Although transdermal drug delivery systems (DDS) offer numerous benefits for patients, including the avoidance of both gastric irritation and first-pass metabolism effect, as well as improved patient compliance, only a limited number of active pharmaceutical ingredients (APIs) can be delivered accordingly. Microneedles (MNs) represent one of the most promising concepts for effective transdermal drug delivery that penetrate the protective skin barrier in a minimally invasive and painless manner. The first MNs were produced in the 90s, and since then, this field has been continually evolving. Therefore, different manufacturing methods, not only for MNs but also MN molds, are introduced, which allows for the cost-effective production of MNs for drug and vaccine delivery and even diagnostic/monitoring purposes. The focus of this review is to give a brief overview of MN characteristics, material composition, as well as the production and commercial development of MN-based systems.

Os protocolos de prevencao sao tecnologias leves usadas no âmbito da saude como forma de prevenir agravos aos pacientes. Esse protocolo traz especificamente os cuidados a serem tomados em relacao as medidas de seguranca, avaliacao e prevencao a integridade da pele dos pacientes acamados ou com declinio de funcionalidade.

This study investigated the behavior of urban-living students related to the salty snacks consumption, and their contribution to salt daily intake. A cross-sectional survey on 1313 urban-living students (16–25 years, 61.4% university students and 38.6% high school students) used a pre-verified questionnaire created specifically for the study. The logistic regression analysis was performed to investigate the factors influencing snack consumption. The results of salt content and the snack consumption frequency were used to evaluate snack contribution to salt intake. All subjects consumed salty snacks, on average several times per week, more often at home and slightly more during periods of intensive studying, with 42% of the participants reporting to consume two or more packages per snacking occasion. Most of the participants consumed such products between main meals, but 10% of them took snacks immediately after the main meal. More high-school students than university students were in the “high snack group” (p < 0.05). The most frequently consumed salty snacks were those with the highest content of salt. Salt intake from snack products for a majority of participants ranged between 0.4 and 1 g/day. The research revealed younger age, home environment and significant contribution to salt intake as critical points in salty snack consumption among urban-living students important for the better understanding of their dietary habits.

Stable isotope-assisted approaches can improve untargeted liquid chromatography-high resolution mass spectrometry (LC-HRMS) metabolomics studies. Here, we demonstrate at the example of chemically stressed wheat that metabolome-wide internal standardization by globally 13C-labeled metabolite extract (GLMe-IS) of experimental-condition-matched biological samples can help to improve the detection of treatment-relevant metabolites and can aid in the post-acquisition assessment of putative matrix effects in samples obtained upon different treatments. For this, native extracts of toxin- and mock-treated (control) wheat ears were standardized by the addition of uniformly 13C-labeled wheat ear extracts that were cultivated under similar experimental conditions (toxin-treatment and control) and measured with LC-HRMS. The results show that 996 wheat-derived metabolites were detected with the non-condition-matched 13C-labeled metabolite extract, while another 68 were only covered by the experimental-condition-matched GLMe-IS. Additional testing is performed with the assumption that GLMe-IS enables compensation for matrix effects. Although on average no severe matrix differences between both experimental conditions were found, individual metabolites may be affected as is demonstrated by wrong decisions with respect to the classification of significantly altered metabolites. When GLMe-IS was applied to compensate for matrix effects, 272 metabolites showed significantly altered levels between treated and control samples, 42 of which would not have been classified as such without GLMe-IS.

Double strand break (DSB) repair mainly occurs through 3 pathways: non-homologous end-joining (NHEJ), alternative end-joining (Alt-EJ), and homologous recombination (HR). We present an assay system that enables simultaneous measurement of all three pathways using Cas9-generated DSBs and next generation sequencing to profile and quantify pathway choice. The assay system has provided several insights. First, absence of the key Alt-EJ factor Pol q only abrogates ~50% of total Alt-EJ. Second, single-strand templated repair (SSTR) requires BRCA1 and MRE11 activity, but not BRCA2, establishing that SSTR commonly used in genome editing is not conventional HR. Third, BRCA1 promotes Alt-EJ usage at two-ended DSBs in contrast to BRCA2. These fundamental differences between BRCA1 and BRCA2 deficiency have implications for therapeutic targeting of HR-deficient cancers. This assay can be used in any system which permits Cas9 delivery and, importantly, allows rapid genotype-to-phenotype correlation in isogenic cell line pairs.

One of the major goals in the European Union for reducing greenhouse gas emissions is the electrification of heat. Therefore, it is expected that the winter peak demand will rise significantly in the next few years. Demand Response could play an important role in reducing the need for network reinforcements by providing flexibility. The major motivation behind this paper is to evaluate the difference in demand flexibility between temperature-dependent consumers using electricity for heating and consumers using other energy sources. In this paper, temperature-dependent consumers are first identified by analyzing their smart metering data with machine learning. Further, the response of consumers is evaluated using probabilistic baseline models. The results show that heat electrification will increase the demand during low temperatures, whereas these consumers will also be able to offer far more flexibility during low temperatures and high demand. To the best of our knowledge, there is no empirical study, that would investigate these using state of the art methods in such detail. The paper presents part of the analyses that were carried out after the real demand response program in the scope of the Slovenian-Japanese NEDO project.

