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Plants and animals are potential sources of food, particularly lipids. They are sources of nutrients for humans, and are used in various applications in food industries. Foods whose lipids consumed, have benefits for animal and human health. Sterols are among the compounds essential to the well-being of living beings. Phytosterols are derived from plants and algae, and zoosterols from animals dominated by cholesterol. Cholesterol is found in small quantities in some plant lipids. Also, cholesterol is produced by herbivorous insects by metabolizing phytosterols. Oilseeds and vegetable oils contain sterols and are the richest natural sources of phytosterols. Vegetables and fruit also contain small quantities. These compounds play an undeniable role in our diet. Foods, particularly vegetable oils, when produced, preserved and used according to established prescriptions, help to ensure consumer health and prevent certain pathologies. Sterols, and in particular phytosterols, play a number of roles in the pharmaceutical field (therapeutic steroids), nutrition (anti-cholesterol, anti-cancer properties). These natural molecules with their nutritional and therapeutic properties have a positive impact on human and animal health, and possibly on vegetative growth (development cycle of plants). The same is true for cholesterol, which has multiple functions in humans and animals. Also, a diet based on plants or their by-products with positive effects on human and animal health is closely in line with the objectives of the 'One health approach'. Indeed, sterols can have adverse effects on health when established standards are not respected. As a result, the health benefits of sterols (cholesterol and phytosterols) require particular attention, given their contribution to the public health problems facing our countries. The aim of the present research is to highlight the health benefits of cholesterol and phytosterols for living organisms, particularly humans, and their contribution to the One Health approach., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Nanostructuration is a promising tool for enhancing the performance of sensors based on electrochemical transduction. Nanostructured materials allow for increasing the surface area of the electrode and improving the limit of detection (LOD). In this regard, inverse opals possess ideal features to be used as substrates for developing sensors, thanks to their homogeneous, interconnected pore structure and the possibility to functionalize their surface. However, overcoming the insulating nature of conventional silica inverse opals fabricated via sol-gel processes is a key challenge for their application as electrode materials. In this work, colloidal assembly, atomic layer deposition and selective surface functionalization are combined to design conductive inverse opals as an electrode material for novel glucose sensing platforms. An insulating inverse opal scaffold is coated with uniform layers of conducting aluminum zinc oxide and platinum, and subsequently functionalized with glucose oxidase embedded in a polypyrrole layer. The final device can sense glucose at concentrations in the nanomolar range and is not affected by the presence of common interferents gluconolactone and pyruvate. This method may also be applied to different conductive materials and enzymes to generate a new class of highly efficient biosensors.

Mapping of the metabolic activity of tumor tissues represents a fundamental approach to better identify the tumor type, elucidate metastatic mechanisms and support the development of targeted cancer therapies. The spatially resolved quantification of Warburg effect key metabolites, such as glucose and lactate, is essential. Miniaturized electrochemical biosensors scanned over cancer cells and tumor tissue to visualize the metabolic characteristics of a tumor is attractive but very challenging due to the limited oxygen availability in the hypoxic environments of tumors that impedes the reliable applicability of glucose oxidase-based glucose micro-biosensors. Herein, the development and application of a new glucose micro-biosensor is presented that can be reliably operated under hypoxic conditions. The micro-biosensor is fabricated in a one-step synthesis by entrapping during the electrochemically driven growth of a polymeric matrix on a platinum microelectrode glucose oxidase and a catalytically active Prussian blue type aggregate and mediator. The as-obtained functionalization improves significantly the sensitivity of the developed micro-biosensor for glucose detection under hypoxic conditions compared to normoxic conditions. By using the micro-biosensor as non-invasive sensing probe in Scanning Electrochemical Microscopy (SECM), the glucose uptake by a breast metastatic adenocarcinoma cell line, with an epithelial morphology, is measured., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Current cancer research is limited by the availability of reliable in vivo and in vitro models that are able to reproduce the fundamental hallmarks of cancer. Animal experimentation is of paramount importance in the progress of research, but it is becoming more evident that it has several limitations due to the numerous differences between animal tissues and real, in vivo human tissues. 3D bioprinting techniques have become an attractive tool for many basic and applied research fields. Concerning cancer, this technology has enabled the development of three-dimensional in vitro tumor models that recreate the characteristics of real tissues and look extremely promising for studying cancer cell biology. As 3D bioprinting is a relatively recently developed technique, there is still a lack of characterization of the chemical cellular microenvironment of 3D bioprinted constructs. In this work, we fabricated a cervical tumor model obtained by 3D bioprinting of HeLa cells in an alginate-based matrix. Characterization of the spheroid population obtained as a function of culturing time was performed by phase-contrast and confocal fluorescence microscopies. Scanning electrochemical microscopy and platinum nanoelectrodes were employed to characterize oxygen concentrations-a fundamental characteristic of the cellular microenvironment-with a high spatial resolution within the 3D bioprinted cervical tumor model; we also demonstrated that the diffusion of a molecular model of drugs in the 3D bioprinted construct, in which the spheroids were embedded, could be measured quantitatively over time using scanning electrochemical microscopy.

The novel coronavirus has affected all regions of the world, but each country has experienced different rates of infection. In West Africa, in particular, infection rates remain low as compared to other parts of the world. This heterogeneity in the spread of COVID-19 raises a lot of questions that are still unanswered. However, some studies point out that people's mobility, size of gatherings, rate of testing, and weather have a great impact on the COVID-19 spread. In this work, we first evaluate the correlation between meteorological parameters and COVID-19 cases using Spearman's rank correlation. Secondly, multi-output Gaussian processes (MOGP) are used to predict the daily confirmed COVID-19 cases by exploring its relationships with meteorological parameters. The number of daily reported COVID-19 cases, as well as, weather variables collected from March 9, 2020, to October 18, 2021, were used in the analysis. The weather variables considered in the analysis are the mean temperature, relative humidity, wind direction, insolation, precipitation, and wind speed. The predicting model was constructed exploiting the correlation between the data of the daily confirmed COVID-19 cases and data of the weather variables. The results show that a significant correlation between the daily confirmed COVID-19 cases was found with humidity, wind direction, wind speed, and insolation. These parameters are used to construct the predictive model using the Multi-Output Gaussian process (MOGP). Different combinations of the data of meteorological parameters together with the data of daily reported COVID-19 cases were used to derive different models. We found that the best predictor is obtained using the combination of Humidity and insolation. This model is then used to predict the daily confirmed COVID-19 cases knowing the humidity and Insolation., (© 2022 The Authors.)