Enhancing the selectivity of gas sensing materials towards specific volatile organic compounds (VOCs) is
challenging due to the chemical simplicity of VOCs as well as the difficulty in interfacing VOC selective
biological elements with electronic components used in the transduction process. We aimed to tune the
selectivity of gas sensing materials through the incorporation of VOC-selective peptides into gel-like gas
sensing materials. Specifically, a peptide (P1) known to discriminate single carbon deviations among benzene
and derivatives, along with two modified versions (P2 and P3), were integrated with gel compositions
containing gelatin, ionic liquid and without or with a liquid crystal component (ionogels and hybrid gels
respectively). These formulations change their electrical or optical properties upon VOC exposure, and were
tested as sensors in an in-house developed e-nose. Their ability to distinct and identify VOCs was evaluated
via a supervised machine learning classifier. Enhanced discrimination of benzene and hexane was detected
for the P1-based hybrid gel. Additionally, complementarity of the electrical and optical sensors was observed
considering that a combination of both their accuracy predictions yielded the best classification results for the
tested VOCs. This indicates that a combinatorial array in a dual-mode e-nose could provide optimal
performance and enhanced selectivity.

Affinity‐triggered assemblies rely on affinity interactions as the driving force to assemble physically‐crosslinked networks. WW domains are small hydrophobic proteins binding to proline‐rich peptides that are typically produced in the insoluble form. Previous works attempted the biological production of the full WW domain in tandem to generate multivalent components for affinity‐triggered hydrogels. In this work, an alternative approach was followed by engineering a 13‐mer minimal version of the WW domain that retains the ability to bind to target proline‐rich peptides. Both ligand and target peptides were produced chemically and conjugated to multivalent polyethylene glycol, yielding two components. Upon mixing, they together form soft biocompatible affinity‐triggered assemblies, stable in stem cell culture media, and displaying mechanical properties in the same order of magnitude as for those hydrogels formed with the full WW protein in tandem.

Non-invasive and fast diagnostic tools based on volatolomics hold great promise in the control of infectious diseases. However, the tools to identify microbial volatile organic compounds (VOCs) discriminating between human pathogens are still missing. Artificial intelligence is increasingly recognised as an essential tool in health sciences. Machine learning algorithms based in support vector machines and features selection tools were here applied to find sets of microbial VOCs with pathogen-discrimination power. Studies reporting VOCs emitted by human microbial pathogens published between 1977 and 2016 were used as source data. A set of 18 VOCs is sufficient to predict the identity of 11 microbial pathogens with high accuracy (77%), and precision (62–100%). There is one set of VOCs associated with each of the 11 pathogens which can predict the presence of that pathogen in a sample with high accuracy and precision (86–90%). The implemented pathogen classification methodology supports future database updates to include new pathogen-VOC data, which will enrich the classifiers. The sets of VOCs identified potentiate the improvement of the selectivity of non-invasive infection diagnostics using artificial olfaction devices.

This paper reports the design, preparation and characterization of cellulose affinity membranes for antibody purification using a new methodology. Cellulose membranes were prepared from polymer-ionic liquid solutions, namely 1-butyl-3-methylimidazolium chloride {([BMIM][Cl])}, by the water induced phase inversion process. After functionalization with a synthetic ligand 2-(3-aminophenol)-6-(4-amino-1-naphthol)-4-chloro-s-triazine (ligand 22/8), these were evaluated as affinity supports for human immunoglobulin G {(IgG).} Membranes were characterized in terms of morphology {(SEM)}, porosity (mercury porosimetry), hydrophilicity (contact angle measurement), transport properties (permeability) and mechanical performance {(DMA).} Membranes prepared with varying cellulose contents (5 and 10&\#xa0;wt.% cellulose in ionic liquid solutions) lead to films with different properties. The 10&\#xa0;wt.% cellulose membrane presented enhanced morphological and mechanical properties, however, the morphology of this membrane was significantly altered after ligand coupling. Adsorption isotherms for human {IgG} onto 10&\#xa0;wt.% matrix activated with ligand 22/8 were obtained. Preliminary results showed that the bovine serum albumin {(BSA)}, a model impurity, did not adsorb onto the membrane while up to 6&\#xa0;mg {IgG/g} was bound and 2&\#xa0;mg {IgG/g} recovered.

