Metal oxide materials have been applied in different fields due to their excellent functional properties. Metal oxides nanostructuration, preparation with the various morphologies, and their coupling with other structures enhance the unique properties of the materials and open new perspectives for their application in the food industry. Chemical gas sensors that are based on semiconducting metal oxide materials can detect the presence of toxins and volatile organic compounds that are produced in food products due to their spoilage and hazardous processes that may take place during the food aging and transportation. Metal oxide nanomaterials can be used in food processing, packaging, and the preservation industry as well. Moreover, the metal oxide-based nanocomposite structures can provide many advantageous features to the final food packaging material, such as antimicrobial activity, enzyme immobilization, oxygen scavenging, mechanical strength, increasing the stability and the shelf life of food, and securing the food against humidity, temperature, and other physiological factors. In this paper, we review the most recent achievements on the synthesis of metal oxide-based nanostructures and their applications in food quality monitoring and active and intelligent packaging.

In recent years, drug manufacturers and researchers have begun to consider the nanobiotechnology approach to improve the drug delivery system for tumour and cancer diseases. In this article, we review current strategies to improve tumour and cancer drug delivery, which mainly focuses on sustaining biocompatibility, biodistribution, and active targeting. The conventional therapy using cornerstone drugs such as fludarabine, cisplatin etoposide, and paclitaxel has its own challenges especially not being able to discriminate between tumour versus normal cells which eventually led to toxicity and side effects in the patients. In contrast to the conventional approach, nanoparticle‐based drug delivery provides target‐specific delivery and controlled release of the drug, which provides a better therapeutic window for treatment options by focusing on the eradication of diseased cells via active targeting and sparing normal cells via passive targeting. Additionally, treatment of tumours associated with the brain is hampered by the impermeability of the blood–brain barriers to the drugs, which eventually led to poor survival in the patients. Nanoparticle‐based therapy offers superior delivery of drugs to the target by breaching the blood–brain barriers. Herein, we provide an overview of the properties of nanoparticles that are crucial for nanotechnology applications. We address the potential future applications of nanobiotechnology targeting specific or desired areas. In particular, the use of nanomaterials, biostructures, and drug delivery methods for the targeted treatment of tumours and cancer are explored.

It has been shown that the gut microbiota plays a central role in human health and disease. A wide range of volatile metabolites present in exhaled breath have been linked with gut microbiota and proposed as a non-invasive marker for monitoring pathological conditions. The aim of this study was to examine the possible correlation between volatile organic compounds (VOCs) in exhaled breath and the fecal microbiome by multivariate statistical analysis in gastric cancer patients (n = 16) and healthy controls (n = 33). Shotgun metagenomic sequencing was used to characterize the fecal microbiota. Breath-VOC profiles in the same participants were identified by an untargeted gas chromatography–mass spectrometry (GC–MS) technique. A multivariate statistical approach involving a canonical correlation analysis (CCA) and sparse principal component analysis identified the significant relationship between the breath VOCs and fecal microbiota. This relation was found to differ between gastric cancer patients and healthy controls. In 16 cancer cases, 14 distinct metabolites identified from the breath belonging to hydrocarbons, alcohols, aromatics, ketones, ethers, and organosulfur compounds were highly correlated with 33 fecal bacterial taxa (correlation of 0.891, p-value 0.045), whereas in 33 healthy controls, 7 volatile metabolites belonging to alcohols, aldehydes, esters, phenols, and benzamide derivatives correlated with 17 bacterial taxa (correlation of 0.871, p-value 0.0007). This study suggested that the correlation between fecal microbiota and breath VOCs was effective in identifying exhaled volatile metabolites and the functional effects of microbiome, thus helping to understand cancer-related changes and improving the survival and life expectancy in gastric cancer patients.

To determine the originality of a typical Italian Parmigiano Reggiano cheese, it is crucial to define and characterize its quality, ripening period, and geographical origin. Different analytical techniques have been applied aimed at studying the organoleptic and characteristic volatile organic compounds (VOCs) profile of this cheese. However, most of the classical methods are time consuming and costly. The aim of this work was to illustrate a new simple, portable, fast, reliable, non-destructive, and economic sensor device S3 based on an array of six metal oxide semiconductor nanowire gas sensors to assess and discriminate the quality ranking of grated Parmigiano Reggiano cheese samples and to identify the VOC biomarkers using a headspace SPME-GC-MS. The device could clearly differentiate cheese samples varying in quality and ripening time when the results were analyzed by multivariate statistical analysis involving principal component analysis (PCA). Similarly, the volatile constituents of Parmigiano Reggiano identified were consistent with the compounds intimated in the literature. The obtained results show the applicability of an S3 device combined with SPME-GC-MS and sensory evaluation for a fast and high-sensitivity analysis of VOCs in Parmigiano Reggiano cheese and for the quality control of this class of cheese.

Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.

The system of sensors of volatile compounds S3 based on nanowires metal oxide semiconductors has been developed to detect anomalous industrial discharges and their spatial location through the continuous qualitative observation of the sewage effluents typical microbiological footprint belonging to the Brescia purification system of A2A CicloIdrico.