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One of the emerging technologies in molecular diagnostics of the last two decades is the use of gold nanoparticles (AuNPs) for biosensors. AuNPs can be functionalized with various biomolecules, such as nucleic acids or antibodies, to recognize and bind to specific targets. AuNPs present unique optical properties, such as their distinctive plasmonic band, which confers a bright-red color to AuNP solutions, and their extremely high extinction coefficient, which makes AuNPs detectable by the naked eye even at low concentrations. Ingenious molecular mechanisms triggered by the presence of a target analyte can change the colloidal status of AuNPs from dispersed to aggregated, with a subsequent visible change in color of the solution due to the loss of the characteristic plasmonic band. This review describes how the optical properties of AuNPs have been exploited for the design of plasmonic biosensors that only require the simple mixing of reagents combined with a visual readout and focuses on the molecular mechanisms involved. This review illustrates selected examples of AuNP-based plasmonic biosensors and promising approaches for the point-of-care testing of various analytes, spanning from the viral RNA of SARS-CoV-2 to the molecules that give distinctive flavor and color to aged whisky.

Ingestion is the preferred way for drug administration. However, many drugs have poor oral bioavailability, warranting the use of injections. Extracellular vesicles (EVs) from cow milk have shown potential utility in improving oral drug bioavailability. However, EVs produced by intestinal epithelial cells have not been investigated for this application. We compared the capacity of cow milk EVs and intestinal epithelial cell-derived counterparts to enhance oral drug bioavailability. EVs were isolated, fluorescently labelled, and loaded with curcumin (CUR) as a model poorly absorbable drug. These were then characterised before testing in an intestinal model (Caco-2). Epithelial cell-derived EVs showed notably higher cell uptake compared to cow milk EVs. Cell uptake was significantly higher in differentiated compared to undifferentiated cells for both types of EVs. While both milk- and cell-derived EVs improved the cell uptake and intestinal permeability of CUR (confirming oral drug bioavailability enhancement potential), epithelial cell EVs demonstrated a superior effect.

Background Logic Learning Machine (LLM) is an innovative method of supervised analysis capable of constructing models based on simple and intelligible rules. In this investigation the performance of LLM in classifying patients with cancer was evaluated using a set of eight publicly available gene expression databases for cancer diagnosis. LLM accuracy was assessed by summary ROC curve ( sROC ) analysis and estimated by the area under an sROC curve ( sAUC ). Its performance was compared in cross validation with that of standard supervised methods, namely: decision tree, artificial neural network, support vector machine (SVM) and k -nearest neighbor classifier. Results LLM showed an excellent accuracy ( sAUC  = 0.99, 95% CI : 0.98–1.0) and outperformed any other method except SVM. Conclusions LLM is a new powerful tool for the analysis of gene expression data for cancer diagnosis. Simple rules generated by LLM could contribute to a better understanding of cancer biology, potentially addressing therapeutic approaches.

Generally, the high diversity of protein properties necessitates the development of unique nanoparticle bio-conjugation methods, optimized for each different protein. Here we describe a universal bio-conjugation approach which makes use of a new recombinant fusion protein combining two distinct domains. The N-terminal part is Glutathione S-Transferase (GST) from Schistosoma japonicum , for which we identify and characterize the remarkable ability to bind gold nanoparticles (GNPs) by forming gold–sulfur bonds (Au–S). The C-terminal part of this multi-domain construct is the SpyCatcher from Streptococcus pyogenes , which provides the ability to capture recombinant proteins encoding a SpyTag. Here we show that SpyCatcher can be immobilized covalently on GNPs through GST without the loss of its full functionality. We then show that GST-SpyCatcher activated particles are able to covalently bind a SpyTag modified protein by simple mixing, through the spontaneous formation of an unusual isopeptide bond. The conjugation of nanoparticles and proteins can require complex optimization for the addition of different proteins. Here, the authors report on the development of a simple isopeptide bond forming method of conjoining gold nanoparticles and fusion proteins.