This paper investigates a secure data exchange between many small distributed consumers/prosumers and the aggregator in the process of energy balancing. It addresses the challenges of ensuring data exchange in a simple, scalable, and affordable way. The communication platform for data exchange is using Ethereum Blockchain technology. It provides a distributed ledger database across a distributed network, supports simple connectivity for new stakeholders, and enables many small entities to contribute with their flexible energy to the system balancing. The architecture of a simulation/emulation environment provides a direct connection of a relational database to the Ethereum network, thus enabling dynamic data management. In addition, it extends security of the environment with security mechanisms of relational databases. Proof-of-concept setup with the simulation of system balancing processes, confirms the suitability of the solution for secure data exchange in the market, operation, and measurement area. For the most intensive and space-consuming measurement data exchange, we have investigated data aggregation to ensure performance optimisation of required computation and space usage.

In this paper, we propose a global digital platform to avoid and combat epidemics by providing relevant real-time information to support selective lockdowns. It leverages the pervasiveness of wireless connectivity while being trustworthy and secure. The proposed system is conceptualized to be decentralized yet federated, based on ubiquitous public systems and active citizen participation. Its foundations lie on the principle of informational self-determination. We argue that only in this way it can become a trustworthy and legitimate public good infrastructure for citizens by balancing the asymmetry of the different hierarchical levels within the federated organization while providing highly effective detection and guiding mitigation measures toward graceful lockdown of the society. To exemplify the proposed system, we choose a remote patient monitoring as use case. This use case is evaluated considering different numbers of endorsed peers on a solution that is based on the integration of distributed ledger technologies and NB-IoT (narrowband IoT). An experimental setup is used to evaluate the performance of this integration, in which the end-to-end latency is slightly increased when a new endorsed element is added. However, the system reliability, privacy, and interoperability are guaranteed. In this sense, we expect active participation of empowered citizens to supplement the more usual top-down management of epidemics.

This article aims to provide a thorough overview of the use of Artificial Intelligence (AI) techniques in studying the gut microbiota and its role in the diagnosis and treatment of some important diseases. The association between microbiota and diseases, together with its clinical relevance, is still difficult to interpret. The advances in AI techniques, such as Machine Learning (ML) and Deep Learning (DL), can help clinicians in processing and interpreting these massive data sets. Two research groups have been involved in this Scoping Review, working in two different areas of Europe: Florence and Sarajevo. The papers included in the review describe the use of ML or DL methods applied to the study of human gut microbiota. In total, 1109 papers were considered in this study. After elimination, a final set of 16 articles was considered in the scoping review. Different AI techniques were applied in the reviewed papers. Some papers applied ML, while others applied DL techniques. 11 papers evaluated just different ML algorithms (ranging from one to eight algorithms applied to one dataset). The remaining five papers examined both ML and DL algorithms. The most applied ML algorithm was Random Forest and it also exhibited the best performances.

To increase the ecological validity of outcomes from laboratory evaluations of hearing and hearing devices, it is desirable to introduce more realistic outcome measures in the laboratory. This article presents and discusses three outcome measures that have been designed to go beyond traditional speech-in-noise measures to better reflect realistic everyday challenges. The outcome measures reviewed are: the Sentence-final Word Identification and Recall (SWIR) test that measures working memory performance while listening to speech in noise at ceiling performance; a neural tracking method that produces a quantitative measure of selective speech attention in noise; and pupillometry that measures changes in pupil dilation to assess listening effort while listening to speech in noise. According to evaluation data, the SWIR test provides a sensitive measure in situations where speech perception performance might be unaffected. Similarly, pupil dilation has also shown sensitivity in situations where traditional speech-in-noise measures are insensitive. Changes in working memory capacity and effort mobilization were found at positive signal-to-noise ratios (SNR), that is, at SNRs that might reflect everyday situations. Using stimulus reconstruction, it has been demonstrated that neural tracking is a robust method at determining to what degree a listener is attending to a specific talker in a typical cocktail party situation. Using both established and commercially available noise reduction schemes, data have further shown that all three measures are sensitive to variation in SNR. In summary, the new outcome measures seem suitable for testing hearing and hearing devices under more realistic and demanding everyday conditions than traditional speech-in-noise tests.

Electricity sector has been facing many changes over the last two decades due to rise in penetration of distributed energy resources that significantly affect the operations of distribution grids. Increase in intermittent electricity production from renewable energy sources, requires activating the flexibility contained in the distributed energy resources. Local electricity market as well as demand response present a mechanism to utilize this flexibility. In this paper, we analyze potentials of energy exchange within energy community created at medium voltage feeder of Elektroprivreda BH – d.d. Sarajevo. We use software tool PVSOL Premium to model prosumers and Python for the analysis of power flows and voltage conditions. As a result, we propose the energy community interaction matrix providing the information about prosumers and consumers as a foundation for automation of local energy exchange within the energy community.