The effect of kefir grains on the proteolysis of major milk proteins in milk kefir and in a culture of kefir grains in pasteurized cheese whey was followed by reverse {phase-HPLC} analysis. The reduction of kappa-, alpha-, and beta-caseins {(CN)}, alpha-lactalbumin {(alpha-LA)}, and beta-lactoglobulin {(beta-LG)} contents during 48 and 90 h of incubation of pasteurized milk {(100mL)} and respective cheese whey with kefir grains (6 and 12 g) at 20 degrees C was monitored. Significant proteolysis of {alpha-LA} and kappa-, alpha-, and beta-caseins was observed. The effect of kefir amount (6 and 12 {g/100mL)} was significant for {alpha-LA} and alpha- and {beta-CN.} {alpha-Lactalbumin} and {beta-CN} were more easily hydrolyzed than {alpha-CN.} No significant reduction was observed with respect to {beta-LG} concentration for 6 and 12 g of kefir in {100mL} of milk over 48 h, indicating that no significant proteolysis was carried out. Similar results were observed when the experiment was conducted over 90 h. Regarding the cheese whey kefir samples, similar behavior was observed for the proteolysis of {alpha-LA} and {beta-LG:} {alpha-LA} was hydrolyzed between 60 and 90% after 12h (for 6 and 12 g of kefir) and no significant {beta-LG} proteolysis occurred. The proteolytic activity of lactic acid bacteria and yeasts in kefir community was evaluated. Kefir milk prepared under normal conditions contained peptides from proteolysis of {alpha-LA} and kappa-, alpha-, and beta-caseins. Hydrolysis is dependent on the kefir:milk ratio and incubation time. {beta-Lactoglobulin} is not hydrolyzed even when higher hydrolysis time is used. Kefir grains are not appropriate as adjunct cultures to increase {beta-LG} digestibility in whey-based or whey-containing foods.

Many successful, recent therapies for life-threatening diseases such as cancer and rheumatoid arthritis are based on the recognition between native or genetically engineered antibodies and cell-surface receptors. Although naturally produced by the immune system, the need for antibodies with unique specificities and designed for single application, has encouraged the search for novel antibody purification strategies. The availability of these products to the end-consumer is strictly related to manufacture costs, particularly those attributed to downstream processing. Over the last decades, academia and industry have developed different types of interactions and separation techniques for antibody purification, affinity-based strategies being the most common and efficient methodologies. The affinity ligands utilized range from biological to synthetic designed molecules with enhanced resistance and stability. Despite the successes achieved, the purification “paradigm” still moves interests and efforts in the continuous demand for improved separation performances. This review will focus on recent advances and perspectives in antibody purification by affinity interactions using different techniques, with particular emphasis on affinity chromatography.

The simultaneous separation and quantification of two casein peptides {(IPP}, {VPP)} presenting potent inhibitory activity of angiotensin-converting-enzyme {(ACE)} and casein in fermented milks was developed. Gradient elution was carried out at a flow-rate of 1 {mL/min}, using a mixture of two solvents. Solvent A was 0.1% {TFA} in water and solvent B was acetonitrile-water-trifluoracetic acid 95:5:0.1. The effluent was monitored by {UV} detector at 214 nm. Calibration curves were constructed in the interval of 0.01-1.0 {mg/mL} for {VPP}, 0.005-1.0 {mg/mL} for {IPP}, and 0.05-3.0 {mg/mL} for casein. R2 invariably exceeded 0.999. The detection limits were 0.004 for {VPP}, 0.002 {mg/mL} for {IPP}, and 0.02 {mg/mL} for casein. Repeatability of the method was evaluated by six consecutive injections of two standard solutions containing {VPP}, {IPP}, and casein. The {RSD} values for concentration were all below 5.08%. Recovery studies were carried out to determine the accuracy of the method. Recoveries ranged between 88 and 98.2%. The methodology was applied, not only, for the monitorization of {VPP}, {IPP}, and casein in commercial fermented milks labeled as presenting antihypertensive properties, but also, in milk with different degrees of fermentation by L. Helveticus, and in other commercial functional fermented milks, such as, those presenting cholesterol lowering properties.