Glues based on starch are widely used for the consolidation of brittle fibres in historic and archaeological textiles. Ageing fabrics are affected by hydrolysis/oxidation and cross-linking of these glues, a decrease of glues’ solubility, the formation of cracks, and discoloration. The hydrolytic action of enzymes on starch-based glues is promising, as molecular recognition offers great selectivity. However, a systematic assessment of the best methods for applying enzymatic formulations has not been explored yet. Here, α-amylase was applied either by pipetting a solution or combining with gellan gel (embedded in the gel or spread on the gel surface). The effectiveness of the different formulations on the removal of potato and wheat starch was evaluated by Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM) and colorimetric measurements. Enzymes dispersed in gel showed weak diffusion at the surface, resulting in poor starch breakdown and removal. On the contrary, amylase applied by pipette and spread on gel resulted in high starch removal selectivity and efficiency, with neither swelling nor damage to the fibres. These results validate protocols for the assessment of the enzymatic activity on glue-consolidated fibres, identify best application methods and confirm the excellent properties of amylase dispersions for the conservation of historic and archaeological textiles.Key points• Application of α-amylase by pipette and combined with gellan gel to remove starch glues from wool.• Systematic assessment of the best application methods following a multi-analytical protocol.• Enzymes dispersed in gel exhibit poor diffusion at the surface, leading to weak starch removal.• Enzymes applied by pipette and spread on gel are efficient in starch cleaning, without damage to the fibres.

Optimizing water distribution both from an energy-saving perspective and from a quality of service perspective is a challenging task since it involves a complex system with many nodes, many hidden variables and many operational constraints. For this reason, water distribution systems need to handle a delicate trade-off between the effectiveness and computational time of the solution. In this paper, we propose a new computationally efficient method, named rule-based control, to optimize water distribution networks without the need for a rigorous formulation of the optimization problem. As a matter of fact, since it is based on a machine learning approach, the proposed method employs only a set of historical data, where the configuration can be labeled according to a quality criterion. Since it is a data-driven approach, it could be applied to any complex network where historical labeled data are available. In particular, rule-based control exploits a rule-based classification method that allows us to retrieve the rules leading to good or bad performances of the system, even without any information about its physical laws. The evaluation of the results on some simulated scenarios shows that the proposed approach is able to reduce energy consumption while ensuring a good quality of the service. The proposed approach is currently used in the water distribution system of the Milan (Italy) water main.

When nanoparticles are introduced into the bloodstream, plasma proteins accumulate at their surface, forming a protein corona. This corona affects the properties of intravenously administered nanomedicines. The firmly bound layer of plasma proteins in direct contact with the nanomaterial is called the “hard corona”. There is also a “soft corona” of loosely associated proteins. While the hard corona has been extensively studied, the soft corona is less understood due to its inaccessibility to analytical techniques. Our study used dynamic light scattering to determine the dissociation constant and thickness of the protein corona formed in solutions of silica or gold nanoparticles mixed with serum albumin, transferrin or prothrombin. Multivariate analysis showed that the nanoparticle material had a greater impact on binding properties than the protein type. Serum albumin had a distinct binding pattern compared to the other proteins tested. This pilot study provides a blueprint for future investigations into the complexity of the soft protein corona, which is key to developing nanomedicines.

