.66-0.75), specificity of 0.78 (95% CI: 0.73-0.81), LR+ of 3.34 (95% CI: 2.02-5.53), LR- of 0.37 (95% CI: 0.32-0.44), DOR of 9.99 (95% CI: 4.69-21.28), and AUC of 0.789 and Q ∗ index of 0.726 demonstrate acceptable diagnostic precision of ANG in identifying BCa. CONCLUSION: This meta-analysis showed that ANG could be a fair biomarker for the diagnosis of BCa patients.

Antimicrobial activities of chitosan/titanium dioxide composites as a biological nanolayer for food preservation: A review.

Packaging is an integral part of food industry that preserves the properties of food during storage. Food spoilage caused by foodborne microorganisms is a public health problem that imposes a significant burden on the healthcare systems. Moreover, packaging based on artificial and chemical materials such as plastic is destructive to the environment. Chitosan can be categorized as an active food packaging material because of its inherent antimicrobial properties and capacity to carry various active components. Combining chitosan and metallic nanoparticles can be used as a practical approach in antimicrobial packaging systems. This strategy has advantages of thermal stability, barrier properties, antioxidant and antimicrobial packaging. Titanium dioxide is one of these nanoparticles that plays a photocatalytic role by releasing reactive oxygen species, thereby leading to the destruction of microorganisms' cell wall and extension of food shelf life. This review elaborates on the antimicrobial applications of chitosan/titanium dioxide nanoparticles films in food packaging systems.

Circulating miR-21 as a potential biomarker in human digestive system carcinoma: a systematic review and diagnostic meta-analysis.

Purpose: Gastrointestinal cancers (GICs) account for about a quarter of cancers. Lately, the circulating microRNAs as a non-invasive biomarker for identifying and monitoring diseases have been recognized. Several studies have examined the role of miR-21 in digestive system carcinoma. We conducted a meta-analysis to assess the diagnostic role of miR-21 in GICs.Methods: Seventeen studies involving 1700 individuals were included in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, DOR, AUC, SROC, and Q* index were calculated based on true-positive, true-negative, false-negative, and false-positive. Moreover, the subgroup analyses have been performed for miR-21 based on sample types (serum/plasma), normalized genes (U6, miR-16, and miR-39), and ethnicity.Results: The pooled sensitivity 0.722 (95% CI: 0.70-0.74), specificity 0.820 (95% CI: 0.801-0.838), PLR 4.375 (95% CI: 3.226-5.933), NLR 0.308 (95% CI: 0.239-0.398), DOR 16.06 (95% CI: 9.732-26.53) as well as AUC 0.86, and Q* index 0.79 represented the high-grade diagnostic precision of miR-21 in identifying GICs (ESCC, GC, CRC, HCC, and PC).Conclusion: This meta-analysis demonstrated that circulating miR-21 levels can be used to monitor the digestive system carcinomas. Therefore, miR-21 can be a useful biomarker of progression and fair diagnosis in GICs patients.

MicroRNA-223 in gastrointestinal cancers: A systematic review and diagnostic meta-analysis.

BACKGROUND: Several studies have been conducted on the diagnostic role of miR-223 in cancers related to the digestive system. However, the diagnostic role of this microRNA in gastrointestinal (GI) cancers has not been fully elucidated. This meta-analysis aimed to accurately assess the diagnostic role of circulating miR-223 in GI cancers. METHODS: A literature search was performed in PubMed/Medline, Science Direct, Web of Science, Google Scholar, Embase and Scopus, up to 1st May 2020 databases. Twelve studies were eligible and included in the analysis. Meta-Disc software was used to calculate the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, area under the curve (AUC) and the summary receiver operating characteristic (SROC) based on true positive, true negative, false negative and false positive for each gastrointestinal cancer separately and in total. RESULTS: Twelve case-control studies were included with 1859 participants (1080 cases and 779 controls). Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio were 0.77 (95% CI: 0.74-0.79), 0.75 (95% CI: 0.72-0.78), 3.04 (95% CI: 2.20-4.18), 0.31 (95% CI: 0.22-0.42) and 10.77 (95% CI: 5.96-19.47), respectively. AUC was 0.83, suggesting a high-grade diagnostic precision of miR-223 in gastrointestinal cancers. Besides, subgroup analyses were performed to assess the diagnostic power of miR-223 based on the type of gastrointestinal cancer, sample type and country via calculating pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio and diagnostic odds ratio. CONCLUSION: Our meta-analysis showed the value of circulating miR-223 levels in the early diagnosis of diverse digestive system carcinomas.

Synthesis and Characterization of Green Zinc Oxide Nanoparticles with Antiproliferative Effects through Apoptosis Induction and MicroRNA Modulation in Breast Cancer Cells.

Changes in the expression of microRNAs can affect cancer cells' viability and behavior and the impact on cancer treatment. In this study, the expression of miR-155-5p, miR-203a-3p, and miR-223-3p in the MCF7 cancer cell line was studied when exposed to ZnO nanoparticles synthesized through a green route. Mentioned ZnO-NPs were well characterized by UV-vis spectroscopy, DLS, XRD, FTIR, FE-SEM, EDX, zeta potential, and AFM analyses. Cellular studies were conducted using ZnO-NPs before miRNA investigations including MTT cytotoxicity test against MCF7, MDA-MB-231, and HFF cell lines. Moreover, apoptosis assays were performed using morphological analysis, fluorescent dyes, flow cytometry, and evaluation of caspase-3 and caspase-8 gene expression. Biological properties such as the antioxidant and antimicrobial activity of these novel ZnO-NPs were considered. MTT assays showed that the inhibitory concentration (IC50) of ZnO-NPs after 24 h was 11.16 µg/mL, 60.08 µg/mL, and 26.3 µg/mL on MCF7, MDA-MB-231, and HFF cells, respectively. The qRT-PCR results showed reduced expression of miR-155-5p, miR-203a-3p, and miR-223-3p when the MCF7 cells were treated with the IC50 concentration of ZnO-NPs (11.16 µg/mL). The antioxidant activity results showed EC50 values at 57.19 µg/mL and 31.5 µg/mL in DPPH and ABTS assays, respectively. The antimicrobial activity of ZnO-NPs was determined on Gram-negative and Gram-positive bacterial strains and fungi using MIC and MBC assays. These NPs had a significant effect in reducing the expression of microRNAs in breast cancer cells. Finally, ZnO-NPs exerted antioxidant and antimicrobial activities.

Development a heuristic method to locate and allocate the medical centers to minimize the earthquake relief operation time.

BACKGROUND: Location-allocation is a combinatorial optimization problem, and is defined as Non deterministic Polynomial Hard (NP) hard optimization. Therefore, solution of such a problem should be shifted from exact to heuristic or Meta heuristic due to the complexity of the problem. Locating medical centers and allocating injuries of an earthquake to them has high importance in earthquake disaster management so that developing a proper method will reduce the time of relief operation and will consequently decrease the number of fatalities. METHODS: This paper presents the development of a heuristic method based on two nested genetic algorithms to optimize this location allocation problem by using the abilities of Geographic Information System (GIS). In the proposed method, outer genetic algorithm is applied to the location part of the problem and inner genetic algorithm is used to optimize the resource allocation. RESULTS: The final outcome of implemented method includes the spatial location of new required medical centers. The method also calculates that how many of the injuries at each demanding point should be taken to any of the existing and new medical centers as well. CONCLUSIONS: The results of proposed method showed high performance of designed structure to solve a capacitated location-allocation problem that may arise in a disaster situation when injured people has to be taken to medical centers in a reasonable time.