Background: The outcome of patients undergoing transcatheter aortic valve replacement (TAVR) can be affected by coexisting tricuspid regurgitation (TR). The aim of the study is to investigate the clinical results of patients undergoing TAVR with or without concomitant significant TR. Methods: Patients undergoing TAVR were divided into two groups according to TR severity: none/mild TR (low-grade) and moderate/severe TR (significant). Data were analysed and compared. Primary endpoint was the mortality 1-year. Secondary endpoints were re-hospitalization and the degree of postoperative and 1-year TR. Results: TAVR procedures were performed in 345 patients between September 2011 and February 2020. Median STS score was 4.3% (IQR: 2.6–7.2), median LVEF was 59.0% (IQR: 45.0–62.0), median aortic area was 0.70cm2 (IQR: 0.60–0.86), median mean gradient was 43.0mmHg (IQR: 36.0–53.0). Before TAVR, 297 patients (86.1%) had low-grade TR and 48 (13.9%) significant TR. Mean age was 82.4 ± 5.7 and 83.8 ± 6.2 years in low-grade and significant TR group, respectively (p = 0.109), with 47.5% (low-grade TR) and 56.3% (significant TR) of female patients (p = 0.279). Patients showed differences in EuroSCORE-II (3.2% (IQR: 1.9–5.7) in low-grade TR vs. 5.6% (IQR: 3.7–8.1) in significant TR; p < 0.001), impaired right ventricular function (3.0% vs. 20.8%; p < 0.001) and pulmonary hypertension (9.1% vs. 39.6%; p < 0.001). Mean valve size was 27.7 ± 2.9 mm. Hospital mortality was 2.0% in low-grade TR and 4.2% in significantTR patients (p = 0.308). Among discharged patients (n = 337), seven patients died within 30 days (2.0% low-grade TR; 2.1% significant TR; logrank test p = 0.154) and 40 were re-hospitalized for heart failure (11.1% low-grade TR; 14.6% significant TR; p = 0.470). After one year, 26 patients died, corresponding to a mortality of 7.9 deaths per 100-person year (95% CI 5.2–12.0) in low-grade TR group and 9.1 deaths per 100-person year (95% CI 3.4–24.3) in significant TR group (logrank test p = 0.815), with HR (low grade vs. significant TR) of 0.87, 95% CI 0.26–2.89. Re-hospitalization for heart failure was 16.5% and 19.6% for low-grade and significant TR, respectively (p = 0.713). Echocardiographic and functional changes over time showed no significant interaction between TR and time. Conclusions: In our experience, patients undergoing TAVR showed similar 30-day and 1-year outcome and re-hospitalization rate, regardless of the degree of concomitant tricuspid regurgitation.

Biostimulants are substances able to improve water and nutrient use efficiency and counteract stress factors by enhancing primary and secondary metabolism. Premise of the work was to exploit raw extracts from leaves (LE) or flowers (FE) of L., to enhance yield and quality of 'Longifolia,' and to set up a protocol to assess their effects. To this aim, an integrated study on agronomic, physiological and biochemical aspects, including also a phenomic approach, has been adopted. Extracts were diluted to 1 or 10 mL L , sprayed onto lettuce plants at the middle of the growing cycle and 1 day before harvest. Control plants were treated with water. Non-destructive analyses were conducted to assess the effect of extracts on biomass with an innovative imaging technique, and on leaf photosynthetic efficiency (chlorophyll fluorescence and leaf gas exchanges). At harvest, the levels of ethylene, photosynthetic pigments, nitrate, and primary (sucrose and total sugars) and secondary (total phenols and flavonoids) metabolites, including the activity and levels of phenylalanine ammonia lyase (PAL) were assessed. Moreover, a preliminary study of the effects during postharvest was performed. Borage extracts enhanced the primary metabolism by increasing leaf pigments and photosynthetic activity. Plant fresh weight increased upon treatments with 10 mL L doses, as correctly estimated by multi-view angles images. Chlorophyll fluorescence data showed that FEs were able to increase the number of active reaction centers per cross section; a similar trend was observed for the performance index. Ethylene was three-fold lower in FEs treatments. Nitrate and sugar levels did not change in response to the different treatments. Total flavonoids and phenols, as well as the total protein levels, the PAL specific activity, and the levels of PAL-like polypeptides were increased by all borage extracts, with particular regard to FEs. FEs also proved efficient in preventing degradation and inducing an increase in photosynthetic pigments during storage. In conclusion, borage extracts, with particular regard to the flower ones, appear to indeed exert biostimulant effects on lettuce; future work will be required to further investigate on their efficacy in different conditions and/or species.

The study deals with intelligible analytics for performance modelling of vehicle platooning. Knowledge extraction with rules is targeted to understand safety regions (collision avoidance) of system parameters. Results are shown by feeding data through simulation to the train of different rule extraction mechanisms. Safety regions are evidenced on test data with statistical error very close to zero.